bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and validation of a semi-automated measurement tool for calculating consistent and reliable surface metrics describing cosmesis in Adolescent Idiopathic Scoliosis
Authors: Suresh Sinduja , Perera Pasan , Izatt Maree T. , Labrom Robert D. , Askin Geoffrey N. , Little J. Paige 
Published: April 2023

AbstractAdolescent Idiopathic Scoliosis (AIS) is a 3D spine deformity that also causes ribcage and torso distortion. While clinical metrics are important for monitoring disorder progression, patients are often most concerned about their cosmesis. The aim of this study was to automate the quantification of AIS cosmesis metrics, which can be measured reliably from patient-specific 3D surface scans (3DSS). An existing database of 3DSS for pre-operative AIS patients treated at the Queensland Children’s Hospital was used to create 30 calibrated 3D virtual models. A modular generative design algorithm was developed on the Rhino-Grasshopper software to measure five key AIS cosmesis metrics from these models—shoulder, scapula and hip asymmetry, torso rotation and head-pelvis shift. Repeat cosmetic measurements were calculated from user-selected input on the Grasshopper graphical interface. InterClass-correlation (ICC) was used to determine intra- and inter-user reliability. Torso rotation and head-pelvis shift measurements showed excellent reliability (> 0.9), shoulder asymmetry measurements showed good to excellent reliability (> 0.7) and scapula and hip asymmetry measurements showed good to moderate reliability (> 0.5). The ICC results indicated that experience with AIS was not required to reliably measure shoulder asymmetry, torso rotation and head-pelvis shift, but was necessary for the other metrics. This new semi-automated workflow reliably characterises external torso deformity, reduces the dependence on manual anatomical landmarking, and does not require bulky/expensive equipment.
Prosocial behavior, social reward and affective state discrimination in adult male and female mice
Authors: Misiołek Klaudia , Klimczak Marta , Chrószcz Magdalena , Szumiec Łukasz , Bryksa Anna , Przyborowicz Karolina , Rodriguez Parkitna Jan , Harda Zofia 
Published: April 2023

AbstractProsocial behavior, defined as voluntary behavior intended to benefit another, has long been regarded as a primarily human characteristic. In recent years, it was reported that laboratory animals also favor prosocial choices in various experimental paradigms, thus demonstrating that prosocial behaviors are evolutionarily conserved. Here, we investigated prosocial choices in adult male and female C57BL/6 laboratory mice in a task where a subject mouse was equally rewarded for entering any of the two compartments of the experimental cage, but only entering of the compartment designated as “prosocial” rewarded an interaction partner. In parallel we have also assessed two traits that are regarded as closely related to prosociality: sensitivity to social reward and the ability to recognize the affective state of another individual. We found that female, but not male, mice increased frequency of prosocial choices from pretest to test. However, both sexes showed similar rewarding effects of social contact in the conditioned place preference test, and similarly, there was no effect of sex on affective state discrimination measured as the preference for interaction with a hungry or relieved mouse over a neutral animal. These observations bring interesting parallels to differences between sexes observed in humans, and are in line with reported higher propensity for prosocial behavior in human females, but differ with regard to sensitivity to social stimuli in males.
Small RNA sequencing of circulating small extracellular vesicles microRNAs in patients with amyotrophic lateral sclerosis
Authors: Kim Jin-Ah , Park Canaria , Sung Jung-Joon , Seo Do-Jin , Choi Seok-Jin , Hong Yoon-Ho 
Published: April 2023

AbstractDysregulation of microRNAs (miRNA) in small extracellular vesicles (sEV) such as exosomes have been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Although circulating cell-free miRNA have been extensively investigated in ALS, sEV-derived miRNAs have not been systemically explored yet. Here, we performed small RNA sequencing analysis of serum sEV and identified 5 differentially expressed miRNA in a discovery cohort of 12 patients and 11 age- and sex-matched healthy controls (fold change > 2, p < 0.05). Two of them (up- and down-regulation of miR-23c and miR192-5p, respectively) were confirmed in a separate validation cohort (18 patients and 15 healthy controls) by droplet digital PCR. Bioinformatic analysis revealed that these two miRNAs interact with distinct sets of target genes and involve biological processes relevant to the pathomechanism of ALS. Our results suggest that circulating sEV from ALS patients have distinct miRNA profiles which may be potentially useful as a biomarker of the disease.
Design of moving average chart and auxiliary information based chart using extended EWMA
Authors: Naveed Muhammad , Azam Muhammad , Nawaz Muhammad Shujaat , Saleem Muhammad , Aslam Muhammad , Saeed Muhammad 
Published: April 2023

