[Progress in complex network theory-based studies on the associations between health-related behaviors and chronic non-communicable diseases]

In recent years, the research focus on health-related behavior and chronic non-communicable diseases has shifted from the analysis on independent effects of multiple causes on a single outcome to the evaluation the complex relationships between multiple causes and multiple effects. Complex network theory, an important branch of system science, considers the relationships among factors in a network and can reveal how health-related behaviors interact with chronic diseases through a series of complex network models and indicators. This paper summarizes the definition and development of complex network theory and its commonly used models, indicators, and case studies in the field of health-related behavior and chronic disease to promote the application of complex network theory in the field of health and provide reference and tools for future research of the relationship between health-related behavior and chronic disease.

[Mediating effects of body mass index and lipid levels on the association between alcohol consumption and hypertension in occupational population]

Objective: To investigate the association between alcohol consumption and hypertension and SBP, DBP and the mediating effects of body mass index (BMI) and lipid level in occupational population, and provide reference for the intervention and prevention of hypertension. Methods: Based on the data of Southwest Occupational Population Cohort from China Railway Chengdu Group Co., Ltd., the information about the demographic characteristics, behavior and lifestyle, blood pressure and lipids level of the participants were collected through questionnaire survey, physical examination and blood biochemical test. Logistic/linear regression was used to analyze the association between alcohol consumption and hypertension, SBP and DBP. The individual and joint mediating effects of BMI, HDL-C, LDL-C, TG, and TC were explored through causal mediating analysis. A network analysis was used to explore the correlation between alcohol consumption, BMI and lipid levels, and hypertension. Results: A total of 22 887 participants were included, in whom 1 825 had newly detected hypertension. Logistic regression analysis found that current/former drinkers had a 33% increase of risk for hypertension compared with never-drinkers (OR=1.33, 95%CI:1.19-1.48). Similarly, alcohol consumption could increase SBP (β=1.05, 95%CI:0.69-1.40) and DBP (β=1.10, 95%CI:0.83-1.38). Overall, BMI and lipid levels could mediate the associations between alcohol consumption and hypertension, SBP and DBP by 21.91%, 28.40% and 22.64%, respectively. BMI and TG were the main mediators, and they were also the two nodes with the highest edge weight and bridge strength centrality in the network of alcohol consumption, BMI, lipid levels and hypertension. Conclusions: Alcohol consumption was associated with increased risk for hypertension, and BMI and TG were important mediators and key nodes in the network. It is suggested that paying attention to the alcohol consumption, BMI and TG might help prevent hypertension in occupational population.

[Association between work environment noise perception and cardiovascular diseases, depressive symptoms, and their comorbidity in occupational population]

Objective: To explore the association between occupational noise perception and cardiovascular disease (CVD), depression symptoms, as well as their comorbidity in occupational population and provide evidence for the prevention and control of physical and mental illnesses. Methods: A cross-sectional survey design was adopted, based on baseline data in population in 28 prefectures in Sichuan Province and Guizhou Province, and 33 districts (counties) in Chongqing municipality from Southwest Occupational Population Cohort from China Railway Chengdu Group Co., Ltd. during October to December 2021. A questionnaire survey was conducted to collect information about noise perception, depressive symptoms, and the history of CVD. Latent profile analysis model was used to determine identify noise perception type, and multinomial logistic regression analysis was conducted to explore the relationship between different occupational noise perception types and CVD, depression symptoms and their comorbidity. Results: A total of 30 509 participants were included, the mean age was (36.6±10.5) years, and men accounted for 82.0%. The direct perception of occupational noise, psychological effects and hearing/sleep impact of occupational noise increased the risk for CVD, depressive symptoms, and their comorbidity. By using latent profile analysis, occupational noise perception was classified into four levels: low, medium, high, and very high. As the level of noise perception increased, the association with CVD, depressive symptoms, and their comorbidity increased. In fact, very high level occupational noise perception were found to increase the risk for CVD, depressive symptoms, and their comorbidity by 2.14 (95%CI: 1.73-2.65) times, 8.80 (95%CI: 7.91-9.78) times, and 17.02 (95%CI: 12.78-22.66) times respectively compared with low-level occupational noise perception. Conclusions: Different types of occupational noise perception are associated with CVD and depression symptom, especially in the form of CVD complicated with depression symptom. Furthermore, the intensity of occupational noise in the work environment should be reduced to lower the risk for physical and mental health.

Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval

In recent years, pre-trained language models (PLMs) have dominated natural language processing (NLP) and achieved outstanding performance in various NLP tasks, including dense retrieval based on PLMs. However, in the biomedical domain, the effectiveness of dense retrieval models based on PLMs still needs to be improved due to the diversity and ambiguity of entity expressions caused by the enrichment of biomedical entities. To alleviate the semantic gap, in this paper, we propose a method that incorporates external knowledge at the entity level into a dense retrieval model to enrich the dense representations of queries and documents. Specifically, we first add additional self-attention and information interaction modules in the Transformer layer of the BERT architecture to perform fusion and interaction between query/document text and entity embeddings from knowledge graphs. We then propose an entity similarity loss to constrain the model to better learn external knowledge from entity embeddings, and further propose a weighted entity concatenation mechanism to balance the impact of entity representations when matching queries and documents. Experiments on two publicly available biomedical retrieval datasets show that our proposed method outperforms state-of-the-art dense retrieval methods. In term of NDCG metrics, the proposed method (called ELK) improves the ranking performance of coCondenser by at least 5% on both two datasets, and also obtains further performance gain over state-of-the-art EVA methods. Though having a more sophisticated architecture, the average query latency of ELK is still within the same order of magnitude as that of other efficient methods.

