Effect of ethanolic extract of Adiantum capillus-veneris in comparison with Gentamicine on 3 pathogenic bacteria in vitro

Objectives: Adiantum capillus-veneris is one of herbs that has been used in traditional medicine of Iran and has mucolytic and antipyretic effects. Antibiotic resistancy is developing against severe bacteria,due to irrational prescription. Therefore, we assessed Adiantum capillus-veneris effects as a medicinal herb on three common bacteria. Methods: Ethanolic extract of Adiantum capillus-veneris was prepared by a pharmacology company with perculation method and was diluted in distilled water to 1/2,1/4 and 1/8 concentration.blank discs were placed in extracts for one day.Then ,the bacteria were cultured in muller hinton agar plate and the discs were placed on them.We used Gentamicine disc as control.After incubation in 37° for 24 hour, the diameter of no growth hallo around the discs were read. Results: The ethanolic extract of Adiantum capillus-veneris herb has no antimicrobial effects on the bacteria. Conclusion: Results of this study suggested that ethanolic extract of Adiantum capillus-veneris has no antimicrobial effects on this three bacteria mentioned above.Because this herb has been used in traditional medicine, we suggest more studies about it

Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation

Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collection and annotation of such datasets comes at significant efforts and associated costs. In addition, visual data might contain private or sensitive information, that makes it equally unsuited for public release. Unfortunately, recent work on membership inference in the broader area of adversarial machine learning and inference attacks on machine learning models has shown that even black box classifiers leak information on the dataset that they were trained on. We show that such membership inference attacks can be successfully carried out on complex, state of the art models for semantic segmentation. In order to mitigate the associated risks, we also study a series of defenses against such membership inference attacks and find effective counter measures against the existing risks with little effect on the utility of the segmentation method. Finally, we extensively evaluate our attacks and defenses on a range of relevant real-world datasets: Cityscapes, BDD100K, and Mapillary Vistas.Comment: Accepted to ECCV 2020. Code at: https://github.com/SSAW14/segmentation_membership_inferenc

Inferring epidemic dynamics using Gaussian process emulation of agent-based simulations

Computational models help decision makers understand epidemic dynamics to optimize public health interventions. Agent-based simulation of disease spread in synthetic populations allows us to compare and contrast different effects across identical populations or to investigate the effect of interventions keeping every other factor constant between ``digital twins''. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geo-spatial perspective using a synthetic population that is constructed based on the U.S. census data. In this paper, we show how Gaussian process regression can be used on FRED-synthesized data to infer the differing spatial dispersion of the epidemic dynamics for two disease conditions that start from the same initial conditions and spread among identical populations. Our results showcase the utility of agent-based simulation frameworks such as FRED for inferring differences between conditions where controlling for all confounding factors for such comparisons is next to impossible without synthetic data.Comment: To be presented in Winter Simulation Conference 2023, repository link: https://github.com/abdulrahmanfci/gpr-ab

Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder

Large-scale models require substantial computational resources for analysis and studying treatment conditions. Specifically, estimating treatment effects using simulations may require a lot of infeasible resources to allocate at every treatment condition. Therefore, it is essential to develop efficient methods to allocate computational resources for estimating treatment effects. Agent-based simulation allows us to generate highly realistic simulation samples. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geospatial perspective using a synthetic population constructed based on the U.S. census data. Given its synthetic population, FRED simulations present a baseline for comparable results from different treatment conditions and treatment conditions. In this paper, we show three other methods for estimating treatment effects. In the first method, we resort to brute-force allocation, where all treatment conditions have an equal number of samples with a relatively large number of simulation runs. In the second method, we try to reduce the number of simulation runs by customizing individual samples required for each treatment effect based on the width of confidence intervals around the mean estimates. In the third method, we use a regression model, which allows us to learn across the treatment conditions such that simulation samples allocated for a treatment condition will help better estimate treatment effects in other conditions. We show that the regression-based methods result in a comparable estimate of treatment effects with less computational resources. The reduced variability and faster convergence of model-based estimates come at the cost of increased bias, and the bias-variance trade-off can be controlled by adjusting the number of model parameters (e.g., including higher-order interaction terms in the regression model).Comment: To be presented in IEEE International Conference on Biomedical and Health Informatics 2023, repository link: https://github.com/abdulrahmanfci/intervention-estimatio

Health monitoring of civil infrastructures by subspace system identification method: an overview

Structural health monitoring (SHM) is the main contributor of the future's smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author's knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated

Effect of Glycyrrhiza glabra (D-reglis tablet) on pain and defecation of patients with irritable bowel syndrome

Background and Aim: Glycyrrhiza glabra has a wide variety of therapeutic effects especially on gastrointestinal (GI) tract and its anti-spasmodic and anti-inflammatory effects have also been reported. This study was preformed to determine the effects of Glycyrrhiza glabra (D-reglis tablet) on the pain and defecation of patients with irritable bowel syndrome (IBS). Materials and Methods: In a randomized double blind clinical trial, 90 patients with IBS referred to gastrointestinal clinic of Shahrekord University of Medical Sciences were randomly selected into case and control groups. Patients in the case group received nortriptyline plus D-reglis (6 tablets in three divided doses for 8 weeks) and patients in the control group received nortriptyline and placebo. During the trial, patients were evaluated for pain severity (based on VAS grade) and defecation condition (with a questionnaire). Data were analyzed by means of SPSS, using relevant statistical tests at the significant level of P<0.05. Results: Although the pain severity showed a decreasing trend in both the case and control groups during the 8 weeks of trial (P0.05). Compared to the control group, patients in the case group spent less time having normal stool (P=0.02) and more time (P=0.02) having hard stool. Conclusion: It seems that Glycyrrhiza glabra has no significant effect on the pain of IBS patients; however, it may improve the diarrhea or exacerbate the constipation in these patients

