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After this COVID winter comes an AI spring – VentureBeat

During boom times, companies focus on growth. In tough times, they seek to improve efficiency. History shows us that after every major economic downturn since the 1980s, businesses relied on digital technology and, specifically, innovations in software technology to return to full productivity with fewer repetitive jobs and less bloat.

The years Ive spent as a VC have convinced me that this is the best time to start an AI-first enterprise, not despite the recession, but because of it. The next economic recovery will both be driven by artificial intelligence and accelerate its adoption.

While the Great Recession is often thought of as a jobless recovery, economists at the National Bureau of Economic Research (NBER) found that the downturn accelerated the shift from repetitive to non-routine jobs at both the high and low ends of the spectrum. So, yes, existing tasks were automated, but companies empowered their employees with data and analytics to augment their judgment to improve productivity and quality, in a virtuous cycle of data and judgment that both increased profitability and created more rewarding work.

Indeed, the highest levels of unemployment during the Great Recession were followed by a surge in enrollment in post-secondary education in analytics and data science as people sought out opportunities to upskill. And the period was followed by a recovery in which despite increased automation unemployment fell to historic lows.

Through no fault of our own, were again thrust into the cycle of recession and recovery. Industries already expect to benefit from improved AI and machine learning in the next recovery. That expectation will create new opportunities for AI entrepreneurs.

Every economic recovery is defined by an emerging software technology and set of applications.

The companies that grew in the lackluster economy of the early 1980s staged the first software IPOs when the economy rebounded in the middle of that decade: Lotus, Microsoft, Oracle, Adobe, Autodesk and Borland.

Packaged software signified a unique turning point in the history of commercial enterprise; the category required little in the way of either CAPEX or personnel costs. Software companies had gross margins of 80% or more, which gave them amazing resilience to grow or shrink without endangering their existence. If entrepreneurs were willing to work for lower wages, software companies could be started quickly with minimal to no outside investment, and if they could find early product-market fit, they could often bootstrap and grow organically.

Those new software companies were perfectly adapted to foster innovation when recessions hit, because high-quality people were available and less expensive, and office space was abundant. At the same time, established companies put new product development on hold while they tried to service and keep existing customers.

I started working as a VC in 1990 for the first venture firm that focused purely on investing in software, Hummer Winblad. While it took hard work and tenacity for John Hummer and Ann Winblad to raise that first fund, their timing as investors turned out to be perfect. A recession began in the second quarter of that year and lasted through Q1 1991.

The software companies coming out of that recession pioneered cost-effective client-server computing. Sybase, which established this trend with its Open Client-Server Interfaces went public in 1991, after growing 54% in the previous year.

By then, universities had graduated many programmers, creating a talent pool for startups. New software developer platforms made those programmers more productive. The 1990s became the first golden era for enterprise computing. One Hummer Winblad company, Arbor Software, invented the category of Online Analytical Processing (OLAP). Another, Powersoft, became the dominant no-code client server development platform. It was the industrys first billion-dollar software acquisition.

The first CRM companies, spawned in that recession, held successful IPOs from 1993 to 1999. This class included Remedy a company that BusinessWeek breathlessly called Americas Number One Top Hot Growth Company in 1996. Scopus, Vantive, and Clarify all grew rapidly and went public or were acquired in this period or shortly thereafter.

That exuberance ended with the dot-com bust in March 2000.

At that time, Salesforce had existed for only a year. Concur was a relatively new company, forced to reinvent itself when its packaged software business collapsed. Many people would have thought their timing was terrible, but they were unhindered by the obligation to service an installed base during the 2001 recession that followed the bust. That left them free to innovate, and they became two of the very first SaaS businesses.

Salesforce went public in 2004, and now has a market cap of about $135 billion. In 2013, Concur sold to SAP for $8.3 billion. Amazon Web Services was also conceived during that recession and launched in July 2002. SaaS and cloud computing leveraged each other for the rest of the decade.

When the sub-prime mortgage crisis brought the entire economy down, companies had to retain customers and improve efficiency goals that are often at odds with each other. The idea of a big data future had already taken root, and forward-thinking executives suspected that the solution was already in their data, if they could only find it. But at the same time, established software companies also cut R&D spending. That opened up fertile ground for newer and more agile analytics companies.

Most software companies saw no growth in 2009, but Omniture, a leader in web analytics, grew more than 80% that year, prompting its acquisition by Adobe for $1.9 billion. Tableau had been founded back in 2003, but it grew slowly until the recession. From 2008-2010, it grew from $13 million to $34 million in sales. Over the same period Splunk went from $9 million to $35 million. Ayasdi, Cloudera, Mapr and Datameer were all launched in the depths of the Great Recession.

Of course, none of those companies could have flourished without data scientists. Just as universities accelerated the creation of software developers in the early 1990s, they again accelerated the creation of analytics experts and data scientists during the Great Recession, which again helped to spur the recovery and drive a decade of economic expansion, job growth, and the longest bull market in American history.

Even before the pandemic, many economists and corporate CFOs felt there was at least a 50% chance of recession in 2020.

Over a year ago, The Parliament the policy magazine published by the EU Parliament predicted that the next recession would usher in a wave of AI. The magazine quoted Mirko Draca, of the London School of Economics as saying, We expect to see another technology surge in the next 10 to 15 years, based on AI and robotics technology.

Those who predicted a mere recession were, to say the least, insufficiently pessimistic. Companies have reduced their labor costs more aggressively than ever to match the suddenness and seriousness of the situation. Once again, theyll rely on automation to boost production when the recovery begins.

The Atlantic Council surveyed over 100 technology experts on the impact that COVID-19 would have on global innovation. Even in the midst of the pandemic, those experts felt that over the next two to five years, data and AI would have more impact than medical bioengineering. The two are not mutually exclusive; Googles Deepmind Technologies recently used its AlphaFold tool to predict complex protein folding patterns, useful in the search for a vaccine.

Companies emerging from this recession will adapt processes to vaccinate their systems against the next pandemic. In response to supply-chain disruptions, Volkswagen is considering increasing its 3D printing capabilities in Germany, which would give the automaker a redundant parts source. The government-run Development Bank of Japan will subsidize the costs of companies that move production back to Japan.

Bringing production back onshore while controlling costs will require significant investment in robotics and AI. Even companies that dont have their own production capacity, such as online retailers, plan to use AI to improve the reliability of complex global supply chains. So a surge in demand for AI talent is inevitable.

In 2018, several major universities announced initiatives to develop that talent. MIT announced the largest-ever commitment to AI from a university: a $1 billion initiative to create a College of Computing. Carnegie-Mellon created the first bachelor of science in artificial intelligence degree program. UCBerkeleyannounced a new division of data science. And Stanford announced ahuman-centered AI initiative.

Dozens more schools have followed suit. Machine learning has moved from obscurity to ubiquity, just as software development did 30 years ago and data science did 10 years ago.

Back in 2017, a couple of my colleagues wrote about the AI risk curve, arguing that the adoption of AI is held back not by technology but by managers perception of the risks involved in replacing a worker (whose performance is known) with an unfamiliar software process.

Recessions increase the pressure on managers to reduce labor costs, and thus increase their tolerance for the risks associated with adopting new technology. Over the next year or two, companies will be more willing to take risks and integrate new technologies into their infrastructure. But the challenges of surviving in the recession will mean that AI-first companies must deliver measurable improvements in quality and productivity.

One relatively new risk that managers must tolerate pertains to data. Even companies that are not yet exploiting their data effectively now recognize it as a valuable resource. As startups deploy AI software systems that prove more accurate and cost-effective than human beings, their early-adopter customers must be more willing to trust them with proprietary data. That will allow AI companies to train new products and make them even smarter. And in return for taking this risk, companies must make their models more transparent, more easily reproducible, and more explainable to their customers, auditors, and regulators.

In the area of food and agriculture, AI will help us to understand and adapt to a changing climate. In infrastructure and security, machine learning models will improve the efficiency, reliability and performance of cloud infrastructure. Better and more dynamic risk models will help companies and the entire financial market handle the next crisis.

A host of new applied-AI companies will be needed in order accomplish all this and, especially, AI-enabling companies creating better developer tools and infrastructure, continuous optimization systems, and products that help disciplines improve data quality, security, and privacy.

Boom times favor established companies. They have the cash flow to fund skunkworks and conduct pure research. But its a truism that R&D spending is one of the first things big companies cut in a recession. As an entrepreneur, the idea of starting a company now of all times might be scary, but that retrenchment by established competitors leaves fresh ground open for you to seed with new ideas.

The first sign of AI spring will come when companies again forecast increased demand and seek to improve productivity. The only way to be there when that opportunity presents itself is to start now.

The best part is you wont just profit from the recovery, youll help to create it.

[VentureBeats Transform 2020 event in July will feature a host of disruptive new AI technologies and companies.]

Mark Gorenberg is founder and managing director at Zetta Venture Partners.

Continued here:
After this COVID winter comes an AI spring - VentureBeat

Recommendation and review posted by Alexandra Lee Anderson

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Coupling chromatin structure and dynamics by live super-resolution imaging – Science Advances


The three-dimensional organization of the eukaryotic genome plays a central role in gene regulation (1). Its spatial organization has been prominently characterized by molecular and cellular approaches including high-throughput chromosome conformation capture (Hi-C) (2) and fluorescent in situ hybridization (3). Topologically associated domains (TADs), genomic regions that display a high degree of interaction, were revealed and found to be a key architectural feature (4). Direct three-dimensional localization microscopy of the chromatin fiber at the nanoscale (5) confirmed the presence of TADs in single cells but also, among others, revealed great structural variation of chromatin architecture (3). To comprehensively resolve the spatial heterogeneity of chromatin, super-resolution microscopy must be used. Previous work showed that nucleosomes are distributed as segregated, nanometer-sized accumulations throughout the nucleus (68) and that the epigenetic state of a locus has a large impact on its folding (9, 10). However, to resolve the fine structure of chromatin, high labeling densities, long acquisition times, and, often, cell fixation are required. This precludes capturing dynamic processes of chromatin in single live cells, yet chromatin moves at different spatial and temporal scales.

The first efforts to relate chromatin organization and its dynamics were made using a combination of photoactivated localization microscopy (PALM) and tracking of single nucleosomes (11). It could be shown that nucleosomes mostly move coherently with their underlying domains, in accordance with conventional microscopy data (12); however, a quantitative link between the observed dynamics and the surrounding chromatin structure could not yet be established in real time. Although it is becoming increasingly clear that chromatin motion and long-range interactions are key to genome organization and gene regulation (13), tools to detect and to define bulk chromatin motion simultaneously at divergent spatiotemporal scales and high resolution are still missing.

