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Assessing Animal Models of Bacterial Pneumonia Used in Investigational New Drug Applications for the Treatment of Bacterial Pneumonia [Experimental Therapeutics]

Animal models of bacterial infection have been widely used to explore the in vivo activity of antibacterial drugs. These data are often submitted to the U.S. Food and Drug Administration to support human use in an investigational new drug application (IND). To better understand the range and scientific use of animal models in regulatory submissions, a database was created surveying recent pneumonia models submitted as part of IND application packages. The IND studies were compared to animal models of bacterial pneumonia published in the scientific literature over the same period of time. In this review, we analyze the key experimental design elements, such as animal species, immune status, pathogens selected, and route of administration, and study endpoints.




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Impact of Daptomycin Dose Exposure Alone or in Combination with {beta}-Lactams or Rifampin against Vancomycin-Resistant Enterococci in an In Vitro Biofilm Model [Susceptibility]

Enterococcus faecium strains are commonly resistant to vancomycin and β-lactams. In addition, E. faecium often causes biofilm-associated infections and these infections are difficult to treat. In this context, we investigated the activity of dosing regimens using daptomycin (DAP) (8, 10, 12, and 14 mg/kg of body weight/day) alone and in combination with ceftaroline (CPT), ampicillin (AMP), ertapenem (ERT), and rifampin (RIF) against 2 clinical strains of biofilm-producing vancomycin-resistant Enterococcus faecium (VREfm), namely, strains S447 and HOU503, in an in vitro biofilm model. HOU503 harbors common LiaS and LiaR substitutions, whereas S447 lacks mutations associated with the LiaFSR pathway. MIC results demonstrated that both strains were susceptible to DAP and resistant to CPT, AMP, ERT, and RIF. The 168-h pharmacokinetic/pharmacodynamic (PK/PD) CDC biofilm reactor models (simulating human antibiotic exposures) were used with titanium and polyurethane coupons to evaluate the efficacy of antibiotic combinations. DAP 12 and 14 achieved bactericidal activity against S447 but lacked such effect against HOU503. Addition of ERT and RIF enhanced DAP activity, allowing DAP 8 and 10 plus ERT or RIF to produce bactericidal activity against both strains at 168 h. While DAP 8 and 10 plus CPT improved killing, they did not reach bactericidal reduction against S447. Combination of AMP, CPT, ERT, or RIF resulted in enhanced and bactericidal activity for DAP against HOU503 at 168 h. Our data provide further support for the use of combinations of DAP with AMP, ERT, CPT, and RIF in infections caused by biofilm producing VREfm. Further research involving DAP combinations against biofilm-producing enterococci is warranted.




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Levonadifloxacin, a Novel Benzoquinolizine Fluoroquinolone, Modulates Lipopolysaccharide-Induced Inflammatory Responses in Human Whole-Blood Assay and Murine Acute Lung Injury Model [Pharmacology]

Fluoroquinolones are reported to possess immunomodulatory activity; hence, a novel benzoquinolizine fluoroquinolone, levonadifloxacin, was evaluated in lipopolysaccharide-stimulated human whole-blood (HWB) and mouse acute lung injury (ALI) models. Levonadifloxacin significantly mitigated the inflammatory responses in an HWB assay through inhibition of proinflammatory cytokines and in the ALI model by lowering lung total white blood cell count, myeloperoxidase, and cytokine levels. The immunomodulatory effect of levonadifloxacin, along with promising antibacterial activity, is expected to provide clinical benefits in the treatment of infections.




