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Abstract Background Hypertension has become a major public health and social problem endangering the health of Chinese residents, and has brought a large economic burden of diseases to families and even the whole society. Promoting the equity of health service utilization for patients with hypertension is of great significance for reducing the occurrence of cardiovascular and cerebrovascular diseases and reducing the economic burden of patients with hypertension. This study aims to analyze the equity and change of outpatient and inpatient service utilization in patients with hypertension, and to explore its influencing factors. Methods Using the data of Household Health Interview Survey from 34 key points of contact for comprehensive healthcare reform in 17 provinces in 2014 and 2016, the equity and change of outpatient and inpatient service utilization of patients with hypertension were analyzed by concentration index, and the contribution degree of each influencing factor to utilization inequality was analyzed by decomposition of concentration index. Results In 2014 and 2016, the standardized concentration index of two-week visits of hypertension patients was 0.0217 and 0.0141, respectively, indicating that the utilization of outpatient services was basically equitable. The standardized concentration index of times of hospitalization was 0.1593 and 0.1364, and the standardized concentration index of hospital length of stay was 0.1517 and 0.1369, respectively. The rich groups utilized more hospitalization services. Compared with 2014, the concentration index of outpatient and inpatient service utilization of patients with hypertension decreased in 2016, indicating that the inequity of outpatient and inpatient service utilization has further improved. The level of family income was the primary factor affecting the inequality of hospitalization utilization. Conclusions The utilization of outpatient services in patients with hypertension was basically equitable; however, wealthier groups had greater utilization of inpatient services. Overall, the degree of inequity has been reduced. Family economic status was the main factor affecting inequality in utilization of medical services. To achieve common prosperity was the most fundamental measure to improve the inequitable utilization of hospitalization services. At this stage, we should improve the medical assistance system and increase the assistance to vulnerable groups, such as the low-income groups.

Abstract Insider threats pose a critical risk to organisations, impacting their data, processes, resources, and overall security. Such significant risks arise from individuals with authorised access and familiarity with internal systems, emphasising the potential for insider threats to compromise the integrity of organisations. Previous research has addressed the challenge by pinpointing malicious actions that have already occurred but provided limited assistance in preventing those risks. In this research, we introduce a novel approach based on bidirectional long short-term memory (BiLSTM) networks that effectively captures and analyses the patterns of individual actions and their sequential dependencies. The focus is on predicting whether an individual would be a malicious insider in a future day based on their daily behavioural records over the previous several days. We analyse the performance of the four supervised learning algorithms on manual features, sequential features, and the ground truth of the day with different combinations. In addition, we investigate the performance of different RNN models, such as RNN, LSTM, and BiLSTM, in incorporating these features. Moreover, we explore the performance of different predictive lengths on the ground truth of the day and different embedded lengths for the sequential features. All the experiments are conducted on the CERT r4.2 dataset. Experiment results show that BiLSTM has the highest performance in combining these features.

Abstract Understanding the properties of explosives is the basis for investigating and analyzing explosion cases. To date, due to the strict legal control of standard explosives and initiators, homemade pyrotechnics composed of oxidizers and fuels have become popular explosive sources of improvised explosive devices (IEDs) threatening greatly social stability and personal safety. The reactivity of pyrotechnics strongly depends on their intrinsic characteristics and operating conditions, which determine the efficiencies of heat and mass transfer between the reaction zone and the unreacted zone. Herein, the tests of thermodynamics, pressurization characteristics, and combustion propagation behaviors are conducted to explore the effects of oxidizer species, particle size, and loading density on the reactivity of homemade binary aluminum-based pyrotechnics. The results show that the pyrotechnics with potassium chlorate (KClO3) have the strongest reactivity with the highest pressurization rate (dp/dt) and the shortest combustion duration. Compared with their counterparts based on aluminum microparticles(mAl), pyrotechnics consisting of Al nanoparticles (nAl) possess superior reactivity as expected, which results from the relatively short heat and mass transfer distances. The nAl-based pyrotechnics have a low reaction exothermic peak temperature, great heat release, great aluminothermic reaction completeness, and high produced peak pressure with several orders of magnitude higher pressurization rate. Increasing the loading density of the pyrotechnics over a certain value can change the dominant mode of heat transfer from convective to conduction, sharply decreasing the pressurization characteristics and combustion front propagation velocities (v p ). The results of theoretical calculations using the NASA-CEA codes show that loading density can alter the reaction process of the pyrotechnics, leading to a decrease in the predicted pressure per unit mass for Al/KNO3 or Al/AP, and an increase for Al/KClO3. For nAl/potassium nitrate (KNO3), the density is between 1.0 and 1.25 g cm−3, across which dp/dt decreases by one order of magnitude from 0.148 to 0.014 MPa ms−1. In addition, v p decreases by three orders of magnitude from 0.040 to 0.078 m s−1. Distinct pressurization behaviors of nAl/AP are observed at a density of 1.5 g cm−3, while the variation in nAl/KClO3 reactivity fluctuates. These results are beneficial for the damage assessment of scenes caused by an explosion and for inversely calculating charge parameters.

In this experiment, goat milk casein was used as raw material. Goat milk casein was treated with different ultrasonic intensities to explore the effects of ultrasonic treatment on the pH, conductivity, Zeta potential, turbidity and other physical and chemical indexes, as well as the functional characteristics of emulsification and foaming. The results showed that compared with untreated group, after ultrasonic treatment, the pH decreased and the conductivity had no significant change (P>0.05). The absolute value of Zeta potential reached the maximum value of 27.85±1.55 mV when treated at 400 W for 2 min. Turbidity was significantly decreased compared with untreated group (P

Abstract Background Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. Methods We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the ‘Deep-RadScore,’ a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. Findings The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. Interpretation This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.