Here, we apply deep learningbased PALM (Deep-PALM) for temporally resolved super-resolution imaging of chromatin in vivo. Deep-PALM acquires a single resolved image in a few hundred milliseconds with a spatial resolution of ~60 nm. We observed elongated ~45- to 90-nm-wide chromatin domain blobs. Using a computational chromosome model, we inferred that blobs are highly dynamic entities, which dynamically assemble and disassemble. Consisting of chromatin in close physical and genomic proximity, our chromosome model indicates that blobs, nevertheless, adopt TAD-like interaction patterns when chromatin configurations are averaged over time. Using a combination of Deep-PALM and high-resolution dense motion reconstruction (14), we simultaneously analyzed both structural and dynamic properties of chromatin. Our analysis emphasizes the presence of spatiotemporal cross-correlations between chromatin structure and dynamics, extending several micrometers in space and tens of seconds in time. Furthermore, extraction and statistical mapping of multiple parameters from the dynamic behavior of chromatin blobs show that chromatin density regulates local chromatin dynamics.

Super-resolution imaging of complex and compact macromolecules such as chromatin requires dense labeling of the chromatin fiber to resolve fine features. We use Deep-STORM, a method that uses a deep convolutional neural network (CNN) to predict super-resolution images from stochastically blinking emitters (Fig. 1A; see Materials and Methods) (15). The CNN was trained to specific labeling densities for live-cell chromatin imaging using a photoactivated fluorophore (PATagRFP); we therefore refer to the method as Deep-PALM. We chose three labeling densities 4, 6, and 9 emitters/m2 per frame in the ON-state to test on the basis of the comparison of simulated and experimental wide-field images (fig. S1A). The CNN trained with 9 emitters/m2 performed significantly worse than the other CNNs and was thus excluded from further analysis (fig. S1B; see Materials and Methods). We applied Deep-PALM to reconstruct an image set of labeled histone protein (H2B-PATagRFP) in human bone osteosarcoma (U2OS) cells using the networks trained on 4 and 6 emitters/m2 per frame (see Materials and Methods). A varying number of predictions by the CNN of each frame of the input series were summed to reconstruct a temporal series of super-resolved images (fig. S1C). The predictions made by the CNN trained with 4 emitters/m2 show large spaces devoid of signal intensity, especially at the nuclear periphery, making this CNN inadequate for live-cell super-resolution imaging of chromatin. While collecting photons from long acquisitions for super-resolution imaging is desirable in fixed cells, Deep-PALM is a live imaging approach. Summing over many individual predictions leads to considerable motion blur and thus loss in resolution. Quantitatively, the Nyquist criterion states that the image resolution R=2/ depends on , the localization density per second, and the time resolution (16). In contrast, motion blur strictly depends on the diffusion constant D of the underlying structure R=4D. There is thus an optimum resolution due to the trade-off between increased emitter sampling and the avoidance of motion blur, which was at a time resolution of 360 ms for our experiments (Fig. 1B and fig. S1D).

(A) Wide-field images of U2OS nuclei expressing H2B-PATagRFP are input to a trained CNN, and predictions from multiple input frames are summed to construct a super-resolved image of chromatin in vivo. (B) The resolution trade-off between the prolonged acquisition of emitter localizations (green line) and motion blur due to diffusion of the underlying diffusion processes (purple line). For our experimental data, the localization density per second is = (2.4 0.1) m2s1, the diffusion constant is D = (3.4 0.8) 103 m2s1 (see fig. S8B), and the acquisition time per frame is = 30 ms. The spatial resolution assumes a minimum (69 5 nm) at a time resolution of 360 ms. (C) Super-resolution images of a single nucleus at time intervals of about 10 s. Scale bars, 2 m. (D) Magnification of segregated accumulations of H2B within a chromatin-rich region. Scale bar, 200 nm. (E) Magnification of a stable but dynamic structure (arrows) over three consecutive images. Scale bars, 500 nm. (F) Fourier ring correlation (FRC) for super-resolved images resulting in a spatial resolution of 63 2 nm. FRC was conducted on the basis of 332 consecutive super-resolved images from two cells. a.u. arbitrary units.

Super-resolution imaging of H2B-PATagRFP in live cells at this temporal resolution shows a pronounced nuclear periphery, while fluorescent signals in the interior vary in intensity (Fig. 1C). This likely corresponds to chromatin-rich and chromatin-poor regions (8). These regions rearrange over time, reflecting the dynamic behavior of bulk chromatin. Chromatin-rich and chromatin-poor regions were visible not only at the scale of the whole nucleus but also at the resolution of a few hundred nanometers (Fig. 1D). Within chromatin-rich regions, the intensity distribution was not uniform but exhibited spatially segregated accumulations of labeled histones of variable shape and size, reminiscent of nucleosome clutches (6), nanodomains (9, 11), or TADs (17). At the nuclear periphery, prominent structures arise. Certain chromatin structures could be observed for ~1 s, which underwent conformational changes during this period (Fig. 1E). The spatial resolution at which structural elements can be observed (see Materials and Methods) in time-resolved super-resolution data of chromatin was 63 2 nm (Fig. 1E), slightly more optimistic than the theoretical prediction (Fig. 1B) (18).

We compared images of H2B reconstructed from 12 frames (super-resolved images) by Deep-PALM in living cells to super-resolution images reconstructed by 8000 frames of H2B in fixed cells (fig. S2, A and B). Overall, the contrast in the fixed sample appears higher, and the nuclear periphery appears more prominent than in images from living cells. However, in accordance with the previous super-resolution images of chromatin in fixed cells (6, 8, 9, 11, 17) and Deep-PALM images, we observe segregated accumulations of signal throughout the nucleus. Thus, Deep-PALM identifies spatially heterogeneous coverage of chromatin, as previously reported (6, 8, 9, 11, 17). We further monitor chromatin temporally at the nanometer scale in living cells.

To quantitatively assess the spatial distribution of H2B, we developed an image segmentation scheme (see Materials and Methods; fig. S3), which allowed us to segment spatially separated accumulations of H2B signal with high fidelity (note S1 and figs. S4 and S5). Applying our segmentation scheme, ~10,000 separable elements, blob-like structures were observed for each super-resolved image (166 resolved images per movie; Fig. 2A). The experimental resolution does not enable us to elucidate their origin and formation because tracking of blobs in three dimensions would be necessary to do so (see Discussion). We therefore turned to a transferable computational model introduced by Qi and Zhang (19), which is based on one-dimensional genomics and epigenomics data, including histone modification profiles and binding sites of CTCF (CCCTC-binding factor). To compare our data to the simulations, super-resolution images were generated from the modeled chromosomes. Within these images, we could identify and characterize chromatin blobs analogously to those derived from experimental data (see Materials and Methods; Fig. 2B).

(A) Super-resolved images show blobs of chromatin (left). These blobs are segmented (see Materials and Methods and note S1) and individually labeled by random color (right). Magnifications of the boxed regions are shown. Scale bars, 2 m (whole nucleus); magnifications, 200 nm. (B) Generation of super-resolution images and blob identification and characterization for a 25million base pair (Mbp) segment of chromosome 1 from GM12878 cells, as simulated in Qi and Zhang (19). Beads (5-kb genomic length) of a simulated polymer configuration within a 200-nm-thick slab are projected to the imaging plane, resembling experimental super-resolved images of live chromatin. Blobs are identified as on experimental data. (C) From the centroid positions, the NND distributions are computed for up to 40 nearest neighbors (blue to red). The envelope of the k-NND distributions (black line) shows peaks at approximately 95, 235, 335, and 450 nm (red dots). (D) k-NND distributions as in (B) for simulated data. (E) Area distribution of experimental and simulated blobs. The distribution is, in both cases, well described by a lognormal distribution with parameters (3.3 2.8) 103 m2 for experimental blobs and (3.1 3.2) 103 m2 for simulated blobs (means SD). PDF, probability density function. (F) Eccentricity distribution for experimental and simulated chromatin blobs. Selected eccentricity values are illustrated by ellipses with the corresponding eccentricity. Eccentricity values range from 0, describing a circle, to 1, describing a line. Prominent peaks arise because of the discretization of chromatin blobs in pixels. The data are based on 332 consecutive super-resolved images from two cells, in each of with ~10,000 blobs were identified.

For imaged (in living and fixed cells) and modeled chromatin, we first computed the kth nearest-neighbor distance (NND; centroid-to-centroid) distributions, taking into account the nearest 1st to 40th neighbors (Fig. 2C and fig. S2, C and D, blue to red). Centroids of the nearest neighbors are (95 30) nm (means SD) apart, consistent with previous and our own super-resolution images of chromatin in fixed cells (9) and slightly further than what was found for clutches of nucleosomes (6). The envelope of all NND distributions (Fig. 2C, black line) shows several weak maxima at ~95, 235, 335, and 450 nm, which roughly coincide with the peaks of the 1st, 7th, 14th, and 25th nearest neighbors, respectively (Fig. 2C, red dots). In contrast, simulated data exhibit a prominent first nearest-neighbor peak at a slightly smaller distance, and higher-order NND distribution decay quickly and appear washed out (Fig. 2D). This hints toward greater levels of spatial organization of chromatin in vivo, which is not readily recapitulated in the used state-of-the-art chromosome model.

Next, we were interested in the typical size of chromatin blobs. Their area distribution (Fig. 2E) fit a log-normal distribution with parameters (3.3 2.8) 103 m2 (means SD), which is in line with the area distribution derived from fixed samples (fig. S2E) and modeled chromosomes. Notably, blob areas vary considerably, as indicated by the high SD and the prominent tail of the area distribution toward large values. Following this, we calculated the eccentricity of each blob to resolve their shape (Fig. 2F and fig. S2F). The eccentricity is a measure of the elongation of a region reflecting the ratio of the longest chord of the shape and the shortest chord perpendicular to it (Fig. 2F; illustrated shapes at selected eccentricity values). The distribution of eccentricity values shows an accumulation of values close to 1, with a peak value of ~0.9, which shows that most blobs have an elongated, fiber-like shape and are not circular. In particular, the eccentricity value of 0.9 corresponds to a ratio between the short and long axes of the ellipse of 1:2 (see Materials and Methods), which results, considering the typical area of blobs in experimental and simulated data, in roughly 92-nm-long and 46-nm-wide blobs on average. A highly similar value was found in fixed cells (fig. S2F). The length coincides with the value found for the typical NND [Fig. 2C; (95 30) nm]. However, because of the segregation of chromatin into blobs, their elongated shape, and their random orientation (Fig. 2A), the blobs cannot be closely packed throughout the nucleus. We find that chromatin has a spatially heterogeneous density, occupying 5 to 60% of the nuclear area (fig. S6, A and B), which is supported by a previous electron microscopy study (20).

