bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uncovering Historical Reservoir Operation Rules and Patterns: Insights From 452 Large Reservoirs in the Contiguous United States
Authors: Li Donghui , Chen Yanan , Lyu Lingqi , Cai Ximing 
Published: August 2024

AbstractReservoir operations are influenced by hydroclimatic variability, reservoir characteristics (i.e., size and purpose), policy regulation, as well as operators' experiences and justification. Data‐driven reservoir operation models based on long‐term historical records shed light on understanding reservoir operation rules and patterns. This study applies generic data‐driven reservoir operation models (GDROMs) developed for 452 data‐rich reservoirs with diversified operation purposes across the CONUS to explore typical operation rules and patterns. We find that the operating policies of any of these reservoirs can be modeled with a small number (1–8) of typical operation modules. The derived modules applied to different conditions of the 452 reservoirs can be categorized into five basic types, that is, constant release, inflow‐driven piecewise constant release, inflow‐driven linear release, storage‐driven piecewise constant release, and storage‐driven nonlinear (or piecewise linear) release. Additionally, a joint‐driven release module, constructed from these five basic types, has been identified. The analysis further shows the module application transition patterns featuring operation dynamics for reservoirs of different operation purposes, sizes, and locations. The typical module types can be used as “Lego” bricks to build operation models, especially for data‐scarce reservoirs. These module types and their application and transition conditions can inform Standard Operation Policy (SOP) and Hedging Policy (HP) with specific inflow, storage, and/or both conditions.
Grounding Zones: The “Inland” Dynamic Interface Between Seawater, Outlet Glaciers, Subglacial Meltwater Routing, and Ice‐Shelf Processes
Authors: Parizek Byron R. 
Published: August 2024

AbstractProjections of sea‐level rise from ice‐sheet shrinkage in a warming world have large uncertainties, linked to limited knowledge of changes at the ocean‐ice sheet interface. This interface most typically is modeled as a grounding line, across which still‐connected ice flows into the ocean to float as an ice shelf, or where icebergs calve from a cliff before the ice begins to float. But, extensive and rapidly increasing evidence shows that this is really a grounding zone, and that processes in this grounding zone omitted from many models could exert major controls on sea‐level rise.
Short‐Term to Inter‐Annual Variability of the Non‐Migrating Tide DE3 From MIGHTI, SABER, and TIDI: Potential Tropospheric Sources and Ionospheric Impacts
Authors: Dhadly Manbharat , Jones McArthur , Emmert John , Drob Douglas , Budzien Scott , Zawdie Kate , McCormack John 
Published: August 2024

AbstractUpward propagating waves of lower atmospheric origin play an important role in coupling terrestrial weather with space weather on daily to inter‐annual timescales. Quantifying their short‐term (<30 days) variability is a difficult challenge because simultaneous observations at multiple local times are needed to sample diurnal cycles. This study demonstrates and validates a short‐term estimation method of the DE3 non‐migrating tide at the equator and then applies the technique to three independent data sets: MIGHTI, SABER, and TIDI. We find that daily DE3 estimates from SABER, MIGHTI, and TIDI at equator agree well with correlation coefficients ranging between 0.76 and 0.85. The daily DE3 amplitude variability is typically ∼7 m/s in zonal winds and ∼3 K in temperature. We also find that daily MLT variations and F‐region ionospheric DE3 from COSMIC‐2 Global Ionospheric Specification (GIS) show a correlation of 0.55–0.65, suggesting that not all ionospheric variability can be attributed to the E‐region dynamo; however, increasing correlation with increasing time‐scale suggests that lower atmospheric variability has pronounced impact on the ionosphere on intra‐seasonal scales. We find that the MLT and the F‐region ionosphere exhibit strong coherent intra‐seasonal oscillations (residual amplitudes upto 50%–60%); their coherency with the MJO in 2020 suggests a possible modulation of the upward propagating DE3 tide related to this major tropical tropospheric weather pattern. In addition, we find stratospheric QBO signatures in the MLT DE3 on inter‐annual scales. This study offers fresh observational insights into the pivotal role of tropospheric weather in shaping variability in the coupled thermosphere‐ionosphere system.
Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin
Authors: Wongchuig S. , Paiva R. , Siqueira V. , Papa F. , Fleischmann A. , Biancamaria S. , Paris A. , Parrens M. , Al Bitar A. 
Published: August 2024

AbstractSatellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research.
The survival and clinical performance of anterior composite resin restorations and posterior indirect and cast restorations used to treat generalised tooth wear
Authors: Shah Sachin , Hemmings Kenneth , Gulamali Akil , Petrie Aviva , Malik Junaid Saleem 
Published: August 2024

AbstractObjective To evaluate the survival and clinical performance of restorative materials used in the rehabilitation of generalised severe tooth wear within a UK NHS postgraduate teaching hospital.Methods The clinical performance of 527 restorations on 20 patients with generalised severe tooth wear was reviewed after a mean period of five years. Anterior teeth were restored with direct composite resin and posterior teeth with indirect restorations. The study used the modified United States Public Health Service criteria for restoration assessment. Survival of the restorations was analysed using Kaplan-Meier survival curves, the log-rank test and the Cox proportional hazards regression analysis.Results The sample included 20 participants: 13 men and 7 women, with a median age of 51.8 years (range: 33-73 years). The median survival time for all restorations was 11.3 years when major failures were considered and 5.9 years for restorations when all types of failure were considered. A median survival time of 5.9 years for composite resin restorations and over seven years for cast restorations was found when considering all failures. Composite resin restorations commonly failed as a result of fracture, wear and marginal discolouration. Factors significantly influencing restoration survival were the material used, aetiology, incisal relationship and tooth location. The biological complications associated with this treatment regime were rare. Patient satisfaction remained generally high, with greatest dissatisfaction related to treatment time.Conclusions The use of anterior composite resin with posterior indirect restorations to treat generalised severe tooth wear is a viable treatment modality with very few major complications.
Untangling the role of RhoA in the heart: protective effect and mechanism
Authors: Miyamoto Shigeki 
Published: August 2024

AbstractRhoA (ras homolog family member A) is a small G-protein that transduces intracellular signaling to regulate a broad range of cellular functions such as cell growth, proliferation, migration, and survival. RhoA serves as a proximal downstream effector of numerous G protein-coupled receptors (GPCRs) and is also responsive to various stresses in the heart. Upon its activation, RhoA engages multiple downstream signaling pathways. Rho-associated coiled-coil-containing protein kinase (ROCK) is the first discovered and best characterized effector or RhoA, playing a major role in cytoskeletal arrangement. Many other RhoA effectors have been identified, including myocardin-related transcription factor A (MRTF-A), Yes-associated Protein (YAP) and phospholipase Cε (PLCε) to regulate transcriptional and post-transcriptional processes. The role of RhoA signaling in the heart has been increasingly studied in last decades. It was initially suggested that RhoA signaling pathway is maladaptive in the heart, but more recent studies using cardiac-specific expression or deletion of RhoA have revealed that RhoA activation provides cardioprotection against stress through various mechanisms including the novel role of RhoA in mitochondrial quality control. This review summarizes recent advances in understanding the role of RhoA in the heart and its signaling pathways to prevent progression of heart disease.
DDX5 deficiency drives non-canonical NF-κB activation and NRF2 expression, influencing sorafenib response and hepatocellular carcinoma progression
Authors: Li Zhili , Kim Woojun , Utturkar Sagar , Yan Bingyu , Lanman Nadia Atallah , Elzey Bennett D. , Kazemian Majid , Yeo Yoon , Andrisani Ourania 
Published: August 2024

