bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks
Authors: Schuiveling Mark , Liu Hong , Eek Daniel , Breimer Gerben E , Suijkerbuijk Karijn P M , Blokx Willeke A M , Veta Mitko 
Published: January 2025

Abstract

Background
Melanoma is an aggressive form of skin cancer in which tumor-infiltrating lymphocytes (TILs) are a biomarker for recurrence and treatment response. Manual TIL assessment is prone to interobserver variability, and current deep learning models are not publicly accessible or have low performance. Deep learning models, however, have the potential of consistent spatial evaluation of TILs and other immune cell subsets with the potential of improved prognostic and predictive value. To make the development of these models possible, we created the Panoptic Segmentation of nUclei and tissue in advanced MelanomA (PUMA) dataset and assessed the performance of several state-of-the-art deep learning models. In addition, we show how to improve model performance further by using heuristic postprocessing in which nuclei classes are updated based on their tissue localization.

Results
The PUMA dataset includes 155 primary and 155 metastatic melanoma hematoxylin and eosin–stained regions of interest with nuclei and tissue annotations from a single melanoma referral institution. The Hover-NeXt model, trained on the PUMA dataset, demonstrated the best performance for lymphocyte detection, approaching human interobserver agreement. In addition, heuristic postprocessing of deep learning models improved the detection of noncommon classes, such as epithelial nuclei.

Conclusion
The PUMA dataset is the first melanoma-specific dataset that can be used to develop melanoma-specific nuclei and tissue segmentation models. These models can, in turn, be used for prognostic and predictive biomarker development. Incorporating tissue and nuclei segmentation is a step toward improved deep learning nuclei segmentation performance. To support the development of these models, this dataset is used in the PUMA challenge.
Four targets: an enhanced framework for guiding causal inference from observational data
Authors: Lu Haidong , Li Fan , Lesko Catherine R , Fink David S , Rudolph Kara E , Harhay Michael O , Rentsch Christopher T , Fiellin David A , Gonsalves Gregg S 
Published: January 2025

Abstract
Observational studies play an increasingly important role in estimating causal effects of a treatment or an exposure, especially with the growing availability of routinely collected real-world data. To facilitate drawing causal inference from observational data, we introduce a conceptual framework centered around “four targets”—target estimand, target population, target trial, and target validity. We illustrate the utility of our proposed “four targets” framework with the example of buprenorphine dosing for treating opioid use disorder, explaining the rationale and process for employing the framework to guide causal thinking from observational data. The “four targets” framework is beneficial for those new to epidemiologic research, enabling them to grasp fundamental concepts and acquire the skills necessary for drawing reliable causal inferences from observational data.
Effects of various physical pretreatments and dextranase on the structure and physicochemical properties of porous sweet potato starch: a comparative study
Authors: Wang Boyan , Luan Wenjing , Li Qiang , Jiang Weihong , Zhang Lei , Lyu Mingsheng , Wang Shujun 
Published: January 2025

Abstract
Porous starch is a type of modified starch that contains a high number of tiny micropores. Due to its natural absorbent properties, porous starch has broad application prospects. Currently, enzymatic hydrolysis is predominantly used for production of porous starch, however, the hydrolysis efficiency is relatively low. In this study, sweet potato porous starch was prepared by combining physical pretreatments with dextranase, and then its adsorption properties were investigated. Ultrasonic, moist heat, and microwave pretreatment methods were used, and the structure, physicochemical properties, and adsorption capacity of resulting porous starches were detected. The microwave-combined enzymatic hydrolysis resulted in a porous starch with more compact pore structure in a shorter time. Additionally, no new chemical groups were observed in the porous starch. The treatment not only increased the solubility of starch to 5.22%, but likewise reduced the swelling power to 6.92%. Furthermore, adsorption capacity of the starch improved remarkably due to increased porosity and specific surface area. Specifically, the oil absorption improved from 51.02 to 102.79%, while the water absorption improved from 58.63 to 120.40%. This study provides an efficient method to produce porous starch from sweet potato with enhanced water and oil absorption capacity, which has promising applications in the food and pharmaceutical industries.
Cutting-edge Nano-therapeutics: silver nanoparticles loaded with ciprofloxacin for powerful antidiabetic, antioxidant, anti-inflammatory, and antibiotic action against resistant pathogenic bacteria
Authors: Laib Ibtissam , Mohammed Hamdi Ali , Laouini Salah Eddine , Bouafia Abderrhmane , Abdullah Mahmood M S , Al-Lohedan Hamad A , Al-Essa Qudama , Trzepieciński Tomasz 
Published: January 2025

