bluuebunny/crossref_metadata_2025 huggingface dataset:
ARIH1 Inhibition Promotes Microtubule Stability and Sensitizes Breast Cancer Cells to Microtubule-Stabilizing Agents
Authors: Elshaer Mohamed , Howley Breege V. , Howe Philip H.
Published: February 2025

Background: Microtubule dynamics play a pivotal role in cancer progression and response to chemotherapeutics. Identifying regulators of microtubule stability can provide new therapeutic targets and predictive biomarkers for cancer treatment. Methods: We investigated the role of ARIH1, an E3 ubiquitin ligase, in breast cancer by analyzing clinical datasets to assess its expression levels and prognostic significance. Functional studies were conducted in breast cancer cell lines to evaluate the impact of ARIH1 depletion on microtubule stability, MAP4 regulation, and paclitaxel sensitivity. Results: Clinical dataset analysis revealed that ARIH1 expression is significantly elevated in breast cancer tissues and correlates with poor prognosis and reduced recurrence-free survival. High ARIH1 expression stratifies patients into high-risk groups, underscoring its potential as a prognostic biomarker. Functional studies demonstrated that ARIH1 loss led to upregulation of MAP4, a microtubule-associated protein, resulting in microtubule stabilization via increased tubulin acetylation and enhanced spindle organization. This stabilization sensitized breast cancer cells to paclitaxel treatment, leading to reduced cell viability, impaired colony formation, and increased apoptosis in ARIH1-deficient cells. Conclusions: Our findings identify ARIH1 as a novel regulator of microtubule dynamics in breast cancer. ARIH1 suppression enhances paclitaxel sensitivity, highlighting its potential as both a therapeutic target and a biomarker for predicting treatment response and patient outcomes in breast cancer.
Absolute Quantification of Nucleotide Variants in Cell-Free DNA via Quantitative NGS: Clinical Application in Non-Small Cell Lung Cancer Patients
Authors: Herbreteau Guillaume , Marcq Marie , Sauzay Chloé , Carpentier Maxime , Pierre-Noël Elise , Pons-Tostivint Elvire , Vallée Audrey , Théoleyre Sandrine , Bizieux Acya , Bennouna Jaafar , Denis Marc G.
Published: February 2025

Background/Objectives: Circulating tumor DNA (ctDNA) analysis is a powerful tool for non-invasive monitoring of tumor burden and treatment response. Reliable quantification methods are critical for the effective use of ctDNA as a tumor biomarker. Digital PCR (dPCR) offers high sensitivity and quantification, but requires the prior knowledge of tumor-specific genomic alterations. Next-generation sequencing (NGS) provides a more comprehensive approach but is semi-quantitative, relying on variant allelic fraction (VAF), which can be influenced by non-tumor cell-free DNA. Methods: We developed a novel quantitative NGS (qNGS) method for absolute quantification of nucleotide variants, utilizing unique molecular identifiers (UMIs) and of quantification standards (QSs), short synthetic DNA sequences modified to include characteristic mutations for unique identification in sequencing data. We evaluated the performance of this method using plasma samples spiked with mutated DNA and plasma pools from cancer patients. We further applied our technique to plasma samples from four non-small cell lung cancer (NSCLC) patients enrolled in the ELUCID trial. Results: Our qNGS approach demonstrated robust linearity and correlation with dPCR in both spiked and patient-derived plasma samples. Notably, the analysis of clinical samples from the ELUCID trial revealed the ability of our method to simultaneously quantify multiple variants in a single plasma sample. Significant differences in ctDNA levels were observed between baseline and post-treatment samples collected after three weeks of front-line therapy. Conclusions: We introduce a novel qNGS method that enables the absolute quantification of ctDNA, independent of non-tumor circulating DNA variations. This technique was applied for the first time to serial samples from NSCLC patients, demonstrating its ability to simultaneously monitor multiple variants, making it a robust and versatile tool for precision oncology.
KRAS Mutations in Cancer: Understanding Signaling Pathways to Immune Regulation and the Potential of Immunotherapy
Authors: Uniyal Priyanka , Kashyap Vivek Kumar , Behl Tapan , Parashar Deepak , Rawat Ravi
Published: February 2025

The Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation is one of the most prevailing mutations in various tumors and is difficult to cure. Long-term proliferation in carcinogenesis is primarily initiated by oncogenic KRAS-downstream signaling. Recent research suggests that it also activates the autocrine effect and interplays the tumor microenvironment (TME). Here, we discuss the emerging research, including KRAS mutations to immune evasion in TME, which induce immunological modulation that promotes tumor development. This review gives an overview of the existing knowledge of the underlying connection between KRAS mutations and tumor immune modulation. It also addresses the mechanisms to reduce the effect of oncogenes on the immune system and recent advances in clinical trials for immunotherapy in KRAS-mutated cancers.
Novel Cold-Active Levansucrase (SacBPk) from Priestia koreensis HL12 for Short-Chain Fructooligosaccharides and Levan Synthesis
Authors: Lekakarn Hataikarn , Phusiri Natthamon , Komonmusik Teeranart , Jaikaew Phuphiphat , Trakarnpaiboon Srisakul , Bunterngsook Benjarat
Published: February 2025

