The multi-frequency decomposition entropy learning for nonlinear fMRI data analysis.

Functional magnetic resonance imaging (fMRI) have been widely adopted to explore the underlying neural mechanisms between psychiatric disorders which share common neurobiology and clinical manifestations. However, the existing studies mainly focus on linear relationships and ignore nonlinear contributions. To address the above issues, we propose a new method named multi-frequency decomposition entropy (MDE) learning for inferring nonlinear functional connectivity between brain regions.

MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI.

Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.

Dual-scale modified BiOBr with enhanced structural self-transformation at wide pH for bifunctional treatment of Cr(VI).

Hexavalent chromium (Cr(VI)) poses threat to both ecosystems and human health. Complex pollution conditions, particularly the pH levels, significantly influence the treatment process of Cr(VI). In this study, BiOBr materials were synthesized with exposed (110) facets and Bi vacancies through dual modifications at both grain and atomic scales.

Resistance mechanisms and prospects of trastuzumab.

Breast cancer that overexpresses Human Epidermal Growth Factor Receptor 2 (HER2+) due to gene amplification or overexpression constitutes 15-20% of all breast cancer cases. Trastuzumab, the first FDA-approved monoclonal antibody targeting HER2, serves as the standard first-line treatment for HER2-positive advanced breast cancer, as recommended by multiple clinical guidelines.Currently, accumulated clinical evidence reveals a considerable degree of variability in the response of HER2+ breast cancer to trastuzumab treatment.

New species group and two new species of the genus Hesperus from China (Coleoptera: Staphylinidae: Philonthina).

A new species group, Hesperus babai group, is proposed based on characters of the subserrate antennae and apical portion of median lobe hatchet-like, along with description of a new species: H. pengi sp. n.

A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6.

Background: Breast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate nature of the underlying biology and its crucial impact on global health. The objective of this research was to perform bioinformatics analyses on breast cancer-related datasets to gain a comprehensive understanding of the molecular mechanisms at play and to identify key genes associated with the disease.

Angelic acid triggers ferroptosis in colorectal cancer cells via targeting and impairing NRF2 protein stability.

Ferroptosis is a unique programmed cell death driven by iron-dependent phospholipid peroxidation. Tumor cells that escape from the conventional therapies appear more sensitive to ferroptosis. Therefore, it is extremely urgent to find safe and efficient active ingredients that induce ferroptosis in tumor cells.

Detecting thyroid nodules along with surrounding tissues and tracking nodules using motion prior in ultrasound videos.

Ultrasound examination plays a crucial role in the clinical diagnosis of thyroid nodules. Although deep learning technology has been applied to thyroid nodule examinations, the existing methods all overlook the prior knowledge of nodules moving along a straight line in the video. We propose a new detection model, DiffusionVID-Line, and design a novel tracking algorithm, ByteTrack-Line, both of which fully leverage the prior knowledge of linear motion of nodules in thyroid ultrasound videos.

Measurements of Atmospheric HO Radicals Using Br-CIMS with Elimination of Potential Interferences from Ambient Peroxynitric Acid.

As a promising direct measurement method of atmospheric hydroperoxyl radicals (HO), bromide chemical ionization mass spectrometry (Br-CIMS) has been first demonstrated by Sanchez et al. ( 2016, , 3851-3861). However, field application of this method is currently still sparse, and there is still a gap between measured HO concentrations and calculated ones derived from the atmospheric equilibrium between HO and peroxynitric acid (HONO).

Labor linkages and flow paths of industry in China.

As economic power increases and market patterns adapt, labor becomes an increasingly significant factor of production. However, there is a dearth of discourse regarding the structural changes that have occurred in the correlation of the labor force across industries, as well as a visual representation of the labor force's movement across industries. To quantify and analyze the correlation effect with greater precision, it is necessary to establish an input-output model as the foundation of analysis, comparing the changes in the total output of the economic system prior to and subsequent to the exclusion using the vertical integration algorithm.

