bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparing Summed Probability Distributions of Shoreline and Radiocarbon Dates from the Mesolithic Skagerrak Coast of Norway
Authors: Roalkvam Isak , Solheim Steinar 
Published: January 2025

Abstract
By developing a new methodology for handling and assessing a large number of shoreline dated sites, this paper compares the summed probability distribution of radiocarbon dates and shoreline dates along the Skagerrak coast of south-eastern Norway. Both measures have previously been compared to elucidate demographic developments in Fennoscandia, but these have not been based on probabilistic methods for shoreline dating. The findings indicate a largely diverging development of the two data sets through the Mesolithic. The number of shoreline dated sites undergoes some process of overall decrease through the period, while the radiocarbon data is characterised by a lacking signal in the earliest parts of the period and then undergoes a logistic growth that quickly plateaus and remains stable for the remainder of the period. The precise nature of this discrepancy will necessitate further substantiation and the method of shoreline dating itself still requires further testing and assessment. Having noted this, we still tentatively suggest that while the number of shoreline dated sites is not devoid of influence from a demographic signal, this measure could be more heavily influenced by mobility patterns. Conversely, we also suggest that the lacking radiocarbon data from the earliest part of the Mesolithic is in part the result of mobility patterns, but that the radiocarbon data could be reflecting population dynamics more directly.
Sorption of palladium using resorcinol–formaldehyde-thiourea discs
Authors: Ajith Nicy , Dalvi Aditi A. , Chavan T. A. , Remya Devi P. S , Swain K. K. 
Published: January 2025

AbstractSorption of Pd from acidic media was investigated using laboratory synthesized resorcinol–formaldehyde discs, functionalized with thiourea. The discs were characterized via SEM–EDS, FT-IR and EDXRF spectrometry. Pd-sorption efficiencies of the discs, evaluated using both inactive and radiotracer solutions, were compared to those of conventional strong anion exchange membranes. It was established that the disc forms are well-suited for batch separations, wherein very large volume reduction factors are the major goals. Among various de-sorbents studied, 1 M NaOH was found to be the best (~ 50% in single step).
Supporting the Advancement of a National Agenda for Pediatric Healthcare Reform: A multi-year Evaluation of a Leadership Education in Neurodevelopmental and Related Disabilities Program
Authors: Koob Caitlin , Griffin Sarah F. , Stuenkel Mackenzie , Cartmell Kathleen B. , Sease Kerry 
Published: January 2025

Abstract

Objectives
To evaluate the implementation and sustainability of the effect of a 1-year Leadership in Education for Neurodevelopmental and related Disabilities (LEND) program in a southeastern state, and to examine its impact on advancing the Maternal Child Health Bureau’s (MCHB) Blueprint for Change—a national agenda for pediatric healthcare reform.

Methods
This study applies the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework to rigorously evaluate LEND implementation and impact between 2018 and 2022. In-depth interviews (N = 24) were conducted among long-term (1-year) LEND trainees, via Zoom, in a southeastern state. A hybrid approach of deductive and inductive thematic analysis was conducted to identify emergent patterns and themes from trainees’ experiences, related to the EPIS constructs and national priorities.

Results

Exploration and Preparation. Trainees identified insights from multidisciplinary discussions and family panels as key facilitators to their development. Implementation. Trainees reported growth in confidence and communication and improving their service delivery, including implementation of a collaborative approach to patient care, family-centered care, and occasionally facilitating their obtainment of leadership positions. Trainees also reported systemic barriers to implementation, including time and financial constraints. Sustainability. Trainees identify their experienced shift in mindset and statewide connections as drivers for sustained change, with suggestions for follow-up events and networking opportunities to enhance the effect of LEND training.

Conclusions for Practice
These results may inform LEND objectives to enhance the statewide network and to advance a national framework for prioritizing family well-being and quality of life and access to services.
Multiplexed single-cell imaging reveals diverging subpopulations with distinct senescence phenotypes during long-term senescence induction
Authors: Sessions Garrett A. , Loops Madeline V. , Diekman Brian O. , Purvis Jeremy E. 
Published: January 2025