[Effects of tibial second toe free flap bridged with blood flow and nerve in the treatment of severe flexion contracture of the proximal interphalangeal joint]

Objective: To investigate the effects of tibial second toe free flap bridged with blood flow and nerve in the treatment of severe flexion contracture of the proximal interphalangeal joint. Methods: A retrospective observational study was conducted. From March 2013 to October 2019, 9 patients with severe flexion contracture (type Ⅲ) of the proximal interphalangeal joint after trauma operation, conforming to the inclusion criteria, were hospitalized in Suzhou Ruihua Orthopaedic Hospital, including 5 males and 4 females, aged from 17 to 62 years. After the contracture tissue affecting the extension of the proximal interphalangeal joint was cut off, and the scar tissue was resected, the size of the volar wound near the proximal interphalangeal joint in extended position was 2.0 cm×1.0 cm-2.5 cm×1.5 cm, with the length of proper digital artery and nerve defect being 1.0-1.5 cm. A free flap of the same size as the wound was cut from the tibial side of the second toe and transplanted to repair the wound, and the defective proper digital artery and nerve was repaired by bridging with the tibial proper plantar digital artery and nerve of about 1.5 cm in length. The full-thickness skin graft was taken from the proximal tibial side of the lower leg to repair the wound at flap donor site. The wound at skin graft donor site was sutured directly. The survival of flap and skin graft was observed after operation. The patients were followed up, and at the last follow-up, the recovery of the affected finger and the second toe, including the donor and recipient areas were observed, the two-point discrimination distances of the flap repaired site and the pulp of the affected finger were observed and measured at the same time, the blood flow patency of bridged vessel of the affected finger was examined by Allen test, and the function of the proximal interphalangeal joint of the affected finger was evaluated according to Chinese Medical Association's standard for the range of motion of proximal interphalangeal joint. Results: The flaps and skin grafts survived smoothly after operation. The follow-up after operation lasted for 5 to 22 months, with a mean of 10 months. At the last follow-up, the flap repaired site had good shape, good color and texture, with the two-point discrimination distance being 9-12 mm, and the two-point discrimination distance of the pulp of the affected finger was 6-10 mm; the Allen test results of the affected fingers were all negative (i.e., the bridged vessels had good blood flow patency), with no recurrence of flexion contracture, and the function of the proximal interphalangeal joint was evaluated as excellent; the skin graft area of the second toe was not ruptured but was a little pigmented, and the flexion and extension activities of toe were good. Conclusions: The tibial second toe free flap bridged with blood flow and nerve has reliable therapeutic effect in the treatment of severe flexion contracture of the proximal interphalangeal joint, and the color and texture of the flap repaired area are good. Bridging to repair the severely contracted proper digital artery and nerve is beneficial to improve the blood supply of the finger body and rebuild the sensation.

Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning

The present study aims to identify suicide risks in major depressive disorders (MDD) patients from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI data of 288 MDD patients, including 110 patients with suicide ideation (SI), 93 patients with suicide attempts (SA), and 85 patients without suicidal ideation or attempts (NS). And we developed interpretable deep neural network models to classify patients in three tasks including SA-versus-SI, SA-versus-NS, and SI-versus-NS, respectively. Furthermore, we interpreted the models by extracting the important features that contributed most to the classification, and further discussed these features or ROI/brain regions.

The Extra-Islet Pancreas Supports Autoimmunity in Human Type 1 Diabetes

In autoimmune Type 1 diabetes (T1D), immune cells progressively infiltrate and destroy the islets of Langerhans - islands of endocrine tissue dispersed throughout the pancreas. However, it is unclear how this process, called 'insulitis', develops and progresses within this organ. Here, using highly multiplexed CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples from pre-T1D, T1D, and non-T1D donors, we examine pseudotemporal-spatial patterns of insulitis and exocrine inflammation within large pancreatic tissue sections. We identify four sub-states of insulitis characterized by CD8 ⁺ T cells at different stages of activation. We further find that exocrine compartments of pancreatic lobules affected by insulitis have distinct cellularity, suggesting that extra-islet factors may make particular lobules permissive to disease. Finally, we identify "staging areas" - immature tertiary lymphoid structures away from islets where CD8 ⁺ T cells appear to assemble before they navigate to islets. Together, these data implicate the extra-islet pancreas in autoimmune insulitis, greatly expanding the boundaries of T1D pathogenesis.

A Review of Fusion Methods for Omics and Imaging Data

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.

A Syntax-enhanced model based on category keywords for biomedical relation extraction

Certain categories in multi-category biomedical relationship extraction have linguistic similarities to some extent. Keywords related to categories and syntax structures of samples between these categories have some notable features, which are very useful in biomedical relation extraction. The pre-trained model has been widely used and has achieved great success in biomedical relationship extraction, but it is still incapable of mining this kind of information accurately. To solve the problem, we present a syntax-enhanced model based on category keywords. First, we prune syntactic dependency trees in terms of category keywords obtained by the chi-square test. It reduces noisy information caused by current syntactic parsing tools and retains useful information related to categories. Next, to encode category-related syntactic dependency trees, a syntactic transformer is presented, which enhances the ability of the pre-trained model to capture syntax structures and to distinguish multiple categories. We evaluate our method on three biomedical datasets. Compared with state-of-the-art models, our method performs better on these datasets. We conduct further analysis to verify the effectiveness of our method.

The identification of treatment-resistant depression patients in electronic health records, a retrospective cohort study in China

Introduction

Previous Electronic Health Records (EHR) based studies adopted various definitions in identifying Treatment-Resistant Depression (TRD) patients. There is a lack of similar attempts among Chinese population which limits the understanding of TRD in China.

Objectives

Assess TRD identification using EHR from a major psychiatric hospital in China.

Methods

This study utilized a retrospective Major Depressive Disorder (MDD) cohort of patients who newly initiated pharmaceutical treatment (2010-2018); follow-up was ended upon 1-year or treatment discontinuation (≥120d without treatment). TRD was first identified based on common clinical definition of two prior regimen failures (change of regimen) with 4-week as regimen adequacy threshold (Def1). Alternative adequacy thresholds of 2-week and 6-week were applied. Based on Def1 (4-week), at least 3 distinctive regimens were additionally required in TRD identification (Def2). Further, a data-driven definition (Def3) based on drug count as having ≥3 antidepressants or ≥1 antipsychotic within 1 year was considered (Cepeda et al., 2018).

Results

From 12257 MDD patients included in the cohort, Def1 identified 633 (5.2%) TRD cases, whereas regimen adequacy thresholds of 2-week and 6-week identified 1772 (14.5%) and 61 (0.5%) cases, respectively. Further, Def2 identified 261 (2.4%) TRD cases. Finally, Def3 yielded 2449 (20.0%) TRD cases, including 1966 exclusive cases that were not identified by Def1.

Conclusions

This study showed different definitions for TRD identification had considerable impact on the number of patients identified among Chinese population, obscuring the comparability among EHR-based TRD studies. As first step, we found the criteria of regimen adequacy as major contributor to the observed variability in China.

Disclosure

No significant relationships.

