bluuebunny/crossref_metadata_2025_split huggingface dataset:
An investigation on the tribological behaviour of biomimetic placoid and cycloid textures on AISI316L stainless steel
Authors: Ahsan Midhat , Bashir Masrat , Saleem Sheikh Shahid 
Published: December 2024

Abstract
Surface texturing is considered as one of the best techniques to improve material performance and characteristics related to its ability to govern exterior properties in specific applications. Contributing to the increase in demand for materials for a variety of use, surface texturing has garnered much attention in recent decades. The objective of this research was to investigate the friction and wear behavior of biomimetic shark-skin (placoid) S1P, trout-skin (cycloid) S2C textures on AISI316L stainless steel surface and untextured surface S3S against commercial AISI52100 steel balls under dry sliding conditions. Nd:YAG laser texturing system was used to create micro-grooves similar to the bionic placoid and cycloid fish scales on AISI316L stainless steel surfaces. To determine their characteristics, the samples were examined using a 3D profilometer with AFM, FESEM, EDS, and XRD. Reciprocating ball on disc tribometer (Rtec-MFT-5000) was used to evaluate the sample’s friction and wear performance. Tribo-tests were performed at two different loads and constant sliding speeds to observe the effect of texturing on the tribological properties of 316L stainless steel. Laser Surface Texturing (LST) was found to be effective in controlling the coefficient of friction and wear volume loss of materials. Sample S2C showed a relatively lower coefficient of friction and wear rate under dry sliding conditions. This paper used the analytical method to compare the tribological behavior of biomimetic placoid and cycloid textures on AISI316L Stainless Steel, and the results revealed that laser surface texturing can be an effective technique to enhance the tribological properties of friction material.
Laryngeal cancer diagnosis based on improved YOLOv8 algorithm
Authors: Nie Xin , Zhang Xueyan , Wang Di , Liu Yuankun , Xing Lumin , Liu Wenjian 
Published: December 2024

Abstract
Laryngeal cancer is the most common malignant tumor in the head and neck region. The larynx, also known as the voice box, plays a crucial role in voice production and ventilation. Enhancing the diagnosis and treatment of laryngeal cancer can significantly improve patients’ prognosis and quality of life. Artificial intelligence (AI) technology shows promise as a valuable tool for diagnosing laryngeal cancer. It not only reduces the burden on endoscopists in interpreting images but also performs screening and diagnosis efficiently and accurately. However, due to the hidden and diverse nature of laryngeal cancer lesions, achieving accuracy and efficiency in AI-based diagnosis presents poses challenges. This study introduces an improved YOLOv8 algorithm named MSEC-YOLO, specifically designed for the detection and classification tasks of laryngeal cancer in endoscopic images. A novel multiscale enhanced convolution module has been introduced to improve the model’s feature extraction capabilities for small-sized targets. Additionally, a tiny fully convolutional network architecture has been employed, reducing the number of model parameters and computational costs while maintaining or enhancing performance, which is crucial for real-time medical imaging analysis. The experiments utilized a real-world endoscopic image dataset from the hospital, and the results indicated that MSEC-YOLO outperformed the original YOLOv8 model and its multi-kernel versions across multiple evaluation metrics, especially in critical categories such as malignant tumors, polyps, and papillomas, demonstrating extremely high precision and recall rates.
Changing mindsets about methotrexate in the rheumatology clinic to reduce side effects and improve adherence: a randomized controlled trial
Authors: Yielder Rachael , Leibowitz Kari , Crum Alia J , Manley Paul , Dalbeth Nicola , Petrie Keith J 
Published: December 2024

Abstract

Background
Patients’ negative expectations about medication can exacerbate side effect burden leading to low adherence and persistence. A novel intervention involves targeting mindsets about non-severe symptoms; reframing them as encouraging signs of medication working.

Purpose
This study aimed to assess whether a brief symptom-mindset intervention can improve symptom experience and adherence in patients starting methotrexate to treat an inflammatory rheumatic disease.

