High-resolution satellite estimates of coal mine methane emissions from local to regional scales in Shanxi, China.

Coal mines are significant anthropogenic sources of methane emissions, detectable and traceable from high spatial resolution satellites. Nevertheless, estimating local or regional-scale coal mine methane emission intensities based on high-resolution satellite observations remains challenging. In this study, we devise a novel interpolation algorithm based on high-resolution satellite observations (including Gaofen5-01A/02, Ziyuan-1 02D, PRISMA, GHGSat-C1 to C5, EnMAP, and EMIT) and conduct assessments of annual mean coal mine methane emissions in Shanxi Province, China, one of the world's largest coal-producing regions, spanning the period 2019 to 2023 across various scales: point-source, local, and regional. We use high-resolution satellite observations to perform interpolation-based estimations of methane emissions from three typical coal-mining areas. This approach, known as IPLTSO (Interpolation based on Satellite Observations), provides spatially explicit maps of methane emission intensities in these areas, thereby providing a novel local-scale coal mine methane emission inventory derived from high-resolution top-down observations. For regional-scale estimation and mapping, we utilize high-resolution satellite data to complement and substitute facility-level emission inventories for interpolation (IPLTSO+GCMT, Interpolation based on Satellite Observations and Global Coal Mine Tracker). We evaluate our IPLTSO and IPLTSO+GCMT estimation with emission inventories, top-down methane emission estimates from TROPOMI observations, and TROPOMI's methane concentration enhancements. The results suggest a notable right-skewed distribution of methane emission flux rates from coal mine point sources. Our IPLTSO+GCMT estimates the annual average coal mine methane emission in Shanxi Province from 2019 to 2023 at 8.9 ± 0.5 Tg/yr, marginally surpassing top-down inversion results from TROPOMI (8.5 ± 0.6 Tg/yr in 2019 and 8.6 ± 0.6 Tg/yr in 2020). Furthermore, the spatial patterns of methane emission intensity delineated by IPLTSO+GCMT and IPLTSO closely mirror those observed in TROPOMI's methane enhancements. Our comparative assessment underscores the superior performance and substantial potential of the developed interpolation algorithm based on high-resolution satellite observations for multi-scale estimation of coal mine methane emissions.

GTAM: a molecular pretraining model with geometric triangle awareness.

MOTIVATION: Molecular representation learning is pivotal for advancing deep learning applications in quantum chemistry and drug discovery. Existing methods for molecular representation learning often fall short of fully capturing the intricate interactions within chemical bonds of 2D topological graphs and the multifaceted effects of 3D geometric conformations. RESULTS: To overcome these challenges, we present a novel contrastive learning strategy for molecular representation learning, named Geometric Triangle Awareness Model (GTAM). This method integrates innovative molecular encoders for both 2D graphs and 3D conformations, enabling the accurate capture of geometric dependencies among edges in graph-based molecular structures. Furthermore, GTAM is bolstered by the development of two contrastive training objectives designed to facilitate the direct transfer of edge information between 2D topological graphs and 3D geometric conformations, enhancing the functionality of the molecular encoders. Through extensive evaluations on a range of 2D and 3D downstream tasks, our model has demonstrated superior performance over existing approaches. AVAILABILITY AND IMPLEMENTATION: The test code and data of GTAM are available online at https://github.com/StellaHxy/GTAM.

Mercury export from a glacier-fed river of Mt. Meili, southeastern Tibetan Plateau.

Mercury (Hg), a global contaminant, can sink into cryosphere and be released into runoff through meltwater. The Tibetan Plateau (TP) has been witnessing ongoing shrinkage of alpine glaciers. However, the export of Hg from melting glacier is still sparsely reported. From October 16, 2020 to October 15, 2021, we conducted daily observations to study the variation in total Hg concentrations and its export to the Mingyong River, a glacier-fed river in southeastern TP. Results showed that the Hg concentrations were high during the monsoon season but low during the non-monsoon period. The Hg in runoff correlated with the concentrations of total suspended particulates (TSP) and dissolved inorganic carbon (DIC) during both monsoon and non-monsoon seasons (p < 0.01), and the correlation of Hg with other parameters showed seasonal variations. The input from meltwater, precipitation, and groundwater to riverine Hg were 8.3 g, 264.4 g, and 71.0 g, respectively, and the total export was 211.0 g (yield: 4.3 g/km2/year) in the hydrological year, indicating that Mingyong catchment act as a sink for Hg. For the entire TP, the annual export of Hg from glacier runoff was estimated to be 947.7 kg/year. Our study highlights the necessity for further investigations on Hg dynamics to understand the changes in the Hg cycle within glaciated aquatic ecosystems.