Abstract
The rise of antibiotic resistance among pathogenic bacteria poses a critical challenge to global healthcare, necessitating innovative therapeutic strategies. This study addresses this gap by developing ciprofloxacin-loaded silver nanoparticles (CIP@Ag NPs), combining robust antibacterial efficacy with additional therapeutic benefits. CIP@Ag NPs were synthesized and characterized through UV–visible spectroscopy, revealing a distinct redshift to 401 nm and a reduced band gap of 2.24 eV, enhancing their photocatalytic and bioactive properties. Structural and morphological integrity was confirmed using XRD and SEM analyses. The nanoparticles exhibited remarkable multifunctionality, with 84% DPPH radical scavenging activity and a fourfold increase in total antioxidant capacity compared to bare Ag NPs. Anti-inflammatory activity was evidenced by 86.43% inhibition of egg albumin denaturation at 800 μg/ml. Additionally, CIP@Ag NPs demonstrated potent antidiabetic effects, achieving 73% α-amylase and 68% α-glucosidase inhibition at 60 μg/mL. Antibacterial assays of CIP@Ag NPs showed significant improvement, with inhibition zones of up to 34 mm against multidrug-resistant strains like Staphylococcus aureus and Escherichia coli, surpassing the efficacy of both Ag NPs and ciprofloxacin individually. These findings underscore the potential of CIP@Ag NPs as a multifunctional nanoplatform, offering a promising solution to combat antibiotic resistance while addressing inflammation, oxidative stress, and diabetes-related complications.
Optimizing deployment model of flexible interconnection systems for new energy 6/10 kV medium-voltage grids
Authors: Feng Kan , Xin Shijin , Zhang Pan , Yang Jian , Fan Changhui , Chen Chong 
Published: January 2025

Abstract
To improve the voltage instability, overloading, and suboptimal economic performance resultant from the integration of flexible interconnections within new energy 6/10 kV medium-voltage distribution grids, we present a novel methodology for the deployment and optimization of flexible interconnection systems. We incorporate generative adversarial networks to assimilate and delineate prototypical deployment scenarios, mitigating voltage disparities. Then, we formulate a two-tiered deployment model for interconnection systems that synchronizes across various time scales, enabling a comprehensive assessment of safety and cost variables intrinsic to the distribution grid framework. Results proved that our refined deployment strategies and models bolster the operational dependability and economic efficacy.
Study on the role of virtual reality technology in the design and evaluation of low-carbon indoor spaces
Authors: Chen Jiarui , Liu Yao , Wu Pengjun 
Published: January 2025

Abstract
The present article analyzes the wind, light environment, and energy consumption of a specific residential unit layout, providing a basis for low-carbon interior design. With the aid of building information modeling and virtual reality (VR) technologies, architectural designers can identify potential environmental issues at an early stage. Furthermore, by simulating the effects of various design proposals, these technologies offer robust support for decision-making. Meanwhile, the incorporation of VR technology not only allows designers to conduct analyses on a two-dimensional plane but also immerses them in a virtual environment where they can experientially engage with the spatial characteristics of the building and its interaction with the surroundings.
Grid capacity planning model for electric vehicle high charging penetration in power distribution networks
Authors: Xu Qing , Wu Zhaobin , Xin Shijin , Niu Haitao , Zhang Pan , Wang Xiaofeng 
Published: January 2025

Abstract
To optimize the grid fluctuation and safety issues caused by high penetration charging of electric vehicles, a novel distribution network capacity planning model is proposed. This model fused traffic-coupled model and dual-layer control strategy for charging scheduling, optimizing the power balance during peak electricity usage and charging station energy storage issues. Then, a stable and efficient distribution network power supply system is established. Experimental results demonstrate that our model achieves a comprehensive energy efficiency of 84.19%, with peak power loads controlled below 300 kW, surpassing similar algorithms. Moreover, it provides a scientifically grounded numerical range for practical planning.
Planning model of a low-carbon landscape garden environment based on PSO-BP
Authors: Han Lin , Li Peng , Ruan Yuting 
Published: January 2025