Objective To discuss the regulating effect of stored red blood cells (RBCs) transfusion on BMDMs in inflammatory conditions, and the relationship between stored RBCs transfusion and inflammatory response induced by bacterial infection. Methods Forty C57BL/6 male mice of 6-8 weeks (18-22 g/mouse) were randomly divided into experimental group and control group. Each mouse was infected with 200 µL Pseudomonas aeruginosa injecting into the tail vein, and 400 µL fresh (storage >14 d) and stored RBCs (storage 0.05). F4/80 of experimental group and control group 2, 4 and 8 hours after RBCs infused were 1.83±0.11 vs 0.75±0.06, 0.46±0.06 vs 0.33±0.06 (P0.05), respectively. iNOS, TNF-α, MCP1 of M1 in liver of experimental group and control group 2, 4 and 8 hours after RBCs infused were respectively: iNOS 3.44±0.20 vs 2.46±0.08, 9.25±0.55 vs 2.67±0.12, 2.80±0.08 vs 2.39 ±0.01; TNF-α 1.69±0.22 vs 1.13±0.03, 1.44±0.24 vs 0.96±0.09, 1.31±0.05 vs 0.96±0.06; MCP1 4.96±0.08 vs 4.28±0.27, 4.63±0.04 vs 2.07±0.09, 2.28±0.19 vs 1.33±0.03 (P0.05). Conclusion Stored RBCs infusion can greatly promote the M1 polarization of BMDMs in liver.

The deployment of network security has become a crucial strategy for the development of modern nations, with skilled professionals serving as the core driving force behind cybersecurity efforts.Cultivating cybersecurity talents and fostering team building are essential for China's talent reserves and strategic capital accumulation.With economic globalization, 0-day vulnerabilities have constantly emerged in information systems, attack chains have become increasingly complex, threat targets were difficult to detect, and hidden viruses varied over time.Talents trained by traditional universities for network security were unable to adapt to innovative production and practical activities in the new fields, as it was confined to their training structure system.In order to break through the shackles of the internal administrative structure and traditional curriculum system of universities, and ease the contradiction between the uncertainty of cyberspace security situation and the standardization of talent cultivation, the talent cultivation models based on Bourdieu's theory were studied, the fusion meaning of cultural capital in the academic field was analyzed and three practical standards were put forward in terms of teaching content, cultivation model and teaching opportunity of cyberspace security talents in combination with the actual cases of the “Fang class”.In terms of education content, standards and open knowledge systems were established to meet the differentiated needs of students.The mentorship approach was employed to construct habitus, fostering students’ ability to adapt flexibly to problems by adopting different cognitive thinking modes.Favorable teaching opportunities were utilized by tutors in various roles to promote efficient integration of individuals and fields, helping students establish a valuable and stable mindset.By leveraging Bourdieu's theory to explore the integration path of academic fields, it has leveraged the advantages of independently cultivating high-quality talents, vigorously promoting technological innovation and the healthy ecological development of the industry.

[This corrects the article DOI: 10.1371/journal.pone.0217185.].

Alternative splicing (AS) is a universal phenomenon in eukaryotes, and it is still challenging to identify AS events. Several methods have been developed to identify AS events, such as expressed sequence tags (EST), microarrays and RNA-seq. However, EST has limitations in identifying low-abundance genes, while microarray and RNA-seq are high-throughput technologies, and PCR-based technology is needed for validation. To overcome the limitations of EST and shortcomings of high-throughput technologies, we established a method to identify AS events, especially for low-abundance genes, by reverse transcription (RT) PCR with gene-specific primers (GSPs) followed by nested PCR. This process includes two major steps: 1) the use of GSPs to amplify as long as the specific gene segment and 2) multiple rounds of nested PCR to screen the AS and confirm the unknown splicing variants. With this method, we successfully identified three new splicing variants, namely, GenBank Accession No. HM623886 for the bdnf gene (GenBank GeneID: 12064), GenBank Accession No. JF417977 for the trkc gene (GenBank GeneID: 18213) and GenBank Accession No. HM623888 for the glb-18 gene (GenBank GeneID: 172485). In addition to its reliability and simplicity, the method is also cost-effective and labor-intensive. In conclusion, we developed an RT-nested PCR method using gene-specific primers to efficiently identify known and novel AS variants. This approach overcomes the limitations of existing methods for detecting rare transcripts. By enabling the discovery of new isoforms, especially for low-abundance genes, this technique can aid research into aberrant splicing in disease. Future studies can apply this method to uncover AS variants involved in cancer, neurodegeneration, and other splicing-related disorders.

Abstract With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.

Objective: Neurologic complications seriously affect the survival rate and quality of life in patients with extracorporeal cardiopulmonary resuscitation (ECPR) undergoing cardiac arrest. This study aimed to repurpose selective hypothermic cerebral perfusion (SHCP) as a novel approach to protect the brains of these patients. Methods: Rats were randomly allocated to Sham, ECPR, and SHCP combined ECPR (CP-ECPR) groups. In the ECPR group, circulatory resuscitation was performed at 6 minutes after asphyxial cardiac arrest by extracorporeal membrane oxygenation. The vital signs were monitored for 3 hours, and body and brain temperatures were maintained at the normal level. In the CP-ECPR group, the right carotid artery catheterization serving as cerebral perfusion was connected with the extracorporeal membrane oxygenation device to achieve selective brain cooling (26-28 °C). Serum markers of brain injury and pathomorphologic changes in the hippocampus were evaluated. Three biological replicates further received RNA sequencing in ECPR and CP-ECPR groups. Microglia activation and inflammatory cytokines in brain tissues and serum were detected. Results: SHCP rapidly reduced the brain-targeted temperature and significantly alleviated nerve injury. This was evident from the reduced brain injury serum biomarker levels, lower pathologic scores, and more surviving neurons in the hippocampus in the CP-ECPR group. Furthermore, more differentially expressed genes for inflammatory responses were clustered functionally according to Kyoto Encyclopedia of Genes and Genomes pathway analysis. And SHCP reduced microglia activation and the release of proinflammatory mediators. Conclusions: Our preliminary data indicate that SHCP may serve as a potential therapy to attenuate brain injury via downregulation of neuroinflammation in patients with ECPR.

