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Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed.

Snoring, as a prevalent symptom, seriously interferes with life quality of patients with sleep disordered breathing only (simple snorers), patients with obstructive sleep apnea (OSA) and their bed partners. Researches have shown that snoring could be used for screening and diagnosis of OSA. Therefore, accurate detection of snoring sounds from sleep respiratory audio at night has been one of the most important parts. Considered that the snoring is somewhat dangerously overlooked around the world, an automatic and high-precision snoring detection algorithm is required. In this work, we designed a non-contact data acquire equipment to record nocturnal sleep respiratory audio of subjects in their private bedrooms, and proposed a hybrid convolutional neural network (CNN) model for the automatic snore detection. This model consists of a one-dimensional (1D) CNN processing the original signal and a two-dimensional (2D) CNN representing images mapped by the visibility graph method. In our experiment, our algorithm achieves an average classification accuracy of 89.3%, an average sensitivity of 89.7%, an average specificity of 88.5%, and an average AUC of 0.947, which surpasses some state-of-the-art models trained on our data. In conclusion, our results indicate that the proposed method in this study could be effective and significance for massive screening of OSA patients in daily life. And our work provides an alternative framework for time series analysis.

High-throughput virtual screening (HTVS) has emerged as a pivotal strategy for identifying high-affinity peptides targeting functional proteins, which are crucial for diagnostic and therapeutic applications. In the HTVS of peptides, expanding the library capacity to enhance peptide sequence diversity, thereby screening out excellent affinity peptide candidates, remains a significant challenge. This study presents a de novo design strategy that leverages directed mutation driven HTVS to evolve vast virtual libraries and screen peptides with ultrahigh affinities for various target proteins. Utilizing a computer-generated library of 104 random 15-mer peptide scaffolds, we employed a self-developed algorithm for parallelized HTVS with Autodock Vina. The top 1% of designs underwent random mutations at a rate of 20% for six generations, theoretically expanding the library to 1014 members. This approach was applied to various protein targets, including a tumor marker (alpha fetoprotein, AFP) and virus surface proteins (SARS-CoV-2 RBD and norovirus P-domain). Starting from the same 104 random 15-mer peptide library, peptides with high affinities in the nanomolar range for three protein targets were successfully identified. The energy-saving and high-efficient design strategy presents new opportunities for the cost-effective development of more effective high-affinity peptides for various environmental and health applications.

DNA methylation plays a significant role in many biological processes. Although some studies of DNA methylation have been performed in woody plant, none is known about the methylation patterns of mulberry ( Morus alba ). In this study, we performed whole genome bisulfite sequencing under drought stress to generate a methylated cytosines map and assessed the effects of the changes on gene expression combined with transcriptomics. We found that the percentage of methylated cytosines varied depending on the local sequence context (CG, CHG and CHH) and external treatment (control, CK; drought stress, DS). The methylation levels under DS were 8.64% higher than that of CK, and differences that were mainly due to the contribution of mCG (6.24%). Additionally, there were 3,243 different methylation and expression associated genes. In addition, methylated genes were enriched within GO subcategories including catalytic activity, cellular process, metabolic process, response to stimulus and regulation of biological process. This is the first study to comprehensively present methylation patterns in mulberry and reveal widespread DNA methylation changes in response to drought stress, which has the potential to enhance our understanding of links between DNA methylation and the modulation of gene expression in plants subjected to abiotic stresses.

