bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic Encoding of Unlabeled Two Dimensional Data Enabling Similarity Searches: Electron Diffusion Regions and Auroral Arcs
Authors: Smith A. W. , Rae I. J. , Stawarz J. E. , Sun W. J. , Bentley S. , Koul A. 
Published: January 2024

AbstractCritically important phenomena in Earth’s magnetosphere often occur briefly, or in small spatial regions. These processes are sampled with orbiting spacecraft or by fixed ground observatories and so rarely appear in data. Identifying such intervals can be an incredibly time consuming task. We apply a novel, powerful method by which two dimensional data can be automatically processed and embeddings created that contain key features of the data. The distance between embedding vectors serves as a measure of similarity. We apply the state‐of‐the‐art method to two example datasets: MMS electron velocity distributions and auroral all sky images. We show that the technique creates embeddings that group together visually similar observations. When provided with novel example images the method correctly identifies similar intervals: when provided with an electron distribution sampled during an encounter with an electron diffusion region the method recovers similar distributions obtained during two other known diffusion region encounters. Similarly, when provided with an interesting auroral structure the method highlights the same structure observed from an adjacent location and at other close time intervals. The method promises to be a useful tool to expand interesting case studies to multiple events, without requiring manual data labeling. Further, the models could be fine‐tuned with relatively small set of labeled example data to perform tasks such as classification. The embeddings can also be used as input to deep learning models, providing a key intermediary step—capturing the key features within the data.
Commencement and Interruption of Relativistic Electron Dropout in the Heart of the Outer Radiation Belt Induced by a Magnetic Cloud Event
Authors: Wei Jiayun , Zou Zhengyang , Zuo Pingbing , Ni Binbin , Ruan Mengsi , Feng Xueshang 
Published: January 2024

AbstractWe analyze and discuss the commencement and interruption of the relativistic electron dropout in the heart of the outer radiation belt (L* = 4–5) induced by a typical magnetic cloud (MC) event. This MC event impinged the Earth’s magnetosphere on 31 October 2012 and caused a moderate geomagnetic storm with a special prolonged initial phase lasting for >13 hr. The relativistic electrons phase space density (PSD) dropout commenced at L* > 5 during the initial phase. The PSD dropout penetrated deep beyond the heart of the radiation belt (reached L* < 4) at the onset of the main phase, while it was partially enhanced with a local PSD maximum around L* = 4.5, thus causing the interruption of the PSD dropout. The dropout became pronounced at L* > ∼4.7 while the local PSD maximum was maintained throughout the main phase. During the recovery phase, the dropout totally disappeared at L* < 5.5 with relativistic electron PSD gradually recovering to pre‐event level or higher. Further investigations on solar wind parameters and plasma waves give evidence that (a) persistent high dynamic pressure and the triggered Ultra‐Low Frequency waves contribute to the dropout of electrons to interplanetary space during the initial phase and the onset of the main phase; (b) local acceleration by chorus waves during the main phase and recovery phase could explain the interruption of the dropout. Our study underlines the persistent high dynamic pressure competing with intense chorus waves in triggering the commencement and interruption of the relativistic electrons PSD dropout in the heart of the outer radiation belt.
Detecting Cold Pool Family Trees in Convection Resolving Simulations
Authors: Hoeller Jannik , Fiévet Romain , Haerter Jan O. 
Published: January 2024

AbstractRecent observations and modeling increasingly reveal the key role of cold pools in organizing the convective cloud field. Several methods for detecting cold pools in simulations exist, but are usually based on buoyancy fields and fall short of reliably identifying the active gust front. The current cold pool (CP) detection and tracking algorithm (CoolDeTA), aims to identify cold pools and follow them in time, thereby distinguishing their active gust fronts and the “offspring” rain cells generated nearby. To accomplish these tasks, CoolDeTA utilizes a combination of thermodynamic and dynamical variables and examines the spatial and temporal relationships between cold pools and rain events. We demonstrate that CoolDeTA can reconstruct CP family trees. Using CoolDeTA we can contrast radiative convective equilibrium (RCE) and diurnal cycle CP dynamics, as well as cases with vertical wind shear and without. We show that the results obtained are consistent with a conceptual model where CP triggering of children rain cells follows a simple birth rate, proportional to a CP's gust front length. The proportionality factor depends on the ambient atmospheric stability and is lower for RCE, in line with marginal stability as traditionally ascribed to the moist adiabat. In the diurnal case, where ambient stability is lower, the birth rate thus becomes substantially higher, in line with periodic insolation forcing—resulting in essentially run‐away mesoscale excitations generated by a single parent rain cell and its CP.
AKT2S128/CCTαS315/319/323-positive cancer-associated fibroblasts (CAFs) mediate focal adhesion kinase (FAK) inhibitors resistance via secreting phosphatidylcholines (PCs)
Authors: Chen Jie , Zhang Lingyuan , Zhu Yuheng , Zhao Di , Zhang Jing , Zhu Yanmeng , Pang Jingyuan , Xiao Yuanfan , Wu Qingnan , Wang Yan , Zhan Qimin 
Published: January 2024

