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We report two structures of the human voltage-gated potassium channel (Kv) Kv1.3 in immune cells alone (apo-Kv1.3) and bound to an immunomodulatory drug called dalazatide (dalazatide-Kv1.3). Both the apo-Kv1.3 and dalazatide-Kv1.3 structures are in an activated state based on their depolarized voltage sensor and open inner gate. In apo-Kv1.3, the aromatic residue in the signature sequence (Y447) adopts a position that diverges 11 Å from other K+ channels. The outer pore is significantly rearranged, causing widening of the selectivity filter and perturbation of ion binding within the filter. This conformation is stabilized by a network of intrasubunit hydrogen bonds. In dalazatide-Kv1.3, binding of dalazatide to the channel's outer vestibule narrows the selectivity filter, Y447 occupies a position seen in other K+ channels, and this conformation is stabilized by a network of intersubunit hydrogen bonds. These remarkable rearrangements in the selectivity filter underlie Kv1.3's transition into the drug-blocked state.

Voltage-gated potassium (Kv) channels modulate the function of electrically-excitable and non-excitable cells by using several types of gates to regulate ion flow through the channels. An important gating mechanism, C-type inactivation, limits ion flow by transitioning Kv channels into a non-conducting inactivated state. Here, we highlight two recent papers, one on the human Kv1.3 channel and the second on the Drosophila Shaker Kv channel, that combined cryogenic electron microscopy and molecular dynamics simulation to define mechanisms underlying C-type inactivation. In both channels, the transition to the non-conducting inactivated conformation begins with the rupture of an intra-subunit hydrogen bond that fastens the selectivity filter to the pore helix. The freed filter swings outwards and gets tethered to an external residue. As a result, the extracellular end of the selectivity filter dilates and K+ permeation through the pore is impaired. Recovery from inactivation may entail a reversal of this process. Such a reversal, at least partially, is induced by the peptide dalazatide. Binding of dalazatide to external residues in Kv1.3 frees the filter to swing inwards. The extracellular end of the selectivity filter narrows allowing K+ to move in single file through the pore typical of conventional knock-on conduction. Inter-subunit hydrogen bonds that stabilize the outer pore in the dalazatide-bound structure are equivalent to those in open-conducting conformations of Kv channels. However, the intra-subunit bond that fastens the filter to the pore-helix is absent, suggesting an incomplete reversal of the process. These mechanisms define how Kv channels self-regulate the flow of K+ by changing the conformation of the selectivity filter.

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease. [ABSTRACT FROM AUTHOR]

This study aimed to estimate the relationship between fish size and otolith size of four creek-associated fishes (Anodontostoma chacunda, Epinephelus diacanthus, Johnius glaucus and Mugil cephalus) collected from the north-eastern Arabian Sea from November 2021 to November 2022. The numerical relations were established using regression between fish size (Total length-TL and Body weight-BW) and otolith dimensions (Length-OL, Weight-Owt, Area-OA, Width-OW and Perimeter-OP). All regressions between the fish size and otolith morphometric measurements were highly correlated (R2 > 0.8), which indicates the robustness of the model. Taxonomists can use findings from this study for species confirmation and reconstruction of past species assemblages. These findings might provide insight into predicting the prey size in food and feeding studies to explore the food web dynamics. [ABSTRACT FROM AUTHOR]

In this article, we propose logarithmic type direct and synthetic estima- tors for the estimation of domain mean under simple random sampling. The properties such as bias and mean square error of the proposed direct and synthetic estimators are obtained up to first order approximation. The efficiency conditions are obtained under which the proposed direct and synthetic estimators outperform their conventional counterparts. The performance of the proposed direct and synthetic estimators is examined with the help of comprehensive computational study using real and artificially drawn populations. Some appropriate suggestions are also provided to the surveyors. [ABSTRACT FROM AUTHOR]

Byline: Vardhipathi. Swathi, Shrishti. Saroha, Jai. Prakash, Shashi. Bhushan BACKGROUND: Nonsteroidal anti-inflammatory drugs (NSAIDs) are associated with kidney damage. In India, only a few reports related to the risk of [...]