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The L-type Ca2+ channel (LTCC, also known as Cav1,2) is involved in the regulation of key neuronal functions, such as dendritic information integration, cell survival, and neuronal gene expression. Clinical studies have shown an association between L-type calcium channels and the onset of depression, although the precise mechanisms remain unclear. The development of depression results from a combination of environmental and genetic factors. DNA methylation, a significant epigenetic modification, plays a regulatory role in the pathogenesis of psychiatric disorders such as posttraumatic stress disorder (PTSD), depression, and autism. In our study, we observed reduced Dnmt3a expression levels in the hippocampal DG region of mice with LPS-induced depression compared to control mice. The antidepressant Venlafaxine was able to increase Dnmt3a expression levels. Conversely, Bay K 8644, an agonist of the L-type Ca2+ channel, partially ameliorated depression-like behaviors but did not elevate Dnmt3a expression levels. Furthermore, when we manipulated DNA methylation levels during Bay K 8644 intervention in depression-like models, we found that enhancing the expression of Dnmt3a could improve LPS-induced depression/anxiety-like behaviors, while inhibiting DNA methylation exacerbated anxiety-like behaviors, the combined use of BAY K 8644 and L-methionine can better improve depressive-like behavior. These findings indicate that DNA methylation plays a role in the regulation of depression-like behaviors by the L-type Ca2+ channel, and further research is needed to elucidate the interactions between DNA methylation and L-type Ca2+ channels.

Purpose: We propose an FDK-based neural network to directly learn the filter from an iterative reconstruction (IR) algorithm and apply the learned filter in the FDK algorithm to obtain a high-quality CBCT reconstruction. Methods: The FDK algorithm is transformed into a linear expression of several matrix multipliers and embedded into neural network layers. Then, the FDK-based neural network framework is built including two fundamental modules and four core network layers. This network model can learn a filter directly from the iteratively reconstructed CBCT images by cascading the network layers of cosine weighting, filtering, backprojection, and leaky rectified linear unit and setting filter as the only trainable parameter. Preliminary and simulation studies performed on abdominal CT datasets are conducted to explore the correctness and effectiveness of the learned filter. Then, the head and neck CT data and Catphan phantom are utilized to demonstrate the generalization performance of the learned filter. Results: Preliminary study shows that the learned filter is consistent with the target filter, and the mean absolute difference is around 0.001. Compared with conventional FDK, the FDK-based neural network shows a better image quality with the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) increasing by 67% and 6%, respectively. In terms of the line-pair slice in the Catphan phantom, the SSIM and PSNR are improved by 13.75% and 42.78%, respectively. Conclusions: The FDK-based neural network can reconstruct high-quality images by directly learning a filter from the label images and provides a new perspective on solving the time-consuming problem of IR methods.

The pathogenicity of grapevine geminivirus A (GGVA), a recently identified DNA virus, to grapevine plants remains largely unclear. Here, we report a new GGVA isolate (named GGVAQN) obtained from grapevine ‘Queen Nina’ plants with severe disease symptoms. The infectious clone of GGVAQN (pXT-GGVAQN) was constructed to investigate its pathogenicity. Nicotiana benthamiana plants inoculated with GGVAQN by agroinfiltration displayed upward leaf curling and chlorotic mottling symptoms. A simple, quick, and efficient method for delivering DNA clones of GGVAQN into grapevine plants was developed, by which Agrobacterium tumefaciens cells carrying pXT-GGVAQN were introduced into the roots of in vitro-grown ‘Red Globe’ grape plantlets with a syringe. By this method, all ‘Red Globe’ grape plants were systemically infected with GGVAQN, and the plants exhibited chlorotic mottling symptoms on their upper leaves and downward curling, interveinal yellowing, and leaf-margin necrosis symptoms on their lower leaves. Our results provide insights into the pathogenicity of GGVA and a simple and efficient inoculation method to deliver infectious viral clones to woody perennial plants.

