bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efecto del aceite esencial de Ruta graveolens (Ruda) en altas concentraciones sobre Staphylococcus aureus subesp aureus cepa ATCC® 25904
Authors: Montero Recalde Mayra Andrea , Aroca Tirado Carlos Enrique , Aroca Tirado Álvaro Jeanpool , Chuquimarca Chuquimarca Maria Angelia , Pujos Anda Julio César , Guevara Minaca Esteban Santiago 
Published: January 2025

La investigación tuvo como objetivo evaluar la eficacia antimicrobiana del aceite esencial de ruda (Ruta graveolens) sobre Staphylococcus aureus subesp aureus cepa ATCC ®25904. La planta fue recolectada del sector de Inapisí de la parroquia de Constantino Fernández, el aceite obtenido fue a través del método de hidrodestilación, se evaluaron 10 concentraciones (10%, 20%, 30%, 40%,50%, 60%, 70%, 80%, 90%, 100%) en dilución con agua bidestilada, cada dilución se manejó con un total de 5 ml y se adicionó tween 80 como emulsificador al 0,1%. Los resultados demostraron que la concentración mínima inhibitoria determinada del aceite esencial se definió en el 30% y con respecto a la concentración mínima bactericida evidenciada por el cero crecimiento de colonias en placa, la cual se la estableció al 40% de concentración; el estudio de sensibilidad antimicrobiana precisó estadísticamente con la prueba de Tukey al 0,5% diferencias significativas entre tratamientos, siendo el mejor al 90% de concentración con 25,25 mm de formación de halos de inhibición, no obstante los tratamientos a partir del 30% presentaron formación de halos de manera importante, concluyendo que el aceite esencial de Ruta graveolens posee eficacia antimicrobiana sobre la cepa evaluada a partir del 30% de concentración.
Características morfológicas e industriales en cultivares de arroz “Vinces UG-03” y “Vinces UG-10” en la costa ecuatoriana
Authors: Painii Montero Vicente Frijoth , Vivas Vivas María Leticia , Durán Mera Christian Alejandro , Santillán Muñóz Olimpa Betzabeth , Barcos Arias Milton Senen 
Published: January 2025

La Universidad de Guayaquil, siguiendo los delineamientos de la política agropecuaria del país, por intermedio del centro de apoyo Vinces de la Universidad de Guayaquil, ejecutó dos proyectos de investigación para el desarrollo de nuevas variedades de arroz; de ello se obtuvieron dos variedades denominadas “Vinces UG-03” y “Vinces UG-10”. El objetivo de esta investigación fue evaluar la morfología y características industriales de dos variedades de arroz. Se utilizó un diseño de bloques completamente al azar con cinco tratamientos y cuatro repeticiones, se realizó un análisis de varianza para la comparación de medias mediante la prueba de Duncan (p=<0,05). Se evaluaron parámetros agronómicos e industrial de acuerdo con el protocolo de la “UPOV” Unión Internacional para la Protección de las Obtenciones Vegetales. De acuerdo con los resultados se determinó que la variedad “Vinces UG-03” en las variables: panícula: longitud (cm), cariópside a la madurez: largo con cáscara (mm), cariópside a la madurez: ancho con cáscara (mm), cariópside a la madurez: peso de mil granos con cáscara (gramos), calidad: calidad industrial: proteínas (%) y calidad industrial: rendimiento (%) presentaron los mayores promedios, lo que conlleva a que esta variedad tiene caracteres distintivos que aportarían al desarrollo agro productivo e industrial arrocero.
Feasibility and Acceptability of a Novel Substance Use Prevention Intervention
Authors: Powell Terrinieka , Smith-Black Bianca , Taye Wubishet 
Published: January 2025

