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One of the first community-based population health studies in the US, the Healthy Nevada Project launched in 2016 with three straightforward goals: conduct sound science, improve health, and save lives. Now among the nations largest such studies, the ground-breaking health and genetics project is three for three.

Developed by theDesert Research Institute Center for Genomic Medicine, the Healthy Nevada Project offers genetic testing at no cost to Nevada residents who want to learn more about their health and genetic profile.

By combining genetic data, environmental data and individual health information, researchers and physicians are gaining new insights into population health, enabling personalised healthcare while improving the health and wellbeing of entire communities in the state.

Painting an accurate portrait of an individual or population to help understand and anticipate health outcomes requires data representing many life factors, including genetics, socioeconomic backgrounds, physical environments, lifestyle behaviours and quality of healthcare.

One of medicines most complicated questions is, how do you predict what someones health outcome is going to be? says Joseph Grzymski, PhD, who serves as principal investigator of the Healthy Nevada Project, chief scientific officer of Renown Health, and research professor of computational biology and genetics at theDesert Research Institute. Its not just genetics, or your blood pressure or where you live, its trying to model all the impacting factors for diseases. The massive challenge of population health studies is to build better predictive models to understand why some people get sick and others dont, why some live to be 90 and above, and determine what that magical equation is.

Working in tandem with experts in environmental data at the Desert Research Institute, the Center for Genomic Medicine fuels the project with de-identified electronic health records. Researchers supplement this with data from the Environmental Protection Agency (EPA), the US Census Bureau, birth and death records, and other data sources to build a population health portrait.

To form connections between participant genetic information and other health factors, data scientists applymachine learningandartificial intelligencecapabilities to DNA results generated byHelix, a partner specialising in population genomics.

Were working to understand how environmental and other factors can help predict who may be at risk, allow for quicker diagnoses and encourage the development of more precise treatments, says Jim Metcalf, chief data scientist of the Healthy Nevada Project. Statistical and machine learning methods, along with the intuitive data visualisations made possible by SAS, have been critical elements.

In addition to using analyticsto identify populations and subpopulations of people who already have a disease in common, project researchers also apply analytics to get in front of diseases before they manifest in individuals.

After a participants voluntary genetic testing, the team checks for risks for many serious genomic conditions, including the top three identified by the Centers for Disease Control and Prevention as medically actionable (CDC Tier 1):

Most individuals affected by these genetic risks arent aware they have them. The project has genetic counsellors who will call our participants if they have a particular mutation and inform them, so they can talk with their physician and make important health decisions, Metcalf says.

Healthy Nevada Project participant Jordan Stiteler says the unexpected phone call saved her life.

Stiteler, a young mother, had family members who had suffered heart attacks and strokes at early ages. When she learned she carried the FH marker, she received guidance and support to help her make healthy lifestyle and medication choices. Soon several other family members joined the study to learn about their genetic risks.

Genetic screening also makes it possible to get in front of a cancer diagnosis. The ideal is to detect these mutations prior to any kind of a tumour becoming untreatable, Metcalf says. We have cases where people have told us, Thank you so much, you saved my life, because they were able to have preventive surgery, or they found a treatable Stage I tumour because of the results of genetic testing. Those are the things we live for in this project.

Since its initial 10,000 adult participants, the Healthy Nevada Project has grown to more than 52,000 individuals and expanded from northern Nevada to Las Vegas and its outlying areas in the southern part of the state.

According to Grzymski, more genome data from more people equates to greater statistical power and accuracy in understanding the links between who you are and your health outcomes. The underpinning of a population genetics study is access to data and then the ability to extract, transform and study the data for any of the myriad health outcomes we want to focus on, he says.

Providing the foundation for those efforts is a SAS platform, which the project runs in an on-premises computing environment.

The strength of the language, the depth, everything that SAS brings has been rock solid, Metcalf says. We have used SAS to comb through, manipulate and extract 200 terabytes of genetics and health records data. Setting the right parameters, we can look through a billion-record table of physician notes with no problem.

A data collection endeavour of this magnitude required cooperation between organisations, care protection of privacy, and a means to gain consent from participants. When executive leadership at Renown realised Desert Research Institute had a cadre of skilled data scientists on staff able to independently ingest and analyse Renowns electronic health records (EHR) data, they made the decision to begin sharing EHR data with the Center for Genomic Medicine at Desert Research Institute. Consequently, Desert Research Institute became a Health Insurance Portability and Accountability Act (HIPAA) business associate of Renowns.

Implementing and supporting processes to ensure patient privacy while facilitating research is a technically challenging and mentally taxing effort.The very real overprint of adhering to HIPAA requirements should not be underestimated in terms of project cost structure and staffing effort. The Healthy Nevada Project team works closely with Renowns compliance department and the Institutional Review Board at the University of Nevada, Reno, to ensure it adheres to the highest standards and practices of maintaining participant privacy. Healthy Nevada Project cohorts typically number in the tens of thousands of participants, if not more. The team is not looking at individuals in the EHR and would have great difficulty doing so as nearly all personally identifiable information is removed from the EHR to create a HIPAA-defined limited dataset as the first step of data ingestion.

Collecting genetic data requires receiving consent from participants via documents approved by the University of Nevada Institutional Review Board. Participants agree to be in the study knowing their genetic information and medical record will be used for medical research. Participating in the study is not mandatory and participants can withdraw at any time for any reason. The consent documents are written at an 8th grade level and are heavily vetted and tested for participant understanding.

The Healthy Nevada Project continues to bring a variety of data sources to the table for insights into population health, including:

The team uses SAS statistical models and analyses to report results to hospital administrators and research to the teams scientific peers for review.

The SAS platform has been the foundation bedrock of the Healthy Nevada Project, Metcalf says. We have immersed ourselves in the machine learning and AI procedures that SAS has and use those on a continual basis.

For example, a hospital wanted to reduce the time patients spend in the post-anesthesia care unit or stepdown room after surgery. To understand why some patients required more time there, the Healthy Nevada Project used a variety of SAS procedures, such as variable selection in the analytic process, to facilitate machine learning, allowing researchers to identify and eliminate possible causes as key factors.

The researchers found that the top factors most directly contributing to time spent in the stepdown room were the anesthesia type used, the patients age and the patients relative health.

The Healthy Nevada Project has elevated Nevadas profile in doing cutting-edge research, using data to deliver evidence-based, publishable results in peer-reviewed scientific journals and databases, says Grzymski. The entire team is proud of the work weve delivered and its impact as we continue to understand what makes people sick or well and enable preventive care.

Using Data and Analytics Across the Research Lifecycle to Improve Population Health read the whitepaper here.

About the author Sarah Newton Sarah is the manager of public sector health policy at SAS, helping governments leverage data and analytics to improve the health and wellbeing of their citizens.Sarah has a masters in public health, as well as extensive experience working on health policy at the federal and state level.Sarah can be contacted at [emailprotected].

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Improving US citizens' health though machine learning and AI - Global Government Forum

The Forza series has always wowed with its stunning visuals and commitment to realism in virtual racing. From the very first game's Mount Panorama Circuit to the third's Rally di Positano, and the fifth's Castillo Del Mar there's a certain, simple charm about hammering expensive, expertly-mirrored real-world cars around gorgeous settings; each one crafted with meticulous care. Forza Motorsport, Turn 10 Studio's latest entry to the enduring racing series, follows suit on all of these points.

But it's not just how Forza Motorsport looks and plays that's setting it up for the next evolution of the racing simulation genre, it's the process it's going through to get there.

"Part of what I love about working at Turn 10, and Xbox in general, is that we tackle weird problems that aren't always looked at in gaming," explains Forza general manager Dan Greenawalt. What the Forza mastermind is chiefly referring to here is Motorsport's new approach to machine learning, and during our hands-off demonstration at the Xbox Games Showcase, it feels like this will change the face of the racer entirely.

Greenawalt explains that the Forza series has long used machine learning to power its AI-controlled characters that player races against. In the pre-Xbox One days, this process was owed to a Bayesian machine learning system that operated on the console's local hard drive; but after this was moved onto the cloud. There, Greenawalt and his team used the network to train the Drivatar.

"Now, with the latest Forza, we've taken machine learning and applied it to build time and not run time or load time; so not while it's running or loading, but actually we're able to do it before the game launches. Instead of having machine learning power the moment-to-moment decisions of the Drivatar, we're having it train the Drivatar to control the car, and then we're using an optimizer to make the lines that Drivatar follows."

