bluuebunny/crossref_metadata_2025_split huggingface dataset:
Telephone-Assisted Self-Help for Caregivers of Adolescents with Attention-Deficit/Hyperactivity Disorder: An Observational Proof-of-Concept Study
Authors: Dose Christina , Roschmann Tina , Kinnen Claudia , Rademacher Christiane , Hautmann Christopher , Döpfner Manfred 
Published: April 2024

AbstractStudies on the treatment of attention-deficit/hyperactivity disorder (ADHD) in adolescence indicate small to moderate treatment effects (e.g., of parent training). Self-help interventions might overcome structural and personal barriers to treatment utilization. The present proof-of-concept study examined the feasibility and effectiveness of a twelve-month, telephone-assisted self-help (TASH) intervention for caregivers of adolescents with ADHD. This intervention comprised 8 booklets on the management of ADHD-related problems and 14 telephone counseling sessions. Sixty-six caregivers participated in the study with a one-group pretest-posttest design (intention-to-treat sample). Of these, forty-three families completed the intervention in accordance with the study protocol (per-protocol sample). Compared to families with early discontinuation (n = 23, drop-out sample), adolescents in the per-protocol sample demonstrated a higher level of caregiver-rated emotional problems and a lower level of quality of life at pre-assessment. Caregivers in the per-protocol sample showed high adherence and reported high satisfaction with the intervention. Analyses of the per-protocol sample yielded significant, moderate to large pre-to-post improvements in caregiver-rated ADHD symptoms (primary outcome; d = 0.81, 95% confidence interval [0.51, 1.11]), oppositional, emotional and behavioral problems and quality of life of the adolescents, and caregiver self-efficacy during the intervention. In intention-to-treat analyses, which considered data of all 66 participants who had originally participated in the study, the pre-post differences in ADHD symptoms, emotional and behavioral problems, and quality of life remained at their significant level. These results provide initial support for the feasibility and effectiveness of the TASH intervention. Its efficacy compared to control conditions remains to be examined.
Structural Origins of the Anisotropic Thermal Expansion of BINOL Crystals
Authors: Waddell Paul G. 
Published: April 2024

AbstractMeasurement of the unit cell of (R)-BINOL over a 200° temperature range (300–100 K) reveals an anisotropic contraction where the c-axis contracts ca. 2.3% compared to a ca. 0.45% contraction of the a and b axes, a ca. six-fold difference in linear thermal expansion coefficient. This contraction corresponds to a decrease in the helical pitch of the 31 screw axis in the [001] direction. The anisotropic nature of the contraction is rationalised by a thorough analysis of intermolecular contacts within the crystal and their impact on the conformation of the molecule and crystal packing.
Graphical Abstract
The crystal structure of (R)-BINOL exhibits a pronounced anisotropic thermal expansion.
Racial and Ethnic Disparities in the Effects of Group Prenatal Care On Identification of Intimate Partner Violence: Findings from a Randomized Controlled Trial of CenteringPregnancy
Authors: Kettrey Heather Hensman , Davis Alyssa J. , Britt Jessica L. , Crockett Amy H. 
Published: April 2024

Abstract
Purpose
Intimate Partner Violence (IPV) during pregnancy can have serious consequences for maternal, infant, and child health. Importantly, the risk and consequences of IPV are greater for Black and Hispanic pregnant individuals than for White pregnant individuals. Thus, identification of IPV and referral to services during pregnancy is important, particularly for Black and Hispanic patients. Continuity of care and patient-centered care are thought to be essential for the identification of IPV in healthcare settings. Thus, we proposed that group prenatal care, which involves prenatal care providers delivering health, education, and support services to patients in a group setting, would create an atmosphere that is conducive to the identification of IPV. We specifically expected to see this effect among Black and Hispanic patients because group prenatal care has been hypothesized to increase the quality of the provider-patient relationship and reduce clinical bias against patients of color.

Methods
We conducted a secondary analysis of data from a randomized controlled trial of CenteringPregnancy (N = 523).

