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Sustainable cropland management requires quantitative and up-to-date information on the spatial pattern of soil organic carbon (SOC) at scales relevant for implementing targeted conservation measures. Spectra-based remote sensing of SOC in croplands is promising, but it requires the extraction of high-quality bare soil pixels that enable spatially continuous coverage. Here, we aim to compare the SOC predictive capability of single-date versus multitemporal compositing of bare soil images in an intensively cultivated region (4,700 km2) of northeast China. A series of 12 bare soil images within 2017–2022 were processed and passed onto three multitemporal compositing approaches (geometric median, and univariate mean and median) to create bare soil mosaics. Both single-date and multitemporal spectral images, together with laboratory-simulated Sentinel-2 benchmark data, were used to develop partial least squares regression, Cubist and random forest models via 100 bootstrapped validations. With Cubist consistently being the best performing algorithm for all three data sources, results show that the 12 single-date images exhibited temporally unstable performance (R2: 0.30–0.67). Among the three compositing approaches, the high-dimensional geometric median composite was the most suitable because of (i) its close resemblance to laboratory reference and robustness to outliers, which yielded a Cubist model (R2: 0.64; RMSE: 2.24 g/kg) outperforming 11 out of 12 single-date models; and (ii) its ability to retain between-band spectral relationships that allowed further incorporation of SOC-relevant bare soil indexes, which led to a further 6.5 % increase in model prediction accuracy. The resultant SOC map highlighted the capability of Sentinel-2 bare soil imaging to reveal field-scale soil degradation patterns. Future work should explore the possibility of extending the purely spectra-based framework to integrated SOC mapping and monitoring with additional soil biophysical and management information.

The evolutionary analysis showed that the GF14 family was conserved, however, there was limited evidence linking GF14s to plant height. In our investigations, we discovered a co-expression relationship between ZmGF14s and functionally characterized genes linked to plant height. In the co-expression network, we identified ZmGF14-3, a gene expression exhibiting a positive correlation with plant height in three maize varieties, we postulated that this gene could be intimately linked to plant height development. Subsequently, we cloned ZmGF14-3 from the maize B73 inbred line and overexpressed it in Arabidopsis, resulting in markedly dwarfed transgenic phenotypes. Measurements of endogenous phytohormones disclosed a significant reduction in concentrations of Gibberellic Acid 7 (GA7) and Indole-3-Acetic Acid (IAA) in the overexpressed Arabidopsis, furthermore, qPCR results highlighted a pronounced decrease in the expression levels of plant height-related genes when compared to the wild type, therefore, it is plausible to posit that ZmGF14-3 plays a pivotal role in regulating the growth and development of maize through interactions with various phytohormone-related genes. Thus, delving into the potential interactions between ZmGF14-3 and these genes holds the promise of yielding valuable insights into the molecular mechanisms underpinning plant height development in maize.

Deep learning has witnessed significant advancements in various tasks and has displayed exceptional performance. However, traditional deep learning techniques often necessitate the utilization of extensive labeled data for training, a requirement that is challenging to fulfill in many real-world scenarios. This limitation has given rise to the field of few-shot learning (FSL). In this paper, we introduce a Distribution Calibration Prototypical Network (DCPNet), aiming to address the limitations of prototypical networks in terms of their weak feature extraction capabilities and the inability of their classifier boundaries to align with the dataset. DCPNet incorporates a parallel hierarchical feature extraction module and a few-shot differentiation loss function to fine-tune the metric learning for better feature representation. This approach employs a parallel approach to extract features based on the semantic depth of image hierarchical extraction and incorporates contrastive learning to achieve feature vector fusion. Furthermore, DCPNet incorporates an improved distribution calibration method that leverages information from the base class dataset to align classifier boundaries with the dataset. To validate our approach, we conducted comparative experiments on datasets such as Mini-Imagenet, Omniglot, and CUB using classical baseline methods. In additional, we conducted ablation experiments on the Mini-Imagenet to assess the performance effectiveness of each component of the model. The results demonstrate that the proposed method presented in this paper outperforms other approaches and offer new insights into the field of few-shot image classification.

Erosion-induced lateral soil redistribution leads to spatially heterogenous soil composition, which can be captured through the distinctive spectral reflectance of soils under varying levels of erosion influence. This points to the potential of using remotely sensed soil spectra to detect severe erosion and deposition hotspots in exposed croplands and, importantly, further differentiate the intra-class spectral variability of moderate erosion that often occupies the largest proportion. Here, we aim to develop a two-step erosion classification and mapping approach based on multitemporal compositing of Sentinel-2 bare soil images of a typical agricultural region (11,500 km2) of northeast China. A random forest classifier was firstly trained against the ground-truth data derived from very high resolution (VHR) imagery in Google Earth, with an overall accuracy of 91 % that allowed for clear delineation of severe erosion and deposition areas based on their distinct topographic and spectral features particularly in the red and red-edge bands. In the second step, the remaining area of moderate erosion (60.30 %) was further differentiated using Iterative Self-Organizing cluster unsupervised classification to yield a five-class soil erosion map at 10 m spatial resolution. The accuracy of the predicted map was successfully validated by independent Caesium-137 (137Cs) and soil organic carbon observations at catchment and regional scales, as revealed by significant inter-class differences in soil redistribution rates estimated from 137Cs inventory. The severe erosion class had a soil loss rate of 5.5 mm yr−1, suggesting that previous assessments have underestimated erosion severity. The spatial accordance of crop growth with soil erosion intensity, particularly in localized settings, further highlighted the potential of bare soil imaging for mapping the spatiotemporal development of soil erosion and its response to targeted sustainable cropland management efforts.

