bluuebunny/crossref_metadata_2025 huggingface dataset:
The Early Study for Nature-Inspired Algorithms in Geophysical Modeling Based on Gravity Method: Prototype Bat Algorithm in 3D Gravity Inversion
Authors: Prasetyo Nugroho , Alawiyah Susanti , Ufaira Aprina Patria , Gunawan Indra
Published: December 2024

The physical properties of the subsurface are a crucial consideration in various geoscientific analyses. Geophysical methods offer insights into the distribution of subsurface physical properties, with varying approximations based on these properties. One approach to developing a subsurface model is inverse modeling, a technique that converts data into models. However, new approaches are needed for gravity inversion methods to obtain models with reduced ambiguity. Stochastic inversion techniques are commonly used to avoid local optima that can occur in gravity data modeling due to the underdetermined nature of the model parameters. Nonetheless, a key constraint of this approach is selecting a range of model parameters that accurately represent the geological conditions. To address this issue, the Bat Algorithm is applied as a meta-heuristic method to stochastic inversion techniques. This method emulates the behavior of microbats in randomly searching for prey in the model space. Through simultaneous communication, all microbats aim to locate the best solution within a global search topology, thereby avoiding the entrapment of local optima. This study aims to develop a novel approach to 3D modeling of gravity data through inversion methods. A 3D grid of stacked rectangular prisms serves as the density model, with perturbation achieved through manipulation of the 3D array index as a density grid. Synthetic testing demonstrates a success rate of over 85% for single and multi-layer array models with a density body in the center of the mesh grid. The random distribution of density originates from the bat’s propagation of the Bat Algorithm in finding the best model. The suggested technique displays potential for expansion towards the modeling of gravity data with greater complexity, including its practical application in field data analyses.
Spreadsheets for Geothermal Resource Classification Based on United Nation Framework Classification (UNFC) and Jailolo Geothermal Field Case Study
Authors: Pratama A B , Pratama H B , Ma’fu A A
Published: December 2024

The United Nations Framework Classification (UNFC) is a resource classification method developed by the United Nations Economic Commission for Europe (UNECE) in collaboration with the International Geothermal Association (IGA). UNFC assesses and classifies geothermal resources based on three aspects: socio-economic factors, project feasibility, and geological knowledge. This uniformity in resource classification, assessment, and comparison is significant for global communication regarding geothermal resources. Implementing UNFC in Indonesia will impact the development and financing of this renewable energy. This paper presents the development of a Microsoft Excel spreadsheet for characterizing geothermal fields in Indonesia using the UNFC classification standard, applied to a specific geothermal field. The spreadsheet categorizes socio-economic factors into axis-E, project feasibility into axis-F, and geological knowledge into axis-G. Case studies were conducted in the Jailolo Geothermal Field, located in Halmahera Regency, North-Maluku Province. The field is at an early stage of development and is included in the government’s drilling program agenda. However, the feasibility of extraction by a defined development project cannot be evaluated due to limited technical data, although site-specific geological studies and exploration activities have identified the potential for an individual deposit with sufficient confidence to warrant drilling or testing. Based on geological studies, the estimated quantities associated with the potential deposit are a low-confidence estimate of 75 MW. The field is categorized as E3.2 for socio-economic aspects, F3.1 for feasibility of extraction based on maturity studies, and G4 for geological studies.
Correlation between N-SPT value and shear wave velocity for clayey soil
Authors: Nurcahyo W D , Prassetyo S H , Simangunsong G M
Published: December 2024

Shear wave velocity (Vs) is an important parameter for tunnel design to evaluate seismic site classification and earthquake hazard at a site. Unfortunately, determining in situ shear wave velocity for large areas is expensive and takes a long time. To overcome these problems, a correlation between the standard penetration test (N-SPT) and Vs is made in order to facilitate predicting Vs from the N-SPT value. N-SPT data were obtained from a total of 41 boreholes ranging from 40 to 85 m in depth. Shear wave velocity was obtained from seismic downhole tests and seismic refraction. The correlation between N-SPT and Vs in this study has a coefficient of regression (R
2) value of 0.901. The correlation in this study is validated using normal consistency ratio, comparison between Vs predicted and Vs measured and a scaled error percent graph. The correlation in this study can be used as a predictor to estimate shear wave velocity for initial design before a survey is conducted.
Effect of core material on random vibration behaviour of foam-based laminated sandwich panels using a modified layerwise theory
Authors: Momeni Saman , Zabihollah Abolghassem
Published: December 2024

