bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neuropsychological task outcomes among survivors of childhood acute lymphoblastic leukemia in Malaysia
Authors: Alias Hamidah , Mohd Ranai Norashikin , Lau Sie Chong Doris , de Sonneville Leo M. J. 
Published: April 2024

AbstractThis study intended to explore the neuropsychological ramifications in childhood acute lymphoblastic leukemia (ALL) survivors in Malaysia and to examine treatment-related sequelae. A case-control study was conducted over a 2-year period. Seventy-one survivors of childhood ALL who had completed treatment for a minimum of 1 year and were in remission, and 71 healthy volunteers were enlisted. To assess alertness (processing speed) and essential executive functioning skills such as working memory capacity, inhibition, cognitive flexibility, and sustained attention, seven measures from the Amsterdam Neuropsychological Tasks (ANT) program were chosen. Main outcome measures were speed, stability and accuracy of responses. Mean age at diagnosis was 4.50 years (SD ± 2.40) while mean age at study entry was 12.18 years (SD ± 3.14). Survivors of childhood ALL underperformed on 6 out of 7 ANT tasks, indicating poorer sustained attention, working memory capacity, executive visuomotor control, and cognitive flexibility. Duration of treatment, age at diagnosis, gender, and cumulative doses of chemotherapy were not found to correlate with any of the neuropsychological outcome measures. Childhood ALL survivors in our center demonstrated significantly poorer neuropsychological status compared to healthy controls.
AI and augmented reality for 3D Indian dance pose reconstruction cultural revival
Authors: Jayanthi J. , Maheswari P. Uma 
Published: April 2024

AbstractThis paper delves into the specialized domain of human action recognition, focusing on the Identification of Indian classical dance poses, specifically Bharatanatyam. Within the dance context, a “Karana” embodies a synchronized and harmonious movement encompassing body, hands, and feet, as defined by the Natyashastra. The essence of Karana lies in the amalgamation of nritta hasta (hand movements), sthaana (body postures), and chaari (leg movements). Although numerous, Natyashastra codifies 108 karanas, showcased in the intricate stone carvings adorning the Nataraj temples of Chidambaram, where Lord Shiva’s association with these movements is depicted. Automating pose identification in Bharatanatyam poses challenges due to the vast array of variations, encompassing hand and body postures, mudras (hand gestures), facial expressions, and head gestures. To simplify this intricate task, this research employs image processing and automation techniques. The proposed methodology comprises four stages: acquisition and pre-processing of images involving skeletonization and Data Augmentation techniques, feature extraction from images, classification of dance poses using a deep learning network-based convolution neural network model (InceptionResNetV2), and visualization of 3D models through mesh creation from point clouds. The use of advanced technologies, such as the MediaPipe library for body key point detection and deep learning networks, streamlines the identification process. Data augmentation, a pivotal step, expands small datasets, enhancing the model’s accuracy. The convolution neural network model showcased its effectiveness in accurately recognizing intricate dance movements, paving the way for streamlined analysis and interpretation. This innovative approach not only simplifies the identification of Bharatanatyam poses but also sets a precedent for enhancing accessibility and efficiency for practitioners and researchers in the Indian classical dance.
Reassessing human MHC-I genetic diversity in T cell studies
Authors: Slieker Roderick C. , Warmerdam Daniël O. , Vermeer Maarten H. , van Doorn Remco , Heemskerk Mirjam H. M. , Scheeren Ferenc A. 
Published: April 2024

AbstractThe Major Histocompatibility Complex class I (MHC-I) system plays a vital role in immune responses by presenting antigens to T cells. Allele specific technologies, including recombinant MHC-I technologies, have been extensively used in T cell analyses for COVID-19 patients and are currently used in the development of immunotherapies for cancer. However, the immense diversity of MHC-I alleles presents challenges. The genetic diversity serves as the foundation of personalized medicine, yet it also poses a potential risk of exacerbating healthcare disparities based on MHC-I alleles. To assess potential biases, we analysed (pre)clinical publications focusing on COVID-19 studies and T cell receptor (TCR)-based clinical trials. Our findings reveal an underrepresentation of MHC-I alleles associated with Asian, Australian, and African descent. Ensuring diverse representation is vital for advancing personalized medicine and global healthcare equity, transcending genetic diversity. Addressing this disparity is essential to unlock the full potential of T cells for enhancing diagnosis and treatment across all individuals.
Genome sequencing identifies “Limestone Canyon virus” as Montaño virus (Hantaviridae: Orthohantavirus montanoense) circulating in brush deermice in New Mexico
Authors: Goodfellow Samuel M. , Nofchissey Robert A. , Morley Valerie J. , Coan Kathryn E. , Schwalm Kurt C. , Cook Joseph A. , Dunnum Jonathan L. , Hanfelt-Goade Diane , Dinwiddie Darrell L. , Domman Daryl B. , Dragoo Jerry W. , Kuhn Jens H. , Bradfute Steven B. 
Published: April 2024

