bluuebunny/crossref_metadata_2025_split huggingface dataset:
Systems immunology-based drug repurposing framework to target inflammation in atherosclerosis
Authors: Amadori Letizia , Calcagno Claudia , Fernandez Dawn M. , Koplev Simon , Fernandez Nicolas , Kaur Ravneet , Mury Pauline , Khan Nayaab S , Sajja Swathy , Shamailova Roza , Cyr Yannick , Jeon Minji , Hill Christopher A. , Chong Peik Sean , Naidu Sonum , Sakurai Ken , Ghotbi Adam Ali , Soler Raphael , Eberhardt Natalia , Rahman Adeeb , Faries Peter , Moore Kathryn J. , Fayad Zahi A. , Ma’ayan Avi , Giannarelli Chiara 
Published: June 2023

AbstractThe development of new immunotherapies to treat the inflammatory mechanisms that sustain atherosclerotic cardiovascular disease (ASCVD) is urgently needed. Herein, we present a path to drug repurposing to identify immunotherapies for ASCVD. The integration of time-of-flight mass cytometry and RNA sequencing identified unique inflammatory signatures in peripheral blood mononuclear cells stimulated with ASCVD plasma. By comparing these inflammatory signatures to large-scale gene expression data from the LINCS L1000 dataset, we identified drugs that could reverse this inflammatory response. Ex vivo screens, using human samples, showed that saracatinib—a phase 2a-ready SRC and ABL inhibitor—reversed the inflammatory responses induced by ASCVD plasma. In Apoe−/− mice, saracatinib reduced atherosclerosis progression by reprogramming reparative macrophages. In a rabbit model of advanced atherosclerosis, saracatinib reduced plaque inflammation measured by [18F]fluorodeoxyglucose positron emission tomography–magnetic resonance imaging. Here we show a systems immunology-driven drug repurposing with a preclinical validation strategy to aid the development of cardiovascular immunotherapies.
Comparative Analysis of the Temporal Aspects of Word-Final Disfluencies, Stuttering-Like Disfluencies, and Nonstuttering-Like Disfluencies
Authors: Sutkowski Stephanie , Scaler Scott Kathleen , Kisenwether Jessica 
Published: June 2023

Purpose:
Repetitions at the end of words, known as word-final disfluencies (WFDs), have long been documented in the speech of individuals with and without other communication disorders. Although these disfluencies are often compared to repetitions at the beginning of words observed in stuttering, it has been suggested that WFDs differ from stuttering in their salient features. WFDs have been observed to present more as nonstuttering-like disfluencies (NSLDs; interjections, phrase repetitions, revisions) than as stuttering-like disfluencies (SLDs) in terms of tension and the individual's awareness of their disfluencies. An acoustic comparison of the different types of disfluency has not yet been explored. The purpose of this study was to compare the durational characteristics of repetitions in individuals with WFDs to those in individuals who exhibit SLDs and NSLDs.

Method:
Speech samples from three school-age boys who presented with WFDs, three school-age boys who stutter, and three preschool-age boys who presented with NSLDs often seen in normal language development were recorded and analyzed. The silent duration between the parent word and the repetition unit of each participant's disfluencies were measured using acoustic analysis software and compared across groups.

Results:
Results indicated that the silent duration between the word and the repeated segment of the disfluencies produced by each group was significantly different from each other.

Conclusion:
Descriptively, the silent duration of WFDs was closer to that of NSLDs than to that of SLDs, suggesting that WFDs may be more closely related to the disfluencies of developing language than to those of stuttering.
Towards trustworthy multi‐modal motion prediction: Holistic evaluation and interpretability of outputs
Authors: Carrasco Limeros Sandra , Majchrowska Sylwia , Johnander Joakim , Petersson Christoffer , Sotelo Miguel Ángel , Fernández Llorca David 
Published: June 2023

AbstractPredicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning. This task is very complex, as the behaviour of road agents depends on many factors and the number of possible future trajectories can be considerable (multi‐modal). Most prior approaches proposed to address multi‐modal motion prediction are based on complex machine learning systems that have limited interpretability. Moreover, the metrics used in current benchmarks do not evaluate all aspects of the problem, such as the diversity and admissibility of the output. The authors aim to advance towards the design of trustworthy motion prediction systems, based on some of the requirements for the design of Trustworthy Artificial Intelligence. The focus is on evaluation criteria, robustness, and interpretability of outputs. First, the evaluation metrics are comprehensively analysed, the main gaps of current benchmarks are identified, and a new holistic evaluation framework is proposed. Then, a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system. To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework, an intent prediction layer that can be attached to multi‐modal motion prediction models is proposed. The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi‐modal trajectories and intentions. The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autonomous vehicles, advancing the field towards greater safety and reliability.
Semantic segmentation via pixel‐to‐center similarity calculation
Authors: Wu Dongyue , Guo Zilin , Li Aoyan , Yu Changqian , Sang Nong , Gao Changxin 
Published: June 2023

AbstractSince the fully convolutional network has achieved great success in semantic segmentation, lots of works have been proposed to extract discriminative pixel representations. However, the authors observe that existing methods still suffer from two typical challenges: (i) The intra‐class feature variation between different scenes may be large, leading to the difficulty in maintaining the consistency between same‐class pixels from different scenes; (ii) The inter‐class feature distinction in the same scene could be small, resulting in the limited performance to distinguish different classes in each scene. The authors first rethink semantic segmentation from a perspective of similarity between pixels and class centers. Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset, which can be regarded as the embedding of the class center. Thus, the pixel‐wise classification amounts to computing similarity in the final feature space between pixels and the class centers. Under this novel view, the authors propose a Class Center Similarity (CCS) layer to address the above‐mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers. The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene, which adapt the large intra‐class variation between different scenes. Specially designed Class Distance Loss (CD Loss) is introduced to control both inter‐class and intra‐class distances based on the predicted center‐to‐center and pixel‐to‐center similarity. Finally, the CCS layer outputs the processed pixel‐to‐center similarity as the segmentation prediction. Extensive experiments demonstrate that our model performs favourably against the state‐of‐the‐art methods.
Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks
Authors: Li Dongji , Xu Shaoyi , Li Yan 
Published: June 2023

AbstractThis paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a large amount of data generated by IoT networks needs to be collected by UAV. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation. To facilitate data collection by UAVs, the data‐distance‐k‐means (d2‐k‐means) algorithm is proposed to divide IoT devices into multiple initial clusters. However, the formulated problem is mixed‐integer joint non‐convex, so it is difficult to solve directly. Since it may be with relatively high computational complexity, as an alternative, a block coordinate descent (BCD)‐based method is designed. To tackle the non‐convex problem, a successive convex approximation (SCA)‐based algorithm is also proposed. Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV‐assisted wireless IoT networks to collect massive amount of data.
Bisimulations for delayed switched Boolean control networks and its application in controllability
Authors: Yu Weiyong , Deng Zhenhua , Guo Qianjin , Liu Qiang 
Published: June 2023

AbstractThis paper investigates bisimulation relations of delayed switched Boolean control networks (DSBCNs), using the semi‐tensor product (STP) approach. Firstly, the notion of bisimulation in DSBCNs is formalized. Subsequently, based on the skeleton matrix of DSBCN, two necessary and sufficient conditions are derived for checking this notion. Moreover, as an application of bisimulation relations, this paper explores the propagation of controllability for DSBCNs through a bisimulation relation. It shows the possibility that the controllability of a DSBCN can be inferred by studying a potentially simpler DSBCN. At last, two examples are proposed to illustrate the efficiency of the obtained results.
Non‐invasive ventilation treatment for patients with chronic obstructive pulmonary disease
Authors: Tehrani Fleur T. , Roum James H. 
Published: June 2023