This study investigates the vibration response of foam-based laminated sandwich panels (FLSP) under random loading. A modified layerwise displacement theory is employed to account for the varying material properties of the layers throughout the thickness. The dynamic behavior of the FLSP is analyzed using random input excitation, focusing on the impact of different foam materials on the random vibration characteristics. Additionally, the effects of the thickness of both composite and foam sections are examined. Key random parameters, including mean, standard deviation, variance, and autocorrelation, are determined for various core materials. A proof-of-concept experimental setup was developed to validate the proposed modified layerwise theory, demonstrating its effectiveness in random vibration analysis of FLSPs. The results indicate that as the softness of the core material increases, the natural frequency of the FLSP decreases. Conversely, higher core stiffness correlates with increased response parameters in random vibration, including mean value, standard deviation, and variance. Furthermore, FLSPs with stiffer core materials exhibit a higher signal correlation compared to those with softer cores.
Does administrative divisions adjustment affect polluting industries dependence? Evidence from township-town merge in China
Authors: Zheng Xiaodan , Li Shangpu , Li Junwen , Sun Yihong
Published: December 2024

Land distribution is intricately linked to economic quality and sustainable growth, influenced by administrative division adjustment (ADA), which can be categorized into various forms, with the township–town merge (TTM) reform being the most common. This study evaluates the effect of TTM on local governments’ dependence on polluting industries from a fiscal decentralization angle, using the differences-in-differences model with transaction data from China’s land market. Findings reveal that TTM increases local governments’ dependence on polluting industries, especially in districts/counties with weaker economic conditions before the COVID-19 pandemic. Evidence also highlights a notable boundary effect of TTM, though its impact is minimal in districts/counties at provincial borders due to their potentially lower public investment inclination. TTM also increases the decentralization level of districts/counties, thus explaining the increase in local government’s dependence on polluting industries. The empirical results have confirmed such a mechanism. However, top-down supervision limits the effect of the reform to a certain extent. This conclusion offers fresh policy insights for leveraging land supply mechanisms to advance environmental governance in centralized developing nations through the lens of ADA.
Optimizing differential privacy in a federated learning framework: strategies for dynamic clipping and privacy allocation
Authors: Liang Zhaoxian , Chen Yonghong
Published: December 2024

Differential privacy has garnered significant attention for its ability to protect the privacy of parameters uploaded in federated learning. Two key factors in differential privacy, threshold clipping and the privacy budget, determine the amount of noise added to the data to achieve privacy protection. However, traditional differential privacy approaches do not account for the unique dynamic characteristics of the federated learning process, often resulting in trade-offs between privacy protection and model performance. To address this limitation, this study proposes a federated learning framework that combines dynamic threshold clipping and adaptive privacy budgeting. By adjusting these parameters in response to the changes during training, our method applies privacy protection more precisely without compromising model performance. The framework effectively balances privacy preservation and model accuracy by dynamically adapting to the training process and data distribution. Experimental results demonstrate that our approach significantly outperforms traditional differential privacy-based federated learning algorithms.
Concrete sleeper crack detection based on image saliency
Authors: Wu Qingjin , An Xiaoxue , Li Liming
Published: December 2024

