System-level Data Research Articles

Abnormal Brain Development in Huntington' Disease Is Recapitulated in the zQ175 Knock-In Mouse Model.

Emerging cellular and molecular studies are providing compelling evidence that altered brain development contributes to the pathogenesis of Huntington’s disease (HD). There has been lacking longitudinal system-level data obtained from in vivo HD models supporting this hypothesis. Our human MRI study in children and adolescents with HD indicates that striatal development differs between the HD and control groups, with initial hypertrophy and more rapid volume decline in HD group. In this study, we aimed to determine whether brain development recapitulates the human HD during the postnatal period. Longitudinal structural MRI scans were conducted in the heterozygous zQ175 HD mice and their littermate controls. We found that male zQ175 HD mice recapitulated the region-specific abnormal volume development in the striatum and globus pallidus, with early hypertrophy and then rapidly decline in the regional volume. In contrast, female zQ175 HD mice did not show significant difference in brain volume development with their littermate controls. This is the first longitudinal study of brain volume development at the system level in HD mice. Our results suggest that altered brain development may contribute to the HD pathogenesis. The potential effect of gene therapies targeting on neurodevelopmental event is worth to consider for HD therapeutic intervention.

Communication Networks on Adopter Farmers of Planting Technology at Cigasong Districts in West Java

The process of adopting technology Jarwo, in addition to the influence of external factors, is also influenced by internal factors, namely the structure of communication network. This research is aimed at analysing the structure of communication network and identifying the role of actors in the communication network of the farmers as the adopter of technology Jarwo . This research was conducted in Cigasong Subdistrict, West Java Province. Research method uses the survey method and data is analysed using communication network analysis. Population determination uses a nominalist approach. Sampling technique uses a small group of samples with a population of 131 farmers. Analysis uses a complete networks analysis consisting of three levels of measurement, i.e. actor level, system level and group level and data processing uses UCINET VI. The results of this research showed that: The structure of the communications network for the farmers adopting technology Jarwo has a low level of cohesiveness; the size of the network is relatively large; the level of tissue density is low; network structure is decentralised; and average distance between fellow actors is quite high, and actors who play an important role in event information sources Jarwo is actor #62 (agricultural extension) and #42 who are farmers as betweenness; on Discussion Events Jarwo , actor #235 who is an actor with centrality levels and high betweenness. This shows that the farmers adopting technology Jarwo prefer to discuss technology Jarwo with fellow farmers and informant neighbours. Keywords: C ommunication n etwork, farmers , innovation , agricultural te ch nology , Jajar Legowo.

Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory

This conceptual research presents a new stochastic model of the dynamics of state-to-state transitions in social systems, the Zhukov–Khvatova model. Employing a mathematical approach based on percolation theory the model caters for random changes, system memory and self-organisation. Curves representing the approach of the system to the percolation threshold differ significantly from the smooth S-shaped curves predicted by existing models, showing oscillations, steps and abrupt steep gradients.The modelling approach is new, working with system level parameters, avoiding reference to node-level changes and modelling a non-Markov process by including self-organisation and the effects (memory) of previous system states over a configurable number of time intervals. Computational modelling is used to demonstrate how the percolation threshold (i.e. the share of nodes which allows information to spread freely within the network) is reached.Possible applications of the model discussed include modelling the dynamics of viewpoints in society during social unrest and elections, changing attitudes in social networks and forecasting the outcome of promotions or uptake of campaigns. The easy availability of system level data (network connectivity, evolving system penetration) makes the model a particularly valuable addition to the toolkit for social sciences, politics, and potentially marketing.

Preparing for the Implementation of an Agnis Enabled Data Reporting System and Comprehensive Research Level Data Repository for All Cellular Therapy Patients