HSRec: Hierarchical self-attention incorporating knowledge graph for sequential recommendation

Modeling user’s fine-grained preferences and dynamic preference evolution from their chronological behaviors are challenging and crucial for sequential recommendation. In this paper, we develop a Hierarchical Self-Attention Incorporating Knowledge Graph for Sequential Recommendation (HSRec). HSRec models not only the user’s intrinsic preferences but also the user’s external potential interests to capture the user’s fine-grained preferences. Specifically, the intrinsic interest module and potential interest module are designed to capture these two preferences respectively. In the intrinsic interest module, user’s sequential patterns are characterized from their behaviors via the self-attention mechanism. As for the potential interest module, high-order paths can be generated with the help of the knowledge graph. Therefore, a hierarchical self-attention mechanism is designed to aggregate the semantic information of user interaction from these paths. Specifically, an entity-level self-attention mechanism is applied to capture the sequential patterns contained in the high-order paths while an interaction-level self-attention mechanism is designed to further capture the semantic information from user interactions. Moreover, according to the high-order semantic relevance, HSRec can explore the user’s dynamic preferences at each time, thus describing the user’s dynamic preference evolution. Finally, experiments conducted on three real world datasets demonstrate the state-of-the-art performance of the HSRec.

Integrated proteome and lipidome analysis of naturally aged safflower seeds varying in vitality

Seed ageing has an important effect on germination and productivity. During natural ageing, seed vigour decreases rapidly but, to date, the molecular mechanisms underlying this decrease have not been fully elucidated. Using omics, some of the details regarding seed vigour decline during natural ageing might be elucidated through integrated analysis. Safflower seed germination and physio-biochemical changes during natural ageing (stored for 4, 16 and 28 months) were determined. Proteome and lipidome profiling during natural seed ageing was performed, and the differentially expressed proteins and lipid metabolite species analysed. The surface and internal structures of cotyledons were observed. An integrating analysis of the proteome and lipidome was also carried out. Natural seed ageing significantly decreased safflower seed germination and vigour. 4,184 proteins and 1,193 lipids were quantified, both of which show huge differences among the different naturally aged seeds. The surface of the cotyledons collapsed and cracked, and the oil bodies become looser during natural ageing. The total content of DAG and PA increased, while the content of TAG and PL (PC, PE, PS, PI and PL) significantly decreased during seeds ageing. Two lipase genes (HH-026818-RA and HH-025320) likely participated in this degradation of lipids. We conclude that the enzymes that participate in glycerolipid metabolism and fatty acid degradation probably lead to the degradation of oil bodies (TAG) and membrane lipids (PC, PE, PS, PI, PG) and, ultimately, destroy the structure, causing a decline in seed vigour during natural seed ageing.

What is an appropriate caesarean delivery rate for China: a multicentre survey

Objective

To assess the current status of caesarean delivery (CD) in China, propose reference CD rates for China overall, and by regions, investigate the main indications for CDs and identify possible areas for safe reduction.

Design

A multicentre cross‐sectional study.

Setting

A total of 94 hospitals across 23 provinces in China.

Population

A total of 73 977 randomly selected deliveries.

Methods

We used a modified Robson classification to characterise CDs in subgroups and by regions, and the World Health Organization (WHO) C‐Model to calculate reference CD rates.

Main outcome measures

CD rates in China.

Results

In 2015–2016, the overall CD rate in China was 38.9% (95% CI 38.6–39.3%). Considering the obstetric characteristics of the population, the multivariable model‐based reference CD rate was estimated at 28.5% (95% CI 28.3–28.8%). Accordingly, an absolute reduction of 10.4% (or 26.7% relative reduction) may be considered. The CD rate varied substantially by region. Previous CD was the most common indication in all regions, accounting for 38.2% of all CDs, followed by maternal request (9.8%), labour dystocia (8.3%), fetal distress (7.7%) and malpresentation (7.6%). Overall, 12.7% of women had prelabour CDs, contributing to 32.8% of the total CDs.

Conclusions

Nearly 39% of births were delivered by caesarean in China but a reduction of this rate by a quarter may be considered attainable. Repeat CD contributed more than one‐third of the total CDs. Given the large variation in maternal characteristics, region‐specific or even hospital‐specific reference CD rates are needed for precision management of CD.

Tweetable abstract

The caesarean rate in 2015–2016 in China was 38.9%, whereas the reference rate was 28.5%.

The caesarean rate in 2015–2016 in China was 38.9%, whereas the reference rate was 28.5%.

Linked article This article is commented on by M Varner, p. 148 in this issue. To view this mini commentary visit https://doi.org/10.1111/1471-0528.16953.

[The prevention and management of approach-specific complications of abdominal aortic balloon occlusion in pelvic and sacral surgery]

Objective: To investigate the causes,prevention and treatment of femoral artery puncture related complications caused by the application of resuscitative endovascular balloon occlusion of the aorta (REBOA) in the resection of pelvic and sacral tumors. Methods: Clinical data of 23 patients with femoral artery puncture related complications who received REBOA in the resection of pelvic and sacral tumors from August 2010 to August 2018 at the Musculoskeletal Tumor Center,Peking University People's Hospital were retrospectively analyzed.There were 8 males and 15 females,with the an age of (37.0±16.2) years (range:15 to 65 years).Arterial access via the Seldinger technique for REBOA was obtained in the right common femoral artery of 18 cases,and in the left of 6 cases.An arterial sheath with a diameter of 11 to 12 F(1 F≈0.33 mm) was used for the patient.The occurrence and treatment of postoperative complications were analyzed. Results: Acute femoral arterial thrombosis occurred in 18 patients,which was managed by open repair 48 hours postoperatively.Among the 349 patients admitted before 2015 who received hemostasis by compression after femoral artery sheath removal,12 patients (3.4%) developed acute femoral artery thrombosis.While the 476 patients admitted after 2015 who used a vascular stapler to close the femoral artery wound,6 patients (1.3%) developed acute femoral artery thrombosis.One case of retroperitoneal hematoma and 1 case of femoral pseudoaneurysm were found and surgically fixed.Postoperative follow-up was (40±18) months (range:13 to 108 months).Three cases with chronic lower extremity ischemia were confirmed by Doppler ultrasonography during 1 to 5 years follow-up.Two of them had minimal symptoms and denied further treatment,while the other one received femoral-femoral artery bypass surgery to restore distal flow for pain and numbness relief. Conclusions: Acute femoral arterial thrombosis was the most common femoral artery puncture.Technique refinement of REBOA,the use of percutaneous suture device and close follow-up can reduce the approach-specific complications,and help to detect and treat the complications timely,which may popularize the clinical application of REBOA.

