bluuebunny/crossref_metadata_2025 huggingface dataset:
Biomechanical Analysis of Limb Coordination in Front-Crawl Among Elite S10 and S12 Para Swimmers: Implications for Performance Optimization
Authors: Yang Luqi , Li Shudong , Wang Shun , Gu Yaodong
Published: December 2024

Para swimmers categorized as S10 and S12 are of particular interest due to their disability grading being closest to that of able-bodied swimmers, making them an ideal group for investigating disparities in limb coordination patterns. This study aimed to investigate whether S10 and S12 para swimmers, whose disability grading for movement and visual ability, respectively, were the closest to that of able-bodied swimmers, would differ in terms of the biomechanics of limb coordination. This study recruited twenty para swimmers (ten with minor limb absence (S10) in the hand and ten with minor visual impairment (S12)). Using panoramic video, the phase duration, stroke length, stroke rate, index of coordination, synchronization, and inter-limb coordination were digitized and compared in the context of a front-crawl sprinting test. The results showed a significantly different duration of the recovery phase for S10 para swimmers at the affected side, where a more random coordination pattern between arm and leg at the pull and push phases was statistically seen. The variation of the inter-limb coordination gradually increased for S10 para swimmers from hand entry to the end of push, but gradually reduced for S12 para swimmers. These results suggest that the same pace was achieved by different hand–leg coordination patterns according to their physical constraints. Consequently, the unique coordination patterns of different para swimmers from this study offer an opportunity to explore the adaptive strategies and biomechanical adjustments that enable optimal performance for para swimmers.
A Comprehensive Objective Evaluation Method for Handwriting Assistive Devices Using a Tablet and Digital Pen for Individuals with Upper Limb Dysfunction
Authors: Li Qian , Gong Rui , Hase Kazunori
Published: December 2024

Traditional evaluations of handwriting assistive devices have primarily relied on user satisfaction surveys. While recent advancements in computer technologies and digital devices have enabled more objective evaluations, these methods often lack comprehensiveness. This study introduces a scientific and systematic evaluation method for evaluating handwriting assistive devices used by individuals with upper limb dysfunction. The proposed method incorporates both writing and drawing tasks, utilizing advanced computer technology and sophisticated sensors in tablets and digital pens to measure key handwriting parameters across ten indicators. These indicators are synthesized into a comprehensive score using the entropy weight method (EWM). Additionally, objective scores were compared with subjective evaluations from participants. A strong positive correlation between objective and subjective evaluations confirms the reliability of our objective method. However, statistical analysis revealed a significant mean difference between the two scoring methods, indicating that the objective method provides a more standardized and quantifiable evaluation, effectively minimizing the influence of personal biases inherent in subjective evaluations. This comprehensive evaluation method not only offers a holistic evaluation of handwriting assistive devices but also addresses the limitations of existing objective methods that tend to focus on singular aspects of performance.
Comparative Analysis of Marine Alternative Fuels for Offshore Supply Vessels
Authors: Park Chybyung , Hwang Insik , Jang Hayoung , Jeong Byongug , Ha Seungman , Kim Joongwon , Jee Jaehoon
Published: December 2024

This paper provides an in-depth analysis of alternative fuels, including liquefied natural gas (LNG), hydrogen, ammonia, and biofuels, assessing their feasibility based on operational requirements, availability, safety concerns, and the infrastructure needed for large-scale adoption. Moreover, it examines hybrid and fully electric propulsion systems, considering advancements in battery technology and the integration of renewable energy sources, such as wind and solar power, to further reduce SOV emissions. Key findings from this research indicate that LNG serves as a viable short- to medium-term solution for reducing GHG emissions in the SOV sector, due to its relatively lower carbon content compared to MDO and HFO. This paper finally insists that while LNG presents an immediate opportunity for emission reduction in the SOV sector, a combination of hydrogen, ammonia, and hybrid propulsion systems will be necessary to meet long-term decarbonisation goals. The findings underscore the importance of coordinated industry efforts, technological innovation, and supportive regulatory frameworks to overcome the technical, economic, and infrastructural challenges associated with decarbonising the maritime industry.
Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network
Authors: Yue Fengli , Sha Zhuo , Sun Hongyun , Chen Dayong , Liu Jinsong
Published: December 2024

