bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exosomes of endothelial progenitor cells repair injured vascular endothelial cells through the Bcl2/Bax/Caspase-3 pathway
Authors: Tan Wei , Li Yanling , Ma Lu , Fu Xinying , Long Qingyin , Yan Fanchen , Li Wanyu , Liu Xiaodan , Ding Huang , Wang Yang , Zhang Wei 
Published: February 2024

AbstractThe main objective of this study is to evaluate the influence of exosomes derived from endothelial progenitor cells (EPC-Exo) on neointimal formation induced by balloon injury in rats. Furthermore, the study aims to investigate the potential of EPC-Exo to promote proliferation, migration, and anti-apoptotic effects of vascular endothelial cells (VECs) in vitro. The underlying mechanisms responsible for these observed effects will also be thoroughly explored and analyzed. Endothelial progenitor cells (EPCs) was isolated aseptically from Sprague–Dawley (SD) rats and cultured in complete medium. The cells were then identified using immunofluorescence and flow cytometry. The EPC-Exo were isolated and confirmed the identities by western-blot, transmission electron microscope, and nanoparticle analysis. The effects of EPC-Exo on the rat carotid artery balloon injury (BI) were detected by hematoxylin and eosin (H&E) staining, ELISA, immunohistochemistry, immunofluorescence, western-blot and qPCR. LPS was used to establish an oxidative damage model of VECs. The mechanism of EPC-Exo repairing injured vascular endothelial cells was detected by measuring the proliferation, migration, and tube function of VECs, actin cytoskeleton staining, TUNEL staining, immunofluorescence, western-blot and qPCR. In vivo, EPC-Exo exhibit inhibitory effects on neointima formation following carotid artery injury and reduce the levels of inflammatory factors, including TNF-α and IL-6. Additionally, EPC-Exo downregulate the expression of adhesion molecules on the injured vascular wall. Notably, EPC-Exo can adhere to the injured vascular area, promoting enhanced endothelial function and inhibiting vascular endothelial hyperplasia Moreover, they regulate the expression of proteins and genes associated with apoptosis, including B-cell lymphoma-2 (Bcl2), Bcl2-associated x (Bax), and Caspase-3. In vitro, experiments further confirmed that EPC-Exo treatment significantly enhances the proliferation, migration, and tube formation of VECs. Furthermore, EPC-Exo effectively attenuate lipopolysaccharides (LPS)-induced apoptosis of VECs and regulate the Bcl2/Bax/Caspase-3 signaling pathway. This study demonstrates that exosomes derived from EPCs have the ability to inhibit excessive carotid intimal hyperplasia after BI, promote the repair of endothelial cells in the area of intimal injury, and enhance endothelial function. The underlying mechanism involves the suppression of inflammation and anti-apoptotic effects. The fundamental mechanism for this anti-apoptotic effect involves the regulation of the Bcl2/Bax/Caspase-3 signaling pathway.
Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs
Authors: Tassoker Melek , Öziç Muhammet Üsame , Yuce Fatma 
Published: February 2024

AbstractIdiopathic osteosclerosis (IO) are focal radiopacities of unknown etiology observed in the jaws. These radiopacities are incidentally detected on dental panoramic radiographs taken for other reasons. In this study, we investigated the performance of a deep learning model in detecting IO using a small dataset of dental panoramic radiographs with varying contrasts and features. Two radiologists collected 175 IO-diagnosed dental panoramic radiographs from the dental school database. The dataset size is limited due to the rarity of IO, with its incidence in the Turkish population reported as 2.7% in studies. To overcome this limitation, data augmentation was performed by horizontally flipping the images, resulting in an augmented dataset of 350 panoramic radiographs. The images were annotated by two radiologists and divided into approximately 70% for training (245 radiographs), 15% for validation (53 radiographs), and 15% for testing (52 radiographs). The study employing the YOLOv5 deep learning model evaluated the results using precision, recall, F1-score, mAP (mean Average Precision), and average inference time score metrics. The training and testing processes were conducted on the Google Colab Pro virtual machine. The test process's performance criteria were obtained with a precision value of 0.981, a recall value of 0.929, an F1-score value of 0.954, and an average inference time of 25.4 ms. Although radiographs diagnosed with IO have a small dataset and exhibit different contrasts and features, it has been observed that the deep learning model provides high detection speed, accuracy, and localization results. The automatic identification of IO lesions using artificial intelligence algorithms, with high success rates, can contribute to the clinical workflow of dentists by preventing unnecessary biopsy procedure.
Applicability analysis of flame height estimation based on Byram’s fireline intensity model under flat and windless conditions
Authors: Zhang Yunlin , Luo Aixia 
Published: February 2024

