Comprehensive Analysis Research Articles

Identification of Stem Cell-related Gene Markers by Comprehensive Transcriptome Analysis to Predict the Prognosis and Immunotherapy of Lung Adenocarcinoma.

Cancer stem cells (CSCs) contribute to metastasis and drug resistance to immunotherapy in lung adenocarcinoma (LUAD), so the stemness evaluation of cancer cells is of great significance. The single-cell RNA sequencing (scRNA-seq) data of the GSE149655 dataset were collected and analyzed. Malignant cells were distinguished by CopyKAT. CytoTRACE score of marker genes in malignant cells was counted by CytoTRACE to construct the stemness score formula. Sample stemness score in TCGA was determined by the formula and divided into high-, medium- and low-stemness score groups. LASSO and COX regression analyses were carried out to screen the key genes related to the prognosis of LUAD from the differentially expressed genes (DEGs) in high- and low-stemness score groups and a risk score model was constructed. Seven types of cells were identified from a total of 4 samples, and 193 marker genes of 3455 malignant cells were identified. There were 1098 DEGs between low- and high-stemness score groups of TCGA, of which CPS1, CENPK, GJB3, and TPSB2 constituted gene signatures. The 4-gene signature could independently evaluate LUAD survival in the training and validation sets and showed an acceptable area under the receiver operator characteristic (ROC) curves (AUCs). This study provides insights into the cellular heterogeneity of LUAD and develops a new cancer stemness evaluation indicator and a 4-gene signature as a potential tool for evaluating the response of LUAD to immune checkpoint blockade (ICB) therapy or antineoplastic therapy.

Phytol and α-Bisabolol Synergy Induces Autophagy and Apoptosis in A549 Cells and Additional Molecular Insights through Comprehensive Proteome Analysis via Nano LC-MS/MS.

Non-Small Cell Lung Cancer (NSCLC) is a malignancy with a significant prevalence and aggressive nature, posing a considerable challenge in terms of therapeutic interventions. Autophagy and apoptosis, two intricate cellular processes, are integral to NSCLC pathophysiology, each affecting the other through shared signaling pathways. Phytol (Phy) and α -bisabolol (Bis) have shown promise as potential anticancer agents individually, but their combined effects in NSCLC have not been extensively investigated. The present study was to examine the synergistic impact of Phy and Bis on NSCLC cells, particularly in the context of autophagy modulation, and to elucidate the resulting differential protein expression using LCMS/ MS analysis. The A549 cell lines were subjected to the patented effective concentration of Phy and Bis, and subsequently, the viability of the cells was evaluated utilizing the MTT assay. The present study utilized real-time PCR analysis to assess the expression levels of crucial apoptotic genes, specifically Bcl-2, Bax, and Caspase-9, as well as autophagy-related genes, including Beclin-1, SQSTM1, Ulk1, and LC3B. The confirmation of autophagy marker expression (Beclin-1, LC3B) and the autophagy-regulating protein SQSTM1 was achieved through the utilization of Western blot analysis. Differentially expressed proteins were found using LC-MS/MS analysis. The combination of Phy and Bis demonstrated significant inhibition of NSCLC cell growth, indicating their synergistic effect. Real-time PCR analysis revealed a shift towards apoptosis, with downregulation of Bcl-2 and upregulation of Bax and Caspase-9, suggesting a shift towards apoptosis. Genes associated with autophagy regulation, including Beclin-1, SQSTM1 (p62), Ulk1, and LC3B, showed significant upregulation, indicating potential induction of autophagy. Western blot analysis confirmed increased expression of autophagy markers, such as Beclin-1 and LC3B, while the autophagy-regulating protein SQSTM1 exhibited a significant decrease. LC-MS/MS analysis revealed differential expression of 861 proteins, reflecting the modulation of cellular processes. Protein-protein interaction network analysis highlighted key proteins involved in apoptotic and autophagic pathways, including STOML2, YWHAB, POX2, B2M, CDA, CAPN2, TXN, ECHS1, PEBP1, PFN1, CDC42, TUBB1, HSPB1, PXN, FGF2, and BAG3, emphasizing their crucial roles. Additionally, PANTHER pathway analysis uncovered enriched pathways associated with the differentially expressed proteins, revealing their involvement in a diverse range of biological processes, encompassing cell signaling, metabolism, and cellular stress responses. The combined treatment of Phy and Bis exerts a synergistic inhibitory effect on NSCLC cell growth, mediated through the interplay of apoptosis and autophagy. The differential protein expression observed, along with the identified proteins and enriched pathways, provides valuable insights into the underlying molecular mechanisms. These findings offer a foundation for further exploration of the therapeutic potential of Phy and Bis in the management of NSCLC.

