bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Beginnings of Psychologist Profession in Argentina: Science, Institutional Context and Society
Authors: Klappenbach Hugo 
Published: March 2023

AbstractThe chapter analyzes the first developments in psychology at the end of the nineteenth century and beginnings of the twentieth century. In those years, psychology appeared to be a knowledge usable for professions like medicine, pedagogy or legal profession. Moreover, the chapter exhibits a very import shift that happened in the middle of twentieth century when some undergraduate programs started in thefieldof psychology. Usually, such programs were not found in psychology, but inpsychotechnicsandprofessional guidance. The chapter analyzes the scientific orientation of such programs, based on French psychology, as well as the institutional and political factors promoting psychological programs. Last, the chapter will expose the re-orientation of psychology education at universities since the second half of the 1960s, when psychology became synonymous withpsychoanalysis. On the one hand, it will analyze the institutional and political background for the constitution of the profession of psychologist. On the other hand, it will exhibit the main ways supporting psychologist psychoanalytically biased profession: epistemological and theoretical approaches within science and psychology education, clinical practices in public institutions, and a virtual repudiation of alternative models of knowing and practicing.
Powershap: A Power-Full Shapley Feature Selection Method
Authors: Verhaeghe Jarne , Van Der Donckt Jeroen , Ongenae Femke , Van Hoecke Sofie 
Published: March 2023

AbstractFeature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper methods. While wrapper methods commonly result in strong predictive performances, they suffer from a large computational complexity and therefore take a significant amount of time to complete, especially when dealing with high-dimensional feature sets. Alternatively, filter methods are considerably faster, but suffer from several other disadvantages, such as (i) requiring a threshold value, (ii) many filter methods not taking into account intercorrelation between features, and (iii) ignoring feature interactions with the model. To this end, we present powershap, a novel wrapper feature selection method, which leverages statistical hypothesis testing and power calculations in combination with Shapley values for quick and intuitive feature selection. Powershap is built on the core assumption that an informative feature will have a larger impact on the prediction compared to a known random feature. Benchmarks and simulations show that powershap outperforms other filter methods with predictive performances on par with wrapper methods while being significantly faster, often even reaching half or a third of the execution time. As such, powershap provides a competitive and quick algorithm that can be used by various models in different domains. Furthermore, powershap is implemented as a plug-and-play and open-source sklearn component, enabling easy integration in conventional data science pipelines. User experience is even further enhanced by also providing an automatic mode that automatically tunes the hyper-parameters of the powershap algorithm, allowing to use the algorithm without any configuration needed.
On the Current State of Reproducibility and Reporting of Uncertainty for Aspect-Based Sentiment Analysis
Authors: Lebmeier Elisabeth , Aßenmacher Matthias , Heumann Christian 
Published: March 2023

AbstractFor the latter part of the past decade, Aspect-Based Sentiment Analysis has been a field of great interest within Natural Language Processing. Supported by the Semantic Evaluation Conferences in 2014–2016, a variety of methods has been developed competing in improving performances on benchmark data sets. Exploiting the transformer architecture behind BERT, results improved rapidly and efforts in this direction still continue today. Our contribution to this body of research is a holistic comparison of six different architectures which achieved (near) state-of-the-art results at some point in time. We utilize a broad spectrum of five publicly available benchmark data sets and introduce a fixed setting with respect to the pre-processing, the train/validation splits, the performance measures and the quantification of uncertainty. Overall, our findings are two-fold: First, we find that the results reported in the scientific articles are hardly reproducible, since in our experiments the observed performance most of the time fell short of the reported one. Second, the results are burdened with notable uncertainty, depending on the data splits, which is why a reporting of uncertainty measures is crucial.
Contextualized Graph Embeddings for Adverse Drug Event Detection
Authors: Gao Ya , Ji Shaoxiong , Zhang Tongxuan , Tiwari Prayag , Marttinen Pekka 
Published: March 2023

