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The early and highly accurate prediction of COVID-19 based on medical images can speed up the diagnostic process and thereby mitigate disease spread; therefore, developing AI-based models is an inevitable endeavor. The presented work, to our knowledge, is the first to expand the model space and identify a better performing model among 10,000 constructed deep transfer learning (DTL) models as follows. First, we downloaded and processed 4481 CT and X-ray images pertaining to COVID-19 and non-COVID-19 patients, obtained from the Kaggle repository. Second, we provide processed images as inputs to four pre-trained deep learning models (ConvNeXt, EfficientNetV2, DenseNet121, and ResNet34) on more than a million images from the ImageNet database, in which we froze the convolutional and pooling layers pertaining to the feature extraction part while unfreezing and training the densely connected classifier with the Adam optimizer. Third, we generate and take a majority vote of two, three, and four combinations from the four DTL models, resulting in DTL models. Then, we combine the 11 DTL models, followed by consecutively generating and taking the majority vote of DTL models. Finally, we select DTL models from Experimental results from the whole datasets using five-fold cross-validation demonstrate that the best generated DTL model, named HC, achieving the best AUC of 0.909 when applied to the CT dataset, while ConvNeXt yielded a higher marginal AUC of 0.933 compared to 0.93 for HX when considering the X-ray dataset. These promising results set the foundation for promoting the large generation of models (LGM) in AI. [ABSTRACT FROM AUTHOR]

The early and highly accurate prediction of COVID-19 based on medical images can speed up the diagnostic process and thereby mitigate disease spread; therefore, developing AI-based models is an inevitable endeavor. The presented work, to our knowledge, is the first to expand the model space and identify a better performing model among 10,000 constructed deep transfer learning (DTL) models as follows. First, we downloaded and processed 4481 CT and X-ray images pertaining to COVID-19 and non-COVID-19 patients, obtained from the Kaggle repository. Second, we provide processed images as inputs to four pre-trained deep learning models (ConvNeXt, EfficientNetV2, DenseNet121, and ResNet34) on more than a million images from the ImageNet database, in which we froze the convolutional and pooling layers pertaining to the feature extraction part while unfreezing and training the densely connected classifier with the Adam optimizer. Third, we generate and take a majority vote of two, three, and four combinations from the four DTL models, resulting in DTL models. Then, we combine the 11 DTL models, followed by consecutively generating and taking the majority vote of DTL models. Finally, we select DTL models from Experimental results from the whole datasets using five-fold cross-validation demonstrate that the best generated DTL model, named HC, achieving the best AUC of 0.909 when applied to the CT dataset, while ConvNeXt yielded a higher marginal AUC of 0.933 compared to 0.93 for HX when considering the X-ray dataset. These promising results set the foundation for promoting the large generation of models (LGM) in AI. [ABSTRACT FROM AUTHOR]

Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims to investigate COVID-19 classification at the lung cellular level in response to SARS-CoV-2. Particularly, differentiating between healthy and infected human alveolar type II (hAT2) cells with SARS-CoV-2. Hence, we explore the feasibility of deep transfer learning (DTL) and introduce a highly accurate approach that works as follows: First, we downloaded and processed 286 images pertaining to healthy and infected hAT2 cells obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than one million images from the ImageNet database to extract features from hAT2 images. Then, it flattens and provides the output feature vectors to a trained, densely connected classifier with the Adam optimizer. The second DTL computation works in a similar manner, with a minor difference in that we freeze the first layers for feature extraction in pre-trained models while unfreezing and jointly training the next layers. The results using five-fold cross-validation demonstrated that TFeDenseNet201 is 12.37× faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance ( P < 2.2 × 10 − 16 from a t-test) compared to an average ACC of 0.937 (F1 of 0.938 and MCC of 0.877) for the counterpart (TFtDenseNet201), showing no significance results ( P = 0.093 from a t-test). [ABSTRACT FROM AUTHOR]

Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server. [ABSTRACT FROM AUTHOR]

Accurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learning useful feature representation. However, existing DL tools are far from perfect and do not provide appropriate interpretation as a guideline to explain and promote superior performance in the target task. Therefore, we provide an interpretable approach for our presented deep transfer learning (DTL) models to overcome such drawbacks, working as follows. We utilize several pre-trained models including SEResNet152, and SEResNeXT101. Then, we transfer knowledge from pre-trained models via keeping the weights in the convolutional base (i.e., feature extraction part) while modifying the classification part with the use of Adam optimizer to deal with classifying healthy controls and T2D based on single-cell gene regulatory network (SCGRN) images. Another DTL models work in a similar manner but just with keeping weights of the bottom layers in the feature extraction unaltered while updating weights of consecutive layers through training from scratch. Experimental results on the whole 224 SCGRN images using five-fold cross-validation show that our model (TFeSEResNeXT101) achieving the highest average balanced accuracy (BAC) of 0.97 and thereby significantly outperforming the baseline that resulted in an average BAC of 0.86. Moreover, the simulation study demonstrated that the superiority is attributed to the distributional conformance of model weight parameters obtained with Adam optimizer when coupled with weights from a pre-trained model. [ABSTRACT FROM AUTHOR]

