Search

Abstract Functional magnetic resonance imaging (fMRI) provides insights into complex patterns of brain functional changes, making it a valuable tool for exploring addiction-related brain connectivity. However, effectively extracting addiction-related brain connectivity from fMRI data remains challenging due to the intricate and non-linear nature of brain connections. Therefore, this paper proposed the Graph Diffusion Reconstruction Network (GDRN), a novel framework designed to capture addiction-related brain connectivity from fMRI data acquired from addicted rats. The proposed GDRN incorporates a diffusion reconstruction module that effectively maintains the unity of data distribution by reconstructing the training samples, thereby enhancing the model’s ability to reconstruct nicotine addiction-related brain networks. Experimental evaluations conducted on a nicotine addiction rat dataset demonstrate that the proposed GDRN effectively explores nicotine addiction-related brain connectivity. The findings suggest that the GDRN holds promise for uncovering and understanding the complex neural mechanisms underlying addiction using fMRI data.

Abstract The utilization of agricultural waste organic materials through composting technology has gained significant traction in agricultural production as an effective means of crop nutrient management. However, the differences in the impact of organic amendments prepared by traditional composting and vermicomposting on soil properties still deserve further research. Based on field experiments conducted in greenhouse, compared to chemical fertilizer treatments as control, we utilized traditional compost (OF) and vermicompost (VcF) derived from agricultural organic waste edible mushroom bran and cow manure (2:8). Variations in soil physiochemical properties, activities of soil enzymes related C and P cycling, abundances and diversities of bacterial 16S rRNA and fungal ITS gene at total DNA level were analyzed. Both compost treatments enhanced soil organic carbon, soil total phosphorus, and soil available P content significantly and also increased the activities of soil α‐glucosidase, β‐glucosidase, acid phosphomonoesterase, and alkaline phosphomonoesterase significantly. The above results suggested that soil C and P transformations were stimulated effectively by both organic amendments. OF and VcF increased the fungal ITS absolute abundances significantly while diversity indices of soil bacterial community increased significantly under both treatments. Correlation analysis indicated that bacterial community composition was strongly correlated with several soil property indexes while fungal community composition was only significantly correlated with soil total phosphorous content. In conclusion, similar to traditional compost, vermicompost significantly improved soil nutrient cycling (especially C and P aspects). In terms of soil microbes, bacteria and fungi showed different responding mechanism to vermicompost: bacteria adjust microbial structure, while fungi tend to proliferated. In consideration of the advantages of vermicompost in technology and economic cost, it could be applied in the subsequent agricultural production more frequently.

In this paper, an engineering quality supervision system with integrated BIM and multiple IT technologies is developed to improve the level and efficiency of engineering quality supervision through a microservice architecture design, realizing the cloud storage management and strong scalability of a large amount of supervision record information data. A BIM model lightweight solution is proposed, data mapping is established between the model and quality supervision work procedures, and the interactive application of the model and business processes is realized. In system regulatory process management, two guidance methods are developed to meet the requirements of easy working and efficient collaboration among stakeholders. The system is developed and applied to a practical project in a management station, where it is shown to simplify the workflow and improve the efficiency of project approval.

Abstract To address the processing scheduling problem involving multiple molds, components, and floors, we propose the Genetic Grey Wolf Optimizer (GGA) as a means to optimize the production scheduling of components in a production line. This approach combines the Grey Wolf algorithm with the genetic algorithm. Previous methods have overlooked the storage requirements arising from the delivery characteristics of prefabricated components, often resulting in unnecessary storage costs. Intelligent algorithms have been demonstrated to be effective in production scheduling, and thus, to enhance the efficiency of prefabricated component production scheduling, our study presents a model incorporating a production objective function. This model takes into account production resources and delivery characteristics constraints. Subsequently, we develop a hybrid algorithm, combining the grey wolf algorithm with the genetic algorithm, to search for the optimal solution with a minimal storage cost. We validate the model using a case study, and the experimental results demonstrate that GAGWO successfully identifies the best precast production schedule. Furthermore, the precast production plan, considering the delivery method, is found to be reasonable.