AbstractAbnormal metabolism is regarded as an oncogenic hallmark related to tumor progression and therapeutic resistance. Present study employed multi-omics, including phosphoproteomics, untargeted metabolomics and lipidomics, to demonstrate that the pAKT2 Ser128 and pCCTα Ser315/319/323-positive cancer-associated fibroblasts (CAFs) substantially release phosphatidylcholines (PCs), contributing to the resistance of focal adhesion kinase (FAK) inhibitors in esophageal squamous cell carcinoma (ESCC) treatment. Additionally, we observed extremely low levels of FAK Tyr397 expression in CAFs, potentially offering no available target for FAK inhibitors playing their anti-growth role in CAFs. Consequently, FAK inhibitor increased the intracellular concentration of Ca2+ in CAFs, promoting the formation of AKT2/CCTα complex, leading to phosphorylation of CCTα Ser315/319/323 sites and eventually enhancing stromal PC production. This activation could stimulate the intratumoral Janus kinase 2 (JAK2)/Signal transducer and activator of transcription 3 (STAT3) pathway, triggering resistance to FAK inhibition. Analysis of clinical samples demonstrated that stromal pAKT2 Ser128 and pCCTα Ser315/319/323 are related to the tumor malignancy and reduced patient survival. Pseudo-targeted lipidomics and further validation cohort quantitatively showed that plasma PCs enable to distinguish the malignant extent of ESCC patients. In conclusion, inhibition of stroma-derived PCs and related pathway could be possible therapeutic strategies for tumor therapy.
Safety and efficacy of immune checkpoint inhibitor rechallenge in advanced non-small cell lung cancer: a retrospective study
Authors: Feng Jia , Chen Xinyi , Wei Jiayan , Weng Yiming , Wang Jingsong , Wang Tong , Song Qibin , Min Peng 
Published: January 2024

AbstractWe conducted a retrospective study to evaluate the efficacy of immune checkpoint inhibitor (ICI) rechallenge in patients with advanced non-small cell lung cancer (NSCLC). The study included 111 patients who had previously received ICI therapy and experienced disease progression. The primary endpoints assessed were overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Our findings revealed that the ICI rechallenge showed promising results in improving patient outcomes. OS (r) is the time from rechallenging with immune checkpoint inhibitors to the last follow-up or death from any cause. The median OS (r) was 14.3 months (95% CI 11.3–17.3 months), with a median PFS (r) of 5.9 months (95% CI 4.1–7.7 months). The ORR was 17.1%; the DCR was 82.3%. Subgroup analysis demonstrated that patients without brain or liver metastases had a longer OS (r) compared to those with metastases (21.6 vs. 13.8 months, χ2 = 3.873, P = 0.046; 20.8 vs. 9.1 months, χ2 = 10.733, P = 0.001, respectively). Moreover, patients without driver gene mutations exhibited significantly longer OS than those with mutations or wild-type patients (22.9 vs. 16.1 vs. 7.5 months, χ2 = 10.710, P = 0.005). Notably, patients who switched to a different ICI during the rechallenge had shorter OS than those who did not change medications (10.4 vs. 21.1 months, χ2 = 9.014, P = 0.003). The incidence of immune-related adverse events did not significantly differ between the two treatment phases. These findings suggest that ICI rechallenge may be a viable therapeutic strategy for select NSCLC patients. Further prospective studies are needed to validate these results and guide treatment decisions for advanced NSCLC.
Dietary supplementation with inulin improves burn-induced skeletal muscle atrophy by regulating gut microbiota disorders
Authors: Gao Shan , Zhao Xiaoshuai , Leng Yan , Xia Zhongyuan 
Published: January 2024

AbstractInulin, as a prebiotic, could modulate the gut microbiota. Burn injury leads to gut microbiota disorders and skeletal muscle catabolism. Therefore, whether inulin can improve burn-induced muscle atrophy by regulating microbiota disorders remains unknown. This study aimed to clarify that inulin intake alleviates gut microbiota disorders and skeletal muscle atrophy in burned rats. Rats were divided into the sham group, burn group, prebiotic inulin intervention group, and pseudo-aseptic validation group. A 30% total body surface area (TBSA) third-degree burn wound on dorsal skin was evaluated in all groups except the sham group. Animals in the intervention group received 7 g/L inulin. Animals in the validation group received antibiotic cocktail and inulin treatment. In our study inulin intervention could significantly alleviate the burn-induced skeletal muscle mass decrease and skeletal myoblast cell apoptosis. Inulin intake increased the abundances of Firmicutes and Actinobacteria but decreased the abundance of Proteobacteria. The biosynthesis of amino acids was the most meaningful metabolic pathway distinguishing the inulin intervention group from the burn group, and further mechanistic studies have shown that inulin can promote the phosphorylation of the myogenesis-related proteins PI3K, AKT and P70S6K and activate PI3K/AKT signaling for protein synthesis. In conclusion, inulin alleviated burn induced muscle atrophy through PI3K/AKT signaling and regulated gut microbiota dysbiosis.
Colloidal stability and dielectric behavior of eco-friendly synthesized zinc oxide nanostructures from Moringa seeds
Authors: José Basílio José Augusto , Shinde Mahendra Devidas 
Published: January 2024

