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ObjectiveThe risk factors of PJK (proximal junctional kyphosis) related to AIS (adolescent idiopathic scoliosis) are inconsistent due to heterogeneity in study design, diagnostic criteria, and population. Therefore, the meta-analysis was conducted to investigate the factors affecting PJK after posterior spinal fusion for AIS patients.MethodsWe implemented a systematic search to obtain potential literature relevant to PJK in AIS surgery. Then, a meta-analysis was performed to assess the incidence of PJK and its risk factors.ResultsWe retrieved 542 articles, and 24 articles were included. The PJK incidence was 17.67%. The use of hooks at UIV (upper instrumented vertebrae) (p = 0.001) could prevent PJK. Before surgery, the larger TK (thoracic kyphosis) (p

A newly developed microseismic (MS) monitoring system was employed in the Tianshan-Shengli tunnel to detect MS activities and then predict and provide early warning of rockburst disasters. The system not only has the advantages of accuracy of artificial analysis but also real-time analysis and warnings. The positioning accuracy for MS events is approximately 5–10 m. A new sensor installation scheme was proposed to achieve fast sensor installation and recovery, taking advantage of semicircular steel tubes and hose clamps. In addition, the rockburst risk level prediction criteria adopted multiple evaluation indexes such as MS event energy and moment magnitude and number, and it revealed that the evolution of maximum energy has a good positive correlation with that of maximum moment magnitude through analyzing the monitored MS events. It also showed that the rockburst generally occurred 2 days after the rock mass was exposed by the tunnel boring machine (TBM) tail shield and belonged to the delayed rockburst category, according to the field statistical results. The preliminary application cases indicated that the rockburst prediction and early warning based on MS monitoring agree with the site survey results. Therefore, the new MS monitoring system is a reliable tool for predicting and providing early warnings of rockburst disasters.

BackgroundPostpartum depression (PPD) is the most common mental illness affecting women after childbirth, and working mothers may be faced with unique challenges. This study aimed to examine the depression status among working postpartum women in 1 year of childbirth and explore the relationship between occupational factors and PPD in urban Beijing, China.MethodsA cross-sectional survey of 554 postpartum women was conducted among ten community health service centers in six urban districts of Beijing, China. Sociodemographic, occupational, childbirth and postpartum information were collected. Depression status was obtained using the Edinburgh Postnatal Depression Scale. Prevalence of postpartum depression (PPD) was assessed in relation to occupational characteristics, and influencing factors were analyzed through logistic regression.ResultsOf the postpartum women, 29.42% met the criteria for depression. PPD prevalence was significantly higher among women employed in commercial enterprises (39.81%). The analysis of influencing factors showed that age, family or personal monthly income, maternity leave, feeding methods, and postpartum care affected the psychological health of occupational women after childbirth.ConclusionPPD prevalence is notably elevated among women employed in commercial enterprises, and specific risk factors contribute to its occurrence. These findings highlight the need for targeted interventions to address these risk factors and prevent PPD in this population.

DNA methylation indicates the individual’s aging, so-called Epigenetic clocks, which will improve the research and diagnosis of aging diseases by investigating the correlation between methylation loci and human aging. Although this discovery has inspired many researchers to develop traditional computational methods to quantify the correlation and predict the chronological age, the performance bottleneck delayed access to the practical application. Since artificial intelligence technology brought great opportunities in research, we proposed a perceptron model integrating a channel attention mechanism named PerSEClock. The model was trained on 24,516 CpG loci that can utilize the samples from all types of methylation identification platforms and tested on 15 independent datasets against seven methylation-based age prediction methods. PerSEClock demonstrated the ability to assign varying weights to different CpG loci. This feature allows the model to enhance the weight of age-related loci while reducing the weight of irrelevant loci. The method is free to use for academics at www.dnamclock.com/#/original.

Aquaculture systems, replete with equipment noise originating from aerators, pumps, feeders, and filtration systems, are known to exert substantial influence on fish behavior and growth. In this research, the focus was directed towards comprehending the impacts of aerator noise on the swimming, feeding, and growth progression of largemouth bass. In the course of a 50-day experimental period, the bass population was segmented into two groups: aerator noise (90.3dB re μPa RMS-1) and ambient noise (70.4dB re μPa RMS-1). The findings indicated discernible disparities in the swimming behavior and feeding between the two groups. Specifically, bass in the noise group maintained greater average angular distance and physical separation from their nearest counterparts than the bass in the ambient group, which were 43.61 ± 1.89° and 85.47 ± 1.72mm for the ambient group and 48.32 ± 0.49° and 97.01 ± 0.57mm for the noise group. Furthermore, the feeding kinetic energy was markedly lower in the noise group as compared to the ambient group. For the first time, the Shannon-Wiener diversity index was leveraged to gauge the diversity of fish swimming behavior, with the results signifying the diversity index of the noise group was 2.69 ± 0.07 higher than that of the ambient group, which was 2.51 ± 0.02. Lastly, the noise group demonstrated compromised growth performance, with a significantly lower average weight as opposed to the ambient group, along with marked variations in the specific growth rate. These findings offer a telling revelation about the profound impacts of aerator noise on the behavioral and growth of largemouth bass, thereby forming a valuable referential base for future research centered on the effects of noise pollution on aquatic organisms.

BackgroundSepsis stands as a dire medical condition, arising when the body’s immune response to infection spirals into overdrive, paving the way for potential organ damage and potential mortality. With intestinal flora’s known impact on sepsis but a dearth of comprehensive data, our study embarked on a two-sample Mendelian randomization analysis to probe the causal link between gut microbiota and their metabolites with severe sepsis patients who succumbed within a 28-day span.MethodsLeveraging data from Genome-wide association study (GWAS) and combining it with data from 2,076 European descendants in the Framingham Heart Study, single-nucleotide polymorphisms (SNPs) were employed as Instrumental Variables (IVs) to discern gene loci affiliated with metabolites. GWAS summary statistics for sepsis were extracted from the UK Biobank consortium.ResultsIn this extensive exploration, 93 distinct genome-wide significant SNPs correlated with gut microbial metabolites and specific bacterial traits were identified for IVs construction. Notably, a substantial link between Coprococcus2 and both the incidence (OR of 0.80, 95% CI: 0.68-0.94, P=0.007) and the 28-day mortality rate (OR 0.48, 95% CI: 0.27-0.85, P=0.013) of sepsis was observed. The metabolite α-hydroxybutyrate displayed a marked association with sepsis onset (OR=1.08, 95% CI: 1.02-1.15, P=0.006) and its 28-day mortality rate (OR=1.17, 95% CI: 1.01-1.36, P=0.029).ConclusionThis research unveils the intricate interplay between the gut microbial consortium, especially the genus Coprococcus, and the metabolite α-hydroxybutyrate in the milieu of sepsis. The findings illuminate the pivotal role of intestinal microbiota and their metabolites in sepsis’ pathogenesis, offering fresh insights for future research and hinting at novel strategies for sepsis’ diagnosis, therapeutic interventions, and prognostic assessments.

IntroductionAflatoxins (AFT) identified as a Group 1 human carcinogen naturally contaminate various types of food and could increase the risk of hepatocellular carcinoma (HCC) through dietary intake. Chongqing municipality is located in Southwest China with subtropical monsoon climate which is conducive to AFT contamination in crops. However, the burden of HCC caused by the dietary exposure of the population in Chongqing to AFT has not been quantified.MethodsThe burden of HCC was estimated in terms of Disability Adjusted Life Year (DALY) using FDA-iRISK software. Dietary exposure to AFT in three food categories including grain and its products, nuts and seeds, and spices was assessed.ResultsThe lifetime average daily dose (LADD) of AFT exposure for the population ranged from 2.40 to 8.25 ng/kg bw/day and 9.51 to 15.10 ng/kg bw/day at the mean and heavy (P95) AFT contamination levels, respectively. Among the three food categories, grain and its products contributed most to AFT exposure of the population. The estimated DALYs related to HCC induced by AFT were 162,000–556,000 and 641,000-1,020,000; the DALY rates were 6.47–22.20 and 25.59–40.72 per 100,000 persons per year; and the population attribution fractions (PAF) were 1.68–5.78% and 6.66–10.60%.DiscussionAlthough the burden of HCC caused by dietary AFT was estimated to be relatively low among the population, the overall health burden might be underestimated owing to the uncertainties of this dataset. Thus, the overall health burden associated with AFT intake should still be of concern in further studies.

