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Objectives: Cigarette smoking is involved in the pathogenesis of head and neck squamous cell carcinoma (HNSCC). However, the underlying molecular mechanisms of cigarette smoking-induced HNSCC carcinogenesis are unclear and may involve cancer stem-like cell generation. We examined the effects of cigarette smoke condensate (CSC) on the formation of cancer stem-like cells, which are rich in octamer-binding transcription factor (OCT)-4, inhibitor of differentiation 1 (ID1), nuclear factor (NF)-κB, and B lymphoma Mo-MLV insertion region 1 homolog (BMI-1). Materials and Methods: We used in vitro, in vivo, and archival human HNSCC tissue analysis to evaluate the effects of CSC on cancer stem-like cell formation. Results: We found that CSC regulated OCT-4 expression, which subsequently regulated ID1 and NF-κB, at the promoter, mRNA, and protein levels in vitro. Furthermore, OCT-4 knockdown with siRNA reduced ID1 expression. ID1 and NF-κB synergistically increased the expression of BMI-1 and stimulated keratinocyte sphere generation. In vivo, ID1 and NF-κB acted together to generate malignant xenograft tumors, which were aggressive locally and systemically metastatic. Clinical data confirmed that ID1- and NF-κB-positive patients had poor clinical outcomes and 5-year disease-free survival. Conclusion: Our data suggest that smoking cigarettes promoted cancer stem-like cell generation in the head and neck area via the OCT-4/ID1/NF-κB/BMI-1 signaling pathway.

To realize spot welding process of the id and base for micro-crystal resonator quickly and accurately, the action sequence is need to be designed for its spot welding device reasonably. The device’...

Supplemental material, sj-pdf-1-imr-10.1177_03000605221093305 for Late venous laceration after inappropriate placement of a left internal jugular hemodialysis catheter: a case report by Xiaohua Hu, Cheng Xue, Bibo Wu, Hua Yu, Congdie Liang and Liming Zhang in Journal of International Medical Research

Additional file 3: Figure S1.

Supplemental material, sj-pdf-1-imr-10.1177_03000605221093305 for Late venous laceration after inappropriate placement of a left internal jugular hemodialysis catheter: a case report by Xiaohua Hu, Cheng Xue, Bibo Wu, Hua Yu, Congdie Liang and Liming Zhang in Journal of International Medical Research

Researchers across nearly every discipline seek to leverage ontologies for knowledge discovery and computational tasks; yet, the number of machine readable materials science ontologies is limited. The work presented in this paper explores the Processing, Structure, Properties and Performance (PSPP) framework for accelerating the development of materials science ontologies. We pursue a case study framed by the creation of an Aerogel ontology and a Battery Cathode ontology and demonstrate the Helping Interdisciplinary Vocabulary Engineer for Materials Science (HIVE4MAT) as a proof of concept showing PSPP relationships. The paper includes background context covering materials science, the PSPP framework, and faceted analysis for ontologies. We report our research objectives, methods, research procedures, and results. The findings indicate that the PSPP framework offers a rubric that may help guide and potentially accelerate ontology development., Comment: Proceeding of 16th International Conference on Metadata and Semantics Research

Additional file 2: Table S2.

Supplemental material, sj-docx-1-tam-10.1177_17588359221122715 for A prospective, open-label, multicenter phase IV clinical trial on the safety and efficacy of lobaplatin-based chemotherapy in advanced breast cancer by Min Yan, Peng Yuan, Quchang Ouyang, Ying Cheng, Guohui Han, Dewei Wang, Li Ran, Tao Sun, Da Zhao, Yuju Bai, Shun’e Yang, Xiaojia Wang, Rong Wu, Xiaohua Zeng, Herui Yao, Xuening Ji, Jun Jiang, Xiaohua Hu, Haifeng Lin, Liping Zheng, Zhitu Zhu, Wei Ge, Junlan Yang, Tongjian Cui, Xiaozhi Zhang, Fangyang Lu, Wenhui Li, Hongyan Xu, Mafei Kang, Ping Gong, Liqun Zou, Jiang Liu, Hongliang Zhang, Hao Yu and Binghe Xu in Therapeutic Advances in Medical Oncology

Additional file 1: Table S1.

