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Botnet detection is an active research topic as botnets are a source of many malicious activities, including distributed denial-of-service (DDoS), click-fraud, spamming, and crypto-mining attacks. However, it is getting more complicated to identify botnets due to the continuous evolution of botnet software and families that harness new types of devices and attack vectors. Recent studies employing machine learning (ML) showed improved performance to detect botnets to some extent, but they are still limited and ineffective with the lack of sequential pattern analysis, which is a key to detect various classes of botnets. In this paper, we propose a novel botnet detection method, built upon Recurrent Variational Autoencoder (RVAE), that effectively captures sequential characteristics of botnet anomalies. We validate the feasibility of the proposed method with the CTU-13 dataset that have been widely employed for botnet detection studies, and show that our method is at least comparable to existing techniques in terms of detection accuracy. In addition, our experimental results show that the proposed method can detect previously unseen botnets by utilizing sequential patterns of network traffic. We will also show how our method can detect botnets in the streaming mode, which is the essential requirement to perform real-time, on-line detection.

Streamflow components (e.g., young water vs old water) and hydrological transit times play an important role in water resource and water quality management. We collected a four-year record of stable isotopes of oxygen and hydrogen (δ18O and δ2H) in precipitation, groundwater and stream water for six catchments in the Yongan watershed of eastern China. The stable isotope records were used to identify spatio-temporal variations in the young water fraction (Fyw, defined as the proportion of the transit-time distribution younger than a threshold age) and mean transit time (MTT) based on sine-wave fitting and convolution integral methods, respectively. The Fyw ranged from 14 to 35% in the Yongan watershed. Cumulative transit time showed contrasting distributions, suggesting considerable heterogeneity and a complex interplay between catchment characteristics. Estimated MTTs ranged from 3.2 to 6.3 years and may be explained by catchment characteristics (e.g., elevation and topographic gradient). Observed spatial trends in MTTs likely result from contrasting contributions of different-aged subsurface water flows across the six catchments. The use of Fyw constraints in estimating MTTs reduced uncertainty in some catchments, suggesting the potential benefits of combining multiple approaches (e.g., Fyw) to optimize the results of traditional calibration methods. The relatively low Fyw and long MTTs highlight the importance of groundwater contributions to streamflow generation and imply a considerable lag time in river water quantity and quality responses to catchment-scale water resource management. Coupling multiple metrics (e.g., Fyw and MTT) and isotope models enhances our understanding of watershed-scale hydrologic processes and hydrograph separation.

We present ADIOS 2, the latest version of the Adaptable Input Output (I/O) System. ADIOS 2 addresses scientific data management needs ranging from scalable I/O in supercomputers, to data analysis in personal computer and cloud systems. Version 2 introduces a unified application programming interface (API) that enables seamless data movement through files, wide-area-networks, and direct memory access, as well as high-level APIs for data analysis. The internal architecture provides a set of reusable and extendable components for managing data presentation and transport mechanisms for new applications. ADIOS 2 bindings are available in C++11, C, Fortran, Python, and Matlab and are currently used across different scientific communities. ADIOS 2 provides a communal framework to tackle data management challenges as we approach the exascale era of supercomputing.

Machine Learning (ML) methods have been widely used in Intrusion Detection Systems (IDS). In particular, many botnet detection methods are based on ML. However, due to the fast-evolving nature of network security threats, it is necessary to frequently retrain the ML tools with up-to-date data, especially because data labeling takes a long time and requires a lot of effort, making it difficult to generate training data. We propose transfer learning as a more effective approach for botnet detection, as it can learn from well curated source data and transfer the knowledge to a target problem domain not seen before. We devise an approach that is effective regardless whether or not the data from the target domain is labeled. More specifically, we train a neural network with the Recurrrent Variation Autoencoder (RVAE) structure on the source data, and use RVAE to compute anomaly scores for data records from the target domain. In an evaluation of this transfer learning framework, we use CTU-13 dataset as a source domain and a fresh set of network monitoring data as a target domain. Tests show that the proposed transfer learning method is able to detect botnets better than semi-supervised learning method that was trained on the target domain data. The area under Receiver Operating Characteristic is 0.810 for transfer learning, and 0.779 for directly using RVAE on the target domain data.

With the growth of 5G wireless technologies and IoT, it become urgent to develop robust network security systems, such as intrusions detection systems (IDS) to keep the networks secure. These IDS systems need to detect unauthorized access and attacks in real-time. However, most of the modern IDS are built based on complex machine learning models that are time-consuming to train. In this work, we propose a methodology using the SHapley Additive exPlanations (SHAP) in combination with tree-based classifiers. SHAP can be used to select consistent and small feature subsets to reduce the execution time and improve classification accuracy. We demonstrate the proposed approach with the Aegean Wi-Fi Intrusion Dataset (AWID) dataset in a series of multi-class classification experiments. Among the four classes ("normal", "injection", "flooding"and "impersonation"), it is well-known that the class impersonation is hard to be classified accurately. Tests show that we can use about 10% of the initial feature set without reducing the overall prediction accuracy. With this reduced set of features, the training time could be reduced as much as a factor of four, while slightly improving the discriminating ability to identify impersonation instances. This study suggests that by reducing the number of features, the classification algorithms are able to focus on key trends that differentiates the "attacks"classes from the "normal"class. Using a reduces subset of features improves IDS's accuracy and performance. Also, SHAP dependence plots capture the relationship between individual features and the classification decision.

Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units, while used by hundreds to thousands of users at the same time. Applications from these large numbers of users have diverse characteristics, such as varying compute, communication, memory, and I/O intensiveness. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, the I/O performance is difficult to predict because the I/O system software is complex, the I/O system is shared among all users, and the I/O operations also heavily rely on networking systems. To improve the prediction of the I/O performance on HPC systems, we propose to integrate information from a number of different system logs and develop a regression-based approach that dynamically selects the most relevant features from the most recent log entries, and automatically select the best regression algorithm for the prediction task. Evaluation results show that our proposed scheme can predict the I/O performance with up to 84% prediction accuracy in the case of the I/O-intensive applications using the logs from CORI supercomputer at NERSC.

