bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linking copepod functional traits to diel vertical migration at the Patagonian shelf-break
Authors: Severo Ayelen , Acha Eduardo Marcelo , Cepeda Georgina Daniela 
Published: September 2024

Abstract

Copepod diel vertical migration (DVM) is a significant phenomenon in marine ecosystems that could have implications for the biological pump and pelagic food webs. DVM has been reported in many regions of the global ocean; nevertheless, their drivers and ecological meaning are not fully understood. This study focused on the daytime and nighttime vertical abundance of select species (and developmental stages) to investigate the role of functional traits such as spawning strategy, body size, trophic group, and feeding mode in DVM at the Patagonian shelf-break (44ºS–47ºS and 60ºW–61ºW). Only females and late copepodites exhibited a normal DVM, being more abundant near the surface during the nighttime and below the thermocline during the daytime. Those species that are sac-spawners, detritivores, carnivores, omnivores, herbivores-omnivores, and cruise-feeders, such as Clausocalanus brevipes, C. laticeps, Aetideus armatus, and Oithona atlantica, were more abundant near the surface during the nighttime and below the thermocline during the daytime. Conversely, those species that are broadcasters, large-sized, herbivores, and filter-feeders, such as Calanus simillimus, Rhincalanus gigas, and Subeucalanus longiceps, did not show consistent DVM patterns, and were more abundant above the thermocline and at the fluorescence maxima, during both daytime and nighttime. Copepod depth selection appears to be influenced by a combination of morphological, physiological, behavioral, and life history traits.
Menopausal hormone therapy and incidence, mortality, and survival of breast cancer subtypes: A prospective cohort study
Authors: Busund Marit Katinka , Ursin Giske , Lund Eiliv , Chen Sairah Lai Fa , Rylander Charlotta 
Published: September 2024

Abstract

Background: Menopausal hormone therapy (MHT) is associated with an increased risk of postmenopausal breast cancer, predominantly the luminal A-like subtype. Little is known about the impact of MHT on deaths from breast cancer subtypes. This study aimed to explore associations between MHT use and the incidence, mortality, and survival of intrinsic-like breast cancer subtypes.
Methods: Data from 160,881 participants with self-reported MHT use from the prospective Norwegian Women and Cancer Study were analyzed. Among them, 7,844 were incident breast cancer cases, and 721 were breast cancer-specific deaths. Cox proportional hazard regression was performed to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for the association between MHT use and the incidence, mortality, and survival of breast cancer subtypes.
Results: MHT use was associated with increased incidence of overall, luminal A-like, and luminal B-like breast cancer, with respective HRs of 1.44 (95% CI: 1.36–1.52), 1.41 (95% CI: 1.31–1.52), and 1.23 (95% CI: 1.09–1.40) among current estrogen-progestin therapy (EPT) users compared with never users. The risk increased by 4%, 4%, and 2% per year of EPT use for overall, luminal A-like, and luminal B-like breast cancers, respectively. Increased risk of overall and luminal A-like breast cancer mortality was also associated with MHT use, with 61% (95% CI: 1.36–1.91) and 115% (95% CI: 1.51–3.05) increased risk among current EPT users compared with non-users. Among patients with breast cancer, pre-diagnostic MHT use was not associated with overall breast cancer survival but was inversely associated with survival from triple-negative breast cancer (TNBC; HR, 0.41; 95% CI: 0.24–0.73 among current users). Results varied significantly according to tumor subtype (pheterogeneity = 0.02).
Conclusions: Our study suggests that MHT use increases the risk of incident and fatal overall, luminal A-like, and incident luminal B-like breast cancer but does not decrease overall survival among patients with breast cancer. Further research is needed to elucidate the mechanisms underlying the differential associations between MHT use and breast cancer mortality and survival, and to explore whether MHT use among patients with TNBC is indeed free from harm.
T-cell diversity and exclusion of blood-derived T-cells in the tumor microenvironment of classical Hodgkin Lymphoma
Authors: Klapper Wolfram , Seifert Nicole , Reinke Sarah , Grund Johanna , Müller-Meinhard Berit , Richter Julia , Heilmann Thorsten , Schloesser Hans , Kotrova Michaela , Brüggemann Monika , Borchmann Peter , Bröckelmann Paul , Altenbuchinger Michael 
Published: September 2024