To evaluate the prevalence, influencing factors, and behavior rules of self-medication in children. Articles on self-medication in children from various electronic databases (PubMed, Cochrane Library, Web of Science, the WHO website ( https://www.who.int/ ), ABI, CNKI, and Wanfang), were searched to August 2022. The single-group meta-analyses of the prevalence, influencing factors, and behavior rules of self-medication in children were performed using Revman 5.3 and Stata 16.0. The overall pooled prevalence of self-medication in children was 57% (95% CI: 0.39-0.75, I² = 100%, P

A 10-year-old girl presented with obvious cyanosis, and the saturation of arterial blood oxygen (SpO2) was decreased to 60.5% in the outpatient examination. Computed tomography angiography (CTA) and echocardiography suggested congenitally corrected transposition of the great arteries (ccTGAs), membranous ventricular septal aneurysm (MVSA), atrial septal defect (ASD), severe pulmonary stenosis (PS), and severe tricuspid regurgitation (TR). Due to the complex pathological anatomical structures, the three-dimensional printed model was used for preoperative assessment. After a comprehensive evaluation was completed, the operation was performed by physiological correction under cardiopulmonary bypass, including the resection of MVSA, repair using the bovine pericardial patch for ASD, and linear valvuloplasty of the tricuspid valve. Due to the special anatomical structures of ccTGA, PS was treated by extracardiac pipe technique. After the operation, the patient recovered well, cyanosis disappeared, SpO2 was up to 96%, and the extracardiac pipe was well-functioning without regurgitation or obstruction.

BackgroundAfter its approval by the European Union in 2011, CytoSorb therapy has been applied to control cytokine storm and lower the increased levels of cytokines and other inflammatory mediators in blood. However, the efficiency of this CytoSorb treatment in patients with coronavirus disease (COVID-19) still remains unclear. To elucidate the Cytosorb efficiency, we conducted a systematic review and single-arm proportion meta-analysis to combine all evidence available in the published literature to date, so that this comprehensive knowledge can guide clinical decision-making and future research.MethodsThe literature published within the period 1 December 2019 to 31 December 2021 and stored in the Cochrane Library, Embase, PubMed, and International Clinical Trials Registry Platform (ICTRP) was searched for all relevant studies including the cases where COVID-19 patients were treated with CytoSorb. We performed random-effects meta-analyses by R software (3.6.1) and used the Joanna Briggs Institute checklist to assess the risk of bias. Both categorical and continuous variables were presented with 95% confidence intervals (CIs) as pooled proportions for categorical variables and pooled means for continuous outcomes.ResultsWe included 14 studies with 241 COVID-19 patients treated with CytoSorb hemadsorption. Our findings reveal that for COVID-19 patients receiving CytoSorb treatment, the combined in-hospital mortality was 42.1% (95% CI 29.5–54.6%, I2 = 74%). The pooled incidence of adjunctive extracorporeal membrane oxygenation (ECMO) support was 73.2%. Both the C-reactive protein (CRP) and interleukin-6 (IL-6) levels decreased after CytoSorb treatment. The pooled mean of the CRP level decreased from 147.55 (95% CI 91.14–203.96) to 92.36 mg/L (95% CI 46.74–137.98), while that of IL-6 decreased from 339.49 (95% CI 164.35–514.63) to 168.83 pg/mL (95% CI 82.22–255.45).ConclusionsThe majority of the COVID-19 patients treated with CytoSorb received ECMO support. In-hospital mortality was 42.1% for the COVID-19 patients who had CytoSorb treatment. Both CRP and IL-6 levels decreased after Cytosorb treatment.

Objective To analyze the safety of homotypic transfusion in military donors with negative unexpected antibody. Methods Blood samples (4 mL/person)of eligible military blood donors from November 2018 to October 2019 in our hospital (also working as forces blood station) were conducted for RBC antigen typing, unexpected antibody screening, direct antiglobulin test and cross-match test using microcolumn gel technology, and the compatibility of homotype blood samples was statistical analyzed. Results A total of 1 577 samples from eligible military blood donors were collected, including A RhD (+ ), B RhD (+ ), O RhD (+ ) and AB RhD (+ ), accounting for 31.39% (495/1 577), 34.37% (542/1 577), 24.10% (380/1 577) and 10.15% (160/1 577), respectively. Six samples presenting positive unexpected antibodies (0.38%, 6/1 577) were screened out, and a total of 7 141 cross-matching tests were performed on 1 571 unexpected antibody negative samples, including A RhD (+ ) [37.36% (2 668/7 141)], B RhD (+ ) [34.81% (2 486/7 141)], O RhD (+ ) [17.71% (1265/7 141)] and AB RhD (+ ) [10.11% (722/7 141)]. There was only 1 case of incompatible cross-matching presented between other donors and clinical patients, and the direct antiglobulin test was 1+ , therefore suspended red blood cells of the donor were scrapped. Conclusion There was high compatibility and good security of homotype transfusion of military blood donors with negative unexpected antibody.