Catalytic pyrolysis and carbonization of walnut shells: poly-generation and enhanced electrochemical performance of carbons.

In this study, walnut shell (WS) was used as feedstock, incorporating lithium carbonate (LC), sodium carbonate (SC), potassium carbonate (PC), and potassium hydroxide (PH) as pyrolysis catalysts and carbonization activators. A one-step method that allows catalytic pyrolysis and carbonization to be carried out consecutively under their respective optimal conditions is employed, enabling the concurrent production of high-quality pyrolysis oil, pyrolysis gas, and carbon materials from biomass conversion. The effects of LC, SC, PC, and PH on the yield and properties of products derived from WS pyrolysis as well as on the properties and performance of the resulting carbon materials were examined.

The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.

The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively.

Distributed cyber attack detection and physical fault diagnosis for a class of interconnected large-scale systems.

In this paper we focus on the distributed cyber attack detection and physical fault diagnosis problem for a class of interconnected large-scale systems (ILSSs). In the proposed scheme, apart from node measurement, edge measurement is also used to construct distributed Kalman filter to estimate the state of each subsystem. The gain matrices of Kalman filter are determined by minimizing the covariance of estimation error in the attack-free and fault-free case, which reduces the false alarm rate of cyber attack detection and physical fault diagnosis.

Bimetal doped Cu-Fe-ZIF-8/g-CN nanocomposites for the adsorption of tetracycline hydrochloride from water.

Bimetal doped Cu-Fe-zeolitic imidazole framework-8 (ZIF-8)/graphitic carbon nitride (GCN) (Cu-Fe-ZIF-8/GCN) nanocomposites were prepared one-pot and ion-exchange methods. The main influencing factors, such as adsorbent concentration, TC concentration, initial pH, and coexisting ions, were evaluated in detail. Due to the suitable pore structures and the presence of multiple interactions on the surface, the nanocomposite showed a high adsorption capacity up to 932 mg g for tetracycline hydrochloride (TC), outperforming ZIF-8 by 4.

RBM10 regulates the tumorigenic potential of human cancer cells by modulating PPM1B and YBX1 activities.

RNA binding protein RBM10 participates in various RNA metabolism, and its decreased expression or loss of function by mutation has been identified in many human cancers. However, how its dysregulation contributes to human cancer pathogenesis remains to be determined. Here, we found that RBM10 expression was decreased in breast tumors, and breast cancer patients with low RBM10 expression presented poorer survival rates.

Melatonin inhibits the formation of intraplaque neovessels in ApoE-/- mice via PPARγ- RhoA-ROCK pathway.

Background: According to former research, the atherosclerotic plaque is thought to be aggravated by intraplaque neovessels (IPN) and intraplaque hemorrhage (IPH). Intriguingly, a lower incidence of IPH was found in plaque treated with melatonin. In this study, we attempted to investigate the impact and underlying mechanism regarding the influences of melatonin upon IPN.

Prediction of F NMR chemical shifts for organic compounds with ORCA.

Accurate assignment of F NMR has long been a challenge, and quantum chemical methods are possible solutions. Herein we reported a scaling method for the prediction of F NMR chemical shift with freely available ORCA program package. Performance of 31 DFT functionals coupled with 11 basis sets were evaluated and influence of geometry optimization was also studied with five functionals coupled with three basis sets.

Real-time 3D shape measurement of dynamic scenes using fringe projection profilometry: lightweight NAS-optimized dual frequency deep learning approach.

Achieving real-time and high-accuracy 3D reconstruction of dynamic scenes is a fundamental challenge in many fields, including online monitoring, augmented reality, and so on. On one hand, traditional methods, such as Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP), are struggling to balance measuring efficiency and accuracy. On the other hand, deep learning-based approaches, which offer the potential for improved accuracy, are hindered by large parameter amounts and complex structures less amenable to real-time requirements.