Concrete sleepers are crucial infrastructure in railway construction and operation. Sleeper cracks may cause the structural damage or functional failure of sleepers, endangering railway operation safety. A third-order residual crack saliency detection network (R3Net-CSD) model is constructed based on ConvNeXt for detecting concrete sleeper cracks in track inspection images. This model can accurately extract the saliency features of rail sleeper crack images. It first uses ConvNeXt to form the backbone network and a block module to form the first residual, followed by a spatial pyramid (dilated convolution group-based spatial pyramid) module based on the anti-residual structure and the dilated convolution group to form the second residual. Finally, it uses a saliency residual (significance residual) module to form the third residual. Simultaneously, combined with a multilevel joint loss function supervision strategy for supervised learning, the R3Net-CSD model fully utilizes the low-level edge information and high-level semantic information of cracks. By using the images collected by a hand-pushed rail inspection vehicle, a dataset of rail sleeper cracks is manually constructed and the developed model is experimentally validated. A large number of experiments demonstrate that the R3Net-CSD model achieves the best results in multiple indicators, including an average absolute error (mean absolute error) of 0.0082, an average accuracy (average precision) of 0.9764, and an area under the curve value of 0.9995. By combining the calculation method of crack geometric parameters, the quantitative indicators of crack length and width are obtained, with a detection accuracy of 0.1 mm. This research provides a new solution for crack detection in concrete sleepers and the measurement of geometric parameters.
Adhesive thickness effect upon the CFRP plate-concrete interface bond behaviours
Authors: Xiong Huixia , Zhang Qian , Guo Shihui
Published: December 2024

Bonding behavior at the interface between concrete and carbon fiber reinforced polymer (CFRP) is very important for the FRP efficiency in concrete structure strengthening. Influences of the adhesive thickness upon BP is rarely studied in the previous literature. In this paper, based upon testing data of 8 single shear specimens, a finite element (FE) method is investigated to analyze debonding in CFRP-concrete interface. By analyzing the specimen failure mode, the FRP strain distributions along bond length, and ultimate bearing capacity (UBC), influences of adhesive thickness on bonding behavior were investigated. Our data prove that the adhesive thickness has significant effects upon bonding behaviors between concrete and CFRP interface. With an increment of adhesive thickness, bond strength increases and reaches the maximum at 3 mm. If the adhesive thickness is > 3 mm, bond strength of the low-grade concrete specimen is reduced. However, the high-grade concrete specimen bond strength continues to increase, but the rate of increase decreases.
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

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

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

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

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

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

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

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

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

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

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 (>30%), literature-based (>50%) and (>80%), and population-based (>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

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

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

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 < 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.
The current landscape of optogenetics for the enhancement of adoptive T-cell therapy
Authors: Smith George R , Lee Max P , Jennings Emma K , James John R
Published: December 2024

Immunotherapy, the medicinal modulation of a host’s immune response to better combat a pathogen or disease, has transformed cancer treatments in recent decades. T-cells, an important component of the adaptive immune system, are further paramount for therapy success. Recent immunotherapeutic modalities have therefore more frequently targeted T-cells for cancer treatments and other pathologies and are termed adoptive T-cell (ATC) therapies. ATC therapies characterize various types of immunotherapies but predominantly fall into three established techniques: tumour-infiltrating lymphocyte, chimeric antigen receptor T-cell, and engineered T-cell receptor therapies. Despite promising clinical results, all ATC therapy types fall short in providing long-term sustained tumour clearance while being particularly ineffective against solid tumours, with substantial developments aiming to understand and prevent the typical drawbacks of ATC therapy. Optogenetics is a relatively recent development, incorporating light-sensitive protein domains into cells or tissues of interest to optically tune specific biological processes. Optogenetic manipulation of immunological functions is rapidly becoming an investigative tool in immunology, with light-sensitive systems now being used to optimize many cellular therapeutic modalities and ATC therapies. This review focuses on how optogenetic approaches are currently utilized to improve ATC therapy in clinical settings by deepening our understanding of the molecular rationale behind therapy success. Moreover, this review further critiques current immuno-optogenetic systems and speculates on the expansion of recent developments, enhancing current ATC-based therapeutic modalities to pave the way for clinical progress.
Estimated total amyloid burden from 18F-florbetaben positron emission tomography predicts all-cause mortality in light-chain cardiac amyloidosis
Authors: Vergaro Giuseppe , Aimo Alberto , Genovesi Dario , Soares Bezerra Lucas , Castiglione Vincenzo , Fabiani Iacopo , Barison Andrea , Panichella Giorgia , Del Giudice Maria Livia , Camerini Lara , Dugo Giovanni , Chubuchna Olena , Giorgetti Assuero , Buda Gabriele , Emdin Michele
Published: December 2024

Aims

The positron emission tomography (PET) tracer 18F-florbetaben is a promising diagnostic tool for light-chain cardiac amyloidosis (AL-CA). A greater cardiac uptake might signal more amyloid burden and a worse outcome. We aimed to assess the prognostic significance of 18F-florbetaben uptake in AL-CA.

