bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compatibilizing of cotton fabric with hydrophobic drug cover layer for anti-inflammatory performance with the implementation of ibuprofen
Authors: Kopańska Aneta , Brzeziński Marek , Gonciarz Weronika , Draczyński Zbigniew 
Published: March 2024

AbstractThis paper presents active analgesic and anti-inflammatory dressings based on cotton woven material with surface functionalization enabling drug implementation. For this purpose, lactide was polymerized on the surface of cotton textiles to achieve better compatibility with hydrophobic drug and polylactide (PLA)-based macromolecules. Subsequently, ibuprofen-loaded PLA and PLA-PEG were implemented through the exhausting method. Such material was tested for cytotoxicity (toward L929 mouse fibroblasts) and anti-inflammatory activity (towards human Hs68 fibroblasts) based on the secretion of pro-inflammatory cytokines IL-1β and TNF-α. The results showed that the drug attachment and its performance are influenced by a combination of mercerization, bleaching and polylactide grafting, and the release of ibuprofen depends on the drug-loaded layer structure. Moreover, we show that cotton woven fabric with ibuprofen-loaded PLA and PLA-PEG cover layers had anti-inflammatory properties. These new dressings may open possibilities for developing prolonged analgesic and anti-inflammatory materials for wound healing or transdermal drug delivery.
A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning
Authors: Guo Jia , Yuan Hao , Shi Binghua , Zheng Xiaofeng , Zhang Ziteng , Li Hongyan , Sato Yuji 
Published: March 2024

AbstractAssistive medical image classifiers can greatly reduce the workload of medical personnel. However, traditional machine learning methods require large amounts of well-labeled data and long learning times to solve medical image classification problems, which can lead to high training costs and poor applicability. To address this problem, a novel unsupervised breast cancer image classification model based on multiscale texture analysis and a dynamic learning strategy for mammograms is proposed in this paper. First, a gray-level cooccurrence matrix and Tamura coarseness are used to transfer images to multiscale texture feature vectors. Then, an unsupervised dynamic learning mechanism is used to classify these vectors. In the simulation experiments with a resolution of 40 pixels, the accuracy, precision, F1-score and AUC of the proposed method reach 91.500%, 92.780%, 91.370%, and 91.500%, respectively. The experimental results show that the proposed method can provide an effective reference for breast cancer diagnosis.
A novel proteomic signature of osteoclast differentiation unveils the deubiquitinase UCHL1 as a necessary osteoclastogenic driver
Authors: Materozzi Maria , Resnati Massimo , Facchi Cecilia , Trudu Matteo , Orfanelli Ugo , Perini Tommaso , Gennari Luigi , Milan Enrico , Cenci Simone 
Published: March 2024

AbstractBone destruction, a major source of morbidity, is mediated by heightened differentiation and activity of osteoclasts (OC), highly specialized multinucleated myeloid cells endowed with unique bone-resorptive capacity. The molecular mechanisms regulating OC differentiation in the bone marrow are still partly elusive. Here, we aimed to identify new regulatory circuits and actionable targets by comprehensive proteomic characterization of OCgenesis from mouse bone marrow monocytes, adopting two parallel unbiased comparative proteomic approaches. This work disclosed an unanticipated protein signature of OCgenesis, with most gene products currently unannotated in bone-related functions, revealing broad structural and functional cellular reorganization and divergence from macrophagic immune activity. Moreover, we identified the deubiquitinase UCHL1 as the most upregulated cytosolic protein in differentiating OCs. Functional studies proved it essential, as UCHL1 genetic and pharmacologic inhibition potently suppressed OCgenesis. Furthermore, proteomics and mechanistic dissection showed that UCHL1 supports OC differentiation by restricting the anti-OCgenic activity of NRF2, the transcriptional activator of the canonical antioxidant response, through redox-independent stabilization of the NRF2 inhibitor, KEAP1. Besides offering a valuable experimental framework to dissect OC differentiation, our study discloses the essential role of UCHL1, exerted through KEAP1-dependent containment of NRF2 anti-OCgenic activity, yielding a novel potential actionable pathway against bone loss.
17 variants interaction of Wnt/β-catenin pathway associated with development of osteonecrosis of femoral head in Chinese Han population
Authors: Shi Chuankai , Li Xin , Sun Yu , Du Zhenwu , Zhang Guizhen , Che Zhenjia , Li Qingyu , Song Shiliang , Guo Jing , Sun Haoyan , Song Yang 
Published: March 2024