Results
We found that group prenatal care does have a significant, positive effect on IPV identification among prenatal care patients, but only for White women. Members of other racial and ethnic groups, who are at increased risk of experiencing IPV and its harmful consequences, do not receive this benefit.

Conclusions
Moving forward, researchers and practitioners should establish whether group prenatal care could be improved by intentionally incorporating a curriculum that directly addresses racial and ethnic disparities in quality of healthcare.

Trial Registration Number and Date
NCT02640638 (Prospectively registered 12/29/2015).
A Spectrally Accurate Step-by-Step Method for the Numerical Solution of Fractional Differential Equations
Authors: Brugnano Luigi , Burrage Kevin , Burrage Pamela , Iavernaro Felice 
Published: April 2024

AbstractIn this paper we consider the numerical solution of fractional differential equations. In particular, we study a step-by-step procedure, defined over a graded mesh, which is based on a truncated expansion of the vector field along the orthonormal Jacobi polynomial basis. Under mild hypotheses, the proposed procedure is capable of getting spectral accuracy. A few numerical examples are reported to confirm the theoretical findings.
Minimizing usage of silane coupling agent for amine-grafted mesoporous silica CO2 adsorbent
Authors: Li Jinrui , Tsunoji Nao , Kumar Rajesh , Sukmana Ndaru Candra , Sadakane Masahiro 
Published: April 2024

AbstractAmine-grafted adsorbents are promising CO2 adsorbents; however, the excessive addition of an amino silane coupling agent during their synthesis increases their production cost. Thus, using low amounts of silane, we synthesized 3-aminopropyltrimethoxysilane (APTMS)-grafted SBA-15 mesoporous silica and evaluated its CO2 adsorption performance. APTMS-grafted SBA-15 samples were prepared using either impregnation or heating–filtration method (grafting). The obtained samples were characterized by X-ray diffraction spectroscopy, transmission electron microscopy, N2 adsorption/desorption, scanning electron microscopy, magic-angle spinning nuclear magnetic resonance, and elemental analysis. The results revealed that the micropores of SBA-15 were preferentially blocked, and APTMS increasingly occupied the mesopores with increasing amine loading. The CO2-adsorption performance of the adsorbents was measured by thermogravimetric analysis under dry conditions. Both synthesis methods achieved high amine immobilization efficiency (78.3–92.2%), as estimated from the amount of silane coupling agents used in the synthesis and that immobilized on the support. The adsorbents prepared by the two methods adsorbed similar amounts of CO2 of approximately 0.5 mmol g− 1 in 400 ppm CO2 and ~ 1.0 mmol g− 1 in 5 vol% CO2. The adsorption amounts attained in this study are comparable to those of previously reported silane-coupling-agent-modified adsorbents that were prepared with more silane. In contrast, the adsorption rate of the samples was affected by the synthesis method, even with similar amine loadings. Nonetheless, the results revealed that even with a low amount of the silane coupling agent, high-performance amine-grafted CO2 adsorbents could be synthesized.
Multi-objective Variational Curves
Authors: Kaya C. Yalçın , Noakes Lyle , Zhang Erchuan 
Published: April 2024

AbstractRiemannian cubics in tension are critical points of the linear combination of two objective functionals, namely the squared $$L^2$$

L
2

norms of the velocity and acceleration of a curve on a Riemannian manifold. We view this variational problem of finding a curve as a multi-objective optimization problem and construct the Pareto fronts for some given instances where the manifold is a sphere and where the manifold is a torus. The Pareto front for the curves on the torus turns out to be particularly interesting: the front is disconnected and it reveals two distinct Riemannian cubics with the same boundary data, which is the first known nontrivial instance of this kind. We also discuss some convexity conditions involving the Pareto fronts for curves on general Riemannian manifolds.
Numerical study on the characterization of NdFeB permanent magnets with fast-neutrons induced (n, n′γ) reactions
Authors: Meleshenkovskii I. , Mauerhofer E. 
Published: April 2024