AbstractForest fire have a serious impact on forest ecosystems, the safety of people’s lives and property, and social stability. The height of surface flames, as the main indicator of forest fire behavior, which is an important parameter for forest fire management. The relationship between fireline intensity and flame height proposed by Byram has been widely used in estimating flame height; however, its applicability to the surface fuel of typical forest stands in the Yunnan–Guizhou Plateau of China has not yet been analyzed. In this study, the surface fuel in the area was taken as the research object, and the flame height of different fuel bed characteristics was measured through an indoor burning experiment. The applicability of three methods—the directly used Byram’s model, corrected model, and re-established prediction model—was analyzed to estimate the flame height in the Yunnan–Guizhou Plateau. We found that the flame height of the typical forest stands in the Yunnan–Guizhou Plateau ranged from 0.05 to 1.2 m and was significantly affected by the moisture content, load, and height of the fuel bed. Although the fireline intensity exhibited a significant linear relationship with the flame height, directly using Byram’s method to predict the flame height of surface fires was impractical, as its mean prediction error exceeded 150%. The mean relative errors of the prediction model obtained by modifying Byram’s method and that based on the characteristics of the fuel bed were both within 15%, which is significantly lower than that of the original Byram’s method. Based on the results of this study, we propose two methods that are suitable for predicting the flame height of surface fires in the typical forests of the Yunnan–Guizhou Plateau in China, which is of great significance for further understanding the relationship between flame height, fireline intensity, and scientific forest fire management.
Effect of multidrug therapy on the prognosis of Mycobacterium avium complex pulmonary disease
Authors: Ushiki Atsuhito , Tanaka Shunnosuke , Yamanaka Miwa , Akahane Jumpei , Ikuyama Yuichi , Komatsu Masamichi , Sonehara Kei , Ichiyama Takashi , Wada Yosuke , Tateishi Kazunari , Kitaguchi Yoshiaki , Hanaoka Masayuki 
Published: February 2024

AbstractMultidrug therapy for Mycobacterium avium complex pulmonary disease (MAC-PD) results in negative sputum cultures. However, the prognostic value of this treatment approach remains unclear. This study aimed to clarify whether multidrug therapy reduces the incidence of events related to MAC-PD and improves the mortality rate. Patients who met the diagnostic criteria for MAC-PD at our hospital between 2003 and 2019 were retrospectively evaluated using medical records. Events related to MAC-PD were defined as hospitalisation for haemoptysis or respiratory infection and the development of chronic respiratory failure. There were 90 and 108 patients in the multidrug and observation groups, respectively. The median observation period was 86 months. Intergroup differences in body mass index, proportion of patients with cavities, and erythrocyte sedimentation rate were not significant. However, the observation group was older with a higher mean age (multidrug group: 62 years, observation group: 69 years; P < 0.001) and had a higher proportion of male patients (multidrug group: 13/90 [14.4%], observation group: 35/108 [32.4%]; P < 0.01). Furthermore, intergroup differences in the incidence of events related to MAC-PD (multidrug group: 26.69/1000 person-years, observation group: 25.49/1000 person-years), MAC-PD-associated mortality rate (multidrug group: 12.13/1000 person-years, observation group: 12.74/1000 person-years), and total mortality (multidrug group: 24.26/1000 person-years, observation group: 29.50/1000 person-years) were not significant. Many patients relapse even after multidrug therapy, and our findings suggest that multidrug therapy has no effect in preventing the onset of respiratory events or prolonging life expectancy.
Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction
Authors: Shoombuatong Watshara , Homdee Nutta , Schaduangrat Nalini , Chumnanpuen Pramote 
Published: February 2024

AbstractThe voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein–protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.
Human-induced intensification of terrestrial water cycle in dry regions of the globe
Authors: Guan Yansong , Gu Xihui , Slater Louise J. , Li Xueying , Li Jianfeng , Wang Lunche , Tang Xiongpeng , Kong Dongdong , Zhang Xiang 
Published: February 2024