Methods
A randomized controlled trial was conducted with patients starting methotrexate. Participants were randomly assigned (1:1) to a mindset intervention or standard information control condition. Symptom mindset was assessed after 4 weeks to check intervention efficacy. The primary outcome was symptom experience after 4 weeks. Secondary outcomes were adherence and motivation to take methotrexate (4 weeks), as well as continuation and C-reactive protein (12 weeks).

Results
Forty-seven participants were randomly assigned to the intervention (n = 24) or control group (n = 23). All participants completed the study. After 4 weeks, compared to the control group, intervention participants endorsed more positive symptom mindsets, experienced less symptom burden (mean difference −2.70 [95% CI, −4.50, −0.90] P = .005), fewer general symptoms (3.53 [−6.99, .79] P = .045) and a similar number of methotrexate-specific symptoms (−0.79 [−2.29, 0.71] P = .295). The intervention group had better motivation and adherence to methotrexate at 4 weeks and better continuation, and C-reactive protein at 12 weeks than the control group. There was no difference in side effect attribution.

Conclusions
In patients starting methotrexate, a mindset intervention reframing the role of non-severe side effects is a promising approach for improving symptom experience and early stage medication persistence.
Histopathological Analysis of Abdominal Skin and Fat After In Vivo Hyperthermia-Induced Lipolysis With a 1064 nm Diode Laser, Radiofrequency, and Electromagnetic Field: A Pilot Study
Authors: Nadarajan Vidhya , Panton Jasmine , Vingan Nicole , Walmsley Christine , Barillas Jennifer , Lazzarini Ariane , Culver Abby , Kenkel Jeffrey , Akgul Yucel 
Published: December 2024

Abstract

Background
Hyperthermic laser lipolysis safely reduces unwanted fat through controlled thermal injury of adipocytes.

Objectives
To assess the effects of 1064 nm laser, single vs multiple treatments, with and without radiofrequency (RF) and pulsed electromagnetic field (PEMF) energy on abdominal tissue.

Methods
Nine volunteers scheduled for elective abdominoplasty were divided into 1 of 3 arms: (1) laser alone, (2) laser with and without RF and PEMF, and (3) multiple vs single laser treatments. Untreated (control) and treated tissues were collected at various time points following treatment. Paraffin sections from surgically excised pannus were evaluated with Masson's trichrome, apoptosis (TUNEL), Collagen 1, Collagen 3, Elastin, and CD68 macrophage markers.

Results
All study arms showed subdermal adipose tissue lysis without compromising epidermal or dermal integrity. Apoptotic adipocytes and macrophage infiltration were present in areas of structural damage. Adjuvant RF and PEMF showed increased macrophage infiltration after 14 days. Multiple 1064 nm treatments induced apoptosis in subcutaneous adipocytes. There were no significant changes in dermal Collagen 1, Collagen 3, and elastin abundance.

Conclusions
The 1064 nm diode laser can destroy adipocytes without harming overlying epidermis. RF enhances lipolytic effects of the laser with increased inflammation and tissue remodeling, whereas PEMF therapy uses electromagnetic fields to stimulate cell function and tissue repair. These findings suggest the 1064 nm diode laser can achieve intended effects.

Level of Evidence: 3 (Therapeutic)
Exploring Available Plastic Surgery Reward Programs and Proposing a Modeled Approach
Authors: Galbraith Logan G , Najafali Daniel , Greco Gregory A , Mehrzad Raman 
Published: December 2024

Abstract

Background
There is a paucity of literature exploring private practice reward programs within plastic and aesthetic surgery. Herein, we explore private practice websites and identify universal commercial programs and practice specific programs being adopted.

Objectives
This study aims to evaluate the current landscape of private practice reward programs by examining their advantages and limitations. Additionally, the analysis proposes an “ideal” loyalty program that could enhance patient engagement, satisfaction, and practice growth.