After rolling, TP2 copper tubes exhibit defects such as sawtooth marks, cracks, and uneven wall thickness after joint drawing, which severely affects the quality of the finished copper tubes. To study the effect of drawing process parameters on wall thickness uniformity, an ultrasonic detection platform for measuring the wall thickness of rolled copper tubes was constructed to verify the accuracy of the experimental equipment. Using the detected data, a finite element model of drawn copper tubes was established, and numerical simulation studies were conducted to analyze the influence of parameters such as outer die taper angle, drawing speed, and friction coefficient on drawing force, maximum temperature, average wall thickness, and wall thickness uniformity. To address the problem of the large number of finite element model meshes and low solution efficiency, the wall thickness uniformity was predicted using a radial basis function (RBF) neural network, and parameter optimization was performed using the particle swarm optimization (PSO) algorithm. The research results show that the RBF neural network can accurately predict wall thickness uniformity, and using the PSO optimization algorithm, the best parameter combination can reduce the wall thickness uniformity after drawing in finite element simulation.
The Concept of Determining a Ship’s Route Based on the Capability Plot and Dijkstra’s Algorithm—Finding the Ship’s Route Between Anchorages
Authors: Wnorowski Jakub , Łebkowski Andrzej
Published: December 2024

Determining the route from the starting point to the destination is one of the first tasks performed when planning a ship’s voyage. Before the computer age, routes were plotted manually by seafarers based on maps. Nowadays, algorithms are used for this purpose, which make it possible to reach any port in the world. In scientific publications, one can mostly find algorithms that generate global routes based on historical weather and traffic data on major sea lanes. Such routes do not take into account the current hydrometeorological conditions in the area where the ship is currently located, so that disturbances generated by environmental forces can increase energy consumption. A solution to the problem can be local routing based on the currently prevailing hydrometeorological conditions. With this approach, it is possible to respond to dynamically changing sea conditions, determine the route along which the impact of environmental forces on the hull will be least severe and minimize fuel and energy consumption. This paper presents an algorithm that determines the local passage route of an offshore ship using the example of a vessel moving to an anchorage to drop anchor. The algorithm defines a grid of points between the start point (the vessel’s current position) and the end point (the anchor position), and then determines the transition weights between each grid point based on the vessel’s capability plots. Finally, a modified Dijkstra algorithm determines the route where the sum of the transition weights will be as small as possible. During the tests, it was found that the time needed to find the passage route depended on the chosen grid density of the waypoints and was as follows: for a 6 × 6 grid—0.05 s, for an 11 × 11 grid—0.36 s, for a 16 × 16 grid—0.47 s and for a 21 × 21 grid—0.85 s. It was also found that the algorithm identified a route where the impact of environmental forces on the ship’s hull was 13% less than the direct route to the destination, resulting in a 7.5% reduction in energy consumption. The operation of the algorithm for determining the passage route was demonstrated in the anchor design tool developed in the Unity3D environment.
Research on State Diagnosis Methods of UHF Partial Discharge Sensors Based on Improved ViT
Authors: Liu Yingyi , Hu Zhenghao , Cheng Lin , Wang Yan , Chen Chuan
Published: December 2024

UHF partial discharge sensors are key equipment for substation monitoring, but they are subject to complex multi-physical field stresses in substation applications, which leads to a significantly higher failure rate among UHF partial discharge sensors used in substations compared to other applications. Effective fault diagnosis is of great significance for improving the safety of substations. In this paper, we propose an improved model based on ViT (Vision Transformer), which effectively identifies the local features of the data by designing a sliding window mechanism, and has a good feature extraction capability for the feature library formed by UHF partial discharge sensors. The experimental results show that the diagnostic accuracy of the improved model, based on the ViT model, can reach 97.6%, which can effectively improve classification accuracy and shorten training times compared with the ViT model.
Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
Authors: Lee Kwang-Sig , Kim Eun Sun
Published: December 2024

This study reviews the recent progress of generative artificial intelligence for gastrointestinal disease (GID) from detection to diagnosis. The source of data was 16 original studies in PubMed. The search terms were ((gastro* [title]) or (endo* [title])) and ((GAN [title/abstract] or (transformer [title/abstract]). The eligibility criteria were as follows: (1) the dependent variable of gastrointestinal disease; (2) the interventions of generative adversarial network (GAN) and/or transformer for classification, detection and/or segmentation; (3) the outcomes of accuracy, intersection of union (IOU), structural similarity and/or Dice; (3) the publication period of 2021–2023; (4) the publication language of English. Based on the results of this study, different generative artificial intelligence methods would be appropriate for different tasks for the early diagnosis of gastrointestinal disease. For example, patch GAN (accuracy 91.9%) in the case of classification, bi-directional cycle GAN (structural similarity 98.8%) in the case of data generation and semi-supervised GAN (Dice 89.4%) in the case of segmentation. Their performance indicators reported varied within 87.1–91.9% for accuracy, 83.0–98.8% for structural similarity and 86.6–89.4% for Dice. Likewise, vision transformer (accuracy 96.9%) in the case of classification, multi-modal transformer (IOU 79.5%) in the case of detection and multi-modal transformer (Dice 89.5%) in the case of segmentation. Their performance measures reported registered a variation within 85.7–96.9% for accuracy, 79.5% for IOU and 77.8–89.5% for Dice. Synthesizing different kinds of generative artificial intelligence for different kinds of GID data would further the horizon of research on this topic. In conclusion, however, generative artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of gastrointestinal disease from detection to diagnosis.
Shelf Life and Organoleptic Attributes of Multifruit Smoothies Treated by Combined Mild Preservation Technologies
Authors: Zakariás Fanni , Hidas Karina Ilona , Kovacs Zoltan , Bázár György , Taczman-Brückner Andrea , Dalmadi István , Kiskó Gabriella
Published: December 2024