Abstract
Cellular senescence is a phenotypic state that contributes to the progression of age-related disease through secretion of pro-inflammatory factors known as the senescence-associated secretory phenotype (SASP). Understanding the process by which healthy cells become senescent and develop SASP factors is critical for improving the identification of senescent cells and, ultimately, understanding tissue dysfunction. Here, we reveal how the duration of cellular stress modulates the SASP in distinct subpopulations of senescent cells. We used multiplex, single-cell imaging to build a proteomic map of senescence induction in human epithelial cells induced to senescence over the course of 31 days. We map how the expression of SASP proteins increases alongside other known senescence markers such as p53, p21, and p16INK4a. The aggregated population of cells responded to etoposide with an accumulation of stress response factors over the first 11 days, followed by a plateau in most proteins. At the single-cell level, however, we identified two distinct senescence cell populations, one defined primarily by larger nuclear area and the second by higher protein concentrations. Trajectory inference suggested that cells took one of two discrete molecular paths from unperturbed healthy cells, through a common transitional subpopulation, and ending at the discrete terminal senescence phenotypes. Our results underscore the importance of using single-cell proteomics to identify the mechanistic pathways governing the transition from senescence induction to a mature state of senescence characterized by the SASP.
Non-parametric Bayesian deep learning approach for whole-body low-dose PET reconstruction and uncertainty assessment
Authors: Levital Maya Fichmann , Khawaled Samah , Kennedy John A. , Freiman Moti 
Published: January 2025

Abstract
Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between image quality and radiation exposure is critical, as reducing the administered dose results in a lower signal-to-noise ratio (SNR) and information loss, which may significantly affect clinical diagnosis. Deep learning (DL) algorithms have recently made significant progress in low-dose (LD) PET reconstruction. Nevertheless, a successful clinical application requires a thorough evaluation of uncertainty to ensure informed clinical judgment. We propose NPB-LDPET, a DL-based non-parametric Bayesian framework for LD PET reconstruction and uncertainty assessment. Our framework utilizes an Adam optimizer with stochastic gradient Langevin dynamics (SGLD) to sample from the underlying posterior distribution. We employed the Ultra-low-dose PET Challenge dataset to assess our framework’s performance relative to the Monte Carlo dropout benchmark. We evaluated global reconstruction accuracy utilizing SSIM, PSNR, and NRMSE, local lesion conspicuity using mean absolute error (MAE) and local contrast, and the clinical relevance of uncertainty maps employing correlation between the uncertainty measures and the dose reduction factor (DRF). Our NPB-LDPET reconstruction method exhibits a significantly superior global reconstruction accuracy for various DRFs (paired t-test,

$$p<0.0001$$

p
<
0.0001

, N=10, 631). Moreover, we demonstrate a 21% reduction in MAE (573.54 vs. 723.70, paired t-test,

$$p<0.0001$$

p
<
0.0001

, N=28) and an 8.3% improvement in local lesion contrast (2.077 vs. 1.916, paired t-test,

$$p<0.0001$$

p
<
0.0001

, N=28). Furthermore, our framework exhibits a stronger correlation between the predicted uncertainty 95th percentile score and the DRF (

$$r^2=0.9174$$

r
2

=
0.9174

vs.

$$r^2=0.6144$$

r
2

=
0.6144

, N=10, 631). The proposed framework has the potential to improve clinical decision-making for LD PET imaging by providing a more accurate and informative reconstruction while reducing radiation exposure.

Graphical abstract
Key Structural Features of Microvascular Networks Leading to the Formation of Multiple Equilibria
Authors: Atkinson George , Ben-Ami Yaron , Maini Philip , Pitt-Francis Joe , Byrne Helen 
Published: January 2025

Abstract
We analyse mathematical models of blood flow in two simple vascular networks in order to identify structural features that lead to the formation of multiple equilibria. Our models are based on existing rules for blood rheology and haematocrit splitting. By performing bifurcation analysis on these simple network flow models, we identify a link between the changing flow direction in key vessels and the existence of multiple equilibria. We refer to these key vessels as redundant vessels, and relate the maximum number of equilibria with the number of redundant vessels. We vary geometric parameters of the two networks, such as vessel length ratios and vessel diameters, to demonstrate that equilibria are uniquely defined by the flow in the redundant vessels. Equilibria typically emerge in sets of three, each having a different flow characteristic in one of the network’s redundant vessels. For one of the three equilibria, the flow within the relevant redundant vessel will be smaller in magnitude than the other two and the redundant vessel will contain few Red Blood Cells (RBCs), if any. For the other two equilibria, the redundant vessel contains RBCs and significant flow in the two available directions. These structural features of networks provide a useful geometric property when studying the equilibria of blood flow in microvascular networks.
Modeling Innate Immunity Causing Chronic Inflammation and Tissue Damage
Authors: Matsuo Kosei , Iwasa Yoh 
Published: January 2025

