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Background Background: Hot springs have been traditionally used as an alternative treatment for a wide range of diseases, including rheumatoid arthritis, bronchial asthma, diabetes, hypertension, psoriasis and atopic dermatitis. However, the clinical effects and therapeutic mechanisms associated with hot springs remain poorly defined. Objective The purpose of this study was to demonstrate the different effects of hot springs on cellular viability and secretion of inflammatory cytokines on keratinocyte in two geographically representative types of hot springs: NaHCO3-type and NaCl-type, which are the most common types in South Korea. Methods We performed WST-1, BrdU measurements, human inflammatory cytokine arrays and enzyme-linked immunosorbent assay in HaCaT cells stimulated with toll-like receptor 3 by polyinosinicpolycytidylic acid. Results The interaction effects of cell viability and cell proliferation were not significantly different regardless of polyinosinic-polycytidylic acid stimulation and cultured hot springs type. Cytokine array and enzymelinked immunosorbent assay analysis showed increased expression of inflammatory cytokines such as interleukin6 and granulocyte-macrophage colony-stimulating factor by polyinosinic-polycytidylic acid stimulation, with expression levels differing according to hot springs hydrochemical composition. Cytokine reduction was not significant. Conclusion The effects and mechanisms of hot springs treatment in keratinocytes were partially elucidated.

Objective Observe the influence of oral nutritional agents rich in soluble dietary (enteral nutritional suspension [TPF‐DM]) on intestinal flora of elderly male subjects with malnutrition. Method Seventy‐eight subjects with good nutrition were considered as the healthy control group. Twenty‐eight male subjects who had malnutrition and were older than 70 years were included and randomly divided into the short‐term (3 months) intervention group (n = 20) and the long‐term (12 months) group (n = 8). They were provided with enteral nutritional suspension (TPF‐DM) 500 mL/day or maximum tolerance dose, so as to observe the changes in nutrition‐related indexes and intestinal flora after the elderly take enteral nutritional suspension (TPF‐DM). Results (1) For elderly male subjects with malnutrition, their body weight, body mass index, hemoglobin, total protein, and albumin were significantly lower than the control group with favorable nutrition. (2) There were obvious differences in intestinal flora between healthy elderly male subjects and those with malnutrition. After the treatment of enteral nutritional suspension (TPF‐DM), intestinal flora of the malnourished elderly subjects showed recovery toward the healthy elderly subjects. The obvious gradient changes of the flora were mainly in the bacteroidetes, firmicutes, and proteobacteria phyla, and the relative abundance of CAG2 clusters in the malnourished group was higher than that in the healthy control group, and the relative abundance decreased after long‐term treatment, and the change approached the healthy control group. The relative abundance of CAG3 and CAG6 clusters in the malnourished group was lower than that in the healthy control group, and the relative abundance increased after long‐term treatment, and the change approached the healthy control group. Conclusion Malnutrition has obvious impact on intestinal flora of the elderly. Enteral nutritional suspension (TPF‐DM) not only prevents the further decline in the state of nutrition but also helps the recovery in intestinal flora of the elderly. Long‐term application can produce better effects., The relative abundance of CAG3 and CAG6 clusters in the malnourished group was lower than that in the healthy control group, and the relative abundance increased after long‐term treatment, and the change approached the healthy control group.

This article reports and analyzes the results of protein contact and distance prediction by our methods in the 14th Critical Assessment of techniques for protein Structure Prediction (CASP14). A new deep learning-based contact/distance predictor was employed based on the ensemble of two complementary coevolution features coupling with deep residual networks. We also improved our multiple sequence alignment (MSA) generation protocol with wholesale meta-genome sequence databases. On 22 CASP14 free modeling (FM) targets, the proposed model achieved a top-L/5 long-range precision of 63.8% and a mean distance bin error of 1.494. Based on the predicted distance potentials, 11 out of 22 FM targets and all of the 14 FM/template-based modeling (TBM) targets have correctly predicted folds (TM-score >0.5), suggesting that our approach can provide reliable distance potentials for ab initio protein folding.

Background:. Acute heart failure (AHF) is the most common disease in emergency departments (EDs). However, clinical data exploring the outcomes of patients presenting AHF in EDs are limited, especially the long-term outcomes. The purposes of this study were to describe the long-term outcomes of patients with AHF in the EDs and further analyze their prognostic factors. Methods:. This prospective, multicenter, cohort study consecutively enrolled 3335 patients with AHF who were admitted to EDs of 14 hospitals from Beijing between January 1, 2011 and September 23, 2012. Kaplan-Meier and Cox regression analysis were adopted to evaluate 5-year outcomes and associated predictors. Results:. The 5-year mortality and cardiovascular death rates were 55.4% and 49.6%, respectively. The median overall survival was 34 months. Independent predictors of 5-year mortality were patient age (hazard ratio [HR]: 1.027, 95 confidence interval [CI]: 1.023–1.030), body mass index (BMI) (HR: 0.971, 95% CI: 0.958–0.983), fatigue (HR: 1.127, 95% CI: 1.009–1.258), ascites (HR: 1.190, 95% CI: 1.057–1.340), hepatic jugular reflux (HR: 1.339, 95% CI: 1.140–1.572), New York Heart Association (NYHA) class III to IV (HR: 1.511, 95% CI: 1.291–1.769), heart rate (HR: 1.003, 95% CI: 1.001–1.005), diastolic blood pressure (DBP) (HR: 0.996, 95% CI: 0.993–0.999), blood urea nitrogen (BUN) (HR: 1.014, 95% CI: 1.008–1.020), B-type natriuretic peptide (BNP)/N-terminal pro-B-type natriuretic peptide (NT-proBNP) level in the third (HR: 1.426, 95% CI: 1.220–1.668) or fourth quartile (HR: 1.437, 95% CI: 1.223–1.690), serum sodium (HR: 0.980, 95% CI: 0.972–0.988), serum albumin (HR: 0.981, 95% CI: 0.971–0.992), ischemic heart diseases (HR: 1.195, 95% CI: 1.073–1.331), primary cardiomyopathy (HR: 1.382, 95% CI: 1.183–1.614), diabetes (HR: 1.118, 95% CI: 1.010–1.237), stroke (HR: 1.252, 95% CI: 1.121–1.397), and the use of diuretics (HR: 0.714, 95% CI: 0.626–0.814), β-blockers (HR: 0.673, 95% CI: 0.588–0.769), angiotensin-converting enzyme inhibitors (ACEIs) (HR: 0.714, 95% CI: 0.604–0.845), angiotensin-II receptor blockers (ARBs) (HR: 0.790, 95% CI: 0.646–0.965), spironolactone (HR: 0.814, 95% CI: 0.663–0.999), calcium antagonists (HR: 0.624, 95% CI: 0.531–0.733), nitrates (HR: 0.715, 95% CI: 0.631–0.811), and digoxin (HR: 0.579, 95% CI: 0.465–0.721). Conclusions:. The results of our study demonstrate poor 5-year outcomes of patients presenting to EDs with AHF. Age, BMI, fatigue, ascites, hepatic jugular reflux, NYHA class III to IV, heart rate, DBP, BUN, BNP/NT-proBNP level in the third or fourth quartile, serum sodium, serum albumin, ischemic heart diseases, primary cardiomyopathy, diabetes, stroke, and the use of diuretics, β-blockers, ACEIs, ARBs, spironolactone, calcium antagonists, nitrates, and digoxin were independently associated with 5-year all-cause mortality.

