bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robotic feet modeled after ungulates improve locomotion on soft wet grounds
Authors: Godon S , Ristolainen A , Kruusmaa M 
Published: November 2024

Abstract
Locomotion on soft yielding grounds is more complicated and energetically demanding than on hard ground. Wet soft ground (such as mud or snow) is a particularly difficult substance because it dissipates energy when stepping and resists extrusion of the foot. Sinkage in mud forces walkers to make higher steps, thus, to spend more energy. Yet wet yielding terrains are part of the habitat of numerous even-toed ungulates (large mammals with split hooves). We hypothesized that split hooves provide an advantage on wet grounds and investigated the behavior of moose legs on a test rig. We found that split hooves of a moose reduce suction force at extrusion but could not find conclusive evidence that the hoof reduces sinkage. We then continued by designing artificial feet equipped with split-hoof-inspired protuberances and testing them under different conditions. These bio-inspired feet demonstrate an anisotropic behavior enabling reduction of sinkage depth up to 46.3%, suction force by 47.6%, and energy cost of stepping on mud by up to 70.4%. Finally, we mounted these artificial feet on a Go1 quadruped robot moving in mud and observed 38.7% reduction of the mechanical cost of transport and 55.0% increase of speed. Those results help us understand the physics of mud locomotion of animals and design better robots moving on wet terrains. We did not find any disadvantages of the split-hooves-inspired design on hard ground, which suggests that redesigning the feet of quadruped robots improves their overall versatility and efficiency on natural terrains.
Material Selection and Optimization of Green Drilling of Aluminium Alloys: MCDM-Pareto ANOVA and T-SRC
Authors: Karthikeyan KMB. , N Sam Richard , S Bharath , L Cyril Moses , NK Eshwar 
Published: November 2024

Abstract
The widespread use of aluminium alloys in the automotive, aerospace, and structural sectors necessitates the establishment of exploratory selection criteria to harness optimality at the various stages of product development. This study utilizes multi-criteria decision-making approaches to determine the most suitable aluminium alloy for thermal friction drilling. Additionally, the process optimization was done using the Taguchi super ranking concept (T-SRC), and the confirmation test using explicit dynamics simulation using ANSYS software. The ideal material selection was evaluated based on several factors, including yield strength, tensile strength, thermal conductivity, impact resistance, density, specific heat capacity, coefficient of thermal expansion, and percentage elongation. The best-ranked AA2024-T4 aluminium alloy underwent an integrated Taguchi Design of Experiments and optimization using Pareto ANOVA and subsequently ANSYS Explicit Dynamics process simulation. The MCDM Expert System derived from AHP, and ranking of materials using TOPSIS, EDAS, and VIKOR proved to be an effective material selection strategy from a set of alternatives for the thermal friction drilling process. By combining Multi-Criteria decision making (MCDM) with process simulation, material selection concerns in different industrial processes can be resolved, therefore eliminating the need for laborious tests.
Archaeological site detection on remote-sensed images using U-net
Authors: Gomes Adriano , Pascoal Rodrigo , Canedo Daniel , Neves António J. R. , Georgieva Petia 
Published: November 2024

Abstract
This project aims to develop a model to identify archeological sites (tumulus and hillforts) on LiDAR data through multiclass semantic segmentation. The data used is from the Alto-Minho region in the north of Portugal and was obtained within the scope of the ODYSSEY project. In this work, it was used a U-net model, that achieved a recall and precision of 64% and 94% (class-wise metrics), respectively, regarding the identification of hillforts, and was not able to identify any of the tumulus presented on the data.
Research on Computer Vision Models for Deep Learning in Autonomous Mobile Robots
Authors: Slavov Danail , Slavova Anastasiya , Hristov Vladimir 
Published: November 2024