Background Dana-Farber Cancer Institute (DFCI) includes both a pediatric and adult transplant program. Though there are many shared resources each program is accredited individually, and they are separate CIBMTR reporting centers. With consistent growth and increasingly demanding reporting requirements, it was determined that one comprehensive internal database that filled the needs of both programs was necessary. Aim/Goals Establish a comprehensive internal database that streamlined multiple unmet needs within two large cellular therapy programs including: • Pre and post therapy case management data • Ability to fulfill all CIBMTR regulatory reporting requirements • A comprehensive research level data repository for all cellular therapy patients Methods • Workgroups were formed to map out the current and future state workflow • Gaps in the workflow and areas of vulnerability and data entry duplication were identified • Testing scripts were developed and data entry into the test environment began • During the testing period end user job aids were created and modified • Entry into historical data systems halted prior to • Electronic and manual data conversion began • Data management staff validated the historical data converted to the new system • End users were trained in preparation of software go-live • Full production database was successfully launched • Throughout this process meetings were held between the pediatric and adult joint working group to share best practices and pinpoint opportunities for collaboration. Conclusions • You don't what you don't know • The amount of time dedicated to rolling out the new system and the magnitude of change that accompanied this was far larger than anyone could anticipate • The time commitment required of team members placed significant constraints on completion of their day-to-day responsibilities • Adapting to the new system with confidence and ease took much longer than originally projected • Data managers shifted pre-transplant data entry to real time, allowing case coordinators and clinical staff to access data during the patient's work-up period • A better understanding of the correlation between the pre and post transplant data was realized Next steps • Continue progress on the work flow mapping project to solidify job roles • Maintain system stability by implementing change control policies • Preserve data integrity with newly developed audit and validation guidelines

Liquid Propulsion System Reliability Estimation using Computational Bayesian Approach with Multi-level Data

Liquid propulsion system (LPS) is a highly reliable and complex system that is used for the military and space applications. It consists of many flight critical components arranged in series configuration. Reliability is the most critical parameter for this system, even one subsystem failure leads to total failure of flight vehicle. Determining the achieved reliability of a LPS is a unique challenge for designer of these systems. The system reliability needs to be estimated with limited number of tests due to the destructive nature of tests, time and cost constraints. In this paper, reliability of LPS was estimated with subsystem test data using computational Bayesian approach. Component level, subsystem level and system level data are considered and a framework is created by combing all information. The reliability of the LPS was calculated using Markov Chain Monte Carlo (MCMC) simulation which has avoided numerical integration. Results are compared with Lindstorm Madden method and Bayesian hybrid method. Computational Bayesian approach can give reasonably better reliability estimate with limited test data.

Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids

The extensive deployment of smart meters in millions of households provides a huge amount of individual electricity consumption data for demand side analysis at a fine granularity. Different from traditional aggregated system-level data, smart meter data is more irregular and unpredictable. As a result, probabilistic load forecasting (PLF), which can provide a better understanding of the uncertainty and volatility in future demand, is critical to constructing energy-efficient and reliable smart grids. In this article, a recently developed technique called Bayesian deep learning is employed to solve this challenging problem. In particular, a novel multitask PLF framework based on Bayesian deep learning is proposed to quantify the shared uncertainties across distinct customer groups while accounting for their differences. Further, a clustering-based pooling method is designed to increase the data diversity and volume for the framework. This not only addresses the problem of overfitting but also improves the predictive performance. Numerical results are presented which demonstrate that the proposed framework provides superior probabilistic forecasting accuracy over conventional methods.

Anti-PD-1 and anti-PD-L1 drugs treatment-related adverse events for patients with cancer: Protocol for an overview of systematic reviews with meta-analyses.

Background:Anti-programmed cell death 1 (PD-1) and anti-programmed cell death ligand 1 (PD-L1) drugs treatment-related adverse events (AEs) are not uniform based on current study for patients with cancer. The study aimed to provide a complete toxicity profile and toxicity spectrum for anti-PD-1 and anti-PD-L1 drugs.Methods:All systematic reviews (SRs) with meta-analyses (MAs) relate to the anti-PD-1 and anti-PD-L1 drugs and SRs will be searched in the database of PubMed, Embase, Cochrane Library, and Web of Science from inception to February 2019. Eligible publications must have reported site, organ, or system level data on treatment-related AEs. The following will extract from each SRs: first author, year of publication, country of origin, number of origin study, number of patients enrolled, participant characteristics, duration of cancer diagnosis, cancer types, detailed description of treatment, and occurrence of AEs. Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) and A Measurements Tool to Assess Systematic Reviews 2 (AMSTAR-2) will be used to assess the reporting and methodological quality of SRs/MAs. The characteristics of the included SRs/MAs and their quality will descriptively summarized using systematically structured tables. A network meta-analysis (NMAs) approach versus a narrative synthesis will be used to examine data synthesis considered. Odds ratios and 95% credibility intervals will be used as summary statistics. Evidence mapping (EM) method will to present the evidence landscape related to anti-PD-1 and anti-PD-L1 drugs treatment-related AEs for patients with cancer.Discussion:The results of the overview will be submitted to a peer-reviewed journal for publication.Ethics and dissemination:Because this study is not a clinical study, and we will search and evaluate only existing sources of literature. So, ethical approval is not required.