AbstractOrthohantaviruses infect distinct eulipotyphlan and rodent reservoirs throughout the world; some rodent orthohantaviruses can cause disease in humans. In the United States, a primary rodent reservoir for the human-pathogenic Sin Nombre virus (SNV) is the western deermouse (Peromyscus sonoriensis; formerly included in Peromyscus maniculatus). Deermice (rodents of genus Peromyscus) carry presumably distinct orthohantaviruses but, although deermice of ten species have been recorded in New Mexico, only SNV has been reported in rodents from that state. Using a set of pan-orthohantavirus primers, we discovered a non-SNV orthohantavirus in a brush deermouse (P. boylii), trapped in central New Mexico in 2019. Sequencing enabled the generation of a consensus coding-complete genome sequence, revealing similarity to the known partial sequences of the unclassified “Limestone Canyon virus (LSCV)” in GenBank and aligning with the information in an unpublished study of wild-caught brush deermice trapped in southwestern New Mexico in 2006. Phylogenetic analysis of these combined data revealed geospatial clades and overall identity of “LSCV”, uncovering its association with the classified Montaño virus (MTNV), which is known to infect Aztec and Orizaba deermice in central Mexico. Our work emphasizes the importance of determining coding-complete viral genome sequences as a framework for rigorous virus classification as the basis for epidemiological studies.
Verb Vocabulary Supports Event Probability Use in Developmental Language Disorder
Authors: Kueser Justin B. , Borovsky Arielle , Deevy Patricia , Muezzinoglu Mine , Outzen Claney , Leonard Laurence B. 
Published: April 2024

Purpose:
Children with developmental language disorder (DLD) tend to interpret noncanonical sentences like passives using event probability (EP) information regardless of structure (e.g., by interpreting “The dog was chased by the squirrel” as “The dog chased the squirrel”). Verbs are a major source of EP information in adults and children with typical development (TD), who know that “chase” implies an unequal relationship among participants. Individuals with DLD have poor verb knowledge and verb-based sentence processing. Yet, they also appear to rely more on EP information than their peers. This paradox raises two questions: (a) How do children with DLD use verb-based EP information alongside other information in online passive sentence interpretation? (b) How does verb vocabulary knowledge support EP information use?

Method:

We created novel EP biases by showing animations of agents with consistent action tendencies (e.g., clumsy vs. helpful actions). We then used eye tracking to examine how this EP information was used during online passive sentence processing. Participants were 4- to 5-year-old children with DLD (
n
= 20) and same-age peers with TD (
n
= 20).

Results:
In Experiment 1, children with DLD quickly integrated verb-based EP information with morphosyntax close to the verb but failed to do so with distant morphosyntax. In Experiment 2, the quality of children's sentence-specific verb vocabulary knowledge was positively associated with the use of EP information in both groups.

Conclusion:
Depending on the morphosyntactic context, children with DLD and TD used EP information differently, but verb vocabulary knowledge aided its use.

Supplemental Material:

https://doi.org/10.23641/asha.25491805
Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review
Authors: Danilatou Vasiliki , Dimopoulos Dimitrios , Kostoulas Theodoros , Douketis James 
Published: April 2024

Background Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.
Methods Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.
Results Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.
Conclusion ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.
A virtual thermometer for ultrahigh-temperature–pressure experiments in a large-volume press
Authors: Feng Bingtao , Xie Longjian , Hou Xuyuan , Liu Shucheng , Chen Luyao , Zhao Xinyu , Li Chenyi , Zhou Qiang , Hu Kuo , Liu Zhaodong , Liu Bingbing 
Published: April 2024

Ultrahigh-temperature–pressure experiments are crucial for understanding the physical and chemical properties of matter. The recent development of boron-doped diamond (BDD) heaters has made such melting experiments possible in large-volume presses. However, estimates of temperatures above 2600 K and of the temperature distributions inside BDD heaters are not well constrained, owing to the lack of a suitable thermometer. Here, we establish a three-dimensional finite element model as a virtual thermometer to estimate the temperature and temperature field above 2600 K. The advantage of this virtual thermometer over those proposed in previous studies is that it considers both alternating and direct current heating modes, the actual sizes of cell assemblies after compression, the effects of the electrode, thermocouple and anvil, and the heat dissipation by the pressure-transmitting medium. The virtual thermometer reproduces the power–temperature relationships of ultrahigh-temperature–pressure experiments below 2600 K at press loads of 2.8–7.9 MN (∼19 to 28 GPa) within experimental uncertainties. The temperatures above 2600 K predicted by our virtual thermometer are within the uncertainty of those extrapolated from power–temperature relationships below 2600 K. Furthermore, our model shows that the temperature distribution inside a BDD heater (19–26 K/mm along the radial direction and &lt;83 K/mm along the longitudinal direction) is more homogeneous than those inside conventional heaters such as graphite or LaCrO3 heaters (100–200 K/mm). Our study thus provides a reliable virtual thermometer for ultrahigh-temperature experiments using BDD heaters in Earth and material sciences.
Ruled by construal?
Authors: Romain Laurence , Hanzlíková Dagmar , Milin Petar , Divjak Dagmar 
Published: April 2024