Metal-Organic Frameworks (MOFs) are a class of modular, porous crystalline materials that have great potential to revolutionize applications such as gas storage, molecular separations, chemical sensing, catalysis, and drug delivery. The Cambridge Structural Database (CSD) reports 10,636 synthesized MOF crystals which in addition contains ca. 114,373 MOF-like structures. The sheer number of synthesized (plus potentially synthesizable) MOF structures requires researchers pursue computational techniques to screen and isolate MOF candidates. In this demo paper, we describe our effort on leveraging knowledge graph methods to facilitate MOF prediction, discovery, and synthesis. We present challenges and case studies about (1) construction of a MOF knowledge graph (MOF-KG) from structured and unstructured sources and (2) leveraging the MOF-KG for discovery of new or missing knowledge., Comment: Accepted by the International Workshop on Knowledge Graphs and Open Knowledge Network (OKN'22) Co-located with the 28th ACM SIGKDD Conference

Supplemental material, sj-docx-1-tam-10.1177_17588359221122715 for A prospective, open-label, multicenter phase IV clinical trial on the safety and efficacy of lobaplatin-based chemotherapy in advanced breast cancer by Min Yan, Peng Yuan, Quchang Ouyang, Ying Cheng, Guohui Han, Dewei Wang, Li Ran, Tao Sun, Da Zhao, Yuju Bai, Shun’e Yang, Xiaojia Wang, Rong Wu, Xiaohua Zeng, Herui Yao, Xuening Ji, Jun Jiang, Xiaohua Hu, Haifeng Lin, Liping Zheng, Zhitu Zhu, Wei Ge, Junlan Yang, Tongjian Cui, Xiaozhi Zhang, Fangyang Lu, Wenhui Li, Hongyan Xu, Mafei Kang, Ping Gong, Liqun Zou, Jiang Liu, Hongliang Zhang, Hao Yu and Binghe Xu in Therapeutic Advances in Medical Oncology

As an acute zoonosis, rabies has a fatality rate of nearly 100%. Since rabies surveillance data over the years show that the rabies epidemic areas in China expand from southern provinces to central and northern provinces, we selected Hubei Province, an area with a high incidence of rabies in Central China, to investigate national rabies prevention and control work by developing a visual analysis platform (VAP-HRHB: http://www.combio-lezhang.online/rabies/index.html). VAP-HRHB employs bioinformatics methods to predict the future developmental trend for rabies incidence and exposure numbers, thereby comprehensively improving the prevention and control of infectious diseases in China.

Sensor-based applications using Big Data are of increasing importance in various fields. A typical example of such use cases is building health-care applications [1], [2]. A typical scenario is where a patient's heart rate is monitored by a smartwatch. A smartphone can then analyze the gathered data and identify patterns in the patient's heart rate. However, if the data analysis is too complex to be performed on a smartphone, the computation could be offloaded to a nearby cloudlet or a remote cloud. A decision usually follows the analysis, and actuation is performed accordingly (e.g., a message is sent to either the patient or the doctor). Developing such an application is intrinsically complex, as the programmer needs to reconcile different APIs specific to different platforms.

A very large number of online cultural heritage (CH) resources is made available through numerous digital libraries. To address the difficulties of discoverability in CH, the common practice is metadata aggregation, where centralized efforts like Europeana facilitate discoverability by collecting the resources' metadata. In the last years, the CH domain has invested in data models for Linked Data (LD) representation of CH metadata. LD, however, also has potential for innovating metadata aggregation. We present the results of a pilot case study within the Europeana Network. In this pilot, the National Library of The Netherlands plays the role of initial data provider, with the Dutch Digital Heritage Network the one of intermediary service providing datasets to Europeana. We analysed the requirements for an LD aggregation solution and defined a workflow that fulfils the same functional requirements as Europeana's current solution. The workflow was put into practice within the pilot and led to the development of several software components for managing datasets, harvesting LD, data analysis and integration. Our analysis of the experience discusses the effort of adopting such an LD approach for data providers and aggregators, the expertise required by CH data analysts, and the supporting tools required for semantic data.

This paper proposes a novel scalable community-based neural framework for graph learning. The framework learns the graph topology through the task of community detection and link prediction by optimizing with our proposed joint SBM loss function, which results from a non-trivial adaptation of the likelihood function of the classic Stochastic Block Model (SBM). Compared with SBM, our framework is flexible, naturally allows soft labels and digestion of complex node attributes. The main goal is efficient valuation of complex graph data, therefore our design carefully aims at accommodating large data, and ensures there is a single forward pass for efficient evaluation. For large graph, it remains an open problem of how to efficiently leverage its underlying structure for various graph learning tasks. Previously it can be heavy work. With our community-based framework, this becomes less difficult and allows the task models to basically plug-in-and-play and perform joint training. We currently look into two particular applications, the graph alignment and the anomalous correlation detection, and discuss how to make use of our framework to tackle both problems. Extensive experiments are conducted to demonstrate the effectiveness of our approach. We also contributed tweaks of classic techniques which we find helpful for performance and scalability. For example, 1) the GAT+, an improved design of GAT (Graph Attention Network), the scaled-cosine similarity, and a unified implementation of the convolution/attention based and the random-walk based neural graph models.

