Telomeres and meiosis in health and disease

Telomeres are important segments of chromosomes that protect chromosome ends from nucleolytic degradation and fusion. At meiosis telomeres display an unprecedented behavior which involves their attachment and motility along the nuclear envelope. The movements become restricted to a limited nuclear sector during the so-called bouquet stage, which is widely conserved among species. Recent observations suggest that telomere clustering involves actin and/or microtubules, and is altered in the presence of impaired recombinogenic and chromosome related functions. This review aims to provide an overview of what is currently known about meiotic telomere attachment, dynamics and regulation in synaptic meiosis.     

The cold case: Are rhinoviruses perfectly adapted pathogens?

Rhinoviruses, which cause common cold, belong to the Picornaviridae family, small non-enveloped viruses (diameter 15-30 nm) containing a single-stranded RNA genome (about 7 kb). Over 100 different rhinoviral serotypes have been identified thus far, establishing rhinoviruses as the most diverse group of Picornaviridae. Based on receptor binding properties, rhinoviruses are divided into two classes: the major group binding to intracellular adhesion molecule-1 and the minor group binding to the very low density lipoprotein receptors. Interactions between virus and the receptor molecules cause a conformational change in the capsid, which is a prerequisite for viral uptake. Rhinoviruses trigger a chemokine response upon infection that may lead to exacerbation of the symptoms of common cold, i.e. asthma and inflammation. The following review aims to summarize the knowledge about rhinoviral infections and discusses therapeutical approaches against this almost perfectly adapted pathogen.     

From endoderm to pancreas: a multistep journey

The formation of the vertebrate pancreas is a complex process that typifies the basic steps of embryonic development. It involves the establishment of competence, specification, signaling from neighboring tissues, morphogenesis, and the elaboration of tissue-specific genetic networks. A full analysis of this multistep process will help us to understand classic principles of embryonic development. Furthermore, this will provide the blueprint for experimental programming of pancreas formation from embryonic stem cells in the context of diabetes cell-therapy. Although in the past decade many studies have contributed to a solid foundation for understanding pancreatogenesis, important gaps persist in our knowledge of early pancreas formation. This review will summarize the current understanding of the early mechanisms coming into play to pattern the "pre-pancreatic" region within the endoderm and, gradually, specify the pancreatic tissue.     

机器学习模型在地下水埋深模拟中的适应性分析

基于京津冀气象、社会资料及地下水埋深数据,构建支持向量机(SVM)、循环神经网络(RNN)和长短期记忆神经网络(LSTM)模型对京津冀地区13个城市地下水埋深进行了模拟,并以确定系数、均方根误差、平均绝对百分比误差、纳什系数对3个模型的适应性进行了评价。结果表明：LSTM模型模拟效果最好,其次是RNN,SVM最差;不同城市基于LSTM模型进行地下水埋深模拟时参数调整最少,适应性最好,SVM模型参数调整最多。将3个模型应用于随机选择的6个测站进行验证,在华北地区浅层地下水埋深模拟方面,LSTM模型模拟精度和可信度最好,适应性最强,是该地区地下水埋深模拟的首选机器学习模型。     

桥梁检测系统中的无线数据采集卡组件

在大跨度桥梁检测中,为了摆脱传统数据采集卡与传感器间导线的限制,我们采用无线数据采集卡组件,实现数据采集和无线传输.采集卡组件由主卡和从卡构成,并分别与计算机和传感器相连.主、从卡之间以“询问———应答”通讯方式,实现一主多从组合,满足实际检测需求.系统启动后,主卡为每一从卡分配互不重叠的时间片,在每个时间片内主卡向特定从卡发送询问字,并接收从卡发回的应答字.主卡通过PCI总线与计算机交换数据.该组件经过测试,性能稳定.     

Do Long‐Run Theory Restrictions Help in Forecasting?

Do long‐run equilibrium relations suggested by economic theory help to improve the forecasting performance of a cointegrated vector error correction model (VECM)? In this paper we try to answer this question in the context of a two‐country model developed for the Canadian and US economies. We compare the forecasting performance of the exactly identified cointegrated VECMs to the performance of the over‐identified VECMs with the long‐run theory restrictions imposed. We allow for model uncertainty and conduct this comparison for every possible combination of the cointegration ranks of the Canadian and US models. We show that the over‐identified structural cointegrated models generally outperform the exactly identified models in forecasting Canadian macroeconomic variables. We also show that the pooled forecasts generated from the over‐identified models beat most of the individual exactly identified and over‐identified models as well as the VARs in levels and in differences. Copyright © 2011 John Wiley & Sons, Ltd.     

Price–Dividend Ratios and Stock Price Predictability

A long‐standing puzzle to financial economists is the difficulty of outperforming the benchmark random walk model in out‐of‐sample contests. Using data from the USA over the period of 1872–2007, this paper re‐examines the out‐of‐sample predictability of real stock prices based on price–dividend (PD) ratios. The current research focuses on the significance of the time‐varying mean and nonlinear dynamics of PD ratios in the empirical analysis. Empirical results support the proposed nonlinear model of the PD ratio and the stationarity of the trend‐adjusted PD ratio. Furthermore, this paper rejects the non‐predictability hypothesis of stock prices statistically based on in‐ and out‐of‐sample tests and economically based on the criteria of expected real return per unit of risk. Copyright © 2011 John Wiley & Sons, Ltd.     

Exploring Survey‐Based Inflation Forecasts

This paper first shows that survey‐based expectations (SBE) outperform standard time series models in US quarterly inflation out‐of‐sample prediction and that the term structure of survey‐based inflation forecasts has predictive power over the path of future inflation changes. It then proposes some empirical explanations for the forecasting success of survey‐based inflation expectations. We show that SBE pool a large amount of heterogeneous information on inflation expectations and react more flexibly and accurately to macro conditions both contemporaneously and dynamically. We illustrate the flexibility of SBE forecasts in the context of the 2008 financial crisis. Copyright © 2011 John Wiley & Sons, Ltd.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.