Nowadays, news text information is exploding, and people need more and more heterogeneous news content. Therefore, news text topic identification is needed to help viewers quickly and accurately screen and filter news related to their interests to save time and energy. The Latent Dirichlet Allocation (LDA) model is the most commonly used method for text topic identification. The optimal number of topics must be specified in advance when using the LDA model to extract topics in previous studies. However, selecting the too-large or the too-small number of topics significantly impacts the final results of LDA topic models, directly determining the quality of topic extraction. Moreover, the news text datasets from social media are very time-sensitive, and the combination of temporal and semantic modeling has not been considered in past studies of news topic identification. This paper proposes an adaptive optimal topic number determination method for fusing semantic and temporal information in news datasets to address the existing problems. Semantic and temporal are first extracted in this method as two different views. Then, density peak clustering of multi-view information fusion is performed based on the two obtained feature vectors. The clustering results are used as the final optimal number of topics. To demonstrate the effectiveness of the proposed method, this paper compares the performance of four traditional methods for determining the optimal number of topics with the performance of this paper’s method on public datasets. The results show that the optimal number of topics considering semantic and temporal factors is significantly better than the other four traditional methods regarding F-value, PMI scores, and MI scores. It performs well in other indicators as well. The above experimental results show that the method proposed in this paper combines the temporal and semantic of news data to determine the optimal number of topics of news text, which can improve the accuracy of selecting the optimal number of topics in the LDA model and the effectiveness of the topic identification of news text to some extent. It can help viewers better understand and utilize the massive news text information. In addition, the method also broadens the idea of identifying and mining unique datasets from multiple perspectives.

α-Keto acids are important raw materials for pharmaceuticals and functional foods, which could be produced from cheap feed stock by whole cell biocatalysts containing L-amino acid deaminases (L-AADs). However, the production capacity is limited by the low activity of L-AADs. The L-AAD mediated redox reaction employs the electron transport chain to transfer electrons from the reduced FADH2 to O2, implying that the interaction between L-AAD and the cell membrane affects its catalytic activity. To improve the catalytic activity of L-AAD from Proteus vulgaris, we redesigned the membrane-bound hydrophobic insertion sequences (INS, residues 325–375) by saturation mutagenesis and high-throughput screening. Mutants D340N and L363N exhibited higher affinity and catalytic efficiency for L-leucine, with half-life 1.62-fold and 1.28-fold longer than that of wild-type L-AAD. D340N catalyzed L-leucine to produce 81.21 g⋅L–1 α-ketoisocaproate, with a bioconversion rate of 89.06%, which was 17.57% higher than that of the wild-type. It is predicted that the mutations enhanced the interaction between the protein and the cell membrane.

Global efforts to restore the world’s degraded croplands require knowledge on the degree and extent of accelerated soil organic carbon (SOC) loss induced by soil erosion. However, the methods for assessing where and to what extent erosion takes place are still inadequate for precise detection of erosion hotspots at high spatial resolution. Drawing on recent advances in multitemporal Sentinel-2 remote sensing to create a bare soil composite that reflects erosion-induced variations in soil spectral signatures, this study attempted to develop a spectra-based soil erosion mapping approach to pinpoint eroded hotspots in a typical catchment located in the black soil region of northeast China as characterized by undulating landscapes. We built a ground-truth dataset consisting of three classes of soils representing Severe, Moderate and Low erosion intensity because of their inter-class contrasts in estimated erosion rates from 137Cs tracing. The spectral separability of different erosion classes was first tested by a combined principal component and linear discriminant analysis (PCA-LDA) against laboratory hyperspectral data and then validated against Sentinel-2-derived broadband spectra. The results show that PCA-LDA produced excellent classification accuracy (Kappa coefficient > 0.9) for both data sources and even more so for Sentinel-2 spectra, highlighting the effectiveness of the multitemporal approach to extract bare soil pixels. Further investigations into the spectral curves enabled identification of distinctive spectral features representative of shifting soil albedo and biochemical composition due to erosion-induced SOC mobilization. A classification scheme comprising the spectral features was applied to the Sentinel-2 bare soil composite for pixel-wise soil erosion mapping, in which 15.9% of the cropland area was detected as erosion hotspots, while the Moderate class occupied 65.4%. Comparing the erosion map to a NDVI map demonstrated the negative impact of soil erosion on crop growth from a spatial perspective, highlighting the potential of the proposed approach to aid targeted cropland management for food security and climate.

To provide experimental basis for the effective development and utilization of Huoshan large-leaf yellow tea resources and the screening of safe and effective active ingredients of large-leaf yellow tea. [Methods] The active substances of Huoshan large-leaf yellow tea were extracted by hot-water extraction, and the freeze-dried powder of Huoshan large-leaf yellow tea was obtained by freeze drying. The antibacterial activity of the extract was preliminarily confirmed using the Oxford cup method, and its antimicrobial spectrum was analyzed using 14 strains. A xylene-induced mouse auricle swelling test was carried out to detect the swelling inhibition rate of the extract and analyze its in-vitro detumescent activity. Then, the antioxidant activity of the extract was identified through a DPPH free radical scavenging capacity test and a ferric reducing antioxidant power assay. [ Results] The extract had significant inhibitory effects on various bacteria. The extract could effectively inhibit the growth of Escherichia coli, Staphylococcus aureus, Enterococcus hirae, Acinetobacter baumannii, Klebsiella pneumonia, Pseudomonas aeruginosa, Bacillus subtilis, and other strains. The diameter of the inhibition zone increased with the increase of sample concentration. The extract had a significant inhibitory effect on auricle swelling induced by xylene in mice. When the concentration of the drug reached 1.0 mg/mL, its inhibition rate on mouse auricle swelling reached 55.2% (P <0.01), slightly lower than the swelling inhibition rate of the aspirin group (66.52%, P < 0.01). The results of the antioxidant test showed that large-leaf yellow tea extract also had strong activity. Within the concentration range of 0.1 -1.0 mg/mL, its DPPH radical scavenging rate increased with the increase of sample concentration. Within the concentration range of 0.1 -1.0 mg/mL, its DPPH radical scavenging rate increased with the increase of sample concentration. When the concentration reached 1.0 mg/mL, the scavenging rate reached 69.75%. The Fe3 * -reduction capacity of the extract also increased with the increase of sample concentration within the concentration range of 0.1 -2.5 mg/mL. When the concentration was 2.5 mg/mL, the reducing power of the extract reached 1.43 ±0.04. However, its DPPH free radical scavenging rate and reducing power were slightly lower than the capacity of Vc at the same concentration. [ Conclusions] The extract of Huoshan large-leaf yellow tea obtained by hot-water extraction had strong activity in many aspects, including inhibiting the growth of various microbes, subsiding swelling in vitro and resisting oxidation. These experimental results provide certain guiding significance for the basic research of Huoshan large-leaf yellow tea extract, as well as experimental data support for the subsequent development of functional foods and drugs of Huoshan large-leaf yellow tea. [ABSTRACT FROM AUTHOR]