Abstract
In cognitive linguistics, grammatical structure is known to be representative of meaning. This is also true of
English articles. In this paper, we argue that the choice of article, when the grammar allows it, is dependent on the wider
discourse context and most importantly on how the speaker construes this context. Using survey data from 181 native speakers of
English, we show that the choice of article depends on the activation of semantic frames and how speakers may choose to highlight
different elements of a frame to construe the situation differently. We rely on Entropy to measure the restrictiveness of a
context and to identify particular contexts in which choice is allowed or inhibited. We find that some contextual features such as
the specificity of the referent are more restrictive while Hearer Knowledge is more open to construal.
Conformal diagrams for stationary and dynamical strong-field hyperboloidal slices
Authors: Vañó-Viñuales Alex 
Published: April 2024

Abstract
Conformal Carter–Penrose diagrams are used for the visualization of hyperboloidal slices, which are smooth spacelike slices reaching null infinity. The focus is on the Schwarzschild black hole geometry in spherical symmetry, whose Penrose diagrams are introduced in a pedagogical way. The stationary regime involves time-independent slices. In this case, different options are given for integrating the height function—the main ingredient for constructing hyperboloidal foliations. The dynamical regime considers slices changing in time, which are evolved together with the spacetime using the eikonal equation. It includes the relaxation of hyperboloidal Schwarzschild trumpet slices and the collapse of a massless scalar field into a black hole, for which Penrose diagrams are presented.
Inverse design of electromagnetic metamaterials: from iterative to deep learning-based methods
Authors: Ma Chen , Wang Zhenyu , Zhang Hui , Yang Fengyuan , Chen Jianlin , Ren Qinghua , Ma Yiming , Wang Nan 
Published: April 2024

Abstract
In recent years, considerable research advancements have emerged in the application of inverse design methods to enhance the performance of electromagnetic (EM) metamaterials. Notably, the integration of deep learning (DL) technologies, with their robust capabilities in data analysis, categorization, and interpretation, has demonstrated revolutionary potential in optimization algorithms for improved efficiency. In this review, current inverse design methods for EM metamaterials are presented, including topology optimization (TO), evolutionary algorithms (EAs), and DL-based methods. Their application scopes, advantages and limitations, as well as the latest research developments are respectively discussed. The classical iterative inverse design methods categorized TO and EAs are discussed separately, for their fundamental role in solving inverse design problems. Also, attention is given on categories of DL-based inverse design methods, i.e. classifying into DL-assisted, direct DL, and physics-informed neural network methods. A variety of neural network architectures together accompanied by relevant application examples are highlighted, as well as the practical utility of these overviewed methods. Finally, this review provides perspectives on potential future research directions of EM metamaterials inverse design and integrated artificial intelligence methodologies.
Particle physics using reactor antineutrinos
Authors: Akindele O A , Bowden N S , Roca C , Xu J , Zhang X , Berryman J M , Carr R , Conant A J , Fernandez-Moroni G , Huber P , Link J M , Langford T J , Littlejohn B R , Ochoa-Ricoux J P , Strigari L , Schoppmann S , Zhang C 
Published: April 2024

Abstract
Nuclear reactors are uniquely powerful, abundant, and flavor-pure sources of antineutrinos that have played a central role in the discovery of the neutrinos and in elucidation of their properties. This continues through a broad range of experiments investigating topics including Standard Model and short-baseline oscillations, beyond-the-Standard-Model physics searches, and reactor flux and spectrum modelling. This Report will survey the state of the reactor antineutrino physics field and summarize the ways in which current and future reactor antineutrino experiments can play a critical role in advancing the field of particle physics in the next decade.
Advanced digital twin-enabled fault diagnosis framework for unmanned vehicle systems
Authors: Li Junfeng , Wang Jianyu 
Published: April 2024