The application of high hydrostatic pressure and mild heat treatment represents preservation processes for extending the shelf life of food products without compromising their quality. The combination of these physical methods at lower applied levels represents a promising approach to preserving the quality of treated products. This study aims to investigate the impact of combined treatments on the quality and storage stability of strawberry, banana, almond milk and avocado smoothies. The total colony count, electronic nose and tongue signals, colour, viscosity and sensory properties were examined over a 14-day storage period at 6 °C. The combined treatments were found to be effective in reducing the total colony count. During the sensory analysis, the impact of storage was the most prominent factor. Both the treatments and storage conditions significantly affected the colour characteristics of the samples. The smoothie samples exhibited pseudoplastic flow behaviour. Both applied treatments resulted in enhanced texture stability of the samples during the storage period. The electronic tongue and nose could differentiate between groups of fresh and stored samples, as well as between control and treated samples.
A Sensor-Fusion-Based Experimental Apparatus for Collecting Touchscreen Handwriting Biometric Features
Authors: Salkanovic Alen , Bačnar David , Sušanj Diego , Ljubic Sandi
Published: December 2024

Using biometric data for user authentication is a frequently addressed subject within the context of computer security. Despite significant advancements in technology, handwriting analysis continues to be the most common method of identifying individuals. There are two distinct types of handwriting recognition: offline and online. The first type involves the identification and interpretation of handwritten content obtained from an image, such as digitized human handwriting. The latter pertains to the identification of handwriting derived from digital writing performed on a touchpad or touchscreen. This research paper provides a comprehensive overview of the proposed apparatus specifically developed for collecting handwritten data. The acquisition of biometric information is conducted using a touchscreen device equipped with a variety of integrated and external sensors. In addition to acquiring signatures, the sensor-fusion-based configuration accumulates handwritten phrases, words, and individual letters to facilitate online user authentication. The proposed system can collect an extensive array of data. Specifically, it is possible to capture data related to stylus pressure, magnetometer readings, images, videos, and audio signals associated with handwriting executed on a tablet device. The study incorporates instances of gathered records, providing a graphical representation of the variation in handwriting among distinct users. The data obtained were additionally analyzed with regard to inter-person variability, intra-person variability, and classification potential. Initial findings from a limited sample of users demonstrate favorable results, intending to gather data from a more extensive user base.
Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
Authors: Blüher Till , Stark Rainer
Published: December 2024

Product Service Systems (PSS) integrate technical systems, digital infrastructure, and digital and physical services to deliver value to customers in a comprehensive way throughout the life cycle of the PSS. While the potential benefits of PSSs, such as economic efficiency and sustainability, are well-recognized, their implementation and evaluation are often hindered by significant complexities and uncertainties, particularly in the early concept phase. This paper introduces a graph-based reasoning approach that enables the evaluation of PSS concepts despite vague and uncertain understanding. By defining key characteristics in the value creation process qualitatively in distinct and probabilistic states, the graph model makes the concept executable and allows for transparent evaluation. The approach actively considers knowledge gaps and variations in the PSS concept, offering insight into how uncertainties and alternative configurations impact system performance. A case study of a PSS for metal powder recycling in additive manufacturing is conducted to validate the method, demonstrating its applicability for PSS concept evaluation.
Recent Progress in Multiplexed Single-Photon Sources
Authors: Adam Peter , Mechler Matyas
Published: December 2024

We review the progress in multiplexed single-photon sources, including overviews on heralded single-photon sources and photon-number-resolving detectors, the various approaches to multiplexing, and their successful experimental realizations. We also summarize the recent results on the theoretical description and optimization of multiplexed single-photon sources, focusing on the procedures and methods that enable the improvement of the performance of these sources.
Synergising Machine Learning and Remote Sensing for Urban Heat Island Dynamics: A Comprehensive Modelling Approach
Authors: Mutani Guglielmina , Scalise Alessandro , Sufa Xhoana , Grasso Stefania
Published: December 2024