Objectives: Non-invasive prenatal testing (NIPT) has been widely used in recent years. According to clinical experience from all hospitals providing prenatal screening services in Beijing, we explored the feasibility of using NIPT for the analysis of common foetal aneuploidies among pregnancies.Methods: In total, 68,763 maternal blood samples were collected from January 2020 to December 2020 at the Beijing prenatal diagnosis agency. Cases with positive screening results by NIPT detection were validated using prenatal diagnosis.Results: In total, 920 cases had a high-risk NIPT result, and 755 cases were shown to be truly positive by a chromosome karyotyping analysis; the prenatal diagnosis rate was 82.07% (755/920). Of the920 cases, there were 164 cases of T21, 70 cases of T18, 38 cases of T13, 360 cases of SCAs and 288 cases of other chromosomal abnormalities. The positive rates of T21, T18, T13, and SCAs were 0.24% (164/68,763), 0.10% (70/68,763), 0.06% (38/68,763) and 0.52% (360/68,763), respectively. The sensitivity and specificity were 98.17% and 99.92% for T21, 96.15% and 99.93% for T18, and 100% and 99.95% for T13, respectively. The PPVs of T21,T18,T13 and SCAs were65.24% (107/164), 35.71% (25/70), 18.42% (7/38) and 31.39% (113/360), respectively. For all indications, there were more higher T21/18/13 in the high-risk group than in the low-risk group (comprising only cases of voluntary request), with a positive rate of 0.46% vs. 0.27% (p < 0.001), sensitivity of 99.16% vs. 91.30% (p = 0.02) and PPV of 56.73%vs.32.81% (p = 0.001), but there was no significant difference in specificity between the groups (p = 0.71). The detection indication with the highest PPV (100%) by NIPT was ultrasound structural abnormalities and ultrasound soft marker abnormalities for T21 and ultrasound structural abnormalities and NT thickening for T18 and T13. The PPVs of different clinical indications of T21 (p = 0.002), T13 (p = 0.04) and SACs (p = 0.02) were statistically significant.Conclusion: The high specificity, efficiency and safety (non-invasiveness) of NIPT can effectively improve the detection rate of common chromosomal aneuploidy, thereby reducing the occurrence of birth defects. We should encourage pregnant women with NIPT-high-risk results to undergo a prenatal diagnosis to determine whether the foetus has chromosomal abnormalities. More importantly, the screening efficiency of NIPT in the low-risk group was significantly lower than that in the high-risk group. Therefore, the use of NIPT in low-risk groups should be fully promoted, and socioeconomic benefits should be considered.

BackgroundA better understanding of the factors and their correlation with clinical first-line nurses’ sleep, fatigue and mental workload is of great significance to personnel scheduling strategies and rapid responses to anti-pandemic tasks in the post-COVID-19 pandemic era.ObjectiveThis multicenter and cross-sectional study aimed to investigate the nurses’ sleep, fatigue and mental workload and contributing factors to each, and to determine the correlation among them.MethodsA total of 1,004 eligible nurses (46 males, 958 females) from three tertiary hospitals participated in this cluster sampling survey. The Questionnaire Star online tool was used to collect the sociodemographic and study target data: Sleep quality, fatigue, and mental workload. Multi-statistical methods were used for data analysis using SPSS 25.0 and Amos 21.0.ResultsThe average sleep quality score was 10.545 ± 3.399 (insomnia prevalence: 80.2%); the average fatigue score was 55.81 ± 10.405 (fatigue prevalence: 100%); and the weighted mental workload score was 56.772 ± 17.26. Poor sleep was associated with mental workload (r = 0.303, P < 0.05) and fatigue (r = 0.727, P < 0.01). Fatigue was associated with mental workload (r = 0.321, P < 0.05). COVID-19 has caused both fatigue and mental workload. As 49% of nurses claimed their mental workload has been severely affected by COVID-19, while it has done slight harm to 68.9% of nurses’ sleep quality.ConclusionIn the post-COVID-19 pandemic era, the high prevalence of sleep disorders and fatigue emphasizes the importance of paying enough attention to the mental health of nurses in first-class tertiary hospitals. Efficient nursing strategies should focus on the interaction of sleep, fatigue and mental workload in clinical nurses. In that case, further research on solutions to the phenomenon stated above proves to be of great significance and necessity.Clinical trial registration[https://clinicaltrials.gov/], identifier [ChiCTR2100053133].

A novel series of benzenesulfonamide derivatives that selectively act on the AT2 receptor have been designed and synthesized. The binding affinity and functional activity were evaluated by radio-ligand binding analysis and cell neurite outgrowth assay, respectively. The compounds 8d, 8h, 8i, 8j, 8l, and 9h exhibited moderate selectivity and affinity for the AT2 receptor. Among them, 8j exhibited agonist activity and 8l displayed similar selectivity to the AT2 receptor with PD123,319. Molecular docking was carried out to analyze the binding mode and binding site between the compound and the AT2 receptor to provide a reference for further development.

Classification is a very important task in data mining and pattern analysis, which have been widely used to solve various real-world problems. To obtain better classification performance, in this paper, we propose a novel classification framework based on multiple weighted class association rules (C-MWCAR), whose key idea is to transform the association among features into a set of class association rules (CARs), then classify unknown instances based on the CARs obtained. Concretely, C-MWCAR consists of a dictionary order-based CAR mining algorithm (DOCMA), a branch-based CAR selection algorithm (BCSA), and a multiple weighted CARs-based classifier (MWCC). Specifically, DOCMA mines the complete set of CARs, from which BCSA further selects a representative and concise set of CARs based on the distribution, coverage, and redundancy of the mined CARs. When classifying an unknown instance, MWCC picks out a set of CARs that are most similar to the given instance and computes the weighted importance of those CARs. Finally, the class label of the given instance will be determined by the similarities between the instance and the CARs and the weighted importance of the CARs. Furthermore, we apply the proposed C-MWCAR to a real-world classification task, i.e., hypertension diagnosis, based on a real dataset of 128 subjects. Experimental results indicate that C-MWCAR outperforms four baseline methods and achieves 93.3%, 93.8%, and 92.7% in terms of accuracy, sensitivity, and specificity, respectively.

Tyrosine phosphorylation, orchestrated by tyrosine kinases and phosphatases, modulates a multi-layered signaling network in a time- and space-dependent manner. Dysregulation of this post-translational modification is inevitably associated with pathological diseases. Our previous work has demonstrated that non-receptor tyrosine kinase FER is upregulated in ovarian cancer, knocking down which attenuates metastatic phenotypes. However, due to the limited number of known substrates in the ovarian cancer context, the molecular basis for its pro-proliferation activity remains enigmatic. Here, we employed mass spectrometry and biochemical approaches to identify insulin receptor substrate 4 (IRS4) as a novel substrate of FER. FER engaged its kinase domain to associate with the PH and PTB domains of IRS4. Using a proximity-based tagging system in ovarian carcinoma-derived OVCAR-5 cells, we determined that FER-mediated phosphorylation of Tyr779 enables IRS4 to recruit PIK3R2/p85β, the regulatory subunit of PI3K, and activate the PI3K-AKT pathway. Rescuing IRS4-null ovarian tumor cells with phosphorylation-defective mutant, but not WT IRS4 delayed ovarian tumor cell proliferation both in vitro and in vivo. Overall, we revealed a kinase-substrate mode between FER and IRS4, and the pharmacological inhibition of FER kinase may be beneficial for ovarian cancer patients with PI3K-AKT hyperactivation.