The implementation of aesthetic education in college physical education is an important part of cultivating comprehensive and harmonious cross-century talents. In its unique way, physical activity can not only cultivate students' feelings, experiences and performances of beauty, but also can imitate sentiment and stimulate students' ability to create beauty.

Clinical guidelines, pathways and protocols are introduced to standardize and provide best-practice care. Analyzing deviations of actual care against the documented best practices is useful to find opportunities for complying better in the future. Prior work demonstrates that deviation analyses should be accurate, automated and insightful but only few studies manage to satisfy all three intentions. In this paper, we manage to reconcile accuracy with automation and insightfulness by combining the previously disconnected steps of checking and mining in compliance analysis software. Results are achieved using an algorithm that consists of three steps. We demonstrate the effectiveness of the algorithm via a real-life case from the intensive care unit of a Dutch hospital.

A double-grating rectangular waveguide with a round beam channel is proposed as the slow-wave interaction structure of a 310GHz travelling wave tube (TWT). We describe the fabrication of the slow wave structure and the assembly method of RF windows.

This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept "user preference" in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling "user preference" and "sentiment" as independent factors. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson's Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of online reviews. Last, as a result of the de-correlation of "user preference" from "sentiment", TSPRA is able to evaluate a new concept "critical aspects", defined as the product aspects seriously concerned by users but negatively commented in reviews. Improvement to such "critical aspects" could be most effective to enhance user experience.

The meaning of the term 'Big Data' is still subject to debate, in spite of being widely used in biomedical publications. This confusion in definition leads to missed opportunities for peers to exchange knowledge and practices. A better understanding of Big Data may help researchers to identify themselves with the Big Data community. In this study, we investigate the most distinguishing features in 'Big Data'-labelled publications by comparing them against publication without this label (non-Big Data), using text mining and machine learning methods. Furthermore, the usage of the term Big Data was analysed over time. Our models could successfully make a distinction between publications labelled with 'Big Data' and those without. The most distinguishing features consisted of terms such as 'omics', 'computing', 'storage', and 'mining'. We observed that publications that do not use the term Big Data may also address topics that fall under well-accepted definitions of Big Data. Trends suggest that, while the use of the term Big Data increased, it is used less reliably now as compared to earlier years.

Mesoscale eddies exist almost everywhere in the ocean and play important roles in the ocean circulation of the world. These eddies may cause sound spread singular regions and bring great influences to the upwater ship and underwater aircraft. Due to the lack of hydrographic survey datasets, study of mesoscale eddies has been greatly restricted. Fortunately, satellite altimeter provided an effective way to study mesoscale eddies. An automatic detection algorithm is introduced to detect mesoscale eddies of specific intensity and spatial/temporal scale based on satellite sea surface height (SSH) data and the algorithm is applied in a strong eddy activity region: the South China Sea and the Northwest Pacific. The algorithm includes four steps. The first step is preprocessing of the SSH image, which includes elimination of error SSH data and interpolation. The second step is to detect suspected mesoscale eddies from preprocessed SSH images by dynamic threshold adjustment and morphological method, and the suspected mesoscale eddy detection includes two procedures: suspected mesoscale eddy core region detection and suspected mesoscale eddy brim extraction. The third step is to pick out mesoscale eddies satisfied with specified criteria from suspected mesoscale eddies. The criteria include three items, that is, intensity criterion, spatial scale, criterion and temporal scale criterion. The last step is algorithm performance analysis and verification. The algorithm has the capability of adaptive parameter adjustment, and can extract mesoscale eddies of interested intensity and spatial/temporal scale. The paper can provide a basis for analyzing space-time characteristics of mesoscale eddy in the South China Sea and the Northwest Pacific.