Silkworm pupa (bombyx mori) protein (SPP) is a potential therapeutic bioactive substance that has anti-tumor activity against breast, liver, and gastric cancers. The aim of this study was to investigate the antitumor effect of SPP on colon cancer nude mice. Using a subcutaneous tumor formation method, we validated the therapeutic effect of SPP on colon cancer nude mice in vivo. Results showed that SPP was cytotoxic to tumor cells. SPP could protect the liver of the nude mice by lowering hepatic oxidative stress and regulating serum inflammation levels by decreasing TNF-α and IL-2 levels while in-creasing INF-γ levels. In addition, diminished Ki-67 protein, enhanced cleaved caspase-3 protein, di-minished Vimentin, enhanced E-cadherin. These findings suggested that SPP’s antitumor activity may be achieved by reducing inflammation, inhibiting tumor proliferation and metastasis, and inducing apoptosis in cancer cells. In the future, SPP could be used as an anticancer drug, potentially providing a new source of drugs for the treatment of colon cancer.

BackgroundAccumulating evidence has suggested that gut microbiota dysbiosis is commonly observed in asthmatics. However, it remains unclear whether dysbiosis is a cause or consequence of asthma. We aimed to examine the genetic causal relationships of gut microbiota with asthma and its three phenotypes, including adult-onset asthma, childhood-onset asthma, and moderate-severe asthma.MethodsTo elucidate the causality of gut microbiota with asthma, we applied two sample Mendelian randomization (MR) based on the largest publicly available genome-wide association study (GWAS) summary statistics. Inverse variance weighting meta-analysis (IVW) was used to obtain the main estimates; and Weighted median, MR-Egger, Robust Adjusted Profile Score (MR-RAPS), Maximum likelihood method (ML), and MR pleiotropy residual sum and outlier (MR-PRESSO) methods were applied in sensitivity analyses. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation.ResultsIn the absence of heterogeneity and horizontal pleiotropy, the IVW method revealed that genetically predicted Barnesiella and RuminococcaceaeUCG014 were positively correlated with the risk of asthma, while the association between genetically predicted CandidatusSoleaferrea and asthma was negative. And for the three phenotypes of asthma, genetically predicted Akkermansia reduced the risk of adult-onset asthma, Collinsella and RuminococcaceaeUCG014 increased the risk of childhood-onset asthma, and FamilyXIIIAD3011group, Eisenbergiella, and Ruminiclostridium6 were correlated with the risk of moderate-severe asthma (all P

Diagnosing the drowning site is a major challenge in forensic practice, particularly when corpses are recovered from flowing rivers. Recently, forensic experts have focused on aquatic microorganisms, including bacteria, which can enter the bloodstream during drowning and may proliferate in corpses. The emergence of 16S ribosomal RNA gene (16S rDNA) amplicon sequencing has provided a new method for analyzing bacterial composition and has facilitated the development of forensic microbiology. We propose that 16S rDNA amplicon sequencing could be a useful tool for inferring drowning sites. Our study found significant differences in bacterial composition in different regions of the Guangzhou section of the Pearl River, which led to differences in bacteria of drowned rabbit lungs at different drowning sites. Using the genus level of bacteria in the lung tissue of drowned rabbits, we constructed a random forest model that accurately predicted the drowning site in a test set with 100% accuracy. Furthermore, we discovered that bacterial species endemic to the water were not always present in the corresponding drowned lung tissue. Our findings demonstrate the potential of a random forest model based on bacterial genus and composition in drowned lung tissues for inferring drowning sites.

Kawasaki disease (KD), an acute febrile systemic vasculitis in children, has become the leading cause of acquired heart disease in developed countries. Recently, the altered gut microbiota was found in KD patients during the acute phase. However, little is known about its characteristics and role in the pathogenesis of KD. In our study, an altered gut microbiota composition featured by the reduction in SCFAs-producing bacteria was demonstrated in the KD mouse model. Next, probiotic Clostridium butyricum (C. butyricum) and antibiotic cocktails were respectively employed to modulate gut microbiota. The use of C. butyricum significantly increased the abundance of SCFAs-producing bacteria and attenuated the coronary lesions with reduced inflammatory markers IL-1β and IL-6, but antibiotics depleting gut bacteria oppositely deteriorated the inflammation response. The gut leakage induced by dysbiosis to deteriorate the host’s inflammation was confirmed by the decreased intestinal barrier proteins Claudin-1, Jam-1, Occludin, and ZO-1, and increased plasma D-lactate level in KD mice. Mechanistically, SCFAs, the major beneficial metabolites of gut microbes to maintain the intestinal barrier integrity and inhibit inflammation, was also found decreased, especially butyrate, acetate and propionate, in KD mice by gas chromatography-mass spectrometry (GC-MS). Moreover, the reduced expression of SCFAs transporters, monocarboxylate transporter 1 (MCT-1) and sodium-dependent monocarboxylate transporter 1 (SMCT-1), was also shown in KD mice by western blot and RT-qPCR analyses. As expected, the decrease of fecal SCFAs production and barrier dysfunction were improved by oral C. butyricum treatment but was deteriorated by antibiotics. In vitro, butyrate, not acetate or propionate, increased the expression of phosphatase MKP-1 to dephosphorylate activated JNK, ERK1/2 and p38 MAPK against excessive inflammation in RAW264.7 macrophages. It suggests a new insight into probiotics and their metabolites supplements to treat KD.

BackgroundOsteoprotegerin (OPG) is a secretory glycoprotein and participates in the progression of atherosclerotic lesions. We aim to explore the relationship between OPG and the prognosis of coronary artery disease (CAD).MethodsPlasma OPG concentrations were measured in 3,766 patients with stable CAD enrolled in the PEACE trial. The PEACE trial (NCT00000558) group followed up the patients and examined their future clinical outcomes.ResultsIn summary, 208 (5.5%) primary outcomes occurred, 295 patients (7.8%) died from all-cause death, 128 (3.4%) died from cardiovascular causes, and 94 (2.5%) experienced heart failure during a median follow-up of 1,892 days. In addition, we found that higher plasma levels of OPG were associated with a higher incidence of all-cause death, cardiovascular death, and heart failure, even after adjusting clinical cofounders.ConclusionIt was demonstrated that elevated plasma OPG levels were associated with an increased incidence of all-cause death, cardiovascular death, and heart failure in patients with stable CAD.Systematic Review Registrationhttps://clinicaltrials.gov/ct2/show/NCT00000558?term=NCT00000558&draw=2&rank=1, identifier: NCT00000558.

Associations between lower birth weight and higher polycystic ovary syndrome (PCOS) risk have been reported in previous observational studies, however, the causal relationship is still unknown. Based on decomposed fetal and maternal genetic effects on birth weight (n = 406,063), we conducted a two-sample Mendelian randomization (MR) analysis to assess potential causal relationships between fetal genome predicted birth weight and PCOS risk using a large-scale genome-wide association study (GWAS) including 4,138 PCOS cases and 20,129 controls. To further eliminate the maternally transmitted or non-transmitted effects on fetal growth, we performed a secondary MR analysis by utilizing genetic instruments after excluding maternally transmitted or non-transmitted variants, which were identified in another birth weight GWAS (n = 63,365 parent-offspring trios from Icelandic birth register). Linkage disequilibrium score regression (LDSR) analysis was conducted to estimate the genetic correlation. We found little evidence to support a causal effect of fetal genome determined birth weight on the risk of developing PCOS (primary MR analysis, OR: 0.86, 95% CI: 0.52 to 1.43; secondary MR analysis, OR: 0.86, 95% CI: 0.54 to 1.39). In addition, a marginally significant genetic correlation (rg = -0.14, se = 0.07) between birth weight and PCOS was revealed via LDSR analysis. Our findings indicated that observed associations between birth weight and future PCOS risk are more likely to be attributable to genetic pleiotropy driven by the fetal genome rather than a causal mechanism.