Role of AOX1 on RXR signaling regulates osteoblastogenesis in hPDLMSCs.

Alveolar bone loss resulting from periodontal disease ultimately leads to tooth loss. Periodontal ligament mesenchymal stem cells (PDLMSCs) are the tissue-specific cells responsible for maintaining and repairing the periodontal ligament, cementum, and alveolar bone. In this study, we explored the role of aldehyde oxidase 1 (AOX1) in regulating the osteoinduction of human periodontal ligament stem cells (hPDLMSCs). hPDLMSCs were isolated from clinically healthy donors, and AOX1 expression was assessed by comparing inducted and non-inducted hPDLMSCs. Remarkably, we observed a significant upregulation of AOX1 expression during osteoinduction, while AOX1 silencing resulted in the enhanced osteogenic potential of hPDLMSCs. Subsequent experiments and analysis unveiled the involvement of retinoid X receptor (RXR) signaling in the inhibition of osteogenesis in hPDLMSCs. Ligands targeting the RXR receptor mirrored the effects of AOX1 on osteogenesis, as evidenced by alterations in alkaline phosphatase (ALP) activity and bone formation levels. Collectively, these findings underscore the potential regulatory role of AOX1 via RXR signaling in the osteogenesis of hPDLMSCs. This elucidation is pivotal for advancing hPDLMSC-based periodontal regeneration strategies and lays the groundwork for the development of targeted therapeutic interventions aimed at enhancing bone formation in the context of periodontal disease.

Development and validation of nomograms to predict survival of neuroendocrine carcinoma in genitourinary system: A population-based retrospective study.

Neuroendocrine carcinoma (NEC) is a rare yet potentially perilous neoplasm. The objective of this study was to develop prognostic models for the survival of NEC patients in the genitourinary system and subsequently validate these models. A total of 7125 neuroendocrine neoplasm (NEN) patients were extracted. Comparison of survival in patients with different types of NEN before and after propensity score-matching (PSM). A total of 3057 patients with NEC, whose information was complete, were extracted. The NEC influencing factors were chosen through the utilization of the least absolute shrinkage and selection operator regression model (LASSO) and the Fine & Gary model (FGM). Furthermore, nomograms were built. To validate the accuracy of the prediction, the efficiency was verified using bootstrap self-sampling techniques and receiver operating characteristic curves. LASSO and FGM were utilized to construct three models. Confirmation of validation was achieved by conducting analyses of the area under the curve and decision curve. Moreover, the FGS (DSS analysis using FGM) model produced higher net benefits. To maximize the advantages for patients, the FGS model disregarded the influence of additional occurrences. Patients are expected to experience advantages in terms of treatment options and survival assessment through the utilization of these models.

Development and validation of nomograms to predict survival of primary cervical lymphoma: A surveillance, epidemiology, and end results (SEER) database analysis.

Due to the rarity of primary cervical lymphoma (PCL), the long-term survival of patients with cervical lymphoma and factors influencing survival are unknown. This study aimed to compare the survivals of patients with PCL and those with other cervical tumors and construct a clinical prediction model to assess the prognosis of patients with PCL. Patients with PCL from the Surveillance, Epidemiology, and End Results database were allocated randomly in a 7:3 ratio to the training and validation sets. Cox proportional hazard and Fine-Gray models were used to verify independent factors influencing overall survival (OS) and disease-specific survival (DSS), and nomograms were constructed. Receiver operating characteristic curve analysis and decision curve analysis (DCA) were used to test the performance and clinical utility of the models, respectively. We included 206 patients with PCL. The areas under the curves (AUCs) and DCA showed that all models had clinical benefits; The models constructed in this study had a predictive performance for patients with PCL. It can guide clinicians to rationalize the treatment plan for patients.

