bluuebunny/crossref_metadata_2025_split huggingface dataset:
A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model
Authors: Xiang Xiuli , Li Xingyu , Zhang Yaoli , Hu Jiang 
Published: March 2024

AbstractDue to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method generally outperformed the other baseline models in all four seasons of the year according to three performance assessment metrics: the normalized root mean square error (RMSE), normalized mean absolute error (MAE) and coefficient of determination (R2). The best RMSE, MAE and R2 reached 0.0195, 0.0128 and 99.72%, respectively, with maximum improvements of 11.32%, 8.57% and 0.38%, respectively, over those of the suboptimal model. Therefore, the model proposed in this paper is effective at improving prediction accuracy. Using the proposed method, this paper concludes with multistep predictions of 3, 6, and 9 steps, which also indicates that the proposed method significantly outperforms the other models.
3D geological and petrophysical modeling of Alam El-Bueib Formation using well logs and seismic data in Matruh Field, northwestern Egypt
Authors: Ali Walaa A. , Deaf Amr S. , Mostafa Taher 
Published: March 2024

AbstractThere are several productive petroleum fields in the North Western Desert (WD) of Egypt, which received extensive investigations regarding their petroleum potential. However, a few studies tackled the Matruh Oil Field, which contains the oil prolific Early Cretaceous Alam El-Bueib Formation (AEB Fm) reservoir. The reservoir intervals of the AEB Fm show substantial lithological variations across the basin. Therefore, it is necessary to analyze the vertical and lateral distributions in terms of their lithological and petrophysical properties. To achieve this objective, wireline logs of four wells and 20-2D seismic lines were used to construct a depth-structure contour map for the studied part of the field. This map was used to build the field’s structure model and to identify the fault patterns in the basin through several seismic lines. Analyses of well logs data and lithology were used to estimate the petrophysical properties of AEB sandstone units AEB-1, AEB-3A, AEB-3C, and AEB-6. Results show that the AEB-6 Unit is the most promising hydrocarbon-bearing unit. It has a net pay of 20–160 feet, a shale volume of 5–20%, an effective porosity of 14–20%, and a hydrocarbon saturation of 70–88%. The structure-depth maps indicate a number of normal faults with two principal NE-SW and NW–SE trends, which probably act as structural traps in the Matruh Oil Field. The constructed structure-depth maps and calculated petrophysical parameters were used to build a three-dimensional reservoir model. A blind well was used to validate the accuracy and reliability of the facies, porosity, and saturation models for the AEB Fm units, ensuring a good match between log-derived data and built models. The AEB Fm shows regional heterogeneous variations in its petrophysical characteristics. It exhibits unconventional reservoir characteristics in a N–S direction and conventional reservoir characteristics in an E–W direction. This observed heterogeneity shows the need to carry out further investigations to comprehensively assess the hydrocarbon potential of AEB Fm in different areas of the Matruh Basin.
Interface engineering for facile switching of bulk-strong polarization in Si-compatible vertical superlattices
Authors: Kumar Pawan , Lee Jun Hee 
Published: March 2024

AbstractFerroelectric thin films incorporating different compositional layers have emerged as a promising approach for enhancing properties and performance of electronic devices. In recent years, superlattices utilizing various interactions between their constituent layers have been used to reveal unusual properties, such as improper ferroelectricity, charged domain walls, and negative capacitance in conventional ferroelectrics. Herein, we report a symmetry scheme based on the interface engineering in which the inherent cell-doubling symmetry allowed atomic distortions (phonons) in any vertically aligned superlattice activate novel interface couplings among atomic distortions of different symmetries and fundamentally improve the ferroelectric properties. In a materialized case, the ionic size difference between Hf4+ and Ce4+ in the HfO2/CeO2 (HCO) ferroelectric/paraelectric superlattice leads to these couplings. These couplings mitigate the phase boundary between polar and non-polar phases, and facilitate polarization switching with a remarkably low coercive field ($${E}_{c}$$

