Assessment of physical activity of the human body considering the thermodynamic system

Correctly dosed physical activity is the basis of a vital and healthy life, but the measurement of physical activity is certainly rather empirical resulting in limited individual and custom activity recommendations. Certainly, very accurate three-dimensional models of the cardiovascular system exist, however, requiring the numeric solution of the Navier–Stokes equations of the flow in blood vessels. These models are suitable for the research of cardiac diseases, but computationally very expensive. Direct measurements are expensive and often not applicable outside laboratories. This paper offers a new approach to assess physical activity using thermodynamical systems and its leading quantity of entropy production which is a compromise between computation time and precise prediction of pressure, volume, and flow variables in blood vessels. Based on a simplified (one-dimensional) model of the cardiovascular system of the human body, we develop and evaluate a setup calculating entropy production of the heart to determine the intensity of human physical activity in a more precise way than previous parameters, e.g. frequently used energy considerations. The knowledge resulting from the precise real-time physical activity provides the basis for an intelligent human–technology interaction allowing to steadily adjust the degree of physical activity according to the actual individual performance level and thus to improve training and activity recommendations.     

Structural Characterization of Biomolecules through Atomistic Simulations Guided by DEER Measurements

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Range expansion of the small spruce bark beetle Ips amitinus: a newcomer in northern Europe

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Resilience of ecosystem processes: a new approach shows that functional redundancy of biological control services is reduced by landscape simplification

Functional redundancy can increase the resilience of ecosystem processes by providing insurance against species loss and the effects of abundance fluctuations. However, due to the difficulty of assessing individual species’ contributions and the lack of a metric allowing for a quantification of redundancy within communities, few attempts have been made to estimate redundancy for individual ecosystem processes. We present a new method linking interaction metrics with metabolic theory that allows for a quantification of redundancy at the level of ecosystem processes. Using this approach, redundancy in the predation on aphids and other prey by natural enemies across a landscape heterogeneity gradient was estimated. Functional redundancy of predators was high in heterogeneous landscapes, low in homogeneous landscapes and scaled with predator specialisation. Our approach allows quantifying functional redundancy within communities and can be used to assess the role of functional redundancy across a wide variety of ecosystem processes and environmental factors.     

Optimal thermotolerance of Bifidobacterium bifidum in gellan-alginate microparticles

The purpose of this research was to encapsulate Bifidobacterium bifidum using gellan, sodium alginate and prebiotics as coating materials, and to maximize the thermotolerance of the probiotics with an optimal combination of the coating materials. The optimal ratio of the coating materials for the microparticles under heat treatments (75 degrees C, 1 min) was obtained by using the response surface method and the sequential quadratic programming technique. Optimization results indicated that 2% sodium alginate mixed with 1% gellan gum as coating materials would produce the highest thermotolerance in terms of B. bifidum count. The verification experiment yielded a result close to the predicted values, with no significant difference (P > 0.05). The results of heat treatments also demonstrated that the addition of gellan gum in the walls of probiotic microcapsules provided improved protection for B. bifidum. These probiotic counts remained at 10(5)-10(6) CFU/g for the microcapsules stored for 2 months, then treated in heat and in simulated gastric fluid.     

Variation, natural selection, and information content--a simulation

In Neo-Darwinism, variation and natural selection are the two evolutionary mechanisms that propel biological evolution. Variation implies changes in the gene pool of a population, enlarging the genetic variability from which natural selection can choose. But in the absence of natural selection, variation causes dissipation and randomization. Natural selection, in contrast, constrains this variability by decreasing the survival and fertility of the less-adapted organisms. The objective of this study is to propose a highly simplified simulation of variation and natural selection, and to relate the observed evolutionary changes in a population to its information content. The model involves an imaginary population of individuals. A quantifiable character allows the individuals to be categorized into bins. The distribution of bins (a histogram) was assumed to be Gaussian. The content of each bin was calculated after one to twelve cycles, each cycle spanning N generations (N being undefined). In a first study, selection was simulated in the absence of variation. This was modeled by assuming a differential fertility factor F that increased linearly from the lower bins (F<1.00) to the higher bins (F>1.00). The fertility factor was applied as a multiplication factor during each cycle. Several ranges of fertility were investigated. The resulting histograms became skewed to the right. In a second study, variation was simulated in the absence of selection. This was modeled by assuming that during each cycle each bin lost a fixed percentage of its content (variation factor Y) to its two adjacent bins. The resulting histograms became broader and flatter, while retaining their bilateral symmetry. Different values of Y were monitored. In a third study, various values of F and Y were combined. Our model allows the straightforward application of Shannon's equation and the calculation of a Shannon-entropy (SE) values for each histogram. Natural selection was, thus, shown to result in a progressive decrease in SE as a function of F. In other words, natural selection, when acting alone, progressively increased the information content of the population. In contrast, variation resulted in a progressive increase in SE as a function of Y. In other words, variation acting alone progressively decreased the information content of a population. When both factors, F and Y, were applied simultaneously, their relative weight determined the progressive change in SE.     

福建省多花黄精生态适宜性区划研究

通过研究多花黄精(Polygonatum cyrtonema)的生态适宜性,为其野生资源保护及人工规范化栽培提供依据。收集237份多花黄精分布位置和55个生态因子,导入最大熵(MaxEnt)模型运算,结合地理信息系统(GIS)分析其适合生长区域。结果表明,影响多花黄精生态适宜性的主要生态因子是3月降水量、年均降水量、6月降水量、5月降水量、4月降水量、2月降水量和最冷季降水量,在福建省具有广泛适宜生长区域。模型预测结果可靠,可为多花黄精野生资源保护提供参考,并为其科学种植提供指导。     

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUG's biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs—the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).     

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

During early embryonic development, cell fate commitment represents a critical transition or"tipping point"of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene–gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regu-latory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the"dark genes"that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes. The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.