Application of deep learning classification model for regional evaluation of roof pressure support evolution effects over time in coal mining face.

Hydraulic support leg pressure serves as a crucial indicator for assessing work face support quality. Current evaluation methods for support quality primarily concentrate on static analyses-like inadequate initial support force, pressure overrun, and uneven bracket force-while neglecting dynamic column pressure changes. This paper introduces a model for assessing hydraulic support quality using deep learning techniques. Real-time data is preprocessed into a spatio-temporal pressure sub-matrix sample, which is then inputted into the model. This process assesses the support quality type and characterizes its dynamic evolution within the area. The model facilitates the identification of dynamic support quality effects in the working face area, aiding operators in making targeted adjustments to hydraulic support status. Experimental results revealed that the optimized LeNet-5 network-adjusting parameters like convolutional layer count, kernel size, and ReLU activation function-achieved the highest classification accuracy of 85.25 % for support quality, surpassed other networks. Furthermore, the improved LeNet-5 network outperformed other networks in both F1 score and recall. Additionally, the improved LeNet-5 network achieved faster convergence to the optimal solution, accelerated training speed. This highlighted its advantages in evaluating the spatio-temporal support quality of hydraulic supports in smart mining operations.

Engineering Plateau Phase Transition in Quantum Anomalous Hall Multilayers.

The plateau phase transition in quantum anomalous Hall (QAH) insulators corresponds to a quantum state wherein a single magnetic domain gives way to multiple domains and then reconverges back to a single magnetic domain. The layer structure of the sample provides an external knob for adjusting the Chern number C of the QAH insulators. Here, we employ molecular beam epitaxy to grow magnetic topological insulator multilayers and realize the magnetic field-driven plateau phase transition between two QAH states with odd Chern number change ΔC. We find that critical exponents extracted for the plateau phase transitions with ΔC = 1 and ΔC = 3 in QAH insulators are nearly identical. We construct a four-layer Chalker-Coddington network model to understand the consistent critical exponents for the plateau phase transitions with ΔC = 1 and ΔC = 3. This work will motivate further investigations into the critical behaviors of plateau phase transitions with different ΔC in QAH insulators.

Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study.

BACKGROUND: Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model. METHODS: A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort. CONCLUSIONS: We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.

MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods.

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML potentials (e.g., KREG, ANI, and MACE). Combinations of QM and ML methods can also be used. While the user can build their own combinations, we provide AIQM1, which is based on Δ-learning and can be used out-of-the-box. We showcase how AIQM1 reproduces the isomerization quantum yield of trans-azobenzene at a low cost. We provide example scripts that, in dozens of lines, enable the user to obtain the final population plots by simply providing the initial geometry of a molecule. Thus, those scripts perform geometry optimization, normal mode calculations, initial condition sampling, parallel trajectories propagation, population analysis, and final result plotting. Given the capabilities of MLatom to be used for training different ML models, this ecosystem can be seamlessly integrated into the protocols building ML models for nonadiabatic dynamics. In the future, a deeper and more efficient integration of MLatom with Newton-X will enable a vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API.

Air-sea exchange of PAHs in the Taiwan Strait: Seasonal dynamics and regulation mechanisms revealed by machine learning approach.

In this study, to understand the seasonal dynamics of air-sea exchange and its regulation mechanisms, we investigated polycyclic aromatic hydrocarbons (PAHs) at the air-sea interface in the western Taiwan Strait in combination with measurements and machine learning (ML) predictions. For 3-ring PAHs and most of 4- to 6-ring, volatilization and deposition fluxes were observed, respectively. Seasonal variations in air-sea exchange flux suggest the influence of monsoon transitions. Results of interpretable ML approach (XGBoost) indicated that volatilization of 3-ring PAHs was significantly controlled by dissolved PAH concentrations (contributed 24.0 %), and the gaseous deposition of 4- to 6-ring PAHs was related to more contaminated air masses originating from North China during the northeast monsoon. Henry's law constant emerged as a secondary factor, influencing the intensity of air-sea exchange, particularly for low molecular weight PAHs. Among environmental parameters, notably high wind speed emerges as the primary factor and biological pump's depletion of PAHs in surface seawater amplifies the gaseous deposition process. The distinct dynamics of exchanges at the air-water interface for PAHs in the western TWS can be attributed to variations in primary emission intensities, biological activity, and the inconsistent pathways of long-range atmospheric transport, particularly within the context of the monsoon transition.

