bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effectiveness of a combination of laccase and green coffee extract on oral malodor: a comparative, randomized, controlled, evaluator-blind, parallel-group trial
Authors: Santos Sylvia L , Holz Caterina , Milleman Kimberly , Milleman Jeffery , Wenqian Gu , Mateo Luis R 
Published: November 2024

Abstract
Oral malodor negatively impacts a person’s quality of life and may affect up to 50% of the population. The aim of this randomized, placebo and no-product controlled, evaluator-blind, proof-of-concept study was to evaluate the effectiveness and safety of the single use of two experimental lozenges containing the laccase enzyme and green coffee extract (with and without flavor) in reducing intrinsic oral malodor. Following 12–16 h of avoidance of oral hygiene,156 generally healthy subjects presented at screening and baseline visits with a mean organoleptic odor intensity (OI) score of ⩾2 and an OralChromaTM reading of ⩾125 parts per billion (ppb) hydrogen sulfide (H2S) gas and were randomly assigned to receive either one of the two experimental lozenges, a placebo lozenge, or no-product. Following the supervised use of the assigned products, subjects’ oral malodor was evaluated using OI assessments and OralChromaTM measurement for volatile sulfur compounds (VSCs) immediately following product use (approximately 5 min), and at 30 min, 1 h, 2 h, 3 h and 4 h. The two experimental lozenges, with and without flavor, showed significant reductions in OI scores compared with the placebo and no-product groups at all time points (p < 0.001). At 5 min post-product use, the experimental lozenges, with and without flavor, were significantly better than the no-product group in reducing the VSCs (p < 0.04). The results of individual VSC components (hydrogen sulfide, methyl mercaptan and dimethyl sulfide) were variable; both experimental lozenges notably reduced hydrogen sulfide and methyl mercaptan levels in most post-use assessments. Four minor adverse events were reported, none of which were directly linked to the product. In conclusion, the experimental lozenges, whether flavored or not, were safe and effective in reducing oral malodor over a span of 4 h, based on organoleptic OI scores.
Clinical Trial No: NCT05950529
Validation of a sensor system for the measurement of breath ammonia using selected-ion flow-tube mass spectrometry
Authors: Wagner Michal , Saad Saliha , Killard Anthony J 
Published: November 2024

Abstract
The measurement of trace breath gases is of growing interest for its potential to provide non-invasive physiological information in health and disease. While instrumental techniques such as selected-ion flow-tube mass spectrometry (SIFT-MS) can achieve this, these are less suitable for clinical application. Sensitive sensor-based systems for breath ammonia could be more widely deployed, but have proven challenging to develop. This work demonstrates the sequential analytical validation of an electrochemical impedance-based sensor system for the measurement of ammonia in breath using SIFT-MS. Qualitative and relative responses between the two methods were comparable, although there were consistent differences in absolute concentration. When tested in artificial breath ammonia, sensors had a relative impedance sensitivity of 3.43 × 10−5 ppbv−1 for each breath in the range of 249–1653 ppbv (r
2 = 0.87, p< 0.05). When correlated with SIFT-MS using human breath (n = 14), ammonia was detected in the range of 100–700 ppbv (r = 0.78, p < 0.001), demonstrating acceptable sensitivity, reproducibility and dynamic range for clinical application.
Permutation-equivariant quantum convolutional neural networks
Authors: Das Sreetama , Caruso Filippo 
Published: November 2024

AbstractThe Symmetric groupSnmanifests itself in large classes of quantum systems as the invariance of certain characteristics of a quantum state with respect to permuting the qubits. Subgroups ofSnarise, among many other contexts, to describe label symmetry of classical images with respect to spatial transformations, such as reflection or rotation. Equipped with the formalism of geometric quantum machine learning, in this study we propose the architectures of equivariant quantum convolutional neural networks (EQCNNs) adherent toSnand its subgroups. We demonstrate that a careful choice of pixel-to-qubit embedding order can facilitate easy construction of EQCNNs for small subgroups ofSn. Our novel EQCNN architecture corresponding to the full permutation groupSnis built by applying all possible QCNNs with equal probability, which can also be conceptualized as a dropout strategy in quantum neural networks. For subgroups ofSn, our numerical results using MNIST datasets show better classification accuracy than non-equivariant QCNNs. TheSn-equivariant QCNN architecture shows significantly improved training and test performance than non-equivariant QCNN for classification of connected and non-connected graphs. When trained with sufficiently large number of data, theSn-equivariant QCNN shows better average performance compared toSn-equivariant QNN . These results contribute towards building powerful quantum machine learning architectures in permutation-symmetric systems.
Gender differences regarding forming the intent to participate in carbon neutral practices: a nationwide sample study of South Korea
Authors: Lee Jin Won , Roh Seungkook 
Published: November 2024

