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Leveraging mutual information in Variational Autoencoders for improved dimensionality reduction of single-cell RNA sequencing data: The scInfoMaxVAE approach.

Comput Biol Chem

August 2025

Laboratory of Regenerative Medicine, Faculty of Biology and Biotechnology, University of Science, Ho Chi Minh City, 700000, Viet Nam; Viet Nam National University, Ho Chi Minh City, 720325, Viet Nam. Electronic address:

Single-cell RNA-seq (scRNA-seq) analysis demands representations that are robust to sparsity and technical noise. We present scInfoMaxVAE, a mutual-information-maximizing variational autoencoder with a zero-inflated count likelihood tailored for scRNA-seq, designed for dimensionality reduction and cell-type classification. We evaluated the model on 12 public scRNA-seq datasets spanning multiple tissues and platforms using a unified pipeline with cell- and gene-level quality control (minimum detected genes), library-size normalization, log-transform, and reference-based cell-type annotation.

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Introduction: In Ethiopia, Maternal, Newborn, and Child Health (MNCH) outcomes have been improving, however, the current level of Maternal and under-five children mortality remains the highest in the world. Despite the rhetoric around the significance of multi-stakeholder engagement as a buzzword in development theories and polices to improve health and other development outcomes, there is limited evidence on how multi-stakeholders intersect and mutually reinforce each other toward the coproduction of improved MNCH outcomes and a resilient community health system. The aim of this manuscript is to examine barriers to and facilitators of coproduction in the context of multi-stakeholder engagement to optimize MNCH outcomes and a resilient community health system in rural Ethiopia.

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An efficient machine-learning framework for predicting protein post-translational modification sites.

Sci Rep

August 2025

Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt.

Post-Translational Modifications (PTMs), particularly lysine 2-hydroxyisobutyrylation (Khib), represent critical regulatory mechanisms governing protein structure and function, with mounting evidence underscoring their important implications in cellular metabolism, transcriptional regulation, and pathological processes. Despite this significance, the experimental identification of Khib sites remains constrained by resource-intensive methodologies and the transient nature of these modifications. To overcome these limitations, we introduce HyLightKhib, a computational framework that leverages Light Gradient Boosting Machine architecture for accurate Khib site prediction.

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Apple-detection performance in orchards degrades markedly under low-light conditions, where intensified noise and non-uniform exposure blur edge cues critical for precise localisation. We propose Knowledge Distillation with Geometry-Consistent Feature Alignment (KDFA), a compact end-to-end framework that couples image enhancement and detection through the following two complementary components: (i) Cross-Domain Mutual-Information-Bound Knowledge Distillation, which maximises an InfoNCE lower bound between daylight-teacher and low-light-student region embeddings; (ii) Geometry-Consistent Feature Alignment, which imposes Laplacian smoothness and bipartite graph correspondences across multiscale feature lattices. Trained on 1200 pixel-aligned bright/low-light image pairs, KDFA achieves 51.

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