Pairwise machine learning-based automatic diagnostic platform utilizing CT images and clinical information for predicting radiotherapy locoregional recurrence in elderly esophageal cancer patients.

OBJECTIVE: To investigate the feasibility and accuracy of predicting locoregional recurrence (LR) in elderly patients with esophageal squamous cell cancer (ESCC) who underwent radical radiotherapy using a pairwise machine learning algorithm. METHODS: The 130 datasets enrolled were randomly divided into a training set and a testing set in a 7:3 ratio. Clinical factors were included and radiomics features were extracted from pretreatment CT scans using pyradiomics-based software, and a pairwise naive Bayes (NB) model was developed. The performance of the model was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). To facilitate practical application, we attempted to construct an automated esophageal cancer diagnosis system based on trained models. RESULTS: To the follow-up date, 64 patients (49.23%) had experienced LR. Ten radiomics features and two clinical factors were selected for modeling. The model demonstrated good prediction performance, with area under the ROC curve of 0.903 (0.829-0.958) for the training cohort and 0.944 (0.849-1.000) for the testing cohort. The corresponding accuracies were 0.852 and 0.914, respectively. Calibration curves showed good agreement, and DCA curve confirmed the clinical validity of the model. The model accurately predicted LR in elderly patients, with a positive predictive value of 85.71% for the testing cohort. CONCLUSIONS: The pairwise NB model, based on pre-treatment enhanced chest CT-based radiomics and clinical factors, can accurately predict LR in elderly patients with ESCC. The esophageal cancer automated diagnostic system embedded with the pairwise NB model holds significant potential for application in clinical practice.

Coagulation and fibrinolytic markers offer utility when distinguishing between benign and malignant gallbladder tumors: A cross-sectional study.

BACKGROUND: The metabolic or proliferative abnormalities that are characteristic of tumor cells can lead to abnormal fibrinolysis or coagulation system activity, with certain tumors exhibiting hypercoagulability or existing in a fibrinolytic state. However, the utility of biomarkers of coagulation and fibrinolysis when seeking to differentiate between benign gallbladder disease and malignant gallbladder tumors remains uncertain. METHODS: This study included a total of 81 patients with benign gallbladder polyps and 94 patients with malignant gallbladder tumors. Pre-biopsy or pretreatment levels of PT, APTT, FIB, D-dimer, FDP, PLT, PIC, TAT, TM, and t-PAIC from these patients were compared using Mann-Whitney tests. The baseline data of the patients were analyzed using chi-square tests, and the diagnostic utility of these biomarkers in distinguishing between benign and malignant gallbladder lesions was evaluated using ROC curves, and Spearman correlation analysis was employed to assess the correlation between these indicators and tumor parameters. RESULTS: The average age of malignant gallbladder tumor group was higher than benign gallbladder polyp group. And the base line analysis showed that there was a statistic difference in age, history of smoking, drinking, biliary tract disease, BMI of over weight between these two groups. In patients with malignant gallbladder tumors, FIB, D-dimer, FDP, PIC, TAT, TM, and t-PAIC levels were significantly elevated relative to those in patients affected by benign gallbladder polyp. The AUC for FIB, D-dimer, and FDP was 0.8469, 0.6514, 0.5950, while for PIC, TAT, TM, t-PAIC and four biomarker combined diagnosed was 0.8455, 0.6554, 0.7130, 0.6806, and 0.8859. Among these, TM was associated with the vascular invasion of tumor patients; TAT and t-PAIC were associated with neural invasion; D-dimer and FDP were related to the maximum tumor diameter; and FDP had a certain correlation with the tumor stage. CONCLUSIONS: In gallbladder tumor patients, conventional coagulation metrics like FIB, D-dimer, and FDP, as well as newer thrombotic indicators such as PIC, TAT, TM, and t-PAIC, were obviously increased. Correlations with tumor parameters suggested their potential as biomarkers to distinguish benign from malignant gallbladder growths.

