bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ancient history education through project-based learning
Authors: Molina-Torres María-Pilar 
Published: September 2024

Abstract
The effectiveness of teaching strategies and resources that promote meaningful content learning is most pronounced when active methods such as project-based learning (PBL) are used to teach Ancient History (Molina, 2020, 53). Therefore, this study focuses on a comprehensive assessment of the methodological and historical competences acquired by students enrolled in the secondary education teacher training programme at the University of Cordoba (Spain). The research, which is non-experimental and quantitative, uses a Likert-type scale and involves the participation of 201 Masters students who have completed the course ‘Learning and Teaching Social Sciences’. The results of the statistical analysis show a positive evaluation of PBL in terms of historical understanding and its effectiveness in improving historical awareness. It is crucial to emphasise the advantages of active and collaborative learning inherent in PBL. However, it is also imperative to acknowledge the challenges that students face in applying their methodological knowledge to the secondary school setting. The transition from theoretical understanding to practical implementation is a significant hurdle for many students aspiring to a career in education. These findings underline the importance of promoting the seamless integration of innovative pedagogical approaches into teacher training programmes in order to effectively address the specific challenges of teaching antiquity in an educational context. Finally, this research was made possible by the Ministry of Science and Innovation's ‘Prueba de Concepto’ project, funded by the European Union under grant number PDC2022-133123-I00, and also by the CLIOGEN project (GINDO-UB/187) on Ancient History.
Effect of Vegetation Restoration on Soil Iron‐Associated Carbon Dynamics: Insights From Different Soil Textures
Authors: Dong Lingbo , Hu Weifang , Wang Defu , Zhang Hailong , Wu Jianzhao , Liao Yang , Li Jiwei , Shangguan Zhouping , Deng Lei 
Published: September 2024

AbstractSoil iron (Fe)‐associated carbon (C) (Fe‐OC) plays a vital role in the soil C cycle due to its high stability, but vegetation restoration might alter the composition and quantity of Fe‐OC by introducing a large amount of plant‐derived C and affecting soil properties. However, how vegetation restoration affects soil Fe‐OC remains unclear. Herein, plant and topsoil samples from grasslands, shrublands, and forestlands across three soil types (loam, loess, and sandy soil) since cropland conversions were collected to address this issue. The results showed soil Fe‐OC content decreased in loam soil but increased in loess and sandy soil following vegetation restoration. Additionally, the Fe‐OC accumulation efficiency induced by vegetation restoration increased with the coarser soil texture. Vegetation restoration promoted the accumulation of Fe‐OC by increasing soil microbial biomass C, dissolved organic C, aromatic‐C, and citric acid, but also disrupted the combination of Fe oxides and C by introducing oxalic acid, reducing Fe oxide content and iron trivalent (Fe(III)). There were two‐sided effects of vegetation restoration on Fe‐OC, but the overall effect depends on the soil types. Moreover, isotopic evidence indicated that microbial source C is the main source of Fe‐OC, but Fe oxides preferentially adsorbed dissolved organic matter (DOM) and root deposits from plants rather than microbial residues and metabolites following vegetation restoration. In addition, Fe oxides preferentially adsorbed aromatic‐C compared to other functional group components. These findings indicated that vegetation restoration in coarser‐texture soils, coupled with selecting species that increase soil microbial biomass, produce more root deposits, and enhance DOM, contribute to the accumulation of soil Fe‐OC.
The K‐Profile Parameterization Augmented by Deep Neural Networks (KPP_DNN) in the General Ocean Turbulence Model (GOTM)
Authors: Yuan Jianguo , Liang Jun‐Hong , Chassignet Eric P. , Zavala‐Romero Olmo , Wan Xiaoliang , Cronin Meghan F. 
Published: September 2024

