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In the article, the authors discuss the brain-wide imaging methods that can be used for pan-brain neuronal connectivity mapping and their potential applications in the big data era of zoology. Also cited are how brain mapping can be used to study the principles of brain wiring and information flow, and the imaging methods used to map rodent brain like serial two-photon tomography and fluorescence micro-optical sectioning tomography.

In this paper, we present a new approach for object segmentation in videos based on Gaussian Mixture Models (GMMs) and Spatial Temporal Markov Random Field (ST-MRF). Instead of considering spatial correlation of a pixel and its neighborhood in traditional spatial MRF models, the proposed method established the ST-MRF model to extend the correlation among adjacent frame pixels. In process of training ST-MRF, the proposed model calculates the means and variances of each partition regions during the sequential frames by updating parameters of GMMs. Moreover, the energy function of ST-MRF is improved by calculating the spatial-temporal pixels' neighboring cliques as referential item. The experiments are compared with some state-of-the-art methods, such as standard GMMs, Meanshift and Fussy C Mean (FCM), on public standard video library and complex videos in real environments. The results demonstrate that our approach has performance improvements on robustness, accuracy, and effectiveness. [ABSTRACT FROM AUTHOR]

XC6422 is a conserved hypothetical protein from Xanthomonas campestris pathovar campestris (Xcc), a Gram-negative yellow-pigmented pathogenic bacterium that causes black rot, one of the major worldwide diseases of cruciferous crops. The protein consists of 220 amino acids and its structure has been determined to 1.6 Å resolution using the multi-wavelength anomalous dispersion (MAD) method. Although it has very low sequence identity to protein sequences in the PDB (less than 20%), the determined structure nevertheless shows that it belongs to the superfamily of serine α/β-hydrolases, with an active site that is fully accessible to solvent owing to the absence of a lid domain. Modelling studies with the serine esterase inhibitor E600 indicate that XC6422 adopts a conserved Ser-His-Asp catalytic triad common to this superfamily and has a preformed oxyanion hole for catalytic activation. These structural features suggest that XC6422 is most likely to be a hydrolase active on a soluble ester or a small lipid. An extra strand preceding the first β-strand in the canonical α/β-hydrolase fold leads to extensive subunit interactions between XC6422 monomers, which may explain why XC6422 crystals of good diffraction quality can grow to dimensions of up to 1.5 mm in a few days. [ABSTRACT FROM AUTHOR]

Xanthomonas campestris pv. campestris is a Gram-negative yellow-pigmented pathogenic bacterium that causes black rot, one of the major worldwide diseases of cruciferous crops. Its genome contains approximately 4500 genes, roughly one third of which have no known structure and/or function. However, some genes of unknown function are highly conserved among several different bacterial genuses. XC6422 is one such conserved hypothetical protein and has been overexpressed in Escherichia coli, purified and crystallized in a variety of forms using the hanging-drop vapour-diffusion method. Crystals grew to approximately 2 × 1.5 × 0.4 mm in size after one week and diffracted to at least 1.6 Å resolution. They belong to the monoclinic space group C2, with one molecule per asymmetric unit and unit-cell parameters a = 75.8, b = 79.3, c = 38.2 Å, β = 109.4°. Determination of this structure may provide insights into the protein's function. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the performance of artificial intelligence and neural networks, a new type of computing architecture-edge computing, came into being. However, when the scale of hybrid intelligent edge systems expands, there are redundant communications between the node and the parameter server; the cost of these redundant communications cannot be ignored. This paper proposes a reputation-based asynchronous model update scheme and formulates the federated learning scheme as an optimization problem. First, the explainable reputation consensus mechanism for hybrid intelligent labeling systems communication is proposed. Then, during the process of local intelligent data annotation, significant challenges in consistency, personalization, and privacy protection posed by the federated recommendation system prompted the development of a novel federated recommendation framework utilizing a graph neural network. Additionally, the method of information interaction model fusion was adopted to address data heterogeneity and enhance the uniformity of distributed intelligent annotation. Furthermore, to mitigate communication delays and overhead, an asynchronous federated learning mechanism was devised based on the proposed reputation consensus mechanism. This mechanism leverages deep reinforcement learning to optimize the selection of participating nodes, aiming to maximize system utility and streamline data sharing efficiency. Lastly, integrating the learned models into blockchain technology and conducting validation ensures the reliability and security of shared data. Numerical findings underscore that the proposed federated learning scheme achieves higher learning accuracy and enhances communication efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intestine is a highly radiation-sensitive organ that could be injured during the radiotherapy for pelvic, abdominal, and retroperitoneal tumors. However, the dynamic change of the intestinal microenvironment related to radiation-induced intestine injury (RIII) is still unclear. Using single-cell RNA sequencing, we pictured a dynamic landscape of the intestinal microenvironment during RIII and regeneration. We showed that the various cell types of intestine exhibited heterogeneous radiosensitivities. We revealed the distinct dynamic patterns of three subtypes of intestinal stem cells (ISCs), and the cellular trajectory analysis suggested a complex interconversion pattern among them. For the immune cells, we found that Ly6c+ monocytes can give rise to both pro-inflammatory macrophages and resident macrophages after RIII. Through cellular communication analysis, we identified a positive feedback loop between the macrophages and endothelial cells, which could amplify the inflammatory response induced by radiation. Besides, we identified different T cell subtypes and revealed their role in immunomodulation during the early stage of RIII through inflammation and defense response relevant signaling pathways. Overall, our study provides a valuable single-cell map of the multicellular dynamics during RIII and regeneration, which may facilitate the understanding of the mechanism of RIII. A scRNA-seq study reveals the dynamic landscape of the intestinal cellular microenvironment during radiation-induced intestine injury and regeneration. [ABSTRACT FROM AUTHOR]