AbstractIn advanced hepatocellular carcinoma (HCC), RNA helicase DDX5 regulates the Wnt/β-catenin-ferroptosis axis, influencing the efficacy of the multi-tyrosine kinase inhibitor (mTKI) sorafenib. DDX5 inhibits Wnt/β-catenin signaling, preventing sorafenib-induced ferroptosis escape. Sorafenib/mTKIs reduce DDX5 expression, correlating with poor patient survival post-sorafenib treatment. Notably, DDX5-knockout in HCC cells activates Wnt/β-catenin signaling persistently. Herein, we investigate the mechanistic impact of Wnt/β-catenin activation resulting from DDX5 downregulation in the progression and treatment of HCC. RNAseq analyses identified shared genes repressed by DDX5 and upregulated by sorafenib, including Wnt signaling genes, NF-κB-inducing kinase (NIK) essential for non-canonical NF-κB (p52/RelB) activation, and cytoprotective transcription factor NRF2. We demonstrate, Wnt/β-catenin activation induced NIK transcription, leading to non-canonical NF-κB activation, which subsequently mediated NRF2 transcription. Additionally, DDX5 deficiency extended NRF2 protein half-life by inactivating KEAP1 through p62/SQSTM1 stabilization. In a preclinical HCC mouse model, NRF2 knockdown or DDX5 overexpression restricted tumor growth upon sorafenib treatment, via induction of ferroptosis. Importantly, DDX5-knockout HCC cells exhibited elevated expression of Wnt signaling genes, NIK, p52/RelB, and NRF2-regulated genes, regardless of sorafenib treatment. Transcriptomic analyses of HCCs from TCGA and the Stelic Animal Model (STAM) of non-alcoholic steatohepatitis revealed elevated expression of these interconnected pathways in the context of DDX5 downregulation. In conclusion, DDX5 deficiency triggers Wnt/β-catenin signaling, promoting p52/RelB and NRF2 activation, thereby enabling ferroptosis evasion upon sorafenib treatment. Similarly, independent of sorafenib, DDX5 deficiency in liver tumors enhances activation and gene expression of these interconnected pathways, underscoring the clinical relevance of DDX5 deficiency in HCC progression and therapeutic response.
DEAD-box ATPase Dbp2 is the key enzyme in an mRNP assembly checkpoint at the 3’-end of genes and involved in the recycling of cleavage factors
Authors: Aydin Ebru , Schreiner Silke , Böhme Jacqueline , Keil Birte , Weber Jan , Žunar Bojan , Glatter Timo , Kilchert Cornelia 
Published: August 2024

AbstractmRNA biogenesis in the eukaryotic nucleus is a highly complex process. The numerous RNA processing steps are tightly coordinated to ensure that only fully processed transcripts are released from chromatin for export from the nucleus. Here, we present the hypothesis that fission yeast Dbp2, a ribonucleoprotein complex (RNP) remodelling ATPase of the DEAD-box family, is the key enzyme in an RNP assembly checkpoint at the 3’-end of genes. We show that Dbp2 interacts with the cleavage and polyadenylation complex (CPAC) and localises to cleavage bodies, which are enriched for 3’-end processing factors and proteins involved in nuclear RNA surveillance. Upon loss of Dbp2, 3’-processed, polyadenylated RNAs accumulate on chromatin and in cleavage bodies, and CPAC components are depleted from the soluble pool. Under these conditions, cells display an increased likelihood to skip polyadenylation sites and a delayed transcription termination, suggesting that levels of free CPAC components are insufficient to maintain normal levels of 3’-end processing. Our data support a model in which Dbp2 is the active component of an mRNP remodelling checkpoint that licenses RNA export and is coupled to CPAC release.
Alpha sensory stimulation modulates theta phase during speech-print associative learning
Authors: Liao Zhijun , Ao Xiya , Sun Yulu , Zhang Manli , Meng Xiangzhi 
Published: August 2024