Organic fertilizers produced from agricultural waste materials using earthworms have many advantages. The impact of vermicompost bio-organic fertilizer (VcF) prepared from waste mushroom bran on the quality of Chinese herbal medicine and soil health deserves attention. We conducted a field experiment to explore the quality of Rhizoma atractylodis and soil, using chemical fertilizer and VcF at dosages of 30 t·ha−1 and 40 t·ha−1. The results demonstrated that VcF significantly increased the contents of atractylenolide II, β-eudesmol, atractylenol, and atractylenolone by 34%, 175%, 72%, and 24%, respectively, along with a 70% increase in yield compared to the control. VcF significantly increased the available phosphorus content and the activity of the soil enzymes (α-Galactosidase, β-Galactosidase, and peroxidase), while reducing the nitrate nitrogen content. The addition of vermicompost did not change the soil bacterial diversity, while it significantly increased the soil fungal diversity. VcF improved the soil fungal diversity and significantly enhanced the relative abundance of the bacterial phylum Actinobacteriota and WPS-2, as well as the fungal phylum Ascomycota. Vermicompost significantly increased the relative abundance of bacteria phylum Actinobacteriota and WPS-2, as well as the fungi phylum Ascomycota. Actinobacteria mainly participate in the decomposition of detritus and the heterotrophic nutrient cycle. A Principal Coordinates Analysis of the soil bacterial and fungi communities suggested a significant impact of vermicompost application on the community structure of the soil bacteria. Conversely, no significant variance was detected in the bacterial community composition across the two levels of vermicompost. This study contributes to the enhancement of fertilization strategies for Atractylodes macrocephala, broadens the application scope of vermicompost, and advances the growth of organic agriculture.

Abstract Soil nitrogen (N) is a vital source of nutrients for maintaining soil fertility and crop production. However, the effect of biochar application rate on the mechanism of organic N transformation and the contribution of enzyme mineralization is still unclear. Therefore, we conducted two 5‐year field experiments in contrasting soils (Phaeozem and Luvisol) with biochar application rate at 0 t hm−2 (CK, 0), 22.5 t hm−2 (D1, 1%), 67.5 t hm−2 (D2, 3%), and 112.5 t hm−2 (D3, 5%) to investigate the potential effects of biochar application rate on soil organic nitrogen (N) turnover and its linkage to enzymatic mineralization in contrasting soil. The results showed that soil organic carbon (SOC) and microbial biomass nitrogen (MBN) contents, microbial biomass carbon to nitrogen ratio (MBC:MBN) and protease activity are significantly influenced by biochar application rate whereas not by soil type. Ammonium nitrogen (NH4+‐N) and nitrate nitrogen (NO3−‐N) contents, and dehydrogenase activity are significantly changed by soil type whereas not by biochar application rate. Based on the redundancy analysis, we found that organic N fractions are associated with MBN, SOC, and protease in Phaeozem, but related to protease activity in Luvisol. Our findings indicate that organic N turnover is not only related to the bioavailability of N but also requires carbon substrates in Phaeozem, whereas the transformation of organic N in Luvisol is dominated by enzymatic mineralization as the relatively low level of bioavailable N.

The breeding of laying hens and broilers in China has increased tremendously. Wet organic fertilizer prepared from hen manure using high-temperature container fermentation preserves high levels of nutrients and a diverse microbial community. We applied low doses of organic fertilizer to peanuts in the black soil area of China’s northeastern region. Based on the calculation of nitrogen content, treatments were set as follows: chemical fertilizer (PCF), organic fertilizer (POF, 4500 kg·ha−1), and replacement of 50% chemical fertilizer with organic fertilizer (PR, 2250 kg·ha−1). Compared to the plots with chemical fertilizers, the use of organic fertilizer and replacing 50% of the chemical fertilizers with organic fertilizer significantly increased peanut yields. Both the organic fertilizer and replacing 50% of the chemical fertilizers with organic fertilizer did not significantly affect the activities of the most tested soil enzymes related to carbon transformation and the absolute abundance of microorganisms. However, they did significantly enhance soil dehydrogenase and α-glucosidase. The community abundance ratio of fungi/bacteria trended downward, leading to soil with a high-fertility bacterial composition. The replacement of 50% chemical fertilizer with organic fertilizer significantly enhanced the species richness and diversity of the bacterial and fungal communities. Organic fertilizer treatment significantly increased the relative abundance of Gemmatimonas and Sphingomonas. The relative abundance of Mycobacterium in the treatment where 50% of the chemical fertilizers were replaced with organic fertilizer was significantly lower than that in the organic fertilizer treatment. PCoA results showed that the low-dose organic fertilizer treatment, replacing 50% of the chemical fertilizers with organic fertilizer, had a significant impact on the composition of soil bacterial communities.

Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer’s disease (AD). However, it is a big challenge to effectively fuse the correlated and complementary information from multimodal neuroimages. In this work, a novel model termed cross-modal transformer generative adversarial network (CT-GAN) is proposed to effectively fuse the functional and structural information contained in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner. Moreover, the swapping bi-attention mechanism is designed to gradually align common features and effectively enhance the complementary features between modalities. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively. The proposed model also provides new insights into detecting AD-related abnormal neural circuits.

Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse structural and functional features in exploring the complex brain network. In this paper, a novel brain structure-function fusing-representation learning (BSFL) model is proposed to effectively learn fused representation from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the decomposition-fusion framework is developed to first decompose the feature space into the union of the uniform and unique spaces for each modality, and then adaptively fuse the decomposed features to learn MCI-related representation. Moreover, a knowledge-aware transformer module is designed to automatically capture local and global connectivity features throughout the brain. Also, a uniform-unique contrastive loss is further devised to make the decomposition more effective and enhance the complementarity of structural and functional features. The extensive experiments demonstrate that the proposed model achieves better performance than other competitive methods in predicting and analyzing MCI. More importantly, the proposed model could be a potential tool for reconstructing unified brain networks and predicting abnormal connections during the degenerative processes in MCI.

Abstract Addiction in the brain is associated with adaptive changes that reshape addiction-related brain regions and lead to functional abnormalities that cause a range of behavioral changes, and functional magnetic resonance imaging (fMRI) studies can reveal complex dynamic patterns of brain functional change. However, it is still a challenge to identify functional brain networks and discover region-level biomarkers between nicotine addiction (NA) and healthy control (HC) groups. To tackle it, we transform the fMRI of the rat brain into a network with biological attributes and propose a novel feature-selected framework to extract and select the features of addictive brain regions and identify these graph-level networks. In this framework, spatial attention recurrent network (SARN) is designed to capture the features with spatial and time-sequential information. And the Bayesian feature selection(BFS) strategy is adopted to optimize the model and improve classification tasks by restricting features. Our experiments on the addiction brain imaging dataset obtain superior identification performance and interpretable biomarkers associated with addiction-relevant brain regions.

Abstract To understand how stover incorporation affects the levels of soil carbon and its biogeochemistry process, we examined soil carbon and amino sugar carbon (AS‐C). Activity of N‐acetylglucosaminidase (NAGase) and the abundance (real‐time quantitative polymerase chain reaction, RT‐qPCR) and communities of the bacterial chitinase‐encoding gene (chiA) were monitored. We implemented a maize cropping system, which included four treatments, i.e., chopped maize stover returned directly (SD), compost produced by maize stover (SC), biochar produced by maize stover (BC), and stover removal (CK). The results showed that BC and SD enhanced soil organic carbon (SOC), microbial biomass carbon:SOC, total AS‐C, glucosamine carbon, galactosamine carbon, NAGase activity, and chiA gene abundance significantly. There were significant positive correlations between the abundance of the soil chiA gene and NAGase activity. Biochar and stover treatments increased the number of total operational taxonomic units (OTUs), unique OTUs, and the chao 1 and ACE richness indices, but decreased the Shannon and Simpson diversity indices. The dominant chiA‐harboring bacterial phyla were Actinobacteria, Proteobacteria, and Bacteroidetes. Some detectable genera in Actinobacteria had high relative abundance, including Streptomyces, Actinoplanes, and Amycolatopsis. PLS‐DA and CCoA revealed that the chiA community in the BC or SD plots was significantly different from that in the CK plots, and differences between BC and SD were not significant. Similar soil properties, diversity, and the abundance of chiA were observed between SC and CK. Greater abundance and diversity of the chiA gene with BC incorporation were associated with higher pH, SOC, and TN concentration. Soil pH was related to the composition and abundances of the chiA‐harboring microbial communities (CCA).

