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This paper mainly introduces a program written in Matlab for determining ground motion parameters. From the three framework modules of input, processing and output, the author combines his understanding and experience to analyze the process of determining design seismic parameters in detail. It uses an Excel file to control the input parameters as a whole, taking into account factors such as peak ground acceleration, site classification, design earthquake grouping, and zoning map adjustment factors. It simplifies the complex process of processing response spectrum data, and achieves automatic calibration of borehole response spectra under different standard spectra recommended by different codes, automatic saving of maps, and exporting of ground motion parameter tables. The program can comprehensively analyze multiple boreholes and multiple response spectrum data for different design reference periods and different probability levels of exceedance at one time, which is very efficient in handling regional seismic safety evaluation and other multi-site engineering projects. The application analysis of examples in seismic safety evaluation work is also presented in the article. The program can not only process bedrock response spectrum, but also analyze the overall or individual response spectrum of boreholes to obtain more reasonable ground motion parameters, which is helpful for the seismic safety evaluation work.

This paper collects relevant information on the newly added Yuping potential seismic source in western Huaihua, and analyzed the seismic hazard of the study area based on 1′×1′ grid points. We mainly discussed the changes in PGA before and after the new potential seismic source in the Hunan-Guizhou border region, and drawing isolines and zoning based on the PGA of site Ⅱ. The results of seismic hazard analysis show that the PGA has been greatly improved inside and 20 km away around the new potential seismic source. When the focal depth is 10 km, the PGA of exceeding probability of 10 in 50 years within the Yuping potential source range increases from 35～40 cm/s2 to 80～95 cm/s2, increase of about 2.3 to 2.4 times. In local areas such as Xinhuang and Zhijiang within the potential source, the basic intensity can reach Ⅶ degrees. And outside the potential source, PGA gradually increases from 35～80 cm/s2. When the focal depth is 15 km, the basic intensity in regions such as Xinhuang and Zhijiang remains at Ⅵ degrees. The article also conducted a preliminary analysis of the seismic capacity of different types of houses in Xinhuang and Zhijiang county in western Huaihua. Additional attention should be paid to the follow-up work of earthquake prevention and disaster reduction.

Introduction: The purpose of this study was to investigate the association of central lean mass distribution with the risk of mortality. Methods: This cohort study included 40,283 UK Biobank participants. Cox proportional hazards regression models were used to estimate the association of central lean mass distribution, i.e., trunk-to-leg lean mass ratio, assessed by dual-energy X-ray absorptiometry, with the risk of mortality. Results: The median age of the participants was 65 years, and 52% were women. During a median follow-up of 4.18 years, 674 participants died, of whom 366 were due to cancer and 126 were due to cardiovascular causes. Compared with the lowest tertile of a trunk-to-leg lean mass ratio, the multivariable-adjusted (age, sex, ethnicity, lifestyle, comorbidities, body mass index, and appendicular muscle mass index) hazards ratios of the highest tertile of trunk-to-leg lean mass ratio were 1.55 (95% CI: 1.23–1.94), 1.69 (95% CI: 1.26–2.26), and 1.14 (95% CI: 0.72–1.80) for all-cause, cancer, and cardiovascular mortality, respectively. Neutrophil-to-lymphocyte ratio mediated 9.3% (95% CI: 3.3%–40.4%) of the association of trunk-to-leg lean mass ratio with all-cause mortality. There was evidence for additive interactions of trunk-to-leg lean mass ratio with older age and poor diet quality for all-cause mortality. Conclusion: Trunk-to-leg lean mass ratio, assessed by dual-energy X-ray absorptiometry, was positively associated with the risks of all-cause and cancer mortality, independent of general obesity and central obesity, in UK middle-aged and older adults. Central lean mass distribution may interact synergistically with aging and poor diet quality to further increase the risk of death.

Abstract Background In this study, we aimed to identify the risk factors in patients with rectal anastomotic re-leakage and develop a prediction model to predict the probability of rectal anastomotic re-leakage after stoma closure. Methods This study was a single-center retrospective analysis of patients with rectal cancer who underwent surgery between January 2010 and December 2020. Among 3225 patients who underwent Total or Partial Mesorectal Excision (TME/PME) surgery for rectal cancer, 129 who experienced anastomotic leakage following stoma closure were enrolled. Risk factors for rectal anastomotic re-leakage were analyzed, and a prediction model was established for rectal anastomotic re-leakage. Results Anastomotic re-leakage after stoma closure developed in 13.2% (17/129) of patients. Multivariable analysis revealed that neoadjuvant chemoradiotherapy (odds ratio, 4.07; 95% confidence interval, 1.17–14.21; p = 0.03), blood loss > 50 ml (odds ratio, 4.52; 95% confidence interval, 1.31–15.63; p = 0.02), and intersphincteric resection (intersphincteric resection vs. low anterior resection: odds ratio, 6.85; 95% confidence interval, 2.01–23.36; p = 0.002) were independent risk factors for anastomotic re-leakage. A nomogram was constructed to predict the probability of anastomotic re-leakage, with an area under the receiver operating characteristic curve of 0.828 in the cohort. Predictive results correlated with the actual results according to the calibration curve. Conclusions Neoadjuvant chemoradiotherapy, blood loss > 50 ml, and intersphincteric resection are independent risk factors for anastomotic re-leakage following stoma closure. The nomogram can help surgeons identify patients at a higher risk of rectal anastomotic re-leakage.

ABSTRACTSuitable features are the key to identifying cashmere and wool fibers, and feature selection is an important step in classification. Existing supervised feature selection methods need to consider the information between fiber features and class labels. Aiming at making up for this deficiency, we propose an unsupervised feature selection method based on k-means clustering, which overcome the difficulty that fiber feature class labels are either unavailable or costly to obtain. Firstly, the subset of fiber features that have been normalized are clustered by the k-means clustering algorithm to obtain the total number of clusters, and the clustering effect is evaluated by the DB Index criterion. Next, the DB value of each feature subset, the correlation of features and the total number of the clustering are considered as the judgment criteria to select the optimal feature subset. Finally, the optimal subset of features obtained by unsupervised feature selection algorithms is fed into a support vector machine for automatic identification and classification of the two fibers. The experimental results show that the method achieves a high recognition rate of 97.25%. It is verified that the unsupervised feature selection method based on k-means clustering is effective for the recognition of cashmere and wool.

ObjectiveThis study aims to analyze the causal relationship between autoimmune thyroiditis (AIT) and inflammatory bowel disease (IBD) using bidirectional Mendelian randomization (MR).MethodsSingle nucleotide polymorphisms were obtained from FinnGen. Exposure-outcome causality was assessed using inverse variance weighted, MR-Egger, and weighted median. MR-Egger intercept, Cochran’s Q, and leave-one-out sensitivity analysis were used to evaluate horizontal pleiotropy, heterogeneity, and robustness, respectively.ResultsForward analysis revealed no significant association between AIT and the risk of ulcerative colitis (UC) (odds ratio [OR] 1.008, 95% confidence interval [CI] 0.986 to 1.03, p = 0.460) or Crohn’s disease (CD) (OR 0.972, 95% CI 0.935 to 1.010, p = 0.143). Reverse analysis showed that UC (OR 0.961, 95% CI 0.783 to 1.180, p = 0.707) was not associated with AIT risk, while CD (OR 2.371, 95% CI 1.526 to 3.683, p < 0.001) was linked to an increased risk of AIT. Intercept analysis and Cochran’s Q test indicated no horizontal pleiotropy or heterogeneity. Sensitivity analysis confirmed the robustness of the MR results.ConclusionThis MR analysis suggests that CD, but not UC, is a risk factor for AIT, whereas AIT is not associated with the risk of IBD. Proactive prevention and treatment of CD can help mitigate the risk of AIT.