Methods and results

Consecutive patients with AL-CA underwent 18F-florbetaben PET scans. Total amyloid burden (TAB; calculated as mean standardized uptake value multiplied by molecular volume) was assessed in the left and right ventricles (LV/RV) in early (5–15′) and late (50–60′) acquisitions. The endpoint was all-cause mortality. Forty patients (median age 69 years, 73% males, Mayo 2004 Stage III in 80%) underwent 18F-florbetaben PET with a median time from tissue biopsy of 21 days (interquartile range, IQR 7–83). Late LV TAB, but not early LV TAB, correlated with N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity (hs)-troponin T. Over 13 months after the PET scan (IQR 5–21), 65% of patients died. A late LV TAB ≥273 cm3 (cut-off derived from spline curve analysis) predicted 18 and 24 month all-cause mortality independently from baseline variables, including NT-proBNP, hs-troponin T, and Mayo 2004 stage. Late RV TAB ≥135 cm3 independently predicted 18 and 24 month all-cause mortality. Patients with both late LV and RV TAB ≥ cut-offs had a shorter survival than those with only LV TAB ≥ cut-off and those with TAB in both ventricles < cut-offs (Log-rank 16.52, P < 0.001).

Conclusion

18F-florbetaben PET imaging offers valuable prognostic information in AL-CA. Values of late TAB measured in the LV and RV are strong predictors of all-cause mortality.
Transcatheter aortic valve replacement for aortic regurgitation following valve sparing root replacement: a case series
Authors: Sharma Harish , Mechery Anthony , Lawton Ewa , Nadir M Adnan , Doshi Sagar N
Published: December 2024

Background

Valve sparing aortic root replacement (VSARR) is a treatment for aortic root dilatation and aortic regurgitation (AR), which preserves the aortic valve. However, AR may recur, and redo surgery often carries high risk. Transcatheter aortic valve replacement (TAVR) can be performed but there is a paucity of literature to guide procedural planning.

Case summary

Two cases are presented herein with recurrence of severe AR following VSARR (David procedure). In both cases, computed tomography (CT) scans demonstrated absence of calcium and a narrow sinus of valsalva. Both cases were considered at prohibitive risk for redo surgery and were successfully treated with TAVR using balloon-expandable valves. The valves were sized based on CT (aiming for moderate oversizing of 10%–11%) and by assessing the anchoring and waist of sizing balloons. Post-procedure aortography and echocardiography revealed no transvalvular or paravalvular regurgitation. Both patients were successfully discharged with follow-up CT scans also showing no migration and preservation of coronary access, together with suitability of future redo TAVR if required.

Discussion

TAVR can be successfully performed after VSARR surgery with a balloon-expandable valve in the absence of aortic valve calcification. Moderate THV oversizing (10%–11%) appears safe and effective. Careful assessment with a sizing balloon is recommended to ensure adequate anchoring, without the need for aggressive oversizing which risks rupture at the graft suture line.
Hypoglycaemia following physical exercise in a patient with novel SLC16A1 variant
Authors: Frampton Ruth , Lewis David , Rahman Yusof , Tchan Michel , Greenfield Jerry R
Published: December 2024

Rare defects in the promoter region of SLC16A1, the gene encoding monocarboxylate transporter 1 (MCT-1), result in exercise-induced hyperinsulinism. In this disorder, inappropriate insulin secretion is triggered by anaerobic exercise with consequent hypoglycaemia. We describe the case of a 41-year-old man presenting with a generalised tonic–clonic seizure and severe hypoglycaemia following strenuous exercise. A subsequent prolonged fast with incorporation of exercise into the protocol demonstrated hyperinsulinaemic hypoglycaemia; genetic testing revealed a variant of unknown significance in the SLC16A1 gene. Administration of subcutaneous octreotide resulted in dose-dependent reduction in hyperinsulinaemia and hypoglycaemia.
Can sexual conflict drive transitions to asexuality? Female resistance to fertilization in a facultatively parthenogenetic insect
Authors: Wilner Daniela , Boldbaatar Jigmidmaa , Miller Soleille M , Burke Nathan W , Bonduriansky Russell
Published: December 2024