A Hierarchical Graph Convolution Network for Representation Learning of Gene Expression Data

The curse of dimensionality, which is caused by high-dimensionality and low-sample-size, is a major challenge in gene expression data analysis. However, the real situation is even worse: labelling data is laborious and time-consuming, so only a small part of the limited samples will be labelled. Having such few labelled samples further increases the difficulty of training deep learning models. Interpretability is an important requirement in biomedicine. Many existing deep learning methods are trying to provide interpretability, but rarely apply to gene expression data. Recent semi-supervised graph convolution network methods try to address these problems by smoothing the label information over a graph. However, to the best of our knowledge, these methods only utilize graphs in either the feature space or sample space, which restrict their performance. We propose a transductive semi-supervised representation learning method called a hierarchical graph convolution network (HiGCN) to aggregate the information of gene expression data in both feature and sample spaces. HiGCN first utilizes external knowledge to construct a feature graph and a similarity kernel to construct a sample graph. Then, two spatial-based GCNs are used to aggregate information on these graphs. To validate the model's performance, synthetic and real datasets are provided to lend empirical support. Compared with two recent models and three traditional models, HiGCN learns better representations of gene expression data, and these representations improve the performance of downstream tasks, especially when the model is trained on a few labelled samples. Important features can be extracted from our model to provide reliable interpretability.

[Incidence and trend of severe postpartum hemorrhage between 2016 and 2019 in China]

Objective: To investigate the incidence and trend of severe postpartum hemorrhage (sPPH) in China, and to provide basic data for the development and evaluation of sPPH prevention and control strategy. Methods: Obstetric data was extracted from annual national representative sampling surveys based on the National Clinical Improvement System. From 2016 to 2019, 2 978, 3 400, 4 576 and 4 594 maternity hospitals with sPPH cases were included for statistics. The annual incidence of sPPH was calculated according to province and type of medical institutions and generalized linear model was emplyed to identify the determinants affecting sPPH incidence. Results: In China, sPPH incidence increased from 0.62% in 2016 to 0.93% in 2018, and was 0.92% in 2019. Eighteen provinces had an inverted U-shaped trend of sPPH over time and most of them had the highest incidence in 2018; ten provinces had an upward trend of sPPH and 3 provinces had a U-shaped trend. In 2019, the top five provinces with the highest sPPH incidence were Yunnan (1.88%), Beijing (1.45%), Jiangsu (1.31%), Guizhou (1.26%), and Ningxia Hui Autonomous Region (1.22%); the top five provinces with the lowest incidence were Henan (0.55%), Jiangxi (0.60%), Inner Mongolia Autonomous Region (0.64%), Liaoning (0.64%) and Gansu (0.69%). In 2019, the sPPH incidence in different types of medical institutions were as follows: tertiary public general hospital (1.15%), tertiary public specialized hospital (1.02%), secondary public general hospital (0.81%), private hospital (0.61%) and secondary public specialized hospital (0.58%). sPPH incidence was positively associated with proportion of twin pregnancies, macrosomia, primipara, and puerpera aged ≥35 years in maternity hospitals (P<0.05). Conclusions: sPPH incidence generally showes an increasing trend from 2016 and is stable at a high level in recent two years in China. It is warranted to further strengthen the monitoring of postpartum hemorrhage, and improve the capability of hierarchical management and treatment in maternity institutions and regions, in order to reduce sPPH incidence and maternal mortality.

GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification

BACKGROUND

Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD. However, there remain challenges to develop an accurate GNN learning model and understand how specific decisions of these graph models are made in brain network analysis.

RESULTS

In this paper, we propose a graph attention network based learning and interpreting method, namely GAT-LI, which learns to classify functional brain networks of ASD individuals versus healthy controls (HC), and interprets the learned graph model with feature importance. Specifically, GAT-LI includes a graph learning stage and an interpreting stage. First, in the graph learning stage, a new graph attention network model, namely GAT2, uses graph attention layers to learn the node representation, and a novel attention pooling layer to obtain the graph representation for functional brain network classification. We experimentally compared GAT2 model's performance on the ABIDE I database from 1035 subjects against the classification performances of other well-known models, and the results showed that the GAT2 model achieved the best classification performance. We experimentally compared the influence of different construction methods of brain networks in GAT2 model. We also used a larger synthetic graph dataset with 4000 samples to validate the utility and power of GAT2 model. Second, in the interpreting stage, we used GNNExplainer to interpret learned GAT2 model with feature importance. We experimentally compared GNNExplainer with two well-known interpretation methods including Saliency Map and DeepLIFT to interpret the learned model, and the results showed GNNExplainer achieved the best interpretation performance. We further used the interpretation method to identify the features that contributed most in classifying ASD versus HC.

CONCLUSION

We propose a two-stage learning and interpreting method GAT-LI to classify functional brain networks and interpret the feature importance in the graph model. The method should also be useful in the classification and interpretation tasks for graph data from other biomedical scenarios.

Prospects and Pitfalls: Next-Generation Tools to Control Mosquito-Transmitted Disease

Mosquito-transmitted diseases, including malaria and dengue, are a major threat to human health around the globe, affecting millions each year. A diverse array of next-generation tools has been designed to eliminate mosquito populations or to replace them with mosquitoes that are less capable of transmitting key pathogens. Many of these new approaches have been built on recent advances in CRISPR/Cas9-based genome editing. These initiatives have driven the development of pathogen-resistant lines, new genetics-based sexing methods, and new methods of driving desirable genetic traits into mosquito populations. Many other emerging tools involve microorganisms, including two strategies involving Wolbachia that are achieving great success in the field. At the same time, other mosquito-associated bacteria, fungi, and even viruses represent untapped sources of new mosquitocidal or antipathogen compounds. Although there are still hurdles to be overcome, the prospect that such approaches will reduce the impact of these diseases is highly encouraging.

Lurbinectedin for the treatment of small cell lung cancer

Small cell lung cancer (SCLC) is a rapidly progressive, aggressive metastatic and lethal subtype of lung cancer. Unfortunately, there has been little progress regarding the development of novel treatments for SCLC. However, lurbinectedin, a transcriptional inhibitor, has emerged as a potential novel treatment for cancer. It produces antitumor efficacy by inhibiting oncogenic transcription activity, inducing the accumulation of DNA double-strand breaks and modulating the tumor microenvironment (TME). Data from phase I/II trials indicates that lurbinectedin has significant antitumor efficacy and tolerable adverse effects in SCLC patients. Furthermore, lurbinectedin is efficacious in platinum-sensitive and platinum-resistant SCLC patients and in those with SCLC relapse after second-line treatment. In 2020, the U.S. Food and Drug Administration (FDA) approved lurbinectedin for the treatment of adult patients with metastatic SCLC or for patients that have received platinum-based chemotherapy. In this review, we discuss the molecular profile and the preclinical and clinical studies of lurbinectedin in the treatment of SCLC patients.