[Objectives] This study was conducted to obtain a Chinese hamster ovary cell line that stably expresses recombinant human coagulation factor X (rhFX), and to induce efficient exprnsion of the target gene with different concentrations of methotrexate (MTX). [Methods] PCR was performed to obtain the rhFX gene, and a recombinant expression plasmid pOptiVEC-rhFX was constructed and subjected to double restriction endonuclease digestion and sequencing identification. CHO-DG44 (DHFR - ) ceHs were transfected by the liposome method, and the target protein was purified by affinity chromatography and detected by SDS-PAGE electrophoresis and Western blot. A ceH line with efifcient and stable expression of the target gene was obtained by increasing the concentration of MTX to select positive clones. [Results] PCR yielded a 1 509 bp rhFX sequence, and the results of double digestion and sequencing showed that the constructed pOptiVEC-rhFX plasmid was correct. After transfection of ceHs, MTX significantly increased protein expression. When MTX reached 1.0 µmol/L, the expression efficiency of the target protein was (9 ±0. 27) µg/ml. The purity of the target protein purified by afinity chromatography was 93%, which could be used for subse¬quent experiments. The exp^sim eflfciency of rhFX in eukaryotic mammalian ceHs was improved by increasing MTX concentration, and an afinity chromatography purification prods for the target protein was preliminarily established. % Conclusions] The results of this study provide data support for the expression and purification of rhFX, and wiil lay a solid foundation for the development of drugs related to rhFX. [ABSTRACT FROM AUTHOR]

Balamuthia mandrillaris is a kind of brain-eating amoeba leading to granulomatous amoebic encephalitis (GAE) with a high mortality rate and limited effective treatment options. Its underlying pathological mechanisms are still poorly understood. Here, we presented a 58-year-old man with Balamuthia mandrillaris meningoencephalitis, who died 33 days after onset. In this unique case, we introduced the use of ventriculoscopy in the treatment of B. mandrillaris meningoencephalitis. Through ventriculoscopy, we observed that the inflammatory response triggered by the disease was diffuse and extensive throughout the ventricles and meninges, not limited to the region of parasite invasion. Furthermore, under the precise guidance of ventriculoscopy, we implemented drug lavage therapy and intraventricular drainage, which, to a certain extent, managed to mitigate the progression of hydrocephalus and intracranial hypertension. Despite the unfortunate outcome of the patient’s death due to brainstem hemorrhage, we reiterate the crucial role that surgical intervention plays in both diagnosing and managing amoebic encephalitis. This underscores the need for continued research and innovation in surgical techniques to enhance patient outcomes and combat this challenging neurological condition.

Abstract Electrolyte plays crucial roles in electrochemical CO2 reduction reaction (e‐CO2RR), yet how it affects the e‐CO2RR performance still being unclarified. In this work, it is reported that Sn‐Zn hybrid oxide enables excellent CO2‐to‐HCOO− conversion in KHCO3 with a HCOO− Faraday efficiency ≈89%, a yield rate ≈0.58 mmol cm−2 h−1 and a stability up to ≈60 h at −0.93 V, which are higher than those in NaHCO3 and K2SO4. Systematical characterizations unveil that the surface reconstruction on Sn‐Zn greatly depends on the electrolyte using: the Sn‐SnO2/ZnO, the ZnO encapsulated Sn‐SnO2/ZnO and the Sn‐SnO2/Zn‐ZnO are reconstructed on the surface by KHCO3, NaHCO3 and K2SO4, respectively. The improved CO2‐to‐HCOO− performance in KHCO3 is highly attributed to the reconstructed Sn‐SnO2/ZnO, which can enhance the charge transportation, promote the CO2 adsorption and optimize the adsorption configuration, accumulate the protons by enhancing water adsorption/cleavage and limit the hydrogen evolution. The findings may provide insightful understanding on the relationship between electrolyte and surface reconstruction in e‐CO2RR and guide the design of novel electrocatalyst for effective CO2 reduction.

Cardiovascular disease (CVD) is the leading cause of human mortality worldwide. Despite Western medicine having made encouraging results in the clinical management of CVD, the morbidity, mortality, and disability rates of the disease remain high. Modern pharmacology has confirmed that traditional Chinese medicine (TCM), characterized by its multi-component, multi-target, and integrity, plays a positive and important role in the prevention and treatment of various CVDs in China, which has notable advantages in stabilizing disease, improving heart function, and enhancing the quality of life. Importantly, TCM is gradually being accepted by the international community due to its low cost, high safety, versatile bioactivity, and low toxicity. Unfortunately, comprehensive studies on the therapeutic effect of TCM on CVD and its mechanisms are very limited, which may restrict the clinical application of TCM in CVD. Therefore, this review is performed to analyze the pathogenesis of CVD, including inflammatory response, oxidative stress, mitochondrial dysfunction, pyroptosis, ferroptosis, dysbiosis of gut microbiota, etc. Moreover, we summarized the latest progress of TCM (formulas, extracts, and compounds) in curing CVD according to published literature from 2018 to 2023, as well as its mechanisms and clinical evidence. In conclusion, this review is expected to provide useful information and reference for the clinical application of TCM in the prevention and treatment of CVD and further drug development of CVD.