It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modification but are general methods that aim to identify protein binding to the genome. In this study, tensor decomposition (TD) and principal component analysis (PCA)-based unsupervised feature extraction with optimized standard deviation were successfully applied to gene expression and DNA methylation. The proposed method was used to identify histone modification. Histone modification along the genome is binned within the region of length L. Considering principal components (PCs) or singular value vectors (SVVs) that PCA or TD attributes to samples, we can select PCs or SVVs attributed to regions. The selected PCs and SVVs further attribute p-values to regions, and adjusted p-values are used to select regions. The proposed method identified various histone modifications successfully and outperformed various state-of-the-art methods. This method is expected to serve as a de facto standard method to identify histone modification. For reproducibility and to ensure the systematic analysis of our study is applicable to datasets from different gene expression experiments, we have made our tools publicly available for download from gitHub. [ABSTRACT FROM AUTHOR]

Integration of gene expression (GE) and protein–protein interaction (PPI) is not straightforward because the former is provided as a matrix, whereas the latter is provided as a network. In many cases, genes processed with GE analysis are refined further based on a PPI network or vice versa. This is hardly regarded as a true integration of GE and PPI. To address this problem, we proposed a tensor decomposition (TD)-based method that can integrate GE and PPI prior to any analyses where PPI is also formatted as a matrix to which singular value decomposition (SVD) is applied. Integrated analyses with TD improved the coincidence between vectors attributed to samples and class labels over 27 cancer types retrieved from The Cancer Genome Atlas Program (TCGA) toward five class labels. Enrichment using genes selected with this strategy was also improved with the integration using TD. The PPI network associated with the information on the strength of the PPI can improve the performance than PPI that stores only if the interaction exists in individual pairs. In addition, even restricting genes to the intersection of GE and PPI can improve coincidence and enrichment. [ABSTRACT FROM AUTHOR]

The use of medical images for colon cancer detection is considered an important problem. As the performance of data-driven methods relies heavily on the images generated by a medical method, there is a need to inform research organizations about the effective imaging modalities, when coupled with deep learning (DL), for detecting colon cancer. Unlike previous studies, this study aims to comprehensively report the performance behavior for detecting colon cancer using various imaging modalities coupled with different DL models in the transfer learning (TL) setting to report the best overall imaging modality and DL model for detecting colon cancer. Therefore, we utilized three imaging modalities, namely computed tomography, colonoscopy, and histology, using five DL architectures, including VGG16, VGG19, ResNet152V2, MobileNetV2, and DenseNet201. Next, we assessed the DL models on the NVIDIA GeForce RTX 3080 Laptop GPU (16GB GDDR6 VRAM) using 5400 processed images divided equally between normal colons and colons with cancer for each of the imaging modalities used. Comparing the imaging modalities when applied to the five DL models presented in this study and twenty-six ensemble DL models, the experimental results show that the colonoscopy imaging modality, when coupled with the DenseNet201 model under the TL setting, outperforms all the other models by generating the highest average performance result of 99.1% (99.1%, 99.8%, and 99.1%) based on the accuracy results (AUC, precision, and F1, respectively). [ABSTRACT FROM AUTHOR]

Overlooked diseases in agriculture severely impact crop growth, which results in significant losses for farmers. Unfortunately, manual field visits for plant disease diagnosis (PDD) are costly and time consuming. Although various methods of PDD have been proposed, many challenges have yet to be investigated, such as early stage leaf disease diagnosis, class variations in diseases, cluttered backgrounds, and computational complexity of the diagnosis system. In this paper, we propose a Convolutional Neural Network (CNN)-based PDD framework (i.e., PDD-Net), which employs data augmentation techniques and incorporates multilevel and multiscale features to create a class and scale-invariant architecture. The Flatten-T Swish (FTS) activation function is utilized to prevent gradient vanishing and exploding problems, while the focal loss function is used to mitigate the impact of class imbalance during PDD-Net training. The PDD-Net method outperforms baseline models, achieving an average precision of 92.06%, average recall of 92.71%, average F1 score of 92.36%, and accuracy of 93.79% on the PlantVillage dataset. It also achieves an average precision of 86.41%, average recall of 85.77%, average F1 score of 86.02%, and accuracy of 86.98% on the cassava leaf disease dataset. These results demonstrate the efficiency and robustness of PDD-Net in plant disease diagnosis. [ABSTRACT FROM AUTHOR]

