Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

“Smart”-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.     

Dynamic genome-scale modeling of Saccharomyces cerevisiae unravels mechanisms for ester formation during alcoholic fermentation

Fermentation employing Saccharomyces cerevisiae has produced alcoholic beverages and bread for millennia. More recently, S. cerevisiae has been used to manufacture specific metabolites for the food, pharmaceutical, and cosmetic industries. Among the most important of these metabolites are compounds associated with desirable aromas and flavors, including higher alcohols and esters. Although the physiology of yeast has been well-studied, its metabolic modulation leading to aroma production in relevant industrial scenarios such as winemaking is still unclear. Here we ask what are the underlying metabolic mechanisms that explain the conserved and varying behavior of different yeasts regarding aroma formation under enological conditions? We employed dynamic flux balance analysis (dFBA) to answer this key question using the latest genome-scale metabolic model (GEM) of S. cerevisiae. The model revealed several conserved mechanisms among wine yeasts, for example, acetate ester formation is dependent on intracellular metabolic acetyl-CoA/CoA levels, and the formation of ethyl esters facilitates the removal of toxic fatty acids from cells using CoA. Species-specific mechanisms were also found, such as a preference for the shikimate pathway leading to more 2-phenylethanol production in the Opale strain as well as strain behavior varying notably during the carbohydrate accumulation phase and carbohydrate accumulation inducing redox restrictions during a later cell growth phase for strain Uvaferm. In conclusion, our new metabolic model of yeast under enological conditions revealed key metabolic mechanisms in wine yeasts, which will aid future research strategies to optimize their behavior in industrial settings.     

Modeling an immune response to influenza A virus infection in alveolar epithelial cells

Influenza A viruses (IAV) have been the cause of several influenza pandemics in history and are a significant threat for the next global pandemic. Hospitalized influenza patients often have excess interferon production and a dysregulated immune response to the IAV infection. Obtaining a better understanding of the mechanisms of IAV infection that induce these harmful effects would help drug developers and health professionals create more effective treatments for IAV infection and improve patient outcomes. IAV stimulates viral sensors and receptors expressed by alveolar epithelial cells, like RIG-I and toll-like receptor 3 (TLR3). These two pathways coordinate with one another to induce expression of type III interferons to combat the infection. Presented here is a queuing theory-based model of these pathways that was designed to analyze the timing and amount of interferons produced in response to IAV single stranded RNA and double-stranded RNA detection. The model accurately represents biological data showing the necessary coordination of the RIG-I and TLR3 pathways for effective interferon production. This model can serve as the framework for future studies of IAV infection and identify new targets for potential treatments.     

Elastic priors to dynamically borrow information from historical data in clinical trials

Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.     

Alginate/chitosan hydrogels as perspective transport systems for cefotaxime

This work reports synthesis of pH-responsive alginate/chitosan hydrogel spheres with the average diameter of 2.0 ± 0.05 mm, which contain cefotaxime that is an antibiotic of the cefalosporine group. The spheres provided the cefotaxime encapsulation efficiency of 95 ± 1%. An in vitro release of cefotaxime from the spheres in the media that simulate human biological fluids in peroral delivery conditions was found to be a pH-dependent process. The analysis of cefotaxime release kinetics by the Korsmeyer–Peppas model revealed a non-Fickian mechanism of its diffusion, which may be related to intermolecular interactions occurring between the antibiotic and chitosan. Conductometry, UV spectroscopy, and IR spectroscopy were used to study complexation of chitosan with cefotaxime in aqueous media with varied pH, characterize the composition of the complexes, and calculate their stability constants. The composition of the cefotaxime–chitosan complexes was found to correspond to the 1.0:4.0 and 1.0:2.0 molar ratios of the components at pH 2.0 and 5.6, respectively. Quantum chemical modeling was used to evaluate energy characteristics of chitosan–cefotaxime complexation considering the influence of a solvent.     

