bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research progress on risk factors of postoperative functional defecation disorder in anorectal malformation
Authors: Xiang Chenzhu , Xiang Xiao , Feng Wei , Wang Yi 
Published: August 2023

AbstractAnorectal malformation (ARM) is a common congenital gastrointestinal malformation in neonates, which has complex pathological changes and unclear etiology. The only treatment modality currently available for ARMs is through surgical treatment to correct the deformity of the anal morphology and restore normal defecation function. In addition, the mortality rates of children with ARMs have significantly decreased as a result of recent advances in medical technology. It is important to note that despite the treatment of perianal anatomical deformities, approximately one‐third of ARM patients continue to experience functional defecation disorder (FDD), which includes fecal incontinence and constipation and negatively impacts the patient's quality of life. There are still numerous questions regarding the exact cause of FDD in ARM patients following surgery. It is generally accepted that the occurrence of FDD may be related to clinical staging, structural changes in the perianal rectal muscles, anomalies of the enteric nervous system, surgical injury, etc. However, some of these factors are still controversial and require additional study to be confirmed. In this paper, we review the risk factors for FDD following surgery in ARMs, which will help us to subsequently develop appropriate interventions to enhance the anal function and improve quality of life of children with ARMs.
Improved synthesis of porous C2N for CO2 conversion
Authors: Liu Bo‐Han , Luo Xian‐Sheng , Deng Han‐Lin , Zhang Zhi‐Hao , Fan Wen‐Hao , Zhang Qi , Huang Mu‐Hua 
Published: August 2023

AbstractSince the initial introduction of porous C2N‐h2D materials by the Baek group in 2015, these materials have exhibited highly promising applications in fields such as semiconductor devices, heterogeneous catalysis, gas storage and separation, biomedicine, and more. However, much of the existing research on C2N materials has been based on theoretical calculations due to the challenges associated with their synthesis. In this study, an enhanced synthesis method for porous C2N materials has been successfully developed, involving the innovative and nonexplosive synthesis of hexaaminobenzene trihydrochloride (HAB·3HCl) as a crucial intermediate, as well as a time‐efficient synthesis of C2N. Rigorous structural characterizations have been conducted, including solid‐state NMR analysis, among others. The resultant C2N material has been effectively employed to improve the efficiency of CO2 conversion reactions. This straightforward protocol for synthesizing C2N materials is poised to stimulate further exploration and application of this promising 2D material in the near future.
Structural characterization of an intrinsically disordered protein complex using integrated small‐angle neutron scattering and computing
Authors: Chen Serena H. , Weiss Kevin L. , Stanley Christopher , Bhowmik Debsindhu 
Published: August 2023

AbstractCharacterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small‐angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue‐specific deuterium labeling SANS experiments, microsecond all‐atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder‐based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB‐disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.
Hyperoside mediates protection from diabetes kidney disease by regulating ROS‐ERK signaling pathway and pyroptosis
Authors: Zhang Kejia , Li MiaoMiao , Yin Kaiwen , Wang Minjie , Dong Qiuchi , Miao Zilan , Guan Yubo , Wu Qi , Zhou Yao 
Published: August 2023

AbstractRenal tubular injury is a key factor in the progression of diabetic kidney disease to end‐stage renal disease. Hyperoside, a natural flavonol glycoside in various plants, is a potentially effective drug for the clinical treatment of diabetic kidney disease. However, the specific mechanisms remain unknown. Therefore, this study will explore the effect and mechanism of hyperoside on renal tubulointerstitium in diabetic kidney disease. db/db mouse (C57BL/KsJ) is a model of type 2 diabetes resulting from Leptin receptor point mutations, with the appearance of diabetic kidney disease. Therefore, db/db mice were used for in vivo experimental studies. In vitro, human renal tubular epithelial cells were incubated with bovine serum albumin to simulate the injury of renal tubular epithelial cells caused by excessive albumin in primary urine. The experimental results showed that hyperoside could improve kidney function and reduce kidney tissue damage in mice, and could inhibit oxidative stress, extracellularly regulated protein kinases 1/2 signaling activation, and pyroptosis in human renal tubular epithelial cells. Therefore, hyperoside inhibited oxidative stress by regulating the activation of the extracellularly regulated protein kinases 1/2/mitogen‐activated protein kinase signaling pathway, thereby alleviating proteinuria‐induced pyroptosis in renal tubular epithelial cells. This study provides novel evidence that could facilitate the clinical application of hyperoside in diabetic kidney disease treatment.
Synbiotic edible films enriched with probiotics and prebiotics: A novel approach for improving the quality and shelf life of sliced cheese
Authors: Ceylan Huriye Gözde , Atasoy Ahmet Ferit 
Published: August 2023