Levansucrases are key enzymes responsible for the synthesis of β-2,6-linked fructans, found in plants and microbes, especially in bacteria. Levansucrases have been applied in the production of levan biopolymer and fructooligosaccharides (FOSs) using sucrose as a substrate as well as in reducing sugar levels in fruit juice. As a result, levansucrases that are active at low temperatures are required for industrial applications to maintain product stability. Therefore, this work firstly reports the novel cold-active levansucrase (SacBPk) isolated from a sucrolytic bacterial strain, P. koreensis HL12. The SacBPk was classified into glycoside hydrolase family 68 subfamily 1 (GH68_1) and comprised a single catalytic domain with the Asp104/Asp267/Glu362 catalytic triad. Interestingly, the recombinant SacBPk demonstrated cold-active levansucrase activity at low temperatures (on ice and 4–40 °C) with the highest specific activity (167.46 U/mg protein) observed at 35 and 40 °C in 50 mM sodium phosphate buffer pH 6.0. SacBPk mainly synthesized levan polymer as the major product (129 g/L, corresponding to 25.8% of total sugar) with a low number of short-chain FOSs (GF2–4) (12.8 g/L, equivalent to 2.5% of total sugar) from 500 g/L sucrose after incubating at 35 °C for 48 h. These results demonstrate the industrial application potential of SacBPk levansucrase for levan and FOSs production.
Effects of Substituent and Scaffold Changes on the Inhibition of Human P5C Reductase by Phenyl-Substituted Aminomethylene Bisphosphonates
Authors: Forlani Giuseppe
Published: February 2025

Being overexpressed in many cancer types and related to tumor invasiveness, the activity of P5C reductase represents a promising target for cancer therapy, yet no effective inhibitors have been identified so far. Several phenyl-substituted aminomethylenebisphosphonic acids had been found to inhibit the plant enzyme in the micro- to millimolar range. The two most active compounds were previously shown to be remarkably active against human P5C reductase (PYCR1, gene ID 5831). To investigate their structure–activity relationships, the human enzyme was heterogously expressed in E. coli, affinity purified and assayed in the presence of increasing concentrations of various aminobisphosphonates differing in substituents on the phenyl ring, using either NADH or NADPH as the electron donor. Some analogues, namely aminoethylenebisphosphonates, hydroxybisphosphonates, aminophosphonates and hydroxyphosphonates, were also evaluated. Results allowed to define the chemical features required for effective inhibition. The aminobisphosphonic moiety was found essential for activity, which was enhanced by the presence of electron-withdrawing substituents on the phenyl ring, provided that an optimal steric hindrance is not exceeded. These results could open up new perspectives on the synthesis of effective inhibitors of human P5C reductase to be used in chemotherapy.
Can AI-Based Video Analysis Help Evaluate the Performance of the Items in the Bayley Scales of Infant Development?
Authors: Ye Dong Hyun , Kim Tae Won , Kim Su Min , Seo Ji Won , Chang Jongyoon , Lee June-Goo , Ko Eun Jae
Published: February 2025

Aims: To develop and evaluate a novel AI-based video analysis tool for the quantitative assessment of “Places Pegs in” and “Blue Board” tasks in the Bayley Scales of Infant Development (BSID-II). Methods: A prospective cohort study was conducted from February 2022 to December 2022, including children aged 12–42 months referred for suspected developmental delay. Participants were evaluated using the BSID-II, and their performances on the two tasks were video recorded and analyzed with the novel tool. Sensitivity and specificity were determined by comparing the tool’s results to standard BSID-II assessments by therapists. Data collected included total time, number of trials, successful trials, and time and spatial intervals for each trial. Children were classified into typically developing (TD) (MDI ≥ 85) and developmental delay (DD) (MDI < 85) groups based on their mental developmental index (MDI). Results: A total of 75 children participated in the study, and the mean values of MDI and PDI for the enrolled children were 88.9 ± 18.7 and 80.0 ± 16.7. The “Places Pegs in” had 86.5% sensitivity and 100% specificity; the “Blue Board” had 96.9% sensitivity and 89.5% specificity. Differences in cumulative successes over time were observed between age groups and TD and DD groups. The tool automatically calculated maximum successes at specific time points. Interpretation: The AI-based tool showed high predictive accuracy for BSID-II tasks in children aged 12–42 months, indicating potential utility for developmental assessments.
SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing
Authors: Alvarado-Robles Gilberto , Espinosa-Vizcaino Isac Andres , Manriquez-Padilla Carlos Gustavo , Saucedo-Dorantes Juan Jose
Published: February 2025

