Which digit is larger? Brain responses to number and size interactions in a numerical Stroop task

When comparing the digits of different physical sizes, the processing of numerical value interacts with the processing of physical size. Given the universal use of Arabic numbers in mathematics and daily life, this study aims to elucidate the cognitive processes involved in the interactions of task-relevant and task-irrelevant features during information processing. We investigated this question by examining event-related potential (ERP) using a modified version of the size congruity comparison, which is a Stroop-like task. Numerical value and physical size were varied independently under task-relevant and task-irrelevant conditions. To better examine how the task-irrelevant features modulated the processing of the task-relevant attributes, a neutral condition was included in both tasks. For the physical task, congruent trials showed a less negative N200 response than neutral trials (indicating a facilitation effect), and incongruent trials elicited a larger N450 and smaller late positive complex (LPC) response than neutral trials (indicating an interference effect). For the numerical task, congruent trials showed a larger LPC response than neutral trials (indicating a facilitation effect). These ERP findings indicate that the sources of the facilitation and interference effects appear in different cognitive processes for each task. We further suggest that language characteristics may be a factor in the superior numerical processing exhibited in this study

Systematic review of the literature and evidence-based recommendations for antibiotic prophylaxis in trauma : results from an italian consensus of experts

Antibiotic prophylaxis is frequently administered in severe trauma. However, the risk of selecting resistant bacteria, a major issue especially in critical care environments, has not been sufficiently investigated. The aim of the present study was to provide guidelines for antibiotic prophylaxis for four different trauma-related clinical conditions, taking into account the risks of antibiotic-resistant bacteria selection, thus innovating previous guidelines in the field

Elevated creatine kinase activity in primary hepatocellular carcinoma

BACKGROUND: Inconsistent findings have been reported on the occurrence and relevance of creatine kinase (CK) isoenzymes in mammalian liver cells. Part of this confusion might be due to induction of CK expression during metabolic and energetic stress. METHODS: The specific activities and isoenzyme patterns of CK and adenylate kinase (AdK) were analysed in pathological liver tissue of patients undergoing orthotopic liver transplantation. RESULTS: The brain-type, cytosolic BB-CK isoenzyme was detected in all liver specimens analysed. Conversely, CK activity was strongly increased and a mitochondrial CK (Mi-CK) isoenzyme was detected only in tissue samples of two primary hepatocellular carcinomas (HCCs). CONCLUSION: The findings do not support significant expression of CK in normal liver and most liver pathologies. Instead, many of the previous misconceptions in this field can be explained by interference from AdK isoenzymes. Moreover, the data suggest a possible interplay between p53 mutations, HCC, CK expression, and the growth-inhibitory effects of cyclocreatine in HCC. These results, if confirmed, could provide important hints at improved therapies and cures for HCC

Selective Decrease of Components of the Creatine Kinase System and ATP Synthase Complex in Chronic Chagas Disease Cardiomyopathy

Chronic Chagas disease cardiomyopathy (CCC) affects millions in endemic areas and is presenting in growing numbers in the USA and European countries due to migration currents. Clinical progression, length of survival and overall prognosis are significantly worse in CCC patients when compared to patients with dilated cardiomyopathy of non-inflammatory etiology. Impairment of energy metabolism seems to play a role in heart failure due to cardiomyopathies. Herein, we have analyzed energy metabolism enzymes in myocardium samples of CCC patients comparing to other non-inflammatory cardiomyopathies. We found that myocardial tissue from CCC patients displays a significant reduction of both myocardial protein levels of ATP synthase alpha and creatine kinase enzyme activity, in comparison to control heart samples, as well as idiopathic dilated cardiomyopathy and ischemic cardiomyopathy. Our results suggest that CCC myocardium displays a selective energetic deficit, which may play a role in the reduced heart function observed in such patients

Eye position affects orienting of visuospatial attention

The ability to detect an incoming visual stimulus is enhanced by knowledge of stimulus location (orienting of visuospatial attention). Although the brain mechanisms at the basis of this enhancement are not yet fully clarified, there is evidence that orienting of attention is accompanied by the activation of oculomotor circuits. It remains unclear, however, whether this oculomotor activity is an epiphenomenon or is functionally related to the attentional process. Attentional benefits are usually measured by the classical Posner paradigm. When subjects fixate centrally and are requested to detect a visual stimulus that could appear in an attended or unattended location, they react faster to stimuli appearing in the attended one. Here, we demonstrate that in monocular vision visuospatial attention was significantly modulated by the position of the eye in the orbit. When the screen was placed 40 degrees to the right or to the left of subjects' sagittal plane, attentional benefits for stimuli appearing in subjects' temporal spatial hemifield dramatically decayed, even if the retinal stimulation was exactly the same as in the classical paradigm. The finding that eyes and attention show a common limit stop point supports their close functional coupling

Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

Bioinformatic techniques targeting gene expression data require specific analysis pipelines with the aim of studying properties, adaptation, and disease outcomes in a sample population. Present investigation compared together results of four numerical experiments modeling survival rates from bladder cancer genetic profiles. Research showed that a sequence of two discretization phases produced remarkable results compared to a classic approach employing one discretization of gene expression data. Analysis involving two discretization phases consisted of a primary discretizer followed by refinement or pre-binning input values before the main discretization scheme. Among all tests, the best model encloses a sequence of data transformation to compensate skewness, data discretization phase with class-attribute interdependence maximization algorithm, and final classification by voting feature intervals, a classifier that also provides discrete interval optimization

Molecular Fingerprint Based and Machine Learning Driven QSAR for Bioconcentration Pathways Determination

Quantitative structure-activity relationship associates molecules’ structural characteristics to their bio-activity, and it can be performed via machine learning to find risky chemicals that accumulate in living organisms. The present analysis focused on investigating how structural information of molecules encoded by molecular fingerprints can be used to predict bioaccumulation pathways. Numerical experiments involved extreme gradient boosting, support vector machines, and neural networks, including spiking neural networks. This investigation might be the first attempt to apply this particular kind of biologically inspired neural network for predicting molecules’ functions from their fingerprints. The computational models forecasted three possible bioaccumulation processes, with support vector machines obtaining the mean peak accuracy and the spiking neural network architectures achieving satisfactory results: the leaky neuron spiking neural network range of outcomes was not statistically different from the accuracies of the support vector machine algorithms. In addition, an algorithm broadly found in chemoinformatics literature as the extreme gradient boosting algorithm fulfilled compatible accuracies with spiking neural networks and support vector machines. This three-class machine learning-driven bioconcentration modeling of chemicals established a foundation for future analysis pipelines focusing on predicting bioaccumulation in biological tissues by investigating molecules’ structures