An Innovative Approach to Modeling Aviation Safety Incidents

Due to the complexity of aviation safety operations, the number of flight incidents continues to rise. The Aviation Safety Reporting System (ASRS) contains the largest collection of such incidents. Efficient and effective analysis of these incidents remains a challenge. This paper proposes a new approach to analyze aviation safety records using deep learning methods to improve incident classification. The proposed approach, CNN-LSTM, combines the characteristics of convolutional neural network (CNN) and long short-term memory (LSTM) neural network, and a distributed computing method to model aviation safety data. The five machine learning methods Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, Multi-layer Perceptron were used to compare with CNN-LSTM. The results show that CNN-LSTM model can significantly improve the accuracy rates of classification for aviation safety incident reports using Word2Vec. The distributed platform in Spark with clusters can make full use of computing resources when processing textual data from ASRS, reducing time-consumption greatly when compared with machine learning algorithms running on a standalone computer. Timely and accurate identification of causes of reported incidents is important. The results of this study demonstrate a new approach to improve both accuracy and efficiency in incident cause identification

A genome-wide linkage and association study using COGA data

BACKGROUND: Genome-wide association will soon be available to use as an adjunct to traditional linkage analysis. We studied alcoholism in 119 families collected by the Collaborative Study on the Genetics of Alcoholism and made available in Genetic Analysis Workshop 14, using genome-wide linkage and association analyses. METHODS: Genome-wide linkage analysis was first performed using microsatellite markers and a region with the strongest linkage evidence was further analyzed using single-nucleotide polymorphisms (SNPs). Family based genome-wide association test was also conducted using the SNPs. RESULTS: Nonparametric linkage analysis revealed weak linkage evidence on chromosome 7, and association analysis identified SNP tsc0515272 on chromosome 3 as significantly associated with alcoholism. CONCLUSION: Linkage analysis may require large sample sizes and high quality genotyping and marker maps to adequately improve power, while association analysis could hold more promise in efforts to identify variants responsible for complex traits

The Gap Seal's Influences on the Leakage and Clamping of Multi-way Directional Valves

Defects coming from processing and assembly inevitably bring geometric and concentricity error to the fitting gap between valve core and frame. To overcome this problem, grooving was proven to be the most simple and effective method to reduce the hydraulic clamping force, and widely used on cylinder valve cores. Theoretically unbalanced forces kept going smaller with the increase of pressure equalizing grooves. When groove width increased, linearly the gap leakage extended. When the width was relatively small leakage was gradually reduced when depth increased. When the width was 1 mm, 1.5 mm or 2 mm, and the depth to width ratio was less than 1, leakage got worse with the increment of depth

The potential of epigallocatechin gallate in the chemoprevention and therapy of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), one of the most notorious malignancies globally, has a high fatality and poor prognosis. Though remarkable breakthroughs have been made in the therapeutic strategies recently, the overall survival of HCC remains unsatisfactory. Consequently, the therapy of HCC remains a great challenge. Epigallocatechin gallate (EGCG), a natural polyphenol extracted from the leaves of the tea bush, has been extensively investigated for its antitumor effects. In this review, we summarize the previous literature to elucidate the roles of EGCG in the chemoprophylaxis and therapy of HCC. Accumulating evidence has confirmed EGCG prevents and inhibits the hepatic tumorigenesis and progression through multiple biological mechanisms, mainly involving hepatitis virus infection, oxidative stress, proliferation, invasion, migration, angiogenesis, apoptosis, autophagy, and tumor metabolism. Furthermore, EGCG enhances the efficacy and sensitivity of chemotherapy, radiotherapy, and targeted therapy in HCC. In conclusion, preclinical studies have confirmed the potential of EGCG for chemoprevention and therapy of HCC under multifarious experimental models and conditions. Nevertheless, there is an urgent need to explore the safety and efficacy of EGCG in the clinical practice of HCC

The crosstalk among the physical tumor microenvironment and the effects of glucose deprivation on tumors in the past decade

The occurrence and progression of tumors are inseparable from glucose metabolism. With the development of tumors, the volume increases gradually and the nutritional supply of tumors cannot be fully guaranteed. The tumor microenvironment changes and glucose deficiency becomes the common stress environment of tumors. Here, we discuss the mutual influences between glucose deprivation and other features of the tumor microenvironment, such as hypoxia, immune escape, low pH, and oxidative stress. In the face of a series of stress responses brought by glucose deficiency, different types of tumors have different coping mechanisms. We summarize the tumor studies on glucose deficiency in the last decade and review the genes and pathways that determine the fate of tumors under harsh conditions. It turns out that most of these genes help tumor cells survive in glucose-deprivation conditions. The development of related inhibitors may bring new opportunities for the treatment of tumors

Dietary inflammatory index, and depression and mortality risk associations in U.S. adults, with a special focus on cancer survivors

