Notes

Assessment of fraction million main care medications from your country wide anti-microbial stewardship program.
Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode-i.e. before the individual had the chance to report on any symptom-our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy.Urban scaling analysis, the study of how aggregated urban features vary with the population of an urban area, provides a promising framework for discovering commonalities across cities and uncovering dynamics shared by cities across time and space. Here, we use the urban scaling framework to study an important, but under-explored feature in this community-income inequality. selleck compound We propose a new method to study the scaling of income distributions by analysing total income scaling in population percentiles. We show that income in the least wealthy decile (10%) scales close to linearly with city population, while income in the most wealthy decile scale with a significantly superlinear exponent. In contrast to the superlinear scaling of total income with city population, this decile scaling illustrates that the benefits of larger cities are increasingly unequally distributed. For the poorest income deciles, cities have no positive effect over the null expectation of a linear increase. We repeat our analysis after adjusting income by housing cost, and find similar results. We then further analyse the shapes of income distributions. First, we find that mean, variance, skewness and kurtosis of income distributions all increase with city size. Second, the Kullback-Leibler divergence between a city's income distribution and that of the largest city decreases with city population, suggesting the overall shape of income distribution shifts with city population. As most urban scaling theories consider densifying interactions within cities as the fundamental process leading to the superlinear increase of many features, our results suggest this effect is only seen in the upper deciles of the cities. Our finding encourages future work to consider heterogeneous models of interactions to form a more coherent understanding of urban scaling.Differential equation models of biochemical networks are frequently associated with a large degree of uncertainty in parameters and/or initial conditions. However, estimating the impact of this uncertainty on model predictions via Monte Carlo simulation is computationally demanding. A more efficient approach could be to track a system of low-order statistical moments of the state. Unfortunately, when the underlying model is nonlinear, the system of moment equations is infinite-dimensional and cannot be solved without a moment closure approximation which may introduce bias in the moment dynamics. Here, we present a new method to study the time evolution of the desired moments for nonlinear systems with polynomial rate laws. Our approach is based on solving a system of low-order moment equations by substituting the higher-order moments with Monte Carlo-based estimates from a small number of simulations, and using an extended Kalman filter to counteract Monte Carlo noise. Our algorithm provides more accurate and robust results compared to traditional Monte Carlo and moment closure techniques, and we expect that it will be widely useful for the quantification of uncertainty in biochemical model predictions.Flying animals resort to fast, large-degree-of-freedom motion of flapping wings, a key feature that distinguishes them from rotary or fixed-winged robotic fliers with limited motion of aerodynamic surfaces. However, flapping-wing aerodynamics are characterized by highly unsteady and three-dimensional flows difficult to model or control, and accurate aerodynamic force predictions often rely on expensive computational or experimental methods. Here, we developed a computationally efficient and data-driven state-space model to dynamically map wing kinematics to aerodynamic forces/moments. This model was trained and tested with a total of 548 different flapping-wing motions and surpassed the accuracy and generality of the existing quasi-steady models. This model used 12 states to capture the unsteady and nonlinear fluid effects pertinent to force generation without explicit information of fluid flows. We also provided a comprehensive assessment of the control authority of key wing kinematic variables and found that instantaneous aerodynamic forces/moments were largely predictable by the wing motion history within a half-stroke cycle. Furthermore, the angle of attack, normal acceleration and pitching motion had the strongest effects on the aerodynamic force/moment generation. Our results show that flapping flight inherently offers high force control authority and predictability, which can be key to developing agile and stable aerial fliers.After more than 1 year into the COVID-19 pandemic, governments worldwide still face the challenge of adopting non-pharmaceutical interventions to mitigate the risks posed by the emergence of new SARS-CoV-2 variants and the lack of a worldwide equitable vaccine allocation. Thus, it becomes crucial to identify the drivers of mobility responses to mitigation efforts during different restriction