Tepotinib hydrochloride for the treatment of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the most devastating cancers with high mortality worldwide. By inhibiting the activity of specific molecular targets in the cancer cells, tyrosine kinase inhibitors (TKIs) have become a standard treatment in combating NSCLC. Tepotinib hydrochloride is an orally bioavailable, mesenchymal-epithelial transition (MET) TKI developed mainly for selected NSCLC patients with METex14 skipping mutations. Tepotinib demonstrated durable clinical response in phase II clinical trials, which led to its approval for use in Japan and breakthrough therapy designation and accelerated approval in the U.S. These progresses highlighted tepotinib as a promising candidate for NSCLC patients. This review summarizes the pharmacological profile of tepotinib, preclinical studies and landmark clinical trials of tepotinib. In addition, we share our perspectives on the future direction of tepotinib as a novel anticancer drug.

[Related factors and interaction on hepatitis C virus infection in patients receiving methadone maintenance treatment in Taiyuan]

Objective: To explore the prevalence of hepatitis C virus (HCV) infection, influence factors and interaction on HCV infection in patients receiving methadone maintenance treatment (MMT) in Taiyuan. Methods: Between April-June 2019, three MMT clinics in Taiyuan were selected to conduct a face-to-face questionnaire survey among MMT patients to collect the information about their socio-demographic characteristics, drug use, MMT, sexual behavior and health status. Software EpiData 3.1 was used for real-time double entry to establish the database. Software SAS 9.4 was used to analyze the data, and χ² test was used for univariate analysis and logistic regression model was used for multivariate and interaction analyses. Results: A total of 903 subjects were surveyed among MMT patients, the male to female ratio of was 7.21∶1(743∶103), and the rate of HCV infection was 12.53% (106/846). After adjusting for the confounding factors, being women (OR=1.936, 95%CI: 1.023-3.662), having sex with drug users (OR=2.073, 95%CI: 1.110-3.871) and injection drug use (OR=7.737, 95%CI: 4.614-12.973) might be the risk factors for HCV infection in patients receiving MMT. The results showed that there were multiplicative interactions among women, having sex with drug user and injection drug use on HCV infection. Conclusions: Being women, having sex with drug user and injection drug use were associated with higher risk for HCV infection in patients receiving MMT in Taiyuan. There were multiplication interactions between being women and having sex with drug user, being female and injection drug use, and having sex with drug use and injection drug use on HCV infection.

Drug utilization of paliperidone in adolescent schizophrenia patients: A retrospective cohort study in China

Introduction

In China, the indications of paliperidone extended in schizophrenia adolescents (12-17 years) was approved by National Medical Products Administration (NMPA) in 2017. But, the utilization of paliperidone in this group needs to be further investigated.

Objectives

To assess paliperidone utilization in schizophrenia adolescents.

Methods

The study employed the electronic medical records (EMRs) database from a psychiatry specialized hospital (PH) and a general hospital (GH), respectively. General information, including birth date, gender, visit date, diagnosis (inpatient and outpatient) with ICD-10 coding, drug characterize, prescription date and dosage, was de-identified and standardized for analysis. Schizophrenia adolescents (ICD-10: F20.x) received at least one prescription of paliperidone between 2018 and 2019 were included in this study. Index date was defined as the date of first identified paliperidone prescription. The patients were followed up until the end of 2019 with the last record, or upon reaching 18 years. The database was analyzed based on days of supply, administration frequency, and daily dose.

Results

Overall, 112 and 117 eligible patients were included in the present study from PH and GH, respectively. The median drug supply was 179.0 days and 44.0 days, respectively, during which median number of prescriptions patients received was 6.0 and 3.0. Paliperidone was mostly initiated alone (57.1% and 88.9%) with frequency of once daily (97.3% and 88.9%), and the median of average daily dose during follow-up was 5.7 mg/day and 6.0 mg/day, respectively.

Conclusions

The duration of paliperidone usage was very different in two hospitals, but the dosages in both hospitals were generally agreed with prescribing information.

Chaperone-mediated autophagy sustains haematopoietic stem-cell function

The activation of mostly quiescent haematopoietic stem cells (HSCs) is a prerequisite for life-long production of blood cells¹. This process requires major molecular adaptations to allow HSCs to meet the regulatory and metabolic requirements for cell division^2-4. The mechanisms that govern cellular reprograming upon stem-cell activation, and the subsequent return of stem cells to quiescence, have not been fully characterized. Here we show that chaperone-mediated autophagy (CMA)⁵, a selective form of lysosomal protein degradation, is involved in sustaining HSC function in adult mice. CMA is required for protein quality control in stem cells and for the upregulation of fatty acid metabolism upon HSC activation. We find that CMA activity in HSCs decreases with age and show that genetic or pharmacological activation of CMA can restore the functionality of old mouse and human HSCs. Together, our findings provide mechanistic insights into a role for CMA in sustaining quality control, appropriate energetics and overall long-term HSC function. Our work suggests that CMA may be a promising therapeutic target for enhancing HSC function in conditions such as ageing or stem-cell transplantation.

A quantitative comparison of time-of-flight momentum microscopes and hemispherical analyzers for time- and angle-resolved photoemission spectroscopy experiments

Time-of-flight-based momentum microscopy has a growing presence in photoemission studies, as it enables parallel energy- and momentum-resolved acquisition of the full photoelectron distribution. Here, we report table-top extreme ultraviolet time- and angle-resolved photoemission spectroscopy (trARPES) featuring both a hemispherical analyzer and a momentum microscope within the same setup. We present a systematic comparison of the two detection schemes and quantify experimentally relevant parameters, including pump- and probe-induced space-charge effects, detection efficiency, photoelectron count rates, and depth of focus. We highlight the advantages and limitations of both instruments based on exemplary trARPES measurements of bulk WSe₂. Our analysis demonstrates the complementary nature of the two spectrometers for time-resolved ARPES experiments. Their combination in a single experimental apparatus allows us to address a broad range of scientific questions with trARPES.