AbstractAn adverse drug event (ADE) is defined as an adverse reaction resulting from improper drug use, reported in various documents such as biomedical literature, drug reviews, and user posts on social media. The recent advances in natural language processing techniques have facilitated automated ADE detection from documents. However, the contextualized information and relations among text pieces are less explored. This paper investigates contextualized language models and heterogeneous graph representations. It builds a contextualized graph embedding model for adverse drug event detection. We employ different convolutional graph neural networks and pre-trained contextualized embeddings as the building blocks. Experimental results show that our methods can improve the performance by comparing recent ADE detection models, suggesting that a text graph can capture causal relationships and dependency between different entities in a document.
Adrenal Diseases
Authors: Francis Isaac R. , Mayo-Smith William W. 
Published: March 2023

AbstractIn this chapter, we will describe practical approaches to the evaluation of adrenal masses detected as incidental masses, as well as in other clinical scenarios in the oncological and non-oncological patient. The most commonly used clinical and imaging techniques and procedures used in the evaluation of these masses and their common appearances will also be described. Current European and US guidelines and their limitations will be briefly addressed.
Was heißt »linke Politik« und was wird aus ihr?
Authors: Kahrs Horst 
Published: March 2023

ZusammenfassungDie modernen Ungleichheitskonflikte um Migration (Innen – Außen), Identität (Wir – Sie), Ökologie (Heute – Morgen) ersetzen nicht den klassischen, von linker Politik bearbeiteten Konflikt Arbeit – Kapital oder den patriarchalen Konflikt Mann – Frau. Vielmehr fordern sie linke Politik heraus, politisch produktive Verbindungen zwischen den Konfliktachsen herzustellen. Die Linke ist dabei in den beiden zurückliegenden Jahrzehnten mehr und mehr in die Defensive geraten gegenüber einer autoritären Rechten, der es zum Beispiel gelungen ist, aus der Verbindung von Identitätsfragen und Migrationsfragen politische Funken zu schlagen. Dass eine dauerhafte emanzipatorische Überwindung des Kapitalismus möglich ist, zählt nicht mehr zu den Gewissheiten linker Politik, wohl aber die Erfahrung, dass es gelingen kann, ihm demokratische sozialstaatliche Zügel anzulegen und Entwicklungsrichtungen etwa durch staatliche Forschungs-, Struktur- und Industriepolitik zu beeinflussen. Für eine erfolgreichere Zukunft linker Politik käme es darauf an, den gemeinsamen linken Wertehorizont erkennbar zu machen, nämlich das Streben nach Verwirklichung universeller Gleichheit von unterschiedlichen Ausgangspunkten aus als Basis für politische Kooperationen und Synergien. Radikaler Pragmatismus als linker Politikmodus verlangt vom Kapitalismus doppelte Anpassung: Dekarbonisierung, um weitere Erderwärmung zu begrenzen und sozio-ökologische Maßnahmen angesichts des stattfindenden Klimawandels.
Zivilgesellschaftliche Wasserstände und ein alternder Meeresspiegel
Authors: Röggla Kathrin 
Published: March 2023