AbstractThis study investigated the effects of fructooligosaccharide (FOS) and inulin (IN) on the properties of sodium caseinate‐based films containing Lactobacillus rhamnosus GG. The optimal formulations were determined and tested for coating sliced cheese during storage. FOS and IN generally increased the thickness and moisture values of the films, while opacity increased with the increase in IN concentration. Tensile strength decreased with FOS and IN, and elongation at break decreased at high prebiotic concentrations. The viability of L. rhamnosus after the drying of the film solution was between 77.67% and 89.91%, and total prebiotic concentration above 2% generally decreased drying stability of L. rhamnosus. The optimal formulations were 0% FOS + 0% IN, 1% FOS + 0% IN and 0% FOS + 1% IN, respectively. These film matrices were found to be suitable carriers for L. rhamnosus. Coating treatments reduced moisture loss, acidity and hardness increase in cheese slices, but decreased luminosity. The probiotic counts in coated cheese were sufficient for therapeutic effect after approximately 20 days.
Associated production of a top-quark pair with two isolated photons at the LHC through NLO in QCD
Authors: Stremmer Daniel , Worek Malgorzata 
Published: August 2023

Abstract
We report on the computation of NLO QCD corrections to top-quark pair production in association with two photons at the LHC. Higher-order effects and photon bremsstrahlung are taken into account in the production and decays of the top-quark pair. Top-quark and W-boson decays are treated in the Narrow Width Approximation conserving spin correlations up to NLO in QCD. This is the first time that the complete set of NLO QCD corrections to the $$ pp\to t\overline{t}\gamma \gamma $$
pp
→
t

t
¯

γγ
process including top-quark decays is calculated. We present results at the integrated and differential cross-section level in the di-lepton and lepton + jet channel. In addition, we investigate the effect of photon bremsstrahlung in $$ t\overline{t} $$
t

t
¯

production and top-quark decays, as well as the mixed contribution. The latter contribution, in which two photons occur simultaneously in the production and decay of the $$ t\overline{t} $$
t

t
¯

pair, proved to be significant at both the integrated and differential cross-section level.
Amplitude-like functions from entire functions
Authors: Duhr Claude , Kshirsagar Chandrashekhar 
Published: August 2023

Abstract
Recently a function was constructed that satisfies all known properties of a tree-level scattering of four massless scalars via the exchange of an infinite tower of particles with masses given by the non-trivial zeroes of the Riemann zeta function. A key ingredient in the construction is an even entire function whose only zeroes coincide with the non-trivial zeroes of the Riemann zeta function. In this paper we show that exactly the same conclusions can be drawn for an infinite class of even entire functions with only zeroes on the real line. This shows that the previous result does not seem to be connected to specific properties of the Riemann zeta function, but it applies more generally. As an application, we show that exactly the same conclusions can be drawn for L-functions other than the Riemann zeta function.
Uniform $$L^{\infty }$$-Estimates for Quasilinear Elliptic Systems
Authors: Vannella Giuseppina 
Published: August 2023

AbstractThe aim of this work is to provide uniform $$L^{\infty }$$

L
∞

-estimates for the solutions of a quite general class of (p, q)-quasilinear elliptic systems depending on two parameters $$\alpha $$
α
and $$\delta $$
δ
.
Beyond data transactions: a framework for meaningfully informed data donation
Authors: Gomez Ortega Alejandra , Bourgeois Jacky , Hutiri Wiebke Toussaint , Kortuem Gerd 
Published: August 2023