IntroductionEpidemiological studies have assessed the correlation between daily dietary branch chain amino acid (BCAA) intakes and the risk of obesity, however, the findings from these studies were inconsistent and investigations among GDM women were few.ObjectiveThe present study was to investigate the associations of daily BCAA intakes with the risks of overweight and abdominal obesity among women with prior gestational diabetes mellitus (GDM) postpartum.MethodWe performed a cross-sectional study of 1,263 women with prior GDM at 1–5 years post-delivery. Logistic regression models were used to estimate the associations of daily dietary intakes of BCAAs with the risks of overweight and abdominal obesity.ResultsThe multivariable-adjusted odds ratios (ORs) across quartiles of daily BCAA intakes postpartum were 1.42 (95% confidence interval [CI] 1.02–1.97), 1.00 (reference), 1.21 (95% CI 0.88–1.68), and 1.31 (95% CI 0.95–1.81) for general overweight, and 1.38 (95% CI 0.99–1.90), 1.00, 1.19 (95% CI 0.86–1.64), and 1.43 (95% CI 1.04–1.98) for abdominal obesity, respectively. Women with the lowest quartile of daily BCAA intakes significantly increased the risks of general overweight (OR 1.49; 95 %CI 1.06–2.09) and abdominal obesity (OR 1.50; 95 %CI 1.08–2.11) compared with women at quartile 2 of daily BCAA intakes after further adjustment of daily energy intake.ConclusionThe present study indicated that daily lower BCAA intakes were associated with increased risks of general overweight and abdominal obesity among women with prior GDM.

Karst ecosystems, especially tropical karst forests, are crucial to the global carbon cycle. In mountainous and hilly areas, elevation-related changes in environment and vegetation often lead to shifts in the accumulation and decomposition of soil organic carbon (SOC). However, the elevational patterns and influencing variables of SOC and its fractions in tropical karst forest ecosystems remain largely unexplored. Here, we analyzed the elevational patterns of SOC and its fractions in the topsoil and subsoil in the tropical seasonal rainforests within typical peak-cluster depression region of Southwest China. Our results indicated that the SOC content was highest at 400 m asl, which was significantly higher than that at 200 m asl (p < 0.05). Overall, SOC content demonstrated an increasing trend with rising elevation. Additionally, SOC content was significantly higher in the topsoil compared to the subsoil (p < 0.05). The majority of SOC fractions exhibited an increase with elevation but decrease with soil depth. Notably, only water-soluble organic carbon (WSOC) displayed a decrease with elevation. Meanwhile, recalcitrant organic carbon (ROC, 54.27%), particulate organic carbon (POC, 30.19%), and easily oxidizable organic carbon (EOC, 16.95%) were the main SOC fractions. Labile organic carbon (LOC) in the karst forest soil was predominantly composed of EOC and POC. Correlation analysis unveiled significant positive correlations between SOC and certain fractions with elevation, soil total nitrogen, and exchangeable magnesium. Conversely, significant negative correlations were observed with soil bulk density (SBD), soil total phosphorus, and litter phosphorus (Litter P). Redundancy analysis indicated that elevation, SBD, and Litter P were the main environmental variables influencing shifts in SOC and its fractions. Structural equation models showed that SOC was primarily directly impacted by soil properties but indirectly impacted by elevation. ROC was mainly associated with the direct effects of soil properties and litterfall, although elevation exerted a substantial impact through indirect pathways. Moreover, LOC was predominantly influenced by the direct impact of soil properties. Therefore, this study demonstrates that SOC and its fractions are strongly influenced by elevation in karst peak-cluster depression regions and have important implications for forest management and sustainable ecosystem development in these regions.

Abstract The Tibetan Plateau contains the largest numbers of glaciers outside the polar regions, yet the initiation times of glacial advances remain unclear. The potential relics of original moraines might be preserved in the sedimentary basins. Here, we present sedimentological and geochronological data from five boreholes located upstream of the Tsangpo Gorge, Eastern Himalayan Syntaxis. Our findings reveal that fluvial sediment accumulation began over 2.5 million of years ago due to enhanced rock uplift downstream, and extensive glacial advances occurred after ~0.75 million of years. Notably, the earliest extensive glacial advance (~0.75 million of years) is considerably younger than the formation of the Tsangpo Gorge (>2.5 million of years). Following the initiation of this glacial advance, the Eastern Himalayan Syntaxis experienced rapid exhumation of approximately 1.3–1.6 kilometers. Such geomorphological processes and exhumation history suggest that rock uplift, rather than glacial damming, played a pivotal role in maintaining the stability of the knickpoints on the southeastern margins of the Tibetan Plateau.

Abstract Seven pieces of gold foils for surface decoration of the lacquerware were excavated in the late Western Han Dynasty tomb in the area of the Jin Yang (晋阳) Ancient City site in Taiyuan (太原), Shanxi (山西), China. These gold foils portray images of the carriage, the leopard, the tigers, the ox, and the dancer with fluttering sleeves, etc., with black lines outlining the contours and red paint depicting vivid patterns. The study used Stereo Microscopes, Metallurgical Microscopy, Environmental Scanning Electron Microscopy coupled with Energy-Dispersive X-ray Spectroscopy (ESEM-EDS), and Laser Raman Spectroscopy (LRS) to analyse the thickness of the gold foils, the alloy composition, the composition of the paint and other surface attachments, and investigate the manufacturing process. As the results show, the thickness of the gold foils is about 26 μm. The composition is a gold-silver alloy with about 96% Au/(Au + Ag) (surface) and 98% Au/(Au + Ag) (cross-section). The metallurgical observation shows that the gold foils underwent heating and forging. The black lines on the front side are Chinese ink lines, with cracking and peeling phenomenon, and parallel polishing lines can be seen at the peeling places, while no polishing lines are observed on the back side. Some red paint made of cinnabar is above the black lines, and the binder is organic. Some lacquer residues are found on the back. According to the results of the study, the manufacturing process of the gold foils applied on the lacquerware is as follows: the gold foils are obtained by heating and forging, the front sides of the gold foils are polished, the shapes are carved out, and the gold foils are pasted on the lacquerware when the lacquerware’s surface is not yet dry. The gold foils are painted with black Chinese ink and red cinnabar pigment. The study’s results offer important references for understanding the manufacturing craft of the gold foils applied on the lacquerware in the late Western Han Dynasty of China and guide the conservation of the lacquerware decorated with painted gold foils.

Abstract The Fennec Fox algorithm (FFA) is a new meta-heuristic algorithm that is primarily inspired by the Fennec fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. The “No free lunch” theorem shows that an algorithm has different effects in the face of different problems, such as: when solving high-dimensional or more complex applications, there are challenges such as easily falling into local optimal and slow convergence speed. To solve this problem with FFA, in this paper, an improved Fenna fox algorithm DEMFFA is proposed by adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, and differential evolution mutation strategies. Firstly, a sin chaotic mapping strategy is added in the initialization stage to make the population distribution more uniform, thus speeding up the algorithm convergence speed. Secondly, in order to expedite the convergence speed of the algorithm, adjustments are made to the factors of the formula whose position is updated in the first stage, resulting in faster convergence. Finally, in order to prevent the algorithm from getting into the local optimal too early and expand the search space of the population, the Cauchy operator mutation strategy and differential evolution mutation strategy are added after the first and second stages of the original algorithm update. In order to verify the performance of the proposed DEMFFA, qualitative analysis is carried out on different test sets, and the proposed algorithm is tested with the original FFA, other classical algorithms, improved algorithms, and newly proposed algorithms on three different test sets. And we also carried out a qualitative analysis of the CEC2020. In addition, DEMFFA is applied to 10 practical engineering design problems and a complex 24-bar truss topology optimization problem, and the results show that the DEMFFA algorithm has the potential to solve complex problems.

