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Abstract Transformer-based methods have achieved excellent results in the field of time series forecasting due to their powerful ability to model sequences and capture their long-term dependencies. However, Transformer-based methods are still computationally inefficient and have high costs when modeling long sequences. In addition, previous work ignored the destruction of the original in-channel features by too many mixed features when performing channel mixing. To address the above issues, we introduce Mamba into the field of time series forecasting. The selective state-space model Mamba models long sequences more efficiently by expanding linearly according to the sequence length. Based on this, we propose Channel Mixing Mamba (CMMamba), which uses bidirectional Mamba to model sequences and uses a channel mixing mechanism to select appropriate mixed features to enhance the feature representation within the original channel. By introducing Mamba, we achieve efficient modeling of sequences. We also propose Deep Convolutional Structure (DCS) to learn cross-channel dependencies and temporal order information. Extensive experimental results on six real-world public datasets demonstrate the effectiveness of CMMamba.

Introduction:: Accurate identification of pulmonary arterial hypertension (PAH) in primary care and rural areas can be a challenging task. However, recent advancements in computer vision offer the potential for automated systems to detect PAH from echocardiography. Objectives:: Our aim was to develop a precise and efficient diagnostic model for PAH tailored to the unique requirements of intelligent diagnosis, especially in challenging locales like high-altitude regions. Methods:: We proposed the Chamber Attention Network (CAN) for PAH identification from echocardiographic images, trained on a dataset comprising 13,912 individual subjects. A convolutional neural network (CNN) for view classification was used to select the clinically relevant apical four chamber (A4C) and parasternal long axis (PLAX) views for PAH diagnosis. To assess the importance of different heart chambers in PAH diagnosis, we developed a novel Chamber Attention Module. Results:: The experimental results demonstrated that: 1) The substantial correspondence between our obtained chamber attention vector and clinical expertise suggested that our model was highly interpretable, potentially uncovering diagnostic insights overlooked by the clinical community. 2) The proposed CAN model exhibited superior image-level accuracy and faster convergence on the internal validation dataset compared to the other four models. Furthermore, our CAN model outperformed the others on the external test dataset, with image-level accuracies of 82.53% and 83.32% for A4C and PLAX, respectively. 3) Implementation of the voting strategy notably enhanced the positive predictive value (PPV) and negative predictive value (NPV) of individual-level classification results, enhancing the reliability of our classification outcomes. Conclusions:: These findings indicate that CAN is a feasible technique for AI-assisted PAH diagnosis, providing new insights into cardiac structural changes observed in echocardiography.

Background: Distinguishing nontuberculous mycobacteria pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB) is a challenge especially in patients with positive sputum smear of acid-fast bacilli (AFB). This study aimed to compare and identify the clinical characteristics between the two diseases among patients with positive sputum AFB. Methods: From February 2017 through March 2021, patients with positive sputum AFB were reviewed in two hospitals of China. Among them, clinical data of NTM-PD and PTB patients was collected and compared. Results: 76 cases of NTM-PD and 92 cases of PTB were included in our study. When compared with PTB, NTM-PD patients were older (59.2 ± 11.4 vs 44.2 ± 19.5 years, P

Recent years witnessed increasing average annual precipitation in northern Xinjiang, amplifying the region’s vulnerability to natural disasters due to its fragile ecology and limited ground infiltration capacity. Therefore, the study of precipitation forecast in northern Xinjiang area is of vital significance to prevent these disasters. This study aims to capture the internal feature dependencies of complex multivariate meteorological data, build a PFformer model based on deep learning by reducing the algorithm time complexity of the short-term precipitation forecast model, use piecewise attention to reduce the algorithm complexity, and enhance the point-by-point dot product attention mechanism in the self-attention model. Data from five meteorological stations in Urumqi, Karamay, Tacheng, Altay, and Hami were used for the experiment. Comparing PFformer with mainstream time series prediction models, performance evaluation using Mean Squared Error (MSE) and Mean Absolute Error (MAE) indicates superior accuracy. Compared with the benchmark model, PFformer’s MSE improved by 7.85% on average and MAE improved by 9.74% on average. It shows potential for improving the accuracy of short-term precipitation forecasting, offering valuable assistance in disaster mitigation and management.

