Correction: Satam et al. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology 2023, 12, 997.

We are very thankful to the commentator for pointing out the issues in the review article by Satam et al [...].

Comparative metabolomics of MCF-7 and MCF-7/TAMR identifies potential metabolic pathways in tamoxifen resistant breast cancer cells.

OBJECTIVES: Breast cancer is the most common cancer and the leading cause of cancer-related death among women. An Estrogen Receptor (ER) antagonist called tamoxifen is used as an adjuvant therapy for ER-positive breast cancers. Approximately 40% of patients develop tamoxifen resistance (TAMR) while receiving treatment. Cancer cells can rewire their metabolism to develop resistant phenotypes, and their metabolic state determines how receptive they are to chemotherapy. METHODS: Metabolite extraction from human MCF-7 and MCF-7/TAMR cells was done using the methanol-methanol-water extraction method. After treating the dried samples with methoxamine hydrochloride in pyridine, the samples were derivatized with 2,2,2-Trifluoro-N-methyl-N-(trimethylsilyl)-acetamide, and Chlorotrimethylsilane (MSTFA + 1% TMCS). The Gas chromatography/mass spectrometry (GC-MS) raw data were processed using MSdial and Metaboanalyst for analysis. RESULTS: Univariate analysis revealed that 35 metabolites were elevated in TAMR cells whereas 25 metabolites were downregulated. N-acetyl-D-glucosamine, lysine, uracil, tyrosine, alanine, and o-phosphoserine were upregulated in TAMR cells, while hydroxyproline, glutamine, N-acetyl-L-aspartic acid, threonic acid, pyroglutamic acid, glutamine, o-phosphoethanolamine, oxoglutaric acid, and myoinositol were found to be downregulated. Multivariate analysis revealed a distinct separation between the two cell lines, as evidenced by their metabolite levels. The enriched pathways of deregulated metabolites included valine, leucine, and isoleucine degradation, Citric Acid Cycle, Warburg effect, Malate-Aspartate shuttle, glucose-alanine cycle, propanoate metabolism, and Phospholipid biosynthesis. CONCLUSION: This study revealed dysregulation of various metabolic processes in TAMR cells, which may be crucial in elucidating the molecular basis of the mechanisms underlying acquired tamoxifen resistance.

Identification of WNK1 as a therapeutic target to suppress IgH/MYC expression in multiple myeloma.

Multiple myeloma (MM) remains an incurable hematological malignancy demanding innovative therapeutic strategies. Targeting MYC, the notorious yet traditionally undruggable oncogene, presents an appealing avenue. Here, using a genome-scale CRISPR-Cas9 screen, we identify the WNK lysine-deficient protein kinase 1 (WNK1) as a regulator of MYC expression in MM cells. Genetic and pharmacological inhibition of WNK1 reduces MYC expression and, further, disrupts the MYC-dependent transcriptional program. Mechanistically, WNK1 inhibition attenuates the activity of the immunoglobulin heavy chain (IgH) enhancer, thus reducing MYC transcription when this locus is translocated near the MYC locus. WNK1 inhibition profoundly impacts MM cell behaviors, leading to growth inhibition, cell-cycle arrest, senescence, and apoptosis. Importantly, the WNK inhibitor WNK463 inhibits MM growth in primary patient samples as well as xenograft mouse models and exhibits synergistic effects with various anti-MM compounds. Collectively, our study uncovers WNK1 as a potential therapeutic target in MM.

Targeting the GPI transamidase subunit GPAA1 abrogates the CD24 immune checkpoint in ovarian cancer.