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[Developmental Biology] Reptiles as a Model System to Study Heart Development

A chambered heart is common to all vertebrates, but reptiles show unparalleled variation in ventricular septation, ranging from almost absent in tuataras to full in crocodilians. Because mammals and birds evolved independently from reptile lineages, studies on reptile development may yield insight into the evolution and development of the full ventricular septum. Compared with reptiles, mammals and birds have evolved several other adaptations, including compact chamber walls and a specialized conduction system. These adaptations appear to have evolved from precursor structures that can be studied in present-day reptiles. The increase in the number of studies on reptile heart development has been greatly facilitated by sequencing of several genomes and the availability of good staging systems. Here, we place reptiles in their phylogenetic context with a focus on features that are primitive when compared with the homologous features of mammals. Further, an outline of major developmental events is given, and variation between reptile species is discussed.




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[TECHNIQUE] Animal Models of Hepatitis C Virus Infection

Hepatitis C virus (HCV) is an important and underreported infectious disease, causing chronic infection in ~71 million people worldwide. The limited host range of HCV, which robustly infects only humans and chimpanzees, has made studying this virus in vivo challenging and hampered the development of a desperately needed vaccine. The restrictions and ethical concerns surrounding biomedical research in chimpanzees has made the search for an animal model all the more important. In this review, we discuss different approaches that are being pursued toward creating small animal models for HCV infection. Although efforts to use a nonhuman primate species besides chimpanzees have proven challenging, important advances have been achieved in a variety of humanized mouse models. However, such models still fall short of the overarching goal to have an immunocompetent, inheritably susceptible in vivo platform in which the immunopathology of HCV could be studied and putative vaccines development. Alternatives to overcome this include virus adaptation, such as murine-tropic HCV strains, or the use of related hepaciviruses, of which many have been recently identified. Of the latter, the rodent/rat hepacivirus from Rattus norvegicus species-1 (RHV-rn1) holds promise as a surrogate virus in fully immunocompetent rats that can inform our understanding of the interaction between the immune response and viral outcomes (i.e., clearance vs. persistence). However, further characterization of these animal models is necessary before their use for gaining new insights into the immunopathogenesis of HCV and for conceptualizing HCV vaccines.




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Brain Metastases: Insights from Statistical Modeling of Size Distribution [ADULT BRAIN]

BACKGROUND AND PURPOSE:

Brain metastases are a common finding on brain MRI. However, the factors that dictate their size and distribution are incompletely understood. Our aim was to discover a statistical model that can account for the size distribution of parenchymal metastases in the brain as measured on contrast-enhanced MR imaging.

MATERIALS AND METHODS:

Tumor volumes were calculated on the basis of measured tumor diameters from contrast-enhanced T1-weighted spoiled gradient-echo images in 68 patients with untreated parenchymal metastatic disease. Tumor volumes were then placed in rank-order distributions and compared with 11 different statistical curve types. The resultant R2 values to assess goodness of fit were calculated. The top 2 distributions were then compared using the likelihood ratio test, with resultant R values demonstrating the relative likelihood of these distributions accounting for the observed data.

RESULTS:

Thirty-nine of 68 cases best fit a power distribution (mean R2 = 0.938 ± 0.050), 20 cases best fit an exponential distribution (mean R2 = 0.957 ± 0.050), and the remaining cases were scattered among the remaining distributions. Likelihood ratio analysis revealed that 66 of 68 cases had a positive mean R value (1.596 ± 1.316), skewing toward a power law distribution.

CONCLUSIONS:

The size distributions of untreated brain metastases favor a power law distribution. This finding suggests that metastases do not exist in isolation, but rather as part of a complex system. Furthermore, these results suggest that there may be a relatively small number of underlying variables that substantially influence the behavior of these systems. The identification of these variables could have a profound effect on our understanding of these lesions and our ability to treat them.




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Studio apartment for sale in Gateway Thao Dien, 50m2, 1BR, modern furniture.

Studio apartment at Gateway Thao Dien for sale has an area of 50m2, the architecture includes 1 bedroom, 1 bathroom, living room, kitchen, dining room, ... with full of luxury furniture. The apartment on the middle floor of Aspen tower is very beautiful, interior with a series of...