AbstractAnthropogenic climate change (ACC) strengthens the global terrestrial water cycle (TWC) through increases in annual total precipitation (PRCPTOT) over global land. While the increase in the average global terrestrial PRCPTOT has been attributed to ACC, it is unclear whether this is equally true in dry and wet regions, given the difference in PRCPTOT changes between the two climatic regions. Here, we show the increase in PRCPTOT in dry regions is twice as fast as in wet regions of the globe during 1961–2018 in both observations and simulations. This faster increase is projected to grow with future warming, with an intensified human-induced TWC in the driest regions of the globe. We show this phenomenon can be explained by the faster warming and precipitation response rates as well as the stronger moisture transport in dry regions under ACC. Quantitative detection and attribution results show that the global increase in PRCPTOT can no longer be attributed to ACC if dry regions are excluded. From 1961–2018, the observed PRCPTOT increased by 5.63%~7.39% (2.44%~2.80%) over dry (wet) regions, and as much as 89% (as little as 5%) can be attributed to ACC. The faster ACC-induced TWC in dry regions is likely to have both beneficial and detrimental effects on dry regions of the globe, simultaneously alleviating water scarcity while increasing the risk of major flooding.
Clonal gene signatures predict prognosis in mesothelioma and lung adenocarcinoma
Authors: Lin Yupei , Burt Bryan M. , Lee Hyun-Sung , Nguyen Thinh T. , Jang Hee-Jin , Lee Claire , Hong Wei , Ripley Robert Taylor , Amos Christopher I. , Cheng Chao 
Published: February 2024

AbstractMalignant pleural mesothelioma (MPM) is a rare but lethal pleural cancer with high intratumor heterogeneity (ITH). A recent study in lung adenocarcinoma has developed a clonal gene signature (ORACLE) from multiregional transcriptomic data and demonstrated high prognostic values and reproducibility. However, such a strategy has not been tested in other types of cancer with high ITH. We aimed to identify biomarkers from multi-regional data to prognostically stratify MPM patients. We generated a multiregional RNA-seq dataset for 78 tumor samples obtained from 26 MPM patients, each with one sample collected from a superior, lateral, and inferior region of the tumor. By integrating this dataset with the Cancer Genome Atlas MPM RNA-seq data, we selected 29 prognostic genes displaying high variability across different tumors but low ITH, which named PRACME (Prognostic Risk Associated Clonal Mesothelioma Expression). We evaluated PRACME in two independent MPM datasets and demonstrated its prognostic values. Patients with high signature scores are associated with poor prognosis after adjusting established clinical factors. Interestingly, the PRACME and the ORACLE signatures defined respectively from MPM and lung adenocarcinoma cross-predict prognosis between the two cancer types. Further investigation indicated that the cross-prediction ability might be explained by the high similarity between the two cancer types in their genomic regions with copy number variation, which host many clonal genes. Overall, our clonal signature PRACME provided prognostic stratification in MPM and this study emphasized the importance of multi-regional transcriptomic data for prognostic stratification based on clonal genes.
Nonequilibrium thermodynamics of quantum coherence beyond linear response
Authors: Rodrigues Franklin L. S. , Lutz Eric 
Published: February 2024

AbstractQuantum thermodynamics allows for the interconversion of quantum coherence and mechanical work. Quantum coherence is thus a potential physical resource for quantum machines. However, formulating a general nonequilibrium thermodynamics of quantum coherence has turned out to be challenging. In particular, precise conditions under which coherence is beneficial to or, on the contrary, detrimental for work extraction from a system have remained elusive. We here develop a generic dynamic-Bayesian-network approach to the far-from-equilibrium thermodynamics of coherence. We concretely derive generalized fluctuation relations and a maximum-work theorem that fully account for quantum coherence at all times, for both closed and open dynamics. We obtain criteria for successful coherence-to-work conversion, and identify a nonequilibrium regime where maximum work extraction is increased by quantum coherence for fast processes beyond linear response.
Neutralizing antibodies after the third COVID-19 vaccination in healthcare workers with or without breakthrough infection
Authors: Reinholm Arttu , Maljanen Sari , Jalkanen Pinja , Altan Eda , Tauriainen Sisko , Belik Milja , Skön Marika , Haveri Anu , Österlund Pamela , Iakubovskaia Alina , Pasternack Arja , Naves Rauno A. , Ritvos Olli , Miettinen Simo , K. Häkkinen Hanni , Ivaska Lauri , Tähtinen Paula A. , Lempainen Johanna , Kantele Anu , Kakkola Laura , Julkunen Ilkka , Kolehmainen Pekka 
Published: February 2024

Abstract
Background
Vaccinations against the SARS-CoV-2 are still crucial in combating the ongoing pandemic that has caused more than 700 million infections and claimed almost 7 million lives in the past four years. Omicron (B.1.1.529) variants have incurred mutations that challenge the protection against infection and severe disease by the current vaccines, potentially compromising vaccination efforts.

