bluuebunny/crossref_metadata_2025_split huggingface dataset:
Toward children-centric AI: a case for a growth model in children-AI interactions
Authors: La Fors Karolina 
Published: October 2022

AbstractThis article advocates for a hermeneutic model for children-AI (age group 7–11 years) interactions in which the desirable purpose of children’s interaction with artificial intelligence (AI) systems is children's growth. The article perceives AI systems with machine-learning components as having a recursive element when interacting with children. They can learn from an encounter with children and incorporate data from interaction, not only from prior programming. Given the purpose of growth and this recursive element of AI, the article argues for distinguishing the interpretation of bias within the artificial intelligence (AI) ethics and responsible AI discourse. Interpreting bias as a preference and distinguishing between positive (pro-diversity) and negative (discriminative) bias is needed as this would serve children's healthy psychological and moral development. The human-centric AI discourse advocates for an alignment of capacities of humans and capabilities of machines by a focus both on the purpose of humans and on the purpose of machines for humans. The emphasis on mitigating negative biases through data protection, AI law, and certain value-sensitive design frameworks demonstrates that the purpose of the machine for humans is prioritized over the purpose of humans. These top–down frameworks often narrow down the purpose of machines to do-no-harm and they miss accounting for the bottom-up views and developmental needs of children. Therefore, applying a growth model for children-AI interactions that incorporates learning from negative AI-mediated biases and amplifying positive ones would positively benefit children’s development and children-centric AI innovation. Consequently, the article explores:What challenges arise from mitigating negative biases and amplifying positive biases in children-AI interactions and how can a growth model address these?To answer this, the article recommends applying a growth model in open AI co-creational spaces with and for children. In such spaces human–machine and human–human value alignment methods can be collectively applied in such a manner that children can (1) become sensitized toward the effects of AI-mediated negative biases on themselves and others; (2) enable children to appropriate and imbue top-down values of diversity, and non-discrimination with their meanings; (3) enforce children’s right to identity and non-discrimination; (4) guide children in developing an inclusive mindset; (5) inform top-down normative AI frameworks by children’s bottom-up views; (6) contribute to design criteria for children-centric AI. Applying such methods under a growth model in AI co-creational spaces with children could yield an inclusive co-evolution between responsible young humans in the loop and children-centric AI systems.
The COVID-19 pandemic and Asian American employment
Authors: Honoré Bo E. , Hu Luojia 
Published: October 2022

AbstractRecent studies have documented the disparate impact of the COVID-19 pandemic on labor market outcomes for different racial groups. This paper adds to this literature by documenting that the employment of Asian Americans—in particular those with no college education—has been especially hard hit by the economic crisis associated with the onset of the pandemic. This can only partly be explained by differences in demographics, local market conditions, and job characteristics, and it also cannot be entirely explained by possible different selection into education levels across ethnic groups. The burden on Asian Americans is primarily borne by those who are not US-born.
Respiratory effects of oral mitragynine and oxycodone in a rodent model
Authors: Henningfield Jack E. , Rodricks Joseph V. , Magnuson Aaron M. , Huestis Marilyn A. 
Published: October 2022

Abstract

Rationale
Kratom derives from Mitragyna speciosa (Korth.), a tropical tree in the genus Mitragyna (Rubiaceae) that also includes the coffee tree. Kratom leaf powders, tea-like decoctions, and commercial extracts are taken orally, primarily for health and well-being by millions of people globally. Others take kratom to eliminate opioid use for analgesia and manage opioid withdrawal and use disorder. There is debate over the possible respiratory depressant overdose risk of the primary active alkaloid, mitragynine, a partial μ-opioid receptor agonist, that does not signal through ß-arrestin, the primary opioid respiratory depressant pathway.

Objectives
Compare the respiratory effects of oral mitragynine to oral oxycodone in rats with the study design previously published by US Food and Drug Administration (FDA) scientists for evaluating the respiratory effects of opioids (Xu et al., Toxicol Rep 7:188–197, 2020).

Methods
Blood gases, observable signs, and mitragynine pharmacokinetics were assessed for 12 h after 20, 40, 80, 240, and 400 mg/kg oral mitragynine isolate and 6.75, 60, and 150 mg/kg oral oxycodone hydrochloride.