Blob dimensions derived from live-cell super-resolution imaging using Deep-PALM are consistent with those found in fixed cells, thereby further validating our method, and in agreement with previously determined size ranges (6, 9). A previously published chromosome model based on Hi-C data (and thus not tuned to display blob-like structures per se) also displays blobs with dimensions comparable to those found here, in living cells. Together, these data strongly suggest the existence of spatially segregated chromatin structures in the sub100-nm range.

The simulations offer to track each monomer (chromatin locus) unambiguously, which is currently not possible to do from experimental data. Since the simulations show blobs comparable to those found in experiment (Fig. 2), simulations help to indicate possible mechanisms leading to the observation of chromatin blobs. For instance, because of the projection of the nuclear volume onto the imaging plane, the observed blobs could simply be overlays of distant, along the one-dimensional genome, noninteracting genomic loci. To examine this possibility, we analyzed the gap length between beads belonging to the same blob along the simulated chromosome. Beads constitute the monomers of the simulated chromosome, and each bead represents roughly 5 kb (19).

The analysis showed that the blobs are mostly made of consecutive beads along the genome, thus implying an underlying domain-like structure, similar to TADs (Fig. 3A). Using the affiliation of each bead to an intrinsic chromatin state of the model (Fig. 3B), it became apparent that blobs along the simulated chromosome consisting mostly of active chromatin are significantly larger than those formed by inactive and repressive chromatin (Fig. 3C). These findings are in line with experimental results (10) and results from the simulations directly (19), thereby validating the projection and segmentation process.

(A) Gap length between beads belonging to the same blob. An exemplary blob with small gap length is shown. The blob is mostly made of consecutive beads being in close spatial proximity. (B) A representative polymer configuration is colored according to chromatin states (red, active; green, inactive; and blue, repressive). (C) The cumulative distribution function (CDF) of clusters within active, inactive, and repressive chromatin. Inset: Mean area of clusters within the three types of chromatin. The distributions are all significantly different from each other, as determined by a two-sample Kolmogorov-Smirnov test (P < 1050). (D) Distribution of the continuous residence time of any monomer within a cluster (0.5 0.3 s; means SD). Inset: Continuous residence time of any monomer within a slab of 200-nm thickness (1.5 1.6 s; means SD). (E) The blob association strength between any two beads is measured as the frequency at which any two beads are found in one blob. The association map is averaged over all simulated configurations (upper triangular matrix; from simulations), and experimental Hi-C counts are shown for the same chromosome segment [lower triangular matrix; from Rao et al. (40)]. The association and Hi-C maps are strongly correlated [Pearsons correlation coefficient (PCC) = 0.76]. (F) Close-up views around the diagonal of Hi-Clike matrices. The association strength is shown together with the inverse distance between beads (top; PCC = 0.85) and with experimental Hi-C counts [bottom; as in (E)]. The data are based on 20,000 polymer configurations.

Since chromatin is dynamic in vivo and in computer simulations, each bead can diffuse in and out of the imaging volume from frame to frame. We estimated that, on average, each bead spent approximately 1.5 s continuously within a slab of 200-nm thickness (Fig. 3D). Furthermore, a bead is, on average, found only 0.55 0.33 s continuously within a blob, which corresponds to one to two experimental super-resolved images (Fig. 3D). These results suggest that chromatin blobs are highly dynamic entities, which usually form and dissemble within less than 1 s. We thus constructed a time-averaged association map for the modeled chromosomes, quantifying the frequency at which each locus is found with any other locus within one blob. The association map is comparable to interaction maps derived from Hi-C (Fig. 3E). Notably, interlocus association and Hi-C maps are strongly correlated, and the association map shows similar patterns as those identified as TADs in Hi-C maps, even for relatively distant genomic loci [>1 million base pairs (Mbp)]. A similar TAD-like organization is also apparent when the average inverse distance between loci is considered (Fig. 3F, top), suggesting that blobs could be identified in super-resolved images because of the proximity of loci within blobs in physical space. The computational chromosome model indicates that chromatin blobs identified by Deep-PALM are mostly made of continuous regions along the genome and cannot be attributed to artifacts originating from the projection of the three-dimensional genome structure to the imaging plane. The simulations further indicate that the blobs associate and dissociate within less than 1 s, but loci within blobs are likely to belong to the same TAD. Their average genomic content is 75 kb, only a fraction of typical TAD lengths in mammalian cells (average size, 880 kb) (4), suggesting that blobs likely correspond to sub-TADs or TAD nanocompartments (17).

To quantify the experimentally observed chromatin dynamics at the nanoscale, down to the size of one pixel (13.5 nm), we used a dense reconstruction of flow fields, optical flow (Fig. 4A; see Materials and Methods), which was previously used to analyze images taken on confocal (12, 14), and structured illumination microscopes (8). We examined the suitability of optical flow for super-resolution on the basis of single-molecule localization images using simulations. We find that the accuracy of optical flow is slightly enhanced on super-resolved images compared to conventional fluorescence microscopy images (note S2 and fig. S7, A to C). Experimental super-resolution flow fields are illustrated on the basis of two subsequent images, between which the dynamics of structural features are apparent to the eye (fig. S7, D and E). On the nuclear periphery, connected regions spanning up to ~500 nm can be observed [fig. S7D (i and ii), marked by arrows]. These structures are stable for at least 360 ms but move from frame to frame. The flow field is shown on top of an overlay of the two super-resolved images and color-coded [fig. S7D (iii); the intensity in frame 1 is shown in green, the intensity in frame 2 is shown in purple, and colocalization of both is white]. Displacement vectors closely follow the redistribution of intensity from frame to frame (roughly from green to purple). Similarly, structures within the nuclear interior (fig. S7E) can be followed by eye, thus further validating and justifying the use of a dense motion reconstruction as a quantification tool of super-resolved chromatin motion.

(A) A time series of super-resolution images (left) is subject to optical flow (right). (B) Blobs of a representative nucleus (see movie S1) are labeled by their NND (left), area (middle), and flow magnitude (right). Colors denote the corresponding parameter magnitude. (C) The average blob area, (D) NND, (E) density, and (F) flow magnitude are shown versus the normalized distance from the nuclear periphery (lower x axis; 0 is on the periphery and 1 is at the center of the nucleus) and versus the absolute distance (upper x axis). Line and shaded area denote the means SE from 322 super-resolved images of two cells. Scale bar, (A) and (B): 3 m.

Using optical flow fields, we linked the spatial appearance of chromatin to their dynamics. Effectively, the blobs were characterized with two structural parameters (NND and area) and their flow magnitude (Fig. 4B). Movie S1 shows the time evolution of those parameters for an exemplary nucleus. Blobs at the nuclear periphery showed a distinct behavior from those in the nuclear interior. In particular, the periphery exhibits a lower density of blobs, but those appear slightly larger and are less mobile than in the nuclear interior (Fig. 4, C to F), in line with previous findings using conventional microscopy (14). The peripheral blobs are reminiscent of dense and relatively immobile heterochromatin and lamina-associated domains (21), which extend only up to 0.5 m inside the nuclear interior. In contrast, blob dynamics increase gradually within 1 to 2 m from the nuclear rim.

To further elucidate the relationship between chromatin structure and dynamics, we analyzed the correlation between each pair of parameters in space and time. Therefore, we computed the auto- and cross-correlation of parameter maps with a given time lag across the entire nucleus (in space) (Fig. 5A). In general, a positive correlation denotes a low-low or a high-high relationship (a variable de-/increases when another variable de-/increases), while, analogously, a negative correlation denotes a high-low relationship. The autocorrelation of NND maps [Fig. 5A (i)] shows a positive correlation; thus, regions exist spanning 2 to 4 m, in which chromatin is either closely packed (low-low) or widely dispersed (high-high). Likewise, blobs of similar size tend to be in spatial proximity [Fig. 5A (iii)]. These regions are not stable over time but rearrange continuously, an observation bolstered by the fact that the autocorrelation diminishes with increasing time lag. The cross-correlation between NND and area [Fig. 5A (ii)] shows a negative correlation for short time lags, suggesting that large blobs appear with a high local density while small ones are more isolated. The correlation becomes slightly positive for time lags 20 s, indicating that big blobs are present in regions that were sparsely populated before and small blobs tend to accumulate in previously densely populated regions. This is in line with dynamic reorganization and reshaping of chromatin domains on a global scale, as observed in snapshots of the Deep-PALM image series (Fig. 1A).

(A) The spatial auto- and cross-correlation between parameters were computed for different time lags. The graphs depict the correlation over space lag for each parameter pair, and different colors denote the time lag (increasing from blue to red). (B) Illustration of the instantaneous relationship between local chromatin density and dynamics. The blob density is shown in blue; the magnitude of chromatin dynamics is shown by red arrows. The consistent negative correlation between NND and flow magnitude is expressed by increased dynamics in regions of high local blob density. Data represent the average over two cells. The cells behave similarly such that error bars are omitted for the sake of clarity.

The flow magnitude is positively correlated for all time lags, while the correlation displays a slight increase for time lags 20 s [Fig. 5A (vi)], which has also been observed previously (8, 12, 22). The spatial autocorrelation of dynamic and structural properties of chromatin are in stark contrast. While structural parameters are highly correlated at short but not at long time scales, chromatin motion is still correlated at a time scale exceeding 30 s. At very short time scales (<100 ms), stochastic fluctuations determine the local motion of the chromatin fiber, while coherent motion becomes apparent at longer times (22). However, there exists a strong cross-correlation between structural and dynamic parameters: The cross-correlation between the NND and flow magnitude shows notable negative correlation at all time lags [Fig. 5A (iv)], strongly suggesting that sparsely distributed blobs appear less mobile than densely packed ones. The area seems to play a negligible role for short time lags, but there is a modest tendency that regions with large blobs tend to exhibit increased dynamics at later time points [10 s; Fig. 5A (v)], likely due to the strong relationship between area and NND.

In general, parameter pairs involving chromatin dynamics exhibit an extended spatial auto- or cross-correlation (up to ~6 m; the lower row of Fig. 5A) compared to correlation curves including solely structural parameters (up to 3 to 4 m). Furthermore, the cross-correlation of flow magnitude and NND does not considerably change for increasing time lag, suggesting that the coupling between those parameters is characterized by an unexpectedly resilient memory, lasting for at least tens of seconds (23). Concomitantly, the spatial correlation of time-averaged NND maps and maps of the local diffusion constant of chromatin for the entire acquisition time enforces their negative correlation at the time scale of ~1 min (fig. S8). Such resilient memory was also proposed by a computational study that observed that interphase nuclei behave similar to concentrated solutions of unentangled ring polymers (24). Our data support the view that chromatin is mostly unentangled since entanglement would influence the anomalous exponent of genomic loci in regions of varying chromatin density (24). However, our data do not reveal a correlation between the anomalous exponent and the time-averaged chromatin density (fig. S8), in line with our previous results using conventional microscopy (14).

