Direct binding of GABAA receptor β2 and β3 subunits to gephyrin

GABAergic transmission is essential to brain function, and a large repertoire of GABA type A receptor (GABA_AR) subunits is at a neuron's disposition to serve this function. The glycine receptor (GlyR)‐associated protein gephyrin has been shown to be essential for the clustering of a subset of GABA_AR. Despite recent progress in the field of gephyrin‐dependent mechanisms of postsynaptic GABA_AR stabilisation, the role of gephyrin in synaptic GABA_AR localisation has remained a complex matter with many open questions. Here, we analysed comparatively the interaction of purified rat gephyrin and mouse brain gephyrin with the large cytoplasmic loops of GABA_AR α1, α2, β2 and β3 subunits. Binding affinities were determined using surface plasmon resonance spectroscopy, and showed an ~ 20‐fold lower affinity of the β2 loop to gephyrin as compared to the GlyR β loop–gephyrin interaction. We also probed in vivo binding in primary cortical neurons by the well‐established use of chimaeras of GlyR α1 that harbour respective gephyrin‐binding motifs derived from the different GABA_AR subunits. These studies identify a novel gephyrin‐binding motif in GABA_AR β2 and β3 large cytoplasmic loops.     

The Adhesion Between a Microvillus-Bearing Cell and a Ligand-Coated Substrate: A Monte Carlo Study

In biology, specific cell adhesion is mediated by receptor–ligand interactions. Consequently, its strength correlates with the strength of single receptor–ligand bonds that can be measured with a variety of techniques. However, whether single receptor–ligand bonds are truly present in an experiment is often a concern. In this paper, we present a Monte Carlo simulation of the adhesion between a microvillus-bearing cell and a ligand-coated substrate. In the simulation, ligands were immobilized on the substrate either uniformly or in clusters of three and seven, while receptors were distributed uniformly on the microvillus tip and they moved randomly on the cellular surface. How ligand clustering affects the adhesion frequency and forward rate constant was studied. Other factors that were studied include receptor aggregation on the microvillus tip, ligand density, receptor density, contact time, and binding pocket size. In the case of uniformly distributed ligands, our simulation results agree well with those obtained from probabilistic analysis. We found that, even with clustered ligands on the substrate, most of the adhesion events were mediated by a single bond if the total adhesion frequency was less than 20%. Besides, ligand clustering decreased the total adhesion frequency and forward rate constant, but increased the single-bond adhesion frequency under comparable conditions. These findings should lend us some assistance in identifying single bonds in cell–substrate or cell–bead adhesion measurements and in illustrating some biological mechanisms that involve clustered ligands.     

Spatial clusters of daytime sleepiness and association with nighttime noise levels in a Swiss general population (GeoHypnoLaus)

Introduction

Daytime sleepiness is highly prevalent in the general adult population and has been linked to an increased risk of workplace and vehicle accidents, lower professional performance and poorer health. Despite the established relationship between noise and daytime sleepiness, little research has explored the individual-level spatial distribution of noise-related sleep disturbances. We assessed the spatial dependence of daytime sleepiness and tested whether clusters of individuals exhibiting higher daytime sleepiness were characterized by higher nocturnal noise levels than other clusters.

山东省平阴县农村地区出生缺陷聚集性分析

目的 分析山东省平阴县农村地区出生缺陷聚集性分布特征,为进一步病因学研究和防治提供线索和依据。方法 收集平阴县2004-2016年出生缺陷登记资料,描述出生缺陷地理分布,计算每个村庄的出生缺陷发病例数,对出生缺陷数据资料进行Poisson分布和负二项分布拟合优度检验。结果 出生缺陷发病数据不符合Poisson分布(χ²=30.33,P<0.001),但符合负二项分布(χ²=4.56,P=0.336)。结论 平阴县农村地区出生缺陷在村级层面上具有空间聚集性。     

Male Inmate Profiles and Their Biological Correlates

Objective:

Borderline and antisocial personality disorders (PDs) share common clinical features (impulsivity, aggressiveness, substance use disorders [SUDs], and suicidal behaviours) that are greatly overrepresented in prison populations. These disorders have been associated biologically with testosterone and cortisol levels. However, the associations are ambiguous and the subject of controversy, perhaps because these heterogeneous disorders have been addressed as unitary constructs. A consideration of profiles of people, rather than of exclusive diagnoses, might yield clearer relationships.

Methods:

In our study, multiple correspondence analysis and cluster analysis were employed to identify subgroups among 545 newly convicted inmates. The groups were then compared in terms of clinical features and biological markers, including levels of cortisol, testosterone, estradiol, progesterone, and sulfoconjugated dehydroepiandrosterone (DHEA-S).

Results:

Four clusters with differing psychiatric, criminal, and biological profiles emerged. Clinically, one group had intermediate scores for each of the tested clinical features. Another group comprised people with little comorbidity. Two others displayed severe impulsivity, PD, and SUD. Biologically, cortisol levels were lowest in the last 2 groups and highest in the group with less comorbidity. In keeping with previous findings reported in the literature, testosterone was higher in a younger population with severe psychiatric symptoms. However, some apparently comparable behavioural outcomes were found to be related to distinct biological profiles. No differences were observed for estradiol, progesterone, or DHEA-S levels.

Conclusions:

The results not only confirm the importance of biological markers in the study of personality features but also demonstrate the need to consider the role of comorbidities and steroid coregulation.     

Correlation between Defects and Electrical Performances of Ion-Irradiated 4H-SiC p–n Junctions

In this study, 4H-SiC p–n junctions were irradiated with 700 keV He⁺ ions in the fluence range 1.0 × 10¹² to 1.0 × 10¹⁵ ions/cm². The effects of irradiation were investigated by current–voltage (I–V) and capacitance–voltage (C–V) measurements, while deep-level transient spectroscopy (DLTS) was used to study the traps introduced by irradiation defects. Modifications of the device’s electrical performances were observed after irradiation, and two fluence regimes were identified. In the low fluence range (≤10¹³ ions/cm²), I–V characteristics evidenced an increase in series resistance, which can be associated with the decrease in the dopant concentration, as also denoted by C–V measurements. In addition, the pre-exponential parameter of junction generation current increased with fluence due to the increase in point defect concentration. The main produced defect states were the Z_1/2, RD_1/2, and EH_6/7 centers, whose concentrations increased with fluence. At high fluence (>10¹³ ions/cm²), I–V curves showed a strong decrease in the generation current, while DLTS evidenced a rearrangement of defects. The detailed electrical characterization of the p–n junction performed at different temperatures highlights the existence of conduction paths with peculiar electrical properties introduced by high fluence irradiation. The results suggest the formation of localized highly resistive regions (realized by agglomeration of point defects) in parallel with the main junction.     

Spatiotemporal Analysis of COVID-19 Incidence Data

(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model’s parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.     

Learning Markov random walks for robust subspace clustering and estimation

Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation. In our framework, both the local pairwise similarity and the global subspace structure can be learnt from the transition probabilities of MRW. We prove that under some suitable conditions, our proposed local/global criteria can exactly capture the multiple subspace structure and learn a low-dimensional embedding for the data, in which giving the true segmentation of subspaces. To improve robustness in real situations, we also propose an extension of the MRW learning model based on integrating transition matrix learning and error correction in a unified framework. Experimental results on both synthetic data and real applications demonstrate that our proposed MRW learning model and its robust extension outperform the state-of-the-art subspace clustering methods.