This study evaluates the effectiveness of sustainable urban regeneration projects in mitigating Urban Heat Island (UHI) effects through a place-based approach. Geographic Information Systems (GIS) and satellite imagery were integrated with machine learning (ML) models to analyse the urban environment, human activities, and climate data in Turin, Italy. A detailed analysis of the ex-industrial Teksid area revealed a significant reduction in Surface Urban Heat Island Intensity (SUHII), with decreases of −0.94 in summer and −0.54 in winter following regeneration interventions. Using 17 variables in the Random Forest model, key determinants influencing SUHII were identified, including building density, vegetation cover, and surface albedo. This study quantitatively highlights the impact of increasing green spaces and enhancing surface materials to improve solar reflectivity, with findings showing a 19.46% increase in vegetation and a 3.09% rise in albedo after mitigation efforts. Furthermore, the results demonstrate that integrating Local Climate Zones (LCZs) into urban planning, alongside interventions targeting these key variables, can further optimise UHI mitigation and assess changes. This comprehensive approach provides policymakers with a robust tool to enhance urban resilience and guide sustainable planning strategies in response to climate change.
Joint Probability Distribution of Extreme Wind Speed and Air Density Based on the Copula Function to Evaluate Basic Wind Pressure
Authors: Zhang Lianpeng , Zhang Zeyu , Wu Chunbing , Ji Xiaodong , Xue Xinyue , Jiang Li , Yang Shihan
Published: December 2024

To investigate an appropriate wind load design for buildings considering dynamic air density changes, classical extreme value and copula theories were utilized. Using wind speed, air temperature, and air pressure data from 123 meteorological stations in Shandong Province from 2004 to 2017, a joint probability distribution model was established for extreme wind speed and air density. The basic wind pressure was calculated for various conditional return periods. The results indicated that the Gumbel and Gaussian mixture model distributions performed well in extreme wind speed and air density fitting, respectively. The joint extreme wind speed and air density distribution exhibited a distinct bimodal pattern. The higher the wind speed was, the greater the air density for the same return conditional period. For the 10-year return period, the air density surpassed the standard air density, exceeding 1.30 kg/m3. The basic wind pressures under the different conditional return periods were more than 10% greater than those calculated from standard codes. Applying the air density based on the conditional return period in engineering design could enhance structural safety regionally.
Assessing the Impacts of Climate Change on Rainfed Maize Production in Burkina Faso, West Africa
Authors: Waongo Moussa , Laux Patrick , Coulibaly Amadou , Sy Souleymane , Kunstmann Harald
Published: December 2024

Smallholder rainfed agriculture in West Africa is vital for regional food security and livelihoods, yet it remains highly vulnerable to climate change. Persistently low crop yields, driven by high rainfall variability and frequent climate hazards, highlight the urgent need for evidence-based adaptation strategies. This study assesses the impact of climate change on maize yields in Burkina Faso (BF) using a calibrated AquaCrop model and recent climate projections. AquaCrop was calibrated using district-level maize yields from 2009 to 2022 and a genetic optimization technique. Climate change impacts were then simulated using two socioeconomic scenarios (SSP2–4.5 and SSP5–8.5) for the periods 2016–2045 and 2046–2075. Climate projections show that Burkina Faso will experience temperature increases of 0.5–3 °C and decreased precipitation, with the most severe rainfall reductions in the country’s southern half, including the crucial southwestern agricultural zone. Maize yields will predominantly decrease across the country, with projected losses reaching 20% in most regions. The southwestern agricultural zone, critical for national food production, faces substantial yield decreases of up to 40% under the SSP5-8.5 scenario. In light of these findings, future research should employ the calibrated AquaCrop model to evaluate specific combinations of adaptation strategies. These strategies include optimized planting windows, field-level water management practices, and optimal fertilizer application schedules, providing actionable guidance for smallholder farmers in West Africa.
Monitoring Indoor Air Quality in Classrooms Using Low-Cost Sensors: Does the Perception of Teachers Match Reality?
Authors: Canha Nuno , Correia Carolina , Mendez Sergio , Gamelas Carla A. , Felizardo Miguel
Published: December 2024

This study intended to understand whether teachers’ perceptions of indoor air quality (IAQ) during classes aligned with the real levels of air pollutants and comfort parameters. For this purpose, an IAQ monitoring survey based on low-cost sensors using a multi-parameter approach was carried out in nine classrooms (a total of 171 monitored classes) in a Portuguese school. In each monitored class, the perception of IAQ reported by the teacher was assessed using a scale from 1 (very bad IAQ) to 10 (very good IAQ). Several exceedances regarding national legislation were found, with temperature being the parameter with a higher percentage of exceedance in all the studied classrooms (46%), followed by PM10 (32%), and then CO2 (27%). Temperature was found to be the only environmental parameter that was significantly associated with lower IAQ perception reported by the teachers, highlighting that typical pollutants such as CO2 (which can be identified as stuffy air) did not contribute to the teachers’ perceptions.
Mechanical Characterization and Modeling of Large-Format Lithium-Ion Battery Cell Electrodes and Separators for Real Operating Scenarios
Authors: Brehm Johannes , Durdel Axel , Kussinger Tobias , Kotter Philip , Altmann Maximilian , Jossen Andreas
Published: December 2024

