Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28) each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%), and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78) indicates the robustness of the present method. Results demonstrate that the multiple morphometric features can be applied to form a rational reproducible individual-based morphological brain network

Analysis of Greenhouse Gas Emissions Characteristics and Emissions Reduction Measures of Animal Husbandry in Inner Mongolia

Global warming has had a profound impact on human life, with animal husbandry being a significant contributor to greenhouse gas emissions and playing a crucial role in the global greenhouse gas budget. Inner Mongolia is a major contributor to these emissions, making it vital to study the link between greenhouse gas emissions and animal husbandry in this region for the purpose of reducing emissions. In this study, the emissions of greenhouse gases (CH4, N2O, and CO2) from livestock and poultry breeding from 2010 to 2020 and the emissions of each city from 2020 were estimated, the emissions characteristics were analysed, and the low carbon emissions reduction technical measures were proposed. The results show that (1) the overall greenhouse gas emissions from 2010 to 2020 in Inner Mongolia showed a fluctuating trend; the main emissions sources were gastrointestinal fermentation and faecal management. The annual average CH4 emissions were 994,400 ta−1, and the annual average N2O emissions were 35,100 ta−1. (2) In 2020, the total emissions of each league city were 38.05 million t equivalent of CO2, and the emissions gradually decreased from east to west, with a significant emissions reduction potential. Based on these findings, this study also proposed technical measures for reducing carbon emissions, offering theoretical support to drive the industrial transformation and upgrading of the livestock industry, and promoting green economic development in Inner Mongolia as part of its carbon peaking and neutrality goals

Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics.

Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted in radiologists visual evaluation. More sophisticated computer algorithms are needed to extract the higher-order information. The purpose of this study was to apply a new deep learning algorithm, the bi-directional convolutional long short-term memory (CLSTM) network, and the radiomics analysis for differential diagnosis of PCa and benign prostatic hyperplasia (BPH). To systematically investigate the optimal amount of peritumoral tissue for improving diagnosis, a total of 9 ROIs were delineated by using 3 different methods. The results showed that bi-directional CLSTM with ± 20% region growing peritumoral ROI achieved the mean AUC of 0.89, better than the mean AUC of 0.84 by using the tumor alone without any peritumoral tissue (p = 0.25, not significant). For all 9 ROIs, deep learning had higher AUC than radiomics, but only reaching the significant difference for ± 20% region growing peritumoral ROI (0.89 vs. 0.79, p = 0.04). In conclusion, the kinetic information extracted from DCE-MRI using bi-directional CLSTM may provide helpful supplementary information for diagnosis of PCa

Structural and functional analysis of the PDZ domains of human HtrA1 and HtrA3

High-temperature requirement A (HtrA) and its homologs contain a serine protease domain followed by one or two PDZ domains. Bacterial HtrA proteins and the mitochondrial protein HtrA2/Omi maintain cell function by acting as both molecular chaperones and proteases to manage misfolded proteins. The biological roles of the mammalian family members HtrA1 and HtrA3 are less clear. We report a detailed structural and functional analysis of the PDZ domains of human HtrA1 and HtrA3 using peptide libraries and affinity assays to define specificity, structural studies to view the molecular details of ligand recognition, and alanine scanning mutagenesis to investigate the energetic contributions of individual residues to ligand binding. In common with HtrA2/Omi, we show that the PDZ domains of HtrA1 and HtrA3 recognize hydrophobic polypeptides, and while C-terminal sequences are preferred, internal sequences are also recognized. However, the details of the interactions differ, as different domains rely on interactions with different residues within the ligand to achieve high affinity binding. The results suggest that mammalian HtrA PDZ domains interact with a broad range of hydrophobic binding partners. This promiscuous specificity resembles that of bacterial HtrA family members and suggests a similar function for recognizing misfolded polypeptides with exposed hydrophobic sequences. Our results support a common activation mechanism for the HtrA family, whereby hydrophobic peptides bind to the PDZ domain and induce conformational changes that activate the protease. Such a mechanism is well suited to proteases evolved for the recognition and degradation of misfolded proteins