With continuous progress in science and technology, a large amount of data are produced in all fields of the world at anytime and anywhere. These data are unmarked and lack marking information, while manual marking is time-consuming and laborious. Herein, this paper introduces a distributed semi-supervised labeling framework. This framework addresses the issue of missing data by proposing an attribute-filling method based on subspace learning. Furthermore, this paper presents a distributed semi-supervised learning strategy that trains sub-models (private models) within each sub-system. Finally, this paper develops a distributed graph convolutional neural network fusion technique with enhanced interpretability grounded on the attention mechanism. This paper assigns weights of importance to the edges of each layer in the graph neural network based on sub-models and public data, thereby enabling distributed and interpretable graph convolutional attention. Extensive experimentation using public datasets demonstrates the superiority of the proposed scheme over other state-of-the-art baselines, achieving a reduction in loss of 50% compared to the original approach. [ABSTRACT FROM AUTHOR]

The synthesis of the cell-wall peptidoglycan during bacterial cell division is mediated by a multiprotein machine, called the divisome. The essential membrane protein complex of FtsB, FtsL and FtsQ (FtsBLQ) is at the heart of the divisome assembly cascade in Escherichia coli. This complex regulates the transglycosylation and transpeptidation activities of the FtsW-FtsI complex and PBP1b via coordination with FtsN, the trigger for the onset of constriction. Yet the underlying mechanism of FtsBLQ-mediated regulation is largely unknown. Here, we report the full-length structure of the heterotrimeric FtsBLQ complex, which reveals a V-shaped architecture in a tilted orientation. Such a conformation could be strengthened by the transmembrane and the coiled-coil domains of the FtsBL heterodimer, as well as an extended β-sheet of the C-terminal interaction site involving all three proteins. This trimeric structure may also facilitate interactions with other divisome proteins in an allosteric manner. These results lead us to propose a structure-based model that delineates the mechanism of the regulation of peptidoglycan synthases by the FtsBLQ complex. The FtsB-FtsL-FtsQ complex is essential for regulating cell wall synthesis during bacterial cell division. Here, authors report the full-length trimeric structure showing a tilted V-shaped conformation and suggest an allosteric regulatory mechanism. [ABSTRACT FROM AUTHOR]