regimes, for planning interventions that are both economically and socially sustainable while effective in controlling an outbreak. Here, using anonymous and privacy-enhanced cell phone data from Italy, we investigate the determinants of spatial variations of reductions in mobility and co-location in response to the adoption and the lift of restrictions, considering both provinces and city neighbourhoods. In large urban areas, our analysis uncovers the desertification of historic city centres, which persisted after the end of the lockdown. Such centre-periphery gradient was mainly associated with differences in educational attainment. At the province level, the local structure of the labour market mainly explained the variations in mobility responses, together with other demographic factors, such as the population's age and sex composition. In the future, targeted interventions should take into account how the ability to comply with restrictions varies across geographical areas and socio-demographic groups.Purpose Evidence supporting free water protocols (FWP) in acute settings is limited and the potential risks and benefits for acutely ill patients are not well understood. This study aimed to observe how and with whom FWPs are implemented in acute stroke and general medical units.Method Mixed methods parallel case study design. Medical and nursing records were evaluated for information pertaining to the implementation of the FWP and outcomes for three patients. Semi-structured interviews conducted with three patient-nurse-speech-language pathologist triads focussed on clinical decision-making and barriers and enablers to FWP implementation. Data were analysed descriptively and triangulated across sources.Result Patients identified as suitable for a FWP had markedly different presentations to those described in the evidence-base and FWP were consequently significantly adapted. Although patients were permitted water, they received and consumed very small amounts. Speech-language pathologists and nurses identified more barriers than enablers to FWP implementation; cognitive impairments, reliance on others and insufficient documentation were perceived as the key barriers, while clear verbal communication was identified as a facilitator.Conclusion Overall the findings suggest FWP implementation in the acute care setting is hindered by a lack of standardised procedures and current evidence-base that would otherwise inform best practice.Mammals have evolved circadian rhythms in internal biological processes and behaviors, such as locomotor activity (LA), to synchronize to the environmental conditions they experience. Photic entrainment of LA has been well established; however, non-photic entrainment, such as ambient temperature (Ta), has received much less attention. To address this dearth of knowledge, we exposed two subterranean endothermic-homeothermic African mole-rat species, the solitary Cape mole-rat (Georychus capensis [GC]) and social Mahali mole-rat (Cryptomys hottentotus mahali [CHM]), to varying Ta cycles in the absence of light. We showed that the LA rhythms of these two species entrain to Ta cycles and that the majority of LA occurred during the coolest 12-h period. LA confined to the coolest Ta periods may be the direct consequence of the poor heat dissipation abilities of African mole-rats brought about by physiological and ecological constraints. Recently, it has been hypothesized that Ta is only a strong zeitgeber for circadian rhythms in species whose thermoregulatory abilities are sensitive to changes in Ta (i.e., heterotherms and ectotherms), which previously has excluded endothermic-homeothermic mammals. However, this study demonstrates that Ta is a strong zeitgeber or entrainer for circadian rhythms of LA in subterranean endothermic-homeothermic mammals as a consequence of their sensitivity to changes in Ta brought about by their poor heat dissipation abilities. This study reinforces the intimate link between circadian rhythms and thermoregulation and conclusively, for the first time, provides evidence that Ta is a strong zeitgeber for endothermic-homeothermic mammals.The Ambrosia Fusarium Clade (AFC) is a monophyletic lineage within clade 3 of the Fusarium solani species complex (FSSC) that currently comprises 19 genealogically exclusive species. These fungi are known or predicted to be farmed by adult female Euwallacea ambrosia beetles as a nutritional mutualism (Coleoptera Scolytinae; Xyleborini). To date, only eight of the 19 AFC species have been described formally with Latin binomials. We describe three AFC species, previously known as AF-8, AF-10, and AF-11, based on molecular phylogenetic analysis of multilocus DNA sequence data and comparative morphological/phenotypic studies. Fusarium duplospermum (AF-8) farmed by E. perbrevis on avocado in Florida, USA, is distinguished by forming two morphologically different types of multiseptate conidia and brownish orange colonies on potato dextrose agar (PDA). Fusarium drepaniforme (AF-10), isolated from an unknown woody host in Singapore and deposited as Herb IMI 351954 in the Royal Botanic Gardens, Kew, UK, under the name F.
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