A framework for optimal control of oscillations and synchrony applied to non-linear models of neural population dynamics.

We adapt non-linear optimal control theory (OCT) to control oscillations and network synchrony and apply it to models of neural population dynamics. OCT is a mathematical framework to compute an efficient stimulation for dynamical systems. In its standard formulation, it requires a well-defined reference trajectory as target state.

Explicitly diverse visual question generation.

Visual question generation involves the generation of meaningful questions about an image. Although we have made significant progress in automatically generating a single high-quality question related to an image, existing methods often ignore the diversity and interpretability of generated questions, which are important for various daily tasks that require clear question sources. In this paper, we propose an explicitly diverse visual question generation model that aims to generate diverse questions based on interpretable question sources.

Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios.

In the realm of infectious disease control, accurate modeling of the transmission dynamics is pivotal. As human mobility and commuting patterns are key components of communicable disease spread, we introduce a novel travel time aware metapopulation model. Our model aims to enhance estimations of disease transmission.

BIRDNN: Behavior-Imitation Based Repair for Deep Neural Networks.

The increasing utilization of deep neural networks (DNNs) in safety-critical systems has raised concerns about their potential to exhibit undesirable behaviors. Consequently, DNN repair/patching arises in response to the times, and it aims to eliminate unexpected predictions generated by flawed DNNs. However, existing repair methods, both retraining- and fine-tuning-based, primarily focus on high-level abstract interpretations or inferences of state spaces, often neglecting the outputs of underlying neurons.

The Future of Phosphoric Acid Production -Why We Have to Leave Trodden Paths.

This paper examines the need for innovation in phosphorus fertilizer production. An important area requiring action is the use of sulfuric acid in the wet chemical process (WCP), which is the dominant process in phosphate fertilizer production. About 50 % of the sulfuric acid produced worldwide is used for fertilizers, and ~95 % of the world's fertilizers are based on sulfuric acid.

Gaze Behavior Reveals Expectations of Potential Scene Changes.

Even if the scene before our eyes remains static for some time, we might explore it differently compared with how we examine static images, which are commonly used in studies on visual attention. Here we show experimentally that the top-down expectation of changes in natural scenes causes clearly distinguishable gaze behavior for visually identical scenes. We present free-viewing eye-tracking data of 20 healthy adults on a new video dataset of natural scenes, each mapped for its potential for change (PfC) in independent ratings.

SPBA-Net point cloud object detection with sparse attention and box aligning.

Object detection in point clouds is essential for various applications, including autonomous navigation, household robots, and augmented/virtual reality. However, during voxelization and Bird's Eye View transformation, local point cloud data often remains sparse due to non-target areas and noise points, posing a significant challenge for feature extraction. In this paper, we propose a novel mechanism named Keypoint Guided Sparse Attention (KGSA) to enhance the semantic information of point clouds by calculating Euclidean distances between selected keypoints and others.

DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition.

Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convolutional neural network, BiLSTM, for enhancer identification.

TIWMFLP: Two-Tier Interactive Weighted Matrix Factorization and Label Propagation Based on Similarity Matrix Fusion for Drug-Disease Association Prediction.

Accurately identifying new therapeutic uses for drugs is crucial for advancing pharmaceutical research and development. Matrix factorization is often used in association prediction due to its simplicity and high interpretability. However, existing matrix factorization models do not enable real-time interaction between molecular feature matrices and similarity matrices, nor do they consider the geometric structure of the matrices.

MAR-YOLOv9: A multi-dataset object detection method for agricultural fields based on YOLOv9.

With the development of deep learning technology, object detection has been widely applied in various fields. However, in cross-dataset object detection, conventional deep learning models often face performance degradation issues. This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment.

Gait analysis in the early stage of Parkinson's disease with a machine learning approach.

Background: Gait disorder is a prominent motor symptom in Parkinson's disease (PD), objective and quantitative assessment of gait is essential for diagnosing and treating PD, particularly in its early stage.

Methods: This study utilized a non-contact gait assessment system to investigate gait characteristics between individuals with PD and healthy controls, with a focus on early-stage PD. Additionally, we trained two machine learning models to differentiate early-stage PD patients from controls and to predict MDS-UPDRS III score.

Artificial intelligence-assisted oculo-gait measurements for cognitive impairment in cerebral small vessel disease.

Introduction: Oculomotor and gait dysfunctions are closely associated with cognition. However, oculo-gait patterns and their correlation with cognition in cerebral small vessel disease (CSVD) remain unclear.

Methods: Patients with CSVD from a hospital-based cohort (n = 194) and individuals with presumed early CSVD from a community-based cohort (n = 319) were included.

A Review of Recent Advances in Cognitive-Motor Dual-Tasking for Parkinson's Disease Rehabilitation.