Shadows in remote sensing images often introduce challenges in accurate segmentation due to their variability in shape, size, and texture. To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. Using the AISD dataset, the proposed method achieved state-of-the-art performance with an Intersection over Union (IoU) of 0.8552, an F1-Score of 0.9219, an Overall Accuracy (OA) of 96.50%, and a Balanced Error Rate (BER) of 5.08%. Comparative analyses demonstrate SDKU-Net’s superior performance against established methods such as U-Net, U-Net++, MSASDNet, and CADDN. Additionally, the model’s efficient training process, requiring only 75 epochs, highlights its potential for resource-constrained applications. These results underscore the robustness and adaptability of SDKU-Net, paving the way for advancements in shadow detection and segmentation across diverse fields.
A Novel, Self-Adaptive, Multiclass Priority Algorithm with VM Clustering for Efficient Cloud Resource Allocation
Authors: Ben Alla Hicham , Ben Alla Said , Ezzati Abdellah , Touhafi Abdellah
Published: February 2025

Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers must handle diverse tasks with distinct priority classes and strict quality of service requirements. To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM clustering for resource allocation. This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. Additionally, we propose a resource prioritization and load-balancing model based on the clustering technique. The proposed work was validated through multiple simulations using the CloudSim simulator, comparing its performance against well-known task scheduling algorithms. The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. Specifically, our work achieved percentage improvements ranging from +0.97% to +26.80% in makespan and +3.68% to +49.49% in energy consumption while also improving other performance metrics, including throughput, resource utilization, and load balancing. This novel model demonstrably enhances task scheduling and resource allocation efficiency, particularly in complex scenarios with tight deadlines and multiclass priorities.
Does Real-World Evidence of the Economic Burden of Lung Cancer in Greece Exist? A Systematic Review of the Literature
Authors: Gourzoulidis George , Kastanioti Catherine , Mavridoglou George , Kotsilieris Theodore , Voudigaris Dikaios , Tzanetakos Charalampos
Published: February 2025

Objective: This systematic literature review aimed to summarize the economic burden of lung cancer in Greece, identify current data gaps, and support the design of future real-world studies. Methods: A systematic search of studies published in English on the cost of lung cancer was performed in MEDLINE-(PubMed), Scopus, and ScienceDirect. The databases were searched until September 2024, and records were screened based on our eligibility criteria. After conducting the initial literature search, the abstracts and full texts of the identified studies were reviewed and evaluated for inclusion based on predefined criteria. Data from the selected studies were then extracted into a standardized form and subsequently synthesized. Results: Seven studies were included in this review. The reported burden was sourced from hospital data and categorized as direct and indirect costs. Most studies (n = 6) reported direct costs, with one study reporting both direct and indirect costs. The total direct medical cost per patient increased from approximately EUR 16,000 in 2015 to EUR 58,974 in 2023, with drug acquisition costs being the key driver of the total direct cost. Additionally, the cost of end-of-life care during the final six months of a patient’s life was estimated to range from EUR 6786 to EUR 7665 per patient, with pharmaceutical costs comprising the largest proportion of the total cost. One study also reported that indirect costs were considerably higher for patients than for family caregivers. Conclusion: The economic burden of lung cancer has increased substantially over the past decade in Greece. The present systematic review emphasizes the critical need for comprehensive real-world studies on the economic burden of lung cancer in Greece. Addressing the current gaps holistically will yield invaluable insights for policymakers and stakeholders.
The Impact of SARS-CoV-2 Infection on Glucose Homeostasis in Hospitalized Patients with Pulmonary Impairment
Authors: Parlițeanu Oana-Andreea , Bălteanu Mara-Amalia , Zaharia Dragoș Cosmin , Constantinescu Tudor , Cristea Alexandra Maria , Dumitrache-Rujinscki Ștefan , Nica Andra Elena , Voineag Cristiana , Alexe Octavian Sabin , Tabacu Emilia , Croitoru Alina , Strâmbu Irina , Nemeș Roxana Maria , Mahler Beatrice
Published: February 2025

Background and Objectives: We conducted a retrospective observational study to evaluate the impact of elevated blood glucose levels in patients with SARS-CoV-2 infection and a prior diagnosis of diabetes mellitus (DM) or newly diagnosed hyperglycemia. Materials and Methods: This study analyzed 6065 patients admitted to the COVID-19 departments of the “Marius Nasta” National Institute of Pulmonology in Bucharest, Romania, between 26 October 2020 and 5 January 2023. Of these, 813 patients (13.40%) were selected for analysis due to either a pre-existing diagnosis of DM or hyperglycemia at the time of hospital admission. Results: The erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) levels were elevated in patients with blood glucose levels exceeding 300 mg/dL. These elevations correlated with the presence of respiratory failure and increased mortality rates. Additionally, oxygen requirements were significantly higher at elevated blood glucose levels (p < 0.001), with a direct relationship between glycemia and oxygen demand. This was accompanied by lower oxygen saturation levels (p < 0.001). Maximum blood glucose levels were associated with the severity of respiratory failure (AUC 0.6, 95% CI: 0.56–0.63, p < 0.001). We identified cut-off values for blood glucose at admission (217.5 mg/dL) and maximum blood glucose during hospitalization (257.5 mg/dL), both of which were associated with disease severity and identified as risk factors for increased mortality. Conclusions: High blood glucose levels, both at admission and during hospitalization, were identified as risk factors for poor prognosis and increased mortality in patients with SARS-CoV-2 infection, regardless of whether the hyperglycemia was due to a prior diagnosis of DM or was newly developed during the hospital stay. These findings underscore the importance of glycemic control in the management of hospitalized COVID-19 patients.
Radiofrequency Echographic Multi Spectrometry—A Novel Tool in the Diagnosis of Osteoporosis and Prediction of Fragility Fractures: A Systematic Review
Authors: Icătoiu Elena , Vlădulescu-Trandafir Andreea-Iulia , Groșeanu Laura-Maria , Berghea Florian , Cobilinschi Claudia-Oana , Potcovaru Claudia-Gabriela , Bălănescu Andra-Rodica , Bojincă Violeta-Claudia
Published: February 2025