Phenolation is one of the effective strategies to synthesize lignin-based polyphenols, improve lignin's properties, and extend its value-added applications in biological, medicinal and cosmetic fields. Herein, by taking the structural feature advantage of lignin, an effective and green strategy was developed to molecularly engineer lignin into a robust lignin-3-(2-hydroxyphenyl)propionate ester (LPPE) derivative via a transesterification reaction between 3,4-dihydrocoumarin (DHC) and the aliphatic hydroxyls in lignin under organocatalysis. The strategy is optimized and the novel derivative was systematically characterized by 1H, 13C and 31P nuclear magnetic resonance (NMR) and Fourier transform infrared (FT-IR) spectroscopy. The findings indicated that the successful introduction of 3-(2-hydroxyphenyl)propionate groups using a OH groups/DHC/organic base molar ratio of 1꞉1꞉0.3 at 120 °C for 6 h increased the content of phenolic hydroxyl groups from 1.793 1 to 3.017 9 mmol/g, and the LPPE exhibited excellent ultraviolet-absorbing and antioxidant performance with up to 90% free radical scavenging activity within 20 min using 5 mg/mL of LPPE. In addition, good biocompatibility and a high Sun protection factor (SPF) value of 40.9 were achieved at 5% (w) dosage of LPPE in the cream, indicating its significant application potential in sunscreen.

Abstract Mitochondria have multiple functions such as supplying energy, regulating the redox status, and producing proteins encoded by an independent genome. They are closely related to the physiology and pathology of many organs and tissues, among which the brain is particularly prominent. The brain demands 20% of the resting metabolic rate and holds highly active mitochondrial activities. Considerable research shows that mitochondria are closely related to brain function, while mitochondrial defects induce or exacerbate pathology in the brain. In this review, we provide comprehensive research advances of mitochondrial biology involved in brain functions, as well as the mitochondria-dependent cellular events in brain physiology and pathology. Furthermore, various perspectives are explored to better identify the mitochondrial roles in neurological diseases and the neurophenotypes of mitochondrial diseases. Finally, mitochondrial therapies are discussed. Mitochondrial-targeting therapeutics are showing great potentials in the treatment of brain diseases.

Abstract Efficiently treating cardiac patients before the onset of a heart attack relies on the precise prediction of heart disease. Identifying and detecting the risk factors for heart disease such as diabetes mellitus, Coronary Artery Disease (CAD), hyperlipidemia, hypertension, smoking, familial CAD history, obesity, and medications is critical for developing effective preventative and management measures. Although Electronic Health Records (EHRs) have emerged as valuable resources for identifying these risk factors, their unstructured format poses challenges for cardiologists in retrieving relevant information. This research proposed employing transfer learning techniques to automatically extract heart disease risk factors from EHRs. Leveraging transfer learning, a deep learning technique has demonstrated a significant performance in various clinical natural language processing (NLP) applications, particularly in heart disease risk prediction. This study explored the application of transformer-based language models, specifically utilizing pre-trained architectures like BERT (Bidirectional Encoder Representations from Transformers), RoBERTa, BioClinicalBERT, XLNet, and BioBERT for heart disease detection and extraction of related risk factors from clinical notes, using the i2b2 dataset. These transformer models are pre-trained on an extensive corpus of medical literature and clinical records to gain a deep understanding of contextualized language representations. Adapted models are then fine-tuned using annotated datasets specific to heart disease, such as the i2b2 dataset, enabling them to learn patterns and relationships within the domain. These models have demonstrated superior performance in extracting semantic information from EHRs, automating high-performance heart disease risk factor identification, and performing downstream NLP tasks within the clinical domain. This study proposed fine-tuned five widely used transformer-based models, namely BERT, RoBERTa, BioClinicalBERT, XLNet, and BioBERT, using the 2014 i2b2 clinical NLP challenge dataset. The fine-tuned models surpass conventional approaches in predicting the presence of heart disease risk factors with impressive accuracy. The RoBERTa model has achieved the highest performance, with micro F1-scores of 94.27%, while the BERT, BioClinicalBERT, XLNet, and BioBERT models have provided competitive performances with micro F1-scores of 93.73%, 94.03%, 93.97%, and 93.99%, respectively. Finally, a simple ensemble of the five transformer-based models has been proposed, which outperformed the most existing methods in heart disease risk fan, achieving a micro F1-Score of 94.26%. This study demonstrated the efficacy of transfer learning using transformer-based models in enhancing risk prediction and facilitating early intervention for heart disease prevention.

ObjectiveWhether the role of post-traumatic stress disorder (PTSD) on type 2 diabetes (T2D) is mediated by obesity or other mediating factors is controversial. This study was designed to assess the impact of PTSD on genetic susceptibility to T2D and mediating factors.MethodsThe datasets for PTSD, T2D, obesity, hypertension, hyperlipidemia, smoking status, and alcohol consumption were obtained from genome-wide association studies. Mendelian randomization (MR) was used to assess exposure-outcome causality, and inverse variance weighted was used as the primary tool for MR analysis. MR-Egger intercept, Cochran’s Q, and leave-one-out sensitivity analysis were employed to assess horizontal pleiotropy, heterogeneity, and robustness, respectively.ResultsThe MR analysis showed that PTSD was associated with increased genetic susceptibility to T2D (OR, 1.036; 95% CI, 1.008-1.064; p = 0.011), obesity (OR, 1.033; 95% CI, 1.016-1.050; p < 0.001), and hypertension (OR, 1.002; 95% CI, 1.000-1.003; p = 0.015), but not not with genetic susceptibility to hyperlipidemia, alcohol consumption, and smoking status (p ≥ 0.05). Mediated effect analysis showed that PTSD increased genetic susceptibility to T2D by increasing genetic susceptibility to obesity and hypertension, with obesity accounting for 9.51% and hypertension accounting for 2.09%. MR-Egger intercept showed no horizontal pleiotropy (p ≥ 0.05). Cochran’s Q showed no heterogeneity (p ≥ 0.05). Leave-one-out sensitivity analysis showed that the results were robust.ConclusionThis MR analysis suggests that PTSD increases the risk of T2D and that this effect is partially mediated by obesity and hypertension. Active prevention and treatment of PTSD can help reduce the risk of T2D.

Paraneoplastic thrombocytosis is common in ovarian cancer and often correlates with disease recurrence. This study aimed to assess the impact of probiotics on platelet counts in ovarian cancer patients. Enrolled patients with paraneoplastic thrombocytosis received oral Bifidobacterium longum JBLC-141 for platelet level modulation. Over a 12-month period, patients were closely monitored for recurrence rates. The study revealed significant differences between the oral probiotic and placebo groups in various parameters, including platelet counts, coagulation functions, inflammatory factors, tumor markers, and gut microbiota composition. However, no significant distinctions were observed during postoperative stabilization. Oral probiotics effectively mitigated platelet count surges and improved coagulation functions in ovarian cancer patients with paraneoplastic thrombocytosis. Positive effects on inflammation, tumor marker levels, and gut microbiota composition were also noted. Further clinical validation is needed to confirm probiotics’ potential in extending the postoperative stabilization period.