ZusammenfassungDie Zivilgesellschaft ist in der politischen Arbeit überhaupt nicht mehr wegzudenken. Ob daraus etwas erwächst, was wirklich einen Unterschied macht, lässt sich schwer abschätzen, es wundert jedenfalls nicht, dass dieses Engagement auch von zahlreichen Institutionen und Verbänden, parteinahen Stiftungen und einzelnen Politiker:innen unterstützt wird. Sie ist ein Ort der Aushandlungen, wie sie für demokratische Prozesse notwendig sind. Aber immer wieder zeigen sich die Begrenztheit, die fragilen Strukturen und Anfälligkeiten für Burnout der zivilgesellschaftlichen Organisation, schließlich hängt die Schlagmacht oft an ganz konkreten Personen. Auch sehen sie sich einem sehr mächtigen politischen und ökonomischen Gegner gegenüber, der sich ganz anders organisieren kann. Aber man soll sich nicht täuschen, auch das sind nicht festsitzende Blöcke, oftmals wird eine Zusammenarbeit auch versucht. Der größte Feind ist dabei die fehlende politische Imagination. Es gibt eine persistierende Unvorstellbarkeit von Veränderung, aus der wiederum eine fehlende Selbstwirksamkeitserfahrung resultiert, in dem Gefühl von zu geringer Handlungsmacht, egal, wo wir uns befinden. Beißt sich die Katze hier in den Schwanz? Aber natürlich – dieser Essay folgt der Kreisförmigkeit, die sich in meinen Gesprächen so deutlich ergeben hat.
Two Unconnected Low-Velocity Zones in the Eastern Boundary of the Sichuan-Yunnan Block Revealed with High-Resolution Ambient Noise Tomography
Authors: Qiu Yu , Fang Lihua , Liu Jing , Fan Liping , Lü Zuoyong , Cai Guangyao , Wang Shiguang 
Published: March 2023

AbstractThe Anninghe-Zemuhe-Xiaojiang fault zone (AZXFZ) is an important boundary fault zone on the southeastern margin of the Tibetan Plateau, with frequent strong earthquakes. Previous studies have imaged widespread low-velocity zones in this area. However, there are still many disputes on the connectivity and genesis of the low-velocity zones. In this study, we obtain the Rayleigh wave phase velocity dispersion curves at 4–25-s periods using observations from 378 broadband stations located near the AZXFZ. The new 3D S-wave velocity model has a lateral resolution of about 30 km in 0–35-km depth and is obtained by direct inversion of surface wave dispersion data. The new results clearly image two low-velocity zones and a high-velocity zone in the middle crust of the study region. The low-velocity zone on the western side of the Lijiang-Xiaojinhe fault is related to the eastward flow of crustal material and the movement of the left lateral strike-slip faults in the Tibetan Plateau, while the low-velocity anomaly distributed along the Daliangshan fault and Xiaojiang fault is the superimposition effect of shear heating of the faults and upwelling of mantle material. The uplift of Gongga Shan is a combination of the continuous accumulation of crustal material in the middle and lower crust of the southeastern margin of the Tibetan Plateau as well as the bending and compression of the Sichuan-Yunnan Block and the Xianshuihe-Anninghe fault zone.
Generative models and Bayesian inversion using Laplace approximation
Authors: Marschall Manuel , Wübbeler Gerd , Schmähling Franko , Elster Clemens 
Published: March 2023

AbstractThe Bayesian approach to solving inverse problems relies on the choice of a prior. This critical ingredient allows expert knowledge or physical constraints to be formulated in a probabilistic fashion and plays an important role for the success of the inference. Recently, Bayesian inverse problems were solved using generative models as highly informative priors. Generative models are a popular tool in machine learning to generate data whose properties closely resemble those of a given database. Typically, the generated distribution of data is embedded in a low-dimensional manifold. For the inverse problem, a generative model is trained on a database that reflects the properties of the sought solution, such as typical structures of the tissue in the human brain in magnetic resonance imaging. The inference is carried out in the low-dimensional manifold determined by the generative model that strongly reduces the dimensionality of the inverse problem. However, this procedure produces a posterior that does not admit a Lebesgue density in the actual variables and the accuracy attained can strongly depend on the quality of the generative model. For linear Gaussian models, we explore an alternative Bayesian inference based on probabilistic generative models; this inference is carried out in the original high-dimensional space. A Laplace approximation is employed to analytically derive the prior probability density function required, which is induced by the generative model. Properties of the resulting inference are investigated. Specifically, we show that derived Bayes estimates are consistent, in contrast to the approach in which the low-dimensional manifold of the generative model is employed. The MNIST data set is used to design numerical experiments that confirm our theoretical findings. It is shown that the approach proposed can be advantageous when the information contained in the data is high and a simple heuristic is considered for the detection of this case. Finally, the pros and cons of both approaches are discussed.
Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset
Authors: Schlaeger Sarah , Drummer Katharina , El Husseini Malek , Kofler Florian , Sollmann Nico , Schramm Severin , Zimmer Claus , Wiestler Benedikt , Kirschke Jan S. 
Published: March 2023