AbstractThe potential of prompt gamma analysis based on inelastic scattering of 2.5 MeV neutrons for a rapid characterization of NdFeB permanent magnets is investigated by means of numerical simulations using an HPGe detector and a CZT detector-array. The results show that rapid assay of a 42 g magnet can be achieved in some minutes when the neutron flux at sample position is about 1.6 × 109 cm−2 s−1 and the detector count rate limited to 500 kcps. Such a high neutron flux could be delivered by a compact 5 MeV proton accelerator with a thick beryllium target for neutron production through the 9Be(p,xn)9Be.
Innovative approach for quantitative determination of ingested microplastics by Daphnia magna: use of differential scanning calorimetry and thermogravimetry
Authors: Procházková Petra , Kalčíková Gabriela , Maršálková Eliška , Zlámalová Gargošová Helena , Kučerík Jiří 
Published: April 2024

AbstractDue to the serious environmental impacts of plastic pollution, some conventional plastics have been replaced with biodegradable alternatives. However, these biodegradable options can also fragment and form microplastics, which can be ingested by and harm various biota. The standard methods for quantifying ingested microplastics involve digestion of the gut or the whole organism using acidic, alkaline, or oxidative processes. However, these aggressive methods may destroy biodegradable microplastics, leading to erroneous results. Therefore, in this study, we employed thermal analysis methods, specifically differential scanning calorimetry (DSC) and thermogravimetry (TG), to quantify the ingestion of poly-3-hydroxybutyrate (P3HB), a biodegradable microplastic, by the freshwater crustacean, Daphnia magna. During chronic experiments, we analysed organisms exposed to P3HB with sizes smaller than 125 and 63 μm, at concentrations ranging from 1.56–25 mg L−1. DSC identified an endothermic peak associated with the melting of semicrystalline P3HB, and its enthalpies were utilised to compute the number/mass of P3HB ingested by D. magna. Notably, shifts in melting points suggested that higher concentrations induced particle agglomeration, and these agglomerates could not penetrate deeply into the organism. The TG approach involved subtracting the mass loss between 200 and 400 °C in D. magna specimens exposed to P3HB suspensions from controls without P3HB exposure. Both methods provided comparable data, revealing that, depending on particle size, individual D. magna ingested up to 10% of their body mass. Our findings indicate that both methods effectively detect P3HB (and potentially other plastic fragments), with DSC demonstrating better sensitivity. While the suggested approach did not enable us to calculate the level of determination or quantification, we were able to demonstrate that DSC can detect P3HB in only one specimen of D. magna exposed to the lowest suspension concentration. This indicates that D. magna exposed to the 63 μm fraction ingested approximately 3 μg of P3HB, whereas those exposed to the 125 μm P3HB ingested around 4 μg of P3HB. The introduced methods expand the possibilities for detecting ingested microplastics and probably also nanoplastics, in zooplankton and possibly also other species.
Utilizing reinforcement learning for de novo drug design
Authors: Gummesson Svensson Hampus , Tyrchan Christian , Engkvist Ola , Haghir Chehreghani Morteza 
Published: April 2024

AbstractDeep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. Recent studies have demonstrated promising performance for string-based generation of novel molecules utilizing reinforcement learning. In this paper, we develop a unified framework for using reinforcement learning for de novo drug design, wherein we systematically study various on- and off-policy reinforcement learning algorithms and replay buffers to learn an RNN-based policy to generate novel molecules predicted to be active against the dopamine receptor DRD2. Our findings suggest that it is advantageous to use at least both top-scoring and low-scoring molecules for updating the policy when structural diversity is essential. Using all generated molecules at an iteration seems to enhance performance stability for on-policy algorithms. In addition, when replaying high, intermediate, and low-scoring molecules, off-policy algorithms display the potential of improving the structural diversity and number of active molecules generated, but possibly at the cost of a longer exploration phase. Our work provides an open-source framework enabling researchers to investigate various reinforcement learning methods for de novo drug design.
Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach
Authors: Bueff Andreas , Belle Vaishak 
Published: April 2024