AbstractThis study centers on the environmentally benign synthesis of zinc oxide nanoparticles (ZnO NPs) derived from Zn (CH3COO)2·2H2O and Moringa seeds. The synthesized nanostructures underwent comprehensive characterization utilizing diverse analytical techniques, encompassing X-ray diffraction (XRD), UV–VIS spectroscopy, field-emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy. XRD measurements coupled with W–H plot transformation unequivocally confirmed the formation of ZnO nanostructures, characterized by an average size of 24.9 nm. UV–VIS spectroscopy, complemented by Kubelka Munk curve analysis, elucidated the direct conduction and determined a bandgap of 3.265 eV. FESEM analysis revealed minimal particle aggregation, showcasing well-defined grain boundaries spanning sizes from 20.4 to 87.7 nm. XPS analysis substantiated the presence of Zn (2p), Zn (3p), Zn (3d), and O (1s). Raman spectroscopy identified E2H as the predominant mode, followed by E1(TO) and (E2H-E2L). ZnO thin films, fabricated via pulsed laser deposition (PLD) and deposited onto silicon (100) substrates, exhibited exemplary morphology and discernible topography, characterized by a normal grain size distribution. Zeta potential tests yielded a value of approximately ($$\mathcalligra{z}$$
z
~ − 43.8 mV), indicative of the commendable stability of the colloidal suspension, likely attributable to low particle aggregation. Dielectric measurements conducted on sintered pellets at 900 °C unveiled elevated capacitance and dielectric constant at low frequencies across the temperature range of 289.935–310 K. These findings affirm the potential utility of environmentally synthesized ZnO for a spectrum of applications, including energy devices and nanofluids.
Characterization of CD90/Thy-1 as a crucial molecular signature for myogenic differentiation in human urine-derived cells through single-cell RNA sequencing
Authors: Kunitake Katsuhiko , Motohashi Norio , Inoue Takafumi , Suzuki Yutaka , Aoki Yoshitsugu 
Published: January 2024

AbstractHuman urine-derived cells (UDCs) are primary cultured cells originating from the upper urinary tract and are known to be multipotent. We previously developed MYOD1-transduced UDCs (MYOD1-UDCs) as a model recapitulating the pathogenesis of Duchenne muscular dystrophy (DMD) caused by a lack of dystrophin. MYOD1-UDCs also allow evaluation of the efficacy of exon skipping with antisense oligonucleotides. However, despite the introduction of MYOD1, some MYOD1-UDCs failed to form myotubes, possibly because of heterogeneity among UDCs. Here, we carried out single-cell RNA-sequencing analyses and revealed that CD90/Thy-1 was highly expressed in a limited subpopulation of UDCs with high myogenic potency. Furthermore, CD90-positive MYOD1-UDCs, but not CD90-negative cells, could form myotubes expressing high levels of myosin heavy chain and dystrophin. Notably, overexpression of CD90 in CD90-negative MYOD1-UDCs did not enhance myogenic differentiation, whereas CD90 suppression in CD90-positive UDCs led to decreased myotube formation and decreased myosin heavy chain expression. CD90 may thus contribute to the fusion of single-nucleated MYOD1-UDCs into myotubes but is not crucial for promoting the expression of late muscle regulatory factors. Finally, we confirmed that CD90-positive MYOD1-UDCs derived from patients with DMD were a valuable tool for obtaining a highly reproducible and stable evaluation of exon skipping using antisense oligonucleotide.
Photocatalytic activity of the biogenic mediated green synthesized CuO nanoparticles confined into MgAl LDH matrix
Authors: Berede Hildana Tesfaye , Andoshe Dinsefa Mensur , Gultom Noto Susanto , Kuo Dong-Hau , Chen Xiaoyun , Abdullah Hairus , Wondimu Tadele Hunde , Wu Yi-nan , Zelekew Osman Ahmed 
Published: January 2024