Revealing Hidden Orbital Pseudospin Texture with Time-Reversal Dichroism in Photoelectron Angular Distributions

We performed angle-resolved photoemission spectroscopy (ARPES) of bulk 2H-WSe_{2} for different crystal orientations linked to each other by time-reversal symmetry. We introduce a new observable called time-reversal dichroism in photoelectron angular distributions (TRDAD), which quantifies the modulation of the photoemission intensity upon effective time-reversal operation. We demonstrate that the hidden orbital pseudospin texture leaves its imprint on TRDAD, due to multiple orbital interference effects in photoemission. Our experimental results are in quantitative agreement with both the tight-binding model and state-of-the-art fully relativistic calculations performed using the one-step model of photoemission. While spin-resolved ARPES probes the spin component of entangled spin-orbital texture in multiorbital systems, we unambiguously demonstrate that TRDAD reveals its orbital pseudospin texture counterpart.

Asymptomatic COVID-19 infection in patients with cancer at a cancer-specialized hospital in Wuhan, China - Preliminary results

OBJECTIVE

Patients with cancer are usually immunosuppressive and susceptible to COVID-19 infection. Asymptomatic COVID-19 cases are infective and cannot be identified by symptom-based screening. There is an urgent need to control virus spread by asymptomatic carriers at cancer centres. We aim to describe the characteristics, screening methods, and outcomes of cancer patients with asymptomatic COVID-19 infection and to further explore anti-tumour treatment for this population.

PATIENTS AND METHODS

We reviewed patients with cancer who were admitted to Hubei Cancer Hospital in Wuhan from February 1, 2020, to April 4, 2020. We collected demographic data, laboratory findings, treatment information, nucleic acid and serum test results, chest computed tomography (CT) information and survival status of cancer patients diagnosed with asymptomatic COVID-19 infection.

RESULTS

A total of 16 cancer patients with asymptomatic COVID-19 infection were confirmed. The most common cancer type was breast cancer. The blood cell counts of most patients were in the normal range. Lymphocytes of 100% of asymptomatic carriers were in the normal range. Thirteen (81.3%) patients were positive for virus-specific IgM antibodies, and three (18.8%) were positive by PCR; only one (6.3%) patient showed novel coronavirus pneumonia features on CT. Three (18.3%) patients died, and the cause of death was considered malignancy caused by delaying anti-tumour treatment.

CONCLUSIONS

Our study shows that the lymphocytes of 100% of asymptomatic carriers were in the normal range. This result indicates that the host immunity of asymptomatic carriers is not significantly disrupted by COVID-19. Single PCR detection is not sufficient to screen among asymptomatic individuals, and a combination of PCR tests, serological tests and CT is of great importance. Unless the tumour is life-threatening or rapidly progressing, we advise restarting active anti-tumour therapy after PCR tests become negative.

Evaluation of Cell's Passability in the ECM Network

The microstructure of the extracellular matrix (ECM) plays a key role in affecting cell migration, especially nonproteolytic migration. It is difficult, however, to measure some properties of the ECM, such as stiffness and the passability for cell migration. On the basis of a network model of collagen fiber in the ECM, which has been well applied to simulate mechanical behaviors such as the stress-strain relationship, damage, and failure, we proposed a series of methods to study the microstructural properties containing pore size and pore stiffness and to search for the possible migration paths for cells. Finally, with a given criterion, we quantitatively evaluated the passability of the ECM network for cell migration. The fiber network model with a microstructure and the analysis method presented in this study further our understanding of and ability to evaluate the properties of an ECM network.

Highly variable upper and abyssal overturning cells in the South Atlantic

The Meridional Overturning Circulation (MOC) is a primary mechanism driving oceanic heat redistribution on Earth, thereby affecting Earth's climate and weather. However, the full-depth structure and variability of the MOC are still poorly understood, particularly in the South Atlantic. This study presents unique multiyear records of the oceanic volume transport of both the upper (<~3100 meters) and abyssal (>~3100 meters) overturning cells based on daily moored measurements in the South Atlantic at 34.5°S. The vertical structure of the time-mean flows is consistent with the limited historical observations. Both the upper and abyssal cells exhibit a high degree of variability relative to the temporal means at time scales, ranging from a few days to a few weeks. Observed variations in the abyssal flow appear to be largely independent of the flow in the overlying upper cell. No meaningful trends are detected in either cell.

A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction

Background

With the rapid development of sequencing technologies, collecting diverse types of cancer omics data become more cost-effective. Many computational methods attempted to represent and fuse multiple omics into a comprehensive view of cancer. However, different types of omics are related and heterogeneous. Most of the existing methods do not consider the difference between omics, so the biological knowledge of individual omics may not be fully excavated. And for a given task (e.g. predicting overall survival), these methods prefer to use sample similarity or domain knowledge to learn a more reasonable representation of omics, but it’s not enough.

Methods

For the purpose of learning more useful representation for individual omics and fusing them to improve the prediction ability, we proposed an autoencoder-based method named MOSAE (Multi-omics Supervised Autoencoder). In our method, a specific autoencoder were designed for each omics according to their size of dimension to generate omics-specific representations. Then, a supervised autoencoder was constructed based on specific autoencoder by using labels to enforce each specific autoencoder to learn both omics-specific and task-specific representations. Finally, representations of different omics that generate from supervised autoencoders were fused in a traditional but powerful way, and the fused representation was used for subsequent predictive tasks.

Results

We applied our method over TCGA Pan-Cancer dataset to predict four different clinical outcome endpoints (OS, PFI, DFI, and DSS). Compared with traditional and state-of-the-art methods, MOSAE achieved better predictive performance. We also tested the effects of each improvement, which all have a positive effect on predictive performance.

Conclusions

Predicting clinical outcome endpoints are very important for precision medicine and personalized medicine. And multi-omics fusion is an effective way to solve this problem. MOSAE is a powerful multi-omics fusion method, which can generate both omics-specific and task-specific representation for given endpoint predictive tasks and improve the predictive performance.

Assessing the impact of drinking water iodine concentrations on the iodine intake of Chinese pregnant women living in areas with restricted iodized salt supply

PURPOSE

The supply of non-iodized salt and the water improvement project have been conducted to reduce the iodine concentration in drinking water in areas with elevated water iodine. We aimed to assess the impact of water iodine concentration (WIC) on the iodine intake of pregnant women in areas with restricted iodized salt supply, and determine the cutoff values of WIC in areas with non-iodized salt supply.