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Apex Legends is adding a horde mode-looking PvE mission next week

Season 5 of Respawn Entertainment’s hero-based battle royale kicks off next week and it looks like they’re doing more than just blowing up the map. This new Apex Legends trailer shows off what appears to be a horde mode PvE quest initiated by the newest Legend of the bunch: Loba. It looks like you’ll be […]




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Call Of Duty: Modern Warfare's crowded April Fool's Shipment playlist returns

Fancy a 1v1 match on Shipment? Of course not. Duels to the death are played out and boring. Subscribing to the view that bigger is indeed always better, Call Of Duty: Modern Warfare has brought back its 10v10 Shipment playlist. After briefly appearing as an April Fool’s jab, Infinity Ward have decided to make 20-player […]




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Software recreates a 3D model of your face from a smartphone video

A program that combines artificial intelligence and geometrical modelling can create an accurate 3D model of your face from a single 20-second video




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Google Duo Courts the PG Crowd With Addition of 'Family Mode'

Google has been folding in a score of updates to help delineate Duo from the dozens of other video chat services available.




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Andrey Markov & Claude Shannon Counted Letters to Build the First Language-Generation Models

Shannon's said: “OCRO HLI RGWR NMIELWIS”



  • robotics
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Tyra Banks Admits Some America’s Next Top Model Moments Were Over the Line

Hindsight is 20/20, even if you’re smizing.




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Call of Duty Modern Warfare Warzone Update nerfs Snake Shot in new COD download



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Call of Duty Modern Warfare Update: Patch Notes revealed for new May 8 download



Call of Duty Modern Warfare players can today download a brand new update for Warzone and Modern Warfare multiplayer. Here are the patch notes for PS4, Xbox One and PCs.




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Hedi Slimane delivers masterclass in modern nostalgia for Celine

Platform sandals meet 40-denier tights as rock’n’roll goes bourgeois at Paris fashion week

At his Celine catwalk show on Friday night, the designer Hedi Slimane distilled what looks to be the most compelling trend of this season of fashion shows. Working title: rock’n’roll bourgeois.

Slim knee-length dresses were worn with slinky chain belts; towering platform sandals with sensible 40-denier semi-sheer tights; slim bootcut velvet trousers worn with Princess-line above-the-knee coats. This was a masterclass in Parisian allure: take one part Avenue Montaigne privilege, and mix with one part backstage pass. Shaken, not stirred.

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New studio Modern Games acquires Beasts of Balance

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The Science of Fear, the Royal Scandal That Made France Modern and Other New Books to Read

The fourth installment in our weekly series spotlights titles that may have been lost in the news amid the COVID-19 crisis




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Which COVID-19 models should we use to make policy decisions?

A new process to harness multiple disease models for outbreak management has been developed by an international team of researchers. The team will immediately implement the process to help inform policy decisions for the COVID-19 outbreak.




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Moderna, Switzerland's Lonza strike deal on potential COVID-19 vaccine

Moderna Inc and Swiss contract drugmaker Lonza Group AG said on Friday they would accelerate the manufacturing of the U.S. drug developer's potential coronavirus vaccine.




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Whirlpool tumble dryer recall list: Which model numbers are affected? How do I check if my machine is at risk?

Whirlpool has announced they will recall thousands more washing machines which could risk causing fires in people's homes.





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‘Moderate becoming good’: my journey to every place in the shipping forecast

From Fair Isle to German Bight, Charlie Connelly has visited all 31 sea areas, but still finds the poetry of the daily radio odyssey mesmerising

The shipping forecast is probably the closest thing we have in the modern age to a national epic. The institution’s rhythms and rituals have changed little since it was first broadcast on New Year’s Day 1924: there is poetry in the daily litany and mystery in its terminology. “The radio’s prayer,” Carol Ann Duffy called it. For Seamus Heaney it was “a sibilant penumbra”.