Background/Objectives: Given the significant economic and social burden of osteoporosis, there is growing interest in developing an efficient alternative to the traditional dual-energy X-ray absorptiometry (DXA). Radiofrequency Echographic Multi Spectrometry (REMS) is an innovative, non-ionizing imaging technique that recently emerged as a viable tool to diagnose osteoporosis and estimate the fragility fracture risk. Nevertheless, its clinical use is still limited due to its novelty and continuing uncertainty of long-term performance. Methods: In order to evaluate the accuracy of the REMS, a systematic review of the English-language literature was conducted. Three databases were searched for relevant publications from 1 January 2015 until 1 December 2024 using the keyword combinations “(radiofrequency echographic multi spectrometry OR REMS) AND (dual-energy X-ray absorptiometry OR DXA)”. The initial search yielded 602 candidate articles. After screening the titles and abstracts following the eligibility criteria, 17 publications remained for full-text evaluation. Results: The reviewed studies demonstrated strong diagnostic agreement between REMS and DXA. Additionally, REMS showed enhanced diagnostic capabilities in cases where lumbar bone mineral density measurements by DXA were impaired by artifacts such as vertebral fractures, deformities, osteoarthritis, or vascular calcifications. REMS exhibited excellent intra-operator repeatability and precision, comparable to or exceeding the reported performance of DXA. The fragility score (FS), a parameter reflecting bone quality and structural integrity, effectively discriminated between fractured and non-fractured patients. Moreover, REMS proved to be a radiation-free option for bone health monitoring in radiation-sensitive populations or patients requiring frequent imaging to assess fracture risk. Conclusions: This current study underscores the robustness of REMS as a reliable method for diagnosing and monitoring osteoporosis and evaluating bone fragility via the FS. It also identifies critical knowledge gaps and emphasizes the need for further prospective studies to validate and expand the clinical applications of REMS across diverse patient populations.
Current Roles of Support Teachers, Analysis of Their Contribution to Inclusive School: A Narrative Review
Authors: Astudillo Lara , Simón Cecilia , Fernández Blázquez Maria Luz M.
Published: February 2025

Support in the context of inclusive education is pivotal, yet many countries struggle with integrating the evolving role of support teachers within inclusive frameworks. For that reason, some challenges and reluctance can be found in relation to the development of the functions of these professionals. Therefore, a way forward would be to clarify the role and responsibilities of support teachers in fostering inclusive educational environments. This paper aims to contribute to this discourse by conducting a narrative review of existing research on the functions of support teachers. Special attention is paid to their contributions in the construction of inclusive educational contexts. The review, which was inspired by the guidelines of the PRISMA statement, yielded 23 final studies. Four professional roles were identified: direct care-assistance, consultative-collaborative, administrative-documentary, and coordinator-leadership. Furthermore, the findings indicate that there is a predominance of individual or small group support practices outside the regular classrooms. The need to implement collaborative practices to generate support networks at both school and community levels is emphasized. Three major themes were identified as the primary challenges: teacher training, collaboration among educators, and school curricula, particularly in the post-primary stages. Orientations for rethinking the role of support teachers from an inclusive approach are outlined in the discussion.
Accessory Mental Foramina in Dry Mandibles: An Observational Study Along with Systematic Review and Meta-Analysis
Authors: Thomaidi Zoi Maria , Tsatsarelis Charalambos , Papadopoulos Vasileios
Published: February 2025

