Can Deep Learning Predict Inducibility of Stress-Responsive Gene Expression from DNA Sequence in Arabidopsis thaliana?

When plant cells are under environmental stress, several chemically distinct reactive oxygen species (ROS) are generated simultaneously in various intracellular compartments and these can cause oxidative damage or act as signals. The conditional flu mutant of Arabidopsis, which generates singlet oxygen in plastids during a dark-to-light transition, has allowed the biological activity of singlet oxygen to be determined, and the criteria to distinguish between cytotoxicity and signalling of this particular ROS to be defined. The genetic basis of singlet-oxygen-mediated signalling has been revealed by the mutation of two nuclear genes encoding the plastid proteins EXECUTER (EX)1 and EX2, which are sufficient to abrogate singlet-oxygen-dependent stress responses. Conversely, responses due to higher cytotoxic levels of singlet oxygen are not suppressed in the ex1/ex2 background. Whether singlet oxygen levels lower than those that trigger genetically controlled cell death activate acclimation is now under investigation.

Because the respiratory chain is the major site of oxidation of the reduced equivalents and of energy production in aerobic cells, its inhibition has severe impact on the cells. Communication pathways from the respiratory chain are required to allow the cell to sense the defect and respond to it. In this work, we studied changes in gene expression induced by the treatment of yeast cells with myxothiazol, an inhibitor of the bc(1) complex, an enzyme of the respiratory chain. The pattern and time-course expression of the genes resemble those of the environmental stress response, a common gene expression program induced by sudden changes in the environment. In addition, the changes were, for most of the genes, mediated through the transcription factors Msn2/4, which play a central role in the cellular response to these stresses. By using a mutant with a myxothiazol-resistant bc(1) complex, we showed that the changes of expression of the majority of the genes was caused by the inhibition of the bc(1) complex but that other stresses might be involved. The expression pattern of CTT1, coding for a cytoplasmic catalase, was further studied. The expression of this gene was largely dependent on Msn2/4 and the inhibition of the cytochrome bc(1). Addition of oxidants of NADH was found to decrease the expression of CTT1 induced by myxothiazol treatment, suggesting that the accumulation of NADH caused by the inhibition of the respiratory chain may be involved in the signaling pathway from the mitochondria to the transcription factor.

BackgroundPathogenesis-related proteins belonging to group 10 (PR10) are elevated in response to biotic and abiotic stresses in plants. Previously, we have shown a drastic salinity-induced increase in the levels of ABR17, a member of the PR10 family, in pea. Furthermore, we have also demonstrated that the constitutive expression of pea ABR17 cDNA in Arabidopsis thaliana and Brassica napus enhances their germination and early seedling growth under stress. Although it has been reported that several members of the PR10 family including ABR17 possess RNase activity, the exact mechanism by which the aforementioned characteristics are conferred by ABR17 is unknown at this time. We hypothesized that a study of differences in transcriptome between wild type (WT) and ABR17 transgenic A. thaliana may shed light on this process.ResultsThe molecular changes brought about by the expression of pea ABR17 cDNA in A. thaliana in the presence or absence of salt stress were investigated using microarrays consisting of 70-mer oligonucleotide probes representing 23,686 Arabidopsis genes. Statistical analysis identified number of genes which were over represented among up- or down-regulated transcripts in the transgenic line. Our results highlight the important roles of many abscisic acid (ABA) and cytokinin (CK) responsive genes in ABR17 transgenic lines. Although the transcriptional changes followed a general salt response theme in both WT and transgenic seedlings under salt stress, many genes exhibited differential expression patterns when the transgenic and WT lines were compared. These genes include plant defensins, heat shock proteins, other defense related genes, and several transcriptional factors. Our microarray results for selected genes were validated using quantitative real-time PCR.ConclusionTranscriptional analysis in ABR17 transgenic Arabidopsis plants, both under normal and saline conditions, revealed significant changes in abundance of transcripts for many stress responsive genes, as well as those related to plant growth and development. Our results also suggest that ABR17 may mediate stress tolerance through the modulation of many ABA- and CK-responsive genes and may further our understanding of the role of ABR17 in mediating plant stress responses.