"What that's allowed us to do is train massive amounts of data so that we can take every car, with every upgrade, and all the tuning options through the wet, through the dry and train that controller so that the AI can make the car do everything it wants it to do. Ultimately, it was about applying the machine learning we were familiar with to a different place."

With that, Greenawalt says applying the machine learning to a different part of Drivatar has led he and his team to the fastest AI with no cheats or hacks, and to a position where they're maximizing their use of the technology in a non-arbitrary way.

Another area in which Forza has excelled throughout its 18-year existence is accessibility, and Motorsport also aims to be the most approachable so far.

Like machine learning, Forza has offered sophisticated assist options throughout its lineage, says Motorsport's creative director, Chris Esaki. Nodding to his colleague standing across the room, Esaki says: "Forza has always had amazing assist, even in the original games. Dan brought the amazing driving line into the world, and he did to simulation racing what Halo has done for shooters, in that he's made it so much more accessible and approachable on control pads."

"Over the years, we've added throttle assist, breaking assist, and turning assist, and you can turn all of those on and off at your will. And we have new additional levels of fidelity around all of those things. We even have one-button driving now, so if you want, you can play the entire game with a single input. If you only want to steer, you can. If you only want to brake, you can too. If you only want to accelerate, that's fine."

Esaki explains that in Forza Motorsport, the above has been evolved further still with the addition of audio assists that help streamline the process more than ever before. In practice, these might tell you when to turn when approaching a corner, or alert you to how near or far away you are to the apex.

He continues: "These audio assist cues can be toggled on and off, to the point where if you turn all of them on, you could literally put a blindfold on and drive around the track. We wanted this to be the most accessible racing simulation ever, no matter what your skill level is. Whether you are near-sighted, whether you're blind, fast or slow, you can have a great time with this game."

Both Esaki and Greenawalt promise more details on the above in the coming months, but at this early stage what Forza Motorsport is working with sounds impressive - all of this, of course, built around the series' signature looks and authentic racing simulation feel.

Forza Motorsport is coming to Xbox Series X, PC, and Xbox One on October 10, 2023.

Link:
Forza Motorsport revolutionizes its use of machine learning to craft ... - Gamesradar

A method that can accurately use machine learning algorithms for Bitcoin price prediction

Bitcoin is probably the most talked about cryptocurrency that is somehow on top of everyones lists when it comes to thinking about investing. However, when it comes to actual investing, most people would like to have some magic ball to see the future instead of exposing themselves to the enormous risk that naturally comes with it. Have you wondered what machine learning could do in this regard? Lets find out. Bitcoin price prediction with machine learning can be made by leveraging the Google Trends search volume index and Baidu media search volume, essential measures of investor attention and media hype that reflect the sentiment in the highly speculative cryptocurrency market.

Also, by integrating gold spot price with regular features such as property, network, trading, and market in the machine learning algorithm, it is possible to develop higher-dimensional features and avoid the problem of simplifying Bitcoin price prediction.

As Bitcoin price fluctuates significantly, machine learning models are applicable and valuable. Various popular machine learning algorithms, including recurrent neural networks, long short-term memory, support vector machines, and random forest models, have therefore been implemented in previous studies.

After the global financial meltdown in 2008, the BTC blockchain was conceived as a new type of currency with a mechanism that can sidestep existing banking systems. Since then, it and other cryptocurrencies have become a prevalent means of exchanging value. Although the platform initially mainly attracted traders who preferred to wager on volatile assets, it has become a new type of investment serving as a keeper of value and protecting against inflation.

Several years ago, retail investors and traders gambled on an increasing price without basing them on reason or facts, which caused previous price oscillations. However, that has changed. As the crypto markets mature, regulatory authorities develop rules specifically for investors. That being said, even though Bitcoin price still fluctuates, many are now considering it the future of the mainstream economy.

The cryptocurrency market is highly volatile, and your investments are at risk. BTC first became available in 2009 and was worthless at the start. Its price increased to US$0.09 in 2010 and US$1.00 by February 2011, after which it surged to US$29.60. The crypto market took a nosedive after that, with BTCs price falling to just US$2.05 by mid-November 2011.

