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Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping requires understanding of the smartphone as a scientific data collection tool. In this pilot study, we detail a procedure for estimating data quality for phone sensor samples and model the relationship between data quality and future symptom-related survey responses in a cohort with schizophrenia. We find that measures of empirical coverage of collected accelerometer and GPS data, as well as survey timing and survey completion metrics, are significantly associated with future survey scores for a variety of symptom domains. We also find evidence that specific measures of data quality are indicative of domain-specific future survey outcomes. These results suggest that for smartphone-based digital phenotyping, metadata is not independent of patient-reported survey scores, and is therefore potentially useful in predicting future clinical outcomes. This work raises important questions and considerations for future studies; we explore and discuss some of these implications., Digital phenotyping: assessing data quality in schizophrenia A pilot study shows that smartphone-collected data from patients with schizophrenia could be used to infer their mental-health status. Using smartphones as scientific data gathering tools holds great promise for understanding some of the behavioral features of psychiatric disorders and could provide an early indication of worsening symptoms. However, few studies have assessed the quality of the collected data, and thus the accuracy of clinical outcome prediction. Patrick Staples at the Harvard T. H. Chan School of Public Health in Boston, MA, and colleagues examined the relationship between data quality and future symptom-related survey responses in 16 patients with schizophrenia. They found that smartphone sensor data as well as phone-use metrics related to the completion of symptom-related surveys were significantly associated with survey results, highlighting the clinical relevance of this approach.

Scientific data often are expensive to produce or impossible to reproduce. Those data may be of great future value for reuse, recombination, and replication by other researchers. However, the potential value of these data can only be achieved if the data producers manage them properly. Visions of data management and the role of the data producer have been constructed by data curators and funders from the top-down, but we have little understanding of what data management looks like on the ground. What do data producers see as their data management responsibilities? The exploratory research reported in this dissertation provides a rich description of data management tasks performed by members of six research groups and members' perception of data management responsibilities. Groups were selected from the Center for Embedded Networked Sensing (CENS), an NSF-funded Science and Technology Research Center, where researchers are already experiencing the data deluge. Document analysis, semi-structured interviews, and field observations were coded and analyzed for emergent themes and used to construct models of data management practices. Significant findings include: (i) these six research groups acquired a diverse array of data (ii) a generalized data life cycle can be applied to practices of these groups, (iii) researchers actively managed their data throughout the data life cycle to support their own use, and (iv) data management tasks were distributed between the members of a research group, and are tied to data handling tasks such as collection, processing, and analysis. The data management tasks performed by researchers are categorized into four core functions: selection for quality, verification for validity, storage for accessibility, and documentation for interpretability. A set of roles and responsibilities were identified for the data producers collaborating on each research project. These findings suggest that including author contribution statements in publications would assist future users of those data in determining who to contact for questions about their creation and context. This study reveals how, when, and why science and technology researchers manage their data and makes recommendations for data management within research groups that will make data more usable and sharable.

In addition to volume and velocity, Big data is also characterized by its variety. Variety in structure and semantics requires new integration approaches which can resolve the integration challenges also for large volumes of data. Data lakes should reduce the upfront integration costs and provide a more flexible way for data integration and analysis, as source data is loaded in its original structure to the data lake repository. Some syntactic transformation might be applied to enable access to the data in one common repository; however, a deep semantic integration is done only after the initial loading of the data into the data lake. Thereby, data is easily made available and can be restructured, aggregated, and transformed as required by later applications. Metadata management is a crucial component in a data lake, as the source data needs to be described by metadata to capture its semantics. We developed a Generic and Extensible Metadata Management System for data lakes (called GEMMS) that aims at the automatic extraction of metadata from a wide variety of data sources. Furthermore, the metadata is managed in an extensible metamodel that distinguishes structural and semantical metadata. The use case applied for evaluation is from the life science domain where the data is often stored only in files which hinders data access and efficient querying. The GEMMS framework has been proven to be useful in this domain. Especially, the extensibility and flexibility of the framework are important, as data and metadata structures in scientific experiments cannot be defined a priori .

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system’s performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals.