Effect of solution temperature on the microstructure and properties of ceramic coating on the surface of 2024 aluminum alloy

This study investigates the influence of the 2024 aluminum alloy was treated with solution before ceramic treatment on the microstructure and characteristics of ceramic coatings applied to 2024 aluminum alloy substrates. The microhardness, corrosion resistance, and microstructural properties of these ceramic coatings were assessed using a microhardness tester, an electrochemical workstation, and a scanning electron microscope. The findings indicate that pre-treatment involving solution treatment significantly enhances the hardness and corrosion resistance of 2024 aluminum alloy ceramic coatings. Notably, when the solution temperature was maintained at 460 °C, the most rapid decrease in current density was observed during the ceramization process, resulting in the attainment of the lowest final stable current density. This particular condition yielded ceramic coatings with optimal hardness and corrosion resistance, with hardness exhibiting a remarkable increase of 48.9 HV, self-corrosion potential rising by 0.207 V, and polarization resistance surging by 5310.7 Ω. Moreover, the surface of the ceramic coating displayed remarkable smoothness and was devoid of discernible defects such as cracks or looseness. In light of these findings, it can be concluded that the optimum solution temperature for achieving these desirable properties is 460 °C. This conclusion is derived from a comprehensive analysis encompassing both the morphology and corrosion resistance of ceramic coatings.

Biomarkers can Predict COVID-19 Disease

Abstract:: Health professionals have been confronted with a series of challenges because of the ongoing pandemic of coronavirus disease 2019 (COVID-19). To save the greatest number of lives possible, it is essential to make a prompt diagnosis and admission to the hospital, as well as to stratify risks, make efficient use of intensive care services, choose appropriate treatments, monitor patients, and ensure a prompt discharge. Laboratory markers, also known as biomarkers, can provide additional information that is objective and has the potential to significantly influence various aspects of patient care. Clinical assessment is necessary, but laboratory markers can provide this information. The COVID-19 virus is not an infection that causes the respiratory system; rather, it is a multisystem disease that is caused by a diffuse system-wide process that involves a complex interplay of the immune, nervous, and endocrine systems in inflammatory and coagulative cascades. A wide variety of potential biomarkers have been uncovered because of a better understanding of the virus's effects on the body and how the body responds to them. Here, the pathophysiology and current data are examined in relation to various kinds of biomarkers, such as immunological and inflammation biomarkers, coagulation and hematological biomarkers, as well as cardiac, biochemical, and other biomarkers. This review provides a comprehensive analysis of the research on the association between biomarkers and clinical characteristics, viral load, treatment efficacy, and how this knowledge might most usefully contribute to patient care.

Blepharoplasty Online: Critical Analysis of Content and Patient Comprehensibility.

Patients frequently turn to online information for decision-making factors about aesthetic procedures. The quality of online medical content is an essential supplement to clinical education. These resources assist patients in understanding the risks, benefits, and appropriateness of their desired procedure. This study examines the breadth and readability of online blepharoplasty information, elucidating its educational utility. A depersonalized Google search was conducted using the Startpage Search Engine, investigating key phrases, "blepharoplasty decision making factors", "eye lift decision making factors", and "eyelid lift decision making factors". The first three pages of results for each search term, totaling 90 links were screened. Data were extracted for various decision-making factors, subspecialty, gender, and readability. Twenty-six websites met inclusion for analysis. Thirteen websites were plastic surgery based, five otolaryngology (ENT), five ophthalmology/oculoplastic, one oral-maxillofacial (OMFS), and two mixed-based practices. Most blepharoplasty webpages identified were that of private practice and male surgeons. Half were subspecialties other than plastic surgery. Thirteen common decision-making factors were identified. The most common factors addressed across all texts were recovery followed by cosmetic and functional goals. The least discussed were genetic factors. Average Readability exceeded the 12th grade. There were no significant differences in readability means among subspecialties. This study examines the online blepharoplasty sphere among US-based practices providing clinical education to patients. No appreciable differences among gender, subspecialty, and readability on decision-making factors were found, highlighting a consistency among surgeons. Most websites fell short of readability standards, however, emphasizing a need for clearer information to patients. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Empowering English as a Foreign Language Education With the Production-Oriented Approach: A Systematic Review