BackgroundPre-eclampsia (PE) is a pregnancy complication associated with maternal and fetal morbidity and mortality. Among the potential pathogenesis discussed, inflammation is considered an essential initiator of PE. Previous studies have compared the levels of various inflammatory biomarkers that indicate the existence of PE; however, the relative levels of pro-inflammatory and anti-inflammatory biomarkers and their dynamic changes during PE progression remain unclear. This knowledge is essential to explain the occurrence and progression of the disease.ObjectiveWe aimed to identify the relationship between inflammatory status and PE using inflammatory biomarkers as indicators. We also discussed the underlying mechanism by which inflammatory imbalance contributes to PE by comparing the relative levels of pro-inflammatory and anti-inflammatory biomarkers. Furthermore, we identified additional risk factors for PE.MethodsWe reviewed PubMed, Embase, and the Cochrane Library for articles published until 15th September 2022. Original articles that investigated inflammatory biomarkers in PE and normal pregnancy were included. We selected healthy pregnant women as controls. The inflammatory biomarkers in the case and control groups were expressed as standardized mean differences and 95% confidence intervals using a random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale. Publication bias was assessed using Egger’s test.ResultsThirteen articles that investigated 2,549 participants were included in this meta-analysis. Patients with PE had significantly higher levels of C-reactive protein (CRP), interleukin (IL)-4, IL-6, IL-8, IL-10, and tumor necrosis factor (TNF) than the controls. CRP and pro-inflammatory cytokine levels were higher than those of anti-inflammatory cytokines. Patients with gestational age > 34 weeks had significantly higher IL-6 and TNF levels. Patients with higher systolic blood pressure had significantly higher IL-8, IL-10, and CRP levels.ConclusionInflammatory imbalance is an independent risk factor for PE development. Impairment of the anti-inflammatory system is a crucial initiating factor for PE development. Failed autoregulation, manifested as prolonged exposure to pro-inflammatory cytokines, leads to PE progression. Higher levels of inflammatory biomarkers suggest more severe symptoms, and pregnant women after 34 weeks of gestation are more susceptible to PE.

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its equal quantification. Since not all mutations elicit the same antitumor rejection, the effect on immunity of neoantigens encoded by different types or locations of somatic mutations may vary. In addition, other typical genomic features, including complex structural variants, are not captured by the conventional TMB metric. Given the diversity of cancer subtypes and the complexity of treatment regimens, this paper proposes that tumor mutations capable of causing various degrees of immunogenicity should be calculated separately. TMB should therefore, be segmented into more exact, higher dimensional feature vectors to exhaustively measure the foreignness of tumors. We systematically reviewed patients’ multifaceted efficacy based on a refined TMB metric, investigated the association between multidimensional mutations and integrative immunotherapy outcomes, and developed a convergent categorical decision-making framework, TMBserval (Statistical Explainable machine learning with Regression-based VALidation). TMBserval integrates a multiple-instance learning concept with statistics to create a statistically interpretable model that addresses the broad interdependencies between multidimensional mutation burdens and decision endpoints. TMBserval is a pan-cancer-oriented many-to-many nonlinear regression model with discrimination and calibration power. Simulations and experimental analyses using data from 137 actual patients both demonstrated that our method could discriminate between patient groups in a high-dimensional feature space, thereby rationally expanding the beneficiary population of immunotherapy.

ObjectiveTo evaluate the effects of two genetic variants in the promoter of the miR-143/145 cluster on the risk of epithelial ovarian cancer (EOC) and the prognosis of EOC patients.Study designGenotypes were determined by the polymerase chain reaction and ligase detection reaction method in 563 EOC patients and 576 healthy women. The expression of miR-143 and miR-145 were detected by quantitative real-time polymerase chain reaction (qRT–PCR) in fifty-two EOC tissues.ResultsThe rs4705342 CC genotype frequencies in EOC patients were higher than those in the controls (P = 0.014). Furthermore, the CC genotype of rs4705342 was associated with an advanced FIGO stage of EOC patients (P = 0.046). Patients with the rs4705342 CC genotype had shorter progression-free survival (PFS) and overall survival (OS) times than those carrying the TT genotype in multivariable analysis adjusting for clinical variables (HR = 1.30, 95% CI = 1.04-1.62, P = 0.020; HR = 1.33, 95% CI = 1.05-1.70, P = 0.020). In addition, the miR-145 levels were lower in EOC tissues with the rs4705342 CC genotype than in those with the TT genotype (P = 0.005).ConclusionThe CC genotype of rs4705342 was related to an increased risk of EOC and poor prognosis of EOC patients, and rs4705342 may serve as a molecular marker for predicting the development of EOC and the clinical outcome of EOC patients.

The rapid development of renewable energy generation aggravates the imbalance between supply and demand in power grid, and exploring the potential of demand side resource can effectively improve such problems. Industrial users (IU) is an important demand response resource of power grid, and mining the load patterns of IU is the basis of studying the demand response ability of IU, which plays an important role in the safe operation and lean management of power grid. Lately, the popularity of advanced metering infrastructures provides data support for studying the load patterns of IU. However, the high dimensionality and the complex non-linear relationship of IU’s load data bring difficulties to the task of clustering. To solve the above problems, this paper proposes a load pattern extraction method based on multidimensional electrical consumption feature construction. Firstly, industrial load characteristic set of IU is created with five load characteristic indexes weighted by improved entropy weight method. In addition, convolutional autoencoder is established to extract the temporal feature of industrial load data which is combined with industrial load characteristic set to build a multidimensional feature set (MFS) for IU and finish multidimensional electrical consumption feature construction (MECFC). Then, MFS is used as the input of Self-Organization Map network to select the initial clustering centers of K-means algorithm, overcoming the problem of local optimal solution, and complete the IU daily load clustering. The experiment shows that the algorithm based on MECFC solves the local optimal problem and have better performance in stability and clustering effect than traditional methods.

Abstract: The development of electronics and software has resulted in the ability of twin agents to act as digital counterparts for the optimization, control, and monitoring of real power grids. When increasing regulation resources are connected to the power grid, is challenging for independent system operators to use a centralized controller to achieve power tracking and balance. Therefore, the present study applied device-level-based digital twins to monitor physical signals for computer-aided design for power tracking. Moreover, a consensus control-based distributed power tracking system is proposed for the physical-model simulation of the power grid. A communication network was also designed for realistic signal exchange. The combination of the proposed distributed power tracking method and communication network can accelerate computational efficiency and protect the privacy of the regulation resources. Finally, the performance of the proposed distributed power tracking method is validated in a simulation system with 10 regulation resources.

ObjectiveThis study aims to highlight the use of robots in surgery and that of tube-assisted minimally invasive surgery for spinal metastases, as well as elaborate on the concept of invasion-controlled surgery (ICS).Summary of backgroundMany patients with spinal metastasis cancer cannot afford serious complications when undergoing traditional open surgery because of their poor physical condition. Robots and minimally invasive technology have been introduced into the field of spine surgery and they have shown significant advantages.MethodsSix patients who underwent robot and working tube-assisted ICS for spinal metastases. Relevant demographic, medical, surgical, and postoperative data were collected from medical records and analyzed.ResultsMean operative time was 3.8 h and the mean length of the surgical incision was 4.9 cm. The mean estimated blood loss was 400 ml. The mean bedtime and hospital length of stay were 3.2 days and 6.5 days, respectively. No obvious complications were reported during treatment. The mean accuracy of screw placement was 98%. The mean time for further system treatment after surgery was 5.8 days. All patients experienced significant pain relief. The mean preoperative visual analog scale (VAS) was 7.83 points. The mean VAS at 1 day, 1 week, and 1 month after surgery were 2.83, 1.83, and 1.17 points, respectively. Frankel grade was improved in five of six patients. One patient preoperatively with Frankel grade D was the same postoperatively.ConclusionThe concept of ICS is suitable for patients with spinal metastases. Robot and working tube-assisted ICS for spinal metastases is one of the safest and most effective treatment methods.