CD24 is frequently overexpressed in ovarian cancer and promotes immune evasion by interacting with its receptor Siglec10, present on tumor-associated macrophages, providing a "don't eat me" signal that prevents targeting and phagocytosis by macrophages. Factors promoting CD24 expression could represent novel immunotherapeutic targets for ovarian cancer. Here, using a genome-wide CRISPR knockout screen, we identify GPAA1 (glycosylphosphatidylinositol anchor attachment 1), a factor that catalyzes the attachment of a glycosylphosphatidylinositol (GPI) lipid anchor to substrate proteins, as a positive regulator of CD24 cell surface expression. Genetic ablation of GPAA1 abolishes CD24 cell surface expression, enhances macrophage-mediated phagocytosis, and inhibits ovarian tumor growth in mice. GPAA1 shares structural similarities with aminopeptidases. Consequently, we show that bestatin, a clinically advanced aminopeptidase inhibitor, binds to GPAA1 and blocks GPI attachment, resulting in reduced CD24 cell surface expression, increased macrophage-mediated phagocytosis, and suppressed growth of ovarian tumors. Our study highlights the potential of targeting GPAA1 as an immunotherapeutic approach for CD24+ ovarian cancers.

Unlocking the enigma of phenotypic drug tolerance: Mechanisms and emerging therapeutic strategies.

In the ongoing battle against antimicrobial resistance, phenotypic drug tolerance poses a formidable challenge. This adaptive ability of microorganisms to withstand drug pressure without genetic alterations further complicating global healthcare challenges. Microbial populations employ an array of persistence mechanisms, including dormancy, biofilm formation, adaptation to intracellular environments, and the adoption of L-forms, to develop drug tolerance. Moreover, molecular mechanisms like toxin-antitoxin modules, oxidative stress responses, energy metabolism, and (p)ppGpp signaling contribute to this phenomenon. Understanding these persistence mechanisms is crucial for predicting drug efficacy, developing strategies for chronic bacterial infections, and exploring innovative therapies for refractory infections. In this comprehensive review, we dissect the intricacies of drug tolerance and persister formation, explore their role in acquired drug resistance, and highlight emerging therapeutic approaches to combat phenotypic drug tolerance. Furthermore, we outline the future landscape of interventions for persistent bacterial infections.

Induction of double-strand breaks with the non-steroidal androgen receptor ligand flutamide in patients on androgen suppression: a study protocol for a randomized, double-blind prospective trial.

BACKGROUND: Prostate cancer remains the most prevalent malignancy and the second-leading cause of cancer-related death in men in the USA. Radiation therapy, typically with androgen suppression, remains a mainstay in the treatment of intermediate- and high-risk, potentially lethal prostate cancers. However, local recurrence and treatment failure remain common. Basic and translational research has determined the potential for using androgen receptor (AR) ligands (e.g., dihydrotestosterone and flutamide) in the context of androgen-deprived prostate cancer to induce AR- and TOP2B-mediated DNA double-strand breaks (DSBs) and thereby synergistically enhance the effect of radiation therapy (RT). The primary aim of this study is to carry out pharmacodynamic translation of these findings to humans. METHODS: Patients with newly diagnosed, biopsy-confirmed localized prostatic adenocarcinoma will be recruited. Flutamide, an oral non-steroidal androgen receptor ligand, will be administered orally 6-12 h prior to prostate biopsy (performed under anesthesia prior to brachytherapy seed implantation). Key study parameters will include the assessment of DNA double-strand breaks by γH2A.x foci and AR localization to the nucleus. The initial 6 patients will be treated in a single-arm run-in phase to assess futility by establishing whether at least 2 subjects from this group develop γH2A.x foci in prostate cancer cells. If this criterion is met, the study will advance to a two-arm, randomized controlled phase in which 24 participants will be randomized 2:1 to either flutamide intervention or placebo standard-of-care (with all patients receiving definitive brachytherapy). The key pharmacodynamic endpoint will be to assess whether the extent of γH2A.x foci (proportion of cancer cells positive and number of foci per cancer cell) is greater in patients receiving flutamide versus placebo. Secondary outcomes of this study include an optional, exploratory analysis that will (a) describe cancer-specific methylation patterns of cell-free DNA in plasma and urine and (b) assess the utility of serum and urine samples as a DNA-based biomarker for tracking therapeutic response. DISCUSSION: This study will confirm in humans the pharmacodynamic effect of AR ligands to induce transient double-strand breaks when administered in the context of androgen deprivation as a novel therapy for prostate cancer. The findings of this study will permit the development of a larger trial evaluating flutamide pulsed-dose sequencing in association with fractionated external beam RT (+/- brachytherapy). The study is ongoing, and preliminary data collection and recruitment are underway; analysis has yet to be performed. TRIAL REGISTRATION: ClinicalTrials.gov NCT03507608. Prospectively registered on 25 April 2018.