Social media platforms such as X, Facebook, and Instagram have become essential avenues for individuals to articulate their opinions, especially during global emergencies. These platforms offer valuable insights that necessitate analysis for informed decision-making and a deeper understanding of societal trends. Sentiment analysis is crucial for assessing public sentiment toward specific issues; however, applying it to dialectal Arabic presents considerable challenges in natural language processing. The complexity arises from the language's intricate semantic and morphological structures, along with the existence of multiple dialects. This form of analysis, also referred to as sentiment classification, opinion mining, emotion mining, and review mining, is the focus of this study, which analyzes tweets from three benchmark datasets: the Arabic Sentiment Tweets Dataset (ASTD), the A Twitter-based Benchmark Arabic Sentiment Analysis Dataset (ASAD), and the Tweets Emoji Arabic Dataset (TEAD). The research involves experimentation with a variety of comparative models, including machine learning, deep learning, transformer-based models, and a transformer-based ensemble model. Feature extraction for both machine learning and deep learning approaches is performed using techniques such as AraVec, FastText, AraBERT, and Term Frequency-Inverse Document Frequency (TF-IDF). The study compares machine learning models such as support vector machine (SVM), naïve Bayes (NB), decision tree (DT), and extreme gradient boosting (XGBoost) with deep learning models such as convolutional neural networks (CNN) and bidirectional long short-term memory (BLSTM) networks. Additionally, it explores transformer-based models such as CAMeLBERT, XLM-RoBERTa, and MARBERT, along with their ensemble configurations. The findings demonstrate that the proposed transformer-based ensemble model achieved superior performance, with average accuracy, recall, precision, and F1-score of 90.4%, 88%, 87.3%, and 87.7%, respectively.

Losing a spouse or life partner is a deeply traumatic event that can have long-term repercussions. Given enough time, however, most surviving partners are able to process their grief. The neural processes that enable people to adapt to their loss remain unknown. To explore this question, scientists often turn to animals that form long-term mating based pair bonds and can be raised in the laboratory. Monogamous prairie voles enter lifelong partnerships where the two individuals live together, prefer to cuddle with each other, and take care of their pups as a team. After having lost their mate, they show signs of distress that eventually subside with time. Sadino et al. examined the biological impact of partner loss in these animals by focusing on the nucleus accumbens, a brain region important for social connections. This involved tracking gene expression – which genes were switched on and off in this area – as the voles established their pair bonds, and then at different time points after one of the partners had been removed. The experiments revealed that establishing a relationship leads to a stable shift in nucleus accumbens gene expression, which may help maintain bonds over time. In particular, genes related to glia (the non-neuronal cells which assist neurons in their tasks) see their expression levels increase, indicating a previously undescribed role for this cell type in regulating pair bonding. Having their partner removed led to an erosion of the gene expression pattern that had emerged during pair bonding; this may help the remaining vole adapt to its loss and go on to form a new bond. In addition, Sadino et al. explored the gene expression of only neurons in the nucleus accumbens and uncovered biological processes distinct from those that occur in glia after partner separation. Together, these results shed light on the genetic and neuronal mechanisms which underlie adaptation to loss; this knowledge could one day inform how to better support individuals during this time.

Losing a spouse or life partner is a deeply traumatic event that can have long-term repercussions. Given enough time, however, most surviving partners are able to process their grief. The neural processes that enable people to adapt to their loss remain unknown. To explore this question, scientists often turn to animals that form long-term mating based pair bonds and can be raised in the laboratory. Monogamous prairie voles enter lifelong partnerships where the two individuals live together, prefer to cuddle with each other, and take care of their pups as a team. After having lost their mate, they show signs of distress that eventually subside with time. Sadino et al. examined the biological impact of partner loss in these animals by focusing on the nucleus accumbens, a brain region important for social connections. This involved tracking gene expression – which genes were switched on and off in this area – as the voles established their pair bonds, and then at different time points after one of the partners had been removed. The experiments revealed that establishing a relationship leads to a stable shift in nucleus accumbens gene expression, which may help maintain bonds over time. In particular, genes related to glia (the non-neuronal cells which assist neurons in their tasks) see their expression levels increase, indicating a previously undescribed role for this cell type in regulating pair bonding. Having their partner removed led to an erosion of the gene expression pattern that had emerged during pair bonding; this may help the remaining vole adapt to its loss and go on to form a new bond. In addition, Sadino et al. explored the gene expression of only neurons in the nucleus accumbens and uncovered biological processes distinct from those that occur in glia after partner separation. Together, these results shed light on the genetic and neuronal mechanisms which underlie adaptation to loss; this knowledge could one day inform how to better support individuals during this time.