BackgroundFew reports have investigated the oncologically safe timing of prophylactic inguinal lymphadenectomy for penile cancer patients with clinically normal inguinal lymph nodes (cN0), particularly those who received delayed surgical treatment.MethodsThe study included pT1aG2, pT1b-3G1-3 cN0M0 patients with penile cancer who received prophylactic bilateral inguinal lymph nodes dissection (ILND) at the Department of Urology of Tangdu Hospital between October 2002 and August 2019. Patients who received simultaneous resection of primary tumor and inguinal lymph nodes were assigned to the immediate group, while the rest were assigned to the delayed group. The optimal timing of lymphadenectomy was determined based on the time-dependent ROC curves. The disease-specific survival (DSS) was estimated based on the Kaplan–Meier curve. Cox regression analysis was used to evaluate the associations between DSS and the timing of lymphadenectomy and tumor characteristics. The analyses were repeated after stabilized inverse probability of treatment weighting adjustment.ResultsA total of 87 patients were enrolled in the study, 35 of them in the immediate group and 52 in the delayed group. The median (range) interval time between primary tumor resection and ILND of the delayed group was 85 (29-225) days. Multivariable Cox analysis demonstrated that immediate lymphadenectomy was associated with a significant survival benefit (HR, 0.11; 95% CI, 0.02–0.57; p = 0.009). An index of 3.5 months was determined as the optimal cut-point for dichotomization in the delayed group. In high-risk patients who received delayed surgical treatment, prophylactic inguinal lymphadenectomy within 3.5 months was associated with a significantly better DSS compared to dissection after 3.5months (77.8% and 0%, respectively; log-rank p

SARS coronavirus 2 (SARS-CoV-2) invades the human body by binding to major receptors such as ACE2 via its S-spike protein, so the interaction of receptor-binding sites has been a hot topic in the development of coronavirus drugs. At present, the clinical progress in monoclonal antibody therapy that occurred early in the pandemic is gradually showing signs of slowing. While recombinant soluble ACE2, as an alternative therapy, has been modified by many engineering methods, both the safety and functional aspects are approaching maturity, and this therapy shows great potential for broadly neutralizing coronaviruses, but its progress in clinical development remains stalled. Therefore, there are still several key problems to be considered and solved for recombinant soluble ACE2 to be approved as a clinical treatment as soon as possible.

By predicting ERα bioactivity and mining the potential relationship between Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) attributes in drug research and development, the development efficiency of specific drugs for breast cancer will be effectively improved and the misjudgment rate of R&D personnel will be reduced. The quantitative prediction model of ERα bioactivity and classification prediction model of Absorption, Distribution, Metabolism, Excretion, Toxicity properties were constructed. The prediction results of ERα bioactivity were compared by XGBoot, Light GBM, Random Forest and MLP neural network. Two models with high prediction accuracy were selected and fused to obtain ERα bioactivity prediction model from Mean absolute error (MAE), mean squared error (MSE) and R2. The data were further subjected to model-based feature selection and FDR/FPR-based feature selection, respectively, and the results were placed in a voting machine to obtain Absorption, Distribution, Metabolism, Excretion, Toxicity classification prediction model. In this study, 430 molecular descriptors were removed, and finally 20 molecular descriptors with the most significant effect on biological activity obtained by the dual feature screening combined optimization method were used to establish a compound molecular descriptor prediction model for ERα biological activity, and further classification and prediction of the Absorption, Distribution, Metabolism, Excretion, Toxicity properties of the drugs were made. Eighty variables were selected by the model ExtraTreesClassifier Classifie, and 40 variables were selected by the model GradientBoostingClassifier to complete the model-based feature selection. At the same time, the feature selection method based on FDR/FPR is also selected, and the three classification models obtained by the two methods are placed into the voting machine to obtain the final model. The experimental results showed that the model‘s evaluation indexes and roc diagram were excellent and could accurately predict ERα bioactivity and Absorption, Distribution, Metabolism, Excretion, Toxicity properties. The model constructed in this study has high accuracy, fast convergence and robustness, has a very high accuracy for Absorption, Distribution, Metabolism, Excretion, Toxicity and ERα classification prediction, has bright prospects in the biopharmaceutical field, and is an important method for energy conservation and yield increase in the future.

The efficiently renewable bioethanol can help to alleviate energy crisis and environmental pollution. Genetically modified strains for efficient use of xylose and developing lignocellulosic hydrolysates play an essential role in facilitating cellulosic ethanol production. Here we present a promising strain GRE3OE via GRE3 overexpressed in a previously reported Saccharomyces cerevisiae strain WXY70. A comprehensive evaluation of the fermentation level of GRE3OE in alkaline-distilled sweet sorghum bagasse, sorghum straw and xylose mother liquor hydrolysate. Under simulated corn stover hydrolysate, GRE3OE produced 53.39 g/L ethanol within 48 h. GRE3OE produced about 0.498 g/g total sugar in sorghum straw hydrolysate solution. Moreover, GRE3OE consumed more xylose than WXY70 in the high-concentration xylose mother liquor. Taken together, GRE3OE could be a candidate strain for industrial ethanol development, which is due to its remarkable fermentation efficiency during different lignocellulosic hydrolysates.

Huanglongbing (HLB), or citrus greening disease, has complex and variable symptoms, making its diagnosis almost entirely reliant on subjective experience, which results in a low diagnosis efficiency. To overcome this problem, we constructed and validated a deep learning (DL)-based method for detecting citrus HLB using YOLOv5l from digital images. Three models (Yolov5l-HLB1, Yolov5l-HLB2, and Yolov5l-HLB3) were developed using images of healthy and symptomatic citrus leaves acquired under a range of imaging conditions. The micro F1-scores of the Yolov5l-HLB2 model (85.19%) recognising five HLB symptoms (blotchy mottling, “red-nose” fruits, zinc-deficiency, vein yellowing, and uniform yellowing) in the images were higher than those of the other two models. The generalisation performance of Yolov5l-HLB2 was tested using test set images acquired under two photographic conditions (conditions B and C) that were different from that of the model training set condition (condition A). The results suggested that this model performed well at recognising the five HLB symptom images acquired under both conditions B and C, and yielded a micro F1-score of 84.64% and 85.84%, respectively. In addition, the detection performance of the Yolov5l-HLB2 model was better for experienced users than for inexperienced users. The PCR-positive rate of Candidatus Liberibacter asiaticus (CLas) detection (the causative pathogen for HLB) in the samples with five HLB symptoms as classified using the Yolov5l-HLB2 model was also compared with manual classification by experts. This indicated that the model can be employed as a preliminary screening tool before the collection of field samples for subsequent PCR testing. We also developed the ‘HLBdetector’ app using the Yolov5l-HLB2 model, which allows farmers to complete HLB detection in seconds with only a mobile phone terminal and without expert guidance. Overall, we successfully constructed a reliable automatic HLB identification model and developed the user-friendly ‘HLBdetector’ app, facilitating the prevention and timely control of HLB transmission in citrus orchards.