2016 saw a gradual increase, with prices reaching over US$900 before the years end. They skyrocketed by the end of December 2017, reaching US$19,345.49. The coin once again gained friction during the start of the COVID-19 pandemic, reaching US$29,000 by the end of 2020. At present, Bitcoin is hovering around US$26k.

As the digital asset market reels from the SEC crackdown on Binance and Coinbase, Bitcoin and other cryptocurrencies are hovering at crucial levels. Bitcoin has lost 1.8% of its value in the last week, and experts forecast that this trend will continue.

Bitcoin has a value of US$26,568.11 with a market cap of US$515B, down by 0.05% overnight. The Bitcoin trading volume has also taken a hit, falling by 20.22% in that same time and now sitting at US$11,783,962,824. With Bitcoin slowly losing its value, analysts foresee a drop below US$26,000.

In conclusion, as you can tell, the future of the cryptocurrency king is still uncertain, and several possible scenarios could play out. Different approaches are available for Bitcoin price prediction but can never be 100% correct. Whatever happens, it will be interesting to see how the crypto market evolves in the next few years.

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Bitcoin Price Prediction: A Machine Learning Approach - Analytics Insight

By Hub staff report

Johns Hopkins scientists have developed a method involving artificial intelligence to visualize and track changes in the strength of synapsesthe connection points through which nerve cells in the brain communicatein live animals. The technique, described in Nature Methods, should lead to a better understanding of how such connections in human brains change with learning, aging, injury, and disease, the scientists say.

"If you want to learn more about how an orchestra plays, you have to watch individual players over time, and this new method does that for synapses in the brains of living animals," says Dwight Bergles, professor in the Department of Neuroscience at the Johns Hopkins University School of Medicine.

Image caption: Thousands of SEP-GluA2 tagged synapses (shown in green) surround a sparsely labeled dendrite (show in magenta) before and after XTC image resolution enhancement. Scale bar is 5 microns.

Image credit: Xu, Y.K.T., Graves, A.R., Coste, G.I. et al. Nat Methods

Bergles co-authored the study with colleagues Adam Charles and Jeremias Sulam, both assistant professors in the Department of Biomedical Engineering, and Richard Huganir, Bloomberg Distinguished Professor at JHU and director of the neuroscience department. All four researchers are members of Johns Hopkins' Kavli Neuroscience Discovery Institute.

Nerve cells transfer information from one cell to another by exchanging chemical messages at synapses, or junctions. In the brain, the authors explain, different life experiences, such as exposure to new environments and learning skills, are thought to induce changes at synapses, strengthening or weakening these connections to allow learning and memory. Understanding how these minute changes occur across the trillions of synapses in our brains is a daunting challenge, but it is central to uncovering how the brain works when healthy and how it is altered by disease.

To determine which synapses change during a particular life event, scientists have long sought better ways to visualize the shifting chemistry of synaptic messaging, necessitated by the high density of synapses in the brain and their small sizetraits that make them extremely hard to visualize even with new state-of-the-art microscopes.

"We needed to go from challenging, blurry, noisy imaging data to extract the signal portions we need to see," Charles says.

To do so, Bergles, Sulam, Charles, Huganir, and their colleagues turned to machine learning, a computational framework that allows flexible development of automatic data processing tools. Machine learning has been successfully applied to many domains across biomedical imaging, and in this case, the scientists leveraged the approach to enhance the quality of images composed of thousands of synapses. Although it can be a powerful tool for automated detection, greatly surpassing human speeds, the system must first be "trained," teaching the algorithm what high quality images of synapses should look like.

In these experiments, the researchers worked with genetically altered mice in which glutamate receptorsthe chemical sensors at synapsesglowed green, or fluoresced, when exposed to light. Because each receptor emits the same amount of light, the amount of fluorescence generated by a synapse in these mice is an indication of the number of synapses, and therefore its strength.

As expected, imaging in the intact brain produced low quality pictures in which individual clusters of glutamate receptors at synapses were difficult to see clearly, let alone to be individually detected and tracked over time. To convert these into higher quality images, the scientists trained a machine learning algorithm with images taken of brain slices (ex vivo) derived from the same type of genetically altered mice. Because these images weren't from living animals, it was possible to produce much higher quality images using a different microscopy technique, as well as low quality imagessimilar to those taken in live animalsof the same views.