AbstractDeep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation and interpretability. Inductive logic programming (ILP) presents an intriguing solution with its data-efficient learning of first-order logic rules. However, ILP grapples with challenges, notably the handling of non-linearity in continuous domains. With the ascent of neuro-symbolic ILP, there’s a drive to mitigate these challenges, synergising deep learning with relational ILP models to enhance interpretability and create logical decision boundaries. In this research, we introduce a neuro-symbolic ILP framework, grounded on differentiable Neural Logic networks, tailored for non-linear rule extraction in mixed discrete-continuous spaces. Our methodology consists of a neuro-symbolic approach, emphasising the extraction of non-linear functions from mixed domain data. Our preliminary findings showcase our architecture’s capability to identify non-linear functions from continuous data, offering a new perspective in neural-symbolic research and underlining the adaptability of ILP-based frameworks for regression challenges in continuous scenarios.
The association of FSCN1 (rs852479, rs1640233) and HOTAIR (rs920778) polymorphisms with the risk of breast cancer in Egyptian women
Authors: Galal Eman Reda , Abdelhakam Dina A. , Ahmed Lamiaa Khalaf , Elhusseny Yasmine , Sayed Sherif El Prince , Eltaweel Noha H. 
Published: April 2024

Abstract

Background
Breast cancer (BC) is one of the most prevalent cancers that contribute to mortality among women worldwide. Despite contradictory findings, considerable evidence suggests that single nucleotide polymorphisms (SNPs) in the FSCN1 and HOTAIR genes may have a causative impact on the development of BC. This case–control study was conducted to evaluate the association of genotype frequency in FSCN1 rs852479, rs1640233, and HOTAIR rs920778 with susceptibility and prognosis of BC, as well as the impact of clinical stages and hormonal features.

Methods and results

FSCN1 (rs852479, rs1640233) and HOTAIR (rs920778) were genotyped using TaqMan real-time PCR assay in 200 BC patients and 200 cancer-free controls, all representing Egyptian women. Genotypic analyses in association with clinicopathological factors and disease risk were assessed. As a result, a significant association with BC risk was observed for CC genotype frequency of FSCN1 rs852479 A > C (OR = 0.395, 95% CI 0.204–0.76, p-value = 0.005). However, no significant correlation was detected between the FSCN1 rs1640233 C > T and HOTAIR rs920778 C > T polymorphic variants and susceptibility to BC. Interestingly, CC genotype of FSCN1 rs1640233 was more likely to progress tumor size and lymph node invasion in BC cases (p-value = 0.04 and 0.02, respectively). Moreover, it was revealed that there was a non-significant correlation between the haplotype distributions of FSCN1 rs852479 and rs1640233 and the probability of BC.

Conclusions
Based on the sample size and genetic characteristics of the subjects involved in the present study, our findings indicated that FSCN1 rs852479 may contribute to BC susceptibility in a sample of the Egyptian population.
Consensus Affinity Graph Learning via Structure Graph Fusion and Block Diagonal Representation for Multiview Clustering
Authors: Gui Zhongyan , Yang Jing , Xie Zhiqiang , Ye Cuicui 
Published: April 2024

AbstractLearning a robust affinity graph is fundamental to graph-based clustering methods. However, some existing affinity graph learning methods have encountered the following problems. First, the constructed affinity graphs cannot capture the intrinsic structure of data well. Second, when fusing all view-specific affinity graphs, most of them obtain a fusion graph by simply taking the average of multiple views, or directly learning a common graph from multiple views, without considering the discriminative property among diverse views. Third, the fusion graph does not maintain an explicit cluster structure. To alleviate these problems, the adaptive neighbor graph learning approach and the data self-expression approach are first integrated into a structure graph fusion framework to obtain a view-specific structure affinity graph to capture the local and global structures of data. Then, all the structural affinity graphs are weighted dynamically into a consensus affinity graph, which not only effectively incorporates the complementary affinity structure of important views but also has the capability of preserving the consensus affinity structure that is shared by all views. Finally, a k–block diagonal regularizer is introduced for the consensus affinity graph to encourage it to have an explicit cluster structure. An efficient optimization algorithm is developed to tackle the resultant optimization problem. Extensive experiments on benchmark datasets validate the superiority of the proposed method.
DialGNN: Heterogeneous Graph Neural Networks for Dialogue Classification
Authors: Yan Yan , Zhang Bo-Wen , Min Peng-hao , Ding Guan-wen , Liu Jun-yuan 
Published: April 2024