The forecast reminds us we’re a maritime nation and its map binds us to our continent, covering not only our own coasts and waters but an area extending from Norway to Portugal to Iceland. There is democracy in its geography, where tiny Fair Isle carries as much heft as mighty Biscay while Lundy, a sliver of rock in the Bristol Channel, is equal in importance to the Irish Sea. And from the salty old seadog in his brine-encrusted fishing boat to the merchant banker on his yacht, the shipping forecast, all seafarers are equally reliant on it.

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Smart Education And Learning Market Size, Share & Trends Analysis Report By Age, By Component, By Learning Mode, By End User, By Region And Segment Forecasts, 2020 - 2027

Smart Education And Learning Market Size, Share & Trends Analysis Report By Age, By Component (Hardware, Software, Service), By Learning Mode, By End User, By Region, And Segment Forecasts, 2020 - 2027Read the full report: https://www.reportlinker.com/p05891723/?utm_source=PRN The global smart education and learning market size is expected to reach USD 680.1 billion by 2027. The market is anticipated to witness a CAGR of 17.9% from 2020 to 2027. Demand for smart education and learning solutions is increasing among the growing population in corporate and academic sectors, owing to benefits such as improved education quality and easy access to educational content. Increasing adoption of consumer electronics, such as smartphones, e-readers, laptops, and e-learning applications, has altered conventional education methodology and has enhanced the efficiency of an individual to learn. Additionally, there are enormous opportunities for advancements in the market, owing to improved internet accessibility.Also, the COVID - 19 outbreak has emerged an opportunity for the market with an increasing number of states and countries closing educational institutes. For instance, over 90.0% of the world's students are not attending their schools due to this pandemic, as mentioned by UNESCO (The United Nations Educational, Scientific, and Cultural Organization). Commonwealth of Learning (COL), an intergovernmental organization of The Commonwealth (Canada), has supported educational institutions and governments in building robust distance education solutions for quality e-learning practices. However, lack of awareness among end-users about the latest technologies and inadequate amount of resources for delivering quality education in developing regions is anticipated to hinder market growth.The simulation-based learning segment is anticipated to exhibit the highest CAGR because this mode enables corporate professional and educational institutions to create a realistic experience in a controlled environment.It also allows professionals and learners to practice, navigate, explore, and obtain more information through a virtual medium before they start working on real-life tasks.Growing awareness among people and the rising popularity of smart education are encouraging solution providers to invest in research and development for creating more reliable, better, and cost-effective solutions. Manufacturers are making substantial investments in developing new products for enhancing the user experience.Smart education and learning market report highlights:• Growing demand for smart educational practices can be accredited to factors, such as reducing expenses of online training, curbing geographic challenges in physically attending classes, and time constraints faced by aspirants• Increasing penetration of the Internet of Things (IoT), enhanced internet accessibility, and rapid adoption of mobile technology have encouraged users to adopt smart education and learning solutions• Innovative techniques, such as gamification, Massive Open Online Courses (MOOCs), microlearning, and adaptive learning, which improve the overall educational process, are expected to drive the market over the projected period• North America accounted for the largest market share in 2019 owing to its large consumer base for e-learning methodsRead the full report: https://www.reportlinker.com/p05891723/?utm_source=PRN About Reportlinker ReportLinker 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: clare@reportlinker.com US: (339)-368-6001 Intl: +1 339-368-6001





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Adult live-streaming site CAM4 exposes millions of models' personal information

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IMD to use dynamic models for forecasts

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Tyra Banks Breaks Her Silence on Problematic America's Next Top Model Moments

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Virtual Commons sitting is start of modernisation, says Speaker Lindsay Hoyle

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What’s Missing in Pandemic Models - Issue 84: Outbreak


In the COVID-19 pandemic, numerous models are being used to predict the future. But as helpful as they are, they cannot make sense of themselves. They rely on epidemiologists and other modelers to interpret them. Trouble is, making predictions in a pandemic is also a philosophical exercise. We need to think about hypothetical worlds, causation, evidence, and the relationship between models and reality.1,2

The value of philosophy in this crisis is that although the pandemic is unique, many of the challenges of prediction, evidence, and modeling are general problems. Philosophers like myself are trained to see the most general contours of problems—the view from the clouds. They can help interpret scientific results and claims and offer clarity in times of uncertainty, bringing their insights down to Earth. When it comes to predicting in an outbreak, building a model is only half the battle. The other half is making sense of what it shows, what it leaves out, and what else we need to know to predict the future of COVID-19.

