Distributed Holistic Clustering on Linked Data

Link discovery is an active field of research to support data integration in the Web of Data. Due to the huge size and number of available data sources, efficient and effective link discovery is a very challenging task. Common pairwise link discovery approaches do not scale to many sources with very large entity sets. We here propose a distributed holistic approach to link many data sources based on a clustering of entities that represent the same real-world object. Our clustering approach provides a compact and fused representation of entities, and can identify errors in existing links as well as many new links. We support a distributed execution of the clustering approach to achieve faster execution times and scalability for large real-world data sets. We provide a novel gold standard for multi-source clustering, and evaluate our methods with respect to effectiveness and efficiency for large data sets from the geographic and music domains

A Proteomics Approach to Investigate Uropathogenic Escherichia coli

Edited version embargoed until 11.09.2020. Full version: Access restricted permanently due to 3rd party copyright restrictions. Restriction set on 11.09.2019 by AW, Doctoral CollegeUropathogenic Escherichia coli (UPEC) is the most commonly known cause of urinary tract infection (UTI). The possession of many virulence factors and global dissemination of multi-drug resistant UPEC strains is posing a critical risk to treatment worldwide. The most virulent UPEC strains have descended from major lineages such as sequence type (ST) 131, ST38 and ST127. Strains of ST127 exhibit the highest virulence potential of most UPEC lineages, but little is known about their pathogenicity. Previous studies have reported the important role of metabolic adaptations in UPEC virulence. This study investigated the metabolic adaptations of UPEC isolates, with a focus on ST127, using a quantitative proteomics approach. We employed a classical shotgun proteomics approach to analyse proteins extracted from multiple strains of UPEC following growth in various environments (Lysogeny broth, artificial urine medium and co-culture with uroepithelial cells). The digested proteins and peptides from all fractions were separated on a Dionex Ultimate 3000 RSLC nano flow system and analysed in an Orbitrap Velos Pro FTMS. Data were processed using Perseus software. Expression and Gene Ontology Enrichment analysis revealed different proteomic profiles of UPEC ST127 strains cultured in LB and AUM. This study showed that environmental changes have an effect on the metabolic pathways expressed by a specific strain. These conditions can be engineered to simulate the variety of environments UPEC isolates may need to survive in. This methodology was then employed to enhance our understanding of the pathogenesis of UPEC. Building on previous work with UPEC paired isolates (urine and blood), proteomics analysis revealed that members of some STs have a higher metabolic potential that could provide virulence advantages. The study also revealed minimal proteomic variations among the paired isolates for some STs. The methods were then applied during co-culture experiments with UPEC and HT1197 bladder epithelial cells. UPEC strain SA189 caused exfoliation effects in HT1197 not seen with other UPEC strains. Analysis of the UPEC SA189 secretome revealed highly abundant bacterial proteins, including hemolysin A. Proteomic analysis is helping to understand the pathogenic potential of UPEC and may facilitate identification of novel diagnostic or therapeutic targets to reduce UTI and potential subsequent bacteraemia.University of Tabuk, Saudi Arabi

Increasing prevalence of gestational diabetes mellitus when implementing the IADPSG criteria : a systematic review and meta-analysis

Aims: Quantify the proportional increase in gestational diabetes (GDM) prevalence when implementing the new International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria compared to prior GDM criteria, and to assess risk factors that might affect the change in prevalence. Methods: A systematic review and meta-analysis was performed of cohort and cross-sectional studies between January 1, 2010 to December 31, 2018 among pregnant women with GDM using IADPSG criteria compared to, and stratified by, old GDM criteria. Web of science, PubMed, EMBASE, Cochrane, Open Grey and Grey literature reports were included. The relative risk for each study was calculated. Subgroup analyses were performed by maternal age, body mass index, study design, country of publication, screening method, sampling method and data stratified according to diagnostic criteria. Results: Thirty-one cohort and cross-sectional studies with 136 705 women were included. Implementing the IADPSG criteria was associated with a 75% (RR 1.75, 95% CI 1.53–2.01) increase in number of women with GDM with evidence of heterogeneity. Conclusions: The IADPSG criteria increase the prevalence of GDM, but allow movement towards more homogeneity. More studies are needed of the benefits, harms, psychological effects and health costs of implementing the IADPSG criteria

Fuzzy decision support systems to diagnose musculoskeletal disorders: A systematic literature review