Recognition of errors in three-dimensional structures of proteins

A major problem in the determination of the three-dimensional structure of proteins concerns the quality of the structural models obtained from the interpretation of experimental data. New developments in X-ray crystallography and nuclear magnetic resonance spectroscopy have acceleratedd the process of structure determination and the biological community is confronted with a steadily increasing number of experimentally determined protein folds. However, in the recent past several experimentally determined protein structures have been proven to contain major errors, indicating that in some cases the interpretation of experimental data is difficult and may yield incorrect models. Such problems can be avoided when computational methods are employed which complement experimental structure determinations. A prerequisite of such computational tools is that they are independent of the parameters obtained from a particular experiment. In addition such techniques are able to support and accelerate experimental structure determinations. Here we present techniques based on knowledge based mean fields which can be used to judge the quality of protein folds. The methods can be used to identify misfolded structures as well as faulty parts of structural models. The techniques are even applicable in cases where only the C_α trace of a protein conformation is available. The capabilities of the technique are demonstrated using correct and incorrect protein folds. © 1993 Wiley-Liss, Inc.     

Beyond the GTP-cap: Elucidating the molecular mechanisms of microtubule catastrophe

Almost 40 years since the discovery of microtubule dynamic instability, the molecular mechanisms underlying microtubule dynamics remain an area of intense research interest. The “standard model” of microtubule dynamics implicates a “cap” of GTP-bound tubulin dimers at the growing microtubule end as the main determinant of microtubule stability. Loss of the GTP-cap leads to microtubule “catastrophe,” a switch-like transition from microtubule growth to shrinkage. However, recent studies, using biochemical in vitro reconstitution, cryo-EM, and computational modeling approaches, challenge the simple GTP-cap model. Instead, a new perspective on the mechanisms of microtubule dynamics is emerging. In this view, highly dynamic transitions between different structural conformations of the growing microtubule end – which may or may not be directly linked to the nucleotide content at the microtubule end – ultimately drive microtubule catastrophe.     

新疆野核桃自然保护区不同坡向丛生野核桃年龄结构及数量动态

通过对新疆野核桃自然保护区丛生野核桃(Juglans regia)进行普查,以径级结构代替年龄结构,采用匀滑技术编制种群静态生命表,并运用时间序列模型预测种群数量动态,从而获得丛生野核桃树的龄级结构与动态。结果表明,不同坡向丛生野核桃以中龄级为主,年龄结构呈两头小中间大的纺锤型;4个坡向种群年龄结构的动态指数Vpi虽大于0,但最大仅为19.10%,且Vpi’极低,最大为1.12%,阳坡种群增长趋势最高,抗干扰能力最强,种群稳定性最好,半阳坡种群增长趋势最弱,抗干扰能力最弱,种群稳定性最差;各坡向种群均存在多个死亡高峰,阳坡最高峰在15龄级,半阳坡在15和18龄级,阴坡和半阴坡均在9龄级,存活曲线更接近Deevey-Ⅱ型;在未来2、5、10龄级后,中龄级个体数量减少,老龄个体呈增加趋势,丛生野核桃种群稳定性难以长期维持。因此对丛生野核桃幼苗的保护非常关键。     

TTLL11 gene is associated with sustained attention performance and brain networks: A genome-wide association study of a healthy Chinese sample

Genetic studies on attention have mainly focused on children with attention-deficit/hyperactivity disorder (ADHD), so little systematic research has been conducted on genetic correlates of attention performance and their potential brain mechanisms among healthy individuals. The current study included a genome-wide association study (GWAS, N = 1145 healthy young adults) aimed to identify genes associated with sustained attention and an imaging genetics study (an independent sample of 483 healthy young adults) to examine any identified genes' influences on brain function. The GWAS found that TTLL11 showed genome-wide significant associations with sustained attention, with rs13298112 as the most significant SNP and the GG homozygotes showing more impulsive but also more focused responses than the A allele carriers. A retrospective examination of previously published ADHD GWAS results confirmed an un-reported, small but statistically significant effect of TTLL11 on ADHD. The imaging genetics study replicated this association and showed that the TTLL11 gene was associated with resting state activity and connectivity of the somatomoter network, and can be predicted by dorsal attention network connectivity. Specifically, the GG homozygotes showed lower brain activity, weaker brain network connectivity, and non-significant brain-attention association compared to the A allele carriers. Expression database showed that expression of this gene is enriched in the brain and that the G allele is associated with lower expression level than the A allele. These results suggest that TTLL11 may play a major role in healthy individuals' attention performance and may also contribute to the etiology of ADHD.