Prediction of gene regulatory connections with joint single-cell foundation models and graph-based learning.

Motivation: Single-cell RNA sequencing (scRNA-seq) data offers unprecedented opportunities to infer gene regulatory networks (GRNs) at a fine-grained resolution, shedding light on cellular phenotypes at the molecular level. However, the high sparsity, noise, and dropout events inherent in scRNA-seq data pose significant challenges for accurate and reliable GRN inference. The rapid growth in experimentally validated transcription factor-DNA binding data has enabled supervised machine learning methods, which rely on known regulatory interactions to learn patterns, and achieve high accuracy in GRN inference by framing it as a gene regulatory link prediction task.

Response dynamics of discrete subiculum→retrosplenial cortex projections underlying trace fear conditioning.

Associating events separated in time depends on the CA1, subiculum (SUB), and retrosplenial cortex (RSP). The degree to which their connectivity and underlying circuit mechanisms contribute to the association of such temporally discontiguous events is not known. Here we showed, using trace fear conditioning (TFC), wherein mice learn to associate tone and shock separated by a temporal trace, that molecularly distinct excitatory VGluT1 and VGluT2 SUB→RSP projections subserve the associative and temporal components of TFC.

Seamless optical cloud computing across edge-metro network for generative AI.

The rapid advancement of generative artificial intelligence (AI) in recent years has profoundly reshaped modern lifestyles, necessitating a revolutionary architecture to support the growing demands for computational power. Cloud computing has become the driving force behind this transformation. However, it consumes significant power and faces computation security risks due to the reliance on extensive data centers and servers in the cloud.

iDDN: determining trans-omics network structure and rewiring with integrative differential dependency networks.

Motivation: Mapping the gene networks that drive disease progression allows identifying molecules that rectify the network by normalizing pivotal regulatory elements. Upon mechanistic validation, these upstream normalizers represent attractive targets for developing therapeutic interventions to prevent the initiation or interrupt the pathways of disease progression. Differential network analysis aims to detect significant rewiring of regulatory network structures under different conditions.

Updates from a single-center phase 2 study of PD-1 inhibitor combined with hypomethylating agent plus CAG regimen in patients with relapsed/refractory acute myeloid leukemia.

Introduction: Anti-PD-1 monotherapy has shown limited clinical efficacy in patients with relapsed/refractory acute myeloid leukemia (r/r AML). Our study aimed to analyze the effectiveness and safety of combining tislelizumab with a hypomethylating agent (HMA) plus CAG regimen in treating patients with r/r AML, with an increased sample size and in comparison, with a historical control group for more reliable data support (ClinicalTrials.gov identifier NCT04541277).

N-acetyl-l-leucine lowers pS129-synuclein and improves synaptic function in models of Parkinson's disease.

N-acetyl-L-leucine (NALL), a derivative of the branched-chain amino acid leucine, has shown therapeutic potential in neurodegenerative diseases, including in prodromal stages of Parkinson's disease (PD). However, the mechanism of its protective effects has been largely unknown. Using discovery-based proteomics, we found that treatment with NALL led to upregulation of lysosomal, mitochondrial, and synaptic proteins in PD patient-derived dopaminergic neurons.

Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques.

DELTA: a method for brain-wide measurement of synaptic protein turnover reveals localized plasticity during learning.

Synaptic plasticity alters neuronal connections in response to experience, which is thought to underlie learning and memory. However, the loci of learning-related synaptic plasticity, and the degree to which plasticity is localized or distributed, remain largely unknown. Here we describe a new method, DELTA, for mapping brain-wide changes in synaptic protein turnover with single-synapse resolution, based on Janelia Fluor dyes and HaloTag knock-in mice.

Asymmetrical estimator for training encapsulated deep photonic neural networks.

Photonic neural networks (PNNs) are fast in-propagation and high bandwidth paradigms that aim to popularize reproducible NN acceleration with higher efficiency and lower cost. However, the training of PNN is known to be challenging, where the device-to-device and system-to-system variations create imperfect knowledge of the PNN. Despite backpropagation (BP)-based training algorithms being the industry standard for their robustness, generality, and fast gradient convergence for digital training, existing PNN-BP methods rely heavily on accurate intermediate state extraction or extensive computational resources for deep PNNs (DPNNs).

IL-8-NF-κB-ALDH1A1 loop promotes the progression of intrahepatic cholangiocarcinoma.

Background: Intrahepatic cholangiocarcinoma (ICC) is a poor prognosis of malignant cancer with high lymph node metastasis and resistance to systemic therapies. Recent studies suggested that the involvement of IL-8 could promote ICC metastasis through epithelial-mesenchymal transition while the ICC-ALDH1A1high subtype is clarified by multi-omics study. The correlation between ALDH1A1 and IL-8 in ICC remains elusive.

Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning.

Motivation: Single-cell RNA sequencing (scRNA-seq) data offers unprecedented opportunities to infer gene regulatory networks (GRNs) at a fine-grained resolution, shedding light on cellular phenotypes at the molecular level. However, the high sparsity, noise, and dropout events inherent in scRNA-seq data pose significant challenges for accurate and reliable GRN inference. The rapid growth in experimentally validated transcription factor-DNA binding data (e.

A feature extraction method for hydrofoil attached cavitation based on deep learning image semantic segmentation algorithm.

Cavitation is a technical challenge for high-speed underwater vehicles, such as nuclear submarines and underwater robots, et al. The cavitation phenomena of hydrofoils are typically studied through water tunnel experiments or numerical simulations, which yield extensive cavitation images. To conveniently extract cavitation features from the massive images, a feature extraction method for hydrofoil cavitation was proposed in this work based on deep learning image semantic segmentation techniques.

Keratinocyte-derived extracellular vesicles in painful diabetic neuropathy.

Painful diabetic neuropathy (PDN) is a challenging complication of diabetes with patients experiencing a painful and burning sensation in their extremities. Existing treatments provide limited relief without addressing the underlying mechanisms of the disease. PDN involves the gradual degeneration of nerve fibers in the skin.

TLS and immune cell profiling: immunomodulatory effects of immunochemotherapy on tumor microenvironment in resectable stage III NSCLC.

Background: The use of programmed death-1 (PD-1) inhibitors in the neoadjuvant setting for patients with resectable stage III NSCLC has revolutionized this field in recent years. However, there is still 40%-60% of patients do not benefit from this approach. The complex interactions between immune cell subtypes and tertiary lymphoid structures (TLSs) within the tumor microenvironment (TME) may influence prognosis and the response to immunochemotherapy.

Network Pharmacology and Metabolomics Reveal Anti-Ferroptotic Effects of Curcumin in Acute Kidney Injury.

Introduction: Acute kidney injury (AKI) is linked to high rates of mortality and morbidity worldwide thereby posing a major public health problem. Evidences suggest that ferroptosis is the primary cause of AKI, while inhibition of monoamine oxidase A(MAOA) and 5-hydroxytryptamine were recognized as the defender of ferroptosis. Curcumin (Cur) is a natural polyphenol and the main bioactive compound of , which has been found nephroprotection in AKI.

Invasive carcinoma segmentation in whole slide images using MS-ResMTUNet.

Identifying the invasive cancer area is a crucial step in the automated diagnosis of digital pathology slices of the breast. When examining the pathological sections of patients with invasive ductal carcinoma, several evaluations are required specifically for the invasive cancer area. However, currently there is little work that can effectively distinguish the invasive cancer area from the ductal carcinoma in situ in whole slide images.

Embracing the informative missingness and silent gene in analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel.

Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots.

This paper presents a map-free navigation approach for industrial automatic mobile robots (AMRs), designed to ensure computational efficiency, cost-effectiveness, and adaptability. Utilizing deep reinforcement learning (DRL), the system enables real-time decision-making without fixed markers or frequent map updates. The central contribution is the Heuristic Dense Reward Shaping (HDRS), inspired by potential field methods, which integrates domain knowledge to improve learning efficiency and minimize suboptimal actions.

Parameter-coupled state space models based on quasi-Gaussian fuzzy approximation.

The accuracy of a fuzzy system's approximation is closely tied to the performance of fuzzy control systems design, while this system's interpretability depends on the description of a mechanical model using human language. This research introduces a quasi-Gaussian membership function characterized by a pair of parameters to achieve the sensitivity of a triangular membership function along with the interpretability of Gaussian membership functions. Consequently, a two-dimensional (2-D) quasi-Gaussian membership function is derived, and a method for establishing quasi-Gaussian fuzzy systems (QGFS) using a rectangular grid is proposed.

Reduction of RAD23A extends lifespan and mitigates pathology in TDP-43 mice.

Protein misfolding and aggregation are cardinal features of neurodegenerative disease (NDD) and they contribute to pathophysiology by both loss-of-function (LOF) and gain-of-function (GOF) mechanisms. This is well exemplified by TDP-43 which aggregates and mislocalizes in several NDDs. The depletion of nuclear TDP-43 leads to reduction in its normal function in RNA metabolism and the cytoplasmic accumulation of TDP-43 leads to aberrant protein homeostasis.

Enhanced pharmacological activities of AKR1C3-activated prodrug AST-3424 in cancer cells with defective DNA repair.

AST-3424 is a novel and highly tumor-selective prodrug. AST-3424 is activated by AKR1C3 to release a toxic bis-alkylating moiety, AST 2660. In this study, we have investigated the essential role of DNA repair in AST-3424 mediated pharmacological activities in vitro and in vivo.