bluuebunny/crossref_metadata_2025 huggingface dataset:
Defense against oxidative stress in Caenorhabditis elegans by dark tea
Authors: Wang Jianxiu , Zhang Kaiheng , Zhang Yaya , Ge Shumin , Zhang Shuhua
Published: January 2024

Dark tea, rich in nutricines including tea polyphenols and free amino acids, is a kind of post-fermented tea. The potential application of nutricines against oxidative damage and senescence, which drives animal health maintenance and disease prevention, has attracted considerable interest. In this study, the effect of dark tea and its effects on longevity and defense against oxidative stress was investigated in the Caenorhabditis elegans (C. elegans) model. Under normal conditions, dark tea extended the lifespan without significant impairment of propagation. It also improved the motility, alleviated the fat accumulation and apoptosis. Additionally, orally administered dark tea could significantly decrease the level of reactive oxygen species (ROS) and resulted in a superior lifespan in H2O2-induced oxidative stressed C. elegans. In antioxidant assays in vitro, dark tea was found to be rich in strong hydroxyl, DPPH and ABTS+ free radical scavenging capacity. Interestingly, mRNA sequence analyses further revealed that dark tea may catalyze intracellular relevant oxidative substrates and synthesize antioxidants through synthetic and metabolic pathways. These results suggest that dark tea is worth further exploration as a potential dietary supplement for the maintenance of animal health and the prevention of related diseases.
The Mediating Role of Digital Competence in the Associations Between the Factors Affecting Healthcare Utilization and Access to Care
Authors: Heponiemi Tarja , Kaihlanen Anu-Marja , Virtanen Lotta , Kainiemi Emma , Saukkonen Petra , Koponen Päivikki , Koskinen Seppo , Elovainio Marko
Published: January 2024

Objectives: To examine with a population-based longitudinal survey design whether poor health, longstanding activity limitation, impaired cognitive functioning, mental distress, or loneliness predict poor access to healthcare and whether digital competence mediates these associations.

Methods: The data were from the longitudinal FinHealth -survey gathered in Finland in 2017 and 2020 including 3,771 respondents (57.1% women). Linear regression analyses were used to examine the associations of factors affecting healthcare utilization with access to care adjusted for age, sex, and education. Counterfactual causal mediation framework was used to examine the mediating role of digital competence in the relationships among these factors and access to healthcare.

Results: Factors affecting healthcare utilization were associated with poor access to care and these associations were partly mediated by low digital competence. Low digital competence mediated 12%, 9% and 8%, of the associations of impaired cognitive functioning, longstanding activity limitation, and loneliness with poor access to care, respectively.

Conclusion: According to our results, one way to improve the access to healthcare among vulnerable groups could be to improve their digital competence.
Utility of the Interferon-Gamma Enzyme-Linked Immunosorbent Spot Assay to Predict Risk of Cytomegalovirus Infection in Kidney Transplant Recipients
Authors: Namsiripongpun Warunyu , Kantachuvesiri Surasak , Bruminhent Jackrapong
Published: January 2024

Non‐specific interferon‐gamma (IFN‐γ) enzyme‐linked immunosorbent (ELISpot) responses after solid organ transplant (SOT) and their relationship with cytomegalovirus (CMV) reactivation have hardly been investigated. Adult kidney transplant (KT) recipients underwent measurement of IFN‐γ‐producing T cells using the ELISpot assay before and 1 month after transplantation. Data for CMV infection episodes were collected. Risk factors for post‐transplant CMV infection, based on IFN‐γ responses, were analyzed using a Cox proportional hazards model. A total of 93 KT recipients were enrolled in the study and 84 evaluable participants remained at 1 month post KT. Thirty-three (39%) recipients developed subsequent CMV infection within 6 months post‐transplant. At 1‐month post‐transplant, IFN‐γ‐producing T cells with <250 spot‐forming units (SFUs)/2.5 × 105 peripheral blood mononuclear cells (PBMCs) were significantly associated with CMV infection (HR 3.1, 95% CI 1.4–7.1, p = 0.007). On multivariable analysis, posttransplant IFN‐γ‐producing T cells with <250 SFUs/2.5 × 105 PBMCs remained independently associated with CMV infection (HR 3.1, 95% CI 1.2–7.8, p = 0.019). Conclusions: KT recipients with low IFN‐γ‐producing T cells measured by the ELISpot assay are more likely to develop CMV infection after transplantation. Therefore, measurement of nonspecific cell-mediated immunity ELISpot responses could potentially stratify recipients at risk of CMV infection (Thai Clinical Trials Registry, TCTR20210216004).
Exogenous Silicon Application Improves Chilling Injury Tolerance and Photosynthetic Performance of Citrus
Authors: Mvondo-She Mireille Asanzi , Mashilo Jacob , Gatabazi Auges , Ndhlala Ashwell Rungano , Laing Mark Delmege
Published: January 2024

