Search

Silvopastoral system has been proposed as a sustainable management system with both ecological and economic benefits compared with open pasture. However, little research compared the ecological impact of silvopastoral system compared with pure forest. Therefore, this study focused on the ecological benefits of silvopastoral system on the soil physicochemical properties under different construction modes and construction periods in China based on meta-analysis. From 29 references we extracted a total of 492 paired data that were used for the meta-analysis. Results showed that silvopastoral system could improve soil physical properties by decreasing soil bulk density and soil pH value. However, no consensus conclusion could be found on soil water content except significant increase in surface soil water content in southern China. Silvopastoral system significantly improved the top-soil nutrient contents. Construction of silvopastoral system significantly increased soil available nitrogen contents of all three soil depths and soil available phosphorus content of 0-40cm soil depth. However, no significant effect could be found for soil available potassium content. Silvopastoral system significantly improved soil organic matter content for all three soil depths. Sub-group analysis showed that 2–3 years after construction of silvopastoral system maximally improved soil available nitrogen and phosphorus contents, while soil available potassium content decreased with the construction period. Our results showed that short-term period construction of silvopastoral systems are effective practices to increase soil nutrient content especially in the temperate climate zone. However, we still need long-term monitoring experiments to verify the long-term ecological effect of silvopastoral system.

Discoloration events caused by loose deposits resuspension in drinking water distribution systems (DWDS) are the main aspects of customer complaints across the world, but the understanding of the potential risks of loose deposits is insufficient. In this study, loose deposits in real DWDS were collected from regions frequently experiencing ‘yellow water’. Cytotoxicity of healthy human liver cells was used to evaluate the toxicity risks of the particle samples. The results showed that the loose deposits would have a realistic discoloration risk (turbidity > 10 NTU) when their concentrations were higher than 10 mg/L. The water sample containing 1,000 mg/L loose deposits had a dark yellow color (100–300 PCU) and cytotoxicity (viability of human liver cells during cytotoxicity tests 59.18–80.69%), while the water sample containing 1 mg/L loose deposits did not have obvious color (97.00%). Particle size showed a stronger correlation with relative viability (r = 0.761) than other properties (specific area, metal content, contact angle, saturation magnetization and electron transfer number). However, it is interesting to note that both turbidity and color had a low correlation with relative viability, thus the toxicity of the particles could not be properly judged using turbidity or color. This study gives an important guidance that although the loose deposits could be visualized during water discoloration, its toxicity risks could not be evaluated through aesthetic indicators. HIGHLIGHTS Loose deposits >10 mg/L had a realistic discoloration risk.; The cell viability of 1,000 mg/L loose deposit particles was 59.18–80.69%.; Particle size showed a stronger correlation with toxicity than other properties.; Toxicity of loose deposits could not be judged using turbidity or color.

Iron particle is one of the key factors inducing discoloration in drinking water distribution system (DWDS), but the mechanism of iron particles on the accumulation of trace organic pollutants in DWDS is not clear. Here, iron-based pipes from real DWDS were used to investigate the perfluorooctanoic acid (PFOA) accumulation mechanisms in DWDS. Results showed that old unlined pipes had a much higher accumulation capacity for PFOA than new pipes. Among the corrosion products in old pipes, Fe

Background: Immune infiltration is a prognostic marker to clinical outcomes in various solid tumors. However, reports that focus on bone and soft tissue sarcoma are rare. The study aimed to analyze and identify how immune components influence prognosis and develop a novel prognostic system for sarcomas. Methods: We retrieved the gene expression data from 3 online databases (GEO, TCGA, and TARGET). The immune fraction was estimated using the CIBERSORT algorithm. After that, we re-clustered samples by K-means and constructed immunoscore by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Next, to confirm the prognostic value, nomograms were constructed. Results: 334 samples diagnosed with 8 tumor types (including osteosarcoma) were involved in our analysis. Patients were next re-clustered into three subgroups (OS, SAR1, and SAR2) through immune composition. Survival analysis showed a significant difference between the two soft tissue groups: patients with a higher proportion of CD8+ T cells, macrophages M1, and mast cells had favorable outcomes (p=0.0018). Immunoscore models were successfully established in OS and SAR2 groups consisting of 12 and 9 cell fractions, respectively. We found immunosocre was an independent factor for overall survival time. Patients with higher immunoscore had poor prognosis (p