OBJECTIVE: To investigate the effects of anti-infection treatment on the expressions of antigen-presenting-related membrane-surface molecules HLA-DR and CD86 in dendritic cells (DCs) in rabbit buccal VX2 squamous cell carcinoma tissue complicated with local inflammation. METHODS: Rabbit buccal VX2 squamous cell carcinoma with local inflammation models that were established by inflammation was induced by inoculation VX2 tumor, mechanical trauma, and drinking of milk with high sugar viscosity. The animals were divided into four groups. Group A (n=12): rabbit buccal VX2 squamous cell carcinoma with local inflammation, procaine penicillin was intramuscularly given, and tinidazole tablets were given by gavage for three consecutive days. Group B (n=12): rabbit buccal VX2 squamous cell carcinoma with local inflammation, normal saline was intramuscularly given, and aspirin were given by gavage for three consecutive days. Group C (n=12): rabbit buccal VX2 squamous cell carcinoma with local inflammation, normal saline was given intramuscularly and by gavage for three consecutive days. Group D (n=10): rabbit buccal VX2 squamous cell carcinoma, normal saline was given intramuscularly and by gavage for three consecutive days. All the rabbits were sacrificed for collection of tumor specimens, and the expression levels of membrane-surface HLA-DR and CD86 in DCs of tumor specimens were detected viaflow cytometry. RESULTS: The positive expression rate of HLA-DR and the double positive expression rate of HLA-DR and CD86 were group A> group D> group B> group C. The positive expression rate of CD86 were group A> group D> group B and group C (P

OSAHS is a common disease with many factors related to the etiology. Airflow plays an important role in the pathogenesis of OSAHS. Previous research has not yielded a sufficient understanding of the relationship between airflow in upper airway and the pathophysiology of OSAHS. Therefore, a better understanding of the flow inside the upper airway in an OSAHS patient is necessary. In this study, ten Chinese adults with OSAHS were recruited. We used the software MIMICS 13.1 to construct 3-dimensional (3-D) models based on the computer tomography scans of them. The numerical simulations were carried out using the software ANSYS 12.0. We found that during the inhalation phase, the vortices and turbulences were located in both the anterior part of the cavity and nasopharynx. But there is no vortex in the whole nasal cavity during the expiratory phase. The airflow velocity is much higher than that of the normal models. The distributions of pressure and wall shear stress are different in two phases. The maximum velocity, pressure and wall shear stress (WSS) are located in velopharynx. It is notable that a strong negative pressure region is found in pharyngeal airway. The maximum velocity is 19.26 ± 12.4 and 19.46 ± 13.1 m/s; the average pressure drop is 222.71 ± 208.84 and 238.5 ± 218.56 Pa and the maximum average WSS is 0.72 ± 0.58 and 1.01 ± 0.61 Pa in inspiratory and expiratory, respectively. The changes of airflow due to the structure changes play an important role in the occurrence of collapse and obstruction of the upper airway, especially, the abnormal pressure changes in velopharyngeal during both inspiratory and expiratory phases. We can say that the airway narrowing in the pharynx may be one of the most important factors driving airway collapse. In addition, the most collapsible region of the pharyngeal airway of the patient with OSAHS may be the velopharynx and oropharynx. In spite of limitations, our results can provide a basis for the further research. On this basis, more about the secret of the pathogenesis of the OSAHS will be revealed.

Actin-based cell motility plays crucial role throughout the lifetime of an organism. While the dendritic nucleation model explains the initiation and organization of the actin network in lamellipodia, two questions need to be answered. In this study, I reconstructed cellular motility in vitro to investigate how actin filaments are organized to coordinate elongation and attachment to leading edge. Using total internal reflection fluorescence microscopy of actin filaments, we tested how profilin, Arp2/3, and capping protein (CP) function together to propel beads or thin glass nanofibers coated with N-WASP WCA domains. During sustained motility, physiological concentrations of Mg²⁺ generated actin filament bundles that processively attached to the nanofiber. Reduction of total Mg²⁺ abolished particle motility and actin attachment to the particle surface without affecting actin polymerization, Arp2/3 nucleation, filament capping, or actin shell formation. Addition of other types of crosslinkers restored both comet tail attachment and particle motility. We propose a model in which polycation-induced filament bundling sustains processive barbed end attachment to the leading edge. I lowered actin, profilin, Arp2/3, and CP concentrations to address the generation of actin filament orientation during the initiation of motility. In the absence of CP, Arp2/3 nucleates barbed ends that grow away from the nanofiber surface and branches remain stably attached to nanofiber. CP addition causes shedding of short branches and barbed end capture by the nanofiber. Barbed end retention by nanofibers is coupled with capping, indicating that WWCA and CP bind simultaneously to barbed ends. In pull-down assays, saturating CP addition only blocks WWCA binding to barbed end by half. Labeled WWCA bound to barbed ends with an affinity of 14 pM and unlabeled WWCA with an affinity of 75 pM. CP addition increased WWCA binding slightly at low CP concentrations and decreased WWCA binding to 50% at high CP concentrations. Molecular models of CP and WH2 domains bound respectively to the terminal and penultimate actin subunit showed no overlap and that CP orientation might blocks WWCA dissociation from the penultimate subunit. Simultaneous binding of CP and WWCA to barbed ends is essential to the establishment of filament orientation at the leading edge. Ph. D.

