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Traditionally, journal subject classification was done manually at varying levels of granularity, depending on the use case for the institution. Subject classification is done to help collate resources by subject enabling the user to discover publications based on different levels of subject specificity. It can also be used to help determine where to publish and the direction a particular author may be pursuing in their research if one wants to track where their work is being published. Currentl

Seamless Internet access under extreme user mobility is highly demanded on high-speed trains and vehicles. However, existing mobile networks (e.g., 4G LTE and 5G NR) cannot reliably satisfy this demand, with a 5.5%-12.6% handover failure ratio at 200–350 km/h. A root cause is that, the 4G/5G handovers have to balance the exploration of more measurements for satisfactory handover and the exploitation for timely handover before the fast-moving user leaves the coverage.We design BaTT, an online learning solution for reliable handovers in extreme mobility. BaTT decomposes the explorationexploitation tradeoff into two multi-armed bandit problems. It uses ϵ-binary-search to optimize the threshold of a serving cell’s signal strength to initiate the handover with $\mathcal{O}(\log J\log T)$ regrets. It further adopts opportunistic Thompson sampling to optimize the sequence of target cells measured for reliable handovers. BaTT can be implemented using the recent Open Radio Access Network (O-RAN) framework in operational 4G LTE and 5G NR. Our evaluations over a dataset from operational LTE networks on the Chinese high-speed rails show a 29.1% handover failure reduction at the speed of 200-350 km/h.

Computer and communication applications such as big data analytics services, social networks, enterprise business and mobile applications often require to simultaneously pass data of different sizes between multiple source and multiple destinations in the network. In practice, the nodes in the communication networks are often connected through multiple intermediate links of different types with varying failure probabilities. Failure of intermediate links can adversely affect data transmission between source and destination nodes; thus, they can impact the quality of service. Therefore, reliability evaluation of such networks is of subtle importance in today’s service dependent world. This article presents an efficient method for reliability evaluation of stochastic flow networks that can pass various demands simultaneously from multiple source nodes to multiple destination nodes. The proposed method has three steps: First step obtains the combined minimal cut sets between the given set of source and destination nodes. Second step generates the set of simultaneous upper boundary flows for the varying demands using the combined minimal cut sets. Third step calculates the network unreliability by applying the Sum of Disjoint Product method on the upper boundary flows. The reliability of the network, i.e., the probability that the network can simultaneously pass the set of demands, is calculated as 1-Unreliability. The MATLAB simulation of the proposed method on bench mark networks show that the proposed approach takes less computational time than the existing method.

Computer and telecommunication networks are stochastic in nature, as each node and arc may have multiple capacity states besides complete failure. Various two-terminal reliability estimation algorithms for such stochastic flow networks are available in literature. These algorithms generate d-minimal cuts from minimal cut-sets of the network, where d is required demand. Different techniques are available in literature to evaluate exact reliability from such d-minimal cuts such as the recursive inclusion–exclusion method. The recursive inclusion–exclusion method has certain redundant computations while evaluating network reliability. This paper proposes a sum of disjoint products technique to minimize redundant computations in exact reliability computation from flow vectors. MATLAB simulation is performed to evaluate the performance of the proposed method and compare it with the existing methods for benchmark networks available in literature. Simulation results show that the proposed method require lesser computational efforts and memory.

