Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

Evolutionary Computation on Road Safety

This study examines the psychological research that focuses on road safety in Smart Cities as proposed by the Vulnerable Road Users (VRUs) sphere. It takes into account qualities such as VRUs’ personal information, their habits, environmental measurements and things data. With the goal of seeing VRUs as active and proactive actors with differentiated feelings and behaviours, we are committed to integrating the social factors that characterize each VRU into our social machinery. As a result, we will focus on the development of a VRU Social Machine to assess VRUs’ behaviour in order to improve road safety. The formal background will be to use Logic Programming to define its architecture based on a Deep Learning approach to Knowledge Representation and Reasoning, complemented with an Evolutionary approach to Computing

Awareness of School Learning Environments

Now, and in the times that follow, student education should focus on developing inclusive skills such as problem-solving and decision-making, where the role of the learning environment plays a crucial part, i.e., it is a process where the screen of the universe of discourse is accomplished in order to consider not only the complex relationships that flow among the objects that populate it, but also its inner structure, co-existing incomplete/unknown or even self-contradictory information or knowledge. As a result, we will focus on the development of an Intelligent Social Machine to assess Learning Environments in high schools, based on factors like School and Disciplinary Climates as well as Parental Involvement. The formal background will be to use Logic Programming to define its architecture based on a Deep Learning-Big Data approach to Knowledge Representation and Reasoning, complemented by an Evolutionary approach to Computing grounded on Virtual Intellects

Myocarditis related to Campylobacter jejuni infection: A case report

BACKGROUND: Myocarditis can develop as a complication of various infections and is most commonly linked to enterovirus infections. Myocarditis is rarely associated with bacterial infections; salmonellosis and shigellosis have been the most frequently reported bacterial cause. We report a case of myocarditis related to Campylobacter jejuni enteritis. CASE PRESENTATION: A 30-year-old previously healthy man presented with a history of prolonged chest pain radiating to the jaw and the left arm. Five days prior to the onset of chest pain, he developed bloody diarrhea, fever and chills. Creatine kinase (CK) and CK-MB were elevated to 289 U/L and 28.7 μg/L. Troponin I was 30.2 μg/L. The electrocardiogram (ECG) showed T wave inversion in the lateral and inferior leads. The chest pain resolved within 24 hours of admission. The patient had a completely normal ECG stress test. The patient was initiated on ciprofloxacin 500 mg po bid when Campylobacter jejuni was isolated from the stool. Diarrhea resolved within 48 hours of initiation of ciprofloxacin. The diagnosis of Campylobacter enteritis and related myocarditis was made based on the clinical and laboratory results and the patient was discharged from the hospital in stable condition. CONCLUSION: Myocarditis can be a rare but severe complication of infectious disease and should be considered as a diagnosis in patients presenting with chest pain and elevated cardiac enzymes in the absence of underlying coronary disease. It can lead to cardiomyopathy and congestive heart failure. There are only a few reported cases of myocarditis associated with Campylobacter infection

The diagnostic accuracy of a laser fluorescence device and digital radiography in detecting approximal caries lesions in posterior permanent teeth: an in vivo study

The aim of this in vivo study was to test the diagnostic accuracy of a pen-type laser fluorescence (LFpen) device in detecting approximal caries lesions, in posterior permanent teeth, at the cavitation and non-cavitation thresholds, and compare it with that of digital bitewing radiography. Thirty patients (aged 18–37), who attended the Faculty of Dentistry at Damascus University for a dental examination, were consecutively screened. Ninety approximal surfaces of posterior permanent teeth without frank cavitations, enamel hypoplasia or restorations were selected and examined using the LFpen (DIAGNOdent pen) and digital bitewing radiography. The reference standard was the visual-tactile inspection, after performing temporary tooth separation, using orthodontic rubber rings, placed for 7 days. The status of included approximal surfaces was recorded as intact/sound, with white/brown spots or cavitated. One trained examiner performed all examinations. There were statistically significant differences in LFpen readings between the three types of approximal surface status (P < 0.001). The optimal cut-off values for detecting approximal caries lesions in posterior permanent teeth were >16 and 8 at the cavitation and non-cavitation thresholds respectively. The sensitivity, specificity and accuracy (measured by the area under the receiver-operating characteristic curve) were 100, 85 and 95 and 92, 90 and 95% at the cavitation and non-cavitation thresholds respectively. The intra-class correlation coefficient for intra-examiner reliability was 0.95. The diagnostic accuracy of the LFpen was significantly higher than that of digital bitewing radiography (P < 0.001). The LFpen’s diagnostic performance was accurate and significantly better than digital bitewing radiography in detecting approximal caries lesions, in posterior permanent teeth. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10103-017-2157-2) contains supplementary material, which is available to authorized users

Mycobacterial dihydrofolate reductase inhibitors identified using chemogenomic methods and in vitro validation.

The lack of success in target-based screening approaches to the discovery of antibacterial agents has led to reemergence of phenotypic screening as a successful approach of identifying bioactive, antibacterial compounds. A challenge though with this route is then to identify the molecular target(s) and mechanism of action of the hits. This target identification, or deorphanization step, is often essential in further optimization and validation studies. Direct experimental identification of the molecular target of a screening hit is often complex, precisely because the properties and specificity of the hit are not yet optimized against that target, and so many false positives are often obtained. An alternative is to use computational, predictive, approaches to hypothesize a mechanism of action, which can then be validated in a more directed and efficient manner. Specifically here we present experimental validation of an in silico prediction from a large-scale screen performed against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. The two potent anti-tubercular compounds studied in this case, belonging to the tetrahydro-1,3,5-triazin-2-amine (THT) family, were predicted and confirmed to be an inhibitor of dihydrofolate reductase (DHFR), a known essential Mtb gene, and already clinically validated as a drug target. Given the large number of similar screening data sets shared amongst the community, this in vitro validation of these target predictions gives weight to computational approaches to establish the mechanism of action (MoA) of novel screening hit