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Complete Overview of Cancer Nanomedicine Market to Witness High Rate of Growth in Forthcoming Years – Fusion Science Academy

A recent market study published by XploreMR titled Endoscopy Fluid Management System Market: Global Industry Analysis 20132017 & Opportunity Assessment 20182028 comprises a comprehensive assessment of the most important market dynamics. Growth prospects of the endoscopy fluid management system market are obtained with maximum precision through thorough research on historic as well as current growth parameters. The report features unique and salient factors that are likely to make a huge impact on the development of the endoscopy fluid management system market during the forecast period. A better understanding of these factors can help market players modify their manufacturing and marketing strategies to envisage maximum growth in the endoscopy fluid management system market in the coming years. The report provides detailed information about the current and future growth prospects of the endoscopy fluid management system market in the most comprehensive way for the better understanding of readers.

Chapter 1 Executive Summary

The report commences with an executive summary of the endoscopy fluid management system market report, which includes the summary of key findings and key statistics of the market. It also includes the market value (US$ million) estimates of the leading segments of the endoscopy fluid management system market.

Chapter 2 Market Introduction

Readers can find detailed taxonomy and definitions in this chapter to understand basic information regarding the endoscopy fluid management system market dynamics, supply chain, list of key distributors and suppliers and list of key market participants.

Chapter 3 Market Overview

Readers can find a detailed opportunity analysis on Endoscopy Fluid Management System Market

Chapter 4 Market Overview

Tracking the market scenario, with key inferences drawn from historical data, current trends, and future prospects. This section provides the Macro-Economic Factors for the market

Chapter 5 Market Background

Readers can find various macro-economic factors associated with the growth of the market. This chapter highlights the key market dynamics, which include the drivers, restraints and trends

Chapter 6 Global Economic Outlook

This section shows the Gross Domestic Product by Region & Country from 2006 2021

Chapter 7 Key Inclusions

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This section of the report includes regulation and reimbursement scenario associated with the market

Chapter 8 North America Endoscopy Fluid Management System Market Analysis 20132017 & Opportunity Assessment 20182028

This chapter includes a detailed analysis of the anticipated growth in the North America endoscopy fluid management system market along with a country-wise assessment for countries in the region, including the U.S. and Canada. Readers can also find information pertaining to the regional trends and regulations prevailing in the North America endoscopy fluid management system market and regional market growth classified on the basis of product type, modality and end user.

Chapter 9 Latin America Endoscopy Fluid Management System Market Analysis 20132017 & Opportunity Assessment 20182028

Readers can find detailed information regarding factors such as pricing analysis and regional trends that are impacting the growth of the Latin America endoscopy fluid management system market. This chapter also includes the growth prospects of the endoscopy fluid management system market in leading LATAM countries such as Brazil, Mexico and rest of the Latin America region.

Chapter 10 Europe Endoscopy Fluid Management System Market Analysis 20132017 & Opportunity Assessment 20182028

Important growth prospects of the endoscopy fluid management system market, on the basis of product type, modality and end user in several European countries such as Germany, the U.K., France, Italy, Spain, Russia, Poland & the rest of Europe have been included in this chapter.

Chapter 11 Asia Pacific Endoscopy Fluid Management System Market Analysis 20132017 & Opportunity Assessment 20182028

China, India, Australia and Japan, being the leading countries in the APEJ region, are the prime subject of assessment in this chapter to obtain the growth prospects of the APEJ endoscopy fluid management system market during the forecast period.

Chapter 12 MEA Endoscopy Fluid Management System Market Analysis 20132017 & Opportunity Assessment 20182028

This chapter provides information on how the endoscopy fluid management system market is expected to grow in major countries of the MEA region, such as Saudi Arabia, South Africa, & Egypt, during the period 20182028.

Chapter 13 Forecast Factors: Relevance and Impact

Forecast factors for the estimation of the entire concerned market are also present in this section.

Chapter 14 Forecast Assumptions

Taken assumptions set for the estimation of the entire concerned market are present in this section.

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Chapter 15 Market Structure Analysis

This chapter includes the market structure by tier of companies for the electrophysiology ablation market. This chapter also includes the company share analysis for various key players in the market.

Chapter 16 Competition Landscape

In this chapter, readers can find a comprehensive list of the leading stakeholders present in the endoscopy fluid management system market along with detailed information about each company, including company overview, revenue shares, strategic overview and recent company developments. Market players featured in the report include Stryker, Olympus Corporation, KARL STORZ SE & Co. KG, Smith & Nephew Plc., Hologic Inc., Richard Wolf GmbH, B. Braun Medical Inc., Cantel Medical Corporation and Medtronic & DePuy Synthes (Johnson & Johnson Services, Inc.).

Chapter 17 Global Endoscopy Fluid Management System Market Analysis (20132017) & Opportunity Assessment (20182028) by Region

This chapter explains how the endoscopy fluid management system market is expected to grow across various geographic regions such as North America, Latin America, Europe, Asia-Pacific Excluding Japan (APEJ), & the Middle East & Africa (MEA).

Chapter 18 Global Endoscopy Fluid Management System Market Analysis (20132017) & Opportunity Assessment (20182028) by Product Type

The endoscopy fluid management system market, on the basis of product type, has been segmented into laparoscopy fluid management systems, laparoscopy suction irrigation pumps, hysteroscopy fluid management systems and hysteroscopy pumps. In this chapter, readers can find information about the key trends and developments in the endoscopy fluid management system market and a market attractiveness analysis based on the product type.

Chapter 19 Global Endoscopy Fluid Management System Market Analysis (20132017) & Opportunity Assessment (20182028) by Modality

On the basis of the modality, the endoscopy fluid management system market has been segmented into floor standing & benchtop. In this chapter, readers can find information about the key trends and developments in the endoscopy fluid management system market and a market attractiveness analysis based on the modality.