Background: Parkinson's disease is primarily characterized by the degeneration of motor neurons, leading to significant impairments in movement. Initially, physical therapy was predominantly employed to address these motor issues through targeted rehabilitation exercises. However, recent research has indicated that cognitive training can enhance the quality of life for patients with Parkinson's.

An effective screening model for subjective cognitive decline in community-dwelling older adults based on gait analysis and eye tracking.

Objective: To evaluate the effectiveness of multimodal features based on gait analysis and eye tracking for elderly people screening with subjective cognitive decline in the community.

Methods: In the study, 412 cognitively normal older adults aged over 65 years were included. Among them, 230 individuals were diagnosed with non-subjective cognitive decline and 182 with subjective cognitive decline.

Anatomical and functional basis of facial expressions and their relationship with emotions in horses.

An emotion is defined as the affective response to a stimulus that leads to specific bodily changes, enabling individuals to react to positive or negative environmental conditions. In the absence of speech, emotions in animals are primarily studied by observing expressive components, such as facial expressions. This review aims to analyze the available literature on the influence of environmental stimuli on measurable behaviors in horses, describing the anatomical components involved in perception at the central nervous system level and the efferent pathways that trigger facial muscle contraction or relaxation, thus altering facial expressions.

The p53 target DRAM1 modulates calcium homeostasis and ER stress by promoting contact between lysosomes and the ER through STIM1.

It is well established that DNA Damage Regulated Autophagy Modulator 1 (DRAM1), a lysosomal protein and a target of p53, participates in autophagy. The cellular functions of DRAM1 beyond autophagy remain elusive. Here, we show p53-dependent upregulation of DRAM1 in mitochondrial damage-induced Parkinson's disease (PD) models and exacerbation of disease phenotypes by DRAM1.

Classification of coronary artery disease using radial artery pulse wave analysis via machine learning.

Background: Coronary artery disease (CAD) is a major global cardiovascular health threat and the leading cause of death in many countries. The disease has a significant impact in China, where it has become the leading cause of death. There is an urgent need to develop non-invasive, rapid, cost-effective, and reliable techniques for the early detection of CAD using machine learning (ML).

A comprehensive approach to evaluate human-machine conflicts in shared steering systems.

The shared control authority between drivers and the steering system may lead to human-machine conflicts, threatening both traffic safety and driving experience of collaborative driving systems. Previous evaluation methods relied on subjective judgment and had a singular set of evaluation criteria, making it challenging to obtain a comprehensive and objective assessment. Therefore, we propose a two-phase novel method that integrates eye-tracking data, electromyography signals and vehicle dynamic features to evaluate human-machine conflicts.

Improved Fast-Response Consensus Algorithm Based on HotStuff.

Recent Byzantine Fault-Tolerant (BFT) State Machine Replication (SMR) protocols increasingly focus on scalability and security to meet the growing demand for Distributed Ledger Technology (DLT) applications across various domains. Current BFT consensus algorithms typically require a single leader node to receive and validate votes from the majority process and broadcast the results, a design challenging to scale in large systems. We propose a fast-response consensus algorithm based on improvements to HotStuff, aimed at enhancing transaction ordering speed and overall performance of distributed systems, even in the presence of faulty copies.

Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns through Human Mobility Models from Real-World Data.

This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal-dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties.

BNIP3-mediated mitophagy attenuates hypoxic-ischemic brain damage in neonatal rats by inhibiting ferroptosis through P62-KEAP1-NRF2 pathway activation to maintain iron and redox homeostasis.

As a major contributor to neonatal death and neurological sequelae, hypoxic-ischemic encephalopathy (HIE) lacks a viable medication for treatment. Oxidative stress induced by hypoxic-ischemic brain damage (HIBD) predisposes neurons to ferroptosis due to the fact that neonates accumulate high levels of polyunsaturated fatty acids for their brain developmental needs but their antioxidant capacity is immature. Ferroptosis is a form of cell death caused by excessive accumulation of iron-dependent lipid peroxidation and is closely associated with mitochondria.

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

Background: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time-consuming and expensive high-throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings.

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers.

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe.

A lightweight Color-changing melon ripeness detection algorithm based on model pruning and knowledge distillation: leveraging dilated residual and multi-screening path aggregation.

Color-changing melons are a kind of cucurbit plant that combines ornamental and food. With the aim of increasing the efficiency of harvesting Color-changing melon fruits while reducing the deployment cost of detection models on agricultural equipment, this study presents an improved YOLOv8s network approach that uses model pruning and knowledge distillation techniques. The method first merges Dilated Wise Residual (DWR) and Dilated Reparam Block (DRB) to reconstruct the C2f module in the Backbone for better feature fusion.