AbstractThe control chart is the most valuable tool in the manufacturing process to track the output process in the industries. Quality specialists always want a visual framework that recognizes sustainable improvements in the monitoring processes. The efficiency of a control chart is increased by utilizing a memory-based estimator or by using any extra information relevant to the key variable. In this study, we present Extended EWMA (EEWMA) and EWMA based monitoring charts for observing the process location using moving average (MA) statistic under two different situations, i.e., when some extra information is known and unknown. We also propose an EEWMA control chart using Auxiliary Information. The output of these charts is evaluated and contrasted to the various existing charts on the basis of average run length (ARL). The comparison indicates that the proposed charts outperform rivals in identifying all types of shifts in the process location parameter. The implementation of these plans is also rendered to incorporate them in a practical situation.
New carbohydrate binding domains identified by phage display based functional metagenomic screens of human gut microbiota
Authors: Akhtar Akil , Lata Madhu , Sunsunwal Sonali , Yadav Amit , LNU Kajal , Subramanian Srikrishna , Ramya T. N. C. 
Published: April 2023

AbstractUncultured microbes represent a huge untapped biological resource of novel genes and gene products. Although recent genomic and metagenomic sequencing efforts have led to the identification of numerous genes that are homologous to existing annotated genes, there remains, yet, an enormous pool of unannotated genes that do not find significant sequence homology to existing annotated genes. Functional metagenomics offers a way to identify and annotate novel gene products. Here, we use functional metagenomics to mine novel carbohydrate binding domains that might aid human gut commensals in adherence, gut colonization, and metabolism of complex carbohydrates. We report the construction and functional screening of a metagenomic phage display library from healthy human fecal samples against dietary, microbial and host polysaccharides/glycoconjugates. We identify several protein sequences that do not find a hit to any known protein domain but are predicted to contain carbohydrate binding module-like folds. We heterologously express, purify and biochemically characterize some of these protein domains and demonstrate their carbohydrate-binding function. Our study reveals several previously unannotated carbohydrate-binding domains, including a levan binding domain and four complex N-glycan binding domains that might be useful for the labeling, visualization, and isolation of these glycans.
Real-time observation of optical rogue waves in spatiotemporally mode-locked fiber lasers
Authors: Teğin Uğur , Wang Peng , Wang Lihong V. 
Published: April 2023

AbstractFiber lasers offer tabletop nonlinear environments to mimic and study the complex dynamics of nature. Optical rogue waves, rarely occurring extreme intensity fluctuations, are one of the many subjects that can be investigated with a fiber laser cavity. Although oceanic rogue waves are a result of spatiotemporal dynamics, the single-mode nature of the fiber laser and the commonly used measurement techniques limit the optical rogue wave studies to only temporal dynamics. In this study, we overcome such limit to observe rogue wave real-time dynamics in spatiotemporally mode-locked fiber lasers by utilizing state-of-the-art compressed ultrafast photography technique. The multimode laser cavity exhibits long-tailed non-Gaussian distributions under relaxed cavity constraints. Single-shot spatiotemporal measurements of rogue events showed that, instead of noise bursts, the cavity produces clean pulses with high-quality beam profiles. Our results indicate that rogue events in spatiotemporally mode-locked fiber lasers undergo nonlinear spatial transformation due to a power-dependent consistent attractor.
Poroelastic stress relaxation, slip stress transfer and friction weakening controlled post-injection seismicity at the Basel Enhanced Geothermal System
Authors: Boyet Auregan , De Simone Silvia , Ge Shemin , Vilarrasa Víctor 
Published: April 2023

AbstractInduced seismicity is a limiting factor for the development of Enhanced Geothermal Systems (EGS). Its causal mechanisms are not fully understood, especially those of post-injection seismicity. To better understand the mechanisms that induced seismicity in the controversial case of the Basel EGS (Switzerland), we perform coupled hydro-mechanical simulation of the plastic response of a discrete pre-existing fault network built on the basis of the monitored seismicity. Simulation results show that the faults located in the vicinity of the injection well fail during injection mainly triggered by pore pressure buildup. Poroelastic stressing, which may be stabilizing or destabilizing depending on the fault orientation, reaches further than pressure diffusion, having a greater effect on distant faults. After injection stops, poroelastic stress relaxation leads to the immediate rupture of previously stabilized faults. Shear-slip stress transfer, which also contributes to post-injection reactivation of distant faults, is enhanced in faults with slip-induced friction weakening.
Beneficial effects of brief early life angiotensin-converting enzyme inhibition wane with time in sheep with solitary functioning kidney
Authors: McArdle Zoe , Singh Reetu R. , Bielefeldt-Ohmann Helle , Moritz Karen M. , Denton Kate M. , Schreuder Michiel F. 
Published: April 2023