Plasma Levels of Organochlorine Pesticides and Tumor Necrosis Factor-Alpha: A Potential Risk Factor for Developing Acquired Aplastic Anemia in the North Indian Population.

Background Pesticide exposure might have a contributory role in the development of acquired aplastic anemia (AA). However, the precise mechanisms of pesticide-induced AA remain unknown. In this case-control study, we conducted a comparative analysis of plasma levels of organochlorine pesticides (OCP) and tumor necrosis factor-alpha (TNF-alpha) between Indian patients diagnosed with AA and an age- and sex-matched control group. Methods This is an observational case-control study conducted at a tertiary care hospital in North India. In this study, 90 subjects were included, out of which 45 were diagnosed with AA according to the criteria of the International Agranulocytosis and Aplastic Anemia Study. Cases were compared with 45 controls. A trained interviewer gave all study subjects a questionnaire to collect data regarding demographic details, exposure to pesticides, and clinical history. Physical examination and routine laboratory investigations of each subject were performed. Both cases and controls were tested for their plasma levels of organochlorines as per established protocol by gas chromatography-mass spectrometry. TNF-alpha level was measured by enzyme-linked immunosorbent assay in each subject. Results There was a significant increase in plasma levels of delta hexachlorocyclohexane (delta HCH) (p = 0.02) and heptachlor (p = 0.00) in patients with AA as compared to controls. We observed nonsignificant trends towards higher levels of beta HCH (p = 0.643), aldrin (p = 0.399), and p,p'-Dichlorodiphenyltrichloroethane (p,p'-DDT) (p = 0.453) in patients with AA when compared to the controls. There were significantly higher TNF-alpha levels (p = 0.024) in cases as compared to the controls. Conclusion Our study concludes that patients with AA exhibited higher levels of delta-HCH, heptachlor, and TNF-alpha in comparison to the control group. There is a significant positive correlation of TNF alpha with OCPs (alpha HCH, lindane, delta HCH, heptachlor, aldrin, p,p'- DDD, and methoxychlor pesticides). These organochlorines may have accumulated in the fatty tissue of bone marrow because of their lipophilic nature. This suggests that they might have served as a neoantigen to trigger an increase in TNF-alpha production, which may have led to disrupted bone marrow function through cell-mediated immunity, leading to AA.

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique.

Detecting code smells may be highly helpful for reducing maintenance costs and raising source code quality. Code smells facilitate developers or researchers to understand several types of design flaws. Code smells with high severity can cause significant problems for the software and may cause challenges for the system's maintainability. It is quite essential to assess the severity of the code smells detected in software, as it prioritizes refactoring efforts. The class imbalance problem also further enhances the difficulties in code smell severity detection. In this study, four code smell severity datasets (Data class, God class, Feature envy, and Long method) are selected to detect code smell severity. In this work, an effort is made to address the issue of class imbalance, for which, the Synthetic Minority Oversampling Technique (SMOTE) class balancing technique is applied. Each dataset's relevant features are chosen using a feature selection technique based on principal component analysis. The severity of code smells is determined using five machine learning techniques: K-nearest neighbor, Random forest, Decision tree, Multi-layer Perceptron, and Logistic Regression. This study obtained the 0.99 severity accuracy score with the Random forest and Decision tree approach with the Long method code smell. The model's performance is compared based on its accuracy and three other performance measurements (Precision, Recall, and F-measure) to estimate severity classification models. The impact of performance is also compared and presented with and without applying SMOTE. The results obtained in the study are promising and can be beneficial for paving the way for further studies in this area.

Lung anomaly detection from respiratory sound database (sound signals).