AbstractDialogue systems have attracted growing research interests due to its widespread applications in various domains. However, most research work focus on sentence-level intent recognition to interpret user utterances in dialogue systems, while the comprehension of the whole documents has not attracted sufficient attention. In this paper, we propose DialGNN, a heterogeneous graph neural network framework tailored for the problem of dialogue classification which takes the entire dialogue as input. Specifically, a heterogeneous graph is constructed with nodes in different levels of semantic granularity. The graph framework allows flexible integration of various pre-trained language representation models, such as BERT and its variants, which endows DialGNN with powerful text representational capabilities. DialGNN outperforms on CM and ECS datasets, which demonstrates robustness and the effectiveness. Specifically, our model achieves a notable enhancement in performance, optimizing the classification of document-level dialogue text. The implementation of DialGNN and related data are shared through https://github.com/821code/DialGNN.
Estimating the natural disaster ınter-event time defition (NIETD) to define compound natural disasters in South Korea
Authors: Kim Kyunghun , Bae Young Hye , Kim Hung Soo 
Published: April 2024

AbstractPrevious study on natural disaster events has been conducted under the assumption that each event is independent of others, which underestimates the risk of natural disasters by ignoring interactions between events. To solve this problem, the concept of compound natural disaster (CND) which is the combination of events has been proposed; however, there is no quantitative method for defining CND. The aims of this study are to examine the estimation methods for IETD (Inter-Event Time Definition), which separates continuous rainfalls into independent rainfall events, and to define NIETD (Natural disaster Inter-Event Time Definition), which is a criterion for determining the independence of natural disasters. This study used the method of average annual number of events for estimating NIETD. Two natural disasters can be defined as CND if the duration between them is less than the NIETD. We estimated the NIETD as 8 days using natural disasters that occurred in South Korea and identified a total of 89 CNDs of 14 different types such as consecutive rainfall events. The largest number of CNDs was caused by the combination of rainfall and typhoon, which also resulted in the most damage. To examine the randomness of event occurrences, we applied a bootstrapping approach and found that there is no evidence of randomness. The frequency analysis showed that CNDs consisting of rainfall and typhoon (7.6years), and consecutive rainfalls (9.4years) had overwhelmingly more frequent occurrences than other types. The CND definition and concept proposed in this study could be useful in the research on CND.
A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning
Authors: Edrich Ann-Kathrin , Yildiz Anil , Roscher Ribana , Bast Alexander , Graf Frank , Kowalski Julia 
Published: April 2024

AbstractMachine learning has grown in popularity in the past few years for susceptibility and hazard mapping tasks. Necessary steps for the generation of a susceptibility or hazard map are repeatedly implemented in new studies. We present a Random Forest classifier-based landslide susceptibility and hazard mapping framework to facilitate future mapping studies using machine learning. The framework, as a piece of software, follows the FAIR paradigm, and hence is set up as a transparent, reproducible and modularly extensible workflow. It contains pre-implemented steps from conceptualisation to map generation, such as the generation of input datasets. The framework can be applied to different areas of interest using different environmental features and is also flexible in terms of the desired scale and resolution of the final map. To demonstrate the functionality and validity of the framework, and to explore the challenges and limitations of Random Forest-based susceptibility and hazard mapping, we apply the framework to a test case. This test case conveys the influence of the training dataset on the generated susceptibility maps in terms of feature combination, influence of non-landslide instances and representativeness of the training data with respect to the area of interest. A comparison of the test case results with the literature shows that the framework works reliably. Furthermore, the results obtained in this study complement the findings of previous studies that demonstrate the sensitivity of the training process to the training data, particularly in terms of its representativeness.