This study presents a novel application-oriented approach to the mechanical characterization and subsequent modeling of porous electrodes and separators in lithium-ion cells to gain a better understanding of their real mechanical operating behavior. An experimental study was conducted on the non-linear stiffness of LiNi0.8Co0.15Al0.05O2 and graphite electrodes as well as PE separators, harvested from large-format lithium-ion cells, using compression tests. The mechanical response of the components was determined for different operating conditions, including nominal stress levels, mechanical loading rates, and mechanical cycles. The presented work describes the test procedure, the experimental setup, and an objective evaluation method, allowing for a detailed summary of the observed mechanical behavior. A distinct nominal stress level and mechanical cycle dependency of the non-linear stiffnesses of the porous materials were found. However, no clear dependency on compression rate was observed. Based on the experimental data, a poroelastic mechanical model was utilized to predict the non-linear behavior of these porous materials under real mechanical operating scenarios with a normalized root-mean-squared error less than 5.5%. The results provide essential new insights into the mechanical behavior of porous electrodes and separators in lithium-ion cells under real operating conditions, enabling the accelerated development of high-performing and safe batteries for various applications.
Deep-Learning Model for Mortality Prediction of ICU Patients with Paralytic Ileus
Authors: Razo Martha , Pishgar Maryam , Galanter William , Darabi Houshang
Published: December 2024

Paralytic Ileus (PI) patients in the Intensive Care Unit (ICU) face a significant risk of death. Current predictive models for PI are often complex and rely on many variables, resulting in unreliable outcomes for such a serious health condition. Predicting mortality in ICU patients with PI is particularly challenging due to the vast amount of data and numerous features involved. To address this issue, a deep-learning predictive framework was developed using the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset, which includes data from 1017 ICU patients with PI. By employing SHAP (SHapley Additive exPlanations) analysis, we were able to narrow down the features to six distinct clinical lab items. The proposed framework, called DLMP (Deep Learning Model for Mortality Prediction of ICU Patients with PI), utilizes these six unique clinical lab items: Anion gap, Platelet, PTT, BUN, Total Bilirubin, and Bicarbonate, along with one demographic variable as inputs to a neural network consisting of only two neuron layers. DLMP achieved an outstanding prediction performance with an AUC score of 0.887, outperforming existing predictive models for ICU patients with PI. The DLMP framework significantly enhances the prediction of mortality for PI patients compared to traditional process mining and machine learning models. This model holds considerable potential for prognosis, enabling families to be better informed about the severity of a patient’s condition and to prepare accordingly. Furthermore, the model is valuable for research purposes and clinical trials.
Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks
Authors: Geravanchizadeh Masoud , Shaygan Asl Amir , Danishvar Sebelan
Published: December 2024

Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system. This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). This approach eliminates the need for manual feature extraction, which is often time-consuming and subjective. Here, the first EEG signals are converted to graphs. We then extract attention information from these graphs using spatial and temporal approaches. Finally, our models are trained with these data. Our model can detect auditory attention in both the spatial and temporal domains. Here, the EEG input is first processed by transformer layers to obtain a sequential representation of EEG based on attention onsets. Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. The new TraGCNN approach, as compared with state-of-the-art attention classification methods from the literature, yields the highest performance in terms of accuracy (80.12%) as a classification metric. Additionally, the proposed model results in higher performance than our previously graph-based model for different lengths of EEG segments. The new TraGCNN approach is advantageous because attenuation detection is achieved from EEG signals of subjects without requiring speech stimuli, as is the case with conventional auditory attention detection methods. Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. The findings of this study have important implications for the understanding and assessment of auditory attention, which is crucial for many applications, such as brain–computer interface (BCI) systems, speech separation, and neuro-steered hearing aid development.
Local Ancestry and Adaptive Introgression in Xiangnan Cattle
Authors: Yan Huixuan , Li Jianbo , Zhang Kunyu , Duan Hongfeng , Sun Ao , Zhang Baizhong , Li Fuqiang , Chen Ningbo , Lei Chuzhao , Yi Kangle
Published: December 2024

Exploring the genetic landscape of native cattle is an exciting avenue for elucidating nuanced patterns of genetic variation and adaptive dynamics. Xiangnan cattle, a native Chinese cattle breed mainly produced in Hunan Province, are well adapted to the high temperature and humidity of the local environment and exhibit strong disease resistance. Herein, we employed whole-genome sequences of 16 Xiangnan cattle complemented by published genome data from 81 cattle. Our findings revealed that Xiangnan cattle are pure East Asian indicine cattle with high genetic diversity and low inbreeding. By annotating the selection signals obtained by the CLR, θπ, FST, and XP-EHH methods, genes associated with immunity (ITGB3, CD55, OTUD1, and PRLH) and heat tolerance (COX4I2, DNAJC18, DNAJC1, EIF2AK4, and ASIC2) were identified. In addition, the considerable introgression from banteng and gaur also contributed to the rapid adaptation of Xiangnan cattle to the environment of Southern China. These results will provide a basis for the further conservation and exploitation of Xiangnan cattle genetic resources.
Integrative Transcriptomic and Target Metabolite Analysis as a New Tool for Designing Metabolic Engineering in Yeast
Authors: Lopez-Barbera Alejandro , Abasolo Nerea , Torrell Helena , Canela Nuria , Fernández-Arroyo Salvador
Published: December 2024