Abstract

Background
Youth exposed to household challenges are among the groups most vulnerable to early substance use. To address the needs of this group, this pilot study assessed the feasibility of participant recruitment and retention, as well as the acceptability of a novel substance use prevention intervention, Better Together, for Black youth exposed to household challenges.
Methods
Participants were recruited using school and community presentations, digital flyers, and referrals. Eighty-nine students from two schools were screened. Participant enrollment, session attendance, and assessment completion were used to determine feasibility. Quantitative and qualitative data were collected after each session; responses were used to assess intervention acceptability. Demographic and substance use data were collected at baseline, post-test, and one-month follow-up.
Results
Fifty-nine (96%) youth were eligible, returned their parental permission forms, and enrolled. Completion rates for assessments were above 90% for all three assessments. Nearly 90% of participants attended at least five sessions; the average attendance was 6.5 sessions across conditions. Session feedback forms showed that most students were happy after each session (61%-85%). Qualitative feedback suggests that students were engaged in discussing the key messages using their own words. There were no significant changes in substance use knowledge or intentions over time or by condition. However, there was a small yet significant increase in substance use behaviors over time.
Conclusions
Better Together may be a promising intervention to prevent substance use among Black youth exposed to household challenges. Findings suggest that recruiting, retaining, and engaging participants in the eight-session intervention is possible. Findings will inform plans to implement a full-scale study to evaluate the efficacy and contextual factors that impact effective program implementation.
Trial registration:
Clinicaltrials.gov: [ID BLINDED FOR REVIEW]. Registered [DATE BLINDED FOR REVIEW] - Retrospectively registered, [LINK BLINDED FOR REVIEW]
Assessing Conformation Validity and Rationality of Deep Learning-Generated 3D Molecules
Authors: Huang Bo , Fan Fan , Xi Bin , Meng Xianghu , Wang Han , Zhang Bowen , Xu Qingbo , Feng Wei , Wang Xiaoman , Zhang Hongbo , Zhou Feng , Liu Zhenming , Zhou Wenbiao 
Published: January 2025

Abstract

Recent advancements in artificial intelligence (AI) have revolutionized the field of 3D molecule generation. However, the lack of effective evaluation methods for 3D conformations limits further improvements. Current techniques, in order to achieve the necessary speed for evaluating large number of AI-generated molecules, often rely on empirical geometric metrics that do not adequately capture various conformational anomalies, or on molecular mechanics (MM) energy metrics that exhibit low accuracy and lack atomic or torsional details. To address this gap, we propose a two-stage approach that achieves both high speed and quantum mechanical (QM) level accuracy. The first stage, termed the validity test, employs an AI-derived force field to identify atoms with elevated energy resulting from implausible neighboring environments. The second stage, known as the rationality test, utilizes a deep learning network trained on data with density functional theory (DFT) accuracy to detect rotatable bonds with high torsional energies. To demonstrate the functionality of our evaluation system, we applied our approach to five recently reported 3D molecule generation AI models across 102 targets in Directory of Useful Decoys-Enhanced (DUD-E) dataset. To facilitate accessibility for the academic community, our method is available as an open-source package.
Central Recruitment mechanism of Itch and Pain Through the Paraventricular Thalamic Nucleus in a Pathway- and Intensity-Dependent Manner
Authors: Li Yun-Qing , Li Jia-Ni , Zhao Liu-Jie , Wu Xue-Mei , Wu Feng-Ling , Sun Han-Xue , Hong Jie , Yan Kai-Li , Li Hui , Chen Tao , Dong Yu-Lin 
Published: January 2025

Abstract

The brain is capable of differentiating itch and pain sensations. However, how does the largely overlapped list of nuclei in itch- and pain-matrixes differentially respond to pruriceptive and nociceptive inputs remains unclear. The present study identified distinct recruitment of the central amygdala nucleus (CeA) and the insular cortex (IC), key nodes in both itch- and pain-matrixes, through a parabrachial nucleus (PBN)-paraventricular thalamic nucleus (PVT) pathway that mitigates itch and heightens pain sensations in an intensity-dependent manner. The PVT was divided into discrete functional neuronal ensembles based on morphological, behavioral and electrophysiological evidence. Moreover, lower stimulus intensity was required in PBN-PVT-CeA pathway activation to simulate itch-suppressing effect and induce allodynia than the PBN-PVT-IC pathway. The current work has elucidated the PVT as an essential information sorting nucleus in the thalamus, which might provide a new framework for understanding central nuclei recruitment mechanism of itch and pain.
Polymorphism Analysis of GDF9 and BMP15 Genes and Their Association With Litter Size in Crossbreed Goats in Malaysia
Authors: Faris Armiyas Shibesh , Rodrigues Kenneth Francis , Zeshan Basit , Sarker Subir , Candyrine Su Chui Len , Rahman Mohammad Mijanur , Kalam Mohamad Asrol , Bhuiyan Md Safiul Alam 
Published: January 2025