On the Feasibility of Malware Authorship Attribution

There are many occasions in which the security community is interested to discover the authorship of malware binaries, either for digital forensics analysis of malware corpora or for thwarting live threats of malware invasion. Such a discovery of authorship might be possible due to stylistic features inherent to software codes written by human programmers. Existing studies of authorship attribution of general purpose software mainly focus on source code, which is typically based on the style of programs and environment. However, those features critically depend on the availability of the program source code, which is usually not the case when dealing with malware binaries. Such program binaries often do not retain many semantic or stylistic features due to the compilation process. Therefore, authorship attribution in the domain of malware binaries based on features and styles that will survive the compilation process is challenging. This paper provides the state of the art in this literature. Further, we analyze the features involved in those techniques. By using a case study, we identify features that can survive the compilation process. Finally, we analyze existing works on binary authorship attribution and study their applicability to real malware binaries.Comment: FPS 201

Sex-Specific Impacts of Exercise on Cardiovascular Remodeling

Cardiovascular diseases (CVD) remain the leading cause of death in men and women. Biological sex plays a major role in cardiovascular physiology and pathological cardiovascular remodeling. Traditionally, pathological remodeling of cardiovascular system refers to the molecular, cellular, and morphological changes that result from insults, such as myocardial infarction or hypertension. Regular exercise training is known to induce physiological cardiovascular remodeling and beneficial functional adaptation of the cardiovascular apparatus. However, impact of exercise-induced cardiovascular remodeling and functional adaptation varies between males and females. This review aims to compare and contrast sex-specific manifestations of exercise-induced cardiovascular remodeling and functional adaptation. Specifically, we review (1) sex disparities in cardiovascular function, (2) influence of biological sex on exercise-induced cardiovascular remodeling and functional adaptation, and (3) sex-specific impacts of various types, intensities, and durations of exercise training on cardiovascular apparatus. The review highlights both animal and human studies in order to give an all-encompassing view of the exercise-induced sex differences in cardiovascular system and addresses the gaps in knowledge in the field

HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information

Development of advance surface Electromyogram (sEMG)-based Human-Machine Interface (HMI) systems is of paramount importance to pave the way towards emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the main focus of recent literature was on development of different Deep Neural Network (DNN)-based architectures that perform Hand Gesture Recognition (HGR) at a macroscopic level (i.e., directly from sEMG signals). At the same time, advancements in acquisition of High-Density sEMG signals (HD-sEMG) have resulted in a surge of significant interest on sEMG decomposition techniques to extract microscopic neural drive information. However, due to complexities of sEMG decomposition and added computational overhead, HGR at microscopic level is less explored than its aforementioned DNN-based counterparts. In this regard, we propose the HYDRA-HGR framework, which is a hybrid model that simultaneously extracts a set of temporal and spatial features through its two independent Vision Transformer (ViT)-based parallel architectures (the so called Macro and Micro paths). The Macro Path is trained directly on the pre-processed HD-sEMG signals, while the Micro path is fed with the p-to-p values of the extracted Motor Unit Action Potentials (MUAPs) of each source. Extracted features at macroscopic and microscopic levels are then coupled via a Fully Connected (FC) fusion layer. We evaluate the proposed hybrid HYDRA-HGR framework through a recently released HD-sEMG dataset, and show that it significantly outperforms its stand-alone counterparts. The proposed HYDRA-HGR framework achieves average accuracy of 94.86% for the 250 ms window size, which is 5.52% and 8.22% higher than that of the Macro and Micro paths, respectively

Generative BIM workspace for AEC conceptual design automation: prototype development

This is an accepted manuscript of an article published by Emerald in Engineering, Construction and Architectural Management on 10/07/2020, available online: https://doi.org/10.1108/ECAM-04-2020-0256 The accepted version of the publication may differ from the final published version.Purpose: The integration and automation of the whole design and implementation process have become a pivotal factor in construction projects. Problems of process integration, particularly at the conceptual design stage, often manifest through a number of significant areas, from design representation, cognition and translation to process fragmentation and loss of design integrity. Whilst building information modelling (BIM) applications can be used to support design automation, particularly through the modelling, amendment and management stages, they do not explicitly provide whole design integration. This is a significant challenge. However, advances in generative design now offer significant potential for enhancing the design experience to mitigate this challenge. Design/methodology/approach: The approach outlined in this paper specifically addresses BIM deficiencies at the conceptual design stage, where the core drivers and indicators of BIM and generative design are identified and mapped into a generative BIM (G-BIM) framework and subsequently embedded into a G-BIM prototype. This actively engages generative design methods into a single dynamic BIM environment to support the early conceptual design process. The developed prototype followed the CIFE “horseshoe” methodology of aligning theoretical research with scientific methods to procure architecture, construction and engineering (AEC)-based solutions. This G-BIM prototype was also tested and validated through a focus group workshop engaging five AEC domain experts. Findings: The G-BIM prototype presents a valuable set of rubrics to support the conceptual design stage using generative design. It benefits from the advanced features of BIM tools in relation to illustration and collaboration (coupled with BIM's parametric change management features). Research limitations/implications: This prototype has been evaluated through multiple projects and scenarios. However, additional test data is needed to further improve system veracity using conventional and non-standard real-life design settings (and contexts). This will be reported in later works. Originality/value: Originality and value rest with addressing the shortcomings of previous research on automation during the design process. It also addresses novel computational issues relating to the implementation of generative design systems, where, for example, instead of engaging static and formal description of the domain concepts, G-BIM actively enhances the applicability of BIM during the early design stages to generate optimised (and more purposeful) design solutions.Published versio