bluuebunny/crossref_metadata_2025_split huggingface dataset:
Handgrip strength asymmetry as a new biomarker for sarcopenia and individual sarcopenia signatures
Authors: Pratt Jedd , Pessanha Ludmilla , Narici Marco , Boreham Colin , De Vito Giuseppe 
Published: September 2023

Abstract
Background
Although handgrip strength (HGS) asymmetry has clinical screening utility, its relevance to sarcopenia is unknown. This study examined the relationship between HGS asymmetry and sarcopenia signatures, and explored the relevance of circulating neural/neuromuscular markers.

Methods
9403 individuals aged 18–92 years participated in this study. Maximal HGS and skeletal muscle index (SMI) were determined using hand dynamometry and DXA. Sarcopenia was diagnosed upon the presence of low HGS and low SMI, according to cohort-specific thresholds. Plasma biomarkers were measured by ELISA in a sub-group of 269 participants aged 50–83 years. Asymmetry was determined as the highest recorded HGS divided by the highest recorded HGS of the opposite hand. Individuals with a ratio > 1.10 were classified as having asymmetrical HGS.

Results
Subjects with asymmetrical HGS had significantly lower SMI (7.67 kg/m2 vs 7.71 kg/m2, p = 0.004) and lower HGS (37.82 kg vs 38.91 kg, p < 0.001) than those with symmetrical HGS. In those aged ≥ 50 years asymmetrical HGS was associated with 2.67 higher odds for sarcopenia [95% confidence interval: (CI) = 1.557–4.561, p < 0.001], 1.83 higher odds for low HGS only (CI 1.427–2.342, p < 0.001), and 1.79 higher odds for low SMI only (CI 1.257–2.554, p = 0.001). HGS asymmetry demonstrated acceptable diagnostic accuracy for sarcopenia (AUC = 0.727, CI 0.658–0.796, p < 0.001). Plasma neural cell adhesion molecule concentrations were 19.6% higher in individuals with asymmetrical HGS (185.40 ng/mL vs 155.00 ng/mL, p < 0.001) than those with symmetrical HGS.

Discussion
Our findings demonstrate the utility of HGS asymmetry as a screening tool that may complement existing strategies seeking to combat sarcopenia. Biomarker analyses suggest that heightened denervation may be an important aetiological factor underpinning HGS asymmetry.
Noneconomic Objectives, Global Value Chains and International Cooperation
Authors: Hoekman Bernard M. , Mavroidis Petros C. , Nelson Douglas R. 
Published: September 2023

AbstractSystemic conflicts increasingly affect the global value chains (GVCs) underpinning globalization by creating policy uncertainty and politicizing trade and investment decisions. Unilateral policies to attain competitiveness and noneconomic objectives (NEOs), including national security, create incentives for international cooperation to attenuate policy spillovers. Recent initiatives seeking to do so are organized around supply chain governance and need not be anchored in trade agreements. Whether such cooperation is feasible and can be designed to be effective in realizing NEOs is unclear. Plurilateral GVC-centered cooperation offers a potential path for states to pursue NEOs and reduce policy uncertainty for international business. Research offers little guidance to policymakers on the design of such cooperation. A key open question is to determine whether explicit market access commitments are necessary to sustain cooperation. Creating mechanisms for the epistemic communities that are concerned with a specific NEO or policy area to interact with stakeholders and lead firms operating international production networks can help inform the design of cooperation to attain NEOs more efficiently.
The impact of power outages on households in Zambia
Authors: Ahmed Imaduddin , Parikh Priti , Munezero Parfait , Sianjase Graham , Coffman D’Maris 
Published: September 2023