In this article, we present a graph-based knowledge representation for biomedical digital library literature clustering. An efficient clustering method is developed to identify the ontology-enriched k-highest density term subgraphs that capture the core semantic relationship information about each document cluster. The distance between each document and the k term graph clusters is calculated. A document is then assigned to the closest term cluster. The extensive experimental results on two PubMed document sets (Disease10 and OHSUMED23) show that our approach is comparable to spherical k-means. The contributions of our approach are the following: (1) we provide two corpus-level graph representations to improve document clustering, a term co-occurrence graph and an abstract-title graph; (2) we develop an efficient and effective document clustering algorithm by identifying k distinguishable class-specific core term subgraphs using terms’ global and local importance information; and (3) the identified term clusters give a meaningful explanation for the document clustering results.

Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this article, we conduct a comparative study on how different term semantic similarity measures including path-based, information-content- based and feature-based similarity measure affect document clustering. Term re-weighting of document vector is an important method to integrate domain ontology to clustering process. In detail, the weight of a term is augmented by the weights of its co-occurred concepts. Spherical k-means are used for evaluate document vector reweighting on two real-world datasets: Disease10 and OHSUMED23. Experimental results on nine different semantic measures have shown that: (1) there is no certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms.

Hardware-in-the-loop simulation is an efficient method for research on radar system.Target’s echo which offered by the simulator should be synchronized with radar on frequency, time, and range bin. However, because simulator needs to take into account of the requirements of various types of radar, it is difficult to make the clock of simulator synchronized with the clock of radar. To solve the problem, synchronous sampling deviation model is established. Influence of sampling deviation on imaging is analyzed. An engineering method is put forward to eliminate the sampling deviation. This method not only provides a reference for simulation system, but also provides a reference for the design of radar system.

There are multiple papers published about different AI models for the COVID-19 diagnosis with promising results. Unfortunately according to the reviews many of the papers do not reach the level of sophistication needed for a clinically usable model. In this paper I go through multiple review papers, guidelines, and other relevant material in order to generate more comprehensive requirements for the future papers proposing a AI based diagnosis of the COVID-19 from chest X-ray data (CXR). Main findings are that a clinically usable AI needs to have an extremely good documentation, comprehensive statistical analysis of the possible biases and performance, and an explainability module., Accepted to AI&BDvsPandemics BIBM 2021 Workshop

Funding Information: This work is supported by the Major Science and Technology Innovation Projects of Key R&D Programs of Shandong Province in 2019 (grant No.2019JZZY010113) Publisher Copyright: © 2021 IEEE. EEG based emotion recognition technology is currently an important concept in artificial intelligence, and also holds great potential in emotional health care. Nevertheless, one major limitation of the prior approaches is they do not capture the relationships between different time-series and channels explicitly, resulting in inevitable low performance, especially in subject-independent recognition settings. In this paper, we propose a novel graph attention network based model to address this issue. Our framework includes dual-pipeline Graph Attention Network layers in parallel to learn the complex dependencies of multi-channel EEG in both temporal and spatial dimensions. The proposed method outperforms other state-of-the-art models on benchmark SEED dataset. Further analysis shows that our method also has good interpretability. As far as we know, it is the first work that introduce graph attention network into EEG based emotion detection research.

