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In order to improve the problem of serious dust pollution in the fully-mechanized mining face, a new air distribution dust control method with air separator installed was proposed for the fully-mechanized mining face of Daliuta Coal Mine in Yulin, Shaanxi Province. Fluent was used to simulate the dust diffusion rule of the new-type air distribution dust control method, and the influences of different distances between the air pressure outlet and the working face and different pressure-pumping ratios on the change of dust diffusion rule were analyzed. When the air pressure outlet is 10-20 m away from the working face, dust accumulates and settles in the bottom of the roadway in the working face area, and the dust concentration is low at the driver’s side, and the dust control effect is better. When the distance between the air pressure outlet and the working face is 10 m and the pressure-pumping ratio is 1.0, the high concentration of dust accumulates in the working face area, and the dust concentration at the driver is controlled below 50 mg/m3, and the dust control effect is better. Through field measurement, the error between dust concentration and simulation results is less than 15.2%.

Background: The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored. Methods: Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM). Results: A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1. Conclusion: The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.

BackgroundThe relationship between dysbiosis of the gastrointestinal microbiota and gastric cancer (GC) has been extensively studied. However, microbiota alterations in GC patients vary widely across studies, and reproducible diagnostic biomarkers for early GC are still lacking in multiple populations. Thus, this study aimed to characterize the gastrointestinal microbial communities involved in gastric carcinogenesis through a meta-analysis of multiple published and open datasets.MethodsWe analyzed 16S rRNA sequencing data from 1,642 gastric biopsy samples and 394 stool samples across 11 independent studies. VSEARCH, QIIME and R packages such as vegan, phyloseq, cooccur, and random forest were used for data processing and analysis. PICRUSt software was employed to predict functions.ResultsThe α-diversity results indicated significant differences in the intratumoral microbiota of cancer patients compared to non-cancer patients, while no significant differences were observed in the fecal microbiota. Network analysis showed that the positive correlation with GC-enriched bacteria increased, and the positive correlation with GC-depleted bacteria decreased compared to healthy individuals. Functional analyses indicated that pathways related to carbohydrate metabolism were significantly enriched in GC, while biosynthesis of unsaturated fatty acids was diminished. Additionally, we investigated non-Helicobacter pylori (HP) commensals, which are crucial in both HP-negative and HP-positive GC. Random forest models, constructed using specific taxa associated with GC identified from the LEfSe analysis, revealed that the combination of Lactobacillus and Streptococcus included alone could effectively discriminate between GC patients and healthy individuals in fecal samples (area under the curve (AUC) = 0.7949). This finding was also validated in an independent cohort (AUC = 0.7712).ConclusionsThis study examined the intratumoral and fecal microbiota of GC patients from a dual microecological perspective and identified Lactobacillus, Streptococcus, Roseburia, Faecalibacterium and Phascolarctobacterium as intratumoral and intestinal-specific co-differential bacteria. Furthermore, it confirmed the validity of the combination of Lactobacillus and Streptococcus as GC-specific microbial markers across multiple populations, which may aid in the early non-invasive diagnosis of GC.

Objective: To investigate the vaginal and gut microbes changes during the carcinogenesis of cervical and the auxiliary diagnostic value. To investigate the effect of microbiome-specific metabolites butyric on cervical cancer cells. Methods: We studied 416 vaginal 16S rRNA sequencing data and 116 gut sequencing data. Reads were processed using VSEARCH. We used Shannon index, Chao1 index, Simpson diversity index, β diversity index, Linear discriminant analysis Effect Size (LEfSe), co-abundance network and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to explore microbiome differences between groups. We constructed random forest models based on genus and verified its discriminant effect. Finally, we used the cell counting kit-8 (CCK-8) method to detect cell proliferation capacity and flow cytometry to detect apoptosis and induction of cell cycle progression. Results: Compared to the non-cancerous population, patients with cervical cancer had unique microbial community characteristics in both vaginal and gut ecological niches. Our predictive model based on genus in two ecological regions achieved high accuracy in the diagnosis of cervical cancer (vaginal model AUC=91.58 %; gut model AUC=99.95 %). Butyric inhibited cervical cancer cell proliferation in a concentration-dependent manner and promoted apoptosis of cancer cells. Conclusion: Significant differences were found in vaginal and gut microbes in patients with cervical cancer compared to the non-cancerous population. The prediction models constructed at the genus level in both ecological sites have good diagnostic value. Microorganisms may be involved in cervical cancer progression in a metabolite-dependent way, and targeting butyric may provide therapeutic options for cervical cancer.