Overall, the spatial cross-correlation between chromatin structure and dynamics indicates that the NND between blobs and their mobility stand in a strong mutual, negative relationship. This relationship, however, concerns chromatin density variations at the nanoscale, but not global spatial density variations such as in euchromatin or heterochromatin (14). These results support a model in which regions with high local chromatin density, i.e., larger blobs are more prevalent and are mobile, while small blobs are sparsely distributed and less mobile (Fig. 5B). Blob density and dynamics in the long-time limit are, to an unexpectedly large extent, influenced by preceding chromatin conformations.

The spatial correlations above were only evaluated pairwise, while the behavior of every blob is likely determined by a multitude of factors in the complex energy landscape of chromatin (19, 22). Here, we aim to take a wider range of available information into account to reveal the principle parameters, driving the observed chromatin structure and dynamics. Using a microscopy-based approach, we have access to a total of six relevant structural, dynamic, and global parameters, which potentially shape the chromatin landscape in space and time (Fig. 6A). In addition to the parameters used above, we included the confinement level as a relative measure, allowing the quantification of transient confinement (see Materials and Methods). We further included the bare signal intensity of super-resolved images and, as the only static parameter, the distance from the periphery since it was shown that dynamic and structural parameters show some dependence on this parameter (Fig. 4). We then used t-distributed stochastic neighbor embedding (t-SNE) (25), a state-of-the-art dimensionality reduction technique, to map the six-dimensional chromatin features (the six input parameters) into two dimensions (Fig. 6A and see note S3). The t-SNE algorithm projects data points such that neighbors in high-dimensional space likely stay neighbors in two-dimensional space (25). Visually apparent grouping of points (Fig. 6B) implies that grouped points exhibit great similarity with respect to all input features, and it is of interest to reveal which subset of the input features can explain the similarity among chromatin blobs best. It is likely that points appear grouped because their value of a certain input feature is considerably higher or lower than the corresponding value of other data points. We hence labeled points in t-SNE maps which are smaller than the first quartile point or larger than the third quartile point. Data points falling in either of the low/high partition of one input feature are colored accordingly for visualization (Fig. 6D; blue/red points, respectively). We then assigned a rank to each of the input features according to their nearest-neighbor fraction (n-n fraction): Since the t-SNE algorithm conserves nearest neighbors, we described the extent of grouping in t-SNE maps by the fraction of nearest neighbors, which fall in either one of the subpopulations of low or high points (illustrated in fig. S9). A high n-n fraction (Fig. 6C) therefore indicates that many points marked as low/high are indeed grouped by t-SNE and are therefore similar. The ranking (from low to high n-n fraction) reflects the potency of a given parameter to induce similar behavior between chromatin blobs with respect to all input features.

(A) The six-dimensional parameter space is input to the t-SNE algorithm and projected to two dimensions. (B) The two-dimensional embedding of an exemplary dataset is shown and colored according to the magnitude of each input feature (blue to red; the parameter average is shown in beige). (C) Points below the first (blue) and above the third (red) quartile points of the corresponding parameter are marked, and the parameters are ranked according to the fraction of nearest neighbors that fall in one of the marked regions. (D) Data points marked below the first or above the third quartile points are labeled according to the feature in which they were marked. Priority is given to the feature with the higher n-n fraction if necessary. (E) t-SNE analysis is carried out for each nucleus over the whole time series, and it is counted how often a parameter ranked first. The results are visualized as a pie chart. The NND predominantly ranks first in about two-thirds of all cases. (F) Marked points in (C) and (D) are mapped back onto the corresponding nuclei, and the CDF over space is shown (means SE). Pie chart and CDF computations are based on 322 super-resolved images from two cells.

The relative frequency at which each parameter ranked first provides an intuitive feeling for the most influential parameters in the dataset (Fig. 6E). The signal intensity plays a negligible role, suggesting that our data are free of potential artifacts related to the bare signal intensity. Furthermore, the blob area and the distance from the periphery likewise do not considerably shape chromatin blobs. In contrast, the NND between blobs was found to be the main factor inducing the observed characteristics in 67% of all-time frames across all nuclei. The flow magnitude and confinement level together rank first in 26% of all cases (11 and 17%, respectively). These numbers suggest that the local chromatin density is a universal key regulator of instantaneous chromatin dynamics. Note that no temporal dependency is included in the t-SNE analysis and, thus, the feature extraction concerns only short-term (360 ms) relationships. The characteristics of roughly one-fourth of all blobs at each time point are mainly determined by similar dynamical features. Mapping chromatin blobs as marked in Fig. 6 (C and D) back to their respective positions inside the nucleus (Fig. 6F) shows that blobs with low/high flow magnitude or confinement level markedly also grouped in physical space, which is highly reminiscent of coherent motion of chromatin (12). In contrast, blobs with extraordinary low or high NND were found interspersed throughout the nucleus, in line with spatial correlation analysis between structural and dynamic features (Fig. 5). Our results point toward a large influence of the local chromatin density on the dynamics of chromatin at the scale of a few hundred nanometers and within a few hundred milliseconds. At longer time and length scales, however, previous results suggest that this relationship is lost (14).

With Deep-PALM, we present temporally resolved super-resolution images of chromatin in living cells. Our technique identified chromatin nanodomains, named blobs, which mostly have an elongated shape, consistent with the curvilinear arrangement of chromatin, as revealed by structured illumination microscopy (8) with typical axes lengths of 45 to 90 nm. A previous study reported ~30-nm-wide clutches of nucleosomes in fixed mammalian cells using STORM nanoscopy (6), while the larger value obtained using Deep-PALM may be attributed to the motion blurring effect in live-cell imaging. However, histone acetylation and methylation marks were shown to form nanodomains of diameter 60 to 140 nm, respectively (9), which includes the computed dimensions for histone H2B using Deep-PALM.

To elucidate the origin of chromatin blobs, we turned to a simulated chromosome model, which displays chromatin blobs similar to our experimental data when seen in a super-resolution reconstruction. The simulations suggest that chromatin blobs consist of continuous genomic regions with an average length of 75 kb, assembling and disassembling dynamically within less than 1 s. Monomers within blobs display a distinct TAD-like association pattern in the long-time limit, suggesting that the identified blobs represent sub-TADs. Transient formation is consistent with recent findings that TADs are not stable structural elements but exhibit extensive heterogeneity and dynamics (3, 5). To experimentally probe the transient assembly of chromatin blobs, it would be interesting to track individual blobs over time. However, this is a nontrivial task. While the size (area/volume) or shape of blobs could be used to establish correspondences between blobs in subsequent frames, the framework needs to be flexible enough to allow for blob deformations since blobs likely arise stochastically and are not rigid bodies. Achieving an even shorter acquisition time per frame in the future could help minimize the influence of blob deformations and make tracking feasible. The second challenge is to distinguish between disassembly and out-of-focus diffusion of a blob. The three-dimensional imaging at sufficient spatial and temporal resolution will be helpful in the future to overcome this hurdle.

Using an optical flow approach to determine the blob dynamics instead, we found that structural and dynamic parameters exhibit extended spatial and temporal (cross-) correlations. Structural parameters such as the local chromatin density (expressed as the NND between blobs) and area lose their correlation after 3 to 4 m and roughly 40 s in the spatial and temporal dimension, respectively. In contrast, chromatin mobility correlations extend over ~6 m and persist during the whole acquisition period (40 s). Extensive spatiotemporal correlation of chromatin dynamics has been presented previously, both experimentally (12) and in simulations (22), but was not linked to the spatiotemporal behavior of the underlying chromatin structure until now. We found that the chromatin dynamics are closely linked to the instantaneous but also to past local structural characterization of chromatin. In other words, the instantaneous local chromatin density influences chromatin dynamics in the future and vice versa. On the basis of these findings, we suggest that chromatin dynamics exhibit an extraordinary long memory. This strong temporal relationship might be established by the fact that stress propagation is affected by the folded chromosome organization (26). Fiber displacements cause structural reconfiguration, ultimately leading to a local amplification of chromatin motion in local high-density environments. This observation is also supported by the fact that increased nucleosome mobility grants chromatin accessibility even within regions of high nucleosome density (27).

Given the persistence at which correlations of chromatin structure and, foremost, dynamics occur in a spatiotemporal manner, we speculate that the interplay of chromatin structure and dynamics could involve a functional relationship (28): Transcriptional activity is closely linked to chromatin accessibility and the epigenomic state (29). Because chromatin structure and dynamics are related, dynamics could also correlate with transcriptional activity (14, 30, 31). However, it is currently unknown whether the structure-dynamics relationship revealed here is strictly mutual or whether it may be causal. Simulations hint that chromatin dynamics follows from structure (22, 23); this question will be exciting to answer experimentally and in the light of active chromatin remodelers to elucidate a potential functional relationship to transcription. Chromatin regions that are switched from inactive to actively transcribing, for instance, undergo a structural reorganization accompanied by epigenetic modifications (32). The mechanisms driving recruitment of enzymes inducing histone modifications such as histone acetyltransferases, deacetylases, or methyltransferases are largely unknown but often involve the association to proteins (33). Their accessibility to the chromatin fiber is inter alia determined by local dynamics (27). Such a structure-dynamics feedback loop would constitute a quick and flexible way to transiently alter gene expression patterns upon reaction to external stimuli or to coregulate distant genes (1). Future work will study how structure-dynamics correlations differ in regions of different transcriptional activity and/or epigenomic states. Furthermore, probing the interactions between key transcriptional machines such as RNA polymerases with the local chromatin structure and recording their (possibly collective) dynamics could shed light into the target search and binding mechanisms of RNA polymerases with respect to the local chromatin structure. Deep-PALM in combination with optical flow paves the way to answer these questions by enabling the analysis of time-resolved super-resolution images of chromatin in living cells.

Human osteosarcoma U2OS expressing H2B-PATagRFP cells were a gift from S. Huet (CNRS, UMR 6290, Institut Gntique et Dveloppement de Rennes, Rennes, France); the histone H2B was cloned, as described previously (34). U2OS cells were cultured in Dulbeccos modified Eagles medium [with glucose (4.5 g/liter)] supplemented with 10% fetal bovine serum (FBS), 2 mM glutamine, penicillin (100 g/ml), and streptomycin (100 U/ml) in 5% CO2 at 37C. Cells were plated 24 hours before imaging on 35-mm petri dishes with a no. 1.5 coverslip-like bottom (ibidi, Biovalley) with a density of 2 105 cells per dish. Just before imaging, the growth medium was replaced by Leibovitzs L-15 medium (Life Technologies) supplemented with 20% FBS, 2 mM glutamine, penicillin (100 g/ml), and streptomycin (100 U/ml).

