Analysis of Differentially Expressed Genes That Aggravate Metabolic Diseases in Depression

Depression is considered the second leading cause of the global health burden after cancer. It is recognized as the most common physiological disorder. It affects about 350 million people worldwide to a serious degree. The onset of depression, inadequate food intake, abnormal glycemic control and cognitive impairment have strong associations with various metabolic disorders which are mediated through alterations in diet and physical activities. The regulatory key factors among metabolic diseases and depression are poorly understood. To understand the molecular mechanisms of the dysregulation of genes affected in depressive disorder, we employed an analytical, quantitative framework for depression and related metabolic diseases. In this study, we examined datasets containing patients with depression, obesity, diabetes and NASH. After normalizing batch effects to minimize the heterogeneity of all the datasets, we found differentially expressed genes (DEGs) common to all the datasets. We identified significantly associated enrichment pathways, ontology pathways, protein–protein cluster networks and gene–disease associations among the co-expressed genes co-expressed in depression and the metabolic disorders. Our study suggested potentially active signaling pathways and co-expressed gene sets which may play key roles in crosstalk between metabolic diseases and depression.

The Role of Phylogenetics in Unravelling Patterns of HIV Transmission towards Epidemic Control: The Quebec Experience (2002–2020)

Phylogenetics has been advanced as a structural framework to infer evolving trends in the regional spread of HIV-1 and guide public health interventions. In Quebec, molecular network analyses tracked HIV transmission dynamics from 2002–2020 using MEGA10-Neighbour-joining, HIV-TRACE, and MicrobeTrace methodologies. Phylogenetics revealed three patterns of viral spread among Men having Sex with Men (MSM, n = 5024) and heterosexuals (HET, n = 1345) harbouring subtype B epidemics as well as B and non-B subtype epidemics (n = 1848) introduced through migration. Notably, half of new subtype B infections amongst MSM and HET segregating as solitary transmissions or small cluster networks (2–5 members) declined by 70% from 2006–2020, concomitant to advances in treatment-as-prevention. Nonetheless, subtype B epidemic control amongst MSM was thwarted by the ongoing genesis and expansion of super-spreader large cluster variants leading to micro-epidemics, averaging 49 members/cluster at the end of 2020. The growth of large clusters was related to forward transmission cascades of untreated early-stage infections, younger at-risk populations, more transmissible/replicative-competent strains, and changing demographics. Subtype B and non-B subtype infections introduced through recent migration now surpass the domestic epidemic amongst MSM. Phylodynamics can assist in predicting and responding to active, recurrent, and newly emergent large cluster networks, as well as the cryptic spread of HIV introduced through migration.

Visual Analysis of Twitter Data to Support Decision-Making in Law Enforcement: An Analytical Study of COVID-19

<p>The COVID-19 epidemic constituted a crisis for health facilities in 2020. This was due to less medical staff available, degrading employment conditions, and higher death rates. These conditions led to tweets (messages posted on Twitter) launching hashtags titled #In_solidarity_with_the_Egyptian_doctors (#متضامن_مع_أطباء_مصر ) to urge medical staff in Egypt to strike for better working conditions. This resulted in less medical care being provided and threats to public security. This study addresses the visual analysis of “Twitter platform” data during the COVID-19 pandemic in Egypt in April 2020 to test documented mechanisms to process mass data and identify accounts that lead the public opinion-gathering processes on Twitter. It analyzes the hierarchical structure and their ideological belonging. The study uses the URL Decoder/Encoder tool to transfer Arabic hashtags into codec symbols. The study deduced that dialogue clusters on Twitter formed Community Cluster Networks in the study sample. Findings proved significant in determining the accounts leading the public opinion-gathering process. They were recognized through the coordination and arrangement function, as well as the hierarchical structure of the group and their intellectual and ideological tendencies. Finally, the study confirmed the increase of decision makers’ opportunities in gathering accurate information and producing high-quality inferences when using multiple open-source analytical tools, especially information visual analysis tools.<br></p>

Visual Analysis of Twitter Data to Support Decision-Making in Law Enforcement: An Analytical Study of COVID-19

<p>The COVID-19 epidemic constituted a crisis for health facilities in 2020. This was due to less medical staff available, degrading employment conditions, and higher death rates. These conditions led to tweets (messages posted on Twitter) launching hashtags titled #In_solidarity_with_the_Egyptian_doctors (#متضامن_مع_أطباء_مصر ) to urge medical staff in Egypt to strike for better working conditions. This resulted in less medical care being provided and threats to public security. This study addresses the visual analysis of “Twitter platform” data during the COVID-19 pandemic in Egypt in April 2020 to test documented mechanisms to process mass data and identify accounts that lead the public opinion-gathering processes on Twitter. It analyzes the hierarchical structure and their ideological belonging. The study uses the URL Decoder/Encoder tool to transfer Arabic hashtags into codec symbols. The study deduced that dialogue clusters on Twitter formed Community Cluster Networks in the study sample. Findings proved significant in determining the accounts leading the public opinion-gathering process. They were recognized through the coordination and arrangement function, as well as the hierarchical structure of the group and their intellectual and ideological tendencies. Finally, the study confirmed the increase of decision makers’ opportunities in gathering accurate information and producing high-quality inferences when using multiple open-source analytical tools, especially information visual analysis tools.<br></p>

Clustering Policies in Japan as an Example of Clustering Strategy

Clustering as a competitive tool allows companies to be in an advantageous position in the sector by cooperating on various issues, especially the exchange of information with each other. Organizations move forward with the cooperation they develop through clusters. In the literature, it has been seen that clusters are considered as a strategy and Competition model tool, considering the benefits they provide. In this study, the concept of clustering is explained within the framework of the concepts of trust and cooperation. Cluster expectations and cooperation in cluster networks are maintained according to the trust relationship between them. In the studies on this subject, it is observed that the clustering policies in Japan, which successfully implement cooperation as a strategy in accordance with the obligations of mutual trust, are taken as an example. For this reason, research on the clustering policies of Japan was included in the continuation of the study. It is believed that the study will contribute to the literature with conceptual explanations.

Inferring transcriptomic cell states and transitions only from time series transcriptome data

Cellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/.

Clusters of Spin Valve Sensors in 3D Magnetic Field of a Label

Magnetic field sensors based on the giant magnetoresistance (GMR) effect have a number of practical current and future applications. We report on a modeling of the magnetoresistive response of moving spin-valve (SV) GMR sensors combined in certain cluster networks to an inhomogeneous magnetic field of a label. We predicted a large variety of sensor responses dependent on the number of sensors in the cluster, their types of interconnections, the orientation of the cluster, and the trajectory of sensor motion relative to the label. The model included a specific shape of the label, producing an inhomogeneous magnetic field. The results can be used for the optimal design of positioning devices.

Evaluation of the network interaction of the participants of the Ulyanovsk-Avia cluster»

The article addresses the problem of identification and analysis of clusters chosen for state support programs. The goal of the paper is to test the possibilities of social network analysis method as a tool to identify and evaluate the activity of clusters supported by the state. To solve this issue, the authors have chosen an aircraft cluster of the Ulyanovsk region — the Consortium «Scientific, educational and production cluster» Ulyanovsk-Avia. The cluster was analyzed using social network analysis based on information collected in the SPARK database. The authors build cluster networks, perform homogeneity calculations, and identify the central nodes. The results obtained allows us to conclude that Ulyanovsk-Avia demonstrates active ties between its companies. However, this interaction cannot be unambiguously classified as a cluster one.