<p style="text-align:justify">The field of English as a Foreign Language (EFL) instruction has gained recognition worldwide, particularly in non-native English-speaking countries. However, traditional teaching methods fall short of meeting the needs of today's EFL learners. In response, the Production-Oriented Approach (POA) has emerged as a new pedagogical approach to address these challenges. Despite its significant impact on EFL education, there is a lack of reviews on the POA. This study conducted a systematic review to explore the POA's effectiveness, challenges, and future trends. Using the PRISMA framework, 31 articles from reputable databases (Web of Science, Education Resources Information Center, Scopus, and Google Scholar) were analyzed, covering the years 2018 to 2023. The study's comprehensive analysis, employing Nvivo, revealed that the POA has immense potential for enhancing English learners' language skills, particularly in reading, speaking, writing, and translating. Moreover, the study highlighted the POA's positive influence on learners' affective factors, including motivation, confidence, critical thinking, and self-directed learning abilities. Nonetheless, the POA faces challenges for both teachers and learners, particularly during the transition from traditional methods and the associated increase in workload. Despite these challenges, this study unequivocally establishes the POA as an effective pedagogical approach for teaching EFL. Additionally, it identifies research gaps that must be addressed to sustain and improve EFL education in the future through further development of the POA.</p>

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

Characteristics of T Cells in Single-Cell Datasets of Peripheral Blood and Cerebrospinal Fluid in Alzheimer's Disease Patients.

Alzheimer's disease (AD) is the most common type of dementia, causing a huge socioeconomic burden. In parallel with the widespread uptake of single-cell RNA sequencing (scRNA-seq) technology, there has been a rapid accumulation of data produced by researching AD at single-cell resolution, which is more conductive to explore the neuroimmune-related mechanism of AD. To explore the potential features of T cells in the peripheral blood and cerebrospinal fluid of AD patients. Two datasets, GSE181279 and GSE134578, were integrated from GEO database. Seurat, Monocle, CellChat, scRepertoire, and singleR packages were mainly employed for data analysis. Our analysis demonstrated that in peripheral blood, T cells were significantly expanded, and these expanded T cells were possessed effector function, such as CD8+TEMRA, CD4+TEMRA, and CD8+TEM. Interestingly, CD8+TEMRA and CD4+TEMRA cells positioned adjacently after dimensions reduction and clustering. Notably, we identified that the expanded T cells were developed from Naïve T cells and TCM cells, and TEM cells was in the intermediate state of this developing process. Additionally, in cerebrospinal fluid of AD patients, the amplified T cells were mainly CD8+TEMRA cells, and the number and strength of communication between CD4+TEM, CD8+TEM, and CD8+TEMRA were decreased in AD patients. Our comprehensive analyses identified the cells in cerebrospinal fluid from AD patients are expanded TEMRA or TEM cells and the TEMRA cells communicating with other immune cells is weakened, which may be an important immune feature that leads to AD.

Comprehensive Analysis of Perioperative Factors Influencing the Risk of Biochemical Recurrence in Patients with Radical Prostatectomy.

To analyze the perioperative factors that influence the risk of biochemical recurrence (BCR) in patients with localized PCa undergoing radical prostatectomy Materials and Methods: A total of 457 patients, operated by 2 surgeons in our high-volume oncological center were included in the initial database. Patients who underwent RP for clinically localized PCa in our clinic from 2016 to 2021 were included in the study. Perioperative data were retrospectively reviewed for this study. Follow-up data including post-operative PSA and adjuvant treatment was prospectively gathered by contacting the patients or from the follow-up consultation. Final database was composed of 366 patients who underwent open or 3D laparoscopic RP. Statistical analysis was performed to emphasize the most powerful parameters that influence the BCR. Results: Accounting for multivariable analysis, 4 parameters were statistically significant: initial PSA (iPSA), Gleason score, vascular involvement and positive surgical margins. For the group of patients with no positive margins, 3 parameters were statistically significant: iPSA above 10,98 ng/mL (AUC=0,71); lymph node involvement and Gleason score. Multivariable Cox regression showed that positive margins and iPSA had a significant impact on the time to BCR. Patients that received adjuvant therapy were excluded from the study. Out of the whole cohort, 27,3% of patients presented BCR. Perioperative factors need to be carefully analyzed and a detailed follow-up needs to be conducted in order to assess the risk of biochemical recurrence, resulting in the optimal time for adjuvant treatment implementation.

Comprehensive Scrna-Seq Analysis and Identification of CD8_+T Cell related Gene Markers for Predicting Prognosis and Drug Resistance of Hepatocellular Carcinoma.

Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis. Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy. We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility. To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

Growing Like a Tree: Finding Trunks From Graph Skeleton Trees.