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes. Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and EasyEnsemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features (4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods (oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models (logistic regression and EasyEnsemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets. Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.

Abstract Background Maternal feeding anxiety (FA) was prevalent during puerperium and might affect infant feeding practices. This study was aimed to investigate the FA status in Chinese postpartum women and its relationship with infant feeding practices (FPs). Methods Participants were from the Mother-Infant Cohort Study of China, in which the dietary and feeding practices, physical and psychiatric health for both mothers and infants were followed up from childbirth to next 2 years. In this study the maternal feeding anxiety (FA) status at 0–3 months postpartum was assessed by Li’s Self-rating Feeding Anxiety Scale (SFAS). Infant feeding practices (FPs) at 0–3 months, including breastfeeding-related behaviors, responsive feeding and infant food refusal were investigated by self-designed questionnaire. Results In total 456 mothers the average feeding anxiety scores (FAS) was 41.02 ± 8.02 (mean ± SD), and maternal FA prevalence were 61.4% (FAS>38) with severe FA being 8.6% (FAS>52) at 0–3 months postpartum. The FAS was related with infant FPs, and lower maternal FAS was significantly related with infant colostrum feeding (40.86 ± 8.02 vs 44.74 ± 11.33, P 53) were more likely to feed infants with bottle (ORs, 95%CI: 2.41, 1.11 ~ 5.19). There were not significant association between FAS and exclusive breastfeeding and responsive feeding practices (P > 0.05). The higher FAS was associated with infant food refusal behaviors, the maternal scores whose infant “never”, “rarely”, “sometimes” and “often” spat out food when feeding were 39.86 ± 8.02, 41.47 ± 8.18, 41.36 ± 7.44 and 42.14 ± 12.03 increasingly (P > 0.05), and the FA prevalence was significantly different among groups (P

Abstract Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness prediction. However, star ratings are used in a way that has either little representation capacity or limited interaction with review texts. As a result, rating information has yet to be fully exploited during the combination. This paper aims to overcome the two drawbacks. A deep interactive architecture is proposed to learn the text–rating interaction (TRI) for helpfulness modeling. TRI enlarges the representation capacity of star ratings while enhancing the influence of rating information on review texts. TRI is evaluated on six real-world domains of the Amazon 5-Core dataset. Extensive experiments demonstrate that TRI can better predict review helpfulness and beat the state of the art. Ablation studies and qualitative analysis are provided to further understand model behaviors and the learned parameters.

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.

Background: In China, congenital heart disease (CHD) is the most common birth defect type, with approximately 13,000 new cases annually. This study aimed to investigate high-risk factors, prenatal screening and prenatal diagnosis as a basis for clinical decisions.Methods: All CHD cases identified from 2018 to 2020 were obtained from the Beijing city birth defect surveillance system and prenatal diagnosis institutions. The prenatal CHD diagnosis was confirmed by fetal echocardiography and amniotic fluid or cord blood genetic examination. Chi-square, odds ratio (OR), 95% confidence interval (CI), and univariate and multivariate logistic analyses were used to explore the high-risk factors, prenatal screening and prenatal diagnosis of CHD. Results: In total, 6,786/594,860 fetuses with CHD were diagnosed by prenatal echocardiography. The average incidence of CHD was 11.4 per 1,000 births, with an increase of 30.7 per 1,000 births from 2018 to 2020 (P < 0.05); the average incidence of complex CHD (CCHD) was 2.02 per 1,000 births, with no significant change from 2018 to 2020 (P > 0.05). Women age ≥35 years (OR 1.06, 95% CI 0.77–1.46) was at higher risk of having babies with CHD than women aged 21–34 years. Overall, CHD incidence increased with maternal age (OR1.03, 95% CI 1.02–1.03). Additionally, women who had a non-local household registration (OR 1.16, 95% CI 1.10–1.22) or had diabetes mellitus (DM) (OR 1.16, 95% CI 0.96–1.25) were at higher risk of CHD. As an independent factor, CCHD was related to maternal age, DM, fetal gender, and maternal education level (all P < 0.05). The prenatal ultrasound screening detection rate of CCHD was 97.59%, which was far higher than that of total CHD (51.67%) (P < 0.001). The prenatal ultrasound diagnosis rate of CCHD was higher than that of simple CHD (P < 0.001), but the coincidence rate in the ultrasound diagnosis of CCHD was lower than that of simple CHD (P < 0.001). Prenatal genetic testing revealed chromosomal abnormalities in 25.62% (279/1089) of CHD cases with indications for a prenatal diagnosis.Conclusions: Maternal age, household registration and DM were related to CHD occurrence. Prenatal ultrasound screening is a highly effective method for CCHD diagnosis, and CHD fetuses should be closely evaluated to exclude chromosomal abnormalities.

Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net.

Social Media has become an integral part of our daily life. Not only it enables collaboration and flow of information but has also become an imperative tool for businesses and governments around the world. All this makes a compelling case for everyone to be on some sort of online social media platform. However, this virtuousness is overshadowed by some of its shortcomings. The manifestation of antisocial behaviour online is a growing concern that hinders participation and cultivates numerous social problems. Antisocial behaviour exists in its various forms such as aggression, disregard for safety, lack of remorse, unlawful behaviour, etc. The paper introduces a deep learning-based approach to detect and classify online antisocial behaviour (ASB). The automatic content classification addresses the issue of scalability, which is imperative when dealing with online platforms. A benchmark dataset was created with multi-class annotation under the supervision of a domain expert. Extensive experiments were conducted with multiple deep learning algorithms and their superior results were validated against the results from the traditional machine learning algorithms. Visually enhanced interpretation of the classification process is presented for model and error analyses. Accuracy of up to 99% in class identification was achieved on the ground truth dataset for empirical validation. The study is an evidence of how the cutting-edge deep learning technology can be utilized to solve a real-world problem of curtailing antisocial behaviour, which is a public health threat and a social problem.

Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This article presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

Twenty novel talmapimod analogues were designed, synthesised and evaluated for the in vivo anti-inflammatory activities. Among them, compound 6n, the most potent one, was selected for exploring the mechanisms underlying its anti-inflammatory efficacy. In RAW264.7 cells, it effectively suppressed lipopolysaccharides-induced (LPS-induced) expressions of iNOS and COX-2. As illustrated by the western blot analysis, 6n downregulated both the NF-κB signalling and p38 MAPK phosphorylation. Further enzymatic assay identified 6n as a potent inhibitor against both p38α MAPK (IC50=1.95 µM) and COX-2 (IC50=0.036 µM). By virtue of the concomitant inhibition of p38α MAPK, its upstream effector, and COX-2, along with its capability to downregulate NF-κB and MAPK-signalling pathways, 6n, a polypharmacological anti-inflammatory agent, deserves further development as a novel anti-inflammatory drug.

Chemotherapy is an important method for treating breast cancer. However, multidrug resistance is one of the major challenges in breast cancer chemotherapy. There is an urgent need to develop novel, effective antitumor strategies that will perfect existing therapeutic regimens. In this study, the double-targeted nanocarrier, Quercetin-3’3-dithiodipropionic acid-Astragalus polysaccharides-Folic acid (QDAF), was successfully synthesized and self-assembled into a neoteric nano-targeted delivery strategy, named nano-pomegranates, and which were utilized to effectively inhibit multidrug resistance in estrogen receptor α (ERα)-positive breast tumor. The outstanding abilities of nano-pomegranates to release the drug in a reducing environment was determined by in vitro release assay. The cellular studies in MCF-7 cells were examined that nano-pomegranates have remarkable efficiencies of enhancing cellular uptake, inhibition and necrosis and apoptosis. In vivo antitumor experiments showed that nano-pomegranates have better anti-tumor effects and lower systemic toxicity than free Cur. In conclusion, nano-pomegranates have great potential in anti-breast cancer treatment.

Abstract Background Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medicine practitioners and the difficulty in improving the service capacity of basic Chinese medicine practitioners. Heterogeneous information networks (HINs) are a kind of graphical model for integrating and modeling real-world information. Through HINs, we can integrate and model the large-scale heterogeneous TCM data into structured graph data and use this as a basis for analysis. Methods Mining categorizations from TCM data is an important task for precision medicine. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network. Results The results from experiments on the Pharmacopoeia of the People’s Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas. Conclusions We use heterogeneous information networks to model TCM data and propose a TCM-HIN. Combining the heterogeneous graph with the probability graph, we proposed the TCM-Clus algorithm, which combines clustering with ranking and classifies traditional Chinese medicine prescriptions. The results of the categorizations can help Chinese medicine practitioners to make clinical decision.

Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user’s mobility and behaviour patterns. In this study, we used location-sharing data from Swarm to explore spatio-temporal, geo-temporal and behaviour patterns within the city of Melbourne. Moreover, we used several tools for different datasets. We used the MeaningCloud tool for sentiment analysis and the LIWC15 tool for psychometric analysis. Also, we employed SPSS software for the descriptive statistical analysis on check-in data to reveal meaningful trends and attain a deeper understanding of human behaviour patterns in the city. The results show that most people do not express strong negative or positive emotions in relation to the places they visit. Behaviour patterns vary based on gender. Furthermore, mobility patterns are different on different days of the week as well as at different times of a day but are not necessarily influenced by the weather.

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.

ObjectivesNon-communicable diseases (NCDs) have become the main cause of mortality in China. In 2009, the Chinese government introduced the Basic Public Health Service (BPHS) program to relieve the rising burden of NCDs through public health measures and delivery of essential medical care. The primary aim of this study was to evaluate the impact of the BPHS program on hypertension control.MethodsThe China National Health Development Research Center (CNHDRC) undertook a Cross-sectional Health Service Interview Survey (CHSIS) of 62,097 people from primary healthcare reform pilot areas across 17 provinces from eastern, central, and western parts of China in 2014. The current study is based on responses to the CHSIS survey from 7,867 participants, who had been diagnosed with hypertension. Multi-variable mixed logit regression analysis was used to estimate the association between BPHS management and uncontrolled hypertension. In a follow-up analysis, generalized structural equation modelling (GSEM) was used to test for mediation of the BPHS program effect through patient compliance with medication.FindingsThe estimated proportion of patients with uncontrolled hypertension was 30% lower (23.2% vs 31.5%) in those participants who were adequately managed under the BPHS program. Other predictors of hypertension control included compliance with medication, self-reported wellbeing, income, educational attainment and exercise; smoking was associated with reduced hypertension control. The significant inverse association between uncontrolled hypertension and age indicates poor outcomes for younger patients. Additional testing suggested that nearly 40% of the effect of BPHS management (95% CI: 28.2 to 51.7) could be mediated by improved compliance with medication; there was also an indication that the effect of management was 30% stronger in districts/counties with established digital information management systems (IMS).ConclusionHypertension control improved markedly following active management through the BPHS program. Some of that improvement could be explained by greater compliance with medication among program participants. This study also identified the need to tailor the BPHS program to the needs of younger patients to achieve higher levels of control in this population. Future investigations should explore ways in which existing healthcare management influences the success of the BPHS program.

