Finszter Géza: Rendészettan. Dialóg Campus Kiadó, 2018

The professional literature of law has been enriched with a stopgap new book: in the series Studia Universitatis Communa issued the publisher Dialóg Campus Kiadó the new work of Géza Finszter. The corpulent book of nearby 500 pages is even as a spectacle admirable. Its author designed it as a textbook but as he mentioned it can be handled as a handbook for law enforcement right, too. Although it includes relatively many legal rule texts that could harm the long-standing character of a handbook. The book consists of an introductory part and of ten chapters with detailed explications.A jogi szakirodalom hézagpótlónak nevezhető újszak könyvvel gazdagodott: a Dialóg Campus Kiadó a Studia Universitatis Communa sorozatban megjelentette Finszter Géza új művét. A testes – közel félezer oldal terjedelmű – kötet látványként is tiszteletet ébreszt. Műfaját tekintve a szerző tankönyvnek szánta ugyan, de maga is rámutat: a rendészeti jog kézi könyvének is megfelelne, bár viszonylag sok jogszabály szöveg található benne, ami egy kézi könyv időtállóságának kétségtelenül árt. A mű egy bevezető részből és az érdemi fejtegetéseket tartalmazó tíz fejezetből áll

Unbalanced synaptic inhibition can create intensity-tuned auditory cortex neurons

Intensity-tuned auditory cortex neurons may be formed by intensity-tuned synaptic excitation. Synaptic inhibition has also been shown to enhance, and possibly even create intensity-tuned neurons. Here we show, using in vivo whole cell recordings in pentobarbital-anesthetized rats, that some intensity-tuned neurons are indeed created solely through disproportionally large inhibition at high intensities, without any intensity-tuned excitation. Since inhibition is essentially cortical in origin, these neurons provide examples of auditory feature-selectivity arising de novo at the cortex.Comment: 22 pages, 5 figure

Role of miR-2392 in driving SARS-CoV-2 infection

MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene regulation that have a major impact on many diseases and provide an exciting avenue toward antiviral therapeutics. From patient transcriptomic data, we determined that a circulating miRNA, miR-2392, is directly involved with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) machinery during host infection. Specifically, we show that miR-2392 is key in driving downstream suppression of mitochondrial gene expression, increasing inflammation, glycolysis, and hypoxia, as well as promoting many symptoms associated with coronavirus disease 2019 (COVID-19) infection. We demonstrate that miR-2392 is present in the blood and urine of patients positive for COVID-19 but is not present in patients negative for COVID-19. These findings indicate the potential for developing a minimally invasive COVID-19 detection method. Lastly, using in vitro human and in vivo hamster models, we design a miRNA-based antiviral therapeutic that targets miR-2392, significantly reduces SARS-CoV-2 viability in hamsters, and may potentially inhibit a COVID-19 disease state in humans

Nonparametric Spatio-Temporal Change Point Analysis for Early Detection in Mammography

One benefit of mammography screening is the early detection of anomalous regions within the mammogram. An automated procedure for performing this detection could be useful in reducing false negative readings and standardizing the interpretation of mammograms. This paper deals with investigating the use of texture features and a nonparametric classification scheme for early detection of developing abnormalities. The texture features used are derived from fractal analysis. The nonparametric classifier is derived from the finite mixture model probability density estimation technique. Preliminary results from a six patient study with a three year mammogram collection period are discussed. INTRODUCTION Computer-aided mammography requires that a method for distinguishing between healthy tissue and anomalous regions be developed [1,2]. This requires a method for characterizing the features which are relevant to this problem. The features we consider are local texture features derived from th..

Dynamic Stochastic Blockmodels: Statistical Models for Time-Evolving Networks

Abstract. Significant efforts have gone into the development of statistical models for analyzing data in the form of networks, such as social networks. Most existing work has focused on modeling static networks, which represent either a single time snapshot or an aggregate view over time. There has been recent interest in statistical modeling of dynamic networks, which are observed at multiple points in time and offer a richer representation of many complex phenomena. In this paper, we propose a state-space model for dynamic networks that extends the well-known stochastic blockmodel for static networks to the dynamic setting. We then propose a procedure to fit the model using a modification of the extended Kalman filter augmented with a local search. We apply the procedure to analyze a dynamic social network of email communication