Ensemble methods for instance-based Arabic language authorship attribution

The Authorship Attribution (AA) is considered as a subfield of authorship analysis and it is an important problem as the range of anonymous information increased with fast growing of internet usage worldwide. In other languages such as English, Spanish and Chinese, such issue is quite well studied. However, in Arabic language, the AA problem has received less attention from the research community due to complexity and nature of Arabic sentences. The paper presented an intensive review on previous studies for Arabic language. Based on that, this study has employed the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method to choose the base classifier of the ensemble methods. In terms of attribution features, hundreds of stylometric features and distinct words using several tools have been extracted. Then, Adaboost and Bagging ensemble methods have been applied on Arabic enquires (Fatwa) dataset. The findings showed an improvement of the effectiveness of the authorship attribution task in the Arabic language

Cryo-EM structure of nucleotide-bound Tel1ATM unravels the molecular basis of inhibition and structural rationale for disease-associated mutations

Yeast Tel1 and its highly conserved human ortholog ataxia-telangiectasia mutated (ATM) are large protein kinases central to the maintenance of genome integrity. Mutations in ATM are found in ataxia-telangiectasia (A-T) patients and ATM is one of the most frequently mutated genes in many cancers. Using cryoelectron microscopy, we present the structure of Tel1 in a nucleotide-bound state. Our structure reveals molecular details of key residues surrounding the nucleotide binding site and provides a structural and molecular basis for its intrinsically low basal activity. We show that the catalytic residues are in a productive conformation for catalysis, but the phosphatidylinositol 3-kinase-related kinase (PIKK) regulatory domain insert restricts peptide substrate access and the N-lobe is in an open conformation, thus explaining the requirement for Tel1 activation. Structural comparisons with other PIKKs suggest a conserved and common allosteric activation mechanism. Our work also provides a structural rationale for many mutations found in A-T and cancer

Modeling cancer genomic data in yeast reveals selection against ATM function during tumorigenesis

The DNA damage response (DDR) comprises multiple functions that collectively preserve genomic integrity and suppress tumorigenesis. The Mre11 complex and ATM govern a major axis of the DDR and several lines of evidence implicate that axis in tumor suppression. Components of the Mre11 complex are mutated in approximately five percent of human cancers. Inherited mutations of complex members cause severe chromosome instability syndromes, such as Nijmegen Breakage Syndrome, which is associated with strong predisposition to malignancy. And in mice, Mre11 complex mutations are markedly more susceptible to oncogene- induced carcinogenesis. The complex is integral to all modes of DNA double strand break (DSB) repair and is required for the activation of ATM to effect DNA damage signaling. To understand which functions of the Mre11 complex are important for tumor suppression, we undertook mining of cancer genomic data from the clinical sequencing program at Memorial Sloan Kettering Cancer Center, which includes the Mre11 complex among the 468 genes assessed. Twenty five mutations in MRE11 and RAD50 were modeled in S. cerevisiae and in vitro. The mutations were chosen based on recurrence and conservation between human and yeast. We found that a significant fraction of tumor-borne RAD50 and MRE11 mutations exhibited separation of function phenotypes wherein Tel1/ATM activation was severely impaired while DNA repair functions were mildly or not affected. At the molecular level, the gene products of RAD50 mutations exhibited defects in ATP binding and hydrolysis. The data reflect the importance of Rad50 ATPase activity for Tel1/ATM activation and suggest that inactivation of ATM signaling confers an advantage to burgeoning tumor cells

An optimized stacking ensemble model for phishing websites detection

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectivel

Antenatal depression among pregnant mothers in Afghanistan: a cross-sectional study