Abstract
Objectives
T2-weighted (w) fat sat (fs) sequences, which are important in spine MRI, require a significant amount of scan time. Generative adversarial networks (GANs) can generate synthetic T2-w fs images. We evaluated the potential of synthetic T2-w fs images by comparing them to their true counterpart regarding image and fat saturation quality, and diagnostic agreement in a heterogenous, multicenter dataset.

Methods
A GAN was used to synthesize T2-w fs from T1- and non-fs T2-w. The training dataset comprised scans of 73 patients from two scanners, and the test dataset, scans of 101 patients from 38 multicenter scanners. Apparent signal- and contrast-to-noise ratios (aSNR/aCNR) were measured in true and synthetic T2-w fs. Two neuroradiologists graded image (5-point scale) and fat saturation quality (3-point scale). To evaluate whether the T2-w fs images are indistinguishable, a Turing test was performed by eleven neuroradiologists. Six pathologies were graded on the synthetic protocol (with synthetic T2-w fs) and the original protocol (with true T2-w fs) by the two neuroradiologists.

Results
aSNR and aCNR were not significantly different between the synthetic and true T2-w fs images. Subjective image quality was graded higher for synthetic T2-w fs (p = 0.023). In the Turing test, synthetic and true T2-w fs could not be distinguished from each other. The intermethod agreement between synthetic and original protocol ranged from substantial to almost perfect agreement for the evaluated pathologies.

Discussion
The synthetic T2-w fs might replace a physical T2-w fs. Our approach validated on a challenging, multicenter dataset is highly generalizable and allows for shorter scan protocols.

Key Points
• Generative adversarial networks can be used to generate synthetic T2-weighted fat sat images from T1- and non-fat sat T2-weighted images of the spine.
• The synthetic T2-weighted fat sat images might replace a physically acquired T2-weighted fat sat showing a better image quality and excellent diagnostic agreement with the true T2-weighted fat images.
• The present approach validated on a challenging, multicenter dataset is highly generalizable and allows for significantly shorter scan protocols.
Aromatic L-amino acid decarboxylase deficiency in countries in the Middle East: a case series and literature review
Authors: Abukhaled Musaad , Al Muqbil Mohammed , Alghamdi Malak Ali , Hundallah Khalid , Suleiman Jehan , Ben-Omran Tawfeg , Alfadhel Majid , Almannai Mohammed , Alsaleh Rehab , Tabarki Brahim 
Published: March 2023

AbstractAromatic L-amino acid decarboxylase (AADC) deficiency is a rare inherited neurometabolic disorder that can lead to severe physical and developmental impairment. This report includes 16 patients from the Middle East and is the largest series of patients with confirmed AADC deficiency from this region reported to date. The patients displayed a range of signs and symptoms at presentation and almost all failed to reach major motor milestones. Missed and delayed diagnoses were common leading to the late introduction of targeted treatments. Eight unique variants were identified in the DDC gene, including six missense and two intronic variants. A previously undescribed variant was identified: an intronic variant between exons 13 and 14 (c.1243-10A>G). The patients were mostly treated with currently recommended medications, including dopamine agonists, vitamin B6, and monoamine oxidase inhibitors. One patient responded well, but treatment outcomes were otherwise mostly limited to mild symptomatic improvements. Five patients had died by the time of data collection, confirming that the condition is associated with premature mortality. There is an urgent need for earlier diagnosis, particularly given the potential for gene therapy as a transformative treatment for AADC deficiency when provided at an early age. Conclusions: Delays in the diagnosis of AADC deficiency are common. There is an urgent need for earlier diagnosis, particularly given the potential for gene therapy as a transformative treatment for AADC deficiency when provided at an early age.