INTRODUCTION: Diabetic eye disease (DED) is a group of eye problems that can affect diabetic people. Such disorders include diabetic retinopathy, diabetic macular edema, cataracts, and glaucoma. Diabetes can damage your eyes over time, which can lead to poor vision or even permanent blindness. Early detection of DED symptoms is therefore essential to prevent escalation of the disease and timely treatment. Research difficulties in early detection of DEDs can so far be summarized as follows: changes in the eye anatomy during its early stage are often untraceable by the human eye due to the subtle nature of the features, where large volumes of fundus images put tremendous pressure on scarce specialist resources, making manual analysis practically impossible.OBJECTIVES: Therefore, methods focused on deep learning have been practiced to promote early detection of DEDs and address the issues currently faced. Despite promising, highly accurate identification of early anatomical changes in the eye using Deep Learning remains a challenge in wide-scale practical application.METHODS: We present conceptual system architecture with pre-trained Convolutional Neural Network combined with image processing techniques to construct an early DED detection system. The data was collected from various publicly available sources, such as Kaggle, Messidor, RIGA, and HEI-MED. The analysis was presented with 13 Convolutional Neural Networks models, trained and tested on a wide-scale imagenet dataset using the Transfer Learning concept. Numerous techniques for improving performance were discussed, such as (i) image processing,(ii) fine-tuning, (iii) volume increase in data. The parameters were recorded against the default Accuracy metric for the test dataset.RESULTS: After the extensive study about the various classification system, and its methods, we found that creating an efficient neural network classifier demands careful consideration of both the network architecture and the data input. Hence, image processing plays a significant role to develop high accuracy diabetic eye disease classifiers.CONCLUSION: This article recognized specific work limitations in the early classification of diabetic eye disease. First, early-stage classification of DED, and second, classification of DR, GL, and DME using a method that causes permanent blindness afterward. Lastly, this study was intended to propose the framework for early automatic DED detection of fundus images through deep learning addressing three main research gaps.

To prepare for the oral and maxillofacial surgery for the facial symmetry of patients, midsagittal plane of skull in brain computed tomography (CT) images is calculated with points manually chosen from skull by doctor. But the extracted midsagittal plane of the skull is different by different doctor. Even the extracted midsagittal plane of the same patient is also different by the same doctor in different times. The manually extracting operation usually takes a long time to increase the doctor's workload. Aimed at this problem, a semi-automatic extracting method for midsagittal plane of skull is proposed in this paper. First, the brain tissue is extracted by region growing method and the oriented bounding box (OBB) of the brain tissue is built. Second, the middle symmetry plane of the OBB of brain tissue is extracted as the initial midsagittal plane, which is updated by the mathematical translation and rotation method. Finally, the symmetrical characteristic of the brain tissue based on the updated symmetry plane is calculated by the mutual information method. This procedure is executed iteratively until the symmetrical characteristic of the brain tissue based on the new symmetry plane is no more different from the previous result. The final extracted symmetry plane is the midsagittal plane of skull in brain CT images of the patient. The midsagittal plane which is extracted manually by doctor is used to compare and evaluate the accuracy of this semi-automatic extracting symmetry plane method. The experimental results from both qualitative and quantitative analyses showed that the method can reach or approach the accuracy of manual extracted, but the stable level of this method is significantly higher than the manual method and this method can shorten the operate time to reduce the doctor's workload.

Domestic violence (DV) is not only a major health and welfare issue but also a violation of human rights. In recent years, domestic violence crisis support (DVCS) groups active on social media have proven indispensable in the support services provided to victims and their families. In the deluge of online-generated content, the significant challenge arises for DVCS groups' to manually detect the critical situation in a timely manner. For instance, the reports of abuse or urgent financial help solicitation are typically obscured by a vast amount of awareness campaigns or prayers for the victims. The state-of-the-art deep learning models with the embeddings approach have already demonstrated superior results in online text classification tasks. The automatic content categorization would address the scalability issue and allow the DVCS groups to intervene instantly with the exact support needed. Given the problem identified, the study aims to: 1) construct the novel “gold standard' dataset from social media with multi-class annotation; 2) perform the extensive experiments with multiple deep learning architectures; 3) train the domain-specific embeddings for performance improvement and knowledge discovery; and 4) produce the visualizations to facilitate models analysis and results in interpretation. The empirical evidence on a ground truth dataset has achieved an accuracy of up to 92% in classes prediction. The study validates an application of cutting edge technology to a real-world problem and proves beneficial to DVCS groups, health care practitioners, and most of all victims.

Constructing ingenious microstructures, such as core–shell, laminate, microcapsule and porous microstructures, is an efficient strategy for tuning the combustion behaviors and thermal stability of energetic materials (EMs). Electrohydrodynamic atomization (EHDA), which includes electrospray and electrospinning, is a facile and versatile technique that can be used to process bulk materials into particles, fibers, films and three-dimensional (3D) structures with nanoscale feature sizes. However, the application of EHDA in preparing EMs is still in its initial development. This review summarizes the progress of research on EMs prepared by EHDA over the last decade. The morphology and internal structure of the produced materials can be easily altered by varying the operation and precursor parameters. The prepared EMs composed of zero-dimensional (0D) particles, one-dimensional (1D) fibers and two-dimensional (2D) films possess precise microstructures with large surface areas, uniformly dispersed components and narrow size distributions and show superior energy release rates and combustion performances. We also explore the reasons why the fabrication of 3D EM structures by EHDA is still lacking. Finally, we discuss development challenges that impede this field from moving out of the laboratory and into practical application.

Abstract Extracting web content is to obtain the required data embedded in web pages, usually including structured records, such as product information, and text content, such as news. Web pages use a large number of HTML tags to organize and to present various information. Both knowing little about the structures of web pages and mixing kinds of information in web pages are making the extraction process very challenging to guarantee extraction performance and extraction adaptability. This study proposes a unified web content extraction framework that can be applied in various web environments to extract both structured records and text content. First, we construct a characteristic container to hold kinds of characteristics related with extraction objectives, including visual text information, content semantics(instead of HTML tag semantics), web page structures, etc. Second, the above characteristics are integrated into an extraction framework for extraction decisions on different web sites. Especially, we put forward different strategies, path aggregation for extracting text content and HMM model for structured records, to locate the extraction area by exploiting both those extraction characteristics. Comparative experiments on multiple web sites with popular extraction methods, including CETR, CETD and CNBE, show that our proposed extraction method can provide better extraction precision and extraction adaptability.

