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.     

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.     

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。     

A new strategy for cellulases application in high temperature industrial scenarios with syringic acid assisting

In the process of bioethanol production, more stable and active cellulase in high temperature condition is required. In this study, syringic acid was applied in cellulase hydrolysis system. At 70°C, TvEG3 activity increased 201.36%, CtBglA activity decreased 72.79% by syringic acid. With syringic acid assisting, TvEG3 thermostability was improved, CtBglA thermostability was reduced. Syringic acid scarcely affected CtCBH. In hydrolysis system with the cellulases containing TvEG3, CtCBH, and CtBglA, the reducing sugar yield improved by 28.37% with syringic acid assisting. With the molecular dynamic simulation in syringic acid system, the backbone root-mean-square deviation (RMSD) and the residue root-mean-square fluctuation (RMSF) of TvEG3, CtCBH reduced, while the RMSD and RMSF of CtBglA increased. The reduction in the number of secondary structures, especially α-helix, caused the structure of CtBglA in the presence of syringic acid to collapse at high temperature. More secondary structures in TvEG3 and more α-helix in CtCBH in the presence of syringic acid make them more stable at high temperatures. These means syringic acid can stabilize TvEG3 and CtCBH structure, destabilize CtBglA structure at high temperature. In summary, this study not only provides insight into cellulase hydrolysis at high temperature with syringic acid assisting but also demonstrates the promoting mechanism of syringic acid.     

ARK2 stabilizes the plus-end of microtubules and promotes microtubule bundling in Arabidopsis

Microtubule dynamics and organization are important for plant cell morphogenesis and development. The microtubule-based motor protein kinesins are mainly responsible for the transport of some organelles and vesicles, although several have also been shown to regulate microtubule organization. The ARMADILLO REPEAT KINESIN (ARK) family is a plant-specific motor protein subfamily that consists of three members (ARK1, ARK2, and ARK3) in Arabidopsis thaliana. ARK2 has been shown to participate in root epidermal cell morphogenesis. However, whether and how ARK2 associates with microtubules needs further elucidation. Here, we demonstrated that ARK2 co-localizes with microtubules and facilitates microtubule bundling in vitro and in vivo. Pharmacological assays and microtubule dynamics analyses indicated that ARK2 stabilizes cortical microtubules. Live-cell imaging revealed that ARK2 moves along cortical microtubules in a processive mode and localizes both at the plus-end and the sidewall of microtubules. ARK2 therefore tracks and stabilizes the growing plus-ends of microtubules, which facilitates the formation of parallel microtubule bundles.     

ALEXANDRIUM SATOANUM SP. NOV. (DINOPHYCEAE) FROM MATOYA BAY,CENTRAL JAPAN1

The gonyaulacoid dinofiagellate Alexandrium satoanum Yuki et Fukuyo sp. nov. is described from Matoya Bay, Pacific coast of central Japan. The species is distinctive in its conical epitheca with almost straight sides and dorsal concavity of the hypotheca. The plate formula is Po, pc, 4′, 6″, 6c, 10s, 5″″, and 2″″, including two accessory plates inside the sulcus. The apical pore plate is triangular and possesses an anterior attachment pore at the right margin. The first apical plate does not make contact with the apical pore plate and lacks a ventral pore. A posterior attachment pore lies at the center of the posterior sulcal plate. In Matoya Bay, vegetative cells occur as solitary cells or sometimes in pairs during late spring and early summer in low concentrations. In connection with this study, the following new combination is proposed: Alexandrium pseudogonyaulax (Biecheler) Horiguchi ex Yuki et Fukuyo comb. nov.     

A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation dynamics

Changes in vegetation structure and biogeography due to climate change feedback to alter climate by changing fluxes of energy, moisture, and momentum between land and atmosphere. While the current class of land process models used with climate models parameterizes these fluxes in detail, these models prescribe surface vegetation and leaf area from data sets. In this paper, we describe an approach in which ecological concepts from a global vegetation dynamics model are added to the land component of a climate model to grow plants interactively. The vegetation dynamics model is the Lund–Potsdam–Jena (LPJ) dynamic global vegetation model. The land model is the National Center for Atmospheric Research (NCAR) Land Surface Model (LSM). Vegetation is defined in terms of plant functional types. Each plant functional type is represented by an individual plant with the average biomass, crown area, height, and stem diameter (trees only) of its population, by the number of individuals in the population, and by the fractional cover in the grid cell. Three time‐scales (minutes, days, and years) govern the processes. Energy fluxes, the hydrologic cycle, and carbon assimilation, core processes in LSM, occur at a 20 min time step. Instantaneous net assimilated carbon is accumulated annually to update vegetation once a year. This is carried out with the addition of establishment, resource competition, growth, mortality, and fire parameterizations from LPJ. The leaf area index is updated daily based on prevailing environmental conditions, but the maximum value depends on the annual vegetation dynamics. The coupling approach is successful. The model simulates global biogeography, net primary production, and dynamics of tundra, boreal forest, northern hardwood forest, tropical rainforest, and savanna ecosystems, which are consistent with observations. This suggests that the model can be used with a climate model to study biogeophysical feedbacks in the climate system related to vegetation dynamics.