[Mechanism of Daotan Xixin Decoction in treating APP/PS1 mice based on high-throughput sequencing technology and bioinformatics analysis].

This study aims to investigate the therapeutic effect and mechanism of Daotan Xixin Decoction on APP/PS1 mice. Twelve APP/PS1 male mice were randomized into four groups: APP/PS1 and low-, medium-, and high-dose Daotan Xixin Decoction. Three C57BL/6 wild-type mice were used as the control group.

Single-cell sequencing reveals that AK5 inhibits apoptosis in AD oligodendrocytes by regulating the AMPK signaling pathway.

Background: Neuroinflammation and abnormal energy metabolism have been shown to significantly contribute to the progression of Alzheimer's disease (AD). Adenylate kinase 5 (AK5), an enzyme predominantly expressed in the brain regulates ATP metabolism, has an unclear role in energy metabolism and neuroinflammation in AD.

Methods: The AD datasets were derived from the GEO public database to analyze the expression levels of AK5 in AD and normal samples and to assess the relationship between AK5 expression and the clinical characteristics of AD patients.

Improvement of the accuracy in measuring cryogenic hot spot mix by multi-channel Ross filter pairs through a two-temperature model of hot spot electron temperature.

In inertial confinement fusion experiments, hot spot mix caused by hydrodynamic instabilities is a critical performance limitation. Currently, multi-channel Ross filter pair imaging is used to quantitatively diagnose the mix mass of cryogenic hot spots driven by 100 kJ energy, but this method brings significant uncertainty. To measure the level of mix more accurately, we have developed a two-temperature model to modify the fitted bremsstrahlung spectra based on the characteristics of cryogenic implosion hot spots.

CRH-YOLO for precise and efficient detection of gastrointestinal polyps.

Gastrointestinal polyps are early indicators of many significant diseases within the digestive system, and timely detection of these polyps is crucial for preventing them. Although clinical gastrointestinal endoscopy and interventions help reduce the risk of malignancy, most current methods fail to adequately address the uncertainties and scale issues associated with the presence of polyps, posing a threat to patients' health. Therefore, this paper proposes a novel single-stage method for polyp detection.

Single-cell and bulk transcriptomic datasets enable the development of prognostic models based on dynamic changes in the tumor immune microenvironment in patients with hepatocellular carcinoma and portal vein tumor thrombus.

Background: Hepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses.

Methods: scRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets.

MCH-PAN: gastrointestinal polyp detection model integrating multi-scale feature information.

The rise of object detection models has brought new breakthroughs to the development of clinical decision support systems. However, in the field of gastrointestinal polyp detection, there are still challenges such as uncertainty in polyp identification and inadequate coping with polyp scale variations. To address these challenges, this paper proposes a novel gastrointestinal polyp object detection model.

A predictive model for classifying college students' academic performance based on visual-spatial skills.

As the application of visual-spatial skills in academic disciplines, vocational fields and daily life is becoming more and more prominent, it is of great theoretical and practical significance how to make use of big data and artificial intelligence technology to conduct research on the relationship between visual-spatial skills and students' grades. This paper explores and analyses from the perspective of artificial intelligence, combining students' visual-spatial skills and students' specific attribute characteristics to construct an expert system, which defines the prediction of academic performance as a classification problem corresponding to the five categories of excellent, good, moderate, passing, and weak, respectively, and based on which a deep neural network-based classification prediction model for students' performance is designed. The experimental results show that visual-spatial skills plays an important role in the professional learning of science and engineering students, while the classification model designed in this paper has high accuracy in the grade prediction process.

A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images.

Colorectal cancer (CRC) is a common digestive system tumor with high morbidity and mortality worldwide. At present, the use of computer-assisted colonoscopy technology to detect polyps is relatively mature, but it still faces some challenges, such as missed or false detection of polyps. Therefore, how to improve the detection rate of polyps more accurately is the key to colonoscopy.

G-YOLO: A YOLOv7-based target detection algorithm for lightweight hazardous chemical vehicles.

Hazardous chemical vehicles are specialized vehicles used for transporting flammable gases, medical waste, and liquid chemicals, among other dangerous chemical substances. During their transportation, there are risks of fire, explosion, and leakage of hazardous materials, posing serious threats to human safety and the environment. To mitigate these possible hazards and decrease their probability, this study proposes a lightweight object detection method for hazardous chemical vehicles based on the YOLOv7-tiny model.

Colorectal image analysis for polyp diagnosis.

Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net.

Registration-free 3D super-resolution generative deep-learning network for fluorescence microscopy imaging.

Volumetric fluorescence microscopy has a great demand for high-resolution (HR) imaging and comes at the cost of sophisticated imaging solutions. Image super-resolution (SR) methods offer an effective way to recover HR images from low-resolution (LR) images. Nevertheless, these methods require pixel-level registered LR and HR images, posing a challenge in accurate image registration.

GRP-YOLOv5: An Improved Bearing Defect Detection Algorithm Based on YOLOv5.