AbstractChronic obstructive pulmonary disease (COPD) affects the lives of millions of patients worldwide. Patients with advanced COPD may require non‐invasive ventilation (NIV) to support the resultant deficiencies of the respiratory system. The purpose of this study was to evaluate the effects of varying the continuous positive airway pressure (CPAP) and oxygen supplementation components of NIV on simulated COPD patients by using an established and detailed model of the human respiratory system. The model used in the study simulates features of advanced COPD including the effects on the changes in ventilation control, increases in respiratory dead space and airway resistance, and the acid–base shifts in the blood seen in these patients over time. The results of the study have been compared with and found to be in general agreement with available clinical data. Our results demonstrate that under non‐emergency conditions, low levels of oxygen supplementation combined with low levels of CPAP therapy seem to improve hypoxemia and hypercapnia in the model, whereas prolonged high‐level CPAP and moderate‐to‐high levels of oxygen supplementation do not. The authors conclude that such modelling may be useful to help guide beneficial interventions for COPD patients using NIV.
Real‐time passenger flow anomaly detection in metro system
Authors: Wei Xiulan , Zhang Yong , Zhang Xinyu , Ge Qibin , Yin Baocai 
Published: June 2023

AbstractReal‐time passenger‐flow anomaly detection at all metro stations is a very critical task for advanced Internet management. Robust principal component analysis (RPCA) based method has often been employed for anomaly detection task of multivariate time series data. However, it ignores the spatio‐temporal features of regular passenger‐flow patterns, resulting in a decrease in the accuracy of anomaly detection. In this paper, RT‐STRPCA model integrating temporal periodicity and spatial similarity is proposed to address the above issues. RT‐STRPCA model detects anomalies by decomposing the observation data into normal component and anomaly component. The spatio‐temporal constraints are taken into account to extract anomalies more accurately. The real‐time anomaly detection are realized by a sliding window. The performance of RT‐STRPCA model is evaluated on synthetic datasets and real‐world datasets. The experimental results on synthetic datasets demonstrate that the method achieves more accurate real‐time anomaly detection than baseline approaches and the result on real‐world datasets verify the utility and effectiveness of the proposed method.
A surface plasmon resonance haemoglobin concentration sensor based on D‐type optical fibre with a graphene‐gold surface architecture
Authors: Shi Zhen‐Jiang , Guo Shi‐Liang , Li Xin , Li Wen‐Chao , Li Zhi‐Quan 
Published: June 2023

AbstractIn this study, a surface plasmon resonance sensor for detecting haemoglobin concentration based on a D‐type optical fibre with a graphene‐gold surface architecture is proposed. The graphene‐gold surface architecture included a 50 nm thick gold film and monolayer graphene film, which were decorated on a 10 mm long D‐type sensing region. The proposed sensor worked in wavelength interrogation mode, with the light wavelength ranging from 400 to 1100 nm. The authors realised the D‐type optical fibre surface polishing process, the gold film vacuum coating process, and the chemical‐vapour‐deposited graphene's wet transfer process. Furthermore, the fabricated sensors were used to detect the refractive index (RI) of haemoglobin samples, which varied from 1.331 to 1.346. Experiment results show that the fitted RI sensitivity of the sensor decorated with gold and graphene reaches 1874.41 nm/RIU, 4.995% higher than that of the sensor decorated only with gold. The concentration sensitivity of the sensor coated with gold and graphene film is 4.96 nm/(g/dL), and the proposed sensor can provide a resolution of 20.2 mg/dL for haemoglobin concentration detection.
A guide to unmanned aerial vehicles performance analysis—the MQ‐9 unmanned air vehicle case study
Authors: Zountouridou Erietta , Kiokes George , Dimeas Aris , Prousalidis John , Hatziargyriou Nikos 
Published: June 2023

