Serologic evidence of previous Campylobacter jejuni infection in patients with the Guillain-Barré syndrome

OBJECTIVE: To determine if patients with the Guillain-Barré syndrome are likely to have had Campylobacter jejuni infection before onset of neurologic symptoms. DESIGN: A case-control study. SETTING: Several university medical centers. PATIENTS: Case patients met clinical criteria for the Guillain-Barré syndrome between 1983 and 1990 and had a serum sample collected and frozen within 3 weeks after onset of neurologic symptoms (n = 118). Disease controls were patients with other neurologic illnesses (n = 56); healthy controls were hospital employees or healthy family members of patients (n = 47). MEASUREMENTS: Serum IgA, IgG, and IgM antibodies to C. jejuni were determined by enzyme-linked immunosorbent assays. Assays were done in a blinded manner. RESULTS: Optical density ratios > or = 2 in two or more immunoglobulin classes were seen in 43 (36%) of patients with the Guillain-Barré syndrome and in 10 (10%) of controls (odds ratio, 5.3; 95% CI, 2.4 to 12.5; P < 0.001). Increasing the optical density ratio or the number of immunoglobulin classes necessary to yield a positive result increased the strength of the association. The number of patients with the Guillain-Barré syndrome who had positive serologic responses was greatest from September to November (P = 0.02). Male patients were three times more likely to have serologic evidence of C. jejuni infection (P = 0.009); the proportion of patients with the syndrome who had a positive serologic response increased with age. CONCLUSIONS: Patients with the Guillain-Barré syndrome are more likely than controls to have serologic evidence of C. jejuni infection in the weeks before onset of neurologic symptoms. Campylobacter jejuni may play a role in the initiation of the Guillain-Barré syndrome in many patients.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

A dynamic variability management approach working with agile product line engineering practices for reusing features

The Journal of Supercomputing - Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both...     

Role of situational method engineering to improve visual information systems in agile distributed environment

Software product prone to continuous evolution due to increase in the use of technology. Therefore, more stakeholders are involved in software evolution increases the cost and complexity. This required optimization of resources and cost to handle evolution with Global Software Development (GSD) to utilize time zones efficiently. The significance challenge of GSD reports: time zone difference, geographical location, communication delays, knowledge sharing, control among stakeholders and development team. Because of these challenges, the requirements for development in GSD environment are also challenge as compared to on site development. Different requirement engineering methods have been used to improve the requirements analysis to deal with ambiguities and inconsistency in large set of requirements. The customization and tailoring of requirements according to changing project’s situations required to improve project development with reusing existing agile methods during requirement engineering phase. Moreover, complex information systems where heterogeneity is inevitable that implies the involvement of divergent stakeholders and necessitate a comprehensive framework to capture multidimensional viewpoints and fulfill aforementioned issues. Therefore, a situational multi-dimensional agile requirement engineering method has been proposed to support team and stakeholders’ viewpoints. The schema of the proposed method is based on challenges recognized by performing Literature Review. Then proposed method has been evaluated via experimental approach and statistical analysis conducted to validated reliability of data collected. This result is significant approved both practically and statistically that the proposed approach ease to use, implement, trained and increased productivity and performance. Hence, the experimental study for the evaluation of the proposed approach results concluded that, proposed approach is the important multimedia tool for supporting organization and distributed development team for information sharing, collaboration, product development.

     

Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application

Principal Component Analysis (PCA) is a statistical process monitoring technique that has been widely used in industrial applications. PCA methods for Fault Detection (FD) use data collected from a steady-state process to monitor T² and Q statistics with a fixed threshold. For the systems where transient values of the processes must be taken into account, the usage of a fixed threshold in PCA method causes false alarms and missing data that significantly compromise the reliability of the monitoring systems. In the present article, a new PCA method based on variance sensitive adaptive threshold (T_vsa) is proposed to overcome false alarms which occur in the transient states according to changing process conditions and the missing data problem. The proposed method is implemented and validated experimentally on an electromechanical system. The method is compared with the conventional monitoring methods. Experimental tests and tabulated results confirm the fact that the proposed method is applicable and effective for both the steady-state and transient operations and gives early warning to operators.     

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...     

Biofilm reduction,cell proliferation,anthelmintic and cytotoxicity effect of green synthesised silver nanoparticle using Artemisia vulgaris extract

Infectious diseases are caused by etiological agents. Nanotechnology has been used to minimise the effect of clinical pathogens which have resistance to antibiotics. In current research synthesis, characterisation and biological activities of green synthesised nanoparticles using Artemisia vulgaris extract have been done. The characterisation of AgNPs was carried out using Fourier transform infrared spectroscopy, UV‐Vis spectrophotometry, and scanning electron microscopy. Anti‐biofilm, cell viability, antibacterial, brine shrimp lethality, and deoxyribonucleic acid protection effects have been screened. UV‐Vis spectra showed the absorption peak of synthesised nanoparticles at 400 nm. FT‐IR indicated the involvement of the functional group in the preparation of AgNPs. SEM showed the spherical shape of AgNPs with 30 nm diameter. Biological screening results revealed the antibacterial effect against clinical bacterial pathogens. Biofilm reduction and cell viability assay also supported the antibacterial effect. Cytotoxicity effect was recorded as 100% at 200 μg/ml through brine shrimp lethality assay. Protein kinase inhibition zones recorded for AgNPs (16 mm bald) compared with A. vulgaris extract (11 mm bald). It has been concluded that green synthesised AgNPs are more effective against infectious pathogens and could be used as a potential source for therapeutic drugs.Inspec keywords: cellular biophysics, toxicology, silver, nanoparticles, nanomedicine, diseases, microorganisms, ultraviolet spectra, visible spectra, Fourier transform infrared spectra, enzymes, molecular biophysicsOther keywords: biofilm reduction, cell proliferation, anthelmintic effect, cytotoxicity effect, green synthesised silver nanoparticle, Artemisia vulgaris extract, infectious diseases, aetiological agents, Fourier transform infrared spectroscopy, UV‐Vis spectrophotometry, scanning electron microscopy, SEM, antibiofilm, cell viability, brine shrimp lethality, deoxyribonucleic acid protection effects, AgNP, cytotoxicity, protein kinase inhibition zones, therapeutic drugs     

Coordinated controller tuning of a boiler turbine unit with new binary particle swarm optimization algorithm

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.     

Experimental investigation on the stability of bride girder against tsunami forces

Bridges are the most useful part in the transportation network. Any disruption of the bridge structures may hamper the whole transportation system. In the last recent tsunamis, numerous number of bridges were structurally damaged by the tsunami waves. Lack of proper provisions of tsunami forces in the design guidelines also contributes to the augmentations of the damage level. Therefore, proper evaluation of the tsunami forces on the bridge girder should be introduced in the design promptly. In this study, laboratory experiments were carried out to reveal the damage mechanism of the bridge girder by assessing the exerted tsunami forces. Both broken and unbroken waves were considered for the analysis. The results showed that measured forces were larger for broken waves than those of unbroken waves. Maximum force of the broken waves was 4.59 times as large as the hydrostatic pressure. Also, waves reached the peak value more rapidly for larger wave heights than those of smaller ones. Additionally, a girder that is placed at a higher position is much vulnerable to tsunami hazard.