E
c

) while preserving the original magnitude of the bulk HfO2 polarization and its scale-free ferroelectric characteristics. We show that the cell-doubled distortions present in any vertical superlattice have unique implications for designing low-voltage ferroelectric switching while retaining bulk-strong charge storing capacities in Si-compatible memory candidates.
The unveiling of a dynamic duo: hydrodynamic cavitation and cold plasma for the degradation of furosemide in wastewater
Authors: Verdini Federico , Abramova Anna , Boffa Luisa , Calcio Gaudino Emanuela , Cravotto Giancarlo 
Published: March 2024

AbstractThe degradation in water of furosemide (FUR), a widely used diuretic drug, was herein reported. The method entails an integrated approach based on the hybridisation of hydrodynamic cavitation (HC) with electrical discharge (ED) plasma technology. This dynamic duo could increase the production of oxidising compounds in water, in particular hydroxyl radicals (OH radicals), by triggering the rapid homolytic decomposition of water molecules and avoiding the addition of external oxidants. This study clearly emphasises the effectiveness of an integrated approach to improve the degradation of pollutants in wastewater originating from active pharmaceutical ingredients (APIs). The results of HC/ED-assisted FUR degradation in the presence of radical scavengers highlight the predominant role of the radical oxidation mechanism at the gas–liquid interface of the cavitation bubble during HC/ED treatment. A comparative analysis of the three technologies—HC alone, HC/ED and UV alone—emphasised the promising potential of hybrid HC/ED as a scalable industrial technology. This is demonstrated by the higher degradation rates (100%, 10 min) when treating large volumes (5L) of wastewater contaminated with FUR (50 mg/L), even in the presence of other APIs.
Alteration in kynurenine pathway metabolites in young women with autoimmune thyroiditis
Authors: Krupa Anna , Łebkowska Agnieszka , Kondraciuk Marcin , Kaminski Karol Adam , Kowalska Irina 
Published: March 2024

AbstractThe kynurenine pathway (KP) of tryptophan degradation includes several compounds that reveal immunomodulatory properties. The present study aimed to investigate the alteration in KP metabolites in young women with autoimmune thyroiditis (AIT) and their associations with thyroid function. The thyroid function tests, antithyroid antibodies measurement and ultrasonography of the thyroid gland have been performed in 57 young women with AIT and 38 age-matched healthy controls. The serum levels of tryptophan, kynurenine (KYN) and its metabolites were determined, and the activity of KP enzymes was calculated indirectly as product-to-substrate ratios. KP was activated and dysregulated in AIT, along with significantly elevated levels of KYN and anthranilic acid (AA), at the expense of the reduction of kynurenic acid (KYNA), which was reflected by the increase in the AA/KYNA ratio (p < 0.001). In univariate and multiple regression analyses, peripheral deiodinase (SPINA-GD) activity in AIT was positively associated with KYNA, AA, and quinolinic acid (QA). The merger of AA, AA/KYNA ratio, QA and SPINA-GD exhibited the highest sensitivity and specificity to predict AIT (p < 0.001) in receiver operating characteristic (ROC) analysis. In conclusion, the serum KYN metabolite profile is dysregulated in young women with AIT and could serve as a new predictor of AIT risk.
The immune regulation and therapeutic potential of the SMAD gene family in breast cancer
Authors: Chen Zhuo , Wang Yu , Lu Xiaodi , Chen Hong , Kong Yiran , Rong Liwei , Wang Guonian 
Published: March 2024

AbstractBreast cancer is a serious threat to human health. The transforming growth factor-β signaling pathway is an important pathway involved in the occurrence and development of cancer. The SMAD family genes are responsible for the TGF-β signaling pathway. However, the mechanism by which genes of the SMAD family are involved in breast cancer is still unclear. Therefore, it is necessary to investigate the biological roles of the SMAD family genes in breast cancer. We downloaded the gene expression data, gene mutation data, and clinical pathological data of breast cancer patients from the UCSC Xena database. We used the Wilcox test to estimate the expression of genes of the SMAD family in cancers. And the biological functions of SMAD family genes using the DAVID website. The Pearson correlation method was used to explore the immune cell infiltration and drug response of SMAD family genes. We conducted in biological experiments vitro and vivo. In this study, we integrated the multi-omics data from TCGA breast cancer patients for analysis. The expression of genes of SMAD family was significantly dysregulated in patients with breast cancer. Except for SMAD6, the expression of other SMAD family genes was positively correlated. We also found that genes of the SMAD family were significantly enriched in the TGF-β signaling pathway, Hippo signaling pathway, cell cycle, and cancer-related pathways. In addition, SMAD3, SMAD6, and SMAD7 were lowly expressed in stage II breast cancer, while SMAD4 and SMAD2 were lowly expressed in stage III cancer. Furthermore, the expression of genes of the SMAD family was significantly correlated with immune cell infiltration scores. Constructing a xenograft tumor mouse model, we found that SMAD3 knockdown significantly inhibited tumorigenesis. Finally, we analyzed the association between these genes and the IC50 value of drugs. Interestingly, patients with high expression of SMAD3 exhibited significant resistance to dasatinib and staurosporine, while high sensitivity to tamoxifen and auranofin. In addition, SMAD3 knockdown promoted the apoptosis of BT-549 cells and decreased cell activity, and BAY-1161909 and XK-469 increased drug efficacy. In conclusion, genes of the SMAD family play a crucial role in the development of breast cancer.
Exploring water-absorbing capacity: a digital image analysis of seeds from 120 wheat varieties
Authors: Khan Tooba , Jamil Muhammad , Ali Aamir , Rasheed Sana , Irshad Asma , Maqsood Muhammad Faisal , Zulfiqar Usman , Chaudhary Talha , Ali M. Ajmal , Elshikh Mohamed S. 
Published: March 2024

AbstractWheat is a staple food crop that provides a significant portion of the world's daily caloric intake, serving as a vital source of carbohydrates and dietary fiber for billions of people. Seed shape studies of wheat typically involve the use of digital image analysis software to quantify various seed shape parameters such as length, width, area, aspect ratio, roundness, and symmetry. This study presents a comprehensive investigation into the water-absorbing capacity of seeds from 120 distinct wheat lines, leveraging digital image analysis techniques facilitated by SmartGrain software. Water absorption is a pivotal process in the early stages of seed germination, directly influencing plant growth and crop yield. SmartGrain, a powerful image analysis tool, was employed to extract precise quantitative data from digital images of wheat seeds, enabling the assessment of various seed traits in relation to their water-absorbing capacity. The analysis revealed significant transformations in seed characteristics as they absorbed water, including changes in size, weight, shape, and more. Through statistical analysis and correlation assessments, we identified robust relationships between these seed traits, both before and after water treatment. Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) were employed to categorize genotypes with similar trait patterns, providing insights valuable for crop breeding and genetic research. Multiple linear regression analysis further elucidated the influence of specific seed traits, such as weight, width, and distance, on water-absorbing capacity. Our study contributes to a deeper understanding of seed development, imbibition, and the crucial role of water absorption in wheat. These insights have practical implications in agriculture, offering opportunities to optimize breeding programs for improved water absorption in wheat genotypes. The integration of SmartGrain software with advanced statistical methods enhances the reliability and significance of our findings, paving the way for more efficient and resilient wheat crop production. Significant changes in wheat seed shape parameters were observed after imbibition, with notable increases in area, perimeter, length, width, and weight. The length-to-width ratio (LWR) and circularity displayed opposite trends, with higher values before imbibition and lower values after imbibition.
Clinical and histopathological characteristics of primary focal segmental glomerulosclerosis in Turkish adults
Authors: Kurultak Ilhan , Gungor Ozkan , Ozturk Savas , Dirim Ahmet Burak , Eren Necmi , Yenigün Ezgi , Dal Elbis Ahbab , Dincer Mevlut Tamer , Bora Feyza , Akgur Suat , Sumnu Abdullah , Dursun Belda , Sipahi Savas , Cetinkaya Hakki , Sahin Idris , Sahin Garip , Yilmaz Murvet , Vatansever Bulent , Aydın Emre , Ulu Memnune Sena , Gundogdu Ali , Ustundag Sedat , Sayarlioglu Hayriye , Kumru Gizem , Elcioglu Omer C. , Aydın Zeki , Selcuk Nedim Yılmaz , Onal Guclu Ceren , Oruc Meric , Kucuk Mehmet , Aktas Nimet , Derici Ulver , Suleymanlar Gultekin 
Published: March 2024

AbstractThe data regarding primary FSGS (pFSGS) from different parts of the world differ. While the prevalence of pFSGS has been increasing in Western countries like the USA, it follows an inconsistent trend in Europe and Asia and a decreasing trend in Far Eastern countries such as China in the last two decades. There are undetermined factors to explain those national and geographic discrepancies. Herein, we aimed to reveal the current prevalence with clinical and histopathological characteristics of pFSGS in Turkish adults. This study includes the biopsy-proven pFSGS patients data recorded between 2009 and 2019, obtained from the national multicenter primary glomerulonephritis registry system of the Turkish Society of Nephrology Glomerular Diseases (TSN-GOLD) database. 850 of the 3875 primer glomerulonephritis patients(21.9%) have pFSGS. The mean age is 40.5 ± 14.2 and 435 (51.2%) of patients are male. Nephrotic syndrome is the most common biopsy indication (59.2%). 32.6% of patients have hematuria, 15.2% have leukocyturia and 7.8% have both. Serum creatinine, albumin, and proteinuria are 1.0 mg/dL (IQR = 0.7–1.4) mg/dl, 3.4 ± 0.9 g/dl, 3400 mg/day(IQR, 1774–5740), respectively. Females have lower mean arterial pressure (− 2.2 mmHg), higher eGFR (+ 10.0 mL/min/1.73 m2), and BMI (+ 1.6 kg/m2) than males. Thickened basal membrane(76.6%) and mesangial proliferation (53.5%) on light microscopy are the major findings after segmental sclerosis. IgM (32.7%) and C3 (32.9%) depositions are the most common findings on immunofluorescence microscopy. IgM positivity is related to lower eGFR, serum albumin, and higher proteinuria. The prevalence of pFSGS is stable although slightly increasing in Turkish adults. The characteristics of the patients are similar to those seen in Western countries.
Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity
Authors: Peng An-zhou , Kong Xiang-Hua , Liu Song-tao , Zhang Hui-fen , Xie Ling-ling , Ma Li-juan , Zhang Qiu , Chen Yong 
Published: March 2024

AbstractThe present study aims to assess the treatment outcome of patients with diabetes and tuberculosis (TB-DM) at an early stage using machine learning (ML) based on electronic medical records (EMRs). A total of 429 patients were included at Chongqing Public Health Medical Center. The random-forest-based Boruta algorithm was employed to select the essential variables, and four models with a fivefold cross-validation scheme were used for modeling and model evaluation. Furthermore, we adopted SHapley additive explanations to interpret results from the tree-based model. 9 features out of 69 candidate features were chosen as predictors. Among these predictors, the type of resistance was the most important feature, followed by activated partial throm-boplastic time (APTT), thrombin time (TT), platelet distribution width (PDW), and prothrombin time (PT). All the models we established performed above an AUC 0.7 with good predictive performance. XGBoost, the optimal performing model, predicts the risk of treatment failure in the test set with an AUC 0.9281. This study suggests that machine learning approach (XGBoost) presented in this study identifies patients with TB-DM at higher risk of treatment failure at an early stage based on EMRs. The application of a convenient and economy EMRs based on machine learning provides new insight into TB-DM treatment strategies in low and middle-income countries.
Thermal vulnerability of sea turtle foraging grounds around the globe
Authors: Goudarzi Forough , Doxa Aggeliki , Hemami Mahmoud-Reza , Mazaris Antonios D. 
Published: March 2024

AbstractAnticipating and mitigating the impacts of climate change on biodiversity requires a comprehensive understanding on key habitats utilized by species. Yet, such information for high mobile marine megafauna species remains limited. Here, we compile a global database comprising published satellite tracking data (n = 1035 individuals) to spatially delineate foraging grounds for seven sea turtle species and assess their thermal stability. We identified 133 foraging areas distributed around the globe, of which only 2% of the total surface is enclosed within an existing protected area. One-third of the total coverage of foraging hotspots is situated in high seas, where conservation focus is often neglected. Our analyses revealed that more than two-thirds of these vital marine habitats will experience new sea surface temperature (SST) conditions by 2100, exposing sea turtles to potential thermal risks. Our findings underline the importance of global ocean conservation efforts, which can meet climate challenges even in remote environments.
Endothelial gene regulatory elements associated with cardiopharyngeal lineage differentiation
Authors: Aurigemma Ilaria , Lanzetta Olga , Cirino Andrea , Allegretti Sara , Lania Gabriella , Ferrentino Rosa , Poondi Krishnan Varsha , Angelini Claudia , Illingworth Elizabeth , Baldini Antonio 
Published: March 2024

AbstractEndothelial cells (EC) differentiate from multiple sources, including the cardiopharyngeal mesoderm, which gives rise also to cardiac and branchiomeric muscles. The enhancers activated during endothelial differentiation within the cardiopharyngeal mesoderm are not completely known. Here, we use a cardiogenic mesoderm differentiation model that activates an endothelial transcription program to identify endothelial regulatory elements activated in early cardiogenic mesoderm. Integrating chromatin remodeling and gene expression data with available single-cell RNA-seq data from mouse embryos, we identify 101 putative regulatory elements of EC genes. We then apply a machine-learning strategy, trained on validated enhancers, to predict enhancers. Using this computational assay, we determine that 50% of these sequences are likely enhancers, some of which are already reported. We also identify a smaller set of regulatory elements of well-known EC genes and validate them using genetic and epigenetic perturbation. Finally, we integrate multiple data sources and computational tools to search for transcriptional factor binding motifs. In conclusion, we show EC regulatory sequences with a high likelihood to be enhancers, and we validate a subset of them using computational and cell culture models. Motif analyses show that the core EC transcription factors GATA/ETS/FOS is a likely driver of EC regulation in cardiopharyngeal mesoderm.
Ranking species in complex ecosystems through nestedness maximization
Authors: Mariani Manuel Sebastian , Mazzilli Dario , Patelli Aurelio , Sels Dries , Morone Flaviano 
Published: March 2024

AbstractIdentifying the rank of species in a complex ecosystem is a difficult task, since the rank of each species invariably depends on the interactions stipulated with other species through the adjacency matrix of the network. A common ranking method in economic and ecological networks is to sort the nodes such that the layout of the reordered adjacency matrix looks maximally nested with all nonzero entries packed in the upper left corner, called Nestedness Maximization Problem (NMP). Here we solve this problem by defining a suitable cost-energy function for the NMP which reveals the equivalence between the NMP and the Quadratic Assignment Problem, one of the most important combinatorial optimization problems, and use statistical physics techniques to derive a set of self-consistent equations whose fixed point represents the optimal nodes’ rankings in an arbitrary bipartite mutualistic network. Concurrently, we present an efficient algorithm to solve the NMP that outperforms state-of-the-art network-based metrics and genetic algorithms. Eventually, our theoretical framework may be easily generalized to study the relationship between ranking and network structure beyond pairwise interactions, e.g. in higher-order networks.