AbstractThis study utilizes Deep Neural Networks (DNN) to improve the K‐Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11‐year turbulence‐resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN‐augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long‐term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each differing in their input and output variables, have been developed and trained. The performance of models utilizing the KPP_DNN schemes is compared to those employing traditional deterministic first‐order and second‐moment closure turbulent mixing parameterizations. Solution comparisons indicate that the simulated mixed layer becomes cooler and deeper when wave effects are included in parameterizations, aligning closer with observations. In the KPP framework, the velocity scale of unresolved shear, which is used to calculate ocean surface boundary layer depth, has a greater impact on the simulated mixed layer than the magnitude of diffusivity does. In the KPP_DNN, unresolved shear depends not only on wave forcing, but also on the mixed layer depth and buoyancy forcing.
Development of the Ionospheric E‐Region Prompt Radio Occultation Based Electron Density (E‐PROBED) Model
Authors: Salinas Cornelius Csar Jude H. , Wu Dong L. , Swarnalingam Nimalan , Emmons Daniel , Qian Liying 
Published: September 2024

AbstractThis work reports the development of the first version of the E‐region Prompt Radio Occultation Based Electron Density (E‐PROBED) Model. This is an empirical model of E‐region electron density (Ne) between 90 and 120 km developed using radio occultation measurements from the COSMIC‐1 mission. This first version captures more than 80% of the observed variability in monthly‐mean latitude‐local time‐altitude E‐region Ne profiles but it does not account for longitudinal variability at constant local‐time. This work also reports a validation of E‐PROBED simulations through comparisons with ionosondes and incoherent scatter radar (ISR) E‐region Ne profiles. E‐PROBED generally agrees with these ground‐based observations during day‐time. During night‐time, there is a large disparity between E‐PROBED and ISR values. Finally, this work compares E‐PROBED with E‐region Ne simulated by the International Reference Ionosphere (IRI) and the Specified Dynamics—Whole Atmosphere Community Climate Model with Ionosphere/Thermosphere eXtension (SD‐WACCM‐X). One of the main differences amongst these models is on the simulation of variabilities that cannot be attributed to photoionization. IRI barely simulates any variability not driven by photoionization. Both E‐PROBED and SD‐WACCM‐X simulates variability not driven by photoionization. Another main difference is in the absolute magnitude of night‐time E‐region Ne values. Both IRI and SD‐WACCM‐X are substantially lower than E‐PROBED. This work first concludes that E‐PROBED can conveniently provide E‐region Ne latitude—local time variabilities and structures that COSMIC‐1 observes. This work also concludes that E‐region Ne have significant non‐photoionization driven variabilities.
Proteogenomic characterization identifies clinical subgroups in EGFR and ALK wild-type never-smoker lung adenocarcinoma
Authors: Kim Hyondeog , Lee Wonyeop , Kim Youngwook , Lee Sang-Jin , Choi Wonyoung , Lee Geon Kook , Park Seung-Jin , Ju Shinyeong , Kim Seon-Young , Lee Cheolju , Han Ji-Youn 
Published: September 2024