Previous laboratory-scale studies have consistently shown that carbon-based conductive materials can notably improve the anaerobic digestion of food waste, typically employing reactors with regular capacity of 1–20 L. Furthermore, incorporating riboflavin-loaded conductive materials can further address the imbalance between fermentation and methanogenesis in anaerobic systems. However, there have been few reports on pilot-scale investigation. In this study, a 10 m2 of riboflavin modified carbon cloth was incorporated into a pilot-scale (2 m3) food waste anaerobic reactor to improve its treatment efficiency. The study found that the addition of riboflavin-loaded carbon cloth can increase the maximum organic loading rate (OLR) by 40% of the pilot-scale reactor, compared to the system using carbon cloth without riboflavin loading, while ensuring efficient operation of the reaction system, effectively alleviating system acidification, sustaining methanogen activity, and increasing daily methane production by 25%. Analysis of the microbial community structure revealed that riboflavin-loaded carbon cloth enriched the methanogenic archaea in the genera of Methanothrix and Methanobacterium, which are capable of extracellular direct interspecies electron transfer (DIET). And metabolic pathway analysis identified the methane production pathway, highly enriched on the reduction of acetic acid and CO2 at riboflavin-loaded carbon cloth sample. The expression levels of genes related to methane production via DIET pathway were also significantly upregulated. These results can provide important guidance for the practical application of food waste anaerobic digestion engineering.

This study utilizes nuclear magnetic resonance (NMR) techniques to monitor complex microstructures and fluid transport, systematically examining fluid distribution and migration during pressure imbibition. The results indicate that increased applied pressure primarily affects micropores and small pores during the initial imbibition stage, enhancing the overall imbibition rate and oil recovery. Higher capillary pressure in the pores strengthens the imbibition ability, with water initially displacing oil from smaller pores. Natural microfractures allow water to preferentially enter and displace oil, thereby reducing oil recovery from these pores. Additionally, clay minerals may induce fracture expansion, facilitating oil flow into the expanding space. This study provides new insights into fluid distribution and migration during pressure imbibition, offering implications for improved oil production in tight reservoirs.

Garden waste, one type of lignocellulosic biomass, holds significant potential for the production of volatile fatty acids (VFAs) through anaerobic fermentation. However, the hydrolysis efficiency of garden waste is limited by the inherent recalcitrance, which further influences VFA production. Granular activated carbon (GAC) could promote hydrolysis and acidogenesis efficiency during anaerobic fermentation. This study developed a strategy to use GAC to enhance the anaerobic fermentation of garden waste without any complex pretreatments and extra enzymes. The results showed that GAC addition could improve VFA production, especially acetate, and reach the maximum total VFA yield of 191.55 mg/g VSadded, which increased by 27.35% compared to the control group. The highest VFA/sCOD value of 70.01% was attained in the GAC-amended group, whereas the control group only reached 49.35%, indicating a better hydrolysis and acidogenesis capacity attributed to the addition of GAC. Microbial community results revealed that GAC addition promoted the enrichment of Caproiciproducens and Clostridium, which are crucial for anaerobic VFA production. In addition, only the GAC-amended group showed the presence of Sphaerochaeta and Oscillibacter genera, which are associated with electron transfer processes. Metagenomics analysis indicated that GAC addition improved the abundance of glycoside hydrolases (GHs) and key functional enzymes related to hydrolysis and acidogenesis. Furthermore, the assessment of major genera influencing functional genes in both groups indicated that Sphaerochaeta, Clostridium, and Caproicibacter were the primary contributors to upregulated genes. These findings underscored the significance of employing GAC to enhance the anaerobic fermentation of garden waste, offering a promising approach for sustainable biomass conversion and VFA production.