High-throughput screening of 222 pesticides in road environments in a megacity of northern China: A new approach to urban population exposure.

A large number of pesticides have been widely manufactured and applied, and are released into the environment with negative impact on human health. Pesticides are largely used in densely populated urban environments, in green zones, along roads and on private properties. In order to characterize the potential exposure related health effects of pesticide and their occurrence in the urban environment, 222 pesticides were screened and quantified in 228 road dust and 156 green-belt soil samples in autumn and spring from Harbin, a megacity in China, using GC-MS/MS base quantitative trace analysis. The results showed that a total of 33 pesticides were detected in road dust and green-belt soil, with the total concentrations of 650 and 236 ng/g (dry weight = dw), respectively. The concentrations of pesticides in road dust were significantly higher than that in green-belt soil. Pesticides in the environment were influenced by the seasons, with the highest concentrations of insecticides in autumn and the highest levels of herbicides in spring. In road dust, the concentrations of highways in autumn and spring (with the mean values of 94.1 and 68.2 ng/g dw) were much lower than that of the other road classes (arterial roads, sub-arterial roads and branch ways). Whereas in the green-belt soil, there was no significant difference in the concentration of pesticides between the different road classes. A first risk assessment was conducted to evaluate the potential adverse health effects of the pesticides, the results showed that the highest hazard index (HI) for a single pesticide in dust and soil was 0.12, the hazard index for children was higher than that for adults, with an overall hazard index of less than 1. Our results indicated that pesticide levels do not have a significant health impact on people.

A novel zwitterionic magnetic nanocomposite developed for non-invasive speciation analysis of inorganic chromium.

3-(2-Aminoethylamino)propyltriethoxysilane and carboxyethylsilanetriol sodium salt were grafted on silica-coated Fe3O4 nanoparticles via sol-gel process to prepare novel amine- and carboxyl-bifunctionalized magnetic nanocomposites (SMNPs-(NH2 + COOH)). After well characterized, this doubly functionalized material was used as magnetic solid-phase extraction (MSPE) adsorbent to separate and enrich inorganic chromium species followed by inductively coupled plasma-mass spectrometry detection. The optimization of MSPE operation parameters including pH was conducted. It is reasonably elucidated that the adsorption mechanisms of zwitterionic SMNPs-(NH2 + COOH) towards chromium species are electrostatic and/or coordination interactions. Cr(VI) and Cr(III) can be adsorbed around pH 3.0 and around 10.0 respectively with strong anti-interference ability not only from other co-existing ions but also from the two labile species each other, and eluted by dilute nitric acid solution. With a 15-fold enrichment factor, the limits of detection of Cr(VI) and Cr(III) were 0.008 and 0.009 µg L-1, respectively, profiting from the maximum adsorption capacities of 7.52 and 6.11 mg g-1. The just one magnetic extraction matrix based speciation scheme possesses excellent convenience and friendliness to Cr(VI) and Cr(III) without any oxidation or reduction prior to capture of these two species. This protocol has been successfully applied to the speciation analysis of inorganic chromium in real-world environmental water samples.

Molecular ferroelectric with low-magnetic-field magnetoelectricity at room temperature.

Magnetoelectric materials, which encompass coupled magnetic and electric polarizabilities within a single phase, hold great promises for magnetic controlled electronic components or electric-field controlled spintronics. However, the realization of ideal magnetoelectric materials remains tough due to the inborn competion between ferroelectricity and magnetism in both levels of symmetry and electronic structure. Herein, we introduce a methodology for constructing single phase paramagnetic ferroelectric molecule [TMCM][FeCl4], which shows low-magnetic-field magnetoelectricity at room temperature. By applying a low magnetic field (≤1 kOe), the halogen Cl‧‧‧Cl distance and the volume of [FeCl4]- anions could be manipulated. This structural change causes a characteristic magnetostriction hysteresis, resulting in a substantial deformation of ~10-4 along the a-axis under an in-plane magnetic field of 2 kOe. The magnetostrictive effect is further qualitatively simulated by density functional theory calculations. Furthermore, this mechanical deformation significantly dampens the ferroelectric polarization by directly influencing the overall dipole configuration. As a result, it induces a remarkable α31 component (~89 mV Oe-1 cm-1) of the magnetoelectric tensor. And the magnetoelectric coupling, characterized by the change of polarization, reaches ~12% under 40 kOe magnetic field. Our results exemplify a design methodology that enables the creation of room-temperature magnetoelectrics by leveraging the potent effects of magnetostriction.