The integrated analysis of multiple gene expression profiles previously measured in distinct studies is problematic since missing both sample matches and common labels prevent their integration in fully data-driven, unsupervised training. In this study, we propose a strategy to enable the integration of multiple gene expression profiles among multiple independent studies with neither labeling nor sample matching using tensor decomposition unsupervised feature extraction. We apply this strategy to Alzheimer's disease (AD)-related gene expression profiles that lack precise correspondence among samples, including AD single-cell RNA sequence (scRNA-seq) data. We were able to select biologically reasonable genes using the integrated analysis. Overall, integrated gene expression profiles can function analogously to prior- and/or transfer-learning strategies in other machine-learning applications. For scRNA-seq, the proposed approach significantly reduces the required computational memory. [ABSTRACT FROM AUTHOR]

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Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes' identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution. Optimizing the standard deviation such that the histogram of P values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes. Our contribution was that we improved these methods so as to be able to select biologically more reasonable differentially expressed genes than the state of art methods that must empirically assume negative binomial distributions and dispersion relation, which is required for the selecting more expressed genes than less expressed ones, which can be achieved by the proposed methods that do not have to assume these. [ABSTRACT FROM AUTHOR]

Identifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit (PP) that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted P-values of 0.1 when the null distribution is generated by gene order shuffling. For this purpose, we newly applied PP to the three data sets to which PCA and TD based unsupervised FE were previously applied; these data sets treated two topics, biomarker identification for kidney cancers (the first two) and the drug discovery for COVID-19 (the thrid one). Then we found the coincidence between PP and PCA or TD based unsupervised FE is pretty well. Shuffling procedures described above are also successfully applied to these three data sets. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time. [ABSTRACT FROM AUTHOR]

A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments. [ABSTRACT FROM AUTHOR]

In the field of gene expression analysis, methods of integrating multiple gene expression profiles are still being developed and the existing methods have scope for improvement. The previously proposed tensor decomposition-based unsupervised feature extraction method was improved by introducing standard deviation optimization. The improved method was applied to perform an integrated analysis of three tissue-specific gene expression profiles (namely, adipose, muscle, and liver) for diabetes mellitus, and the results showed that it can detect diseases that are associated with diabetes (e.g., neurodegenerative diseases) but that cannot be predicted by individual tissue expression analyses using state-of-the-art methods. Although the selected genes differed from those identified by the individual tissue analyses, the selected genes are known to be expressed in all three tissues. Thus, compared with individual tissue analyses, an integrated analysis can provide more in-depth data and identify additional factors, namely, the association with other diseases. [ABSTRACT FROM AUTHOR]

Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma. Traditionally, a dermatologist utilizes a microscope to inspect and then provide a report on a biopsy for diagnosis; however, this diagnosis process is not easy and requires experience. Hence, there is a need to facilitate the diagnosis process while still yielding an accurate diagnosis. For this purpose, artificial intelligence techniques can assist the dermatologist in carrying out diagnosis. In this study, we considered the detection of melanoma through deep learning based on cutaneous image processing. For this purpose, we tested several convolutional neural network (CNN) architectures, including DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNet, and evaluated the associated deep learning models on graphical processing units (GPUs). A dataset consisting of 7146 images was processed using these models, and we compared the obtained results. The experimental results showed that GoogleNet can obtain the highest performance accuracy on both the training and test sets (74.91% and 76.08%, respectively). [ABSTRACT FROM AUTHOR]

Automated diagnosis for the quality of bovine in vitro-derived embryos based on imaging data is an important research problem in developmental biology. By predicting the quality of embryos correctly, embryologists can (1) avoid the time-consuming and tedious work of subjective visual examination to assess the quality of embryos; (2) automatically perform real-time evaluation of embryos, which accelerates the examination process; and (3) possibly avoid the economic, social, and medical implications caused by poor-quality embryos. While generated embryo images provide an opportunity for analyzing such images, there is a lack of consistent noninvasive methods utilizing deep learning to assess the quality of embryos. Hence, designing high-performance deep learning algorithms is crucial for data analysts who work with embryologists. A key goal of this study is to provide advanced deep learning tools to embryologists, who would, in turn, use them as prediction calculators to evaluate the quality of embryos. The proposed deep learning approaches utilize a modified convolutional neural network, with or without boosting techniques, to improve the prediction performance. Experimental results on image data pertaining to in vitro bovine embryos show that our proposed deep learning approaches perform better than existing baseline approaches in terms of prediction performance and statistical significance. [ABSTRACT FROM AUTHOR]