Large high-performance computers (HPC) are expensive tools responsible for supporting thousands of scientific applications. However, it is not easy to determine the best set of configurations for workloads to best utilize the storage and I/O systems. Users typically use the default configurations provided by the system administrators, which typically results in poor performance. In an effort to identify application characteristics more important to I/O performance, we applied several machine learning techniques to characterize these applications. To identify the features that are most relevant to the I/O performance, we evaluate a number of different feature selection methods, e.g., Mutual information regression and F regression, and develop a novel feature selection method based on Min-max mutual information. These feature selection methods allow us to sift through a large set of the real-world workloads collected from NERSC's Cori supercomputer system, and identify the most important features. We employ a number of different clustering algorithms, including KMeans, Gaussian Mixture Model (GMM) and Ward linkage, and measure the cluster quality with Davies Boulder Index (DBI), Silhouette and a new Combined Score developed for this work. The cluster evaluation result shows that the test dataset could be best divided into three clusters, where cluster 1 contains mostly small jobs with operations on standard I/O units, cluster 2 consists of middle size parallel jobs dominated by read operations, and cluster 3 include large parallel jobs with heavy write operations. The cluster characteristics suggest that using parallel I/O library MPI IO and a large number of parallel cores are important to achieve high I/O throughput.

We consider data sources connected to a software defined network (SDN) with heterogeneous link access rates. Deadline-driven data transfer requests are made to a centralized network controller that schedules pacing rates of sources and meeting the request deadline has a pre-assigned value. The goal of the scheduler is to maximize the aggregate value. We design a scheduler (RL-Agent) based on Deep Deterministic Policy Gradient (DDPG). We compare our approach with three heuristics: (i) PFAIR, which shares the bottleneck capacity in proportion to the access rates, (ii) VDRatio, which prioritizes flows with high value-to-demand ratio, and (iii) VBEDF, which prioritizes flows with high value-to-deadline ratio. For equally valued requests and homogeneous access rates, PFAIR is the same as an idealized TCP algorithm, while VBEDF and VDRatio reduce to the Earliest Deadline First (EDF) and the Shortest Job First (SJF) algorithms, respectively. In this scenario, we show that RL-Agent performs significantly better than PFAIR and VDRatio and matches and in over-loaded scenarios out-performs VBEDF. When access rates are heterogeneous, we show that the RL-Agent performs as well as VBEDF even though the RL-Agent has no knowledge of the heterogeneity to start with. For the value maximization problems, we show that the RL-Agent out-performs the heuristics for both homogeneous and heterogeneous access networks. For the general case of heterogeneity with different values, the RL-Agent performs the best despite having no prior knowledge of the heterogeneity and the values, whereas the heuristics have full knowledge of the heterogeneity and VDRatio and VBEDF have partial knowledge of the values through the ratios of value to demand and value to deadline, respectively.

Quickly identifying astronomical transients from synoptic surveys is critical to many recent astrophysical discoveries. However, each of the data processing pipelines in these surveys contains dozens of stages with highly varying time and space requirements. Properly predicting the resources required to run these pipelines is critical for the allocation of computing resources and reducing the discovery response time. We propose a machine learning strategy for this prediction task and demonstrate its effectiveness using a set of timing measurements from the intermediate Palomar Transient Factory (iPTF) workflow. The proposed model utilizes the spatiotemporal correlation of astronomical images, where nearby patches of the sky (space) are likely to have a similar number of objects of interest and workflows executed in the recent past (time) are likely to use a similar amount of time because the machines and data storage systems are likely to be in similar states. We capture the relationship among these spatial and temporal features in a Bayesian network and study how they impact the prediction accuracy. This Bayesian network helps us to identify the most influential features for predictions. With proper features, our models achieve errors close to the random variance boundary within batches of images taken at the same time, which can be regarded as the intrinsic limit of prediction accuracy.

Daily transportation mode choice is largely habitual, but transitions between life events may disrupt travel habits and can shift choices between alternative transportation modes. Although much is known about general mode switches following life event transitions, less is understood about differences that may exist between subpopulations, especially from a long-term perspective. Understanding these differences will help planners and policymakers introduce more targeted policy interventions to promote sustainable transportation modes and inform longer-term predictions. Extending beyond existing literature, we use data collected from a retrospective survey to investigate the effects of life course events on mode use situated within different long-term life trajectory contexts. We apply a machine-learning method called joint social sequence clustering to define five distinct and interpretable cohorts based on trajectory patterns in family and career domains over their life courses. We use these patterns as an innovative contextual system to investigate (1) the heterogeneous effects of life events on travel mode use and (2) further differentiation between gender and generation groups in these life event effects. We find that events occurring relatively early in life are more strongly associated with changes in mode-use behavior, and that mode use can also be affected by the relative order of events. This timing and order effect can have lasting impacts on mode use aggregated over entire life cycles: members of our ‘‘Have-it-alls’’ cohort—who finish their education, start working, partner up, and have children early in life—ramp up car use at each event, resulting in the highest rate of car use occurring the earliest among all the cohorts. Women drive more when having children primarily when their family formation and career formation are intertwined early in life, and younger generations rely relatively more on car use during familial events when their careers have a later start.

Meijing Shen,* Xianghao Chen,* Jingyue He, Lin Xiong, Rengui Tian, Guangwu Yang, He Zha, Kaifeng Wu Department of Laboratory Medicine, the First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Kaifeng Wu; He Zha, Department of Laboratory Medicine, the First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, People’s Republic of China, Email kiphoonwu@126.com; zhahe666@126.comPurpose: Carbapenem-resistant Klebsiella pneumoniae (CRKP) has seriously threatened public health worldwide. This study aimed to investigate the antimicrobial resistance patterns, sequence types (STs), virulence and carbapenemase genes of CRKP isolates from patients in Zunyi, China.Methods: CRKP isolates were collected from the First People’s Hospital of Zunyi between January 2018 and December 2020. Antimicrobial susceptibility was determined using a VITEK® 2 analyzer and confirmed using either the broth dilution method, Kirby–Bauer method, or E-test assays. Carbapenemase production was examined using a modified carbapenem inactivation method. STs of the studied isolates were determined by multilocus sequence typing, and the presence of carbapenemase and virulence genes was examined using polymerase chain reaction assays.Results: In total, 94 CRKP isolates were collected. All studied isolates produced carbapenemase, and the most common carbapenemase gene was New Delhi metallo-β-lactamase (NDM; 72.3%), followed by Klebsiella pneumoniae carbapenemase (KPC; 24.5%), and Verona integron-encoded metallo-β-lactamase (VIM; 3.2%). Of the studied isolates, 74.3% exhibited multidrug-resistant (MDR) phenotype, and 25.7% were either pandrug-resistant (PDR) or extensively drug-resistant (XDR) phenotypes. The most prevalent sequence type was ST2407 (37.2%), followed by ST76 (21.3%) and ST11 (11.7%). The NDM gene was present in 97.1% of ST2407 isolates and 90.0% of ST76 isolates, whereas the KPC gene was present in 90.9% of ST11 isolates. The majority of the isolates carried wabG, uge, and fimH virulence genes, with prevalence rates of 94.7%, 92.6%, and 94.7%, respectively.Conclusion: This study describes NDM-producing ST2407 and ST76, as well as KPC-producing ST11, as the major clonal types of CRKP isolates in Zunyi, China. All CRKP isolates were resistant to multiple types of antibiotics, and the majority of isolates carried carbapenemase and virulence genes. Clonal spread of NDM-producing CRKP ST2407 and ST76, and KPC-producing CRKP ST11 should be strictly monitored.Keywords: Klebsiella pneumoniae, antimicrobial resistance patterns, virulence genes, sequence types, carbapenemase genes

