bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relationship between blood amyloid A and resting magnetic resonance functional brain connections in patients with obstructive sleep apnea–hypopnea syndrome
Authors: Wang Xiang , Bai Zhiyu , He Yaqing , Wu Qiaozhen 
Published: May 2022

Abstract

Objective
The aim of this study was to analyze the relationship between serum amyloid A (SAA) concentrations in patients with obstructive sleep apnea–hypopnea syndrome (OSAHS) and their magnetic resonance imaging (MRI) of resting brain function.

Methods

Male patients with OSAHS were enrolled from January to June 2019 in Suzhou Ninth People’s Hospital Affiliated to Soochow University, and nineteen healthy male volunteers were selected as the normal control group. The patients with OSAHS were divided into mild, moderate, and severe groups according to their apnea-hypopnea index (AHI). Early in the morning after the polysomnography (PSG), blood samples were collected and serum levels of serum amyloid A (SAA) were measured by enzyme-linked immunosorbent assay. All subjects were scored by the Auditory Verbal Learning Test (AVLT) scale. Resting brain function images of healthy male volunteers and patients in the severe group were collected by 3.0 T magnetic resonance scanner. SPSS25.0 software was used for statistical analysis.

Results
The SAA of the OSAHS group (n = 43) were higher than those of control group (n = 19). The scores of AVLT-immediate and AVLT-delay in the severe OSAHS group were lower than those in the control group (P < 0.05), and it was negatively correlated with SAA. In the severe OSAHS group, the rest state Function Connection (rsFC) in temporal lobe, marginal lobe, and frontal lobe was lower than that in the control group (P < 0.05) and was significantly negatively correlated with SAA. The rsFC in bilateral parietal lobes was higher than that in the control group (P < 0.05), was significantly positively correlated with SAA, and was negatively correlated with AVLT-delay.

Conclusions
The significant increase in SAA concentration in patients with OSAHS correlated with brain rsFC intensity, providing a reference role for the diagnosis, treatment, and prognosis of cognitive dysfunction in patients with OSAHS.
A Latent Variable Mixed-Effects Location Scale Model with an Application to Daily Diary Data
Authors: Blozis Shelley A. 
Published: May 2022

AbstractA mixed-effects location scale model allows researchers to study within- and between-person variation in repeated measures. Key components of the model include separate variance models to study predictors of the within-person variance, as well as predictors of the between-person variance of a random effect, such as a random intercept. In this paper, a latent variable mixed-effects location scale model is developed that combines a longitudinal common factor model and a mixed-effects location scale model to characterize within- and between-person variation in a common factor. The model is illustrated using daily reports of positive affect and daily stressors for a large sample of adult women.
Learning intraoperative organ manipulation with context-based reinforcement learning
Authors: D’Ettorre Claudia , Zirino Silvia , Dei Neri Niccolò , Stilli Agostino , De Momi Elena , Stoyanov Danail 
Published: May 2022

Abstract
Purpose
Automation of sub-tasks during robotic surgery is challenging due to the high variability of the surgical scenes intra- and inter-patients. For example, the pick and place task can be executed different times during the same operation and for distinct purposes. Hence, designing automation solutions that can generalise a skill over different contexts becomes hard. All the experiments are conducted using the Pneumatic Attachable Flexible (PAF) rail, a novel surgical tool designed for robotic-assisted intraoperative organ manipulation.

Methods
We build upon previous open-source surgical Reinforcement Learning (RL) training environment to develop a new RL framework for manipulation skills, rlman. In rlman, contextual RL agents are trained to solve different aspects of the pick and place task using the PAF rail system. rlman is implemented to support both low- and high-dimensional state information to solve surgical sub-tasks in a simulation environment.

Results
We use rlman to train state of the art RL agents to solve four different surgical sub-tasks involving manipulation skills using the PAF rail. We compare the results with state-of-the-art benchmarks found in the literature. We evaluate the ability of the agent to be able to generalise over different aspects of the targeted surgical environment.