Translocation of two-dimensional active polymers through nanopores using Langevin dynamics simulations.

The translocation of polymers through nanopores is a complex process influenced by various factors. In this study, the translocation behavior of a two-dimensional active polymer chain, comprised of a head active Brownian particle (ABP) and a tail passive polymer chain, through a nanopore is studied using Langevin dynamics simulations. Results show that the effect of the self-propulsion force of the ABP on the translocation differs significantly from the driving force inside the pore for traditional polymer translocations. Specifically, the translocation time τ initially increases with increasing the magnitude fs of the self-propulsion force and then decreases with a further increase in fs. A small fs lowers the potential barrier for the translocation and thus promotes slow translocations, whereas a large fs directly pulls the polymer chain through the nanopore following the scaling relation τ ∝ fs-1. Moreover, two asymptotic scaling relations between τ and polymer length N, τ ∝ Nα, are found, with the exponent α of about 2.5 for small fs or long N and the exponent α of about 1.4 for short active polymers with large fs. We discover that the slow rotation of the ABP accelerates the translocation process.

Regulation of Protein Conformation Enables Cell-Selective Targeting of Virus-Mimicking Nanoparticles for siRNA Therapy of Glioblastoma.

Orthotopic glioblastoma (GBM) has an aggressive growth pattern and complex pathogenesis, becoming one of the most common and deadly tumors of the central nervous system (CNS). The emergence of RNA therapies offers promise for the treatment of GBM. However, the efficient and precise delivery of RNA drugs to specific tumor cells in the brain with high cellular heterogeneity remains ongoing. Here, a strategy is proposed to regulate protein conformation through lipid nanoenvironments to custom-design virus-mimicking nanoparticles (VMNs) with excellent selective cell targeting capabilities, leading to efficient and precise delivery of small interfering RNA for effective treatment of GBM. The optimized VMNs not only retain the ability to cross the blood-brain barrier and release the RNA by lysosomal escape like natural viruses but also ensure precise enrichment in the GBM area. This study lays the conceptual foundation for the custom design of VMNs with superior cell-selective targeting capabilities and opens up the possibility of RNA therapies for the efficient treatment of GBM and CNS tumors.

The potential association between metabolic disorders and pulmonary tuberculosis: a Mendelian randomization study.

BACKGROUND: Metabolic disorders (MetDs) have been demonstrated to be closely linked to numerous diseases. However, the precise association between MetDs and pulmonary tuberculosis (PTB) remains poorly understood. METHOD: Summary statistics for exposure and outcomes from genome-wide association studies (GWASs) for exposures and outcomes were obtained from the BioBank Japan Project (BBJ) Gene-exposure dataset. The 14 clinical factors were categorized into three groups: metabolic laboratory markers, blood pressure, and the MetS diagnostic factors. The causal relationship between metabolic factors and PTB were analyzed using two-sample Mendelian Randomization (MR). Additionally, the direct effects on the risk of PTB were investigated through multivariable MR. The primary method employed was the inverse variance-weighted (IVW) model. The sensitivity of this MR analysis was evaluated using MR-Egger regression and the MR-PRESSO global test. RESULTS: According to the two-sample MR, HDL-C, HbA1c, TP, and DM were positively correlated with the incidence of active TB. According to the multivariable MR, HDL-C (IVW: OR 2.798, 95% CI 1.484-5.274, P = 0.001), LDL (IVW: OR 4.027, 95% CI 1.140-14.219, P = 0.03) and TG (IVW: OR 2.548, 95% CI 1.269-5.115, P = 0.009) were positively correlated with the occurrence of PTB. TC (OR 0.131, 95% CI 0.028-0.607, P = 0.009) was negatively correlated with the occurrence of PTB. We selected BMI, DM, HDL-C, SBP, and TG as the diagnostic factors for metabolic syndrome. DM (IVW, OR 1.219, 95% CI 1.040-1.429 P = 0.014) and HDL-C (IVW, OR 1.380, 95% CI 1.035-1.841, P = 0.028) were directly correlated with the occurrence of PTB. CONCLUSIONS: This MR study demonstrated that metabolic disorders, mainly hyperglycemia, and dyslipidemia, are associated with the incidence of active pulmonary tuberculosis.