Purpose To explore (i) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1-weighted scans related to gradient nonlinearity (GNL) and subject positioning; (ii) the effect of vendor-implemented GNL corrections; and (iii) easily applicable methods that can be used to retrospectively correct data. Methods A multiple sclerosis patient was scanned at seven sites using 3T MRI scanners with the same 3D T1-weighted protocol without GNL-distortion correction. Two healthy subjects and a phantom were additionally scanned at a single site with varying table positions. The 2D and 3D vendor-implemented GNL-correction algorithms and retrospective methods based on (i) phantom data fit, (ii) normalization with C2 vertebral body diameters, and (iii) the Jacobian determinant of nonlinear registrations to a template were tested. Results Depending on the positioning of the subject, GNL introduced up to 15% variability in UCCA measurements from volumetric brain T1-weighted scans when no distortion corrections were used. The 3D vendor-implemented correction methods and the three proposed methods reduced this variability to less than 3%. Conclusions Our results raise awareness of the significant impact that GNL can have on quantitative UCCA studies, and point the way to prospectively and retrospectively managing GNL distortions in a variety of settings, including clinical environments. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Objective Fully or partially automated spinal cord gray matter segmentation techniques for spinal cord gray matter segmentation will allow for pivotal spinal cord gray matter measurements in the study of various neurological disorders. The objective of this work was multi-fold: (1) to develop a gray matter segmentation technique that uses registration methods with an existing delineation of the cord edge along with Morphological Geodesic Active Contour (MGAC) models; (2) to assess the accuracy and reproducibility of the newly developed technique on 2D PSIR T1 weighted images; (3) to test how the algorithm performs on different resolutions and other contrasts; (4) to demonstrate how the algorithm can be extended to 3D scans; and (5) to show the clinical potential for multiple sclerosis patients. Methods The MGAC algorithm was developed using a publicly available implementation of a morphological geodesic active contour model and the spinal cord segmentation tool of the software Jim (Xinapse Systems) for initial estimate of the cord boundary. The MGAC algorithm was demonstrated on 2D PSIR images of the C2/C3 level with two different resolutions, 2D T2* weighted images of the C2/C3 level, and a 3D PSIR image. These images were acquired from 45 healthy controls and 58 multiple sclerosis patients selected for the absence of evident lesions at the C2/C3 level. Accuracy was assessed though visual assessment, Hausdorff distances, and Dice similarity coefficients. Reproducibility was assessed through interclass correlation coefficients. Validity was assessed through comparison of segmented gray matter areas in images with different resolution for both manual and MGAC segmentations. Results Between MGAC and manual segmentations in healthy controls, the mean Dice similarity coefficient was 0.88 (0.82–0.93) and the mean Hausdorff distance was 0.61 (0.46–0.76) mm. The interclass correlation coefficient from test and retest scans of healthy controls was 0.88. The percent change between the manual segmentations from high and low-resolution images was 25%, while the percent change between the MGAC segmentations from high and low resolution images was 13%. Between MGAC and manual segmentations in MS patients, the average Dice similarity coefficient was 0.86 (0.8–0.92) and the average Hausdorff distance was 0.83 (0.29–1.37) mm. Conclusion We demonstrate that an automatic segmentation technique, based on a morphometric geodesic active contours algorithm, can provide accurate and precise spinal cord gray matter segmentations on 2D PSIR images. We have also shown how this automated technique can potentially be extended to other imaging protocols.

IMPORTANCE: Primary care is increasingly delivered at or near workplaces, yet utilization and cost of employer-sponsored primary care services remain unknown. OBJECTIVE: To compare the health care utilization and cost of an employer-sponsored on-site, near-site, and virtual comprehensive primary care service delivery model with those of traditional community-based primary care. DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study of 23 518 commercially insured employees and dependents of an engineering and manufacturing firm headquartered in southern California was performed from January 1, 2016, to July 1, 2019. A subset of the population with most (≥50%) primary care visits through employer-sponsored on-site, near-site, or virtual care clinics was matched to a subset not having most such visits through the employer-sponsored clinics using propensity score matching (n = 1983 each). In sensitivity analyses, employees were matched to dependents at neighboring firms that lacked access to the employer-sponsored primary care delivery model (additional n = 1680). EXPOSURES: Integrated primary care, mental health, and physical therapy delivered through on-site, near-site, and virtual clinics. MAIN OUTCOMES AND MEASURES: Utilization (inpatient, outpatient, emergency department, pharmaceutical, radiology, and laboratory visits per 1000 member-months) and spending (2019 costs per member per month in US dollars) by service type. RESULTS: A total of 23 518 individuals (mean [SD] age, 27 [15] years; 14 604 [62.1%] male) were included in the full population sample and had been enrolled in the employer-sponsored health plan for a mean of 29 months (interquartile range, 14-48 months). Of eligible members, 5292 (22.5%) used the employer-sponsored services, with 2305 (9.8%) using them for most of their primary care. The mean (SD) cost of employer-sponsored service delivery was $87 ($32) per member month. Among the matched populations (mean [SD] age, 31 [11] years; 3349 [84.5%] male) of primary users vs control individuals, total spending was 45% lower per member per month (95% CI, 35%-55%; cost difference, −$167 per member per month; 95% CI, −$204 to −$130; P

Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study.

PURPOSE: To explore: (1) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1-weighted scans related to gradient nonlinearity (GNL) and subject positioning; (2) the effect of vendor-implemented GNL-corrections; and (3) easily applicable methods that can be used to retrospectively correct data. METHODS: A multiple sclerosis patient was scanned at 7 sites using 3T MRI scanners with the same 3D T1-weighted protocol without GNL-distortion correction. Two healthy subjects and a phantom were additionally scanned at a single site with varying table positions. 2D and 3D vendor-implemented GNL-correction algorithms and retrospective methods based on: (1) phantom data fit; (2) normalization with C2 vertebral body diameters; and (3) the Jacobian determinant of non-linear registrations to a template, were tested. RESULTS: Depending on positioning of the subject, GNL introduced up to 15% variability in UCCA measurements from volumetric brain T1-weighted scans when no distortion corrections were utilized. 3D vendor-implemented correction methods and the three proposed methods reduced this variability to less than 3%. CONCLUSION: Our results raise awareness of the significant impact that GNL can have on quantitative UCCA studies and point the way to prospectively and retrospectively managing GNL-distortions in a variety of settings, including clinical environments.

We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine-that is, the application of information technology to medicine-has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real-time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence-based medicine, support shared decision making, and ultimately lead to improved outcomes.

Importance Although multiple HLA alleles associated with multiple sclerosis (MS) risk have been identified, genotype-phenotype studies in the HLA region remain scarce and inconclusive. Objectives To investigate whether MS risk-associated HLA alleles also affect disease phenotypes. Design, Setting, and Participants A cross-sectional, case-control study comprising 652 patients with MS who had comprehensive phenotypic information and 455 individuals of European origin serving as controls was conducted at a single academic research site. Patients evaluated at the Multiple Sclerosis Center at University of California, San Francisco between July 2004 and September 2005 were invited to participate. Spinal cord imaging in the data set was acquired between July 2013 and March 2014; analysis was performed between December 2014 and December 2015. Main Outcomes and Measures Cumulative HLA genetic burden (HLAGB) calculated using the most updated MS-associated HLA alleles vs clinical and magnetic resonance imaging outcomes, including age at onset, disease severity, conversion time from clinically isolated syndrome to clinically definite MS, fractions of cortical and subcortical gray matter and cerebral white matter, brain lesion volume, spinal cord gray and white matter areas, upper cervical cord area, and the ratio of gray matter to the upper cervical cord area. Multivariate modeling was applied separately for each sex data set. Results Of the 652 patients with MS, 586 had no missing genetic data and were included in the HLAGB analysis. In these 586 patients (404 women [68.9%]; mean [SD] age at disease onset, 33.6 [9.4] years), HLAGB was higher than in controls (median [IQR], 0.7 [0-1.4] and 0 [−0.3 to 0.5], respectively; P = 1.8 × 10 −27 ). A total of 619 (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. No significant difference was observed between relapsing-onset MS and primary progressive MS. A higher HLAGB was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women with relapsing-onset MS (standard β = −1.20 × 10 −1 ; P = 1.7 × 10 −2 and standard β = −1.67 × 10 −1 ; P = 2.3 × 10 −4 , respectively), which were driven mainly by the HLA-DRB1*15:01 haplotype. In addition, we observed the distinct role of the HLA-A*24:02 - B*07:02 - DRB1*15:01 haplotype among the other common DRB1*15:01 haplotypes and a nominally protective effect of HLA-B*44:02 to the subcortical gray atrophy (standard β = −1.28 × 10 −1 ; P = 5.1 × 10 −3 and standard β = 9.52 × 10 −2 ; P = 3.6 × 10 −2 , respectively). Conclusions and Relevance We confirm and extend previous observations linking HLA MS susceptibility alleles with disease progression and specific clinical and magnetic resonance imaging phenotypic traits.