ObjectiveThe relationship between diabetes mellitus (DM) and autism spectrum disorder (ASD) remains controversial. This study aimed to analyze the causal relationship between different types of DM and ASD by bidirectional Mendelian randomization (MR).MethodsSingle nucleotide polymorphisms for type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), gestational diabetes mellitus (GDM), and ASD were obtained from genome-wide association studies. Subsequently, inverse variance weighted, MR-Egger, and weighted median were used to test the exposure-outcome causality. Finally, MR-Egger’s intercept, Cochran’s Q, and leave-one-out method were used to assess horizontal pleiotropy, heterogeneity, and sensitivity of the results, respectively.ResultsThe positive analysis showed that T2DM was associated with an increased risk of ASD, whereas neither T1DM nor GDM was associated with the risk of ASD. The reverse analysis showed that ASD was associated with an increased risk of T2DM, while it was not associated with the risk of either T1DM or GDM. MR-Egger intercept showed no horizontal pleiotropy (p > 0.05) for these results. Cochran’s Q showed no heterogeneity expect for the results of T1DM on the risk of ASD, and leave-one-out sensitivity analysis showed these results were robust.ConclusionThis MR analysis suggests that T2DM and ASD are reciprocal risk factors and that they may create an intergenerational risk cycling in female patients. Aggressive prevention and treatment of T2DM and ASD help to break the trap of this risk cycling. Additionally, this study does not support a causal relationship between T1DM and ASD, as well as GDM and ASD. And more studies are needed in the future to continue to explore the interactions and underlying mechanisms between different types of DM and ASD.

Objective: To evaluate the application value of combining spectral imaging and metal artifact reduction (MAR) in head and neck CTA after the embolization of intracranial aneurysms. Methods: We collected 37 patients who experienced embolization of intracranial aneurysms then received spectral imaging of head and neck CTA. Monochromatic images with energy ranging from 70～140 keV, 120 kVp-like mixed energic images, 70～140 keV MAR images, and 120 kVp-like MAR images were generated. The region of interest was placed on the area near the coil and with the most serious metal artifact. CT attenuation and standard deviation were measured, and artifact index (AI) and signal-noise ratio (SNR) were calculated. Two radiologists independently subjectively evaluated the metal artifact and the display of surrounding vessels using Likert 5 scales. The subjective scores and objective parameters between MAR and non-MAR images were compared. The Wilcoxon ranking test, paired sample t test, and independent sample t test were utilized to compare parameters between the groups. Results: MAR images had significantly lower AI than did non-MAR images for all eight monochromatic energies. When energies ranged from 80~110 keV, SNR was higher for MAR images than for non-MAR images, and the difference was statistically significant. With same energies, MAR images had higher artifact and vessel display scores than did non-MAR images. For non-MAR images, the different coil diameters did not make a statistical difference in AI and vessel display scores. For MAR images, a larger coil diameter (>8.79 mm) led to higher AI and lower vessel display scores than did normal diameters (≤8.79 mm). Conclusion: The combination of spectral imaging and MAR could effectively reduce the metal artifact of implants for the embolization of intracranial aneurysms and improve the surrounding vessel display. Moreover, the metal artifact reduction effect was more significant for the coils with smaller diameters.

This is an article in the field of earth sciences and mining processing engineering. Potash ore occurs in the salt layer of Cambrian Hormuz Formation, The ore body is in dancing band with great thickness variation. The ore type is chloride-type sylvite ore, the main ore mineral is sylvite, with KCl content of 2.5%～35.20%. The Cambrian is a lagoon facies deposit. The continuous evaporation of potassium-bearing brine in the Persian Gulf formed a series of marine evaporite sequence containing potassium salt.Affected by the early Alpine structureactivity, the salt layer was compressed to form a salt diapir structure.Using the test flow of one roughing, one scavenger, two concentrating and Coarse ore washing, good test indexes have been achieved, and potassium chloride products containing 87.44% KCl (wet basis) (91.14% dry basis) or 88.97% KCl (wet basis) (93.69% dry basis) can be obtained, with KCl recovery rates of 85.93% and 81.70% , respectively.

Peptides are a particular molecule class with inherent attributes of some small-molecule drugs and macromolecular biologics, thereby inspiring continuous searches for peptides with therapeutic and/or agrochemical potentials. However, the success rate is decreasing, presumably because many interesting but less-abundant peptides are so scarce or labile that they are likely ‘overlooked’ during the characterization effort. Here, we present the biochemical characterization and druggability improvement of an unprecedented minor fungal RiPP (ribosomally synthesized and post-translationally modified peptide), named acalitide, by taking the relevant advantages of metabolomics approach and disulfide-bridged substructure which is more frequently imprinted in the marketed peptide drug molecules. Acalitide is biosynthetically unique in the macrotricyclization via two disulfide bridges and a protease (AcaB)-catalyzed lactamization of AcaA, an unprecedented precursor peptide. Such a biosynthetic logic was successfully re-edited for its sample supply renewal to facilitate the identification of the in vitro and in vivo antiparkinsonian efficacy of acalitide which was further confirmed safe and rendered brain-targetable by the liposome encapsulation strategy. Taken together, the work updates the mining strategy and biosynthetic complexity of RiPPs to unravel an antiparkinsonian drug candidate valuable for combating Parkinson's disease that is globally prevailing in an alarming manner.

To solve the problem that the traditional dimensionality reduction methods cannot be directly applied to multivariate time series, and for the existing approaches, it is difficult to ensure the effectiveness of dimensionality reduction while significantly reducing the dimension, an unsupervised dimensionality reduction method of multivariate time series based on global and local scatter was proposed.Firstly, a feature series extraction method was proposed to extract the upper triangular elements of the co-variance matrix of each multivariate time series and combine them into a feature sequence.Then, based on the idea of “minimum local scatter and maximum global scatter,” an unsupervised dimensionality reduction model was presented, which preserved the global information as much as possible while maintaining the local nearest neighbor relationship.Using the feature sequence as the input, the sum of the neighborhood variances of all sample points was minimized, and the variance of all the neighborhood centroids were maximized.The projection matrix obtained by solving the proposed model could be used to perform the dimensionality reduction.Finally, the proposed method was evaluated with experiments on 20 public data sets.The results show that the proposed method can significantly reduce the dimension of multivariate time series, while ensuring the effectiveness of dimensionality reduction.

Soil metabolites are critical in regulating the dynamics of ecosystem structure and function, particularly in fragile karst ecosystems. Clarification of response of soil metabolism to vegetation succession in karst areas will contribute to the overall understanding and management of karst soils. Here, we investigated the metabolite characteristics of karst soils with different vegetation stages (grassland, brushwood, secondary forest and primary forest) based on untargeted metabolomics. We confirmed that the abundance and composition of soil metabolites altered with vegetation succession. Of the 403 metabolites we found, 157 had significantly varied expression levels across vegetation soils, including mainly lipids and lipid-like molecules, phenylpropanoids and polyketides, organic acids and derivatives. Certain soil metabolites, such as maltotetraose and bifurcose, were sensitive to vegetation succession, increasing significantly from grassland to brushwood and then decreasing dramatically in secondary and primary forests, making them possible indicators of karst vegetation succession. In addition, soil metabolic pathways, such as galactose metabolism and biosynthesis of unsaturated fatty acids, also changed with vegetation succession. This study characterized the soil metabolic profile in different vegetation stages during karst secondary succession, which would provide new insights for the management of karst soils.