Feed has the greatest impact on the carbon footprint of the aquaculture, and also determines the water quality in aquaculture to a great extent. Making appropriate feeding control strategies is one of the most effective ways to promote cleaner production as well as fish welfare in aquaculture. Reliable and accurate fish appetite grading especially based on time-limited data is a prerequisite for achieving high-precision and reasonable feeding control in practical production. To date, however, few efforts have been done on this challenge. For these, regarding Micropterus salmoides as the experimental fish, a novel and practical method, based on a modified kinetic energy feature-based graph convolutional network (GCN), was developed in this study. First, graphs were constructed based on the extracted modified kinetic energy features and their temporal correlation. Then, with the help of a series of the convolution and global pooling operations, a GCN model was customized based on the constructed graphs. Following this, the customized GCN model was enriched by the self-attention pooling mechanism and customized network structure. Results show that the proposed GCN-based approach outperforms other typical state-of-the-art methods in fish appetite grading, and the grading accuracy obtained here could be 98.60% using only the first 4.2 seconds as well as the first 8.3 seconds of input data, which is not much different from that (98.89%) using full-length (25 second-long) input data. What’s more, compared to the recurrent neural network (RNN)-based method which performance is closest to our method, the space complexity of the proposed approach here can better satisfy the requirements of real aquaculture, in which the quantity of the trainable parameters here is only 6.4% ~ 31.8% of the RNN-based method. In summary, the proposed modified kinetic energy feature-based GCN approach is favorable for the appetite grading of fish like Micropterus salmoides with time-limited data, which is a promising approach in dealing with feeding control tasks and alleviating the water environmental burden in aquaculture.

BackgroundConcerning the changes in the prevalence of neurodevelopmental disorders (NDDs), we estimate the prevalence of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), intellectual disorder (ID), and learning disability (LD) among US children and adolescents aged 3–17 years in 2019 and 2020.MethodsThe study includes 14,983 US children and adolescents aged 3–17 years in 2019 and 2020 from the National Health Interview Survey (NHIS). Parents were interviewed about whether their children ever and/or currently had NDDs diagnosed. Prevalence estimates of NDDs were calculated with a survey-based weighting scheme. Logistic regression models were used to estimate the associations between NDDs prevalence and subgroups.ResultsThe weighted prevalence of ADHD, ASD, ID, and LD was 8.5% (95% CI: 7.9–9.2%), 2.9% (95% CI: 2.6–3.4%), 1.4% (95% CI: 1.2–1.7%), and 6.4% (95% CI: 5.8–7.0%), respectively. A higher prevalence of ADHD, ASD, ID, and LD was observed in boys, those who ever had anxiety or depression symptoms, those with lower family income, those living in a rented house, ever been bullied, and ever lived with anyone mentally ill.ConclusionThe study found the prevalence of ADHD, ASD, ID, and LD was different by demographics, comorbidity/mental problems, household/parental characteristics, and stressful life events.

BackgroundTraumatic brainstem injury (TBSI) is one of the forms of brain injury and has a very high mortality rate. Understanding the molecular mechanism of injury can provide additional information for clinical treatment.Materials and methodsIn this study, we detected transcriptome, proteomics, and metabolome expression changes in the brainstem of TBSI rats, and comprehensively analyzed the underlying mechanisms of TBSI.ResultsAfter TBSI, there was significant diffuse axonal injury (DAI) in the brainstem of rats. A total of 579 genes, 70 proteins, and 183 metabolites showed significant changes in brainstem tissue. Through molecular function and pathway analysis, the differentially expressed genes, proteins, and metabolites of TBSI were mainly attributed to neural signal regulation, inflammation, neuroprotection, and immune system. In addition, a comprehensive analysis of transcripts, proteins, and metabolites showed that the genes, proteins, and metabolic pathways regulated in the brainstem after TBSI were involved in neuroactive ligand-receptor interaction. A variety of GCPR-regulated pathways were affected, especially GAGA’s corresponding receptors GABAA, GABAB, GABAC, and transporter GAT that were inhibited to varying degrees.ConclusionThis study provides insights into the development of a rapid diagnostic kit and making treatment strategies for TBSI.

Chordoma is a rare malignant bone tumor that mainly occurs in the sacrum and the clivus/skull base. Surgical resection is the treatment of choice for chordoma, but the local recurrence rate is high with unsatisfactory prognosis. Compared with other common tumors, there is not much research and individualized treatment for chordoma, partly due to the rarity of the disease and the lack of appropriate disease models, which delay the discovery of therapeutic strategies. Recent advances in modern techniques have enabled gaining a better understanding of a number of rare diseases, including chordoma. Since the beginning of the 21st century, various chordoma cell lines and animal models have been reported, which have partially revealed the intrinsic mechanisms of tumor initiation and progression with the use of next-generation sequencing (NGS) techniques. In this study, we performed a systematic overview of the chordoma models and related sequencing studies in a chronological manner, from the first patient-derived chordoma cell line (U-CH1) to diverse preclinical models such as the patient-derived organoid-based xenograft (PDX) and patient-derived organoid (PDO) models. The use of modern sequencing techniques has discovered mutations and expression signatures that are considered potential treatment targets, such as the expression of Brachyury and overactivated receptor tyrosine kinases (RTKs). Moreover, computational and bioinformatics techniques have made drug repositioning/repurposing and individualized high-throughput drug screening available. These advantages facilitate the research and development of comprehensive and personalized treatment strategies for indicated patients and will dramatically improve their prognoses in the near feature.

Perivascular epithelioid cell tumors (PEComas) are rare mesenchymal tumors arising from perivascular epithelial cells. There was no standard treatment for unresectable PEComa before 2021. For a low incidence and a rarely curable disease, development of new therapy is essential. A 45-year-old female was diagnosed with malignant renal PEComa (likely with TFE3 rearrangement) that underwent rapid progression after 10 months of surgery. The patient then received the tyrosine kinase inhibitor (TKI) Apatinib, and the tumor remained stable for 15 months before another progression. The patient then received the MTOR inhibitor everolimus that alleviated her symptoms but the tumor went into remission again after another 15 months. This result suggests that antagonizing the vascular endothelial growth factor receptor (VEGFR) pathway be a useful strategy for malignant PEComas, along with the MTOR pathway inhibition that had recently been approved for the rare tumor.

BackgroundThe inflammation and nutrition status are crucial factors influencing the outcome of patients with gastric cancer. This study aims to investigate the prognostic value of the preoperative prognostic nutritional index (PNI) in patients with stage I–III gastric cancer undergoing robotic radical gastrectomy combined with Enhanced Recovery after Surgery (ERAS), and further to create a clinical prognosis prediction model.Study525 patients with stage I–III gastric cancer who underwent ERAS combined with RRG from July 2010 to June 2018 were included in this work, and were divided randomly into training and validating groups in a 7-to-3 ratio. The association between PNI and overall survival (OS) was assessed by Kaplan-Meier analysis and the log-rank test. Independent risk factors impacting postoperative survival were analyzed with the Cox proportional hazards regression model. A nomogram for predicting OS was constructed based on multivariate analysis, and its predictive performance was evaluated using Harrell's concordance index (C-index), calibration plots, ROC curve, decision curve analysis (DCA), and time-dependent ROC curve analysis.ResultsSurvival analyses revealed the presence of a significant correlation between low preoperative PNI and shortened postoperative survival (P = 0.001). According to multivariate analysis, postoperative complications (P

BackgroundPrevious studies revealed that maternal smoking exposure during pregnancy was an essential risk factor for offspring developing attention-deficit/hyperactivity disorder (ADHD). The impact of paternal smoking exposure 1 year before pregnancy on offspring ADHD risk is still unclear.MethodsThe present study included 2,477 school-age children and their parents from the Shanghai Child and Adolescent Health Cohort who had complete data for offspring ADHD diagnosis and parents' smoking exposure before and during pregnancy information. A multivariate logistic regression model and Firth's logistic regression model were used to determine the associations of paternal smoking and parental smoke exposure patterns before and during pregnancy with offspring ADHD risk.ResultsChildren whose fathers smoked before pregnancy had a higher risk of developing ADHD [odds ratio (OR) = 2.59, 95% confidence interval (CI): 1.35–4.98] compared to those whose fathers had never been exposed to smoking. Similarly, parents who were exposed to smoking or second-hand smoke before pregnancy had 1.96 times (OR = 1.96, 95% CI: 1.19–3.22) more likely to have offspring with ADHD. Moreover, children whose parents were exposed to smoking both before and during pregnancy were 2.01 times (OR = 2.01, 95% CI: 1.29–3.12) more likely to develop ADHD.ConclusionPaternal smoking before pregnancy and parental smoking exposure 1 year ahead of and throughout pregnancy were all risk factors for offspring developing ADHD.