Succinylation is an important posttranslational modification of proteins, which plays a key role in protein conformation regulation and cellular function control. Many studies have shown that succinylation modification on protein lysine residue is closely related to the occurrence of many diseases. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. In this study, we develop a new model, IFS-LightGBM (BO), which utilizes the incremental feature selection (IFS) method, the LightGBM feature selection method, the Bayesian optimization algorithm, and the LightGBM classifier, to predict succinylation sites in proteins. Specifically, pseudo amino acid composition (PseAAC), position-specific scoring matrix (PSSM), disorder status, and Composition of k -spaced Amino Acid Pairs (CKSAAP) are firstly employed to extract feature information. Then, utilizing the combination of the LightGBM feature selection method and the incremental feature selection (IFS) method selects the optimal feature subset for the LightGBM classifier. Finally, to increase prediction accuracy and reduce the computation load, the Bayesian optimization algorithm is used to optimize the parameters of the LightGBM classifier. The results reveal that the IFS-LightGBM (BO)-based prediction model performs better when it is evaluated by some common metrics, such as accuracy, recall, precision, Matthews Correlation Coefficient (MCC), and F -measure.

Iron particles present in drinking water distribution systems (DWDSs) could cause discoloration, while organic pollutants in DWDSs, such as perfluorooctanoic acid (PFOA), could be enriched by iron particles. However, little is known about the enhanced effects of PFOA and iron particles in DWDSs. To fill in these knowledge gaps, herein, iron-PFOA (FEP) particles were generated using residual chlorine as an oxidant in drinking water conditions and then separated into different sizes (ranging from small to large: FEP-S, FEP-M ，and FEP-L). FEP-S harbored the greatest cytotoxicity among the sizes. Interestingly, our data revealed that the PFOA released from FEP particles transformed into PFOS (perfluorooctane sulfonate) upon digestion in the gastrointestinal environment (GI), and FEP-L bored the strongest transformation, showing a toxicity profile that was distinct from that of FEP-S. Furthermore, mechanistic studies revealed that FEP per se should be accountable for the conversion of PFOA to PFOS dependent on the generation of hydroxyl radicals (·OH) in GI, and that FEP-L revealed the greatest production of ·OH. Collectively, these results showed how iron particles and PFOA could result in enhanced toxicity effects in drinking water: (i) PFOA could increase the toxicity of iron particles by reducing particle size and inducing higher generation of ·OH; (ii) iron particles could induce the transformation of PFOA into more toxic PFOS through digestion.

Objective. This study is aimed at comparing the uterine fibroids patients’ postoperative living quality between ultrasound-guided high-intensity focused ultrasound (HIFU) and laparoscopic myomectomy. Materials and Methods. A total of 164 patients were included with uterine fibroids who underwent laparoscopic myomectomy and HIFU in Cangzhou Central Hospital from September 2020 to November 2021. This study divided these objects into HIFU group and laparoscopic group, and both groups were followed up 6 months after surgery. After obtaining the results, Uterine Fibroid Symptom and health-related Quality Of Life questionnaire (UFS-QOL) and 36-Item Short Form Health Survey (SF-36) were performed before and after treatment to assess patient outcome. Results. After treatments, the living quality in both groups was significantly improved compared with that before surgery, which had statistical significant ( P < 0.05 ). After treatment, the scores of the two scales in HIFU group were significantly better than those in the laparoscopic group ( P < 0.05 ). Conclusion. In comparison with laparoscopic myomectomy, ultrasound-guided high-intensity focused ultrasound could improve the life quality of patients more effectively than traditional laparoscopic myomectomy and was helpful to the recovery and prognosis of uterine fibroids after treatment. The outcomes will provide a reference for clinicians to select a more appropriate treatment for uterine fibroids.