ObjectiveThe effect of mental disorders (MD) on cardiovascular disease (CVD) remains controversial, and this study aims to analyze the causal relationship between eight MD and CVD by Mendelian randomization (MR).MethodsSingle nucleotide polymorphisms of attention-deficit/hyperactivity disorder (ADHD), anorexia nervosa (AN), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), depression, obsessive-compulsive disorder (OCD), schizophrenia (SCZ), and CVD were obtained from UK Biobank and FinnGen. Exposure-outcome causality was tested using inverse variance weighted (IVW), MR-Egger, and weighted median. Horizontal pleiotropy and heterogeneity were assessed by MR-Egger intercept and Cochran's Q, respectively, while stability of results was assessed by leave-one-out sensitivity analysis.ResultsMR analysis showed that ANX (IVW [odds ratio (OR) 1.11, 95% confidence intervals (CI) 1.07–1.15, p 0.05). Intercept analysis showed no horizontal pleiotropy (p > 0.05). Cochran's Q showed no heterogeneity except for BD (p = 0.035). Sensitivity analysis suggested that these results were robust.ConclusionsANX, ASD, and depression are associated with an increased risk of CVD, whereas AN, ADHD, BD, OCD, and SCZ are not causally associated with CVD. Active prevention and treatment of ANX, ASD, and depression may help reduce the risk of CVD.

ObjectiveThe role of different immune cells in autism spectrum disorders (ASD) is still controversial. The purpose of this study was to evaluate the causal effects of different immune cell phenotypes on ASD via Mendelian randomization (MR).MethodsDatasets of immune cell phenotypes were obtained from the European Bioinformatics Institute, and datasets of ASD were obtained from the IEU Open GWAS project. Single nucleotide polymorphisms were selected based on the assumptions of association, independence, and exclusivity. Inverse variance weighted was utilized as the main method for MR analysis. MR-Egger was employed to assess the horizontal pleiotropy of the results. Cochran’s Q and leave-one-out method were used for heterogeneity analysis and sensitivity analysis of the results, respectively.ResultsMR analysis showed that TD CD8br AC [odds ratio (OR), 1.137; 95% confidence interval (CI), 1.031–1.254; p = 0.010], CD8br %leukocyte (OR, 1.142; 95% CI, 1.067–1.223; p < 0.001), CD8br and CD8dim %leukocyte (OR, 1.117; 95% CI, 1.032–1.210; p = 0.006), naive CD8br %T cell (OR, 1.052; 95% CI, 1.004–1.104; p = 0.035), CD28− CD8dim %T cell (OR, 1.097; 95% CI, 1.038–1.158; p < 0.001), CD127− CD8br AC (OR, 1.086; 95% CI, 1.006–1.171; p = 0.034), CD45 on CD8br (OR, 1.059; 95% CI, 1.021–1.099; p = 0.002), CD3 on HLA DR+ CD8br (OR, 1.098; 95% CI, 1.041–1.158; p < 0.001), CD4 on activated Treg (OR, 1.048; 95% CI, 1.001–1.096; p = 0.046), CD3 on CD39+ resting Treg (OR, 1.070; 95% CI, 1.012–1.131; p = 0.018), IgD+ CD38− %lymphocyte (OR, 1.103; 95% CI, 1.023–1.190; p = 0.011), CD62L− plasmacytoid DC %DC (OR, 1.046; 95% CI, 1.001–1.093; p = 0.046), and FSC-A on plasmacytoid DC (OR, 1.075; 95% CI, 1.003–1.153; p = 0.042) were associated with increased genetic susceptibility to ASD. MR-Egger displayed no horizontal pleiotropy (p ≥ 0.05). Cochran’s Q revealed no heterogeneity of results (p ≥ 0.05). Sensitivity analysis indicated that the results were robust.ConclusionThis MR analysis revealed 13 immune cell phenotypes associated with increased genetic susceptibility to ASD and emphasized the importance of CD8 T cells and Tregs, which provides new directions for the pathogenesis and drug research of ASD.

ObjectiveThe impact of hepatitis B virus (HBV) on the risk of type 2 diabetes (T2D) remains a controversial topic. This study aims to analyze the causal relationship between HBV and T2D using Mendelian randomization (MR).MethodsSingle nucleotide polymorphisms on chronic hepatitis B (CHB), liver fibrosis, liver cirrhosis, and T2D were obtained from BioBank Japan Project, European Bioinformatics Institute, and FinnGen. Mendelian randomization was utilized to evaluate exposure-outcome causality. Inverse variance weighted was used as the primary method for MR analysis. To assess horizontal pleiotropy and heterogeneity, we conducted MR-Egger intercept analysis and Cochran’s Q test, and the robustness of the MR analysis results was evaluated through leave-one-out sensitivity analysis.ResultsMR analysis revealed that CHB was associated with a decreased genetic susceptibility to T2D (OR, 0.975; 95% CI, 0.962–0.989; p 0.05). Cochran’s Q showed no heterogeneity (p > 0.05). Leave-one-out sensitivity analysis showed that the results were robust.ConclusionCHB has the potential to act as a protective factor for T2D, but its effectiveness is constrained by viral load and disease stage. This protective effect diminishes or disappears as viral load decreases, and it transforms into a risk factor with the progression to liver fibrosis and cirrhosis.

Abstract Recognizing cashmere and wool fibers has been a challenging problem in the textile industry due to their similar morphological structure, chemical composition, and physicochemical properties. Traditional manual methods for identifying these fibers are inefficient, labor-intensive, and inaccurate. To address these issues, we present a novel method for recognizing cashmere and wool fibers using an improved version of ShuffleNetV2 and Transfer Learning, which we implement as a new cashmere and wool classification network (CWCNet).The approach leverages depthwise separable dilated convolution to extract more feature information for fiber classification. We also introduce a new activation function that enhances the nonlinear representation of the model and allows it to more fully extract negative feature information. Experimental results demonstrate that CWCNet achieves an accuracy rate of up to 98.438% on our self-built dataset, which is a 2.084% improvement over the original ShuffleNetV2 model. Furthermore, our proposed method outperforms classical models such as EfficientNetB0, MobileNetV2, Wide-ResNet50, and ShuffleNetV2 in terms of recognition accuracy while remaining lightweight.The method is capable of extracting more information on fiber characteristics and has the potential to replace manual labor as technological advancements continue to be made. This will greatly benefit engineering applications in the textile industry by providing more efficient and accurate fiber classification.

Inspired by classical experiments that uncovered the inherent properties of light waves, Young’s Double-Slit Experiment (YDSE) optimization algorithm represents a physics-driven meta-heuristic method. Its unique search mechanism and scalability have attracted much attention. However, when facing complex or high-dimensional problems, the YDSE optimizer, although striking a good balance between global and local searches, does not converge as fast as it should and is prone to fall into local optimums, thus limiting its application scope. A fractional-order boosted hybrid YDSE, called FYDSE, is proposed in this article. FYDSE employs a multi-strategy mechanism to jointly address the YDSE problems and enhance its ability to solve complex problems. First, a fractional-order strategy is introduced into the dark edge position update of FYDSE to ensure more efficient use of the search potential of a single neighborhood space while reducing the possibility of trapping in a local best. Second, piecewise chaotic mapping is constructed at the initial stage of the population to obtain better-distributed initial solutions and increase the convergence rate to the optimal position. Moreover, the low exploration space is extended by using a dynamic opposition strategy, which improves the probability of acquisition of a globally optimal solution. Finally, by introducing the vertical operator, FYDSE can better balance global exploration and local exploitation and explore new unknown areas. The numerical results show that FYDSE outperforms YDSE in 11 (91.6%) of cec2022 sets. In addition, FYDSE performs best in 8 (66.6%) among all algorithms. Compared with the 11 methods, FYDSE obtains the optimal best and average weights for the 20-bar, 24-bar, and 72-bar truss problems, which proves its efficient optimization capability for difficult optimization cases.