Abstract
For a society to achieve the goal of carbon neutrality, the public must also participate in practices that reduce carbon emissions in their daily lives. This study investigates how males and females differ in forming the intent to participate in such practices. Our analysis of a nationwide survey sample taken from South Korea shows that (1) females have a higher rate of intent to participate in carbon neutral practices; however, (2) the positive effect of the perceived necessity of carbon neutrality on this intent is greater among males. These findings suggest that although males are more restrained regarding their view of carbon neutral practices, their intent to participate in such practices can be greatly increased if they perceive carbon neutrality as necessary. The findings contribute to the existing research on the role of gender in environmental psychology and provide implications for communicating essential information regarding carbon neutral behavior to the general public.
Regulation of protonation in aramid nanofiber films for improved mechanical properties
Authors: Choi Yuseung , Shin Gilyong , Lee Ju Hwan , Kim Hyeong Jun , Jun so Byeong , Lee Hoyeon , Jeon Jei Gyeong , Kang Tae June 
Published: November 2024

Abstract
Aramid nanofibers (ANFs) are a strong and heat resistant nanomaterial that can be isolated from commercial poly(p-phenylene terephthalamide) (PPTA) fibers. These nanofibers allow bottom-up self-assembly to form macroscale structures in various form factors through a simple reprotonation process. However, the mechanical properties of these reassembled ANF structures often fall short of those of PPTA fibers, mainly due to insufficient packing, non-uniform microstructures, and low crystallinity. In this study, we present ANF films with improved mechanical properties prepared by a repeated spin-coating technique combined with a reprotonation process using deionized water and formic acid at concentrations ranging from 20 to 60 wt.%. Extensive analyses were performed on the resulting ANF films to evaluate their surface and cross-sectional morphologies, chemical bonds and compositions, thermal stabilities, and mechanical properties. The fabricated ANF films exhibited remarkable performance, with an elastic modulus of 6.7 GPa, tensile strength of 680 MPa, and toughness of 7.7 MJ m−3, while maintaining the inherent thermal stability of PPTA fibers. These properties significantly exceed those of previously reported ANF films, broadening the potential applications for ANF films in various fields.
Investigation on novel ultralight weight thermally insulated fireproof composite
Authors: Burai Suvam , Srikrishnan A R , Bhowmik Shantanu 
Published: November 2024

Abstract
This study highlights the performance of ultra-lightweight fireproof composite. Polyacrylonitrile based carbon fiber (CF) has been reinforced in Polyetherimide (PEI) polymer to develop the composite. The Surfac2e of CF and PEI film was modified by low-pressure plasma to improve the bonding strength between matrix and reinforcement. The polymeric composite was fabricated by compression molding with a pressure of 2 bar, temperature of 380 °C and holding time of 30 min. CF/PEI composite was used to make a hybrid composite by layering of silicone foam in between the layers. The hybrid composite was exposed to a Bunsen burner under sustained flame for a duration of 10 min. The composite panel’s flame-facing side reached 676.2 °C after 10 min of fire exposure, while the temperature on the other side only reached 58.2 °C. The fabricated hybrid composite was exposed to very low temperature in order to test its ability of thermal insulation under extreme cold temperature. Over the specific period of testing, the temperature of the dry ice decreased from 25 °C to −3.1 °C. After exposure to fire, only minimal loss of material was observed. The hybrid composite of carbon fiber and PEK film, sandwiched between silicone foam, exhibits excellent fire resistance due to its high limiting oxygen index. This composite is considered to be among the best thermally insulating and fire-resistant materials. Thermogravimetric analysis of carbon fiber and PEI-Carbon fiber composite was performed to determine the optimal processing temperature of compression molding for the composite, upon heating, it showed a modest weight decrease of 6.053%. The composite shows a significant improvement of impact resistance, compressive strength and thermal stability. A simulation model was developed under Ansys fluent software for both heating and cooling. The analysis of developed model also shows similar results.
TIA-UNet: transformer-enhanced deep learning for adolescent idiopathic scoliosis spinal x-ray image segmentation
Authors: Li Zhiwu , Deng Shuangcheng , Xue Zhilong , Zhang Jinlong , Hua Junfei , Li Gang , Yang Yang , Li Aijing , Wang Junyang 
Published: November 2024