Findings
Oxycodone administration produced significant dose-related respiratory depressant effects and pronounced sedation with one death each at 60 and 150 mg/kg. Mitragynine did not yield significant dose-related respiratory depressant or life-threatening effects. Sedative-like effects, milder than produced by oxycodone, were evident at the highest mitragynine dose. Maximum oxycodone and mitragynine plasma concentrations were dose related.

Conclusions
Consistent with mitragynine’s pharmacology that includes partial µ-opioid receptor agonism with little recruitment of the respiratory depressant activating β-arrestin pathway, mitragynine produced no evidence of respiratory depression at doses many times higher than known to be taken by humans.
Intracortical facilitation and inhibition in human primary motor cortex during motor skill acquisition
Authors: Ho Kelly , Cirillo John , Ren April , Byblow Winston D. 
Published: October 2022

AbstractThe primary motor cortex (M1) is critical for movement execution, but its role in motor skill acquisition remains elusive. Here, we examine the role of M1 intracortical circuits during skill acquisition. Paired-pulse transcranial magnetic stimulation (TMS) paradigms of short-interval intracortical facilitation (SICF) and inhibition (SICI) were used to assess excitatory and inhibitory circuits, respectively. We hypothesised that intracortical facilitation and inhibition circuits in M1 would be modulated to support acquisition of a novel visuomotor skill. Twenty-two young, neurologically healthy adults trained with their nondominant hand on a skilled and non-skilled sequential visuomotor isometric finger abduction task. Electromyographic recordings were obtained from the nondominant first dorsal interosseous (FDI) muscle. Corticomotor excitability, SICF, and SICI were examined before, at the midway point, and after the 10-block motor training. SICI was assessed using adaptive threshold-hunting procedures. Task performance improved after the skilled, but not non-skilled, task training, which likely reflected the increase in movement speed during training. The amplitudes of late SICF peaks were modulated with skilled task training. There was no modulation of the early SICF peak, SICI, and corticomotor excitability with either task training. There was also no association between skill acquisition and SICF or SICI. The findings indicate that excitatory circuitries responsible for the generation of late SICF peaks, but not the early SICF peak, are modulated in motor skill acquisition for a sequential visuomotor isometric finger abduction task.
Traditions, anomalies, mistakes and recommendations in infrared spectrum measurement for wood
Authors: Tolvaj Laszlo 
Published: October 2022

AbstractThis paper deals with the difficulties of infrared spectroscopy measurement and suggests ways of dealing with them. Many problems appear when applying ATR (attenuated total reflection) measurement for determining the absorbance spectrum of wood, especially the highly porous nature of wood which does not fulfil the requirements of ATR measurement. Correct ATR spectrum determination requires wavelength dependence correction, but some authors miss out doing this. Normalisation of the infrared (IR) spectrum is a useful data manipulation method for correct evaluation of the spectra, but the incorrect normalisation can destroy the spectrum preventing the evaluation of the spectrum appropriately. Examples are given to teach the correct normalisation process. The difference spectrum method is an excellent tool to present the changes in IR spectra, but only a few scientists use it. Usage of wavenumbers during IR spectrum presentation is a traditional method nowadays. However, the usage of wavelength gives a more expressive spectrum presentation than wavenumber if the whole wavelength interval is presented in one diagram.
Role of brain 2-[18F]fluoro-2-deoxy-D-glucose-positron-emission tomography as survival predictor in amyotrophic lateral sclerosis
Authors: Canosa Antonio , Martino Alessio , Manera Umberto , Vasta Rosario , Grassano Maurizio , Palumbo Francesca , Cabras Sara , Di Pede Francesca , Arena Vincenzo , Moglia Cristina , Giuliani Alessandro , Calvo Andrea , Chiò Adriano , Pagani Marco 
Published: October 2022

Abstract
Purpose
The identification of prognostic tools in amyotrophic lateral sclerosis (ALS) would improve the design of clinical trials, the management of patients, and life planning. We aimed to evaluate the accuracy of brain 2-[18F]fluoro-2-deoxy-D-glucose-positron-emission tomography (2-[18F]FDG-PET) as an independent predictor of survival in ALS.