Precision fermentation processes, especially when using edited microorganisms, demand accuracy in the bioengineering process to maximize the desired outcome and to avoid adverse effects. The selection of target sites to edit using CRISPR/Cas9 can be complex, resulting in non-controlled consequences. Therefore, the use of multi-omics strategies can help in the design, selection and efficiency of genetic editing. In this study, we present a multi-omics approach based on targeted metabolite analysis and transcriptomics for the designing of CRISPR/Cas9 in baker’s yeast as a more efficient strategy to select editing regions. Multi-omics shows potential to reveal new metabolic bottlenecks and to elucidate new metabolic fluxes, which could be a key factor in minimizing the metabolic burden in edited microorganisms. In our model, we focus our attention on the isoprenoid synthesis due to their industrial interest. Targeted metabolite detection combined with a transcriptomic analysis revealed hydroxymethylglutaryl-CoA reductases (HMGs) as the best target gene to induce an increase in isoprenoid synthesis. Thus, an extra copy of HMG1 was introduced using, for the first time, the synthetic UADH1 promoter. The multi-omics analysis of the recombinant strain results in an accurate assessment of yeast behavior during the most important growth phases, highlighting the metabolic burden, Crabtree effect or the diauxic shift during culture.
Cervical Cancer Cells Use the CD95 and IL-2 Pathways to Promote Their Proliferation and Survival
Authors: Gutiérrez-Hoya Adriana , Ortiz-Garrido Ivan , Salazar-Valencia Itzel , Romero-Hernández Christopher , Valle-Mendiola Arturo , Weiss-Steider Benny , Soto-Cruz Isabel
Published: December 2024

Cervical cancer is a global health problem; therapies focused on eliminating tumour cells and strengthening different immunotherapies are in development. However, it has been observed that cervical tumour cells can evade cell death mechanisms and generate immune system molecules to promote their proliferation and metastasis. In this context, we analysed the role of the IL-2 and CD95 pathways, essential molecules in activating the immune system and eliminating tumour cells. However, it is important to analyse their role in cervical tumour cells because these cells could be using these pathways to proliferate. In this study, we found that SiHa and HeLa cells respond to treatment, with 10 IU/mL of IL-2 inducing their proliferation and 100 IU/mL of IL-2 decreasing their proliferation. We also observed that they express a high percentage of the CD95 receptor and its ligand (CD95L) and that treatment with CD95 agonist antibodies at low doses increases cell proliferation. Furthermore, simultaneous treatment with high doses of IL-2 plus CD95 agonist antibody positively regulates LC3B accumulation. We did not observe apoptosis under any of the treatments carried out. In conclusion, cervical tumour cells can use the IL-2 and CD95 pathways to induce their proliferation and potentially activate cytoprotective mechanisms for survival.
Exploring Causal Associations Between Serum Inflammatory Markers and Female Reproductive Disorders: A Mendelian Randomisation Study
Authors: Alesi Simon , Teede Helena , Moran Lisa , Enticott Joanne , De Silva Kushan , Mousa Aya
Published: December 2024

Although inflammation may disrupt immunoendocrine crosstalk essential for female reproductive function, causal links to disorders like polycystic ovary syndrome (PCOS) and endometriosis remain unestablished. This study aimed to utilise Mendelian randomisation (MR) methods to explore causal associations between serum inflammatory markers and common reproductive disorders, aiming to identify novel mechanisms and potential avenues for treatment. Total causal effects of serum inflammatory markers (interleukins, monocyte chemoattractant protein-1, etc.) on female reproductive disorders in large sample cohorts of Finnish ancestry were assessed using univariable two-sample MR methods, including the inverse variance weighted (IVW) method as the primary analysis, with relevant quality assessments (e.g., leave-one out, heterogeneity, and horizontal pleiotropy testing). The main outcome measures were PCOS (642 cases and 118,228 controls) and endometriosis (8288 cases and 68,969 controls) from the FINNGEN cohort. Monocyte chemoattractant protein-1/C-C motif chemokine ligand demonstrated a positive causal association with polycystic ovary syndrome (odds ratio [95% CI]: 1.48 [1.10, 2.00], p = 0.0097), while higher interleukin-9 levels were positively associated with endometriosis (1.15 [1.02, 1.30], p = 0.0277), both via the IVW method. These markers should be investigated as key candidates for future research into the mechanistic pathways underpinning these conditions.
Hematological and Biochemical Characteristics Associated with Cytogenetic Findern Alterations in Adult Patients with Acute Lymphoblastic Leukemia (ALL) from the Northern Region of Brazil
Authors: Duarte Dejair da Silva , Teixeira Eliel Barbosa , de Oliveira Marcelo Braga , Carneiro Thiago Xavier , Leão Lucyana Barbosa Cardoso , Mello Júnior Fernando Augusto Rodrigues , Carneiro Debora Monteiro , Nunes Patricia Ferreira , Cohen-Paes Amanda , Alcantara Diego Di Felipe Ávila , Khayat André Salim , Burbano Rommel Mario Rodriguez
Published: December 2024