Abstract Background 5-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key importance in genetic and pathological studies. However, traditional experimental methods for identifying 5mC sites are time-consuming and costly, so there is an urgent need to develop computational methods to automatically detect and identify these 5mC sites. Results Deep learning methods have shown great potential in the field of 5mC sites, so we developed a deep learning combinatorial model called i5mC-DCGA. The model innovatively uses the Convolutional Block Attention Module (CBAM) to improve the Dense Convolutional Network (DenseNet), which is improved to extract advanced local feature information. Subsequently, we combined a Bidirectional Gated Recurrent Unit (BiGRU) and a Self-Attention mechanism to extract global feature information. Our model can learn feature representations of abstract and complex from simple sequence coding, while having the ability to solve the sample imbalance problem in benchmark datasets. The experimental results show that the i5mC-DCGA model achieves 97.02%, 96.52%, 96.58% and 85.58% in sensitivity (Sn), specificity (Sp), accuracy (Acc) and matthews correlation coefficient (MCC), respectively. Conclusions The i5mC-DCGA model outperforms other existing prediction tools in predicting 5mC sites, and it is currently the most representative promoter 5mC site prediction tool. The benchmark dataset and source code for the i5mC-DCGA model can be found in https://github.com/leirufeng/i5mC-DCGA .

Abstract The objective of this study was to evaluate and compare the effectiveness of three different types of bariatric surgeries, namely, sleeve gastrectomy (SG), one-anastomotic gastric bypass (OAGB), and single anastomosis sleeve ileal (SASI) bypass, in the treatment of metabolic syndrome (MS). The optimal approach for managing MS remains uncertain, and thus this study aimed to provide a recent analysis of the efficacy of these surgical procedures. This retrospective study evaluated data of individuals who underwent SG, OAGB, and SASI bypass. The primary outcome measures included weight, body mass index (BMI), glucolipid metabolic index, and the occurrence of treatment-related complications within 6 to 12 months post-surgery. A total of 324 patients were included in this study. Of these, 264 patients underwent SG, 30 underwent OAGB, and 30 underwent SASI bypass. A significant decrease in weight was observed at the 6-month and 12-month marks following all three surgical procedures. Of these, patients who underwent SASI bypass exhibited the greatest reduction in weight and BMI post-surgery. Furthermore, the SASI bypass was associated with a significantly higher percentage of total weight loss (%TWL) and excess body mass index loss (%EBMIL) compared to SG and OAGB. Patients who underwent OAGB and SASI bypass demonstrated notable improvements in type 2 diabetes mellitus (T2DM). Patients who underwent SASI bypass and OAGB experienced greater postoperative comfort and reported fewer complaints of discomfort compared to the other procedure. Based on the retrospective analysis of the data, SASI bypass was associated with greater reductions in weight and BMI, higher percentages of %TWL and %EBMIL, and better improvement in T2DM compared to SG and OAGB. Therefore, both SASI bypass and OAGB were found to be more effective than SG in the treatment of MS.

Abstract The influence of environmental factors like smoking and alcohol on myopia and astigmatism is controversial. However, due to ethical concerns, alternative study designs are urgently needed to assess causal inference, as mandatory exposure to cigarettes and alcohol is unethical. Following comprehensive screenings, 326 single nucleotide polymorphisms (SNPs) related to myopia and astigmatism were included in the dataset. To validate the causal association between exposures such as cigarette smoking, alcohol consumption, and coffee intake, and outcomes namely astigmatism and myopia, five regression models were employed. These models encompassed MR-Egger regression, random-effects inverse-variance weighted (IVW), weighted median estimator (WME), weighted model, and simple model. The instrumental variables utilized in these analyses were the aforementioned SNPs. Apply Cochran’s Q test to determine heterogeneity of SNPs; if heterogeneity exists, focus on IVW model results. The IVW model showed a 1.379-fold increase in the risk of astigmatism (OR = 1.379, 95%CI 0.822~2.313, P = 0.224) and a 0.963-fold increase in the risk of myopia (OR = 0.963, 95%CI 0.666~1.393, P = 0.841) for each unit increase in smoking. For each unit increase in coffee intake, the risk of astigmatism increased 1.610-fold (OR = 1.610, 95%CI 0.444~5.835, P = 0.469) and the risk of myopia increased 0.788-fold (OR = 0.788, 95%CI 0.340~1.824, P = 0.578). For each additional unit of alcohol consumption, the risk of astigmatism increased by 0.763-fold (OR = 0.763, 95%CI 0.380~1.530, P = 0.446), and none of the differences were statistically significant. However, for each unit of alcohol consumption, the risk of myopia increased by 1.597 times, and the difference was statistically significant (OR = 1.597, 95%CI 1.023~2.493, P = 0.039). The findings indicate that alcohol consumption is a risk factor for myopia but smoking and coffee intake do not affect its development. Additionally, there is no association between smoking, alcohol consumption, coffee intake, and the risk of astigmatism.

Serratia marcescens, as a Gram-negative opportunistic pathogen, is a rare cause of peritonitis and has worse clinical outcomes than Gram-positive peritonitis. In this case report, we describe a case of Serratia marcescens associated peritonitis that was successfully cured without catheter removal. A 40-year-old male patient with peritoneal dialysis who worked in the catering industry was admitted to the hospital for 16 hours after the discovery of cloudy peritoneal dialysate and abdominal pain. Ceftazidime and cefazolin sodium were immediately given intravenously as an empirical antibiotic regimen. After detecting Serratia marcescens in the peritoneal diasate culture, the treatment was switched to ceftazidime and levofloxacin. The routine examination of peritoneal dialysate showed a significant decrease in white blood cells, the peritoneal dialysate became clear, and the peritoneal dialysis catheter was retained. The patient was treated for 2 weeks and treated with oral antibiotics for 1 week. It is necessary to further strengthen the hygiene of work environment to prevent Serratia marcescens infection in peritoneal dialysis patients. We recommend that patients with Serratia marcescens associated peritonitis should be treated with a combination of antibiotics as early as possible empirically, and at the same time, the peritoneal dialysis fluid culture should be improved, and the antibiotic regimen should be timely adjusted according to the drug sensitivity results. For patients with clinical symptoms for more than 3 days, considering the strong virulence of Serratia marcescens, whether to use meropenem directly or not can provide a reference for clinical decision-making. Further clinical studies are needed to achieve more precise anti-infective treatment.