Yang Liu,1 Chao Zhang,2 Qianwen Wang,1 Kangze Wu,3 Zhouyi Sun,1 Zhe Tang,1 Bo Zhang3 1The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, 322000, People’s Republic of China; 2Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, 442000, People’s Republic of China; 3The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, People’s Republic of ChinaCorrespondence: Bo Zhang, The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, Zhejiang, 310000, People’s Republic of China, Tel/Fax +86-0571-87783563, Email jjs10@zju.edu.cnBackground: This study aimed to evaluate the global colorectal cancer(CRC) trend and the relevant risk factors from 1990 to 2019 and for better policymaking and resource allocation.Methods: Data on CRC, including incidence, mortality and disability adjusted life year (DALY) rates, were extracted from the 2019 Global Burden of Disease (GBD) study. The estimated annual percentage changes (EAPCs) were calculated to assess the temporal trend of incidence, mortality and DALYs. The Bayesian age-period-cohort model(BAPC) was used to predict the future burden of CRC.Results: In 2019, a total of 2.17 million CRC cases were reported worldwide, a 157% increase from 1990. In high-social demographic index (SDI) regions, the trend of age-standardized incidence rate(ASIR) tended to decrease, while the proportion of people under 50 years of age tended to increase. Although the number of deaths and DALYs increased, the age-standardized death rate (ASDR) and age-standardized DALY rate decreased. The CRC burden was growing fastest in middle-SDI regions, especially in East Asia, followed by low SDI regions. In addition, the milk intake, High-BMI and high fasting plasma glucose play a more important role in on CRC. The predicted cases and deaths in global continued to increase to 2044. And there is an upward trend in ASIR for both men and women.Conclusion: In developed regions, the CRC burden continues to decrease, while the CRC burden become more and more severe in developing regions. Overall, the burden of CRC will rising in the near future. Therefore, reasonable resource allocation and prevention policies should be implemented. Developing countries needs more attention.Keywords: global burden of disease, colorectal cancer, disability-adjusted life year, estimated annual percentage change, age-standardized incidence rate, Bayesian age-period-cohort

Today, scientific experiments and simulations produce massive amounts of heterogeneous data that need to be stored and analyzed. Given that these large datasets are stored in many files, formats and locations, how can scientists find relevant data, duplicates or similarities? In this context, we concentrate on developing algorithms to compare similarity of time series for the purpose of search, classification and clustering. For example, generating accurate patterns from climate related time series is important not only for building models for weather forecasting and climate prediction, but also for modeling and predicting the cycle of carbon, water, and energy. We developed the methodology and ran an exploratory analysis of climatic and ecosystem variables from the FLUXNET2015 dataset. The proposed combination of similarity metrics, nonlinear dimension reduction, clustering methods and validity measures for time series data has never been applied to unlabeled datasets before, and provides a process that can be easily extended to other scientific time series data. The dimensionality reduction step provides a good way to identify the optimum number of clusters, detect outliers and assign initial labels to the time series data. We evaluated multiple similarity metrics, in terms of the internal cluster validity for driver as well as response variables. While the best metric often depends on a number of factor, the Euclidean distance seems to perform well for most variables and also in terms of computational expense.

The demands of increasingly large scientific application workflows lead to the need for more powerful supercomputers. As the scale of supercomputing systems have grown, the prediction of fault tolerance has become an increasingly critical area of study, since the prediction of system failures can improve performance by saving checkpoints in advance. We propose a real-time failure detection algorithm that adopts an event-based prediction model. The prediction model is a convolutional neural network that utilizes both traditional event attributes and additional spatio-temporal features. We present a case study using our proposed method with six years of reliability, availability, and serviceability event logs recorded by Mira, a Blue Gene/Q supercomputer at Argonne National Laboratory. In the case study, we have shown that our failure prediction model is not limited to predict the occurrence of failures in general. It is capable of accurately detecting specific types of critical failures such as coolant and power problems within reasonable lead time ranges. Our case study shows that the proposed method can achieve a F1 score of 0.56 for general failures, 0.97 for coolant failures, and 0.86 for power failures.

Prediction of daily transportation mode use (car, public transit, or active travel) is a important task in transportation research. Unlike statistical models that impose a predetermined model structure, machine learning models are learned from the data, making them more flexible with higher prediction accuracy. However, prediction of mid-to long-term habitual modes still largely relies on traditional statistical analysis using small samples of cross-sectional data. Low interpretability of 'black-box' machine learning models limits their usefulness for generating behavior insights needed for designing appropriate interventions. This paper, leveraging a set of unique longitudinal life course data, is the first use case to demonstrate machine learning methods applied for both predicting and interpreting regularly used travel modes. We combine sequence clustering and tree-based machine learning methods coupled with TreeExplainer to predict and interpret habitual travel modes using mid-to long-term predictors. Five life course clusters are derived to provide evaluation and interpretation contexts. This allows us to improve upon a recently developed TreeExplainer method to better distinguish predictor importance locally and globally; and predictor interactions across subpopulations within distinctive life history contexts. Our results demonstrate a promising step toward interpretable machine learning applications to mid-to long-term prediction of travel modes for transportation planning.