AbstractPatients with lung adenocarcinoma who have never smoked (NSLA) and lack key driver mutations, such as those in the EGFR and ALK genes, face limited options for targeted therapies. They also tend to have poorer outcomes with immune checkpoint inhibitors than lung cancer patients who have a history of smoking. The proteogenomic profile of nonsmoking lung adenocarcinoma patients without these oncogenic driver mutations is poorly understood, which complicates the precise molecular classification of these cancers and highlights a significant area of unmet clinical need. This study analyzed the genome, transcriptome, and LC‒MS/MS-TMT-driven proteome data of tumors obtained from 99 Korean never-smoker lung adenocarcinoma patients. NSLA tumors without EGFR or ALK driver oncogenes were classified into four proteogenomic subgroups: proliferation, angiogenesis, immune, and metabolism subgroups. These 4 molecular subgroups were strongly associated with distinct clinical outcomes. The proliferation and angiogenesis subtypes were associated with a poorer prognosis, while the immune subtype was associated with the most favorable outcome, which was validated in an external lung cancer dataset. Genomic-wide impacts were analyzed, and significant correlations were found between copy number alterations and both the transcriptome and proteome for several genes, with enrichment in the ERBB, neurotrophin, insulin, and MAPK signaling pathways. Proteogenomic analyses suggested several targetable genes and proteins, including CDKs and ATR, as potential therapeutic targets in the proliferation subgroup. Upregulated cytokines, such as CCL5 and CXCL13, in the immune subgroup may serve as potential targets for combination immunotherapy. Our comprehensive proteogenomic analysis revealed the molecular subtypes of EGFR- and ALK-wild-type NSLA with significant unmet clinical needs.
Clinical correlates of a high cardiorespiratory risk score for very low birth weight infants
Authors: Kausch Sherry L. , Slevin Claire C. , Duncan Amanda , Fairchild Karen D. , Lake Douglas E. , Keim-Malpass Jessica , Vesoulis Zachary A. , Sullivan Brynne A. 
Published: September 2024

Abstract
Background
A pulse oximetry warning system (POWS) to analyze heart rate and oxygen saturation data and predict risk of sepsis was developed for very low birth weight (VLBW) infants.

Methods
We determined the clinical correlates and positive predictive value (PPV) of a high POWS score in VLBW infants. In a two-NICU retrospective study, we identified times when POWS increased above 6 (POWS spike). We selected an equal number of control times, matched for gestational and chronologic age. We reviewed records for infection and non-infection events around POWS spikes and control times. We calculated the frequencies and PPV of a POWS spike for infection or another significant event.

Results
We reviewed 111 POWS spike times and 111 control times. Days near POWS spikes were more likely to have clinical events than control days (77% vs 50%). A POWS spike had 52% PPV for suspected or confirmed infection and 77% for any clinically significant event. Respiratory deterioration occurred near more POWS spike times than control times (34% vs 18%).

Conclusions
In a retrospective cohort, infection and respiratory deterioration were common clinical correlations of a POWS spike. POWS had a high PPV for significant clinical events with or without infection.

Impact

There are significant gaps in understanding the best approach to implementing continuous sepsis prediction models so that clinicians can best respond to early signals of deterioration.

Infection and respiratory deterioration were common clinical events identified at the time of a high predictive model score.

Understanding the clinical correlates of a high-risk early warning score will inform future implementation efforts.
Dietary intake and risk of metabolic syndrome in long-term survivors of pediatric allogeneic hematopoietic stem cell transplantation
Authors: Pedersen Louise Lindkvist , Gerbek Tina , Sørum Maria Ebbesen , Muhic Ena , Christiansen Terkel , Kok Karin , Sørensen Kaspar , Mølgaard Christian , Müller Klaus 
Published: September 2024

AbstractWe explored the dietary intake and metabolic syndrome (MetS) in 85 survivors of pediatric stem cell transplantation (median age 30 years, median follow-up time 20 years). Overall, the distribution of fatty acid deviated from the recommendations with a higher intake of saturated fat and a lower intake of unsaturated fat but was comparable to that of the background population. The prevalence of MetS was 27%, corresponding to that of the elderly background population. We compared the intake of macronutrients between those with MetS and those without MetS and found that overall fat intake was higher in patients with MetS (36.7E% [range, 27.2–51.2E%] vs. 33,5E% (range, 23.4–45.1E%), P = 0.016). Within the subgroup of patients treated with total body irradiation (TBI), we found a higher fat intake in those with MetS (36.8E% (range, 27.2–51.2E%) versus 32.0E% (range, 24.6–42.1E%), P = 0.013). This was confirmed in a multivariate analysis adjusted for TBI, sex, and age at follow-up (OR 1.20 (1.06–1.39), P = 0.008). Our findings suggest that conditioning with the use of TBI may induce a state of hypersensitivity to the potentially harmful effects of fat in the diet and suggest that this risk of MetS after TBI treatment may be modifiable by dietary changes.
Acid-activatable photosensitizers for photodynamic therapy using self-aggregates of chlorophyll‒peptide conjugates
Authors: Nagatani Miyu , Yoshikawa Masaru , Tsukiji Shinya , Higuchi Masahiro , Tamiaki Hitoshi , Matsubara Shogo 
Published: September 2024