IntroductionA higher risk for depression and mortality is associated with the inflammatory potential of diet measured through the Dietary Inflammatory Index (DII). The roles of DII in the risk of depression and death in cancer survivors were unclear. We aimed to examine the association between energy-adjusted DII (E-DII) score and risk of depression, and mortality using data from the 2007–2018 National Health and Nutrition Examination Survey (NHANES), with a special focus on cancer survivors.MethodsThe 24-h dietary recall interview was used as a basis to calculate the E-DII score and the Patient Health Questionnaire-9 (PHQ-9) was used to measure the depressive outcomes. Logistic regression analyses were performed to determine the association between quartiles of E-DII score and depression. Cox proportional hazard regression and competing risk analyses were used to estimate the risks of quartiles of E-DII score or depression on mortality.ResultsA total of 27,447 participants were included; including 24,694 subjects without cancer and 2,753 cancer survivors. The E-DII score and depression were not distributed differently between the two groups. However, the E-DII scores were positively associated with within each group’s depression (all P trend < 0.001) and participants with higher E-DII scores had a higher risk of depression (subjects without cancer: ORQ4vsQ1: 2.17, 95% CI: 1.75–2.70; cancer survivors: ORQ4vsQ1: 1.78, 95% CI: 1.09–2.92). The median follow-up time were 87 person-months, a total of 1,701 (4.8%) and 570 (15.2%) all-cause deaths in subjects without cancer and cancer survivors were identified by the end of 2019. The highest E-DII scores quartile was associated with the highest risk of all-cause (HRQ4vsQ1: 1.90, 95% CI: 1.54–2.35) and cardiovascular disease (CVD) cause death (HRQ4vsQ1: 2.50, 95% CI: 1.69–2.3.7) in the subjects without cancer. Moreover, participants with depressive symptoms had higher all-cause mortality (HR: 1.29, 95% CI: 1.04–1.59). No significant correlation was found for E-DII scores or depression with all-cause, cancer-cause or CVD-cause mortality in cancer survivors.ConclusionOur findings demonstrate that E-DII score was positively associated with depression risk. A higher E-DII score or depressive symptom may increase the risks of all-cause and CVD-cause mortality only among general subjects

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature

Pengaruh sense of school belonging terhadap student's misbehavior

Penelitian ini bertujuan untuk mengetahu pengaruh sense of school belonging terhadap student’s misbehavior. Penelitian ini merupakan penelitian korelasional dengan menggunakan teknnik pengumpulan data berupa skala likert yaitu skala sense of school belonging dan skala student’s misbehavior masing masing terdiri dari 30 aitem yang sudah melalui uji coba. Skala sense of school belonging memiliki reabilitas sebesar 0,899 sedangkan skala student’s misbehavior memiliki reabilitas sebesar 0,924. Subjek penelitian berjumlah 144 siswa dari jumlah populasi sebesar 576 siswa. Pengambilan data menggunakan simple random sampling. Hasil penelitian menujukkan bahwa terdapat pengaruh sense of school belonging terhadap student’s misbehavior dengan nilai signifikansi 0,000 < 0,05. Dalam table model summary pada analisis regresi linier sederhana, sense of school belonging memberikan pengaruh sebesar 17,7% terhadap student’s misbehavior. Pada table correlation, terdapat nilai koerfisien korelasi sebesar -0,420 yang berarti semakin tinggi sense of school belonging maka semakin rendah student’s misbehavior yang dilakukan oleh siswa

Molecular Ecology of Pyrethroid Knockdown Resistance in Culex pipiens pallens Mosquitoes

Pyrethroid insecticides have been extensively used in China and worldwide for public health pest control. Accurate resistance monitoring is essential to guide the rational use of insecticides and resistance management. Here we examined the nucleotide diversity of the para-sodium channel gene, which confers knockdown resistance (kdr) in Culex pipiens pallens mosquitoes in China. The sequence analysis of the para-sodium channel gene identified L1014F and L1014S mutations. We developed and validated allele-specific PCR and the real-time TaqMan methods for resistance diagnosis. The real-time TaqMan method is more superior to the allele-specific PCR method as evidenced by higher amplification rate and better sensitivity and specificity. Significant positive correlation between kdr allele frequency and bioassay-based resistance phenotype demonstrates that the frequency of L1014F and L1014S mutations in the kdr gene can be used as a molecular marker for deltamethrin resistance monitoring in natural Cx. pipiens pallens populations in the East China region. The laboratory selection experiment found that L1014F mutation frequency, but not L1014S mutation, responded to deltamethrin selection, suggesting that the L1014F mutation is the key mutation conferring resistance to deltamethrin. High L1014F mutation frequency detected in six populations of Cx. pipens pallens suggests high prevalence of pyrethroid resistance in Eastern China, calling for further surveys to map the resistance in China and for investigating alternative mosquito control strategies

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase&nbsp;1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation&nbsp;disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age&nbsp; 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score&nbsp; 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc&nbsp;= 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N&nbsp;= 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in&nbsp;Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in&nbsp;Asia&nbsp;and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701