bluuebunny/crossref_metadata_2025_split huggingface dataset:
The protective effect of ginsenoside Rg1 against sepsis-induced lung injury through PI3K-Akt pathway: insights from molecular dynamics simulation and experimental validation
Authors: Zhong Kaiqiang , Huang Yingui , Chen Rui , Pan Qiusha , Li Jun , Xi Xiaotu 
Published: July 2024

AbstractSepsis-induced acute lung injury (SALI) poses a significant threat with high incidence and mortality rates. Ginsenoside Rg1 (GRg1), derived from Ginseng in traditional Chinese medicine, has been found to reduce inflammation and protect lung epithelial cells against tissue damage. However, the specific roles and mechanisms by which GRg1 mitigates SALI have yet to be fully elucidated. In this context, we employed a relevant SALI mouse model, alongside network pharmacology, molecular docking, and molecular dynamics simulation to pinpoint GRg1's action targets, complemented by in vitro assays to explore the underlying mechanisms. Our research shows that GRg1 alleviates CLP-induced SALI, decreasing lung tissue damage and levels of serum proinflammatory factor IL-6, TNF-α, and IL-1β, also enhancing the survival rate of CLP mice. A total of 116 common targets between GRg1 and ALI, with specific core targets including AKT1, VEGFA, SRC, IGF1, ESR1, STAT3, and ALB. Further in vitro experiments assessed GRg1's intervention effects on MLE-12 cells exposed to LPS, with qRT-PCR analysis and molecular dynamics simulations confirming AKT1 as the key target with the favorable binding activity for GRg1. Western blot results indicated that GRg1 increased the Bcl-2/Bax protein expression ratio to reduce apoptosis and decreased the high expression of cleaved caspase-3 in LPS-induced MLE-12 cells. More results showed significant increases in the phosphorylation of PI3K and AKT1. Flow cytometric analysis using PI and Annexin-V assays further verified that GRg1 decreased the apoptosis rate in LPS-stimulated MLE-12 cells (from 14.85 to 6.54%, p < 0.05). The employment of the AKT1 inhibitor LY294002 confirmed these trends, indicating that AKT1’s inhibition negates GRg1’s protective effects on LPS-stimulated MLE-12 cells. In conclusion, our research highlights GRg1's potential as an effective adjunct therapy for SALI, primarily by inhibiting apoptosis in alveolar epithelial cells and reducing pro-inflammatory cytokine secretion, thus significantly enhancing the survival rates of CLP mice. These beneficial effects are mediated through targeting AKT1 and activating the PI3K-AKT pathway.
Epigenetically associated IGF2BP3 upregulation promotes cell proliferation by regulating E2F1 expression in hepatocellular carcinoma
Authors: Liu Chenghao , Zhuo Yicheng , Yang Xiaofeng , Yang Chen , Shu Min , Hou Bowen , Hou Jun , Chen Xueling , Wang Lianghai , Wu Xiangwei 
Published: July 2024

AbstractRNA-binding proteins (RBPs) are a class of proteins that primarily function by interacting with different types of RNAs and play a critical role in regulating the transcription and translation of cancer-related genes. However, their role in the progression of hepatocellular carcinoma (HCC) remains unclear. In this study, we analyzed RNA sequencing data and the corresponding clinical information of patients with HCC to screen for prognostic RBPs. Insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) was identified as an independent prognostic factor for liver cancer. It is upregulated in HCC and is associated with a poor prognosis. Elevated IGF2BP3 expression was validated via immunohistochemical analysis using a tissue microarray of patients with HCC. IGF2BP3 knockdown inhibited the proliferation of Hep3B and HepG2 cells, whereas IGF2BP3 overexpression promoted the expansion of HuH-7 and MHCC97H cells. Mechanistically, IGF2BP3 modulates cell proliferation by regulating E2F1 expression. DNA hypomethylation of the IGF2BP3 gene may increase the expression of IGF2BP3, thereby enhancing cell proliferation in HCC. Therefore, IGF2BP3 may act as a novel prognostic biomarker and a potential therapeutic target for HCC.
Numerical simulation of blasting behavior of rock mass with cavity under high in-situ stress
Authors: Rong Hai , Li Nannan , Cao Chen , Wang Yadi , Li Jincheng , Li Mingda 
Published: July 2024