Abstract
We propose a computer vision model for semantic and instance segmentation, based on YOLOv8 architecture and partial convolution modules. Transfer learning on the Cityscapes dataset is performed and results show comparable to the SOTA model accuracy. Furthermore, a speed comparison in terms of inference time is conducted, the results of which show that the proposed model is faster when processing larger input images using smaller batch sizes. Our research enhances the potentialities for further improvement in application of fast deep learning models in mobile robotics for complex vision tasks assisting their intelligent behaviour. It also suggests opportunities to increase the suitability of the improved models when implemented on low-resource hardware, such as edge devices and single-board computers, which in turn can facilitate integration of intelligent features and into various autonomous mobile robots in tasks such as self-driving, avoiding and detouring obstacles, optimal trajectory/path selection, increasing safety and reliability.
CMC/Gel/GO 3D-printed cardiac patches: GO and CMC improve flexibility and promote H9C2 cell proliferation, while EDC/NHS enhances stability
Authors: Arıcı Şule , Kamali Ali Reza , Ege Duygu 
Published: November 2024

Abstract
In this research, carboxymethyl cellulose (CMC)/gelatin (Gel)/graphene oxide (GO)-based scaffolds were produced by using extrusion-based 3D printing for cardiac tissue regeneration. Rheological studies were conducted to evaluate the printability of CMC/Gel/GO inks, which revealed that CMC increased viscosity and enhanced printability. The 3D-printed cardiac patches were crosslinked with N-(3-dimethylaminopropyl)-n’-ethylcarbodiimide hydrochloride (EDC)/N-hydroxysuccinimide (NHS) (100:20 mM, 50:10 mM, 25:5 mM) and then characterized by mechanical analysis, electrical conductivity testing, contact angle measurements and degradation studies. Subsequently, cell culture studies were conducted to evaluate the viability of H9C2 cardiomyoblast cells by using the Alamar Blue assay and fluorescence imaging. A high concentration of EDC/NHS (100:20 mM) led to the stability of the patches; however, it drastically reduced the flexibility of the scaffolds. Conversely, a concentration of 25:5 mM resulted in flexible but unstable scaffolds in phosphate buffer saline solution. The suitable EDC/NHS concentration was found to be 50:10 mM, as it produced flexible, stable, and stiff cardiac scaffolds with high ultimate tensile strength. Mechanical characterization revealed that % strain at break of C15/G7.5/GO1 exhibited a remarkable increase of 61.03% compared to C15/G7.5 samples. The improvement of flexibility was attributed to the hydrogen bonding between CMC, Gel and GO. The electrical conductivity of 3D printed CMC/Gel/GO cardiac patches was 7.0 × 10−3 S cm−1, demonstrating suitability for mimicking the desired electrical conductivity of human myocardium. The incorporation of 1 wt% of GO and addition of CMC concentration from 7.5 wt% to 15 wt% significantly enhanced relative % cell viability. Overall, although this research is at its infancy, CMC/Gel/GO cardiac patches have potential to improve the physiological function of cardiac tissue.
The anti-fouling activity of solvothermally synthesized Nb2O5-coated Fe ship strip on optimal interactions at the targeted interfaces of barnacle attachments -An Insilico study
Authors: Vanangamudi Aravindhan , Punniyakoti Sathyanarayanan 
Published: November 2024

Abstract
Fouling is a major issue occurring in water-going vessels, such as ships that cause increased surface roughness and drag resistance. The fouling organisms produce extracellular polymeric substances (EPS), which negatively impact water-going vessels. The settlement-inducing protein complex (SIPC) is a contact pheromone that promotes the gregarious settling of barnacle larvae (cyprids). The SIPC can be found in both adult barnacle cuticles and cyprids as transient adhesive secretions (footprints). The presence of SIPC in the footprints plays a critical role during the initial adhesion, which facilitates further settlement. The adsorption of of SIPC on Iron/Fe ship strip(FSS) surface was often found to be irreversible even after physical treatements. For the antifouling studies, Nb2O5 coated FSS were constructed and simulated to analyze the interaction of barnacles Aacp20K protein. For simulation studies, the homology model of barnacles Aacp20K protein is fabricated using the SWISS automated comparative modeling platform. The result of homology model showed a good 3D secondary structure of Aacp20K protein, especially 7q1y template protein. Adsorption location analysis results illustrate that the surface of the FSS coated with Nb2O5 film disfavour the binding of SIPC inhibiting the binding of barnacle cuticles and cyprids. For validating the simulation results, Nb2O5 nanostructure film was synthesized using a solvothermal process and characterized using XRD,SEM and EDS. Furthermore, the wetting behaviour was studied experimentally. The simulations and experimental results indicate Nb2O5-coated FSS as potent anti-fouling surfaces.
Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II
Authors: Mehdi Rahimifar Mohammad , Wingering Quentin , Gouin-Ferland Berthié , Coffee Ryan , Therrien Audrey C 
Published: November 2024