The application of intelligent equipment and technologies such as robots and unmanned vehicles is an important part of the construction of intelligent mines, and has become China’s national coal energy development strategy and the consensus of the coal industry. Environment perception and instant positioning is one of the key technologies destined to realize unmanned and autonomous navigation in underground coal mines, and simultaneous location and mapping (SLAM) is an effective method of deploying this key technology. The underground space of a coal mine is long and narrow, the environment is complex and changeable, the structure is complex and irregular, and the lighting is poor. This is a typical unstructured environment, which poses a great challenge to SLAM. This paper summarizes the current research status of underground coal mine map construction based on visual SLAM and Lidar SLAM, and analyzes the defects of the LeGO-LOAM algorithm, such as loopback detection errors or omissions. We use SegMatch to improve the loopback detection module of LeGO-LOAM, use the iterative closest point (ICP) algorithm to optimize the global map, then propose an improved SLAM algorithm, namely LeGO-LOAM-SM, and describe its principle and implementation. The performance of the LeGO-LOAM-SM was also tested using the KITTI dataset 00 sequence and SLAM experimental data collected in two coal mine underground simulation scenarios, and the performance indexes such as the map construction effect, trajectory overlap and length deviation, absolute trajectory error (ATE), and relative pose error (RPE) were analyzed. The results show that the map constructed by LeGO-LOAM-SM is clearer, has a better loopback effect, the estimated trajectory is smoother and more accurate, and the translation and rotation accuracy is improved by approximately 5%. This can construct more accurate point cloud map and low drift position estimation, which verifies the effectiveness and accuracy of the improved algorithm. Finally, to satisfy the navigation requirements, the construction method of a two-dimensional occupancy grid map was studied, and the underground coal mine simulation environment test was carried out. The results show that the constructed raster map can effectively filter out outlier noise such as dynamic obstacles, has a mapping accuracy of 0.01 m, and the required storage space compared with the point cloud map is reduced by three orders of magnitude. The research results enrich the SLAM algorithm and implementation in unstructured environments such as underground coal mines, and help to solve the problems of environment perception, real-time positioning, and the navigation of coal mine robots and unmanned vehicles.

Background The use of mulberry leaves has long been limited to raising silkworms, but with the continuous improvement of mulberry ( Morus alba ) resource development and utilization, various mulberry leaf extension products have emerged. However, the fresh leaves of mulberry trees have a specific window of time for picking and are susceptible to adverse factors, such as drought stress. Therefore, exploring the molecular mechanism by which mulberry trees resist drought stress and clarifying the regulatory network of the mulberry drought response is the focus of the current work. Results In this study, natural and drought-treated mulberry grafted seedlings were used for transcriptomic and proteomic analyses (CK vs. DS9), aiming to clarify the molecular mechanism of the mulberry drought stress response. Through transcriptome and proteome sequencing, we identified 9889 DEGs and 1893 DEPs enriched in stress-responsive GO functional categories, such as signal transducer activity, antioxidant activity, and transcription regulator activity. KEGG enrichment analysis showed that a large number of codifferentially expressed genes were enriched in flavonoid biosynthesis pathways, hormone signalling pathways, lignin metabolism and other pathways. Through subsequent cooperation analysis, we identified 818 codifferentially expressed genes in the CK vs. DS9 comparison group, including peroxidase ( POD ), superoxide dismutase ( SOD ), aldehyde dehydrogenase ( ALDHs ), glutathione s-transferase ( GST ) and other genes closely related to the stress response. In addition, we determined that the mulberry gene MaWRKYIII8 (XP_010104968.1) underwent drought- and abscisic acid (ABA)-induced expression, indicating that it may play an important role in the mulberry response to drought stress. Conclusions Our research shows that mulberry can activate proline and ABA biosynthesis pathways and produce a large amount of proline and ABA, which improves the drought resistance of mulberry. MaWRKYIII8 was up-regulated and induced by drought and exogenous ABA, indicating that MaWRKYIII8 may be involved in the mulberry response to drought stress. These studies will help us to analyse the molecular mechanism underlying mulberry drought tolerance and provide important gene information and a theoretical basis for improving mulberry drought tolerance through molecular breeding in the future.