AbstractApplying 10 Hz (α-rate) sensory stimulation, not 5 Hz (θ-rate), prior to introducing novel speech-print pairs can reset the phase of θ oscillations and enhance associative learning. This rapid gain indicates coordinated mechanisms to regulate attentional/cognitive resources (α oscillations) and facilitate memory storage (θ oscillations) early in learning. The present findings may inform educational practices for children with reading difficulties.
Selenium nanoparticle inclusion in broiler diets for enhancing sustainable production and health
Authors: Elkhateeb Fatma S. O. , Ghazalah Abdallah A. , Lohakare Jayant , Abdel-Wareth Ahmed A. A. 
Published: August 2024

AbstractThis study aimed to evaluate the effects of dietary supplementation of nanoparticles of Selenium (Nano-Se) on productive performance, nutrient digestibility, carcass criteria, selenium retention, blood biochemistry, and histopathological examination of broiler chicken. A total of 192 1-day-old male broiler chickens (Cobb 500) were randomly assigned to one of four treatment diets, with each diet given to six replicates of eight chicks. The birds were randomly assigned to one of four treatment groups, each of which included Nano-Se at levels of 0, 0.2, 0.3, or 0.4 mg/kg. The feeding experiment lasted 35 days. Nano-Se addition to broiler diets at 0.2 and 0.3 mg/kg enhanced body weight and body weight gain linearly compared to the control diet and 0.4 mg/kg. The apparent digestibility coefficient of ether extracts linearly increased with increasing Nano-Se levels up to 0.4 mg/kg. Increasing Nano-Se decreased serum cholesterol, triglycerides, alanine aminotransaminase, aspartate aminotransaminase, and creatinine in broiler chickens. Also, serum antioxidants showed a significant increase with increasing Nano-Se levels. As Nano-Se levels were supplemented, improvements in cooking loss, water-holding capacity, and antioxidants were observed as compared to the control. Additionally, a noticeable improvement in meat quality was observed regarding the obtained meat characters. It was preferred to use low doses of Nano-Se (0.3 mg/kg), as tissue retention of Se for both meat and liver was more comparable to the control. In conclusion, nutritional supplementation with Nano-Se increased growth performance, nutrient digestibility, selenium retention, meat quality, blood biochemistry, histological indices, and antioxidant activity of broiler chickens. Overall, the best performance of broilers was observed with Nano-Se supplementation at 0.3 mg/kg, highlighting its potential as a novel supplement for broiler diets.
Trajectory optimization of UAV-IRS assisted 6G THz network using deep reinforcement learning approach
Authors: Saleh Amany M. , Omar Shereen S. , Abd El-Haleem Ahmed M. , Ibrahim Ibrahim I. , Abdelhakam Mostafa M. 
Published: August 2024

AbstractTerahertz (THz) wireless communication is a promising technology that will enable ultra-high data rates, and very low latency for future wireless communications. Intelligent Reconfigurable Surfaces (IRS) aiding Unmanned Aerial Vehicle (UAV) are two essential technologies that play a pivotal role in balancing the demands of Sixth-Generation (6G) wireless networks. In practical scenarios, mission completion time and energy consumption serve as crucial benchmarks for assessing the efficiency of UAV-IRS enabled THz communication. Achieving swift mission completion requires UAV-IRS to fly at maximum speed above the ground users it serves. However, this results in higher energy consumption. To address the challenge, this paper studies UAV-IRS trajectory planning problems in THz networks. The problem is formulated as an optimization problem aiming to minimize UAVs-IRS mission completion time by optimizing the UAV-IRS trajectory, considering the energy consumption constraint for UAVs-IRS. The proposed optimization algorithm, with low complexity, is well-suited for applications in THz communication networks. This problem is a non-convex, optimization problem that is NP-hard and presents challenges for conventional optimization techniques. To overcome this, we proposed a Deep Q-Network (DQN) reinforcement learning algorithm to enhance performance. Simulation results show that our proposed algorithm achieves performance compared to benchmark schemes.
Modular control of vertebrate axis segmentation in time and space
Authors: Seleit Ali , Brettell Ian , Fitzgerald Tomas , Vibe Carina , Loosli Felix , Wittbrodt Joachim , Naruse Kiyoshi , Birney Ewan , Aulehla Alexander 
Published: August 2024