Abstract
New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s−1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.
RecGOBD: accurate recognition of gene ontology related brain development protein functions through multi-feature fusion and attention mechanisms
Authors: Xia Zhiliang , Ma Shiqiang , Li Jiawei , Guo Yan , Jiang Limin , Tang Jijun 
Published: November 2024

Abstract

Motivation
Protein function prediction is crucial in bioinformatics, driven by the growth of protein sequence data from high-throughput technologies. Traditional methods are costly and slow, underscoring the need for computational solutions. While deep learning offers powerful tools, many models lack optimization for brain development datasets, critical for neurodevelopmental disorder research. To address this, we developed RecGOBD (Recognition of Gene Ontology-related Brain Development protein function), a model tailored to predict protein functions essential to brain development.

Result
RecGOBD targets 10 key gene ontology (GO) terms for brain development, embedding protein sequences associated with these terms. Leveraging advanced pre-trained models, it captures both sequence and structure data, aligning them with GO terms through attention mechanisms. The category attention layer enhances prediction accuracy. RecGOBD surpassed five benchmark models in AUROC, AUPR, and Fmax metrics and was further used to predict autism-related protein functions and assess mutation impacts on GO terms. These findings highlight RecGOBD’s potential in advancing protein function prediction for neurodevelopmental disorders.

Availability and implementation
All Python codes associated with this study are available at https://github.com/ZL-Xia/RECGOBD.git.
Assessing variable importance in survival analysis using machine learning
Authors: Wolock C J , Gilbert P B , Simon N , Carone M 
Published: November 2024

SUMMARY
Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction task at hand. For example, in HIV vaccine trials, participant baseline characteristics are used to predict the probability of HIV acquisition over the intended follow-up period, and investigators may wish to understand how much certain types of predictors, such as behavioral factors, contribute toward overall predictiveness. Time-to-event outcomes such as time to HIV acquisition are often subject to right censoring, and existing methods for assessing variable importance are typically not intended to be used in this setting. We describe a broad class of algorithm-agnostic variable importance measures for prediction in the context of survival data. We propose a nonparametric efficient estimation procedure that incorporates flexible learning of nuisance parameters, yields asymptotically valid inference, and enjoys double-robustness. We assess the performance of our proposed procedure via numerical simulations and analyze data from the HVTN 702 vaccine trial to inform enrollment strategies for future HIV vaccine trials.
Dual-energy metal artefact reduction for iodine-125 seed identification in postimplant CT after prostate brachytherapy
Authors: Suzuki Chiaki , Matsubara Kosuke , Ujihara Yuta , Isogai Kenta 
Published: November 2024

Abstract

Objectives
To assess the metal artefact reductions of dual-energy CT high-energy virtual monochromatic images (VMI) combined with the Single-Energy Metal Artifact Reduction (SEMAR) (CANON MEDICAL SYSTEMS, Otawara, Japan) processing techniques for iodine (I)-125 seed identification in postimplant CT after prostate brachytherapy

Methods
Dual-energy acquisition with fast tube voltage switching was performed on a prostate phantom with simulated seeds and six clinical cases treated with I-125 prostate brachytherapy. The images were retrospectively reconstructed at VMI energy levels of 65–200 keV and with and without SEMAR (SEMAR and non-SEMAR images). To estimate seed swelling, the calibre of iodine-125 seed was calculated as the full width at half maximum. The metal artefacts were evaluated using the artefact index (AI). The dose distributions were calculated and were compared among the high-energy VMI (SEMAR and non-SEMAR images) and low-energy VMI (SEMAR images).

