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The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans. However, this process costs an immense number of resources and is time consuming. Each reservoir prediction experiment needs tens or hundreds of simulation runs, taking several hours or days to finish. In this paper, we attempt to overcome these issues by creating machine learning and deep learning models to expedite the process of forecasting oil and gas production. The dataset was provided by the leading oil producer, Saudi Aramco. Our approach reduced the time costs to a worst-case of a few minutes. Our study covered eight different ML and DL experiments and achieved its most outstanding R2 scores of 0.96 for XGBoost, 0.97 for ANN, and 0.98 for RNN over the other experiments.

Background: In livestock breeding, oocyte cryopreservation is crucial for preserving and transferring superior genetic traits. This study was conducted to examine the additional effect of melatonin to maturation and vitrification media on the in vitro developmental capacity, mitochondrial distribution, and intensity of buffalo oocytes. The study involved obtaining ovaries from a slaughterhouse and conducting two phases. In the first phase, high-quality oocytes were incubated in a maturation medium with or without 10 −9 M melatonin for 22 h (at 38.5°C in 5% CO 2 ). Matured oocytes were fertilized in vitro and cultured in SOF media for seven days. In the second phase, vitrified in vitro matured oocytes were stored in vitrified media (basic media (BM) containing a combination of cryoprotectants (20% Ethyl Glycol and 20% Dimethyl sulfoxide), with or without melatonin, and then stored in liquid nitrogen. Normal vitrified/thawed oocytes were fertilized in vitro and cultured as described. Finally, the matured oocytes from the fresh and vitrified/thawed groups, both with and without melatonin, were stained using DAPI and Mitotracker red to detect their viability (nuclear maturation), mitochondrial intensity, and distribution using a confocal microscope. The study found that adding 10 −9 M melatonin to the maturation media significantly increased maturation (85.47%), fertilization rate (84.21%)cleavage (89.58%), and transferable embryo (48.83%) rates compared to the group without melatonin (69.85%,79.88%, 75.55%, and 37.25% respectively). Besides that, the addition of melatonin to the vitrification media improved the recovery rate of normal oocytes (83.75%), as well as the cleavage (61.80%) and transferable embryo (27.00%) rates when compared to the vitrified TCM group (67.46%, 51.40%, and 17.00%, respectively). The diffuse mitochondrial distribution was higher in fresh with melatonin (TCM + Mel) (80%) and vitrified with melatonin (VS2 + Mel groups) (76.70%), Furthermore, within the same group, while the mitochondrial intensity was higher in the TCM + Mel group (1698.60) than other group. In conclusion, Melatonin supplementation improves the developmental competence and mitochondrial distribution in buffalo oocytes in both cases(in vitro maturation and vitrification).

Germinal matrix hemorrhage (GMH) is a critical condition affecting premature infants, commonly diagnosed through cranial ultrasound imaging. This study presents an advanced deep learning approach for automated GMH grading using the YOLOv8 model. By analyzing a dataset of 586 infants, we classified ultrasound images into five distinct categories: Normal, Grade 1, Grade 2, Grade 3, and Grade 4. Utilizing transfer learning and data augmentation techniques, the YOLOv8 model achieved exceptional performance, with a mean average precision (mAP50) of 0.979 and a mAP50-95 of 0.724. These results indicate that the YOLOv8 model can significantly enhance the accuracy and efficiency of GMH diagnosis, providing a valuable tool to support radiologists in clinical settings.

Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteristics, such as leaf shape, flower structure, and seed and fruit characters. Artificial intelligence (AI), machine learning, and especially deep learning can also play an instrumental role in plant taxonomy by automating the process of categorizing plant species based on the available features. This study investigated transfer learning techniques to analyze images of plants and extract features that can be used to cluster the species hierarchically using the k-means clustering algorithm. Several pretrained deep learning models were employed and evaluated. In this regard, two separate datasets were used in the study comprising of seed images of wild plants collected from Egypt. Extensive experiments using the transfer learning method (DenseNet201) demonstrated that the proposed methods achieved superior accuracy compared to traditional methods with the highest accuracy of 93% and F1-score and area under the curve (AUC) of 95%, respectively. That is considerable in contrast to the state-of-the-art approaches in the literature.

The deep learning techniques have been playing an important role in the identification and classification problems such as diseases in medical science, marketing in the industry, manufacturing in engineering, and identification in plant taxonomy science. Fruit identification and its family classification is among one of the areas that needs more emphasis for the sake of automation. With this inspiration, fruit images for 52 species belonging to four different families (Apiaceae, Brassicaceae, Asteraceae, and Apocynaceae) have been used in this study to build a deep learning analysis dataset. Further, the dataset has been augmented to 3800 images, divided to 2660 images for training and 1440 for testing, and different 14 fruit images belonging to the same families have been used for prediction of the testing module. A novel Convolution Neural Network (CNN) model architecture has been proposed to extract the fruit features, classify each image with its family, and use the trained model to predict that the new fruits belong to the same four families. The maximum accuracy obtained for the training and testing module was 99.82%. The prediction for this module succeeded by 93% since all fruits’ success predicted was attained except one from the family number 2 (Brassicaceae). The same dataset was applied to two different models to evaluate our proposed model, the Deep learning model, aka Residual Neural Network, 20 layers (ResNet-20), and Support Vector Machine (SVM). The proposed CNN model achieved higher accuracy and efficiency than the ResNet-20 and SVM.

Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Though it can occur in both genders, it is far more common in women. It is a disease in which the patient’s body cells in the breast start growing abnormally. It has various kinds (e.g., invasive ductal carcinoma, invasive lobular carcinoma, medullary, and mucinous), which depend on which cells in the breast turn into cancer. Traditional manual methods used to detect breast cancer are not only time consuming but may also be expensive due to the shortage of experts, especially in developing countries. To contribute to this concern, this study proposed a cost-effective and efficient scheme called AMAN. It is based on deep learning techniques to diagnose breast cancer in its initial stages using X-ray mammograms. This system classifies breast cancer into two stages. In the first stage, it uses a well-trained deep learning model (Xception) while extracting the most crucial features from the patient’s X-ray mammographs. The Xception is a pertained model that is well retrained by this study on the new breast cancer data using the transfer learning approach. In the second stage, it involves the gradient boost scheme to classify the clinical data using a specified set of characteristics. Notably, the experimental results of the proposed scheme are satisfactory. It attained an accuracy, an area under the curve (AUC), and recall of 87%, 95%, and 86%, respectively, for the mammography classification. For the clinical data classification, it achieved an AUC of 97% and a balanced accuracy of 92%. Following these results, the proposed model can be utilized to detect and classify this disease in the relevant patients with high confidence.

The number of social media users has increased. These users share and reshare their ideas in posts and this information can be mined and used by decision-makers in different domains, who analyse and study user opinions on social media networks to improve the quality of products or study specific phenomena. During the COVID-19 pandemic, social media was used to make decisions to limit the spread of the disease using sentiment analysis. Substantial research on this topic has been done; however, there are limited Arabic textual resources on social media. This has resulted in fewer quality sentiment analyses on Arabic texts. This study proposes a model for Arabic sentiment analysis using a Twitter dataset and deep learning models with Arabic word embedding. It uses the supervised deep learning algorithms on the proposed dataset. The dataset contains 51,000 tweets, of which 8,820 are classified as positive, 37,360 neutral, and 8,820 as negative. After cleaning it will contain 31,413. The experiment has been carried out by applying the deep learning models, Convolutional Neural Network and Long Short-Term Memory while comparing the results of different machine learning techniques such as Naive Bayes and Support Vector Machine. The accuracy of the AraBERT model is 0.92% when applying the test on 3,505 tweets.

P-chloroaniline is a polar organochlorine compound and an important member of aromatic amines, widely used in various industries, including pesticides, dyes, and pharmaceuticals. Exposure to high levels of P-chloroaniline can cause severe damage to the liver and kidneys and negatively affect the central nervous system. Recently, concerns have been raised about P-chloroaniline as a contaminant in paracetamol pharmaceutical formulations. Interestingly, this impurity is neither controlled in the API nor in the finished paracetamol products. Consequently, a rapid, sensitive, and selective liquid chromatography mass spectrometry (LC/MS) method was developed and applied as a limit test for the determination of P-chloroaniline in 11 paracetamol pharmaceutical formulations. The LC separation was carried out with a C18 column at 35 ℃ with a mobile phase of 0.1% Methanol: 0.1% formic acid (50:50 v/v). Additionally, P-chloroaniline ions were monitored at 127.9/93 and 127.9/111.0 as qualifier and quantifier ions, respectively, using an ESI ion source. The method was validated as a limit test according to ICH Q2 (R1) guidelines and showed high sensitivity and specificity for the determination of P-chloroaniline in paracetamol formulations. Implementing the method for commercial sample analysis revealed its suitability as a new strategy to ensure product compliance with the quality standards.

Purpose. Breast cancer is a heterogeneous disease. Exploring new prognostic and therapeutic targets in patients with breast cancer is essential. This study investigated the expression of MET, ESR1, and ESR2 genes and their association with clinicopathologic characteristics and clinical outcomes in patients with breast cancer. Methods. The METABRIC dataset for breast cancer was obtained from the cBioPortal public domain. Gene expression data for MET, ESR1, and ESR2, as well as the putative copy number alterations (CNAs) for MET were retrieved. Results. The MET mRNA expression levels correlated inversely with the expression levels of ESR1 and positively with the expression levels of ESR2 (r = −0.379, p<0.001 and r = 0.066, and p=0.004, respectively). The ESR1 mRNA expression was significantly different among MET CNAs groups p<0.001. Patients with high MET/ESR1 coexpression had favorable clinicopathologic tumor characteristics and prognosticators compared to low MET/ESR1 coexpression in terms of greater age at diagnosis, reduced Nottingham Prognostic Index, lower tumor grade, hormone receptor positivity, HER2-negative status, and luminal subtype p<0.001. In contrast, patients with high MET/ESR2 coexpression had unfavorable tumor features and advanced prognosticators compared to patients with low MET/ESR2 coexpression p<0.001. No significant difference in overall survival was observed based on the MET/ESR coexpression status. However, when data were stratified based on the treatment type (chemotherapy and hormonal therapy), survival was significantly different based on the coexpression status of MET/ESR. Conclusions. Findings from our study add to the growing evidence on the potential crosstalk between MET and estrogen receptors in breast cancer. The expression of the MET/ESR genes could be a novel prognosticator and calls for future studies to evaluate the impact of combinational treatment approaches with MET inhibitors and endocrine drugs in breast cancer.