Low-temperature stress is an important limiting factor affecting citrus growth and fruit yields. Therefore, increasing citrus cold stress tolerance may enhance the growth, yield, and quality of citrus production in marginal areas. The objective of this study was to determine the efficacy of silicon (Si) fertilizer application on cold-tolerance enhancement in citrus. Two citrus cultivars (Delta and Nules) were subjected to Si fertilization (control, 1000 mg L−1) and cold-stress temperature treatments (control and 0 °C for 72 h) using a 2 × 2 × 2 factorial treatment structure with six replications. Leaf gas exchange and chlorophyll fluorescence parameters, such as net photosynthetic rate (A), stomatal conductance (gs), transpiration rate (Tr), internal CO2 concentration (Ci), intrinsic water-use efficiency (iWUE), minimal fluorescence (Fo), maximum fluorescence (Fm), maximum quantum efficiency of PSII primary photochemistry of dark-adapted leaves (Fv/Fm), maximum quantum efficiency of PSII primary photochemistry of dark-/light-adapted leaves (F’v/F’m), electron transport rate (ETR), non-photochemical quenching (NPQ), and the relative measure of electron transport to oxygen molecules (ETR/A), were measured. The application of Si drenching to trees that were subsequently exposed to cold stress reduced gs, Tr, and Ci but improved iWUE and Fo in both cultivars compared to the Si-untreated trees. In addition, specific adaptation mechanisms were found in the two citrus species; NPQ and ETR were improved in Si-treated Valencia trees, while A, Fm, and ETR/A were improved in Clementine trees under chilling stress conditions. The current research findings indicate the potential of Si application to enhance cold stress tolerance in citrus, which can provide a strategy for growing citrus in arid and semi-arid regions that may experience cold stress. Overall, after the application of silicon drenching, the cold-sensitive citrus Valencia cultivar became as cold-tolerant as the cold-tolerant Clementine cultivar.
Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels
Authors: Wu Jingming , Bai Tiecheng , Li Xu
Published: January 2024

Chlorophyll content is highly susceptible to environmental changes, and monitoring these changes can be a crucial tool for optimizing crop management and providing a foundation for research in plant physiology and ecology. This is expected to deepen our scientific understanding of plant ecological adaptation mechanisms, offer a basis for improving agricultural production, and contribute to ecosystem management. This study involved the collection of hyperspectral data, image data, and SPAD data from jujube leaves. These data were then processed using SG smoothing and the isolated forest algorithm, following which eigenvalues were extracted using a combination of Pearson’s phase relationship method and the Partial Least Squares Regression–continuous projection method. Subsequently, seven methods were employed to analyze the results, with hyperspectral data and color channel data used as independent variables in separate experiments. The findings indicated that the integrated BPNN-RF-Ridge Regression algorithm provided the best results, with an R2 of 0.8249, MAE of 2.437, and RMSE of 2.9724. The inclusion of color channel data as an independent variable led to a 3.2% improvement in R2, with MAE and RMSE increasing by 1.6% and 3.9%, respectively. These results demonstrate the effectiveness of integrated methods for the determination of chlorophyll content in jujube leaves and underscore the potential of using multi-source data to improve the model fit with a minimal impact on errors. Further research is warranted to explore the application of these findings in precision agriculture for jujube yield optimization and income-related endeavors, as well as to provide insights for similar studies in other plant species.
AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning
Authors: Tomášik Juraj , Zsoldos Márton , Oravcová Ľubica , Lifková Michaela , Pavleová Gabriela , Strunga Martin , Thurzo Andrej
Published: January 2024

In the age of artificial intelligence (AI), technological progress is changing established workflows and enabling some basic routines to be updated. In dentistry, the patient’s face is a crucial part of treatment planning, although it has always been difficult to grasp in an analytical way. This review highlights the current digital advances that, thanks to AI tools, allow us to implement facial features beyond symmetry and proportionality and incorporate facial analysis into diagnosis and treatment planning in orthodontics. A Scopus literature search was conducted to identify the topics with the greatest research potential within digital orthodontics over the last five years. The most researched and cited topic was artificial intelligence and its applications in orthodontics. Apart from automated 2D or 3D cephalometric analysis, AI finds its application in facial analysis, decision-making algorithms as well as in the evaluation of treatment progress and retention. Together with AI, other digital advances are shaping the face of today’s orthodontics. Without any doubts, the era of “old” orthodontics is at its end, and modern, face-driven orthodontics is on the way to becoming a reality in modern orthodontic practices.
Boesenbergia rotunda and Its Pinostrobin for Atopic Dermatitis: Dual 5-Lipoxygenase and Cyclooxygenase-2 Inhibitor and Its Mechanistic Study through Steady-State Kinetics and Molecular Modeling
Authors: Liana Desy , Eurtivong Chatchakorn , Phanumartwiwath Anuchit
Published: January 2024