BackgroundFOSB is reported to be an oncogene in a variety of tumors. However, the expression and role of FOSB in glioma remain obscure. In this study, we aimed to explore the expression of FOSB in glioma and its biological role in glioblastoma multiforme (GBM).MethodsWestern blot, immunohistochemical staining, and quantitative real-time polymerase chain reaction (RT-qPCR) were used to detect the expression of FOSB in clinical samples. FOSB was knocked down in cells to determine the effects of FOSB on the phenotypic changes of tumors by plate cloning, CCK-8 assay, and Transwell assay. Finally, subcutaneous tumorigenesis in nude mice was used to observe the tumorigenesis of glioma cell lines after the knockdown of the FOSB gene.ResultsFOSB expression was higher in glioma compared with normal brain tissue. After the downregulation of FOSB, the expression of cleaved caspase-3 increased. Plate cloning and CCK-8 experiments showed that the proliferation of glioma cell lines decreased. The Transwell assay demonstrated that the glioblastoma cell lines had lower migration ability after the knockdown of FOSB. Finally, the tumor volume of U87 glioma cells in group sh-FOSB was smaller than that in the control group. The TUNEL staining in vitro showed that the apoptosis of sh-FOSB glioma cells increased.ConclusionFOSB was highly expressed in glioma tissues. The viability of glioma cells decreased, and the ability of glioma cells to proliferate and migrate was reduced when FOSB was downregulated. Hence, FOSB may promote the development and migration of gliomas.

Most of the current development of artificial intelligence is based on brain cognition, however, this replication of biology cannot simulate the subjective emotional and mental state changes of human beings. Due to the imperfections of existing artificial intelligence, this manuscript summarizes and clarifies that artificial intelligence system combined with cognitive psychology is the research direction of artificial intelligence. It aims to promote the development of artificial intelligence and give computers human advanced cognitive abilities, so that computers can recognize emotions, understand human feelings, and eventually achieve dialog and empathy with humans and other artificial intelligence. This paper emphasizes the development potential and importance of artificial intelligence to understand, possess and discriminate human mental states, and argues its application value with three typical application examples of human–computer interaction: face attraction, affective computing, and music emotion, which is conducive to the further and higher level of artificial intelligence research.

The flowers of tea plants (Camellia sinensis), as well as tea leaves, contain abundant secondary metabolites and are big potential resources for the extraction of bioactive compounds or preparation of functional foods. However, little is known about the biosynthesis and transcriptional regulation mechanisms of those metabolites in tea flowers, such as terpenoid, flavonol, catechins, caffeine, and theanine. This study finely integrated target and nontarget metabolism analyses to explore the metabolic feature of developing tea flowers. Tea flowers accumulated more abundant terpenoid compounds than young leaves. The transcriptome data of developing flowers and leaves showed that a higher expression level of later genes of terpenoid biosynthesis pathway, such as Terpene synthases gene family, in tea flowers was the candidate reason of the more abundant terpenoid compounds than in tea leaves. Differently, even though flavonol and catechin profiling between tea flowers and leaves was similar, the gene family members of flavonoid biosynthesis were selectively expressed by tea flowers and tea leaves. Transcriptome and phylogenetic analyses indicated that the regulatory mechanism of flavonol biosynthesis was perhaps different between tea flowers and leaves. However, the regulatory mechanism of catechin biosynthesis was perhaps similar between tea flowers and leaves. This study not only provides a global vision of metabolism and transcriptome in tea flowers but also uncovered the different mechanisms of biosynthesis and transcriptional regulation of those important compounds.

PurposeTo investigate the relationship of muscle atrophy and fat infiltration around the hip joint with areal bone mineral density (aBMD) in each subregion of the proximal femur.Materials and methodsIn total, 144 participants (66 women and 78 men) were examined by quantitative computed tomography (QCT), and areal bone mineral density (aBMD) of the femoral neck (FN), trochanter (TR), and intertrochanter (IT) of the proximal femur were obtained. The cross-sectional area (CSA) and proton density fat fraction (PDFF) of the gluteus maximus (G.MaxM), gluteus medius (G.MedM), gluteus minimus (G.MinM), and iliopsoas (IliopM) were obtained via magnetic resonance imaging (MRI) using the mDIXON-Quant sequence. A multivariate generalized linear model was used to evaluate the correlation of the CSA and PDFF of muscles with aBMD in all subregions of the proximal femur.ResultsThe FN integral (Int) aBMD was significantly associated with the G.MaxM CSA (men: P = 0.002; women: P = 0.008) and PDFF (men: P < 0.001; women: P = 0.047). Some muscle indexes were related to the FN aBMD in males or females, including the CSA of G.MedM, G.MinM, and IliopM as well as the PDFF of IliopM and G.MinM. Associations of hip muscle parameters with the TR Int aBMD in both males and females were observed, including G.MaxM CSA (men: P < 0.001; women: P = 0.028) and G.MaxM PDFF (men: P = 0.031; women: P = 0.038). Other muscle indexes, including G.MedM and IliopM, were related to the TR aBMD, mainly affecting the aBMD of TR cortical (Cort) and TR Int. The IT Int aBMD and IT Cort aBMD showed significant correlation with the muscle indexes of G. MaxM, IliopM, and G.MedM, including the PDFF and CSA in males and females. Further, more indicators of the G.MedM and IliopM correlated with the TR and IT aBMD compared to the FN aBMD.ConclusionsThe CSA of gluteus muscles and iliopsoas had a positive association with the aBMD in the proximal femur, and the PDFF of gluteus muscles and iliopsoas had a negative correlation with the aBMD in the proximal femur. In addition, there was an interaction of the proximal femur aBMD with the muscle size and fatty infiltration of hip muscles.

The gut microbiome and its interaction with host have been implicated as the causes and regulators of colorectal cancer (CRC) pathogenesis. However, few studies comprehensively investigate the compositions of gut bacteria and their interactions with host at the early inflammatory and cancerous stages of CRC. In this study, mouse fecal samples collected at inflammation and CRC were subjected to microbiome and metabolome analyses. The datasets were analyzed individually and integratedly using various bioinformatics approaches. Great variations in gut microbiota abundance and composition were observed in inflammation and CRC. The abundances of Bacteroides, S24-7_group_unidifineted, and Allobaculum were significantly changed in inflammation and CRC. The abundances of Bacteroides and Allobaculum were significantly different between inflammation and CRC. Furthermore, strong excluding and appealing microbial interactions were found in the gut microbiota. CRC and inflammation presented specific fecal metabolome profiling. Fecal metabolomic analysis led to the identification and quantification of 1,138 metabolites with 32 metabolites significantly changed in CRC and inflammation. 1,17-Heptadecanediol and 24,25,26,27-Tetranor-23-oxo-hydroxyvitamin D3 were potential biomarkers for CRC. 3α,7β,12α-Trihydroxy-6-oxo-5α-cholan-24-oic Acid and NNAL-N-glucuronide were potential biomarkers for inflammation. The significantly changed bacterial species and metabolites contribute to inflammation and CRC diagnosis. Integrated microbiome and metabolomic analysis correlated microbes with host metabolites, and the variated microbe-metabolite association in inflammation and CRC suggest that microbes facilitate tumorigenesis of CRC through interfering host metabolism.