AbstractWith the shift of coal seam mining to the deep, the in-situ stress of coal and rock mass increases gradually. High ground stress can limit the generation of rock cracks caused by blasting, and blasting usually shows different crushing states than low stress conditions. In order to study the blasting expansion rule of rock mass with cavity under high ground stress and the rock mass fracture state under different side stress coefficients. In this paper, the effective range of blasting and the stress distribution under blasting load are analyzed theoretically. The RHT (Riedel-Hiermaier-Thoma) model is used to numerically simulate the blasting process of rock mass with cavity under different ground stress, and the influence of ground stress and lateral pressure coefficient on the crack growth of rock mass is studied. The results show that when there is no ground stress, the damage cracks in rock mass are more concentrated in the horizontal direction and the fracture development tends to the direction where the holes are located, which confirms the guiding effect and stress concentration effect of the holes in rock mass, which helps to promote the crack penetration between the hole and the hole. The length difference of horizontal and vertical damage cracks in rock mass increases with the increase of horizontal and vertical stress difference. Under the same lateral stress coefficient, the larger the horizontal and vertical stress difference is, the stronger the inhibition effect on crack formation is. For blasting of rock mass with high ground stress, the crack formation length between gun holes decreases with the increase of stress level, and the crack extends preferentially in the direction of higher stress. Therefore, the placement of gun holes along the direction of greater stress and the shortening of hole spacing are conducive to the penetration of cracks between gun holes and empty holes. The research can provide reference for rock breaking behavior of deep rock mass blasting.
Hybrid YSGOA and neural networks based software failure prediction in cloud systems
Authors: Kaur Ramandeep , Vaithiyanathan Revathi 
Published: July 2024

AbstractIn the realm of cloud computing, ensuring the dependability and robustness of software systems is paramount. The intricate and evolving nature of cloud infrastructures, however, presents substantial obstacles in the pre-emptive identification and rectification of software anomalies. This study introduces an innovative methodology that amalgamates hybrid optimization algorithms with Neural Networks (NN) to refine the prediction of software malfunctions. The core objective is to augment the purity metric of our method across diverse operational conditions. This is accomplished through the utilization of two distinct optimization algorithms: the Yellow Saddle Goat Fish Algorithm (YSGA), which is instrumental in the discernment of pivotal features linked to software failures, and the Grasshopper Optimization Algorithm (GOA), which further polishes the feature compilation. These features are then processed by Neural Networks (NN), capitalizing on their proficiency in deciphering intricate data patterns and interconnections. The NNs are integral to the classification of instances predicated on the ascertained features. Our evaluation, conducted using the Failure-Dataset-OpenStack database and MATLAB Software, demonstrates that the hybrid optimization strategy employed for feature selection significantly curtails complexity and expedites processing
Serum microRNAs as new biomarkers for detecting subclinical hemolysis in the nonacute phase of G6PD deficiency
Authors: Boonpeng Kanyarat , Shibuta Tatsuki , Hirooka Yoshitaka , Kulkeaw Kasem , Palasuwan Duangdao , Umemura Tsukuru 
Published: July 2024

AbstractGlucose-6-phosphate dehydrogenase (G6PD) deficiency is one of the most common enzymopathies worldwide. Patients with G6PD deficiency are usually asymptomatic throughout their life but can develop acute hemolysis after exposure to free radicals or certain medications. Several studies have shown that serum miRNAs can be used as prognostic biomarkers in various types of hemolytic anemias. However, the impact of G6PD deficiency on circulating miRNA profiles is largely unknown. The present study aimed to assess the use of serum miRNAs as biomarkers for detecting hemolysis in the nonacute phase of G6PD deficiency. Patients with severe or moderate G6PD Viangchan (871G > A) deficiency and normal G6PD patients were enrolled in the present study. The biochemical hemolysis indices were normal in the three groups, while the levels of serum miR-451a, miR-16, and miR-155 were significantly increased in patients with severe G6PD deficiency. In addition, 3D analysis of a set of three miRNAs (miR-451a, miR-16, and miR-155) was able to differentiate G6PD-deficient individuals from healthy individuals, suggesting that these three miRNAs may serve as potential biomarkers for patients in the nonhemolytic phase of G6PD deficiency. In conclusion, miRNAs can be utilized as additional biomarkers to detect hemolysis in the nonacute phase of G6PD deficiency.
Activation of AMPD2 drives metabolic dysregulation and liver disease in mice with hereditary fructose intolerance
Authors: Andres-Hernando Ana , Orlicky David J. , Kuwabara Masanari , Fini Mehdi A. , Tolan Dean R. , Johnson Richard J. , Lanaspa Miguel A. 
Published: July 2024