Human 5-lipoxygenase (5-LOX) and cyclooxygenase-2 (COX-2) are potential targets for suppressing pruritic skin inflammation in atopic dermatitis (AD). In addition, Staphylococcus aureus colonization and oxidative stress worsen AD skin conditions. We aimed to investigate anti-inflammatory activity, using 5-LOX and COX-2 inhibitions, and the anti-staphylococcal, and antioxidant potentials of several medicinal plants bio-prospected from traditional medicine related to AD pathogenesis. Essential oils and hexane fractions were prepared and analyzed using gas chromatography–mass spectrometry. Boesenbergia rotunda hexane extract displayed anti-Staphylococcus aureus (MIC = 10 µg/mL) and antioxidant activities (IC50 = 557.97 and 2651.67 µg/mL against DPPH and NO radicals, respectively). A major flavonoid, pinostrobin, was further nonchromatographically isolated. Pinostrobin was shown to be a potent 5-LOX inhibitor (IC50 = 0.499 µM) compared to nordihydroguaiaretic acid (NDGA; IC50 = 5.020 µM) and betamethasone dipropionate (BD; IC50 = 2.077 µM) as the first-line of AD treatment. Additionally, pinostrobin inhibited COX-2 (IC50 = 285.67 µM), which was as effective as diclofenac sodium (IC50 = 290.35 µM) and BD (IC50 = 240.09 µM). This kinetic study and molecular modeling showed the mixed-type inhibition of NDGA and pinostrobin against 5-LOX. This study suggests that B. rotunda and its bioactive pinostrobin have promising properties for AD therapy.
Antioxidant and Anti-Inflammatory Properties of Quail Yolk Oil via Upregulation of Superoxide Dismutase 1 and Catalase Genes and Downregulation of EIGER and Unpaired 2 Genes in a D. melanogaster Model
Authors: Ismaila Muhammad Sani , Sanusi Kamaldeen Olalekan , Iliyasu Uwaisu , Imam Mustapha Umar , Georges Karla , Sundaram Venkatesan , Jones Kegan Romelle
Published: January 2024

Quail egg yolk oil (QEYO) has a rich history of medicinal use, showcasing heightened antioxidant and bioactive properties in our prior studies. This positions QEYO as a promising candidate for therapeutic and cosmetic applications. In this investigation, QEYO was extracted using ethanol/chloroform and 2-propanol/hexane solvents. GC–MS and FTIR analyses quantified 14 major bioactive compounds in the ethanol/chloroform fraction and 12 in the 2-propanol/hexane fraction. Toxicity evaluations in fruit flies, spanning acute, sub chronic, and chronic exposures, revealed no adverse effects. Negative geotaxis assays assessed locomotor activity, while biochemical assays using fly hemolymph gauged antioxidant responses. Real-time PCR revealed the relative expression levels of the antioxidant and anti-inflammatory genes. FTIR spectra indicated diverse functional groups, and the GC–MS results associated bioactive compounds with the regulation of the anti-inflammatory genes EIGER and UPD2. While no significant change in SOD activities was noted, male flies treated with specific QEYO doses exhibited increased catalase activity and total antioxidant capacity, coupled with a significant decrease in their malondialdehyde levels. This study offers valuable insights into the bioactive compounds of QEYO and their potential regulatory roles in gene expression.
Prediction Models for Mechanical Properties of Cement-Bound Aggregate with Waste Rubber
Authors: Zvonarić Matija , Benšić Mirta , Barišić Ivana , Dokšanović Tihomir
Published: January 2024

The high stiffness of cement-bound aggregate (CBA) is recognized as its main drawback. The stiffness is described by the modulus of elasticity, which is difficult to determine precisely in CBA. Incorporating rubber in these mixtures reduces their stiffness, but mathematical models of the influence of rubber on the mechanical characteristics have not previously been defined. The scope of this research was to define a prediction model for the compressive strength (fc), dynamic modulus of elasticity (Edyn) and static modulus of elasticity (Est) based on the measured ultrasonic pulse velocity as a non-destructive test method. The difference between these two modules is based on the measurement method. Within this research, the cement and waste rubber content were varied, and the mechanical properties were determined for three curing periods. The Edyn was measured using the ultrasonic pulse velocity (UPV), while the Est was determined using three-dimensional digital image correlation (3D DIC). The influence of the amount of cement and rubber and the curing period on the UPV was determined. The development of prediction models for estimating the fc and Est of CBA modified with waste rubber based on the non-destructive test results is highlighted as the most significant contribution of this work. The curing period was statistically significant for the prediction of the Est, which points to the development of CBA elastic properties through different stages during the cement-hydration process. By contrast, the curing period was not statistically significant when estimating the fc, resulting in a simplified, practical and usable prediction model.
Calculation and Analysis of Load Transfer Characteristics of Tensile Anchors for Geotechnical Anchoring Systems
Authors: Cheng Zhiyuan , Wang Yimin , Zhang Kunbiao , Wei Daidong
Published: January 2024