AbstractAs we navigate physical (e.g., supermarket) and digital (e.g., social media) systems, we generate personal data about our behavior. Researchers and designers increasingly rely on this data and appeal to several approaches to collect it. One of these is data donation, which encourages people to voluntarily transfer their (personal) data collected by external parties to a specific cause. One of the central pillars of data donation is informed consent, meaning people should be adequately informed about what and how their data will be used. However, can we be adequately informed when it comes to donating our data when many times we don’t even know it is being collected and, even more so, what exactly is being collected? In this paper, we investigate how to foster (personal) data literacy and increase donors’ understanding of their data. We introduce a Research through Design approach where we define a data donation journey in the context of speech records, data collected by Google Assistant. Based on the data donation experiences of 22 donors, we propose a data donation framework that understands and approaches data donation as an encompassing process with mutual benefit for donors and researchers. Our framework supports a donation process that dynamically and iteratively engages donors in exploring and understanding their data and invites them to (re)evaluate and (re)assess their participation. Through this process, donors increase their data literacy and are empowered to give meaningfully informed consent.
The nature of compact radio sources: the case of FR 0 radio galaxies
Authors: Baldi Ranieri D. 
Published: August 2023

AbstractRadio-loud compact radio sources (CRSs) are characterised by morphologicalcompactnessof the jet structure centred on the active nucleus of the galaxy. Most of the local elliptical galaxies are found to host a CRS with nuclear luminosities lower than those of typical quasars,$$\lesssim 10^{42}\, \text{erg}\, \text{s}^{-1}$$≲1042ergs-1. Recently, low-luminosity CRSs with a LINER-like optical spectrum have been named Fanaroff–Riley (FR) type 0 to highlight their lack of substantially extended radio emission at kpc scales, in contrast with the other Fanaroff–Riley classes, full-fledged FR Is and FR II radio galaxies. FR 0s are the most abundant class of radio galaxies in the local Universe, and characterised by a higher core dominance, poorer Mpc-scale environment and smaller (sub-kpc scale, if resolved) jets than FR Is. However, FR 0s share similar host and nuclear properties with FR Is. A different accretion–ejection paradigm from that in place in FR Is is invoked to account for the parsec-scale FR 0 jets. This review revises the state-of-the-art knowledge about FR 0s, their nature, and which open issues the next generation of radio telescopes can solve in this context.
Parkinsonism in complex neurogenetic disorders: lessons from hereditary dementias, adult-onset ataxias and spastic paraplegias
Authors: Aloisio Simone , Satolli Sara , Bellini Gabriele , Lopriore Piervito 
Published: August 2023

AbstractParkinsonism is a syndrome characterized by bradykinesia in combination with either rest tremor, rigidity, or both. These features are the cardinal manifestations of Parkinson’s disease, the most common cause of parkinsonism, and atypical parkinsonian disorders. However, parkinsonism can be a manifestation of complex neurological and neurodegenerative genetically determined disorders, which have a vast and heterogeneous motor and non-motor phenotypic features. Hereditary dementias, adult-onset ataxias and spastic paraplegias represent only few of this vast group of neurogenetic diseases. This review will provide an overview of parkinsonism’s clinical features within adult-onset neurogenetic diseases which a neurologist could face with. Understanding parkinsonism and its characteristics in the context of the aforementioned neurological conditions may provide insights into pathophysiological mechanisms and have important clinical implications, including diagnostic and therapeutic aspects.
Can direct finance within a servitizing supply chain reduce the moral hazard associated with the servitized user’s care of the product?
Authors: Bian Yaya , Reimann Marc , Zhang Weihua 
Published: August 2023

AbstractServitizing business models can provide economic benefits in a supply chain by utilizing products more efficiently. However, the transfer of the burden to keep the product operable from the user to the servitizing provider may cause issues. As a result of no longer owning the product, a user may be less inclined to taking care of it thereby increasing the maintenance effort for the servitizing provider. This may induce additional financial stress on the latter, specifically if it is a small company with lack of budget to begin with. In this study we analyze whether direct finance within a supply chain, where the user lends money to the servitizing provider, can alleviate this problem, when compared with a more traditional bank finance option. We find that improved access to finance indeed enables the servitizing provider to induce high effort by the user through lower servitizing fees. This also makes the servitizing model economically more attractive for both firms involved. Besides those economic implications, direct finance increases consumer demand and surplus, while at the same time resource consumption for satisfying this increased demand increases.
New approach in the reuse of modified ground tire rubber as thermal and acoustic insulation to be used in civil engineering
Authors: Colom X. , Girbau J. , Marin M. , Formela K. , Saeb M. R. , Carrillo F. , Cañavate J. 
Published: August 2023