AbstractHereditary fructose intolerance (HFI) is a painful and potentially lethal genetic disease caused by a mutation in aldolase B resulting in accumulation of fructose-1-phosphate (F1P). No cure exists for HFI and treatment is limited to avoid exposure to fructose and sugar. Using aldolase B deficient mice, here we identify a yet unrecognized metabolic event activated in HFI and associated with the progression of the disease. Besides the accumulation of F1P, here we show that the activation of the purine degradation pathway is a common feature in aldolase B deficient mice exposed to fructose. The purine degradation pathway is a metabolic route initiated by adenosine monophosphate deaminase 2 (AMPD2) that regulates overall energy balance. We demonstrate that very low amounts of fructose are sufficient to activate AMPD2 in these mice via a phosphate trap. While blocking AMPD2 do not impact F1P accumulation and the risk of hypoglycemia, its deletion in hepatocytes markedly improves the metabolic dysregulation induced by fructose and corrects fat and glycogen storage while significantly increasing the voluntary tolerance of these mice to fructose. In summary, we provide evidence for a critical pathway activated in HFI that could be targeted to improve the metabolic consequences associated with fructose consumption.
Sea ice choke points reduce the length of the shipping season in the Northwest Passage
Authors: Cook Alison J. , Dawson Jackie , Howell Stephen E. L. , Holloway Jean E. , Brady Mike 
Published: July 2024

AbstractArctic sea ice has shifted from a perennial (older, thicker ice) to a seasonal (younger, thinner) ice regime, leading to the increasingly common belief that shipping through Canada’s Northwest Passage is becoming more viable. Here, we use the Risk Index Outcome values derived from the Polar Operational Limit Assessment Risk Indexing System and analyze recent changes to shipping season lengths along individual sections of the Northwest Passage routes from 2007 to 2021. Results show that multi-year ice flushed southward from high-latitude regions maintains the so-called choke points along certain route sections, reducing overall shipping season length. There is considerable spatiotemporal variability in shipping season lengths along the southern and northern routes. Specifically, parts of the northern route exhibit a decrease of up to 14 weeks over the 15 years. The variability of shipping season and, in particular, the shortening of the season will impact not only international shipping but also resupply and the cost of food in many Arctic communities, which require a prompt policy response.
Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID
Authors: Hadley Emily , Yoo Yun Jae , Patel Saaya , Zhou Andrea , Laraway Bryan , Wong Rachel , Preiss Alexander , Chew Rob , Davis Hannah , Brannock M. Daniel , Chute Christopher G. , Pfaff Emily R. , Loomba Johanna , Haendel Melissa , Hill Elaine ,   N3C and RECOVER consortia, Moffitt Richard 
Published: July 2024

Abstract
Background
Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood. We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection.

Methods
We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative. We calculate summary statistics, effect sizes, and Kaplan–Meier curves to better understand COVID-19 reinfections.

Results
Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p-value: <0.0001) with a medium effect size (Cramer’s V: 0.20, DoF = 3). Individuals who experienced severe initial and first reinfection were older in age and at a higher mortality risk than those who had mild initial infection and reinfection.

Conclusions
In a large patient cohort, we find that the severity of reinfection appears to be associated with the severity of initial infection and that Long COVID diagnoses appear to occur more often following initial infection than reinfection in the same epoch. Future research may build on these findings to better understand COVID-19 reinfections.
Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts
Authors: Zang Chengxi , Hou Yu , Schenck Edward J. , Xu Zhenxing , Zhang Yongkang , Xu Jie , Bian Jiang , Morozyuk Dmitry , Khullar Dhruv , Nordvig Anna S. , Shenkman Elizabeth A. , Rothman Russell L. , Block Jason P. , Lyman Kristin , Zhang Yiye , Varma Jay , Weiner Mark G. , Carton Thomas W. , Wang Fei , Kaushal Rainu 
Published: July 2024

Abstract
Background
SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors.