Methods
A review was performed to identify available rewards programs and models. Private practice websites were examined for available rewards models. Commercial loyalty programs were cross referenced to determine practices leveraging them. Data collected included private practice reward program type, region, and total surgeons at the practice. Advantages and areas of improvement were analyzed of existing programs and discussions with private practice surgeons generated proposals of an ideal loyalty program model.

Results
There were no articles on plastic surgery rewards programs. Private practice websites suggested three universal reward systems including Allē, Aspire, and Evolus. A total of four private practices with custom reward programs were identified.

Conclusions
Universal reward systems are point-based and often lack excitement for the consumer. These benefits accumulate points across different providers, but often lack practice specific reward opportunities. While beneficial, most commercially available loyalty programs do not integrate into electronic medical records (EMRs) to track usage and challenges exist in tracking patient utilization. The ideal platform harmonizes the advantages of commercial loyalty programs with tailored practice-specific rewards while having the capacity to integrate with the EMR and capture patient utilization.
STLBRF: an improved random forest algorithm based on standardized-threshold for feature screening of gene expression data
Authors: Feng Huini , Ju Ying , Yin Xiaofeng , Qiu Wenshi , Zhang Xu 
Published: December 2024

Abstract
When the traditional random forest (RF) algorithm is used to select feature elements in biostatistical data, a large amount of noise data and parameters can affect the importance of the selected feature elements, making the control of feature selection difficult. Therefore, it is a challenge for the traditional RF algorithm to preserve the accuracy of algorithm results in the presence of noise data. Generally, directly removing noise data can result in significant bias in the results. In this study, we develop a new algorithm, standardized threshold, and loops based random forest (STLBRF), and apply it to the field of gene expression data for feature gene selection. This algorithm, based on the traditional RF algorithm, combines backward elimination and K-fold cross-validation to construct a cyclic system and set a standardized threshold: error increment. The algorithm overcomes the shortcomings of existing gene selection methods. We compare ridge regression, lasso regression, elastic net regression, the traditional RF algorithm, and our improved RF algorithm using three real gene expression datasets and conducting a quantitative analysis. To ensure the reliability of the results, we validate the effectiveness of the genes selected by these methods using the Random Forest classifier. The results indicate that, compared to other methods, the STLBRF algorithm achieves not only higher effectiveness in feature gene selection but also better control over the number of selected genes. Our method offers reliable technical support for feature expression analysis and research on biomarker selection.
stHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial regularization
Authors: Wang Runqing , Dai Qiguo , Duan Xiaodong , Zou Quan 
Published: December 2024

Abstract
Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular spatial information, greatly enhancing our understanding of cellular interactions within tissues. Accurate identification of spatial domains is crucial for comprehending tissue organization. However, the effective integration of spatial location and gene expression still faces significant challenges. To address this challenge, we propose a novel self-supervised graph representation learning framework named stHGC for identifying spatial domains. Firstly, a hybrid neighbor graph is constructed by integrating different similarity metrics to represent spatial proximity and high-dimensional gene expression features. Secondly, a self-supervised graph representation learning framework is introduced to learn the representation of spots in ST data. Within this framework, the graph attention mechanism is utilized to characterize relationships between adjacent spots, and the self-supervised method ensures distinct representations for non-neighboring spots. Lastly, a spatial regularization constraint is employed to enable the model to retain the structural information of spatial neighbors. Experimental results demonstrate that stHGC outperforms state-of-the-art methods in identifying spatial domains across ST datasets with different resolutions. Furthermore, stHGC has been proven to be beneficial for downstream tasks such as denoising and trajectory inference, showcasing its scalability in handling ST data.
Genetics and resource availability shape divergence in life history and behaviour between locally adapted populations of Atlantic mollies (Poecilia mexicana, Poeciliidae)
Authors: Coffin John L , Williams Bethany L , Tobler Michael 
Published: December 2024