CAPE: A cross-layer framework for accurate microprocessor power estimation

State-of-the-art system-level simulators can deliver fast power estimates for microprocessor designs, but often at the expense of reduced accuracy. The inaccuracies mainly stem from incorrect or over-simplified modeling of the target architecture. On the other hand, modern register-transfer level (RTL) simulators are cycle-accurate but overwhelmingly time consuming for most real-life workloads. Consequently, the design community often has to make a compromise between accuracy and speed. In this work, we propose a novel cross-layer power estimation (CAPE) technique that carefully integrates system-level and RTL profiling data for the target design in order to attain better accuracy. Our proposed methodology first leverages the SimPoint tool to transform a workload into weighted simulation points. We, then, present two different strategies to represent the critical segment of an application - either with a workload-specific simulation point (CAPE-WSSP) or, with the highest-weighted simulation point (CAPE-HWSP). Next, we profile the critical simulation point with an RTL simulator for maximum accuracy, while the other simulation points are simulated at system-level for fast evaluation. Finally, we input the integrated set of profiling data to the power simulator (McPAT). Our evaluation results show that CAPE can improve the power estimation accuracy by up to 15% for individual simulation points and by ∼8% for the full application, compared to that of a system-level only simulation scheme while adding minimal runtime overhead.

Politische Systeme und Wählerverhalten: eine Diskussion der international vergleichenden Mehrebenenforschung

A significant body of internationally comparative research has been accumulated over the past decade that seeks a better understanding of electoral behavior by combining survey data on voters with system-level data on their countries in complex multilevel designs. The current paper offers a state-of-the-art review of this rapidly evolving landscape of research, which thus far has been mainly guided by two questions: (i) Which conditions promote voter turnout that is high and more egalitarian, thus giving citizens an equal say in politics? And (ii) Which conditions promote electoral choices that are in line with voters’ own interests, thus enhancing the role of elections as instruments for holding governments to account? While some studies primarily help to consolidate the field of comparative electoral behavior by taking more appropriate methodological approaches, others demonstrate with unprecedented clarity how individuals’ voting behavior is systematically moderated by institutional as well as socioeconomic features of country contexts. Whether and how you select among electoral candidates depends critically on where you live—this is the powerful message of cross-national multilevel research on voting behavior. By identifying important sources of heterogeneity in voters’ decision-making, this line of research profoundly questions the homogeneity assumption that has been a hallmark of electoral studies for decades.

Evidence-based intervention to reduce avoidable hospital admissions in care home residents (the Better Health in Residents in Care Homes (BHiRCH) study): protocol for a pilot cluster randomised trial

IntroductionAcute hospital admission is distressing for care home residents. Ambulatory care sensitive conditions, such as respiratory and urinary tract infections, are conditions that can cause unplanned hospital admission but may...