Brain community detection is an efficient method to represent the communities of brain networks. However, time-variable functions of the brain and the intricate brain community structure impose a great challenge on it. In this paper, a time-sequential graph adversarial learning (TGAL) framework is proposed to detect brain communities and characterize the structure of communities from brain networks. In the framework, a novel time-sequential graph neural network is designed as an encoder to extract efficient graph representations by spatio-temporal attention mechanism. Since it is difficult to capture the community structure, the measurable modularity loss is used to optimize by maximizing the modularity of the community. In addition, the framework employs an adversarial scheme to guide the learning of representation. The effectiveness of our model is shown through experiments on the real-world brain network datasets, and the great performance of brain community detection demonstrates the advantage of the proposed framework.

As a new preparation process of nanofibers, electrospinning technology developed by bionic spider spinning can further improve the molecular structure and mechanical properties of nanofibers, and obtain nanofibers and base membrane products with better mechanical properties. The spider spinning needs minimal energy consumption though it has a very long liquid transport tube. Because spider silk has high mechanical properties and wide industrial applications, so a kind of new biomimetic electrospinning technology which harnessed a very long spiral-needle was proposed. Then poly(lactic acid)PLA/graphite oxide(GO)/dopamine (DA) nanofiber membrane was fabricated by the new biomimetic electrospinning technology. The results showed that compared with the pure PLA nanofiber membranes, the average pore size of PLA/GO porous nanofiber membranes decreased, the total number of pores increased, and the fracture strength and elongation at break increased. At the same time, the spun nanofibers were also more uniform. The pore size distribution of nanofiber was more dense after adding DA, and the fracture strength and elongation at break of nanofiber increased, while the nanofiber was also more even. After adding DA, the fracture strength was greatly increased and the elongation at break was similar to that of pure PLA nanofiber. The nanofiber membrane was obtained after oxidative polymerization of DA into poly(dopamine) (PDA), a layer of nanofiber membrane was attached to the surface of nanofiber. The performance of PLA/GO/PDA nanofiber membranes prepared based on PLA/GO/DA nanofiber membranes was superior to that of PLA/GO/PDA nanofiber membranes prepared based on PLA/GO nanofiber membranes, and its adsorption rate after 24 h was as high as 98.81%.

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial.

Highly active antiretroviral therapy (ART) can effectively inhibit virus replication and restore immune function in most people living with human immunodeficiency virus (HIV). However, an important proportion of patients fail to achieve a satisfactory increase in CD4+ T cell counts. This state is called incomplete immune reconstitution or immunological nonresponse (INR). Patients with INR have an increased risk of clinical progression and higher rates of mortality. Despite widespread attention to INR, the precise mechanisms remain unclear. In this review, we will discuss the alterations in the quantity and quality of CD4+ T as well as multiple immunocytes, changes in soluble molecules and cytokines, and their relationship with INR, aimed to provide cellular and molecular insights into incomplete immune reconstitution.

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children’s eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents.Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models.Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed.Result: The best linear model created using HM had an R2, S2, and R2cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q2 (0.503), R2 (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model’s reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability.Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.

Abstract Renal inflammation is a critical pathophysiological characteristic of diabetic kidney disease (DKD). The mechanism of the inflammatory response is complicated, and there are few effective treatments for renal inflammation that can be used clinically. Insulin-like growth factor-binding protein 5 (IGFBP5) is an important secretory protein that is related to inflammation and fibrosis in several tissues. Studies have shown that the IGFBP5 level is significantly upregulated in DKD. However, the function of IGFBP5 and its mechanism in DKD remain unclear. Here, we showed that IGFBP5 levels were significantly increased in the kidneys of diabetic mice. Ablation of IGFBP5 alleviated kidney inflammation in DKD mice. Mechanistically, IGFBP5 increased glycolysis, which was characterized by increases in lactic acid and the extracellular acidification rate, by activating the transcription factor early growth response 1 (EGR1) and enhancing the expression of PFKFB3 in endothelial cells. Furthermore, a mutation in PFKFB3 attenuated renal inflammation in DKD mice. Taken together, we provided evidence that IGFBP5 enhanced kidney inflammation through metabolic reprogramming of glomerular endothelial cells. Our results provide new mechanistic insights into the effect of IGFBP5 on kidney and highlight potential therapeutic opportunities for IGFBP5 and the metabolic regulators involved in DKD.