Graph Convolutional Network (GCN) has achieved significant success in many graph representation learning tasks. GCN usually learns graph representations by performing Neighbor Aggregation (NA) and Feature Transformation (FT) operations. Deep Adaptive Graph Neural Network (DAGNN) improves NA operation in the aggregation-transformation decoupled GCN, which enables the model to obtain a large receiver field. However, the problem with GCN is that when the model is trained to a deeper level, the performance decreases. In particular, the influence of NA and FT on model degradation has not been fully considered. In this work, we propose a new decoupled GCN architecture to enhance the performance of deep GCN. First, we conduct an experimental analysis of the impact of NA and FT operations on the degradation of the deep GCN model. Subsequently, we propose an Adaptive Aggregation-Transform Decoupled Graph Convolutional Network (AATD-GCN) which divides the model into two depths $D_{NA}$ and $D_{FT}$ , and proposes improved approaches in NA and FT, respectively. The AATD-GCN reduces the influence of NA and FT operations on performance degradation of deep GCN, and obtains a large receiver field while extracting complex feature information. We investigate the requirements of model depth $D_{NA}$ and $D_{FT}$ for graphs of different structures and sizes. Finally, the effectiveness of the proposed architecture is verified through extensive experiments on real-world datasets, the experimental results show that AATD-GCN is superior in terms of accuracy and robustness.

Nitrous oxide (N2O) is not only recognized as a potent greenhouse gas, but it is also used in industry as a clean energy source. In this study, different electrode materials of carbon felt and graphite were equipped in the ΔnosZ P. aeruginosa/microbial electrolysis cell (MEC) systems to explore the optimization mechanism for long-term N2O recovery during incineration leachate treatment. The carbon felt group showed a better performance in N2O recovery across 45 days of operation. The N2O conversion efficiency was above 80% and the proportion of N2O in biogas accounted for 80.6% in the carbon felt group. qRT-PCR analysis was conducted to evaluate the expression of genes involved in denitrification (norB) and electroactivity (phzG, phzM, and phzH) of ΔnosZ P. aeruginosa. The results showed a significant upregulation in the suspended biomass (day 21) and the electron-attached biomass (day 45) from the carbon felt-equipped reactor, which was highly related to the opportunity of biomass exposed to the phenazine derivatives. By the carbon felt optimization in the system, 82.6% of the Pseudomonas genus survived after 45 days of operation. These results indicate that the carbon felt electrode has a more sustainable performance for N2O recovery in the ΔnosZ P. aeruginosa/MEC system.

Abstract Background Tuberculous pleural effusion (TPE) patients usually have elevated D-dimer levels. The diagnostic performance of D-dimer in predicting pulmonary embolism (PE) in the TPE population is unclear. This study aimed to assess the diagnostic performance of D-dimer for PE in the TPE population and explore its potential mechanism. Methods We retrospectively analysed patients who were admitted to Xinhua Hospital and Weifang Respiratory Disease Hospital with confirmed TPE between March 2014 and January 2020. D-dimer levels were compared between patients with and without PE. To test the diagnostic performance of D-dimer in predicting PE, receiver operating characteristic curve analysis was performed. Positive predictive value (PPV) and negative predictive value (NPV) were also reported. To explore the potential mechanism of PE in TPE, inflammatory biomarkers were compared between PE and non-PE patients. Results This study included 248 patients (170 males and 78 females) aged 43 ± 20.6 years. Elevated D-dimer levels (≥ 0.5 mg/L) were detected in 186/248 (75%) patients. Of the 150 patients who underwent computed tomography pulmonary angiography, 29 were diagnosed with PE. Among the TPE population, the PE patients had significantly higher D-dimer levels than the non-PE patients (median, 1.06 mg/L vs. 0.84 mg/L, P