AbstractThe genes of Wnt/β-catenin pathway may have potential roles in fat accumulation of Non-traumatic osteonecrosis of the femoral head (ONFH), but the effects of their variants in the pathway on ONFH development have been remained unclear. To explore the potential roles of the variants in the development of ONFH, we completed the investigation of the paired interactions as well as their related biological functions of 17 variants of GSK3β, LRP5, and FRP4 genes etc. in the pathway. The genotyping of the 17 variants were finished by MASS ARRAY PLATFORM in a 560 ONFH case–control system. The association of variants interactions with ONFH risk and clinical traits was evaluated by logistic regression analysis etc. and bioinformatics technology. The results showed that the genotype, allele frequency, and genetic models of Gsk3β rs334558 (G/A), SFRP4 rs1052981 (A/G), and LRP5 rs312778 (T/C) were significantly associated with the increased and decreased ONFH risk and clinical traits, respectively (P < 0.001–0.0002). Particularly, the paired interactions of six variants as well as eight variants also showed statistically increased and decreased ONFH risk, bilateral hip lesions risk and stage IV risk of ONFH, respectively (P < 0.044–0.004). Our results not only at the first time simultaneously showed exact serum lipid disorder and abnormal platelet function of ONFH in the same study system with the 17 variants polymorphisms of Wnt/β-catenin pathway but also shed light on the variants closely intervening the lipid disorder and abnormal coagulation of ONFH.
Low to moderate prenatal alcohol exposure and neurodevelopment in a prospective cohort of early school aged children
Authors: Muggli Evelyne , Halliday Jane , Hearps Stephen , Nguyen Thi-Nhu-Ngoc , Penington Anthony , Thompson Deanne K. , Spittle Alicia , Forster Della A. , Lewis Sharon , Elliott Elizabeth J. , Anderson Peter J. 
Published: March 2024

AbstractEvidence is strong for adverse fetal effects of high level or chronic prenatal alcohol exposure (PAE), but many pregnant women continue to drink at lower levels. The ‘Asking Questions about Alcohol in pregnancy’ prospective cohort aimed to determine the neurodevelopmental consequences at 6–8 years of age of low to moderate PAE. 1570 women from seven public antenatal clinics in Melbourne, Australia, provided information on frequency and quantity of alcohol use, and obstetric, lifestyle and socio-environmental confounders at four gestation timepoints. PAE was classified into five trajectories plus controls. At 6–8 years, 802 of 1342 eligible families took part and completed a questionnaire (60%) and 696 children completed neuropsychological assessments (52%). Multiple linear regressions examined mean outcome differences between groups using complete case and multiple imputation models. No meaningful relationships were found between any of the PAE trajectories and general cognition, academic skills, motor functioning, behaviour, social skills, social communication, and executive function. Maternal education most strongly influenced general cognition and academic skills. Parenting behaviours and financial situation were associated with academic skills, behaviour, social skills and/or executive function. The lack of association between PAE and neurodevelopment at 6–8 years may partly be explained by cumulative positive effects of socio-environmental factors.
Axial compression tests on CFRP strengthened CFS plain angle short columns
Authors: Vivek K. S. , Dar Mohammad Adil , Ali M. I. , Manohar M. , Sreedhar Babu T. 
Published: March 2024

AbstractA comprehensive test program was performed to experimentally investigate the effect of CFRP strengthening on the axial strength and stability of CFS plain angle short columns subjected to monotonic axial compression. A total of 28 specimens were tested by varying the CFRP strengthening configurations for different column heights. Both uni-directional (CF_UD) and bi-directional (CF_BD) CFRP were considered. The influence of various parameters such as the type of CFRP, fiber orientation, and number of CFRP layers was investigated and discussed in detail. For single layer (ply) of CFRP, CF_UD-0° strengthening configuration resulted in maximum increase of axial capacity by 58.33% and 45.72% (in comparison to bare steel specimens), corresponding to 0.5 m and 1.0 m column lengths respectively. All the bare steel and skin-strengthened specimens failed predominantly due to torsional–flexural buckling mode. Additional layer of CFRP wrapping was found to enhance the axial capacity further and CF_UD-0°/BD was found to possess greater capacity in the case of double layer of CFRP. Adopting cardboard in-fill in addition to CF_UD-0° wrap has prevented the torsional mode of buckling and resulted in a peak increase of axial capacity by 192.55% and 240.61% corresponding to 500 mm and 100 mm long specimens, respectively.
Development of antibacterial collagen membranes with optimal silver nanoparticle content for periodontal regeneration
Authors: Takallu Sara , Kakian Farshad , Bazargani Abdollah , Khorshidi Hooman , Mirzaei Esmaeil 
Published: March 2024

AbstractThe effective control of pathogenic bacteria is crucial in the restoration of periodontal tissue affected by periodontitis. Guided tissue regeneration (GTR) membranes are commonly used to aid in the repair of periodontal defects. Therefore, there is a clear advantage in developing antibacterial periodontal membranes that can effectively eliminate infections and promote tissue regeneration. This study aimed to create a collagen membrane with optimal content of silver nanoparticles (AgNPs) for effective antibacterial properties and minimal toxicity to mammalian cells. Ascorbic acid-reduced AgNPs were incorporated into collagen at the ratio of 0.5%, 1%, 2%, and 3% (based on total dry weight). Collagen/AgNPs hydrogels were compressed and freeze-dried to form membranes and then were characterized. Antibacterial activity was tested against Fusobacterium nucleatum and Enterococcus faecalis, and membrane cytocompatibility was accomplished on human gingival fibroblasts. Membranes with 2% and 3% AgNPs exhibited significant antibacterial activity, while 1% showed minimal activity and 0.5% and 0% showed none. HGF cells on the 3% AgNPs membrane had poor viability, proliferation, and adhesion, but 0%, 0.5%, 1%, and 2% AgNPs membranes showed desirable cellular behavior. In conclusion, the collagen membrane with 2% AgNPs demonstrated both antibacterial capacity and excellent cytocompatibility, making it a promising choice for periodontal treatments, especially in GTR approaches.
Study on the effect of different high-voltage electric field polarization process parameters on the vitality of dried chili pepper seeds
Authors: Sun Sheng , Hu Bin , Wu Xinming , Luo Xin , Guo Mengyu , Liu Hanjun 
Published: March 2024

AbstractTo study the effect of different high-voltage electric field polarisation treatment process parameters on the viability of seeds of dried chili peppers. In this study, a high-voltage electrostatic polarisation treatment system was constructed to carry out experiments on the effects of different high-voltage electric field polarisation treatment process parameters on the viability of dried chili seeds. Conduct one-way tests to determine the preferred polarisation method and the preferred interval for output voltage and polarisation time. Two-factor, five-level central combination test with output voltage and polarization time as test factors and seed conductivity as a response indicator. Determining the better combination of parameters for output voltage and polarization time; Conducting seed germination trials to validate the effectiveness of the polarisation process. The results of the one-way test showed that: Negative-voltage polarisation was more effective than positive-voltage polarisation and alternating positive–negative-voltage polarisation in promoting seed vigor, with a better output voltage in the range of 10–14 kV, and a better polarisation time in the range of 20–40 s; The results of orthogonal tests showed that: Under the condition of negative voltage polarisation treatment, the output voltage of 12.08 kV and polarisation time of 30.32 s was the better parameter combination, at which the seed conductivity was minimum 159.87 uS/(cm g). Analyzing the function of cell membrane selective semi-permeability by seed conductivity change and revealing the mechanism of seed viability enhancement by high voltage electric field polarisation treatment; In the seed germination test, compared with the control group, seed germination potential increased by 9.09%, germination rate increased by 20.45%, germination index increased by 3.49, and vigor index increased by 41.66 under high-voltage electrostatic polarisation treatment, and all vigor indexes were significantly improved. The results of this study can provide a basis for the selection of processes and parameters for subsequent high-voltage electric field polarisation treatment of crop seeds.
Spatial and temporal variation in surface nitrate and phosphate in the Northern Gulf of Mexico over 35 years
Authors: Acosta Kailani G. , Juhl Andrew R. , Subramaniam Ajit , Duhamel Solange 
Published: March 2024

AbstractDissolved inorganic nutrient concentrations in the surface waters (0 to 5 m) of the Northern Gulf of Mexico (NGoM) were analyzed from 1985 to 2019 (> 10,000 observations) to determine spatiotemporal trends and their connection to nutrients supplied from the Mississippi/Atchafalaya River (MAR). In the NGoM, annual mean dissolved inorganic P (DIP) concentrations increased significantly over time, while dissolved inorganic N (DIN) concentrations showed no temporal trend. With greater salinity, mean DIN:DIP decreased from above the Redfield ratio of 16 to below it, reflecting DIN losses and the more conservative behavior of DIP with salinity. Over the same time period, annual mean P (total dissolved P, DIP, dissolved organic P) loading from the MAR to the NGoM significantly increased, annual mean DIN and total dissolved N loading showed no temporal trend, and dissolved organic N loading significantly decreased. Though DIP increased in the MAR, MAR DIP alone was insufficient to explain the surface distribution of DIP with salinity. Therefore, increases in surface DIP in the NGoM are not simply a reflection of increasing MAR DIP, pointing to temporal changes in other DIP sources. The increase in NGoM DIP suggests greater N limitation for phytoplankton, with implications for N fixation and nutrient management.
Intensification and shutdown of deep convection in the Labrador Sea were caused by changes in atmospheric and freshwater dynamics
Authors: Yashayaev Igor 
Published: March 2024

AbstractLabrador Sea winter convection forms a cold, fresh and dense water mass, Labrador Sea Water, that sinks to the intermediate and deep layers and spreads across the ocean. Convective mixing undergoes multi-year cycles of intensification (deepening) and relaxation (shoaling), which have been also shown to modulate long-term changes in the atmospheric gas uptake by the sea. Here I analyze Argo float and ship-based observations to document the 2012-2023 convective cycle. I find that the highest winter cooling for the 1994-2023 period was in 2015, while the deepest convection for the 1996-2023 period was in 2018. Convective mixing continued to deepen after 2015 because the 2012-2015 winter mixing events preconditioned the water column to be susceptible to deep convection in three more years. The progressively intensified 2012-2018 winter convections generated the largest and densest class of Labrador Sea Water since 1995. Convection weakened afterwards, rapidly shoaling by 800 m per year in the winters of 2021 and 2023. Distinct processes were responsible for these two convective shutdowns. In 2021, a collapse and an eastward shift of the stratospheric polar vortex, and a weakening and a southwestward shift of the Icelandic Low resulted in extremely low surface cooling and convection depth. In 2023, by contrast, convective shutdown was caused by extensive upper layer freshening originated from extreme Arctic sea-ice melt due to Arctic Amplification of Global Warming.
Discovery of excellent ultraviolet nonlinear optical materials in chlorates and bromates with highly stereochemically active lone pairs
Authors: Hu Chun-Li , Chen Qian-Qian , Kong Fang , Mao Jiang-Gao 
Published: March 2024

The long-neglected stereochemically active lone pair groups ClO3−and BrO3−are demonstrated to be important UV NLO functional motifs based on the fact that AClO3 and ABrO3 possess high SHG effects and short λPM and are promising UV NLO materials.
TTMRI: Multislice texture transformer network for undersampled MRI reconstruction
Authors: Zhang Xiaozhi , Zhou Liu , Wan Yaping , Ling Bingo Wing‐Kuen , Xiong Dongping 
Published: March 2024

AbstractMagnetic resonance imaging (MRI) is a non‐interposition imaging technique that provides rich anatomical and physiological information. Yet it is limited by the long imaging time. Recently, deep neural networks have shown potential to significantly accelerate MRI. However, most of these approaches ignore the correlation between adjacent slices in MRI image sequences. In addition, the existing deep learning‐based methods for MRI are mainly based on convolutional neural networks (CNNs). They fail to capture long‐distance dependencies due to the small receptive field. Inspired by the feature similarity in adjacent slices and impressive performance of Transformer for exploiting the long‐distance dependencies, a novel multislice texture transformer network is presented for undersampled MRI reconstruction (TTMRI). Specifically, the proposed TTMRI is consisted of four modules, namely the texture extraction, correlation calculation, texture transfer and texture synthesis. It takes three adjacent slices as inputs, in which the middle one is the target image to be reconstructed, and the other two are auxiliary images. The multiscale features are extracted by the texture extraction module and their inter‐dependencies are calculated by the correlation calculation module, respectively. Then the relevant features are transferred by the texture transfer module and fused by the texture synthesis module. By considering inter‐slice correlations and leveraging the Transformer architecture, the joint feature learning across target and adjacent slices are encouraged. Moreover, TTMRI can be stacked with multiple layers to recover more texture information at different levels. Extensive experiments demonstrate that the proposed TTMRI outperforms other state‐of‐the‐art methods in both quantitative and qualitative evaluationsions.
Haltungs- und Bewegungsasymmetrien im Säuglingsalter
Authors: Sacher Robby 
Published: March 2024

ZusammenfassungObwohl ca. 20% aller Säuglinge Haltungs- und Bewegungsasymmetrien der Kopf-, Rumpf-,
und Extremitätenhaltung aufweisen, existiert keine für diesen Symptomkomplex
einheitliche Diagnose. Für einige therapeutische Herangehensweisen liegen
kontrollierte Studien vor, für andere Erfahrungsberichte. Doch welche Therapieformen
sind am besten untersucht, welche am erfolgversprechendsten? „Best Practice“ oder
Vielfalt? Eine Antwort könnte sein: „Good Practice“ mit Augenmaß.
Accelerating defect predictions in semiconductors using graph neural networks
Authors: Rahman Md Habibur , Gollapalli Prince , Manganaris Panayotis , Yadav Satyesh Kumar , Pilania Ghanshyam , DeCost Brian , Choudhary Kamal , Mannodi-Kanakkithodi Arun 
Published: March 2024

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.