Abstract

The Tumor Microenvironment (TME) in classical Hodgkin lymphoma (HL) contains abundant immune cells and only few neoplastic Hodgkin and Reed-Sternberg cells (HRSC). We analyzed the T-cell receptor (TCR) repertoire to detect T-cell expansion in the TME and blood. In contrast to solid cancer tissue, T-cells in the TME of HL are highly polyclonal at first diagnosis and show only minor clonal expansion during anti-PD1 immune check-point blockade (ICB). At relapse and during ICB, pre-amplified T-cell populations increase in the TME of solid cancers but to a much lesser extent in HL. In contrast, T-cell populations in the peripheral blood of HL patients display higher clonality than healthy controls reaching clonality levels comparable to solid cancer or CMV-infection. However, pre-amplified blood T-cells in HL patients show only minor additional clonal expansion during ICB. Moreover, blood-derived T-cells do not repopulate the TME of HL at relapse or during ICB to the same extent as observed in solid cancers. Thus, the T-cell repertoire in the TME of HL appears unique in its polyclonality and the exclusion of clonally expanded T-cells from the peripheral blood. Exclusion of clonally expanded tumor-specific T-cells from the TME may present a novel mechanism of immune evasion in HL.
Conditional Similarity Triplets Enable Covariate-Informed Representations of Single-Cell Data
Authors: Chen Chi-Jane , Yi Haidong , Stanley Natalie 
Published: September 2024

Abstract

Single-cell technologies enable comprehensive profiling of diverse immune cell-types through the measurement of multiple genes or proteins per cell. In order to translate data from immune profiling assays into powerful diagnostics, machine learning approaches are used to compute per-sample immunological summaries, or featurizations that can be used as inputs to models for outcomes of interest. Current supervised learning approaches for computing per-sample representations are optimized based only on the outcome variable to be predicted and do not take into account clinically-relevant covariates that are likely to also be measured. Here we expand the optimization problem to also take into account such additional patient covariates to directly inform the learned per-sample representations. To do this, we introduce CytoCoSet, a set-based encoding method, which formulates a loss function with an additional triplet term penalizing samples with similar covariates from having disparate embedding results in per-sample representations. Overall, incorporating clinical covariates leads to improved prediction of clinical phenotypes.
Multiobjective Optimization and Kinetic Modelling of Anaerobic Baffled Reactor Integrated with Activated Sludge Reactor Treating Pulp and Paper Mill Wastewater
Authors: Mahmoudian-Boroujerd Laleh , Karimi-Jashni Ayoub , Azadi Sama 
Published: September 2024

Abstract

Pulp and paper mill wastewater treatment poses a significant challenge due to the presence of numerous refractory pollutants, necessitating the need for effective treatment methods. This study aims at multiobjective optimization of an anaerobic baffled reactor (ABR) combined with an activated sludge reactor (ASR) for pulp and paper wastewater treatment. The optimization approach minimizes the hydraulic retention time (HRT) while maximizing the ABR system's organic loading rate (OLR) and COD and BOD removal efficiency. Optimization efforts identified the optimum conditions for the ABR as an OLR of 6.2 g/L/d and an HRT of 3.2 d. Under these conditions, the remarkable COD removal efficiency of 92% and BOD removal efficiency of 95% were achieved in the ABR, demonstrating the system's robust performance in reducing the pollutant load of the wastewater. The integrated ABR-ASR also exhibited outstanding removal efficiencies for various parameters in the optimum conditions. Specifically, COD, BOD, TSS, turbidity, and color displayed removal efficiencies of 95%, 97%, 92%, 98%, and 92%, respectively. These findings underscore the versatility of the integrated system in addressing a spectrum of pollutants present in pulp and paper wastewater. Furthermore, the rate of substrate consumption was investigated using the modified Stover-Kincannon model. The saturation value constant (KB) and the maximum utilization rate (Umax) values for ABR were found to be 7.95 g/L/d and 5.5 g/L/d, respectively, while for ASR, these values were 0.69 g/L/d and 0.15 g/L/d. This research advances our understanding of the synergistic potential of the ABR-ASR in treating high-strength industrial wastewater.
Optimizing Vehicular Edge Computing: Graph-Based Double-DQN Approaches for Intelligent Task Offloading
Authors: Ullah Ihsan , Youn-Hee Han 
Published: September 2024