Abstract

The goat industry plays a crucial role in Malaysia's economy, with breeds such as Katjang and Boer being the integral to mutton supply. However, achieving self-sufficiency in goat meat production is still challenging due to the insufficient local carcass supply. To address this issue, a crossbreeding program has been initiated to improve reproductive performance in local breeds. In mammals, there are BMP15 and GDF9 genes which are the components of TGF-β superfamily that are key regulators of ovulation and litter size. This study aimed to improve reproductive performance in crossbred goats from Katjang and Boer populations in Sabah, Malaysia, by analyzing genetic variations in the BMP15 and GDF9 genes. A total of sixty female cross-Katjang goats, with recorded litter size and parities were sampled. Five milliliters of blood were taken from the jugular vein using K2-EDTA tubes. Genetic analysis was performed using conventional PCR at Biotechnology Research Institute, University Malaysia Sabah, followed by sequencing at Apical Scientific, Malaysia. Four novel polymorphic loci were identified within the GDF9 gene: G320C, A352G, G366T, and G375T. All of these loci resulted in missense mutations, causing a change in amino acid sequences. Notably, the A352G and G375T loci showed significant associations with litter size, with the AG genotype at position 352 and the GG genotype at position 375 emerging as potential markers for selective breeding. However, the polymorphic information content (PIC)values indicated low genetic diversity (< 0.25) at these loci. Interestingly, no polymorphisms were detected in the BMP15 gene, suggesting a monomorphic state (wild-type genotype) in the Cross-Katjang goat population. This finding indicates that BMP15 may not play a significant role in litter size variation in this crossbreed, shifting the focus towards GDF9 as a more relevant genetic marker for enhancing reproductive traits. This study provides important insights into the genetic factors influencing reproductive traits in crossbred goats, with potential applications in marker-assisted selection to improve reproductive and productivity performance.
Angle-Tuned Gross-Neveu Quantum Criticality in Twisted Bilayer Graphene: A Quantum Monte Carlo Study
Authors: Huang Cheng , Parthenios Nikolaos , Ulybyshev Maksim , Zhang Xu , Assaad Fakher , Classen Laura , Meng Zi Yang 
Published: January 2025

Abstract

Twisted bilayber graphene (TBG) has revealed fascinating quantum many-body states, and unstrained chargeneutral TBG at 1.08° is understood, both experimentally and theoretically , as a correlated insulator due to the interplay of long-range Coulomb interactions and the quantum metrics of flat bands. However, the fate of the ground state as one continuously tunes the twisted angle is still largely unknown. Here, theoretically, by employing a newly developed momentum-space continuous-field quantum Monte Carlo method that can fully take into account the long-range Coulomb interactions and quantum metrics of flat bands with system sizes that were not possible before , we show that charge-neutral TBG realizes an angle-tuned quantum phase transition with Gross-Neveu criticality at Θc ~ 1.20(1)°, from the analyses of single-particle spectra, free energy and the order parameter of inter-valley coherence. This shows the exciting possibility that by tuning away from 1.08°, pristine TBG can mimic a fundamental phase transition of Dirac fermions.
Evaluation of Women’s Knowledge about Risk Factors and Early Detection of Breast Cancer among Women Working in Governmental Sector in Gaza Strip, Palestine
Authors: hassouna Eman suliman 
Published: January 2025

Abstract

Purpose: The purpose of the study was to evaluate women's knowledge about signs and symptoms, risk factors and early detection of breast cancer at governmental sector in Gaza Strip.
Materials &Methods:The study utilized a cross-sectional design. The sample included 253 women employed in the ministries in Gaza Strip. Data were collected through a pre-tested self-administered questionnaire.
Results: The results showed no significant association between knowledge about BC and age, marital status, and level of education. Participated women were able to recognize signs and symptoms of BC. They were knowledgeable about risk factors, screening and early detection, the factors that increase the possibility of BC, and measures to prevent development of BC.
Conclusion: The researcher expects that this study may help in increasing the attention about screening and early detection of BC in order to improve the outcome.
Large Language Models Enable Textual Interpretation of Image-Based Astronomical Transient Classifications
Authors: Stoppa Fiorenzo , Bulmus Turan , Bloemen Steven , Smartt Stephen , Groot Paul , Vreeswijk Paul , Smith Ken 
Published: January 2025