What is Known:• Aromatic L-amino acid decarboxylase deficiency is a rare neurometabolic disorder that can lead to severe physical and developmental impairment.• Currently recommended medications provide mostly mild symptomatic improvements.

What is New:• The clinical presentation of sixteen patients with confirmed AADC deficiency varied considerably and almost all failed to reach major motor milestones.• There is an urgent need for earlier diagnosis, given the potential for gene therapy as a transformative treatment for AADC deficiency when provided at an early age.
Combined effect of graded Thera-Band and scapular stabilization exercises on shoulder adhesive capsulitis post-mastectomy
Authors: Aboelnour Nancy H. , Kamel FatmaAlzahraa H. , Basha Maged A. , Azab Alshimaa R. , Hewidy Islam M. , Ezzat Mohamed , Kamel Noha M. 
Published: March 2023

Abstract

Purpose

The main aim of the trial was to assess the combined impact of graded Thera-Band strengthening exercises and scapular stabilization exercises on shoulder pain, physical function, and quality of life (QoL) in post-mastectomy adhesive capsulitis (AC).

Methods
Seventy females with unilateral post-mastectomy AC partook in the trial. Participants were subdivided equally into two groups at random. Both groups obtained the traditional physical therapy program; in addition, the intervention group received graded Thera-Band exercises for shoulder muscles and scapular stabilization exercises 5 days a week for 8 weeks. Range of motion (ROM) and muscle power of shoulder were assessed by digital goniometer and handheld dynamometer, respectively. Disability of the Arm, Shoulder, and Hand questionnaire (DASH) was utilized for assessment of shoulder function and visual analogue scale (VAS) for pain measurement while short-form (SF-36) for QoL assessment. All evaluation data was recorded prior to the trial and at the eighth week of interventions for both groups.

Results
All participants achieved improvements in shoulder ROM, muscle power, pain, and all aspects of QoL; however, higher statistical improvements were reported in all measurements with respect to strengthening exercises group (p < 0.001).

Conclusion
The addition of graded Thera-Band strengthening exercises and scapular stabilization exercises in post-mastectomy AC rehabilitation program has significant benefits in shoulder function and patients’ QoL.
Trial registration: This study is retrospectively registered at ClinicalTrials.gov NCT05311839.
Evaluating the Efficacy of the Family Check-Up Online to Improve Parent Mental Health and Family Functioning in Response to the COVID-19 Pandemic: A Randomized Clinical Trial
Authors: Connell Arin M. , Stormshak Elizabeth A. 
Published: March 2023

AbstractThis study evaluated the effects of an enhanced version of the Family Check-Up Online (FCU-O), adapted to address parent and family functioning in response to the COVID-19 pandemic. In order to increase accessibility, the FCU-O was delivered as a web-based application coupled with online coaching support, a service delivery model that is consistent with pandemic-related limitations for in-person intervention, as well as the limited staffing and resources available in many schools and health care settings despite the increased need for mental health services driven by the pandemic. This registered clinical trial (blinded) tested the effects of the intervention on parental mental health, parenting behaviors, and family functioning from pre-treatment to 2-month follow-up. Families were randomly assigned to receive the FCU-O (N = 74) or to a wait-list control condition (N = 87). Random assignment to the FCU-O was associated with significant improvements in parental well-being, including reduced anxiety, depression, and perceived stress. Further, the FCU-O predicted significant improvements in adaptive parenting skills (e.g. less negative/coercive parenting, greater proactive parenting), and enhancements in family-relational functioning (e.g. improved coparenting). Effect sizes were small to moderate in magnitude (partial eta squared values between 0.03 and 0.11). The results indicate that online delivery of a family-centered intervention may represent a promising approach for addressing pandemic-related impacts on parent and family functioning.