Abstract Inspired by the successful application of the q‐Weibull distribution in other research fields, we took the lead to use it in the field of medical devices in this work. The parameter estimation of the q‐Weibull distribution was performed using the probability plot method. The CT failure data from Nanfang Hospital in Guangzhou, China, were used to study the reliability of CT equipment at two levels: the CT system and its seven main components. In terms of evaluation accuracy, the mean squared error, Akaike's information criterion, and the determination coefficient were used to compare the accuracy of fitting of different distribution models. The results show that the accuracy of fitting the q‐Weibull distribution is higher than that of the Weibull distribution in terms of determination coefficient and mean squared error. When considering the complexity of the model, the fit accuracy of the Weibull distribution is better. The results were analyzed using reliability and failure rate plots. The q‐Weibull distribution gives a good fit for the failure data of the CT system and components. Though the Weibull distribution fits better in a few cases, the q‐Weibull distribution can describe the entire “bathtub curve” with only a set of parameters. The findings of this study can be extended to other medical devices.

Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p < 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p < 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.

The L-type Ca2+ channel (LTCC, also known as Cav1,2) is involved in the regulation of key neuronal functions, such as dendritic information integration, cell survival, and neuronal gene expression. Clinical studies have shown an association between L-type calcium channels and the onset of depression, although the precise mechanisms remain unclear. The development of depression results from a combination of environmental and genetic factors. DNA methylation, a significant epigenetic modification, plays a regulatory role in the pathogenesis of psychiatric disorders such as posttraumatic stress disorder (PTSD), depression, and autism. In our study, we observed reduced Dnmt3a expression levels in the hippocampal DG region of mice with LPSinduced depression compared to control mice. The antidepressant Venlafaxine was able to increase Dnmt3a expression levels. Conversely, Bay K 8644, an agonist of the L-type Ca2+ channel, partially ameliorated depression-like behaviors but did not elevate Dnmt3a expression levels. Furthermore, when we manipulated DNA methylation levels during Bay K 8644 intervention in depression-like models, we found that enhancing the expression of Dnmt3a could improve LPS-induced depression/anxiety-like behaviors, while inhibiting DNA methylation exacerbated anxiety-like behaviors, the combined use of BAY K 8644 and L-methionine can better improve depressive-like behavior. These findings indicate that DNA methylation plays a role in the regulation of depression-like behaviors by the L-type Ca2+ channel, and further research is needed to elucidate the interactions between DNA methylation and L-type Ca2+ channels. [ABSTRACT FROM AUTHOR]

Abstract Background The frequent interactions of rodents with humans make them a common source of zoonotic infections. Brandt's vole is the dominant rodent species of the typical steppe in Inner Mongolia, and it is also an important pest in grassland. Objectives: To obtain an initial unbiased measure of the microbial diversity and abundance in the blood and intestinal tracts and to detect the pathogens carried by wild Brandt's voles in Hulun Buir, Inner Mongolia. Methods Twenty wild adult Brandt's voles were trapped using live cages, and 12 intestinal samples were collected for metagenomic analysis and 8 blood samples were collected for meta‐transcriptomic analysis. We compared the sequencing data with pathogenic microbiota databases to analyse the phylogenetic characteristics of zoonotic pathogens carried by wild voles. Results A total of 122 phyla, 79 classes, 168 orders, 382 families and 1693 genera of bacteria and a total of 32 families of DNA and RNA viruses in Brandt's voles were characterized. We found that each sample carried more than 10 pathogens, whereas some pathogens that were low in abundance were still at risk of transmission to humans. Conclusion This study improves our understanding of the viral and bacterial diversity in wild Brandt's voles and highlights the multiple viral and bacterial pathogens carried by this rodent. These findings may serve as a basis for developing strategies targeting rodent population control in Hulun Buir and provide a better approach to the surveillance of pathogenic microorganisms in wildlife.

To better assess the practical value and avoid potential risks of the traditionally medicinal and edible basidiomycete Schizophyllum commune, which may arise from undescribed metabolites, a combination of elicitors was introduced for the first time to discover products from cryptic and low-expressed gene clusters under laboratory cultivation. Treating S. commune NJFU21 with the combination of five elicitors led to the upregulated production of a class of unusual linear diterpene-derived variants, including eleven new ones (1–11), along with three known ones (12–14). The structures and stereochemistry were determined by 1D and 2D NMR, HRESIMS, ECD, OR and VCD calculations. Notably, the elongation terminus of all the diterpenes was decorated by an unusual butenedioic acid moiety. Compound 1 was a rare monocyclic diterpene, while 2–6 possessed a tetrahydrofuran moiety. The truncated metabolites 4, 5 and 13 belong to the trinorditerpenes. All the diterpenes displayed approximately 70% scavenging of hydroxyl radicals at 50 μM and null cytotoxic activity at 10 μM. In addition, compound 1 exhibited potent antifungal activity against the plant pathogenic fungi Colletotrichum camelliae, with MIC values of 8 μg/mL. Our findings indicated that this class of diterpenes could provide valuable protectants for cosmetic ingredients and the lead compounds for agricultural fungicide development.