In most of the studies focusing on the conformity of voluntary vaccination decisions, the conformity was always directly modeled as a conformity-driven strategy-updating rule. However, the utility of an individual can also be influenced by the group identity or discrimination behaviors associated with strategy conformity in realities. Thus, a novel utility model of the vaccination game is first formulated in which the influence of strategy conformity is considered. Then, we use the spatial evolutionary game theory to study the dynamics of individual vaccination strategies under the influence of strategy conformity on the scale-free network. The results show that moderate strategy conformity and a high herd immunity threshold have a significant positive effect on vaccination behaviors when the initial vaccination fraction is low. Moreover, for a high initial vaccination fraction, the strong strategy conformity and high herd immunity threshold are more conducive to encourage vaccination behavior. To analyze the model sensitivity, experiments are conducted in the small world network and square lattice network. In addition, we performed the sensitivity analysis on vaccination effectiveness. Finally, the generality of strategy conformity effect is investigated when the myopic strategy updating rule is adopted in the whole population. The result shows that vaccination behaviors can also be promoted under the condition of moderate strategy conformity and low initial vaccination fraction.

Catalytic transformation of low-cost glycerol to value-added lactic acid (LA) is considered as one of the most promising technologies for the upgradation of glycerol into renewable products. Currently, research studies reveal that anaerobic transformation of glycerol to LA could also obtain green H2 with the same yield of LA. However, the combined value-added utilization of released H2 with high selectivity of LA during glycerol conversion under mild conditions still remains a grand challenge. In this perspective, for the first time, we conducted a comprehensive and critical discussion on current strategies for combined one-pot/tandem dehydrogenation of glycerol to LA with catalytic transfer hydrogenation of H2 acceptors (such as CO2) to other chemicals. The aim of this overview was to provide a general guidance on the atomic economic reaction pathway for upgrading low-cost glycerol and CO2 to LA as well as other chemicals.

The diatom test is a forensic technique that can provide supportive evidence in the diagnosis of drowning but requires the laborious observation and counting of diatoms using a microscopy with too much effort, and therefore it is promising to introduce artificial intelligence (AI) to make the test process automatic. In this article, we propose an artificial intelligence solution based on the YOLOv5 framework for the automatic detection and recognition of the diatom genera. To evaluate the performance of this AI solution in different scenarios, we collected five lab-grown diatom genera and samples of some organic tissues from drowning cases to investigate the potential upper/lower limits of the capability in detecting the diatoms and recognizing their genera. Based on the study of the article, a recall score of 0.95 together with the corresponding precision score of 0.9 were achieved on the samples of the five lab-grown diatom genera via cross-validation, and the accuracy of the evaluation in the cases of kidney and liver is above 0.85 based on the precision and recall scores, which demonstrate the effectiveness of the AI solution to be used in drowning forensic routine.

The overlying rock movement and mining pressure in the fully mechanized caving face with a large dip angle were systematically investigated according to theoretical analysis, similar material simulation, numerical calculation, and field monitoring. The following conclusions were obtained: 1) the theoretical analysis showed that the roof movement characteristics at different positions of the working face are quite different. The mining pressure in the upper section of the working face is primarily controlled by the structural instability of the main roof. The stable structure of the main roof is easily formed in the middle and lower sections of the working face, and the mining pressure is mostly controlled by the collapse of the immediate roof. 2) The results of similar material simulations indicated that the height of the fracture zone in different areas of the working face is different due to the influence of a large dip angle. The height of the fracture zone formed in the upper section of the working face is significantly larger than that formed in the lower section of the working face. 3) The numerical calculation suggested that the residual coal pillar of the overlying coal seam has a certain influence on the mining pressure of the 9-301 working face, making the advanced abutment pressure in the range of 80 m close to the main gate under the coal pillar more obvious. 4) The field pressure monitoring results demonstrated that the influence range of the advanced abutment pressure close to the upper part of the working face is greater than that close to the lower part of the working face, and the peak point is closer to the mining rib. Affected by the overlying residual coal pillar, the hydraulic support resistance and the peak value of advanced abutment pressure in the lower part of the working face are greater than those in the upper part. Both the initial and periodic pressure intervals of the upper section of the working face are smaller than those of the lower section. The results of this research will provide a scientific basis for the reasonable determination of the control measures for the mining pressure.

Co-pyrolysis of plastics and biomass can effectively improve the quality of bio-oil and solve the problem of plastic pollution. However, synergistic effect of co-pyrolysis on kinetics and the role of biomass H/Ceff in co-pyrolysis are still not conclusive. In this work, the co-pyrolysis synergistic effects of three different hydrogen-to-carbon ratio (H/Ceff) of biomass-rice husk (RH), sugarcane bagasse (SUG), and poplar wood (PW) with hydrogen-rich polypropylene (PP) were studied using a thermogravimetric method. The total synergy degree (φ) and the difference between experimental and theoretical weight losses (ΔW) were defined, and the activation energies of various experimental materials were calculated by the isoconversional method. The results showed that the addition of PP reduced the dependence of product species on biomass H/Ceff during co-pyrolysis. The synergistic effect of biomass and PP was related to biomass types, pyrolysis temperature, and mass ratio of biomass to PP. The mixture of SUG and PP showed positive synergistic effect at all mass ratios. Simultaneously, at the low temperature of pyrolysis, the synergistic effect is inhibited in all mixtures, which might be due to the melting of PP. Kinetic analysis showed that the activation energy could be reduced by 11.14–31.78% by co-pyrolysis with biomass and PP. A multi-step mechanism was observed in both the pyrolysis of a single sample and the co-pyrolysis of a mixture, according to Criado’s schematic analysis.

Fungal cellulases usually contain a family 1 carbohydrate-binding module (CBM1), and its role was considered to recognize the substrate specifically. This study testified that the CBM1s derived from cellobiohydrolase I of Trichoderma reesei, Penicillium oxalicum, and Penicillium funiculosum could be used as an effective accessory protein in cellulase cocktails to enhance the saccharification of lignocellulose, and its enhancement effect was significantly superior to some reported accessory proteins, such as bovine serum albumin (BSA). The promoting effects of the CBM1s were related to not only the CBM1 sources and protein dosages, but also the substrate characteristics and solid consistency during enzymatic hydrolysis. The adsorption capacity of the CBM1s, the adsorption kinetic of TrCBM from T. reesei and cellobiohydrolase, endoglucanase, and β-glucosidase from P. oxalicum, and the effect of adding TrCBM on enzyme activities of free cellulases in the hydrolysis system were investigated, and the binding conformations and affinities of CBM1s to cellulose and lignin were predicted by molecular docking. It was speculated that the higher affinity of the CBM1s to lignin than cellulases could potentially enable the CBM1s to displace cellulase adsorbed on lignin or to preferentially adsorb onto lignin to avoid ineffective adsorption of cellulase onto lignin, which enhanced cellulase system efficiency during enzymatic hydrolysis of lignocellulose.

BackgroundThe efficacy of topical minoxidil (MX) alone on female pattern hair loss (FPHL) is limited. Combination therapy based on topical MX is currently expected to provide better outcomes.ObjectivesThis study aimed to assess whether the combined therapies including MX plus oral spironolactone (SPT) and MX plus microneedling (MN) have advantages in efficacy and safety over topical MX alone on mild-to-moderate FPHL with normal hormone levels in the blood and regular menstrual cycle.MethodsA prospective, single-center, parallel-group, evaluator blinded, randomized trial including 120 non-menopause women with proven FPHL (Sinclair class II-III) was performed in China. Patients were randomly assigned to three groups, namely, the MX group (5% topical MX alone, once daily), the MX + SPT group (MX plus SPT 80–100 mg daily), and the MX+MN group (MX plus MN every 2 weeks, 12 sessions). The change from the baseline to week 24 was assessed in hair growth (hair density and diameter under dermoscope), scalp tissue structure (epidermal thickness, dermis thickness, and average hair follicle diameter under ultrasound biomicroscopy), physician's global assessment (using a 7-point global-assessment scale and Sinclair's stage change), patient evaluation (Women's Androgenetic Alopecia Quality of Life Questionnaire and Sinclair's hair-shedding score) and side effects.ResultsIn total, 115 participants completed the trial. At week 24, the hair density increased most in MX + MN group and increased least in MX group (p < 0.001 for MX + MN group vs. MX + SPT group; p = 0.009 for MX + SPT group vs. MX group). The hair shaft diameter significantly increased in all groups (p < 0.001, respectively), but there were no significant differences among the three groups (p = 0.905). The epidermal thickness and average hair follicle diameter only increased in MX + MN group. Dermis thickness increased in all groups, but there were no significant differences among the three groups. Both physician's and patient assessments showed improvement in all three groups. Scalp pruritus was the most common side effect. The MX + SPT group had the most reported adverse effects.LimitationsThe main limitations of this study are the relatively small sample size, the exclusion of severe FPHL patients, and the potential bias from unblinded treatments among the 3 groups.ConclusionTopical MX combined with MN is a better choice than either MX plus oral SPT or MX alone for the treatment of mild-to-moderate FPHL patients.

