Bacteriological and Molecular Identification of Thermophilic Campylobacters of Animal and Human Origins in Beni-Suef Governorate, Egypt

Thermophilic species of the genus Campylobacter are generally considered commensals of livestock and the leading cause of bacterial food-borne zoonoses. The present study was delineated to clarify the role of Campylobacter species as a diarrheagenic pathogen in animals and man and to investigate the fecal carriage rate of Campylobacters in animals and in-contact humans. A total number of 78 fecal samples were collected from diarrheic and non-diarrheic cattle (n=26), sheep (n=28) and humans (n=24). Samples were enriched in Preston broth, followed by streaking on selective Campylobacter agar base medium. The suspected colonies were tested morphologically and biochemically. Campylobacter spp. was recovered from 29 (37.17%) out of 78 fecal samples (34.61%, 42.85% and 33.33%) for cattle, sheep and humans, respectively. Positive correlation between the occurrence of diarrhea and the isolation of Campylobacters was observed in samples of human origin while in adult ruminants particularly sheep, high fecal carriage rate was observed in non-diarrheic animals. The isolates were identified to genus and species levels by polymerase chain reaction targeting the 16S rRNA gene, the mapA gene and the ceuE gene which revealed that all of isolates were Campylobacter jejuni. These findings pose a significant epidemiological implication where cattle and sheep act as vehicles of, and excrete Campylobacter jejuni which is capable of causing disease in the local community in the area of investigation

Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail

While the notion of age of information (AoI) has recently emerged as an important concept for analyzing ultra-reliable low-latency communications (URLLC), the majority of the existing works have focused on the average AoI measure. However, an average AoI based design falls short in properly characterizing the performance of URLLC systems as it cannot account for extreme events that occur with very low probabilities. In contrast, in this paper, the main objective is to go beyond the traditional notion of average AoI by characterizing and optimizing a URLLC system while capturing the AoI tail distribution. In particular, the problem of vehicles' power minimization while ensuring stringent latency and reliability constraints in terms of probabilistic AoI is studied. To this end, a novel and efficient mapping between both AoI and queue length distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem. Simulation results shows a nearly two-fold improvement in terms of shortening the tail of the AoI distribution compared to a baseline whose design is based on the maximum queue length among vehicles, when the number of vehicular user equipment (VUE) pairs is 80. The results also show that this performance gain increases significantly as the number of VUE pairs increases.Comment: Accepted in IEEE GLOBECOM 2018 with 7 pages, 6 figure

Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach

In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using polynomial function to showcase an approach for deriving strong initial value ranges of numerical attributes and use a non-overlapping distribution for categorical attributes such that the entire similarity range [0,1] is utilized. We use an open source dataset for demonstrating modelling and development of the similarity measures and will present a case-based reasoning (CBR) system that can be used to search for the most relevant similar cases

Synthesis and Biological activity of 1,3-Thiazolylidenehydrazinylidene ethylpyridiniumbromide monohydrate, 1,3-Thiazolylidenehydraziniumbromide and 1,3-Thiazolylidenehydrazine derivatives

1,3-Thiazolylidenehydrazinylidene ethylpyridinium bromide monohydrate, 1,3-thiazolylidenehydrazinium bromide and 1,3-thiazolylidenehydrazine derivatives were synthesized by heterocyclization of 2-(1-substituted ethylidene) hydrazinecarbothioamides, characterized and screened for their anti-bacterial activities. The structures of synthesized compounds were established by spectroscopic (IR, 1H, 13C-NMR, Mass) and X-ray analyses

Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier.We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders