Search

Gulf Pidgin Arabic (GPA) is a simplified communication system established by the non-Arabic immigrants to Gulf countries in the Middle East. In the past decades, millions of people with no Arabic skills have come from countries such as Bangladesh, India, Indonesia, Pakistan and the Philippines for job opportunities. They needed to try to speak Arabic in order to communicate with their employers and this communication system has evolved into a pidgin. GPA is based on Gulf Arabic (GA), the variety of Arabic spoken in the Gulf states: Bahrain, Oman, Qatar, United Arab Emirates, Kuwait, and nearby areas.

The current article argues that the multiple nominative constructions of the Japanese type do not exist in Standard Arabic. Based on evidence from binding, A′-interception and Case, the article shows that the so-called ‘broad subject’ is a clitic left-dislocated element base generated in the A′-domain. The article thus follows the spirit of the analysis proposed by Landau (e.g., Landau, Idan. 2009. Against broad subjects in Hebrew. Lingua 119(1). 89–101), who denies the existence of broad subjects in Hebrew, concluding that broad subjects do not exist in Semitic languages at all.

Colon cancer (CC) is a significant cause of death worldwide, particularly in Saudi Arabia. To increase the accuracy of diagnosis and treatment, it is important to discover new specific biomarkers for CC. The main objectives of this research are to identify potential specific biomarkers for the early diagnosis of CC by analyzing the expressions of eight cancer testis (CT) genes, as well as to analyze how epigenetic mechanisms control the expression of these genes in CC cell lines. Tissue samples were collected from 15 male patients with CC tissues and matched NC tissues for gene expression analysis. The expression levels of specific CT genes, including ADAD1, DMRTC2, PRSS54, SYCE1, SYCP1, TEX101, TEX48, and TMPRSS12, were assessed using quantitative techniques. To validate the gene expression patterns, we used publicly available CC statistics. To investigate the effect of inhibition of DNA methylation and histone deacetylation on CT gene expression, in vitro experiments were performed using HCT116 and Caco-2 cell lines. There was no detected expression of the genes neither in the patient samples nor in NC tissues, except for TEX48, which exhibited upregulation in CC samples compared to NC tissues in online datasets. Notably, CT genes showed expression in testis samples. In vitro, experiments demonstrated significant enhancement in mRNA expression levels of ADAD1, DMRTC2, PRSS54, SYCE1, SYCP1, TEX101, TEX48, and TMPRSS12 following treatment with 5-aza-2'-deoxycytidine and trichostatin A in HCT116 and Caco-2 cell lines. Epigenetic treatments modify the expression of CT genes, indicating that these genes can potentially be used as biomarkers for CC. The importance of conducting further research to understand and target epigenetic mechanisms to improve CC treatment cannot be overemphasized. [ABSTRACT FROM AUTHOR]

Bisphenol A (BPA) is a monomer widely used in our living environment to manufacture many chemical products. Mammalian cells are exposed to it at high levels, which has toxic and estrogenic consequences. Moreover, BPA exposure during prenatal and neonatal development results in defects in the male and female reproductive systems, abnormal meiotic development in fetal oocytes, and pregnancy complications. The aim of the current study was to study the adverse effects of the oral administration of BPA on the liver and testis of adult male rats with the protective roles of herbal teas. A comparative study was conducted on 40 adult male Wistar rats (six weeks old, weighing 100-120 g). Groups were coded as RT/BPA, GT/BPA, and BT/BPA, for red, green, and blue herbal teas, respectively. Several evaluations such as weights, hepatic enzyme markers, lipid profile, and oxidative nitrosative stress for the liver and testis were conducted. Results indicated that exposure to BPA significantly increased oxidative nitrosative stress and damaged the hepatic tissue on the liver and testis. The ALT GT/BPA group detected the highest values for AST (61.87 mU/mL), ALP (2.48 mU/mL), LDH (1153.11 U/L), and GGT (49.72 U/L); RT/BPA group detected the lowest values for AST (58.71 mU/mL), ALP (1.51 mU/mL), and GGT (21.12 U/L). Similarly, the RT/BPA and BT/BPA groups were detected (4.11 and 4.12 mg/100 mL) for creatinine levels as compared to the vehicle control group. While rats in the BT/BPA and GT/BPA groups showed similar values to the vehicle group (45.02 and 118.28 mg/dI) for HDL and TC quantities, respectively. The present research established the protective role of herbal teas due to the antioxidant properties, especially of green tea, which preserved GPx value (88.22 nmol/mg), testosterone (1.80 ng/mL); and red tea, which preserved CAT value (101.18 U/mg); while blue tea preserved MDA and SOD values (0.81 nmol/mg and 7.18 U/mg), respectively. BPA oral administration with herbal teas reduced toxicity on the liver and testis due to free radical scavenging effects. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA's Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on "ML in IoT" from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning. [ABSTRACT FROM AUTHOR]