In order to explore the problems of load transfer and anchorage mechanisms of tensile anchors under pull-out load for geotechnical anchoring systems, a step-wise mathematical model is established which considers the linear–nonlinear shear stress and shear displacement of the anchorage segment, using an elasto-plastic constitutive model. The displacement, axial force, and shear stress of the anchorage interface in different stages (elastic, plastic, and debonding) are analyzed and solutions are derived. And the theoretical solutions for the ultimate pull-out load of the anchor at each stage are also presented. Two in situ pull-out tests are used to verify and apply these findings in engineering. The results show that the stepwise composite model could reflect the bonding, softening and residual characteristics of the anchoring interface. In the process of the pull-out load increasing, the pulling end of the anchor initially enters the plastic stage and the debonding stage, respectively, and the failure of the anchor occurs at the pulling end, and as the axial force transfers down deeper, the damage gradually spreads deeper. The axial force distribution of the anchorage section is a monotonically decreasing curve, and the peak point of the shear stress gradually moves deeper. The calculation results of the axial force distribution curve and load–displacement curve of the anchor are in good agreement with the measured values, which verifies the rationality and reliability of the theoretical prediction method. This method can provide a theoretical reference for the load transfer analysis and design of tension anchors for geotechnical anchoring systems.
Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China
Authors: Zhou Xue , Zhang Zhen , Yang Weidong , Liu Qingkuan
Published: January 2024

To investigate the susceptibility of drifting snow along the Ya’an–Qamdo section of the railway, which is located in a high-altitude and cold plateau in Southwest China with scarce meteorological information, the Weather Research and Forecasting Model (WRF) is used in this paper to simulate the spatio-temporal distribution of meteorological data. According to the varying terrain, the railway section from Ya’an to Qamdo is divided into two regions along 100.8° E for double-layer nested simulation. The original land use data of the WRF model are used in region 1. Due to the increased number of mountains in region 2, the original data are replaced by the MCD12Q1v006 land use data, and the vertical direction layers are densified near the ground to increase simulation accuracy. The simulated results are compared with the observation data. It is found that after densification, the results have been significantly improved. The results obtained by the WRF model can accurately simulate the change trends of temperature, rainfall, and wind speed, and the correlation coefficients are relatively high, which verifies the accuracy of WRF for simulating complex terrain regions. The simulation results further indicate that approximately 300 km of the Ya’an–Qamdo railway may experience drifting snow. Among them, no drifting snow events occur in Ya’an County, and the areas with higher probability are located at the border between Luding County and Tianquan County, followed by Kangding area. The remaining areas have a probability of less than 10%. The WRF model demonstrates its capability in the drifting snow protection of railways with limited meteorological data.
RETRACTED: Prasanna et al. Continual Learning Approach for Continuous Data Stream Analysis in Dynamic Environments. Appl. Sci. 2023, 13, 8004
Authors: Prasanna K. , Khan Mudassir , Alshahrani Saeed M. , Kiran Ajmeera , Phanindra Kumar Reddy P. , Alymani Mofadal , Chinna Babu J.
Published: January 2024

The journal retracts the article “Continual Learning Approach for Continuous Data Stream Analysis in Dynamic Environments” [...]
Testing Protective Clothing with an Emphasis on Subjective Opinions
Authors: Kubás Jozef , Kollár Boris , Ristvej Jozef , Strelcová Stanislava , Petrlová Katarína , Stolínová Adéla
Published: January 2024

This paper focuses on the testing and evaluation of special anti-chemical protective suits. The testing and evaluation focused on the subjective aspect of the protective suits, which is best perceived by their wearers; however, the subjective aspect only accounts for a certain proportion of the overall evaluation. For this reason, the proposed objective evaluation criteria are also presented in the thesis. Four types of anti-chemical protective suits were selected for testing. In this way, it was possible to compare their design specificities. An older type of civil protection suit and three modern types were selected for evaluation. Multi-criteria decision making and the expert method were used to develop the criteria. Mathematical relationships were used to evaluate the test results. A methodological procedure was developed to integrate the established subjective criteria in order to test. The results of the testing confirmed the construction and design advances of the protective suits over the older type of civil protection suit. At the same time, they pointed out possible shortcomings in the work and execution of the movements of the individual selected protective suits. By evaluating the data obtained, the most suitable protective suit was selected that best met the subjective criteria. However, these results need to be supplemented by an assessment of the objective criteria in the final evaluation. This paper also presents the characteristics of the selected protective suits in terms of technical parameters and objective criteria. The results of the study can further serve as a complementary way of evaluating the quality of protective suits. It is the subjective evaluation that is essential in a comprehensive assessment of the quality of protective suits, as the user himself often perceives the quality of the product differently from the manufacturer.
The Task of Post-Editing Machine Translation for the Low-Resource Language
Authors: Rakhimova Diana , Karibayeva Aidana , Turarbek Assem
Published: January 2024

