Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical sciences.The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases.The use of IIF for detecting autoimmune diseases is widespread in different medical areas.Nearly 80 different types of autoimmune diseases have existed in various body parts.The IIF has been used for image classification in both ways,manually and by using the Computer-Aided Detection(CAD)system.The data scientists conducted various research works using an automatic CAD system with low accuracy.The diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)approach.The baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune diseases.The technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the body.In the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of TL.Data augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL models.These models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,respectively.Therefore,DenseNet-121 shows the highest performance with suitable analysis of autoimmune diseases.The overall performance highlighted that TL is a suitable and enhanced technique compared to its counterparts.Also,the proposed technique is used to detect autoimmune diseases with a minimal margin of errors and flaws.

应用ImageJ程序包进行材料显微组织图像处理、分析和可视化的一种框架(英文)

显微组织图像(例如胞、粒子与晶粒等)的数字图像处理、分割和分析,对于获取显微组织特征的三维信息非常重要。已有数种商用和共享程序包可以用于图像的处理和分析。"ImageJ"即其中之一,其长期广泛采用及其可扩展插件形式已使其成为许多不同应用领域科学家选用的工具。它包含了处理、分割、重建和可视化材料显微结构所需要的几乎所有基本的和最新的功能以及图像分析工具(例如‘Particle Analyzer’,‘3D object counter’,‘3D Roi Manager’),以用于成组粒子的复杂统计处理。虽然它在生物医学研究领域很受欢迎,被认为是一种有用的和有效的开放源码的图像处理与分析软件,但是在材料科学领域对其所知甚少。面向材料学界,本文尤其是那些没有图像处理和分析经验的材料科学与工程专业人员,在简要介绍ImageJ的基础上,提出了将其应用于在三维空间中处理、分割和分析显微组织结构连续切片图像的一个通用框架。

Analysis of 1246 Cases of Orbital Lesions: A Study of 17 Years

Purpose: The aim of this study is to present an analysis of orbital lesions by classifying them according to the site of origin in patients of all ages presenting at a tertiary care eye hospital from 1996 till 2012. Methods: 1637 patients were initially enrolled in this descriptive case series. Clinical data of 1246 patients who completed the study during 17 years were analyzed. Orbital lesions of the patients were examined and managed medically or surgically as per requirement. The histopathological reports of these patients were used to classify the lesions. Results: Out of all cases 54.57% (n = 680) were neoplastic and 45.42% (n = 566) were nonneoplastic lesions. Amongst the neoplastic lesions 86.17% (n = 586) were malignant and 13.82% (n = 94) were benign. Primary orbital lesions were the most common orbital lesions being 963 (77.29%) followed by secondary orbital lesions being 232 (18.62%), hematopoietic reticuloendothelial being 47 (3.77%) and metastatic lesions being 04 (0.32%). Conclusion: Orbital lesions are more common in adults as compared to children. Neoplastic lesions are more common than nonneoplastic lesions, and amongst the neoplastic lesions malignant lesions are more common than benign ones. Primary orbital lesions are the most common orbital lesions followed by secondary orbital lesions, lesions of the hematopoietic reticuloendothelial system and metastatic lesions.

Variation in patient dose due to differences in calibration and dosimetry protocols

For precise and accurate patient dose delivery,the dosimetry system must be calibrated properly according to the recommendations of standard dosimetry protocols such as TG-51 and TRS-398. However, the dosimetry protocol followed by a calibration laboratory is usually different from the protocols that are followed by different clinics, which may result in variations in the patient dose.Our prime objective in this study was to investigate the effect of the two protocols on dosimetry measurements.Dose measurements were performed for a Co-60 teletherapy unit and a high-energy Varian linear accelerator with 6 and 15 MV photon and 6, 9, 12, and 15 MeV electron beams, following the recommendations and procedures of the AAPM TG-51 and IAEA TRS-398 dosimetry protocols. The dosimetry systems used for this study were calibrated in a Co-60 radiation beam at the Secondary Standard Dosimetry Laboratory(SSDL) PINSTECH,Pakistan, following the IAEA TRS-398 protocol. The ratio of the measured absorbed doses to water in clinical setting,D_w(TG-51/TRS-398), was 0.999 and 0.997 for 6 and15 MV photon beams,whereas these ratios were 1.013,1.009, 1.003, and 1.000 for 6, 9, 12, and 15 MeV electron beams, respectively. This difference in the absorbed dosesto-water D_w ratio may be attributed mainly due to beam quality(K_Q) and ion recombination correction factor.