Background: Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 10 2 – 10 5 features. In particular, appropriate methods to weight individual omics datasets are unclear, and the approach adopted has substantial consequences for feature selection. In this study, we extended a recently proposed kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) method to integrate multi-omics datasets obtained from common samples in a weight-free manner. Method: KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets. Results: The proposed advanced KTD-based unsupervised FE method showed comparative performance to that of the previously proposed KTD method, as well as tensor decomposition-based unsupervised FE, but required reduced memory and central processing unit time. Moreover, this advanced KTD method, specifically designed for multi-omics analysis, attributes P values to features, which is rare for existing multi-omics–oriented methods. Conclusions: The sample R code is available at https://github.com/tagtag/MultiR/. [ABSTRACT FROM AUTHOR]

The histone group added to a gene sequence must be removed during mitosis to halt transcription during the DNA replication stage of the cell cycle. However, the detailed mechanism of this transcription regulation remains unclear. In particular, it is not realistic to reconstruct all appropriate histone modifications throughout the genome from scratch after mitosis. Thus, it is reasonable to assume that there might be a type of "bookmark" that retains the positions of histone modifications, which can be readily restored after mitosis. We developed a novel computational approach comprising tensor decomposition (TD)-based unsupervised feature extraction (FE) to identify transcription factors (TFs) that bind to genes associated with reactivated histone modifications as candidate histone bookmarks. To the best of our knowledge, this is the first application of TD-based unsupervised FE to the cell division context and phases pertaining to the cell cycle in general. The candidate TFs identified with this approach were functionally related to cell division, suggesting the suitability of this method and the potential of the identified TFs as bookmarks for histone modification during mitosis. [ABSTRACT FROM AUTHOR]

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is a critical pandemic that has affected human communities worldwide, and there is an urgent need to develop effective drugs. Although there are a large number of candidate drug compounds that may be useful for treating COVID-19, the evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds such as C646, chelerythrine chloride, canertinib, BX-795, sorafenib, sorafenib, QL-X-138, radicicol, A-443654, CGP-60474, alvocidib, mitoxantrone, QL-XII-47, geldanamycin, fluticasone, atorvastatin, quercetin, motexafin gadolinium, trovafloxacin, doxycycline, meloxicam, gentamicin, and dibromochloromethane. The screen also identified ivermectin, which was first identified as an anti-parasite drug and recently the drug was included in clinical trials for SARS-CoV-2. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19. [ABSTRACT FROM AUTHOR]

Gene expression profiles of tissues treated with drugs have recently been used to infer clinical outcomes. Although this method is often successful from the application point of view, gene expression altered by drugs is rarely analyzed in detail, because of the extremely large number of genes involved. Here, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to the gene expression profiles of 24 mouse tissues treated with 15 drugs. TD-based unsupervised FE enabled identification of the common effects of 15 drugs including an interesting universal feature: these drugs affect genes in a gene-group-wide manner and were dependent on three tissue types (neuronal, muscular, and gastroenterological). For each tissue group, TD-based unsupervised FE enabled identification of a few tens to a few hundreds of genes affected by the drug treatment. These genes are distinctly expressed between drug treatments and controls as well as between tissues in individual tissue groups and other tissues. We also validated the assignment of genes to individual tissue groups using multiple enrichment analyses. We conclude that TD-based unsupervised FE is a promising method for integrated analysis of gene expression profiles from multiple tissues treated with multiple drugs in a completely unsupervised manner. [ABSTRACT FROM AUTHOR]

Although single-cell RNA sequencing (scRNA-seq) technology is newly invented and a promising one, but because of lack of enough information that labels individual cells, it is hard to interpret the obtained gene expression of each cell. Because of insufficient information available, unsupervised clustering, for example, t -distributed stochastic neighbor embedding and uniform manifold approximation and projection, is usually employed to obtain low-dimensional embedding that can help to understand cell–cell relationship. One possible drawback of this strategy is that the outcome is highly dependent upon genes selected for the usage of clustering. In order to fulfill this requirement, there are many methods that performed unsupervised gene selection. In this study, a tensor decomposition (TD)-based unsupervised feature extraction (FE) was applied to the integration of two scRNA-seq expression profiles that measure human and mouse midbrain development. TD-based unsupervised FE could select not only coincident genes between human and mouse but also biologically reliable genes. Coincidence between two species as well as biological reliability of selected genes is increased compared with that using principal component analysis (PCA)-based FE applied to the same data set in the previous study. Since PCA-based unsupervised FE outperformed the other three popular unsupervised gene selection methods, highly variable genes, bimodal genes, and dpFeature, TD-based unsupervised FE can do so as well. In addition to this, 10 transcription factors (TFs) that might regulate selected genes and might contribute to midbrain development were identified. These 10 TFs, BHLHE40, EGR1, GABPA, IRF3, PPARG, REST, RFX5, STAT3, TCF7L2, and ZBTB33, were previously reported to be related to brain functions and diseases. TD-based unsupervised FE is a promising method to integrate two scRNA-seq profiles effectively. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) algorithms aim to improve the prediction performance in a target task (e.g. the prediction of cisplatin sensitivity in triple-negative breast cancer patients) via transferring knowledge from auxiliary data of a related task (e.g. the prediction of docetaxel sensitivity in breast cancer patients), where the distribution and even the feature space of the data pertaining to the tasks can be different. In real-world applications, we sometimes have a limited training set in a target task while we have auxiliary data from a related task. To obtain a better prediction performance in the target task, supervised learning requires a sufficiently large training set in the target task to perform well in predicting future test examples of the target task. In this paper, we propose a TL approach for cancer drug sensitivity prediction, where our approach combines three techniques. First, we shift the representation of a subset of examples from auxiliary data of a related task to a representation closer to a target training set of a target task. Second, we align the shifted representation of the selected examples of the auxiliary data to the target training set to obtain examples with representation aligned to the target training set. Third, we train machine learning algorithms using both the target training set and the aligned examples. We evaluate the performance of our approach against baseline approaches using the Area Under the receiver operating characteristic (ROC) Curve (AUC) on real clinical trial datasets pertaining to multiple myeloma, nonsmall cell lung cancer, triple-negative breast cancer, and breast cancer. Experimental results show that our approach is better than the baseline approaches in terms of performance and statistical significance. [ABSTRACT FROM AUTHOR]