High entropy alloys (HEAs), which is at the expense of high cost compared to traditional alloy, should not be confined to the mechanical properties, but should be employed to devise a novel combination with unique functional and mechanical performances. In this work, high entropy alloy nitride (HEAN) is utilized as a novel double absorption layer to improve solar absorption in the high temperature solar selective absorbing coatings (SSACs). Our primary motivation is to lower thermal emittance (e) and enhance solar absorptance (α). In order to realize this goal, coating design (CODE) software is employed to design and optimize the proposed HEAN based SSACs using appropriate dielectric function model. The ultimate as-deposited coating shows good optical properties with a high α value of 0.965 and a low e value of 0.086 (at 82 °C). The estimate of thermal stability tests indicates that HEAN based SSACs has the ability to resist instability in high working temperature, which keeps good optical properties (α = 0.925, e = 0.070) after annealing at 600 °C for 10 h.

Solar selective absorber coatings (SSACs), with the characteristics of efficient harvesting and conversion of solar energy into the heat work fluid, are considered to be one of the key components f...

Recent advances in high-entropy alloys have spurred many breakthroughs in the fields of high-temperature materials and optical materials and they provide incredible application potentialities for photothermal conversion systems. Solar-selective absorbers (SSAs), as key components, play a vital role in photothermal conversion efficiency and service life. The most pressing problem with SSAs is their inconsistent optical performance, an instability constraint induced by thermal stress. A feasible method of improving performance stability is the introduction of high-entropy materials, such as high-entropy alloy nitrides. In this study, enabled by an intrinsic MoTaTiCrN absorption layer, the solar configuration achieves greatly enhanced, exceptional thermotolerance and optical properties, leading to the formation of a scalable, highly efficient, and cost-effective structure. Computational and experimental approaches are employed to achieve optimum preparation parameters for thicknesses and constituents. The crystal structure of high-entropy ceramic MoTaTiCrN is fully investigated, including thickness-dependent crystal nucleation. High-temperature and long-term thermal stability tests demonstrate that our proposed SSA is mechanically robust and chemically stable. Moreover, a low thermal emittance (15.86%) at 500 °C promotes the photothermal conversion efficiency. In addition, due to the exceptional spectral selectivity (α/ε = 92.3/6.5%), thermal robustness (550 °C for 168 h), and photothermal conversion efficiency (86.9% at 550 °C under 100 sun), it is possible for our proposed SSA to enhance the practical realization of large-area photothermal conversion applications, especially for concentrated solar power systems.

The most recent advances in high-entropy materials provide impetus for the development of high-performance materials, simultaneously providing high-temperature robustness and excellent functional properties owing to the high configurational entropy and distorted lattices. Thus, in this work, double-layer high-entropy alloy nitride HfNbTaTiZrN with a well-designed metal content gradient is employed to fabricate a solar selective absorber coating (SSAC). We extensively investigated whether the coating meets the definition of a high-entropy material from the points of elemental analysis and phase structure, combined with thermodynamic calculation. Remarkably, the new, tailored SSAC exhibits an exceptionally high solar absorptance (α = 96%) and a suppressed thermal emittance (e = 8.2%) at 82 °C and omnidirectional absorption. Investigations of the long-term thermal stability indicate that the HfNbTaTiZrN-based SSAC due to the entropy-driven structural stabilization could endure heat treatment at 600 °C for 168 h, retaining a performance criterion (PC) value less than 0.05, which implies the feasibility of practical applications. More importantly, the photothermal conversion efficiency (η), which is utilized to quantitatively evaluate the performance at elevated temperatures, reaches 90.1% at a working temperature of 550 °C under 100 suns, and even after annealing at 600 °C for 168 h, the efficiency drop is still less than 5%. Overall, the combination of thermal robustness and photothermal conversion efficiency at working temperatures less than 600 °C provides significant potential to maximize solar energy harvesting and pioneers an opportunity to explore simultaneous multifunctional applications of high-entropy alloys.

The functional application of high entropy alloys, which emerge as a novel multi-principal component alloy, has been constantly expanding in the field of solar photothermal harvesting from optical materials to high temperature materials due to their excellent properties. A solar spectrally selective absorbing coating requires such materials that show not only outstanding optical properties but also prominent thermal stability. It is desirable but challenging to fabricate high performance solar absorbing coatings. A prospective strategy for the preparation of solar absorbing coatings is the introduction of high entropy alloys. In this work, we have developed a simple, cost-conscious, scalable and highly efficient solar selective absorbing coating using a single layer high entropy nitride AlCrTaTiZrN by choosing appropriate elemental compositions. The as-deposited coatings show an outstanding spectral selectivity of α/e = 92.8%/5.1% or 91.6%/5.1%. High temperature thermal stability tests are performed in depth, indicating that the coating could endure annealing treatment at 800 °C for 2 h with insignificant reflectance spectrum variation. A prolonged period of thermal stability characterization presents that this coating could maintain good optical performance after annealing at 650 °C for 300 h. Furthermore, high temperature thermal emittance can be suppressed to as low as 15.4% at 600 °C, which contributes to the state-of-the-art photothermal conversion efficiency under 100 suns (87.7% at 600 °C, and 84.6% at 650 °C). Overall, the high entropy nitride AlCrTaTiZrN based coating contributes to good thermal stability and optical properties, showing great potential for extensive applications in solar energy harvesting.

