COVID-19 and Its Impact on Onset and Progression of Parkinson’s and Cognitive Dysfunction

In the COVID-19 pandemic, neurological complications have emerged as a significant cause of morbidity and mortality. A wide range of neurological manifestations ranging from cognitive or memory disturbances, headache, loss of smell or taste, confusion, and disabling strokes have been reported during and post COVID conditions. The COVID-19 virus can utilize two possible pathways for invasion into the brain, either through retrograde axonal transport (olfactory route) or by crossing the blood-brain barrier (BBB). Furthermore, the production of SARS-CoV-2-associated cytokines, such as interleukin (IL)-6, IL-17, IL-1b, and tumor necrosis factor (TNF), is able to disrupt the BBB. The neuroinvasive nature of SARS-CoV-2 has a more severe impact on patients with preexisting neurological manifestations such as Parkinson’s disease (PD). Pathological features of PD include selective loss of dopaminergic neurons in the substantia nigra pars compacta and aggregation of α-syn proteins present in neurons. Interaction between SARS-COV-2 infection and α-synuclein might have long-term implications on the onset of Parkinsonism by the formation of toxic protein clumps called amyloid fibrils—a hallmark of Parkinson’s. Molecular modeling is an emerging tool to predict potential inhibitors against the enzyme α-synuclein in neurodegenerative diseases by using plant bioactive molecules

Observation of multiple doubly degenerate bands in ¹⁹⁵Tl

The High-spin states in 195 Tl, populated through the 185,187 Re( 13 C, xn) fusion evaporation reaction at the beam energy of 75 MeV, were studied using the Indian National Gamma Array (INGA). More than 50 new γ transitions have been placed in the proposed level scheme which is extended up to the excitation energy of ≈ 5.6 MeV and spin =22.5ħ . Two pairs of degenerate bands based on two different quasi-particle configurations have been identified in this nucleus indicating the first observation of such bands in an odd- A nucleus in A∼190 region and signify the first evidence of multiple chiral bands in a nucleus in this region. The total Routhian surface calculations predict triaxial shapes for both the configurations and thereby, support the experimental observation. The importance of multiple neutron holes in the i13/2 orbital and the stability of shapes for these two configurations have been discussed.Financial support of Department of Science & Technology, Govt. of India for clover detectors of INGA (Grant No. IR/S2/PF-03/2003-II) is greatfully acknowledged. One of the authors (S. Bhattacharya) acknowledges with thanks the financial support received as Raja Ramanna Fellowship from the Department of Atomic Energy, Govt. of India. T.R and Md. A.A acknowledge with thanks the financial support received as research fellows from the Department of Atomic Energy (DAE), Govt. of India

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Search for leptophobic Z ' bosons decaying into four-lepton final states in proton-proton collisions at root s=8 TeV

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Measurements of differential production cross sections for a Z boson in association with jets in pp collisions at root s=8 TeV

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Search for high-mass diphoton resonances in proton-proton collisions at 13 TeV and combination with 8 TeV search

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Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

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