Background: Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. Results: In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. Conclusions: We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance. [ABSTRACT FROM AUTHOR]

Arbuscular mycorrhizal fungi (AMF) constitute a key functional group of soil biota that can greatly contribute to crop productivity and ecosystem sustainability. They improve nutrient uptake and enhance the ability of plants to cope with abiotic stresses. The presence of AMF in coffee ( Coffea arabica L.) plant roots have been reported in several locations but not in Saudi Arabia despite the fact that coffee has been in cultivation here since ancient times. The objective of the present study was to investigate the diversity of AMF communities colonizing the roots of coffee trees growing in two sites of Fifa Mountains (south-west Saudi Arabia): site 1 at 700 m altitude and site 2 at 1400 m. The AMF large subunit rDNA regions (LSU) were subjected to nested PCR, cloning, sequencing, and phylogenetic analysis. Microscopic observations indicated higher mycorrhizal intensity (24.3%) and spore density (256 spores/100 g of soil) in site 2 (higher altitude). Phylogenetic analysis revealed 10 phylotypes, six belonging to the family Glomeraceae, two to Claroideoglomercea, one to Acaulosporaceae and one to Gigasporaceae family. Glomus was the dominant genus at both sites and the genus Gigaspora was detected only at site 2. This is the first study reporting the presence of AMF in coffee roots and the composition of this particular mycorrhizal community in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool. [ABSTRACT FROM AUTHOR]

Stem succulents Euphorbia fractiflexa and Euphorbia triaculeata (Euphorbiaceae) are common arido-active perennials in arid regions of Arabian Peninsula. Work presented in this paper aimed at investigating Crassulacean Acid Metabolism (CAM) in these two species by studying diurnal and seasonal changes in stomatal conductance and cell sap acidity. Chlorophyll fluorescence was measured to elucidate effect of drought and high irradiance on photosynthetic apparatus. Pigment profile analysis was carried out to provide comparative view of photoprotective mechanisms in these two species. Results indicated that these two Euphorbia species are obligate CAM plants that shift to CAM-idling in response to protracted drought. Results also revealed stress induced changes in activity and antenna size of Photosystem II. Changes in Photosystem II associated with increased non-photochemical quenching of chlorophyll fluorescence reflecting operation of photoprotective excess energy dissipation mechanisms. Pigment profile analysis revealed that photoprotection in E. fractiflexa relies on operation of xanthophyll cycle while that in E. triaculeata relies on anthocyanin accumulation. [ABSTRACT FROM AUTHOR]

A series of poly(DL-lactide-co-glycolic acid) (PLGA) with poly(vinylalcohol-co-ethylene) (PEVAL) blends were prepared by solution casting method. The miscibility, thermal and mechanical properties have been investigated using FTIR, DSC, and DMA techniques. The miscibility of this pair of polymers throughout compositions was proved by these methods through the single Tgand the presence of interactions between the constituents. The TGA analysis revealed three degradation zones and no sensible enhancement in the thermal stability of PLGA was noted with addition of PEVAL content. The SEM analysis revealed that the draying method dramatically influence the surface morphology of copolymers and blend. The cross section micrograph of blend scaffold containing 50 wt% of PEVAL presents microcavities of diameter pores ranged between 70 and 170 µm interconnected and uniformly distributed in the polymer matrix. [ABSTRACT FROM AUTHOR]