METHODS

Overall, 534 pregnant women who attended routine antenatal outpatient visits in Zibo Maternal and Child Health Hospital in Gaoqing County were recruited. The 24-h urine iodine excretion (UIE) in 534 samples and the iodine concentration in 534 drinking water samples were estimated. Urinary iodine excretion, daily iodine intake, and daily iodine intake from drinking water (WII) were calculated. The relationship between WIC and daily iodine take was analyzed.

RESULTS

The median WIC, spot urine iodine concentration (UIC), and 24-h UIE were 17 (6, 226) μg/L, 145 (88, 267) μg/L, and 190 (110, 390) μg/day, respectively. A significant positive correlation was found between WIC and UIE (R² = 0.265, p < 0.001) and UIC (R² = 0.261, p < 0.001). The contribution rate of WII to total iodine intake increased from 3.0% in the group with WIC of < 10 μg/L to 45.7% in the group with WIC of 50-99 μg/L.

CONCLUSION

The iodine content in drinking water is the major iodine source in pregnant women living in high-water iodine areas where iodized salt supply is restricted. The contribution rate of daily iodine intake from drinking water increases with the increase in water iodine concentration.

[Characteristics of methadone maintenance treatment clinic patients and influencing factors for HBsAg positivity based on Bayesian network model]

Objective: To understand the characteristics and explore the influencing factors of HBsAg positivity in methadone maintenance treatment (MMT) clinic patients. Methods: A face to face interview and medical record review were conducted in 1 040 patients at three MMT clinics in Guangxi from September to November in 2014. The questionnaire information included general demographic characteristics, drug use history, MMT status, sexual behaviors, and health status, etc. Blood samples were collected from the patients at the same time for the detections of the level of HBsAg, anti-HBs and anti-HCV. By using χ(2) test, unconditional logistic regression model and Bayesian network model the influencing factors for HBsAg positivity in MMT clinic patients and the complex network relationship among these factors were explored. Results: A total of 1 031 MMT clinic patients were surveyed, the HBsAg positive rate was 11.35% (117/1 031). The anti-HCV positive rate was 71.77% (740/1 031), among the anti-HCV positive patients, the HBsAg positive rate was 10.27% (76/740). After adjusting for the confounding factors, anti-HBs positive persons might not be HBsAg positive (OR=0.05, 95%CI: 0.03-0.09), and anti-HCV positive persons might not be HBsAg positive too (OR=0.30, 95%CI: 0.17-0.52) compared with anti-HBs negative and anti-HCV negative persons, respectively. The persons with family history of hepatitis B virus infection were more likely to be HBsAg positive compared those with no such family history (OR=5.30, 95%CI: 2.68-10.52). Bayesian network model analysis results showed that family history of hepatitis B virus infection and anti-HBs were directly related with HBsAg positivity. Anti-HCV, intravenous drug use in the past three months and other drug using during treatment were indirectly related with HBsAg positivity. Conclusions: Anti-HBs, family history of hepatitis B virus infection, anti-HCV, intravenous drug use in past three months and other drug use during treatment were related with the HBsAg positivity in MMT clinic patients. So, it is necessary to enhance health education, improve health awareness and decrease high risk behaviors to reduce the rate of HBV infection.

Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study

Background

The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction. It is challenging to avoid noise and extract cross-sentence relations.

Objective

This study aimed to avoid errors by dividing the document-level dataset, verify that a self-attention structure can extract biomedical relations in a document with long-distance dependencies and complex semantics, and discuss the relative benefits of different entity pretreatment methods for biomedical relation extraction.

Methods

This paper proposes a new data preprocessing method and attempts to apply a pretrained self-attention structure for document biomedical relation extraction with an entity replacement method to capture very long-distance dependencies and complex semantics.

Results

Compared with state-of-the-art approaches, our method greatly improved the precision. The results show that our approach increases the F1 value, compared with state-of-the-art methods. Through experiments of biomedical entity pretreatments, we found that a model using an entity replacement method can improve performance.

Conclusions

When considering all target entity pairs as a whole in the document-level dataset, a pretrained self-attention structure is suitable to capture very long-distance dependencies and learn the textual context and complicated semantics. A replacement method for biomedical entities is conducive to biomedical relation extraction, especially to document-level relation extraction.

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

LCQS: an efficient lossless compression tool of quality scores with random access functionality

Background

Advanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio. However, the main drawbacks of these compressors are the problem of weak robustness which means unstable or even unavailable results of sequencing files and the problem of slow compression speed. Meanwhile, some compressors attempt to construct a fine-grained index structure to solve the problem of slow random access decompression speed. However, they solve the problem at the sacrifice of compression speed and at the expense of large index files, which makes them inefficient and impractical. Therefore, an efficient lossless compressor of quality scores with strong robustness, high compression ratio, fast compression and random access decompression speed is urgently needed and of great significance.

Results

In this paper, based on the idea of maximizing the use of hardware resources, LCQS, a lossless compression tool specialized for quality scores, was proposed. It consists of four sequential processing steps: partitioning, indexing, packing and parallelizing. Experimental results reveal that LCQS outperforms all the other state-of-the-art compressors on all criteria except for the compression speed on the dataset SRR1284073. Furthermore, LCQS presents strong robustness on all the test datasets, with its acceleration ratios of compression speed increasing by up to 29.1x, its file size reducing by up to 28.78%, and its random access decompression speed increasing by up to 2.1x. Additionally, LCQS also exhibits strong scalability. That is, the compression speed increases almost linearly as the size of input dataset increases.

Conclusion

The ability to handle all different kinds of quality scores and superiority in compression ratio and compression speed make LCQS a high-efficient and advanced lossless quality score compressor, along with its strength of fast random access decompression. Our tool LCQS can be downloaded from https://github.com/SCUT-CCNL/LCQSand freely available for non-commercial usage.