Currently, most chemical transmission equipment relies on bearings to support rotating shafts and to transmit power. However, bearing defects can lead to a series of failures in the equipment, resulting in reduced production efficiency. To prevent such occurrences, this paper proposes an improved bearing defect detection algorithm based on YOLOv5.

A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks.

Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach.

Research on epidemic spread model based on cold chain input.

In recent years, the new type of coronary pneumonia (COVID-19) has become a highly contagious disease worldwide, posing a serious threat to the public health. This paper is based on the SEIR model of the new coronavirus pneumonia, considering the impact of cold chain input and re-positive on the spread of the virus in the COVID-19. In the process of model design, the food cold chain and re-positive are used as parameters, and its stability is analyzed and simulated.

Personalized Sliding Window Recommendation Algorithm Based on Sequence Alignment.

With the explosive growth of the amount of information in social networks, the recommendation system, as an application of social networks, has attracted widespread attention in recent years on how to obtain user-interested content in massive data. At present, in the process of algorithm design of the recommending system, most methods ignore structural relationships between users. Therefore, in this paper, we designed a personalized sliding window for different users by combining timing information and network topology information, then extracted the information sequence of each user in the sliding window and obtained the similarity between users through sequence alignment.

An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19.

At present, the Corona Virus Disease 2019 (COVID-19) is ravaging the world, bringing great impact on people's life safety and health as well as the healthy development of economy and society, so the research on the prediction of the development trend of the epidemic is crucial. In this paper, we focus on the prevention and control of epidemic using the relevant technologies in the field of artificial intelligence and signal analysis. With the unknown principle of epidemic transmission, we first smooth out the complex and variable epidemic data through the empirical mode decomposition model to obtain the change trends of epidemic data at different time scales.

Pattern formation in a four-ring reaction-diffusion network with heterogeneity.

In networks of nonlinear oscillators, symmetries place hard constraints on the system that can be exploited to predict universal dynamical features and steady states, providing a rare generic organizing principle for far-from-equilibrium systems. However, the robustness of this class of theories to symmetry-disrupting imperfections is untested in free-running (i.e.

Efficacy of Morphine Combined with Mechanical Ventilation in the Treatment of Heart Failure with Cardiac Magnetic Resonance Imaging under Artificial Intelligence Algorithms.

This study was aimed at exploring the efficacy of morphine combined with mechanical ventilation in the treatment of heart failure with artificial intelligence algorithms. The cardiac magnetic resonance imaging (MRI) under the watershed segmentation algorithm was proposed, and the local grayscale clustering watershed (LGCW) model was designed in this study. A total of 136 patients with acute left heart failure were taken as the research objects and randomly divided into the control group (conventional treatment) and the experimental group (morphine combined with mechanical ventilation), with 68 cases in each group.

Ensemble of coupling forms and networks among brain rhythms as function of states and cognition.

The current paradigm in brain research focuses on individual brain rhythms, their spatiotemporal organization, and specific pairwise interactions in association with physiological states, cognitive functions, and pathological conditions. Here we propose a conceptually different approach to understanding physiologic function as emerging behavior from communications among distinct brain rhythms. We hypothesize that all brain rhythms coordinate as a network to generate states and facilitate functions.

Polyp Detection from Colorectum Images by Using Attentive YOLOv5.

Background: High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas.

Application of network link prediction in drug discovery.

Background: Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery.

Research on multi-effect evaporation salt prediction based on feature extraction.

In the multi-effect evaporation salt making process, the smooth operation of the salt making process is crucial. As the salt production process continues, many unstable factors will cause the salt production process not to proceed smoothly. These factors can be discovered in advance by predicting the salt production data, thus, it is of great significance to predict the multi-effect evaporation salt production data.

Attractor-state itinerancy in neural circuits with synaptic depression.

Neural populations with strong excitatory recurrent connections can support bistable states in their mean firing rates. Multiple fixed points in a network of such bistable units can be used to model memory retrieval and pattern separation. The stability of fixed points may change on a slower timescale than that of the dynamics due to short-term synaptic depression, leading to transitions between quasi-stable point attractor states in a sequence that depends on the history of stimuli.

A novel scheme for essential protein discovery based on multi-source biological information.

Mining essential protein is crucial for discovering the process of cellular organization and viability. At present, there are many computational methods for essential proteins detecting. However, these existing methods only focus on the topological information of the networks and ignore the biological information of proteins, which lead to low accuracy of essential protein identification.

An Early Intestinal Cancer Prediction Algorithm Based on Deep Belief Network.

The incidence of colorectal cancer (colorectal cancer, CRC) in China has increased in recent years, and its mortality rate has become one of the highest among all cancers. CRC also increasingly affects people's health and quality of life, and the workloads of medical doctors have further increased due to the lack of sufficient medical resources in China. The goal of this study was to construct an automated expert system using a deep learning technique to predict the probability of early stage CRC based on the patient's case report and the patient's attributes.