Methods
We analyzed serum samples taken up to 9 months post third dose from 432 healthcare workers. Enzyme-linked immunosorbent assays (ELISA) and microneutralization tests (MNT) were used to assess the prevalence of vaccine-induced neutralizing antibodies against various SARS-CoV-2 Omicron variants.

Results
In this serological analysis we show that SARS-CoV-2 vaccine combinations of BNT162b2, mRNA-1273, and ChAdOx1 mount SARS-CoV-2 binding and neutralizing antibodies with similar kinetics, but with differing neutralization capabilities. The most recent Omicron variants, BQ.1.1 and XBB.1.5, show a significant increase in the ability to escape vaccine and infection-induced antibody responses. Breakthrough infections in thrice vaccinated adults were seen in over 50% of the vaccinees, resulting in a stronger antibody response than without infection.

Conclusions
Different three-dose vaccine combinations seem to induce considerable levels of neutralizing antibodies against most SARS-CoV-2 variants. However, the ability of the newer variants BQ1.1 and XBB 1.5 to escape vaccine-induced neutralizing antibody responses underlines the importance of updating vaccines as new variants emerge.
Technical indicator enhanced ultra‐short‐term wind power forecasting based on long short‐term memory network combined XGBoost algorithm
Authors: Zheng Yingying , Guan Shijie , Guo Kailei , Zhao Yongning , Ye Lin 
Published: February 2024

AbstractThe growing integration of renewable energy sources into the power grid has introduced unprecedented uncertainty. Ensuring an appropriately scheduled reserve is essential to accommodate renewable energy's intermittent and volatile nature. This study introduces an innovative approach to ultra‐short‐term wind power forecasting, which relies on feature engineering and a hybrid model. The effectiveness of this proposed method is showcased through a case study involving a utility‐scale wind farm in Inner Mongolia, China. The findings indicate that the hybrid model, which combines the XGBoost (Extreme Gradient Boosting) algorithm and LSTM (Long Short‐Term Memory) network with KDJ (Stochastic Oscillator), and MACD (Moving Average Convergence and Divergence), achieves the highest forecasting accuracy. Specifically, the proposed model yields a normalized mean absolute error of 0.0396 for wind power forecasting. The modelling and forecasting process takes approximately 550 s. Furthermore, the suggested method is employed to predict wind power and wind speed for a wind farm in the USA. The experimental results consistently indicate that the proposed model maintains a dependable performance across various raw datasets; it is suitable for use in power system operations.
The radiative and dynamical impact of clouds in the atmosphere of the hot Jupiter WASP-43 b
Authors: Teinturier L. , Charnay B. , Spiga A. , Bézard B. , Leconte J. , Mechineau A. , Ducrot E. , Millour E. , Clément N. 
Published: February 2024

Context. Hot Jupiters are tidally locked gaseous exoplanets that exhibit large day-night temperature contrasts. Their cooler nightsides are thought to host clouds, as has been suggested by numerous observations. However, the exact nature of these clouds, their spatial distribution, and their impact on atmospheric dynamics, thermal structure, and spectra is still unclear.
Aims. We investigate the atmosphere of WASP-43 b, a short period hot Jupiter recently observed with James Webb Space Telescope (JWST), to understand the radiative and dynamical impact of clouds on the atmospheric circulation and thermal structure. We aim to understand the impact of different kinds of condensates potentially forming in WASP-43 b, with various sizes and atmospheric metallicities.
Methods. We used a 3D global climate model (GCM) with a new temperature-dependent cloud model that includes radiative feedbacks coupled with hydrodynamical integrations to study the atmospheric properties of WASP-43 b. We produced observables from our GCM simulations and compared them to spectral phase curves from various observations to derive constraints on the atmospheric properties.
Results. We show that clouds have a net warming effect, meaning that the greenhouse effect caused by clouds is stronger than the albedo cooling effect. We show that the radiative effect of clouds has various impacts on the dynamical and thermal structure of WASP-43 b. Depending on the type of condensates and their sizes, the radiative-dynamical feedback will modify the horizontal and vertical temperature gradient and reduce the wind speed. For super-solar metallicity atmospheres, fewer clouds form in the atmosphere, leading to a weaker feedback. Comparisons with spectral phase curves observed with HST, Spitzer, and JWST indicate that WASP-43 b's nightside is cloudy and rule out sub-micron Mg2SiO4 cloud particles as the main opacity source. Distinguishing between cloudy solarand cloudy super-solar-metallicity atmospheres is not straightforward, and further observations of both reflected light and thermal emission are needed.