Abstract
A particle swarm optimization-back propagation neural network (PSO-BP) is proposed. First, we collect and preprocess the planning data for low-carbon landscape environment to ensure accuracy and consistency of data; then we propose the PSO-BP model that combines the global optimization characteristics of particle swarm algorithm and the nonlinear mapping capability of the backpropagation neural network. Empirical studies show that this model can effectively reduce energy consumption and carbon emissions, thus improving the ecological environment quality, improve the ecological service function, and effectively promote the sustainable development of low-carbon landscape buildings.
Machine learning-assisted evaluation of PVSOL software using a real-time rooftop PV system: a case study in Kocaeli, Turkey, with a focus on diffuse solar radiation
Authors: Aksoy Tırmıkçı Ceyda , Yavuz Cenk , Özkurt Cem , Çarklı Yavuz Burcu 
Published: January 2025

Abstract
Reducing energy-related CO2 emissions is vital for global climate targets, with Net Zero Energy Buildings (NZEBs) playing a key role. This study evaluates PVSOL software’s accuracy in simulating a rooftop photovoltaic (PV) system in an NZEB in Kocaeli, Turkey. A machine learning model enhanced result reliability using local weather data. The system’s first-year performance ratio was 81.9%, close to the theoretical 84.53%. The 435 600 USD investment is expected to be recovered in 11.42 years, while PVSOL predicts 14.9 years. The findings confirm PVSOL’s reliability for rooftop PV systems, emphasizing their effectiveness in CO2 reduction and energy transition efforts.
Microbial competition for iron determines its availability to the ferrous wheel
Authors: Strzepek Robert F , Latour Pauline , Ellwood Michael J , Shaked Yeala , Boyd Philip W 
Published: January 2025

Abstract
Iron plays a pivotal role in regulating ocean primary productivity. Iron is supplied from diverse sources such as the atmosphere and the geosphere, and hence iron biogeochemical research has focused on identifying and quantifying such sources of “new” iron. However, the recycling of this new iron fuels up to 90% of the productivity in vast oceanic regions. Evidence points to the key role of microbes in mediating this recycling, referred to as the “ferrous wheel”, that remobilises iron initially supplied to ocean biota. In the iron-limited subantarctic waters of the Southern Ocean, iron uptake is dominated by microbes smaller than 2 μm and exhibits seasonal and depth-related variations. The microbial community within the &lt;2 μm size fraction comprises heterotrophic bacteria and picophytoplankton, both competing for iron. Here, we dissect the demand component of the ferrous wheel by separately assessing iron uptake by heterotrophic bacteria and photoautotrophic picophytoplankton. To explore the seasonal and depth-related variability in iron uptake, the influence of light on iron uptake in both bacterial and phytoplankton communities was examined. We observed that picoeukaryote phytoplankton demonstrated iron uptake rates 10 times greater than those observed in bacteria when normalized to biomass. Light was shown to stimulate iron uptake by 8- to 16-fold in phytoplankton and by 4- to 8-fold in heterotrophic bacteria. These results highlight the unexpectedly significant role of picoeukaryotic phytoplankton in driving the speed of the ferrous wheel, with implications for iron recycling across diurnal cycles, different oceanic depths, and seasonally.
Prediction of hip fracture by high-resolution peripheral quantitative computed tomography in older Swedish women
Authors: Jaiswal Raju , Pivodic Aldina , Zoulakis Michail , Axelsson Kristian F , Litsne Henrik , Johansson Lisa , Lorentzon Mattias 
Published: January 2025