Abstract Background and objective Musculoskeletal disorders (MSDs) are one of the most important causes of disability with a high prevalence. The accurate and timely diagnosis of these disorders is often difficult. Clinical decision support systems (CDSSs) can help physicians to diagnose diseases quickly and accurately. Given the ambiguous nature of MSDs, fuzzy logic can be helpful in designing the CDSSs knowledge bases. The present study aimed to review the studies on fuzzy CDSSs to diagnose MSDs. Methods A comprehensive search was conducted in Medline, Scopus, Cochrane Library, and ISI Web of Science databases to identify relevant studies published until March 15, 2016. Studies were included in which CDSSs were developed using fuzzy logic to diagnose MSDs, and tested their accuracy using real data from patients. Results Of the 3188 papers examined, 23 papers included according to the inclusion criteria. The results showed that among all the designed CDSSs only one (CADIAG-2) was implemented in the clinical environment. In about half of the included studies (52%), CDSSs were designed to diagnose inflammatory/infectious disorder of the bone and joint. In most of the included studies (70%), the knowledge was extracted using a combination of three methods (acquiring from experts, analyzing the data, and reviewing the literature). The median accuracy of fuzzy rule-based CDSSs was 91% and it was 90% for other fuzzy models. The most frequently used membership functions were triangular and trapezoidal functions, and the most used method for inference was the Mamdani. Conclusions In general, fuzzy CDSSs have a high accuracy to diagnose MSDs. Despite the high accuracy, these systems have been used to a limited extent in the clinical environments. To design of knowledge base for CDSSs to diagnose MSDs, rule-based methods are used more than other fuzzy methods. Keywords Musculoskeletal disorders Decision support systems Fuzzy logic Diagnose Revie

Identifying Optimal Launch Sites of High-Altitude Latex-Balloons using Bayesian Optimisation for the Task of Station-Keeping

Station-keeping tasks for high-altitude balloons show promise in areas such as ecological surveys, atmospheric analysis, and communication relays. However, identifying the optimal time and position to launch a latex high-altitude balloon is still a challenging and multifaceted problem. For example, tasks such as forest fire tracking place geometric constraints on the launch location of the balloon. Furthermore, identifying the most optimal location also heavily depends on atmospheric conditions. We first illustrate how reinforcement learning-based controllers, frequently used for station-keeping tasks, can exploit the environment. This exploitation can degrade performance on unseen weather patterns and affect station-keeping performance when identifying an optimal launch configuration. Valuing all states equally in the region, the agent exploits the region's geometry by flying near the edge, leading to risky behaviours. We propose a modification which compensates for this exploitation and finds this leads to, on average, higher steps within the target region on unseen data. Then, we illustrate how Bayesian Optimisation (BO) can identify the optimal launch location to perform station-keeping tasks, maximising the expected undiscounted return from a given rollout. We show BO can find this launch location in fewer steps compared to other optimisation methods. Results indicate that, surprisingly, the most optimal location to launch from is not commonly within the target region. Please find further information about our project at https://sites.google.com/view/bo-lauch-balloon/

Identifying Optimal Launch Sites of High-Altitude Latex-Balloons using Bayesian Optimisation for the Task of Station-Keeping

Station-keeping tasks for high-altitude balloons show promise in areas such as ecological surveys, atmospheric analysis, and communication relays. However, identifying the optimal time and position to launch a latex high-altitude balloon is still a challenging and multifaceted problem. For example, tasks such as forest fire tracking place geometric constraints on the launch location of the balloon. Furthermore, identifying the most optimal location also heavily depends on atmospheric conditions. We first illustrate how reinforcement learning-based controllers, frequently used for station-keeping tasks, can exploit the environment. This exploitation can degrade performance on unseen weather patterns and affect station-keeping performance when identifying an optimal launch configuration. Valuing all states equally in the region, the agent exploits the region's geometry by flying near the edge, leading to risky behaviours. We propose a modification which compensates for this exploitation and finds this leads to, on average, higher steps within the target region on unseen data. Then, we illustrate how Bayesian Optimisation (BO) can identify the optimal launch location to perform station-keeping tasks, maximising the expected undiscounted return from a given rollout. We show BO can find this launch location in fewer steps compared to other optimisation methods. Results indicate that, surprisingly, the most optimal location to launch from is not commonly within the target region. Please find further information about our project at https://sites.google.com/view/bo-lauch-balloon/