Chest or upper body auscultation has long been considered a useful part of the physical examination going back to the time of Hippocrates. However, it did not become a prevalent practice until the invention of the stethoscope by Rene Laennec in 1816, which made the practice suitable and hygienic. Pulmonary disease is a kind of sickness that affects the lungs and various parts of the respiratory system. Lung diseases are the third largest cause of death in the world. According to the World Health Organization (WHO), the five major respiratory diseases, namely chronic obstructive pulmonary disease (COPD), tuberculosis, acute lower respiratory tract infection (LRTI), asthma, and lung cancer, cause the death of more than 3 million people each year worldwide. Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963(96.3%), 0.932,(93.2%) and 0.948(94.8%), respectively.

Next-Generation Sequencing Technology: Current Trends and Advancements.

The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.

Aging, oxidative stress and degenerative diseases: mechanisms, complications and emerging therapeutic strategies.

Aging accompanied by several age-related complications, is a multifaceted inevitable biological progression involving various genetic, environmental, and lifestyle factors. The major factor in this process is oxidative stress, caused by an abundance of reactive oxygen species (ROS) generated in the mitochondria and endoplasmic reticulum (ER). ROS and RNS pose a threat by disrupting signaling mechanisms and causing oxidative damage to cellular components. This oxidative stress affects both the ER and mitochondria, causing proteopathies (abnormal protein aggregation), initiation of unfolded protein response, mitochondrial dysfunction, abnormal cellular senescence, ultimately leading to inflammaging (chronic inflammation associated with aging) and, in rare cases, metastasis. RONS during oxidative stress dysregulate multiple metabolic pathways like NF-κB, MAPK, Nrf-2/Keap-1/ARE and PI3K/Akt which may lead to inappropriate cell death through apoptosis and necrosis. Inflammaging contributes to the development of inflammatory and degenerative diseases such as neurodegenerative diseases, diabetes, cardiovascular disease, chronic kidney disease, and retinopathy. The body's antioxidant systems, sirtuins, autophagy, apoptosis, and biogenesis play a role in maintaining homeostasis, but they have limitations and cannot achieve an ideal state of balance. Certain interventions, such as calorie restriction, intermittent fasting, dietary habits, and regular exercise, have shown beneficial effects in counteracting the aging process. In addition, interventions like senotherapy (targeting senescent cells) and sirtuin-activating compounds (STACs) enhance autophagy and apoptosis for efficient removal of damaged oxidative products and organelles. Further, STACs enhance biogenesis for the regeneration of required organelles to maintain homeostasis. This review article explores the various aspects of oxidative damage, the associated complications, and potential strategies to mitigate these effects.

Advancing cellular immunotherapy with macrophages.

Cell-based immunotherapies have become an exciting avenue for cancer treatment, particularly CAR T cells, which have shown great success in treating hematological malignancies. However, the limited success of T cell-based approaches in treating solid tumors has sparked interest in alternative cell types that could be used for solid tumor immunotherapy. Recent research has pointed to macrophages as a potential solution, given their ability to infiltrate solid tumors, exhibit a strong anti-tumor response, and persist long-term in the tumor microenvironment. Although early attempts with ex-vivo activated macrophage-based therapies failed to translate into clinical success, the field has revolutionized with the recent development of chimeric antigen receptor-expressing macrophages (CAR-M). While CAR-M therapy has reached the clinical trial stage, several challenges still need to be overcome before the therapy can become a reality. Here we review the evolution of macrophage-based cell therapy and evaluate recent studies and developments, emphasizing the potential of macrophages as cellular therapeutics. Furthermore, we also discuss the challenges and opportunities associated with using macrophages as a basis for therapeutic interventions.

Appraisal of the Possible Role of PPARγ Upregulation by CLA of Probiotic Pediococcus pentosaceus GS4 in Colon Cancer Mitigation.