AbstractHow the timing of development is linked to organismal size is a longstanding question. Although numerous studies have reported a correlation of temporal and spatial traits, the developmental or selective constraints underlying this link remain largely unexplored. We address this question by studying the periodic process of embryonic axis segmentation in-vivo in Oryzias fish. Interspecies comparisons reveal that the timing of segmentation correlates to segment, tissue and organismal size. Segment size in turn scales according to tissue and organism size. To probe for underlying causes, we genetically hybridised two closely related species. Quantitative analysis in ~600 phenotypically diverse F2 embryos reveals a decoupling of timing from size control, while spatial scaling is preserved. Using developmental quantitative trait loci (devQTL) mapping we identify distinct genetic loci linked to either the control of segmentation timing or tissue size. This study demonstrates that a developmental constraint mechanism underlies spatial scaling of axis segmentation, while its spatial and temporal control are dissociable modules.
Interactome of the HIV-1 proteome and human host RNA
Authors: Schynkel Tinus , Snippenberg Willem van , Verniers Kimberly , Jang Gwendolyn M , Krogan Nevan J , Mestdagh Pieter , Vandekerckhove Linos , Trypsteen Wim 
Published: August 2024

AbstractThe human immunodeficiency virus (HIV-1) is highly dependent on a variety of host factors. Beside proteins, host RNA molecules are reported to aid HIV-1 replication and latency maintenance. Here, we implement multiple workflows of native RNA immunoprecipitation and sequencing (nRIPseq) to determine direct host RNA interaction partners of all 18 HIV-1 (poly)proteins. We identify 1,727 HIV-1 protein – human RNA interactions in the Jurkat cell line and 1,558 interactions in SupT1 cells for a subset of proteins, and discover distinct cellular pathways that seem to be used or controlled by HIV-1 on the RNA level: Tat binds mRNAs of proteins involved in the super elongation complex (AFF1-4, Cyclin-T1). Correlation of the interaction scores (based on binding abundancy) allows identifying the highest confidence interactions, for which we perform a small-scale knockdown screen that leads to the identification of three HIV-1 protein binding RNA interactors involved in HIV-1 replication (AFF2, H4C9 and RPLP0).
A predictive SD‐WAN traffic management method for IoT networks in multi‐datacenters using deep RNN
Authors: Nazemi Absardi Zeinab , Javidan Reza 
Published: August 2024

AbstractDeploying the Internet of Things (IoT) in integrated edge‐cloud environments exposes the IoT traffic data to performance issues such as delay, bandwidth limitation etc. Recently, Software‐Defined Wide Area Network (SD‐WAN) has emerged as an architecture that originates from the Software‐Defined Network (SDN) paradigm and provides solutions for networking multiple data centers by allowing network administrators to manage and control network layers. In this article, an SDWAN‐based policy for traffic management in IoT is introduced in which the Quality of Service (QoS) metrics such as end‐to‐end delay and bandwidth utilization are optimized. The proposed method implements the traffic management policy in the SDWAN controller. When the IoT traffic flows reach the SDWAN infrastructure network, graph search algorithms are performed to find the near‐optimal paths that affect the end‐to‐end delay of traffic flows. Because of the ability of deep learning to process complex data, a deep RNN model is used to predict the network state information, such as link latency and available bandwidth, before the traffic flows reach the infrastructure network. The proposed method consists of four key modules to predict the routes for future time intervals: (a) an SD‐WAN topology updater unit that checks the link changes and availability, (b) the network state information collector, which collects the network state information to create a dataset, (c) the learning unit, which trains a deep RNN model using the created dataset, and (d) the route predictor unit, which uses the trained model to predict the network state information using a heuristic algorithm to determine the routes. The simulation results showed that the deep RNN model can achieve high accuracy and low Mean Absolute Error (MAE), and the proposed method outperforms shortest‐path algorithms in terms of latency. At the same time, the available bandwidth is almost fairly distributed among all network links.