AbstractGreat efforts are devoted for integrating Renewable Energy Sources (RES) on the propulsion system of Unmanned Air Vehicles (UAVs). This is applicable to small UAVs, having a horizon to expand to large UAVs within the next decades. For the conventional propulsion systems to be replaced, the required power needs of the aircraft should first be examined. The required power depends on the UAV's flight stage from takeoff to landing, whereas its calculation varies with regard to the propulsion system type, which is separated into two main categories, the propelled‐driven engines and the jet engines. Two obstacles are arising. First, the parameters needed for the power estimation are commercially sensitive and second the analysis alters with respect to the engine's type. This paper focuses on the mathematical analysis of the required power for both the main propulsion systems, presented extensively for each flight phase, proposing, at the same time, a parameter estimation method which is applicable to any UAV type. The main purpose of this paper is to act as a guide for the calculation of any type UAV's required power at any flight stage. To validate the analysis, the MQ‐9 Reaper/Predator B, a High Altitude Long Endurance (HALE) turboprop UAV produced by General Atomics is analysed. Its unknown parameters are estimated and based on a selected flight profile, the required power in terms of horsepower from takeoff to landing is assessed, for two scenarios regarding the aircraft's initial weight. In the first scenario the UAV has the maximum gross takeoff weight, whereas in the second scenario it does not carry any payload. The estimated required shaft power per flight phase for each scenario is then marked into the TPE331‐10 turboprop engine characteristic curves.
The post-disk (or primordial) spin distribution of M dwarf stars
Authors: Gehrig L. , Gaidos E. , Güdel M. 
Published: June 2023

Context. The rotation periods of young low-mass stars after disks have dissipated (≲-pagination10 Myr) but before magnetized winds have removed significant angular momentum is an important launch point for gyrochronology and models of stellar rotational evolution; the rotation of these stars also regulates the magnetic activity and the intensity of high-energy emission that affects any close-in planets. A recent analysis of young M dwarf stars suggests a distribution of specific angular momentum (SAM) that is mass-independent, but the physical basis of this observation is unclear.
Aims. We investigate the influence of an accretion disk on the angular momentum (AM) evolution of young M dwarfs, whose parameters govern the AM distribution after the disk phase, and whether this leads to a mass-independent distribution of SAM.
Methods. We used a combination of protostellar spin and implicit hydrodynamic disk evolution models to model the innermost disk (∼0.01 AU), including a self-consistent calculation of the accretion rate onto the star, non-Keplerian disk rotation, and the influence of stellar magnetic torques over the entire disk lifetime. We executed and analyzed over 500 long-term simulations of the combined stellar and disk evolution.
Results. We find that above an initial rate of Ṁcrit ∼ 10−8 M⊙ yr−1, accretion “erases” the initial SAM of M dwarfs during the disk lifetime, and stellar rotation converges to values of SAM that are largely independent of initial conditions. For stellar masses > 0.3 M⊙, we find that observed initial accretion rates Ṁinit are comparable to or exceed Ṁcrit. Furthermore, stellar SAM after the disk phase scales with the stellar magnetic field strength as a power law with an exponent of −1.1. For lower stellar masses, Ṁinit is predicted to be smaller than Ṁcrit and the initial conditions are imprinted in the stellar SAM after the disk phase.
Conclusions. To explain the observed mass-independent distribution of SAM, the stellar magnetic field strength has to range between 20 G and 500 G (700 G and 1500 G) for a 0.1 M⊙ (0.6 M⊙) star. These values match observed large-scale magnetic field measurements of young M dwarfs and the positive relation between stellar mass and magnetic field strength agrees with a theoretically motivated scaling relation. The scaling law between stellar SAM, mass, and the magnetic field strength is consistent for young stars, where these parameters are constrained by observations. Due to the very limited number of available data, we advocate for efforts to obtain more such measurements. Our results provide new constraints on the relation between stellar mass and magnetic field strength and they can be used as initial conditions for future stellar spin models, starting after the disk phase.
The star in RNO 54: A possible post-FUor with a short faint jet
Authors: Magakian T. Yu. , Movsessian T. A. , Andreasyan H. R. , Moiseev A. V. , Uklein R. I. 
Published: June 2023