Abstract
A child with a congenital solitary functioning kidney (SFK) may develop kidney disease from early in life due to hyperfiltration injury. Previously, we showed in a sheep model of SFK that brief angiotensin-converting enzyme inhibition (ACEi) early in life is reno-protective and increases renal functional reserve (RFR) at 8 months of age. Here we investigated the long-term effects of brief early ACEi in SFK sheep out to 20 months of age. At 100 days gestation (term = 150 days) SFK was induced by fetal unilateral nephrectomy, or sham surgery was performed (controls). SFK lambs received enalapril (SFK+ACEi; 0.5 mg/kg, once daily, orally) or vehicle (SFK) from 4 to 8 weeks of age. At 8, 14 and 20 months of age urinary albumin excretion was measured. At 20 months of age, we examined basal kidney function and RFR via infusion of combined amino acid and dopamine (AA+D). SFK+ACEi resulted in lower albuminuria (∼40%) at 8 months, but not at 14 or 20 months of age compared with vehicle-SFK. At 20 months, basal GFR (∼13%) was lower in SFK+ACEi compared with SFK, but renal blood flow (RBF), renal vascular resistance (RVR) and filtration fraction were similar to SFK. During AA+D, the increase in GFR was similar in SFK+ACEi and SFK animals, but the increase in RBF was greater (∼46%) in SFK+ACEi than SFK animals. Brief ACEi in SFK delayed kidney disease in the short-term but these effects were not sustained long-term.
Energy function‐guided histogram analysis for interrupted sampling repeater jamming suppression
Authors: Wang Zijian , Li Jiamu , Yu Wenbo , Luo Yunhua , Zhao Yao , Yu Zhongjun 
Published: April 2023

AbstractInterrupted sampling repeater jamming (ISRJ) is a novel intra‐pulse coherent jamming based on digital radio frequency memory (DRFM). By repeatedly sampling and retransmitting the radar transmitting signal fragments, a series of false targets can be formed after pulse compression (PC), posing a severe threat to modern radar systems. Inspired by the energy function method and histogram analysis in image processing, an adaptive time‐frequency (TF) filtering method is proposed in this letter. The ISRJ‐contaminated regions can be accurately determined in the TF image after histogram analysis and subsequent energy accumulation. Guided by the energy function, the proposed method can automatically adjust the intensity threshold in TF image histogram analysis and therefore reveals better robustness compared with other competing methods. Simulations have verified the effectiveness and robustness of the proposed method against ISRJ under various circumstances.
AARN: Anchor‐guided attention refinement network for inshore ship detection
Authors: Liu Di , Zhang Yan , Zhao Yan , Shi Zhiguang , Zhang Jinghua , Zhang Yu , Ling Feng , Zhang Yi 
Published: April 2023

AbstractInshore ship detection is an important task in several fields, for example, maritime transportation, maritime supervision, and port management. However, due to the diversified categories and locations of different ships and interference of complex surroundings, capturing discriminative characteristics of multi‐scale inshore ships for accurate detection is still challenging. Here, an anchor‐guided attention refinement network (AARN) is proposed to alleviate the problems by prominently designing an attention feature filter module (AFFM) and an anchor‐guided alignment detection module (AADM). In AFFM, of which the attention supervision generated from high‐level semantic features, is used to highlight informative target cues and suppress background interference when establishing a four‐layer feature pyramid. In AADM, the anchor‐aligned features are adopted to eventually identify potential inshore ships, which both alleviates misalignment between refined anchors and pyramidal features and improves the performance further. Extensive experiments conducted on the public Seaships7000 dataset verify the contributions of the proposed modules and the effectiveness of our method for detecting multi‐scale inshore ships in comparison to both domain‐specific and general CNN‐based methods.
DCIFPN: Deformable cross‐scale interaction feature pyramid network for object detection
Authors: Xiao Junrui , Jiang He , Li Zhikai , Gu Qingyi 
Published: April 2023

AbstractExploiting multi‐scale features is one of the most effective methods to recognize objects of different scales in object detection. Since image pyramid is time‐consuming, Feature Pyramid Network (FPN) becomes the most popular component used for obtaining pyramidal features. Despite its effectiveness, there still exist some intrinsic defects. In this work, it is attributed to insufficient information flow and a Deformable Cross‐scale Interaction Feature Pyramid Network (DCIFPN) is proposed, which aims to promote the information transfer process with content‐aware sampling and dynamic aggregation weights. More specifically, Deformable Semantic Enhancement Module (DSEM) is designed that can construct accurate information flow with dynamic aggregation weights. In addition, Deformable Spatial Refinement Module (DSRM) is proposed to enhance high‐level features with low‐level location details. When DCIFPN is deployed on RetinaNet and FCOS with ResNet‐50, the performance is improved by 1.6 AP and 1.1 AP, respectively, on the challenging MS COCO benchmark. Apart from one‐stage detectors, DCIFPN is also applicable to two‐stage methods such as Faster R‐CNN and Mask R‐CNN. Further experiments on Pascal VOC and CrowdHuman datasets can verify the effectiveness and generalization of the method.
Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm
Authors: Qiu Yue , Zhou Suyang , Xia Dong , Gu Wei , Sun Kaiyu , Han Gaoyan , Zhang Kang , Lv Hongkun 
Published: April 2023