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency.

Comprehending the impacts of land-use type on soil nutrition and stoichiometry in watersheds is crucial for effective regional ecosystem management. However, a deeper understanding of the influence of land-use type on soil stoichiometry in karst agricultural watersheds is still lacking. Here, we analyzed the contents, stoichiometries, and drivers of topsoil C, N, and P in a karst agricultural watershed in China, focusing on six land-use types: paddy fields, dry farmland, tussock land, shrubland, shrubby tussock land, and woodland. We found that woodland exhibited significantly higher soil organic carbon (SOC) content than other land-use types except shrubland. Moreover, woodland exhibited the highest total nitrogen (TN) and total phosphorus (TP) contents compared with other land-use types. C/N and N/P ratios did not vary significantly with land-use type, whereas dry farmland (18.68) showed a significantly lower C/P ratio than woodland (39), shrubland (39.92), and paddy fields (34.87). In addition, our results revealed that soil pH, catalase and invertase activity, and bacterial and actinomycetes abundance significantly influenced C, N, and P content and stoichiometry. These findings reveal that interactions between multiple biotic and abiotic factors drive variability in soil stoichiometry, offering valuable insight for land improvement and ecological management in karst agricultural watersheds.

The recently introduced coati optimization algorithm suffers from drawbacks such as slow search velocity and weak optimization precision. An enhanced coati optimization algorithm called CMRLCCOA is proposed. Firstly, the Sine chaotic mapping function is used to initialize the CMRLCCOA as a way to obtain better-quality coati populations and increase the diversity of the population. Secondly, the generated candidate solutions are updated again using the convex lens imaging reverse learning strategy to expand the search range. Thirdly, the Lévy flight strategy increases the search step size, expands the search range, and avoids the phenomenon of convergence too early. Finally, utilizing the crossover strategy can effectively reduce the search blind spots, making the search particles constantly close to the global optimum solution. The four strategies work together to enhance the efficiency of COA and to boost the precision and steadiness. The performance of CMRLCCOA is evaluated on CEC2017 and CEC2019. The superiority of CMRLCCOA is comprehensively demonstrated by comparing the output of CMRLCCOA with the previously submitted algorithms. Besides the results of iterative convergence curves, boxplots and a nonparametric statistical analysis illustrate that the CMRLCCOA is competitive, significantly improves the convergence accuracy, and well avoids local optimal solutions. Finally, the performance and usefulness of CMRLCCOA are proven through three engineering application problems. A mathematical model of the hypersonic vehicle cruise trajectory optimization problem is developed. The result of CMRLCCOA is less than other comparative algorithms and the shortest path length for this problem is obtained.

The stability and deformation during trenching of ultra-deep diaphragm walls have a greater impact on the construction of diaphragm walls. The conventional limit equilibrium method, known as the vertical slices method, assumes homogeneity within the rock and soil mass, making it challenging to account for their stratification. Based on the limit equilibrium method, the horizontal strip method takes into account the stratification of the foundation soil. Based on the actual project, considering the different mud unit weights and heights, the horizontal strip method was used to analyze the stability of the groove trench and analyze the deformation law of the groove trench by the finite element method. The results indicate the following: The horizontal strip method can effectively assess the influence law of mud weight and height on the safety coefficient of groove trench stabilization. The higher the unit weight and level of slurry, the greater the wall safety coefficient. Moreover, the mud unit weight obtained by the horizontal strip method is about 12.70~12.64 kN/m3, which is close to the actual maximum mud weight of 12.5 kN/m3. The estimated mud unit weight aligns more closely with actual conditions. Additionally, through finite element analysis, the deformation law of the groove trench of ultra-deep diaphragm walls has been analyzed and summarized. The soil around the groove trench does not move inward, which shows three different deformation modes with different depths.

Abstract Astrocytes contribute to chronic neuroinflammation in a variety of neurodegenerative diseases, including Parkinson's disease (PD), the most common movement disorder. However, the precise role of astrocytes in neuroinflammation remains incompletely understood. Herein, we show that regulator of G-protein signaling 5 (RGS5) promotes neurodegenerative process through augmenting astrocytic tumor necrosis factor receptor (TNFR) signaling. We found that selective ablation of Rgs5 in astrocytes caused an inhibition in the production of cytokines resulting in mitigated neuroinflammatory response and neuronal survival in animal models of PD, whereas overexpression of Rgs5 had the opposite effects. Mechanistically, RGS5 switched astrocytes from neuroprotective to pro-inflammatory property via binding to the receptor TNFR2. RGS5 also augmented TNFR signaling-mediated pro-inflammatory response by interacting with the receptor TNFR1. Moreover, interrupting RGS5/TNFR interaction by either RGS5 aa 1–108 or small molecular compounds feshurin and butein, suppressed astrocytic cytokine production. We showed that the transcription of astrocytic RGS5 was controlled by transcription factor early B cell factor 1 whose expression was reciprocally influenced by RGS5-modulated TNF signaling. Thus, our study indicates that beyond its traditional role in G-protein coupled receptor signaling, astrocytic RGS5 is a key modulator of TNF signaling circuit with resultant activation of astrocytes thereby contributing to chronic neuroinflammation. Blockade of the astrocytic RGS5/TNFR interaction is a potential therapeutic strategy for neuroinflammation-associated neurodegenerative diseases.

The gas sensitivity of platinum doped TiO2 (101) to two kinds on polyvinyl chloride decomposition production, CO and HCl, were explored based on density functional theory. The binding energy, adsorption distance, charge transfer, and density of states distribution were simulated and discussed. The results suggest that the O oriented mode is probably the best mode for the CO gas molecules adsorption on the Pt-TiO2 and H oriented mode for the HCl adsorption system. HCl and CO molecules obtain electrons from Pt doped TiO2 surface. At room temperature, the recovery time of CO and HCl is 1.9743 × 10-10, 1.2094 × 10-10 respectively. HCl is easier to dissociate from Pt doped TiO2 surface with a little lower energy barrier compared to CO. This work provides theoretical adsorption information of Pt-TiO2 as gas sensor material for HCl and CO detection in the application of online condition monitoring and defect diagnosis in the power cables.

