New insights into the regulation of synaptic transmission and plasticity by the endoplasmic reticulum and its membrane contacts

Mammalian neurons are highly compartmentalized yet very large cells. To provide each compartment with its distinct properties, metabolic homeostasis and molecular composition need to be precisely coordinated in a compartment-specific manner. Despite the importance of the endoplasmic reticulum (ER) as a platform for various biochemical reactions, such as protein synthesis, protein trafficking, and intracellular calcium control, the contribution of the ER to neuronal compartment-specific functions and plasticity remains elusive. Recent advances in the development of live imaging and serial scanning electron microscopy (sSEM) analysis have revealed that the neuronal ER is a highly dynamic organelle with compartment-specific structures. sSEM studies also revealed that the ER forms contacts with other membranes, such as the mitochondria and plasma membrane, although little is known about the functions of these ER-membrane contacts. In this review, we discuss the mechanisms and physiological roles of the ER structure and ER-mitochondria contacts in synaptic transmission and plasticity, thereby highlighting a potential link between organelle ultrastructure and neuronal functions.     

Biomarkers for Alzheimer's Disease – An Overview

Alzheimer's disease (AD) is a complex neurodegenerative disorder with a significant global impact on public health. The emergence of atypical clinical phenotypes challenges traditional diagnostic approaches, necessitating a deeper exploration of biomarkers for accurate identification. The US Food and Drug Administration (FDA) classification of biomarkers and their integration into different stages of AD provide a structured framework for their application in research and clinical settings. Within the context of AD drug development, biomarkers are essential for participant selection, target engagement evaluation, and assessment of pathological hallmarks, including Aβ and tau protein abnormalities. The incorporation of nanoparticles with a biodegradable approach introduces innovative strategies to address the complexities of AD. This paper extensively discusses biomarkers associated with synaptic dysfunction, neuroinflammation, and glial activation, recognizing their significance in elucidating disease mechanisms. Common pathologies such as synuclein and TDP-43 further underscore the multifaceted nature of AD. Current biomarkers for AD diagnosis, encompassing cerebral spinal fluid (CSF) biomarkers and various imaging modalities, reflect the ongoing efforts to enhance early detection and monitoring. Intriguingly, novel biomarkers continue to emerge, offering promising avenues for improved understanding and intervention. Current review provides a comprehensive survey of biomarkers for AD, elucidating their diverse roles across different aspects of the disease. By highlighting their contributions to diagnosis, drug development, and mechanistic insights, this overview underscores the importance of biomarker research in the pursuit of effective AD management and treatment strategies.     

带有未知输入的降阶观测器的设计

本文讨论了带未知输入的线性时不变系统的状态重构问题 .在一种特定的坐标变换下 ,给定了该系统存在降阶观测器的充分必要条件 .与已有结果相比 ,该条件比相应的文献所给出的条件弱 ,并且本文的条件是系统本身的 ,因而也更合理     

Input-to-state stability of nonlinear systems with hybrid inputs and delayed impulses

This paper addresses input-to-state stability (ISS) and integral input-to-state stability (iISS) problem of impulsive systems with hybrid inputs and delayed impulses. By adopting Lyapunov function method, sufficient conditions for ISS/iISS are established, and the impact of time delay in hybrid impulses, that is, the stabilizing impulses and destabilizing impulses, are further studied. Moreover, several examples are given and numerical simulations are performed to illustrate their usefulness.     

On classifying inputs and outputs in DEA: A revised model

Cook and Zhu [Cook, W.D., Zhu, J., 2007. Classifying inputs and outputs in data envelopment analysis. European Journal of Operational Research 180, 692–699] introduced a new method to determine whether a measure is an input or an output. In practice, however, their method may produce incorrect efficiency scores due to a computational problem as result of introducing a large positive number to the model. This note introduces a revised model that does not need such a large positive number.     