Methods
In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests.

Results
We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7–0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6).

Conclusions
This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.
The Ising model celebrates a century of interdisciplinary contributions
Authors: Macy Michael W. , Szymanski Boleslaw K. , Hołyst Janusz A. 
Published: July 2024

AbstractThe centennial of the Ising model marks a century of interdisciplinary contributions that extend well beyond ferromagnets, including the evolution of language, volatility in financial markets, mood swings, scientific collaboration, the persistence of unintended neighborhood segregation, and asymmetric hysteresis in political polarization. The puzzle is how anything could be learned about social life from a toy model of second order ferromagnetic phase transitions on a periodic network. Our answer points to Ising’s deeper contribution: a bottom-up modeling approach that explores phase transitions in population behavior that emerge spontaneously through the interplay of individual choices at the micro-level of interactions among network neighbors.
Adaptive control of quadrotor suspended load systems with variable payload and wind disturbances
Authors: Wang Ruiying , Shen Jun , Qiu Hongling , Zhu Bohao 
Published: July 2024

AbstractThis paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications.
Innovative hull cleaning robot design and control by Laguerre base model predictive control for impedance and vibration management
Authors: Madanipour Vahid , Najafi Farid 
Published: July 2024

AbstractThe intricate and unpredictable nature of underwater environments and disturbances necessitates the use of model predictive control for the effective operation and inspection of remotely operated vehicles (ROVs). This paper presented an innovative suspension system for a hull‐cleaning robot to control impedance while reducing the vibration of ROV brushes in the presence of environmental disturbances and uncertainties. The use of a model predictive controller that utilizes Laguerre functions results significant reduction in tracking time, and the efficiency of the proposed controller is demonstrated through successful impedance tracking in Z‐direction and vibration reduction in Z and Y directions of the robot in an uncertain environment with disturbance. A prototype robot is built and the controller performance is validated in a real condition and modal analysis theory output with experimental data. The results highlight the effectiveness of the designed suspension system and the developed MPC for real‐world applications where environmental conditions are unpredictable or subject to change while the robot is needed to clean the surface perfectly without scratching the hull.
Firing pattern manipulation of neuronal networks by deep unfolding‐based model predictive control
Authors: Aizawa Jumpei , Ogura Masaki , Shimono Masanori , Wakamiya Naoki 
Published: July 2024

AbstractThe complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding‐based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding‐based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks.
Emotion recognition in user‐generated videos with long‐range correlation‐aware network
Authors: Yi Yun , Zhou Jin , Wang Hanli , Tang Pengjie , Wang Min 
Published: July 2024

AbstractEmotion recognition in user‐generated videos plays an essential role in affective computing. In general, visual information directly affects human emotions, so the visual modality is significant for emotion recognition. Most classic approaches mainly focus on local temporal information of videos, which potentially restricts their capacity to encode the correlation of long‐range context. To address this issue, a novel network is proposed to recognize emotions in videos. To be specific, a spatio‐temporal correlation‐aware block is designed to depict the long‐range correlations between input tokens, where the convolutional layers are used to learn the local correlations and the inter‐image cross‐attention is designed to learn the long‐range and spatio‐temporal correlations between input tokens. To generate diverse and challenging samples, a dual‐augmentation fusion layer is devised, which fuses each frame with its corresponding frame in the temporal domain. To produce rich video clips, a long‐range sampling layer is designed, which generates clips in a wide range of spatial and temporal domains. Extensive experiments are conducted on two challenging video emotion datasets, namely VideoEmotion‐8 and Ekman‐6. The experimental results demonstrate that the proposed method obtains better performance than baseline methods. Moreover, the proposed method achieves state‐of‐the‐art results on the two datasets. The source code of the proposed network is available at: https://github.com/JinChow/LRCANet.