Background/Objectives: The mental foramen (MF) constitutes a passage for mental nerves and vessels, and it is a crucial anatomical landmark in the body of the mandible. The accessory mental foramen (AMF) is a small, addable foramen proximate to the MF, and it is mainly located posteriorly. The AMF is a rare anatomical variation in human mandibles that must be taken into consideration throughout dental and surgical operations. We aimed to assess the incidence and perform morphological and morphometric analyses of AMFs in the human dry mandibles of the Greek population, in addition to a relevant systematic review and meta-analysis of global data. Methods: We studied 114 human adult dry mandibles of unknown gender and age available from the Laboratory of Anatomy, Medical School, Democritus University of Thrace, Greece. We used the search term “accessory mental foramen” in the PubMed, Scopus, and Google Scholar databases to detect all publications of the last 50 years reporting the prevalence and morphology of AMFs in dry mandibles; the search ended on 13 January 2025. Quality assessments were performed using the relevant Joanna Briggs Institute tool. Data were synthesized with the random-effects REML model after Freeman–Tukey double arcsine transformationusing STATA 18. No external funding was received. The PROSPERO CRD is 42025638135. Results: According to our data, the MF was present in all observed mandibles, and it was bilateral. Nine AMFs (five right/four left; five round/four oval; six posterior/three anterior to the MF) were found in seven mandibles (five single and two double), and all were unilateral. AMFs presented a mean diameter of 0.96 ± 0.43 mm and mean distances of 4.12 ± 2.15 mm from the MF, 12.68 ± 4.10 mm from the alveolar ridge, and 11.92 ± 1.57 mm from the lower border of the mandible. Furthermore, 27 publications were included in the meta-analysis; the combined AMF prevalence was 6.1% (95% CI: 4.8–7.6%; I2 60%), the combined mean vertical axis was 1.18 ± 0.61 mm, and the combined mean distance from the MF was 3.64 ± 2.29 mm. Bilateral AMFs were detected in 2.1% of AMF cases. An oval shape was described in 37.3% of AMFs. No publication bias was detected. Conclusions: AMFs are not considered rare, and they are occasionally bilateral or even multiple in number. Moreover, they demonstrate considerable variation regarding their size, shape, anddistance from the MF, alveolar ridge, and lower border of the mandible. Dental surgeons must be aware of AMFs’ anatomical variations during surgical and anesthetic planning in order to effectively prevent or mitigate the risk of postoperative complications, such as pain, anesthesia, injury, and other adverse outcomes.
Comparison of Four Ni-Ti Rotary Systems: Dental Students’ Perceptions in a Multi-Center Simulated Study
Authors: Puleio Francesco , Tosco Vincenzo , Monterubbianesi Riccardo , Pirri Rosario , Alibrandi Angela , Pulvirenti Daria , Simeone Michele
Published: February 2025

Background/Objectives: Nickel–titanium (Ni-Ti) rotary instruments have significantly enhanced the efficiency and safety of root canal shaping. However, the variety of Ni-Ti systems, characterized by differences in alloy composition, taper design, and heat treatment, may influence their usability, particularly for novice operators. This study aimed to evaluate the perceptions of dental students using four Ni-Ti rotary systems (MTwo, SlimShaper Pro, ProTaper Gold, and HyFlex EDM) in a simulated environment. Methods: Forty dental students from two universities participated in the study and completed a standardized preoperative training session. Root canal shaping was performed on resin teeth models, and an 11-item questionnaire was used to assess various parameters, including flexibility, ease of use, and the ability to maintain working length. Results: The results indicated significant differences in student feedback. SlimShaper Pro and HyFlex EDM were preferred for their flexibility, reduced resistance, and ease of instrument transitions, likely due to their smaller tapers and martensitic alloys. MTwo was rated the least flexible and more challenging to use. Conclusions: These findings underline the importance of user-friendly Ni-Ti systems in pre-clinical training to reduce procedural difficulties and operator stress. Further research should validate these findings in clinical settings to improve the training of novice operators.
Similar Ehlers–Danlos Syndrome Profiles Produced by Variants in Multiple Collagen Genes
Authors: Tonk Sahil S. , Wilson Golder N.
Published: February 2025

Background: Despite increased attention to double-jointedness or joint hypermobility as seen in connective tissue dysplasias like Ehlers–Danlos syndrome, improved clinical DNA correlations are needed to reduce decadal delays in diagnosis. Methods: To this end, patterns of history (among 80) and physical (among 40) findings are compared for 121 Ehlers–Danlos syndrome patients with recurring variants in collagen type I, II, III, V, VI, VII, IX, XI, and XII genes and novel ones in type XV, XVII, XVIII, and XXVII. Results: A recognizable tissue laxity–dysautonomia profile that transcended collagen biochemical class, triple helix component, mutation type, or presence of accessory DNA variants was defined with a few exceptions. Patients with novel variations experienced severe symptoms at younger ages (6–10 versus 14–18 years) and those with collagen type III variations had more than one significant difference in finding frequencies (spinal disk issues 75% versus 49%; bloating-reflux 100% versus 69%; migraines or menorrhagia 92% versus 53%). Conclusions: These results suggest that collagen DNA variants of diverse gene and molecular type can demonstrate EDS disposition and hasten its diagnosis when distress and disease become manifest.
Federated Learning and Semantic Communication for the Metaverse: Challenges and Potential Solutions
Authors: Bian Yue , Zhang Xin , Luosang Gadeng , Renzeng Duojie , Renqing Dongzhu , Ding Xuhui
Published: February 2025