The fermentation of a soil-derived fungus Acremonium sp. led to the isolation of thirteen ascochlorin congeners through integrated genomic and Global Natural Product Social (GNPS) molecular networking. Among the isolated compounds, we identified two unusual bicyclic types, acremochlorins O (1) and P (2), as well as two linear types, acremochlorin Q (3) and R (4). Compounds 1 and 2 contain an unusual benzopyran moiety and are diastereoisomers of each other, the first reported for the ascochlorins. Additionally, we elucidated the structure of 5, a 4-chloro-5-methylbenzene-1,3-diol with a linear farnesyl side chain, and confirmed the presence of eight known ascochlorin analogs (6–13). The structures were determined by the detailed interpretation of 1D and 2D NMR spectroscopy, MS, and ECD calculations. Compounds 3 and 9 showed potent antibacterial activity against Staphylococcus aureus and Bacillus cereus, with MIC values ranging from 2 to 16 μg/mL.

The comprehensive carrying capacity of land is a key indicator for judging the sustainable development of nature and economy. Conducting comprehensive studies on this land capacity can provide the government with valuable information for decision-making. To divide and utilize land in a more reasonable and scientific manner, this study constructed an evaluation model based on the Random Forest and Support Vector Machine algorithms. The objective is to establish a more precise evaluation model and conduct a more comprehensive evaluation of the indicators. Based on the experimental results, it can be concluded that the carrying capacity of all five systems increased between 2014 and 2022. System Dexhibited the fastest increase in carrying capacity, with a growth rate of 58.43%, while system R recorded a growth rate of 46.29%. The difference in data before and after spatial processing in a certain region was analyzed: the average error of the five regions was relatively small, indicating that the processed values can accurately express spatial visualization information. The accuracy of Random forest support vector machine evaluation model is higher than that of support vector machine, which verifies the optimization effect of Random forest algorithm on support vector machine. In the comparative experiment of four models, each error evaluation parameter from the constructed evaluation module was superior to those from other models. These results illustrate the superior applicability of the proposed research model in terms of comprehensive land bearing capacity. The study optimized the evaluation model for comprehensive carrying capacity of land. This model proposes countermeasures and suggestions for the assessment of land carrying capacity and provides decision-making references for the protection and development of land resources.

Global climate change is one of the major challenges facing the world, and the spatial optimization of land use patterns has been regarded as critical in realizing carbon mitigation. In this study, the linear programming model and the Markov Chain model are integrated in different scenarios to optimize land use structure for low-carbon development. The land use pattern is then simulated through the adjusted convolutional neural network and cellular automata model, taking Guangzhou City as the case study area. The results reveal that construction land with high economic efficiency will increase its area, and the reaming types will experience slight changes, in 2035 in the natural development scenario and the economic priority scenario. Ecological land such as forest land, grassland, and water is partly occupied by construction land in the urban–rural fringe areas. The total carbon emissions decrease by 2.32% and 1.57% in these two scenarios. In the low-carbon-oriented scenario, the expansion of construction land is restricted, and the forest land and grassland undergo great expansion. The total carbon emission decreases by 18.95%—a figure much larger than that in the natural development scenario and the economic priority scenario. Our paper embeds the needs and constraints in land spatial planning into the spatial optimization of the land use pattern, which provides valuable references for low-carbon city development in the future.