AbstractPhotodynamic therapy is useful due to its high antitumor efficacy, spatiotemporal selectivity, and noninvasiveness and has garnered significant attention in the field of cancer treatment. When photoexcited by light irradiation, photosensitizers produce reactive oxygen species (ROS) that damage tumor tissues. However, photosensitizers can also accumulate in normal tissues, leading to side effects such as skin photosensitivity. To mitigate these side effects, we report the development of chlorophyll‒peptide conjugates as tumor-selective photosensitizers. These conjugates bearing histidine and lysine residues self-assemble into nanoparticles that are expected to accumulate selectively in tumors and reduce ROS generation through self-quenching under the neutral conditions typical of normal tissues. In contrast, these aggregated conjugates partially disassemble under weakly acidic conditions, such as those found in tumor tissues, resulting in phototoxicity. We anticipate that these acid-activatable conjugates have the potential to serve as cancer-selective photosensitizers, thereby reducing phototoxicity in normal tissues.
Gene-level alignment of single-cell trajectories
Authors: Sumanaweera Dinithi , Suo Chenqu , Cujba Ana-Maria , Muraro Daniele , Dann Emma , Polanski Krzysztof , Steemers Alexander S. , Lee Woochan , Oliver Amanda J. , Park Jong-Eun , Meyer Kerstin B. , Dumitrascu Bianca , Teichmann Sarah A. 
Published: September 2024

Abstract
Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.
Structural basis for translational control by the human 48S initiation complex
Authors: Petrychenko Valentyn , Yi Sung-Hui , Liedtke David , Peng Bee-Zen , Rodnina Marina V. , Fischer Niels 
Published: September 2024

Abstract
The selection of an open reading frame (ORF) for translation of eukaryotic mRNA relies on remodeling of the scanning 48S initiation complex into an elongation-ready 80S ribosome. Using cryo-electron microscopy, we visualize the key commitment steps orchestrating 48S remodeling in humans. The mRNA Kozak sequence facilitates mRNA scanning in the 48S open state and stabilizes the 48S closed state by organizing the contacts of eukaryotic initiation factors (eIFs) and ribosomal proteins and by reconfiguring mRNA structure. GTPase-triggered large-scale fluctuations of 48S-bound eIF2 facilitate eIF5B recruitment, transfer of initiator tRNA from eIF2 to eIF5B and the release of eIF5 and eIF2. The 48S-bound multisubunit eIF3 complex controls ribosomal subunit joining by coupling eIF exchange to gradual displacement of the eIF3c N-terminal domain from the intersubunit interface. These findings reveal the structural mechanism of ORF selection in human cells and explain how eIF3 could function in the context of the 80S ribosome.
A deep learning dataset for metal multiaxial fatigue life prediction
Authors: Chen Shuonan , Bai Yongtao , Zhou Xuhong , Yang Ao 
Published: September 2024

AbstractMultiaxial fatigue failure of metals, a common issue in industrial production, often leads to significant losses. Recently, many researchers have applied deep learning methods to predict the multiaxial fatigue life of metals, achieving promising results. Due to the high costs of fatigue testing, training data for deep learning is scarce and labor-intensive to collect. This study meets this need by creating a large-scale, high-quality dataset for multiaxial fatigue life prediction, consisting of 1167 samples from 40 materials collected from literature. The dataset includes key mechanical properties (elastic modulus, yield strength, tensile strength, Poisson’s ratio) and 48 loading paths, along with additional relevant information (composition ratios, processing conditions). Common deep learning models validated the dataset’s effectiveness. This dataset aims to support researchers applying deep learning to fatigue life prediction, addressing the long-standing issue of data scarcity, thereby advancing the intersection of artificial intelligence and metal fatigue research.
Disulfiram attenuates cell and tissue damage and blood‒brain barrier dysfunction after intracranial haemorrhage by inhibiting the classical pyroptosis pathway
Authors: Xu Chen , Jiang Fangchao , Mao Yuanfu , Wei Wan , Song Jihe , Jia Feihong , Du Xinshu , Zhong Di , Li Guozhong 
Published: September 2024

