Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.     

Forest resistance to drought in a humid tropical dipterocarp forest in the Western Ghats of India

Droughts are linked to tree die-offs in the biodiverse humid tropics. We assessed drought response of a Dipterocarp Forest and found a marginal decrease in tree survival, indicating drought resistance. Understory and emergent species were sensitive to drought. Urgent focus is needed to understand drought impacts and plant physiological responses in South Asian forests.     

Chiral symmetry breaking: Experimental results and computer analysis of a liquid-phase autoxidation

The autoxidation of tetralin is treated as a model reaction system to define the applicability of stereospecific autocatalysis. This concept, predicting a spontaneous amplification of enantiomeric excess generated by an autocatalytic chemical reaction, is used in several theoretical models as an explanation for the origin of natural optical activity. The reaction system investigated obeys the basic criteria of these models: a chiral intermediate (tetralin hydroperoxide) is produced from an achiral substrate (tetralin) via an autocatalytic pathway where the feedback mechanism is expected to generate a state of broken chiral symmetry. In order to test the amplification capacity of this reaction a computer analysis of the kinetic scheme is performed. This simulation is derived from the known kinetic scheme of autoxidation and is validated by fitting the experimentally observed data of hydroperoxide evolution. Calculations show that this model allows powerful amplification of enantiomeric excess and a transient amplification of the optical rotation. It is also demonstrated that the model system exhibits pronounced sensitivity toward any loss of absolute configuration of the involved chiral species. Since an amplification effect results exclusively at a high degree of stereoselectivity, it is concluded that stereospecific autocatalysis is possible in systems which show template reactions, crystallization, or colloidal effects. © 1993 Wiley-Liss, Inc.     

A mechanistic spatio-temporal modeling of COVID-19 data

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.     

单增李斯特菌胎盘感染的体内外模型研究进展

单核细胞增生李斯特氏菌（Listeria monocytogenes）是重要的食源性致病菌,能引发人类的李斯特菌病,是全球公共卫生问题之一。该菌易感染孕妇,引起胎儿和新生儿的侵袭性李斯特菌病,严重威胁母婴健康。因此,建立有效的单增李斯特菌感染胎盘体内外模型,解析和探究单增李斯特菌经胎盘感染机制,是预防和控制单增李斯特菌感染母婴的关键所在。本文综述了可用于研究单增李斯特菌母婴感染的体内外胎盘模型,总结和讨论了各类模型的优势和局限性;并着重分析了体外三维胎盘屏障模型在单增李斯特菌感染方面的研究进展和未来研究方向。以期为深入解析该菌经胎盘感染的途径、发病机制提供支持,并为预防和控制母婴李斯特菌病提供科学参考。     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

Inferring landscape resistance to gene flow when genetic drift is spatially heterogeneous

In connectivity models, land cover types are assigned cost values characterizing their resistance to species movements. Landscape genetic methods infer these values from the relationship between genetic differentiation and cost distances. The spatial heterogeneity of population sizes, and consequently genetic drift, is rarely included in this inference although it influences genetic differentiation. Similarly, migration rates and population spatial distributions potentially influence this inference. Here, we assessed the reliability of cost value inference under several migration rates, population spatial patterns and degrees of population size heterogeneity. Additionally, we assessed whether considering intra-population variables, here using gravity models, improved the inference when drift is spatially heterogeneous. We simulated several gene flow intensities between populations with varying local sizes and spatial distributions. We then fit gravity models of genetic distances as a function of (i) the ‘true’ cost distances driving simulations or alternative cost distances, and (ii) intra-population variables (population sizes, patch areas). We determined the conditions making the identification of the ‘true’ costs possible and assessed the contribution of intra-population variables to this objective. Overall, the inference ranked cost scenarios reliably in terms of similarity with the ‘true’ scenario (cost distance Mantel correlations), but this ‘true’ scenario rarely provided the best model goodness of fit. Ranking inaccuracies and failures to identify the ‘true’ scenario were more pronounced when migration was very restricted (<4 dispersal events/generation), population sizes were most heterogeneous and some populations were spatially aggregated. In these situations, considering intra-population variables helps identify cost scenarios reliably, thereby improving cost value inference from genetic data.     

Some population consequences of variation in preference among individual predators

A number of foraging studies have demonstrated that populations of predators rarely consist of individuals with identical preferences for particular types of prey. Variation among predators can lead to frequency-dependent changes in population preference, because those predators mat prefer the rarer type of prey generally have the greatest influence on population preference. In this study we develop a series of theoretical models which demonstrate how anti-apostatic selection (i.e. selection against the rare form) can arise out of (a) bimodal and (b) normal variation in preference among individuals of the same species. We show that population level anti-apostatic selection can occur even when individual predators show pro-apostatic selection (i.e. selection against the common form). Furthermore, patterns of population prey selection that arise out of variation in preference can potentially be pro-apostatic over one range of relative densities and anti-apostatic over another range of relative densities. Finally, we examine a case study involving predation by female waterboatmen Notonecta glauca and show that the variation in preference in this species is large enough to generate higher anti-apostatic selection than would be expected from the diet selected by the average individual.