Abstract
Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity where precise diagnosis is crucial for developing effective treatment strategies. Traditional manual x-ray image analysis is time-consuming and highly dependent on the operator’s expertise, thus constraining diagnostic efficiency and accuracy. This study aimed to develop an automated thoracolumbar spine segmentation method utilizing deep learning to enhance the efficiency and accuracy of AIS diagnosis. We introduced TIA-UNet, an innovative network architecture that combines Convolutional Neural Networks (CNN) with Transformer models. By integrating the IR Block and DA Block, TIA-UNet was optimized for feature extraction and multi-scale information fusion. The model underwent training and validation using our established Adolescent Scoliosis Medical Dataset (ASMD). TIA-UNet attained a Dice similarity coefficient of 90. 02%, a Mean Intersection over Union (MIoU) of 81. 96%, and a Hausdorff Distance (HD) of 4. 09, significantly surpassing current state-of-the-art methods such as UNet and TransUNet. Moreover, TIA-UNet exhibited superior computational efficiency regarding parameter count, inference time, and floating-point operations (FLOPs). As an automated medical image segmentation algorithm, TIA-UNet enhanced segmentation accuracy while maintaining high computational efficiency, demonstrating significant potential for clinical diagnostic applications. This study provides compelling evidence supporting the utilization of deep learning techniques in medical image analysis.
Exploring the thermal conductivity of green filamentous algae natural fibre: an experimental investigation
Authors: G R Sajin , S Gijoy , S Rejin 
Published: November 2024

Abstract
The use of non-renewable resources in thermal insulation has significant environmental impacts. Another major problem faced by the ecosystem is the invasive growth of water weeds. Making sustainable products from waterweeds helps to prevent its overgrowth that disrupts the balance of the ecosystem. This study explores the viability of Green Filamentous Algae (GFA) as an eco-friendly thermal insulation material. GFA was collected from water bodies, cleaned, and processed into two forms: untreated (UTD) and alkali-treated (TD). Experimental investigations were conducted to evaluate the physical properties of GFA, including density, self-ignition temperature, water absorption, moisture absorption, flammability, and thermal conductivity. Results show that the untreated GFA had a density of 402.52 kg m−3, while the treated GFA exhibited a slightly lower density of 392.32 kg m−3. The self-ignition temperature for both untreated and treated GFA was measured between 275 °C and 280 °C. The water absorption capacity was higher in treated GFA (654.7%) compared to untreated (493.83%). Moisture absorption capacity was 15.14% for untreated GFA and 17.47% for treated GFA. Flammability tests revealed a burning rate of 20.026 mm min−1, placing GFA in the combustibility classification 1 (CC1). Thermal conductivity values were found to be 0.308 W m−1K−1 (UTD) and 0.273 W m−1K−1 (TD), making treated GFA a promising candidate for sustainable insulation applications. This study demonstrates the potential of GFA as a bio-based insulation material and highlights future improvements to enhance its moisture resistance and thermal performance.
Environmental impacts of floating solar panels on freshwater systems and their techno-ecological synergies
Authors: M G Rocha Sofia , Armstrong Alona , Thackeray Stephen J , Hernandez Rebecca R , M Folkard Andrew 
Published: November 2024

Abstract
Renewable energy sources, such as floating photovoltaic systems (FPVs), are crucial to mitigating the climate crisis. FPV deployments on freshwater bodies are rapidly growing, as they avert land-use change, operate with increased efficiency, and potentially improve water availability by reducing evaporation and the frequency of algal blooms. However, understanding of the ecological consequences for water bodies is very limited despite the variable and far-reaching range of potential impacts. Here, we bring novel insight by combining an established theoretical approach—techno-ecological synergies (TES)—with robust understanding of water body functioning, to direct sustainable FPV deployments. Specifically, we show the potential to integrate consideration of both energy and ecological systems for FPV deployment decisions based on current evidence and fundamental understanding of freshwater body states and processes. Based on 422 pieces of evidence from a systematic review of known physical (n = 283), chemical (n = 96) and biological (n = 43) effects of FPV on freshwater systems, we outline eight potential TES that could be realised through FPV location, design, and operation decisions. There was most evidence (n = 114) for the Water Use Efficiency TES, which all reported decreased evaporation rates, or increased water savings, due to panel shading. We highlight a lack of understanding of chemical and biological effects of FPV on hosting water bodies, as well as a need for comprehensive studies in which physical, chemical, and biological aspects of water body dynamics are integrated. Finally, we detail research priorities to ensure future FPV deployments bring benefit for both energy and water bodies. Ultimately, integrated energy and water body system knowledge, FPV deployments could mitigate both the climate and ecological emergencies, with notable benefits for society.
An unbiased comparison of immunoglobulin sequence aligners
Authors: Konstantinovsky Thomas , Peres Ayelet , Polak Pazit , Yaari Gur 
Published: November 2024