BackgroundNinety-eight percent of documented cases of the zoonotic disease human monkeypox (MPX) were reported after 2001, with especially dramatic global spread in 2022. This longitudinal study aimed to assess spatiotemporal risk factors of MPX infection and predict global epidemiological trends.MethodTwenty-one potential risk factors were evaluated by correlation-based network analysis and multivariate regression. Country-level risk was assessed using a modified Susceptible-Exposed-Infectious-Removed (SEIR) model and a risk-factor-driven k-means clustering analysis.ResultsBetween historical cases and the 2022 outbreak, MPX infection risk factors changed from relatively simple [human immunodeficiency virus (HIV) infection and population density] to multiple [human mobility, population of men who have sex with men, coronavirus disease 2019 (COVID-19) infection, and socioeconomic factors], with human mobility in the context of COVID-19 being especially key. The 141 included countries classified into three risk clusters: 24 high-risk countries mainly in West Europe and Northern America, 70 medium-risk countries mainly in Latin America and Asia, and 47 low-risk countries mainly in Africa and South Asia. The modified SEIR model predicted declining transmission rates, with basic reproduction numbers ranging 1.61–7.84 in the early stage and 0.70–4.13 in the current stage. The estimated cumulative cases in Northern and Latin America may overtake the number in Europe in autumn 2022.ConclusionsIn the current outbreak, risk factors for MPX infection have changed and expanded. Forecasts of epidemiological trends from our modified SEIR models suggest that Northern America and Latin America are at greater risk of MPX infection in the future.

Hepatic encephalopathy (HE)–a major complication of liver disease–has been found to increase the risk of olfactory dysfunction, which may be attributed to elevated levels of ammonia/ammonium in the blood and cerebrospinal fluid. However, the cellular mechanisms underlying hyperammonemia-induced olfactory dysfunction remain unclear. By performing patch-clamp recordings of mitral cells (MCs) in the mouse olfactory bulb (OB), we found that 3 mM ammonium (NH4+) increased the spontaneous firing frequency and attenuated the amplitude, but synaptic blockers could prevent the changes, suggesting the important role of glutamate receptors in NH4+-induced hyperexcitability of MCs. We also found NH4+ reduced the currents of voltage-gated K+ channel (Kv), which may lead to the attenuation of spontaneous firing amplitude by NH4+. Further studies demonstrated NH4+ enhanced the amplitude and integral area of long-lasting spontaneous excitatory post-synaptic currents (sEPSCs) in acute OB slices. This enhancement of excitatory neurotransmission in MCs occurred independently of pre-synaptic glutamate release and re-uptake, and was prevented by the exocytosis inhibitor TAT-NSF700. In addition, an NH4+-induced increasement in expression of NR1 and GluR1 was detected on cytoplasmic membrane, indicating that increased trafficking of glutamate receptors on membrane surface in MCs is the core mechanism. Moreover, NH4+-induced enhanced activity of glutamate receptors in acute OB slices caused cell death, which was prevented by antagonizing glutamate receptors or chelating intracellular calcium levels. Our study demonstrates that the enhancement of the activity and recruitment of glutamate receptor directly induces neuronal excitotoxicity, and contributes to the vulnerability of OB to acute hyperammonemia, thus providing a potential pathological mechanism of olfactory defects in patients with hyperammonemia and HE.

The preventive monitoring and sustainable conservation of large-scale cultural heritage sites require satellite-based Earth observations. In this study, we present the first monitoring and thematic mapping results of the Badaling Great Wall (Beijing, China) using deformation indicators calculated from very high-resolution (VHR) multi-temporal spotlight TerraSAR-X data. The proposed coarse–fine search algorithm achieved high computational efficiency for calculating the unknown parameters of the two-tier network persistent scatterer synthetic aperture radar (SAR) interferometry (PSInSAR) approach. The spatiotemporal deformation anomalies, characterized by the absolute velocity, deformation deviation and acceleration, are informative to identify suspected hotspots for prioritizing monitoring activities. We provide an understandable method for thematic mapping and subsequent sustainable conservation of heritage sites by synergistically exploiting impacts from natural degradation and the tourism industry. We determine that the optimum tourist capacity of the site could be 1.0 million per month based on comparing pre- and post-COVID data (2019–2020). This study demonstrates the potential and performance of spaceborne PSInSAR tools for the intelligent management of large-scale architectural heritage sites by integrating InSAR deformation products with environmental and social data.

