ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention.

The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.

MMGAT: a graph attention network framework for ATAC-seq motifs finding.

BACKGROUND: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. RESULTS: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. CONCLUSIONS: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

Hypothalamic CRF neurons facilitate brain reward function.

Aversive stimuli activate corticotropin-releasing factor (CRF)-expressing neurons in the paraventricular nucleus of hypothalamus (PVNCRF neurons) and other brain stress systems to facilitate avoidance behaviors. Appetitive stimuli also engage the brain stress systems, but their contributions to reward-related behaviors are less well understood. Here, we show that mice work vigorously to optically activate PVNCRF neurons in an operant chamber, indicating a reinforcing nature of these neurons. The reinforcing property of these neurons is not mediated by activation of the hypothalamic-pituitary-adrenal (HPA) axis. We found that PVNCRF neurons send direct projections to the ventral tegmental area (VTA), and selective activation of these projections induced robust self-stimulation behaviors, without activation of the HPA axis. Similar to the PVNCRF cell bodies, self-stimulation of PVNCRF-VTA projection was dramatically attenuated by systemic pretreatment of CRF receptor 1 or dopamine D1 receptor (D1R) antagonist and augmented by corticosterone synthesis inhibitor metyrapone, but not altered by dopamine D2 receptor (D2R) antagonist. Furthermore, we found that activation of PVNCRF-VTA projections increased c-Fos expression in the VTA dopamine neurons and rapidly triggered dopamine release in the nucleus accumbens (NAc), and microinfusion of D1R or D2R antagonist into the NAc decreased the self-stimulation of these projections. Together, our findings reveal an unappreciated role of PVNCRF neurons and their VTA projections in driving reward-related behaviors, independent of their core neuroendocrine functions. As activation of PVNCRF neurons is the final common path for many stress systems, our study suggests a novel mechanism underlying the positive reinforcing effect of stressful stimuli.

2.5D transfer deep learning model for segmentation of contrast-enhancing lesions on brain magnetic resonance imaging of multiple sclerosis and neuromyelitis optica spectrum disorder.

Background: Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are the two mimic autoimmune diseases of the central nervous system, which are rare in East Asia. Quantitative detection of contrast-enhancing lesions (CELs) on contrast-enhancing T1-weighted magnetic resonance (MR) images is of great significance for assessing the disease activity of MS and NMOSD. However, it is challenging to develop automatic segmentation algorithms due to the lack of data. In this work, we present an automatic segmentation model of CELs based on Fully Convolutional with Attention DenseNet (FCA-DenseNet) and transfer learning strategy to address the challenge of CEL quantification in small-scale datasets. Methods: A transfer learning approach was employed in this study, whereby pretraining was conducted using 77 MS subjects from the open access datasets (MICCAI 2016, MICCAI 2017, ISBI 2015) for white matter hyperintensity segmentation, followed by fine-tuning using 24 MS and NMOSD subjects from the local dataset for CEL segmentation. The proposed FCA-DenseNet combined the Fully Convolutional DenseNet and Convolutional Block Attention Module in order to improve the learning capability. A 2.5D data slicing strategy was used to process complex 3D MR images. U-Net, ResUNet, TransUNet, and Attention-UNet are used as comparison models to FCA-DenseNet. Dice similarity coefficient (DSC), positive predictive value (PPV), true positive rate (TPR), and volume difference (VD) are used as evaluation metrics to evaluate the performances of different models. Results: FCA-DenseNet outperforms all other models in terms of all evaluation metrics, with a DSC of 0.661±0.187, PPV of 0.719±0.201, TPR of 0.680±0.254, and VD of 0.388±0.334. Transfer learning strategy has achieved success in building segmentation models on a small-scale local dataset where traditional deep learning approaches fail to train effectively. Conclusions: The improved FCA-DenseNet, combined with transfer learning strategy and 2.5D data slicing strategy, has successfully addressed the challenges in constructing deep learning models on small-scale datasets, making it conducive to clinical quantification of brain CELs and diagnosis of MS and NMOSD.

Non-peptide orphanin receptor antagonist activity in rat myocardial ischemia-induced cardiac arrhythmias.