AbstractNo single treatment significantly reduces the mortality rate and improves neurological outcomes after intracerebral haemorrhage (ICH). New evidence suggests that pyroptosis-specific proteins are highly expressed in the perihaematomal tissues of patients with ICH and that the disulfiram (DSF) inhibits pyroptosis. An ICH model was established in C57BL/6 mice by intracranial injection of collagenase, after which DSF was used to treat the mice. Cell model of ICH was constructed, and DSF was used to treat the cells. HE, TUNEL, Nissl, FJC and IF staining were performed to evaluate the morphology of brain tissues; Western blotting and ELISA were performed to measure the protein expression of NOD-like receptor protein 3 (NLRP3)/Caspase-1/gasdermin D (GSDMD) classical pyroptosis pathway and Toll-likereceptor4 (TLR4)/nuclear factor-kappaB (NF-κB) inflammatory signaling pathway and blood‒brain barrier-associated factoes, and the wet/dry weight method was used to determine the brain water content. The expression of proteins related to the NLRP3/Caspase-1/GSDMD pathway and the TLR4/NF-κB pathway was upregulated in tissues surrounding the haematoma compared with that in control tissues; Moreover, the expression of the blood–brain barrier structural proteins occludin and zonula occludens-1 (ZO-1) was downregulated, and the expression of Aquaporin Protein-4 (AQP4) and matrix metalloprotein 9 (MMP-9) was upregulated. DSF significantly inhibited these changes, reduced the haematoma volume, decreased the brain water content, reduced neuronal death and degeneration and improved neurological function after ICH. ICH activated the classical pyroptosis pathway and TLR4/NF-κB inflammatory pathway, disruped the expression of blood–brain barrier structural proteins, and exacerbated brain injury and neurological dysfunction. DSF inhibited these changes and exerted the therapeutic effects on pathological changes and dysfunction caused by ICH.
Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis
Authors: Nemat Hoda , Khadem Heydar , Elliott Jackie , Benaissa Mohammed 
Published: September 2024

AbstractAccurate prediction of blood glucose level (BGL) has proven to be an effective way to help in type 1 diabetes management. The choice of input, along with the fundamental choice of model structure, is an existing challenge in BGL prediction. Investigating the performance of different data-driven time series forecasting approaches with different inputs for BGL prediction is beneficial in advancing BGL prediction performance. Limited work has been made in this regard, which has resulted in different conclusions. This paper performs a comprehensive investigation of different data-driven time series forecasting approaches using different inputs. To do so, BGL prediction is comparatively investigated from two perspectives; the model’s approach and the model’s input. First, we compare the performance of BGL prediction using different data-driven time series forecasting approaches, including classical time series forecasting, traditional machine learning, and deep neural networks. Secondly, for each prediction approach, univariate input, using BGL data only, is compared to a multivariate input, using data on carbohydrate intake, injected bolus insulin, and physical activity in addition to BGL data. The investigation is performed on two publicly available Ohio datasets. Regression-based and clinical-based metrics along with statistical analyses are performed for evaluation and comparison purposes. The outcomes show that the traditional machine learning model is the fastest model to train and has the best BGL prediction performance especially when using multivariate input. Also, results show that simply adding extra variables does not necessarily improve BGL prediction performance significantly, and data fusion approaches may be required to effectively leverage other variables’ information.