Chapter 20 Global Endoscopy Fluid Management System Market Analysis (20132017) & Opportunity Assessment (20182028) by End User

On the basis of the end user, the endoscopy fluid management system market has been segmented into hospitals, ambulatory surgical centers, specialty clinics & diagnostic centers. In this chapter, readers can find information about the key trends and developments in the endoscopy fluid management system market and a market attractiveness analysis based on the end user.

Chapter 21 Global Endoscopy Fluid Management System Market Analysis (20132017) & Opportunity Assessment (20182028)

This chapter explains how the endoscopy fluid management system market is expected to grow across the period of 20182028.

Chapter 22 Assumptions and Acronyms

This chapter includes a list of acronyms and assumptions that provide a base to the information and statistics included in the report.

Chapter 23 Research Methodology

This chapter helps readers understand the research methodology followed to obtain various conclusions and important qualitative and quantitative information regarding the endoscopy fluid management system market.

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Most engineered nanoparticles enter tumours through cells not between them, U of T researchers find – News@UofT

University of Toronto researchers have discovered that an active rather than passive process dictates which nanoparticles enter solid tumours, upending decades of thinking in the field of cancer nanomedicine and pointing toward more effective nanotherapies.

The prevailing theory in cancer nanomedicine an approach that enables more targeted therapies than standard chemotherapy has been that nanoparticles mainly diffuse passively into tumours through tiny gaps between cells in the endothelium, which lines the inner wall of blood vessels that support tumour growth.

The researchers previously showed thatless than one per centof nanoparticle-based drugs typically reach their tumour targets. In the current study, they found that among nanoparticles that do penetrate tumours, more than 95 per cent pass through endothelial cells not between gaps among those cells.

Our work challenges long-held dogma in the field and suggests a completely new theory, saysAbdullah Syed, a co-lead author on the study and post-doctoral researcher in the lab ofWarren Chan, a professor at theInstitute of Biomaterials and Biomedical Engineeringand theDonnelly Centre for Cellular and Biomolecular Research.

We saw many nanoparticles enter the endothelial cells from blood vessels and exit into the tumour in various conditions. Endothelial cells appear to be crucial gatekeepers in the nanoparticle transport process.

The findings were recently published in thejournalNature Materials.

From left to right: U of T researchers Jessica Ngai, Shrey Sindhwani, Abdullah Syed and Benjamin Kingston (photo by Qin Dai)

Syed compares nanoparticles to people trying to get into popular restaurants on a busy night. Some restaurants dont require a reservation, while others have bouncers who check if patrons made reservations, he says. The bouncers are a lot more common than researchers thought, and most places only accept patrons with a reservation.

The researchers established that passive diffusion was not the mechanism of entry with multiple lines of evidence. They took over 400 images of tissue samples from animal modelsand saw few endothelial gaps relative to nanoparticles. They observed the same trend using 3D fluorescent imaging and live-animal imaging.

Similarly, they found few gaps between endothelial cells in samples from human cancer patients.

The group then devised an animal model that completely stopped the transportation of nanoparticles through endothelial cells. This allowed them to isolate the contribution of passive transport via gaps between endothelial cells, which proved to be miniscule.

The researchers posit several active mechanisms by which endothelial cells might transport nanoparticles into tumours, including binding mechanisms, intra-endothelial channels and as-yet undiscovered processes all of which they are investigating.

Meanwhile, the results have major implications for nanoparticle-based therapeutics.

These findings will change the way we think about delivering drugs to tumours using nanoparticles, saysShrey Sindhwani, also a co-lead author on the paper and an MD/PhD student in the Chan lab. A better understanding of the nanoparticle transport phenomenon will help researchers design more effective therapies.

The research included collaborators from U of Ts department of physics in the Faculty of Arts & Science, Cold Spring Harbor Laboratory In New York and the University of Ottawa. The study was funded by the Canada Research Chairs Program, Canadian Cancer Society, Natural Sciences and Engineering Research Council of Canadaand the Canadian Institutes of Health Research.

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Healthcare IT Market trends research and projections –

The consumerization in healthcare information technology has reached a tipping point, the impact of which has been felt across healthcare IT market. The need to ensure comfort and security for patients has brought about a major transformation in the medical sphere, leading to a path-breaking intersection of IT and healthcare. The deployment of IT has equipped the healthcare industry with nanomedicine, virtual healthcare, 3D printing, robot-assisted surgery, and more. These advancements, aided by the investments by the governments worldwide, have brought about a massive change in the healthcare IT industry outlook.

The global healthcare IT market has also gained traction on account of innumerable parameters, prominent among them being, the increasing geriatric populace and the changing consumer lifestyles. Indeed, these have led to a spate of diseases worldwide, consequently surging the demand for a highly sophisticated healthcare IT network in order to lower errors in administration processes and ensure efficient medical data and patient record management.

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Consumer expectations have also changed in the last decade or so, leading to healthcare providers focusing on prioritizing efficient management of healthcare data. This has subsequently led to the implementation of innovative technologies in the medical ecosphere, augmenting the revenue graph of the global healthcare IT market. Indeed, estimates claim that healthcare IT industry is expected to exceed $441.8 billion by the year 2025.

While numerous IT solutions are deployed in the healthcare space, one of the most significant ones is that of electronic health records. Undeniably, healthcare IT industry has gained much via rapid adoption of the EHR technology by healthcare specialists in the U.S. and other economies. According to the National Electronic Health Records Survey, 2017, approximately 9 out of 10 office-based physicians had adopted any type of EHR, while certified EHRs were adopted by 4 out of 5 office-based physicians. Since 2008, the rate of EHR adoption has more than doubled from 42 percent to nearly 86 percent in 2017. The accelerated adoption of EHR will thus drive the growth graph of healthcare IT industry from electronic health records. As a matter of fact, estimates claim that EHR-based healthcare IT market size will cross $97.8 billion by 2025.

