Forecasts by Application (Drug Discovery, Precision Medicine, Medical Imaging & Diagnostics, Research), by Technology (Machine Learning, Other Technologies), by Offering (Hardware, Software, Services), by Deployment (Cloud, On-Premises) AND Regional and Leading National Market Analysis PLUS Analysis of Leading AI Companies AND COVID-19 Recovery ScenariosNew York, April 15, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence (AI) in Pharmaceutical Market Report 2021-2031" - https://www.reportlinker.com/p06061831/?utm_source=GNW How Adoption of Artificial Intelligence Impacting Pharmaceutical Industry? The global pharmaceuticals industry is in the throes of transition, both clinical trials and regulatory approvals have been challenged by the proliferation of specialized drugs catering to smaller patient groups. In order to boost results in drug discovery, clinical trials, and research and development, the pharmaceutical and life sciences companies are switching to robotic process control, artificial intelligence and machine learning. All these factors are anticipated to propel demand for artificial intelligence in pharmaceutical across the globe. Artificial Intelligence Anticipated to Revolutionize Several Aspects of Pharmaceutical Industry The way drugs are made, prescribed and ingested today will be standardized by artificial intelligence. Many facets of the pharmaceuticals and life sciences industry will also be revolutionized but will not heal sickness or replace physicians. Understanding the main aim of artificial intelligence, which is to improve human capability and accomplishment instead of challenging it, would dispel much of the technology’s concerns and put out its excellent ability to serve humanity. Which Factors are Fueling AI in Pharmaceuticals Industry Growth? . Growing Complexity of Modern Pharmacology . Growing Demand for Viable Therapeutic Candidates . Improves Overall R&D Productivity . Concerns Associated with Rising Capital Requirements in Drug Discovery . Increasing Awareness Related to Artificial Intelligence Among Pharmaceutical Manufacturers Which Factors are Restraining Growth? . Lack of Skilled Professionals . Limited Availability of Datasets UNIQUE COVID-19 VARIATIONS- only available in this Visiongain report are dedicated analysis of 4 different rebound scenarios of how the market will develop - no matter how COVID-19 affects the economy. To access the data contained in this document please email email@example.com How do prominent players strengthen their position throughout the world? You must read this newly updated report if you are involved in this sector. The report from Visiongain shows you potential revenues up to 2031, evaluate information, trends, opportunities and business outlooks. Discover how to stay ahead Our 350+ page report provides 500+ tables and charts/graphs. Read on to discover the most lucrative areas in the industry and the future market prospects. Our new study lets you assess forecasted sales at overall world market and regional level. See financial results, trends, opportunities, and revenue predictions. Much opportunity remains in this growing AI in Pharmaceuticals Market. See how to exploit the opportunities. Forecasts to 2031 and other analyses reveal the commercial prospects . In addition to revenue forecasting to 2031, our new study provides you with recent results, growth rates, and market shares. . You find original analyses, with business outlooks and developments. . Discover qualitative analyses (including market dynamics, drivers, opportunities, restraints and challenges), SWOT Analysis, PEST Analysis, Porter’s Analysis, product profiles and commercial developments. Discover sales predictions for the world market and submarkets Application . Drug Discovery . Precision Medicine . Medical Imaging & Diagnostics . Research Technology . Machine Learning . Other Technologies Offering . Hardware . Software . Services Deployment . Cloud . On-Premises In addition to the revenue predictions for the overall world market and segments, you will also find revenue forecasts for 5 regional and 13 leading national markets: By Region (Segmental Breakdown for All the Regions) . North America - U.S. - Canada . Europe - Germany - France - UK - Italy - Spain - Rest of Europe . Asia Pacific - China - Japan - India - Rest of Asia Pacific . RoW Need industry data? Please contact us today. Leading companies and the potential for market growth Overall world revenue for AI in Pharmaceuticals Market will surpass $xx billion in 2021, our work calculates. We predict strong revenue growth through to 2031. Our work identifies which organizations hold the greatest potential. Discover their capabilities, progress, and commercial prospects, helping you stay ahead. Prospects for established firms and those seeking to enter the market- including company profiles for 16 of