Abstract
The socioeconomic burden of hip fractures, the most severe osteoporotic fracture outcome, is increasing and the current clinical risk assessment lacks sensitivity. This study aimed to develop a method for improved prediction of hip fracture by incorporating measurements of bone microstructure and composition derived from high-resolution peripheral quantitative computed tomography (HR-pQCT). In a prospective cohort study of 3028 community-dwelling women aged 75 to 80, all participants answered questionnaires and underwent baseline examinations of anthropometrics and bone by dual x-ray absorptiometry (DXA) and HR-pQCT. Medical records, a regional x-ray archive, and registers were used to identify incident fractures and death. Prediction models for hip, major osteoporotic fracture (MOF), and any fracture were developed using Cox proportional hazards regression and machine learning algorithms (neural network, random forest, ensemble, and XGBoost). In the 2856 (94.3%) women with complete HR-pQCT data at 2 tibia sites (distal and ultra-distal), the median follow-up period was 8.0 years, and 217 hip, 746 MOF, and 1008 any type of incident fracture occurred. In Cox regression models adjusted for age, BMI, clinical risk factors (CRF), and femoral neck bone mineral density (FN BMD) the strongest predictors of hip fracture were tibia total volumetric BMD and cortical thickness. The performance of the Cox regression-based prediction models for hip fracture was significantly improved by HR-pQCT (time-dependent AUC; area under receiver operating characteristic curve at 5 years of follow-up 0.75 [0.64-0.85]), compared to a reference model including CRFs and FN BMD (AUC = 0.71 [0.58-0.81], p&lt;.001) and a FRAX risk score model (AUC = 0.70 [0.60-0.80], p&lt;.001). The Cox regression model for hip fracture had a significantly higher accuracy than the neural network-based model, the best-performing machine learning algorithm, at clinically relevant sensitivity levels. We conclude that the addition of HR-pQCT parameters improves the prediction of hip fractures in a cohort of older Swedish women.
Opportunistic measurement of sagittal abdominal diameter with bone densitometry predicts death and cardiovascular events
Authors: Leslie William D , Zarzour Fatima , Lix Lisa M , Binkley Neil , Lewis Joshua R , Schousboe John T 
Published: January 2025

Abstract
Supine sagittal abdominal diameter (SAD), also known as abdominal height, has been proposed as a simple measure for assessing abdominal adiposity. We aimed to determine whether SAD from dual-energy X-ray absorptiometry (DXA) performed for osteoporosis assessment predicts major adverse cardiovascular events (MACE) using the population-based DXA registry for the Province of Manitoba, Canada. The study population comprised 72,974 individuals aged 40 years and older with baseline DXA assessment between February 1999 and March 2018. Incident MACE (composite of all-cause mortality, acute myocardial infarction [MI], non-hemorrhagic stroke) was ascertained from linked healthcare databases. During mean 8.4 years follow-up (611,862 person-years), 14,457 (18.8%) individuals experienced incident MACE. Risk stratification was greatest with SAD/weight ratio, with area under the curve (AUC) for MACE and its components ranging from 0.582 for acute MI to 0.620 for death (all p&lt;0.001), all significantly better than with body mass index (BMI) (p&lt;0.001). In multivariable-adjusted models, each SD increase in SAD/weight was associated with increased risk for MACE (hazards ratio [HR] 1.20, 95% CI 1.18-1.22), death (HR 1.22, 95% CI 1.20-1.25), acute MI (HR 1.19, 95% CI 1.14- 1.24), and stroke (HR 1.17, 95% CI 1.12-1.22). A linear gradient was seen across SAD/weight quintiles (all p-trend&lt;0.001), with adjusted HR for MACE 1.61 (95% CI 1.50-1.72) for highest versus lowest quintile. Results were similar when further adjusted for BMI, in non-obese and obese individuals (p-interaction for obesity = 0.141), and in both women and men (p-interaction for sex = 0.471). In conclusion, SAD measured opportunistically at the time of DXA testing is predictive of death and major cardiovascular events in individuals undergoing osteoporosis assessment.
Forensic Assessment of Kinship, Genomic Ancestry, and Natural History of an Iconic Tiger of Harlem-New York City
Authors: Wu Zhen-Bo , Sui Zheng-Yan , Peng Lan-Hui , Ghemrawi Mirna , Duncan George , Jones Henry , O’Brien Stephen J , Luo Shu-Jin 
Published: January 2025

Abstract
In the fall of 2003, a two-year-old tiger named Ming, weighing some four hundred pounds, was discovered living in an apartment in Harlem, New York. Ming’s rescue by NYPD was witnessed, recalled, and venerated by scores of neighbors. The tiger’s history and ancestry stimulated considerable media interest, investigative sleuthing, and forensic genomic analyses. The Harlem tiger’s subspecies makeup, his relationship to his putative sibling named Cheeky living in Homestead, Florida, and his genetic distinctiveness from wild tigers was assessed by Whole Genome Sequence (WGS) analyses of trace materials from plucked whiskers. Verified Subspecies Ancestry (VSA) of Ming and Cheeky was determined by comparing their WGS to SNP annotation from WGS of 35 voucher (pure subspecies) tiger specimens from six living subspecies. Genome-wide structure analyses based on 3,422,109 SNPs and a subset of 6,724 Ancestry-Informative Markers (AIMS) showed that Ming has an admixed genetic background from five subspecies: Indochinese tiger (Panthera tigris corbetti 35 ~ 40%), Bengal tiger (P. t. tigris 17 ~ 23%), Sumatran tiger (P. t. sumatrae 12 ~ 14%), Amur tiger (P. t. altaica ~ 10%), and Malayan tiger (P. t. jacksoni 1 ~ 10%). Cheeky is confirmed to be a full sibling to Ming and displayed an admixed genetic background with similar subspecies proportions as Ming’s. The forensic assessment of the tigers’ subspecies composition, kinship, and recent history of animal transaction provides an analytical pipeline and promises to assist in tiger conservation effort worldwide through standardized genomic analysis of tigers or tiger products with unknown origin.
Transcriptional regulation of development by SMAX1-LIKE proteins, targets of strigolactone and karrikin/KAI2 ligand signaling
Authors: Chang Sun Hyun , George Wesley , Nelson David C 
Published: January 2025