Recent evidence has shown that demyelination occurs along with axonal degeneration in spinal cord injury (SCI) during the secondary injury phase. Oligodendrocyte precursor cells (OPC) are present in the lesions but fail to differentiate into mature oligodendrocytes and form new myelin. Given the limited recovery of neuronal functions after SCI in adults without effective treatment available so far, it remains unknown whether enhancing OPC differentiation and myelination could benefit the recovery of SCI. To show the significance of myelin regeneration after SCI, the injury was treated with clemastine in the rat model. Clemastine is an FDA-approved drug that is potent in promoting oligodendrocyte differentiation and myelination in vivo, for four weeks following SCI. Motor function was assessed using sloping boards and grid walking tests and scored according to the Basso, Beattie, and Bresnahan protocol. The myelin integrity and protein expression were evaluated using transmission electron microscopy and immunofluorescence, respectively. The results indicated that clemastine treatment preserves myelin integrity, decreases loss of axons and improves functional recovery in the rat SCI model. The presented data suggest that myelination-enhancing strategies may serve as a potential therapeutic approach for the functional recovery in SCI.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with a high mortality rate caused by . The aspartate aminotransferase to platelet ratio index (APRI) is a biomarker of liver injury and inflammation. This study aimed to examine the correlation between APRI and SFTS prognosis using clinical data analysis and attempt to explain its prognostic significance through metabolic analysis. Data from hospitalized patients with a confirmed diagnosis of SFTS virus infection at Wuhan Union Hospital were retrospectively collected. The low and high APRI groups were 1:1 matched using propensity score matching (PSM) analysis. Fresh plasma was collected from patients with SFTS on admission and used for metabolic tests. A total of 617 patients with SFTS who met the inclusion criteria were selected for analysis. Survival analysis revealed that patients with SFTS with high APRI (> 35.3) had a substantially higher death rate than those with low APRI (≤ 35.3). Receiver operating characteristic analysis showed the predictive performance of APRI for SFTS prognosis is 0.77, with a 95% CI of 0.73-0.80, which was superior to NLR (area under the curve (AUC): 0.65), platelet-to-lymphocyte ratio (AUC: 0.54), and systemic immune-inflammation index (AUC: 0.58). The prognostic value and predictive performance of APRI were more substantial after PSM than before PSM. Metabolomic testing identified several differential serum metabolites, with alanine, aspartate, glutamate, glycerophospholipid, and tryptophan metabolism being the most important metabolic pathways. A high APRI score was associated with relatively higher mortality in patients with SFTS, and its predictive performance for the survival outcome of SFTS was superior to that of well-recognized inflammatory scores. Alanine, aspartate, and glutamate metabolism are involved in the progression of SFTS.

Fabricating Ti alloy based dental implants with defined porous scaffold structure is a promising strategy for improving the osteoinduction of implants. In this study, we use Laser Beam Melting (LBM) 3D printing technique to fabricate porous Ti6Al4V dental implant prototypes with three controlled pore sizes (200, 350 and 500 μm). The mechanical stress distribution in the surrounding bone tissue is characterized by photoelastography and associated finite element simulation. For in-vitro studies, experiments on implants’ biocompatibility and osteogenic capability are conducted to evaluate the cellular response correlated to the porous structure. As the preliminary results, porous structured implants show a lower stress-shielding to the surrounding bone at the implant neck and a more densed distribution at the bottom site compared to the reference implant. From the cell proliferation tests and the immunofluorescence images, 350 and 500 μm pore sized implants demonstrate a better biocompatibility in terms of cell growth, migration and adhesion. Osteogenic genes expression of the 350 μm group is significantly increased alone with the ALP activity test. All these suggest that a pore size of 350 μm provides an optimal provides an optimal potential for improving the mechanical shielding to the surrounding bones and osteoinduction of the implant itself.

Background Trichinosis is one of the most widespread parasitic infections worldwide. Trichinella spiralis not only infects humans but can also utilize wild anddomestic animals as hosts. The serine protease inhibitors secreted by Trichinella spiralis play a critical role in its invasion and immune evasion. Serpins can effectively inhibit host proteases, although the host can mount a strongimmune response against to these inhibitors. Results In this study we analyzed the crystal structures of the serine protease inhibitors from Trichinella spiralis and Trichinella pseudospiralis , revealing that both serpins exhibit.structural characteristics typical of serine protease inhibitors. The similarity of both “breach” region and “shutter” region of the two serpins are very high, but the “hinge” region are different, the “hinge” of Tp-serpin is closed, while of Ts-serpin was partially inserted into sheet-A, suggesting that Tp-serpin had higher inhibition activity. Using alpha chymotrypsin as Ts-serpin and Tp-serpin protease targets, the two serpins enzyme inhibition activity were measured separately, by measuring the secondary inhibition rate constant, half inhibitory concentration IC50, inhibition of stoichiometric number parameters and confirmed both the serine protease inhibitory activity, and Tp-serpin slightly higher than that of Ts-serpin, but no inhibition activity of P1-P1’ mutant. Conclusion In this study, the mechanism of enzyme inhibition activity of serpin was studied by means of structural biology and biochemistry comprehensively. These discoveries provide a theoretical foundation for a deeper understanding of the inhibition mechanisms of serpins and for the development of new drugs and vaccines against Trichinella spiralis infection.