AbstractAs global average temperatures rise, so does the frequency and intensity of El Niño-induced droughts, which in turn threaten the reliability of hydropower. 1.4 billion people live in countries where hydropower constitutes more than a quarter of the electricity production and which have experienced El Niño droughts, meaning many more power outages can be expected around the world. Little research has been conducted on the impact of power outages on mental health. This study takes Zambia as its case study to examine the impact that El Niño droughts have had on the lives of householders connected to a highly hydropower-dependant electricity grid, and includes the impact it has had on their physical and self-reported mental health. Using 54 online responses to a survey, we found that the greatest impacts of outages spoiled food, compromised entertainment, compromised ability to work and limitation in cooking options. More than a fifth of respondents reported experiencing self-reported depression to a major degree or all of the time due to power outages, with individuals writing their own responses that they felt debilitated, experienced reduced communication and reduced activities, and stress. Using Bayesian inference, we found that changes in sleeping patterns arising from power outages was a statistically significant predictor of self-reported depression. 63% of surveyed households were willing to pay approximately USD 0.10/kWh as of the end of 2019, about double the tariff that they did, to ensure reliable electricity supply. Household income was a statistically significant predictor of willingness to pay more.
Personalized Re-ranking for Recommendation with Mask Pretraining
Authors: Han Peng , Zhou Silin , Yu Jie , Xu Zichen , Chen Lisi , Shang Shuo 
Published: September 2023

AbstractRe-ranking is to refine the candidate ranking list of recommended items, such that the re-ranked list attracts users to purchase or click more items than the candidate one without re-ranking. Items in the candidate list are often ranked by their relevance to users’ interests. It is thus important to exploit the mutual influence between items in the re-ranking process. Existing re-ranking models focus on only the pairwise influence between two items, and have limited capability to exploit the local mutual influence in a group of items. Users often show successive interests on a group of relevant items, e.g., mobile phone, phone covers, wireless headset, namely scene. We propose a novel re-ranking model that jointly exploits the local mutual influence in scenes and the global mutual influence between different scenes. Scene representations are learned by GNN and multi-head attention, where GNN aims to learn local mutual influence while multi-head attention is to learn global mutual influence. To study the interaction between users and scenes, matrix factorization on users is utilized to obtain the user preference, which can be further applied to scenes to compute the scene scores. The final re-ranking list is generated by sorting the predicted scores of all scenes. To further mine user history information and item related user information, we also develop the extension pretraining module which relies on mask mechanism to support users and items high-quality embedding generation. We conduct a comprehensive evaluation on several real-world datasets. The experimental results demonstrate that our model substantially outperforms existing approaches.
Learning with Small Data: Subgraph Counting Queries
Authors: Zhao Kangfei , He Zongyan , Yu Jeffrey Xu , Rong Yu 
Published: September 2023

AbstractDeep Learning (DL) has been widely used in many applications, and its success is achieved with large training data. A key issue is how to provide a DL solution when there is no large training data to learn initially. In this paper, we explore a meta-learning approach for a specific problem, subgraph isomorphism counting, which is a fundamental problem in graph analysis to count the number of a given pattern graph, p, in a data graph, g, that matches p. There are various data graphs and pattern graphs. A subgraph isomorphism counting query is specified by a pair, (g, p). This problem is NP-hard and needs large training data to learn by DL in nature. We design a Gaussian Process (GP) model which combines Graph Neural Network with Bayesian nonparametric, and we train the GP by a meta-learning algorithm on a small set of training data. By meta-learning, we can obtain a generalized meta-model to better encode the information of data and pattern graphs and capture the prior of small tasks. With the meta-model learned, we handle a collection of pairs (g, p), as a task, where some pairs may be associated with the ground-truth, and some pairs are the queries to answer. There are two cases. One is there are some with ground-truth (few-shot), and one is there is none with ground-truth (zero-shot). We provide our solutions for both. In particular, for zero-shot, we propose a new data-driven approach to predict the count values. Note that zero-shot learning for our regression tasks is difficult, and there is no hands-on solution in the literature. We conducted extensive experimental studies to confirm that our approach is robust to model degeneration on small training data, and our meta-model can fast adapt to new queries by few-shot and zero-shot learning.
Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance
Authors: Ashwin S. H. , Naveen Raj Rashmi 
Published: September 2023