Wi-Fi has become the wireless networking standard that allows short- to medium-range device to connect without wires. For the last 20 year, the Wi-Fi technology has so pervasive that most devices in use today are mobile and connect to the internet through Wi-Fi. Unlike wired network, a wireless network lacks a clear boundary, which leads to significant Wi-Fi network security concerns, especially because the current security measures are prone to several types of intrusion. To address this problem, machine learning and deep learning methods have been successfully developed to identify network attacks. However, collecting data to develop models is expensive and raises privacy concerns. The goal of this paper is to evaluate a federated learning approach that would alleviate such privacy concerns. This initial work on intrusion detection is performed in a simulated environment. Once proven feasible, this process would allow edge devices to collaboratively update global anomaly detection models, without sharing sensitive training data. On a set of tests with the AWID intrusion detection data set, we show that our federated approach is effective in terms of classification accuracy, computation cost, as well as communication cost.

We consider a science network that runs applications requiring data transfers to be completed within a given deadline. The underlying network is a software defined network (SDN) that supports fine grain real-time network telemetry. Deadline-aware data transfer requests are made to a centralized network controller that schedules the flows by setting pacing rates of the deadline flows and metering the background traffic at the ingress routers. The goal of the scheduling algorithm is to maximize the number of flows that meet the deadline while maximizing the network utilization. In this paper, we develop a Reinforcement Learning (RL) agent based network controller and compare its performance with well-known heuristics. For a network consisting of a single bottleneck link, we show that the RL-agent based network controller performs as well as Earliest Deadline First (EDF), which is known to be optimal. We also show that the RL-agent performs significantly better than an idealized TCP protocol in which the bottleneck link capacity is equally shared among the competing flows. We also study the sensitivity of the RL-agent controller for different parameter settings and reward functions.

Kui Wu,&ast; Hui-Hui Ke,&ast; Wei Gong, Hua Hu, Li Chen Department of Obstetrics and Gynecology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Li Chen; Hua Hu, Department of Obstetrics and Gynecology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, 2800 Gongwei Road, Shanghai, 201399, People’s Republic of China, Tel +86 21 5802 2995, Email chenli8369@163.com; Huhua20212022@163.comAim: To assess the impact of maternal pre-pregnancy body mass index (BMI) on gestational diabetes mellitus (GDM) based on different pre-pregnancy hemoglobin levels.Methods: This retrospective cohort study included 1289 pregnant women between June 2020 and January 2022. Clinical data were collected by reviewing their medical and antepartum screening records between 24 and 28 gestational weeks, including pre-pregnancy BMI and pre-pregnancy hemoglobin (Hb) levels. The diagnosis of GDM mainly depended on oral glucose tolerance test (OGTT) during 24– 28 weeks. Restricted cubic spline (RCS) was used to investigate the association between the pre-pregnancy Hb level and the risk of GDM. Univariate and multivariate logistic regression analyses were applied to evaluate the relative risk of GDM.Results: Of the 1289 included pregnant women, 187 (14.5%) women were diagnosed with GDM in this study. The pre-pregnancy Hb level was significantly associated with GDM risk, and the pre-pregnancy Hb level of 123 g/L was identified as the threshold to stratify and assess the association between the GDM risk and the pre-pregnancy BMI. For women with a pre-pregnancy Hb level ≥ 123 g/L, the pre-pregnancy BMI showed a significant association with GDM risk, and the estimated incidence rate of GDM was 7.7%, 14.8%, 36.3% and 44% for underweight, normal-weight, overweight and obese pregnant women, respectively. After adjusting for potential influencing factors of GDM, the respective relative risk was 1.0 (reference), 2.04 (95% CI 0.84, 4.99), 7.06 (2.66, 18.61), and 10.77 (2.85, 40.63) (P for trend < 0.001).Conclusion: In pregnant women with a pre-pregnancy Hb level ≥ 123 g/L, pre-pregnancy BMI showed a more significant association with GDM risk as compared with those with a pre-pregnancy Hb level < 123 g/L.Keywords: pregnancy, body mass index, hemoglobin, gestational diabetes mellitus, overweight, obese

Siyan Liu,1 Jing Li,2 Lin Gu,3 Kunzhe Wu,2 Hua Xing1 1Department of Breast Surgery, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China; 2Department of Scientific Research Center, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China; 3Breast Surgery, Jilin Province Tumor Hospital, Changchun, People’s Republic of ChinaCorrespondence: Hua Xing, Department of Breast Surgery, China-Japan Union Hospital of Jilin University, Xian Tai Street, No. 126, Changchun, Jilin, 130033, People’s Republic of China, Tel +86 139 4412 7842, Email xingh@jlu.edu.cnAbstract: Triple-negative breast cancer (TNBC) exhibits high recurrence and mortality rates because of the lack of effective treatment targets. Surgery and traditional chemotherapy are the primary treatment options. Immunotherapy shows high potential for treating various cancers but exhibits limited efficacy against TNBC as a monotherapy. Chemoimmunotherapy has broad prospects for applications for cancer treatment conferred through the synergistic immunomodulatory and anti-tumor effects of chemotherapy and immunotherapeutic strategies. However, improving the efficacy of synergistic therapy and reducing the side effects of multiple drugs remain to be the main challenges in chemoimmunotherapy against TNBC. Nanocarriers can target both cancer and immune cells, promote drug accumulation, and show minimal toxicity, making them ideal delivery systems for chemotherapeutic and immunotherapeutic agents. In this review, we introduce the immunomodulatory effects of chemotherapy and combined mechanisms of chemoimmunotherapy, followed by a summary of nanoparticle-mediated chemoimmunotherapeutic strategies used for treating TNBC. This up-to-date synthesis of relevant findings in the field merits contemplation, while considering avenues of investigation to enable advances in the field.Keywords: chemotherapy, drug-delivery system, immunotherapy, mechanism, targeted delivery