Methods
A prospective cohort study enrolled 418 ALS patients, who underwent brain 2-[18F]FDG-PET at diagnosis and whose survival time was available. We discretized the survival time in a finite number of classes in a data-driven fashion by employing a k-means-like strategy. We identified “hot brain regions” with maximal power in discriminating survival classes, by evaluating the Laplacian scores in a class-aware fashion. We retained the top-m features for each class to train the classification systems (i.e., a support vector machine, SVM), using 10% of the ALS cohort as test set.

Results
Data were discretized in three survival profiles: 0–2 years, 2–5 years, and > 5 years. SVM resulted in an error rate < 20% for two out of three classes separately. As for class one, the discriminant clusters included left caudate body and anterior cingulate cortex. The most discriminant regions were bilateral cerebellar pyramid in class two, and right cerebellar dentate nucleus, and left cerebellar nodule in class three.

Conclusion
Brain 2-[18F]FDG-PET along with artificial intelligence was able to predict with high accuracy the survival time range in our ALS cohort. Healthcare professionals can benefit from this prognostic tool for planning patients’ management and follow-up. 2-[18F]FDG-PET represents a promising biomarker for individual patients’ stratification in clinical trials. The lack of a multicentre external validation of the model warrants further studies to evaluate its generalization capability.
State and physiology behind personality in arthropods: a review
Authors: Rádai Zoltán , Kiss Johanna , Nagy Nikoletta A. , Somogyi Anna Ágnes , Fülöp Attila , Tóth Zsófia , Babits Melinda Alexáné , Németh Zoltán 
Published: October 2022

AbstractIn the endeavour to understand the causes and consequences of the variation in animal personality, a wide range of studies were carried out, utilising various aspects to make sense of this biological phenomenon. One such aspect integrated the study of physiological traits, investigating hypothesised physiological correlates of personality. Although many of such studies were carried out on vertebrates (predominantly on birds and mammals), studies using arthropods (mainly insects) as model organisms were also at the forefront of this area of research. In order to review the current state of knowledge on the relationship between personality and the most frequently studied physiological parameters in arthropods, we searched for scientific articles that investigated this relationship. In our review, we only included papers utilising a repeated-measures methodology to be conceptually and formally concordant with the study of animal personality. Based on our literature survey, metabolic rate, thermal physiology, immunophysiology, and endocrine regulation, as well as exogenous agents (such as toxins) were often identified as significant affectors shaping animal personality in arthropods. We found only weak support for state-dependence of personality when the state is approximated by singular elements (or effectors) of condition. We conclude that a more comprehensive integration of physiological parameters with condition may be required for a better understanding of state’s importance in animal personality. Also, a notable knowledge gap persists in arthropods regarding the association between metabolic rate and hormonal regulation, and their combined effects on personality. We discuss the findings published on the physiological correlates of animal personality in arthropods with the aim to summarise current knowledge, putting it into the context of current theory on the origin of animal personality.
Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI
Authors: Pradella Maurice , Scott Michael B. , Omer Muhammad , Hill Seth K. , Lockhart Lisette , Yi Xin , Amir-Khalili Alborz , Sojoudi Alireza , Allen Bradley D. , Avery Ryan , Markl Michael 
Published: October 2022

Abstract
Objectives
Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to the performance of a deep learning (DL) application for fully-automated VCD and flow quantification and corrected semi-automated analysis (corSAA).

Methods
We included 97 consecutive patients (age = 52.9 ± 16 years, 41 female) with 2D-CINE-PC-MRI imaging on 1.5T MRI systems at sinotubular junction (STJ), and 28/97 also received 2D-CINE-PC at main pulmonary artery (PA). A cardiovascular radiologist performed MA (reference) and corSAA (built-in tool) in commercial software for all cardiac time frames (median: 20, total contours per analysis: 2358 STJ, 680 PA). DL-analysis automatically performed VCD, followed by net flow (NF) and peak velocity (PV) quantification. Contours were compared using Dice similarity coefficients (DSC). Discrepant cases (> ± 10 mL or > ± 10 cm/s) were reviewed in detail.