Abstract

Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing between real astrophysical signals (e.g., explosive events, variable stars) and bogus imaging artifacts remains challenging. Convolutional neural networks (CNNs) are effective for such real-bogus classification in optical imaging data; however, their reliance on latent representations makes it difficult to discern the underlying physical reasoning behind each classification. Here, we show that large language models (LLMs) achieve accuracy comparable to CNNs on three major optical transient survey datasets (Pan-STARRS, MeerLICHT, and ATLAS) while simultaneously providing direct, human-readable descriptions for every transient. Using only 15 examples and a concise set of instructions, Google's LLM, Gemini, achieves a 93\% average accuracy across these datasets which have quite diverse resolution and pixel scales. This is the first demonstration of a successful application of an LLM to imaging data from optical transient surveys and it eliminates the need for extensive and complex labeled sets. Furthermore, we demonstrate that a second LLM can evaluate the coherence of the first LLM classifications, thus guiding iterative improvements by indicating problematic examples. This opens up new possibilities: rather than laboriously training a network from scratch, one can simply define the desired output characteristics and rely on the LLM to deliver them. Furthermore, by generating textual descriptions of observed features, LLMs enable users to query classifications as if navigating an annotated catalog, rather than deciphering abstract latent spaces. As next-generation telescopes and surveys further increase data streams, LLM-based classification could help bridge the gap between automated detection and transparent, human-level understanding.
Diallel Analysis of Maize (Zea mays L.) Inbred Lines for Yield and Yield Components Under Acidic Soil Conditions in Western Ethiopia
Authors: Asfawu Fekadu , Azmach Girum , Matiwos Temesgen 
Published: January 2025

Abstract

Soil acidity, particularly due to aluminum toxicity, limits maize production in Western Ethiopia, emphasizing the need for acid-tolerant maize varieties. This study assessed the general combining ability (GCA) and specific combining ability (SCA) of seven maize inbred lines, selected based on their aluminum tolerance identified through preliminary hydroponic screening. Using a half-diallel mating design (Griffing, Method 4, Model 1), 21 single cross hybrids were created from these inbred lines and evaluated under acid soil conditions in Migna Kersa Kebele, Leka Dulecha district, East Wollega Zone, Oromia, during the 2023 cropping season. Significant genotype variation (P < 0.05) was observed across most traits, indicating their suitability for breeding. Both GCA and SCA effects were significant (P < 0.05), reflecting the role of both additive and non-additive gene actions in trait inheritance. Inbred lines VL144091, VL153179, and VL144011 showed positive and significant GCA effects for grain yield, indicating their potential as valuable contributors to acid soil tolerance. Specific hybrid combinations, including VL144091 x VL153179 and VL144091 x VL144011, demonstrated positive SCA effects for grain yield. These findings offer valuable insights into the genetic mechanisms of acid soil tolerance in maize, aiding in the selection of parental lines and hybrids for developing resilient maize varieties.
Leveraging Domain Motif Assembler for Multi-objective, Multi-domain and Explainable Molecular Design
Authors: Tian Yonghong , Chen Zijun , Wang Yu , Lv Liuzhenghao , Li Hao , Lin Zongying , Yuan Li 
Published: January 2025