Aim and Objective: Missense mutation (MM) may lead to various human diseases by disabling proteins. Accurate prediction of MM is important and challenging for both protein function annotation and drug design. Although several computational methods yielded acceptable success rates, there is still room for further enhancing the prediction performance of MM. Materials and Methods: In the present study, we designed a new feature extracting method, which considers the impact degree of residues in the microenvironment range to the mutation site. Stringent cross-validation and independent test on benchmark datasets were performed to evaluate the efficacy of the proposed feature extracting method. Furthermore, three heterogeneous prediction models were trained and then ensembled for the final prediction. By combining the feature representation method and classifier ensemble technique, we reported a novel MM predictor called TargetMM for identifying the pathogenic mutations from the neutral ones. Results: Comparison outcomes based on statistical evaluation demonstrate that TargetMM outperforms the prior advanced methods on the independent test data. The source codes and benchmark datasets of TargetMM are freely available at https://github.com/sera616/TargetMM.git for academic use.

Active learning algorithms attempt to overcome the labeling bottleneck by asking queries from large collection of unlabeled examples. Existing batch mode active learning algorithms suffer from three limitations: (1) The methods that are based on similarity function or optimizing certain diversity measurement, in which may lead to suboptimal performance and produce the selected set with redundant examples. (2) The models with assumption on data are hard in finding images that are both informative and representative. (3) The problem of noise labels has been an obstacle for algorithms. In this paper, we propose a novel active learning method that makes embeddings of labeled examples to those of unlabeled ones and back via deep neural networks. The active scheme makes correct association cycles that end up at the same class from that the association was started, which considers both the informativeness and representativeness of examples, as well as being robust to the noise labels. We apply our active learning method to semi-supervised classification and clustering. The submodular function is designed to reduce the redundancy of the selected examples. Specifically, we incorporate our batch mode active scheme into the classification approaches, in which the generalization ability is improved. For semi-supervised clustering, we try to use our active scheme for constraints to make fast convergence and perform better than unsupervised clustering. Finally, we apply our active learning method to data filtering. To validate the effectiveness of the proposed algorithms, extensive experiments are conducted on diversity benchmark datasets for different tasks, i.e., classification, clustering, and data filtering, and the experimental results demonstrate consistent and substantial improvements over the state-of-the-art approaches.

Whether the anemia increases the risk of mortality in patients with acute heart failure (AHF) remains unclear. This study aims to explore the relationship between anemia and outcomes in patients with AHF including subgroup analysis. This study included 3279 patients with hemoglobin available from the Beijing Acute Heart Failure Registry (Beijing AHF Registry) study. The primary endpoint was all-cause mortality in 1 year, and the secondary endpoint was 1-year all-cause events including all-cause death and readmission. Logistic regression models were applied to describe related variables of anemia in patients with AHF. Multivariate Cox proportional hazards models described associations of anemia with clinical outcomes in the overall cohort and subgroups. 45.4% of the patients were found anemic. They were older and had more comorbidities than non-anemic patients. Variables including older age, female, chronic kidney dysfunction (CKD), lower hematocrit, lower albumin, with loop diuretics applied, without beta-blockers, angiotensin-converting enzyme inhibitors /angiotensin receptor blockers (ACEIs/ARBs) and spironolactone applied in the emergency department (ED) were associated with anemia in AHF patients. Anemic patients had higher 1-year mortality (38.4% vs. 27.2%, p

Background New-onset arrhythmias and sleep disturbances are frequently observed during the postoperative period in patients undergoing thoracic surgery. Objective We evaluated the effectiveness of a single-dose stellate ganglion block (SGB) to prevent the occurrence of arrhythmias in patients undergoing thoracic surgery for cancer. Design Randomised controlled double-blind study. Setting Single university hospital. Patients Ninety patients with lung cancer or oesophagal cancer scheduled for elective video-assisted thoracoscopic surgery were randomly randomised into one of two equal groups (the SGB group and control group, n = 40 each). Interventions Patients received a single dose of 5 ml of 0.5% ropivacaine during ultrasound-guided SGB before induction of general anaesthesia in the SGB group. Main outcome measures Holter ECG was continuously monitored during the first 48 postoperative hours, and sleep state was monitored during the first two postoperative nights. Results The incidences of postoperative supraventricular tachycardias were lower in the SGB group compared with the control group during the first 48 postoperative hours; 11.6 (5/43) vs. 31.8% (14/44), respectively, P = 0.023 (odds ratio 0.28, 95% confidence interval 0.09 to 0.87). The SGB also prolonged the total sleep time and increased the sleep efficiency during the first two postoperative nights. The duration of stage N2 sleep was longer in the SGB group compared with the control group [28 (interquartile range, 14 to 58) to 94 (interquartile range, 69 to 113) min, P = 0.016] on the first postoperative night. There were no differences in the duration of stage N1 and N3 sleep (P = 0.180, 0.086, respectively) on the first postoperative night, and the duration of stage N1, N2 and N3 sleep (P = 0.194, 0.057, 0.405, respectively) on the second postoperative night between the groups. Conclusion A pre-operative SGB effectively prevented the occurrence of postoperative supraventricular tachycardias and improves the objective sleep quality in patients undergoing thoracic surgery for cancer. Trial registration number ChiCTR-1900023064.

Background To develop a nomogram to predict cancer-specific survival (CSS) in patients with metastatic testicular germ cell tumors (mTGCTs). Methods Data were obtained from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox regression models were used to identify factors associated with CSS. Survival times between different groups were compared using Kaplan-Meier survival curves and the log-rank test. A nomogram visualization model was established using the R language to predict survival rates. Harrell’s concordance index (C-index), the area under the receiver operating characteristic curve (AUC) and calibration plots were used to assess the performance of the model. Results We analyzed the data of 949 patients. The median follow-up time was 32 months (range 0 to 83 months), and 224 (23.60%) patients died before the last follow-up, of whom 193 (20.33%) died of mTGCTs. The site of distant metastases was an independent prognostic factor for CSS. Compared to patients without involvement of the corresponding organ, patients with bone, brain, liver, and lung involvement had worse CSS. We also found that age, histological type, surgery, radiation therapy, chemotherapy, metastatic site and insurance status affected the CSS of patients with mTGCTs. We used these prognostic factors to construct our nomogram. Harrell’s C-index for CSS was 0.739. The AUC and calibration plots indicated good performance of the nomogram. Conclusions A nomogram for predicting CSS in patients with mTGCTs has been developed, which can help patients and clinicians accurately predict mortality risk and recommend personalized treatment modalities.