Fan Zhang,1,&ast; Jiayu Liang,1 You Lu,2,&ast; Yongquan Tang,3 Shengzhuo Liu,1 Kan Wu,1 Fuxun Zhang,1 Yiping Lu,1 Zhihong Liu,1 Xianding Wang1 1Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China; 2Department of Pediatrics, West China Second University Hospital, Chengdu, Sichuan University, Chengdu, 610041, People’s Republic of China; 3Department of Pediatric Urology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xianding Wang; Jiayu Liang, Email xiandingwang66@126.com; drliangjiayu@163.comBackground: Cathepsin Z (CTSZ) is a cathepsin family member that plays a dual role in the adhesion and migration of immune and tumor cells.Methods: The expression pattern of CTSZ in clear cell renal cell carcinoma (ccRCC) was observed by immunohistochemistry and validated by using double-labeling immunofluorescence. Publicly available single-cell sequencing data was used to further define the cell type-specific CTSZ expression in ccRCC. Methylation modification, immune infiltration, and tumor-related signaling enrichment analyses involving CTSZ were performed using multi-omics data. Data from two independent cohorts of anti-programmed death-1 (PD-1) therapeutic clinical trials were used to investigate correlations between CTSZ levels and treatment responses.Results: CTSZ was upregulated in ccRCC tissues compared with adjacent normal tissues at the RNA but not in ccRCC cells. Immunohistochemistry indicated that CTSZ was expressed in tumors infiltrated with lymphocytes. Double immunofluorescence demonstrated that CTSZ was co-expressed with CD68 but not CD8. Single-cell transcriptome data showed macrophage-specific expression of CTSZ in ccRCC. High CTSZ expression was significantly correlated with the enrichment of interferon-γ, epithelial‐to‐mesenchymal transition, cell cycle, apoptosis pathways, and B cell, macrophage, neutrophil, and dendritic cell infiltrations, as well as the expression of immune checkpoints CTLA4, LAG3, HAVCR2, PDCD1LG2, PDCD1, TIGIT, and SIGLEC15. Hypomethylation modification of cg02744249, cg02744249, and cg22145559 were negatively correlated with CTSZ expression, suggesting an epigenetic mechanism for the regulation of CTSZ expression. Clinically, CTSZ levels were associated with the prognosis of patients with ccRCC (hazard ratio=1.5, P=0.007). Notably, patients with higher CTSZ expression had a worse prognosis with anti-PD-1 monotherapy (hazard ratio=1.51, P=0.039).Conclusion: Macrophage-specific CTSZ was associated with activation of epithelial‐to‐mesenchymal transition, cell cycle signatures, and a higher infiltration level of B cells, macrophages, neutrophils, and dendritic cells in the tumor microenvironment. High expression of CTSZ could be considered as a prognostic and treatment response biomarker for patients with ccRCC receiving anti-PD-1 immunotherapy.Keywords: ccRCC, CTSZ, macrophage, single-cell, prognosis

Limin Yan1 &ast;, Dedong Xu2 &ast;, Ying Xiao,1 Mingming Dai,1 Ting Wang,1 Xinhong Zhuang,3 Kunliang Wu4 1Department of Neurology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China; 2Department of Neurosurgery, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China; 3Department of Nephrology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China; 4Department of Infectious Diseases, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xinhong Zhuang, Department of Nephrology, The Second Affiliated Hospital of Hainan Medical University, No. 48, Baishuitang Road, Haikou, Hainan, 570311, People’s Republic of China, Tel +86-0890-66809015, Email zhuang_xinhong@163.com Kunliang Wu, Department of Infectious Diseases, The Second Affiliated Hospital of Hainan Medical University, No. 48, Baishuitang Road, Haikou, Hainan, 570311, People’s Republic of China, Tel +86-0890-66809015, Email kun4821583@163.comPurpose: NAD(P)H: Quinone Oxidoreductase 1 gene (NQO1) polymorphism is associated with the risk of cardiovascular disease. This study was designed to investigate the relationship between NQO1 gene polymorphism and ischemic stroke susceptibility in Chinese Han nationality.Patients and Methods: One hundred and forty-one patients diagnosed with ischemic stroke and 139 matched control groups were recruited in this study. The polymorphism distribution of rsl800566 locus and rs10517 locus of NQO1 gene was genotyped via TaqMan assay, and the concentration of Oxidized low-density lipoprotein (ox-LDL) in the blood of the subjects was detected by enzyme linked immunosorbent assay (ELISA). The relationship between the polymorphism distribution and the susceptibility to ischemic stroke was evaluated.Results: The frequency distribution of the three genotypes of NQO1 rs1800566 between the case group and the control group was statistically significant, and cases carrying CT and TT genotype were less likely to suffer from ischemic stroke. Compared with individuals carrying T allele, C allele carriers have higher risk of ischemic stroke. However, there was no significant difference in frequency distribution among the three genotypes of NQO1 rs10517 between controls and patients.Conclusion: The NQO1 rs1800566 C allele may be a novel marker associated with ischemic stroke susceptibility in Chinese Han population. Polymorphism of rsl800566 locus in NQO1 gene may be protective against ischemic stroke risk.Keywords: genetic polymorphism, ischemic stroke, NQO1, oxidized low-density lipoprotein