Abstract
Phenotypic variation is common along environmental gradients, but it is often not known to what extent it results from genetic differentiation between populations or phenotypic plasticity. We studied populations of a livebearing fish that have colonized streams rich in toxic hydrogen sulphide (H2S). There is strong phenotypic differentiation between adjacent sulphidic and non-sulphidic populations. In this study, we varied food availability to pregnant mothers from different populations to induce maternal effects, a form of plasticity, and repeatedly measured life-history and behavioural traits throughout the ontogeny of the offspring. Genetic differentiation affected most of the traits we measured, in that sulphidic offspring tended to be born larger, mature later, have lower burst swimming performance, be more exploratory, and feed less effectively. In contrast, maternal effects impacted few traits and at a smaller magnitude, although offspring from poorly provisioned mothers tended to be born larger and be more exploratory. Population differences and maternal effects (when both were present) acted additively, and there was no evidence for population differences in plasticity. Overall, our study suggests that phenotypic divergence between these populations in nature is caused primarily by genetic differentiation and that plasticity mediated by maternal effects accentuates but does not cause differences between populations.
Consistency of common spatial estimators under spatial confounding
Authors: Gilbert Brian , Ogburn Elizabeth L , Datta Abhirup 
Published: December 2024

Abstract
This paper addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially-structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel main result on the in-fill consistency of the generalized least squares estimator using a working covariance matrix from a Matérn or square exponential kernel, in the presence of spatial confounding. The result holds under very mild assumptions, accommodating any exposure with some nonspatial variation, any spatially continuous fixed confounder function, and non-Gaussian errors in both the exposure and outcome. Finally, we prove that spatial estimators from generalized least squares, Gaussian process regression, and spline models that are consistent under confounding by a fixed function will also be consistent under endogeneity or confounding by a random function, ie, a stochastic process. We conclude that, contrary to claims in some literature on spatial confounding, traditional spatial estimators are capable of estimating linear exposure effects under spatial confounding as long as there is some noise in the exposure. We support our theoretical arguments with simulation studies.
Evaluation of a surrogate virus neutralization assay for detecting neutralizing antibodies against SARS-CoV-2 in an African population
Authors: Nkinda Lilian , Shayo Victoria , Masoud Salim , Barabona Godfrey , Ngare Isaac , Kunambi Ponsian P , Nkuwi Emmanuel , Kamori Doreen , Msafiri Frank , Osati Elisha , Hassan Frank Eric , Kisuse Juma , Kidenya Benson , Mfinanga Sayoki , Senkoro Mbazi , Ueno Takamasa , Lyamuya Eligius , Balandya Emmanuel 
Published: December 2024

Abstract
The global resurgence of coronaviruses and the move to incorporate COVID-19 vaccines into the expanded program for immunization have warranted for a high-throughput and low-cost assay to measure and quantify mounted neutralizing antibodies as an indicator for protection against SARS-CoV-2. Hence, we evaluated the surrogate-virus-neutralization-assay (sVNT) as an alternative assay to the pseudo-virus neutralization assay (pVNT). The sVNT was used to measure neutralizing antibodies among 119 infected and/or vaccinated blood samples, against wild-type SARS-CoV-2 (WT) and the Omicron-variant with reference to the pVNT. Four different cut-offs were assessed for suitability in distinguishing neutralizers: the manufacturer (&gt;30%), literature-based (&gt;50%) and (&gt;80%), and population-based (&gt;27.69%). The obtained data was analyzed using “R” through its integrated development environments; JAMOV and R-Studio. Using the WT strain, only the population-based cut-off was able to differentiate neutralizers from non-neutralizers beyond chance, with an area under the curve (AUC) of 0.833 (95%CI, 0.505–1.0; P = .049). Applying the population-based cut-off, improved the sensitivity to 100% from 91.4% obtained from the manufacturer cut-off (P = .002). However, the specificity remained low (67%). The negative-predictive-value also improved to 100% vs 16.4% (P = .006), but there was no difference in the positive-predictive-value (99.1% vs 99.1%) (P = .340). When we used the Omicron-variant, the sVNT titers were not able to predict the neutralizers and non-neutralizers with reference to pVNT (AUC of 0.649) (P = .221). The sVNT assay is a potential alternative for screening individuals harboring potent neutralizing antibody with high sensitivity, although we recommend continuous improvement of the assay in line with the viral mutations. Further, we recommend that individual users establish a population-based cut-off while using the sVNT assay.
Vulnerability, Immigration Detention and (Carceral) Reform
Authors: Turnbull Sarah 
Published: December 2024