Abstract
SUPPRESSOR OF MAX2 1 (SMAX1) and SMAX1-LIKE (SMXL) proteins comprise a family of plant growth regulators that includes downstream targets of the karrikin (KAR)/KAI2 ligand (KL) and strigolactone (SL) signaling pathways. Following the perception of KAR/KL or SL signals by α/β hydrolases, some types of SMXL proteins are polyubiquitinated by an E3 ubiquitin ligase complex containing the F-box protein MORE AXILLARY GROWTH2 (MAX2)/DWARF3 (D3), and proteolyzed. Because SMXL proteins interact with TOPLESS (TPL) and TPL-related (TPR) transcriptional corepressors, SMXL degradation initiates changes in gene expression. This simplified model of SMXL regulation and function in plants must now be revised in light of recent discoveries. It has become apparent that SMXL abundance is not regulated by KAR/KL or SL alone, and that some SMXL proteins are not regulated by MAX2/D3 at all. Therefore, SMXL proteins should be considered signaling hubs that integrate multiple cues. Here we review the current knowledge of how SMXL proteins impose transcriptional regulation of plant development and environmental responses. SMXL proteins can bind DNA directly and interact with transcriptional regulators from several protein families. Multiple mechanisms of downstream genetic control by SMXL proteins have been identified recently that do not involve the recruitment of TPL/TPR, expanding the paradigm of SMXL function.
A complex interplay between histone variants and DNA methylation
Authors: Edera Alejandro , Quadrana Leandro 
Published: January 2025

Abstract
Nucleosomes, the chromatin building blocks, play an important role in controlling DNA and chromatin accessibility. Nucleosome remodeling and the incorporation of distinct histone variants confer unique structural and biochemical properties, influencing the targeting of multiple epigenetic pathways, particularly DNA methylation. This stable epigenetic mark suppresses transposable element expression in plants and mammals, serving as an additional layer of chromatin regulation. In this review, we explore recent advances in our understanding of the complex interplay between histone variants and DNA methylation in plants, and discuss the role that chromatin remodeling plays in coordinating histone exchange and methylation of DNA.
Substantive equality for women in distribution of matrimonial property: Uganda’s contribution-based approach
Authors: Lubaale Emma Charlene 
Published: January 2025

Abstract
This article explores the potential for a contribution-based approach to the division of matrimonial property by drawing on Uganda’s approach. Uganda lacks a legislative framework for dividing matrimonial property upon divorce, leaving courts with the discretion to determine distribution. In the 2022 Court of Appeal decision in Ambayo v Aserua, the Court established several key principles: marriage does not guarantee an automatic half-share in matrimonial property; a spouse’s share depends on their contribution; homemakers' contributions should be valued based on comparable market services; and contributions towards a spouse’s education during marriage should be deducted from the beneficiary spouse’s total claim. This decision requires parties to demonstrate the extent of their contributions to matrimonial property. However, the historical undervaluation of homemaking and the difficulty in proving such contributions raise concerns about the protection of homemakers, often women, in property distribution upon divorce. This article examines key rulings in Ambayo to assess the potential for achieving substantive equality for women under this regime. The unique contribution of this article is its use of the Ambayo rulings to develop strategies that leverage a contribution-based approach to promote substantive equality while also highlighting limitations. It concludes that while Uganda’s contribution-based approach shows promise for achieving substantive equality, several obstacles persist. These include the lack of tangible proof for certain homemaking roles, the low market value assigned to care and domestic work, and the difficulties in valuing unquantifiable aspects of care and domestic tasks.