Purpose: The purposeofthis dissertation is to investigate the feasibilityofusingtestsofrobustnessin peer review. This study involved selecting three high-impact papers which featured open data and utilized bioinformatic analyses but provided no source code and refactoring these to allow external survey participants to swap tools, parameters, and data subsets to evaluate therobustnessand underlying validityofthese analyses. Technical advances that have taken place in recent years - scientific computing infrastructure has matured to support the distributionofreproducible computational analyses - enable this approach. These advances, along with cultural shifts encompassing open data and open code initiatives, promise to address technical stumbling blocks that have contributed to the "reproducibility crisis." To take full advantageofthese developments toward improving scientific quality, authors, reviewers, and publishers must integrate reproducible analysis into the peer review process. Seven existing major case study types - reproduction, replication, refactor,robustnesstest, survey, census, and case narrative - have been invaluable toward establishing reproducibility as a serious and independent areaofresearch.Ofparticular interest are refactors, in which an existing analysis with abstract methods is reimplemented by a third party, androbustnesstests, which involve the manipulationoftools, parameters, and data to assess the scientific validityofan analysis. This thesis describes efforts totestthe feasibilityofrobustnesstesting in the contextofin silico peer review. The contributions described are complemented with extensive source code. Design and Methods: A multi-method approach was employed for this study consistingofuser surveys andtestsofrobustness- hands-on, self-directed software development exercises. Three high-impact genomics publications with open data, but no source code, were selected, refactored, and distributed to active study participants who acted as quasi-external reviewers. The processofthe refactor was used to evaluate the limitationsofreproducibility using conventional tools and to study how best to present analyses for peer review, and thetestsofrobustnesswere employed under the hypothesis this practice would help to evaluate the underlying validityofan analysis. Three different approaches were taken in thesetestsofrobustness- a faithful reproductionofthe original manuscript into a framework that could be manipulated by participants, a workflow-library approach in which participants were encouraged to employ modern "off-the-shelf" pre-built pipelines to triangulatetests, and an advisor-led approach in which senior experts suggested alternate tools to be implemented and I generated a report for their evaluation. Findings: The refactors andtestsofrobustnessproduced numerous discoveries both in termsofthe underlying scientific content and, more importantly, into the strengths and weaknessofthe threerobustnessapproaches (faithful/workflow-library/advisor-led) and pain points in the analytic stack, which may be addressed with appropriate software and metadata. The principal findings are that the faithful approach may often discourage aggressiverobustnesstesting becauseofthe inertia imposed by the existing framework, the workflow-library approach is efficient but can prove inconclusive, and the advisor-led approach may be most practical for journals but requires a higher levelofcommunication to be effective. The vast majorityoftime in all these refactors was spent on sample metadata management, particularly organizing sample groupsofbiological and technical replicates to produce the numerous and varied tool input manifests. Practical Implications: Reproducibility-enabled in silico peer review is substantially more time-consuming than traditional manuscript peer review and will require economic, cultural, and technical change to bring to reality. The work presented here could contribute to developing new models to minimize the increased effortofthis typeofpeer review while incentivizing reproducibility. Value: This study provides practical guidance toward designing the futureofreproducibility-enabled in silico peer review, which is a logical extensionofthe computational reproducibility afforded by technical advances in dependency management, containerization, pipeline frameworks, and notebooks.

Early detection of adverse events at hospitals could be useful in terms of reducing costs, morbidity, and mortality. Therefore, in this paper, we present a personalized real-time hybrid model based on Nonlinear Autoregressive Exogenous (NARX) model and Ensemble Learning (EL) to predict patients' severity during hospitalization at Emergency Departments (ED). This model utilizes vital signs of patients, including Pulse Rate (PR), Respiratory Rate (RR), Arterial Blood Oxygen Saturation (SpO2) and Systolic Blood Pressure (SBP), which are collected automatically during the treatment to predict the illness severity of hospitalized patients at ED in the next hour based on their vital signs of the previous two hours. Two EL algorithms, including Random Forest (RF) and Adaptive Boosting (AdaBoost) are considered to build hybrid models. The performance of NARX-EL models is compared with Auto Regressive Integrated Moving Average (ARIMA), combination of NARX and Linear Regression (LR), Support Vector Regression (SVR) and K-Nearest Neighbors Regression (KNN). The results show that our proposed hybrid models can predict patients' severity with significantly higher accuracy. It is also found that NARX-RF has the best performance in the prediction of sudden changes and unexpected adverse events in patients' vital signs (R 2 score =0.978, NRMSE =6.16%).

Road damages are of great interest for federal road authorities and their infrastructure management as well as the automated driving task and thus safety and comfort of vehicle occupants. Therefore, we are investigating the automatic detection of different types of road damages by images from a front-facing camera in the vehicle. The data basis of our work is provided by the ’IEEE BigData Cup Challenge’ and its dataset ’RDD-2020’ with a large number of labelled images from Japan, India and the Czech Republic. Our Deep Learning approach utilizes the pre-trained Faster Region Based Convolutional Neural Networks (R-CNN). In the first step, we classify the destination of the image followed by expert networks for each region. Between the explanation of our applied Deep Learning methodology, some remaining sources of errors are discussed and further, partly failed approaches during our development period are displayed, which could be of interest for future work. Our results are convincing and we are able to achieve an F1 score of 0 . 487 across all regions for longitudinal and lateral cracks, alligator cracks and potholes.