Accurately predicting resistance spot welding (RSW) quality is essential for the manufacturing process. In this study, the RSW process signals of 2219/5A06 aluminum alloy under two assembly conditions (including gap and spacing) were analyzed, and then artificial intelligence modeling was carried out. To improve the performance and efficiency of RSW quality evaluation, this study proposed a multi-signal fusion method that was performed by combining principal component analysis and a correlation analysis. A backpropagation neural network (BPNN) model was optimized using the sine-chaotic-map-improved sparrow search algorithm (SSA), and the input and output of the model were the variables after multi-signal fusion and the button diameter, respectively. Compared with the standard BPNN model, the Sine-SSA-BP model reduced the MAE by 42.33%, MSE by 51.84%, and RMSE by 31.45%. Its R2 coefficient reached 0.6482, which is much higher than that of BP (0.2464). According to various indicators (MAE, MSE, RMSE, and R2), the evaluation performance of the Sine-SSA-BP model was better than that of the standard BPNN model. Compared with other models (BP, GA-BP, PSO-BP, SSA-BP, and Sine-PSO-BP), the evaluation performance of the Sine-SSA-BP model was best, which can successfully predict abnormal spot welds.

Leg Length Discrepancy (LLD) is a critical problem which not only impacts the quality of life but also causes other physical ailments such as limping and severe back pain. Most people had a non-negligible difference in the length of their two legs, and 0.1% of the population with differences of 20 mm or more are diagnosed as a difference in limb length. The Ilizarov external fixator is commonly used in treating LLD as conventional therapeutic equipment, but often causes serious complications that cannot be prevented. Therefore, intramedullary leg-lengthening treatment has become popular in distraction osteogenesis to eliminate extracorporeal surgery. This paper presents a study on the design and optimization of a novel electromagnetic-driven Intramedullary Skeletal Distraction Robot (ISDR) with robust mechanical stiffness and surplus electromagnetic driving force. Compared with PRECICE, the split structure of ISDR eases the optimal design and manufacturing difficulties to strengthen mechanical stiffness, and the electromagnetic configuration allows improving the distraction force by adjusting the Permanent Magnet Brushless Direct Current (PMBLDC) motor parameters. ISDR, which is implanted in the medullary cavity, has the Von-Mises stress of 952.15 MPa, and the first mode of natural frequency is 28.823 Hz indicating that it can withstand the load during the walking gait phases. On the other hand, the ISDR distraction force encounters resistance from muscle fibers, and an average driving torque of 9 Nmm ensures its distraction. Based on the results, ISDR is proven secure and reliable during and after leg-lengthening treatment, which can significantly reduce lifestyle disruption and medical complications.

Studies have shown that illuminance and correlated colour temperature (CCT) are strongly correlated with body responses such as circadian rhythm, alertness, and mood. It is worth noting that these responses show a complex and variable coupling, which needs to be solved using accurate mathematical models for the regulation of indoor light parameters. Therefore, in this study, by weighing the evaluations of visual comfort, alertness, valence, and arousal of mood, a multi-objective optimisation mathematical model was developed with constraints conducive to the healthy rhythm. The problem was solved with the multi-objective evolutionary algorithm based on the decomposition differential evolution (MOEA/D-DE) algorithm. Taking educational space as the analysis goal, a dual-parameter setting strategy for illuminance and CCT covering four modes was proposed: focused learning, comfortable learning, soothing learning, and resting state, which could provide a scientific basis for the regulation of the lighting control system. The alertness during class time reached 3.01 compared to 2.34 during break time, showing a good light facilitation effect. The proposed mathematical model and analysis method also have the potential for application in the lighting design and control in other spaces to meet the era of intelligent, highly flexible, and sustainable buildings.

In order to promote the development and construction of smart cities, the massive equipment requirements of sensing terminals increased the pressure on urban site resource allocation. The light pole is suitable for carrying various urban functional equipment to form a smart street lighting system, which can provide rich site resources for the large-scale construction of urban functional facilities such as 5G micro base stations. However, the selection and combination of equipment mounted in the smart street lighting system only focus on the functional superposition at the physical level, without considering the relevance of each subsystem in practical application scenarios. Therefore, this study proposed a 5G micro base station location model based on a smart street lighting system. The correlation and cooperativity between 5G micro base stations and mounted devices were fully considered, and a universal system-level location selection index was developed to realize rational utilization of urban space site resources and intelligent linkage between subsystems. The results showed that the model is significantly effective for functional areas with different road network characteristics and provides practical, robust, effective, and accurate help for similar location selection problems.