Acute lymphoblastic leukemia (ALL) is an aggressive neoplasm derived from B and/or T cell lineage (B-ALL; T-ALL). For the first time, this study describes, cytogenetically, the karyotypic alterations in adults with ALL in the northern region of Brazil and their relationship with hematological and biochemical characteristics. Through banding analyses, immunophenotyping, as well as hematological and biochemical examination data obtained directly from patients’ records, we found that chromosome 21 aneuploidy was the most frequent. The cytogenetic structural alterations observed with the highest incidence among the patients were: t(9;22), t(4;11), t(1;19), del(6q), and del(9p). In patients presenting with chromosome alterations, we verified that patients with t(4;11) have elevated red blood cell levels and patients with del(9p) presented with distinct and high values of hematological parameters compared to other patients. Regarding biochemical alterations, we observed that patients with translocations (4;11) and del(6q) presented with elevated urea levels compared to other patients, highlighting its relationship to kidney changes and patient prognosis. Thus, our study highlights that variations in hematological and biochemical data are associated with specific cytogenetic changes and other factors, which may impact the prognosis of adult patients with ALL.
Proteomic Profile Regulated by the Immunomodulatory Jusvinza Drug in Neutrophils Isolated from Rheumatoid Arthritis Patients
Authors: Hernández-Cedeño Mabel , Rodríguez-Ulloa Arielis , Ramos Yassel , González Luis J. , Serrano-Díaz Anabel , Zettl Katharina , Wiśniewski Jacek R. , Martinez-Donato Gillian , Guillen-Nieto Gerardo , Besada Vladimir , Domínguez-Horta María del Carmen
Published: December 2024

Jusvinza is an immunomodulatory drug composed of an altered peptide ligand (APL) designed from a novel CD4+ T cell epitope of human heat shock protein 60 (HSP60), an autoantigen involved in the pathogenesis of rheumatoid arthritis (RA). The peptide induces regulatory T cells and decreases levels of TNF-α and IL-17; pre-clinical and phase I clinical studies support its use for the treatment of RA. This peptide was repositioned for the treatment of COVID-19 patients with signs of hyperinflammation. Neutrophils play a pathogenic role in both RA and severe forms of COVID-19. To add novel evidence about the mechanism of action of Jusvinza, the proteomic profile regulated by this peptide of neutrophils isolated from four RA patients was investigated using LC-MS/MS and bioinformatics analysis. A total of 149 proteins were found to be differentially modulated in neutrophils treated with Jusvinza. The proteomic profile regulated by Jusvinza is characterized by the presence of proteins related to RNA splicing, phagocytosis, endocytosis, and immune functions. In response to Jusvinza treatment, several proteins that regulate the NF-κB signaling pathway were differentially modulated, supporting the peptide’s anti-inflammatory effect. Proteins related to metabolic pathways that supply ATP for cellular functions or lipid metabolites with immunoregulatory properties were also identified. Additionally, several structural components of neutrophil extracellular traps (NETs) were decreased in Jusvinza-treated cells, supporting its impairment of this biological process. Of note, these findings were validated by in vitro experiments which confirmed that Jusvinza decreased NET formation. Such results provide evidence of the molecular mechanism of action and support the therapeutic potentialities of Jusvinza to treat other diseases characterized by hyperinflammation besides RA and COVID-19.
Integrative Multi-Omics Approach in Vascular Ehlers–Danlos Syndrome: Further Insights into the Disease Mechanisms by Proteomic Analysis of Patient Dermal Fibroblasts
Authors: Chiarelli Nicola , Cinquina Valeria , Zoppi Nicoletta , Bertini Valeria , Maddaluno Marianna , De Leonibus Chiara , Settembre Carmine , Venturini Marina , Colombi Marina , Ritelli Marco
Published: December 2024