Abstract
This article examines the British government’s recent concern around ‘vulnerable detainees’ and reforms to immigration detention, looking specifically at the evolving ‘adults at risk in immigration detention’ policy. It critically explores the notion of ‘vulnerability’ and the production of the ‘vulnerable detainee’ as a certain kind of carceral subject who is constituted as less deserving of detention. The article considers what the lessons learned from histories of carceral reform can offer the current immigration detention reform movement. By unpacking the notion of the ‘vulnerable detainee’, it encourages critical reflection on the potential limitations of reform efforts and how seemingly good intentions can be co-opted by the state to further solidify the power to deprive non-citizens of their liberty.
Characteristics maximization through halogenation in hexa-peri-hexabenzocoronene utilizing quantum inspired technology
Authors: Hashiguchi Kazuhiro , Maruo Akito , Soeda Takeshi , Iwane Shinji , Jippo Hideyuki 
Published: December 2024

Abstract
This article demonstrates a high-speed search for polycyclic aromatic hydrocarbons with desirable properties, combining Fujitsu's Quantum-inspired Computing Digital Annealer (DA) with density functional theory (DFT) calculations. The target compound is a nanographene based on hexa-peri-hexabenzocoronene (HBC), known for its wide π-conjugated plane, enabling high electron conductivity and molecular alignment. We optimized halogen substituents (F, Cl) in HBC to maximize dipole moment and minimize lowest unoccupied molecular orbital (LUMO), calculated by DFT. A bit representation was used for 18 hydrogen atom substitutions. Factorization Machines (FM) were employed for formulation, and optimization was performed using DA (FM-DA). However, its dependence on initial data led to the development of FM-DA&GA, incorporating Genetic Algorithm (GA) for parallel recommendations. FM-DA&GA effectively searched through 236 (approximately 68.7 billion) bit combinations, finding the optimal solution. These bit combinations correspond to 318 (approximately 387 million) chemical structures, if chemical symmetry is disregarded. The combined approach of DA's fast search on the FM model and GA's global search prevented local solution traps, enabling a more efficient and comprehensive exploration of the solution space. This synergistic effect represents a powerful tool for efficient material discovery, particularly in overcoming the limitations of traditional optimization methods.
Complete suppression of fluorescence from a 9,10-bis(phenylethynyl)anthracene-based ring upon [2]catenane formation
Authors: Thuluvanchery Fazil Salim , Tamaoki Nobuyuki , Sagara Yoshimitsu 
Published: December 2024

Abstract
We report the synthesis and characterization of a [2]catenane consisting of one fluorescent ring incorporating a 9,10-bis(phenylethynyl)anthracene and the other ring containing 2 naphthalene diimides as quenchers. The catenane was constructed via a modified Huisgen 1,3-dipolar cycloaddition between terminal alkyne and azide groups, introduced into precursors of the quencher ring in the presence of the fluorescent ring. In chloroform, the absorption spectra of the catenane showed a broad band between 500 and 700 nm, attributed to charge-transfer interactions between the fluorophore and quenchers. Titration experiments using a quencher ring and linear model compounds of the fluorescent ring suggested that the fluorophore in [2]catenane is sandwiched between the 2 quenchers. This configuration led to substantial shifts in the ¹H NMR signals of the fluorophore, compared to the free 9,10-bis(phenylethynyl)anthracene of the ring before catenane formation. While the fluorescent ring exhibited a high fluorescence quantum yield (Φ = 0.91), this was completely quenched upon catenane formation (Φ &lt; 0.01). This pronounced fluorescence quenching makes the system a promising candidate for the development of supramolecular mechanophores capable of on/off fluorescence switching in response to applied force.