Imaging of H2B-PAtagRFP in living U2OS cells was carried out on a fully automated Nikon Ti-E/B PALM (Nikon Instruments) microscope. The microscope is equipped with a full incubator enclosure with gas regulation to maintain a temperature of ~37C for normal cell growth during live-cell imaging. Image sequences of 2000 frames were recorded with an exposure time of 30 ms per frame (33.3 frames/s). For Deep-PALM imaging, a relatively low power (~50 W/cm2 at the sample) was applied for H2B-PATagRFP excitation at 561 nm and then combined with the 405 nm (~2 W/cm2 at the sample) to photoactivate the molecules between the states. Note that for Deep-PALM imaging, switched fluorophores are not required to stay as long in the dark state as for conventional PALM imaging. We used oblique illumination microscopy (11) combined with total internal reflection fluorescence (TIRF) mode to illuminate a thin layer of 200 nm (axial resolution) across the nucleus. The reconstruction of super-resolved images improves the axial resolution only marginally (fig. S1, E and F). Laser beam powers were controlled by acoustic optic-modulators (AA Opto-Electronic). Both wavelengths were united into an oil immersion 1.49-NA (numerical aperture) TIRF objective (100; Nikon). An oblique illumination was applied to acquire image series with a high signal-to-noise ratio. The fluorescence emission signal was collected by using the same objective and spectrally filtered by a Quad-Band beam splitter (ZT405/488/561/647rpc-UF2, Chroma Technology) with a Quad-Band emission filter (ZET405/488/561/647m-TRF, Chroma Technology). The signal was recorded on an electron-multiplying charge-coupled device camera (Andor iXon X3 DU-897, Andor Technology) with a pixel size of 108 nm. For axial correction, Perfect Focus System was applied to correct for defocusing. NIS-Elements software was used for acquiring the images.

The same cell line (U2OS expressing H2B-PAtagRFP), as in live-cell imaging, was used for conventional PALM imaging. Before fixation, cells were washed with phosphate-buffered saline (PBS) (three times for 5 min each) and then fixed with 4% paraformaldehyde (Sigma-Aldrich) diluted in PBS for 15 min at room temperature. A movie of 8000 frames was acquired with an exposure time of 30 ms per frame (33.3 frames/s). In comparison to Deep-PALM imaging, a relatively higher excitation laser of 561 nm (~60 W/cm2 at the sample) was applied to photobleach H2B-PATagRFP and then combined with the 405 nm (~2.5 W/cm2 at the sample) for photoactivating the molecules. We used the same oblique illumination microscopy combined with TIRF system, as applied in live-cell imaging.

PALM images from fixed cells were analyzed using ThunderSTORM (35). Super-resolution images were constructed by binning emitter localizations into 13.5 13.5 nm pixels and blurred by a Gaussian to match Deep-PALM images. The image segmentation was carried out as on images from living cells (see below).

The CNN was trained using simulated data following Nehme et al. (15) for three labeling densities (4, 6, and 9 emitters/m2 per frame). Raw imaging data were checked for drift, as previously described (12). The detected drift in raw images is in the range of <10 nm and therefore negligible. The accuracy of the trained net was evaluated by constructing ground truth images from the simulated emitter positions. The structural similarity index is computed to assess the similarity between reconstructed and ground truth images (36)SSIM=x,y(2xx+C1)(2xy+C2)(x2+y2+C1)(x2+y2+C2)(1)where x and y are windows of the predicted and ground truth images, respectively, and denote their local means and SD, respectively, and xy denotes their cross-variance. C1 = (0.01L)2 and C2 = (0.03L)2 are regularization constants, where L is the dynamic range of the input images. The second quantity to assess CNN accuracy is the root mean square error between the ground truth G and reconstructed image RRMSE=1NN(RG)2(2)where N is the number of pixels in the images. After training, sequences of all experimental images were compared to the trained network, and predictions of single Deep-PALM images were summed to obtain a final super-resolved image. An up-sampling factor of 8 was used, resulting in an effective pixel size of 108 nm/8 = 13.5 nm. A blind/referenceless image spatial quality evaluator (37) was used to determine the optimal number of predictions to be summed. For visualization, super-resolved images were convolved with a Gaussian kernel ( = 1 pixel) and represented using a false red, green, and blue colormap. The parameters of the three trained networks are available at

Fourier ring correlation (FRC) is an unbiased method to estimate the spatial resolution in microscopy images. We follow an approach similar to the one described by Nieuwenhuizen et al. (38). For localization-based super-resolution techniques, the set of localizations is divided into two statistically independent subsets, and two images from these subsets are generated. The FRC is computed as the statistical correlation of the Fourier transforms of both subimages over the perimeter of circles of constant frequency in the frequency domain. Deep-PALM, however, does not result in a list of localizations, but in predicted images directly. The set of 12 predictions from Deep-PALM were thus split into two statistically independent subsets, and the method described by Nieuwenhuizen et al. (38) was applied.

The super-resolved images displayed isolated regions of accumulated emitter density. To quantitatively assess the structural information implied by this accumulation of emitters in the focal plane, we developed a segmentation scheme that aims to identify individual blobs (fig. S3). A marker-assisted watershed segmentation was adapted to accurately determine blob boundaries. For this purpose, we use the raw predictions from the deep CNN without convolution (fig. S3A). The foreground in this image is marked by regional maxima and pixels with very high density (i.e., those with I > 0.99 Imax; fig. S3B). Since blobs are characterized by surrounding pixels of considerably less density, the Euclidian distance transform is computed on the binary foreground markers. Background pixels (i.e., those pixels not belonging to any blobs) are expected to lie far away from any blob center, and thus, a good estimate for background markers are those pixels being furthest from any foreground pixel. We hence compute the watershed transform on the distance transform of foreground markers, and the resulting watershed lines depict background pixels (fig. S3C). Equipped with fore- and background markers (fig. S3D), we apply a marker-controlled watershed transform on the gradient of the input image (fig. S3E). The marker-controlled watershed imposes minima on marker pixels, preventing the formation of watershed lines across marker pixels. Therefore, the marker-controlled watershed accurately detects boundaries and blobs that might not have been previously marked as foreground (fig. S3F). Last, spurious blobs whose median- or mean intensity is below 10% of the maximum intensity are discarded, and each blob is assigned a unique label for further correspondence (fig. S3G). The area and centroid position are computed for each identified blob for further analysis. This automated segmentation scheme performs considerably better than other state-of-the-art algorithms for image segmentation because of the reliable identification of fore- and background markers accompanied by the watershed transform (note S1).

Centroid position, area, and eccentricity were computed. The eccentricity is computed by describing the blobs as an ellipseE=1a2/b2(3)where a and b are the short and long axes of the ellipse, respectively.

We chose to use a computational chromatin model, recently introduced by Qi and Zhang (19), to elucidate the origin of experimentally determined chromatin blobs. Each bead of the model covers a sequence length of 5 kb and is assigned 1 of 15 chromatin states to distinguish promoters, enhancers, quiescent chromatin, etc. Starting from the simulated polymer configurations, we consider monomers within a 200-nm-thick slab through the center of the simulated chromosome. To generate super-resolved images as those from Deep-PALM analysis, fluorescence intensity is ascribed to each monomer. Monomer positions are subsequently discretized on a grid with 13.5-nm spacing and convolved with a narrow point-spread function, which results in images closely resembling experimental Deep-PALM images of chromatin. Chromatin blobs were then be identified and characterized as on experimental data (Fig. 2, A and B). Mapping back the association of each bead to a blob (if any) allows us to analyze principles of blob formation and maintenance using the distance and the association strength between each pair of monomers, averaged over all 20,000 simulated polymer configurations.

The radial distribution function g(r) (also pair correlation function) is calculated (in two dimensions) by counting the number of blobs in an annulus of radius r and thickness dr. The result is normalized by the bulk density = n/A, with the total number of blobs n and, A, the area of the nucleus, and the area of the annulus, 2r drdn(r)=g(r)2rdr(4)

Super-resolved images of chromatin showed spatially distributed blobs of varying size, but the resolved structure is too dense for state-of-the-art single-particle tracking methods to track. Furthermore are highly dynamic structures, assembling and dissembling within one to two super-resolved frames (Fig. 3D), which makes a single-particle tracking approach unsuitable. Instead, we used a method for dynamics reconstruction of bulk macromolecules with dense labeling, optical flow. Optical flow builds on the computation of flow fields between two successive frames of an image series. The integration of these flow fields from super-resolution images results in trajectories displaying the local motion of bulk chromatin with temporal and high spatial resolution. Further, the trajectories are classified into various diffusion models, and parameters describing the underlying motion are computed (14). Here, we use the effective diffusion coefficient D (in units of m2/s), which reflects the magnitude of displacements between successive frames (the velocity of particles or monomers in the continuous limit) and the anomalous exponent (14). The anomalous exponent reflects whether the diffusion is free ( = 1, e.g., for noninteracting particles in solution), directed ( > 1, e.g., as the result from active processes), or hindered ( < 1, e.g., because of obstacles or an effective back-driving force). Furthermore, we compute the length of constraint Lc, which is defined as the SD of the trajectory positions with respect to its time-averaged position. Denoting R(t; R0), the trajectory at time t originating from R0, the expression reads Lc(R0) = var(R(t; R0))1/2, where var denotes the variance. The length of constraint is a measure of the length scale explored of the monomer during the observation period. A complementary measure is the confinement level (39), which computes the inverse of the variance of displacements within a sliding window of length : C / var(R(t; R0)), where the sliding window length is set to four frames (1.44 s). Larger values of C denote a more confined state than small ones.

The NND and the area, as well as the flow magnitude, were calculated and assigned to the blobs centroid position. To calculate the spatial correlation between parameters, the parameters were interpolated from the scattered centroid positions onto a regular grid spanning the entire nucleus. Because not every pixel in the original super-resolved images is assigned a parameter value, we chose an effective grid spacing of five pixels (67.5 nm) for the interpolated parameter maps. After interpolation, the spatial correlation was computed between parameter pairs: Let r = (x, y)T denote a position on a regular two-dimensional grid and f(r, t) and g(r, t) two scalar fields with mean zero and variance one, at time t on that grid. The time series of parameter fields consist of N time points. The spatial cross-correlation between the fields f and g, which lie a lag time apart, is then calculated asC(,)=1Ntx,yf(r,t)g(r+,t+)x,yf(r,t)g(r,t+)(5)where the space lag is a two-dimensional vector = (x, y)T. The sums in the numerator and denominator are taken over the spatial dimensions; the first sum is taken over time. The average is thus taken over all time points that are compliant with time lag . Subsequently, the radial average in space is taken over the correlation, thus effectively calculating the spatial correlation C(, ) over the space lag =x2+y2. If f = g, then the spatial autocorrelation is computed.