Activated carbon was prepared from date pits via chemical activation with HPO. The effects of activating agent concentration and activation temperature on the yield and surface area were studied. The optimal activated carbon was prepared at 450 °C using 55 % HPO. The prepared activated carbon was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric-differential thermal analysis, and Brunauer, Emmett, and Teller (BET) surface area. The prepared date pit-based activated carbon (DAC) was used for the removal of bromate (BrO). The concentration of BrO was determined by ultra-performance liquid chromatography-mass tandem spectrometry (UPLC-MS/MS). The experimental equilibrium data for BrO adsorption onto DAC was well fitted to the Langmuir isotherm model and showed maximum monolayer adsorption capacity of 25.64 mg g. The adsorption kinetics of BrO adsorption was very well represented by the pseudo-first-order equation. The analytical application of DAC for the analysis of real water samples was studied with very promising results. [ABSTRACT FROM AUTHOR]

An ultra high performance liquid chromatography with mass spectrometry method has been developed for the simultaneous separation, identification and determination of 22 phenolic constituents in honey from various floral sources from Yemen. Solid-phase extraction was used for extraction of the target phenolic constituents from honey samples, while multiwalled carbon nanotubes were used as solid-phase adsorbent. The chromatographic separation of all phenolic constituents was performed on a BEH C18 column using a linear gradient elution with a binary mobile phase mixture of aqueous 0.1% formic acid and methanol. The quantitation was carried out in selected ion reaction monitoring acquisition mode. The total amount of phenolic acids, flavonoids and other phenols in each analyzed honey was found in the range of 338-3312, 122-5482 and 2.4-1342 μg/100 g of honey, respectively. 4-Hydroxybenzoic acid was found to be the major phenolic acid. The main detected flavonoid was chrysin, while cinnamic acid was found to be the major other phenol compound. The regeneration of solid phase adsorbent to be reused and recovery results confirm that the proposed method could be potentially used for the routine analysis of phenolic constituents in honey extract. [ABSTRACT FROM AUTHOR]

Background Programs based on hash tables and Burrows-Wheeler are very fast for mapping short reads to genomes but have low accuracy in the presence of mismatches and gaps. Such reads can be aligned accurately with the Smith-Waterman algorithm but it can take hours and days to map millions of reads even for bacteria genomes. Results We introduce a GPU program called MaxSSmap with the aim of achieving comparable accuracy to Smith-Waterman but with faster runtimes. Similar to most programs MaxSSmap identifies a local region of the genome followed by exact alignment. Instead of using hash tables or Burrows-Wheeler in the first part, MaxSSmap calculates maximum scoring subsequence score between the read and disjoint fragments of the genome in parallel on a GPU and selects the highest scoring fragment for exact alignment. We evaluate MaxSSmap's accuracy and runtime when mapping simulated Illumina E.coli and human chromosome one reads of different lengths and 10% to 30% mismatches with gaps to the E.coli genome and human chromosome one. We also demonstrate applications on real data by mapping ancient horse DNA reads to modern genomes and unmapped paired reads from NA12878 in 1000 genomes. Conclusions We show that MaxSSmap attains comparable high accuracy and low error to fast Smith-Waterman programs yet has much lower runtimes. We show thatMaxSSmap can map reads rejected by BWA and NextGenMap with high accuracy and low error much faster than if Smith-Waterman were used. On short read lengths of 36 and 51 both MaxSSmap and Smith-Waterman have lower accuracy compared to at higher lengths. On real dataMaxSSmap produces many alignments with high score and mapping quality that are not given by NextGenMap and BWA. The MaxSSmap source code in CUDA and OpenCL is freely available from http://www.cs.njit.edu/usman/MaxSSmap. [ABSTRACT FROM AUTHOR]

Leafless stem succulent Euphorbia triaculeata Forssk. is abundant in arid regions southwest of the Arabian Peninsula. These regions are characterized by short wet season with erratic rainfall and long dry season with high temperature and high irradiance. Field observations indicated that the plant survives the long dry season and acquires a red stem colour under high irradiance and protracted drought. Our work aimed at investigating survival of this arido-active species in its natural habitat by studying diurnal and seasonal changes in stomatal conductance, cell sap acidity, and chlorophyll fluorescence. Results showed that E. triaculeata is obligate crassulacean acid metabolism (CAM) plant. Under protracted drought, low stomatal conductance and dampening of CAM denoted a shift to CAM-idling. Observed stress-induced reduction of Photosystem II activity occurred in concomitance with increased non-photochemical quenching of chlorophyll fluorescence and increased anthocyanin content. These results reflected operation of a photoprotective mechanism involving interplay of non-photochemical energy dissipation via xanthophyll cycle and alleviation of oxidative stress by anthocyanin. It was concluded that E. triaculeata survives in its arid habitat by performing obligate CAM, shifting to CAM-idling under protracted drought, increasing non-photochemical excess energy dissipation, and accumulating anthocyanin pigment for its antioxidant attributes. [ABSTRACT FROM AUTHOR]