Prediction is about forecasting the future, or, when comparing scenarios, projecting several hypothetical futures. Because epidemiology informs public health directives, predicting is central to the field. Epidemiologists compare hypothetical worlds to help governments decide whether to implement lockdowns and social distancing measures—and when to lift them. To make this comparison, they use models to predict the evolution of the outbreak under various simulated scenarios. However, some of these simulated worlds may turn out to misrepresent the real world, and then our prediction might be off.

In his book Philosophy of Epidemiology, Alex Broadbent, a philosopher at the University of Johannesburg, argues that good epidemiological prediction requires asking, “What could possibly go wrong?” He elaborated in an interview with Nautilus, “To predict well is to be able to explain why what you predict will happen rather than the most likely hypothetical alternatives. You consider the way the world would have to be for your prediction to be true, then consider worlds in which the prediction is false.” By ruling out hypothetical worlds in which they are wrong, epidemiologists can increase their confidence that they are right. For instance, by using antibody tests to estimate previous infections in the population, public health authorities could rule out the hypothetical possibility (modeled by a team at Oxford) that the coronavirus has circulated much more widely than we think.3

One reason the dynamics of an outbreak are often more complicated than a traditional model can predict is that they result from human behavior and not just biology.

Broadbent is concerned that governments across Africa are not thinking carefully enough about what could possibly go wrong, having for the most part implemented coronavirus policies in line with the rest of the world. He believes a one-size-fits-all approach to the pandemic could prove fatal.4 The same interventions that might have worked elsewhere could have very different effects in the African context. For instance, the economic impacts of social distancing policies on all-cause mortality might be worse because so many people on the continent suffer increased food insecurity and malnutrition in an economic downturn.5 Epidemic models only represent the spread of the infection. They leave out important elements of the social world.

Another limitation of epidemic models is that they model the effect of behaviors on the spread of infection, but not the effect of a public health policy on behaviors. The latter requires understanding how a policy works. Nancy Cartwright, a philosopher at Durham University and the University of California, San Diego, suggests that “the road from ‘It works somewhere’ to ‘It will work for us’ is often long and tortuous.”6 The kinds of causal principles that make policies effective, she says, “are both local and fragile.” Principles can break in transit from one place to the other. Take the principle, “Stay-at-home policies reduce the number of social interactions.” This might be true in Wuhan, China, but might not be true in a South African township in which the policies are infeasible or in which homes are crowded. Simple extrapolation from one context to another is risky. A pandemic is global, but prediction should be local.

Predictions require assumptions that in turn require evidence. Cartwright and Jeremy Hardie, an economist and research associate at the Center for Philosophy of Natural and Social Science at the London School of Economics, represent evidence-based policy predictions using a pyramid, where each assumption is a building block.7 If evidence for any assumption is missing, the pyramid might topple. I have represented evidence-based medicine predictions using a chain of inferences, where each link in the chain is made of an alloy containing assumptions.8 If any assumption comes apart, the chain might break.