Childhood neurogenic stuttering due to bilateral congenital abnormality in globus pallidus: A case report and review of the literature

Objective The basal ganglia are a group of structures that act as a cohesive functional unit. They are situated at the base of the forebrain and are strongly connected with the cerebral cortex and thalamus. Some speech disorders such as stuttering can resulted from disturbances in the circuits between the basal ganglia and the language motor area of the cerebral cortex. Stuttering consists of blocks, repetitive, prolongation or cessation of speech. We present a 7.5 -year-old male child with bilateral basal ganglia lesion in globus pallidus with unclear reason. The most obvious speech disorders in patient was stuttering, but also problems in swallowing, monotone voice, vocal tremor, hypersensitivity of gag reflex and laryngeal dystonia were seen. He has failed to respond to drug treatment, so he went on rehabilitation therapy when his problem progressed. In this survey, we investigate the possible causes of this type of childhood neurogenic stuttering. © 2016, Iranian Child Neurology Society. All rights reserved

Adsorption of toxic dye using red seaweeds from synthetic aqueous solution and its application to industrial wastewater effluents

This study investigated the potential application of dried powder from red seaweed Pterocladia capillacea as an eco-friendly adsorbent for removing Crystal Violet Dye (CV dye) from a synthetic solution. The adsorption conditions for the adsorbent were determined, in batch conditions, by changing different experimental parameters such as initial CV dye concentrations (5, 10, 20, 30, and 40 mg L–1), contact time (15, 30, 60, 120, and 180 min.), adsorbent doses (0.025, 0.05, 0.1, 0.2, and 0.3 g), temperature (25, 35, 45, and 55°C), and pH (3, 5, 7, 9, and 11). The adsorption mechanisms of CV dye onto the P. capillacea biomass were examined using various analytical techniques such as FTIR, BET, UV–Visible, and SEM. These characterizations suggest the average BET surface area of P. capillacea was 87.17 m2 g–1 and a pore volume of 0.10368 cc g−1. Moreover, according to the FTIR study, the dye has been deposited inside the adsorbent’s pores after adsorption. The adsorption behavior of the adsorbent was investigated by performing both kinetic and equilibrium isothermal studies in batch conditions at 25°C. Also, the thermodynamic factors showed the exothermic nature and physisorption of the adsorption process, which tends to be spontaneous at lower temperatures. In addition, Langmuir, Dubinin-Radushkevich, Freundlich, and Tempkin isotherm models were selected to evaluate the adsorption of CV dye on P. capillacea. The equilibrium adsorption data were best represented by the Freundlich, indicating multilayer adsorption on the heterogeneous surface. The qe experiment and calculation values for the Pseudo-Second-Order and interparticle diffusion kinetic models were determined. The results showed that, under optimum conditions P. capillacea exhibited 98% removal of CV dye from synthetic wastewater. Moreover, it will help to regenerate the adsorbents that can be reused to adsorb CV dye ions and develop a successful adsorption process. Finally, this study concluded that the dried powdered form of P. capillacea is an attractive source for adsorbing CV dye from aqueous solution

Maximum-likelihood synchronization and channel estimation with multiuser detection in GFDMA

Accurate estimation and correction of channel distortions and carrier frequency offset (CFO) are of a great importance in any multicarrier communication system. Hence, in this paper, we propose data-aided CFO and channel estimation techniques for both multiuser uplink and downlink of the generalized frequency division multiple access (GFDMA). Our proposed solutions jointly estimate the CFO and channel responses based on the maximum-likelihood criterion. To simplify the implementation of the proposed estimation algorithms, we suggest a preamble composed of two similar Zadoff-Chu training sequences in a generalized frequency division multiplexing block. It is worth mentioning that our proposed technique can estimate both integer and fractional CFO values without any limitation on the acquisition range of CFO. In the uplink phase, each user aligns its carrier frequency with the base station using the estimated CFO in the downlink. However, the CFO estimates may get outdated for the uplink transmission. Thus, residual CFOs may still remain in the received signal at the base station. While being trivial in the downlink, CFO correction is a challenging task in the uplink. Thus, we also propose a joint CFO correction and channel equalization technique for the uplink of GFDMA systems. Finally, we evaluate our proposed estimation and correction algorithms in terms of estimation mean square error and bit error rate performance through simulations