The association between three prevalent autoimmune disorders and the likelihood of developing prostate cancer: a Mendelian randomization study.

Numerous studies establish a significant correlation between autoimmune disorders (AIDs) and prostate cancer (PCa). Our Mendelian randomization (MR) analysis investigates the potential connection between rheumatoid arthritis (RA) and PCa, aiming to confirm causal links between systemic lupus erythematosus (SLE), hyperthyroidism, and PCa. Summary statistics from genome-wide association studies provided data on PCa and three AIDs. MR analysis, using IVW as the main approach, assessed causal relationships, validated by sensitivity analysis. IVW revealed a correlation between genetically anticipated RA and PCa, notably in Europeans (OR = 1.03; 95% CI 1.01-1.04, p = 2*10-5). Evidence supported a lower PCa risk in individuals with SLE (OR = 0.94; 95% CI 0.91-0.97, p = 2*10-4) and hyperthyroidism (OR = 0.02; 95% CI 0.001-0.2, p = 2*10-3). Weighted mode and median confirmed these findings. No pleiotropic effects were observed, and MR heterogeneity tests indicated dataset homogeneity. Our study establishes a causal link between RA, SLE, hyperthyroidism, and PCa.

Westerlies-driven transboundary transport of atmospheric mercury to the north-central Tibetan Plateau.

The transboundary mercury (Hg) pollution has caused adverse effects on fragile ecosystems of the Tibetan Plateau (TP). Yet, knowledge of transport paths and source regions of atmospheric Hg on the inland TP remains poor. Continuous measurements of atmospheric total gaseous mercury (TGM) were conducted in the central TP (Tanggula station, 5100 m a.s.l., June-October). Atmospheric TGM level at Tanggula station (1.90 ± 0.30 ng m-3) was higher than the background level in the Northern Hemisphere. The identified high-potential source regions of atmospheric TGM were primarily located in the northern South Asia region. TGM concentrations were lower during the Indian summer monsoon (ISM)-dominant period (1.81 ± 0.25 ng m-3) than those of the westerly-receding period (2.18 ± 0.40 ng m-3) and westerly-intensifying period (1.91 ± 0.26 ng m-3), contrary to the seasonal pattern in southern TP. The distinct TGM minima during the ISM-dominant period indicated lesser importance of ISM-transported Hg to Tanggula station located in the northern boundary of ISM intrusion, compared to stations in proximity to South and Southeast Asia source regions. Instead, from the ISM-dominant period to the westerly-intensifying period, TGM concentrations showed an increasing trend as westerlies intensified, indicating the key role of westerlies in transboundary transport of atmospheric Hg to the inland TP.

Protocol for automated N-glycan sequencing using mass spectrometry and computer-assisted intelligent fragmentation.

Biological functions of glycans are intimately linked to fine details in branches and linkages, which make structural identification extremely challenging. Here, we present a protocol for automated N-glycan sequencing using multi-stage mass spectrometry (MSn). We describe steps for release/purification and derivation of glycans and procedures for MSn scanning. We then detail "glycan intelligent precursor selection" to computationally guide MSn experiments. The protocol can be used for both discrete individual glycans and isomeric glycan mixtures. For complete details on the use and execution of this protocol, please refer to Sun et al.,1 Huang et al.,2 and Huang et al.3.

Renal pelvis metastasis following surgery for breast angiosarcoma: a case report and literature review.

Renal metastasis of breast angiosarcoma is rare. This article reports the medical records of a patient diagnosed with breast angiosarcoma who underwent radical mastectomy and was found to have multiple lung metastases 3 years after surgery and renal pelvic metastasis 4 years after surgery. The patient underwent robot-assisted laparoscopic radical nephroureterectomy and sleeve resection of the intramural segment of the ureter, and postoperative pathology and immunohistochemical staining confirmed the diagnosis of renal pelvic metastasis of breast angiosarcoma. The patient received anlotinib for lung metastases following surgery and was followed up for 4 months after surgery. Currently, the patient has symptoms of coughing and hemoptysis but no other discomfort. The diagnosis and treatment of this rare malignant tumor remain challenging.