Aim Detailed longitudinal evaluation of corneal and other ophthalmological features of patients with lipoid proteinosis (LP). Methods Ophthalmological examinations, chart review, ultrasound biomicroscopy, corneal confocal microscopic examinations with Nidek confoScan 4 and direct sequencing of the extracellular matrix protein 1 gene in individuals from three consanguineous Saudi families with LP. Results Seven individuals affected with LP (four female and three male subjects) were evaluated together with nine unaffected parents and siblings. All affected individuals had homozygous mutations in extracellular matrix protein 1. Four patients were examined frequently (every 6 months) beginning in infancy and early childhood. Globe and vision were normal in all individuals, and moniliform blepharosis always appeared after the age of 4 years. Prominent corneal nerves were detected in all patients regardless of age and were more apparent in patients with more severe genetic mutations. Conversely, the severity of moniliform blepharosis seemed age-dependent rather than genotype-related. Conclusion Prominent corneal nerves can be helpful in the early diagnosis of LP and should be added to the list of LP ophthalmological diagnostic features. [ABSTRACT FROM AUTHOR]

Several species of the stem succulent Caralluma (Apocynaceae) are abundant perennials in arid regions of the Arabian Peninsula. These arid regions have a short wet season with erratic rainfall and are characterized by harsh climatic conditions of high temperature, high evaporation and sand storms. Work presented in this paper aimed at investigating importance of Crassulacean Acid Metabolism (CAM) for survival of three Caralluma species in their natural habitat. Investigations involved studying stomatal characteristics, stomatal diffusive conductance, chlorophyll fluorescence, and CAM in three species of Caralluma, namely C. acutangula (Decne.) N.E.Br., C. edulis (Edgew.) Benth. ex Hook.f., and C. subulata (Forssk.) Decne. Microscopic examination revealed a pattern of stomatal characteristics typical of CAM plants in these three Caralluma species. Results showed that these three Caralluma species were obligate CAM plants exhibiting this mode of photosynthesis during both the wet and the dry seasons. Under protracted water stress during the long dry season very low values of stomatal diffusive conductance and dampening of CAM acidification-deacidification cycles denoted the tendency of these three Caralluma species to shift from the obligate CAM physiotype to CAM-idling mode. Chlorophyll fluorescence measurements indicated that protracted water stress induced a reduction in Photosystem II (PSII) antenna efficiency and quantum yield in the three studied Caralluma species. This reduction of PSII activity occurred in concomitance with a marked rise in non-photochemical quenching of chlorophyll fluorescence denoting operation of non-photochemical energy dissipating mechanisms known to be important for photoprotection of the photosynthetic apparatus. [ABSTRACT FROM AUTHOR]

During field studies in sand dune areas and some habitats close to wetland ecosystems of the Tihama region of Jazan Province in south-western Saudi Arabia, specimens of 3 interesting species belonging to the genera Odyssea (Poaceae), Sesbania (Papilionaceae), and Sesamum (Pedaliaceae) were collected; they were identified as Odyssea mucronata (Forssk.) Stapf, Sesbania sericea (Willd.) Link., and Sesamum alatum Thonn. A review of the literature revealed that these tropical African taxa were never before recorded for the flora of Saudi Arabia. O. mucronata is represented by a relatively large isolated population. The other 2, S. sericea and S. alatum--components of a semi wetland ecosystem and sandy areas, respectively--are represented in the Arabian Peninsula by 2 relict populations. Morphology, habitat descriptions, illustrations, and taxonomic commentary of all 3 taxa are presented. [ABSTRACT FROM AUTHOR]

Abstract: During field studies in sand dune areas and some habitats close to wetland ecosystems of the Tihama region of Jazan Province in south-western Saudi Arabia, specimens of 3 interesting species belonging to the genera Odyssea (Poaceae), Sesbania (Papilionaceae), and Sesamum (Pedaliaceae) were collected; they were identified as Odyssea mucronata (Forssk.) Stapf, Sesbania sericea (Willd.) Link., and Sesamum alatum Thonn. A review of the literature revealed that these tropical African taxa were never before recorded for the flora of Saudi Arabia. O. mucronata is represented by a relatively large isolated population. The other 2, S. sericea and S. alatum--components of a semi wetland ecosystem and sandy areas, respectively--are represented in the Arabian Peninsula by 2 relict populations. Morphology, habitat descriptions, illustrations, and taxonomic commentary of all 3 taxa are presented. [ABSTRACT FROM AUTHOR]