Production Application Performance Data Streaming for System Monitoring

In this article, we present an approach to streaming collection of application performance data. Practical application performance tuning and troubleshooting in production high-performance computing (HPC) environments requires an understanding of how applications interact with the platform, including (but not limited to) parallel programming libraries such as Message Passing Interface (MPI). Several profiling and tracing tools exist that collect heavy runtime data traces either in memory (released only at application exit) or on a file system (imposing an I/O load that may interfere with the performance being measured). Although these approaches are beneficial in development stages and post-run analysis, a systemwide and low-overhead method is required to monitor deployed applications continuously. This method must be able to collect information at both the application and system levels to yield a complete performance picture.In our approach, an application profiler collects application event counters. A sampler uses an efficient inter-process communication method to periodically extract the application counters and stream them into an infrastructure for performance data collection. We implement a tool-set based on our approach and integrate it with the Lightweight Distributed Metric Service (LDMS) system, a monitoring system used on large-scale computational platforms. LDMS provides the infrastructure to create and gather streams of performance data in a low overhead manner. We demonstrate our approach using applications implemented with MPI, as it is one of the most common standards for the development of large-scale scientific applications.We utilize our tool-set to study the impact of our approach on an open source HPC application, Nalu. Our tool-set enables us to efficiently identify patterns in the behavior of the application without source-level knowledge. We leverage LDMS to collect system-level performance data and explore the correlation between the system and application events. Also, we demonstrate how our tool-set can help detect anomalies with a low latency. We run tests on two different architectures: a system enabled with Intel Xeon Phi and another system equipped with Intel Xeon processor. Our overhead study shows our method imposes at most 0.5% CPU usage overhead on the application in realistic deployment scenarios.

Crowding in the emergency department in the absence of boarding \u2013 a transition regression model to predict departures and waiting time

BackgroundCrowding in the emergency department (ED) is associated with increased mortality, increased treatment cost, and reduced quality of care. Crowding arises when demand exceed resources in the ED and a first sign may be increasing waiting time. We aimed to quantify predictors for departure from the ED, and relate this to waiting time in the ED before departure.MethodsWe utilised administrative data from the ED and calculated number of arrivals, departures, and the resulting queue in 30 min time steps for all of 2013 (N = 17,520). We build a transition model for each time step using the number of past departures and pre-specified risk factors (arrivals, weekday/weekend and shift) to predict the expected number of departures and from this the expected waiting time in the ED. The model was validated with data from the same ED collected March through August 2014.ResultsWe found that the number of arrivals had the greatest independent impact on departures with an odds ratio of 0.942 (95%CI: 0.937;0.948) corresponding to additional 7 min waiting time per new arrival in a 30 min time interval with an a priori time spend in the ED of two hours. The serial correlation of departures was present up to one and a half hour previous but had very little effect on the estimates of the risk factors. Boarding played a negligible role in the studied ED.ConclusionsWe present a transition regression model with high predictive power to predict departures from the ED utilising only system level data. We use this to present estimates of expected waiting time and ultimately crowding in the ED. The model shows good internal validity though further studies are needed to determine generalisability to the performance in other settings.

Collecting core data in physician-staffed pre-hospital helicopter emergency medical services using a consensus-based template: international multicentre feasibility study in Finland and Norway

BackgroundComparison of services and identification of factors important for favourable patient outcomes in emergency medical services (EMS) is challenging due to different organization and quality of data. The purpose of the present study was to evaluate the feasibility of physician-staffed EMS (p-EMS) to collect patient and system level data by using a consensus-based template.MethodsThe study was an international multicentre observational study. Data were collected according to a template for uniform reporting of data from p-EMS using two different data collection methods; a standard and a focused data collection method. For the standard data collection, data were extracted retrospectively for one year from all FinnHEMS bases and for the focused data collection, data were collected prospectively for six weeks from four selected Norwegian p-EMS bases. Completeness rates for the two data collection methods were then compared and factors affecting completeness rates and template feasibility were evaluated. Standard Chi-Square, Fisher’s Exact Test and Mann-Whitney U Test were used for group comparison of categorical and continuous data, respectively, and Kolomogorov-Smirnov test for comparison of distributional properties.ResultsAll missions with patient encounters were included, leaving 4437 Finnish and 128 Norwegian missions eligible for analysis. Variable completeness rates indicated that physiological variables were least documented. Information on pain and respiratory rate were the most frequently missing variables with a standard data collection method and systolic blood pressure was the most missing variable with a focused data collection method. Completeness rates were similar or higher when patients were considered severely ill or injured but were lower for missions with short patient encounter. When a focused data collection method was used, completeness rates were higher compared to a standard data collection method.ConclusionsWe found that a focused data collection method increased data capture compared to a standard data collection method. The concept of using a template for documentation of p-EMS data is feasible in physician-staffed services in Finland and Norway. The greatest deficiencies in completeness rates were evident for physiological parameters. Short missions were associated with lower completeness rates whereas severe illness or injury did not result in reduced data capture.