Whole-brain mesoscale mapping in primates has been hindered by large brain sizes and the relatively low throughput of available microscopy methods. Here, we present an approach that combines primate-optimized tissue sectioning and clearing with ultrahigh-speed fluorescence microscopy implementing improved volumetric imaging with synchronized on-the-fly-scan and readout technique, and is capable of completing whole-brain imaging of a rhesus monkey at 1 × 1 × 2.5 µm3 voxel resolution within 100 h. We also developed a highly efficient method for long-range tracing of sparse axonal fibers in datasets numbering hundreds of terabytes. This pipeline, which we call serial sectioning and clearing, three-dimensional microscopy with semiautomated reconstruction and tracing (SMART), enables effective connectome-scale mapping of large primate brains. With SMART, we were able to construct a cortical projection map of the mediodorsal nucleus of the thalamus and identify distinct turning and routing patterns of individual axons in the cortical folds while approaching their arborization destinations. A whole monkey brain is imaged at high resolution in 100 hours. [ABSTRACT FROM AUTHOR]

The speed of high-resolution optical imaging has been a rate-limiting factor for meso-scale mapping of brain structures and functional circuits, which is of fundamental importance for neuroscience research. Here, we describe a new microscopy method of Volumetric Imaging with Synchronized on-the-fly-scan and Readout (VISoR) for high-throughput, high-quality brain mapping. Combining synchronized scanning beam illumination and oblique imaging over cleared tissue sections in smooth motion, the VISoR system effectively eliminates motion blur to obtain undistorted images. By continuously imaging moving samples without stopping, the system achieves high-speed 3D image acquisition of an entire mouse brain within 1.5 hours, at a resolution capable of visualizing synaptic spines. A pipeline is developed for sample preparation, imaging, 3D image reconstruction and quantification. Our approach is compatible with immunofluorescence methods, enabling flexible cell-type specific brain mapping and is readily scalable for large biological samples such as primate brains. Using this system, we examined behaviorally relevant whole-brain neuronal activation in 16 c-Fos-shEGFP mice under resting or forced swimming conditions. Our results indicate the involvement of multiple subcortical areas in stress response. Intriguingly, neuronal activation in these areas exhibits striking individual variability among different animals, suggesting the necessity of sufficient cohort size for such studies. [ABSTRACT FROM AUTHOR]

Background. Intermittent hypoxia (IH) induced by obstructive sleep apnea (OSA) is a leading factor affecting cardiovascular fibrosis. Under IH condition, smooth muscle cells (SMAs) respond by dedifferentiation, which is associated with vascular remodelling. The expression of prolyl 4-hydroxylase domain protein 3 (PHD3) increases under hypoxia. However, the role of PHD3 in OSA-induced SMA dedifferentiation and cardiovascular fibrosis remains uncertain. Methods. We explored the mechanism of cardiovascular remodelling in C57BL/6 mice exposed to IH for 3 months and investigated the mechanism of PHD3 in improving the remodelling in vivo and vitro. Results. In vivo remodelling showed that IH induced cardiovascular fibrosis via SMC dedifferentiation and that fibrosis improved when PHD3 was overexpressed. In vitro remodelling showed that IH induced SMA dedifferentiation, which secretes much collagen I. PHD3 overexpression in cultured SMCs reversed the dedifferentiation by degrading and inactivating HIF-1α. Conclusion. OSA-induced cardiovascular fibrosis was associated with SMC dedifferentiation, and PHD3 overexpression may benefit its prevention by reversing the dedifferentiation. Therefore, PHD3 overexpression has therapeutic potential in disease treatment. [ABSTRACT FROM AUTHOR]

Copyright of SCIENCE CHINA Information Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Apoptosis is an important process to maintain cellular homeostasis. Deregulated apoptosis has linked to a number of diseases, such as inflammatory diseases, neurodegenerative disorder, and cancers. A major signaling complex in the death receptor signaling pathway leading to apoptosis is death-induced signaling complex (DISC), which is regulated mainly by death effector domain (DED)-containing proteins. There are seven DED-containing proteins in human, including FADD, c-FLIP, caspase-8, caspase-10, DEDD, DEDD2, and PEA-15. The main players in DISC formation employ tandem DEDs for regulating signaling complex formation. The regulatory mechanism of signaling complex formation is important and yet remains unclear. Interestingly, three caspase recruitment domain (CARD)-containing members, which belong to the same DD superfamily as DED-containing proteins, also contains similar tandem CARDs. Recent structural studies have shown that tandem CARDs are essential for the formation of a helical signaling complex. This review summarizes recent structural studies on DED-containing proteins and especially discusses the studies on tandem DEDs and tandem CARDs, which suggest new mechanisms of signaling complex assembly. [ABSTRACT FROM AUTHOR]

