Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

Impact of High Deductible Health Plans on Diabetes Care Quality and Outcomes: Systematic Review

ObjectiveTo provide a review of the impact of high deductible health plans (HDHPs) on the utilizations of services required for optimal management of diabetes and subsequent health outcomes.MethodsSystematic literature review of studies published between January 1, 2000, and May 7, 2021, was conducted that examined the impact of HDHP on diabetes monitoring (eg, recommended laboratory and surveillance testing), routine care (eg, ambulatory appointments), medication management (eg, medication initiation, adherence), and acute health care utilization (eg, emergency department visits, hospitalizations, incident complications).ResultsOf the 303 reviewed articles, 8 were relevant. These studies demonstrated that HDHPs lower spending at the expense of reduced high-value diabetes monitoring, routine care, and medication adherence, potentially contributing to the observed increases in acute health care utilization. Additionally, patient out-of-pocket costs for recommended screenings doubled, and total health care expenditures increased by 49.4% for HDHP enrollees compared with enrollees in traditional health plans. Reductions in disease monitoring and routine care and increases in acute health care utilization were greatest in lower-income patients. None of the studies examined the impact of HDHPs on access to diabetes self-management education, technology use, or glycemic control.ConclusionAlthough HDHPs reduce some health care utilization and costs, they appear to do so at the expense of limiting high-value care and medication adherence. Policymakers, providers, and payers should be more cognizant of the potential for negative consequences of HDHPs on patients’ health.     

Exclosure fences around nests of imperiled Florida Grasshopper Sparrows reduce rates of predation by mammals

Increasing nest survival by excluding predators is a goal of many bird conservation programs. However, new exclosure projects should be carefully evaluated to assess the potential risks of disturbance. We tested the effectiveness of predator exclosure fences (hereafter, fences) for nests of critically endangered Florida Grasshopper Sparrows (Ammodramus savannarum floridanus) at a dry prairie site (Three Lakes; 2015–2018) and a pasture site (the Ranch; 2015–2016) in Osceola County, Florida, USA. We installed fences at nests an average of 8 days after the start of incubation, and nest abandonment after fence installation was rare (2 of 149 installations). Predation was the leading cause of failure for unfenced nests at both sites (48–73%). At Three Lakes, nest cameras revealed that mammals and snakes were responsible for 61.5% and 38.5% of predation events, respectively, at unfenced nests. Fences reduced the daily probability of predation (0.016 for fenced nests vs. 0.074 for unfenced nests). The probability that a fenced nest would survive from discovery to fledging was more than double that of unfenced nests (60.4% vs. 27.7%). However, we found no difference in daily nest survival at the Ranch between the year before nests were fenced (2015; 0.874) and the year when all but one nest were fenced (2016; 0.867) because red imported fire ants (Solenopsis invicta) were responsible for 86% of predation events at fenced nests at the Ranch. The use of cameras at fenced nests revealed that site‐specific differences in nest predators explained variation in fence efficiency between sites. Our fence design may be useful for other species of grassland birds, but site‐specific predator communities and species‐specific response of target bird species to fences should be assessed before installing fences at other sites.     

Priors for the Bayesian star paradox

We show that the Bayesian star paradox, first proved mathematically by Steel and Matsen for a specific class of prior distributions, occurs in a wider context including less regular, possibly discontinuous, prior distributions.     

Spatial and temporal factors associated with nest survival of Gray Flycatchers in managed ponderosa pine forests

Gray Flycatchers (Empidonax wrightii) breed in a variety of habitats in the arid and semi‐arid regions of the western United States, but little is known about their breeding biology, especially in the northern portion of their range where they nest in ponderosa pine (Pinus ponderosa) forests. From May to July 2014 and 2015, we conducted surveys for singing male Gray Flycatchers along the eastern slope of the Cascade Range in Washington, U.S.A, monitored flycatcher nests, and quantified nest‐site vegetation. We used a logistic‐exposure model fit within a Bayesian framework to model the daily survival probability of flycatcher nests. During the 2 yr of our study, we monitored 141 nests, with 93% in ponderosa pines. Mean clutch size was 3.6 eggs and the mean number of young fledged per nest was 3.2. Predation accounted for 90% of failed nests. We found a positive association between daily nest survival and both nest height and distance of nest substrates from the nearest tree. Flycatchers that locate their nests higher above the ground and further from adjacent trees may be choosing the safest alternative because higher nests may be less exposed to terrestrial predators and nests in trees that are farther from other trees may be less exposed to arboreal predators such as jays (Corvidae) that may forage in patches with connected canopies. Nests in trees farther from other trees may also allow earlier detection of approaching predators and thus aid in nest defense.     

Effects of the supply levels and ratios of nitrogen and phosphorus on seed traits of Chenopodium glaucum

全球氮沉降不仅改变土壤氮和磷的有效性, 同时也改变氮磷比例。氮磷供应量、比例及其交互作用可能会影响植物种子性状。该研究在内蒙古草原基于沙培盆栽实验种植灰绿藜(Chenopodium glaucum), 设置3个氮磷供应量水平和3个氮磷比例的正交实验来探究氮磷供应量、比例及其交互作用对灰绿藜种子性状的影响。结果发现氮磷供应量对种子氮浓度、磷浓度和萌发率影响的相对贡献(15%-24%)大于氮磷比例(3%-7%), 而种子大小只受氮磷比例的影响。同时氮磷供应量和比例之间的交互作用显著影响种子氮浓度和磷浓度。同等氮磷比例情况下, 低量养分供应提高种子氮浓度、磷浓度和萌发率。氮磷比例只有在养分匮乏的环境中才会对种子大小和萌发率产生显著影响。总之, 灰绿藜种子不同性状对氮或磷限制的敏感性不同, 同时种子性状也对养分限制表现出适应性和被动响应。     

Portrait of the Patient as a Young Man: An Exploration of the Use of Photographs in Hospital

The display of personal photographs in hospital is a common practice that has yet to be rigorously examined. The photographs displayed are subject to interpretation by the viewer and may lead to misunderstandings or miscommunication if clarification of meaning is not sought. This paper explores a range of possible meanings that the display of photographs in hospital may hold, based on a case study of a 15 year old boy hospitalised with a life threatening illness. Further research is needed into the actual meanings attributed to the display of photographs in hospital by patients and family members.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

The Amount of BCL6 in B Cells Shortly after Antigen Engagement Determines Their Representation in Subsequent Germinal Centers

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Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data

Summary .  We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introducing a   ℓ₂   penalty in the usual QR check function to shrink the subject-specific intercepts and slopes toward the common population values. The informative missing data are assumed to be related to the longitudinal outcome process through the shared latent random effects. We assess the performance of the proposed method using simulation studies, and illustrate it with data from a pediatric AIDS clinical trial.