Facultatively parthenogenetic animals could help reveal the role of sexual conflict in the evolution of sex. Although each female can reproduce both sexually (producing sons and daughters from fertilized eggs) and asexually (typically producing only daughters from unfertilized eggs), these animals often form distinct sexual and asexual populations. We hypothesized that asexual populations are maintained through female resistance as well as the decay of male traits. We tested this via experimental crosses between individuals descended from multiple natural sexual and asexual populations of the facultatively parthenogenic stick insect Megacrania batesii. We found that male-paired females descended from asexual populations produced strongly female-biased offspring sex ratios resulting from reduced fertilization rates. This effect was not driven by incompatibility between diverged genotypes but, rather, by both genotypic and maternal effects on fertilization rate. Furthermore, when females from asexual populations mated and produced sons, those sons had poor fertilization success when paired with resistant females, consistent with male trait decay. Our results suggest that resistance to fertilization resulting from both maternal and genotypic effects, along with male sexual trait decay, can hinder the invasion of asexual populations by males. Sexual conflict could thus play a role in the establishment and maintenance of asexual populations.
CAT-Posterior Mean Site Frequencies Improves Phylogenetic Modeling Under Maximum Likelihood and Resolves Tardigrada as the Sister of Arthropoda Plus Onychophora
Authors: Giacomelli Mattia , Vecchi Matteo , Guidetti Roberto , Rebecchi Lorena , Donoghue Philip C J , Lozano-Fernandez Jesus , Pisani Davide
Published: December 2024

Tardigrada, the water bears, are microscopic animals with walking appendages that are members of Ecdysozoa, the clade of molting animals that also includes Nematoda (round worms), Nematomorpha (horsehair worms), Priapulida (penis worms), Kinorhyncha (mud dragons), Loricifera (loricated animals), Arthropoda (insects, spiders, centipedes, crustaceans, and their allies), and Onychophora (velvet worms). The phylogenetic relationships within Ecdysozoa are still unclear, with analyses of molecular and morphological data yielding incongruent results. Accounting for across-site compositional heterogeneity using mixture models that partition sites in frequency categories, CATegories (CAT)-based models, has been shown to improve fit in Bayesian analyses. However, CAT-based models such as CAT-Poisson or CAT-GTR (where CAT is combined with a General Time Reversible matrix to account for replacement rate heterogeneity) have proven difficult to implement in maximum likelihood. Here, we use CAT-posterior mean site frequencies (CAT-PMSF), a new method to export dataset-specific mixture models (CAT-Poisson and CAT-GTR) parameterized using Bayesian methods to maximum likelihood software. We developed new maximum likelihood-based model adequacy tests using parametric bootstrap and show that CAT-PMSF describes across-site compositional heterogeneity better than other across-site compositionally heterogeneous models currently implemented in maximum likelihood software. CAT-PMSF suggests that tardigrades are members of Panarthropoda, a lineage also including Arthropoda and Onychophora. Within Panarthropoda, our results favor Tardigrada as sister to Onychophora plus Arthropoda (the Lobopodia hypothesis). Our results illustrate the power of CAT-PMSF to model across-site compositionally heterogeneous datasets in the maximum likelihood framework and clarify the relationships between the Tardigrada and the Ecdysozoa.
Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis
Authors: Atkins Kieran , Garzón-Martínez Gina A , Lloyd Andrew , Doonan John H , Lu Chuan
Published: December 2024

Deep learning can revolutionise high-throughput image-based phenotyping by automating the measurement of complex traits, a task that is often labour-intensive, time-consuming, and prone to human error. However, its precision and adaptability in accurately phenotyping organ-level traits, such as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effectiveness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using images from a multiparent advanced generation intercross (MAGIC) mapping family. We trained an instance segmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong performance with an average precision of 88.0% for detection and 55.9% for segmentation. Quantitative trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on automated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scalable and provides high-quality phenotype data, facilitating genetic analysis and gene discovery, as well as advancing crop breeding research.