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Regionally speaking, it has been forecast that the United States will crop up as a prime growth avenue for the global healthcare IT industry, primarily driven by numerous investments in medical care infrastructure and government mandates. More than half a decade ago, The U.S. Department of Health and Human Services (HHS) had mandated the adoption of information technology by healthcare providers. A substantial growth has also been recorded in terms of investments by the U.S. government in healthcare IT since 2008.

A few years ago, the U.S. government had made an investment of about $20 billion through the HITECH (Health Information Technology for Economic and Clinical Health) Act, for setting up electronic health records. Aided by numerous government initiatives and the escalating need for an efficient healthcare management system, the U.S. healthcare IT industry is estimated to record substantial revenues by 2025.

Speaking of government initiatives, yet another regional ground touted to garner extensive proceeds in healthcare IT market is the United Kingdom. In December 2018, a new collaboration had been announced between the government and the life sciences industry, backed by a government fund worth 79 million, in order to study 5 million people and develop AI-centric diagnostic tests. Back in 2014, the UK government has also announced an investment of $5.4 billion in healthcare IT for a five-year period. It comes as no surprise therefore, that the UK healthcare IT market, powered by government initiatives, will reach $24.7 billion by the year 2025.

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With the increasing prevalence of diseases and the subsequently rising demand for a sophisticated medical infrastructure, healthcare IT market contenders have been working to bring forth a slew of advancements in their service portfolio. For instance, McKesson Corporation has recently collaborated with technology leader Navigating Cancer to provide an enhanced Patient Relationship Management (PRM) platform for oncologists.

IT aided healthcare has come a long way since its inception. With massive changes in the technological landscape, many more innovations have been touted to disrupt the healthcare space. Powered by huge investments and the incorporation of advanced technologies in medical care management, healthcare IT market is expected to chart out a lucrative growth map in the forthcoming years.

Table Of Content

Chapter 1. Methodology

1.1. Methodology

1.2. Market definition

1.3. Forecast parameters

1.4. Data sources

1.4.1. Secondary Paid sources Unpaid sources

1.4.2. Primary

Chapter 2. Executive Summary

2.1. Healthcare information technology industry 3600 synopsis, 2014 - 2025

2.1.1. Business trends

2.1.2. Solution trends

2.1.3. End-use trends

2.1.4. Regional trends

Chapter 3. Healthcare Information Technology Industry Insights

3.1. Industry segmentation

3.2. Industry landscape, 2014 - 2025

3.3. Industry impact forces

3.3.1. Growth drivers Growing investment towards healthcare infrastructure development in Asia Pacific region Growing adoption of artificial intelligence High adoption of electronic health records in developed countries such as the U.S. Favorable government initiatives Increasing demand for cost-saving in healthcare delivery

3.3.2. Industry pitfalls & challenges High cost associated with implementation and maintenance Security and privacy concerns

3.4. Growth potential analysis

3.4.1. By solution

3.4.2. By end-use

3.5. Regulatory landscape

3.5.1. U.S.

3.5.2. Europe

3.5.3. China

3.6. Technology landscape

3.7. Porter's analysis

3.8. Competitive landscape, 2017

3.8.1. Strategy dashboard

3.9. PESTEL analysis

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Neon Tiger, a vegan cocktail bar set in the year 2048, is coming to King Street this spring – Charleston City Paper

The flickering outline of a roaring tiger stares out from a black screen. The neon orange and pink pulsates, like a power line is fighting to fuel the light. "It's a glitch in the Matrix," says John Adamson of his new vegan cocktail concept Neon Tiger. "It doesn't adhere to the rules of the Matrix."

Adamson explains that his new restaurant will be set in the year 2048 when it opens at 654 King St. (formerly Juliet) in early spring. Less whimsy and more end times, this is the world Adamson believes we will have to grapple with if humans continue to kill, consume, and imprison animals.

The restaurant will be entirely plant-based, with locally sourced booze, no cans or bottles, and as little waste as possible. Prolific Toronoto-based activist and vegan chef Doug McNish serves as Neon Tiger's consultant.

"We have pretty grand plans," says Adamson. Neon Tiger will be a B Corp, an entity that functions as a business while also meeting standards for social responsibility and sustainability. "It's all about education, for me as an activist, you have to play to your strengths and my strength is creating and designing restaurant concepts."

Adamson has a been a vegan for two-and-a-half years. The day he decided to change his way of life, Adamson says he was ready to turn his restaurant, The Rarebit, into a vegan-only eatery. "There would be no greater statement for the movement," he says. But it wasn't practical, so the restaurateur decided to sell his popular King Street joint and put money toward a new venture. Serendipitously, Neon Tiger's landlord is also a vegan.

"Designing those spaces [Rarebit and The Americano] from my head, it's just what I enjoy. This one happens to be more important than any I've ever done."

Adamson is ready for keyboard warriors to attack his animal-free restaurant the outspoken activist is used to getting flack from meat eaters. "The funny thing, well it's not funny, but the interesting thing about veganism is you have so many people who want to fight you on it, but you are fighting for them. Animal liberation is human liberation."

He says his goal since becoming vegan was to "create a space for people to have a cruelty free meal." The response from fellow vegans in the hospitality industry has been great, says Adamson. Turns out there are plenty of front and back of house workers who desire an animal-free work place, but haven't been able to pursue this goal and still keep a roof over their heads.

If you don't buy into the whole "veganism will save the world" thing, that's OK says Adamson. "You only need about 10 or 15 percent of the population think about any movement in history. We're just racing for that 10 percent."

According to a Forbes analysis in 2018 based on a Science mag report, "Since livestock production is the single largest contributor of emissions around the globe (more than planes, trains and cars combined), removing it from out food system could allow the planet to regenerate. Raising animals for food is also the largest contributor to wildlife extinction around the world."

Whether you're a vegan, on the fence, or an adamant consumer of animal products, Adamson hopes you'll check out Neon Tiger. It will be open nightly until 2 a.m. with a "sexy, lounge-y feel" that also happens to be mid apocalypse themed.