Abstract
Mathematical models of immune responses have traditionally focused on adaptive immunity and pathogen-immune dynamics. However, recent advances in immunology have highlighted the critical role of innate immunity. In response to physical damage or pathogen attacks, innate immune cells circulating throughout the body rapidly migrate from blood vessels and accumulate at the site of injury, triggering inflammation. These cells engulf, break down, and eliminate pathogens. This innate immune response occurs much faster than adaptive immune responses, which require time for cell activation and proliferation. While inflammation helps eliminate pathogens, it can sometimes lead to chronic inflammation by triggering excessive immune responses, ultimately causing tissue damage. In this study, we examine a simple dynamical model of innate immunity. The analysis indicates that when an infection occurs, it triggers inflammation, which activates the innate immune system and initiates the activation cycle. Consequently, pathogens may be eradicated, leaving behind persistent chronic inflammation. Alternatively, the pathogens may not be eradicated, with their abundance either stabilizing at a positive level or oscillating indefinitely. The dynamics exhibit both transcritical and Hopf bifurcations. When innate immunity is activated in the absence of inflammation, pathogens are eradicated more easily, and the likelihood of oscillations in inflammation, immune responses, and pathogen abundance is reduced.
Suppressive effect of vitamin K2 (menatetrenone) against bone mineral density loss after radiotherapy in uterine cancer patients
Authors: Kaneyasu Yuko , Fujiwara Hisaya , Akita Tomoyuki , Tanaka Junko , Shibata Yuuka , Nakagawa Tomio , Koh Iemasa , Hirata Eiji , Hyodo Maki , Miyamoto Tadashi , Murakami Yuji , Nishibuchi Ikuno , Imano Nobuki , Nagata Yasushi , Kudo Yoshiki 
Published: January 2025

Abstract

Purpose
This study aimed to investigate whether vitamin K2 (menatetrenone) suppresses bone mineral density (BMD) loss in the irradiated region after radiotherapy (RT) in uterine cancer patients.

Materials and Methods
Our study included 34 patients who underwent whole pelvic irradiation for uterine cancer between 2001 and 2010. The patients were categorized in two groups: (1) Vitamin K2 (45 mg/day) administration group (group A) with 18 cases and (2) non-administered group (group B) with 16 cases. The duration of vitamin K2 administration was 1 year or longer. BMD was measured before and immediately, 3 months, 6 months, 1 year, 1 year or more after RT.

Results
Regarding change rate in the BMD of L3-L4 which was outside the irradiated field, no significant changes were observed in BMD after radiation in either groups compared to BMD before radiotherapy. Regarding change rate in BMD of L5-S1 which was inside the irradiated field, BMD reduced significantly at 6 months after radiotherapy compared to BMD before the start of radiotherapy in Group B (P = 0.0234). However, no significant change was seen in group A. Grade 2 and 3 insufficiency fractures appeared in both groups, one in each. Regarding outside the irradiation field, one patient developed compression fracture in L2 in group B, none occurred in group A.

Conclusion
We suggest that vitamin K2 could suppress the decrease in BMD due to whole pelvic radiotherapy. Further studies are needed in the future to improve quality of life such as the prevention of insufficiency fractures.
Key Technologies for Machine Vision for Picking Robots: Review and Benchmarking
Authors: Xiao Xu , Jiang Yiming , Wang Yaonan 
Published: January 2025

Abstract
The increase in precision agriculture has promoted the development of picking robot technology, and the visual recognition system at its core is crucial for improving the level of agricultural automation. This paper reviews the progress of visual recognition technology for picking robots, including image capture technology, target detection algorithms, spatial positioning strategies and scene understanding. This article begins with a description of the basic structure and function of the vision system of the picking robot and emphasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural environment. Subsequently, various image processing techniques and vision algorithms, including color image analysis, three-dimensional depth perception, and automatic object recognition technology that integrates machine learning and deep learning algorithms, were analysed. At the same time, the paper also highlights the challenges of existing technologies in dynamic lighting, occlusion problems, fruit maturity diversity, and real-time processing capabilities. This paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of picking. At the same time, this paper also introduces innovative research, such as the application of convolutional neural networks (CNNs) for accurate fruit detection and the development of event-based vision systems to improve the response speed of the system. At the end of this paper, the future development of visual recognition technology for picking robots is predicted, and new research trends are proposed, including the refinement of algorithms, hardware innovation, and the adaptability of technology to different agricultural conditions. The purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricultural robotics, including current achievements, existing challenges and future development prospects.
Counterfactual Learning on Graphs: A Survey
Authors: Guo Zhimeng , Wu Zongyu , Xiao Teng , Aggarwal Charu , Liu Hui , Wang Suhang 
Published: January 2025

Abstract
Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs. To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning. We divide existing methods into four categories based on problems studied. For each category, we provide background and motivating examples, a general framework summarizing existing works and a detailed review of these works. We point out promising future research directions at the intersection of graph-structured data, counterfactual learning, and real-world applications. To offer a comprehensive view of resources for future studies, we compile a collection of open-source implementations, public datasets, and commonly-used evaluation metrics. This survey aims to serve as a “one-stop-shop” for building a unified understanding of graph counterfactual learning categories and current resources.
Using neuroimaging to identify sex differences in adults with sports-related concussion: a systematic review
Authors: Macleod Harry , Smith Clare L. , Laycock Robin 
Published: January 2025