A conformational search of 5,12-dihydro-5,12-ethanonaphthacene-13-carbaldehyde predicted the presence of twelve conformations. The geometry of the twelve conformations established at the B3LYP/6-31G* level showed only six unique ones. Vibrational frequencies were calculated at the B3LYP/6-31G* level. The calculated vibrational frequencies enabled us to interpret the appearance of two bands corresponding to the C=O stretching mode of 5,12-dihydro-5,12-ethanonaphthacene-13-carbaldehyde. The first band corresponded to the 5,12-dihydro-5,12-ethanonaphthacene-13-carbaldehyde structure where the aldehyde group O atom was above the benzene or naphthalene ring. The other band was due to the O atom of the aldehyde group pointing out of the benzene or naphthalene ring. [ABSTRACT FROM AUTHOR]

Purpose: To identify the microbiological spectrum and visual outcome of bleb-associated endophthalmitis. Methods: The medical records of patients treated for bleb-associated endophthalmitis were reviewed. Results: Seventy-five patients were identified. Vitreous cultures were positive in 48 (64%) patients. The most common isolates were Streptococcus species in 24 (50%) eyes, gram-negative bacteria in 10 (20.8%) eyes, and Staphylococcus epidermidis in 7 (14.6%) eyes. Good visual outcome was achieved in 33 (44%) patients. On univariate analysis, shorter interval from onset of symptoms and treatment ( p = .005), better initial visual acuity ( p = .004), clear cornea at presentation ( p = .048), isolation of less virulent organisms ( p < .001), and absence of diabetes mellitus ( p = .048) were significant positive predictors for good visual outcome. Conclusions: Bleb-associated endophthalmitis is associated with severe visual impairment. Clinical features associated with better visual outcome include early presentation, better presenting visual acuity, clear cornea at presentation, culture of a less virulent organism, and absence of diabetes mellitus. [ABSTRACT FROM AUTHOR]

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course. [ABSTRACT FROM AUTHOR]

Analysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address this difficult problem. The technique can successfully integrate single-cell multiomics data composed of gene expression, DNA methylation, and accessibility. Although the last two have large dimensions, as many as ten million, containing only a few percentage of nonzero values, TD-based unsupervised FE can integrate three omics datasets without filling in missing values. Together with UMAP, which is used frequently when embedding single-cell measurements into two-dimensional space, TD-based unsupervised FE can produce two-dimensional embedding coincident with classification when integrating single-cell omics datasets. Genes selected based on TD-based unsupervised FE are also significantly related to reasonable biological roles. [ABSTRACT FROM AUTHOR]

Abstract: Acute angle closure glaucoma is unexpected complication following laser in situ keratomileusis (LASIK). We are reporting a 49-years-old lady that was presented to the emergency department with acute glaucoma in both eyes soon after LASIK correction. Diagnosis was made on detailed clinical history and examination, slit lamp examination, intraocular pressure measurement and gonioscopy. Laser iridotomy in both eyes succeeded in controlling the attack and normalizing the intraocular pressure (IOP) more than 6months of follow-up. Prophylactic laser iridotomy is essential for narrow angle patients before LASIK surgery if refractive laser surgery is indicated. [Copyright &y& Elsevier]

The large p small n problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of features is more than 100,000 whereas the number of samples is as small as about 100, hence constituting a typical large p small n problem. The proposed TD-based unsupervised FE outperformed other conventional supervised feature selection methods, random forest, categorical regression (also known as analysis of variance, or ANOVA), penalized linear discriminant analysis, and two unsupervised methods, multiple non-negative matrix factorization and principal component analysis (PCA) based unsupervised FE when applied to synthetic datasets and four methods other than PCA based unsupervised FE when applied to multiomics datasets. The genes selected by TD-based unsupervised FE were enriched in genes known to be related to tissues and transcription factors measured. TD-based unsupervised FE was demonstrated to be not only the superior feature selection method but also the method that can select biologically reliable genes. To our knowledge, this is the first study in which TD-based unsupervised FE has been successfully applied to the integration of this variety of multiomics measurements. [ABSTRACT FROM AUTHOR]

The underlying mechanisms of eating disorders (EDs) are very complicated and still poorly understood. The pathogenesis of EDs may involve the interplay of multiple genes. To investigate the dysregulated gene pathways in EDs we analyzed gene expression profiling in dorsolateral prefrontal cortex (DLPFC) tissues from 15 EDs cases, including 3 with anorexia nervosa (AN), 7 with bulimia nervosa (BN), 2 AN-BN cases, 3 cases of EDs not otherwise specified, and 102 controls. We further used a weighted gene co-expression network analysis to construct a gene co-expression network and to detect functional modules of highly correlated genes. The functional enrichment analysis of genes in co-expression modules indicated that an altered mitochondrial oxidative phosphorylation process may be involved in the pathogenesis of EDs. [ABSTRACT FROM AUTHOR]