Jichan Shi,1,&ast; Gexin Gao,2,&ast; Zhijie Yu,3,&ast; Kaihuai Wu,4 Youquan Huang,5 Lian-Peng Wu,6 Zhengxing Wu,1 Xinchun Ye,1 Chaochao Qiu,1 Xiangao Jiang1 1Department of Infectious Disease, Wenzhou Central Hospital, The Dingli Clinical Institute of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, People’s Republic of China; 2Department of Nursing School, Wenzhou Medical University, Wenzhou, Zhejiang, 325000, People’s Republic of China; 3Department of Hematology, Wenzhou Key Laboratory of Hematology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China; 4Departments of Infectious Diseases, Taishun People’s Hospital, Wenzhou, 325000, People’s Republic of China; 5Departments of Infectious Diseases, Yongjia People’s Hospital, Wenzhou, 325000, People’s Republic of China; 6Department of Laboratory, Wenzhou Central Hospital, The Dingli Clinical Institute of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiangao Jiang, Department of Infectious Disease of Wenzhou Central Hospital, The Dingli Clinical Institute of Wenzhou Medical University, 252 Baili East Road, Lucheng District, Wenzhou, Zhejiang, 325000, People’s Republic of China, Tel +86 13587691822, Fax +86 577 88070007, Email jiang_xiangao@126.comBackground: Tuberculosis (TB) is still the single pathogen infectious disease with the largest number of deaths worldwide. The relationship that intestinal microbiota disorder and de novo fatty acid synthesis metabolism have with disease progression in multi-drug resistant TB (MDR-TB) has not yet been fully studied.Objective: To investigate the effects of long periods of MDR-TB, pre-extensively drug-resistant TB (pre-XDR-TB), or rifampicin-resistant TB (RR-TB) on gut microbiome dysbiosis and advanced disease.Methods: The sample was chosen between March 2019 and September 2019 in Wenzhou Central Hospital and comprised 11 patients with pre-XDR-TB, 23 patients with RR-TB, and 28 patients with MDR-TB. Healthy individuals were chosen as the control group (CK group). An overnight fast blood sample was drawn via venipuncture into tubes without anticoagulant. For analysis, 300 mg of faeces from patients from the same group was mixed and analysed using DNA extraction, NGS sequencing, and bioinformatics. A QIAamp Fecal DNA Mini Kit was used to isolate the DNA. The extracted DNA was stored at − 20°C.Results: Advanced TB was concurrent with an elevated level of the proportions of acetyl-CoA carboxylase (ACC1) to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and fatty acid synthase (FASN) to GAPDH in de novo fatty acids synthesis, and Eubacterium, Faecalibacterium, Roseburia, and Ruminococcus were increased significantly in RR-TB patients compared to healthy individuals, whereas their abundance in the pre-XDR-TB and MDR-TB groups showed little change in comparison with the control group. Proteobacteria levels were greatly increased in the RR-TB and MDR-TB patient groups but not in the patients with pre-XDR-TB or the healthy subjects. The pre-XDR-TB group exhibited alterations of the intestinal microbiome: coliform flora showed the highest abundance of Verrucomicrobiales, Enterobacteriales, Bifidobacteriales and Lactobacillales. De novo fatty acids synthesis was enhanced in patients and was associated with the gut microbiome dysbiosis induced by the antimicrobials, with Bacteroidetes, Bacteroidales, and Bacteroidaceae displaying the most important correlations on a phylum, order, and family level, respectively.Conclusion: The progression to advanced TB was observed to be a result of the interaction between multiple interrelated pathways, with gut–lung crosstalk potentially playing a role in patients with drug-resistant TB.Keywords: multi-drug resistant TB disease, gut commensal, microbial imbalance, de novo fatty acids synthesis, microbiome biosignature alterations

For in transit processing, one of the fundamental challenges is the efficient movement of data from producers to consumers. Exploiting the flexibility offered by the SENSEI generic in situ framework, we have developed a number of different in transit data transport mechanisms. In this work, we focus on the transport mechanism that leverages the HDF5 parallel I/O library, and investigate the performance characteristics of this transport mechanism. For in transit use cases at scale on HPC platforms, one might expect that an in transit data transport mechanism that uses faster layers of the storage hierarchy, such as DRAM memory, would always outperform a transport that uses slower layers of the storage hierarchy, such as an NVRAM-based persistent storage presented as a distributed file system. However, our test results show that the performance of the transport using NVRAM is competitive with the transport that uses socket-based data movement across varying levels of producer and consumer concurrency.

Many scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query, to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating-point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word-Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.

In high-performance computing (HPC) environments, an appropriate amount of hardware resources must be used for the best parallel I/O performance. For this reason, HPC users are provided with tunable parameters to change the HPC configurations, which control the amounts of resources. However, some users are not well aware of a relationship between the parallel I/O performance and the HPC configuration, and they thus fail to utilize these parameters. Even if users who know the relationship, they have to run an application under every parameter combination to find the setting for the best performance, because each application shows different performance trends under different configurations. The paper shows the result of analyzing the I/O performance trends for HPC users to find the best configurations with minimal efforts. We divide the parallel I/O characteristic into independent and collective I/Os and measure the I/O throughput under various configurations by using synthetic workload, IOR benchmark. Through the analysis, we have figured out that the parallel I/O performance is determined by the trade-off between the gain from the parallelism of increased OSTs and the loss from the contention for shared resources. Also, this performance trend differs depending on the I/O characteristic. Our evaluation shows that HPC applications also have similar performance trends as our analysis.

Sensors typically record their measurements using more precision than the accuracy of the sensing techniques. Thus, experimental and observational data often contain noise that appears random and cannot be easily compressed. This noise increases storage requirement as well as computation time for analyses. In this work, we describe a line of research to develop data reduction techniques that preserve the key features while reducing the storage requirement. Our core observation is that the noise in such cases could be characterized by a small number of patterns based on statistical similarity. In earlier tests, this approach was shown to reduce the storage requirement by over 100-fold for one-dimensional sequences. In this work, we explore a set of different similarity measures for multidimensional sequences. During our tests with standard quality measures such as Peak Signal to Noise Ratio (PSNR), we observe that the new compression methods reduce the storage requirements over 100-fold while maintaining relatively low errors in PSNR. Thus, we believe that this is an effective strategy to construct data reduction techniques.

In this work, we study the use of decision tree-based models to predict the transfer rates in different parts of the data pipeline that sends experiment data from Linac Coherent Light Source (LCLS) at SLAC National Accelerator Laboratory (SLAC) to National Energy Research Scientific Computing Center (NERSC). The system monitoring the data pipeline collects a number of characteristics such as the file size, source file system, start time and so on, all of which are known at the start of the file transfer. However, these static variables do not capture the dynamic information such as current state of the networking system. In this work, we explore a number of different ways to capture the state of the network and other dynamic information. We find that in addition to using static features, using these dynamic features can improve the transfer performance predictions by up to 10-15%. We additionally study a couple of different well-known decision-tree based models and find that Gradient-Tree Boosting algorithm performs better overall.

Accurately predicting network behavior is beneficial for TCP congestion control, and can help improve routing, allocating network resources, and optimizing network designs. This task is challenging because many factors could affect network traffic, such as the number of network sessions and synthetic reordering. There are also many ways to measure the network state, such as the number of retransmissions per flow and packet duplication. For this work, we use a set of passive TCP flow measurements collected at a major computer center on multiple data transfer nodes (DTN). To assist the operations of the computer network, we propose to detect abnormally slow network transfers in real-time. The proposed system breaks the network monitoring logs into fixed-size chunks and employs a state of art classifier to identify the slow time windows. This method will be validated on real large datasets collected from several DTNs. The proposed method is able to generate models to quickly detect large intervals of low performing network transfers, which require attention from network engineers.