A novel polycyclic quinazoline and three quinolines alkaloids from marine-derived fungus trichoderma longibrachiatum QD01 with anti-bacterial and anti-quorum sensing activities.

A novel polycyclic quinazoline alkaloid (1) along with one new natural quinoline alkaloid (2) and two known quinoline alkaloids (3,4) were isolated from the marine-derived fungus Trichoderma longibrachiatum QD01. Structural determinations of those isolates were established by comprehensive spectroscopic analyses and literature comparison. Single-crystal X-ray diffraction analysis of novel compound verified its structure and stereochemistry, representing the first characterised crystal structure of a trimeric-type of tetrahydroquinazoline. Compound 4 exhibited potential antibacterial and anti-quorum sensing activity against C. violaceum and C. violaceum CV026. The sub-MIC of 4 observably decreased the violacein production in C. violaceum CV026 by 55% on 15 µg/mL. Furthermore, molecular docking results revealed that 4 has stronger binding interactions with CviR receptor than ligand C6-HSL with lower binding energy of -8.68 kcal/mol. Hydrogen bond and π-π interactions formed by Trp84, Tyr88, Trp111, and Phe126 were predicted to play an important role in the inhibition against C. violaceum CV026.

Synthesizing Pd-based high entropy alloy nanoclusters for enhanced oxygen reduction.

We report the synthesis of uniform Pd-based high-entropy alloy clusters via rapid Joule heating. The quinary PdMnFeCuNi clusters exhibit 4.95 times higher mass activity than the Commercial Pt/C for the oxygen reduction reaction, and outstanding stability with only 2 mV decay in the half-wave potential after 20 000 cycles of testing.

Protein-centric omics analysis reveals circulating complements linked to non-viral liver diseases as potential therapeutic targets.

BACKGROUND/AIMS: To evaluate the causal correlation between complement components and non-viral liver diseases and their potential use as druggable targets. METHODS: We conducted Mendelian randomization (MR) to assess the causal role of circulating complements in the risk of non-viral liver diseases. A complement-centric protein interaction network was constructed to explore biological functions and identify potential therapeutic options. RESULTS: In the MR analysis, genetically predicted levels of complement C1q C chain (C1QC) were positively associated with the risk of autoimmune hepatitis (odds ratio 1.125, 95% confidence interval 1.018-1.244), while complement factor H-related protein 5 (CFHR5) was positively associated with the risk of primary sclerosing cholangitis (PSC;1.193, 1.048- 1.357). On the other hand, CFHR1 (0.621, 0.497-0.776) and CFHR2 (0.824, 0.703-0.965) were inversely associated with the risk of alcohol-related cirrhosis. There were also significant inverse associations between C8 gamma chain (C8G) and PSC (0.832, 0.707-0.979), as well as the risk of metabolic dysfunction-associated steatotic liver disease (1.167, 1.036-1.314). Additionally, C1S (0.111, 0.018-0.672), C7 (1.631, 1.190-2.236), and CFHR2 (1.279, 1.059-1.546) were significantly associated with the risk of hepatocellular carcinoma. Proteins from the complement regulatory networks and various liver diseaserelated proteins share common biological processes. Furthermore, potential therapeutic drugs for various liver diseases were identified through drug repurposing based on the complement regulatory network. CONCLUSION: Our study suggests that certain complement components, including C1S, C1QC, CFHR1, CFHR2, CFHR5, C7, and C8G, might play a role in non-viral liver diseases and could be potential targets for drug development.

Molecular characteristics and pathogenicity of a novel chicken astrovirus variant.