Abstract
The control system of unmanned vehicles must demonstrate strong capability to promptly diagnose and address system faults. Such a capability can improve transportation efficiency, ensure the smooth execution of production tasks, and to a certain extent, mitigate the risk of human casualties. To ensure the upkeep of unmanned vehicles and address the diagnostic requirements of control systems, this study integrates traditional wheeled vehicle control systems with digital twin (DT) technology to establish a framework for control system fault diagnosis and maintenance, with the primary objective of fulfilling the fault diagnosis task. By this framework, a method for detecting faults in unmanned vehicle control systems based on DT technology has been developed. This method involves the design of a data-driven model using multiple sensors and the application of a DT-improved particle filter fault diagnosis algorithm, utilizing a multi-domain model approach. A case study of the proposed method and simulation results are presented to illustrate its feasibility.
Selection of noise models for GNSS coordinate time series based on model averaging algorithm
Authors: Huan Yueyang , Chang Guobin , Huang Yangjin , Feng Yong , Zhu Yuhua , Yang Shuoqi 
Published: April 2024

Abstract
In the field of global navigation satellite system (GNSS) time series noise analysis, appropriately modeling the noise components plays an important role in determining the velocity of GNSS sites and quantifying the uncertainty associated with the velocity estimation. Over the years, researchers have focused on only one optimal noise model, while other noise models that show similar performance to the optimal model have been ignored. We investigated whether these ignored noise models can be made use of to describe the noise in the GNSS time series after applying a model averaging algorithm. The experimental data were derived from 28 International GNSS Service (IGS) sites in the California region of the United States and 110 IGS sites worldwide. The results showed that for the GNSS time series of 28 IGS sites in the California, 79%, 68%, and 75% of the site components can be applied the model averaging algorithm in the east/north/up (E/N/U) directions, respectively. Based on it, the east direction showed the best performance, with 50% of the site components obtaining more conservative velocity uncertainty after applying the model averaging algorithm compared to the optimal noise model. For GNSS time series of 110 IGS stations worldwide, the model averaging algorithm demonstrates excellent performance in all the E/N/U directions. In the E/N/U directions, 86%, 94%, and 57% of the site components can apply the model averaging algorithm. Building upon this, 77%, 65%, and 62% of the site components achieve more conservative velocity uncertainty in the E/N/U directions compared to the optimal noise model. To fully validate the feasibility of the model averaging algorithm, we also tested GNSS time series of varying lengths and different thresholds of the model averaging algorithm. In summary, the model averaging algorithm performs exceptionally well in the noise analysis of GNSS time series. It helps prevent overly optimistic estimation results.
An existence theory for superposition operators of mixed order subject to jumping nonlinearities
Authors: Dipierro Serena , Perera Kanishka , Sportelli Caterina , Valdinoci Enrico 
Published: April 2024

Abstract
We consider a superposition operator of the form

∫

[
0
,
1
]

(
−
Δ
)

s

u

d
μ

(
s
)

,

for a signed measure µ on the interval of fractional exponents

[
0
,
1
]

, joined to a nonlinearity whose term of homogeneity equal to one is ‘jumping’, i.e. it may present different coefficients in front of the negative and positive parts. The signed measure is supposed to possess a positive contribution coming from the higher exponents that overcomes its negative contribution (if any). The problem taken into account is also of ‘critical’ type, though in this case the critical exponent needs to be carefully selected in terms of the signed measure µ. Not only the operator and the nonlinearity considered here are very general, but our results are new even in special cases of interest and include known results as particular subcases. The possibility of considering operators ‘with the wrong sign’ is also a complete novelty in this setting.
RF antenna helicity dependent particle heating in a helicon source
Authors: Stevenson K J , Gilbert T J , Good T N , Paul M , Shi P , Nirwan R , Srivastav P , Steinberger T E , Scime E E 
Published: April 2024

Abstract
Experiments have demonstrated that ion phenomena, such as the lower hybrid resonance, play an important role in helicon source operation. Damping of the slow branch of the bounded whistler wave at the edge of a helicon source (i.e. the Trivelpiece-Gould mode) has been correlated with the creation of energetic electrons, heating of ions at the plasma edge, and anisotropic ion heating. Here we present ion velocity distribution function measurements, electron density and temperature measurements, and magnetic fluctuation measurements on both sides of an

m
=

|

1
|

helical antenna in a helicon source as a function of the driving frequency, magnetic field strength, and magnetic field orientation relative to the antenna helicity. Significant electron and ion heating (up to two times larger) occurs on the side of the antenna consistent with the launch of the

m
=
+
1

mode. The electron and ion heating occurs within one electron skin depth of the plasma edge, where slow wave damping is expected. The source parameters for enhanced particle heating are also consistent with lower hybrid resonance effects, which can only occur for Trivelpiece-Gould wave excitation.