Abstract

In vehicular edge computing, optimizing task offloading is crucial to balance computational needs, reduce delays, and cut costs amid the dynamic challenges of the vehicular environment, including vehicle mobility, network topology, and computing resource variability.
This study introduces a task offloading scheme enabling vehicles to dynamically choose local execution or offloading to nearby vehicles, edge servers, or the cloud.
The primary goal is to optimize task offloading by simultaneously minimizing cost and delay.
Achieving this involves integrating graph convolutional networks and deep reinforcement learning, enhancing decision-making efficiency and network representation for agents.
The fusion of Graph Convolutional Networks with Double-DQN strengthens overall network representation and decision-making.
The optimization challenge is formally structured as a Markov Decision Process.
Simulation results highlight the proposed scheme's superiority, showcasing its effectiveness in achieving cost-efficiency by maximizing resource utilization, minimizing costs, and optimizing task offloading while reducing task rejection.
Experimental study of vortex dynamics in eccentric collision of vortex rings
Authors: Wang YuZe , Chen Xin , Wang BoFu , Qiu Xiang , Liu Yulu , Zhou Quan 
Published: September 2024

Abstract

This study investigates eccentrically colliding vortex rings using Particle Image Velocimetry (PIV) measurements. Under a fixed Reynolds number, we explore how eccentricity influences flow characteristics and behavior. Diverse collision dynamics at various eccentricities are revealed by analyzing flow structures from Lagrangian and Eulerian frames. When the eccentricity H/Rring = 0, frontal collisions cause significant deformation and breakup due to induced radial flow between oppositely rotating vortices. Increasing eccentricity (H/Rring = 0.893) leads to similar dynamics with noticeable trajectory deviations. At H/Rring = 1.786, momentum exchange altered the trajectory of the vortex rings and led to material exchange between them. The opposing flow induced by the neighboring vortex ring increases the vortex strength, and the vortex ring is eventually stretched and broken apart gradually. Further increasing to H/Rring = 2.679, the vortex cores merged, underwent subsequent splitting, 1 and involved material exchange. They eventually returned to their initial state. The vortex strength experiences an initial increase, followed by a decrease, and then a recovery. At the highest eccentricity (H/Rring = 3.572) and beyond, there is no interaction between the vortex rings. This study provides insights into vortex dynamics during eccentric collisions, highlighting eccentricity’s role in shaping flow dynamics.
Environment-friendly lead-free Cs3Bi2Br9 perovskite quantum dots as fluorescent probes for rapid detection of oxytetracycline via inner filter effect
Authors: Liu Jiali , Li Chen , Zhang Shen , Liu Xinni , Wei Xiao , Gao Yue , Wang Fei , Yan Mengwei , Wang Jiaqi , Kamuti Diana 
Published: September 2024

Abstract

All-inorganic perovskite quantum dots have sparked a research boom due to their excellent optical properties, however, their own strong ionicity and lead toxicity have hindered further development in the field of sensing. In this study, we have solved the toxicity problem of lead-based perovskite quantum dots by replacing lead with green metal bismuth. Meanwhile, due to the ligand-passivation effect of oleylamine and oleic acid, we successfully synthesized highly stable bismuth-based perovskite quantum dots(Cs3Bi2Br9 PQDs)in ethanol, and constructed the environment-friendly fluorescence sensor for the quantitative detection of OTC for the first time. The results demonstrated that the fluorescence quenching degree of Cs3Bi2Br9 PQDs showed a good linear relationship with the concentration of OTC within the range of 2.0 ~ 18 µM, and the detection limit was 0.432 µM. By studying fluorescence lifetime, absorption spectroscopy, and evaluation of internal filtration parameters., it was proved that the sensing mechanism is caused by the inner filter effect owing to the overlapping of fluorescence emission spectrum of Cs3Bi2Br9 PQDs and UV absorption spectrum of OTC. Moreover, Cs3Bi2Br9 PQDs fluorescent sensor had good selectivity and anti-interference ability. It is believed that this work will open up a new way for lead-free perovskite quantum dot fluorescence sensor in the field of analytical detection.
Exploring the genetic diversity of Lebanon’s underexploited grapevine resources
Authors: Merheb Joe , Chalak Lamis , Roux Catherine , Laucou Valérie , Ouaini Naim , Beyrouthy Marc El , Touma Joe-Assad , Lacombe Thierry , This Patrice 
Published: September 2024