Aim: To investigate the relationship between hemoglobin levels and the progression of IgA nephropathy (IgAN). Methods: In a two-center cohort of 1,828 cases with biopsy-proven IgAN, we examined the association of hemoglobin levels with the primary outcome of a composite of all-cause mortality or kidney failure defined as a 40% decline in eGFR, or ESKD (defined as eGFR 2 or need for kidney replacement therapy including hemodialysis, peritoneal dialysis, or kidney transplantation), or the outcome of kidney failure, assessed using Cox and logistic regression models, respectively, with adjustment for confounders. Results: At baseline, mean age, eGFR, and hemoglobin levels were 33.75 ± 11.03 years, 99.70 ± 30.40 mL/min/1.73 m2, and 123.47 ± 18.36 g/L, respectively. During a median of approximately 7-year follow-up, 183 cases reached the composite outcome. After adjustment for demographic and IgAN-specific covariates and treatments, a lower quartile of hemoglobin was nonlinearly associated with an increased risk of the primary outcome or kidney failure in the Cox proportional hazards models (primary outcome: HR for quartile 3 vs. 4, 1.37; 95% CI, 0.83–2.25; HR for quartile 2 vs. 4, 1.18; 95% CI, 0.68–2.07; HR for quartile 1 vs. 4, 1.91; 95% CI, 1.15–3.17; kidney failure: HR for quartile 3 vs. 4, 1.39; 95% CI, 0.84–2.31; HR for quartile 2 vs. 4, 1.20; 95% CI, 0.68–2.11; HR for quartile 1 vs. 4, 1.83; 95% CI, 1.09–3.07) in the fully adjusted model. Then, hemoglobin levels were transformed to a binary variable for fitting the model according to the criteria for anemia of 110 g/L in the women and 120 g/L in men in China. The participants in the anemia group had an increased risk of developing outcomes compared with the nonanemia group in both genders (primary outcome: male: HR, 1.64; 95% CI, 1.01–2.68; female: HR, 1.68; 95% CI, 1.02–2.76; kidney failure: male: HR, 1.60; 95% CI, 0.97–2.64; female: HR, 1.58; 95% CI, 0.95–2.61) in the fully adjusted model. Conclusions: A low level of hemoglobin was nonlinearly associated with IgAN progression. The anemic IgAN patients presented a higher risk of developing poor outcomes compared with the nonanemic patients.

Background To develop a nomogram for predicting cancer-specific survival (CSS) of patients with non-metastatic primary adenocarcinoma of the bladder (NMACB). Methods We used a retrospective cohort study design. Patient data were obtained from the SEER database, univariate and multivariate Cox regression analyses were performed to identify factors associated with CSS. A nomogram visualization model was established using R language software to predict survival rate. Harrell’s concordance index (C-index), area under the receiver operating characteristic (ROC) curve (AUC) in addition to calibration plots were used to assess the performance of the model. Results A total of 1,635 patients were included in the study. A multivariate Cox regression model indicated that age, histological type, grade, stage, and surgery were independent covariates associated with CSS. Using these prognostic factors, a nomogram was constructed. Harrell’s C indices for CSS were 0.729 in the training cohort and 0.716 in the validation cohort. AUC values were 0.769, 0.735 and 0.724 for 1, 3, and 5-year in the training cohort, and 0.738, 0.727 and 0.713 for 1, 3 and 5-year in the validation cohort, respectively. The AUC values and calibration plots indicated that the nomogram provided good predictive performance. Conclusions A nomogram for predicting CSS in patients with NMACB was developed to assist clinicians in the accurate prediction of mortality risk to allow them to recommend a personalized treatment modality.

Sample pretreatment is an important part in detection and analysis of samples, especially for those with low initial concentration. Preconcentration technology can improve the accuracy of analytical method and expand its application range. However, conventional sample preconcentration technology is often cumbersome, costly, time-consuming, and relies on expensive equipment. Paper-based microfluidic sample preconcentration technique has many advantages such as easy preparation, low cost and high efficiency, which can simplify operation and reduce detection cost. This paper focuses on the research and application progress of paper-based microfluidic sample preconcentration in recent years, including enrichment principle, selection of paper-based materials, design of device structure, and the application of this technology. Finally, the development trend of this technique was prospected.

Protein-DNA interactions play an important role in biological processes. Accurately identifying DNA-binding residues is a critical but challenging task for protein function annotations and drug design. Although wet-lab experimental methods are the most accurate way to identify DNA-binding residues, they are time consuming and labor intensive. There is an urgent need to develop computational methods to rapidly and accurately predict DNA-binding residues. In this study, we propose a novel sequence-based method, named PredDBR, for predicting DNA-binding residues. In PredDBR, for each protein, its position-specific frequency matrix (PSFM), predicted secondary structure (PSS), and predicted probabilities of ligand-binding residues (PPLBR) are first generated as three feature sources. Secondly, for each feature source, the sliding window technique is employed to extract the matrix-format feature of each residue. Then, we design two strategies, i.e., SR and AVE, to separately transform PSFM-based and two predicted feature source-based, i.e., PSS-based and PPLBR-based, matrix-format features of each residue into three cube-format features. Finally, after serially combining the three cube-format features, the ensemble classifier is generated via applying bagging strategy to multiple base classifiers built by the framework of 2D convolutional neural network. Experimental results demonstrate that PredDBR outperforms several state-of-the-art sequenced-based DNA-binding residue predictors.