Results
The blooming artefacts decreased at higher energy levels. In addition, the SEMAR process markedly reduced AI, which helped reduce overestimation of high dose ranges in the treatment planning dose map.

Conclusions
The locations and number of iodine-125 seed were clearly identified in the dose distribution map of the treatment planning using 200 keV VMI with SEMAR.

Advances in knowledge
The high-energy VMI of the dual-energy CT in combination with SEMAR is appropriate for the postimplant planning process of I-125 prostate brachytherapy.
Multiple delayed-onset metachronous ileal stenoses after transcatheter arterial embolization using N-butyl-2-cyanoacrylate for upper gastrointestinal bleeding: factors of complication and importance of plain CT evaluations
Authors: Sekikawa Zenjiro , Kamide Hiroyuki , Kobayashi Yusuke , Terauchi Miki 
Published: November 2024

Abstract
A 63-year-old man underwent transcatheter arterial embolization (TAE) using a mixture of N-butyl-2-cyanoacrylate (NBCA) and iodized oil to treat acute gastrointestinal (GI) bleeding. The procedure was initially successful; however, the patient developed ileus &gt;1 month later and subsequently underwent several surgeries to treat the multiple metachronous ileal stenoses. The flux of a small amount of off-target glue was the primary cause of these complications. As the patient had few symptoms in the first month post-TAE, however, affirming the diagnosis took time. A detailed review of plain CT scans was a decisive factor in achieving the final diagnosis. This case demonstrates that TAE using an NBCA–iodized oil mixture effectively treats acute GI bleeding. However, a complication such as off-target embolization is likelier to occur because of a combination of certain factors such as vascular anatomy, complexity of the procedure, and NBCA dilution. Close observation using plain CT should be performed for the identification of off-target embolization occurrence even in cases of successful TAE.
dmTGS: Precise Targeted Enrichment Long-Read Sequencing Panel for Tandem Repeat Detection
Authors: Yang Kang , Liu Yue , Zhang Ji , Yu Qian , Xu Feng , Liu Jiyuan , Li Yuting , Zhang Xiaojie , Wang Zhiqiang , Wang Ning , Li Yuezhen , Shi Yan , Chen Wan-Jin 
Published: November 2024

Abstract

Background
Tandem repeats (TRs) are abundant in the human genome and associated with repeat expansion disorders. Our study aimed to develop a tandem repeat panel utilizing targeted long-read sequencing to evaluate known TRs associated with these disorders and assess its clinical utility.

Methods
We developed a targeted long-read sequencing panel for 70 TR loci, termed dynamic mutation third-generation sequencing (dmTGS), using the PacBio Sequel II platform. We tested 108 samples with suspected repeat expansion disorders and compared the results with conventional molecular methods.

Results
For 108 samples, dmTGS achieved an average of 8000 high-fidelity reads per sample, with a mean read length of 4.7 kb and read quality of 99.9%. dmTGS outperformed repeat-primed-PCR and fluorescence amplicon length analysis-PCR in distinguishing expanded from normal alleles and accurately quantifying repeat counts. The method demonstrated high concordance with confirmatory methods (rlinear = 0.991, P &lt; 0.01), and detected mosaicism with sensitivities of 1% for FMR1 CGG premutation and 5% for full mutations. dmTGS successfully identified interruptive motifs in genes that conventional methods had missed. For variable number TRs in the PLIN4 gene, dmTGS identified precise repeat counts and sequence motifs. Screening 57 patients with suspected genetic muscular diseases, dmTGS confirmed repeat expansions in genes such as GIPC1, NOTCH2NLC, NUTM2B-AS1/LOC642361, and DMPK. Additionally, dmTGS detected CCG interruptions in CTG repeats in 8 myotonic dystrophy type 1 patients with detailed characterization.