Conclusion
We have shown that the rlman framework can support the training of different RL algorithms for solving surgical sub-task, analysing the importance of context information for generalisation capabilities. We are aiming to deploy the trained policy on the real da Vinci using the dVRK and show that the generalisation of the trained policy can be transferred to the real world.
A Study of the Effect of Alloying Elements and Temperature on Nitrogen Solubility in Industrial Stainless Steelmaking
Authors: Pitkälä Jyrki , Holappa Lauri , Jokilaakso Ari 
Published: May 2022

Abstract
The role of nitrogen varies in different stainless steels from a harmful impurity to a valuable alloying element. Therefore, the strict control of nitrogen content is a very important issue in the steel making process. Better understanding about the thermodynamics of nitrogen solubility is the basis for this control. The aim of this study was to determine whether the nitrogen solubility at atmospheric pressure at any given composition and temperature in an Argon Oxygen Decarburization (AOD) converter could be reliably predicted. This was done by comparing different equations for nitrogen solubility to industrial measurements of several different steel grades with a wide range of compositions and temperatures. The test set consisted of 100 heats to ensure reproducibility and sufficient variability. The focus was on examining the effect of the main alloying elements (Cr, Ni, Mo, Mn) and temperature on nitrogen solubility. Nitrogen solubility is increased most by the chromium content, followed by manganese and molybdenum, whereas nickel decreases the solubility. The interaction effect of an alloying element on nitrogen declines at increasing temperatures, resulting in a negative temperature dependence, i.e., the maximum nitrogen solubility is obtained near the liquidus temperature. The study showed that most of the examined thermodynamic equations predicted the nitrogen solubility quite well in common stainless steels and even in high alloyed steels. On the other hand, clear discrepancies were observed for steels with a high manganese content as well as with strongly deviating temperatures. Therefore, based on the present measurements and the literature data, a new equation was developed for predicting the solubility of nitrogen in a wide range of stainless steels. This equation can be coupled with the existing process models to control the AOD process.
Research Progress of Catalysts and Initiators for Promoting the Cracking of Endothermic Hydrocarbon Fuels
Authors: Liu Yiyao , Chen Ran , Liu Jie , Zhang Xiangwen 
Published: May 2022

AbstractCatalytic/initiated cracking of endothermic hydrocarbon fuels is an effective technology for cooling a hypersonic aircraft with a high Mach number (over 5). Catalysts and initiators can promote fuel cracking at low temperatures, increase fuel conversion and the heat sink capacity, and suppress coke deposition, thereby reducing waste heat. Catalysts mainly include metal oxide catalysts, noble metal catalysts and metal nanoparticles, zeolite catalysts, nanozeolite catalysts, and coating catalysts. Moreover, initiators roughly include nitrogenous compounds, oxygenated compounds, and hyperbranched polymer initiators. In this review, we aim to summarize the catalysts and initiators for cracking endothermic hydrocarbon fuels and their mechanisms for promoting cracking. This review will facilitate the development of the synthesis and exploration of catalysts and initiators.
Efficacy and Dosage Pattern of Sacubitril/Valsartan in Chinese Heart Failure with Reduced Ejection Fraction Patients
Authors: Cheang Iokfai , Shi Shi , Lu Xinyi , Liao Shengen , Zhu Xu , Su Xi , Lu Qi , Yuan Jing , Xu Dachun , Zhang Min , Dai Cuilian , Wang Jingfeng , Yuan Fang , Zhao Yan , Zhou Jingmin , Li Xinli 
Published: May 2022

AbstractThis study aims to investigate the dosage pattern, efficacy, and safety of sacubitril/valsartan (Sac/Val) in Chinese heart failure with reduced ejection fraction (HFrEF) patients regarding real-world settings. Patients from 27 centers with a confirmed diagnosis of HFrEF and initiated Sac/Val treatment were enrolled. The primary objective was to evaluate the dosage pattern and change of heart failure status. In a final cohort of 983 patients, outpatient Sac/Val treatment demonstrated a similar beneficial effect in NT-proBNP and cardiac function. After initiating the treatment, overall and sub-population showed similar safety and efficacy. Patients who received a higher dose of Sac/Val (> 200 mg/d) demonstrated better improvement in LV function and reduction of NT-proBNP regardless of adjustment. Among Chinese HFrEF patients, Sac/Val showed a comparable reduction in NT-proBNP and improvement in cardiac function. Data further support guideline recommendations of Sac/Val in Chinese population. Optimal up-titration might provide further benefits. Further long-term and prognostic studies are needed.
Graphical abstract
Near-Peer Teaching in Radiation Oncology: a Proof of Principle Study for Learning Treatment Planning
Authors: Walls Gerard M. , Ellis Rachel , Lynch Sophie , Flynn Margaret A. , McCann Gemma , Jellett Lucy J. , Harrison Claire 
Published: May 2022