In recent years, machine translation has made significant advancements; however, its effectiveness can vary widely depending on the language pair. Languages with limited resources, such as Kazakh, Uzbek, Kalmyk, Tatar, and others, often encounter challenges in achieving high-quality machine translations. Kazakh is an agglutinative language with complex morphology, making it a low-resource language. This article addresses the task of post-editing machine translation for the Kazakh language. The research begins by discussing the history and evolution of machine translation and how it has developed to meet the unique needs of languages with limited resources. The research resulted in the development of a machine translation post-editing system. The system utilizes modern machine learning methods, starting with neural machine translation using the BRNN model in the initial post-editing stage. Subsequently, the transformer model is applied to further edit the text. Complex structural and grammatical forms are processed, and abbreviations are replaced. Practical experiments were conducted on various texts: news publications, legislative documents, IT sphere, etc. This article serves as a valuable resource for researchers and practitioners in the field of machine translation, shedding light on effective post-editing strategies to enhance translation quality, particularly in scenarios involving languages with limited resources such as Kazakh and Uzbek. The obtained results were tested and evaluated using specialized metrics—BLEU, TER, and WER.
Saturated Boiling Enhancement of Novec-7100 on Microgrooved Surfaces with Groove-Induced Anisotropic Properties
Authors: Lin Ho-Ching , Kang Cheng-Hsin , Cheng Hui-Chung , Chang Tien-Li , Chen Ping-Hei
Published: January 2024

The effects of the anisotropic properties (wettability and roughness) of microgrooved surfaces on heat transfer were experimentally investigated during pool boiling using Novec-7100 as a working fluid. The idea for introducing the concept of anisotropic wettability in boiling experiments draws inspiration from biphilic surfaces. The investigation is also motivated by two-phase immersion cooling, which involves phase-change heat transfer, using a dielectric liquid as a working fluid. Very few studies have focused on the effects of surfaces with anisotropic properties on boiling performance. Thus, this study aims to examine the pool-boiling heat transfer performance on surfaces with microgroove-induced anisotropic properties under the saturation condition. A femtosecond-laser texturing method was employed to create microgrooved surfaces with different groove spacings. The results indicated that anisotropic properties affected the heat transfer coefficient and critical heat flux. Relative to the plain surface, microgrooved surfaces enhanced the heat transfer performance due to the increased number of bubble nucleation sites and higher bubble detachment frequency. An analysis of bubble dynamics under different surface conditions was conducted with the assistance of high-speed images. The microgrooved surface with a groove spacing of 100 μm maximally increased the BHTC by 37% compared with that of the plain surface. Finally, the CHF results derived from experiments were compared with related empirical correlations. Good agreement was achieved between the results and the prediction correlation.
Ecological Importance of Alkaline Phosphatase Activity and Acid Phosphatase Activity in Lakes with Different Catchment Use Structures
Authors: Janicka Ewelina , Kanclerz Jolanta , Agaj Tropikë
Published: January 2024

Surface waters in Europe and across other continents have been affected by anthropogenic activities, leading to changes in the ecological state of lakes due to the continuous phenomenon of eutrophication. This study assesses the activity of enzymes secreted in bottom sediments collected from two lakes, aiming to determine the interaction between bottom sediments and water based on samples collected from lake bottoms and overlying water. The study measured the production of secreted enzymes via alkaline phosphatase (ALP) and acidic phosphatase (ACP) in two distinct environments. Sample collection encompassed two lakes within two diverse catchment types (agroforestry and forest) during spring and summer. The findings revealed higher ACP values than ALP in both lake environments, with higher enzyme values recorded in the lake with a typical forest catchment area. High values of enzymes were related to the pollutant’s input into the lakes, suggesting that anthropogenic activities may have a substantial influence on the studied lakes. Principal component analysis (PCA) allowed the identification of a negative correlation between the enzymatic activity of phosphorus in bottom sediments and the content of organic phosphorus (org-P) in overlying waters only within the forest catchment, where the angle between the vectors representing primary variables is close to 180 degrees. The conducted statistical analysis demonstrated significant distinctions among the lakes concerning most of the examined parameters.
Research on the Evolution Network Model of the Landslide Disaster Chain: A Case Study of the Baige Landslide
Authors: Gao Feng , Gao Xiu , Yang Chun , Li Jielin
Published: January 2024