"The idea is in 2048 the only tiger youll have will be representations of these animals. It also brings that human element it's like a slight to humanity of course we'd only be left with neon ... It's a responsibility we had that we completely neglected and failed."

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What is The Game Changers about? Why everyone is talking about the vegan documentary – RadioTimes

In case you hadnt noticed, theres been a lot of discussion in recent times about veganism, its nutritional benefits and environmental advantages. In this Netflix documentary, several world-famous athletes and bodybuilders have their say

See below for everything you need to know about Netflixs The Game Changers

The documentary follows British UFC fighter James Wilks as he travels around the world to discover the optimal diet for human performance, particularly looking into the benefits of a plant-based diet. Along the way, he interviews scientists, special ops soldiers, action stars and some of the biggest names in sport.

The Game Changers makes some pretty big claims, suggesting that a plant-based diet is actually better for improving performance and strength than eating meat.

Its not one set of dietary guidelines for improving your performance as an athlete, another one for reversing heart disease, reversing diabetes, said Dr Dean Ornish.Its the same for all of them.

The documentary also shows several burly world-class athletes who have achieved astonishing feats which they attribute to a plant-based diet.

When I made the switch to a plant-based diet, I qualified for my third Olympic team, I broke two American records, said weightlifter Kendrick Farris.I was like man, I should have done this a long while ago!

During the film, Wilks discovers that the Roman gladiators were mostly vegetarian, and after taking part in a seven-day vegan challenge New York firefighters find they had apparently reduced cholesterol and blood pressure.

It has also hit the headlines recently as the documentary allegedly convinced the CEO of Greggs to turn vegan, surely guaranteeing that vegan sausage rolls are here to stay (sorry Piers Morgan).

The documentary is available to watch now on Netflix. You can also watch The Game Changers on Amazon.

Appropriately for a documentary about strength, the film features several heavyweight action stars and athletes.

Arnold Schwarzenegger, Jackie Chan, Lewis Hamilton, Novak Djokovic and James Cameron all feature to discuss the ideal diet for peak human strength and performance.

The Game Changes is on Netflix now.

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How vegetarianism is going back to its roots in Africa – The Guardian

In the meat-loving capital of Burkina Faso, customers at a small roadside joint eat bean balls, grilled tofu skewers and peanut butter rice while a report about chickens unfit for consumption being dumped on the street airs on the midday news.

A sign above the door proudly welcomes customers: Vegetarian restaurant Nasa. Food for the love of health. In Ouagadougous first plant-based restaurant, there are no knives on the tables.

The place is full of regular customers who greet Christine Tapsoba, the owner, like an old friend. But it wasnt always like this. At the start, it wasnt easy. People thought it was weird, they didnt know how we could make food without using meat, she says. Some days, we could open the restaurant and sell nothing.

In the years since Nasa opened in 2004, her clientele has grown exponentially, drawn in initially by giveaways of her popular barbecued tofu skewers.

Plant-based diets have also spread across the west, with vegan restaurants and products seeing meteoric rises in sales. But global meat consumption is still increasing, with burgeoning urban middle classes across Africa, Asia and Latin America powering the demand.

Across Africa, a growing number of plant-based restaurants are following in Tapsobas footsteps in response to health and environmental challenges. Happy Cow, an app that helps vegetarians and vegans find places to eat around the world, lists more than 900 restaurants with vegan options across Africa. More than half of these were added in the past two years. Thirty fully vegan restaurants have been listed since the start of 2018.

Demand has been way up in most major cities. Its awesome times for those who like to eat plant-based, says Eric Brent, Happy Cows founder. Some of the catalysts have been vegan documentaries, popular YouTubers [including in South Africa], and plant-based alternatives to meat and dairy, he adds.

South Africa has been at the forefront of this push, with veganism booming in Cape Town and Johannesburg. Cities such as Nairobi in Kenya, and Accra in Ghana, today boast a dozen meat-free restaurants. In Dakar, the Senegalese capital, upmarket seaside restaurants are quickly adding salad bowls and aubergine sandwiches to their otherwise meat- and fish-filled menus.

The continent is also at the forefront of some of the challenges veganism hopes to ease. Conditions such as heart disease and cancer have now overtaken infectious diseases such as cholera and measles to become the biggest drain on Africas economies, according to the World Health Organization. Much of the continent is already feeling the effects of the climate crisis a common reason for reducing meat intake as more regular and unpredictable droughts and floods wreak havoc for farmers and regularly claim lives.

Our ancestors didnt eat as much meat. It is through colonisation that we learned these crazy meat-eating practices

Many of its advocates, however, argue that veganism is not a new trend it is simply a return to traditional African diets. I particularly think its important to spread veganism around Africa because it originated in Africa, says Nicola Kagoro, a chef working in South Africa and Zimbabwe. Our ancestors didnt eat as much meat. It is through colonisation that we learned these crazy meat-eating practices. Kagoro founded the African Vegan on a Budget movement to show Africans vegan diets can be affordable and filling. She also cooks for female vegan armed rangers group the Akashinga, who fight elephant poaching in Zimbabwe.

In research on the worlds healthiest diets, published in the Lancet in 2015, west African countries such as Mali, Chad, Senegal and Sierra Leone, which boasted diets rich in fruits, vegetables and whole grains, topped the list. Ethiopian cuisine relies on plant-based foods such as the sourdough flatbread injera, lentils and beans, and many of the countrys Orthodox Christians take part in regular fasts during which meals are served without any animal products.

Still, the trend is slow to take hold. Its hard to spread the vegan practice around Africa because Africans love their meat, says Kagoro, who is known as Chef Cola. The challenge is because Africans think meat is a form of showing wealth.

With Nasa, Tapsoba helps the few Burkinabe vegetarians of Ouagadougou navigate an often difficult path to a meat-free life. When a vegetarian is here and I am told they struggle to find something to eat, immediately I rise up to help them, she says.