Results
DL was successfully applied to 97% (121/125) of the 2D-CINE-PC-MRI series (STJ: 95/97, 98%, PA: 26/28, 93%). Compared to MA, mean DSC were 0.91 ± 0.02 (DL), 0.94 ± 0.02 (corSAA) at STJ, and 0.85 ± 0.08 (DL), 0.93 ± 0.02 (corSAA) at PA; this indicated good to excellent DL-performance. Flow quantification revealed similar NF at STJ (p = 0.48) and PA (p > 0.05) between methods while PV assessment was significantly different (STJ: p < 0.001, PA: p = 0.04). A detailed review showed noisy voxels in MA and corSAA impacted PV results. Overall, DL analysis compared to human assessments was accurate in 113/121 (93.4%) cases.

Conclusions
Fully-automated DL-analysis of 2D-CINE-PC-MRI provided flow quantification at STJ and PA at expert level in > 93% of cases with results being available instantaneously.

Key Points
• Deep learning performed flow quantification on clinical 2D-CINE-PC series at the sinotubular junction and pulmonary artery at the expert level in > 93% of cases.
• Location detection and contouring of the vessel boundaries were performed fully-automatic with results being available instantaneously compared to human assessments which approximately takes three minutes per location.
• The evaluated tool indicates usability in daily practice.
SAM-X: sorting algorithm for musculoskeletal x-ray radiography
Authors: Hinterwimmer Florian , Consalvo Sarah , Wilhelm Nikolas , Seidl Fritz , Burgkart Rainer H. H. , von Eisenhart-Rothe Rüdiger , Rueckert Daniel , Neumann Jan 
Published: October 2022

Abstract
Objective
To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity.

Methods
In total, 42,608 unstructured and pseudonymized radiographs were retrieved from the PACS of a musculoskeletal tumor center. In phase 1, imaging data were sorted into 1000 clusters by a self-supervised model. A human-in-the-loop radiologist assigned weak, semantic labels to all clusters and clusters with the same label were merged. Three hundred thirty-two non-musculoskeletal clusters were discarded. In phase 2, the initial model was modified by “injecting” the identified labels into the self-supervised model to train a classifier. To provide statistical significance, data split and cross-validation were applied. The hold-out test set consisted of 50% external data. To gain insight into the model’s predictions, Grad-CAMs were calculated.

Results
The self-supervised clustering resulted in a high normalized mutual information of 0.930. The expert radiologist identified 28 musculoskeletal clusters. The modified model achieved a classification accuracy of 96.2% and 96.6% for validation and hold-out test data for predicting the top class, respectively. When considering the top two predicted class labels, an accuracy of 99.7% and 99.6% was accomplished. Grad-CAMs as well as final cluster results underlined the robustness of the proposed method by showing that it focused on similar image regions a human would have considered for categorizing images.

Conclusion
For efficient dataset building, we propose an accurate deep learning sorting algorithm for classifying radiographs according to their anatomical entity in the assessment of musculoskeletal diseases.

Key Points
• Classification of large radiograph datasets according to their anatomical entity.
• Paramount importance of structuring vast amounts of retrospective data for modern deep learning applications.
• Optimization of the radiological workflow and increase in efficiency as well as decrease of time-consuming tasks for radiologists through deep learning.
Pectus excavatum in motion: dynamic evaluation using real-time MRI
Authors: Gräfe Daniel , Lacher Martin , Martynov Illya , Hirsch Franz Wolfgang , Voit Dirk , Frahm Jens , Busse Harald , Sesia Sergio Bruno , Krämer Sebastian , Zimmermann Peter 
Published: October 2022

Abstract
Objectives
The breathing phase for the determination of thoracic indices in patients with pectus excavatum is not standardized. The aim of this study was to identify the best period for reliable assessments of morphologic indices by dynamic observations of the chest wall using real-time MRI.

Methods
In this prospective study, patients with pectus excavatum underwent morphologic evaluation by real-time MRI at 3 T between January 2020 and June 2021. The Haller index (HI), correction index (CI), modified asymmetry index (AI), and modified eccentricity index (EI) were determined during free, quiet, and forced breathing respectively. Breathing-related differences in the thoracic indices were analyzed with the Wilcoxon signed-rank test. Motion of the anterior chest wall was analyzed as well.