In the context of Western China’s unique geography, recurrent landslide disasters pose substantial threats to both resident safety and economic stability. The escalating frequency of these incidents emphasizes the critical need for innovative disaster research, particularly focused on the concept of a disaster chain. This research aims to enhance disaster preparedness and management strategies with the ultimate goal of minimizing losses. On the basis of predecessors, this study changes the previous analysis forms of single or partial disaster events, innovatively collects all secondary disaster events derived from the landslide disaster chain, and builds an evolutionary network model. In concrete terms, our study concentrates on the Baige landslide within the Qinghai-Tibet Plateau, pinpointing sub-hazard events as crucial disaster nodes within the landslide. By establishing directed connections, we have developed a comprehensive landslide disaster chain evolution network model firmly grounded in the principles of disaster chain dynamics and complex network theory. This model encompasses 31 distinct disaster nodes and 77 connecting edges. To assess the inherent risks in the landslide catastrophe chain, we conducted a thorough analysis considering node access degree and clustering coefficients. Critical nodes driving economic losses, such as floods, debris flows, secondary landslides, and downstream water damage, were identified. Additionally, we isolated vulnerable connections within the evolving network, evaluating the susceptibility of each edge. Our research underscores the significance of proactive measures, including pre-disaster monitoring, early warning systems, and timely post-disaster information dissemination. Implementing these actions can play a pivotal role in mitigating the impact of landslide disasters, preserving lives and sustaining regional prosperity.
A Survey of Incremental Deep Learning for Defect Detection in Manufacturing
Authors: Mohandas Reenu , Southern Mark , O’Connell Eoin , Hayes Martin
Published: January 2024

Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase. This paper reviews how new process, training or validation information is rigorously incorporated in real time when detection exceptions arise during inspection. In particular, consideration is given to how new tasks, classes or decision pathways are added to existing models or datasets in a controlled fashion. An analysis of studies from the incremental learning literature is presented, where the emphasis is on the mitigation of process complexity challenges such as, catastrophic forgetting. Further, practical implementation issues that are known to affect the complexity of deep learning model architecture, including memory allocation for incoming sequential data or incremental learning accuracy, is considered. The paper highlights case study results and methods that have been used to successfully mitigate such real-time manufacturing challenges.
Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
Authors: Li Zoe , Huang Ruili , Xia Menghang , Patterson Tucker A. , Hong Huixiao
Published: January 2024

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.
ATP-Binding Cassette Subfamily G Member 2 in Acute Myeloid Leukemia: A New Molecular Target?
Authors: Damiani Daniela , Tiribelli Mario
Published: January 2024

Despite the progress in the knowledge of disease pathogenesis and the identification of many molecular markers as potential targets of new therapies, the cure of acute myeloid leukemia remains challenging. Disease recurrence after an initial response and the development of resistance to old and new therapies account for the poor survival rate and still make allogeneic stem cell transplantation the only curative option. Multidrug resistance (MDR) is a multifactorial phenomenon resulting from host-related characteristics and leukemia factors. Among these, the overexpression of membrane drug transporter proteins belonging to the ABC (ATP-Binding Cassette)-protein superfamily, which diverts drugs from their cellular targets, plays an important role. Moreover, a better understanding of leukemia biology has highlighted that, at least in cancer, ABC protein’s role goes beyond simple drug transport and affects many other cell functions. In this paper, we summarized the current knowledge of ABCG2 (formerly Breast Cancer Resistance Protein, BCRP) in acute myeloid leukemia and discuss the potential ways to overcome its efflux function and to revert its ability to confer stemness to leukemia cells, favoring the persistence of leukemia progenitors in the bone marrow niche and justifying relapse also after therapy intensification with allogeneic stem cell transplantation.
A Retrospective Study on tDCS Treatment in Patients with Drug-Resistant Chronic Pain
Authors: Pérez-Borrego Yolanda A. , Soto-León Vanesa , Brocalero-Camacho Ángela , Oliviero Antonio , Carrasco-López Carmen
Published: January 2024

Background. Transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) has an analgesic effect superior to a placebo in chronic pain. Some years ago, tDCS was implemented at the Hospital Nacional of Paraplegics (Toledo, Spain) to treat patients with pharmacological resistance to chronic pain. Objective. The main objectives of this study with tDCS were (1) to confirm the safety of one-year treatment; (2) to estimate the number of patients after one year in treatment; (3) to describe the effects of tDCS on the pain intensity during one-year treatment; and (4) to identify factors related to treatment success. Methods. This was a retrospective study conducted at the National Hospital for Paraplegics with 155 patients with pharmacologically resistant chronic pain. Anodal tDCS was applied over the M1 for 20 min at 1.5 mA for 10 treatment sessions from Monday to Friday (Induction phase), followed by 2–3 sessions per month (Maintenance phase). Pain intensity was assessed using a Visual Analogue Scale (VAS). Results. Anodal tDCS on M1 confirmed the reduction in the pain intensity. Moreover, 58% of outpatients completed one year of treatment. Only the VAS values obtained during the baseline influenced the response to treatment. Patients with a very high VAS at the baseline were more likely to not respond adequately to tDCS treatment. Conclusions. Anodal tDCS over M1 is an adequate therapy (safe and efficient) to treat drug-resistant chronic pain. Moreover, pain intensity at the start of treatment could be a predictor of patients’ continuity with tDCS for at least one year.
Target-Following Control of a Biomimetic Autonomous System Based on Predictive Reinforcement Learning
Authors: Wang Yu , Wang Jian , Kang Song , Yu Junzhi
Published: January 2024