Fog computing has the potential to be an energy-efficient alternative to cloud computing for guaranteeing latency requirements of Latency-critical (LC) IoT services. However, even in fog computing low energy-efficiency of homogeneous multi-core server processors can be a major contributor to energy wastage. Recent studies have shown that Heterogeneous Multi-core Processors (HMPs) can improve energy efficiency of servers by adapting to dynamic load changes of LC-services. However, proposed approaches optimize energy only at a single server level. In our work, we demonstrate that optimization at the cluster-level across many HMP-servers can offer much greater energy savings through optimal work distribution across the HMP-servers while still guaranteeing the Service Level Objectives (SLO) of LC-services. In this paper, we present Greeniac, a cluster-level task manager that employs Reinforcement Learning to identify optimal configurations at the server- A nd cluster-levels for different workloads. We develop a server-level service scheduler and a cluster-level load balancing module to assign services and distribute tasks across HMP servers based on the learned configurations. In addition to meeting the required SLO targets, Greeniac achieves up to 28% energy saving compared to best-case cluster scheduling techniques with local HMP-aware scheduling on a 4-server fog cluster, with potentially larger savings in a larger cluster.

In high-performance computing, storage is a shared resource and used by all users with many different application requirements and knowledge of storage. Consequently, the optimal storage configuration varies according to the I/O behavior of each application. While system logs are helpful resources in understanding the storage behavior, it is non-trivial for each user to analyze the logs and adjust complex configurations. Even for experienced users, it is difficult to understand the full stack of I/O systems and find the optimal configuration for the specific application. In this work, we analyzed the I/O activities of CORI which is an HPC system in National Energy Research Scientific Computing Center (NERSC). The result of our analysis shows that most users do not adjust storage configurations and use the default settings. Also, it shows that only a few applications are executed repeatedly in the HPC environment. Based on this result, we have developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm, which utilizes system log information and widely adapted rules to adjust storage configurations automatically without any user intervention. DCA-IO utilizes existing system logs and does not require any modifications in code or an additional library. To demonstrate the effectiveness of DCA-IO, we have performed experiments using I/O kernels of the real applications in both isolated small-sized Lustre environment and CORI. Our experimental result shows that the use of our scheme can lead to improvements in the performance of HPC applications by up to 75% in an isolated environment and 50% in a real HPC environment without user intervention.

The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

Accurately predicting network traffic volume is beneficial for congestion control, improving routing, allocating network resources and network optimization. Traffic congestion happens when a network device is receiving more data packets than its processing capability. The number of retransmissions per flow, packet duplication and synthetic reordering can seriously degrade the overall TCP performance. An unsupervised/supervised technique to accurately identify TCP anomalies occurring during file transfers based on passive measurements of TCP traffic collected using Tstat is proposed. This method will be validated on real large datasets collected from several data transfer nodes. The preliminary results indicate that the percentage of TCP anomalies correlate well with the average throughput in any given time window.

The Linac Coherent Light Source (LCLS) located at SLAC National Accelerator Laboratory has been essential to over 1023 publications since 2009. The LCLS produces vast quantities of data - thousands of gigabytes per experiment. The data must be analyzed and stored at large data centers to be available to the world-wide user community. Due to the vast quantities of data flowing through the network, many abnormal data transfers remain unnoticed. This work focuses on identifying network failures that could slow down the data transfer process. This work aims to develop a diagnostic tool to detect when network transfers become anomalously slow. The tool uses an algorithm based on the hampel filter to detect poor performance and alert SLAC administrators to bottlenecks in each phase of the workflow. We will describe our experience of preparing the data and modifying the hampel filter to enhance its effectiveness. We found that applying a heuristic to the algorithm in conjunction with parsing the data along key features improved performance.

Scientific data analysis typically involves reading massive amounts of data that was generated by simulations, experiments, and observations. Performance of reading such large volumes of data from disk-based file systems is often poor because of the slow and mechanical components in the disks. Recent supercomputing systems are adding non-volatile storage layers in a hierarchy to handle the performance gap between fast main memory and slow disk-based storage. Software libraries for managing this hierarchy not only need efficient reading of data but also reduce user-involvement for cross-layer data movement. Furthermore, these libraries need to support array data access patterns into hierarchical storage management as scientific data is often organized in array-based data structures. Existing software typically manage individual storage layers requiring significant manual process in moving data among them. In this paper, we introduce a new array caching in hierarchical storage (ARCHIE) to accelerate array data analysis in a seamless fashion. ARCHIE evaluates array access patterns and prefetches data with array semantics between storage layers. Our evaluation shows that ARCHIE outperforms state-of-the-art file systems, i.e., Lustre and DataWarp, on a production supercomputing system by up to 5.8× in accessing data by scientific analysis applications.

Chunkang Liu,1,&ast; Kunzhe Wu,2,&ast; Huan Gao,3 Jianyang Li,3 Xiaohua Xu3 1Department of Gastrointestinal Surgery, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China; 2Department of Scientific Research Center, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China; 3Department of Nephrology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaohua Xu, Email xu_xh@jlu.edu.cnAbstract: Diabetic nephropathy (DN), a severe microvascular complication of diabetes mellitus (DM), is the most common form of chronic kidney disease (CKD) and a leading cause of renal failure in end-stage renal disease. No currently available treatment can achieve complete cure. Traditional treatments have many limitations, such as painful subcutaneous insulin injections, nephrotoxicity and hepatotoxicity with oral medication, and poor patient compliance with continual medication intake. Given the known drawbacks, recent research has suggested that nanoparticle-based drug delivery platforms as therapeutics may provide a promising strategy for treating debilitating diseases such as DN in the future. This administration method provides multiple advantages, such as delivering the loaded drug to the precise target of action and enabling early prevention of CKD progression. This article discusses the development of the main currently used nanoplatforms, such as liposomes, polymeric NPs, and inorganic NPs, as well as the prospects and drawbacks of nanoplatform application in the treatment of CKD.Keywords: diabetic nephropathy, DN, nanoplatform, route of administration