Results
A total of 56 patients (11 females and 45 males, median age 15.4 years, interquartile range 14.3–16.9) were included. In quiet expiration, the median HI in the cohort equaled 5.7 (4.5–7.2). The median absolute differences (Δ) in the thoracic indices between peak inspiration and peak expiration were ΔHI = 1.1 (0.7–1.6, p < .001), ΔCI = 4.8% (1.3–7.5%, p < .001), ΔAI = 3.0% (1.0–5.0%, p < .001), and ΔEI = 8.0% (3.0–14.0%, p < .05). The indices varied significantly during different inspiratory phases, but not during expiration (p > .05 each). Furthermore, the dynamic evaluation revealed three distinctive movement patterns of the funnel chest.

Conclusions
Real-time MRI reveals patterns of chest wall motion and indicate that thoracic indices of pectus excavatum should be assessed in the end-expiratory phase of quiet expiration.

Key Points
• The thoracic indices in patients with pectus excavatum depend on the breathing phase.
• Quiet expiration represents the best breathing phase for determining thoracic indices.
• Real-time MRI can identify different chest wall motion patterns in pectus excavatum.
Application of diffusion microstructure imaging in musculoskeletal radiology — translation from head to shoulders
Authors: Rau Alexander , Jungmann Pia M. , Diallo Thierno D. , Reisert Marco , Kellner Elias , Eisenblaetter Michel , Bamberg Fabian , Jung Matthias 
Published: October 2022

Abstract
Objectives
Quantitative MRI techniques, such as diffusion microstructure imaging (DMI), are increasingly applied for advanced tissue characterization. We determined its value in rotator cuff (RC) muscle imaging by studying the association of DMI parameters to isometric strength and fat fraction (FF).

Methods
Healthy individuals prospectively underwent 3T-MRI of the shoulder using DMI and chemical shift encoding-based water-fat imaging. RC muscles were segmented and quantitative MRI metrics (V-ISO, free fluid; V-intra, compartment inside of muscle fibers; V-extra, compartment outside of muscle fibers, and FF) were extracted. Isometric shoulder strength was quantified using specific clinical tests. Sex-related differences were assessed with Student’s t. Association of DMI-metrics, FF, and strength was tested. A factorial two-way ANOVA was performed to compare the main effects of sex and external/internal strength-ratio and their interaction effects on quantitative imaging parameters ratios of infraspinatus/subscapularis.

Results
Among 22 participants (mean age: 26.7 ± 3.1 years, 50% female, mean BMI: 22.6 ± 1.9 kg/m2), FF of the individual RC muscles did not correlate with strength or DMI parameters (all p > 0.05). Subjects with higher V-intra (r = 0.57 to 0.87, p < 0.01) and lower V-ISO (r = −0.6 to −0.88, p < 0.01) had higher internal and external rotation strength. Moreover, V-intra was higher and V-ISO was lower in all RC muscles in males compared to female subjects (all p < 0.01). There was a sex-independent association of external/internal strength-ratio with the ratio of V-extra of infraspinatus/subscapularis (p = 0.02).

Conclusions
Quantitative DMI parameters may provide incremental information about muscular function and microstructure in young athletes and may serve as a potential biomarker.

Key Points
• Diffusion microstructure imaging was successfully applied to non-invasively assess the microstructure of rotator cuff muscles in healthy volunteers.
• Sex-related differences in the microstructural composition of the rotator cuff were observed.
• Muscular microstructural metrics correlated with rotator cuff strength and may serve as an imaging biomarker of muscular integrity and function.
Study of ferromagnetic and ferroelectric properties of nanocrystalline Bi2Mn4O10
Authors: Fareed Shereef A. , Ibrahim Makram , Hannora Ahmed E. , El-Desoky M. M. 
Published: October 2022

AbstractNano-crystalline bismuth manganese oxide Bi2Mn4O10 was prepared from Bi2O3 and MnO2 by mechanochemical technique at different milling times followed by heat treatment at 1073 K. The crystal structural were obtained using X-ray diffraction (XRD) and TEM. We obtained the variation of the crystallite size and micro-strain with milling time. The magnetic parameters include the saturation magnetization, coercivity and squareness ratio were obtained by vibrating sample magnetometer (VSM). Each of the coercive field, Squareness ratio and saturation magnetization increased with increasing the milling time. The electrical conduction of the prepared samples was evaluated by AC conductivity measurements at different temperature, the dielectric parameters were plotted against temperature at different frequencies. The conduction mechanism is described by the correlated barrier hopping model (CBH). The recoverable energy density and loss energy was obtained to calculate the storage energy efficiency.