Quercetin improved spatial memory dysfunctions in rat model of intracerebroventricular streptozotocin-induced sporadic Alzheimer’sdisease

Background: Alzheimer’s disease (AD) is one of the most common neurodegenerative syndromes characterized by a progressive decline in the spatial memory. There are convincing evidences on the neuroprotective effects of flavonoids against AD. Aims and Objective: To determine the effect of quercetin on the acquisition and retention of spatial memory in a rat model of AD. Materials and Methods: Twenty-four male Wistar rats were divided into four groups (six in each): group I: control rats receiving intracerebroventricular (ICV) injection of normal saline, group II: rats induced AD by ICV injection of streptozotocin (STZ; 3 mg/kg bilaterally; twice, on days 1 and 3), and groups III and IV: ICV-STZ AD rats treated intraperitoneally (IP) with 40 and 80 mg/kg/day quercetin, respectively, over a period of 12 days. Then, the rats were trained with four trials per day for five consecutive days in the Morris water maze (MWM). On the sixth day, the memory retention was evaluated. Result: The ICV-STZ AD groups showed a significant impairment in the acquisition and retrieval of spatial memory when compared with the control group (P < 0.001). In the AD groups, the escape latency during the training trials showed a significant decrease (P < 0.001). Meanwhile, during the MWM task, theseratsspentmoretimeinthetargetquadrant in probe trials when compared with the controls. Conclusion: Quercetin acted as a spatial memory enhancer in ICV-STZ–induced AD rats. Hence, this flavonoid can be considered potentially as a promising agent for developing prophylactic and therapeutic neuroprotection. This neuroprotective effect of quercetin may be attributed to its antioxidant and scavenging properties. © 2015 Hamid Sepehri

On sign-symmetric signed graphs

A signed graph is said to be sign-symmetric if it is switching isomorphic to its negation. Bipartite signed graphs are trivially sign-symmetric. We give new constructions of non-bipartite sign-symmetric signed graphs. Sign-symmetric signed graphs have a symmetric spectrum but not the other way around. We present constructions of signed graphs with symmetric spectra which are not sign-symmetric. This, in particular answers a problem posed by Belardo, Cioabă, Koolen, and Wang (2018)

A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months

Background: Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings. Methods: In this study, we examined the capability of a machine learning-based model in predicting �favorable� or �unfavorable� outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, and accuracy. A ten-fold cross-validation method was used to estimate these indices. Results: Overall, the developed models showed excellent performance with the area under the curve around 0.81, sensitivity and specificity of around 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are �Glasgow coma scale motor response,� �pupillary reactivity,� and �age.� Conclusions: Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set. © 2022 Korean Society of Critical Care Medicine. All right reserved

An android application for estimating muscle onset latency using surface EMG signal

Background: Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs. Objective: Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG. Material and Methods: A multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too. Results: Evaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error <10. MOL values estimated by these two systems are linearly related, the correlation coefficient value ~ 0.93. These evaluations revealed that the presented app performed as well as MegaWin 3.0 software in estimating MOL. Conclusion: Recent advances in smart portable devices such as mobile phones have shown the great capability of facilitating and decreasing the cost of analyzing biomedical signals, particularly in academic environments. Here, we developed an Android app for estimating MOL via analyzing the surface EMG signal. Performance is promising to use the app for teaching or research purposes. © 2019, Shiraz University of Medical Sciences. All right reserved

An automated coding and classification system with supporting database for effective design of manufacturing systems

The philosophy of group technology (GT) is an important concept in the design of flexible manufacturing systems and manufacturing cells. Group technology is a manufacturing philosophy that identifies similar parts and groups them into families. Beside assigning unique codes to these parts, group technology developers intend to take advantage of part similarities during design and manufacturing processes. GT is not the answer to all manufacturing problems, but it is a good management technique with which to standardize efforts and eliminate duplication. Group technology classifies parts by assigning them to different families based on their similarities in: (1) design attributes (physical shape and size), and/or (2) manufacturing attributes (processing sequence). The manufacturing industry today is process focused; departments and sub units are no longer independent but are interdependent. If the product development process is to be optimized, engineering and manufacturing cannot remain independent any more: they must be coordinated. Each sub-system is a critical component within an integrated manufacturing framework. The coding and classification system is the basis of CAPP and the functioning and reliability of CAPP depends on the robustness of the coding system. The proposed coding system is considered superior to the previously proposed coding systems, in that it has the capability to migrate into multiple manufacturing environments. This article presents the design of a coding and classification system and the supporting database for manufacturing processes based on both design and manufacturing attributes of parts. An interface with the spreadsheet will calculate the machine operation costs for various processes. This menu-driven interactive package is implemented using dBASE-IV. Part Family formation is achieved using a KAMCELL package developed in TURBO Pascal.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46606/1/10845_2004_Article_BF00123696.pd

Spectral symmetry in conference matrices

A conference matrix of order n is an n× n matrix C with diagonal entries 0 and off-diagonal entries ± 1 satisfying CC⊤= (n- 1) I. If C is symmetric, then C has a symmetric spectrum Σ (that is, Σ = - Σ) and eigenvalues ±n-1. We show that many principal submatrices of C also have symmetric spectrum, which leads to examples of Seidel matrices of graphs (or, equivalently, adjacency matrices of complete signed graphs) with a symmetric spectrum. In addition, we show that some Seidel matrices with symmetric spectrum can be characterized by this construction