Flow networks in which each component is associated with multiple capacities are known as Stochastic flow networks. These networks are prone to partial or complete failures and are often subjected to unavailability. To estimate two-terminal reliability for such stochastic flow networks several algorithms exists in literature. Most of these algorithms takes huge computational time to compute network reliability even for moderate sized networks. The known efficient cutset based stochastic flow network reliability estimation algorithm developed till date can be presented as follows (1) Maximal flows for demand d, of the network are generated using the minimal cuts and capacities of the components. (2) Removes the non-maximal flows to obtain the set of upper boundary flows for demand d. (3) Determines unreliability from the upper boundary flows from which reliability is calculated as 1-unreliability of the network. In this work, we present an algorithm that preorders the minimal cuts and calculates the network unreliability using the ordered minimal cuts. Our algorithm reduces the computational time compared to the existing algorithm. We provide an example to illustrate the proposed method. Matlab simulation is performed to compare the proposed method with the existing methods using the standard benchmark networks available in literature. Simulation results show that the proposed method takes lesser computation time and memory.

Mindcontrol is an open-source web-based dashboard to quality control and curate neuroimaging data. At Neurohackweek 2016, a group assembled to add new features to the Mindcontrol interface. Contributors used Python, Javascript, and Git to configure Mindcontrol for the ABIDE and CoRR open datasets, and add new types of plots to the interface. All contributions are freely available online, and the code is being actively maintained at http://www.github.com/akeshavan/mindcontrol.

Cloud computing brings in a lot of advantages for enterprise IT infrastructure; virtualization technology, which is the backbone of cloud, provides easy consolidation of resources, reduction of cost, space and management efforts. However, security of critical and private data is a major concern which still keeps back a lot of customers from switching over from their traditional in-house IT infrastructure to a cloud service. Existence of techniques to physically locate a virtual machine in the cloud, proliferation of software vulnerability exploits and cross-channel attacks in-between virtual machines, all of these together increases the risk of business data leaks and privacy losses. This work proposes a framework to mitigate such risks and engineer customer trust towards enterprise cloud computing. Everyday new vulnerabilities are being discovered even in well-engineered software products and the hacking techniques are getting sophisticated over time. In this scenario, absolute guarantee of security in enterprise wide information processing system seems a remote possibility; software systems in the cloud are vulnerable to security attacks. Practical solution for the security problems lies in well-engineered attack mitigation plan. At the positive side, cloud computing has a collective infrastructure which can be effectively used to mitigate the attacks if an appropriate defense framework is in place. We propose such an attack mitigation framework for the cloud. Software vulnerabilities in the cloud have different severities and different impacts on the security parameters (confidentiality, integrity, and availability). By using Markov model, we continuously monitor and quantify the risk of compromise in different security parameters (e.g.: change in the potential to compromise the data confidentiality). Whenever, there is a significant change in risk, our framework would facilitate the tenants to calculate the Mean Time to Security Failure (MTTSF) cloud and allow them to adopt a dynamic mitigation plan. This framework is an add-on security layer in the cloud resource manager and it could improve the customer trust on enterprise cloud solutions.