Abstract

Lebanon's well-documented history in grape cultivation and winemaking dates back millennia, intertwining with its cultural heritage and landscape. However, Lebanon's grapevine genetic resources have received limited attention until recent years. This study presents a comprehensive exploration and characterization of Lebanese local underexploited resources, beginning with the collection of 276 accessions prospected throughout the country, with an emphasis on old/abandoned vines and vineyards. Molecular and morphological analyses were conducted on these accessions: a total of 22 nuclear simple sequence repeat (SSR) markers and 3 chloroplastic SSR markers were employed to assess their genetic diversity. The results revealed 102 distinct genotypes, with 63 genotypes exhibiting original genetic signatures previously undocumented in international databases, highlighting the unique genetic richness of Lebanon's grapevine germplasm. Subsequent morphological characterization using a set of OIV descriptors provided valuable insights into the phenotypic traits of these newly found grape varieties. Parentage analysis facilitated the elucidation of genetic relationships among the accessions, and the construction of genetic pedigrees. Comparative analysis positioned Lebanese accessions within the Eastern Mediterranean group, suggesting shared genetic affinities with regional counterparts. This comprehensive study underscores the importance of Lebanon's grapevine genetic resources for conservation, breeding, and future research endeavors in viticulture. The conservation and sustainable utilization of these genetic resources is not only for preserving the nation's viticultural heritage but also for fostering vinification innovation and resilience in the face of changing environmental conditions.
Elemental Analysis for the Optimization of Plant Secondary Metabolites Using Cannabis sativa as a Model Organism
Authors: Lukomski Brendan 
Published: September 2024

Abstract

When cultivating medicinally valuable plants, it is often beneficial to assess production of secondary metabolites as these can act as markers for desired traits. Cannabis is one such medicinally valuable plant that has gained much attention recently due to its growing availability. Chemometric methods can be applied to understand the relationships governing the production of these compounds and optimize cultivation toward certain ends. In the current study, thirteen elements were quantitatively measured in the leaves of cannabis plants using inductively coupled plasma mass spectroscopy and an Elementar vario macro cube. Correlation analysis, principal components analysis, and K-means clustering were utilized to describe and elucidate trends in the dataset. Moderately positive, monotonic correlations were found between magnesium, boron, and calcium, along with nitrogen, sulfur, and copper. PCA was used to corroborate these relationships. Clustering analysis was able to identify three distinct groups to which strains could be mapped with a relatively high degree of resolution when compared to cultivator identifiers. These findings suggest similar methods of introduction and elemental incorporation into the strains of these distinct groups. The methods utilized in the current study serve as the basis for the future development of methods that may be utilized in the optimization of secondary metabolite production.
Analysis of stresses and settlement for strip footings resting on jointed rock masses
Authors: Showkati Alan 
Published: September 2024

Abstract

Large-scale engineering structures with substantial loads, such as dams, towers, and bridges, are currently being constructed on rocks as a good load-bearing foundation. Because many rocks are jointed and thus inherently discontinuous, the analysis of stress and settlement is crucial to ensure a safe and reliable foundation design with no potential for failure or excessive settlement. This paper presents a new analytical solution based on the complex variable method for calculating stresses and settlements under vertically loaded strip footings. The rock mass was assumed to be elastic and intersected by two perpendicular joint sets parallel and normal to the rock surface. The jointed rock was modeled as an equivalent orthotropic continuum to obtain the analytical solution, and the theory of anisotropic elasticity was applied. Comparisons with the available isotropic solutions and results from the FEM models are also provided to verify the accuracy of the proposed solution. Finally, a parametric study is conducted to investigate the effects of various joint characteristics and Poisson’s ratios on the solutions.
SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants
Authors: Jiang Yu , Yang Qimeng , Sun Jianfeng , Wang Xinyu , Wang Jiong , Liu Quanzhong , Ru Jinlong , Zhang Xin , Wang Sizhe , Hao Ran , Bian Peipei , Dai Xuelei , Gong Mian , Zhang Zhuangbiao , Wang Ao , Bai Fengting , Li Ran , Cai Yudong 
Published: September 2024

