Epidemiological Study of Cutaneous Leishmaniasis in Neyshabur County, East of Iran (2011-2017)

BACKGROUND: Cutaneous Leishmaniasis (CL) isn’t a deadly disease, but it has always been taken into consideration due to the long-term involvement of patients with skin. Various factors can play an intervening role in increasing the rate of disease. The present study aimed to evaluate the prevalence and associated factors of disease from 2011-2017 and provide appropriate control strategies for reducing its incidence in Neyshabur county. METHODS: All patients with CL, who had medical records in the health centres of Neyshabur from 2011 to 2017, were examined for conducting this analytical-descriptive study. Data were analyzed by descriptive statistics and chi-square test at a significant level of 0.95 using SPSS V22. RESULTS: Findings indicated that the highest annual incidence was in 2016 (229 patients), and the least incidence was in 2014 (100 patients). The majority of patients were under 10 years of age and 51.7% of patients were male. About 59.5% of patients were living in cities and 35% of them were living in North of Neyshabur city. Hands were the most affected part of the body (56.0%) followed by trunk (1.3%). Most patients (69.9%) were treated with topical regimens. CONCLUSION: This study showed that CL was hypo-endemic in Neyshabur. Also, the disease was more prevalent in urban areas. Therefore, appropriate health measures to improve environmental conditions, public health educations, and the public awareness of the positive impact of early diagnosis of disease in the success of treatment (especially for inhabitance suburbanite) are essential

Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization

Age Effects on Decision-Making, Drift Diffusion Model

Training can improve human decision-making performance. After several training sessions, a person can quickly and accurately complete a task. However, decision-making is always a trade-off between accuracy and response time. Factors such as age and drug abuse can affect the decision-making process. This study examines how training can improve the performance of different age groups in completing a random dot motion (RDM) task. The participants are divided into two groups: old and young. They undergo a three-phase training and then repeat the same RDM task. The hierarchical drift-diffusion model analyzes the subjects' responses and determines how the model's parameters change after training for both age groups. The results show that after training, the participants were able to accumulate sensory information faster, and the model drift rate increased. However, their decision boundary decreased as they became more confident and had a lower decision-making threshold. Additionally, the old group had a higher boundary and lower drift rate in both pre and post-training, and there was less difference between the two group parameters after training

Operative Treatment of Acute Distal Femur Fractures: Review of literature

Fractures of the distal femur may be extra articular or have an intra articular component. Mismanagement of any of these fractures can result in abnormalities of alignment of the load-bearing axis of lower limb and/or rotational deformities. Essentially all supracondylar femur fractures require operative intervention because of the severe potential risks of prolonged bed rest. Yet, despite their proven track record and benefits over older implants, technical errors are common and must be overcome with proper preoperative planning and intra-operative attention to details. The goal of this study was   to present an update on the management of these fracture

Residual Information of Previous Decision Affects Evidence Accumulation in Current Decision

Bias in perceptual decisions can be generally defined as an effect which is controlled by factors other than the decision-relevant information (e.g., perceptual information in a perceptual task, when trials are independent). The literature on decision-making suggests two main hypotheses to account for this kind of bias: internal bias signals are derived from (a) the residual of motor signals generated to report a decision in the past, and (b) the residual of sensory information extracted from the stimulus in the past. Beside these hypotheses, this study suggests that making a decision in the past per se may bias the next decision. We demonstrate the validity of this assumption, first, by performing behavioral experiments based on the two-alternative forced-choice (TAFC) discrimination of motion direction paradigms and, then, we modified the pure drift-diffusion model (DDM) based on the accumulation-to-bound mechanism to account for the sequential effect. In both cases, the trace of the previous trial influences the current decision. Results indicate that the probability of being correct in the current decision increases if it is in line with the previously made decision even in the presence of feedback. Moreover, a modified model that keeps the previous decision information in the starting point of evidence accumulation provides a better fit to the behavioral data. Our findings suggest that the accumulated evidence in the decision-making process after crossing the bound in the previous decision can affect the parameters of information accumulation for the current decision in consecutive trials

A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans

Converging reports indicate that face images are processed through specialized neural networks in the brain –i.e. face patches in monkeys and the fusiform face area (FFA) in humans. These studies were designed to find out how faces are processed in visual system compared to other objects. Yet, the underlying mechanism of face processing is not completely revealed. Here, we show that a hierarchical computational model, inspired by electrophysiological evidence on face processing in primates, is able to generate representational properties similar to those observed in monkey face patches (posterior, middle and anterior patches). Since the most important goal of sensory neuroscience is linking the neural responses with behavioral outputs, we test whether the proposed model, which is designed to account for neural responses in monkey face patches, is also able to predict well-documented behavioral face phenomena observed in humans. We show that the proposed model satisfies several cognitive face effects such as: composite face effect and the idea of canonical face views. Our model provides insights about the underlying computations that transfer visual information from posterior to anterior face patches