the major companies involved in the AI in Pharmaceuticals Market. Some of the companies profiled in this report include are Microsoft Corporation, NVIDIA Corporation, IBM Corporation, Alphabet Inc., Atomwise, Inc., Deep Genomics, Cloud Pharmaceuticals, Inc., Insilico Medicine, BenevolentAI, Exscientia, Biosymetrics, Euretos, Insitro, Cyclica, Biovista, and OWKIN, INC. Key Questions Answered by this Report . What is the current size of the overall global AI in Pharmaceuticals market? How much will this market be worth from 2021 to 2031? . What are the main drivers and restraints that will shape the overall AI in Pharmaceuticals market over the next ten years? . What are the main segments within the overall AI in Pharmaceuticals market? How much will each of these segments be worth for the period 2021 to 2031? How will the composition of the market change during that time, and why? . What factors will affect that industry and market over the next ten years? . What are the largest national markets for the world AI in Pharmaceuticals? What is their current status and how will they develop over the next ten years? What are their revenue potentials to 2031? . How will market shares of the leading national markets change by 2031, and which geographical region will lead the market in 2031? . Which are the leading companies and what are their activities, results, developments, and prospects? . What are the main trends that will affect the world AI in Pharmaceuticals market between 2021 and 2031? . What are the main strengths, weaknesses, opportunities, and threats for the market? . How will the global AI in Pharmaceuticals market evolve over the forecasted period, 2021 to 2031? . How will market shares of prominent national markets change from 2021, and which countries will lead the market in 2031, achieving highest revenues and fastest growth? Read the full report: https://www.reportlinker.com/p06061831/?utm_source=GNWAbout ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.__________________________ CONTACT: Clare: firstname.lastname@example.org US: (339)-368-6001 Intl: +1 339-368-6001
To better understand these surprising results, we reanalyzed the associated data. We were unable to reproduce the original findings, nor could we identify reliably cycling genes. We conclude that there is insufficient evidence to support circadian transcriptional rhythms in the absence of Bmal1. Recently, Ray et al. (1) reported transcriptional rhythmicity in mouse tissues lacking BMAL1. BMAL1 is a core component of the circadian molecular oscillator (2) whose deletion is associated with loss of physiological and molecular rhythms (3).
A year into the severe acute respiratory syndrome coronavirus 2 pandemic, we are experiencing waves of new variants emerging. Some of these variants have worrying functional implications, such as increased transmissibility or antibody treatment escape. Lythgoe et al. have undertaken in-depth sequencing of more than 1000 hospital patients' isolates to find out how the virus is mutating within individuals. Overall, there seem to be consistent and reproducible patterns of within-host virus diversity. The authors observed only one or two variants in most samples, but a few carried many variants. Although the evidence indicates strong purifying selection, including in the spike protein responsible for viral entry, the authors also saw evidence for transmission clusters associated with households and other possible superspreader events. After transmission, most variants fizzled out, but occasionally some initiated ongoing transmission and wider dissemination. Science , this issue p. [eabg0821] ### INTRODUCTION Genome sequencing at an unprecedented scale during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is helping to track spread of the virus and to identify new variants. Most of this work considers a single consensus sequence for each infected person. Here, we looked beneath the consensus to analyze genetic variation within viral populations making up an infection and studied the fate of within-host mutations when an infection is transmitted to a new individual. Within - host diversity offers the means to help confirm direct transmission and identify new variants of concern. ### RATIONALE We sequenced 1313 SARS-CoV-2 samples from the first wave of infection in the United Kingdom. We characterized within-host diversity and dynamics in the context of transmission and ongoing viral evolution. ### RESULTS Within-host diversity can be described by the number of intrahost single nucleotide variants (iSNVs) occurring above a given minor allele frequency (MAF) threshold. We found that in lower-viral-load samples, stochastic sampling effects resulted in a higher variance in MAFs, leading to more iSNVs being detected at any threshold. Based on a subset of 27 pairs of high-viral-load replicate RNA samples (>50,000 uniquely mapped veSEQ reads, corresponding to a cycle threshold of ~22), iSNVs with a minimum 3% MAF were highly reproducible. Comparing samples from two time points from 41 individuals, taken on average 6 days apart (interquartile ratio 2 to 10), we observed a dynamic process of iSNV generation and loss. Comparing iSNVs among 14 household contact pairs, we estimated transmission bottleneck sizes of one to eight viruses. Consensus differences between individuals in the same household, where sample depth allowed iSNV detection, were explained by the presence of an iSNV at the same site in the paired individual, consistent with direct transmission leading to fixation. We next focused on a set of 563 high-confidence iSNV sites that were variant in at least one high-viral-load sample (>50,000 uniquely mapped); low-confidence iSNVs unlikely to represent genomic diversity were excluded. Within-host diversity was limited in high-viral-load samples (mean 1.4 iSNVs per sample). Two exceptions, each with >14 iSNVs, showed variant frequencies consistent with coinfection or contamination. Overall, we estimated that 1 to 2% of samples in our dataset were coinfected and/or contaminated. Additionally, one sample was coinfected with another coronavirus (OC43), with no detectable impact on diversity. The ratio of nonsynonymous to synonymous ( dN/dS ) iSNVs was consistent with within-host purifying selection when estimated across the whole genome [ dN/dS = 0.55, 95% confidence interval (95% CI) = 0.49 to 0.61] and for the Spike gene ( dN/dS = 0.60, 95% CI = 0.45 to 0.82). Nevertheless, we observed Spike variants in multiple samples that have been shown to increase viral infectivity (L5F) or resistance to antibodies (G446V and A879V). We observed a strong association between high-confidence iSNVs and a consensus change on the phylogeny (153 cases), consistent with fixation after transmission or de novo mutations reaching consensus. Shared variants that never reached consensus (261 cases) were not phylogenetically associated. ### CONCLUSION Using robust methods to call within-host variants, we uncovered a consistent pattern of low within-host diversity, purifying selection, and narrow transmission bottlenecks. Within-host emergence of vaccine and therapeutic escape mutations is likely to be relatively rare, at least during early infection, when viral loads are high, but the observation of immune-escape variants in high-viral-load samples underlines the need for continued vigilance. ![Figure] Diagram showing low SARS-CoV-2 within-host genetic diversity and narrow transmission bottleneck. Individuals with high viral load typically have few, if any, within-host variants. Narrow transmission bottlenecks mean that the major variant in the source individual was typically transmitted and the minor variants lost. Occasionally, the minor variant was transmitted, leading to a consensus change, or multiple variants were transmitted, resulting in a mixed infection. Credit: FontAwesome, licensed under CC BY 4.0. Extensive global sampling and sequencing of the pandemic virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have enabled researchers to monitor its spread and to identify concerning new variants. Two important determinants of variant spread are how frequently they arise within individuals and how likely they are to be transmitted. To characterize within-host diversity and transmission, we deep-sequenced 1313 clinical samples from the United Kingdom. SARS-CoV-2 infections are characterized by low levels of within-host diversity when viral loads are high and by a narrow bottleneck at transmission. Most variants are either lost or occasionally fixed at the point of transmission, with minimal persistence of shared diversity, patterns that are readily observable on the phylogenetic tree. Our results suggest that transmission-enhancing and/or immune-escape SARS-CoV-2 variants are likely to arise infrequently but could spread rapidly if successfully transmitted. : /lookup/doi/10.1126/science.abg0821 : pending:yes
If you've been a reader of Sprudge for any reasonable amount of time, you've no doubt by now ready multiple articles about how coffee is potentially beneficial for some particular facet of your health. The stories generally go like this: "a study finds drinking coffee is associated with a X% decrease in [bad health outcome]" followed shortly by "the study is observational and does not prove causation." In a new study in the American Heart Association's journal Circulation: Heart Failure, researchers found a link between drinking three or more cups of coffee a day and a decreased risk of heart failure. This study used machine learning to get to its conclusion, and it may significantly alter the utility of this sort of study in the future. As reported by the New York Times, the new study isn't exactly new at all.