An assumption can involve, for example, the various factors supporting an intervention. Cartwright writes that “policy variables are rarely sufficient to produce a contribution [to some outcome]; they need an appropriate support team if they are to act at all.” A policy is only one slice of a complete causal pie.9 Take age, an important support factor in causal principles of social distancing. If social distancing prevents deaths primarily by preventing infections among older individuals, wherever there are fewer older individuals there may be fewer deaths to prevent—and social distancing will be less effective. This matters because South Africa and other African countries have younger populations than do Italy or China.10

The lesson that assumptions need evidence can sound obvious, but it is especially important to bear in mind when modeling. Most epidemic modeling makes assumptions about the reproductive number, the size of the susceptible population, and the infection-fatality ratio, among other parameters. The evidence for these assumptions comes from data that, in a pandemic, is often rough, especially in early days. It has been argued that nonrepresentative diagnostic testing early in the COVID-19 pandemic led to unreliable estimates of important inputs in our epidemic modeling.11

Epidemic models also don’t model all the influences of the pathogen and of our policy interventions on health and survival. For example, what matters most when comparing deaths among hypothetical worlds is how different the death toll is overall, not just the difference in deaths due to the direct physiological effects of a virus. The new coronavirus can overwhelm health systems and consume health resources needed to save non-COVID-19 patients if left unchecked. On the other hand, our policies have independent effects on financial welfare and access to regular healthcare that might in turn influence survival.

A surprising difficulty with predicting in a pandemic is that the same pathogen can behave differently in different settings. Infection fatality ratios and outbreak dynamics are not intrinsic properties of a pathogen; these things emerge from the three-way interaction among pathogen, population, and place. Understanding more about each point in this triangle can help in predicting the local trajectory of an outbreak.

In April, an influential data-driven model, developed by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, which uses a curve-fitting approach, came under criticism for its volatile projections and questionable assumption that the trajectory of COVID-19 deaths in American states can be extrapolated from curves in other countries.12,13 In a curve-fitting approach, the infection curve representing a local outbreak is extrapolated from data collected locally along with data regarding the trajectory of the outbreak elsewhere. The curve is drawn to fit the data. However, the true trajectory of the local outbreak, including the number of infections and deaths, depends upon characteristics of the local population as well as policies and behaviors adopted locally, not just upon the virus.

Predictions require assumptions that in turn require evidence.

Many of the other epidemic models in the coronavirus pandemic are SIR-type models, a more traditional modelling approach for infectious-disease epidemiology. SIR-type models represent the dynamics of an outbreak, the transition of individuals in the population from a state of being susceptible to infection (S) to one of being infectious to others (I) and, finally, recovered from infection (R). These models simulate the real world. In contrast to the data-driven approach, SIR models are more theory-driven. The theory that underwrites them includes the mathematical theory of outbreaks developed in the 1920s and 1930s, and the qualitative germ theory pioneered in the 1800s. Epidemiologic theories impart SIR-type models with the know-how to make good predictions in different contexts.

For instance, they represent the transmission of the virus as a factor of patterns of social contact as well as viral transmissibility, which depend on local behaviors and local infection control measures, respectively. The drawback of these more theoretical models is that without good data to support their assumptions they might misrepresent reality and make unreliable projections for the future.

One reason why the dynamics of an outbreak are often more complicated than a traditional model can predict, or an infectious-disease epidemiology theory can explain, is that the dynamics of an outbreak result from human behavior and not just human biology. Yet more sophisticated disease-behavior models can represent the behavioral dynamics of an outbreak by modeling the spread of opinions or the choices individuals make.14,15 Individual behaviors are influenced by the trajectory of the epidemic, which is in turn influenced by individual behaviors.

“There are important feedback loops that are readily represented by disease-behavior models,” Bert Baumgartner, a philosopher who has helped develop some of these models, explains. “As a very simple example, people may start to socially distance as disease spreads, then as disease consequently declines people may stop social distancing, which leads to the disease increasing again.” These looping effects of disease-behavior models are yet another challenge to predicting.