The graph data structure offers a highly expressive way of representing many real-world constructs such as social networks, chemical compounds, the world wide web, street maps, etc. In essence, any collection of entities and the relationships between them can be modelled using a graph, thus preserving more information about the real-world objects than a simple vector space model. An issue that arises when operating on collections of graphs, however, is that most statistical analysis and machine learning methods expect their input data to be in the form of multidimensional vectors, where all items can be compared with each other using well-understood metrics such as Euclidean or Manhattan distance. This paper presents a variety of approaches for computing distances between graphs with known node correspondence, with the aim of applying those measures alongside clustering algorithms to discover patterns in a given dataset. The performance of each distance measure is then evaluated through its ability to identify communities of graphs with similar features. We show that because the considered distance metrics highlight different structural properties, the method that produces the highest quality result will depend on the characteristics of the processed graph population.

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0 [13], a modern data standard for collaborative science across the neurophysiology community.

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.

Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding applications such as question answering, text summarization and information extraction. Recently, the public availability of big datasets such as Stanford Natural Language Inference (SNLI) and SciTail, has made it feasible to train complex neural NLI models. Particularly, Bidirectional Long Short-Term Memory networks (BiLSTMs) with attention mechanisms have shown promising performance for NLI. In this paper, we propose a Combined Attention Model (CAM) for NLI. CAM combines the two attention mechanisms: intra-attention and inter-attention. The model first captures the semantics of the individual input premise and hypothesis with intra-attention and then aligns the premise and hypothesis with inter-sentence attention. We evaluate CAM on two benchmark datasets: Stanford Natural Language Inference (SNLI) and SciTail, achieving 86.14% accuracy on SNLI and 77.23% on SciTail. Further, to investigate the effectiveness of individual attention mechanism and in combination with each other, we present an analysis showing that the intra- and inter-attention mechanisms achieve higher accuracy when they are combined together than when they are independently used.

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare.

The content generated by users on social media is rich in personal information that can be mined to construct accurate user profiles, and subsequently used for tailored advertising or other personalized services. Facebook has recently come under scrutiny after a third party gained access to the data of millions of users and mined it to construct psychographical profiles, which were allegedly used to influence voters in elections. As part of a possible solution to avoid data breaches while still being able to perform meaningful machine learning (ML) on social media data, we propose a privacy-preserving algorithm for k-nearest neighbor (kNN) [1] , one of the oldest ML methods, used traditionally in collaborative filtering recommender systems.

The combination of Condition Based monitoring techniques with the predictive capabilities of neural networks represents a topic of central importance when it comes to maximizing production profits and consequently reducing costs and downtime. The ability to plan the best strategy based on the prediction of potential damaging events can represent a significant contribution, especially for the maintenance function. In fact, optimization of the management of the equipment is a fundamental step to guarantee the competitiveness of companies in the current market. In this paper, a tool based on the implementation of Radial Basis Function Neural Networks was developed to support the maintenance function in the decision-making process. In addition to providing an indication of the status of the equipment, the current approach provides an additional level of information in terms of predicting the confidence interval around the prediction of the neural network. The confidence interval combined with the prediction of the future state of the equipment can be of fundamental importance in order to avoid strategic decisions based on a low level knowledge of the system status or prediction performance of the applied algorithm. The developed tool is tested on the prediction of a naval propulsion system gas turbine performance decay, where the statuses of both the turbine and the compressor of the system are predicted as well as predicting their confidence intervals.

Many machine learning (ML) applications rely on large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data. The emergence of consumer-grade, low-cost brain-computer interfaces (BCIs) and corresponding software development kits1 is bringing the use of BCI within reach of application developers. The access that BCI applications have to neural signals rightly raises privacy concerns. Application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other personal data [1]. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users.