Abstract TP487: Data Dashboard: Making the Electronic Medical Record Work for You

Background: Stroke care is becoming increasingly complex in the 21st century. In order to assure optimal patient outcomes, our health network developed a stroke care pathway based on evidence-based practices and expert consensus. Overall goals for the project include increased adherence to practice standards and defined process metrics. This project is part of a larger institutional initiative for specific disease states. An integral aspect of the project was the development and implementation of a dashboard allowing for a global view of the stroke population as well as the ability to drill down to the patient or provider level. Purpose: The purpose of the dashboard is to use data to evaluate trends and develop data driven interventions. This tool is available to key stakeholders including administration, quality, program leadership and care providers. Methods: The pathway initiative is a collaboration between quality, clinical, and data analytic teams. It used a multidisciplinary approach and included input from services throughout the care continuum. Clinical pathways for stroke alert, reperfusion, neurointerventional care, and nursing care were developed and recently launched. The dashboard auto-populates from discreet fields within the electronic medical record. It is an informational tool that allows for data transparency through the use of interactive charts and graphs which provides temporal trending. Results: Utilization of this approach supports system level data trending and analysis. Preliminary results demonstrate improved provider engagement, reduction in variability in several care processes, and data transparency through accessible real-time reporting. Conclusions: Disease specific dashboards using automatic data feeds from the electronic medical record allow for evaluation of program efficiencies, population trends, and provider level data without additional manual abstraction or analysis. Evaluating data in real time allows for identification of variations in practice, real-time interventions targeting identified variances, as well as determining the effectiveness of interventions on quality and cost of care.

Unknown Vulnerability Risk Assessment Based on Directed Graph Models: A Survey

Nowadays, vulnerability attacks occur frequently. Due to the information asymmetry between attackers and defenders, vulnerabilities can be divided into known and unknown. Existing researches mainly focus on the risk assessment of known vulnerabilities. However, unknown vulnerabilities are more threatening and harder to detect. Therefore, unknown vulnerability risk assessment deserves the widespread attention. To model the exploit process, directed graph models are applied to vulnerability risk assessment. And security metrics are used to quantify the exploitability of vulnerabilities. In this paper, according to the data source of nodes, related works of unknown vulnerability risk assessment based on directed graph models are divided into two types. One is based on network-level data, the other is based on system-level data. The former is to visualize the network status, while the latter is to reflect the running process of the system. The concept and purpose of these directed graph models are given at first. Then, these models are analyzed from three aspects, including advantages, flaws and solutions. After that, challenges and solutions of unknown vulnerability risk assessment based on directed graph models are given. Meantime, security metrics for unknown vulnerability risk assessment based on directed graph models are summarized and classified. Finally, future work directions of unknown vulnerability risk assessment are discussed from the perspective of techniques and application trends. Consequently, this paper can fill in the lack of current survey on unknown vulnerability risk assessment based on directed graph models.

Testosterone levels and sexual function in drug - naïve first – episode nonaffective psychosis patients compared with healthy controls

State-of-the-art system-level simulators can deliver fast power estimates for microprocessor designs, but often at the expense of reduced accuracy. The inaccuracies mainly stem from incorrect or over-simplified modeling of the target architecture. On the other hand, modern register-transfer level (RTL) simulators are cycle-accurate but overwhelmingly time consuming for most real-life workloads. Consequently, the design community often has to make a compromise between accuracy and speed. In this work, we propose a novel cross-layer power estimation (CAPE) technique that carefully integrates system-level and RTL profiling data for the target design in order to attain better accuracy. Our proposed methodology first leverages the SimPoint tool to transform a workload into weighted simulation points. We, then, present two different strategies to represent the critical segment of an application - either with a workload-specific simulation point (CAPE-WSSP) or, with the highest-weighted simulation point (CAPE-HWSP). Next, we profile the critical simulation point with an RTL simulator for maximum accuracy, while the other simulation points are simulated at system-level for fast evaluation. Finally, we input the integrated set of profiling data to the power simulator (McPAT). Our evaluation results show that CAPE can improve the power estimation accuracy by up to 15% for individual simulation points and by ∼8% for the full application, compared to that of a system-level only simulation scheme while adding minimal runtime overhead.