Abstract
Radiotherapy techniques are expanding in range and complexity; therefore, protecting learning environments where residents nurture treatment planning skills is critical. The evidence base for ‘near-peer’ teaching (NPT), where professionals at a similar career stage assist in each other’s learning, is growing in hospital-based disciplines, but has not been reported in radiation oncology. The feasibility of a resident-led teaching programme for developing treatment planning skills was investigated herein with quality improvement (QI) methodology. Following consultation with attendings (n = 10) and all residents (n = 17) at the two cancer centres in the region, a regular NPT session focused on planning skills was initiated at the largest centre, with video-linking to the second centre. Tutorials were case-based and pitched at the level of qualifying examinations. Plan–Do–Study–Act (PDSA) cycles were designed based on primary and secondary improvement drivers derived by group consensus among residents, with tutorials adopted accordingly. Participation, content, and satisfaction were monitored for 20 months. Six PDSA cycles reformed the tutorial format, leading to logistical and pedagogical benefits including interprofessional contributions and enhanced interactivity. Tutorials occurred on 85% prescribed occasions (n = 45) during the subsequent 18-month follow-up, with 25 distinct tumour sites featured. Resident participation and satisfaction increased, independent of resident seniority. Tutorials were paused for the first 2 months of the SARS-CoV-2 pandemic only. A high-quality and cost-effective regional, trainee-led teaching programme on treatment planning was feasible and cost-effective in this study.
Experimental study on CO2 capture by using n-butylamine to plug the gas channeling to enhanced oil recovery
Authors: Wang Chengjun , Ma Bin , Zhang Lei 
Published: May 2022

AbstractThe national policy of peak carbon dioxide emission and carbon neutrality has pointed out the technological direction for the development of the petroleum industry in China. In order to efficiently utilize CO2 gas source to enhance oil recovery, n-butylamine is taken as the plugging channeling agent for experiment study to plug the produced channeling-path during the process of CO2 flooding in ultra-low permeability reservoir. The contents of the experiment included three parts: reaction mechanism of n-butylamine with CO2, evaluation of the injection performance of n-butylamine, and the extent of enhanced oil recovery after plugging the gas channeling by using n-butylamine. Reaction product of n-butylamine and CO2 is white solid, which is a type of organic urea so that it can be used to plug the gas channeling. N-butylamine has a good injection performance after adding protecting slug on the condition of high temperature. 80% of the whole volume of core can be spread after injecting 0.3 PV of n-butylamine. During plugging and displacement experiment of heterogeneous cores, oil recovery can be greatly enhanced by 25–30% after injecting n-butylamine. Experimental results show that it can provide a new train of thought for the gas injection development of fractured, heterogeneous and ultra-low permeability reservoirs by using n-butylamine to plug the high permeability area.
Integrating blue: How do we make nationally determined contributions work for both blue carbon and local coastal communities?
Authors: Dencer-Brown Amrit Melissa , Shilland Robyn , Friess Daniel , Herr Dorothée , Benson Lisa , Berry Nicholas J. , Cifuentes-Jara Miguel , Colas Patrick , Damayanti Ellyn , García Elisa López , Gavaldão Marina , Grimsditch Gabriel , Hejnowicz Adam P. , Howard Jennifer , Islam Sheikh Tawhidul , Kennedy Hilary , Kivugo Rahma Rashid , Lang’at Joseph K. S. , Lovelock Catherine , Malleson Ruth , Macreadie Peter I. , Andrade-Medina Rosalía , Mohamed Ahmed , Pidgeon Emily , Ramos Jorge , Rosette Minerva , Salim Mwanarusi Mwafrica , Schoof Eva , Talukder Byomkesh , Thomas Tamara , Vanderklift Mathew A. , Huxham Mark 
Published: May 2022