AbstractThe global concern over water pollution caused by organic pollutants such as methylene blue (MB) and other dyes has reached a critical level. Herein, the Allium cepa L. peel extract was utilized to fabricate copper oxide (CuO) nanoparticles. The CuO was combined with MgAl-layered double hydroxides (MgAl-LDHs) via a co-precipitation method with varying weight ratios of the CuO/LDHs. The composite catalysts were characterized and tested for the degradation of MB dye. The CuO/MgAl-LDH (1:2) showed the highest photocatalytic performance and achieved 99.20% MB degradation. However, only 90.03, 85.30, 71.87, and 35.53% MB dye was degraded with CuO/MgAl-LDHs (1:1), CuO/MgAl-LDHs (2:1), CuO, and MgAl-LDHs catalysts, respectively. Furthermore, a pseudo-first-order rate constant of the CuO/MgAl-LDHs (1:2) was 0.03141 min−1 while the rate constants for CuO and MgAl-LDHs were 0.0156 and 0.0052 min−1, respectively. The results demonstrated that the composite catalysts exhibited an improved catalytic performance than the pristine CuO and MgAl-LDHs. The higher photocatalytic performances of composite catalysts may be due to the uniform distribution of CuO nanoparticles into the LDH matrix, the higher surface area, and the lower electron and hole recombination rates. Therefore, the CuO/MgAl-LDHs composite catalyst can be one of the candidates used in environmental remediation.
Prognostic and immune predictive roles of a novel tricarboxylic acid cycle-based model in hepatocellular carcinoma
Authors: Zeng Yifan , Yu Tao , Jiang Shuwen , Wang Jinzhi , Chen Lin , Lou Zhuoqi , Pan Liya , Zhang Yongtao , Ruan Bing 
Published: January 2024

AbstractHepatocellular carcinoma (HCC) is the most prevalent type of liver cancer. Since the tricarboxylic acid cycle is widely involved in tumor metabolic reprogramming and cuproptosis, investigating related genes may help to identify prognostic signature of patients with HCC. Data on patients with HCC were sourced from public datasets, and were divided into train, test, and single-cell cohorts. A variety of machine learning algorithms were used to identify different molecular subtypes and determine the prognostic risk model. Our findings revealed that the risk score (TRscore), based on the genes OGDHL, CFHR4, and SPP1, showed excellent predictive performance in different datasets. Pathways related to cell cycle and immune inflammation were enriched in the high-risk group, whereas metabolism-related pathways were significantly enriched in the low-risk group. The high-risk group was associated with a greater number of mutations of detrimental biological behavior and higher levels of immune infiltration, immune checkpoint expression, and anti-cancer immunotherapy response. Low-risk patients demonstrated greater sensitivity to erlotinib and phenformin. SPP1 was mainly involved in the interaction among tumor-associated macrophages, T cells, and malignant cells via SPP1–CD44 and SPP1–(ITGA5 + ITGB1) ligand-receptor pairs. In summary, our study established a prognostic model, which may contribute to individualized treatment and clinical management of patients with HCC.
Response score-based protein structure analysis for cancer prediction aided by the Internet of Things
Authors: Alruwaili Omar , Yousef Amr , Jumani Touqeer A. , Armghan Ammar 
Published: January 2024

AbstractMedical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively.
Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome
Authors: Hennocq Quentin , Willems Marjolaine , Amiel Jeanne , Arpin Stéphanie , Attie-Bitach Tania , Bongibault Thomas , Bouygues Thomas , Cormier-Daire Valérie , Corre Pierre , Dieterich Klaus , Douillet Maxime , Feydy Jean , Galliani Eva , Giuliano Fabienne , Lyonnet Stanislas , Picard Arnaud , Porntaveetus Thantrira , Rio Marlène , Rouxel Flavien , Shotelersuk Vorasuk , Toutain Annick , Yauy Kevin , Geneviève David , Khonsari Roman H. , Garcelon Nicolas 
Published: January 2024

AbstractThe field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9–99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729–0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.
Identifying the need for infection-related consultations in intensive care patients using machine learning models
Authors: Zwerwer Leslie R. , Luz Christian F. , Soudis Dimitrios , Giudice Nicoletta , Nijsten Maarten W. N. , Glasner Corinna , Renes Maurits H. , Sinha Bhanu 
Published: January 2024

AbstractInfection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive. Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records. Data was retrieved from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. Infection-related consultations were predicted using logistic regression, random forest, gradient boosting machines, and long short-term memory neural networks (LSTM). Overall, 7.8% of admitted patients received an infection-related consultation. Time-sensitive modelling approaches performed better than static approaches. Using LSTM resulted in the prediction of infection-related consultations in the next clinical shift (up to eight hours in advance) with an area under the receiver operating curve (AUROC) of 0.921 and an area under the precision recall curve (AUPRC) of 0.541. The successful prediction of infection-related consultations for ICU patients was done without the use of classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care as well as outcome for critically ill patients with (suspected) infections.