Abstract Background The monophyly of flatfishes has not been supported in many molecular phylogenetic studies. The monophyly of Pleuronectoidei, which comprises all but one family of flatfishes, is broadly supported. However, the Psettodoidei, comprising the single family Psettodidae, is often found to be most closely related to other carangimorphs based on substantial sequencing efforts and diversely analytical methods. In this study, we examined why this particular result is often obtained. Results The mitogenomes of five flatfishes were determined. Select mitogenomes of representative carangimorph species were further employed for phylogenetic and molecular clock analyses. Our phylogenetic results do not fully support Psettodes as a sister group to pleuronectoids or other carangimorphs. And results also supported the evidence of long-branch attraction between Psettodes and the adjacent clades. Two chronograms, derived from Bayesian relaxed-clock methods, suggest that over a short period in the early Paleocene, a series of important evolutionary events occurred in carangimorphs. Conclusion Based on insights provided by the molecular clock, we propose the following evolutionary explanation for the difficulty in determining the phylogenetic position of Psettodes: The initial diversification of Psettodes was very close in time to the initial diversification of carangimorphs, and the primary diversification time of pleuronectoids, the other suborder of flatfishes, occurred later than that of some percomorph taxa. Additionally, the clade of Psettodes is long and naked branch, which supports the uncertainty of its phylogenetic placement. Finally, we confirmed the monophyly of flatfishes, which was accepted by most ichthyologists.

Abstract Background Given that the CCDC92 (coiled-coil domain containing 92) was important in insulin resistance, we sought to investigate whether the CCDC92 rs825476 SNP is associated with the risk of CHD in Chinese Han population. Methods Rs11057401 was genotyped for 817 patients with CHD and 724 age- and sex-matched controls using PCR-based Invader assay with the probe sets designed and synthesized by third wave. Results Patients were found to have a significantly higher frequency of AA than the controls (23.5% vs. 14.7%, OR = 1.60, p = 0.000), and the frequency of allele A was found to be remarkably higher in the patients than the controls (48.1% vs. 40.3%, OR = 1.19, p = 0.000). Multivariate logistic analysis showed that the incidence of CHD was positively correlated with hyperlipidemia, T2D and rs11057401 AA/AT genotypes. The FPG, TC, and ApoA1 levels in the CHD patients were different among the AA, AT and TT genotypes (P

Diet is believed to play a major role in maternal recovery, postpartum weight retention (PPWR) is one of the challenges for Chinese women. However, the association between puerperal women's diet and PPWR remained unclear and complicated in China. The study assessed the dietary quality of puerperal women using adjusted Chinese Dietary Balance Index-16 (DBI-16) and explored its associations with PPWR. Participants were enrolled in the Mother-Infant Cohort Study of China. Dietary intake and demographic characteristics were obtained by a semi-quantitative food frequency questionnaire and a self-designed questionnaire at 0-3 months postpartum. PPWR was calculated by the weight difference at 0-3months and 6-8months postpartum minus pre-pregnancy weight. Dietary quality was assessed using adjusted DBI-16. 316 puerperal women were enrolled. According to adjusted DBI-16, 84.8% of participants had an insufficient dietary intake (vegetables 84.8%, fruits 91.8%, dairy 87.3%, soybean 61.4% and aquatic foods 79.4%, respectively), 67.1% had an excessive intake (cereals 60%, meat 57.3% and eggs 64.9%, respectively), 98.4% had an imbalanced diet consumption. PPWR at 0-3, and 6-8 months were 6.0 (±5.1) kg and 5.2 (±7.7) kg, and the percentage of PPWR (≥5kg) were 63.0% and 52.8% respectively. Multivariable linear regression showed the intake of fish and shrimp at 0-3 months postpartum was negatively associated with PPWR at 6-8 months (β = -0.114, SE = 0.279, p < 0.05). The diet quality of Chinese puerperal women was unreasonable and imbalanced. Fish intake tended to be a favorable factor for postpartum weight loss.

This paper attempts to estimate diagnostically relevant measure, i.e., Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme. The features exploited for retinal vessel characterization are based on statistical measures of histogram, different filter responses of images and local gradient information. The feature selection process is based on two feature ranking approaches (Pearson Correlation Coefficient technique and Relief-F method) to rank the features followed by use of maximum classification accuracy of three supervised classifiers (k-Nearest Neighbor, Support Vector Machine and Naïve Bayes) as a threshold for feature subset selection. Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme, i.e., decision fusion of multiple classifiers. The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance. The system is tested on three databases, a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies. Overall, an accuracy of 90.45%, 93.90% and 87.82% is achieved in retinal blood vessel separation with 0.0565, 0.0650 and 0.0849 mean error in Arteriovenous Ratio calculation for Local, INSPIRE-AVR and VICAVR dataset, respectively.

At present, with the development of an aging society and an increase in the number of elderly people, in order to ensure the ability and enthusiasm of the elderly to live independently, it is necessary to ensure that they can understand the world in a normal way. More and more elderly people have cognitive impairment, and virtual reality (VR) technology is more effective in cognitive diagnosis and treatment than traditional methods. This review article describes some studies on cognitive diagnosis and training for the elderly, and puts forward some suggestions for current studies, in the hopes that VR technology can be better applied to cognitive diagnosis and training.

Objective This study was performed to evaluate the clinical efficacy and safety of a novel surgical procedure in treating tracheal or bronchial compression related to severe congenital heart disease. Methods The clinical data of 28 patients with tracheal or bronchial compression related to severe congenital heart disease were retrospectively analyzed. In the control group, 12 patients underwent surgery for congenital cardiac malformations. In the treatment group, 16 patients underwent surgery for congenital cardiac malformations combined with partial resection of the pulmonary artery wall. The cardiothoracic ratio, pulmonary arterial pressure, left ventricular end-diastolic dimension, diameter of the pulmonary artery, and diameter of the trachea in the stenotic segment were quantitatively measured before and 9 days after the operation. Results The diameter of the pulmonary artery and diameter of the trachea in the stenotic segment were almost restored to the normal range in the treatment group. Patients in the treatment group recovered more rapidly and effectively than those in the control group. Conclusion Partial resection of the pulmonary artery wall is an efficacious and safe technique in the treatment of tracheal or bronchial compression related to severe congenital heart disease.