Objective The aim of this study was to study the relationship between adverse pregnant outcomes(APO) with chronic hepatitis B virus (HBV) infection in pregnant women. Method From 2017 to 2019, we studied HBsAg (+) pregnant women and HBsAg (-) who gave birth at our hospital in Guangzhou City, China. We compared of the outcomes of pregnant women with HBsAg(+) or HBsAg(-). Further, among HBsAg(+) pregnant women, We compared of the outcomes of pregnant women with HBeAg(+) group or HBeAg(-) group, HBV DNA above 2×10e5IU/mL group or HBV DNA below 2×10e5IU/mL) group, respectively. Finally, multivariate logistic regression analysis was used to evaluate the independent association between HBV infection and the risk of developing APO. Result First, Our research Indicates that the rates of gestational diabetes mellitus (GDM), intrahepatic cholestasis of pregnancy (ICP), premature rupture of membrane (PROM), Fetal distress (FD), Oligohydramnios, Premature delivery (PD), Low birth weight (LBW), Meconium contamination (MC), Neonatal hyperbilirubinemia(NH) in HBsAg(+) group were higher than those in HBsAg(-) group (P5 IU/mL were higher than those in HBV DNA below 2×10e5 IU/mL group (P

Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for predicting transcription factor binding sites on 690 ChIP-seq datasets. More specifically, MAResNet combines the bottom-up and top-down attention mechanisms and a state-of-the-art feed-forward network (ResNet), which is constructed by stacking attention modules that generate attention-aware features. In particular, the multi-scale attention mechanism is utilized at the first stage to extract rich and representative sequence features. We further discuss the attention-aware features learned from different attention modules in accordance with the changes as the layers go deeper. The features learned by MAResNet are also visualized through the TMAP tool to illustrate that the method can extract the unique characteristics of transcription factor binding sites. The performance of MAResNet is extensively tested on 690 test subsets with an average AUC of 0.927, which is higher than that of the current state-of-the-art methods. Overall, this study provides a new and useful framework for the prediction of transcription factor binding sites by combining the funnel attention modules with the residual network.

Little is known about the comorbidities in actinic keratosis patients. To evaluate the association of actinic keratosis with certain malignancies. All patients with actinic keratosis (n = 61,438) and age- and sex-matched control subjects (n = 307,190) at a 5:1 ratio were enrolled using data from the Korean National Health Insurance Service between the years 2007 and 2014. In subjects with actinic keratosis, overall cancer incidence was higher than that for controls after income level, habitat, diabetes, hypertension, and dyslipidemia were adjusted (Hazard Ratio [HR] = 1.43 [95% confidence interval 1.38–1.47]). The positive association of specific cancers were observed in the following order: skin cancer (HR = 3.43 [2.47–4.75]), oral cavity and pharyngeal cancer (HR = 1.99 [1.57–2.52]), lymphoma (HR = 1.59 [1.28–1.96]), leukemia (HR = 1.35 [1.03–1.77]), prostate cancer (HR = 1.35 [1.21–1.51]), renal cancer (HR = 1.29 [1.02–1.63]), liver cancer (HR = 1.21 [1.09–1.35]), thyroid cancer (HR = 1.20 [1.05–1.38]), and gastric cancer (HR = 1.13 [1.03–1.23]). Although further research on pathologic mechanism is needed, the implications of a positive correlation between actinic keratosis and internal organ malignancies has great significance.

Excessive application of nitrogen fertilizer has caused the reduction of nitrogen fertilizer utilization efficiency, resource waste and environmental problems in our country. There are disadvantages of long sampling and heavy workload using the traditional methods of acid absorption and box method for farmland ammonia volatilization measurement. In order to meet the application needs for on-line automatic measurement of ammonia volatilization in farmland, an in-situ automatic measurement device for ammonia volatilization was designed combined with laser spectroscopy technology. The main body of the measurement device is an integrated sampling box, with the advantages of long optical path measurement, heat resistance and corrosion resistance. The control circuit of the sampling box used ARM as the main controller for environmental data monitoring, automatic opening and closing of the cover and lens temperature controlling. The ammonia volatilization results were analyzed by the data acquisition and processing system after the laser passed through the sampling box to obtain ammonia spectroscopy signal. The device was used for observation at Fengqiu Ecological Station, CAS. The results showed that the ammonia volatilization rose rapidly after 90 minutes for nitrogen fertilizer applying, and it decreased gradually to a low value after 270 minutes. According to the experimental results, the continuous measurement time for cover closing can be set to 3 minutes, and the cover opening time can be set to 3 to 5 minutes. The working performance of the automatic measurement device was verified through these experiments, with the advantages of low power consumption, high measurement sensitivity, and no gas sampling. It realizes insitu automatic measurement for ammonia volatilization in farmland with the natural respiration condition of the soil.

Background and Objective Ocular torsion, the eye movements to rotating around the line of sight, has not been well investigated regarding the influence of refractive errors. The purpose of this study was to investigate the effect of uncorrected ametropia on ocular torsion induced by fixation distances. Methods Seventy-two subjects were classified according to the type of their refractive error, and ocular torsion of the uncorrected eye was compared based on changes induced by different fixation distances. Ocular torsion was measured using a slit-lamp biomicroscope equipped with an ophthalmic camera and a half-silvered mirror. Results In all groups, excyclotorsion values increased as the fixation distance decreased, but the myopia and astigmatism groups had larger amounts of ocular torsion than the emmetropia group. In addition, as the amount of uncorrected myopia and astigmatism increased, the amount of ocular torsion increased. Conclusion Since the amount of ocular torsion caused by a change to a shorter fixation distance was larger when the refractive error was uncorrected, we suggest that ametropia should be fully corrected in patients frequently exposed to ocular torsion due to changes in fixation distance.

Protein subcellular localization plays a crucial role in characterizing the function of proteins and understanding various cellular processes. Therefore, accurate identification of protein subcellular location is an important yet challenging task. Numerous computational methods have been proposed to predict the subcellular location of proteins. However, most existing methods have limited capability in terms of the overall accuracy, time consumption and generalization power. To address these problems, in this study, we developed a novel computational approach based on human protein atlas (HPA) data, referred to as PScL-HDeep, for accurate and efficient image-based prediction of protein subcellular location in human tissues. We extracted different handcrafted and deep learned (by employing pretrained deep learning model) features from different viewpoints of the image. The step-wise discriminant analysis (SDA) algorithm was applied to generate the optimal feature set from each original raw feature set. To further obtain a more informative feature subset, support vector machine–based recursive feature elimination with correlation bias reduction (SVM-RFE + CBR) feature selection algorithm was applied to the integrated feature set. Finally, the classification models, namely support vector machine with radial basis function (SVM-RBF) and support vector machine with linear kernel (SVM-LNR), were learned on the final selected feature set. To evaluate the performance of the proposed method, a new gold standard benchmark training dataset was constructed from the HPA databank. PScL-HDeep achieved the maximum performance on 10-fold cross validation test on this dataset and showed a better efficacy over existing predictors. Furthermore, we also illustrated the generalization ability of the proposed method by conducting a stringent independent validation test.