Abstract

Designing molecules to meet multiple objectives simultaneously is a significant challenge, rooted in the inherent complexity of structure-property relationships and the limited availability of labeled molecular data. Compounding this difficulty, the black-box nature of deep learning models hampers interpretability, undermining confidence in their applications and restricting broader industrial adoption. To address these limitations, we introduce Domain Motif Assembler (DM-Assembler), a novel framework that employs a score-based matching model to guide the selection and assembly of molecular motifs. DM-Assembler excels in generating high-performance molecules that satisfy multi-objective constraints across various domains, drastically reducing the combinatorial complexity of chemical space by leveraging domain motifs and diffusion models on discrete fragment representations. Comprehensive experimental evaluations reveal three distinct advantages of DM-Assembler. First, it delivers exceptional performance in designing molecules with optimized composite metrics, achieving an average improvement of approximately 56\% across four benchmark domains, while extending beyond the property boundaries of its training data. Second, it generates molecular distributions that closely match domain-specific datasets, recording the lowest KL divergence among 149 descriptors encompassing multi-scale topological structures and biochemical properties—outperforming the second-best method by roughly 19\%. Third, we developed a post-hoc interpretability method based on 149 molecular descriptors, revealing DM-Assembler’s superior ability to fit physicochemical properties compared to atom-based models and topological structures compared to non-diffusion models, highlighting its advantages in molecular generation tasks. Furthermore, DM-Assembler demonstrates outstanding conditional sampling capabilities, seamlessly integrating auxiliary scorers or domain motif priors for efficient and accurate conditional generation. This enables deeper qualitative exploration of structure-activity relationships and accelerates pharmacophore discovery. Overall, we anticipate that DM-Assembler will advance trust-worthy and multi-objective molecular design across diverse disciplines, fostering significant progress in related research fields.
Mass Production of Gradient Perovskite Nanowires via Microscale Thermal Engineering
Authors: Yang Yiming , Li Jianliang , Li Jing , Xu Jiao , Liu Weili , Tan Conghui , An Meiqi , Yang Shuai , Bao Yanan , Tang Huayi , Bao Han , Luo Yingmin , Fang Yurui 
Published: January 2025

Abstract

The bandgap engineering of halide perovskites at microscopic level is challenging due to fabrication complexity, environmental sensitivity and material stability. Here we report a facile approach to high-yield compositional graded perovskite nanowires (NWs) via vapor-phase anion exchange methods. Using rationally engineered thermal inhomogeneity along the length of single NWs, arrays of NWs with compositional gradient across tens of micrometers can be readily mass-produced via bottom-up as well as top-down exchange strategies. These exchanged NWs exhibit well-preserved single-crystallinity for efficient optical and electrical transport, while their halogen stoichiometry, fluorescence, and energy band structure demonstrate apparent axial gradient. Detailed analysis of elemental distribution and thermal simulation reveal that the ultralow thermal conductivity together with reduced dimensionality leads to microscale temperature gradient, which is further converted to compositional gradient upon anion exchange. In addition, the gradient NWs show excellent optoelectronic features suitable for further integration into functional devices. This work provides guidelines for composition manipulation of perovskites through thermal engineering, extending their applications in ultracompact microspectrometers, spectral imaging sensors, and other miniaturized optoelectronic devices.
Coping with the Dunkelflaute: Power system implications of variable renewable energy droughts in Europe
Authors: Kittel Martin , Roth Alexander , Schill Wolf-Peter 
Published: January 2025

Abstract

Coping with prolonged periods of low availability of wind and solar power, also referred to as "Dunkelflaute", emerges as a key challenge for realizing a decarbonized European energy system fully based on renewable energy sources. Here we investigate the role of long-duration electricity storage and geographical balancing in dealing with such variable renewable energy droughts. To this end, we combine renewable availability time series analysis and power sector modeling, using 36 historical weather years. We find that extreme drought events define long-duration storage operation and investment. The most extreme event in Europe occurred in the winter of 1996/97. Assuming policy-relevant interconnection, long-duration storage of 351TWh or 7% of yearly electricity demand is required to deal with this event. As it affects many countries simultaneously, a storage capacity of 159 TWh or 3% of yearly electricity demand remains required even in the extreme case of unconstrained geographical balancing. Before and during Dunkelflaute events, we find complex interactions of long-duration storage with other flexibility options. Sensitivity analyses illustrate that firm zero-emission generation technologies would only moderately reduce long-duration storage needs. Thus, policymakers and system planners should prepare for a rapid expansion of long-duration storage capacity to safeguard the renewable energy transition in Europe. We further argue that using multiple weather years that include pronounced renewable energy droughts is required for weather-resilient energy system modeling.