AbstractBlue Carbon Ecosystems (BCEs) help mitigate and adapt to climate change but their integration into policy, such as Nationally Determined Contributions (NDCs), remains underdeveloped. Most BCE conservation requires community engagement, hence community-scale projects must be nested within the implementation of NDCs without compromising livelihoods or social justice. Thirty-three experts, drawn from academia, project development and policy, each developed ten key questions for consideration on how to achieve this. These questions were distilled into ten themes, ranked in order of importance, giving three broad categories of people, policy & finance, and science & technology. Critical considerations for success include the need for genuine participation by communities, inclusive project governance, integration of local work into national policies and practices, sustaining livelihoods and income (for example through the voluntary carbon market and/or national Payment for Ecosystem Services and other types of financial compensation schemes) and simplification of carbon accounting and verification methodologies to lower barriers to entry.
Quintessential effects on quasiperiodic oscillations in 4D Einstein–Gauss–Bonnet gravity
Authors: Rayimbaev Javlon , Tadjimuratov Pulat , Ahmedov Bobomurat , Palvanov Satimbay 
Published: May 2022

AbstractDynamics of test particles in the space-time of a quintessential black hole (BH) in a novel four-dimensional Einstein–Gauss–Bonnet (EGB) theory is studied. First, we analyse the properties of the space-time of the BH and possible values of the coupling $$\alpha $$
α
and quintessential parameters q that allows the existence of the event horizon. The scalar invariants of the space-time and effects of the parameters on innermost stable circular orbits (ISCOs) radius are also studied. Moreover, fundamental frequencies such as Keplerian and harmonic oscillations are investigated together with applications to quasi-periodic oscillations (QPOs) where the powerful way to test gravity theories using astrophysical observations of twin-peak QPOs is discussed.
New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted Gaussian mixture modelling
Authors: Vijh Surbhi , Kumar Sumit , Saraswat Mukesh 
Published: May 2022

AbstractThe progress in digital histopathology for computer-aided diagnosis leads to advancement in automated histopathological image classification system. However, heterogeneity and complexity in structural background make it a challenging process. Therefore, this paper introduces robust and reliable new bag-of-feature framework. The optimal visual words are obtained by applying proposed reinforcement cat swarm optimization algorithm. Moreover, the frequency of occurrence of each visual words is depicted through histogram using new weighted Gaussian mixture modelling method. Reinforcement cat swarm optimization algorithm is evaluated on the IEEE CEC 2017 benchmark function problems and compared with other state-of-the-art algorithms. Moreover, statistical test analysis is done on acquired mean and the best fitness values from benchmark functions. The proposed classification model effectively identifies and classifies the different categories of histopathological images. Furthermore, the comparative experimental result analysis of proposed reinforcement cat swarm optimization-based bag-of-feature is performed on standard quality metrics measures. The observation states that reinforcement cat swarm optimization-based bag-of-feature outperforms the other methods and provides promising results.
End-to-end human inspired learning based system for dynamic obstacle avoidance
Authors: Jafri S. M. Haider , Kala Rahul 
Published: May 2022

AbstractAs a first, the paper proposes modelling and learning of specific behaviors for dynamic obstacle avoidance in end-to-end motion planning. In the literature many end-to-end methods have been used in simulators to drive a car and to apply the learnt strategies to avoid the obstacles using the lane changing, following the vehicle as per the traffic rules, driving in-between the lane boundaries, and many more behaviors. The proposed method is designed to avoid obstacles in the scenarios where a dynamic obstacle is headed directly towards the robot from different directions. To avoid the critical encounter of the dynamic obstacles, we trained a novel deep neural network (DNN) with two specific behavioral obstacle avoidance strategies, namely“head-on collision avoidance”and“stop and move”. These two strategies of obstacle avoidance come from the human behavior of obstacle avoidance. Looking at the current frame only, for a very similar visual display of the scenario, the two strategies have contrasting outputs and overall outcomes that makes learning very difficult. A random data recording over general simulations is unlikely to record the corner cases of both behaviors that rarely occur, and a behavior-specific training used in this paper intensifies the same cases for a better learning of the robot in such corner cases. We calculate the intention of the obstacle, whether it will move or not. This proposed method is compared with three state-of-the-art methods of motion planning, namely Timed-Elastic Band, Dynamic Window Approach and Nonlinear Probabilistic Velocity Obstacle. The proposed method beats all the state-of-the-art methods used for comparisons.
Bioinspired MXene-Based User-Interactive Electronic Skin for Digital and Visual Dual-Channel Sensing
Authors: Cao Wentao , Wang Zheng , Liu Xiaohao , Zhou Zhi , Zhang Yue , He Shisheng , Cui Daxiang , Chen Feng 
Published: May 2022