Losing a spouse or life partner is a deeply traumatic event that can have long-term repercussions. Given enough time, however, most surviving partners are able to process their grief. The neural processes that enable people to adapt to their loss remain unknown. To explore this question, scientists often turn to animals that form long-term mating based pair bonds and can be raised in the laboratory. Monogamous prairie voles enter lifelong partnerships where the two individuals live together, prefer to cuddle with each other, and take care of their pups as a team. After having lost their mate, they show signs of distress that eventually subside with time. Sadino et al. examined the biological impact of partner loss in these animals by focusing on the nucleus accumbens, a brain region important for social connections. This involved tracking gene expression – which genes were switched on and off in this area – as the voles established their pair bonds, and then at different time points after one of the partners had been removed. The experiments revealed that establishing a relationship leads to a stable shift in nucleus accumbens gene expression, which may help maintain bonds over time. In particular, genes related to glia (the non-neuronal cells which assist neurons in their tasks) see their expression levels increase, indicating a previously undescribed role for this cell type in regulating pair bonding. Having their partner removed led to an erosion of the gene expression pattern that had emerged during pair bonding; this may help the remaining vole adapt to its loss and go on to form a new bond. In addition, Sadino et al. explored the gene expression of only neurons in the nucleus accumbens and uncovered biological processes distinct from those that occur in glia after partner separation. Together, these results shed light on the genetic and neuronal mechanisms which underlie adaptation to loss; this knowledge could one day inform how to better support individuals during this time.

Plants live under various biotic and abiotic stress conditions, and to cope with the adversity and severity of these conditions, they have developed well-established resistance mechanisms. These mechanisms begin with the perception of stimuli, followed by molecular, biochemical, and physiological adaptive measures. Tomato (Solanum lycopersicum) is a globally significant vegetable crop that experiences several biotic and abiotic stress events that can adversely impact its quality and production. Mitogen-activated protein kinases (MAPKs) in tomato plants have crucial functions of mediating responses to environmental cues, internal signals, defense mechanisms, cellular processes, and plant development and growth. MAPK cascades respond to various environmental stress factors by modulating associated gene expression, influencing plant hormone synthesis, and facilitating interactions with other environmental stressors. Here, we review the evolutionary relationships of 16 tomato SlMAPK family members and emphasize on recent studies describing the regulatory functions of tomato SlMAPKs in both abiotic and biotic stress conditions. This review could enhance our comprehension of the MAPK regulatory network in biotic and abiotic stress conditions and provide theoretical support for breeding tomatoes with agronomic traits of excellent stress resistance.

Irony and Sarcasm Detection (ISD) is a crucial task for many NLP applications, especially sentiment and opinion mining. It is also considered a challenging task even for humans. Several studies have focused on employing Deep Learning (DL) approaches, including building Deep Neural Networks (DNN) to detect irony and sarcasm content. However, most of them concentrated on detecting sarcasm in English rather than Arabic content. Especially studies concerning deep neural networks, including convolutional neural networks (CNN) and recurrent neural network (RNN) architectures. This paper investigates several deep learning approaches, including DNNs and fine-tuned pretrained transformer-based language models, for identifying Arabic sarcastic tweets. In addition, it presents a comprehensive evaluation of the impact of data preprocessing techniques and several pretrained word embedding models on the performance of the proposed deep models. Two shared tasks' datasets on Arabic sarcasm detection are used to develop, fine-tune, and evaluate the different techniques and methods presented in this paper. Results on the first dataset showed that fine-tuned pretrained transformer-based language model outperformed the developed DNNs. The proposed DNN models obtained comparable performance on the second dataset to the fine-tuned models. Results also proved the necessity of applying preprocessing techniques with the various Deep Learning approaches for better detection performance of these models.

Plant Stress and Biotechnology

Detecting and removing hateful speech in various online social media is a challenging task. Researchers tried to solve this problem by using both classical and deep learning methods, which are found to have limitations in terms of the requirement of extensive hand-crafted features, model architecture design, and pretrained embeddings, that are not very proficient in capturing semantic relations between words. Therefore, in this paper, we tackle the problem using Transformer-based pretrained language models which are specially designed to produce contextual embeddings of text sequences. We have evaluated two such models—RoBERTa and XLNet—using four publicly available datasets from different social media platforms and compared them to the existing baselines. Our investigation shows that the Transformer-based models either surpass or match all of the existing baseline scores by significant margins obtained by previously used models such as 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM). The Transformer-based models proved to be more robust by achieving native performance when trained and tested on two different datasets. Our investigation also revealed that variations in the characteristics of the data produce significantly different results with the same model. From the experimental observations, we are able to establish that Transformer-based language models exhibit superior performance than their conventional counterparts at a fraction of the computation cost and minimal need for complex model engineering. Received: 28 August 2021 | Revised: 20 October 2021 | Accepted: 29 October 2021 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.