Targeting cathepsin S promotes activation of OLF1-BDNF/TrkB axis to enhance cognitive function.

BACKGROUND: Cathepsin S (CTSS) is a cysteine protease that played diverse roles in immunity, tumor metastasis, aging and other pathological alterations. At the cellular level, increased CTSS levels have been associated with the secretion of pro-inflammatory cytokines and disrupted the homeostasis of Ca2+ flux. Once CTSS was suppressed, elevated levels of anti-inflammatory cytokines and changes of Ca2+ influx were observed. These findings have inspired us to explore the potential role of CTSS on cognitive functions. METHODS: We conducted classic Y-maze and Barnes Maze tests to assess the spatial and working memory of Ctss-/- mice, Ctss+/+ mice and Ctss+/+ mice injected with the CTSS inhibitor (RJW-58). Ex vivo analyses including long-term potentiation (LTP), Golgi staining, immunofluorescence staining of sectioned whole brain tissues obtained from experimental animals were conducted. Furthermore, molecular studies were carried out using cultured HT-22 cell line and primary cortical neurons that treated with RJW-58 to comprehensively assess the gene and protein expressions. RESULTS: Our findings reported that targeting cathepsin S (CTSS) yields improvements in cognitive function, enhancing both working and spatial memory in behavior models. Ex vivo studies showed elevated levels of long-term potentiation levels and increased synaptic complexity. Microarray analysis demonstrated that brain-derived neurotrophic factor (BDNF) was upregulated when CTSS was knocked down by using siRNA. Moreover, the pharmacological blockade of the CTSS enzymatic activity promoted BDNF expression in a dose- and time-dependent manner. Notably, the inhibition of CTSS was associated with increased neurogenesis in the murine dentate gyrus. These results suggested a promising role of CTSS modulation in cognitive enhancement and neurogenesis. CONCLUSION: Our findings suggest a critical role of CTSS in the regulation of cognitive function by modulating the Ca2+ influx, leading to enhanced activation of the BDNF/TrkB axis. Our study may provide a novel strategy for improving cognitive function by targeting CTSS.

FGF13 enhances the function of TRPV1 by stabilizing microtubules and regulates acute and chronic itch.

Itching is an aversive somatosensation that triggers the desire to scratch. Transient receptor potential (TRP) channel proteins are key players in acute and chronic itch. However, whether the modulatory effect of fibroblast growth factor 13 (FGF13) on acute and chronic itch is associated with TRP channel proteins is unclear. Here, we demonstrated that conditional knockout of Fgf13 in dorsal root ganglion neurons induced significant impairment in scratching behaviors in response to acute histamine-dependent and chronic dry skin itch models. Furthermore, FGF13 selectively regulated the function of the TRPV1, but not the TRPA1 channel on Ca2+ imaging and electrophysiological recordings, as demonstrated by a significant reduction in neuronal excitability and current density induced by TRPV1 channel activation, whereas TRPA1 channel activation had no effect. Changes in channel currents were also verified in HEK cell lines. Subsequently, we observed that selective modulation of TRPV1 by FGF13 required its microtubule-stabilizing effect. Furthermore, in FGF13 knockout mice, only the overexpression of FGF13 with a tubulin-binding domain could rescue TRP channel function and the impaired itch behavior. Our findings reveal a novel mechanism by which FGF13 is involved in TRPV1-dependent itch transduction and provide valuable clues for alleviating pathological itch syndrome.

Value of Molecular Typing Combined with Integrated Positron Emission Tomography/Magnetic Resonance Imaging in Risk Stratification of Endometrial Cancer.