This study investigates the high-quality data processing technology, immersive experience mechanisms, and large-scale access in the Metaverse, concurrently ensuring robust privacy and security. We commence with a comprehensive analysis of the Metaverse’s service requirements, followed by an exploration of its principal technologies. Furthermore, we evaluate the feasibility and potential benefits of integrating semantic communication to enhance the service quality of the Metaverse. A federated semantic communication framework is proposed, integrating semantic data transmission, semantic digital twins, and a Metaverse construction model trained through federated learning. We proceed to assess the performance of our proposed framework through simulations, highlighting the notable enhancements in transmission efficiency, recovery effectiveness, and intelligent recognition ability afforded by semantic communication for the Metaverse. Notably, the framework achieves outstanding compression efficiency with minimal information distortion (0.055), which decreases transmission delays and improves the immersion quality within the Metaverse. Finally, we identify future challenges and propose potential solutions for advancing semantic communication, federated learning, and Metaverse technologies.
Integrated Low Cost, LoRa-Based, Real Time Fluid Infusion Flask Monitoring System
Authors: Mitropoulos Spyridon , Rimpas Dimitrios , Katsoulis Stylianos , Hloupis George , Christakis Ioannis
Published: February 2025

Manual intravenous (IV) monitoring delays, put patients at risk, as the reaction time of nursing staff can be critical to the patient’s health. The widespread use of LoRa networks today is a reality. The deployment of devices and applications based on LoRa networks in healthcare environments, such as hospital facilities, is of great interest and can offer both time savings for medical and nursing staff and improvements in medical care. In this work an integrated low-cost, real-time monitoring system for fluid infusion based on a LoRa network is presented. The measured (monitoring) data are the weight of the fluid infusion flask and the number of fluid drops. The design of the system and the affordability of the materials (low-cost devices) give the possibility for immediate application in healthcare environments. As the system consists of low-cost sensors, and given that it is intended for health purposes, extensive research has been carried out on the evaluation and reliability of the measurements. The proposed system is intended for medical care; in this sense it should have the lowest possible measurement error. The evaluation of the system has revealed a polynomial equation as a corrective factor for weight and shows an improvement of the error from 2% of the raw measurements to 0.6% of the corrected measurements, while regarding the calculation of the weight from the measurement of the droplets, it shows an error of 1.6%. The proposed system contributes directly to both the valuable time of the medical staff and the improvement of patient care. The evolution of technology should be applied to the health sector and low-cost and internet of things (IoT) devices can be applied to healthcare after thorough evaluation and calibration procedures.
Analysis and Control Design of a Step-Up/Step-Down Converter for Battery-Discharge Voltage Regulation
Authors: Villanueva-Loredo Juan A. , Martinez-Rodriguez Panfilo R. , Rodriguez-Cortés Christopher J. , Langarica-Cordoba Diego , Hernández-Gómez Ángel , Guilbert Damien
Published: February 2025

In recent years, step-up/step-down DC–DC converters have been required in renewable energy processing and energy storage applications. In the latter, lithium-ion batteries have played an important role since they have been the most usable source in portable equipment and electric vehicles. This paper proposes a novel step-up/step-down converter with continuous input current and a model-based control scheme. Moreover, the converter design highlights based on steady-state analysis are obtained for a proper parameter selection of the converter. Thus, a fourth-order average system model is obtained to design a multiloop control law. The proposed control is designed in such a way that the control scheme results in an inner current loop on the input current and an outer control loop on the output voltage. Finally, experimental results are performed to assess the performance of the proposed power converter and the control law. An experimental setup with an efficiency of 95 % is implemented to validate the theoretical analysis. The intended application is to regulate a voltage fluctuation from 200 V to 250 V to a constant voltage of 200 V with a nominal power of 533 W.
Frame-Stacking Method for Dark Digital Holographic Microscopy to Acquire 3D Profiles in a Low-Power Laser Environment
Authors: Koga Takahiro , Nakamura Kosei , Kim Hyun-Woo , Cho Myungjin , Lee Min-Chul
Published: February 2025

Digital Holographic Microscopy (DHM) is a method of converting hologram images into three-dimensional (3D) images by image processing, which enables us to obtain the detailed shapes of the objects to be observed. Three-dimensional imaging of the microscopic objects by DHM can contribute to the early diagnosis and the detection of the diseases in the medical field by observing the shape of the cells. DHM requires several experimental components. One of them is the laser, which is a problem because its high power may cause the deformation and the destruction of the cells and the death of the microorganisms. Since the greatest advantage of DHM is the detailed geometrical information of the object by 3D measurement, the loss of such information is a serious problem. To solve this problem, a Neutral Density (ND) filter has been used to reduce power after the laser irradiation. However, the image acquired by the image sensor becomes too dark to obtain sufficient information, and the effect of noise increased due to the decrease in the amount of light. Therefore, in this paper, we propose the Frame-Stacking Method (FSM) for dark DHM for reproducing 3D profiles that enable us to observe the shape of the objects from the images taken in low-power environments when the power is reduced. The proposed method realizes highly accurate 3D profiles by the frame decomposition of the low-power videos into images and superimposing and rescaling the obtained low-power images. On the other hand, the continuous irradiation of the laser beam for a long period may destroy the shape of the cells and the death of the microorganisms. Therefore, we conducted experiments to investigate the relationship between the number of superimposed images corresponding to the irradiation time and the 3D profile, as well as the characteristics of the power and the 3D profile.
Precision Forecasting in Colorectal Oncology: Predicting Six-Month Survival to Optimize Clinical Decisions
Authors: Lee Jaehyuk , Cho Youngchae , Kyung Yeunwoong , Kim Eunchan
Published: February 2025