We denote as global parameters those that reflect the structural and dynamic behavior of chromatin spatially resolved in a time-averaged manner. Examples involve the diffusion constant, the anomalous exponent, the length of constraint, but also time-averaged NND maps, etc. (fig. S8). Those parameters are useful to determine time-universal characteristics. The spatial correlation between those parameters is equivalent to the expression given for temporally varying parameters when the temporal dimension is omitted, effectively resulting in a correlation curve C().

The distance from the periphery, intensity, their NND, area, flow magnitude, and confinement level of each identified blob form the six-dimensionalinput feature space for t-SNE analysis. The parameters for each blob (n = 3,260,232; divided into subsets of approximately 10,000) were z-transformed before the t-SNE analysis. The t-SNE analysis was performed using MATLAB and the Statistics and Machine Learning Toolbox (Release 2017b; The MathWorks Inc., Natick, MA, USA) with the Barnes-Hut approximation. The algorithm was tested using different distance metrics and perplexity values and showed robust results within the examined ranges (note S3 and fig. S10).

Acknowledgments: We acknowledge support from the Ple Scientifique de Modlisation Numrique, ENS de Lyon for providing computational resources. We thank B. Zhang (Massachusetts Institute of Technology) for providing data of simulated chromosomes and S. Kocanova (LBME, CBI-CNRS; University of Toulouse) for providing PALM videos for fixed cells. We thank H. Babcock (Harvard University), A. Seeber (Harvard University), and M. Tamm (Moscow State University) for valuable feedback on the manuscript. Funding: This publication is based upon work from COST Action CA18127, supported by COST (European Cooperation in Science and Technology). This work is supported by Agence Nationale de la Recherche (ANR) ANDY and Sinfonie grants. Author contributions: H.A.S. designed and supervised the project. R.B. designed the data analysis and wrote the code. H.A.S. carried out experimental work. R.B. carried out the data analysis. H.A.S. and R.B. interpreted results. H.A.S., R.B., and K.B. wrote the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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LifeGaines Medical and Aesthetics Purchases 3 of the Latest and Best Lumenis Products to Serve Clients; a Laser Hair Removal System, a Tattoo Removal…

LifeGaines has been designated a 'Center of Excellence' by Lumenis for offering its new products such as Splendor X, PiQo4, and AcuPulse

Boca Raton, Florida--(Newsfile Corp. - June 30, 2020) - LifeGaines in Boca Raton just purchased 3 Lumenis products, a laser hair removal system and pigment lesion eraser called Splendor X, a tattoo removal device called the Piq04, and the AcuPulse, a CO2 laser system that removes "spider veins".

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LifeGaines has been just awarded the designation of "Center of Excellence" as they offer a collection of Lumenis services. These different services are described below.

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Medical Wellness Market to Record Exponential Growth Owing to High Demand Through COVID 19 Pandemic – 3rd Watch News

Wellness describes itself as complete physical mental and social well-being. It comprises all the components used to lead a healthy life. Wellness is multidirectional and constitutes social, emotional, physical, spiritual, intellectual and emotional wellbeing. According to National Wellness Institute, two more component of wellness includes cultural and environmental wellness. Mental health and well-being are an integral and essential component of health. Wellness goes further than disease or disability and highlights the maintenance and improvement of health and well-being of the person. Wellness includes activities that improve health, enhance the quality of life and increase the levels of well-being of the person. Different types of wellness include workplace wellness, wellness tourism, lifestyle wellness and others. In order to help prevent disease, reduce stress, and enhance the overall quality of life Global Wellness Institute (GWI) organizes e Global Spa & Wellness Summit (GSWS) annually, that brings together leaders and visionaries to discuss various aspects of health and wellbeing.

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Nowadays, people are focusing on preventive, proactive aspects of wellness, wellness economy incorporates industrial sector that enables consumers to incorporate wellness into their lives. Consumers are inclining towards preventive measures to prevent diseases and maintain good health. The key sector of wellness includes wellness tourism, fitness, complementary & alternative medicine, lifestyle wellness, rejuvenation and spa industry, workplace wellness and others.

Wellness is self-responsibility and is opening new opportunities for wellness market as due to increase in geriatric population, rise in disease population due to sedentary lifestyle, new research on wellness procedures using alternative medicines, expansion of consumer base and wellness industries, tourism is growing, that will incorporate wellness into travel, shift of consumers towards personal care products are some of the factors that will drive the medical wellness market. The awareness about medical wellness will help consumers, spread wellness to homes and their workplace and help the right way to exercise, include healthy eating in their diet, focus on preventive and personalized health and others. Lack of awareness about medical wellness, rise in products and services of wellness industry, lack of workforce and others are some of the factors restraining the market growth.

The global medical wellness market is segmented on basis of wellness sector, distribution channel and geography:

Segment by Wellness Sector

Segment by Distribution Channel

The global medical wellness market is segmented into wellness sector and distribution channel. Based on the wellness sector, the medical wellness market is segmented into complementary and alternative medicine, beauty care and anti-aging (surgical and non-surgical), preventative and personalized medicine, healthy eating, nutrition and weight loss, rejuvenation and others. The beauty care and anti- aging segment will dominate the wellness market due to rise in number of aesthetics procedures and increase in number of beauty care wellness sectors. Based on the end user, the medical wellness market is segmented as franchise and company owned outlets. The global medical wellness market is going to increase significantly is near future due to shift of consumers towards proactive approaches and include wellness in day to day life.

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By regional presence, the global medical wellness market is segmented into five broad regions viz. North America, Latin America, Europe, Asia-Pacific, and the Middle East & Africa. North America market is expected to dominate in terms of revenue share, owing to the high availability of advanced products and services, wellness tourism, expenditures growth, , increasing penetration of leading companies in the region along with increase in patient population. Significant economic development has led to an increase in healthcare availability in Asia Pacific region, growing number of multi-specialty care centers, rejuvenation and fitness centers and penetration of global players in Asia is expected to fuel the medical wellness market

Some of the major players in medical wellness market are Enrich Hair & Skin Solutions, VLCC Wellness Center, Guardian Lifecare, Healthkart, WTS International, The Body Holiday, Bon Vital, Biologique Recherch, MINDBODY Inc., Massage Envy, ClearCost Health, Golds Gym International, Inc., World Gym, Spafinder Wellness 365, Kaya Skin Clinic, Body master and others

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Nutraceutical Products Market 2019 | How The Industry Will Witness Substantial Growth In The Upcoming Years | Exclusive Report By MRE – Cole of Duty

The Global Nutraceutical Products Market is expected to exceed more than US$ 404.48 Billion by 2024 at a CAGR of 7.32% in the given forecast period.

Market Research Engine has published a new report titled as Nutraceutical Products Market Size By Type (Food, Beverages, Dietary Supplements, Dairy Products, Infant Nutrition Products), By Distribution Channel (Conventional Stores, Specialty Stores, Drugstores & Pharmacies), By Source (Proteins & Amino Acids, Probiotics, Phytochemicals & Plant Extracts, Fibers & Specialty Carbohydrates, Omega-3 Fatty Acids, Vitamins, Prebiotics, Carotenoids, Minerals), By Region (North America, Europe, Asia-Pacific, Rest of the World), Market Analysis Report, Forecast 2018-2024.

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The term nutraceutical has been derived from 2 terms, nutrition and pharmaceutical. It is a lot of an advert term that is employed for any food merchandise, or a part of food supplements that has a medical or health profit instead of simply basic nutrition. Such practical food merchandise contains varied minerals, vitamins, dietary fibers, hydrolysed proteins, prebiotics and probiotics that are terribly useful within the provision of nutrition to a persons body. Nutraceutical merchandise are offered within the sort of sports drinks, farm merchandise, snacks and pre-packaged diet meals. Nutraceuticals are terribly capable and versatile in nature as theyre utilized in various industries like pharmaceutical food & beverages, animal feed additives, and private care.

The global nutraceutical ingredients market is projected to register a major growth rate over a forecast amount because the demand for nutraceutical ingredients is hyperbolic thanks to the rising awareness and consciousness regarding the healthy diet as well as nutraceutical ingredients. Increasing prevalence of chronic diseases and rising aging population worldwide is that the major mode issue boosts the demand for the nutraceutical ingredient and drives the worldwide nutraceutical ingredients market. The increasing use of nutraceutical ingredients within the pharmaceutical medicine thanks to its effective properties like anti-aging conjointly drives the expansion of the worldwide nutraceutical ingredient market.

The proteins & amino acids section is projected to carry the most important market share throughout the forecast amount. Proteins area unit chiefly utilized in industrial applications because of their nutrition ARY and useful properties. Their potential to extend nutrition ARY levels makes them one among the key ingredients within the nutraceutical merchandise market. Additionally, intensive R&D on proteins and therefore the edges of their properties have semiconductor diode to the identification of innovative uses within the attention and pharmaceutical industries. what is more, the role of amino acids within the growth of organisms to make sturdy muscular tissues, development of organs, and best functioning of the system has semiconductor diode to their increasing usage in snacks, ready-to-eat, and convenience foods.

The global Nutraceutical Products market is segregated on the basis of Type as Food, Beverages, Dietary Supplements, Dairy Products, and Infant Nutrition Products. Based on Distribution Channel the global Nutraceutical Products market is segmented in Conventional Stores, Specialty Stores, and Drugstores & Pharmacies. Based on Source the global Nutraceutical Products market is segmented in Proteins & Amino Acids, Probiotics, Phytochemicals & Plant Extracts, Fibers & Specialty Carbohydrates, Omega-3 Fatty Acids, Vitamins, Prebiotics, Carotenoids, Minerals, and Others.

The global Nutraceutical Products market report provides geographic analysis covering regions, such as North America, Europe, Asia-Pacific, and Rest of the World. The Nutraceutical Products market for each region is further segmented for major countries including the U.S., Canada, Germany, the U.K., France, Italy, China, India, Japan, Brazil, South Africa, and others.

Competitive Rivalry

Kraft Heinz Company, The Hain Celestial Group, Conagra, General Mills, Kelloggs, Nestl, Natures Bounty, Amway, Hero Group, Barilla, and others are among the major players in the global Nutraceutical Products market. The companies are involved in several growth and expansion strategies to gain a competitive advantage. Industry participants also follow value chain integration with business operations in multiple stages of the value chain.