Background: Approximately one in five pregnant women experience antenatal depression globally. The purpose of the present study was to estimate the prevalence of antenatal depression and explore its relationship between various demographic variables, recent sexual engagement, and recent adverse life events among pregnant Afghan women. Methods: A cross-sectional survey study was carried out between January, 2023 and April 2023 among 460 women aged 15–45 years who were recruited using convenience sampling from Herat province (Afghanistan). Logistic regression models were utilized to explore the relationship between antenatal depression and socio-demographic characteristics among the participants. Results: The prevalence of antenatal depression symptoms was 78.5%. Multiple regression analysis indicated that antenatal depression was significantly associated with (i) being aged 30–45 years (AOR: 4.216, 95% CI: 1.868–9.515, p = .001), (ii) being of low economic status (AOR:2.102, 95% CI: 1.051–4.202, p = .036), (iii) not being employed (AOR: 2.445, 95% CI:1.189–5.025, p = .015), (iv) not having had sex during the past seven days (AOR: 2.335, 95% CI: 1.427–3.822, p = .001), and (v) not experiencing a traumatic event during the past month (AOR:0.263, 95% CI: 0.139–0.495, p < .001). Conclusion: The present study provides insight into the factors associated with the high prevalence of antenatal depression among pregnant Afghan women (e.g., demographic variables, recent adverse life events, and recent sexual engagement). It highlights the urgency of addressing antenatal depression in Afghanistan and provides a foundation for future research and interventions aimed at improving the mental health and well-being of pregnant women in the Afghan context

Phishing websites detection by using optimized stacking ensemble model

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent

CHROMIUM STATUS IN DIABETES MELLITUS

Fasting serum chromium, total cholesterol HDL-cholesterol, LDL-cholesterol, triacytglycerot and blood sugar were determined in fifty two diabetic patients with no other organic diseases anil compared with those obtained from a control group including fourty two healthy volunteers matched for age, sex ami body mass irutex (BMI). Fasting serum chromium and HDL-cholesterol were significantly lower in patients than in controls (p&amp;lt;0.0001 and p&amp;lt;0.001 respectively), but the mean triacytglycerot concentration was significantly higher in patients than in controls (p&amp;lt;002). Mean total cholesterol and LDL-cholesterol values were not significantly different in the two groups. Mean intake of energy, proteins, fats and chromium, estimated by the 24 hr dietary recall method were not significantly different in the two groups. We demonstrated that despite an adequate intake of chromium, the fasting serum chromium was lower in diabetic patients than in control subjects. Chromium deficiency in diabetic patients may act as a contributing factor in aggravating the disease&apos;s complications

RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification

Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different types of resolutions (radiometric, spatial, spectral, and temporal), modes of imaging, and sensor types, and this range of options often makes the process of analyzing and interpreting such data more difficult. In this paper, which is the first study of its kind, we propose a novel distributed deep learning-based approach for the classification of big remote sensing images. Specifically, we propose Distributed Convolutional-Neural-Networks for handling RS image classification (RS-DCNN). The first step is to prepare the training dataset for RS-DCNN. Then, to ensure a data-parallel training on the top of the Apache Spark framework, a pixel-based convolutional-neural-network model across the big data cluster is performed using BigDL. Experiments are conducted on a real dataset covering many regions of Saudi Arabia and the results demonstrate high classification accuracy at a faster speed than other state-of-the-art classification methods

Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for VANET

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET

The particle size of drug nanocarriers dictates the fate of neurons; Critical points in neurological therapeutics

Neurological disorders and diseases are on the rise in the world, while pharmacists are being encouraged to encapsulate drugs into the nanocarriers. The critical key question is which size of nanocarrier has a promising neurotherapeutic effect. In the present study, FTY-720, an FDA approved drug, was encapsulated into O/W nanocarriers. SEM and DLS data indicated in ultrasonication and stirring methods resulted in spherical nanocarriers with a particle size of 60 and 195 nm (nF60 and nF195), respectively. Further to investigate the effect of particle size on neuronal cells, MTT assay, PI flow-cytometry, LDH release, and NO production examinations were performed. Results showed that small nanocarriers increased cell viability along with the decline of dead cells, while both nanocarriers decreased LDH release and NO production as compared to the conventional drug. Notably, qRT-PCR and western blotting data related to apoptotic markers indicated in the increase of cell mortality in cells treated by nF190 was not due to the increase of apoptosis and Bax/Bcl2 ratio. It is worth mentioning that integrin α5 as a cell surface receptor involves in neuritogenesis was over-expressed in neuronal cells treated by small nanocarriers. However, nF60 increased PTK2 over-expression along with neurite outgrowth, as well. In other words, nanocarriers at the size of 60 nm are preferred to 195 nm as a drug carrier in neurotherapy due to profound impacts on neural cells. Thanks to small nanocarrier broad positive action on neural viability and neurite outgrowth. The present study discloses a pharmaceutical strategy to design drugs based on their particle size efficiency. © 2020 IOP Publishing Ltd