Objective: In this study, we investigated the value of molecular typing combined with integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) semi-quantitative indices in endometrial cancer risk stratification. Methods: A retrospective study was conducted on 86 patients who were pathologically diagnosed with endometrial cancer and underwent surgical treatment after curettage at the Department of Obstetrics and Gynecology, Xuanwu Hospital, Capital Medical University between January 2017 and March 2023. Prior to surgery, each patient underwent integrated PET/MRI examination. The postoperative samples were subjected to pathological diagnosis, immunohistochemistry, and POLE gene sequencing. The differences in clinicopathological features between the four molecular subtypes and the differences in integrated PET/MRI semi-quantitative indexes (SUV max, ADC min) between the four molecular subtypes were analyzed. The cutoff value of molecular typing combined with integrated PET/MRI semi-quantitative indices for endometrial cancer risk stratification was determined. Results: There were statistically significant differences in pathological types and tumor grades among the four molecular subtypes of endometrial cancer. The values of the four integrated PET/MRI semi-quantitative indices (SUV max and ADC min) of the molecular subtypes were statistically different. The SUV max was greater in the p53abn mutation group than in the POLE mutation group (P < 0.05). The ADC minimum of the POLE mutation group and the MMR-d group was lower than the NSMP group (P < 0.05). Molecular typing combined with the integrated PET/MRI semi-quantitative SUV max index can predict the low/medium risk group of endometrial cancer and the medium-high/high risk group, and the cut-off value of SUV max for predicting the risk of early endometrial cancer was 14.72 (sensitivity 66.7%, specificity 68.7%). Conclusion: Molecular typing combined with integrated PET/MRI semi-quantitative indicators is useful to achieve risk stratification in patients diagnosed with endometrial cancer and guide individualized treatment.

METTL5 promotes cell proliferation, invasion, and migration by up-regulating Toll-like receptor 8 expression in colorectal cancer.

BACKGROUND: N6-methyladenosine (m6A) modification represents the predominant alteration found in eukaryotic messenger RNA and plays a crucial role in the progression of various tumors. However, despite its significance, the comprehensive investigation of METTL5, a key m6A methyltransferase, in colorectal cancer (CRC) remains limited. AIM: To investigate the role of METTL5 in CRC. METHODS: We assessed METTL5 expression levels in clinical samples obtained from CRC patients as well as in CRC cell lines. To elucidate the downstream targets of METTL5, we performed RNA-sequencing analysis coupled with correlation analysis, leading us to identify Toll-like receptor 8 (TLR8) as a potential downstream target. In vitro functional assessments of METTL5 and TLR8 were conducted using CCK-8 assays, scratch assays, as well as assays measuring cell migration and invasion. RESULTS: Our findings reveal a pronounced upregulation of METTL5 expression in both CRC cells and tissues, which correlated significantly with an unfavorable prognosis. In vitro experiments unequivocally demonstrated the oncogenic role of METTL5, as evidenced by its promotion of CRC cell proliferation, invasion, and migration. Notably, we identified TLR8 as a downstream target of METTL5, and subsequent down-regulation of TLR8 led to a significant inhibition of CRC cell proliferation, invasion, and tumor growth. CONCLUSION: The heightened expression of METTL5 in CRC is strongly associated with clinicopathological features and a poor prognosis, thereby underscoring its potential utility as a critical marker for facilitating early diagnosis and prognostication in CRC.

Comparison of genomic prediction methods for early growth traits of Inner Mongolia cashmere goats based on multi trait models.

