Using ecological networks to answer questions in global biogeography and ecology

Ecological networks have classically been studied at site and landscape scales, yet recent efforts have been made to collate these data into global repositories. This offers an opportunity to integrate and upscale knowledge about ecological interactions from local to global scales to gain enhanced insights from the mechanistic information provided by these data. By drawing on existing research investigating patterns in ecological interactions at continental to global scales, we show how data on ecological networks, collected at appropriate scales, can be used to generate an improved understanding of many aspects of ecology and biogeography—for example, species distribution modelling, restoration ecology and conservation. We argue that by understanding the patterns in the structure and function of ecological networks across scales, it is possible to enhance our understanding of the natural world.     

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.     

Some new results on Cox–Czanner divergence and their applications in survival studies

In the present communication, we propose a quantile-based measure for the divergence between two survival functions. This can also be used in a dynamic way where the divergence between survival functions varies with time. Several new properties of the proposed measure are investigated with suitable examples. The behavior of the measure for various reliability models is also investigated. A real data analysis is employed to compare the relative efficacy of two treatment groups using the proposed divergence measure.     

定量分析调控人类免疫缺陷病毒潜伏与激活的动力学机制

目的 治疗艾滋病最大的障碍在于无法根除人类免疫缺陷病毒（HIV）潜伏于人体细胞所形成的病毒存储库。构建描述病毒存储库建立分子机制的动力学模型需考虑生物体内的噪声环境和多重影响因素,本文通过一种全新的动力学结构分解方法将随机微分方程的确定性部分与随机性噪声分开,从而在仅需分析常微分方程不动点的情况下即可判断不同药物靶点的作用效果。方法 使用连续的随机微分方程构建了HIV转录过程的动力学模型,简化了描述系统所需方程的维度,增大了模型的可探索空间,在此基础上,通过计算得到的势能函数和概率分布函数直观表示病毒潜伏与激活的不同表达状态以及它们之间的关系。结果 定量分析了不同动力学参数对系统稳态和势函数的影响程度,分别得到了系统处于双稳态和单稳态时的参数范围,并将不同因素对动力系统分岔的影响程度与生物学实验结果对比,验证了本工作的理论基础。结论 本文突破了以往离散、随机的方法,可以通过常微分方程定量分析HIV转录调控的动力学机制,有利于推广到处理高维情况,进一步研究艾滋病在生物体内的发生发展,从而指导设计实验寻找临床上的治疗方案。     

具有节点偏置的高阶神经网络模型

在汪涛文献基础上提出了一个具有节点偏置的高阶神经网络模型、给出了模型的哈密顿量和学习算法,证明了学习算法的收敛性,该模型能对每一神经元自动引入一个节点偏置使得网络能够存储所有学习图样包括相关图样,其存储容量远高于Hebb—rule—like学习算法下的高阶神经网络模型.对由30个神经元组成的二阶神经网络进行了计算机仿真,结果证实了上述结论.此外,对初始突触强度对学习效果的影响和不同存储图样数目下的平均吸引半径进行了仿真计算并分析了所得结果.新模型的特点使其具有良好的应用前景     

采用Reichardt运动检测器和Boltzmann Machine神经网络的运动计算

建立了一个探讨灵长类视皮层从Ｖ１区到ＭＴ区的运动信息加工原理的计算模型,这个过程的突出特征是视觉运动信息经过了从局部检测进步到整体感知。模型的第一层由用于抽提运动模式的局部速度以及结构性质的Ｒｅｉｃｈａｒｄｔ运动检测器组成,进一步的加工是通过Ｂｏｌｔｚｍａｎｎ Ｍａｃｈｉｎｅ神经网络来实现的。这种网络的学习算法具有局部更新的显著性质,在学习阶段,网络不断地修改联结权重以形成对于记录在网络的显单元上     

生长抑素在糖尿病大鼠胰腺外分泌功能降低中的作用

本实验观察到,用链佐霉素造成大鼠糖尿病模型,其胰腺组织中生长抑素含量明显升高。大鼠皮下注射生长抑素(100μg·kg~(-1)·d~(-1))5d,其胰腺组织中淀粉酶的含量明显降低。体外胰腺灌流也表明,生长抑素能抑制CCK—8刺激引起的胰腺淀粉酶释放。以上结果提示,糖尿病时胰腺组织中生长抑素含量的增加可能在胰腺外分泌功能降低中有一定的作用。     

N-Heterocyclic Carbene-Palladium Polymers Network: Recyclable Pre-catalyst for Effective Suzuki–Miyaura Coupling Reaction in Water

Rational design of high-efficiency N-heterocyclic carbene (NHC) palladium catalyst is of great importance to modern organic synthesis, especially in chemical and pharmaceutical industries. Herein, we fabricate a polymer network containing N-heterocyclic carbene palladium (PNNHC-Pd) catalytic active sites via an immobilization process. The N-heterocyclic carbene palladium can serve as a promising linkage of polymer network as well as an effective catalytic active site owing to its structural preference and strong σ-donating ability with palladium species. The results display that N-heterocyclic carbene palladium disperses homogeneously in polymer network, thus rendering PNNHC-Pd excellent catalytic activity, high stability and superior reusability in palladium-catalyzed Suzuki–Miyaura coupling reaction in aqueous medium. This work provides a new insight into the development of heterogenization of homogeneous catalysts based on polymer network.     

Insights into circular RNAs: Biogenesis,function and their regulatory roles in cardiovascular disease

As a distinctive member of the noncoding RNA family, circular RNAs (circRNAs) are generated from single-stranded, covalently closed structures and are ubiquitous in mammalian cells and tissues. Due to its atypical circular architecture, it was conventionally deemed insignificant dark matter for a prolonged duration. Nevertheless, studies conducted over the last decade have demonstrated that this abundant, structurally stable and tissue-specific RNA has been increasingly relevant in diverse diseases, including cancer, neurological disorders, diabetes mellitus and cardiovascular diseases (CVDs). Therefore, regulatory pathways controlled by circRNAs are widely involved in the occurrence and pathological processes of CVDs through their function as miRNA sponges, protein sponges and protein scaffolds. To better understand the role of circRNAs and their complex regulatory networks in CVDs, we summarize current knowledge of their biogenesis and function and the latest research on circRNAs in CVDs, with the hope of paving the way for the identification of promising biomarkers and therapeutic strategies for CVDs.