Rubidium and cesium are critical strategic elements, and their development and utilization are of great significance. In this study, a magnesium ammonium phosphate (MAP) adsorbent was prepared and characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, and Fourier transform infrared spectroscopy (FTIR). The adsorption performance of the adsorbent for Rb+ and Cs+ in solution was investigated. The results showed that the adsorbent exhibited high adsorption capacities of 2.83 mol/g for Rb+ and 4.37 mol/g for Cs+. In simulated brine, the adsorbent demonstrated excellent selectivity for Cs+. Kinetic and thermodynamic studies indicated that the adsorption process followed a pseudo-second order kinetic model and Langmuir isotherm model. The primary adsorption mechanism was an ion exchange. The development of this adsorbent holds significant promise for the extraction of rubidium and cesium from liquid resources.

Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. The EEG characteristic analysis was performed during left and right turns, involving the calculation of the energy ratio of beta waves and Shannon entropy. The psychological workload of pilots during different flight phases was quantified as well. Based on the EEG characteristics, the pilots’ psychological workload was classified through the use of a support vector machine (SVM). The study results showed significant changes in the energy ratio of beta waves and Shannon entropy during left and right turns compared to the cruising phase. Additionally, the pilots’ psychological workload was found to have increased during these turning phases. Using support vector machines to detect the pilots’ psychological workload, the classification accuracy for the training set was 98.92%, while for the test set, it was 93.67%. This research holds significant importance in understanding pilots’ psychological workload.

Abstract Background The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large‐scale meta‐analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine‐learning classifier. Methods In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony‐related functions. Results The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top‐ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota‐associated functions between cancer‐affected and healthy individuals that were primarily associated with metabolic disturbances. Conclusions GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC‐specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.

Abstract Background Winter wheat grain samples from 185 sites across southern Shanxi region were processed and analyzed using a non-destructive approach. For this purpose, spectral data and protein content of grain and grain powder were obtained. After combining six types of preprocessed spectra and four types of multivariate statistical models, a relationship between hyperspectral datasets and grain protein is presented. Results It was found that the hyperspectral reflectance of winter wheat grain and powder was positively correlated with the protein contents, which provide the possibility for hyperspectral quantitative assessment. The spectral characteristic bands of protein content in winter wheat extracted based on the SPA algorithm were proved to be around 350–430 nm; 851–1154 nm; 1300–1476 nm; and 1990–2050 nm. In powder samples, SG-BPNN had the best monitoring effect, with the accuracy of R v 2 = 0.814, RMSEv = 0.024 g/g, and RPDv = 2.318. While in case of grain samples, the SG-SVM model exhibited the best monitoring effect, with the accuracy of R v 2 = 0.789, RMSEv = 0.026 g/g, and RPDv = 2.177. Conclusions Based on the experimental findings, we propose that a combination of spectral pretreatment and multivariate statistical modeling is helpful for the non-destructive and rapid estimation of protein content in winter wheat. Graphical Abstract

Abstract Background Hypothalamic hamartoma (HH) is a congenital non-progressive lesion of hypothalamus during fetal development. Mass-like lesions in different anatomical locations often develop a variously disabling course presenting with cognitive decline, psychiatric symptoms, as well as multiple seizure types. As a rare disease, HH is relatively common in infants and children, but it is extremely rare in adults. Case presentation We reported a case of adult-onset hypothalamic hamartoma, and summarized and analyzed relevant reports and studies of HH worldwide. The patient had clinical manifestations characterized by multiple seizure forms. After stereotactic radiofrequency thermocoagulation and drug treatment, the condition was effectively controlled. The patient was followed up till October 2022, with no recurrence of seizures. Conclusions Epilepsy caused by HH can resemble that of temporal lobe seizures, as HH forms a complex epileptogenic network with other regions of the brain through anatomical and functional connections. Early treatment of HH can provide better control of the symptoms of epilepsy, and patients with longer disease courses may have more complications.

