bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weakened Bayesian Calibration for Tactile Temporal Order Judgment in Individuals with Higher Autistic Traits
Authors: Wada Makoto , Umesawa Yumi , Sano Misako , Tajima Seiki , Kumagaya Shinichiro , Miyazaki Makoto 
Published: January 2022

AbstractPrevious psychophysical studies reported a positive aftereffect in tactile temporal order judgments, which can be explained by the Bayesian estimation model (‘Bayesian calibration’). We investigated the relationship between Bayesian calibration and autistic traits in participants with typical development (TD) and autism spectrum disorder (ASD). Bayesian calibration was weakened in TD participants with high autistic traits, consistent with the ‘hypo-priors’ hypothesis for autistic perceptions. The results from the ASD group were generally observed as a continuation of those from the TD groups. Meanwhile, two ASD participants showed irregularly large positive or negative aftereffects. We discussed the mechanisms behind the general results among TD and ASD participants and two particular results among ASD participants based on the Bayesian estimation model.
Landing Site Detection for Autonomous Rotor Wing UAVs Using Visual and Structural Information
Authors: Chatzikalymnios Evangelos , Moustakas Konstantinos 
Published: January 2022

AbstractThe technology of unmanned aerial vehicles (UAVs) has increasingly become part of many civil and research applications in recent years. UAVs offer high-quality aerial imaging and the ability to perform quick, flexible and in-depth data acquisition over an area of interest. While navigating in remote environments, UAVs need to be capable of autonomously landing on complex terrains for security, safety and delivery reasons. This is extremely challenging as the structure of these terrains is often unknown, and no prior knowledge can be leveraged. In this study, we present a vision-based autonomous landing system for rotor wing UAVs equipped with a stereo camera and an inertial measurement unit (IMU). The landing site detection algorithm introduces and evaluates several factors including terrain’s flatness, inclination and steepness. Considering these features we compute map metrics that are used to obtain a landing-score map, based on which we detect candidate landing sites. The 3D reconstruction of the scene is acquired by stereo processing and the pose of the UAV at any given time is estimated by fusing raw data from the inertial sensors with the pose obtained from stereo ORB-SLAM2. Real-world trials demonstrate successful landing in unknown and complex terrains such as suburban and forest areas.
Low-cost Imaging of Fluorescent DNA in Agarose Gel Electrophoresis using Raspberry Pi cameras
Authors: Abid Hassan Ali , Ong Jian Wern , Lin Eric Shen , Song Zhixiong , Liew Oi Wah , Ng Tuck Wah 
Published: January 2022

AbstractLow-cost analytical solutions built around microcomputers like the Raspberry Pi help to facilitate laboratory investigations in resource limited venues. Here, three camera modules (V1.3 with and without filter, as well as NoIR) that work with this microcomputer were assessed for their suitability in imaging fluorescent DNA following agarose gel electrophoresis. Evaluation of their utility was based on signal-to-noise (SNR) and noise variance metrics that were developed. Experiments conducted with samples were subjected to Polymerase Chain Reaction (PCR), and the amplified products were separated using gel electrophoresis and stained with Midori green. Image analysis revealed the NoIR camera performed the best with SNR and noise variance values of 21.7 and 0.222 respectively. In experiments conducted using UV LED lighting to simulate ethidium bromide (EtBr) excitation, the NoIR and V1.3 with filter removed cameras showed comparable SNR values.
A Comparative Study on Methods of Distinction Between Near- and Far-Side Defects as Techniques Used Alongside with the Magnetic Flux Leakage Testing
Authors: Usarek Zbigniew , Chmielewski Marek , Piotrowski Leszek 
Published: January 2022

AbstractResults of the finite element analysis show that a far-side defect in a steel plate, with the depth greater by 10% of the plate thickness than a near-side defect, can produce a very similar magnetic flux leakage (MFL) signal. Due to the fact that a measurement of MFL itself can lead to misclassification of a far-side defect as a near-side one, and thus to underestimation of its depth, a comparative study of three complementary magnetic techniques was performed. The following techniques were studied: surface topology air-gap reluctance system (STARS), residual magnetic flux leakage (RMFL) and stray magnetic flux leakage (SMFL). Numerical results showed that in the case of the STARS and SMFL, defect signatures in signals were observed for investigated near-side defects, but not for far-side defects. The signature of the far-side defect in the RMFL was observed, however its peak-to-peak value was only about 8% of the value corresponding to the near-side defect.
Minding our Minds: Obsessive-Compulsiveness, Psychiatry, and Psychology
Authors: Blum Lawrence D. 
Published: January 2022