And with patience, free tofu, and a growing awareness of the consequences of meaty diets, she hopes to convince others to join her.

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How DeepMind is unlocking the secrets of dopamine and protein folding with AI – VentureBeat

Demis Hassabis founded DeepMind with the goal of unlocking answers to some of the worlds toughest questions by recreating intelligence itself. His ambition remains just that an ambition but Hassabis and colleagues inched closer to realizing it this week with the publication of papers in Natureaddressing two formidable challenges in biomedicine.

The first paper originated from DeepMinds neuroscience team, and it advances the notion that an AI research development might serve as a framework for understanding how the brain learns. The other paper focuses on DeepMinds work with respect to protein folding work which it detailed in December 2018. Both follow on the heels of DeepMinds work in applying AI to the prediction of acute kidney injury, or AKI, and to challenging game environments such as Go, shogi, chess, dozens of Atari games, and Activision Blizzards StarCraft II.

Its exciting to see how our research in [machine learning] can point to a new understanding of the learning mechanisms at play in the brain, said Hassabis. [Separately, understanding] how proteins fold is a long-standing fundamental scientific question that could one day be key to unlocking new treatments for a whole range of diseases from Alzheimers and Parkinsons to cystic fibrosis and Huntingtons where misfolded proteins are believed to play a role.

In the paper on dopamine, teams hailing from DeepMind and Harvard investigated whether the brain represents possible future rewards not as a single average but as a probability distribution a mathematical function that provides the probabilities of occurrence of different outcomes. They found evidence of distributional reinforcement learning in recordings taken from the ventral tegmental area the midbrain structure that governs the release of dopamine to the limbic and cortical areas in mice. The evidence indicates that reward predictions are represented by multiple future outcomes simultaneously and in parallel.

The idea that AI systems mimic human biology isnt new. A study conducted by researchers at Radboud University in the Netherlands found that recurrent neural networks (RNNs) can predict how the human brain processes sensory information, particularly visual stimuli. But, for the most part, those discoveries have informed machine learning rather than neuroscientific research.

In 2017, DeepMind built an anatomical model of the human brain with an AI algorithm that mimicked the behavior of the prefrontal cortex and a memory network that played the role of the hippocampus, resulting in a system that significantly outperformed most machine learning model architectures. More recently, DeepMind turned its attention to rational machinery, producing synthetic neural networks capable of applying humanlike reasoning skills and logic to problem-solving. And in 2018, DeepMind researchers conducted an experiment suggesting that the prefrontal cortex doesnt rely on synaptic weight changes to learn rule structures, as once thought, but instead uses abstract model-based information directly encoded in dopamine.

Reinforcement learning involves algorithms that learn behaviors using only rewards and punishments as teaching signals. The rewards serve to reinforce whatever behaviors led to their acquisition, more or less.

As the researchers point out, solving a problem requires understanding how current actions result in future rewards. Thats where temporal difference learning (TD) algorithms come in they attempt to predict the immediate reward and their own reward prediction at the next moment in time. When this comes in bearing more information, the algorithms compare the new prediction against what it was expected to be. If the two are different, this temporal difference is used to adjust the old prediction toward the new prediction so that the chain becomes more accurate.

Above: When the future is uncertain, future reward can be represented as a probabilitydistribution. Some possible futures are good (teal), others are bad (red).

Image Credit: DeepMind

Reinforcement learning techniques have been refined over time to bolster the efficiency of training, and one of the recently developed techniques is called distributional reinforcement learning.

The amount of future reward that will result from a particular action is often not a known quantity, but instead involves some randomness. In such situations, a standard TD algorithm learns to predict the future reward that will be received on average, while a distributional reinforcement algorithm predicts the full spectrum of rewards.

Its not unlike how dopamine neurons function in the brains of animals. Some neurons represent reward prediction errors, meaning they fire i.e., send electrical signals upon receiving more or less reward than expected. Its called the reward prediction error theory a reward prediction error is calculated, broadcast to the brain via dopamine signal, and used to drive learning.

Above: Each row of dots corresponds to adopamine cell, and each color corresponds to a different reward size.

Image Credit: DeepMind

Distributional reinforcement learning expands upon the canonical reward prediction error theory of dopamine. It was previously thought that reward predictions were represented only as a single quantity, supporting learning about the mean or average of stochastic (i.e., randomly determined) outcomes, but the work suggests that the brain in fact considers a multiplicity of predictions. In the brain, reinforcement learning is driven by dopamine, said DeepMind research scientist Zeb Kurth-Nelson. What we found in our paper is that each dopamine cell is specially tuned in a way that makes the population of cells exquisitely effective at rewiring those neural networks in a way that hadnt been considered before.

One of the simplest distributional reinforcement algorithms distributional TD assumes that reward-based learning is driven by a reward prediction error that signals the difference between received and anticipated rewards. As opposed to traditional reinforcement learning, however, where the prediction is represented as a single quantity the average over all potential outcomes weighted by their probabilities distributional reinforcement uses several predictions that vary in their degree of optimism about upcoming rewards.

A distributional TD algorithm learns this set of predictions by computing a prediction error describing the difference between consecutive predictions. A collection of predictors within apply different transformations to their respective reward prediction errors, such that some predictors selectively amplify or overweight their reward errors. When the reward prediction error is positive, some predictors learn a more optimistic reward corresponding to a higher part of the distribution, and when the reward prediction is negative, they learn more pessimistic predictions. This results in a diversity of pessimistic or optimistic value estimates that capture the full distribution of rewards.

Above: As a population, dopamine cells encode the shape of the learned reward distribution:We can decode the distribution of rewards from their firing rates. The gray shaded area is the true distribution of rewards encountered in the task.

Image Credit: DeepMind

For the last three decades, our best models of reinforcement learning in AI have focused almost entirely on learning to predict the average future reward. But this doesnt reflect real life, said DeepMind research scientist Will Dabney. [It is in fact possible] to predict the entire distribution of rewarding outcomes moment to moment.