The Nutraceutical Products Market has been segmented as below:

Nutraceutical Products Market, By Type

Nutraceutical Products Market, By Distribution Channel

Nutraceutical Products Market, By Source

Nutraceutical Products Market, By Region

Nutraceutical Products Market, By Company

The report covers:

Report Scope:

The global Nutraceutical Products market report scope includes detailed study covering underlying factors influencing the industry trends.

The report covers analysis on regional and country level market dynamics. The scope also covers competitive overview providing company market shares along with company profiles for major revenue contributing companies.

The report scope includes detailed competitive outlook covering market shares and profiles key participants in the global Nutraceutical Products market share. Major industry players with significant revenue share include Kraft Heinz Company, The Hain Celestial Group, Conagra, General Mills, Kelloggs, Nestl, Natures Bounty, Amway, Hero Group, Barilla, and others.

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DXIN Joins Hands with TCI to Develop a Variety of Premium Functional Foods for Beauty Purposes – PRNewswire

DXIN, since 2014, has launched numerous sought-after products and achieved considerable sales growth with social commerce, so far the hottest e-commerce trend worldwide. Social commerce connects consumers around the world based on interpersonal communication of user experience and users' direct feedback on social media platforms. The key to the success of this trending business model lies in that consumers can truly feel the effectiveness of products and that the products really help improve their lives. Thus, DXIN thoroughly investigates market expectations, rigorously controls product quality, and collaborates withTCI (TWE:8436),international ODM supplier to develop competitive products of exceptional quality and make the brand widely recognized by consumers, who are willing to recommend products by DXIN to their friends and followers on social networks.

2020 is a crucial year for DXIN's further business expansion. DXIN, from its' years of experience in social commerce, has discovered that the combination of internal and external treatment, a highly-valued strategy in traditional Chinese medicine, is the ultimate skincare solution. Therefore, DXIN joins hands with TCI, a world-famous scientific R&D manufacturer sophisticated in health and skin care, to develop a variety of premium functional foods for beauty purposes, including the anti-aging Collagen Cubilose Drink, the skin-whitening Pomegranate and Rosa roxburghii Tablet, the fat-burning Vegetable Fruits and Vegetables Baking Powder, and the immunity-boosting Coniferous Cherry Probiotic Powder. Exceeding consumers' expectations, these groundbreaking products with incredible health effects have literally made DXIN a market leader whose launches of new products are always expected by consumers. Currently, DXIN and TCI have embarked on the co-development of the energy-boosting Polygonatum sibiricum drink for males, the blueberry eye care jelly, which will be produced at the Sunrise Park in Pingtung, Taiwan, and the blood-enriching red jinseng drink.

DXIN has indicated in an interview that its products, such as the Cubilose Drink easy to take in modern life, the fermented Fruits and Vegetables Powder, and Probiotic Powder, are produced based on the integration of Chinese traditional herbal treatment and the R&D partner TCI's unique extraction technology. DXIN believes that with the strict quality control in accordance with international standards and innovative strategy in formula design, its brand-new collection of functional foods will again take social commerce platforms by storm and create a better future for the health and beauty of consumers and that DXIN is definitely becoming a China-based pioneer navigating the global skin care market.


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Overnight Face Mask Market 2020: Global Industry Analysis By Size, Share, Growth, Trends And Forecast To 2026 – Cole of Duty

Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Overnight Face Mask market.

Trusted Business Insights presents an updated and Latest Study on Overnight Face Mask Market 2019-2026. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Overnight Face Mask market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

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Industry Insights, Market Size, CAGR, High-Level Analysis: Overnight Face Mask Market

The global overnight face mask market size was valued at USD 18.2 billion in 2018 and is anticipated to expand at a CAGR of 7.1% over the forecast period. Growing incidences of various skin conditions such as ageing, wrinkles, and dryness are driving the consumers to increasingly spend on various skin care solutions that offer prolonged treatment. Overnight face masks have been gaining an increasing traction among consumers who look for convenient skin care products at home that do not demand additional time investment. Moreover, increased consumer demand for mental relaxation is expected to drive the manufacturers to develop overnight face products infused with functional ingredients, thereby propelling the growth of the global market in the near future.Impact of busy and modern lifestyles due to long work hours, pollution, work life pressures, and inappropriate diets is taking a visible toll on the skin, thereby resulting in premature damage. Considering the fact that adequate sleep is an essential remedy, consumers are also looking for skin care products including overnight face masks that penetrate more effectively when at rest.

This scenario has triggered the increasing application of sleep masks among the consumers. In the last few years, bubble masks had gained significant popularity in the Asian beauty trends, which resulted in manufacturers creating their own unique formulas. For instance, brands such as Origins and e.l.f. Cosmetics introduced their hydrating bubble mask and foaming deep cleansing masks respectively that included deep hydration and cleansing agents. These product innovations are anticipated to drive the market for overnight face masks.Increasing preference for overnight masks infused with essential oils and natural and fruit based ingredients is driving continuous product innovations. For instance, ESPA Overnight Hydration Therapy is made of relaxing essential oils and seaweeds extracts that revive the skin moisture content overnight. Lanieges Sleeping Mask is a popular fruit based overnight face rejuvenating product. Laniege offers few varieties in the night mask category.Women have been the major demand generators in the overnight face mask market. However, men are increasingly considered to be the high potential consumers by brand managers, particularly who are into online distribution or have high-end product positioning. Shaving often leads to roughness and dryness, hence sheet masks are generally preferred by men for a well moisturized skin. Moreover, a wide range of overnight face masks for men are available with specific solutions such as anti-aging, wrinkle free, and skin lightening. Some of the common overnight face masks include watermelon glow sleeping mask, Elizabeth Ardens superstart probiotic boost skin renewal bio cellulose mask, and Saturn sulfur acne treatment mask by Sunday Riley.Distribution Channel InsightsConvenience stores accounted for the largest share of 46.8% in 2018. These include beauty, drug, and general stores. Growth of the skin care industry has driven the manufacturers to distribute their products widely across their retail beauty stores and independent beauty and drug stores. These stores are easily accessible to the customers and ensure high visibility of products including overnight face masks. Moreover, presence of an in-store associate guides the customers in choosing the correct product, thereby easing out their purchase decisions.Online distribution channel is expected to expand at the fastest CAGR of 8.8% over the forecast period. Growing demand for beauty care products has been driving the existing as well new manufacturers to introduce their online distribution channels to cater to the large customer base. Moreover, high demand for Korean as well as other international based overnight face mask products is expected to popularize the use of online shopping sites. Moreover, online retailers such as Sephora and e.l.f Cosmetics have started creating their own formulas in this category in order to cater to the continuous demand of the consumers.

Product Insights of Overnight Face Mask Market

Cream and gel based masks accounted for the largest share of more than 55.0% in 2018. This is the most preferred type due to its easy and hassle free application. Creams and gel forms penetrate deeply in the skin, thereby resulting in effective skin repair. Companies are investing in introducing lightweight cream and gel based masks to provide a relaxing effect on the skin while sleeping. For instance, LOreal Paris had introduced a jelly based mask made with French grape seed extracts. This product repairs the dullness and enhances the hydration level of the skin with its double hyaluronic acids, thereby giving a soft and supple effect to the face.

Sheet based overnight face masks are expected to witness significant growth in the next few years due to its quick and hassle free application. Face sheets are usually infused with cooling ingredients that relaxes the muscles and effectively revives the damaged skin. These overnight face sheets usually include hyaluronic acid and enriching vitamins in high content that seep in quickly as compared to creams. These face sheets provide easy hydration and add extra moisture content to energize dry skin. Sheet masks can be used as a daily skincare routine as compared to creams, hence it is expected to witness increased application in the near future.

Regional Insights of Overnight Face Mask Market

Asia Pacific dominated the global market, accounting for a share of more than 35.0% in 2018 primarily due to increased use of overnight face masks in countries such as China and Korea. Growing demand for skin care products such as face masks in these countries has been driving the product innovations. Consumers in these countries prefer intensive skin care solutions and daily grooming routines, wherein application of overnight sheet based masks serve as an essential product. The market includes a wide variety of masks with an array of enriching ingredients derived from traditional Chinese medicine extracts, coupled with vitamins and minerals.North America is expected to expand at the highest CAGR of 7.5% over the forecast period. Companies are emphasizing on increasing the product penetration in U.S. owing to growing consumer awareness about skin care solutions and high demand for Korean beauty products. Moreover, celebrity influencers are playing a crucial role in popularizing the skin care trends in this region, which is expected to contribute to this market growth.

Market Share Insights of Overnight Face Mask Market

The global market is highly competitive in nature. Some of the major players operating in the market for overnight face mask are LOreal Paris; Laniege; e.l.f. Cosmetics; Inc.; The Body Shop; Peter Thomas Roth Labs LLC.; Lotus Herbals; Lakme Cosmetics; Innisfree; The Estee Lauder Companies Inc.; Vichy Laboratories; and Avon Products, Inc. Companies dealing in this beauty care category are actively involved in introducing new products to cater to the varied needs of the customers. Continuous research and development is carried out to introduce products based on natural ingredients. For instance, Innisfree is a naturalism oriented cosmetic brand that offers a wide range of sleeping or overnight face masks infused with natural ingredients, such as its green tea sleeping pack is made of green tea seed oil.

Segmentations, Sub Segmentations, CAGR, & High-Level Analysis overview of Overnight Face Mask Market Research ReportThis report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2014 to 2025. For the purpose of this study, this market research report has segmented the global overnight face mask market report on the basis of product, distribution channel, and region:

Product Outlook (Revenue, USD Billion, 2019 2030)

Creams & Gels


Distribution Channel Outlook (Revenue, USD Billion, 2019 2030)

Supermarkets & Hypermarkets

Convenience Stores


Quick Read Table of Contents of this Report @ Overnight Face Mask Market Size, Share, Global Market Research and Industry Forecast Report, 2025 (Includes Business Impact of COVID-19)

Trusted Business InsightsShelly ArnoldMedia & Marketing ExecutiveEmail Me For Any ClarificationsConnect on LinkedInClick to follow Trusted Business Insights LinkedIn for Market Data and Updates.US: +1 646 568 9797UK: +44 330 808 0580

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We Need To Change the Narrative Surrounding Vegetarianism – Study Breaks

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In recent years, vegetarianism and veganism have become increasingly accepted as healthy alternatives to an omnivorous lifestyle. Scientific studies proclaiming the health benefits of a meatless lifestyle, as well as documentaries such as Food, Inc. that expose the moral problems of industrial meat production and consumption, have inspired many to adopt a vegetarian diet. Although, people are not only accepting plant-based diets anymore they are also evangelizing them.

I have been supportive of plant-based living for years: In middle school, I committed to eating only humanely raised meat before going fully vegetarian during high school. I was, and still am, especially influenced by the ethical ramifications of a meatless diet. American factory farming horrifies me: The enclosure of animals in small spaces without room to graze and frequent antibiotic and hormone injections that harm the health of animals are just two examples of inhumane industrial farming practices.