Molecular Docking and Pharmacological Property Analysis of Phytochemicals from <i>Clitoria ternatea</i>as Potent Inhibitors of Cell Cycle Checkpoint Proteins in the Cyclin/CDK Pathway in Cancer Cells

Cancer comprises a group of diseases which are involved in the aberrant growth of the cells causing disruption of normal body function. Due to the lack of proper sophisticated treatments this nasty disease leads to the death of most of the patients affected with it. Moreover, treatments like chemotherapy involve other post-treatment complications which make them unfavorable for extended use. Medicinal plants possess many phytochemicals of great therapeutic value and many of them are effective in killing cancer cells. These compounds working by variety of mechanisms and in most of the cases exhibit their anticancer potentiality by inhibiting many proteins involved in cell growth and division. Molecular docking is a computational approach which facilitates the finding of the best molecule from a group which may bind with the highest affinity with the intended target by providing a virtual biological system. This process works on the basis of specific algorithm and involves scoring function to rank the molecules that fit with the target. This study has been designed to investigate the potentiality of four phytochemicals from Clitoria ternatea—Kaempferol, Myricetin, P-Hydroxycinnamic acid and Quercetin as inhibitors of two cell cycle checkpoint proteins—Cyclin Dependent Kinase-2 (CDK-2) and Cyclin Dependent Kinase-6 (CDK-6) in Cyclin/CDK pathway. Quercetin and Myricetin docked with higher affinity with CDK-2 and CDK-6 respectively. Drug likeness property analysis and ADME/T test impose computational approach to investigate physicochemical and pharmacological properties of candidate drug molecules. P-Hydroxycinnamic acid performed well in both drug likeness property analysis and ADME/T than Quercetin and Myricetin. So, P-Hydroxycinnamic acid is the best finding of this experiment.

Climatic Changes and Their Effect on Wildlife of District Dir Lower, Khyber Pakhtunkhwa, Pakistan

Climatic changes and their impact are increasingly evident in Pakistan,especially in the mountainous regions. Mountain ecosystems are consideredto be sensitive indicators of global warming;even slight variationsin temperature can lead to significant shifts in local climate, which can,in turn, drastically affect the natural environment, subsequently alteringpeople’s lifestyle and wildlife habitats. The targeted area for the presentresearch was Lower Dir District, Pakistan. The study gathered the requiredinformation from primary and secondary sources. Secondary dataon temperature and precipitation were obtained from various sources,i.e., local CBO, including WWF Pakistan. Based on information gatheredon climate change and wildlife, a detailed questionnaire was designed.Results showed that no regular pattern of the increase was found in temperaturefrom 2010 to 2018;the same was noticed in the rainfall decreasepattern. Results also showed that the leading causes behind climaticchanges are an increase in greenhouse gases due to pollution by industries,vehicles, crushing plants, deforestation, and some natural phenomenasuch as floods. The study showed that more than 80% of the respondentsagreed that climatic effects have a significant impact on wildlife, i.e.,the existence of wildlife falls in danger due to climatic changes as it maylead to habitat change, making it difficult for the survival and adaptationof the wildlife. Hence, in consequence, it leads to migration, low growthrate, an increase in morbidity and mortality rate, and finally leading to theextinction of the species or population. It is concluded from the study thatpeople are severely noticing the climatic change and its leading causesare greenhouse gases and deforestation. To control climatic changes andwildlife extinction, we need an appropriate policy for forest conservation,wildlife conservation, prevent hunting, industrial pollution control, vehiclepollution control, increase in plantation, awareness of policy for thecontrol of climatic changes, etc.