‘Quality evaluation based on color grading’ is one of the features used in Chinese medicine discrimination. In order to assess the feasibility of electronic eye (E-eye) in implementing ‘quality evaluation based on color grading’, the present study applied an IRIS VA400 E-eye to test 58 batches of Corni Fructus samples. Their optical data were acquired and combined with their corresponding classes. A total of four quality discrimination models were produced according to discrimination analysis (DA), least squares support vector machine (LS-SVM), partial least squares-discrimination analysis (PLS-DA), and principal component analysis-discrimination analysis (PCA-DA). The accuracy rate of the aforementioned 4 cross evaluation models were 86.21%, 89.66%, 81.03% and 91.38%, respectively. Therefore, the PCA-DA method was used to build the final discrimination model for classifying Corni Fructus or discriminating its quality.

This paper discusses how the Open Science has constituted a new approach to the scientific knowledge generation process, based on the collaborative form that scientific production is being created and outreach. Traditional research outcomes such as papers, dissertations and theses, although available in open access, can be compared to a black box, under the Actor-Network Theory (ANT) perspective, since they are considered the final and closed result of a determined researcher's or a research group's analysis. This paper, mainly bibliographic, uses bibliographical and bibliometric research as methods of data treatment, followed by a systematic review of the literature. The aim is to highlight how the research data availability has agency within the science process and the scholarly communication. To this end, some of ANT's concepts are used to identify the role of research data within the network, built of human and nonhuman elements together. As results, this paper evidence how the research data availability provides new information resources, allowed mainly by the scientific activity feedback. A Ciência Aberta tem constituído uma nova abordagem para o processo de geração de conhecimento científico, com base na forma colaborativa que a produção científica tem sido criada e comunicada. Os tradicionais resultados de pesquisa como artigos, dissertações e teses, ainda que disponíveis em acesso aberto podem ser comparados à uma caixa-preta, de acordo com a perspectiva da Teoria Ator-Rede (TAR), constituindo o resultado acabado da análise de um determinado pesquisador ou grupo de pesquisadores. Este artigo, eminentemente bibliográfico a partir de descritores, utiliza a pesquisa bibliográfica e bibliométrica como métodos de tratamento dos dados, seguido de revisão sistemática da literatura. O objetivo busca evidenciar como a disponibilidade dos dados de pesquisa interfere no processo de geração e de comunicação científica. Para tal utiliza-se das concepções da TAR na identificação do papel dos dados de pesquisa dentro de uma rede, composta por elementos humanos e não humanos. Como resultados, demonstra-se a partir do ciclo de vida dos dados de pesquisa, de que forma a disponibilidade destes produz novos recursos de informação, viabilizada pela retroalimentação da atividade científica.

International audience; Limitations in current DBMSs prevent their wide adoption in scientific applications. In order to make scientific applications benefit from DBMS support, enabling declarative data analysis and visualization over scientific data, we present an in-memory array DBMS system called SAVIME. In this work we describe the system SAVIME, along with its data model. Our preliminary evaluation show how SAVIME, by using a simple storage definition language (SDL) can outperform the state-of-the-art array database system, SciDB, during the process of data ingestion. We also show that is possible to use SAVIME as a storage alternative for a numerical solver without affecting its scalability.

Current Supplier-Manufacturer (SM) networks are highly complex and susceptible to local and global disruptions, due to connectivity and interdependency among suppliers and manufacturers. Resiliency of supply chains is critical for organizations to remain operational in the face of disruptive events. Existing quantitative analyses oversimplify the mutualistic nature of SM networks, in which failure of individual entities affects not only the directly connected entities but also those connected indirectly. In this work we investigate resiliency of SM networks using the quantitative methods employed to study mutualistic ecological systems. Much like in ecological systems, catastrophic failures of SM networks are difficult to predict due to high dimensionality of their interactive space. To address this, first we create a bipartite representation and generate a multidimensional nonlinear model that captures the dynamics of a SM network. We transform the multidimensional model into a two-dimensional model without sacrificing the model’s ability to predict the point of collapse. We extensively validate the model using real-world global automotive SM networks. We observe that the resiliency of a SM network depends on both the network structure and parameters. The current work offers a means for designing resilient supply chains that can remain robust to local and global perturbations.

Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patient-specific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC.Primary tumor growth was best described by a gompertzian model for the pre-diagnosis history, followed by a tumor growth inhibition model during treatment. Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1-5.3 years). Multiple metastatic models were assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4 - 5.7 months and onset of BMs 14 - 19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.