In addition, the environmental benefits of reducing meat consumption are enormous: Although carbon dioxide is by far the biggest culprit in global greenhouse gas emissions, methane a gas that cows produce is much more potent. Continuing factory farming at its current rate of production will only increase the amount of methane in the atmosphere.

We need to be doing what we can to phase out factory farming, to support the ethical treatment of animals and to improve the health of ourselves and our planet with plant-based food. My problem with the movement is the way this message is being conveyed.

The narrative surrounding vegetarianism, previously contained within personal conversations and doctor visits, is now publicly steeped in moral superiority. It says, If youre a good person, youll stop eating meat altogether. Dont get me wrong what we eat is political. Meat consumption has real environmental and ethical impacts beyond ourselves. In the era of COVID-19, in which the virus is severely affecting meat packing workers, shutting down industrial farming facilities and leading farmers to euthanize animals, it is clear that the American food systems current reliance on meat is damaging our communities.

Jonathan Safran Foer makes this argument in his recent New York Times op-ed, titled The End of Meat Is Here. On the whole, its a good article; I agree with his stance that the coronavirus has exposed the degree to which we seriously need to reduce our meat consumption. He advocates urgency and appeals to a world in which farmers were not myths, tortured bodies were not food and the planet was not the bill at the end of the meal. I think most of us want to live in that world. But his message that everyone needs to cut out meat completely is harmful.

First and foremost, your diet impacts your physical and mental well-being, and those who advocate a complete transition to a no-meat society do not do justice to this fact. Safran Foer addresses the health impacts of vegetarianism, writing, Dont we need animal protein? No. We can live longer, healthier lives without it. Most American adults eat roughly twice the recommended intake of protein including vegetarians, who consume 70 percent more than they need.

This is all true. What is also true and what Safran Foer fails to mention is that vegetarian diets, although beneficial for our hearts and kidneys, can lead to vitamin deficiencies that impact health. Nutrients only found in animal products, such as vitamin B-12 (cobalamin), are essential for maintaining good health. A study published in 2016 concluded that compared to non-vegetarians, vegetarians have reduced body mass index (BMI), serum cholesterol, serum glucose and blood pressure with a lower mortality rate due to ischemic heart disease. However, underestimating the correct supplementation of cobalamin (Cbl) can nullify these benefits.

In addition, vegetarianism can have a serious impact on mental health. A study published in April 2020 observed a distinct correlation between meat abstention and poor psychological health, declaring that Our study does not support meat avoidance as a strategy to benefit psychological health. And even with this new research, studies on vegetarianism and physical health still outnumber those on psychological health by a wide margin.

Claiming that vegetarianism is for everyone ignores these facts and fails to recognize the role of diet in mental health treatment. It also perpetuates the one-size-fits-all approach to health, which neglects the impact of individual circumstances like stress level, socioeconomic status and past trauma on well-being. Ignoring these factors makes it difficult for individuals to identify and seek out treatments for chronic physical and mental health issues.

When I went vegetarian, I didnt know that my new diet could cause vitamin deficiencies. I felt the impact of these deficiencies my first year of college, when I was suddenly dealing with heightened anxiety that manifested itself not only through worried thoughts, but also through other physical symptoms. I went down a frustrating path of doctor visits and medical tests in search of treatments for my new chronic health issues, and had to transition out of a vegetarian diet to start remedying the imbalance I felt in my mind and body. Now, about to enter my senior year of college, Im still dealing with these symptoms and grappling with how to eat ethically while consuming meat.

I do not share my story to scare people away from vegetarianism; rather, I want to help others reduce their meat consumption while avoiding an experience like mine. No one can responsibly recommend a vegetarian diet to others without also informing them of the potential risks.

There are ways that we can eat less meat, even forgo meat completely, while being tuned into individual health concerns. If you do want to go vegetarian, make sure you do your research on what vitamins you may have a harder time getting from your diet once you abstain from meat. Find out how you can get these nutrients in other foods, and take supplements where diet alone falls short. Adolescents and those with preexisting conditions need to be especially careful about making sure what they eat is supporting their specific needs.

That being said, transitioning to a vegetarian diet isnt for everyone, and for those who cannot fathom giving up meat, there are still ways to eat more sustainably. The Reducetarian Foundation has plenty of resources for those who want to improve the health of humans, animals and the environment without ditching meat completely. Even just skipping meat one day a week can have a beneficial impact; according to the Natural Resources Defense Council Health Campaigns Director Sujatha Bergen, the environmental impact of the average American reducing their meat intake by one hamburger per week would be equivalent to eliminating a years worth of tailpipe emissions from ten million cars.

In addition, try to buy humanely raised meat whenever you can, looking out for labels that designate animals as hormone-free, free-range, and grass-fed. These labels themselves can be misleading, so check out resources such as American Grass-Fed that have lists of certified providers of grass-fed beef in each state. And when in doubt, buy from local farms.

We need to educate ourselves about the ways that we can eat more ethically, and eating less meat is one of the best ways to do that. However, we also need to encourage plant-based eating in a way that allows individuals to best take care of themselves and their families. Physical and mental health are inseparable, and we cannot detach vegetarianism from personal well-being.

So, just as we should eat responsibly, we should also advocate responsibly. Health is personal, and when it comes to diet recommendations, no one should take the moral high ground.

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Heres why Im slowly switching to vegetarianism Helen Martin – Edinburgh News

Seven per cent of the UK population are vegetarians and Helen is tempted to join their ranks

THIS year, since March, must have been the worst time across Scotland and other countries with the virus, presenting a fleet of fears, risks, fatalities, tragedies, depression, dreams and ambitions shattered, income losses and anxious wondering about the future.

Theres been little to cheer us up. One suspected cause of coronavirus, the disgusting wet markets of China, started up another booming thread of bad news on TV and social media relating to animal cruelty. Why would we want to learn about all that right now?

One of the most upsetting for many was the coverage of the Chinese Yulin Dog Meat Festival, including pictures of cats and dogs, starving, yelling, injured and packed tightly with hundreds crammed into metal cages, before being roasted alive.

Then came the worst revelations of trophy hunting, with young children being taught to shoot arrows at bear cubs or young deer fawns, and adults happily promoting their killing of giraffes, lions and rhinos, etc.

How working animals, from monkeys and elephants to donkeys and dogs, were tortured by their captors or owners was bad enough and perhaps another, ghastly, closer-to-home, expos was how some animals were produced and dealt with, even in the UK, for food.

A lot of that on social media has been posted by vegans. My first reaction was that it was all so horrific and tearful I wanted to block them. But no, I signed all the petitions, retweeted the things while trying as much as possible to avoid the pictures.

My Irish family were farmers. I knew they werent as cruel as the stuff I was seeing now. I worked with them! So, not surprisingly, eating meat had been normal and delicious for me for 60 years! Id often rejected the idea of veganism. But for some reason during this Covid-19 lockdown, Ive slowly begun to switch to vegetarianism.

I do mean slowly. About four meals a week contain no meat. Ive discovered vegan cheese for some recipes but still use some made from milk. I still use dairy milk for some things but tried the options such as almond and coconut, and finally settled on oat milk.

Non-meat soya mince is so good for chilli or curry, it tastes as good as beef. Im about to experiment with jackfruit, a real fruit that takes on a meaty texture and taste in spicy dishes. Pasta with veg ingredients is easy. And my animal-loving husband is going along with it all.

When restaurants open, well try vegetarian and vegan joints and learn more. Some people do a sudden switch to veggie or vegan but will we ever totally rule out animal contents? Could I reject haggis, steak and bacon forever? To be honest, I dont know.

Id certainly want intensive, factory farming banned, and compassionate, ethical farming to take over whether I really changed diet or not. Im sure many meat eaters would prefer that too, no matter how more expensive it was.

According to Finder UK, currently seven per cent of the population are vegetarian, four per cent are pescatarian and two per cent are vegan, all of which amount to 6.7 million. And by the end of 2020, all these statistics are predicted to double (although not everyone completely sticks to their new diet).

But heres the moral and emotional, insoluble problem. The spend on meaty dog and cat-food in the UK is estimated annually at up to 4 billion. (For my dog and cat I spend about 40 a week.) Add zoos, sanctuaries and shelters and the meat which is always necessary especially for those who love and feed animals.

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Low T Therapy Market Report 2020 Global Industry Analysis, Trends, Market Size and Forecasts Up to 2027: AbbVie, Endo International, Eli Lilly – 3rd…

The Global Low T Therapy Market Research Report provides customers with a complete analytical study that provides all the details of key players such as company profile, product portfolio, capacity, price, cost, and revenue during the forecast period from 2020 to 2027. The report provides a full assessment. Low T Therapy market with future trends, current growth factors, meticulous opinions, facts, historical data and statistically supported and industry-validated market data.

This Low T Therapy market research provides a clear explanation of how this market will impress growth during the mentioned period. This study report scanned specific data for specific characteristics such as Type, Size, Application and End User. There are basic segments included in the segmentation analysis that are the result of SWOT analysis and PESTEL analysis.

To Learn More About This Report, Request a Sample Copy:* The sample copy includes: Report Summary, Table of Contents, Segmentation, Competitive Landscape, Report Structure, Methodology.

AbbVie, Endo International, Eli Lilly, Pfizer, Actavis (Allergan), Bayer, Novartis, Teva, Mylan, Upsher-Smith, Ferring Pharmaceuticals, Kyowa Kirin, Acerus Pharmaceuticals are some of the major organizations dominating the global market.(*Note: Other Players Can be Added per Request)

Key players in the Low T Therapy market were identified through a second survey, and their market share was determined through a primary and second survey. All measurement sharing, splitting, and analysis were solved using a secondary source and a validated primary source. The Low T Therapy market report starts with a basic overview of the Industry Life Cycle, Definitions, Classifications, Applications, and Industry Chain Structure, and when used together, how key players can meet market coverage, offered characteristics, and customer needs It helps to understand.

The report also makes some important suggestions for new Low T Therapy market projects before evaluating their feasibility. Overall, this report covers Low T Therapy market Sales, Price, Sales, Gross Profit, Historical Growth,and Future Prospects. It provides facts related to the widespread merger, acquisition, partnership, and joint venture activities on the market.

This report includes market size estimates of value (million US $) and trading volume (K MT). The top-down and bottom-up approaches are used to estimate and validate the market size of the Low T Therapy market, estimating the size of various other subordinate markets in the overall market. All ratio sharing, splitting, and analysis were determined using the secondary source and the identified primary source.

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Remarkable Attributes of Low T Therapy Market Report:

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