It is a highly complex and daunting challenge we face. That’s nothing unusual for doctors and public health experts, who are used to grappling with uncertainty. I remember what that uncertainty felt like when I was training in medicine. It can be discomforting, especially when confronted with a deadly disease. However, uncertainty need not be paralyzing. By spotting the gaps in our models and understanding, we can often narrow those gaps or at least navigate around them. Doing so requires clarifying and questioning our ideas and assumptions. In other words, we must think like a philosopher.

Jonathan Fuller is an assistant professor in the Department of History and Philosophy of Science at the University of Pittsburgh. He draws on his dual training in philosophy and in medicine to answer fundamental questions about the nature of contemporary disease, evidence, and reasoning in healthcare, and theory and methods in epidemiology and medical science.

References

1. Walker, P., et al. The global impact of COVID-19 and strategies for mitigation and suppression. Imperial College London (2020).

2. Flaxman, S., et al. Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London (2020).

3. Lourenco, J., et al. Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic. medRxiv:10.1101/2020.03.24.20042291 (2020).

4. Broadbent, A., & Smart, B. Why a one-size-fits-all approach to COVID-19 could have lethal consequences. TheConversation.com (2020).

5. United Nations. Global recession increases malnutrition for the most vulnerable people in developing countries. United Nations Standing Committee on Nutrition (2009).

6. Cartwright, N. Will this policy work for you? Predicting effectiveness better: How philosophy helps. Philosophy of Science 79, 973-989 (2012).

7. Cartwright, N. & Hardie, J. Evidence-Based Policy: A Practical Guide to Doing it Better Oxford University Press, New York, New York (2012).

8. Fuller, J., & Flores, L. The Risk GP Model: The standard model of prediction in medicine. Studies in History and Philosophy of Biological and Biomedical Sciences 54, 49-61 (2015).

9. Rothman, K., & Greenland, S. Causation and causal inference in epidemiology. American Journal Public Health 95, S144-S50 (2005).

10. Dowd, J. et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences 117, 9696-9698 (2020).

11. Ioannidis, J. Coronavirus disease 2019: The harms of exaggerated information and non‐evidence‐based measures. European Journal of Clinical Investigation 50, e13222 (2020).

12. COVID-19 Projections. Healthdata.org. https://covid19.healthdata.org/united-states-of-america.

13. Jewell, N., et al. Caution warranted: Using the Institute for Health metrics and evaluation model for predicting the course of the COVID-19 pandemic. Annals of Internal Medicine (2020).

14. Nardin, L., et al. Planning horizon affects prophylactic decision-making and epidemic dynamics. PeerJ 4:e2678 (2016).

15. Tyson, R., et al. The timing and nature of behavioural responses affect the course of an epidemic. Bulletin of Mathematical Biology 82, 14 (2020).

Lead image: yucelyilmaz / Shutterstock


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Watkins Family Hour: Brother Sister review – a model of sibling harmony

(Family Hour/Thirty Tigers)
Sean and Sara Watkins are back and in reflective mood

California’s Sean and Sara Watkins are akin to royalty in American folk circles, firstly as founding members of the hugely successful Nickel Creek, and secondly as hosts of an 18-year residency at LA’s Largo club, where they perform alongside invited guests. Brother Sister draws on both strands of their history. Like its self-titled 2015 predecessor, the album sets aside the pizzazz of Nickel Creek for a down-home approach, but instead of boisterous, star-studded cover versions come five original songs and a minimal musical palette.

Alternating on lead, the pair’s vocals remain a model of sibling harmony, while the interplay between Sean’s intricate guitar picking and Sara’s elegant fiddle is similarly impressive – the breakneck bluegrass instrumental Bella and Ivan is a case in point. Mostly, however, the mood is reflective. Lafayette and Miles of Desert Sand chronicle the search for a better life, and Fake Badge, Real Gun is an artful snipe at Trump – “Throw your tantrums but the truth will be waiting”. Warren Zevon’s forlorn Accidentally Like a Martyr fits in neatly, while Charley Jordan’s ribald Keep It Clean is a gleeful example of a Largo session.

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