Abstract

Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly occurring at repetitive genomic regions, from short-read sequencing data remains challenging. Here, we introduce SVLearn, a machine-learning approach for genotyping bi-allelic SVs. It exploits a dual-reference strategy to engineer a curated set of genomic, alignment, and genotyping features based on a reference genome in concert with an allele-based alternative genome. Using 38,613 human-derived SVs, we show that SVLearn significantly outperforms four state-of-the-art tools, with precision improvements of up to 15.61% for insertions and 13.75% for deletions in repetitive regions. On two additional sets of 121,435 cattle SVs and 113,042 sheep SVs, SVLearn demonstrates a strong generalizability to cross-species genotype SVs with a weighted genotype concordance score of up to 90%. Notably, SVLearn enables accurate genotyping of SVs at low sequencing coverage, which is comparable to the accuracy at 30× coverage. Our studies suggest that SVLearn can accelerate the understanding of associations between the genome-scale, high-quality genotyped SVs and diseases across multiple species.
Planning with emission models to reduce carbon footprint of new reservoirs
Authors: Janus Tomasz , Barry Christopher , Win Shelly , Kuriakose Jaise 
Published: September 2024

Abstract

Reservoirs collectively contribute 1–2 percent of global man-made greenhouse gas emissions, although
their individual emissions can vary widely. Reservoir emission models have considerably advanced our
understanding of the lifetime carbon impacts of reservoirs globally and inherently, offer means to inform judicious planning of new investments. Their widespread adoption is nonetheless hindered by high manual processing requirements, wide prediction uncertainties, and linkages to geospatial drivers that can be obscure for planners. Here, an automated methodology that overcomes and ameliorates these constraints is demonstrated by application to planning hydropower expansion in Myanmar. We show potential hydropower emission reductions of 0.94 MtCO2e /year, while conserving 239 km2 of forest and arable land, and reducing barriers in lower river reaches from 28 to 7. We highlight the benefits of combining detailed empirical models with clear explanations for increasing transparency towards enhancing stakeholder acceptance, informed decision-making, and effective policy design.
Gender differences in attentional processes and attractiveness evaluation models during gait observation
Authors: Tanabe Hiroko , Yamamoto Kota 
Published: September 2024

Abstract

Introduction:
Physical attractiveness plays a crucial role in building interpersonal relationships and in daily communication. Attractiveness is perceived through nonverbal information regarding one’s morphological features, posture, movement, and behavior. Selective pressures throughout our species’ evolutionary history have shaped sex differences in the evaluation of physical attractiveness. However, research on the process of body attractiveness perception has been limited to static information involving body images. Therefore, a better understanding of the attractiveness perception process in the real world requires an appreciation of the attractiveness perception mechanism of physical movement.
Methods
This study examined the attractiveness perception of 30-second walking animations, as well as gender differences in gaze behavior and statistical models of attractiveness evaluation. We recruited 16 men and 17 women and made gender comparisons of fixation ratio to each gaze area (head, trunk, hip, leg, and others). Furthermore, the standardized estimates of the statistical models were qualitatively compared between male and female observers.
Results
Male observers were highly fixated on the walkers’ trunk, whereas female observers tended to shift their attention from the trunk to the legs, especially when observing high-preference animations. The statistical model for attractiveness evaluation, which used gait parameters for each gender, showed the tendency that when assessing attractiveness, male observers placed greater weight on the walkers’ trunk silhouette, whereas female observers prioritized parameters requiring whole-body observation.
Discussion
Gender differences in gaze behavior were observed in the assessment and perception of human movement attractiveness; such differences may reflect the evaluation model for each gender. The results suggest that men assess female gait attractiveness based on observations of the reproductive regions of the female body. In contrast, women perceive other women as potential competitors and assess female gait attractiveness based on beauty standards, which are shaped by sociocultural environments and the walker’s psychological state. Our findings are the first step toward understanding the process of perceiving the attractiveness of physical movement and are expected to help generate attractive biological motions.