Aims. The aim of the present study is to analyze the spectra of an unusual pre-main sequence star in the cometary nebula RNO 54, which is suspected by several researchers to be a FUori-like object.
Methods. We performed long-slit spectroscopy of the star using the 6m telescope with the SCORPIO-2 multi-mode focal reducer.
Results. We discover a short (~4″ or ~6000 AU) and faint emission shock-excited jet from this star, probably oriented toward the long axis of the nebular ellipse. The spectral type of the star is estimated as G0-2 II; we confirm the split of the Li I absorption line, which is a typical sign indicating a FUori-like spectrum. Our analysis of the available data reveals an almost null photometric variability over at least the last 20 years. The lower limit of the bolometric luminosity of the star is estimated as 300 L⊙. Our study supports the classification of RNO 54 as a FUor-like star in the long post-outburst stage.
Well ev-covered trees
Authors: Boutrig Razika , Chellali Mustapha 
Published: June 2023

An edge in a graph G = (V, E) is said to ev-dominate the vertices incident to it as well as the vertices adjacent to these incident vertices. A subset F ⊆ E is an edge-vertex dominating set (or simply, ev-dominating set) if every vertex is ev-dominated by at least one edge of F. The ev-domination number γev(G) is the minimum cardinality of a ev-dominating set of G. An ev-dominating set is independent if its edges are independent. The independent ev-domination number iev(G) is the minimum cardinality of an independent ev-dominating set and the upper independent ev-domination number βev(G) is the maximum cardinality of a minimal independent ev-dominating set of G. In this paper, we show that for every nontrivial tree T, γev(T) = iev(T) ≤ γ(T) ≤ βev(T), where γ(T) is the domination number of T. Moreover, we provide a characterization of all trees T with iev(T) = βev(T), which we call well ev-covered trees, as well as a characterization of all trees T with γev(T) = iev(T) = γ(T).
Centering on People: How Hearing Care Professionals Can Adapt to Consumers' Need and Outcomes
Authors: Brice Sophie , Timmer Barbra H. B. , Barr Caitlin 
Published: June 2023

AbstractAudiology is experiencing exponential growth in technology, service, and provision options. These advancements give hearing care professionals the opportunity to revise, potentially improve, and adapt to the modern hearing care landscape to better serve the modern consumer. Consumer needs guide care planning and delivery, with the goal of achieving outcomes that are important to both the consumer and the clinician. The changes available to the hearing care industry can also enable consumers' needs to be identified and served in a more holistic and personalized manner than has previously been possible. The purpose of this article is to explain and encourage hearing care professionals to adopt a mindset of doing whatever is reasonable and clinically appropriate to meet the need and desires of the consumer by implementing choice in service, technology, and channel across whichever model of care adopted by a provider.
Deep Learning for the Assessment of Facial Nerve Palsy: Opportunities and Challenges
Authors: Boochoon Kieran , Mottaghi Ali , Aziz Aya , Pepper Jon-Paul 
Published: June 2023

AbstractAutomated evaluation of facial palsy using machine learning offers a promising solution to the limitations of current assessment methods, which can be time-consuming, labor-intensive, and subject to clinician bias. Deep learning-driven systems have the potential to rapidly triage patients with varying levels of palsy severity and accurately track recovery over time. However, developing a clinically usable tool faces several challenges, such as data quality, inherent biases in machine learning algorithms, and explainability of decision-making processes. The development of the eFACE scale and its associated software has improved clinician scoring of facial palsy. Additionally, Emotrics is a semiautomated tool that provides quantitative data of facial landmarks on patient photographs. The ideal artificial intelligence (AI)-enabled system would analyze patient videos in real time, extracting anatomic landmark data to quantify symmetry and movement, and estimate clinical eFACE scores. This would not replace clinician eFACE scoring but would offer a rapid automated estimate of both anatomic data, similar to Emotrics, and clinical severity, similar to the eFACE. This review explores the current state of facial palsy assessment, recent advancements in AI, and the opportunities and challenges in developing an AI-driven solution.