Abstract Phase transition and floral induction in citrus requires several years of juvenility after germination. Such a long period of juvenility has been a major hindrance to its genetic improvement program. Studies have shown that miR156 along with its downstream genes SQUAMOSA PROMOTER BINDING PROTEINS (SBP) and SBP-LIKE (SPL) mediate the phase transition and floral induction process in plants. Our current study has systematically analyzed SPLs in 15 different citrus-related species, systematically annotated them based on their close homology to their respective Arabidopsis orthologs, and confirmed the functional attributes of the selected members in floral precocity. The majority of the species harbored 15 SPLs. Their cis-element assessment suggested the involvement of the SPLs in diverse developmental and physiological processes in response to different biotic and abiotic cues. Among all, SPL5, SPL9, and SPL11 stood out as consistently differentially expressed SPLs in the adult and young tissues of different citrus-related species. Independent overexpression of their F. hindsii orthologs (FhSPL5, FhSPL9, and FhSPL11) brought an enhanced expression of endogenous FLOWERING LOCUS T leading to the significantly precocious flowering in transgenic Arabidopsis lines. Future study of the genes in the citrus plant itself is expected to conclude the assessments made in the current study. Graphical Abstract

Abstract Background Alterations in DNA methylation (DNAm) have been reported to be a mechanism by which bariatric surgeries resulted in considerable metabolic improvements. Previous studies have mostly focused on change in DNAm following weight-loss interventions, yet whether DNAm prior to intervention can explain the variability in glycemic outcomes has not been investigated. Here, we aim to examine whether baseline DNAm is differentially associated with glycemic outcomes induced by different types of weight-loss interventions. Methods Participants were 75 adults with severe obesity who underwent non-surgical intensive medical intervention (IMI), adjustable gastric band (BAND) or Roux-en-Y gastric bypass (RYGB) (n = 25 each). Changes in fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c) were measured at 1-year after intervention. DNAm was quantified by Illumina 450 K arrays in baseline peripheral blood DNA. Epigenome-wide association studies were performed to identify CpG probes that modify the effects of different weight-loss interventions on glycemic outcomes, i.e., changes in FPG and HbA1c, by including an interaction term between types of intervention and DNAm. Models were adjusted for weight loss and baseline clinical factors. Results Baseline DNAm levels at 3216 and 117 CpGs were differentially associated with changes in FPG and HbA1c, respectively, when comparing RYGB versus IMI. Of these, 79 CpGs were significant for both FPG and HbA1c. The identified genes are enriched in adaptive thermogenesis, temperature homeostasis and regulation of cell population proliferation. Additionally, DNAm at 6 CpGs was differentially associated with changes in HbA1c when comparing RYGB versus BAND. Conclusions Baseline DNAm is differentially associated with glycemic outcomes in response to different types of weight-loss interventions, independent of weight loss and other clinical factors. Such findings provided initial evidence that baseline DNAm levels may serve as potential biomarkers predictive of differential glycemic outcomes in response to different types of weight-loss interventions.

Objectives To explore associations between adverse birth outcomes and childhood overweight at 3–8 years of age.Design A prospective cohort study.Setting Six central urban districts of Tianjin, China.Participants 1681 woman–child pairs.Methods 1681 woman–child pairs were followed up for 8 years in Tianjin, China. Demographic and clinical information including birth outcomes was collected longitudinally, commencing from first antenatal care visit till postpartum period. Offspring height and weight were measured at 3–8 years of age. High and low weight/length ratios (WLR) at birth were, respectively, defined as ≥90th and ≤10th gestational week and sex-specific percentiles. Overweight for children at 3–5 and 6–8 years of age were, respectively, defined as body mass index (BMI)-for-age and -sex above the 2 z-score and 1 z-score curves of the WHO’s child growth standards. Binary logistic regression analysis was used to obtain ORs and 95% CI with a stepwise backward selection method to select independent predictors.Primary outcomes measures Childhood overweight.Results Of 1681 children, 10.7% (n=179) and 27.8% (n=468) developed overweight at 3–5 and 6–8 years of age, respectively. Large for gestational age (LGA) was associated with increased risk of overweight at 3–5 years of age (aOR: 1.86, 95% CI: 1.27 to 2.72) while high WLR at birth was associated with increased risk of overweight at 6–8 years of age (1.82, 1.41 to 2.34). Low WLR at birth was associated with decreased risk of overweight at 6–8 years of age (0.52, 0.30 to 0.90).Conclusions LGA and high WLR at birth predicted childhood overweight at 3–5 and 6–8 years of age, respectively. Low WLR at birth was associated with decreased risk of childhood overweight at 6–8 years of age.

Abstract Locus coeruleus (LC) dysfunction is involved in the pathophysiology of depression; however, the neural circuits and specific molecular mechanisms responsible for this dysfunction remain unclear. Here, it is shown that activation of tyrosine hydroxylase (TH) neurons in the LC alleviates depression‐like behaviors in susceptible mice. The dorsolateral septum (dLS) is the most physiologically relevant output from the LC under stress. Stimulation of the LCTH‐dLSSST innervation with optogenetic and chemogenetic tools bidirectionally can regulate depression‐like behaviors in both male and female mice. Mechanistically, it is found that brain‐derived neurotrophic factor (BDNF), but not norepinephrine, is required for the circuit to produce antidepressant‐like effects. Genetic overexpression of BDNF in the circuit or supplementation with BDNF protein in the dLS is sufficient to produce antidepressant‐like effects. Furthermore, viral knockdown of BDNF in this circuit abolishes the antidepressant‐like effect of ketamine, but not fluoxetine. Collectively, these findings underscore the notable antidepressant‐like role of the LCTH‐dLSSST pathway in depression via BDNF‐TrkB signaling.

ObjectiveThe specific benefit and selection of acupoints in acupuncture for diabetic kidney disease (DKD) remains controversial. This study aims to explore the specific benefits and acupoints selection of acupuncture for DKD through meta-analysis and data mining.MethodsClinical trials of acupuncture for DKD were searched in eight common databases. Meta-analysis was used to evaluate its efficacy and safety, and data mining was used to explore its acupoints selection.ResultsMeta-analysis displayed that compared with the conventional drug group, the combined acupuncture group significantly increased the clinical effective rate (risk ratio [RR] 1.35, 95% confidence interval [CI] 1.20 to 1.51, P < 0.00001) and high-density lipoprotein cholesterol (mean difference [MD] 0.36, 95% CI 0.27 to 0.46, P < 0.00001), significantly reduced the urinary albumin (MD –0.39, 95% CI –0.42 to –0.36, P < 0.00001), urinary microalbumin (MD –32.63, 95% CI –42.47 to –22.79, P < 0.00001), urine β2-microglobulin (MD –0.45, 95% CI –0.66 to –0.24, P < 0.0001), serum creatinine (MD –15.36, 95% CI –21.69 to –9.03, P < 0.00001), glycated hemoglobin A1c (MD –0.69, 95% CI –1.18 to –0.19, P = 0.006), fasting blood glucose (MD –0.86, 95% CI –0.90 to –0.82, P < 0.00001), 2h postprandial plasma glucose (MD –0.87, 95% CI –0.92 to –0.82, P < 0.00001), total cholesterol (MD –1.23, 95% CI –2.05 to –0.40, P = 0.003), triglyceride (MD –0.69, 95% CI –1.23 to –0.15, P = 0.01), while adverse events were comparable. Data mining revealed that CV12, SP8, SP10, ST36, SP6, BL20, BL23, and SP9 were the core acupoints for DKD treated by acupuncture.ConclusionAcupuncture improved clinical symptoms, renal function indices such as uALB, umALB, uβ2-MG, and SCR, as well as blood glucose and blood lipid in patients with DKD, and has a favorable safety profile. CV12, SP8, SP10, ST36, SP6, BL20, BL23, and SP9 are the core acupoints for acupuncture in DKD, and this program is expected to become a supplementary treatment for DKD.