Investigating and comparing the dynamic patterns of the business value of information technology over time

The long-debated issue of the business value of information technology (IT) to the firm (country) has received a great deal of attention in the literature. But the studies have rarely examined the dynamic patterns of the IT value as measured by the firm’s productive efficiency over time. The objective of this paper is to apply the three-factor constant elasticity of substitution (CES) time-varying stochastic production frontier models and use the same data set as used in several previous studies to investigate the dynamic patterns of IT value over time in connection with the issues of inputs substitution and complement and the productivity paradox. This paper adopts two analytical skills, collective and individual, to analyze the empirical results. Collectively, we find that the dynamic patterns of IT value are closely related to the substitution and complement of three inputs and the IT productivity paradox. Individually, we identify five common dynamic patterns of the IT value measured by productive efficiency and interpret their implications for the productivity paradox as summarized in a two by two matrix of practical applications and strategies. This matrix accounts for four different scenarios of the relationship between the average productive efficiency and the IT productivity paradox, illustrates some practical applications by the analytical results, and provides some business insights and managerial strategies for IT decision makers and PO/IS managers. This represents a new contribution to the understanding of the dynamic influence of IT investments upon the value of IT over time.     

加工贸易产业升级的影响因素——基于发展中国家面板数据的分析

本文在Feenstra的要素连续统模型基础上,引入普遍技术水平和配套产业发展水平因素,提出了发展中国家加工贸易产业升级的理论模型。利用1992-2007年13个发展中国家的面板数据进行计量分析之后,我们发现,FDI流入、配套产业发展水平和普遍技术水平的提高都会对发展中国家加工贸易的产业升级产生正向影响,其中,配套产业发展水平的提高起到最主要的推动作用。     

Measuring returns to scale in DEA models when the firm is regulated

In the standard framework of data envelopment analysis (DEA) models, the returns to scale are fully characterized using the multiplier on the convexity constraint of inefficient decision making units (DMU) using the projection of the input–output vector on the frontier. In this note, we investigate how the returns to scale measurements in DEA models are affected by the presence of regulatory constraints. These additional constraints change the role played by the convexity constraint. In order to avoid biased estimation of the returns to scale, we show that the interaction between the regulatory and the convexity constraints has to be taken into account.     

Nonlinear neural networks. I. General theory

A neural network is called nonlinear if the introduction of new data into the synaptic efficacies has to be performed through anonlinear operation. The original Hopfield model is linear, whereas, for instance, clipped synapses constitute a nonlinear model. Here a general theory is presented to obtain the statistical mechanics of a neural network with finitely many patterns and arbitrary (symmetric) nonlinearity. The problem is reduced to minimizing a free energy functional over all solutions of a fixed-point equation with synaptic kernelQ. The case of clipped synapses with bimodal and Gaussian probability distribution is analyzed in detail. To this end, a simple theory is developed that gives the spectrum ofQ and thereby all the solutions that bifurcate from the high-temperature phase.     

基于抑制性突触可塑性的神经元放电率自稳态机制

神经元放电率自稳态是指大脑神经网络的放电率维持在相对稳定的状态.大量实验研究发现神经元放电率自稳态是神经电活动的重要特征,并且放电率自稳态是实现神经信息处理及维持正常脑功能的基础,因此放电率自稳态的研究是神经科学领域的重要科学问题.脑神经网络是一个高度复杂的动态系统,存在大量输入扰动信号及由于动态链接导致的参数摄动,因此如何建立并维持神经元放电率自稳态及其鲁棒性仍有待深入研究.反馈神经回路是皮层神经网络的典型连接模式,抑制性突触可塑性对神经元放电率自稳态具有重要的调控作用.本文通过构建包含抑制性突触可塑性的反馈神经回路模型对神经元放电率自稳态及其鲁棒性进行计算研究.结果表明:在抑制性突触可塑性的作用下,神经元放电率可自适应地跟踪目标放电率,从而取得放电率自稳态;在有外部输入干扰和参数摄动的情况下,神经元放电率具有良好的抗扰动性能,表明放电率自稳态具有很强的鲁棒性;理论分析揭示了抑制性突触可塑性学习规则是神经元放电率自稳态的神经机制;仿真分析进一步揭示了学习率及目标放电率对放电率自稳态建立过程具有重要影响.