One of the causes of sudden cardiac death is arrhythmia after acute myocardial ischemia. After ischemia, endogenous orphanin (N/OFQ) plays a role in the development of arrhythmias. It is discussed in this paper how nonpeptide orphanin receptor (ORL1) antagonists such as J-113397, SB-612111 and compound-24 (C-24) affect arrhythmia in rats following acute myocardial ischemia and what the optimal concentrations for these antagonists are. The electrocardiogram of the rat was recorded as part of the experiment. The concentrations of tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß) in the myocardium were measured following euthanasia. Following the use of three antagonists, we found the lowest inflammatory factor concentrations and the smallest number of ischemic arrhythmia episodes. All of them had a small impact on cardiac function. LF/HF values were significantly reduced in all three antagonist groups, suggesting that they are involved in the regulation of sympathetic nerves. In conclusion, pretreatment with the three antagonist groups can effectively reduce the concentration of TNF-α and IL-1ß, and the occurrence of arrhythmias after ischemia can also be significantly reduced. Inflammation and sympathetic activity may be related to the mechanism of action of antagonists.

Proteomic Analysis Reveals the Vital Role of Synaptic Plasticity in the Pathogenesis of Temporal Lobe Epilepsy.

Temporal lobe epilepsy (TLE) is a chronic neurological disorder that is often resistant to antiepileptic drugs. The pathogenesis of TLE is extremely complicated and remains elusive. Understanding the molecular mechanisms underlying TLE is crucial for its diagnosis and treatment. In the present study, a lithium-pilocarpine-induced TLE model was employed to reveal the pathological changes of hippocampus in rats. Hippocampal samples were taken for proteomic analysis at 2 weeks after the onset of spontaneous seizure (a chronic stage of epileptogenesis). Isobaric tag for relative and absolute quantization (iTRAQ) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) technique was applied for proteomic analysis of hippocampus. A total of 4173 proteins were identified from the hippocampi of epileptic rats and its control, of which 27 differentially expressed proteins (DEPs) were obtained with a fold change > 1.5 and P < 0.05. Bioinformatics analysis indicated 27 DEPs were mainly enriched in "regulation of synaptic plasticity and structure" and "calmodulin-dependent protein kinase activity," which implicate synaptic remodeling may play a vital role in the pathogenesis of TLE. Consequently, the synaptic plasticity-related proteins and synaptic structure were investigated to verify it. It has been demonstrated that CaMKII-α, CaMKII-ß, and GFAP were significant upregulated coincidently with proteomic analysis in the hippocampus of TLE rats. Moreover, the increased dendritic spines and hippocampal sclerosis further proved that synaptic plasticity involves in the development of TLE. The present study may help to understand the molecular mechanisms underlying epileptogenesis and provide a basis for further studies on synaptic plasticity in TLE.

How to Develop Entrepreneurial Talent More Effectively? A Comparison of Different Entrepreneurship Educational Methods.

Recently, scholars have begun to shift focus toward the effectiveness of different teaching methods for entrepreneurship education. However, the establishment of a unified and clear standard for the division of entrepreneurship educational methods remains unfulfilled, affecting the accuracy of research conclusions. In the present study, for the first time, the aim was to divide the entrepreneurship educational method into the classroom teaching method (CTM) and the extracurricular activity method (EAM) from the perspective of competency level training. On the basis of the modified planning behavior theory, the influence of entrepreneurship education on entrepreneurial intention (EI) was explored. In the present study, 514 college students of 14 universities in China were surveyed. The results reveal that the CTM and EAM had a direct positive bearing on EI, with indirect impact exerted by attitude toward entrepreneurship (ATE) and perceived behavioral control (PBC). Although the direct effects of the two teaching methods were similar, EAM could effectively improve ATE and PBC, thereby resulting in a positive effect on EI to a greater extent. Further observations were made that the participation of research University students in CTM was significantly lower than that of applied University students, leading to lower EI. Additionally, higher EI could be attributed to the more active participation in EAM of male students than female students, while no significant difference was indicated between different majors in EI. The results are of significant reference value for promoting the reform of entrepreneurship education and improving the quality of entrepreneurship education in colleges and universities.

Study on the Influence Mechanism of Virtual Simulation Game Learning Experience on Student Engagement and Entrepreneurial Skill Development.