Biological fish often swim in a schooling manner, the mechanism of which comes from the fact that these schooling movements can improve the fishes’ hydrodynamic efficiency. Inspired by this phenomenon, a target-following control framework for a biomimetic autonomous system is proposed in this paper. Firstly, a following motion model is established based on the mechanism of fish schooling swimming, in which the follower robotic fish keeps a certain distance and orientation from the leader robotic fish. Second, by incorporating a predictive concept into reinforcement learning, a predictive deep deterministic policy gradient-following controller is provided with the normalized state space, action space, reward, and prediction design. It can avoid overshoot to a certain extent. A nonlinear model predictive controller is designed and can be selected for the follower robotic fish, together with the predictive reinforcement learning. Finally, extensive simulations are conducted, including the fix point and dynamic target following for single robotic fish, as well as cooperative following with the leader robotic fish. The obtained results indicate the effectiveness of the proposed methods, providing a valuable sight for the cooperative control of underwater robots to explore the ocean.
Prognostic Significance of Preoperative Inflammation Markers on the Long-Term Outcomes in Peritoneal Carcinomatosis from Ovarian Cancer
Authors: Balescu Irina , Eftimie Mihai , Petrea Sorin , Diaconu Camelia , Gaspar Bogdan , Pop Lucian , Varlas Valentin , Hasegan Adrian , Martac Cristina , Bolca Ciprian , Stoian Marilena , Stroescu Cezar , Zgura Anca , Bacalbasa Nicolae
Published: January 2024

Ovarian cancer remains one of the most lethal gynaecological malignancies affecting women worldwide; therefore, attention has been focused on identifying new prognostic factors which might help the clinician to select cases who could benefit most from surgery versus cases in which neoadjuvant systemic therapy followed by interval debulking surgery should be performed. The aim of the current paper is to identify whether preoperative inflammation could serve as a prognostic factor for advanced-stage ovarian cancer. Material and methods: The data of 57 patients who underwent to surgery for advanced-stage ovarian cancer between 2014 and 2020 at the Cantacuzino Clinical Hospital were retrospectively reviewed. The receiver operating characteristic curve was used to determine the optimal cut-off value of different inflammatory markers for the overall survival analysis. The analysed parameters were the preoperative level of CA125, monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and systemic inflammation index (SII). Results: Baseline CA125 > 780 µ/mL, NLR ≥ 2.7, MLR > 0.25, PLR > 200 and a systemic immune inflammation index (SII, defined as platelet × neutrophil–lymphocyte ratio) ≥ 84,1000 were associated with significantly worse disease-free and overall survival in a univariate analysis. In a multivariate analysis, MLR and SII were significantly associated with higher values of overall survival (p < 0.0001 and p = 0.0124); meanwhile, preoperative values of CA125, PLR and MLR were not associated with the overall survival values (p = 0.5612, p = 0.6137 and p = 0.1982, respectively). In conclusion, patients presenting higher levels of MLR and SII preoperatively are expected to have a poorer outcome even if complete debulking surgery is performed and should be instead considered candidates for neoadjuvant systemic therapy followed by interval surgery.
Structure and Microchannel Catalytic Bed Performance of Silver Thin Films Prepared by Electroplating
Authors: Yang Yong , Ye Yinghua , Shen Ruiqi
Published: January 2024

The morphology of catalysts in microchannels plays a crucial role in the orbital maneuvering and networking applications of micro/nano satellites using hydrogen peroxide as a unit propellant. In this paper, a microfluidic reaction chip was designed and fabricated to detect the reaction rate of the catalytic decomposition of hydrogen peroxide solution by a microchannel catalytic bed. In addition, a silver thin film prepared by constant-current electroplating was used as a substrate for the microchannel catalyst. The results show that the ratio of surface area to area of silver film and the average particle size of silver particles have a significant positive correlation on the reaction rate of catalytic decomposition, while the thickness, silver content, and surface roughness of the silver film have no significant effect on the reaction rate of catalytic decomposition. The catalytic performance of the microchannel catalytic bed of silver thin film is greatly influenced by the conditions of electroplating, namely, the electroplating temperature (T), time (t), and current (I). And when I = 0.3 mA, t = 180 s, and T = 60 °C, the microchannel catalytic bed of the silver film prepared by electroplating reaches the optimal reaction rate for the catalytic decomposition of hydrogen peroxide solution. This study has the best process parameters for the design and optimization of heterogeneous catalysts applied to microfluidic reactors.
Prostate Cancer Detection in Colombian Patients through E-Senses Devices in Exhaled Breath and Urine Samples
Authors: Durán Acevedo Cristhian Manuel , Carrillo Gómez Jeniffer Katerine , Cuastumal Vasquez Carlos Alberto , Ramos José
Published: January 2024