Near-space high-altitude balloon-based platforms have a series of advantages and provide superior conditions for optical observation. In order to ensure the stability of the optical axis of the optical detection load and stable tracking of the target, a near-space high-altitude balloon-based high-precision pointing and tracking system was designed, which can compensate for changes in the pitch angle and azimuth angle of the platform during flight. The system includes a primary platform stable pointing system and a secondary precise tracking system. In the finished flight experiment, the primary platform pointing system and secondary precise tracking system on the balloon-based observation platform worked normally, providing a guarantee for the coronagraph’s stable tracking and detection of the sun. The primary platform pointing system can realize ±1° pointing accuracy, and the simulated accuracy of the secondary precise tracking system is 4″, which guaranteed that the coronagraph obtained more than 20,000 images. In subsequent works, we will upgrade and optimize the whole system and conduct our next flight experiment in the future.

The promoter is a key DNA sequence whose primary function is to control the initiation time and the degree of expression of gene transcription. Accurate identification of promoters is essential for understanding gene expression studies. Traditional sequencing techniques for identifying promoters are costly and time-consuming. Therefore, the development of computational methods to identify promoters has become critical. Since deep learning methods show great potential in identifying promoters, this study proposes a new promoter prediction model, called iPro2L-DG. The iPro2L-DG predictor, based on an improved Densely Connected Convolutional Network (DenseNet) and a Global Attention Mechanism (GAM), is constructed to achieve the prediction of promoters. The promoter sequences are combined feature encoding using C2 encoding and nucleotide chemical property (NCP) encoding. An improved DenseNet extracts advanced feature information from the combined feature encoding. GAM evaluates the importance of advanced feature information in terms of channel and spatial dimensions, and finally uses a Full Connect Neural Network (FNN) to derive prediction probabilities. The experimental results showed that the accuracy of iPro2L-DG in the first layer (promoter identification) was 94.10% with Matthews correlation coefficient value of 0.8833. In the second layer (promoter strength prediction), the accuracy was 89.42% with Matthews correlation coefficient value of 0.7915. The iPro2L-DG predictor significantly outperforms other existing predictors in promoter identification and promoter strength prediction. Therefore, our proposed model iPro2L-DG is the most advanced promoter prediction tool. The source code of the iPro2L-DG model can be found in https://github.com/leirufeng/iPro2L-DG.

BackgroundPain is a common symptom in multiple sclerosis (MS), especially neuropathic pain, which has a significant impact on patients’ mental and physical health and quality of life. However, risk factors that related to neuropathic pain, still remain unclear.ObjectiveThe study aimed to explore the risk factors of neuropathic pain among MS patients.Materials and methodsThis retrospective study examined the consecutive patients diagnosed with MS in the Department of Neurology of Guangdong Provincial Hospital of Chinese Medicine between August 2011 and October 2022. Neuropathic pain was defined as “pain arising as a direct consequence of a lesion or disease affecting the somatosensory system”. Demographic and clinical features were obtained from the electronic system of the hospital.ResultsOur cohort revealed that the prevalence of patients with neuropathic pain in MS was 34.1%. The results indicated that the longer the spinal lesions, the greater the neuropathic pain risks (2-4: OR, 13.3(2.1-82), >5: OR, 15.2(2.7-86.8), p for tread: 0.037). Meanwhile, multivariate regression analysis showed that cervical and thoracic lesions (OR 4.276, 95% CI 1.366-13.382, P = 0.013), upper thoracic lesions (T1-T6) (OR 3.047, 95% CI 1.018-9.124, P = 0.046) were positively correlated with neuropathic pain, while basal ganglia lesions (OR 0.188, 95% CI 0.044-0.809, P = 0.025) were negatively correlated with neuropathic pain among MS patients.ConclusionExtended spinal lesions (≥3 spinal lesions), cervical and thoracic lesions, upper thoracic lesions were independent risk factors of neuropathic pain among MS patients. Furthermore, our study found that the longer the spinal lesions, the greater the neuropathic pain risks.

Abstract Viral RNA-host protein interactions are indispensable during RNA virus transcription and replication, but their detailed structural and dynamical features remain largely elusive. Here, we characterize the binding interface for the SARS-CoV-2 stem-loop 3 (SL3) cis-acting element to human TIA1 protein with a combined theoretical and experimental approaches. The highly structured SARS-CoV-2 SL3 has a high binding affinity to TIA1 protein, in which the aromatic stacking, hydrogen bonds, and hydrophobic interactions collectively direct this specific binding. Further mutagenesis studies validate our proposed 3D binding model and reveal two SL3 variants have enhanced binding affinities to TIA1. And disruptions of the identified RNA-protein interactions with designed antisense oligonucleotides dramatically reduce SARS-CoV-2 infection in cells. Finally, TIA1 protein could interact with conserved SL3 RNA elements within other betacoronavirus lineages. These findings open an avenue to explore the viral RNA-host protein interactions and provide a pioneering structural basis for RNA-targeting antiviral drug design.