AbstractThe concern for the amount of end-of-life tires generated each year has arisen from constant research directed to their valorisation. Herein we propose a new material, which is constituted by GTR with a binder, as acoustic and also as a thermal insulator for civil engineering. The insulator can also include the fibre mat present in the tire, seldomly considered as a recyclable sub-product. To provide insight into the insulating behaviour of these materials, four mathematical models have been tested and compared with the experimental results of thermal conductivity. The Lewis-Nielsen modelization presented good accuracy with deviations of less than 3%. A statistical analysis has also been conducted on the experimental data showing that the parameter with more effect on thermal conductivity is thickness (differences up to 43%) being particle size, less important (ca 6%). In acoustic properties, different effects can be observed depending on the frequency range, being the density the most relevant. From the mathematical, statistical and experimental analysis can be deduced that good insulation properties would be achieved in materials with: low density, porous; including mat and thick. The effect of these parameters causes variations of thermal conductivity from 0.189 to 0.117 W/m·K and in sound absorption coefficient from 0.06 to 0.6.
Learning modular language-conditioned robot policies through attention
Authors: Zhou Yifan , Sonawani Shubham , Phielipp Mariano , Ben Amor Heni , Stepputtis Simon 
Published: August 2023

AbstractTraining language-conditioned policies is typically time-consuming and resource-intensive. Additionally, the resulting controllers are tailored to the specific robot they were trained on, making it difficult to transfer them to other robots with different dynamics. To address these challenges, we propose a new approach called Hierarchical Modularity, which enables more efficient training and subsequent transfer of such policies across different types of robots. The approach incorporates Supervised Attention which bridges the gap between modular and end-to-end learning by enabling the re-use of functional building blocks. In this contribution, we build upon our previous work, showcasing the extended utilities and improved performance by expanding the hierarchy to include new tasks and introducing an automated pipeline for synthesizing a large quantity of novel objects. We demonstrate the effectiveness of this approach through extensive simulated and real-world robot manipulation experiments.
Limited top–down effects of feral cats on rodent dynamics in a seabird colony
Authors: Scomparin Cyril , Geale Caitlan , Johnson Christopher N. , Jones Menna E. 
Published: August 2023

AbstractControl of invasive predators is a priority to protect island biodiversity. Understanding the responses of other species in multi-species invaded food webs is important to avoid unintended consequences. We use an intensive 2-year cat-trapping program in the vicinity of seabird colonies on Bruny Island, Tasmania, to investigate its effectiveness in reducing feral cat density and whether cat control influenced the behaviour and abundance of native and invasive mammal species. Cat density before control was extremely high around this seasonally rich food resource, much higher than on mainlands. Cat density was reduced 5.4-fold by control showing that trapping is effective in reducing cat density in this focussed landscape context. We found no direct effect of cat reduction on the abundance or behaviour of native or invasive mammalian prey species. Recruitment of invasive black rats and native swamp rats increased on the seabird colonies after the shearwater breeding season, and cats responded by increasing their presence on the colonies relative to surrounding areas. This suggests cascading bottom-up effects from a lagged productivity pulse provided by breeding seabirds which would require nutrient sampling to confirm. Our results highlight the complexity of subsequent effects of an invasive predator control on the broader ecosystem.
Resilience to extreme weather events and local financial structure of prefecture-level cities in China
Authors: Peters Vinzenz , Wang Jingtian , Sanders Mark 
Published: August 2023

AbstractWe study the local economic impacts of extreme weather events and the role of local finance in economic resilience. We use data on the physical intensities of extreme wind and precipitation events for 284 prefecture-level cities in China between 2004 and 2013. We estimate impulse response functions using a bias-corrected method of moments estimator to capture the dynamic responses of affected cities up to 5 years after such events. We find that extreme precipitation events depress the growth of local GDP per capita for multiple years, while the negative effects of storms vanish after the first year. We then use this model to measure the economic resilience of cities to extreme weather events. Regressions of economic resilience on indicators of the local financial structure suggest that cities with higher levels of debt are less resilient. Moreover, the presence of state-owned commercial banks appears to be instrumental to regional economic resilience. As extreme weather events are expected to become more frequent and severe due to climate change, our results inform the emerging debate about regional economic resilience to weather-related shocks.
Prediction of shear behavior of glass FRP bars-reinforced ultra-highperformance concrete I-shaped beams using machine learning
Authors: Ahmed Asif , Uddin Md Nasir , Akbar Muhammad , Salih Rania , Khan Mohammad Arsalan , Bisheh Hossein , Rabczuk Timon 
Published: August 2023

AbstractThis study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.