Prunella vulgaris L. (P. vulgaris, Labiatae) is a perennial medicinal and edible plant widely used in China, Korea, Japan and Europe. The reddish brown spica of P. vulgaris (Prunellae Spica), which is collected in summer, has been commonly used in traditional medicine and food industry, while it is also used with whole grass in Europe and Taiwan. To clarify the regulatory pathways and mechanism of quality formation in P. vulgaris, targeted metabolomic, transcriptomic, and proteomic analyses of Prunellae Spica samples from five consecutive developmental stages were carried out. The results showed that terpenoids were mainly synthesized in the maturity stage of Prunellae Spica, with the key enzymes and coding genes in downstream pathways being mainly expressed during ripening, while related enzymes in the upstream pathway showed the opposite pattern. Flavonoids mainly accumulated before ripening, with highly expressed pathway enzymes and coding genes. The accumulation of phenylpropanoids was relatively active throughout the development process. Rosmarinic acid (RA) and its synthetic intermediate products mainly accumulated via more active pathway enzymes and coding genes before ripening. The regulatory factors and metabolites related to RA synthesis were mainly enriched in phenylpropanoid biosynthesis, plant hormone signal transduction, plant pathogen interaction, oxidative phosphorylation, and endoplasmic reticulum protein processing pathways.

Protein fold recognition contribute to comprehend the function of proteins, which is of great help to the gene therapy of diseases and the development of new drugs. Researchers have been working in this direction and have made considerable achievements, but challenges still exist on low sequence similarity datasets. In this study, we propose the ASFold-DNN framework for protein fold recognition research. Above all, four groups of evolutionary features are extracted from the primary structures of proteins, and a preliminary selection of variable parameter is made for two groups of features including ACC _HMM and SXG _HMM, respectively. Then several feature selection algorithms are selected for comparison and the best feature selection scheme is obtained by changing their internal threshold values. Finally, multiple hyper-parameters of Full Connected Neural Network are fully optimized to construct the best model. DD, EDD and TG datasets with low sequence similarities are chosen to evaluate the performance of the models constructed by the framework, and the final prediction accuracy are 85.28, 95.00 and 88.84 percent, respectively. Furthermore, the ASTRAL186 and LE datasets are introduced to further verify the generalization ability of our proposed framework. Comprehensive experimental results prove that the ASFold-DNN framework is more prominent than the state-of-the-art studies on protein fold recognition. The source code and data of ASFold-DNN can be downloaded from https://github.com/Bioinformatics-Laboratory/project/tree/master/ASFold.

As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58–83.38% and an area under the curve (AUC) of 81.39–91.04%. Furthermore, the independent datasets achieved an ACC of 72.95–83.04% and an AUC of 80.79–91.09%, which shows an excellent generalization ability of our proposed method.

As one of the most prevalent posttranscriptional modifications of RNA, N7-methylguanosine (m7G) plays an essential role in the regulation of gene expression. Accurate identification of m7G sites in the transcriptome is invaluable for better revealing their potential functional mechanisms. Although high-throughput experimental methods can locate m7G sites precisely, they are overpriced and time-consuming. Hence, it is imperative to design an efficient computational method that can accurately identify the m7G sites. In this study, we propose a novel method via incorporating BERT-based multilingual model in bioinformatics to represent the information of RNA sequences. Firstly, we treat RNA sequences as natural sentences and then employ bidirectional encoder representations from transformers (BERT) model to transform them into fixed-length numerical matrices. Secondly, a feature selection scheme based on the elastic net method is constructed to eliminate redundant features and retain important features. Finally, the selected feature subset is input into a stacking ensemble classifier to predict m7G sites, and the hyperparameters of the classifier are tuned with tree-structured Parzen estimator (TPE) approach. By 10-fold cross-validation, the performance of BERT-m7G is measured with an ACC of 95.48% and an MCC of 0.9100. The experimental results indicate that the proposed method significantly outperforms state-of-the-art prediction methods in the identification of m7G modifications.