Conclusions
dmTGS accurately detects repeat sizes and interruption motifs associated with repeat expansion disorders and demonstrates superior performance compared to conventional molecular methods.
Estimating Reference Change Values Using Routine Patient Data: A Novel Pathology Database Approach
Authors: Røys Eirik Åsen , Viste Kristin , Kellmann Ralf , Guldhaug Nora Alicia , Alaour Bashir , Sylte Marit Sverresdotter , Torsvik Janniche , Strand Heidi , Marber Michael , Omland Torbjørn , Theodorsson Elvar , Jones Graham Ross Dallas , Aakre Kristin Moberg 
Published: November 2024

Abstract

Background
The reference change value (RCV) is calculated by combining the within-subject biological variation (CVI) and local analytical variation (CVA). These calculations do not account for the variation seen in preanalytical conditions in routine practice or CVI in patients presenting for treatment. As a result, the RCVs may not reflect routine practice or align with clinicians’ experiences. We propose a novel RCV approach based on routine patient data that is potentially more clinically relevant.

Methods
This study used the refineR algorithm to determine RCVs using serial patient data extracted from a local Laboratory Information System (LIS). The model was applied to biomarkers with a range of result ratio distributions varying from normal to log-normal. Results were compared against conventional formula-based RCVs using CVI estimates from a state-of-the-art biological variation study. Monte Carlo simulations were also used to validate the LIS data approach.

Results
The RCVs estimated from LIS data were: 11-deoxycortisol (men): −70%/+196%, 17-hydroxyprogesterone (men): −49%/+100%, albumin: −10%/+11%, androstenedione (men): −47%/+96%, cortisol (men): −54%/+51%, cortisone (men): −32%/+51%, creatinine: −16%/+14%, phosphate (women): −23%/+29%, phosphate (men): −27%/+29%, testosterone (men): −38%/+60%. The formula-based RCV estimates showed similar but slightly lower results, and the Monte Carlo simulations confirmed the applicability of the new approach.

Conclusions
RCVs may be estimated from patient results without prior assumptions about the shape of the ratios between serial results. Laboratories can determine RCVs based on local practice and population.
Impact of genetic risk information for cardiovascular disease on behavioural changes, psychological responses and risk factor modification: a systematic review
Authors: Chen Ruofei (Trophy) , Pearson Vincent , Suebkinorn Orathai , Bulto Lemma N , Pincha Baduge Mihirika , Anderson Alice , Nelson Adam J , Zoungas Sophia , Clark Robyn A , Nicholls Stephen J 
Published: November 2024

Abstract

Aims
Cardiovascular disease (CVD) remains a significant public health concern, influenced by both genetic susceptibility and lifestyle factors. Integrating genetic risk information into clinical practice shows promise but has yielded mixed results regarding its impact on CVD prevention and management. This systematic review aimed to assess the impact of providing genetic CVD risk information on health behaviours, psychological outcomes, and risk factors.

Methods and results
Following Joanna Briggs Institute methodology and PRISMA 2020 guidelines, four electronic databases and two trial registries were searched for randomized controlled trials evaluating the impact of genetic risk information on the CVD risk profile. Data were synthesized using a narrative synthesis approach. Of the 3596 articles retrieved, 11 studies were eligible. Genetic risk information showed modest improvements in dietary behaviour but had inconclusive effects on physical activity and medication adherence. Minimal changes in psychological outcomes were noted, including a slight decrease in depression. The impact on traditional risk factors, such as systolic blood pressure and total cholesterol, was also limited. Bias across all studies was noted.

Conclusion
Genetic CVD risk information has limited effects on clinical outcomes and psychological factors, despite its potential to encourage some health behaviour changes. These findings suggest that genetic risk information alone may not be sufficient to significantly reduce cardiovascular risk, highlighting the need for further research to better understand its long-term effects.