AbstractLocal integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms.
An adaptive multivariate Student's t‐process recursive method for hypersonic glide vehicle trajectory prediction
Authors: Xue Xirui , Huang Shucai , Wei Daozhi 
Published: April 2023

AbstractThe trajectory prediction of the hypersonic glide vehicle (HGV) can provide hit point information for early warning systems, which is of great significance for near‐space defence operations. However, the heavy‐tailed noise caused by abnormal environmental disturbance and abrupt changes in vehicle trajectory seriously affects the accuracy of HGV trajectory prediction. To solve the problem of trajectory prediction for HGV under heavy‐tailed noise, we propose an adaptive multivariate Student's t‐process regression method called aEM‐MVTPR. Firstly, a heavy‐tailed noise model is developed using the Student's t‐distribution. Secondly, a multivariate Student's t‐process regression (MVTPR) for HGV trajectory prediction is derived, and the method can learn the HGV trajectory time series features and mine the correlations among the trajectory variables. Finally, to further improve the method's robustness, we use the accelerated expectation maximisation algorithm and Pearson correlation analysis to adaptively estimate and adjust the initial values of the MVTPR parameters. The simulation experimental results show that the proposed method has more accurate predicted values of trajectory variables than multivariate Gaussian process regression (MVGPR) under heavy‐tailed noise conditions. In addition, the ability of the aEM‐MVTPR to adaptively adjust the parameter makes it more robust than the MVTPR under different noise environments.
Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter
Authors: Abdollahzadeh Mohammad , Mollaee Hasan , Ghamari Seyyed Morteza , Khavari Fatemeh 
Published: April 2023

AbstractDC/ DC Boost converter has a right half‐plane zero structure called a non‐minimum phase system, which presents several challenging constraints for designing well‐behaved control techniques. The Fractional‐Order concept as a beneficial scheme provides several advantages, such as lower sensitivity to noise and parametric variation. For this purpose, a Fractional‐order Proportional‐Integrated‐Derivative (FOPID) controller is designed for the Boost converter. On the other hand, for wider ranges of disturbances, including parametric variations, load uncertainty, supply voltage variation, and noise, this approach shows an unsuitable practical application based on its fixed gain values; therefore, the control parameters need to be optimized again to provide ideal operations. An Artificial Neural Network structure (ANNs) is adopted here to optimize the gains of the FOPID in challenging conditions. This method suffers from higher complexity and slower dynamics in practical applications. A single‐layer ANNs is designed to reduce the complexity; also, particle swarm optimization (PSO) algorithm is utilized in this decision‐making structure to provide better results with its online mechanism. Furthermore, the Black‐box technique is applied for the proposed system, which does not require an accurate mathematical model resulting in a lower computational burden, easy implementation, and lower dependency on the states of the model. The Artificial Neural Network structure can optimize the FOPID gains real‐time, even in severe challenging conditions, efficiently. To better examine the superiority of the proposed method, conventional Proportional‐Integrated‐Derivative (PID) and the FOPID controllers are proposed to drive a comparison with this work, which are tuned by the PSO optimization algorithm. The evaluation of simulation results demonstrates that the proposed control scheme is suitable not only for preserving stability but also for compensating for the disturbances and uncertainties in the Boost converter, properly.
Formalisation de la scénarisation d’un dispositif de formation avec patients simulés : la méthode SIMPS (Sujets/Instructions au candidat/Mesures/Patient/Support technique)
Authors: Burnier Isabelle , Launay Frédéric , Duveau Mickaël , Gosset Marjolaine 
Published: April 2023

Contexte : La scénarisation des activités de simulation est très importante. Beaucoup de gabarits sont proposés pour la simulation en immersion clinique simulée (ICS) mais peu sont réellement adaptés à la simulation avec patient simulé (PS). De plus, peu de lignes directrices proposent une réelle démarche de rédaction. Objectifs : Proposer une méthode de scénarisation basée sur une expérience de 15 ans en cliniques simulées. Méthodes : Une démarche empirique d’analyse de pratiques a été mise en œuvre, aboutissant à la formation d’une démarche méthodique « Sujets/Instructions au candidat/Mesures/Patient/Support technique » (SIMPS). Conclusion : La méthode SIMPS facilite la rédaction selon des étapes précises tout en respectant l’intégration des concepts pédagogiques liés à l’apprentissage par simulation. Avec la méthode SIMPS, n’importe quel clinicien qui désire participer à l’enseignement par simulation avec PS pourra se sentir guidé et en confiance. Des recherches doivent être menées pour évaluer cette méthode auprès des rédacteurs de scénarios de simulation.