In drinking water, residual chlorine is very likely to coexist with iron particles. However, the understanding of the effect of residual chlorine on iron particle formation in relation to different drinking water conditions is limited. Here, we found a significant competitive relationship between residual chlorine and dissolved oxygen (DO) during iron particle formation. Composition gave a good indicator of the extent of oxidation; with DO being the sole oxidant, the most abundant iron crystal in the particles formed was FeOOH (79.12%) followed by Fe3O4 (20.88%); in the presence of residual chlorine, however, the oxidation states of the iron crystals shifted upwards; the composition became as follows: FeOOH (50.64%), Fe3O4 (27.96%) and Fe2O3 (21.40%). The morphology of the particles formed showed interesting trends. In the presence of residual chlorine, the iron particles became denser and the specific surface area decreased. Particle size also exhibited a positive correlation with the chlorine concentration. The iron particle's specific surface areas formed with and without residual chlorine were 41.11m2/g and 61.30 m2/g, respectively. Linear regression also showed a good linear relationship between concentrations of iron ion and residual chlorine and the turbidity of the resulting water sample. Residual chlorine affects iron particle formation through different aspects, especially so when considering the interaction between residual chlorine with other oxidants and the resulting change in morphology. Runs of solutions with a high chlorine concentration through the pipe network could potentially act as a maintenance treatment.

Understanding the function of protein is conducive to research in advanced fields such as gene therapy of diseases, the development and design of new drugs, etc. The prerequisite for understanding the function of a protein is to determine its tertiary structure. The realization of protein structure classification is indispensable for this problem and fold recognition is a commonly used method of protein structure classification. Protein sequences of 40% identity in the ASTRAL protein classification database are used for fold recognition research in current work to predict 27 folding types which mostly belong to four protein structural classes: α , β , α / β and α + β . We extract features from primary structure of protein using methods covering DSSP, PSSM and HMM which are based on secondary structure and evolutionary information to convert protein sequences into feature vectors that can be recognized by machine learning algorithm and utilize the combination of LightGBM feature selection algorithm and incremental feature selection method (IFS) to find the optimal classifiers respectively constructed by machine learning algorithms on the basis of tree structure including Random Forest, XGBoost and LightGBM. Bayesian optimization method is used for hyper-parameter adjustment of machine learning algorithms to make the accuracy of fold recognition reach as high as 93.45% at last. The result obtained by the model we propose is outstanding in the study of protein fold recognition.

Introduction: Prostate adenocarcinoma is the most frequently diagnosed malignancy, particularly for people >70 years old. The main challenge in the treatment of advanced neoplasm is bone metastasis and therapeutic resistance for known oncology drugs. Novel treatment methods to prolong the survival time and improve the life quality of these specific patients are required. The present study attempted to screen potential therapeutic compounds for the tumor through bioinformatics approaches, in order to provide conceptual treatment for this malignant disease. Methods: Differentially expressed genes were obtained from the Gene Expression Omnibus database and submitted into the Connectivity Map database for the detection of potentially associated compounds. Target genes were extracted from the search results. Functional annotation and pathway enrichment were performed for the confirmation. Survival analysis was used to measure potential therapeutic effects. Results: It was revealed that 3 compounds (vanoxerine, tolnaftate, and gabexate) may help to prolong the disease-free survival time from tumor metastasis of patients with the tumor. A total of 6 genes [also-keto reductase family 1 member C3 (AKR1C3), collagen type III α 1 chain (COL3A1), lipoprotein lipase (LPL), glucuronidase, β pseudogene 11 (GUSBP11), apolipoprotein E (APOE), and collagen type I α 1 chain (COL1A1)] were identified to be the potential therapeutic targets for the aforementioned compounds. Conclusion: In the present study, it was speculated that 3 compounds may function as the potential therapeutic drugs of bone metastatic prostate adenocarcinoma; however, further studies verifying vitro and in vivo are necessary.

A real-time exposure dose control algorithm for deep ultraviolet (DUV) excimer lasers in a step-and-scan optical lithography is presented. By establishing an abstract scan exposure model and analyzing the pulse-to-pulse energy fluctuation characteristics of DUV excimer lasers, a real-time dose regulation is implemented based on closed-loop feedback control, which especially focuses on reducing the effect of pulse energy overshot and pulse-to-pulse stochastic fluctuation. The experiment conducted on an ArF excimer laser with wavelength of 193 nm, repetition rate of 4 kHz, and pulse energy of 5 mJ confirms that such a real-time dose control algorithm is able to achieve a dose accuracy of above 0.89% even with only 20 pulses. It is fully expected that this algorithm will not only meet increasingly stringent dose accuracy requirements for sub-half-micron lithography, but also be helpful to improve lithography throughput as well as efficiency.