An efficient and precise genome-editing approach is in high demand in any molecular biology or cell biology laboratory worldwide. However, despite a recent rapid progress in the toolbox tailored for precise genome-editing, including the base editors and prime editors, there is still a need for a cost-effective knock-in (KI) approach amenable for long donor DNA cargos with high efficiency. By harnessing the high-efficient double-strand break (DSB) repair pathway of microhomology-mediated end joining, we previously showed that a specially designed 3′-overhang double-strand DNA (odsDNA) donor harboring 50-nt homology arm (HA) allows high-efficient exogenous DNA KI when combined with CRISPR-Cas9 technology. The lengths of the 3′-overhangs of odsDNA donors could be manipulated by the five consecutive phosphorothioate (PT) modifications. In this protocol, we detail the stepwise procedures to conduct the LOCK (Long dsDNA with 3′-Overhangs mediated CRISPR Knock-in) method for gene-sized (~1–3 kb) KI in mammalian cells.Graphical overview Improvement of large DNA fragment knock-in rates by attaching odsDNA donors to Cas9-PCV2 fusion protein

The stomach was once considered a sterile organ until the discovery of Helicobacter pylori (HP). With the application of high-throughput sequencing technology and macrogenomics, researchers have identified fungi and fivemajor bacterial phyla within the stomachs of healthy individuals. These microbial communities exert regulatory influence over various physiological functions, including energy metabolism and immune responses. HP is a well-recognized risk factor for gastric cancer, significantly altering the stomach’s native microecology. Currently, numerous studies are centered on the mechanisms by which HP contributes to gastric cancer development, primarily involving the CagA oncoprotein. However, aside from exogenous infections such as HP and EBV, certain endogenous dysbiosis can also lead to gastric cancer through multiple mechanisms. Additionally, gut microbiota and its metabolites significantly impact the development of gastric cancer. The role of microbial therapies, including diet, phages, probiotics and fecal microbiota transplantation, in treating gastric cancer should not be underestimated. This review aims to study the mechanisms involved in the roles of exogenous pathogen infection and endogenous microbiota dysbiosis in the development of gastric cancer. Also, we describe the application of microbiota therapy in the treatment and prognosis of gastric cancer.

The pure-MBBR process was applied to remove ammonia in a full-scale micro-polluted-water treatment plant with a daily treatment capacity of 260 × 104 m3/d, Guangdong, China. The relationship between treatment efficiency, physical and chemical properties and microbial diversity in the process of biofilm growth was explored, and the oxygen transfer model of biofilm was established. The results show that the effluent of two-stage pure MBBR process is stable and up to standard after 10 days' incubation. The nitrification loads of two-stage biofilm was stable on the 14th day. The biomass and biofilm thickness lagged behind the nitrification load, and reached a relatively stable level on the 28th day. The species richness of biofilm basically reached a stable level on the 21st day, and the microbial diversity of primary biofilm was higher. In the primary and secondary stage at different periods, the relative abundance of dominant nitrifying bacteria Nitrospira reaches 8.48–13.60%, 6.48–9.27%, and Nitrosomonas reaches 2.89–5.64%, 0.00–3.48%. The pure MBBR system mainly adopts perforated aeration. Through the cutting and blocking of bubbles by suspended carriers, the oxygen transfer rate of the system was greatly improved. HIGHLIGHTS The MBBR process was integrated into a micro-polluted-water treatment plant of 2.6 million tons daily.; The nitrification load, biomass, and biofilm thickness stabilized after 10, 14, and 28 d, respectively.; The dominant microorganisms at genus level were Nitrospira, Hyphomicrobium, Pedomicrobium, Nitrosomonas, and Pedobacter.;