Colorectal cancer (CRC) has a relatively high five-year survival rate compared to other cancers; however, this rate drops significantly in patients with malignant CRC. One critical factor in palliative care decision-making is the ability to accurately predict patient survival, with the six-month survival period commonly used as a threshold. In this study, we evaluated the performance of five machine learning models—logistic regression, decision tree, random forest, multilayer perceptron, and extreme gradient boosting (XGBoost)—in predicting six-month survival for patients with malignant CRC using a publicly available synthetic dataset containing 11,774 samples and 51 features. The models were trained and validated using five-fold cross-validation, and the synthetic minority oversampling technique (SMOTE) was applied to address class imbalance. Among the models, XGBoost demonstrated the highest performance, achieving 95% accuracy, precision, recall, and F1-score, along with 90% specificity. Feature importance analysis identified smoking status and surgical history as key factors influencing model predictions. These findings highlight the potential of tree-based machine learning models in supporting timely and informed palliative care decisions, while also providing insights into handling data imbalance and optimizing model parameters in survival prediction tasks.
DMR-SCL: A Design and Verification Framework for Redundancy-Based Resilient Asynchronous Sleep Convention Logic Circuits
Authors: Datta Mithun , Mazumder Dipayan , Bodoh Alexander C. , Sakib Ashiq A.
Published: February 2025

The digital integrated circuit (IC) design industry is continuously evolving. However, the rapid advancements in technology are accompanied by major reliability concerns. Conventional clock-based synchronous designs become exceedingly susceptible to transient errors, caused by radiation rays, power jitters, electromagnetic interferences (EMIs), and/or other noise sources, primarily due to aggressive device and voltage scaling. quasi-delay-insensitive (QDI) asynchronous (clockless) circuits demonstrate inherent robustness against such transient errors, owing to their unique architecture. However, they are not completely immune. This article presents a hardened QDI Sleep Convention Logic (SCL) asynchronous architecture, which can fully recover from radiation-induced single-event effects such as single-event upset (SEU) and single-event latch-up (SEL). Multiple benchmark circuits are designed based on the proposed architecture. The simulation results indicate that the proposed designs offer substantial energy savings per operation, dissipate substantially less power during idle phases, and have lower area footprints in comparison to designs based on an existing resilient Null Convention Logic (NCL) architecture at the cost of increased latency. In addition, a formal verification framework for the proposed architecture is also presented. The performance and scalability of the proposed verification scheme are demonstrated using several multiplier benchmark circuits of varying width.
Multimodal Emotion Recognition Method Based on Domain Generalization and Graph Neural Networks
Authors: Xie Jinbao , Wang Yulong , Meng Tianxin , Tai Jianqiao , Zheng Yueqian , Varatnitski Yury I.
Published: February 2025

In recent years, multimodal sentiment analysis has attracted increasing attention from researchers owing to the rapid development of human–computer interactions. Sentiment analysis is an important task for understanding dialogues. However, with the increase of multimodal data, the processing of individual modality features and the methods for multimodal feature fusion have become more significant for research. Existing methods that handle the features of each modality separately are not suitable for subsequent multimodal fusion and often fail to capture sufficient global and local information. Therefore, this study proposes a novel multimodal sentiment analysis method based on domain generalization and graph neural networks. The main characteristic of this method is that it considers the features of each modality as domains. It extracts domain-specific and cross-domain-invariant features, thereby facilitating cross-domain generalization. Generalized features are more suitable for multimodal fusion. Graph neural networks were employed to extract global and local information from the dialogue to capture the emotional changes of the speakers. Specifically, global representations were captured by modeling cross-modal interactions at the dialogue level, whereas local information was typically inferred from temporal information or the emotional changes of the speakers. The method proposed in this study outperformed existing models on the IEMOCAP, CMU-MOSEI, and MELD datasets by 0.97%, 1.09% (for seven-class classification), and 0.65% in terms of weighted F1 score, respectively. This clearly demonstrates that the domain-generalized features proposed in this study are better suited for subsequent multimodal fusion, and that the model developed here is more effective at capturing both global and local information.
Pseudo-Twin Neural Network of Full Multi-Layer Perceptron for Ultra-Short-Term Wind Power Forecasting
Authors: Yang Yulong , Wang Jiaqi , Chen Baihui , Yan Han
Published: February 2025

In recent wind power forecasting studies, deep neural networks have shown powerful performance in estimating future power from wind power data. In this paper, a pseudo-twin neural network model of full multi-layer perceptron is proposed for power forecasting in wind farms. In this model, the input wind power data are divided into physical attribute data and historical power data. These two types of input data are processed separately by the feature extraction module of the pseudo-twin structure to obtain physical attribute features and historical power features. To ensure comprehensive integration and establish a connection between the two types of extracted features, a feature mixing module is introduced to cross-mix the features. After mixing, a set of multi-layer perceptrons is used as a power regression module to forecast wind power. In this paper, simulation research is carried out based on measured data. The proposed model is compared with mainstream models such as CNN, RNN, LSTM, GRU, and hybrid neural network. The results show that the MAE and RMSE of the single-step forecasting of the proposed model are reduced by up to 21.88% and 16.85%, respectively. Additionally, the MAE and RMSE of the 1 h rolling forecasting (six steps ahead) are reduced by up to 31.58% and 40.92%, respectively.
Common-Mode Filter for Transformer-Less Split-Phase Neutral Grounded Inverter
Authors: Bagawade Snehal , Pahlevani Majid
Published: February 2025