AbstractNumerous accidents and fatalities occur every year across the world as a result of the reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to these factors, autonomous cars have attracted enormous attention as a potentially game-changing technology to address a number of persistent problems in the transportation industry. Autonomous vehicles need to be modeled as intelligent agents with the capacity to observe, and perceive the complex and dynamic environment on the road, and decide an action with the highest priority to the lives of people in every scenarios. The proposed deep deterministic policy gradient-based sequential decision algorithm models the autonomous vehicle as a learning agent and trains it to drive on a lane, overtake a static and a moving vehicle, and avoid collisions with obstacles on the front and right side. The proposed work is simulated using a TORC simulator and has shown the expected performance under the above-said scenarios.
The long-term effects of multidrug immunosuppressive protocols based on calcineurin inhibitors and conversion to rapamycin on the morphology, apoptosis, and proliferation of rat salivary glands
Authors: Grabowska Marta , Kędzierska-Kapuza Karolina , Kram Andrzej , Gill Kamil , Teresiński Leszek , Sipak Olimpia , Piasecka Małgorzata 
Published: September 2023

Abstract
Background
The effect of multidrug immunosuppressive protocols on the salivary glands is still unknown. This study aimed to determine the influence of immunosuppressive regimens based on calcineurin inhibitors (CNIs) and conversion to rapamycin on the morphology, apoptosis, and proliferation of rat salivary glands.

Methods
Male rats received cyclosporin A (CsA), tacrolimus (FK-506), mycophenolate mofetil (MMF), rapamycin (Rapa), and prednisone (Pre) according to three-drug protocols: CMP (CsA, MMF, and Pre), CMP/R (CsA, MMF, and Pre with conversion to Rapa), TMP (FK-506, MMF, and Pre), and TMP/R (FK-506, MMF, and Pre with conversion to Rapa). Morphological and immunohistochemical and quantitative analyses of the salivary glands were performed.

Results
Structural changes in salivary glands were observed in all experimental groups, especially in the submandibular gland. In the salivary glands, the percentages of collagen fibers and TUNEL-, Ki67- and PCNA-positive cells were higher in the experimental groups vs. the control but were lower in the CMP/R and TMP/R groups vs. the CMP and TMP groups, with the exception of collagen fibers in the parotid gland in the TMP/R group vs. the TMP group.

Conclusions
Long-term administration of CNIs in triple regimens and after conversion to rapamycin monotherapy, causes morphological changes in the salivary glands of rats. Immunosuppressive treatment based on CNIs is associated with an increase in collagen accumulation. The effects of the conversion of treatment with CNIs to rapamycin in immunosuppressive protocols in rat salivary glands lead to decreased fibrosis, apoptosis, and proliferation. These changes may possibly prevent abnormalities resulting from the application of CNIs.
Antipathogenic copper coatings: electrodeposition process and microstructure analysis
Authors: Bigos Agnieszka , Bugajska Monika , Kwiecien Izabella , Janusz-Skuza Marta , Szczerba Maciej , Ozga Piotr , Wierzbicka-Miernik Anna , Dyner Marcin , Misztela Andrzej , Dyner Aneta , Wojewoda-Budka Joanna 
Published: September 2023

Abstract
Copper coatings are an important group of decorative-protective materials characterised by high corrosion resistance, excellent thermal and electrical conductivity, which lately gained more significance due to their antimicrobial activity. The main aim of the presented research was to electrodeposit homogenous copper coatings from the non-cyanide electrolyte solution in galvanostatic conditions on steel (1.4024) and nickel (Ni201) substrates, commercially used for surgical instruments. The effect of substrate finishes used in the production line, by shot peening with glass balls, corundum treatment and brushing on the coatings surface formation, was investigated. The substrates’ and coatings’ microstructural properties were analyzed by scanning and transmission electron microscopy, atomic force microscopy, and X-ray diffraction analysis. The current efficiency of the copper reduction on nickel and steel substrates was found to be above 95%. The copper layers adhere to both substrates, except those deposited on a brushed surface. Regardless of the substrate used, they have a nanocrystalline structure with an average crystallite size of 30 nm. Moreover, the coating surface morphology, which affects the nature of interaction with microorganisms, was effectively modified by the appropriate substrate finishing without changing the electrodeposition conditions.
Sampling locally, hypothesis globally: accurate 3D point cloud registration with a RANSAC variant
Authors: Cheng Yuxin , Huang Zhiqiang , Quan Siwen , Cao Xinyue , Zhang Shikun , Yang Jiaqi 
Published: September 2023