AbstractUser-interactive electronic skin (e-skin) that could convert mechanical stimuli into distinguishable outputs displays tremendous potential for wearable devices and health care applications. However, the existing devices have the disadvantages such as complex integration procedure and lack of the intuitive signal display function. Here, we present a bioinspired user-interactive e-skin, which is simple in structure and can synchronously achieve digital electrical response and optical visualization upon external mechanical stimulus. The e-skin comprises a conductive layer with a carbon nanotubes/cellulose nanofibers/MXene nanohybrid network featuring remarkable electromechanical behaviors, and a stretchable elastomer layer, which is composed of silicone rubber and thermochromic pigments. Furthermore, the conductive nanohybrid network with outstanding Joule heating performance can generate controllable thermal energy under voltage input and then achieve the dynamic coloration of silicone-based elastomer. Especially, such an innovative fusion strategy of digital data and visual images enables the e-skin to monitor human activities with evermore intuition and accuracy. The simple design philosophy and reliable operation of the demonstrated e-skin are expected to provide an ideal platform for next-generation flexible electronics.
Impacting Change in Classroom Literacy Instruction: A Further Investigation of the Better Start Literacy Approach
Authors: Scott Amy , Gillon Gail , McNeill Brigid , Gath Megan 
Published: May 2022

AbstractA controlled intervention study supported the effectiveness of teachers implementing an integrated intervention (Better Start Literacy Approach; BSLA) to accelerate foundational literacy skills for children in Year 1 with low levels of oral language ability in a community with significant challenges to effective teaching and learning (Gillon et al., 2019). As part of an implementation approach, the current study aimed to investigate whether teachers from less challenging contexts can successfully implement the Better Start Literacy Approach with reduced support from researchers. Two schools with a total of 93 Year 0/1 children participated in the teacher-led classroom literacy intervention, with 20% of sample classified as linguistically diverse. A series of research questions explored the impact of the intervention on children’s foundational literacy skills. Repeated measures general linear models demonstrated a positive impact of the intervention for the research group compared to the control group. Further analysis demonstrated the intervention was equally effective for linguistically diverse learners. The findings have important implications for better understanding the effectiveness of the BSLA in differing contexts and for linguistically diverse learners, further adding to the research for this literacy intervention.
Dissecting Human Gonadal Cell Lineage Specification and Sex Determination Using a Single-Cell RNA-Seq Approach
Authors: Wang Rui , Liu Xixi , Li Li , Yang Ming , Yong Jun , Zhai Fan , Wen Lu , Yan Liying , Qiao Jie , Tang Fuchou 
Published: May 2022

AbstractGonadal somatic cells are the main players in gonad development and are important for sex determination and germ cell development. Here, using a time-series single-cell RNA sequencing (scRNA-seq) strategy, we analyzed fetal germ cells (FGCs) and gonadal somatic cells in human embryos and fetuses. Clustering analysis of testes and ovaries revealed several novel cell subsets, including POU5F1+SPARC+ FGCs and KRT19+ somatic cells. Furthermore, our data indicated that the bone morphogenetic protein (BMP) signaling pathway plays cell type-specific and developmental stage-specific roles in testis development and promotes the gonocyte-to-spermatogonium transition (GST) in late-stage testicular mitotic arrest FGCs. Intriguingly, testosterone synthesis function transitioned from fetal Sertoli cells to adult Leydig cells in a stepwise manner. In our study, potential interactions between gonadal somatic cells were systematically explored and we identified cell type-specific developmental defects in both FGCs and gonadal somatic cells in a Turner syndrome embryo (45, XO). Our work provides a blueprint of the complex yet highly ordered development of and the interactions among human FGCs and gonadal somatic cells.