Yachen Ji,&ast; Xiangjun Xu,&ast; Kangfei Wu, Yi Sun, Hao Wang, Yapeng Guo, Ke Yang, Junfeng Xu, Qian Yang, Xianjun Huang, Zhiming Zhou Department of Neurology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xianjun Huang; Zhiming Zhou, Department of Neurology, The First Affiliated Hospital of Wannan Medical College, 2# East Zheshan Road, Wuhu, 241000, People’s Republic of China, Tel +86-25-80860124, Fax +86-25-84664563, Email doctorhuangxj@hotmail.Com; neuro_depar@hotmail.ComPurpose: The systemic immune inflammatory index (SII), as a new marker, is widely used to predict the disease prognosis. We investigated the predictive value of SII for malignant cerebral edema (MCE) and whether postoperative MCE mediates the relationship between SII and functional prognosis in patients undergoing endovascular thrombectomy (EVT).Patients and Methods: A total of 829 patients with anterior circulation large-vessel occlusive stroke (LVOS) were registered, and 675 (81.4%) met the inclusion criteria. We collected baseline data upon admission, including SII. Postoperative computed tomography was performed to assess the presence and grading of cerebral edema (CED), and MCE was defined as a CED score of 3. A good prognosis was defined as a modified Rankin Scale (mRS) score of 0– 2 at the 90-day follow-up.Results: A total of 132 patients developed MCE after EVT. The patients were divided into MCE and non-MCE groups, and univariate and multifactorial analyses were performed. Among these risk factors, an elevated SII was independently correlated with the occurrence of MCE. In addition, the receiver operating characteristic (ROC) curve was used to assess the predictive capability of SII levels for prognosis. The area under the ROC was 0.69, and the optimal critical value was 2.14. In addition, postoperative MCE may partially account for the poorer functional prognosis of patients with elevated SII (regression coefficient changed by 40.3%).Conclusion: The SII is an independent predictor of malignant brain edema after EVT. Postoperative MCE is partly the reason for the poorer prognosis in patients with elevated SII.Keywords: acute ischemic stroke, endovascular treatment, systemic immune inflammatory index, malignant cerebral edema

Yinhui Pei, Xiaoling Chen, Yiwei Tan, Xiuping Liu, Fang Duan, Kaili Wu State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, People’s Republic of ChinaCorrespondence: Kaili Wu, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yan-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, People’s Republic of China, Email wukaili@mail.sysu.edu.cn Fang Duan, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yan-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, People’s Republic of China, Email duanfangg@126.comPurpose: This study aims to assess the testing methods used to detect fungal pathogens as well as the pathogenic profiles and drug susceptibility among fungal isolates from ocular sources collected in a tertiary eye hospital.Methods: The laboratory test records of patients with suspected ocular fungal infection from January 2010 to December 2019 at a tertiary eye care center in southern China were retrospectively evaluated. The isolated pathogens identified by smear examination and culture combined with microscopy were analyzed. For 2017– 2019 fungal isolates, their antifungal susceptibilities to four antifungal agents were evaluated.Results: A total of 22,233 specimens (16,315 for culture and 5918 for direct smear examination) from 16,209 individuals were assessed, and 3317 specimens (2169 for culture and 1148 for smear examination) from 2395 patients had confirmed fungal infection. The mean age of patients was 50.6± 14.2 years and 63.4% were male. The positive rate of smear examination was 19.4% and significantly higher than 13.3% of fungal culture (p< 0.001). Of 5812 patients who were simultaneously examined for culture and smear staining, 897 (15.43%) had positive findings for fungi. Among the 2420 positive findings, 2138 (88.3%) were from the cornea, and 133 (5.5%) were from intraocular samples. Fusarium spp. (40.5%) and Aspergillus spp. (22.0%) were the major fungal genera among the molds, whereas Candida spp. (4.6%) constituted the major yeast. Fusarium spp. had a lower antifungal susceptibility than Aspergillus spp. against voriconazole and amphotericin. And yeast spp. had the highest susceptibility to caspofungin.Conclusion: This study provided a 10-year assessment of fungi in ocular infections in southern China, giving support to epidemiological understanding and guiding empiric antimicrobial therapy.Keywords: fungal infection, eye, corneal scrape, fungal culture, keratomycosis, antifungal susceptibility, endophthalmitis

Traffic flow prediction is a key component of an intelligent transportation system. Accurate traffic flow prediction provides a foundation for other tasks, such as signal coordination and travel time forecasting. There are many known methods in literature for the short-term traffic flow prediction problem, but their efficacy depends heavily on the traffic characteristics. It is difficult, if not impossible, to pick a single method that works well over time. In this paper, we present an automated framework to address this practical issue. Instead of selecting a single method, we combine predictions from multiple methods to generate a consensus traffic flow prediction. We propose an ensemble learning model that exploits the temporal characteristics of the data, and balances the accuracy of individual models and their mutual dependence through a covariance-regularizer. We additionally use a pruning scheme to remove anomalous individual predictions. We apply our proposed model to multi-step-ahead arterial roadway flow prediction. In tests, our method consistently outperforms recently published ensemble prediction methods based on ridge regression and lasso. Our method also produces steady results even when the standalone models and other ensemble methods make wildly exaggerated predictions.

Ruhan Liu,1,2,&ast; Chenyang Li,1,&ast; Huajun Xu,1 Kejia Wu,1 Xinyi Li,1 Yupu Liu,1 Jie Yuan,1 Lili Meng,1 Jianyin Zou,1 Weijun Huang,1 Hongliang Yi,1 Bin Sheng,2 Jian Guan,1 Shankai Yin1 1Department of Otolaryngology Head and Neck Surgery and Shanghai Key Laboratory of Sleep Disordered Breathing & Otolaryngological Institute of Shanghai Jiao Tong University, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, People’s Republic of China; 2Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bin Sheng; Jian Guan, Tel +86 13124853285, Email shengbin@sjtu.edu.cn; guanjian0606@sina.comPurpose: Misdiagnosis and missed diagnosis of sleep-disordered breathing (SDB) is common because polysomnography (PSG) is time-consuming, expensive, and uncomfortable. The use of recording methods based on the oxygen saturation (SpO2) signals detected by wearable devices is impractical and inaccurate for extracting signal features and detecting apnoeic events. We propose a method to automatically detect the apnoea-based SpO2 signal segments and compute the apnoea–hypopnea index (AHI) for SDB screening and grading.Patients and Methods: First, apnoea-related desaturation segments in raw SpO2 signals were detected; global features were extracted from whole night signals. Then, the SpO2 signal segments and global features were fed into a bi-directional long short-term memory convolutional neural network model to identify apnoea-related and non-apnoea-related events. The apnoea-related segments were used to assess the AHI.Results: The model was trained on 500 individuals and tested on 8131 individuals from two public hospitals and one private centre. In the testing data, the classification accuracy for apnoea-related segments was 84.3%. Individuals with SDB (AHI 15) were identified with a mean accuracy of 88.95%.Conclusion: Using automatic SDB detection based on SpO2 signals can accurately screen for SDB.Keywords: sleep apnea hypopnea syndrome, AHI, SDB severity classification, Bi-LSTM-CNN, desaturation events