AbstractObsessive–compulsive features are commonly found in high-achieving people including psychiatrists, psychologists, and scientists. These traits have a substantial but unrecognized cultural influence on psychiatric and psychological science and practice. This article reviews obsessive–compulsive mechanisms and discusses the ways they both promote and impede psychiatric and psychological science and practice. It examines them in relation to two of the dominant psychiatric and psychological paradigms of our era, the Diagnostic and Statistical Manual (DSM), and Cognitive-Behavioral Therapy. Finally, the article suggests that better awareness of our collective obsessive–compulsive tendencies can facilitate a cultural shift toward a broader, more useful science of mind and brain, as well as therapies informed by more comprehensive scientific understanding.
The Effectiveness of Two Implementation Strategies for Improving Teachers’ Delivery of an Evidenced-based HIV Prevention Program
Authors: Wang Bo , Deveaux Lynette , Cottrell Lesley , Li Xiaoming , Adderley Richard , Dorsett Barbara , Firpo-Triplett Regina , Koci Veronica , Marshall Sharon , Forbes Nikkiah , Stanton Bonita 
Published: January 2022

Abstract
Background
Effective implementation strategies are needed to enhance the success of evidence-based prevention programs. The current study evaluates the effects of two implementation strategies on teachers’ implementation of an evidenced-based HIV intervention.

Methods
Using our 7-item pre-implementation school screening tool, we identified teachers who were at-risk for not implementing the Focus on Youth HIV-risk reduction intervention curriculum which targets grade six through grade 8 students. After completing a two-day curriculum workshop, 81 low- and moderate-performing teachers were randomly assigned to one of four experimental conditions and were asked to teach the two-month intervention curriculum. This optimization trial examines the impact of two implementation strategies: biweekly monitoring/feedbacks (BMF) and site-based assistance/mentorship (SAM). The primary outcome is implementation fidelity defined as number of core activities taught. Linear mixed-effects model was used to examine the association of the implementation strategies with implementation fidelity.

Results
BMF and SAM were significantly associated with teachers’ implementation fidelity. Teachers who received both BFM and SAM taught the greatest numbers of core activities (15 core activities on average), followed by teachers who received either BMF (6.9 activities) or SAM (7.9 activities). Teachers who did not receive BMF or SAM taught the lowest numbers (4.1 activities). Teachers’ sustained implementation of FOYC in the prior school year was related to increased implementation fidelity during the optimization trial. Teachers’ confidence in implementing five core activities, attitudes toward sex education in schools, and perceived principal support were significantly related to increased self-efficacy, which in turn was related to teachers’ fidelity of implementation before the optimization trial.

Conclusion
BMF and SAM are effective in promoting teachers’ implementation of youth evidence-based interventions. Researchers and future program implementers should consider teacher training, teachers’ attitudes toward sex education, perceived principal support, and self-efficacy when attempting to maintain the effects of teacher-delivered interventions in schools.
Unconstrained binary models of the travelling salesman problem variants for quantum optimization
Authors: Salehi Özlem , Glos Adam , Miszczak Jarosław Adam 
Published: January 2022

AbstractQuantum computing is offering a novel perspective for solving combinatorial optimization problems. To fully explore the possibilities offered by quantum computers, the problems need to be formulated as unconstrained binary models, taking into account limitation and advantages of quantum devices. In this work, we provide a detailed analysis of the travelling salesman problem with time windows (TSPTW) in the context of solving it on a quantum computer. We introduce quadratic unconstrained binary optimization and higher-order binary optimization formulations of this problem. We demonstrate the advantages of edge-based and node-based formulations of the TSPTW problem. Additionally, we investigate the experimental realization of the presented methods on a quantum annealing device. The provided results pave the path for utilizing quantum computer for a variety of real-world tasks which can be cast in the form of travelling salesman problem with time windows.
Prevalence of FRAX risk factors and the osteoporosis treatment gap among women ≥ 70 years of age in routine primary care across 8 countries in Europe
Authors: McCloskey Eugene , Rathi Jeetandera , Heijmans Stephane , Blagden Mark , Cortet Bernard , Czerwinski Edward , Hadji Peyman , Payer Juraj , Palmer Kerry , Stad Robert , O’Kelly James , Papapoulos Socrates 
Published: January 2022