Abstract Background The HIV/AIDS (human immunodeficiency virus/acquired immune deficiency syndrome) remains a global threat to health. Suicidal ideation has been a serious public health problem among people living with HIV (PLWH). However, the suicide prevention mechanism among PLWH still unclear. This study aims to analyze the suicidal ideation and its related factors in PLWH, and further explore the relationships between suicidal ideation and depression, anxiety and perceived social support. Methods This is a cross-sectional study. A total of 1146 PLWH were investigated by the general information questionnaire, the perceived social support scale (PSSS), the Beck scale for suicide ideation of Chinese version (BSI-CV), the generalized anxiety disorder scale-2 (GAD-2) and the patient health questionnaire-2 (PHQ-2) though the WeChat in China in 2018. By using statistical description and the binary unconditional logistic regression, we assessed the incidence of suicidal ideation and its related factors in PLWH. Besides, the intermediary effect of social support between anxiety, depression and suicidal ideation were explored by the stepwise test and Bootstrap method. Results The incidence of suicide ideation was 54.0% (619/1146) among the PLWH in the last week or during the most serious depression. Binary logistic regression analysis results showed that the PLWH who with short time for HIV positive diagnosis (aOR (adjusted odd ratio) = 1.754, 95% CI (confidence interval):1.338–2.299), low monthly income (aOR = 1.515, 95%CI:1.098–2.092), other chronic diseases except HIV (aOR = 1.555, 95%CI:1.134–2.132), irregular lovers (aOR = 1.369, 95%CI:1.021–1.837), anxiety (aOR = 2.711, 95%CI:1.767–4.161), depression (aOR = 1.614, 95%CI:1.078–2.417), low PSSS (aOR = 2.139, 95%CI:1.345–3.399) had high risk of suicide ideation.The social support played a mediating role between the anxiety (the mediating effect accounted for 30.43% of the total effect), depression (the mediating effect accounted for 23.76% of the total effect) and the suicide ideation among PLWH. Conclusion The incidence of suicide ideation of PLWH was high. Anxiety, depression, and social support are the key factors of suicide ideation of PLWH. Social support plays a partial mediating role between anxiety, depression and suicidal ideation, which provides a new approach for prevention of suicidal ideation in PLWH and should be known widely for people to prevent suicide.

The synthesis of betalain using microorganisms is an innovative developmental technology, and the excavation of microorganisms closely related to betalain can provide certain theoretical and technical support to this technology. In this study, the characteristics of soil microbial community structures and their functions in the rhizospheres of white-fleshed dragon fruit (Hylocereus undatus) and red-fleshed dragon fruit (Hylocereus polyrhizus) were analyzed. The results show that the soil bacterial and fungal compositions in the rhizospheres were shaped differently between H. undatus and H. polyrhizus. Bacterial genera such as Kribbella and TM7a were the unique dominant soil bacterial genera in the rhizospheres of H. undatus, whereas Bradyrhizobium was the unique dominant soil bacterial genus in the rhizospheres of H. polyrhizus. Additionally, Myrothecium was the unique dominant soil fungal genus in the rhizospheres of H. polyrhizus, whereas Apiotrichum and Arachniotus were the unique dominant soil fungal genera in the rhizospheres of H. undatus. Moreover, TM7a, Novibacillus, Cupriavidus, Mesorhizobium, Trechispora, Madurella, Cercophora, and Polyschema were significantly enriched in the rhizospheres of H. undatus, whereas Penicillium, Blastobotrys, Phialemonium, Marasmius, and Pseudogymnoascus were significantly enriched in the rhizospheres of H. polyrhizus. Furthermore, the relative abundances of Ascomycota and Penicillium were significantly higher in the rhizospheres of H. polyrhizus than in those of H. undatus.

The 5-methylcytosine (5mC) in the promoter region plays a significant role in biological processes and diseases. A few high-throughput sequencing technologies and traditional machine learning algorithms are often used by researchers to detect 5mC modification sites. However, high-throughput identification is laborious, time-consuming and expensive; moreover, the machine learning algorithms are not so advanced. Therefore, there is an urgent need to develop a more efficient computational approach to replace those traditional methods. Since deep learning algorithms are more popular and have powerful computational advantages, we constructed a novel prediction model, called DGA-5mC, to identify 5mC modification sites in promoter regions by using a deep learning algorithm based on an improved densely connected convolutional network (DenseNet) and the bidirectional GRU approach. Furthermore, we added a self-attention module to evaluate the importance of various 5mC features. The deep learning-based DGA-5mC model algorithm automatically handles large proportions of unbalanced data for both positive and negative samples, highlighting the model's reliability and superiority. So far as the authors are aware, this is the first time that the combination of an improved DenseNet and bidirectional GRU methods has been used to predict the 5mC modification sites in promoter regions. It can be seen that the DGA-5mC model, after using a combination of one-hot coding, nucleotide chemical property coding and nucleotide density coding, performed well in terms of sensitivity, specificity, accuracy, the Matthews correlation coefficient (MCC), area under the curve and Gmean in the independent test dataset: 90.19%, 92.74%, 92.54%, 64.64%, 96.43% and 91.46%, respectively. In addition, all datasets and source codes for the DGA-5mC model are freely accessible at https://github.com/lulukoss/DGA-5mC.

The rapid development of unmanned aerial vehicles (UAVs) provides a new application mode for gas remote sensing. Compared with fixed observation and vehicle-mounted platforms, a Fourier transform infrared spectrometer (FTIR) integrated in the UAV can monitor chemical gases across a large area, can collect data from multiple angles in three-dimensional space, and can operate in contaminated or hazardous environments. The unmanned helicopter has a larger payload and longer endurance than the rotary-wing drone, which relaxes the weight, size and power consumption limitations of the spectrometer. A FTIR remote sensing system integrated in an unmanned helicopter was developed. In order to solve the data acquisition and analysis problem caused by vibration and attitude instability of the unmanned helicopter, a dual-channel parallel oscillating mirror was designed to improve the stability of the interferometer module, and a robust principal component analysis algorithm based on kernel function was used to separate background spectrum and gas features. The flight experiment of sulfur hexafluoride gas detection was carried out. The results show that the system operates stably and can collect and identify the target spectrum in real time under the motion and hovering modes of an unmanned helicopter, which has broad application prospects.

A herringbone gear transmission system with star gear train is taken as the research object. In order to find the cause of system resonance， a modal-analyzing 3D finite element model is established to analyze the modal characteristics of herringbone gear transmission system of star gear train. Firstly， according to the experimental results of star gear train， the resonance caused by rotational frequency excitation and meshing frequency excitation is the main part of the vibration amplitude of the system. According to this phenomenon， a 3D finite element model is established based on the meshing stiffness matrix to analyze the modal characteristics of the system. The results show that the rotational frequency resonance mode and meshing frequency resonance mode obtained by simulation are uniquely corresponding to the experiment data， and the meshing frequency resonance mode is mainly represented by the fifth pitch diameter vibration mode of the inner gear and the torsional vibration mode of the coupled gear pair. This finite element model provides a valuable reference method for resonance prediction and resonance cause analysis of herringbone gear transmission system with star gear train.