Private Data Acquisition Method Based on System-Level Data Migration and Volatile Memory Forensics for Android Applications

Nowadays, many Android applications enable data encryption to protect the security of private data, making it difficult for investigators to access the clear data even if they have already obtained the application database. Volatile memory dynamically presents the current state of applications and OS, which contains a store of valuable information including the plain text of application data, and it is a significant analysis object in the field of digital forensics. Over the past decade, some forensics researchers have proposed a number of volatile memory acquisition methods for Android mobile devices and have made valuable contributions. However, most of the existing methods are subjecting to severe restrictions in real investigation environment and only can be applied to pre-prepared devices, resulting in these methods are impractical. In order to address this problem, this paper proposes an Android application memory data acquisition method, called PASM, which can be applied to unprepared Android devices. PASM makes use of system-level data migration function provided by Android manufacturers to migrate and load the application private data into an intermediate device. The intermediate device is pre-flashed with a custom kernel providing the function of volatile memory forensics, so that the application private data can be acquired from the volatile memory of the intermediate device. We select thirty privacy-sensitive applications as the test objects and build seven different experiment scenarios to acquire the private data stored in memory image dumped by PASM. The experiment results show that PASM is able to acquire part of private data stored in volatile memory, and more importantly, PASM has the ability to overcome most of the limitations in the real world, which is more practical than existing Android memory acquisition methods.

System-Level Approach for Identifying Main Uncertainty Sources in Pavement Construction Life-Cycle Assessment for Quantifying Environmental Impacts

Poor data quality in pavement construction life-cycle inventory (LCI) causes uncertainty in quantifying the associated environmental impact through life-cycle assessment (LCA). To reduce such LCA uncertainty while enhancing the reliability, several studies have been conducted on a screening procedure based on a quality assessment of the LCI input data to identify main sources of the resulting uncertainty. However, they often create additional uncertainty in the screening process and thus result in erroneous outcomes in identifying main uncertainty sources. This paper proposes a new system-level approach that enables the identification of main uncertainty sources through input data quality assessment upon reducing additional uncertainty. Based on the proposed preset criteria and by leveraging environmental emission quantities associated with each process, the authors first propose to achieve a consistent weighting process and then derive the system-level aggregated data quality indicator (ADQI). By utilizing the ADQI, system-level LCA uncertainty information is obtained through a modified beta distribution. The proposed method was evaluated through case studies on real-world pavement construction projects of the Illinois Tollway, and the main uncertainty sources, named key processes, were identified through sensitivity analyses. In the case studies, the plant operation, cement production, and binder production were identified as key processes in the given pavement construction project, contributing more than half of the total uncertainty resulting from poor data quality. Based on these findings, the proposed work is expected to help practitioners improve the reliability of pavement construction LCA through uncertainty-informed decision making to better reflect real project characteristics in the identified key processes.

Comparative safety of immune checkpoint inhibitors in cancer: systematic review and network meta-analysis

ObjectiveTo provide a complete toxicity profile, toxicity spectrum, and a safety ranking of immune checkpoint inhibitor (ICI) drugs for treatment of cancer.DesignSystematic review and network meta-analysis.Data sourcesElectronic databases (PubMed, Embase,...

CBVMI: A container-based virtualization method for instruments

Intelligent instruments, which dominate the network test system, typically have certain redundant resources even when the test task has ongoing operations. Thus, if these surplus resources can be utilized by the centralized manager and the test task scheduler to conduct test data pre-processing or system-level test data processing, the performance and efficiency of the test system could be greatly improved.This paper proposed a novel method called CBVMI, a Container-Based Virtualization Method for Instruments, which can make full use of these surplus resources of intelligent instruments. Meanwhile, it can achieve effective isolation between virtual resources to avoid interference of test tasks while maintaining necessary network interconnection among virtual resources. Furthermore, CBVMI provides one method for the network bandwidth allocation, and ultimately utilizes the network bandwidth to achieve efficient information transmission among virtual resources. To the best of our knowledge, this is the first work to introduce virtualization techniques into the research of intelligent instruments. The proposed method was evaluated on a real physical platform. The experimental results show that CBVMI can efficiently improve the utilization of system resources.