A proton therapy facility is under development at Huazhong University of Science and Technology (HUST). To meet the need for fast energy changes during treatments, a beamline control system (BCS) has been designed and implemented. The BCS coordinates and controls various beamline devices by adopting a distributed architecture divided into three layers: the client, server, and device layers. Among these, the design of the server layer is the key to realize fast energy changes. The server layer adopts the submodule programming paradigm and optimizes the data interface among modules, allowing the main workflow to be separated from the device workflow and data. Furthermore, this layer uses asynchronous, multithreaded, and thread-locking methods to improve the system's ability to operation efficiently and securely. Notably, to evaluate the changing energy status over time, a dynamic node update method is adopted, which can dynamically adjust the update frequency of variable nodes. This method not only meets the demand for fast updates on energy changes but also reduces the server's communication load in the steady state. This method is tested on a virtual platform, and the results are as expected.

IntroductionA growing body of evidence indicates that the dysbiosis of both mammary and intestinal microbiota is associated with the initiation and progression of breast tumors. However, the microbial characteristics of patients with breast tumors vary widely across studies, and replicable biomarkers for early-stage breast tumor diagnosis remain elusive.MethodsWe demonstrate a machine learning-based method for the analysis of breast tissue and gut microbial differences among patients with benign breast disease, patients with breast cancer (BC), and healthy individuals using 16S rRNA sequence data retrieved from eight studies. QIIME 2.0 and R software (version 3.6.1) were used for consistent processing. A naive Bayes classifier was trained on the RDP v16 reference database to assign taxonomy using the Vsearch software.ResultsAfter re-analyzing with a total of 768 breast tissue samples and 1,311 fecal samples, we confirmed that Halomonas and Shewanella were the most representative genera of BC tissue. Bacteroides are frequently and significantly enriched in the intestines of patients with breast tumor. The areas under the curve (AUCs) of random forest models were 74.27% and 68.08% for breast carcinoma tissues and stool samples, respectively. The model was validated for effectiveness via cohort-to-cohort transfer (average AUC =0.65) and leave-one-cohort-out (average AUC = 0.66). The same BC-associated biomarker Clostridium_XlVa exists in the tissues and the gut. The results of the in-vitro experiments showed that the Clostridium-specific-related metabolite deoxycholic acid (DCA) promotes the proliferation of HER2-positive BC cells and stimulates G0/G1 phase cells to enter the S phase, which may be related to the activation of peptide-O-fucosyltransferase activity functions and the neuroactive ligand–receptor interaction pathway.DiscussionThe results of this study will improve our understanding of the microbial profile of breast tumors. Changes in the microbial population may be present in both the tissues and the gut of patients with BC, and specific markers could aid in the early diagnosis of BC. The findings from in-vitro experiments confirmed that Clostridium-specific metabolite DCA promotes the proliferation of BC cells. We propose the use of stool-based biomarkers in clinical application as a non-invasive and convenient diagnostic method.

The relationship between gut microbiota (GM) and mental health is one of the focuses of psychobiology research. In recent years, the microbial-gut-brain axis (MGBA) concept has gradually formed about this bidirectional communication between gut and brain. But how the GM is involved in regulating brain function and how they affect emotional disorders these mechanisms are tenuous and limited to animal research, and often controversial. Therefore, in this review, we attempt to summarize and categorize the latest advances in current research on the mechanisms of GM and depression to provide valid information for future diagnoses and therapy of mental disorders. Finally, we introduced some antidepressant regimens that can help restore gut dysbiosis, including classic antidepressants, Chinese materia medica (CMM), diet, and exogenous strains. These studies provide further insight into GM’s role and potential pathways in emotion-related diseases, which holds essential possible clinical outcomes for people with depression or related psychiatric disorders. Future research should focus on clarifying the causal role of GM in disease and developing microbial targets, applying these findings to the prevention and treatment of depression.