Statistical analysis of large and sparse graphs is a challenging problem in data science due to the high dimensionality and nonlinearity of the problem. This paper presents a fast and scalable algorithm for partitioning such graphs into disjoint groups based on observed graph distances from a set of reference nodes. The resulting partition provides a low-dimensional approximation of the full distance matrix which helps to reveal global structural properties of the graph using only small samples of the distance matrix. The presented algorithm is inspired by the information-theoretic minimum description principle. We investigate the performance of this algorithm for selected real data sets and for synthetic graph data sets generated using stochastic block models and power-law random graphs, together with analytical considerations for sparse stochastic block models with bounded average degrees., Comment: IEEE BigData 2018 Conference Workshop, Advances in High Dimensional Big Data, 10.-13.12. 2018, Seattle USA

A method for compression of large graphs and matrices to a block structure is further developed. Szemer\'edi's regularity lemma is used as a generic motivation of the significance of stochastic block models. Another ingredient of the method is Rissanen's minimum description length principle (MDL). We continue our previous work on the subject, considering cases of missing data and scaling of algorithms to extremely large size of graphs. In this way it would be possible to find out a large scale structure of a huge graphs of certain type using only a tiny part of graph information and obtaining a compact representation of such graphs useful in computations and visualization., Comment: Accepted for publication in: Fourth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining, December 11, 2017, Bosto U.S.A

The recent boom of big data, coupled with the challenges of its processing and storage gave rise to the development of distributed data processing and storage paradigms like MapReduce, Spark, and NoSQL databases. With the advent of cloud computing, processing and storing such massive datasets on clusters of machines is now feasible with ease. However, there are limited tools and approaches, which users can rely on to gauge and comprehend the performance of their big data applications deployed locally on clusters, or in the cloud. Researchers have started exploring this area by providing benchmarking suites suitable for big data applications. However, many of these tools are fragmented, complex to deploy and manage, and do not provide transparency with respect to the monetary cost of benchmarking an application. In this paper, we present Plug And Play Bench, an infrastructure aware abstraction built to integrate and simplify the deployment of big data benchmarking tools on clusters of machines. PAPB automates the tedious process of installing, configuring and executing common big data benchmark workloads by containerising the tools and settings based on the underlying cluster deployment framework. Our proof of concept implementation utilises HiBench as the benchmark suite, HDP as the cluster deployment framework and Azure as the cloud platform. The paper further illustrates the inclusion of cost metrics based on the underlying Microsoft Azure cloud platform., Comment: 8 pages, Published as a workshop paper in 2017 IEEE International Conference on Big Data in Boston Dec 11 - 14

The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the classification of the entire graph. Whilst there is considerable work on the first problem, the efficient and accurate classification of massive graphs into one or more classes has, thus far, received less attention. In this paper we propose the Deep Topology Classification (DTC) approach for global graph classification. DTC extracts both global and vertex level topological features from a graph to create a highly discriminate representation in feature space. A deep feed-forward neural network is designed and trained to classify these graph feature vectors. This approach is shown to be over 99% accurate at discerning graph classes over two datasets. Additionally, it is shown to be more accurate than current state of the art approaches both in binary and multi-class graph classification tasks.

Recent technological advances in modern healthcare have lead to the ability to collect a vast wealth of patient monitoring data. This data can be utilised for patient diagnosis but it also holds the potential for use within medical research. However, these datasets often contain errors which limit their value to medical research, with one study finding error rates ranging from 2.3%???26.9% in a selection of medical databases. Previous methods for automatically assessing data quality normally rely on threshold rules, which are often unable to correctly identify errors, as further complex domain knowledge is required. To combat this, a semantic web based framework has previously been developed to assess the quality of medical data. However, early work, based solely on traditional semantic web technologies, revealed they are either unable or inefficient at scaling to the vast volumes of medical data. In this paper we present a new method for storing and querying medical RDF datasets using Hadoop Map / Reduce. This approach exploits the inherent parallelism found within RDF datasets and queries, allowing us to scale with both dataset and system size. Unlike previous solutions, this framework uses highly optimised (SPARQL) joining strategies, intelligent data caching and the use of a super-query to enable the completion of eight distinct SPARQL lookups, comprising over eighty distinct joins, in only two Map / Reduce iterations. Results are presented comparing both the Jena and a previous Hadoop implementation demonstrating the superior performance of the new methodology. The new method is shown to be five times faster than Jena and twice as fast as the previous approach.

With more and more large networks becoming available, mining and querying such networks are increasingly impor- tant tasks which are not being supported by database models and querying languages. This paper wants to alleviate this situation by proposing a data model and a query language for facilitating the analysis of networks. Key features in- clude support for executing external tools on the networks, flexible contexts on the network each resulting in a different graph, primitives for querying subgraphs (including paths) and transforming graphs. The data model provides for a closure property, in which the output of every query can be stored in the database and used for further querying. Acceptance rate: 14,5% ispartof: pages:485-494 ispartof: Proceedings of the ACM Conference on Information and Knowledge Management pages:485-494 ispartof: ACM Conference on Information and Knowledge Management location:Hong Kong, China date:2 Nov - 6 Nov 2009 status: published