The subject of this article is Image Super-Resolution (ISR) using deep learning techniques. ISR is a rapidly evolving research area in computer science that focuses on producing high-resolution images from one or more low-resolution sources. It has garnered substantial interest due to its broad applications in areas such as medical imaging, remote sensing, and multimedia. The rise of deep learning techniques has brought a revolution in ISR, providing superior performance and computational efficiency compared to traditional methods and driving further advancements in overcoming the challenges associated with enhancing image resolution. The goal of this study is to enhance the quality of super-resolved images by developing a novel deep learning approach. Specifically, we explore the integration of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to address the inherent challenges of producing high-quality images from low-resolution data. This study aims to push the boundaries of ISR by combining these architectures for greater precision and visual fidelity. The tasks are as follows: 1) design and implement a hybrid model using CNNs and GANs for image super-resolution tasks; 2) train the model on benchmark datasets like Set5, Set14, DIV2K, and specialized datasets such as X-ray images; 3) assess the model’s performance using numerical metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM); 4) to compare the proposed method against existing state-of-the-art ISR techniques and demonstrate its superiority. The following results were obtained in this study: Our deep learning model, which integrates the Super-Resolution Convolutional Neural Network (SRCNN) and the Super-Resolution Generative Adversarial Network (SRGAN), demonstrated significant performance improvements. The CNN successfully learned to map low-resolution image patches to their high-resolution counterparts, and the GAN further refined the images, enhancing both precision and visual quality. The evaluation metrics yielded highly promising results, with Peak Signal-to-Noise Ratio (PSNR) reaching up to 36.1368 dB and Structural Similarity Index Measure (SSIM) reaching 0.9670. These values exceed the benchmarks set by contemporary ISR methods, thus validating the superiority and effectiveness of our approach in the field of image super-resolution. Conclusions. This study demonstrated the potential of combining CNN and GAN in the domain of image super-resolution. The proposed model exhibits significant advancements over existing ISR methods, offering higher accuracy and improved image quality. The findings confirm the efficiency of deep learning methods in overcoming traditional imaging challenges, making the proposed model valuable for both academic research and practical applications in ISR.

Through the power of new sensing technologies, we are increasingly digitizing the real world. However, instruments produce unstructured data, mainly in the form of point clouds for 3D data and images for 2D data. Nevertheless, many applications (such as navigation, survey, infrastructure analysis) need structured data containing objects and their geometry. Various computer vision approaches have thus been developed to structure the data and identify objects therein. They can be separated into model-driven, data-driven, and knowledge-based approaches. Model-driven approaches mainly use the information on the objects contained in the data and are thus limited to objects and context. Among data-driven approaches, we increasingly find deep learning strategies because of their autonomy in detecting objects. They identify reliable patterns in the data and connect these to the object of interest. Deep learning approaches have to learn these patterns in a training stage. Knowledge-based approaches use characteristic knowledge from different domains allowing the detection and classification of objects. The knowledge must be formalized and substitutes the training for deep learning. Semantic web technologies allow the management of such human knowledge. Deep learning and knowledge-based approaches have already shown good results for semantic segmentation in various examples. The common goal but the different strategies of the two approaches engaged our interest in doing a comparison to get an idea of their strengths and weaknesses. To fill this knowledge gap, we applied two implementations of such approaches to a mobile mapping point cloud. The detected object categories are car, bush, tree, ground, streetlight and building. The deep learning approach uses a convolutional neural network, whereas the knowledge-based approach uses standard semantic web technologies such as SPARQL and OWL2to guide the data processing and the subsequent classification as well. The LiDAR point cloud used was acquired by a mobile mapping system in an urban environment and presents various complex scenes, allowing us to show the advantages and disadvantages of these two types of approaches. The deep learning and knowledge-based approaches produce a semantic segmentation with an average F1 score of 0.66 and 0.78, respectively. Further details are given by analyzing individual object categories allowing us to characterize specific properties of both types of approaches.

Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as—omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms.

Integrating rapid responses to 1,25-dihydroxyvitamin D 3 with transcriptional changes in osteoblasts: Ca 2+ regulated pathways to the nucleus