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer’s representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue–residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

There have been no epidemiological studies identifying associations between systemic inflammatory diseases and actinic keratosis. This study used a large nationwide database to investigate the associations between actinic keratosis and systemic inflammatory diseases. Records of patients over 20 years of age newly diagnosed with actinic keratosis (n = 64,659) from 2012 to 2017 were analysed. A control population of individuals without actinic keratosis, matched for age, sex, and year of claim, who visited an outpatient clinic, was sampled at a ratio of 1:1 (n = 64,659). Both cohorts were analysed for the presence of systemic inflammatory diseases within at least 5 years prior to diagnosis of actinic keratosis. Patients with actinic keratosis exhibited higher odds ratios for rheumatoid arthritis (1.336; 95% confidence interval (95% CI) 1.161-1.537)) and psoriasis (1.513; 95% CI 1.435-1.595) compared with the control group on multivariate analysis. However, the proportions of Behçet's disease, Crohn's disease, ulcerative colitis, and multiple sclerosis in the actinic keratosis group were not statistically significant.

The accurate prediction of the relative solvent accessibility of a protein is critical to understanding its 3D structure and biological function. In this study, a novel deep multi-view feature learning (DMVFL) framework that integrates three different neural network units, i.e., bidirectional long short-term memory recurrent neural network, squeeze-and-excitation, and fully-connected hidden layer, with four sequence-based single-view features, i.e., position-specific scoring matrix, position-specific frequency matrix, predicted secondary structure, and roughly predicted three-state relative solvent accessibility probability, is developed to accurately predict relative solvent accessibility information of protein. On the basis of this newly developed framework, one new protein relative solvent accessibility predictor was proposed and called DMVFL-RSA, which employs a customized multiple feedback mechanism that helps to extract discriminative information embedded in the four single-view features. In benchmark tests on TEST524 and CASP14-derived (CASP14set) datasets, DMVFL-RSA outperforms other existing state-of-the-art protein relative solvent accessibility predictors when predicting two-state (exposure threshold of 25%), three-state (exposure thresholds of 9% and 36%), and four-state (exposure thresholds of 4%, 25%, and 50%) discrete values. For real-valued prediction on TEST524 and CASP14set, DMVFL-RSA has also gained high Pearson correlation coefficient values, indicating a positive correlation between the predicted and native relative solvent accessibility. Detailed analyses show that the major advantages of DMVFL-RSA lie in the high efficiency of the DMVFL framework, the applied multiple feedback mechanism, and the strong sensitivity of the sequence-based features. The web server of DMVFL-RSA is freely available at https://jun-csbio.github.io/DMVFL-RSA/for academic use. The standalone package of DMVFL-RSA is downloadable at https://github.com/XueQiangFan/DMVFL-RSA.

DNA N6-methyladenine is an important type of DNA modification that plays important roles in multiple biological processes. Despite the recent progress in developing DNA 6mA site prediction methods, several challenges remain to be addressed. For example, although the hand-crafted features are interpretable, they contain redundant information that may bias the model training and have a negative impact on the trained model. Furthermore, although deep learning (DL)-based models can perform feature extraction and classification automatically, they lack the interpretability of the crucial features learned by those models. As such, considerable research efforts have been focused on achieving the trade-off between the interpretability and straightforwardness of DL neural networks. In this study, we develop two new DL-based models for improving the prediction of N6-methyladenine sites, termed LA6mA and AL6mA, which use bidirectional long short-term memory to respectively capture the long-range information and self-attention mechanism to extract the key position information from DNA sequences. The performance of the two proposed methods is benchmarked and evaluated on the two model organisms Arabidopsis thaliana and Drosophila melanogaster. On the two benchmark datasets, LA6mA achieves an area under the receiver operating characteristic curve (AUROC) value of 0.962 and 0.966, whereas AL6mA achieves an AUROC value of 0.945 and 0.941, respectively. Moreover, an in-depth analysis of the attention matrix is conducted to interpret the important information, which is hidden in the sequence and relevant for 6mA site prediction. The two novel pipelines developed for DNA 6mA site prediction in this work will facilitate a better understanding of the underlying principle of DL-based DNA methylation site prediction and its future applications.

BACKGROUND AND OBJECTIVES To assess whether fecal pH might be an indicator of pouchitis during the postoperative period in hereditary colorectal cancer (CRC) patients who have undergone ileal pouch anal anastomosis (IPAA). METHODS Five consecutive daily pH values of stool samples from 31 familial adenomatous polyposis (FAP) patients and 32 metachronous Lynch syndrome patients who underwent IPAA procedures were reviewed. Patients with pouchitis (pouchitis group, n = 22) were compared with patients without pouchitis (nonpouchitis group, n = 41). A receiver operating characteristic (ROC) analysis was performed to determine the indicative potential of fecal pH for pouchitis. A Mantel-Cox test was also performed to evaluate the survival status of patients with or without pouchitis. RESULTS Pouchitis was noted in 22 (34.9%) of 63 patients after IPAA. The significance of each daily average fecal pH value and the 5-day overall average fecal pH value was compared between the two groups (P

We report the results of residue-residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)-based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact-map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end-to-end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free-modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the top L/5 long-range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems.

Objective.Behçet disease (BD) is a chronic inflammatory multiorgan disease. An increased risk of cardiovascular disease (CVD) and heightened death rate with BD have been suggested, but to our knowledge, a nationwide large-scale study has not been conducted to date. This study aimed to determine the overall CV risk and death rate in patients with BD versus controls using the Korean National Health Insurance Service claim database.Methods.Patients with BD (n = 5576) with no previous history of CVD were selected from 2010 to 2014. An age- and sex-matched control population of individuals without BD (n = 27,880) was randomly sampled at a ratio of 5:1. Both cohorts were followed for incident CVD or all-cause death until 2015.Results.The risks of myocardial infarction (HR 1.72, 95% CI 1.01–2.73) and stroke (HR 1.65, 95% CI 1.09–2.50) were significantly higher in patients with BD than in controls. Patients with BD also had a significantly higher risk of all-cause death (HR 1.82, 95% CI 1.40–2.37) compared to controls.Conclusion.Korean patients with BD had a higher overall risk of CVD than did those without BD. Therefore, patients with BD must be carefully monitored for the potential development of CVD to ensure that appropriate early treatments are delivered.