Microbial electrochemical CO2 reduction and in-situ biogas upgrading can effectively reduce the CO2 content in biogas produced during anaerobic digestion, thereby reducing CO2 emissions and achieving carbon reduction. pH is an important indicator in this process as it can significantly change the solubility and forms of CO2 in the aquatic phase. This study comprehensively evaluated the optimal pH value from the perspectives of methane upgrading performance and electron utilization efficiency and observed and analyzed the morphology of the biofilm on the electrode surface and the microbial community in the cathodic region under optimal conditions. The results showed that the optimal pH was 6.5; methane content reached ~88.3% in the biogas; methane production reached a maximum of 22.1 ± 0.1 mmol·d−1, with an increase in methane production compared to the control group reaching a maximum of 1.7 mmol·d−1; and CO2 conversion rate reached ~22.9%. A dense biofilm with a thickness of 51.3 μm formed on the electrode surface, with Methanobacterium being the dominant genus, with a high relative abundance of 69.3%, and Geobacter had a relative abundance of 20.1%. The above findings have important guiding significance for the practical application of methane upgrading.

A solar flare is one of the most intense solar activities, and flare forecasting is necessary to avoid its destructive impact on the near-Earth space environment and technological infrastructure. Previous studies have demonstrated the importance of the photospheric magnetic field in the occurrence of flares. Therefore, most of the input data in traditional solar flare forecasting models are magnetograms of active regions. The magnetic field of the photosphere is routinely measured and observed, but the magnetic field of the corona is not. Hence, the goal of our work is to test whether precursors can be extracted from coronal multiwavelength images of active regions and to build a flare-forecasting model. Therefore, we investigated the effect of using extreme ultraviolet (EUV) images (at 94, 131, 171, 193, 211, and 335 Å) of the active region on solar flare forecasting. We generated a data set consisting of EUV images of the active regions observed by the Solar Dynamics Observatory/Atmospheric Imaging Assembly from 2010 to 2016. Based on this data set, a deep-learning method was used to extract precursors from EUV multiwavelength images. The test results of the forecasting model were discussed and analyzed, and the following conclusions were drawn. (1) Each wavelength achieved good results using the EUV multiwavelength images for flare forecasting. The 94 Å wavelength demonstrated the best result among the single-wavelength results. (2) Among the combined multiwavelength results, the best fusion results were obtained for all six wavelengths.

ABSTRACT Geobacter sulfurreducens is a model microbe for elucidating the mechanisms for extracellular electron transfer in several biogeochemical cycles, bioelectrochemical applications, and microbial metal corrosion. Multiple lines of evidence previously suggested that electrically conductive pili (e-pili) are an essential conduit for long-range extracellular electron transport in G. sulfurreducens. However, it has recently been reported that G. sulfurreducens does not express e-pili and that filaments comprised of multi-heme c-type cytochromes are responsible for long-range electron transport. This possibility was directly investigated by examining cells, rather than filament preparations, with atomic force microscopy. Approximately 90% of the filaments emanating from wild-type cells had a diameter (3 nm) and conductance consistent with previous reports of e-pili harvested from G. sulfurreducens or heterologously expressed in Escherichia coli from the G. sulfurreducens pilin gene. The remaining 10% of filaments had a morphology consistent with filaments comprised of the c-type cytochrome OmcS. A strain expressing a modified pilin gene designed to yield poorly conductive pili expressed 90% filaments with a 3-nm diameter, but greatly reduced conductance, further indicating that the 3-nm diameter conductive filaments in the wild-type strain were e-pili. A strain in which genes for five of the most abundant outer-surface c-type cytochromes, including OmcS, were deleted yielded only 3-nm-diameter filaments with the same conductance as in the wild type. These results demonstrate that e-pili are the most abundant conductive filaments expressed by G. sulfurreducens, consistent with previous functional studies demonstrating the need for e-pili for long-range extracellular electron transfer. IMPORTANCE Electroactive microbes have significant environmental impacts, as well as applications in bioenergy and bioremediation. The composition, function, and even existence of electrically conductive pili (e-pili) has been one of the most contentious areas of investigation in electromicrobiology, in part because e-pili offer a mechanism for long-range electron transport that does not involve the metal cofactors common in much of biological electron transport. This study demonstrates that e-pili are abundant filaments emanating from Geobacter sulfurreducens, which serves as a model for long-range extracellular electron transfer in direct interspecies electron transfer, dissimilatory metal reduction, microbe-electrode exchange, and corrosion caused by direct electron uptake from Fe(0). The methods described in this study provide a simple strategy for evaluating the distribution of conductive filaments throughout the microbial world with an approach that avoids artifactual production and/or enrichment of filaments that may not be physiologically relevant.