Distributional reinforcement learning is simple in its execution, but its highly effective when used with machine learning systems its able to increase performance by a factor of two or more. Thats perhaps because learning about the distribution of rewards gives the system a more powerful signal for shaping its representation, making it more robust to changes in the environment or a given policy.

The study, then, sought to determine whether the brain uses a form of distributional TD. The team analyzed recordings of dopamine cells in 11 mice that were made while the mice performed a task for which they received stimuli. Five mice were trained on a variable-probability task, while six were trained on a variable-magnitude task. The first group was exposed to one of four randomized odors followed by a squirt of water, an air puff, or nothing. (The first odor signaled a 90% chance of reward, while the second, third, and fourth odors signaled a 50% chance of reward, 10% chance of reward, and 90% chance of reward, respectively.)

Dopamine cells change their firing rate to indicate a prediction error, meaning there should be zero prediction error when a reward is received thats the exact size a cell predicted. With that in mind, the researchers determined the reversal point for each cell the reward size for which a dopamine cell didnt change its firing rate and compared them to see if there were any differences.

They found that some cells predicted large amounts of reward, while others predicted little reward, far beyond the differences that might be expected from variability. They again saw diversity after measuring the degree to which the different cells exhibited amplifications of positive versus negative expectations. And they observed that the same cells that amplified their positive prediction errors had higher reversal point, indicating they were tuned to expect higher reward volumes.

Above: Complex 3D shapes emerge from a string of amino acids.

Image Credit: DeepMind

In a final experiment, the researchers attempted to decode the reward distribution from the firing rates of the dopamine cells. They report success: By performing inference, they managed to reconstruct a distribution that was a match to the actual distribution of rewards in the task in which the mice were engaged.

As the work examines ideas that originated within AI, its tempting to focus on the flow of ideas from AI to neuroscience. However, we think the results are equally important for AI, said DeepMind director of neuroscience research Matt Botvinick. When were able to demonstrate that the brain employs algorithms like those we are using in our AI work, it bolsters our confidence that those algorithms will be useful in the long run that they will scale well to complex real-world problems and interface well with other computational processes. Theres a kind of validation involved: If the brain is doing it, its probably a good idea.

The second of the two papers details DeepMinds work in the area of protein folding, which began over two years ago. As the researchers note, the ability to predict a proteins shape is fundamental to understanding how it performs its function in the body. This has implications beyond health and could help with a number of social challenges, like managing pollutants and breaking down waste.

The recipe for proteins large molecules consisting of amino acids that are the fundamental building block of tissues, muscles, hair, enzymes, antibodies, and other essential parts of living organisms are encoded in DNA. Its these genetic definitions that circumscribe their three-dimensional structure, which in turn determines their capabilities. Antibody proteins are shaped like a Y, for example, enabling them to latch onto viruses and bacteria, while collagen proteins are shaped like cords, which transmit tension between cartilage, bones, skin, and ligaments.

But protein folding, which occurs in milliseconds, is notoriously difficult to determine from a corresponding genetic sequence alone. DNA contains only information about chains of amino acid residues and not those chains final form. In fact, scientists estimate that because of the incalculable number of interactions between the amino acids, it would take longer than 13.8 billion years to figure out all the possible configurations of a typical protein before identifying the right structure (an observation known as Levinthals paradox).

Thats why instead of relying on conventional methods to predict protein structure, such as X-ray crystallography, nuclear magnetic resonance, and cryogenic electron microscopy, the DeepMind team pioneered a machine learning system dubbed AlphaFold. It predicts the distance between every pair of amino acids and the twisting angles between the connecting chemical bonds, which it combines into a score. A separate optimization step refines the score through gradient descent (a mathematical method of improving the structure to better match the predictions), using all distances in aggregate to estimate how close the proposed structure is to the right answer.

The most successful protein folding prediction approaches thus far have leveraged whats known as fragment assembly, where a structure is created through a sampling process that minimizes a statistical potential derived from structures in the Protein Data Bank. (As its name implies, the Protein Data Bank is an open source repository of information about the 3D structures of proteins, nucleic acids, and other complex assemblies.) In fragment assembly, a structure hypothesis is modified repeatedly, typically by changing the shape of a short section while retaining changes that lower the potential, ultimately leading to low potential structures.

With AlphaFold, DeepMinds research team focused on the problem of modeling target shapes from scratch without drawing on solved proteins as templates. Using the aforementioned scoring functions, they searched the protein landscape to find structures that matched their predictions and replaced pieces of the protein structure with new protein fragments. They also trained a generative system to invent new fragments, which they used along with gradient descent optimization to improve the score of the structure.

The models trained on structures extracted from the Protein Data Bank across 31,247 domains, which were split into train and test sets comprising 29,427 and 1,820 proteins, respectively. (The results in the paper reflect a test subset containing 377 domains.) Training was split across eight graphics cards, and it took about five days to complete 600,000 steps.

The fully trained networks predicted the distance of every pair of amino acids from the genetic sequences it took as its input. A sequence with 900 amino acids translated to about 400,000 predictions.

Above: The top figure features the distance matrices for three proteins, where the brightness of each pixel represents the distance between the amino acids in the sequence comprising the protein. The bottom row shows the average of AlphaFolds predicted distancedistributions.

Image Credit: DeepMind

AlphaFold participated in the December 2018 Critical Assessment of protein Structure Prediction competition (CASP13), a competition that has been held every every two years since 1994 and offers groups an opportunity to test and validate their protein folding methods. Predictions are assessed on protein structures that have been solved experimentally but whose structures have not been published, demonstrating whether methods generalize to new proteins.