Abstract
Concussion is a common injury in sports that causes neurological damage, leading to memory loss and difficulty concentrating. Insufficient recovery time may result in significant long-term harm to individuals. Several neuroimaging techniques have been used to understand the pathophysiological changes following concussion, and how long individuals need to recover before returning to play. Despite the progress in neuroimaging concussion research, few studies have considered whether females sustain different effects on the brain and how recovery from concussion might differ from males. Thus, we conducted a systematic review of the existing literature to highlight sex differences in concussion with neuroimaging techniques. By searching four different databases, studies were selected if they used a neuroimaging technique to examine sex differences following concussion in athletes over the age of 18. After screening 2295 studies from an initial search, 15 were found to match the selection criteria. Nine papers established some difference between males and females, however many of these studies were not designed to specifically examine sex differences, and hence conclusions in this regard are somewhat limited. A further common limitation among these papers was a lack of whole brain scans, instead relying on regions of interest analyses, which reduces the ability to compare studies effectively. The current systematic review has highlighted the need for future studies to specifically consider whether, and how sex influences the impact and trajectory of brain recovery from concussion. This can then help to inform suitable amendments to current concussion return-to-play protocols for male and female athletes.
Effects of green and chemically synthesized ZnO nanoparticles on Capsicum annuum under drought stress
Authors: Guzel Deger Aysin , Çevik Sertan , Kahraman Oskay , Turunc Ersan , Yakin Ayşegül , Binzet Rıza 
Published: January 2025

Abstract
Nanoparticle applications have recently become a popular method due to increase the tolerance levels of plants to environmental stresses. In this study, changes induced by exogenous application of green synthesized and commercially purchased zinc oxide nanoparticles (ZnO NPs) (100, 500, 1000 mg/L) to drought-stressed Capsicum annuum plants were studied through comprehensive morphologic, anatomic, physiological and biochemical analyzes. While drought decreased relative water content (RWC) and leaf water potential (LWP), it increased proline, thiobarbituric acid reactive substances (TBARS) content and antioxidant enzyme activities. Exogenous ZnO NPs increased RWC and LWP but they decreased TBARS and proline contents in drought-stressed plants. Furthermore, the data obtained in this study revealed that the response of the antioxidant enzyme varies depending on the concentration of the nanoparticles and the type of synthesis. However, green synthesized ZnO NPs applications (100 and 500 mg/L) were found to be much more effective in reducing the adverse effects of drought stress than those synthesized chemically. The results of this study indicated that although the response of Capsicum annuum plants to ZnO NPs applications varied according to the type and concentration under drought conditions, nanoparticle applications did not lead to a consistent stress response.
Determination of the Time-frequency Features for Impulse Components in EEG Signals
Authors: Filimonova Natalia , Specovius-Neugebauer Maria , Friedmann Elfriede 
Published: January 2025

Abstract
Accurately identifying the timing and frequency characteristics of impulse components in EEG signals is essential but limited by the Heisenberg uncertainty principle. Inspired by the visual system’s ability to identify objects and their locations, we propose a new method that integrates a visual system model with wavelet analysis to calculate both time and frequency features of local impulses in EEG signals. We develop a mathematical model based on invariant pattern recognition by the visual system, combined with wavelet analysis using Krawtchouk functions as the mother wavelet. Our method precisely identifies the localization and frequency characteristics of the impulse components in EEG signals. Tested on task-related EEG data, it accurately detected blink components (0.5 to 1 Hz) and separated muscle artifacts (16 Hz). It also identified muscle response durations (298 ms) within the 1 to 31 Hz range in emotional reaction studies, offering insights into both individual and typical emotional responses. We further illustrated how the new method circumvents the uncertainty principle in low-frequency wavelet analysis. Unlike classical wavelet analysis, our method provides spectral characteristics of EEG impulses invariant to time shifts, improving the identification and classification of EEG components.
Using multi-orbit cyclic subspace codes for constructing optical orthogonal codes
Authors: Özbudak Ferruh , Santonastaso Paolo , Zullo Ferdinando 
Published: January 2025

Abstract
We present a new application of multi-orbit cyclic subspace codes to construct large optical orthogonal codes, with the aid of the multiplicative structure of finite fields extensions. This approach is different from earlier approaches using combinatorial and additive (character sum) structures of finite fields. Consequently, we immediately obtain new classes of optical orthogonal codes with different parameters.