Background: Although several studies have reported a correlation between Behcet’s disease and Parkinson’s disease, this association has not yet been clarified. Objectives: The aim of this study is to determine the risk of Parkinson’s disease in patients with Behcet’s disease. Methods: Behcet’s disease patients (n = 17,179; mean age, 51.12±8.09 years; male, 34.95%) without known previous diagnosis of Parkinson’s disease were selected from the Korean National Health Insurance Database between 2010 and 2013. An age and gender-matched control population of individuals without Behcet’s disease was randomly sampled at a ratio of 3:1 (n = 34,575). Both cohorts were followed for development of Parkinson’s disease until 2015. Results: Among a total of 11,525 Behcet’s disease patients, 50 (0.43%) developed Parkinson’s disease, whereas only 51 among the 34,575 individuals in the non- Behcet’s disease group developed Parkinson’s (0.15%, P < 0.0001). The incidence of Parkinson’s disease was significantly higher in Behcet’s disease patients than in controls. In addition, during the follow-up period, patients with Behcet’s disease were more likely to be diagnosed with Parkinson’s disease than those in the non- Behcet’s disease group. Conclusion: Behcet’s disease patients had a higher risk of Parkinson’s disease compared to non- Behcet’s disease controls. Physicians should carefully monitor patients with Behcet’s disease for potential development of Parkinson’s disease.

Unsteady turbulent flow simulations were performed based on the Reynolds-averaged Navier–Stokes (RANS) equations to investigate flow oscillation due to three-dimensional (3D) configuration of a Mach 1.5 supersonic open cavity flow with a length-to-depth ratio of 3. Two-dimensional (2D) and 3D unsteady simulation results were analyzed and compared with experimental data and Rossiter’s empirical prediction data. The three-dimensional cavity width-to-depth ratio (W/D) was 1, 3.8 and 7.6. Computational results indicated that pressure oscillation in the 2D flow was generated by a single-flow structure, whereas a multiple-flow structure generated multiple oscillation peaks in the 3D flow. The flow structure in the 3D cavity was investigated. For the 2D flow case, the cavity internal pressure wave was directly synchronized with the free shear layer. In the 3D flow case, an unstable spanwise flow due to the sidewall was observed. This spanwise fluctuation produced additional pressure oscillations coupled with the streamwise internal pressure wave. The numerical results indicate that the spanwise flow reduces the propagation speed of the internal pressure waves and the intensity of the corresponding pressure fluctuation.

This study analysed genomic mutations in basal cell carcinoma using whole exome sequencing of DNA specimens obtained from 20 Korean patients. Histological evaluation determined that 15 (75%) were low-risk basal cell carcinomas, and 5 (25%) were high-risk basal cell carcinomas. Seventy-five percent of the basal cell carcinomas harboured somatic mutations in hedge­hog pathway genes (PTCH1, 40% and SMO, 50%) and 45% harboured mutations in TP53. LRP1B was the most frequently mutated gene in high-risk basal cell carcinomas, SMO was the most frequently mutated gene in low-risk basal cell carcinomas. Specifically, LRP1B, ROS1, PTCH1, KMT2C, NSD1 and ARID1A mutations were more frequent in high-risk basal cell carcinomas than in low-risk basal cell carcinomas. However, copy number gains of the ROS1 gene were observed only in low-risk basal cell carcinomas. Other basal cell carcinoma related genes found in this study include: KDR, KMT2D, FAT1, FAT4, GRIN2A, ERBB4, NOTCH2, PDE4DIP, TET1, ZFHX3 and PREX2. These results provide insight into basal cell carcinoma in non-Caucasians.

Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have achieved significant success. Nevertheless, the current state-of-the-art computational methods have some drawbacks associated with the use of limited datasets with insufficient experimental data. To address this, we propose a novel transfer learning-based method, termed SAResNet, which combines the self-attention mechanism and residual network structure. More specifically, the attention-driven module captures the position information of the sequence, while the residual network structure guarantees that the high-level features of the binding site can be extracted. Meanwhile, the pre-training strategy used by SAResNet improves the learning ability of the network and accelerates the convergence speed of the network during transfer learning. The performance of SAResNet is extensively tested on 690 datasets from the ChIP-seq experiments with an average AUC of 92.0%, which is 4.4% higher than that of the best state-of-the-art method currently available. When tested on smaller datasets, the predictive performance is more clearly improved. Overall, we demonstrate that the superior performance of DNA-protein binding prediction on DNA sequences can be achieved by combining the attention mechanism and residual structure, and a novel pipeline is accordingly developed. The proposed methodology is generally applicable and can be used to address any other sequence classification problems.

ObjectiveTo compare the ocular counter-roll (OCR) measured using iris images during binocular fixation and head tilt with OCR measured via fundus photography.MethodsFifty-three healthy college students participated in this study. The mean OCR was measured by collection of iris images and fundus images under seven head tilt conditions (0 degrees; 10, 20, and 30 degrees right; and 10, 20, and 30 degrees left). Three iris images (crossed pupil center, pupil center, and pupil periphery) were taken using a slit-lamp biomicroscope with an ophthalmic camera and a half-silvered mirror; fundus images were collected via fundus photography. The mean OCR values were compared between images taken with each method.ResultsNo iris images or head tilt conditions revealed any significant differences in mean OCR comparison with fundus images. The mean difference in OCR was smallest, and the correlation was greatest, between the crossed pupil center and fundus images.ConclusionA half-silvered mirror and iris images can replace fundus photography for the measurement of OCR.

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

Knowledge of protein-ATP interaction can help for protein functional annotation and drug discovery. Accurately identifying protein-ATP binding residues is an important but challenging task to gain the knowledge of protein-ATP interactions, especially for the case where only protein sequence information is given. In this study, we propose a novel method, named DeepATPseq, to predict protein-ATP binding residues without using any information about protein three-dimension structure or sequence-derived structural information. In DeepATPseq, the HHBlits-generated position-specific frequency matrix (PSFM) profile is first employed to extract the feature information of each residue. Then, for each residue, the PSFM-based feature is fed into two prediction models, which are generated by the algorithms of deep convolutional neural network (DCNN) and support vector machine (SVM) separately. The final ATP-binding probability of the corresponding residue is calculated by the weighted sum of the outputted values of DCNN-based and SVM-based models. Experimental results on the independent validation data set demonstrate that DeepATPseq could achieve an accuracy of 77.71%, covering 57.42% of all ATP-binding residues, while achieving a Matthew's correlation coefficient value (0.655) that is significantly higher than that of existing sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis show that the major advantage of DeepATPseq lies at the combination utilization of DCNN and SVM that helps dig out more discriminative information from the PSFM profiles. The online server and standalone package of DeepATPseq are freely available at: https://jun-csbio.github.io/DeepATPseq/for academic use.