AlphaFold won the 2018 CASP13 by predicting the most accurate structure for 24 out of 43 proteins. DeepMind contributed five submissions chosen from eight structures produced by three different variations of the system, all of which used potentials based on the AI model distance predictions, and some of which tapped structures generated by the gradient descent system. DeepMind reports that AlphaFold performed particularly well in the free modeling category, creating models where no similar template exists. In point of fact, it achieved a summed z-score a measure of how well systems perform against the average of 52.8 in this category, ahead of 36.6 for the next-best model.

The 3D structure of a protein is probably the single most useful piece of information scientists can obtain to help understand what the protein does and how it works in cells, wrote head of the UCL bioinformatics group David Jones, who advised the DeepMind team on parts of the project. Experimental techniques to determine protein structures are time-consuming and expensive, so theres a huge demand for better computer algorithms to calculate the structures of proteins directly from the gene sequences which encode them, and DeepMinds work on applying AI to this long-standing problem in molecular biology is a definite advance. One eventual goal will be to determine accurate structures for every human protein, which could ultimately lead to new discoveries in molecular medicine.

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The Importance of Understanding TargetProtein Interactions in Drug Discovery – Technology Networks

Youre unwell, you see a doctor, they prescribe you a medicine and you take it. But how exactly is that drug having an effect? What is its mechanism of action? Drugs exhibit their effects through specific protein-target interactions.

But in some cases, there may not be a treatment available. In approximately 30% of cases, drugs fail during clinical development, and toxicity which can be caused by off-target binding is often to blame.

Andrew Lynn, Chief Executive Officer at Fluidic Analytics discusses why understanding proteintarget interactions is so important, the common challenges researchers face when attempting to determine these interactions, and touches on the relationship between the drug "attrition rate" crisis and the off-target effects of drugs.

Laura Lansdowne (LL): Could you discuss the importance of understanding proteintarget interactions in drug discovery, and the implications of not knowing your target?Andrew Lynn (AL): Understanding proteintarget interactions is crucial we are talking about the difference between finding a lifesaving drug/therapy and wasting hundreds of millions of dollars developing a drug with the wrong mechanism of action.A recent paper from Jason Sheltzers group showed that ten anticancer drugs undergoing clinical trials had a completely different mechanism of action from the one originally attributed to them. Briefly, when the protein targeted by each of the drugs was removed from cancer cells, the group expected the drugs to stop working. But what they found was that the drugs continued to work as normal and thus had to be working through off-target binding.This is crucial because it means potentially there are many more drugs out there that are working through off-target binding; it also means that many other drug candidates that have previously been disregarded may have unrecognized promise. This problem is about to become even more acute as research expands into conditions with difficult targets like Alzheimer's disease.The way in which we discover the exact mechanism of action between proteins and potential drug candidates needs better technologies for characterizing on-target and off-target interactions We cannot discover new information relying solely on technologies that have fallen short for decades.LL: What challenges do drug discovery researchers face when trying to identify targetprotein interactions?AL: Drug discovery and development is a lengthy, complex and costly process with a high degree of uncertainty whether a drug will succeed. The two biggest challenges are: First, not understanding the pathophysiology of many disorders, such as neurodegenerative disorders, which makes target identification challenging. Second, the lack of validated diagnostic and therapeutic biomarkers to objectively detect and measure biological states.At the heart of both challenges is the ability to characterize protein-drug target interactions. Unfortunately, the methods currently employed by researchers to do this research are outdated.

An example of this can be seen when scientists try to characterize interactions involving intrinsically disordered proteins (IDPs) such as the ones associated with Parkinsons disease. Current characterization methods modify proteins by fixing them to a surface or putting them in artificial environments. So, its no surprise that many drugs are great at targeting proteins with these modifications but poor at targeting these same proteins as they exist in vivo in solution and not tethered to an artificial surface.

This is why were building new tools and methods for researchers to more accurately characterize binding events in solution: to better understand how drugs interact with their protein targets in their native environment.

LL: What is microfluidic diffusional sizing and how can this be used to measure the binding affinity of proteinprotein interactions?AL: Microfluidic diffusional sizing (MDS) characterizes proteins and their interactions in solution based on the size (or more specifically hydrodynamic radius) of proteins and protein complexes as they diffuse within a microfluidic laminar flow. Characterizing in solution avoids artefacts from surfaces or matrices; gathering information about size to give crucial insights into stoichiometry, on- and off-target binding, oligomerization and folding.

MDS can be used to measure binding affinity by tracking changes in the size of a protein as it binds at different concentrations. The size of the complex can also give a strong indication of whether the protein is forming a protein-target complex at the expected size (on-target binding) or something with a completely different or unexpected size (off-target binding). A major additional advantage of MDS is that, because of the absence of surfaces or matrices, it can be used to characterize binding involving difficult targets such as intrinsically disordered proteins and membrane proteins.

LL: Could you discuss the relationship between the drug "attrition rate" crisis and the off-target effects of drugs?AL: Compound failure rates due to toxicity before human testing is very high. A recent review from a top-20 pharma company cited toxicity as the reason why, between 2005-2010, 82% of drugs were rejected at the preclinical stage and 35% in phase 2a. Overall, concerns surrounding toxicity account for as much as 30% of drug attrition occurring during the clinical stage of development.For many potential drugs, toxicity is due to off-target binding. By employing new methods to characterize drug candidates binding to protein targets in native conditions, we can identify off-target binding more effectively. This could help save billions of dollars in development costs and reduce the attrition rate we are currently facing.

LL: There has currently been very limited success in the development of effective therapies for Alzheimers disease (AD). Could you touch on some of the successes and highlight the molecules of interest in AD as well as the challenges related to their study.AL: One recent success is the anti-amyloid drug, aducanumab. After Biogen re-examined the data from the clinical trials, they found that exposure to high doses of Aducanumab reduced clinical decline in patients exhibiting early stages of Alzheimers disease.If approved, aducanumab would become the first therapy to slow the cognitive decline that accompanies Alzheimer's disease. This a massive step forward and a much-needed source of hope for patients and their families.But aducanumab doesnt cure Alzheimers disease. A major challenge impeding the development of further AD drugs is the ability to understand the mechanism of action via which candidate drugs interact with targets. Amyloid- is known to be a particularly difficult-to-characterize peptide, and even aducanumab doesnt have a well-understood mechanism of action. Any breakthroughs in being able to characterize how it or other Alzheimers disease drugs interact with difficult targets would be a major breakthrough in drug development.However, the majority of Alzheimers patients do not carry the dominantly inherited genetic mutation for the disease, and we dont know why amyloid proteins aggregate within their brains.