The message-passing paradigm has served as the foundation of graph neural networks (GNNs) for years, making them achieve great success in a wide range of applications. Despite its elegance, this paradigm presents several unexpected challenges for graph-level tasks, such as the long-range problem, information bottleneck, over-squashing phenomenon, and limited expressivity. In this study, we aim to overcome these major challenges and break the conventional "node- and edge-centric" mindset in graph-level tasks. To this end, we provide an in-depth theoretical analysis of the causes of the information bottleneck from the perspective of information influence. Building on the theoretical results, we offer unique insights to break this bottleneck and suggest extracting a skeleton tree from the original graph, followed by propagating information in a distinctive manner on this tree. Drawing inspiration from natural trees, we further propose to find trunks from graph skeleton trees to create powerful graph representations and develop the corresponding framework for graph-level tasks. Extensive experiments on multiple real-world datasets demonstrate the superiority of our model. Comprehensive experimental analyses further highlight its capability of capturing long-range dependencies and alleviating the over-squashing problem, thereby providing novel insights into graph-level tasks.

In-Domain GAN Inversion for Faithful Reconstruction and Editability.

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution.

Global Emergence of SARS-CoV2 Infection and Scientific Interventions to Contain its Spread.

The global pandemic caused by COVID-19 posed a significant challenge to public health, necessitating rapid scientific interventions to tackle the spread of infection. The review discusses the key areas of research on COVID-19 including viral genomics, epidemiology, pathogenesis, diagnostics, and therapeutics. The genome sequencing of the virus facilitated the tracking of its evolution, transmission dynamics, and identification of variants. Epidemiological studies have provided insights into disease spread, risk factors, and the impact of public health infrastructure and social distancing measures. Investigations of the viral pathogenesis have elucidated the mechanisms underlying immune responses and severe manifestations including the long-term effects of COVID-19. Overall, the article provides an updated overview of the diagnostic methods developed for SARS-CoV-2 and discusses their strengths, limitations, and appropriate utilization in different clinical and public health settings. Furthermore, therapeutic approaches including antiviral drugs, immunomodulatory therapies, and repurposed medications have been investigated to alleviate disease severity and improve patient outcomes. Through a comprehensive analysis of these scientific efforts, the review provides an overview of the advancements made in understanding and tackling SARS-CoV-2, while underscoring the need for continued research to address the evolving challenges posed by this global health crisis.

Organic acid and aromatic compounds create distinctive flavor in the blackening process of jujube

Previous studies demonstrated jujube blackening effectively increased cyclic adenosine phosphate and triterpene acid levels, improving its nutritional value. However, compositional changes during this process require further elucidation. The objective aimed to analyze compositional transformations during this process with SEM, TPA, UPLC- MS, E-nose. Results showed decreased hardness, springiness, and chewiness coupled with increased gumminess over blackening durations. Untargeted omics analysis revealed increases of 2-aminooctadec-8-ene-1,3,4-trioland carbendazim. Targeted organic acid analysis showed initial citric acid accumulation (1481.62 to 1645.78 mg/kg) in the first 24 h, then declines to 1072.96 mg/kg. Meanwhile, oxalic and lactic acids steadily rose, peaking at 96–120 h before slightly decreasing. E-nose analysis implied alterations in organic sulfide aromatics engendered the characteristic flavors. Organic acid fluctuations likely resulted from sugar biotransformation and thermal degradation. These comprehensive analyses demonstrate jujube blackening imparts a rich and unique flavor, providing theoretical support for investigating the mechanisms and products underlying this process.

Knowledge mapping of translanguaging in education: A scientometric analysis using CiteSpace

The study of translanguaging in education has drawn increasing attention from scholars, particularly in the past decade. The present research conducted a comprehensive scientometric analysis using the CiteSpace application to investigate the multifaceted phenomenon of translanguaging in the context of education. It addresses the pressing need for quantitative review efforts in the domain of translanguaging in education by employing visualization as an innovative means of presenting bibliometric data. This paper analyzes 680 relevant journal articles published between 2010 and 2022, identifies three phases in the development of translanguaging research, and highlights topics such as bilingual education, higher education, and language-in-education policy. The primary findings, presented through statistical analyses and succinct commentaries, are organized into four sections to address the research questions including 1) general situation, 2) leading geographical locations, 3) co-citation analysis, and 4) keywords co-occurrence analysis. The findings reveal the importance of exploiting students' linguistic repertoires and home languages in education to enhance learning and promote a sense of belonging. This review offers valuable resources for subsequent research in the domain of translanguaging in education that is founded on a better understanding of multilingualism and its significance in contemporary educational landscapes.