It is well-established that the genetic diversity, regional prevalence, and broad host range of astroviruses significantly impact the poultry industry. In July 2022, a small-scale commercial broiler farm in China reported cases of growth retardation and a 3% mortality rate. From chickens displaying proventriculitis and pancreatitis, three chicken astroviruses (CAstV) isolates were obtained and named SDAU2022-1-3. Complete genomic sequencing and analysis revealed the unique characteristics of these isolates from known CAstV strains in ORF1a, ORF1b, and ORF2 genes, characterized by an unusually high variability. Analysis of amino acid mutations in ORF1a, ORF1b, and ORF2 indicated that the accumulation of these mutations played a pivotal role in the emergence of the variant strain. Inoculation experiments demonstrated that affected chickens exhibited liver and kidney enlargement, localized proventricular hemorrhage, and a dark reddish-brown appearance in about two-thirds of the pancreas. Histopathological examination unveiled hepatic lymphocytic infiltration, renal tubular epithelial cell swelling, along with lymphocytic proventriculitis and pancreatitis. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis indicated viremia and viral shedding at 3 days post-infection (dpi). The proventriculus displayed the highest viral loads, followed by the liver, kidney, duodenum, and pancreas. Liver parameters (AST and ALT) and kidney parameters (UA and UN) demonstrated mild damage consistent with earlier findings. While the possibility of new mutations in the ORF2 gene of CAstV causing proventriculitis and pancreatitis warrants further investigation, these findings deepen our comprehension of CAstV's pathogenicity in chickens. Additionally, they serve as valuable references for subsequent research endeavors.

EMNGly: predicting N-linked glycosylation sites using the language models for feature extraction.

MOTIVATION: N-linked glycosylation is a frequently occurring post-translational protein modification that serves critical functions in protein folding, stability, trafficking, and recognition. Its involvement spans across multiple biological processes and alterations to this process can result in various diseases. Therefore, identifying N-linked glycosylation sites is imperative for comprehending the mechanisms and systems underlying glycosylation. Due to the inherent experimental complexities, machine learning and deep learning have become indispensable tools for predicting these sites. RESULTS: In this context, a new approach called EMNGly has been proposed. The EMNGly approach utilizes pretrained protein language model (Evolutionary Scale Modeling) and pretrained protein structure model (Inverse Folding Model) for features extraction and support vector machine for classification. Ten-fold cross-validation and independent tests show that this approach has outperformed existing techniques. And it achieves Matthews Correlation Coefficient, sensitivity, specificity, and accuracy of 0.8282, 0.9343, 0.8934, and 0.9143, respectively on a benchmark independent test set.

Development and validation of machine-learning models for the difficulty of retroperitoneal laparoscopic adrenalectomy based on radiomics.

Objective: The aim is to construct machine learning (ML) prediction models for the difficulty of retroperitoneal laparoscopic adrenalectomy (RPLA) based on clinical and radiomic characteristics and to validate the models. Methods: Patients who had undergone RPLA at Shanxi Bethune Hospital between August 2014 and December 2020 were retrospectively gathered. They were then randomly split into a training set and a validation set, maintaining a ratio of 7:3. The model was constructed using the training set and validated using the validation set. Furthermore, a total of 117 patients were gathered between January and December 2021 to form a prospective set for validation. Radiomic features were extracted by drawing the region of interest using the 3D slicer image computing platform and Python. Key features were selected through LASSO, and the radiomics score (Rad-score) was calculated. Various ML models were constructed by combining Rad-score with clinical characteristics. The optimal models were selected based on precision, recall, the area under the curve, F1 score, calibration curve, receiver operating characteristic curve, and decision curve analysis in the training, validation, and prospective sets. Shapley Additive exPlanations (SHAP) was used to demonstrate the impact of each variable in the respective models. Results: After comparing the performance of 7 ML models in the training, validation, and prospective sets, it was found that the RF model had a more stable predictive performance, while xGBoost can significantly benefit patients. According to SHAP, the variable importance of the two models is similar, and both can reflect that the Rad-score has the most significant impact. At the same time, clinical characteristics such as hemoglobin, age, body mass index, gender, and diabetes mellitus also influenced the difficulty. Conclusion: This study constructed ML models for predicting the difficulty of RPLA by combining clinical and radiomic characteristics. The models can help surgeons evaluate surgical difficulty, reduce risks, and improve patient benefits.

Development and validation of nomograms to predict survival of primary adrenal lymphoma: a population-based retrospective study.