The prevalence of colon cancer (CC) is increasing at the endemic scale, which is accompanied by subsequent morbidity and mortality. Although there have been noteworthy achievements in the therapeutic strategies in recent years, the treatment of patients with CC remains a formidable task. The current study focused on to study role of biohydrogenation-derived conjugated linoleic acid (CLA) of probiotic Pediococcus pentosaceus GS4 (CLAGS4) against CC, which induced peroxisome proliferator-activated receptor gamma (PPARγ) expression in human CC HCT-116 cells. Pre-treatment with PPARγ antagonist bisphenol A diglycidyl ether has significantly reduced the inhibitory efficacy of enhanced cell viability of HCT-116 cells, suggesting the PPARγ-dependent cell death. The cancer cells treated with CLA/CLAGS4 demonstrated the reduced level of Prostaglandin E2 PGE2 in association with reduced COX-2 and 5-LOX expressions. Moreover, these consequences were found to be associated with PPARγ-dependent. Furthermore, delineation of mitochondrial dependent apoptosis with the help of molecular docking LigPlot analysis showed that CLA can bind with hexokinase-II (hHK-II) (highly expressed in cancer cells) and that this association underlies voltage dependent anionic channel to open, thereby causing mitochondrial membrane depolarization, a condition that initiates intrinsic apoptotic events. Apoptosis was further confirmed by annexin V staining and elevation of caspase 1p10 expression. Taken all together, it is deduced that, mechanistically, the upregulation of PPARγ by CLAGS4 of P. pentosaceus GS4 can alter cancer cell metabolism in association with triggering apoptosis in CC.

Development impact bonds in developing countries: an emerging innovation for achieving social outcomes.

This research focuses on the development of impact bonds (DIBs), a newly emerging alternative financial innovative product for financing social results in developing countries. DIBs are an agreement between several parties working towards a development goal. The idea of DIBs has not been thoroughly investigated. The various dimensions of DIBs, including their structure, evolution, conduct, and performance, are the focus of this research. This study examines the idea of DIBs specifically concerning developing countries based on an analytical review of grey literature. According to our research, when it comes to DIBs, policymakers have prioritised their policies more towards health, followed by employment and training, education, poverty reduction, and agriculture.

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues.

The fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs.

Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review.

Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and tools. Research has spawned in various directions to yield effective malware detection mechanisms. Since malware can adopt different ways to attack and hide, accurate analysis is the key to detecting them. Like any usual mobile app, malware requires permission to take action and use device resources. There are 235 total permissions that the Android app can request on a device. Malware takes advantage of this to request unnecessary permissions, which would enable those to take malicious actions. Since permissions are critical, it is important and challenging to identify if an app is exploiting permissions and causing damage. The focus of this article is to analyze the identified studies that have been conducted with a focus on permission analysis for malware detection. With this perspective, a systematic literature review (SLR) has been produced. Several papers have been retrieved and selected for detailed analysis. Current challenges and different analyses were presented using the identified articles.

Emerging Trends in Immunotherapy for Cancer.

Recent advances in cancer immunology have enabled the discovery of promising immunotherapies for various malignancies that have shifted the cancer treatment paradigm. The innovative research and clinical advancements of immunotherapy approaches have prolonged the survival of patients with relapsed or refractory metastatic cancers. Since the U.S. FDA approved the first immune checkpoint inhibitor in 2011, the field of cancer immunotherapy has grown exponentially. Multiple therapeutic approaches or agents to manipulate different aspects of the immune system are currently in development. These include cancer vaccines, adoptive cell therapies (such as CAR-T or NK cell therapy), monoclonal antibodies, cytokine therapies, oncolytic viruses, and inhibitors targeting immune checkpoints that have demonstrated promising clinical efficacy. Multiple immunotherapeutic approaches have been approved for specific cancer treatments, while others are currently in preclinical and clinical trial stages. Given the success of immunotherapy, there has been a tremendous thrust to improve the clinical efficacy of various agents and strategies implemented so far. Here, we present a comprehensive overview of the development and clinical implementation of various immunotherapy approaches currently being used to treat cancer. We also highlight the latest developments, emerging trends, limitations, and future promises of cancer immunotherapy.

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study.

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015-2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated.

Deep Learning-Based Defect Prediction for Mobile Applications.

Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.