This work consists of a study to detect prostate cancer using E-senses devices based on electronic tongue and electronic nose systems. Therefore, two groups of confirmed prostate cancer and control patients were invited to participate through urine and exhaled breath samples, where the control patients group was categorized as Benign Prostatic Hyperplasia, Prostatitis, and Healthy patients. Afterward, the samples were subsequently classified using Pattern Recognition and machine learning methods, where the results were compared through clinical history, obtaining a 92.9% success rate in the PCa and control samples’ classification accuracy by using eTongue and a 100% success rate of classification using eNose.
Cytotoxic Effect of Andrographis paniculata Associated with 2-Aminoethyl Dihydrogen Phosphate in Triple-Negative Breast Cells
Authors: Laiso Rosa Andrea Nogueira , Ferreira Julia Carolina , Rici Rose Eli Grassi , Cabral Laertty Garcia de Sousa , Maria Durvanei Augusto
Published: January 2024

Cancer stands out as a major global public health concern and a significant impediment to increasing life expectancy worldwide. Natural bioactives derived from plants are renowned for their efficacy in treating various types of cancer. Andrographis paniculata (Burm.f.) is a well-known plant traditionally employed in diverse medical systems across the globe. The 2-AEH2P monophosphoester, a molecule intricately involved in phospholipid turnover, demonstrates antiproliferative effects across a broad spectrum of cancer types. This study aims to assess the antitumor, antiproliferative, and pharmacological effects of andrographolide at different concentrations, both individually and in conjunction with 2-aminoethyl dihydrogen phosphate. The cytotoxicity of the treatments was evaluated using the colorimetric MTT method, cell cycle phases, mitochondrial electrical potential, and markers expression via flow cytometry, while the pharmacological effects were assessed using SynergyFinder software 3.0. Treatments with A. paniculata, isolated at concentrations of 10%, 30%, and 50% of andrographolide, induced cell death in tumor cells, resulting in a reduction in mitochondrial electrical potential and alterations in cell cycle phases, particularly a decrease in the population of MDA MB-231 cells in the G0/G1 phase. The combination treatments exhibited significant cytotoxicity toward tumor cells, with minimal toxicity observed in normal fibroblast cells FN1. This led to a reduction in mitochondrial electrical potential and cell cycle arrest in the S phase for MDA MB-231 cells. Across all concentrations, the combined treatments demonstrated a synergistic pharmacological effect, underscoring the efficacy of the association. There was a change in the markers involved in cell death, such as p53, caspase 3, Bcl-2, and cytochrome c, suggesting the induction of regulated cell death. Markers associated with progression and proliferation, such as cyclin D1 and p21, corroborate the findings for cytotoxicity and cell cycle arrest.
The Scenario of Clays and Clay Minerals Use in Cosmetics/Dermocosmetics
Authors: Sarruf Fernanda Daud , Contreras Vecxi Judith Pereda , Martinez Renata Miliani , Velasco Maria Valéria Robles , Baby André Rolim
Published: January 2024

The use of clays in beauty care comes from ancient times, with therapeutic use since prehistory, and it is considerably relevant in the current cosmetic industry worldwide. In our review, we described types of clay and clay minerals used in cosmetics and dermocosmetics, compositions, usages as active compounds and cosmetic ingredients/starting materials, and observations about formulation techniques. From this review, we observed that although much scientific and specialized literature has reported the characterization of clays, only some involved efficacy tests when incorporated into cosmetic products, mainly concerning haircare applications. Our review could be considered and encouraged in the coming years to provide scientific and technical information for the cosmetic industry regarding the multifunctional use of clays and clay minerals.
Locking-Enabled Security Analysis of Cryptographic Circuits
Authors: Upadhyaya Devanshi , Gay Maël , Polian Ilia
Published: January 2024

Hardware implementations of cryptographic primitives require protection against physical attacks and supply chain threats. This raises the question of secure composability of different attack countermeasures, i.e., whether protecting a circuit against one threat can make it more vulnerable against a different threat. In this article, we study the consequences of applying logic locking, a popular design-for-trust solution against intellectual property piracy and overproduction, to cryptographic circuits. We show that the ability to unlock the circuit incorrectly gives the adversary new powerful attack options. We introduce LEDFA (locking-enabled differential fault analysis) and demonstrate for several ciphers and families of locking schemes that fault attacks become possible (or consistently easier) for incorrectly unlocked circuits. In several cases, logic locking has made circuit implementations prone to classical algebraic attacks with no fault injection needed altogether. We refer to this “zero-fault” version of LEDFA by the term LEDA, investigate its success factors in-depth and propose a countermeasure to protect the logic-locked implementations against LEDA. We also perform test vector leakage assessment (TVLA) of incorrectly unlocked AES implementations to show the effects of logic locking regarding side-channel leakage. Our results indicate that logic locking is not safe to use in cryptographic circuits, making them less rather than more secure.