Background: Dominant mutations in COL3A1 are known to cause vascular Ehlers–Danlos syndrome (vEDS) by impairing extracellular matrix (ECM) homeostasis. This disruption leads to the fragility of soft connective tissues and a significantly increased risk of life-threatening arterial and organ ruptures. Currently, treatments for vEDS are primarily symptomatic, largely due to a limited understanding of its underlying pathobiology and molecular mechanisms. Methods: In this study, we conducted a comprehensive analysis of the intracellular proteome of vEDS fibroblasts, integrating these findings with our previous transcriptome results to identify key molecular pathways that drive the disease. Additionally, we explored the therapeutic potential of inhibiting miR-29b-3p as a proof of concept. Results: Our integrative multi-omics analysis revealed complex pathological networks, emphasizing the critical role of miRNAs, particularly miR-29b-3p, in impairing ECM organization, autophagy, and cellular stress responses, all of which contribute to the pathogenesis of vEDS. Notably, the inhibition of miR-29b-3p in vEDS fibroblasts resulted in the upregulation of several differentially expressed target genes involved in these critical processes, as well as increased protein expression of essential ECM components, such as collagen types V and I. These changes suggest potential therapeutic benefits aimed at improving ECM integrity and restoring intracellular homeostasis. Conclusions: Overall, our findings advance our understanding of the complex biological mechanisms driving vEDS and lay a solid foundation for future research focused on developing targeted and effective treatment strategies for this life-threatening disorder.
Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives
Authors: Molla Getnet , Bitew Molalegne
Published: December 2024

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which mainly encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.
DENV-1 Infection of Macrophages Induces Pyroptosis and Causes Changes in MicroRNA Expression Profiles
Authors: Zhang Qinyi , Yu Sicong , Yang Zhangnv , Wang Xingxing , Li Jianhua , Su Lingxuan , Zhang Huijun , Lou Xiuyu , Mao Haiyan , Sun Yi , Fang Lei , Yan Hao , Zhang Yanjun
Published: December 2024

Background: Dengue virus (DENV) is the most widespread mosquito-borne virus, which can cause dengue fever with mild symptoms, or progress to fatal dengue hemorrhagic fever and dengue shock syndrome. As the main target cells of DENV, macrophages are responsible for the innate immune response against the virus. Methods: In this study, we investigated the role of pyroptosis in the pathogenic mechanism of dengue fever by examining the level of pyroptosis in DENV-1-infected macrophages and further screened differentially expressed microRNAs by high-throughput sequencing to predict microRNAs that could affect the pyroptosis of the macrophage. Results: Macrophages infected with DENV-1 were induced with decreased cell viability, decreased release of lactate dehydrogenase and IL-1β, activation of NLRP3 inflammasome and caspase-1, cleavage of GSDMD to produce an N-terminal fragment bound to cell membrane, and finally induced macrophage pyroptosis. MicroRNA expression profiles were obtained by sequencing macrophages from all periods of DENV-1 infection and comparing with the negative control. Sixty-three microRNAs differentially expressed in both the early and later stages of infection were also identified. In particular, miR-223-3p, miR-148a-3p, miR-125a-5p, miR-146a-5p and miR-34a-5p were recognized as small molecules that may be involved in the regulation of inflammation. Conclusions: In summary, this study aimed to understand the pathogenic mechanism of DENV through relevant molecular mechanisms and provide new targets for dengue-specific therapy.
Effects of Route Complexity and Lighting on Route Following in Alzheimer’s Disease and Posterior Cortical Atrophy
Authors: Carton Amelia M. , Frost Chris , Poole Teresa , Yang Biao , McCarthy Ian D. , Suzuki Tatsuto , Holloway Catherine , Serougne Robin , Boampong Derrick , Sullivan Mary Pat , Tyler Nick , Crutch Sebastian , Yong Keir X. X.
Published: December 2024

Objective: Visual processing deficits arising in dementia are associated with particular functional disability. This study aimed to investigate the effects of the built environment on mobility and navigation in people with dementia-related visual loss. Methods: Participants with posterior cortical atrophy (PCA; “visual-variant Alzheimer’s”; n = 11), typical Alzheimer’s disease (tAD; N = 10), and controls (n = 13) repeatedly walked down routes within a simplified real-world setting. Participant groups were of comparable age and gender. Routes were of different complexity (straight, U-shaped, and S-shaped), overhead lighting levels (low and high) and with or without a dynamic LED (light-emitting diode) cue (trial n = 24). Ratios of walking times for each experimental condition (each complex route vs the straight route, high lighting vs low, and LED cue vs no cue) were compared between participant groups. Kinematic measures were produced from a total of 10,813 steps using wearable inertial measurement units (IMUs). Results: The walking time ratios relating to route complexity were higher in the PCA group than in controls: 30.3% (95% CI [13.5%, 49.5%] higher for U-shaped vs straight and 31.9% [21.1%, 55.3%] for S-shaped vs straight, averaged over other conditions). The analogous results relating to route complexity for the tAD group were intermediate between those for the PCA and control groups. There was no evidence that walking time ratios differed according to lighting level or the presence of the LED cue. Conclusions: Findings contribute to evidence-based design for dementia-friendly environments, emphasizing consequences of environmental complexity for functional independence and mobility in people with dementia-related visual loss. Findings inform recommendations for environmental design to support the independence of individuals with dementia.