Taking a herringbone gear transmission system with star gear train as the research object and based on the finite node method，a system-level dynamics model considering shaft-bearing-rigid rotor and the meshing of gears is established. The dynamic characteristics of the system are analyzed based on the RMS of vibration velocity at the bearings with the rotation speeds. Then the theoretical analysis results are compared with the experimental data to verify the reliability of the proposed model. After that，based on the analysis of vibration characteristics and modal characteristics of the gear system，resonance localization and resonance cause analysis are carried out. The results show that the theoretical analysis is in good agreement with experimental results. This study provides a valuable reference method for resonance prediction and resonance cause analysis of herringbone gear transmission system of star gear train.

IntroductionThiopurines, azathiopurine (AZA) and mercaptopurine (6-MP) have been regularly used in the treatment of inflammatory bowel disease (IBD). Despite optimized dosage adjustment based on the NUDT15 genotypes, some patients still discontinue or change treatment regimens due to thiopurine-induced leukopenia.MethodsWe proposed a prospective observational study of lipidomics to reveal the lipids perturbations associated with thiopurine-induced leukopenia. One hundred and twenty-seven IBD participants treated with thiopurine were enrolled, twenty-seven of which have developed thiopurine-induced leucopenia. Plasma lipid profiles were measured using Ultra-High-Performance Liquid Chromatography-Tandem Q-Exactive. Lipidomic alterations were validated with an independent validation cohort (leukopenia n = 26, non-leukopenia n = 74).ResultsUsing univariate and multivariate analysis, there were 16 lipid species from four lipid classes, triglyceride (n = 11), sphingomyelin (n = 1), phosphatidylcholine (n = 1) and lactosylceramide (n = 3) identified. Based on machine learning feature reduction and variable screening strategies, the random forest algorithm established by six lipids showed an excellent performance to distinguish the leukopenia group from the normal group, with a model accuracy of 95.28% (discovery cohort), 79.00% (validation cohort) and an area under the receiver operating characteristic (ROC) curve (ROC-AUC) of 0.9989 (discovery cohort), 0.8098 (validation cohort).DiscussionOur novel findings suggested that lipidomic provided unique insights into formulating individualized medication strategies for thiopurines in IBD patients.

A herringbone gear transmission system is taken as the research object. A method of gear system-level modeling and simulation analysis is proposed. Based on the finite element node method，a dynamic model considering rigid rotor-shaft-bearing and the meshing of gears is established. Then the resonance speed of the system is analyzed and the result is compared with the experimental data to verify the reliability and limitations of the finite element node model. Then a 3D finite element model used for natural frequency calculation and vibration mode analysis is established. The modeling of gear meshing is used in finite element node method. Finally，combined with the modal analysis and response analysis，the theoretical analysis is compared with the experimental data. And the cause of experimental resonance is determined and resonance localization is realized，which provides direction for decreasing the vibration amplitude and avoiding the vibration.

Background: 5-methylcytosine (m5C) is a key post-transcriptional modification that plays a critical role in RNA metabolism. Owing to the large increase in identified m5C modification sites in organisms, their epigenetic roles are becoming increasingly unknown. Therefore, it is crucial to precisely identify m5C modification sites to gain more insight into cellular processes and other mechanisms related to biological functions. Although researchers have proposed some traditional computational methods and machine learning algorithms, some limitations still remain. In this study, we propose a more powerful and reliable deep-learning model, im5C-DSCGA, to identify novel RNA m5C modification sites in humans. Methods: Our proposed im5C-DSCGA model uses three feature encoding methods initially—one-hot, nucleotide chemical property (NCP), and nucleotide density (ND)—to extract the original features in RNA sequences and ensure splicing; next, the original features are fed into the improved densely connected convolutional network (DenseNet) and Convolutional Block Attention Module (CBAM) mechanisms to extract the advanced local features; then, the bidirectional gated recurrent unit (BGRU) method is used to capture the long-term dependencies from advanced local features and extract global features using Self-Attention; Finally, ensemble learning is used and full connectivity is used to classify and predict the m5C site. Results: Unsurprisingly, the deep-learning-based im5C-DSCGA model performed well in terms of sensitivity (Sn), specificity (SP), accuracy (Acc), Matthew’s correlation coefficient (MCC), and area under the curve (AUC), generating values of 81.0%, 90.8%, 85.9%, 72.1%, and 92.6%, respectively, in the independent test dataset following the use of three feature encoding methods. Conclusions: We critically evaluated the performance of im5C-DSCGA using five-fold cross-validation and independent testing and compared it to existing methods. The MCC metric reached 72.1% when using the independent test, which is 3.0% higher than the current state-of-the-art prediction method Deepm5C model. The results show that the im5C-DSCGA model achieves more accurate and stable performances and is an effective tool for predicting m5C modification sites. To the authors’ knowledge, this is the first time that the improved DenseNet, BGRU, CBAM Attention mechanism, and Self-Attention mechanism have been combined to predict novel m5C sites in human RNA.

Abstract As an attractive technique for measuring water vapor, the Global Navigation Satellite System (GNSS) faces additional challenges in dynamic applications such as in the open sea. We present a new method of retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP), which uses GNSS data from multiple antennas and incorporates the constraints of known baseline vector and common tropospheric delay. The 4‐day shipborne dynamic experiment along the China coast demonstrates that the baseline vector constraint shortens the convergence time of positioning and atmospheric parameters, and also slightly improves their accuracies. The common tropospheric delay constraint helps to provide compromised, more robust, and sometimes more accurate PWV estimates. An evaluation with radiosonde‐derived PWVs shows that the combination of the two constraints achieves the best accuracy reaching 4.2 mm. This method helps to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting.