BackgroundCognitive frailty is the coexistence of physical frailty and mild cognitive impairment. Research shows that cognitive frailty is related to an increased risk of hospitalization, mortality, disability, and dementia. Diabetes and hypertension are common risk factors for physical frailty and cognitive impairment. However, the factors influencing cognitive frailty in the elderly with hypertension and diabetes are still unclear. This study aimed to investigate the possible factors influencing cognitive frailty in the elderly with hypertension and diabetes.MethodsA cross-sectional study was conducted. We evaluated people over 60 years with hypertension and diabetes who underwent physical examination in Wuxi Xin'an Community Health Service Center. Frail scale, Montreal Cognitive Assessment-Basic and clinical dementia rating were used to assess cognitive frailty. We collected demographic characteristics, hypertension and diabetes-related laboratory indicators of the participants. We also used various scales to assess the overall health status of the elderly.ResultsApproximately 20.8% of the participants were determined to have cognitive frailty in elderly adults with hypertension and diabetes. These participants were older, had a lower monthly income, and included a higher proportion of peasants. They also had a higher level of depression (p = 0.037), higher risk of falls (p = 0.000), higher risk of malnutrition (p = 0.002), poorer ability to perform activities of daily living (ADL) (p = 0.000), and less social support (p = 0.030). Multivariate regression analysis was used to further assess the factors for cognitive frailty. After adjusting for possible confounders, age and ADL score emerged as risk factors, whereas high monthly income decreased the risk of cognitive frailty.ConclusionCognitive frailty is correlated with age, income, and ability to perform daily living activities in the elderly with diabetes and hypertension. Closer attention to the elderly who have low income and poor self-care ability may play an important role in the early prevention of cognitive frailty and even dementia.

This study aimed to evaluate the osteotoxicity of polychlorinated biphenyls in murine osteoblastic MC3T3-E1 cells, and to explore the underlying mechanism focused on oxidative stress. The cells were exposed to Aroclor 1254 at concentrations of 2.5-20 µmol/L, and then cell viability, oxidative stress, intracellular calcium concentration, osteocalcin content, and calcium nodules formation were measured. Aroclor 1254 reduced cell viability and induced overproduction of intracellular reactive oxygen species in a dose-dependent manner. Activity of superoxide dismutase was decreased, and malondialdehyde content was promoted after exposure. Moreover, inhibitory effects of Aroclor 1254 on calcium metabolism and mineralization of osteoblasts were observed, as indicated by reduction of the intracellular calcium concentration, osteocalcin content, and modules formation rate. The decreased expression of osteocalcin, alkaline phosphatase, bone sialoprotein, and transient receptor potential vanilloid 6 further confirmed the impairment of Aroclor 1254 on calcium homeostasis and osteoblast differentiation. Addition of the antioxidant N-acetyl-L-cysteine partially restored the inhibitory effects on calcium metabolism and mineralization. In general, Aroclor 1254 exposure reduces calcium homeostasis, osteoblast differentiation and bone formation, and oxidative stress plays a vital role in the underlying molecular mechanism of osteotoxicity.

Abstract The 2019 coronavirus disease (COVID-19) outbreak caused by the SARS-CoV-2 virus is an ongoing global health emergency. However, the virus’ pathogenesis remains unclear, and there is no cure for the disease. We investigated the dynamic changes of blood immune response in patients with COVID-19 at different stages by using 5’ gene expression, T cell receptor (TCR), and B cell receptors (BCR) V(D)J transcriptome analysis at a single-cell resolution. We obtained single-cell mRNA sequencing (scRNA-seq) data of 341,420 peripheral blood mononuclear cells (PBMCs) and 185,430 clonotypic T cells and 28,802 clonotypic B cells from 25 samples of 16 patients with COVID-19 for dynamic studies. In addition, we used three control samples. We found expansion of dendritic cells (DCs), CD14+ monocytes, and megakaryocytes progenitor cells (MP)/platelets and a reduction of naïve CD4+ T lymphocytes in patients with COVID-19, along with a significant decrease of CD8+ T lymphocytes, and natural killer cells (NKs) in patients in critical condition. The type I interferon (IFN-I), mitogen-activated protein kinase (MAPK), and ferroptosis pathways were activated while the disease was active, and recovered gradually after patient conditions improved. Consistent with this finding, the mRNA level of IFN-I signal-induced gene IFI27 was significantly increased in patients with COVID-19 compared with that of the controls in a validation cohort that included 38 patients and 35 controls. The concentration of interferon-α (IFN-α) in the serum of patients with COVID-19 increased significantly compared with that of the controls in an additional cohort of 215 patients with COVID-19 and 106 controls, further suggesting the important role of the IFN-I pathway in the immune response of COVID-19. TCR and BCR sequences analyses indicated that patients with COVID-19 developed specific immune responses against SARS-CoV-2 antigens. Our study reveals a dynamic landscape of human blood immune responses to SARS-CoV-2 infection, providing clues for therapeutic potentials in treating COVID-19.