This cross-modality data collection framework enabled the team to develop an enhancement algorithm that can produce higher resolution images from low quality ones, similar to the images collected from living mice. In this way, data collected from the intact brain can be significantly enhanced and able to detect and track individual synapses (in the thousands) during multiday experiments.

To follow changes in receptors over time in living mice, the researchers then used microscopy to take repeated images of the same synapses in mice over several weeks. After capturing baseline images, the team placed the animals in a chamber with new sights, smells, and tactile stimulation for a single five-minute period. They then imaged the same area of the brain every other day to see if and how the new stimuli had affected the number of glutamate receptors at synapses.

Although the focus of the work was on developing a set of methods to analyze synapse level changes in many different contexts, the researchers found that this simple change in environment caused a spectrum of alterations in fluorescence across synapses in the cerebral cortex, indicating connections where the strength increased and others where it decreased, with a bias toward strengthening in animals exposed to the novel environment.

The studies were enabled through close collaboration among scientists with distinct expertise, ranging from molecular biology to artificial intelligence, who don't normally work closely together. The researchers are now using this machine learning approach to study synaptic changes in animal models of Alzheimer's disease, and they believe the method could shed new light on synaptic changes that occur in other disease and injury contexts.

"We are really excited to see how and where the rest of the scientific community will take this," Sulam says.

The experiments in this study were conducted by Yu Kang Xu, a PhD student and Kavli Neuroscience Discovery Institute fellow at JHU; Austin Graves, assistant research professor in biomedical engineering at JHU; and Gabrielle Coste, a neuroscience PhD student at JHU. This research was funded by the National Institutes of Health (RO1 RF1MH121539).

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Machine learning helps scientists see how the brain adapts to ... - The Hub at Johns Hopkins

A chilled CBD-infused Labatt Breweries beverage is coming to a market near you this December.

Fluent Beverage Company, the joint-partnership between the massive brewer Anheuser-Busch Inbev NV (EBR:ABI) and global cannabis pioneer Tilray Inc. (NASDAQ:TLRY), announced this week it will commercialize a non-alcoholic, CBD-infused beverage for Canadians likely hitting markets in December 2019.

Beer drinkers will know Anheuser-Busch by its Canadian subsidiary Labatt Breweries, which employs over 3,400 canucks and brews Budweiser, Kokanee, Stella Artois, Corona, Palm Bay and Mikes Hard Lemonade, to name a few.

The joint venture was announced in December 2018 when High Park, a wholly-owned subsidiary of Tilray, and Labatt partnered to research a non-alcoholic drink containing weed cannabinoids tetrahydrocannabinol (THC) and cannabidiol (CBD).

Each company is investing up to $50 million in the partnership, according to Benzinga.

The companies need more time to research beverages containing THC and will only be providing CBD-drinks in December, Fluents chief executive Jorn Socquet told the Canadian Press.

THC, the intoxicating compound in cannabis, is unstable and degrades too quickly for a reasonable shelf life whereas CBD, the non-intoxicating compound, remains potent and stable for longer, said Socquet.

What the drink will actually look like, taste like, or smell like isnt being revealed, but Socquet told the Canadian Press the non-alcoholic CBD-infused drink will likely be sparkling, slightly sweet and tea-like.

The partnership between Labatt and Tilray comes after two similar beer and weed partnership announcements from August 2019.

Molson Coors Brewing Co. (TSX:TPX.B) and Quebec-based HEXO Corp. (NYSE:HEXO) are partnering to get cannabis-infused non-acloholic drinks to Canadians, and Constellation Brands Inc.(NYSE:STZ)(NYSE:STZ.B) bought a 38 per cent majority share of Canopy Growth Corp. (NYSE:CGC)(TSE:WEED) in August to invest in a similar venture.

Canadians wont be able to crack a cold CBD one till the government passes the second wave of cannabis legalization, set for October 17 which will legalize beverages, edibles, vapes and topicals. Even then consumers will have to wait 60 days while companies give a mandatory notice to Health Canada before drinks sales kick off.

If everything goes according to plan, expect the tsunami of CBD-drinks to hit one week before Christmas.

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Finlay Minerals to use machine-learning to increase exploration success in British Columbia project - Mugglehead