It follows that there wont be a single cause but rather many causes. Thus, the common consensus is that there wont be a single miracle drug that cures Alzheimers disease for everyone.

Andrew Lynn was speaking with Laura Elizabeth Lansdowne, Senior Science Writer, Technology Networks.

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Gocycle to partner with nutrition brand Fuel10k to promote benefits of e-bikes – Bike Biz

Gocycle is set to partner with protein breakfast brand Fuel10k to increase awareness of how e-bikes can help more people to lead an active lifestyle.

Fuel10k will give away five fast-folding Gocycle GX electric bikes as part of its biggest-ever on-pack promotion between January and April.

The GX will feature on three million of the brands high-protein breakfast drinks and porridge pots in outlets nationwide.

Richard Thorpe, Gocycle designer and founder, said: We are really excited about the opportunity to spread the message of the enormous health benefits of e-bikes to millions of people across the UK. E-bikes are the perfect travel solution for people who want to lead a more active and sustainable lifestyle and above all they are fun!

This partnership is all about fuelling more people to lead a more active lifestyle in the long-term. E-bikes are a great way to get back out onto two wheels. Having the electrical assistance on tap removes many of the daunting elements of cycling and encourages more people to cycle more of the time which can only be a good thing.

Individuals can enter the competition by purchasing a Fuel10k breakfast drink or porridge pot that features a Gocycle on the packaging. They will be presented with a unique code which they can enter on Fuel10ks competition site to be in with a chance of winning a fast-folding Gocycle GX and other prizes such as sports T-shirts, water bottles or discount codes.

Scott Chassels, Fuel10k managing director, added: We are an increasingly time-poor society and everyone seems to be busier than ever, but that shouldnt be at the detriment of our health. Fuel10k exists to give people a better for you, protein-based, breakfast on-the-go, which helps them to maximise the precious little time they have in the morning and fuel their active day ahead.

We are really excited by this partnership as e-bikes can really enhance the lifestyles of busy people by helping them to have a healthier, more sustainable and speedier commute.

The fast-folding Gocycle GX is available to order now online and through select resellers throughout US, Canada, UK, and EU.

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How Much Does it Really Cost to Be a Vegan? – VEGWORLD Magazine

A vegan lifestyle has a reputation for being expensive, but thanks to Joybirds research, the cost of following a vegan diet doesn't have to break the bank.

The Cost of a Vegan Diet vs a Non-Vegan Diet

Health and wellness are often the first things that come to mind when you hear the word resolution, especially this time of year. The Joybird team spent time exploring some of the most popular trends in diet and exercise, including the potential benefits of a vegan diet, in which a person does not consume any animal products (a.k.a. No dairy, eggs, meat, etc.). However, veganism has a reputation for being expensive, and people are often discouraged by the potential price before they even give it a shot. So, Joybird shares their exploration of how much more expensive a vegan diet is versus a non-vegan diet.

The Research

The Joybird team collected prices from local online groceries in every state to find the average cost of 10 common food items that appear on weekly grocery lists outside of produce, along with their vegan substitutes. They compared the total average cost for the 10 non-vegan and comparable vegan items to calculate the cost difference between the two grocery lists in each state. They even included items that would need a substitute, so produce wasnt included in the study.

The non-vegan items in the study include Greek yogurt, ground beef patties, shredded mozzarella cheese, ice cream, spreadable butter, chicken nuggets, coffee creamer, turkey slices, whole milk, and Italian sausage. The vegan items include dairy-free yogurt, meat-free burger patties, dairy-free shredded mozzarella cheese, non-dairy ice cream, buttery spread, meat-free chicken nuggets, almond milk coffee creamer, veggie turkey slices, almond milk, and tofurkey Italian sausage.

The prices come from Walmart groceries in up to 10 zip codes in each state, in both urban and rural areas in each state. The numbers reflect prices only, taken from the retailers, and do not include any additional taxes or fees that may be incurred. Pricing data was, unfortunately, unavailable for Hawaii.

What They Found

The national average difference between the vegan and non-vegan food items came in at $12.02. 22 states difference fell above the national average, with the rest falling below.

The state with the largest cost difference between the vegan and non-vegan items is Alaska with an average difference of $14.84. The next four states with the largest difference in cost are Arkansas ($14.53), Arizona ($14.31), Michigan ($13.57), and Wyoming ($13.23).

The state with the smallest cost difference between the vegan and non-vegan items is Louisiana with an average difference of $9.82. The next four states with the smallest difference in cost are Massachusetts ($10.52), Nevada ($10.60), New Hampshire ($10.66), and California ($10.68). It was most surprising to see a state like California in the bottom 5 since theyre often known for higher than average prices, but its likely that they have a larger vegan population, so they need to cater to that accordingly.

The Joybird team also compared the average cost difference in each U.S. region. The Northeast has the smallest difference in price in vegan and non-vegan items with an average of $11.41. The Midwest and the West tied for the most expensive, with a difference of $12.26, which is still only slightly higher than the national average.

You can see the details of how your states average costs for non-vegan and vegan food items compare to the rest of the country in the chart below.


Overall, there ended up being no great difference in cost between the non-vegan and vegan food items, showing you dont have to break the bank to adopt a healthier eating plan!

Whether you choose to try out a vegan or vegetarian diet in the New Year or opt for a meat-friendly meal plan instead, your food choices should be a reflection of who you are and what you believe in.

Thank you @Joybird for contributing this article! Find the source here:

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