Traditional Chinese Medicinal (TCM) compounds provide a plethora of natural chemiome for structure based novel drug discovery against unexplored targets of important diseases. One such disease is Secondary Spinal cord Injury (SSCI), a condition secondary to initial Spinal cord Injury (SCI) caused by a trauma. In SSCI oxidative stress and inflammation play a pivotal role in aggravating neural damage at the site of trauma. To look into it reactive oxygen species (ROS) inhibition is a good strategy. Our Study here focuses on finding novel ROS inhibitors from in-house TCM compound library using advanced structure based drug discovery methods. From Virtual screening, Molecular Docking, Molecular Dynamics Simulation and MM-PBSA calculations a single ROS inhibitor was proposed for targeting SSCI. Our study provides a platform for future structure based drug discoveries in the field of treating SCI by targeting SSCI pathways.

Gut microbial alteration is closely associated with brain disorders including cognitive impairment (CI). Gut microbes have the potential to predicate the development of diseases. However, the gut microbial markers for CI remain to be elucidated. In this study, the gut microbial alterations were assessed using16S rRNA sequencing, and identified the gut microbial markers using a random forest model. The results showed that there were significant gut microbial differences between the control and CI groups based on beta diversity (p < 0.002). Patients with CI had higher abundances of Actinobacteria and Proteobacteria but lower proportions of Bcateroidetes and Firmicutes vs. that in the control group. Patients had 39 special genera and the control subjects had 11 special genera. Furthermore, 11 genera such as Blautia, Roseburia, and Lactococcus and 18 genera such as Lactobacillus, Ruminococcus 2, and Akkermansia were the differential taxa in the control and CI groups, respectively. Gene functions related to nutrient metabolisms were upregulated in patients with CI. This suggested that the huge differences in gut microbes between the two groups and gut microbiota had the potential to predicate the development of CI. Based on machine learning results, 15 genera such as Lactobacillus, Bifidobacterium, and Akkermansia were selected as the optimal marker set to predicate CI with an area under curve (AUC) value of 78.4%. The results revealed the gut microbial markers for CI and provided a potential diagnosis tool to prevent the development of CI in the elderly.

In China, with the expansion of the prefabricated construction market, the demand for precast concrete (PC) components is increasing and the construction of production plants to meet this need is expanding. To address the problem of siting PC component plants in China's construction industry, a combination of qualitative and quantitative analysis using principal component analysis (PCA) and geographic information system (GIS) is proposed for their siting. Taking the site selection process of a PC component plant in Wuhan, Hubei Province, China as an example, the diamond model is combined with six dimensions of environmental factors, requirements, regional competition, related and supporting industries, technological innovation and policies and regulations to construct an indicator system for PC component plant site selection. In the preliminary selection stage, PCA was applied to analyze the site selection indexes to obtain the overall score and ranking of each district for the selection of PC component plants in Wuhan, and it was concluded that three districts, namely, Xinzhou, Hannan, and Huangpi, were more suitable for the construction of component plants. In the precise selection stage, based on the availability of data, Hannan was selected among the three areas suitable for site selection for GIS analysis, using the visual-spatial analysis function of GIS to quantify the four geographical information indicators of environmental factors, and the results showed that Area C in Hannan was the most suitable area. The results show that the method has high accuracy and good applicability.