AbstractCorrespondence-based six-degree-of-freedom (6-DoF) pose estimation remains a mainstream solution for 3D point cloud registration. However, the heavy outliers pose great challenges to this problem. In this paper, we propose a random sample consensus (RANSAC) variant based on sampling locally and hypothesis globally (SLHG) for 6-DoF pose estimation and 3D point cloud registration. The key novelties are efficient sampling by guiding the sampling process locally and accurate pose estimation by generating hypotheses with global information. SLHG first generates a correspondence subset via compatibility clustering on the initial set. Second, locally guided graph sampling is performed. Third, 6-DoF hypotheses are generated by incorporating global information with a voting scheme. The best hypothesis serves as the estimation result by repeating the second and third steps. Extensive experiments on four popular datasets and comparisons with state-of-the-art methods confirm that: SLHG manages to 1) achieve accurate registrations with a few iterations, and 2) yield better accuracy performance than most competitors.
Enhancements in Cloud Condensation Nuclei Activity From Turbulent Fluctuations in Supersaturation
Authors: Anderson Jesse C. , Beeler Payton , Ovchinnikov Mikhail , Cantrell Will , Krueger Steven , Shaw Raymond A. , Yang Fan , Fierce Laura 
Published: September 2023

AbstractThe effect of aerosols on the properties of clouds is a large source of uncertainty in predictions of weather and climate. These aerosol‐cloud interactions depend critically on the ability of aerosol particles to form cloud droplets. A challenge in modeling aerosol‐cloud interactions is the representation of interactions between turbulence and cloud microphysics. Turbulent mixing leads to small‐scale fluctuations in water vapor and temperature that are unresolved in large‐scale atmospheric models. To quantify the impact of turbulent fluctuations on cloud condensation nuclei (CCN) activation, we used a high‐resolution Large Eddy Simulation of a convective cloud chamber to drive particle‐based cloud microphysics simulations. We show small‐scale fluctuations strongly impact CCN activity. Once activated, the relatively long timescales of evaporation compared to fluctuations causes droplets to persist in subsaturated regions, which further increases droplet concentrations.
Characterizing the Importance of Porosity of Large Woody Debris Accumulations at Single Bridge Piers on Localized Scour
Authors: Panici D. , Kripakaran P. 
Published: September 2023

AbstractThe accumulation of large woody debris (LWD) at bridge piers is a serious hazard to the structural integrity of bridges across watercourses worldwide. The exacerbated scour that can directly result from LWD accumulations can lead to major structural damage or even catastrophic collapse. Recent research has led to empirical equations to estimate the scour depth for given LWD accumulation size; however these are mostly based on experimental tests with prismatic and impervious solid LWD accumulations, ignoring field observations that have shown that accumulations are neither impervious nor prismatic but are porous with inverted conical shapes. In this study, we therefore investigate the effects of porous LWD accumulations having shapes commonly observed in the field on scour holes. Results reveal that LWD size and shape, and flow characteristics are the primary factors influencing the erosion of sediments at the base of bridge piers. However, the porosity of accumulations is also observed to have a considerable effect on the size and maximum depth of scour holes. In particular, porous LWD reduce the maximum scour depth by up to 50% (and on average in the range of 5%–25%) relative to the respective solid impervious accumulation. The results shown in this study also provide a practical tool for arriving at more realistic and less conservative estimates of scour depths at bridge piers when affected by LWD accumulations.
Quantifying the Rightward Bias Extent of Tropical Cyclones' Cold Wakes
Authors: Ma Zhanhong , Yan Xinyue , Fei Jianfang 
Published: September 2023

AbstractAlthough the cold wake of tropical cyclones (TCs) is recognized to be typically rightward biased relative to TC track (in the North Hemisphere), it remains unclear to what extent the rightward bias should be for specified TCs. Based on numerical simulations and observational statistics, this study shows that the distance of cold wake rightward bias is closely related to latitude, translation speed and radius of maximum wind (RMW) of a TC, but is independent from maximum surface wind and radial wind profile outside the RMW, namely TC intensity and size. A semi‐empirical equation is derived to represent the rightward bias extent of cold wakes. For TCs with slower translation speed, smaller RMW, or at higher latitudes, the cold wakes are less asymmetric relative to the TC track. Atmospheric simulations also show that the TC tends to be weaker and more asymmetric as the cold wake is closer to TC center.
Impact of Domain Size on Tropical Precipitation Within Explicit Convection Simulations
Authors: Jones Richard W. , Sanchez Claudio , Lewis Huw , Warner James , Webster Stuart , Macholl Joshua 
Published: September 2023

