Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS) for the computational analyses of high speed reacting flows

The principal objective is to extend the boundaries within which large eddy simulations (LES) and direct numerical simulations (DNS) can be applied in computational analyses of high speed reacting flows. A summary of work accomplished during the last six months is presented

The Autism Related Protein Contactin-Associated Protein-Like 2 (CNTNAP2) Stabilizes New Spines: An In Vivo Mouse Study.

The establishment and maintenance of neuronal circuits depends on tight regulation of synaptic contacts. We hypothesized that CNTNAP2, a protein associated with autism, would play a key role in this process. Indeed, we found that new dendritic spines in mice lacking CNTNAP2 were formed at normal rates, but failed to stabilize. Notably, rates of spine elimination were unaltered, suggesting a specific role for CNTNAP2 in stabilizing new synaptic circuitry

Experimental Study on the Effect of Excitation Type on the Output-Only Modal Analysis Results

Output-only Modal Analysis (OMA) has found extensive use in the identification of dynamic properties of structures. This study aims to investigate the effect of excitation force on the accuracy of modal parameters. For this purpose, the modal parameters of a simply supported beam are obtained through the Experimental Modal Analysis (EMA) and the OMA method using three different types of artificial and natural excitations, namely a shaker, acoustic waves, and environmental noise. Frequency Domain Decomposition (FDD) technique is used to identify dynamic characteristics. Finally, these results are compared with those obtained by the analytical method and the EMA method. The results demonstrated the following: 1) Acoustic excitation presents the natural frequencies with the smallest errors in comparison with the analytical results. 2) Inaccuracy is observed at certain natural frequencies during the excitation with a shaker with respect to the connecting point between the shaker and the beam. 3) Modal Assurance Criterion (MAC) showed that the mode shapes extracted by the acoustic excitations are more similar to the analytical results

IAVS: Intelligent Active Network Vulnerability Scanner

Network security needs to be assured through runtime active evaluating and assessment. However, active vulnerability scanners suffer from serious deficiencies such as heavy scan traffic during the reconnaissance phase, uncertainty in the environment, and heavy reliance on experts. Generating a blind heavy load of attack packets not only causes usage of network resources, but it also increases the probability of detection by target defense systems and causes failure in finding vulnerabilities. Furthermore, environmental uncertainty increases pointless attempts of vulnerability scanners, which wastes time. Utilizing a decision-making method devised for uncertainty conditions, we present Intelligent Active Network Vulnerability Scanner (IAVS). IAVS is implemented as an extension on Hail Mary, the automatic execution mechanism in the Metasploit toolkit. IAVS learns from previous vulnerability exploitation attempts to select exploit codes purposefully. IAVS not only reduces the role of experts in the process of vulnerability testing, but it also decreases the volume of scanning requests during the reconnaissance phase by integrating the reconnaissance and exploitation phases. Our experimental results indicate a successful decrease in failed attempts. It is also demonstrated that improvements in the results of IAVS correspond directly to the rate of similarity among different vulnerabilities in systems of the target network; that is, the higher the similarity, the better the results of IAVS. Our experiments compared the results of IAVS and those of Hail Mary without the IAVS extension; these results show that IAVS improved Hail Marys successful attempts by around 37%.

DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data

Detailed behavioral analysis is key to understanding the brain-behavior relationship. Here, we present deep learning-based methods for analysis of behavior imaging data in mice and humans. Specifically, we use three different convolutional neural network architectures and five different behavior tasks in mice and humans and provide detailed instructions for rapid implementation of these methods for the neuroscience community. We provide examples of three dimensional (3D) kinematic analysis in the food pellet reaching task in mice, three-chamber test in mice, social interaction test in freely moving mice with simultaneous miniscope calcium imaging, and 3D kinematic analysis of two upper extremity movements in humans (reaching and alternating pronation/supination). We demonstrate that the transfer learning approach accelerates the training of the network when using images from these types of behavior video recordings. We also provide code for post-processing of the data after initial analysis with deep learning. Our methods expand the repertoire of available tools using deep learning for behavior analysis by providing detailed instructions on implementation, applications in several behavior tests, and post-processing methods and annotated code for detailed behavior analysis. Moreover, our methods in human motor behavior can be used in the clinic to assess motor function during recovery after an injury such as stroke