Machine learning study on time-temperature-transformation diagram of carbon and low-alloy steel

Time-temperature-transformation(TTT)diagram plays a critical role in designing appropriate heat treatment process of steels by describing the relationship among holding time,temperature,and quantities of phase transformation.Making predictions for TTT diagrams of new steel rapidly and accurately is therefore of much practical importance,especially for costly and time-consuming experimental determination.Here,TTT diagrams for carbon and low-alloy steels were predicted using machine learning methods.Five commonly used machine learning(ML)algorithms,backpropagation artificial neural network(BP network),LibSVM,k-nearest neighbor,Bagging,and Random tree,were adopted to select appropriate models for the prediction.The results illustrate that Bagging is the optimal model for the prediction of pearlite transformation and bainite transformation,and BP network is the optimal model for martensite transformation.Finally,the ML framework composed of Bagging and BP network models was applied to predict the entire TTT diagram.Additionally,the ML models show superior performance on the prediction of testing samples than the commercial software JMatPro.

基于光致变色效应的BaMgSiO4:Bi3+陶瓷的发光性质调控及信息的可逆写入、擦除和读出研究（英文）

光存储技术与传统固态存储和磁存储相比有许多优点,如成本低、可重复性存储.因此,光存储的需求正在持续增长.本文研究了铋掺杂的硅酸镁钡陶瓷的光致变色及光致发光现象.在紫外线(254 nm)和532 nm激光交替辐照下,实现了陶瓷在灰色与粉红色之间的可逆转变.研究证实MgSiO4:Bi^3+陶瓷的可逆光致变色来源于基质中的氧空位陷阱对电子的俘获和释放.基于可逆光致变色效应在陶瓷表面制作不同颜色的图案,实现了光信号的双模式存储和擦除.同时,在光致变色过程中实现了光致发光的可逆调控;通过这种可逆的发光调控行为,实现存储信息的读出;此外,变色和漂白的程度与光照射时间有关,因此可实现多通路编码存储,有望提高光存储容量.

Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels

Continuous cooling transformation diagrams in synthetic weld heat-affected zone(SH-CCT diagrams)show the phase transition temperature and hardness at different cooling rates,which is an important basis for formulating the welding process or predicting the performance of welding heat-affected zone.However,the experimental determination of SH-CCT diagrams is a time-consuming and costly process,which does not conform to the development trend of new materials.In addition,the prediction of SHCCT diagrams using metallurgical models remains a challenge due to the complexity of alloying elements and welding processes.So,in this study,a hybrid machine learning model consisting of multilayer perceptron classifier,k-Nearest Neighbors and random forest is established to predict the phase transformation temperature and hardness of low alloy steel using chemical composition and cooling rate.Then the SH-CCT diagrams of 6 kinds of steels are calculated by the hybrid machine learning model.The results show that the accuracy of the classification model is up to 100%,the predicted values of the regression models are in good agreement with the experimental results,with high correlation coefficient and low error value.Moreover,the mathematical expressions of hardness in welding heat-affected zone of low alloy steel are calculated by symbolic regression,which can quantitatively express the relationship between alloy composition,cooling time and hardness.This study demonstrates the great potential of the material informatics in the field of welding technology.

Neighborhood topological effect on grain topology-size relationship in three-dimensional polycrystalline microstructures

A new grain topology-size relationship in three-dimensional(3D)polycrystalline microstructures has recently been established by considering the effects of non-random first nearest neighbor grains.In this contribution,a generalized form for this relationship is presented by considering the interactions of kth(k=1,2,3…)nearest neighbor grains,and large scale Monte Carlo-Potts model simulation is used to investigate the general neighborhood topological effect on the topology-size relationship.The results show that,unlike their first nearest neighbors(k=1),the topological correlations of 3D grains with their kth layers(k 2)of nearest-neighbors may have trivial effect on the topology-size relationship.