With the emergence of the COVID-19 pandemic, virtual simulation games have provided an effective teaching method for online entrepreneurship education. By exploring the mechanisms that influence student engagement and learning outcomes from different perspectives, such as game design, team and individual perspectives, numerous scholars have demonstrated that such a teaching method can effectively improve students' engagement and learning performance. However, the existing studies are relatively scattered, and there is a scarcity of studies in which the effects of said factors are considered. Based on the learning process 3P model (presage-process-product) proposed by Biggs (1993), students' perceived experience of game design, teamwork and self-efficacy were taken as variables in the early learning stage in the present study, and the influence mechanism of virtual simulation game learning experience on students' engagement and entrepreneurial skill development was explored, so as to close the gap in existing research. In the present study, 177 college students from Chinese universities were surveyed and the data were surveyed using AMOS 23.0 software. Although the empirical results show that students' "goal and feedback" and "alternative" experience of game design did not have a significant positive impact on students' engagement, there was a direct and significant effect the development of entrepreneurial skills. Students' experience of teamwork and general self-efficacy could not only directly and significantly affect the development of entrepreneurial skills, but also indirectly affect the development of entrepreneurial skills through learning engagement. The research results are practically significant for teachers in the selection and development of virtual simulation games, can be effectively applied in teaching process management, and can improve students' engagement and learning performance.

Hydration and strength development in blended cement with ultrafine granulated copper slag.

This study aims at evaluating the effect of ultrafine granulated copper slag (UGCS) on hydration development of blended cement and mechanical properties of mortars. The UGCS with the median particle size of 4.78 µm and BET surface area of 1.31 m2/g was used as a cement replacement to prepare blended cements. Hydration heat emission of blended cement and mechanical performance of mortars were investigated by using isothermal calorimetry and strength tests, respectively. X-ray diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) were applied to the analysis of pozzolanic reaction and hydration products. The results illustrate that UGCS has influence on the hydration heat evolution of blended cement due to its filler effect and pozzolanic reaction. The cumulative hydration heat of blended cement is reduced by partial cement replacement with UGCS. The test mortar prepared by using blended cements with 30 wt. % UGCS shows a retardation of strength development with a low value at early ages (7 days) and a rapid growth at later ages (28 days). The 90-day compressive strength of test mortar is 45.0 MPa close to that of the control mortar (49.5 MPa). The obtained results from XRD and TGA analysis exhibit an increase in calcium hydroxide (CH) consumption and calcium silicate hydrates (C-S-H) formation in blended cement pastes with curing time. The cement replacement with UGCS induces changes in microstructure of blended cement paste and chemical composition of hydration products.

Mechanical Activation of Granulated Copper Slag and Its Influence on Hydration Heat and Compressive Strength of Blended Cement.

Mechanical activation of granulated copper slag (GCS) is carried out in the present study for the purposes of enhancing pozzolanic activity for the GCS. A vibration mill mills the GCS for 1, 2, and 3 h to produce samples with specific surface area of 0.67, 1.03 and 1.37 m²/g, respectively. The samples are used to replace 30% cement (PC) to get 3 PC-GCS binders. The hydration heat and compressive strength are measured for the binders and derivative thermogravimetric /thermogravimetric analysis (DTG/TGA), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) are used to characterize the paste samples. It is shown that cumulative heat and compressive strength at different ages of hydration and curing, respectively, are higher for the binders blending the GCS milled for a longer time. The compressive strength after 90 d of curing for the binder with the longest milling time reaches 35.7 MPa, which is higher than the strength of other binders and close to the strength value of 39.3 MPa obtained by the PC pastes. The percentage of fixed lime by the binder pastes at 28 days is correlated with the degree of pozzolanic reaction and strength development. The percentage is higher for the binder blending the GCS with longer milling time and higher specific surface area. The pastes with binders blending the GCS of specific surface area of 0.67 and 1.37 m²/g fix lime of 15.20 and 21.15%, respectively. These results together with results from X-ray diffraction (XRD), FTIR, and SEM investigations demonstrate that the mechanical activation via vibratory milling is an effective method to enhance the pozzolanic activity and the extent for cement substitution by the GCS as a suitable supplementary cementitious material (SCM).