Register for a free or VIP pass today. The past several years have made it clear that AI and machine learning are not a panacea when it comes to fair outcomes. Applying algorithmic solutions to social problems can magnify biases against marginalized peoples; undersampling populations always results in worse predictive accuracy. But bias in AI doesn't arise from the datasets alone. Problem formulation, or the way researchers fit tasks to AI techniques, can contribute.
Children who struggle with memory issues and have a poor attention span are more likely to develop mental health conditions when they become adults, study shows. Researchers from the University of Birmingham analysed data from a cohort of 13,988 individuals born in 1991 and 1992 and re-examined over decades. They set out to look for any links between childhood cognitive problems such as lack of control and memory issues, and later life mental health conditions. They found that poor attention span in eight year olds could lead to depression at 18, and memory problems at ten could lead to hypomania when they are 22 years old. Targeting specific markers in childhood for early treatment may help to minimise the risk of developing certain psychopathological problems later in life.
A treatment that combines an artificial intelligence therapist with psychedelic drugs to treat depression and addiction has been approved for clinical trials. Life science firm atai and digital therapeutics specialist Psyber are working on a brain computer interface (BCI) based around an electroencephalogram (EEG). The system will allow for the automated monitoring and assessment of patients who have been prescribed psychedelic drugs to treat mental health conditions. The device will record electrical activity in the brain for real-time interpretation of emotional, behavioural, and mental states, providing the patient with live feedback. Dr Srinivas Rao, Co-founder and Chief Scientific Officer at atai told MailOnline the goal was to democratise therapy and provide direct, and instant treatment to people in rural locations that may not have access to a licensed psychiatrist.
We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer’s Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.
A team of University of Illinois researchers estimated the mortality costs associated with air pollution in the U.S. by developing and applying a novel machine learning-based method to estimate the life-years lost and cost associated with air pollution exposure. Scholars from the Gies College of Business at Illinois studied the causal effects of acute fine particulate matter exposure on mortality, health care use and medical costs among older Americans through Medicare data and a unique way of measuring air pollution via changes in local wind direction. The researchers--Tatyana Deryugina, Nolan Miller, David Molitor and Julian Reif--calculated that the reduction in particulate matter experienced between 1999-2013 resulted in elderly mortality reductions worth $24 billion annually by the end of that period. Garth Heutel of Georgia State University and the National Bureau of Economic Research was a co-author of the paper. "Our goal with this paper was to quantify the costs of air pollution on mortality in a particularly vulnerable population: the elderly," said Deryugina, a professor of finance who studies the health effects and distributional impact of air pollution.
The Conference on Neural Information Processing Systems, held in 2019 in Vancouver, Canada, is the largest in the discipline of artificial intelligence. Artificial intelligence (AI) researchers are hoping to use the tools of their discipline to solve a growing problem: how to identify and choose reviewers who can knowledgeably vet the rising flood of papers submitted to large computer science conferences. In most scientific fields, journals act as the main venues of peer review and publication, and editors have time to assign papers to appropriate reviewers using professional judgment. But in computer science, finding reviewers is often by necessity a more rushed affair: Most manuscripts are submitted all at once for annual conferences, leaving some organizers only a week or so to assign thousands of papers to a pool of thousands of reviewers. This system is under strain: In the past 5 years, submissions to large AI conferences have more than quadrupled, leaving organizers scrambling to keep up.