Rotation-Agnostic Image Representation Learning for Digital Pathology

This paper addresses complex challenges in histopathological image analysis through three key contributions. Firstly, it introduces a fast patch selection method, FPS, for whole-slide image (WSI) analysis, significantly reducing computational cost while maintaining accuracy. Secondly, it presents PathDino, a lightweight histopathology feature extractor with a minimal configuration of five Transformer blocks and only 9 million parameters, markedly fewer than alternatives. Thirdly, it introduces a rotation-agnostic representation learning paradigm using self-supervised learning, effectively mitigating overfitting. We also show that our compact model outperforms existing state-of-the-art histopathology-specific vision transformers on 12 diverse datasets, including both internal datasets spanning four sites (breast, liver, skin, and colorectal) and seven public datasets (PANDA, CAMELYON16, BRACS, DigestPath, Kather, PanNuke, and WSSS4LUAD). Notably, even with a training dataset of 6 million histopathology patches from The Cancer Genome Atlas (TCGA), our approach demonstrates an average 8.5% improvement in patch-level majority vote performance. These contributions provide a robust framework for enhancing image analysis in digital pathology, rigorously validated through extensive evaluation. Project Page: https://rhazeslab.github.io/PathDino-Page/Comment: 23 pages, 10 figures, 18 tables. Histopathological Image Analysi

The relationship between learning style preferences and gender, educational major and status in first year medical students: A survey study from Iran

Background: Identifying and employing appropriate learning styles could play an important role in selecting teaching styles in order to improve education. Objectives: This study aimed to determine the relationship between learning styles preferences and gender, educational major and status in first year students at Isfahan University of Medical Sciences. Patients and Methods: A cross-sectional study employing the visual-aural-read/write-kinesthetic (VARK) learning style's questionnaire was done on 184 first year students of medicine, pharmacy, dentistry, nursing and health services management at Isfahan University of Medical Sciences in 2012. The validity of the questionnaire was assessed through experts' views and reliability was calculated using Cronbach's alpha coefficients (α = 0.86). Data were analyzed using the SPSS ver.18 software and x2 test. Results: Out of 184 participants who responded to and returned the questionnaire, 122 (66.3) were female; more than two-thirds (68.5) of the enrolled students were at the professional doctorate level (medicine, pharmacy, dentistry) and 31.5 at the undergraduate level (nursing and health services management). Eighty-nine (48.4) students preferred a single-modal learning style. In contrast, the remaining 95 students (51.6) preferred multi-modal learning styles. A significant relationship between gender and single modal learning styles (P = 0.009) and between status and learning styles (P = 0.04) was observed. Conclusions: According to the results, male students preferred to use the kinesthetic learning style more than females, while, female students preferred the aural learning style. Knowledge about the learning styles of students at educational institutes is valuable and helps solve learning problems among students, and allows students to become better learners. © 2015, Iranian Red Crescent Medical Journal

Preclinical characterization of the efficacy and safety of biologic N‑001 as a novel pain analgesic for post‑operative acute pain treatment

Inhibition of actin remodeling in nerves modulates action potential propagation and therefore could be used to treat acute pain. N-001 is a novel protein analgesic engineered from several C. Botulinum toxins. N-001 targets sensory neurons through ganglioside GT1b binding and ADP-ribosylates G-actin reducing actin remodeling. The activity and efficacy of N-001 was evaluated previously in vitro and in a mouse inflammatory pain model. To assess the relevance of N-001 for treatment of acute post-surgical pain, the current study evaluated the efficacy of N-001 in a mouse hind-paw incision model by periincisional and popliteal nerve block administration combined with mechanical testing. N-001 provided relief of pain-like behavior over 3 days and 2 days longer than the conventional long-acting anesthetic bupivacaine. Preclinical safety studies of N-001 indicated the drug produced no toxic or adverse immunological reactions over multiple doses in mice. These results combined with past targeting results encourage further investigation of N-001 as an analgesic for post-operative pain management with the potential to function as a differential nociceptor-specific nerve block