Summary: Bradyrhizobium is a genus of nitrogen-fixing bacteria, with some species producing nodules in leguminous plants. Investigations into Bradyrhizobium have recently revealed its substantial genetic resources and agricultural benefits, but a comprehensive survey of its genetic diversity and functional properties is lacking. Using a panel of various strains (N = 278), this study performed a comparative genomics analysis to anticipate genes linked with symbiotic nitrogen fixation. Bradyrhizobium’s pan-genome consisted of 84,078 gene families, containing 824 core genes and 42,409 accessory genes. Core genes were mainly involved in crucial cell processes, while accessory genes served diverse functions, including nitrogen fixation and nodulation. Three distinct genetic profiles were identified based on the presence/absence of gene clusters related to nodulation, nitrogen fixation, and secretion systems. Most Bradyrhizobium strains from soil and non-leguminous plants lacked major nif/nod genes and were evolutionarily more closely related. These findings shed light on Bradyrhizobium’s genetic features for symbiotic nitrogen fixation.

ObjectiveThe effect of tea on gout and uric acid is still controversial. This study aims to analyze the effect of tea intake on genetic predisposition to gout, idiopathic gout, gout due to impairment of renal function as well as uric acid by Mendelian randomization (MR).MethodsForty independent single nucleotide polymorphisms (SNPs) associated with tea intake were selected from UK Biobank. SNPs for uric acid were obtained from BioBank Japan, SNPs for gout were obtained from UK Biobank, and SNPs for gout due to impairment of renal function and idiopathic gout were derived from FinnGen. The causal relationship of exposure-outcome was tested using inverse variance weighted, MR-Egger and weighted median. MR-Egger intercept was employed to assess horizontal pleiotropy, Cochran’s Q test was used to assess heterogeneity, and leave-one-out sensitivity analysis was utilized to analyze the stability of the results.ResultsThe results of MR analysis showed that tea intake was negatively associated with gout due to impairment of renal function (OR 0.997, 95% CI 0.994 to 0.999, P = 0.017), whereas there was no causal association with gout, idiopathic gout, and uric acid (P > 0.05), for which sensitivity analysis suggested that these results were robust.ConclusionsThere was a genetic predisposition effect of increased tea intake on the reduced risk of gout due to impairment of renal function, whereas there was no such effect on gout, idiopathic gout, and uric acid. Tea intake may become an important option in the dietary treatment of gout due to impairment of renal function.

Peripheral bacterial infections without impaired blood–brain barrier integrity have been attributed to the pathogenesis of Parkinson's disease (PD). Peripheral infection promotes innate immune training in microglia and exacerbates neuroinflammation. However, how changes in the peripheral environment mediate microglial training and exacerbation of infection-related PD is unknown. In this study, we demonstrate that GSDMD activation was enhanced in the spleen but not in the CNS of mice primed with low-dose LPS. GSDMD in peripheral myeloid cells promoted microglial immune training, thus exacerbating neuroinflammation and neurodegeneration during PD in an IL-1R-dependent manner. Furthermore, pharmacological inhibition of GSDMD alleviated the symptoms of PD in experimental PD models. Collectively, these findings demonstrate that GSDMD-induced pyroptosis in myeloid cells initiates neuroinflammation by regulating microglial training during infection-related PD. Based on these findings, GSDMD may serve as a therapeutic target for patients with PD.

Abstract As biological macromolecules, proteins are involved in important cellular functions ranging from DNA replication and biosynthesis to metabolic signalling and environmental sensing. Protein sequencing can help understand the relationship between protein function and structure, and provide key information for disease diagnosis and new drug design. Nanopore sensors are a novel technology to achieve the goal of label‐free and high‐throughput protein sequencing. In recent years, nanopore‐based biosensors have been widely used in the detection and analysis of biomolecules such as DNA, RNA, and proteins. At the same time, computer simulations can describe the transport of proteins through nanopores at the atomic level. This paper reviews the applications of nanopore sensors in protein sequencing over the past decade and the solutions to key problems from a computer simulation perspective, with the aim of pointing the way to the future of nanopore protein sequencing.

Abstract Background The relationship between glucose fluctuation and the risk of cardiovascular disease (CVD) in patients with diabetes remains elusive. Glycated hemoglobin (HbA1c) variability is a key parameter of glucose fluctuation. Methods PubMed, Cochrane Library, Web of Science, and Embase were searched up to 1 July 2022. Studies reporting associations of HbA1c variability (HbA1c‐SD), coefficient of variation of HbA1c (HbA1c‐CV), and HbA1c variability score [HVS] with the risk of CVD among patients with diabetes were included. We used three different insights (a high‐low value meta‐analysis, a study‐specific meta‐analysis, and a non‐linear dose–response meta‐analysis) to explore the relationship between HbA1c variability and CVD risk. A subgroup analysis was also performed to screen the potential confounding factors. Results A total of 14 studies with 254 017 patients with diabetes were eligible. The highest HbA1c variability was significantly associated with increased risks of CVD (HbA1c‐SD, risk ratio [RR] 1.45; HbA1c‐CV, RR 1.74; HVS, RR 2.46; all p 1 (all p

Embedded systems in production equipment and Internet of Things (IoT) sensors on production lines are one of the elements that constitute an industrial cyber-physical system. In this paper, an in-depth study and analysis of the optimization of blasting parameters and prediction of vibration effects in open pit mines using deep neural network arithmetic are present. Based on the deep neural network research and analysis of the relationship between blasting parameters and rock fragmentation, a prediction model for blasting parameters and fragmentation for the East Open Pit Mine was established, and sensitivity analysis was performed on blasting parameters, and the unit consumption of explosives and the perforation rate were established. It was found that the average relative errors of both numerical simulation results and depth prediction results were no more than 10%, while the average relative errors of Sadowski's formula prediction results were more than 20%. The results show that the neural network optimized by a genetic algorithm and the numerical simulation has the highest accuracy in predicting the blasting result parameters. The research model and results obtained in this paper can be used as a reference guide for engineering practice.

Background: Long noncoding RNA (lncRNA) is widely acknowledged for its crucial role in the biological processes of various human cancers. MCF2L antisense RNA 1 (MCF2L-AS1) is a newly identified lncRNA, which remains unexplored in the context of cancer. Methods: MCF2L-AS1 expression was examined using qRT-PCR analysis. The impact of MCF2L-AS1 on LUAD cell growth was assessed through CCK-8, colony formation, EdU, caspase-3 activity, TUNEL, Western blot, and transwell assays. The interaction between miR-874-3p and MCF2L-AS1 or STAT3 was confirmed by RIP, luciferase reporter, and RNA pull-down assays. Results: Our study demonstrated the overexpression of MCF2L-AS1 in LUAD cells. Functionally, the silencing of MCF2L-AS1 hindered cell proliferation, migration, and invasion, while promoting cell apoptosis. Notably, the depletion of MCF2L-AS1 was associated with decreased cisplatin resistance. Mechanistically, MCF2L-AS1 was identified as an upstream gene of miR-874-3p, negatively regulating its expression. Following this, STAT3, the downstream target of miR-874-3p, was identified. Additionally, the expression of STAT3 was inversely related to miR-874-3p and positively regulated by MCF2L-AS1. A restoration assay suggested that MCF2L-AS1 promoted LUAD cell growth by sponging miR-874-3p and modulating STAT3 expression. Intriguingly, STAT3 was subsequently confirmed as a transcription factor that binds to the MCF2L-AS1 promoter, thereby enhancing its transcription. Conclusions: The MCF2L-AS1/miR-874-3p/STAT3 feedback loop plays a significant role in LUAD cell growth and cisplatin resistance.