Current state of knowledge and theories on second language reading

There is a thorny controversy regarding defining second language (L2) reading based on the required levels of interacting with the decoded texts and including theories that could prevent or facilitate the L2 process. This research attempts to fill the gap through providing a comprehensive analysis to the current state of knowledge and theories on second language reading; schema theory, orthographic depth hypothesis, socio-cultural theory, as well as influence from prior educational experiences, the concept of common underlying proficiency, and cross-language interference. Understanding such second language reading-related theories and hypotheses could enhance redirecting the attention to more practicality and reliability in the field as well as justifying insufficient learning outcomes of second language reading; dedicated particularly to Saudi EFL learners.

Network Pharmacology Along with Molecular Docking to Explore the Mechanism of Danshen Injection against Anthracycline-induced Cardiotoxicity and Transcriptome Validation.

Although anthracyclines have demonstrated efficacy in cancer therapy, their utilization is constrained by cardiotoxicity. In contrast, Danshen injection (DSI), derived from Salvia miltiorrhiza, has a longstanding tradition of being employed to ameliorate cardiovascular ailments, including anthracycline- induced cardiotoxicity (AIC). Nonetheless, there is a notable dearth of comprehensive systematic investigation into the molecular mechanisms underlying DSI's effects on AIC. Consequently, this study was undertaken to explore the underlying mechanism by which DSI acted against AIC. Employing network pharmacology approach, the current investigation undertook a comprehensive analysis of the impact of DSI on AIC, which was further validated by transcriptome sequencing with in vitro AIC model. Additionally, molecular docking was conducted to evaluate the binding of active ingredients to core targets. A total of 3,404 AIC-related targets and 12 active ingredients in DSI, including chrysophanol, luteolin, tanshinone IIA, isoimperatorin, among others, were collected by differentially expressed analysis and database search, respectively. The network pharmacology and enrichment analysis suggested 102 potential targets and 29 signaling pathways associated with the protective effect of DSI on AIC. Three core targets (CA12, NOS3, and POLH) and calcium signaling pathways were further validated by transcriptomic analysis of the in-vitro model. The high affinity of the active ingredients binding to corresponding targets was confirmed by molecular docking. The present study suggested that DSI might exert a cardioprotective effect on AIC via the inhibition of CA12, NOS3, and POLH, as well as the modulation of calcium signaling. Further experiments are warranted to verify the findings.

Deep Learning for Iris Recognition: A Survey

In this survey, we provide a comprehensive review of more than 200 articles, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition.

Optimized Transition Metal Phosphides for Direct Seawater Electrolysis: Current Trends.

Seawater electrolysis presents a viable route for sustainable large-scale hydrogen production, yet its practical application is hindered by several technical challenges. These include the sluggish kinetics of hydrogen evolution, poor stability, cation deposition at the cathode, electrode corrosion, and competing chloride oxidation at the anode. To overcome these obstacles, the development of innovative electrocatalysts is crucial. Transition metal phosphides (TMPs) have emerged as promising candidates owing to their superior catalytic performance and tunable structural properties. This review provides a comprehensive analysis of recent progress in the structural engineering of TMPs tailored for efficient seawater electrolysis. We delve into the catalytic mechanisms underpinning hydrogen and oxygen evolution reactions in different pH conditions, along with the detrimental side reactions that impede hydrogen production efficiency. Several methods to prepare TMPs are then introduced. Additionally, detailed discussions on structural modifications and interface engineering tactics are presented, showcasing strategies to enhance the activity and durability of TMP electrocatalysts. By analyzing current research findings, our review aims to inform ongoing research endeavors and foster advancements in seawater electrolysis for practical and ecologically sound hydrogen generation.

Impact of transfer learning compared to convolutional neural networks on fruit detection

Smart farming, also known as precision agriculture or digital farming, is an innovative approach to agriculture that utilizes advanced technologies and data-driven techniques to optimize various aspects of farming operations. One smart farming activity, fruit classification, has broad applications and impacts across agriculture, food production, health, research, and environmental conservation. Accurate and reliable fruit classification benefits various stakeholders, from farmers and food producers to consumers and conservationists. In this study, we conduct a comprehensive comparative analysis to assess the performance of a Convolutional Neural Network (CNN) model in conjunction with four transfer learning models: VGG16, ResNet50, MobileNet-V2, and EfficientNet-B0. Models are trained once on a benchmark dataset called Fruits360 and another time on a reduced version of it to study the effect of data size and image processing on fruit classification performance. The original dataset reported accuracy scores of 95%, 93%, 99.8%, 65%, and 92.6% for these models, respectively. While accuracy increased when trained on the reduced dataset for three of the employed models. This study provides valuable insights into the performance of various deep learning models and dataset versions, offering guidance on model selection and data preprocessing strategies for image classification tasks.