In this study, an Escherichia coli (E. coli) whole-cell biosensor for the specific detection of bioavailable arsenic was developed by placing a green fluorescent protein (GFP) reporter gene under the control of the ArsR1 (GSU2952) regulatory circuit from Geobacter sulfurreducens. E. coli cells only emitted green fluorescence in the presence of arsenite and were more sensitive to arsenite when they were grown in M9 supplemented medium compared to LB medium. Under optimal test conditions, the Geobacter arsR1 promoter had a detection limit of 0.01 μM arsenite and the GFP expression was linear within a range of 0.03–0.1 μM (2.25–7.5 μg/l). These values were well below World Health Organization’s drinking water quality standard, which is 10 μg/l. The feasibility of using this whole-cell biosensor to detect arsenic in water samples, such as arsenic polluted tap water and landfill leachate was verified. The biosensor was determined to be just as sensitive as atomic fluorescence spectrometry. This study examines the potential applications of biosensors constructed with Geobacter ArsR-Pars regulatory circuits and provides a rapid and cost-effective tool that can be used for arsenic detection in water samples.

The reuse of the sewage is an effective way to solve the shortage of water resources, but disinfection by-products (DBPs) caused by chlorination may bring potential ecological and health risks to the supplied water. In this study, the occurrence and potential ecological risk of DBPs in SH River in Beijing were evaluated. Four kinds of DBPs were detected in 84 samples by GC-MS, including THM, CH, CTC and TCAN, whose detection rates were 100%, 100%, 100% and 2.38%, respectively. Combining with the relevant standard limitation and corresponding threshold values in China, and the reported concentration in domestic and foreign literatures, the results showed that the number of samples which [THM], [CTC] and [CH] exceeded the threshold values in relevant standard for 23.81%, 100.00% and 89.29%, respectively. CTC showed the highest excess times than the threshold value with [CTC]max was 356.46 μg/L. In addition, the temporal and spatial characteristics of identified DBPs were studied. [THM], [CTC] and [CH] all exhibited the highest concentration in Aug., which was as the same as the variation trend of air and water temperature. With the increase of sampling distance, [THM] and [CTC] fluctuated greatly, and the background values in SH River were higher due to the supplement of the reclaimed water. [CH] and [TCAN] gradually decreased, which may be due to that they were more prone to volatilize in the channel and be degraded by aquatic microorganisms. In addition, the occurrence situation in S2 and S7, were in the order of CTC > CH > THM. Hence, the rank of the occurrence situation of identified DBPs was CTC > CH > THM > TCAN. Multivariate analysis showed that THM was significantly positively correlated with CTC and their sources were similar. Moreover, they were all affected by solution pH and DO. Potential ecological risk assessment indicated that the rank of identified DBPs ecological risk was CTC > THM > CH > TCAN. Among them, the risk level of CTC and THM were high in both daily and extreme situations. Therefore, the potential ecological risk caused by DBPs should be fully considered in the process of reclaimed water supplying landscape water, such as urban river. If a higher level of the ecological risk management is needed, THM, CTC and CH, especially CTC, should be considered firstly.