The human intestine is home to a variety of microorganisms. In healthy populations, the intestinal flora shares a degree of similarity and stability, and they have a role in the metabolism, immunological response, and physiological function of key organs. With the rapid advent of high-throughput sequencing in recent years, several researchers have found that dysbiosis of the human gut microflora potentially cause physical problems and gynecological malignancies among postmenopausal women. Besides, dysbiosis hinders tumor treatment. Nonetheless, the importance of maintaining homeostatic gut microbiota and the effective use of probiotics in the treatment of gynecological malignancies should not be disregarded. Moreover, intestinal flora regulation and the involvement of probiotics as well as associated biologically active substances in gynecological malignancies could be an adjuvant treatment modality related to surgery and chemoradiotherapy in the future. Herein, this article aims to review the potential relationship between gut microorganisms and postmenopausal status as well as gynecologic malignancies; then the relationship between gut microbes and early screening as well as therapeutic aspects. Also, we describe the role of probiotics in the prevention, treatment, and prognosis of gynecologic malignancies.

The traditional hematite depressant starch has the disadvantages of poor solubility and high viscosity. In this study, a novel hematite depressant, pullulan, with better performance and flotation effect than starch was found. The structure, molecular weight and viscosity of pullulan were determined for its characterization. The results of flotation tests revealed that the flotation separation of hematite-quartz by pullulan was better than that of starch for hematite-quartz. The selective depression mechanism of pullulan on hematite was studied by contact angle measurement, zeta potential measurement, FT-IR analyses and XPS analyses. Overall, the weak adsorption of pullulan on quartz did not affect the adsorption of DOPA on the quartz surface. However, it was adsorbed strongly on hematite surface and hindered the adsorption of DOPA. This selective adsorption led to a much greater hydrophobicity of quartz than hematite in the reverse flotation, resulting in the separation of quartz and hematite. Pullulan was adsorbed by chemical bonding between its hydroxyl group and iron sites on the hematite surface. Thus, compared with starch, pullulan was easy to dissolve in water, had low viscosity and good flotation effect, so it was a better hematite depressant.

Due to poor predictability of resources and difficulty in perception of task execution status, traditional Automatic Guide Vehicle (AGV) scheduling systems need a lot of extra time in the charging process. To solve this problem, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed, including four functions, namely the knowledge support system, the scheduling model, the scheduling optimization, and the scheduling simulation. With the features of virtual reality data interaction, symbiosis, and fusion from the digital twin technology, the proposed DTDAS method can solve the AGV charging problem in the AGV scheduling system, effectively improving the operating efficiency of the workshop. An AGV scheduling process in a discrete manufacturing workshop is taken as a case study to verify the effectiveness of the proposed method. The results show that, compared with the traditional AGV scheduling method, the DTDAS method proposed in this article can reduce makespan 10.7% and reduce energy consumption by 1.32%.

CRISPR/Cas9 genome-editing tools have tremendously boosted our capability of manipulating the eukaryotic genomes in biomedical research and innovative biotechnologies. However, the current approaches that allow precise integration of gene-sized large DNA fragments generally suffer from low efficiency and high cost. Herein, we developed a versatile and efficient approach, termed LOCK (Long dsDNA with 3'-Overhangs mediated CRISPR Knock-in), by utilizing specially designed 3'-overhang double-stranded DNA (odsDNA) donors harboring 50-nt homology arm. The length of the 3'-overhangs of odsDNA is specified by the five consecutive phosphorothioate modifications. Compared with existing methods, LOCK allows highly efficient targeted insertion of kilobase-sized DNA fragments into the mammalian genomes with low cost and low off-target effects, yielding >fivefold higher knock-in frequencies than conventional homologous recombination-based approaches. This newly designed LOCK approach based on homology-directed repair is a powerful tool suitable for gene-sized fragment integration that is urgently needed for genetic engineering, gene therapies, and synthetic biology. [ABSTRACT FROM AUTHOR]