Abstract
Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is critical for our understanding of the adaptive immune system’s dynamics in health and disease. Reliable analysis of AIRR-seq data depends on accurate rearranged immunoglobulin (Ig) sequence alignment. Various Ig sequence aligners exist, but there is no unified benchmarking standard representing the complexities of AIRR-seq data, obscuring objective comparisons of aligners across tasks. Here, we introduce GenAIRR, a modular simulation framework for generating Ig sequences alongside their ground truths. GenAIRR realistically simulates the intricacies of V(D)J recombination, somatic hypermutation, and an array of sequence corruptions. We comprehensively assessed prominent Ig sequence aligners across various metrics, unveiling unique performance characteristics for each aligner. The GenAIRR-produced datasets, combined with the proposed rigorous evaluation criteria, establish a solid basis for unbiased benchmarking of immunogenetics computational tools. It sets up the ground for further improving the crucial task of Ig sequence alignment, ultimately enhancing our understanding of adaptive immunity.
Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering
Authors: Shu Zhenqiu , Xia Min , Tan Kaiwen , Zhang Yongbing , Yu Zhengtao 
Published: November 2024

Abstract
Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https://github.com/szq0816/scMMN.
Making PBPK models more reproducible in practice
Authors: Domínguez-Romero Elena , Mazurenko Stanislav , Scheringer Martin , Martins dos Santos Vítor A P , Evelo Chris T , Anton Mihail , Hancock John M , Županič Anže , Suarez-Diez Maria 
Published: November 2024

Abstract
Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model’s behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.
Quantifying defective and wild-type viruses from high-throughput RNA sequencing
Authors: Muñoz-Sánchez Juan C , Olmo-Uceda María J , Oteo José-Ángel , Elena Santiago F 
Published: November 2024

Abstract

Motivation
Defective viral genomes (DVGs) are variants of the wild-type (wt) virus that lack the ability to complete autonomously an infectious cycle. However, in the presence of their parental (helper) wt virus, DVGs can interfere with the replication, encapsidation, and spread of functional genomes, acting as a significant selective force in viral evolution. DVGs also affect the host’s immune responses and are linked to chronic infections and milder symptoms. Thus, identifying and characterizing DVGs is crucial for understanding infection prognosis. Quantifying DVGs is challenging due to their inability to sustain themselves, which makes it difficult to distinguish them from the helper virus, especially using high-throughput RNA sequencing. An accurate quantification is essential for understanding their very dynamical interactions with the helper virus.

Results
We present a method to simultaneously estimate the abundances of DVGs and wt genomes within a sample by identifying genomic regions with significant deviations from the expected sequencing depth. Our approach involves reconstructing the depth profile through a linear system of equations, which provides an estimate of the number of wt and DVG genomes of each type. Until now, in silico methods have only estimated the DVG-to-wt ratio for localized genomic regions. This is the first method that simultaneously estimates the proportions of wt and DVGs genome wide from short-reads RNA sequencing.

Availability and implementation
The Matlab code and the synthetic datasets are freely available at https://github.com/jmusan/wtDVGquantific.
One Health on islands: Tractable ecosystems to explore the nexus between human, animal, terrestrial, and marine health
Authors: Fountain-Jones Nicholas M , Hutson Kate S , Jones Menna , Nowak Barbara F , Turnbull Alison , Younger Jane , O'Reilly Malgorzata , Watkins Emma , Guernier-Cambert Vanina , Cooley Louise , Hamede Rodrigo 
Published: November 2024

Abstract
Islands play a central role in understanding the ecological and evolutionary processes that shape life but are rarely used to untangle the processes that shape human, animal, and environmental health. Islands, with their discrete human and animal populations, and often well-studied ecological networks, serve as ideal natural laboratories for exploring the complex relationships that shape health across biomes. Relatively long coastlines and, in some cases, low lying topography also make islands sentinels for climate change. In this article, we examine the potential of islands as valuable laboratories and research locations for understanding the One Health nexus. By delving into the challenges faced in island settings, we provide valuable insights for researchers and policymakers aiming to globally promote and apply One Health principles. Ultimately, recognizing the interconnected health of humans, animals, and the environment on islands contributes to efforts aimed at promoting global health and sustainability.