We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more frequently. Such an approach will result in more reliable and economical ways to monitor rail infrastructure. The dataset also includes corresponding GPS positions of the trains, environmental conditions (including temperature, wind, weather, and precipitation), and track maintenance logs. The data, which is stored in a MAT-file format, can be conveniently loaded for various potential uses, such as validating anomaly detection and data fusion as well as investigating environmental influences on train responses., Design Type(s)time series design • observation design • data collection and processing objectiveMeasurement Type(s)public infrastructure • positionTechnology Type(s)Accelerometer • GPS navigation systemFactor Type(s)temporal_interval • temperature • wind • weather • precipitationSample Characteristic(s)Pittsburgh • anthropogenic habitat Machine-accessible metadata file describing the reported data (ISA-Tab format)

Author(s): Yildiz, B; Wu, K; Byna, S; Shoshani, A | Abstract: 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.

The paper describes the use of electrical impedance spectroscopy (EIS) for characterizing the impedance of polyaniline doped polyacrylonitrile fibres. The electrical impedance of fibres samples was measured by means of high impedance analyser and modeled by four types of models: Debye, Cole-Cole, Davidson-Cole and Havriliak-Negami. The fitting errors are presented for all model types. The model parameters are correlated with chemical substances additives and processing which were used during the fibres production. The presented results show that the fractional-order models reflect properly the impedance spectra of fibres samples.

Modern scientific instruments are acquiring data at ever-increasing rates, leading to an exponential increase in the size of data sets. Taking full advantage of these acquisition rates will require corresponding advancements in the speed and efficiency of data analytics and experimental control. A significant step forward would come from automatic decision-making methods that enable scientific instruments to autonomously explore scientific problems—that is, to intelligently explore parameter spaces without human intervention, selecting high-value measurements to perform based on the continually growing experimental data set. Here, we develop such an autonomous decision-making algorithm that is physics-agnostic, generalizable, and operates in an abstract multi-dimensional parameter space. Our approach relies on constructing a surrogate model that fits and interpolates the available experimental data, and is continuously refined as more data is gathered. The distribution and correlation of the data is used to generate a corresponding uncertainty across the surrogate model. By suggesting follow-up measurements in regions of greatest uncertainty, the algorithm maximally increases knowledge with each added measurement. This procedure is applied repeatedly, with the algorithm iteratively reducing model error and thus efficiently sampling the parameter space with each new measurement that it requests. We validate the method using synthetic data, demonstrating that it converges to faithful replica of test functions more rapidly than competing methods, and demonstrate the viability of the approach in an experimental context by using it to direct autonomous small-angle (SAXS) and grazing-incidence small-angle (GISAXS) x-ray scattering experiments.

Cognition has been found to constrain several aspects of human behaviour, such as the number of friends and the number of favourite places a person keeps stable over time. This limitation has been empirically defined in the physical and social spaces. But do people exhibit similar constraints in the digital space? We address this question through the analysis of pseudonymised mobility and mobile application (app) usage data of 400,000 individuals in a European country for six months. Despite the enormous heterogeneity of apps usage, we find that individuals exhibit a conserved capacity that limits the number of applications they regularly use. Moreover, we find that this capacity steadily decreases with age, as does the capacity in the physical space but with more complex dynamics. Even though people might have the same capacity, applications get added and removed over time. In this respect, we identify two profiles of individuals: app keepers and explorers, which differ in their stable (keepers) vs exploratory (explorers) behaviour regarding their use of mobile applications. Finally, we show that the capacity of applications predicts mobility capacity and vice-versa. By contrast, the behaviour of keepers and explorers may considerably vary across the two domains. Our empirical findings provide an intriguing picture linking human behaviour in the physical and digital worlds which bridges research studies from Computer Science, Social Physics and Computational Social Sciences.