A three-leg full-bridge inverter is conventionally used to generate split-phase AC voltage. If the neutral phase of such an inverter is grounded, then parasitic currents of significant magnitude appear in the ground circuit. This issue arises primarily due to the presence of high-frequency common-mode voltage between the output AC terminals and the DC-bus terminals. In this paper, split DC-bus capacitors are introduced in the conventional inverter circuit to attenuate the common-mode switching voltage. The addition of the capacitive filter forms a second-order low-pass filter for common-mode voltage and attenuates the magnitude of the switching-frequency component of common-mode voltage by around 40 dB. The proposed inverter is thereby able to generate a transformer-less neutral grounded split-phase AC voltage supply for an off-grid application. The simulation and experimental results of a 12 kW lab prototype are presented for verifying the proposed converter circuit topology.
VehiCast: Real-Time Highway Traffic Incident Forecasting System Using Federated Learning and Vehicular Ad Hoc Network
Authors: Alnami Hani , Mohzary Muhammad
Published: February 2025

Road safety is a critical concern, as accidents happen globally. Despite efforts to enhance roads and enforce stricter driving rules, the number of accidents remains high. These issues arise from distracted driving, speeding, and driving under the influence. In the United States, fatal accidents increased by 16% from 2018 to 2022. The number of deaths rose from 36,835 in 2018 to 42,795 in 2022. This trend reveals a critical need for new solutions to reduce traffic incidents and improve road safety. Machine learning (ML) can help make roads safer and reduce traffic-related deaths. This paper presents an ML-based real-time highway traffic incident forecasting system named “VehiCast”. VehiCast utilizes vehicular ad hoc networks (VANETs) and federated learning (FL) to collect real-time traffic data, such as average delay, average speed, and the total number of vehicles across several highway zones, to enhance traffic incident prediction accuracy in real-time. Our extensive experimental results showcase that VehiCast reaches an impressive prediction accuracy of 91%, highlighting the power of innovation and determination.
Learning the N-Input Parity Function with a Single-Qubit and Single-Measurement Sampling
Authors: Tsili Antonia , Maragkopoulos Georgios , Mandilara Aikaterini , Syvridis Dimitris
Published: February 2025

The parity problem, a generalization of the XOR problem to higher-dimensional inputs, is a challenging benchmark for evaluating learning algorithms, due to its increased complexity as the number of dimensions of the feature space grows. In this work, a single-qubit classifier is developed, which can efficiently learn the parity function from input data. Despite the qubit model’s simplicity, the solution landscape created in the context of the parity problem offers an attractive test bed for exploring optimization methods for quantum classifiers. We propose a new optimization method called Ensemble Stochastic Gradient Descent (ESGD), with which density matrices describing batches of quantum states are incorporated into the loss function. We demonstrate that ESGD outperforms both Gradient Descent and Stochastic Gradient Descent given the aforementioned problem. Additionally, we show that applying ESGD with only one measurement per data input does not lead to any performance degradation. Our findings not only highlight the potential of a single-qubit model, but also offer valuable insights into the use of density matrices for optimization. Further to this, we complement the outcome with interesting results arising by the employment of a Doubly Stochastic Gradient Descent for training quantum variational circuits.
Video Captioning Method Based on Semantic Topic Association
Authors: Fu Yan , Yang Ying , Ye Ou
Published: February 2025

To address the issue in encoder–decoder-based video captioning method where semantic topics are overlooked in guiding the video captioning, leading to a semantic mismatch between video content and generated captions, a video captioning method based on semantic topic association is proposed. The core of this method lies in the designed semantic topic module, which is capable of capturing more detailed topic information on objects and actions in videos, and establishing a bridge between video representations and semantic topics, thereby selectively guiding the generation of video descriptions. During the decoding phase, an optimized NGPT kernel sampling decoding strategy is employed, constructing an NGPT model based on Top-P kernel sampling to jointly decode semantic information and generate captions. This model fully leverages the topic information in the video, narrowing the “semantic gap”, mitigating redundancy issues during decoding, and making the generated text descriptions that are more aligned with the topic content of the video. Ultimately, experiments were carried out using two publicly accessible datasets: MSVD and MSR-VTT. The findings reveal that our proposed approach surpasses several leading methods, achieving enhancements of 5.3% in BLEU4, 3.1% in METEOR, 2.4% in ROUGE_L, and 13.1% in CIDEr on the MSVD dataset, as well as improvements of 0.9%, 0.4%, 1.5%, and 3.3%, respectively, on the MSR-VTT dataset. Therefore, the video captioning method based on semantic topic association can extract more topic information, thereby improving the effectiveness of video caption generation.