Objective: This study aimed to predict the spatial and temporal distribution pattern of Oncomelania hupensis (O. hupensis) on a fine scale based on ecological niche models, so as to provide insights into O. hupensis surveillance.Methods: Geographic distribution and environmental variables of O. hupensis in Suzhou City were collected from 2016 to 2020. Five machine learning algorithms were used, including eXtreme gradient boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosted (GBM), and C5.0 algorithms, to predict the distribution of O. hupensis and investigate the relative contribution of each environmental variable. The accuracy of the five ecological niche models was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with ten-fold cross-validation.Results: Five models predicted that the potential distribution of O. hupensis was in southwestern areas of Wuzhong, Wujiang, Taichang, and Xiangcheng counties. The AUC of RF, XGB, GBM, SVM, and C5.0 algorithms were 0.8233, 0.8051, 0.7938, 0.7897, and 0.7282, respectively. Comparing the predictive results and the truth of O. hupensis distribution in 2021, XGB and GBM models were shown to be more effective. The six greatest contributors to predicting potential O. hupensis distribution included silt content (13.13%), clay content (10.21%), population density (8.16%), annual accumulated temperatures of ≥0°C (8.12%), night-time lights (7.67%), and average annual precipitation (7.23%).Conclusions: Environmental factors play a key role in the spatial and temporal distribution pattern of O. hupensis. The XGB and GBM machine learning algorithms are effective and highly accurate for fine-scale prediction of potential O. hupensis distribution, which provides insights into the surveillance of O. hupensis.

The bladder patch constructed with the bladder acellular matrix (BAM) and adipose-derived stem cells (ASCs) was incubated with the omentum for bladder reconstruction in a rat model of bladder augmentation cystoplasty. A self-designed perfusion system and five different decellularization protocols were used to prepare the BAM. Finally, an optimal protocol (group C) was screened out by comparing the cell nucleus residue, collagen structure preservation and biologically active components retention of the prepared BAM. ASCs-seeded (BAM-ASCs group) and unseeded BAM (BAM group) were incubated with the omentum for 7 days to promote neovascularization and then perform bladder reconstruction. Hematoxylin and eosin and Masson’s trichrome staining indicated that the bladder patches in the BAM-ASCs group could better regenerate the bladder wall structure compared to the BAM group. Moreover, immunofluorescence analyses demonstrated that the ASCs could promote the regeneration of smooth muscle, neurons and blood vessels, and the physiological function (maximal bladder capacity, max pressure prior to voiding and bladder compliance) restoration in the BAM-ASCs group. The results demonstrated that the self-designed perfusion system could quickly and efficiently prepare the whole bladder scaffold and confirmed that the prepared BAM could be used as the scaffold material for functional bladder tissue engineering applications.

Accurate recognition of patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) is important for the subsequent treatment and rehabilitation. Recently, with the fast development of artificial intelligence (AI), AI-assisted diagnosis has been widely used. Feature selection as a key component is very important in AI-assisted diagnosis. So far, many feature selection methods have been developed. However, few studies consider the stability of a feature selection method. Therefore, in this study, we introduce a frequency-based criterion to evaluate the stability of feature selection and design a pipeline to select feature selection methods considering both stability and discriminability. There are two main contributions of this study: (1) It designs a bootstrap sampling-based workflow to simulate real-world scenario of feature selection. (2) It develops a decision graph to determine the optimal combination of supervised and unsupervised feature selection both considering feature stability and discriminability. Experimental results on the ADNI dataset have demonstrated the feasibility of our method.

With the access to a large number of intermittent and fluctuating new energy sources in the low-voltage distribution network, the complex relationship between producers and consumers makes the node voltage stability and other problems in the power system increasingly prominent. Aiming at the voltage stability problem caused by the intermittent and fluctuation of the new power system, based on the high-precision measurement data of PMU, this article used the model predictive control algorithm to realize regional voltage optimization and improve the stability of node voltage. First, the voltage sensitivity matrix is calculated using PMU measurement data and grid structure state information. Second, active power, reactive power, and voltage were taken as the input of the MPC algorithm, and the optimal compensation voltage instruction was obtained by rolling optimization, and the node voltage was compensated by SVG and SVC. Finally, a simulation experiment was carried out on the MATLAB/Simulink simulation platform, and the experimental results verified the correctness and effectiveness of the proposed control method.

Polyploid breeding is an effective approach to improve plant biomass and quality. Both fast growth and dwarf types of in vitro or ex vitro plants are produced after polyploidization. However, little is known regarding the dwarf type mechanism in polyploids grown in vitro. In this study, the morphological and cytological characteristics were measured in tetraploid and diploid hybrid sweetgum (Liquidambar styraciflua × L. formosana) with the same genetic background. RNA sequencing (RNA-Seq) was used to analyse shoot and root variations between tetraploid and diploid plants; important metabolites were validated. The results showed that the shoot and root lengths were significantly shorter in tetraploids than in diploids after 25 d of culture. Most tetraploid root cells were wider and more irregular, and the length of the meristematic zone was shorter, while tetraploid cells were significantly larger than diploid cells. Differentially expressed genes (DEGs) were significantly enriched in the plant growth and organ elongation pathways, such as plant hormone biosynthesis and signal transduction, sugar and starch metabolism, and cell cycles. Hormone biosynthesis and signal transduction genes, such as YUCCA, TAA1, GH3, SAUR, CPS, KO, KAO, GA20ox, GA3ox, BAS1 and CYCD3, which help to regulate organ elongation, were generally downregulated. The auxin, gibberellin, and brassinolide (BL) contents in roots and stems were significantly lower in tetraploids than in diploids, which may greatly contribute to slow growth in the roots and stems of tetraploid regenerated plants. Exogenous gibberellic acid (GA3) and indole-3-acetic acid (IAA), which induced plant cell elongation, could significantly promote growth in the stems and roots of tetraploids. In summary, comparative transcriptomics and metabolite analysis showed that the slow growth of regenerated tetraploid hybrid sweetgum was strongly related to auxin and gibberellin deficiency. Our findings provide insights into the molecular mechanisms that underlie dwarfism in allopolyploid hybrid sweetgum.

Radiotherapy is one of the important treatments for malignant tumors. The precision of radiotherapy is affected by the respiratory motion of human body, so real-time motion tracking for thoracoabdominal tumors is of great significance to improve the efficacy of radiotherapy. This paper aims to establish a highly precise and efficient prediction model, thus proposing to apply a depth prediction model composed of multi-scale enhanced convolution neural network and temporal convolutional network based on empirical mode decomposition (EMD) in respiratory prediction with different delay times. First, to enhance the precision, the unstable original sequence is decomposed into several intrinsic mode functions (IMFs) by EMD, and then, a depth prediction model of parallel enhanced convolution structure and temporal convolutional network with the characteristics specific to IMFs is built, and finally training on the respiratory motion dataset of 103 patients with malignant tumors is conducted. The prediction precision and time efficiency of the model are compared at different levels with those of the other three depth prediction models so as to evaluate the performance of the model. The result shows that the respiratory motion prediction model determined in this paper has superior prediction performance under different lengths of input data and delay time, and, furthermore, the network update time is shortened by about 60%. The method proposed in this paper will greatly improve the precision of radiotherapy and shorten the radiotherapy time, which is of great application value.