Pulmonary hypertension is a fatal disease, however reliable prognostic tools are lacking. Heart rhythm complexity analysis is derived from non-linear heart rate variability (HRV) analysis and has shown excellent performance in predicting clinical outcomes in several cardiovascular diseases. However, heart rhythm complexity has not previously been studied in pulmonary hypertension patients. We prospectively analyzed 57 patients with pulmonary hypertension (31 with pulmonary arterial hypertension and 26 with chronic thromboembolic pulmonary hypertension) and compared them to 57 age- and sex-matched control subjects. Heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) and linear HRV parameters were analyzed. The patients with pulmonary hypertension had significantly lower mean RR, SDRR, pNN20, VLF, LF, LF/HF ratio, DFAα1, MSE slope 5, scale 5, area 1–5 and area 6–20 compared to the controls. Receiver operating characteristic curve analysis showed that heart rhythm complexity parameters were better than traditional HRV parameters to predict pulmonary hypertension. Among all parameters, scale 5 had the greatest power to differentiate the pulmonary hypertension patients from controls (AUC: 0.845, P

Raman spectroscopy shows promise as a tool for timely diagnostics via in-vivo spectroscopy of the eye, for a number of ophthalmic diseases. By measuring the inelastic scattering of light, Raman spectroscopy is able to reveal detailed chemical characteristics, but is an inherently weak effect resulting in noisy complex signal, which is often difficult to analyse. Here, we embraced that noise to develop the self-optimising Kohonen index network (SKiNET), and provide a generic framework for multivariate analysis that simultaneously provides dimensionality reduction, feature extraction and multi-class classification as part of a seamless interface. The method was tested by classification of anatomical ex-vivo eye tissue segments from porcine eyes, yielding an accuracy >93% across 5 tissue types. Unlike traditional packages, the method performs data analysis directly in the web browser through modern web and cloud technologies as an open source extendable web app. The unprecedented accuracy and clarity of the SKiNET methodology has the potential to revolutionise the use of Raman spectroscopy for in-vivo applications.

Many scientific experiments are conducted in space; therefore, it is critical to understand the microgravity environment of a space laboratory. The first Chinese cargo ship, Tianzhou-1 (TZ-1), entered space on 20 April, 2017 and later joined with the Tiangong-2 (TG-2) Chinese space laboratory. TZ-1 carried a high-precision electrostatic suspension accelerometer system (ES-ACC) for measuring the microgravity acceleration on the spacecraft and a microgravity-active vibration system (MAIS), which contained flexible quartz accelerometers (Q-ACC). The ES-ACC was able to provide a reduced-disturbance environment for the MAIS. The purpose of these two instruments was to validate novel technologies and as an opportunity to record the microgravity acceleration of TZ-1 and TG-2 in detail during spacecraft operation in different flight modes, with or without vibration isolation. The acceleration data were analyzed comprehensively in a time–frequency–amplitude spectrogram. Some periodical disturbances with orbital period and irregular signals related to certain in-orbit events were observed. After reducing those disturbances, the microgravity levels on TZ-1 and TG-2 could be resolved to better than 10−6 m/s2 in the root mean square in the frequency of 0.01–10 Hz. These accurate measurements aboard the Chinese space laboratory will provide valuable information to optimize working conditions for scientific experiments.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p

Colon cancer has been well studied using a variety of molecular techniques, including whole genome sequencing. However, genetic markers that could be used to predict lymph node (LN) involvement, which is the most important prognostic factor for colon cancer, have not been identified. In the present study, we compared LN(+) and LN(−) colon cancer patients using differential gene expression and network analysis. Colon cancer gene expression data were obtained from the Cancer Genome Atlas and divided into two groups, LN(+) and LN(−). Gene expression networks were constructed using LASSO (Least Absolute Shrinkage and Selection Operator) regression. We identified hub genes, such as APBB1, AHSA2, ZNF767, and JAK2, that were highly differentially expressed. Survival analysis using selected hub genes, such as AHSA2, CDK10, and CWC22, showed that their expression levels were significantly associated with the survival rate of colon cancer patients, which indicates their possible use as prognostic markers. In addition, protein-protein interaction network, GO enrichment, and KEGG pathway analysis were performed with selected hub genes from each group to investigate the regulatory relationships between hub genes and LN involvement in colon cancer; these analyses revealed differences between the LN(−) and LN(+) groups. Our network analysis may help narrow down the search for novel candidate genes for the treatment of colon cancer, in addition to improving our understanding of the biological processes underlying LN involvement. All R implementation codes are available at journal website as Supplementary Materials.