Inner Mongolia cashmere goat is an excellent livestock breed formed through long-term natural selection and artificial breeding, and is currently a world-class dual-purpose breed producing cashmere and meat. Multi trait animal model is considered to significantly improve the accuracy of genetic evaluation in livestock and poultry, enabling indirect selection between traits. In this study, the pedigree, genotype, environment, and phenotypic records of early growth traits of Inner Mongolia cashmere goats were used to build multi trait animal model., Then three methods including ABLUP, GBLUP, and ssGBLUP wereused to estimate the genetic parameters and genomic breeding values of early growth traits (birth weight, weaning weight, average daily weight gain before weaning, and yearling weight). The accuracy and reliability of genomic estimated breeding value are further evaluated using the five fold cross validation method. The results showed that the heritability of birth weight estimated by three methods was 0.13-0.15, the heritability of weaning weight was 0.13-0.20, heritability of daily weight gain before weaning was 0.11-0.14, and the heritability of yearling weight was 0.09-0.14, all of which belonged to moderate to low heritability. There is a strong positive genetic correlation between weaning weight and daily weight gain before weaning, daily weight gain before weaning and yearling weight, with correlation coefficients of 0.77-0.79 and 0.56-0.67, respectively. The same pattern was found in phenotype correlation among traits. The accuracy of the estimated breeding values by ABLUP, GBLUP, and ssGBLUP methods for birth weight is 0.5047, 0.6694, and 0.7156, respectively; the weaning weight is 0.6207, 0.6456, and 0.7254, respectively; the daily weight gain before weaning was 0.6110, 0.6855, and 0.7357 respectively; and the yearling weight was 0.6209, 0.7155, and 0.7756, respectively. In summary, the early growth traits of Inner Mongolia cashmere goats belong to moderate to low heritability, and the speed of genetic improvement is relatively slow. The genetic improvement of other growth traits can be achieved through the selection of weaning weight. The ssGBLUP method has the highest accuracy and reliability in estimating genomic breeding value of early growth traits in Inner Mongolia cashmere goats, and is significantly higher than that from ABLUP method, indicating that it is the best method for genomic breeding of early growth weight in Inner Mongolia cashmere goats.

Deciphering DED assembly mechanisms in FADD-procaspase-8-cFLIP complexes regulating apoptosis.

Fas-associated protein with death domain (FADD), procaspase-8, and cellular FLICE-inhibitory proteins (cFLIP) assemble through death-effector domains (DEDs), directing death receptor signaling towards cell survival or apoptosis. Understanding their three-dimensional regulatory mechanism has been limited by the absence of atomic coordinates for their ternary DED complex. By employing X-ray crystallography and cryogenic electron microscopy (cryo-EM), we present the atomic coordinates of human FADD-procaspase-8-cFLIP complexes, revealing structural insights into these critical interactions. These structures illustrate how FADD and cFLIP orchestrate the assembly of caspase-8-containing complexes and offer mechanistic explanations for their role in promoting or inhibiting apoptotic and necroptotic signaling. A helical procaspase-8-cFLIP hetero-double layer in the complex appears to promote limited caspase-8 activation for cell survival. Our structure-guided mutagenesis supports the role of the triple-FADD complex in caspase-8 activation and in regulating receptor-interacting protein kinase 1 (RIPK1). These results propose a unified mechanism for DED assembly and procaspase-8 activation in the regulation of apoptotic and necroptotic signaling across various cellular pathways involved in development, innate immunity, and disease.

Occurrence, seasonal variations, and fate of household and personal care chemicals in a wastewater treatment plant with Bacillus bioreactor process.

Household and personal care chemicals (HPCCs) constitute a significant component of everyday products, with their global usage on the rise. HPCCs are eventually discharged into municipal wastewater treatment plants (WWTPs). However, the behaviors of HPCCs inside the Bacillus Bioreactor (BBR) process, including their prevalence, fate, and elimination mechanisms, remain underexplored. Addressing this gap, our study delves into samples collected from a BBR process at a significant WWTP in the northeast of China. Our results spotlight the dominance of linear alkylbenzene sulfonates (LASs) in the influent with concentrations ranging between 238 and 789 µg/L, much higher than the other HPCC concentrations, and remained dominant in the subsequent treatment units. After treatment using the BBR process, the concentrations of HPCCs in the effluent were diminished. Examination of different treatment units underscores the grit chamber removed over 60% of higher-concentration HPCCs, while the performance of the (RBC) tank needs to be improved. Except for the ultraviolet radiation (UV)-filters, seasonal variations exert minimal impact on the concentrations and removal efficiencies of other HPCCs in the BBR process. According to the mass balance analysis, the important mechanisms for HPCC removal were biodegradation and sludge adsorption. Also, the octocrylene (OCT) concerns raised by the environmental risk assessment of the HPCCs residuals in the final effluent, indicate a moderate risk to the surrounding aquatic environment (0.1 < RQ < 1), whereas other HPCCs have a lower risk level (RQ < 0.1). Overall, the research offers new perspectives on the fate and elimination mechanisms of HPCCs throughout the BBR process.