While it is known that accurate evaluation of overall survival (OS) and disease-specific survival (DSS) for patients with primary adrenal lymphoma (PAL) can affect their prognosis, no stable and effective prediction model exists. This study aimed to develop prediction models to evaluate survival. This study enrolled 5448 patients with adrenal masses from the SEER Program. The influencing factors were selected using the least absolute shrinkage and selection operator regression model (LASSO) and Fine and Gray model (FGM). In addition, nomograms were constructed. Receiver operating characteristic curves and bootstrap self-sampling methods were used to verify the discrimination and consistency of the nomograms. The independent influencing factors for PAL survival were selected by LASSO and FGM, and three models were built: the OS, DSS, and FGS (DSS analysis by FGM) model. The areas under the curve and decision curve analyses indicated that the models were valid. This study developed survival prediction models to predict OS and DSS of patients with PAL. The FGS model was more accurate than the DSS model in the short term. Above all, these models should offer benefits to patients with PAL in terms of the treatment modality choice and survival evaluation.

Tensile-Strained Holey Pd Metallene toward Efficient and Stable Electrocatalysis.

Noble metal-based metallenes are attracting intensive attention in energy catalysis, but it is still very challenging to precisely control the surface structures of metallenes for higher catalytic properties on account of their intrinsic thermodynamic instability. Herein, the synthesis of tensile-strained holey Pd metallene by oxidative etching is reported using hydrogen peroxide, which exhibits highly enhanced catalytic activity and stability in comparison with normal Pd metallene toward both oxygen reduction reaction and formic acid oxidation. The pre-prepared Pd metallene functions as a catalyst to decompose hydrogen peroxide, and the Pd atoms in amorphous regions of Pd metallene are preferentially removed by the introduced hydrogen peroxide during the etching process. The greatly enhanced ORR activity is mainly determined by the strong electrostatic repulsion between intermediate O* and the dopant O, which balances the adsorption strength of O* on Pd sites, ultimately endowing a weakened adsorption energy of O* on TH-Pd metallene. This work creates a facile and economical strategy to precisely shape metallene-based nanoarchitectures with broad applications for energy systems and sensing devices.

Predicting the antigenic evolution of SARS-COV-2 with deep learning.

The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infections. Gaining insights into potential antigenic changes is critical but challenging due to the vast sequence space. Here, we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithms to predict the viral fitness landscape and explore antigenic evolution via in silico directed evolution. By analyzing existing SARS-CoV-2 variants, MLAEP accurately infers variant order along antigenic evolutionary trajectories, correlating with corresponding sampling time. Our approach identified novel mutations in immunocompromised COVID-19 patients and emerging variants like XBB1.5. Additionally, MLAEP predictions were validated through in vitro neutralizing antibody binding assays, demonstrating that the predicted variants exhibited enhanced immune evasion. By profiling existing variants and predicting potential antigenic changes, MLAEP aids in vaccine development and enhances preparedness against future SARS-CoV-2 variants.

An accurate, efficient, and economical identification technology for black twig borer based on species-specific cytochrome C oxidase subunit I PCR assay.

Xylosandrus compactus (Eichhoff) (Coleoptera: Curculionidae, Scolytinae) is a worldwide invasive species that causes huge economic loss and environmental damage in many countries. Traditional morphological characteristics make it hard to identify scolytines due to their tiny size. Besides, the intercepted insect samples are incomplete, and the limitation of insect (larvae and pupae) morphology makes morphological identification more difficult. The majority of the damage is caused by adults and fungi that serve as nutrition for their larvae. They destroy plant trunks, branches, and twigs, affecting plant transport tissues in both weak and healthy plants. An accurate, efficient, and economical molecular identification technique for X. compactus not restricted by professional taxonomic knowledge is necessary. In the present study, a molecular identification tool based on the mitochondrial DNA gene, cytochrome C oxidase subunit I (COI) was developed. A species-specific COI (SS-COI) PCR assay was designed to identify X. compactus regardless of the developmental stage. Twelve scolytines commonly found in eastern China, namely Xylosandrus compactus, X. crassiusculus, X. discolor, X. germanus, X. borealis, X. amputates, X. eupatorii, X. mancus, Xyleborinus saxesenii, Euwallacea interjectus, E. fornicatus, and Acanthotomicus suncei, were included in the study. Additionally, specimens of X. compactus from 17 different areas in China, as well as a specimen collected from the United Stated, were also analyzed. Results demonstrated the accuracy and high efficiency of the assay, regardless of the developmental stage or the type of specimen. These features provide a good application prospect for fundamental departments and can be used to prevent the harmful consequences of the spread of X. compactus.