As an essential task in protein structure and function prediction, protein fold recognition has attracted increasing attention. The majority of the existing machine learning-based protein fold recognition approaches strongly rely on handcrafted features, which depict the characteristics of different protein folds; however, effective feature extraction methods still represent the bottleneck for further performance improvement of protein fold recognition. As a powerful feature extractor, deep convolutional neural network (DCNN) can automatically extract discriminative features for fold recognition without human intervention, which has demonstrated an impressive performance on protein fold recognition. Despite the encouraging progress, DCNN often acts as a black box, and as such, it is challenging for users to understand what really happens in DCNN and why it works well for protein fold recognition. In this study, we explore the intrinsic mechanism of DCNN and explain why it works for protein fold recognition using a visual explanation technique. More specifically, we first trained a VGGNet-based DCNN model, termed VGGNet-FE, which can extract fold-specific features from the predicted protein residue–residue contact map for protein fold recognition. Subsequently, based on the trained VGGNet-FE, we implemented a new contact-assisted predictor, termed VGGfold, for protein fold recognition; we then visualized what features were extracted by each of the convolutional layers in VGGNet-FE using a deconvolution technique. Furthermore, we visualized the high-level semantic information, termed fold-discriminative region, of a predicted contact map from the localization map obtained from the last convolutional layer of VGGNet-FE. It is visually confirmed that VGGNet-FE could effectively extract distinct fold-discriminative regions for different types of protein folds, thereby accounting for the improved performance of VGGfold for protein fold recognition. In summary, this study is of great significance for both understanding the working principle of DCNNs in protein fold recognition and exploring the relationship between the predicted protein contact map and protein tertiary structure. This proposed visualization method is flexible and applicable to address other DCNN-based bioinformatics and computational biology questions. The online web server of VGGfold is freely available at http://csbio.njust.edu.cn/bioinf/vggfold/.

Objective: Aim to the relationship between adverse pregnant outcomes with chronic hepatitis B virus (HBV) infection in pregnant women. Simultaneously, assess the incidence of adverse pregnancy outcomes (APO) among different serum HBV status in pregnant women. Method: From 2017 to 2019, we studied HBsAg (+) pregnant women and HBsAg (-) who gave birth at our hospital in Guangzhou City, China. We compared of the incidence of pregnant women with HBsAg(+) or HBsAg(-). Further, among HBsAg(+) pregnant women, We compared of the incidence of pregnant women with HBeAg(+) group or HBeAg(-) group, high HBV DNA loads (HBV DNA≥2×10^5IU/mL) group or low HBV DNA loads (HBV DNA＜2×10^5IU/mL) group, respectively. Finally, multivariate logistic regression analysis was used to evaluate the independent association between HBV infection and the risk of developing APO.Result: First, Our research Indicates that the rates of gestational diabetes mellitus (GDM), intrahepatic cholestasis of pregnancy (ICP), premature rupture of membrane (PROM), Fetal distress (FD), Oligohydramnios, Premature delivery (PD), Low birth weight (LBW), Meconium contamination (MC), Neonatal hyperbilirubinemia(NH) in HBsAg(+) group were higher than those in HBsAg(-) group (PPConclusions: Chronic HBV infection can increase the rate of host adverse pregnancy outcomes (APO). The maternal viral load and HBeAg status were significantly associated with the incidence of GDM and ICP. Maternal HBsAg carrier had an independent risk for GDM and ICP.

Protein-DNA interactions exist ubiquitously and play important roles in the life cycles of living cells. The accurate identification of DNA-binding proteins (DBPs) is one of the key steps to understand the mechanisms of protein-DNA interactions. Although many DBP identification methods have been proposed, the current performance is still unsatisfactory. In this study, a new method, called TargetDBP+, is developed to further enhance the performance of identifying DBPs. In TargetDBP+, five convolutional features are first extracted from five feature sources, i.e., amino acid one-hot matrix (AAOHM), position-specific scoring matrix (PSSM), predicted secondary structure probability matrix (PSSPM), predicted solvent accessibility probability matrix (PSAPM), and predicted probabilities of DNA-binding sites (PPDBSs); second, the five features are weightedly and serially combined using the weights of all of the elements learned by the differential evolution algorithm; and finally, the DBP identification model of TargetDBP+ is trained using the support vector machine (SVM) algorithm. To evaluate the developed TargetDBP+ and compare it with other existing methods, a new gold-standard benchmark data set, called UniSwiss, is constructed, which consists of 4881 DBPs and 4881 non-DBPs extracted from the UniprotKB/Swiss-Prot database. Experimental results demonstrate that TargetDBP+ can obtain an accuracy of 85.83% and precision of 88.45% covering 82.41% of all DBP data on the independent validation subset of UniSwiss, with the MCC value (0.718) being significantly higher than those of other state-of-the-art control methods. The web server of TargetDBP+ is accessible at http://csbio.njust.edu.cn/bioinf/targetdbpplus/; the UniSwiss data set and stand-alone program of TargetDBP+ are accessible at https://github.com/jun-csbio/TargetDBPplus.

Background: The distribution knowledge structure and pattern of the literature on labor analgesia in PubMed were examined. Methods: Scientific papers on labor analgesia published from 1 January, 2000 to 31 June, 2020 were retrieved. The extracted MeSH items were quantitatively analyzed by the Bibliographic Item Co-Occurrence Matrix Builder (BICOMB), and the high frequency MeSH items were identified. In gCLUTO software, repeated bisection method was used to Mountain visualisation, and the visual matrix was established. By constructing high-frequency MeSH terms co-occurrence matrix, strategic diagram and social network are further completed. Results: The search strategy yielded 2870 papers, and the number of papers published annually had changed slightly during the study period. Among all extracted MeSH terms, 42 high-frequency MeSH terms were identified by consensus, and were divided into six categories by diclustering analysis. In the strategic diagram, the methods of labor analgesia, drug doses, and routes of administration were properly presented. In contrast, statistical and numerical data on obstetric analgesia were relatively underdeveloped, and management of pain during labor was undeveloped. In the social network analysis, the position status of each component was determined by the centrality values. Conclusions: The findings on labor analgesia are relatively divergent, and the six research categories outlined in this study reflect the publication trends in the field of labor analgesia to some extent. Our quantitative bibliometric research across a 20-year span depicts the overall direction of the latest topics and provides some hints for researchers when launching new projects.