CARD subfamily is the second largest subfamily in the DD superfamily that plays important roles in regulating various signaling pathways, including but not limited to NF-kB activation signaling, apoptosis signaling and inflammatory signaling. The CARD subfamily contains 33 human CARD-containing proteins, regulating the assembly of many signaling complexes, including apoptosome, inflammsome, nodosome, the CBM complex, PIDDosome, the TRAF2 complex, and the MAVS signalosome, by homotypic CARD-CARD interactions. The mechanism of how CARDs find the right binding partner to form a specific complex remains unclear. This review uses different classification schemes to update the classification of CARD-containing proteins. Combining the classification based on domain structures, functions, associated signaling complexes, and roles would help better understand the structural and function diversity of CARD-containing proteins. This review also summarizes recent structural studies on CARDs. Especially, the CARD-containing complexes can be divided into the homodimeric, heterodimeric, oligomeric, filamentous CARD complexes and the CARD-ubiquitin complex. This review will give an overview of the versatile roles of CARDs in regulating signaling transduction, as well as the therapeutic drugs targeting CARD-containing proteins. [ABSTRACT FROM AUTHOR]

Development of multifunctional nanoscale coordination polymers (NCPs) allowing for T1- and T2-weighted targeted magnetic resonance (MR) imaging of tumors could significantly improve the diagnosis accuracy. In this study, nanoscale coordination polymers (NCPs) with a diameter of ≈80 nm are obtained with 1,1′-dicarboxyl ferrocene (Fc) as building blocks and magnetic gadolinium(III) ions as metallic nodes using a nanoprecipitation method, then further aminated through silanization. The amine-functionalized Fc-Gd@SiO2 NCPs enable the covalent conjugation of a fluorescent rhodamine dye (RBITC) and an arginine-glycine-aspartic acid (RGD) peptide as a targeting ligand onto their surface. The formed water-dispersible Fc-Gd@SiO2(RBITC)-RGD NCPs exhibit a low cytotoxicity, as confirmed by MTT assay. They have a longitudinal relaxivity ( r1) of 5.1 mM−1 s−1 and transversal relaxivity ( r2) of 21.7 mM−1 s−1, suggesting their possible use as both T1-positive and T2-negative contrast agents. In vivo MR imaging experiments show that the signal of tumor over-expressing high affinity αvβ3 integrin from T1-weighted MR imaging is positively enhanced 47±5%, and negatively decreased 33±5% from T2-weighted MR imaging after intravenous injection of Fc-Gd@SiO2(RBITC)-RGD NCPs. [ABSTRACT FROM AUTHOR]

This paper investigates the reliability behavior of IGZO TFT with different widths under AC gate and drain stress. Device of larger width suffers from worse current degradation. By comparing the contact resistance after stress such behavior can be attributed to the damaged contact region by large current during stress. [ABSTRACT FROM AUTHOR]

With the growing use of information technology in all life domains, hacking has become more negatively effective than ever before. Also with developing technologies, attacks numbers are growing exponentially every few months and become more sophisticated so that traditional IDS becomes inefficient detecting them. This paper proposes a solution to detect not only new threats with higher detection rate and lower false positive than already used IDS, but also it could detect collective and contextual security attacks. We achieve those results by using Networking Chatbot, a deep recurrent neural network: Long Short Term Memory (LSTM) on top of Apache Spark Framework that has an input of flow traffic and traffic aggregation and the output is a language of two words, normal or abnormal. We propose merging the concepts of language processing, contextual analysis, distributed deep learning, big data, anomaly detection of flow analysis. We propose a model that describes the network abstract normal behavior from a sequence of millions of packets within their context and analyzes them in near real-time to detect point, collective and contextual anomalies. Experiments are done on MAWI dataset, and it shows better detection rate not only than signature IDS, but also better than traditional anomaly IDS. The experiment shows lower false positive, higher detection rate and better point anomalies detection. As for prove of contextual and collective anomalies detection, we discuss our claim and the reason behind our hypothesis. But the experiment is done on random small subsets of the dataset because of hardware limitations, so we share experiment and our future vision thoughts as we wish that full prove will be done in future by other interested researchers who have better hardware infrastructure than ours. [ABSTRACT FROM AUTHOR]