Enhancers play a crucial role in controlling gene transcription and expression. Therefore, bioinformatics puts many emphases on predicting enhancers and their strength. It is vital to create quick and accurate calculating techniques because conventional biomedical tests take too long time and are too expensive. This paper proposed a new predictor called iEnhancer-DCSV built on a modified densely connected convolutional network (DenseNet) and an improved convolutional block attention module (CBAM). Coding was performed using one-hot and nucleotide chemical property (NCP). DenseNet was used to extract advanced features from raw coding. The channel attention and spatial attention modules were used to evaluate the significance of the advanced features and then input into a fully connected neural network to yield the prediction probabilities. Finally, ensemble learning was employed on the final categorization findings via voting. According to the experimental results on the test set, the first layer of enhancer recognition achieved an accuracy of 78.95%, and the Matthews correlation coefficient value was 0.5809. The second layer of enhancer strength prediction achieved an accuracy of 80.70%, and the Matthews correlation coefficient value was 0.6609. The iEnhancer-DCSV method can be found at https://github.com/leirufeng/iEnhancer-DCSV. It is easy to obtain the desired results without using the complex mathematical formulas involved.

Sclerotinia sclerotiorum causes white mold, leading to substantial losses on a wide variety of hosts around the world. Many genes encoding effector proteins play important roles in the pathogenesis of S. sclerotiorum. Therefore, establishment of a transformation system for the exploration of gene function is necessarily significant. Here, we introduce a modified protocol to acquire protoplasts for transformation and generate knockout strains, which completements the transformation system of S. sclerotiorum.

Mesoscale eddies (MEs) affect the transport and redistribution of oceanic matter and energy. The long-lived and long-distance propagation of individual eddies has garnered extensive attention; however, short-lived MEs (< 7 days) have been widely overlooked. In this study, the basic features of short-lived MEs and their spatial-temporal variations in a tropical eddy-rich region were extracted and analyzed for the first time. Short-lived cyclonic and anticyclonic eddies (CEs/AEs) were found to be widespread in two eddy belts in the tropical region of the western Pacific warm pool (WPWP). The CEs and AEs were formed by the shear instability between large-scale circulations and were distributed on both sides of the North Equatorial Countercurrent, with significant differences in spatial distribution. The variations in sea surface temperature, mixed layer depth, and surface chlorophyll-a concentration in the core of the WPWP were spatially and temporally related to the development of the two eddy belts. This new insight into short-lived MEs in the tropical region contributes to our current understanding of ocean eddies. The potential impacts of short-lived MEs on climate change, global air–sea interactions, and tropical cyclone formation should receive adequate attention and further assessment in future research.

Seawalls are vital for protecting coastal areas. However, the seabed in front of seawalls may undergo severe scouring. This can result in destabilization of the seawall structure, the underlying mechanisms of which remains unclear. Therefore, an integrated observation system consisting of acoustic and optical instruments was deployed in areas with severe seabed scouring. This observation system was used to observe sediment dynamics elements such as waves, currents, tides, turbulence and suspended sediment concentration (SSC) for 31 days in winter. Using advanced time–frequency analysis techniques including wavelet transform and spectrum analysis, we examined the dynamic factors associated with sediment resuspension and transport. In calm weather conditions, a notable increase in SSC was not observed indicating that the tidal dynamics were not sufficient for sediment suspension. During high winds, the SSC increased sharply to 12,222 mg/L, and the sediment vertical diffusion flux induced by turbulence was coupled to SSC, indicating that the increased SSC was predominantly attributed to local resuspension. Consistent temporal distribution of turbulence-induced sediment vertical diffusion flux and momentum flux in high wavelet power spectra highlights the important role of turbulence in sediment dynamics. Enhanced longshore currents during high wave conditions intensified sediment transportation. Horizontal net sediment fluxes notably increased to 769 t/m2 per day during winter gales, which had a significant effect on seabed erosion. This study reveals the key processes associated with seabed scouring in front of seawalls during gale events.

Tropical cyclones (TCs) play important roles in the evolution of beaches. The response of beaches to a single tropical cyclone (TC) has been well reported, however, few studies have compared the responses of the same beach to TCs with different characteristics. Taking Haiyang Beach in the northern China Seas as a case study, the beach profiles before and after two TCs, Lekima and Bavi, which passed through the west and east sides of the study area, were investigated. The beach exhibited erosion at the upper foreshore and minor deposition near the mean high water level (MHW) after Lekima with subaqueous sandbars formed in the nearshore zone, whereas all the beach profiles showed little changes after Bavi. A high-resolution FVCOM model was established and eleven sensitive experiments were conducted to simulate hydrodynamic changes induced by TCs with different tracks, intensity and duration. Then the Xbeach model was used to display the beach evolution due to different TCs conditions. It is found that the significant storm surge and strong seaward bottom current induced by landward wind of the TCs passed through the west side of the study area should responsible for the beach profile changes. Negligible storm surge rise and landward bottom current related to the seaward wind of eastern path TCs can only result in little change of the beach. Swell contributes most to the wave height while has less effect on the beach changes. In this study, we conclude that the effects of TCs on beaches are primarily dependent on relative position of a beach and TCs’ tracks.

Udder conformation traits interact with cow milk yield, and it is essential to study the udder characteristics at different levels of production to predict milk yield for managing cows on farms. This study aims to develop an effective method based on instance segmentation and an improved neural network to divide cow production groups according to udders of high- and low-yielding cows. Firstly, the SOLOv2 (Segmenting Objects by LOcations) method was utilized to finely segment the cow udders. Secondly, feature extraction and data processing were conducted to define several cow udder features. Finally, the improved CNN-LSTM (Convolution Neural Network-Long Short-Term Memory) neural network was adopted to classify high- and low-yielding udders. The research compared the improved CNN-LSTM model and the other five classifiers, and the results show that CNN-LSTM achieved an overall accuracy of 96.44%. The proposed method indicates that the SOLOv2 and CNN-LSTM methods combined with analysis of udder traits have the potential for assigning cows to different production groups.