Association of the OPRM1 Variant rs1799971 (A118G) with Non-Specific Liability to Substance Dependence in a Collaborative de novo Meta-Analysis of European-Ancestry Cohorts

Peer reviewe

A Computational Approach to Identifying miRNAs Implicated in Drosophila Neurodevelopment

miRNAs are known to regulate many aspects of neurodevelopment. They participate at different stages of this process from early embryogenesis to adult stage. Their various and specific functions begin to be unraveled in many model systems. Important part of neurogenesis, which generates mature neurons from progenitor cells, is the nerve growth and the formation of synapses. As they underlie the neuronal network formation, perturbation of their proper regulation causes different neuro-developmental diseases in human. In our study we used the model organism Drosophila to identify by a computational approach miRNAs, targeting genes, which control axonal growth and synaptogenesis. We screened preselected groups of genes, known to regulate these processes and identified several micro-RNAs as likely candidates for their expression control. We found five miRNAs, which have been reported earlier to associate with dFMRP (Drosophila Fragile X mental Retardation Protein 1) and which target only a small number of specific genes. We also identified several new miRNA candidates likely implicated in synaptogenesis

Clunio balticus Heimbach 1978

<i>Clunio balticus</i> and <i>Clunio ponticus</i> sampling <p> Two male adults of both <i>C</i>. <i>balticus</i> and <i>C</i>. <i>ponticus</i> (preserved in 96% ethanol) were analysed. <i>C</i>. <i>balticus</i> was collected from the Atlantic Ocean seashore about 20 km south of Bergen in western Norway, in the Kviturdvikpollen (60 o 16´N, 5 o 15´E), reared by Dr. F. Heimbach and sent to Dr. P. Michailova on 09.05.1978. <i>C</i>. <i>ponticus</i> was collected while it was floating on the water surface of the Black Sea coast near Varna beach, Bulgaria, St. Konstantin and Helena Resorts (4313’45’N and 28 0’30’E) on 26.VI.2019. Head, thorax, legs and hypopygium of the examined adults (where diagnostic characters to identify the species are present) dissected and mounted on a microscope slide with Canada Balsam, after dehydration with acetic acid and clarification with butanol (Lencioni <i>et al</i>. 2017). Genomic DNA extracted from the abdomen. The mounted samples identified to species level according to Moubayed-Breil & Michailova (2020) and Moubayed-Breil <i>et al.</i> (2020). Morphological voucher specimens and the DNA vouchers of each specimen are deposited in Valeria Lencioni’s collection at the Science Museum of Trento (Italy).</p>Published as part of <i>Michalova, Paraskeva, Lencioni, Valeria, Nenov, Momchil & Nikolov, Svetoslav, 2021, Can DNA barcoding be used to identify closely related Clunio Haliday, 1855 species (Diptera: Chironomidae, Orthocladiinae)?, pp. 1-8 in Zootaxa 4927 (1)</i> on page 2, DOI: 10.11646/zootaxa.4927.1.1, <a href="http://zenodo.org/record/4533806">http://zenodo.org/record/4533806</a&gt

Detection and Analysis of Critical Dynamic Properties of Oligodendrocyte Differentiation

In this paper, we derive a four-dimensional ordinary differential equation (ODE) model representing the main interactions between Sox9, Sox10, Olig2 and several miRNAs, which drive the process of (olygodendrocyte) differentiation. We utilize the Lyapunov–Andronov theory to analyze its dynamical properties. Our results indicated that the strength of external signaling (morphogenic gradients shh and bmp), and the transcription rate of mOlig2 explain the existence of stable and unstable (sustained oscillations) behavior in the system. Possible biological implications are discussed

Detecting IMSI-Catcher Using Soft Computing

Lately, from a secure system providing adequate user’s protection of confidentiality and privacy, the mobile communication has been degraded to be a less trustful one due to the revelation of IMSI catchers that enable mobile phone tapping. To fight against these illegal infringements there are a lot of activities aiming at detecting these IMSI catchers. However, so far the existing solutions are only device-based and intended for the users in their self-protection. This paper presents an innovative network-based IMSI catcher solution that makes use of machine learning techniques. After giving a brief description of the IMSI catcher the paper identifies the attributes of the IMSI catcher anomaly. The challenges that the proposed system has to surmount are also explained. Last but least, the overall architecture of the proposed Machine Learning based IMSI catcher Detection system is described thoroughly

Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today’s level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment

New Insights into the Hendra Virus Attachment and Entry Process from Structures of the Virus G Glycoprotein and Its Complex with Ephrin-B2

<div><p>Hendra virus and Nipah virus, comprising the genus <em>Henipavirus</em>, are recently emerged, highly pathogenic and often lethal zoonotic agents against which there are no approved therapeutics. Two surface glycoproteins, the attachment (G) and fusion (F), mediate host cell entry. The crystal structures of the Hendra G glycoprotein alone and in complex with the ephrin-B2 receptor reveal that henipavirus uses Tryptophan 122 on ephrin-B2/B3 as a “latch” to facilitate the G-receptor association. Structural-based mutagenesis of residues in the Hendra G glycoprotein at the receptor binding interface document their importance for viral attachments and entry, and suggest that the stability of the Hendra-G-ephrin attachment complex does not strongly correlate with the efficiency of viral entry. In addition, our data indicates that conformational rearrangements of the G glycoprotein head domain upon receptor binding may be the trigger leading to the activation of the viral F fusion glycoprotein during virus infection.</p> </div

Structure of the HeV-G dimer.

<p>The secondary structure elements of the two molecules are colored in cyan and green. The axes of the two six-blade β-propellers are approximately perpendicular to each other. Disulfide bonds are illustrated as yellow sticks. Asparagine-linked carbohydrate modifications (glycosylations) are illustrated as grey spheres.</p

Four hydrophobic residues of the ephrin-B2 G-H loop insert in four hydrophobic HeV-G pockets.

<p>The four ephrin residues (F117, P119, L121 and W122) are illustrated as purple sticks. The HeV-G pockets are shown as a yellow surface. The residues defining these pockets are shown as yellow lines and are labeled.</p