The seven members of the insulin-like growth factor (IGF) binding protein family (IGFBPs) were initially considered to be the regulatory proteins of IGFs in the blood circulation, mainly as the subsequent reserve for bidirectional regulation of IGF function during environmental changes. However, in recent years, IGFBPs has been found to have many functions independent of IGFs. The role of IGFBPs in regulating transcription, inducing cell migration and apoptosis is closely related to the occurrence and development of kidney disease. IGFBP-1, IGFBP-3, IGFBP-4 are closely associated with diabetes and diabetic nephropathy. IGFBP-3, IGFBP-4, IGFBP-5, IGFBP-6 are involved in different kidney disease such as diabetes, FSGS and CKD physiological process as apoptosis proteins, IGFBP-7 has been used in clinical practice as a biomarker for early diagnosis and prognosis of AKI. This review focuses on the differential expression and pathogenesis of IGFBPs in kidney disease.

Precast concrete (PC) slabs are widely used in the assembly of concrete residential buildings. The PC slabs are manufactured at the factory and then arranged in stacks for transport to the construction site for assembly. Currently, optimization of the stacking plans for PC slabs focuses on yard-space utilization and transportation efficiency and rarely considers the assembly sequence; secondary sequencing of prefabricated elements is required during construction to meet the lifting scheme, which leads to increased construction preparation time and risk of worker injury. To enable stacking crews to generate stacking plans rapidly and systematically to improve the on-site lifting efficiency of the components, this paper proposes a storage-location allocation model with two objectives: reduce secondary-sorting workload and increase stacking stability for PC slabs. At the same time, it must match the characteristics of the problem. To prevent the solution from falling into the local optimum during the evolution of the particle swarm optimization algorithm, we introduce an elitist learning strategy, which can improve the solutions when the group converges. Finally, we verify our allocation model and optimization algorithm through example simulations. The simulation results show that, compared with the traditional method, the stacking plans generated by this method have a lower secondary-sorting workload and higher stacking stability when using the same number of storage racks.

In this paper, we investigate the resource optimization algorithm design in a multicarrier relay system with simultaneous wireless information and power transfer (SWIPT). The relay is capable of harvesting energy from the source's signals by using the power splitting method. The non-ideal energy consumption including both the non-ideal power amplifier and non-ideal circuit power consumption is considered. First, we study the transmission rate maximization problem (TRMP) in an asymmetric decode-and-forward (DF) relay transmission, where the transmission power at the source, the transmission power at the relay, the power splitting ratio, and the transmission time are jointly optimized. The formulated problem is a non-convex problem, and it is generally quite difficult to solve it. By exploiting the structure of the problem, we propose two methods (logarithmic operation on constraints and logarithmic change of variables) to transform it into the corresponding difference of convex (DC) optimization problems. Then, we extend the TRMP to an amplify-and-forward (AF) relay transmission. Furthermore, we propose an effective algorithm to solve the DC optimization problem and prove that the algorithm can converge to a stationary point. Finally, extensive simulations are conducted to verify the performance of the proposed algorithm. The simulation results show that the asymmetric DF relay transmission achieves the highest sum rate and the AF relay transmission achieves a much lower sum rate than both the asymmetric and symmetric DF relay transmissions under different conditions.

Abstract Aiming at how to improve the efficiency of image edge detection, an image edge detection method based on least squares support vector machine (LSSVM) and cellular automata is proposed. Firstly, a new kernel function is constructed based on the Gauss radial basis kernel and polynomial kernel, which enables the LSSVM to fit the gray values of the image pixels accurately. Then, the gradient operator of the image is deduced, and the gradient value of the image is obtained by convolution with the gray value of the image. Then, the cellular automata evolves the gradient value according to the designed local rules to locate and detect the image edge. Simulation results show that the proposed edge detection algorithm is effective, and the new algorithm has higher detection performance than Sobel and Canny algorithms.

To address the issues of long testing periods and small sample sizes while evaluating the service life of heavy-load self-lubricating liners, we propose a succinct method based on Monte Carlo simulation that is significantly fast and requires a small sample size. First, the support vector regression algorithm was applied to fit the degradation trajectories of the wear depth, and the first and second characteristic parameter vectors of the wear depth as well as the corresponding distribution models were obtained. Next, sample expansion was performed using Monte Carlo simulation and the inverse transform method. Finally, based on the failure criterion of the self-lubricating liner, the service lives and distribution models of the expanded samples were obtained; subsequently, the corresponding reliability life indices were provided. Our results indicate that when the expanded sample was large enough, the proposed prediction method exhibited a relatively high prediction accuracy. Therefore, these results provide theoretical support for shortening the testing cycle used to evaluate the service life of self-lubricating liners and for accelerating the research and development of self-lubricating spherical plain bearing products.