Molecular cloning and characterization of Y-box gene (Rpybx) from Manila clam and its expression analysis in different strains under low-temperature stress

Manila clam, Ruditapes philippinarum, is an economically important marine bivalve species. Y-box proteins are members of the cold shock proteins family and highly conserved from bacteria to humans. In this study, a novel Y-box gene (Rpybx) was cloned from the Manila clam and gene expression profiling was performed on three shell color strains (white, zebra and white zebra) and two wild populations (Southern and Northern) of R. philippinarum. The complete ORF length of Rpybx is 1367 bp, encoding 253 amino acids residues. Based on the amino acid sequence analysis and phylogenetic analysis, the Rpybx gene was identified as a member of the invertebrate Y-box proteins family. Rpybx has a similar tertiary structure to human Y-box protein YB-1. The Rpybx mRNA levels were analyzed by qPCR under acute and gradually varied cold stress. Under acute low-temperature stress, the expression of Rpybx mRNA in gills and hepatopancreas was significantly increased in all selected strains and populations (P < 0.05). The Northern population showed the lowest relative expression level of Rpybx. The expressions of Rpybx were greatly upregulated in gills and hepatopancreas of different stains and populations at 5 or -2°C under gradually varied temperature stress (P < 0.05). The results shed light on the biological function of the Rpybx gene in defending against low-temperature challenge and further exploring the molecular mechanism of cold tolerance and resistance in R. philippinarum.

Monitoring spatiotemporal changes in chaperone-mediated autophagy in vivo

Autophagy malfunctioning occurs in multiple human disorders, making attractive the idea of chemically modulating it with therapeutic purposes. However, for many types of autophagy, a clear understanding of tissue-specific differences in their activity and regulation is missing because of lack of methods to monitor these processes in vivo. Chaperone-mediated autophagy (CMA) is a selective type of autophagy that until now has only been studied in vitro and not in the tissue context at single cell resolution. Here, we develop a transgenic reporter mouse that allows dynamic measurement of CMA activity in vivo using image-based procedures. We identify previously unknown spatial and temporal differences in CMA activity in multiple organs and in response to stress. We illustrate the versatility of this model for monitoring CMA in live animals, organotypic cultures and cell cultures from these mice, and provide practical examples of multiorgan response to drugs that modulate CMA.

Variant-Kudu: An Efficient Tool kit Leveraging Distributed Bitmap Index for Analysis of Massive Genetic Variation Datasets

The storage and analysis of massive genetic variation datasets in variant call format (VCF) become a great challenge with the rapid growth of genetic variation data in recent years. Traditional single process based tool kits become increasingly inefficient when analyzing massive genetic variation data. While emerging distributed storage technology such as Apache Kudu offers attractive solution, it is demanded to develop distributed storage tool kit for VCF dataset. In this article, we present Variant-Kudu, an efficient genome tool kit for storing and analyzing massive genetic variation datasets. Based on a new distributed scheme, the genetic variation data would be segmented and stored in Kudu on multinode. With this scheme, data can be randomly accessed at low latency and scanned efficiently. Aiming at reducing the queries' execution time, a strategy of distributed bitmap index is proposed and a parallel query method is designed, which expedite analyses of massive genetic variation data. Variant-Kudu is a scalable tool kit to analyze massive genetic variation datasets, and our experiments demonstrate that Variant-Kudu achieves high performance on a multinode cluster.

Symbiotic versus nonsymbiotic optimization for spatial and temporal degrees of freedom in pair creation

The field-induced decay of the quantum vacuum state associated with the creation of electron-positron pairs can be caused independently by either multiphoton transitions or by tunneling processes. The first mechanism is usually induced by appropriate temporal variations of the external field while the second (Schwinger-like) process occurs if a static but spatially dependent electric field is of supercritical strength. The ultimate goal is to construct an optimal space-time profile of an electromagnetic field that can maximize the creation of particle pairs. The simultaneous optimization of parameters that characterize the spatial and temporal features of both fields suggests that the optimal two-field configuration can be remarkably similar to that predicted from two independent optimizations for the spatial and temporal fields separately.

Time- and momentum-resolved photoemission studies using time-of-flight momentum microscopy at a free-electron laser

Time-resolved photoemission with ultrafast pump and probe pulses is an emerging technique with wide application potential. Real-time recording of nonequilibrium electronic processes, transient states in chemical reactions, or the interplay of electronic and structural dynamics offers fascinating opportunities for future research. Combining valence-band and core-level spectroscopy with photoelectron diffraction for electronic, chemical, and structural analyses requires few 10 fs soft X-ray pulses with some 10 meV spectral resolution, which are currently available at high repetition rate free-electron lasers. We have constructed and optimized a versatile setup commissioned at FLASH/PG2 that combines free-electron laser capabilities together with a multidimensional recording scheme for photoemission studies. We use a full-field imaging momentum microscope with time-of-flight energy recording as the detector for mapping of 3D band structures in (k_x, k_y, E) parameter space with unprecedented efficiency. Our instrument can image full surface Brillouin zones with up to 7 Å^-1 diameter in a binding-energy range of several eV, resolving about 2.5 × 10⁵ data voxels simultaneously. Using the ultrafast excited state dynamics in the van der Waals semiconductor WSe₂ measured at photon energies of 36.5 eV and 109.5 eV, we demonstrate an experimental energy resolution of 130 meV, a momentum resolution of 0.06 Å^-1, and a system response function of 150 fs.

Association of Circulating Retinol and α-TOH Levels with Cognitive Function in Aging Subject with Type 2 Diabetes Mellitus

OBJECTIVES

Malnutrition of vitamin A (retinol) and vitamin E (α-tocopherol, α-TOH) was observed in type 2 diabetes mellitus (T2DM) or dementia patients. However, how these vitamins affect cognitive function of subjects with T2DM was seldom reported. The objective of this study was to determine the association of circulating retinol and α-TOH with cognition in aging subjects with T2DM.

METHODS

A total of 448 T2DM subjects and 448 age, gender and education matched control subjects (aged 55-75 years) were included in the study. Demographic characters of the participants were collected. Food frequency questionnaire (FFQ) method was used to collect dietary intake information. To assess the status of cognition, the MoCA test was used. Circulating retinol and α-TOH levels were compared between T2DM and non-T2DM subjects. Correlation of circulating retinol and α-TOH levels with cognitive function was analyzed in T2DM subjects. The effect of serum retinol and α-TOH levels on the risk of MCI in T2DM patients was explored.

RESULTS

We found that T2DM-MCI subjects demonstrate lower serum retinol level than T2DM-nonMCI subjects (P < 0.01). Serum retinol level was positively correlated to cognitive function in T2DM subject (P < 0.05). T2DM subjects with higher circulating retinol level demonstrate higher cognitive scores in visual and executive, attention, language, memory and delayed recall domains (P < 0.05).

CONCLUSION

Diminished circulating retinol predicts an increased risk of MCI in T2DM patients. Our findings provide suggestions that optimal retinol nutritional status might benefit cognition and decrease the risk of MCI in aging subjects with T2DM.

A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.