AbstractWe investigate the sensitivity of modeled tropical precipitation accumulation, intensity and structures to the extent of convection‐permitting limited area model (LAM) domain size. Our comparison focusses on two LAM domains, with identical physical parameterization schemes and using 2.2 km grid spacing. One LAM domain spans almost the full tropical belt while the other focusses on southeast Asia. We show that the LAMs both capture the complex diurnal cycle of precipitation and that the timing and intensity of precipitation are comparable with satellite observations. Systematic differences between the LAMs are largest within ∼1,000 km of the western and eastern boundaries of the southeast Asia LAM. This is due to convective spin‐up at the western boundary of the southeast Asia LAM and a lack of propagating deep convection. We highlight that showing the added value of global storm‐resolving models by comparing with LAMs will help to accelerate their operational implementation.
Typical Synoptic Patterns Responsible for Summer Regional Hourly Extreme Precipitation Events Over the Middle and Lower Yangtze River Basin, China
Authors: Zeng Jingwen , Huang Anning , Wu Peili , Huang Danqing , Zhang Yan , Tang Jian , Zhao Dajun , Yang Ben , Chen Shuang 
Published: September 2023

AbstractBased on the hourly rainfall gauge data and ERA5 reanalysis for the period 1980–2020, typical synoptic patterns responsible for summer regional hourly extreme precipitation events (RHEPE) over the middle and lower Yangtze River basin have been objectively identified using a circulation clustering method. It is found that the Meiyu front with different locations and intensities imbedded in the East Asian summer monsoon, and landfalling typhoons are the leading contributors. As the dominant synoptic pattern, the Meiyu front pattern is associated with ∼92% of the total RHEPE occurrence and can be categorized into a southerly strong‐Meiyu type and a northerly weak‐Meiyu type. The RHEPE occurrence shows a predominant morning peak associated with the southerly strong‐Meiyu type and a secondary late afternoon peak related to the northerly weak‐Meiyu type, in which the Meiyu front is pushed northward by the strengthened western North Pacific subtropical high accompanied by accelerated low‐level southwesterly flow.
Parametric Dependence of Topside Ionospheric Empirical Scale Height and Electron Density Profiles in NeQuick2 Model Over the Equatorial and Low Latitudes and Its Consequences on the Estimation of TEC
Authors: Venkatesh K. , Pallamraju D. , Pant T. K. , Suryawanshi P. 
Published: September 2023

AbstractConstructing realistic vertical electron density profiles is a crucial step in accurately estimating the ionospheric total electron content (TEC) in empirical models. Inadequate representation of electron density profiles leads to under‐ or over‐estimation of TEC in model outputs. One of the widely used ionospheric empirical model, the NeQuick2 exhibits large uncertainties over equatorial and low latitudes, particularly in the topside ionosphere. The NeQuick2 model employs semi Epstein type of polynomials to characterize the topside ionospheric structure, wherein, the scale height (H) is the key parameter. NeQuick2 model estimates H value using empirical formulations, which contain three major parameters namely, Ho—the scale height at the F‐layer peak, g—height gradient in H, and r—which controls the increase in H at higher altitudes. In this study, a systematic analysis has been carried out with particular focus on the equatorial and low latitudes to explore the variability of topside scale height and electron density profiles on the above three empirical parameters using COSMIC observations over Trivandrum and Ahmedabad, in India. Model uncertainties in estimating Ho and their impacts on the estimation of TEC are investigated. Data assimilation analysis has been carried out to obtain a quantitative understanding on the effect of deviations in topside scale height on the uncertainties in accurate estimation of TEC over equatorial and low latitudes. This study reveals the impact of scale height uncertainties on errors in modeled TEC and demonstrates the need for improvement in empirical formulations for improving accuracies in ionospheric modeling over equatorial and low latitude sectors.