The remaining useful life (RUL) prediction of self-lubricating spherical plain bearings is essential for replacement decision-making and the reliability of high-end equipment. The high-frequency swing self-lubricating liner (HSLL) is the key component of self-lubricating spherical plain bearings under high-frequency oscillation conditions. In this study, a RUL prediction method was proposed based on the Wiener process and grey system theory. First, the predictive processing of the wear depth was carried out using the grey model GM(1,1) to reduce the randomness and enhance the inherent regularity of the life test data. A degradation process model was established and the RUL was predicted online with the model parameter estimates based on the Bayesian updating strategy. Finally, examples were provided to elaborate the RUL prediction of the HSLL. The results show that the prediction accuracy of the proposed RUL prediction model is higher than that of the simple Wiener process during the entire residual life cycle of the HSLL. Based on the original wear data, the prediction accuracy of the RUL exhibited a strong dependence on prior samples and was relatively low owing to the larger deviation of the wear rate between the test sample and prior samples.

In this paper, we present an automated skeletal maturity recognition system that takes a single hand radiograph as an input and finally output the bone age prediction. Unlike the conventional manually diagnostic methods, which are laborious, fallible, and time-consuming, the proposed system takes input images and generates classification results directly. It first accurately detects the distal radius and ulna areas from the hand and wrist X-ray images by a faster region-based convolutional neural network (CNN) model. Then, a well-tuned CNN classification model is applied to estimate the bone ages. In the experiment section, we employed a data set of 1101 hand and wrist radiographs and conducted comprehensive experiments on the proposed system. We discussed the model performance according to various network configurations, multiple optimization algorithms, and different training sample amounts. After parameter optimization, the proposed model is finally achieved 92% and 90% classification accuracies for radius and ulna grades, respectively.

Background/Aims: Osteosarcoma (OS) is the second leading cause of cancer-related death in children and young adults. Chemoresistance is the most important cause of treatment failure in OS, largely resulting from presence of cancer stem cells (CSCs). However, CSCs isolated from cancer cell lines do not necessarily represent those from primary human tumors due to accumulation of genetic aberrations that increase with passage number. Therefore, studies on CSCs from primary OS may be more important for understanding the mechanisms driving the chemoresistance of CSCs in OS. Methods: We established a primary culture of OS cells, known as C1OS, from freshly resected tumor tissue. We further isolated CSCs from C1OS cells (C1OS-CSCs). We analyzed the effects of bufalin, a traditional Chinese medicine, on the stemness of C1OS-CSCs. We also analyzed the microRNA (miR) targets of bufalin on the stemness of C1OS-CSCs. Moreover, we examined these findings in the OS specimen. Results: Bufalin inhibited the stemness of C1OS-CSCs. Moreover, we found that miR-148a appeared to be a target of bufalin, and miR-148a further regulated DNMT1 and p27 to control the stemness of OS cells. This mechanism was further confirmed in OS specimen. Conclusion: Our data suggest that bufalin may be a promising treatment for OS, and its function may be conducted through regulation of miR-148a.

Dynamic spectrum sharing has drawn intensive attention in cognitive radio networks. The secondary users are allowed to use the available spectrum to transmit data if the interference to the primary users is maintained at a low level. Cooperative transmission for secondary users can reduce the transmission power and thus improve the performance further. We study the joint subchannel pairing and power allocation problem in relay-based cognitive radio networks. The objective is to maximize the sum rate of the secondary user that is helped by an amplify-and-forward relay. The individual power constraints at the source and the relay, the subchannel pairing constraints, and the interference power constraints are considered. The problem under consideration is formulated as a mixed integer programming problem. By the dual decomposition method, a joint optimal subchannel pairing and power allocation algorithm is proposed. To reduce the computational complexity, two suboptimal algorithms are developed. Simulations have been conducted to verify the performance of the proposed algorithms in terms of sum rate and average running time under different conditions.

Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.