Background Osteoporosis is a common, complex disease of bone with a strong heritable component, characterized by low bone mineral density, microarchitectural deterioration of bone tissue and an increased risk of fracture. Due to limited drug selection for osteoporosis and increasing morbidity, mortality of osteoporotic fractures, osteoporosis has become a major health burden in aging societies. Current researches for identifying specific loci or genes involved in osteoporosis contribute to a greater understanding of the pathogenesis of osteoporosis and the development of better diagnosis, prevention and treatment strategies. However, little is known about how most causal genes work and interact to influence osteoporosis. Therefore, it is greatly significant to collect and analyze the studies involved in osteoporosis-related genes. Unfortunately, the information about all these osteoporosis-related genes is scattered in a large amount of extensive literature. Currently, there is no specialized database for easily accessing relevant information about osteoporosis-related genes and miRNAs. Methods We extracted data from literature abstracts in PubMed by text-mining and manual curation. Moreover, a local MySQL database containing all the data was developed with PHP on a Windows server. Results OsteoporosAtlas (http://biokb.ncpsb.org/osteoporosis/), the first specialized database for easily accessing relevant information such as osteoporosis-related genes and miRNAs, was constructed and served for researchers. OsteoporosAtlas enables users to retrieve, browse and download osteoporosis-related genes and miRNAs. Gene ontology and pathway analyses were integrated into OsteoporosAtlas. It currently includes 617 human encoding genes, 131 human non-coding miRNAs, and 128 functional roles. We think that OsteoporosAtlas will be an important bioinformatics resource to facilitate a better understanding of the pathogenesis of osteoporosis and developing better diagnosis, prevention and treatment strategies.

Background. Fibrosis is a highly dynamic process caused by prolonged injury, deregulation of the normal processes of wound healing, and extensive deposition of extracellular matrix (ECM) proteins. During fibrosis process, multiple genes interact with environmental factors. Over recent decades, tons of fibrosis-related genes have been identified to shed light on the particular clinical manifestations of this complex process. However, the genetics information about fibrosis is dispersed in lots of extensive literature. Methods. We extracted data from literature abstracts in PubMed by text mining, and manually curated the literature and identified the evidence sentences. Results. We presented FibroAtlas, which included 1,439 well-annotated fibrosis-associated genes. FibroAtlas 1.0 is the first attempt to build a nonredundant and comprehensive catalog of fibrosis-related genes with supporting evidence derived from curated published literature and allows us to have an overview of human fibrosis-related genes.

Tetra acetyl ethylene diamine (TAED) was used as an activator in H2O2 bleaching to improve bleaching efficiency. The present work was aimed at confirming different activations for various H2O2/TAED bleaching processes, including the addition of acetic anhydride and the step-addition of sodium hydroxide. The results showed that an acetic anhydride dosage of 1%, an acetic anhydride treatment time of 10 min, and an addition time of 45 min were the optimal treatment conditions. The optimum processes of NaOH step-addition treatment in H2O2 bleaching and in H2O2/TAED bleaching also were confirmed, respectively. The o-quinone contents of H2O2 bleached lignin and H2O2/TAED bleached lignin were determined. The results indicated that H2O2/TAED bleached lignin has a lower o-quinone content than H2O2 bleached lignin, which is one of the reasons for the H2O2/TAED bleaching process having better bleaching efficiency than H2O2 bleaching.

The crystal structure of the title compound, {[Na(C7H5ClNO2)(H2O)3]·H2O}n, features polymeric chains along [010]. The Na+ cation is octahedrally coordinated by four bridging water molecules, a terminal water molecule and an O atom derived from a monodentate carboxylate ligand. Adjacent polyhedra share two O...O edges. The polymeric chains are linked into a three-dimensional network via O—H...O and O—H...N hydrogen bonds.