Uniformly high order accurate essentially non-oscillatory schemes 3

In this paper (a third in a series) the construction and the analysis of essentially non-oscillatory shock capturing methods for the approximation of hyperbolic conservation laws are presented. Also presented is a hierarchy of high order accurate schemes which generalizes Godunov's scheme and its second order accurate MUSCL extension to arbitrary order of accuracy. The design involves an essentially non-oscillatory piecewise polynomial reconstruction of the solution from its cell averages, time evolution through an approximate solution of the resulting initial value problem, and averaging of this approximate solution over each cell. The reconstruction algorithm is derived from a new interpolation technique that when applied to piecewise smooth data gives high-order accuracy whenever the function is smooth but avoids a Gibbs phenomenon at discontinuities. Unlike standard finite difference methods this procedure uses an adaptive stencil of grid points and consequently the resulting schemes are highly nonlinear

Motor symptoms in Parkinson's disease: A unified framework

Parkinson’s disease (PD) is characterized by a range of motor symptoms. Besides the cardinal symptoms (akinesia and bradykinesia, tremor and rigidity), PD patients show additional motor deficits, including: gait disturbance, impaired handwriting, grip force and speech deficits, among others. Some of these motor symptoms (e.g., deficits of gait, speech, and handwriting) have similar clinical profiles, neural substrates, and respond similarly to dopaminergic medication and deep brain stimulation (DBS). Here, we provide an extensive review of the clinical characteristics and neural substrates of each of these motor symptoms, to highlight precisely how PD and its medical and surgical treatments impact motor symptoms. In conclusion, we offer a unified framework for understanding the range of motor symptoms in PD. We argue that various motor symptoms in PD reflect dysfunction of neural structures responsible for action selection, motor sequencing, and coordination and execution of movement

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale

Topographical Response of Retinal Neovascularization to Aflibercept or Panretinal Photocoagulation in Proliferative Diabetic Retinopathy Post Hoc Analysis of the CLARITY Randomized Clinical Trial

Importance Eyes with proliferative diabetic retinopathy have a variable response to treatment with panretinal photocoagulation (PRP) or anti–vascular endothelial growth factor agents. The location of neovascularization (NV) is associated with outcomes (eg, patients with disc NV [NVD] have poorer visual prognosis than those with NV elsewhere [NVE]). Objective To investigate the distribution of NV in patients with proliferative diabetic retinopathy and the topographical response of NV to treatment with aflibercept or PRP. Design, Setting, and Participants This post hoc analysis of the phase 2b randomized clinical single-masked multicenter noninferiority Clinical Efficacy and Mechanistic Evaluation of Aflibercept for Proliferative Diabetic Retinopathy (CLARITY) trial was conducted from November 1, 2019, to September 1, 2020, among 120 treatment-naive patients with proliferative diabetic retinopathy to evaluate the topography of NVD and NVE in 4 quadrants of the retina on color fundus photography at baseline and at 12 and 52 weeks after treatment. Exposures In the CLARITY trial, patients were randomized to receive intravitreal aflibercept (2 mg/0.05 mL at baseline, 4 weeks, and 8 weeks, and as needed from 12 weeks onward) or PRP (completed in initial fractionated sessions and then on an as-needed basis when reviewed every 8 weeks). Main Outcomes and Measures Main outcomes were per-retinal quadrant frequencies of NV at baseline and frequencies of patterns of regression, recurrence, and new occurrence at 12-week and 52-week unmasked follow-up. Results The study included 120 treatment-naive patients (75 men; mean [SD] age, 54.8 [14.6] years) with proliferative diabetic retinopathy (there was a 1:1 ratio of eyes to patients). At baseline, NVD with or without NVE was observed in 42 eyes (35.0%), and NVE only was found in 78 eyes (65.0%); NVE had a predilection for the nasal quadrant (64 [53.3%]). Rates of regression with treatment were higher among eyes with NVE (89 of 102 [87.3%]) compared with eyes with NVD (23 of 43 [53.5%]) by 52 weeks, with NVD being more resistant to either treatment with higher rates of persistence than NVE (20 of 39 [51.3%] vs 29 of 100 [29.0%]). Considering NVE, the regression rate in the temporal quadrant was lowest (32 of 42 [76.2%]). Eyes treated with aflibercept showed higher rates of regression of NVE compared with those treated with PRP (50 of 52 [96.2%] vs 39 of 50 [78.0%]; difference, 18.2% [95% CI, 5.5%-30.8%]; P = .01), but no difference was found for NVD (11 of 17 [64.7%] vs 12 of 26 [46.2%]; difference, 18.6% [95% CI, −11.2% to 48.3%]; P = .23). Conclusions and Relevance This post hoc analysis found that NVD is less frequent but is associated with more resistance to currently available treatments than NVE. Aflibercept was superior to PRP for treating NVE, but neither treatment was particularly effective against NVD by 52 weeks. Future treatments are needed to better target NVD, which has poorer visual prognosis. Trial Registration isrctn.org Identifier: ISRCTN3220758

A multi-step nucleation process determines the kinetics of prion-like domain phase separation

The nucleation mechanisms of biological protein phase separation are poorly understood. Here, the authors perform time-resolved SAXS experiments with the low-complexity domain (LCD) of hnRNPA1 and uncover multiple kinetic regimes on the micro- to millisecond timescale. Initially, individual proteins collapse. Nucleation then occurs via two steps distinguished by their protein cluster size distributions

Virus-induced gene silencing database for phenomics and functional genomics in Nicotiana benthamiana

Virus-induced gene silencing (VIGS) is an important forward and reverse genetics method for the study of gene function in many plant species, especially Nicotiana benthamiana. However, despite the widespread use of VIGS, a searchable database compiling the phenotypes observed with this method is lacking. Such a database would allow researchers to know the phenotype associated with the silencing of a large number of individual genes without experimentation. We have developed a VIGS phenomics and functional genomics database (VPGD) that has DNA sequence information derived from over 4,000 N. benthamiana VIGS clones along with the associated silencing phenotype for approximately 1,300 genes. The VPGD has a built-in BLAST search feature that provides silencing phenotype information of specific genes. In addition, a keyword-based search function could be used to find a specific phenotype of interest with the corresponding gene, including its Gene Ontology descriptions. Query gene sequences from other plant species that have not been used for VIGS can also be searched for their homologs and silencing phenotype in N. benthamiana. VPGD is useful for identifying gene function not only in N. benthamiana but also in related Solanaceae plants such as tomato and potato. The database is accessible at http://vigs.noble.org.Noble Research Institute and NSF IOS-102564

A batch-service queueing model with a discrete batch Markovian arrival process

Queueing systems with batch service have been investigated extensively during the past decades. However, nearly all the studied models share the common feature that an uncorrelated arrival process is considered, which is unrealistic in several real-life situations. In this paper, we study a discrete-time queueing model, with a server that only initiates service when the amount of customers in system (system content) reaches or exceeds a threshold. Correlation is taken into account by assuming a discrete batch Markovian arrival process (D-BMAP), i.e. the distribution of the number of customer arrivals per slot depends on a background state which is determined by a first-order Markov chain. We deduce the probability generating function of the system content at random slot marks and we examine the influence of correlation in the arrival process on the behavior of the system. We show that correlation merely has a small impact on the threshold that minimizes the mean system content. In addition, we demonstrate that correlation might have a significant influence on the system content and therefore has to be included in the model

ICON: A System for Implementing Constraints in Object-based Networks

In today's Network Management scenario, the network operator's interface to the network is through a Management Information Base (MIB). The MIB stores all management related data such as configuration information, performance statistics, and trouble logs and so on. Configuration management, which is at the core of network management, is implemented through the MIB in a three step process: making updates to MIB data elements, checking the validity of the updates, propagating the effects of the updates to the network elements. While all three steps need to be executed efficiently for the MIB to serve its intended goal, the second step of checking update validity is especially important from the management viewpoint. For example, if an operator mistakenly configures a ninth port on an eight port card, it is essential that the MIB should both detect and prevent this error. Allowing such operations to go through would have adverse impact on the performance of the network (since it increases the network management traffic). Therefore, we focus primarily on the problem of checking the validity of updates to MIB data elements, which can be viewed as a specific instance of the general problem of constraint management in database systems. We introduce the design of ICON (Implementing Constraints in Object-based Networks), a proposed constraint management system. In ICON, constrains are expressed through rules. Each rule is composed of an event, a condition, and an action. Occurrence of the event triggers the rule, the condition is a boolean check, and the action is executed if the condition is satisfied. Rules and events are also treated as objects so that they can be created, modified, and deleted like other objects, thus providing a uniform view of rules and events in an OO context. The OO paradigm results in an extensible and a reusable system. To our knowledge, not much work has been done in this area and this paper would trigger further research in this area

Sustainable and Reliable Healthcare Automation and Digitization using Machine Learning Techniques

Healthcare 4.0 takes significant benefits while aligned with Industry 4.0. Mainly citing the recent and existing pandemic, the need for Industry Internet of Things (IIoT), automation, digitalization, and induction of machine learning techniques for forecasting and prediction have been the technologies to rely on. On these lines, digitization and automation in the healthcare industry have been practical tools to accelerate diagnosis and provide handy second opinions to practitioners. Sustainability in health care has several objectives, like reduced cost and low emission rate, while promising effective outcomes and ease of diagnosis. In this paper, such an attempt has been made to employ deep learning techniques to predict the phase of brain tumors. The deep learning methods help practitioners to correlate patients' status with similar subjects and assess and predict future anomalies due to brain tumors. Popular datasets have been employed for modeling the prediction process. Machine learning has been the most successful tool for handling supervised classification while dealing with complex patterns. The study aims to apply this machine learning technique to classifying images of brains with different types of tumors: meningioma, glioma, and pituitary. The simulation is performed in a python environment, and analysis is carried out using standard metrics