Abstract

Summary

We studied whether elderly women at risk for fractures receive primary care treatment to prevent fracture. We found that across Europe, women at risk are often not identified, and less than half of such women receive appropriate treatment. Finally, women diagnosed with osteoporosis are much more likely to receive treatment.

Purpose
To examine the relationship between risk factors for fragility fracture (FF) and osteoporosis (OP) treatment gap in elderly women across Europe, and compare the prevalence of risk factors between countries.

Methods
Demographic and clinical information was collected from women ≥ 70 years visiting primary care physicians in Belgium, France, Germany, Ireland, Poland, Slovakia, Switzerland, and the UK. Increased risk of FF was defined by the presence of 1 or more criteria (history of fracture, 10-year fracture probability, or T-score ≤  − 2.5).

Results
There were 3798 women in total. Treatment gap (proportion at increased risk of FF not receiving treatment for OP) varied from 53.1 to 90.8% across countries, and the proportion of patients at increased risk of FF varied from 41.2 to 76.1%. Across countries, less than 50% of patients with increased risk of FF had a diagnosis of OP. Previous fracture was the most common risk factor, with similar prevalence across most countries; other risk factors varied widely. The treatment gap was reduced in patients with an OP diagnosis in all countries, but this reduction varied from 36.5 to 79.4%. The countries with the lowest rates of bone densitometry scans (Poland, France, and Germany; 8.3–12.3%) also had the highest treatment gap (82.2 to 90.8%).

Conclusions
This study highlights differences across Europe in clinical risk factors for fracture, rates of densitometry scanning, and the rates of OP diagnosis. More emphasis is needed on risk assessment to improve the identification and treatment of elderly women at risk for fracture.
Intraoperative margin assessment by wireless signals in thoracoscopic anterior (S3) segmentectomy using a radiofrequency identification marker
Authors: Yutaka Yojiro , Ohsumi Akihiro , Nakajima Daisuke , Hamaji Masatsugu , Menju Toshi , Date Hiroshi 
Published: January 2022

AbstractDespite the use of near-infrared thoracoscopy with intravenous indocyanine green, intraoperative assessment of the surgical margin for the resection of non-palpable tumors located near the intersegmental plane requires highly advanced surgical skill for the prevention of local recurrence. Because the demarcation line is limited to the pleural surface, to overcome uncertainty in tumor palpation for deeply located small-sized lesions, other supplemental localization techniques have been proposed. Here, we present a novel surgical technique using radiofrequency identification markers for intraoperative assessment of the lateral surgical margin in segmentectomy.
Students’ collaborative creative process and its phases in mathematics: an explorative study using dual eye tracking and stimulated recall interviews
Authors: Schindler Maike , Lilienthal Achim J. 
Published: January 2022

AbstractIn the age of artificial intelligence where standard problems are increasingly processed by computers, creative problem solving, the ability to think outside the box is in high demand. Collaboration is also increasingly significant, which makes creative collaboration an important twenty-first-century skill. In the research described in this paper, we investigated students’ collaborative creative process in mathematics and explored the collaborative creative process in its phases. Since little is known about the collaborative creative process, we conducted an explorative case study, where two students jointly worked on a multiple solution task. For in-depth insight into the dyad’s collaborative creative process, we used a novel research design in mathematics education, DUET SRI: both students wore eye-tracking glasses during their collaborative work for dual eye-tracking (DUET) and they each participated in a subsequent stimulated recall interview (SRI) where eye-tracking videos from their joint work served as stimulus. Using an inductive data analysis method, we then identified the phases of the students’ collaborative creative process. We found that the collaborative creative process and its phases had similarities to those previously found for solo creative work, yet the process was more complex and volatile and involved different branches. Based on our findings, we present a tentative model of the dyad’s collaborative process in its phases, which can help researchers and educators trace and foster the collaborative creative process more effectively.
Ultrasensitive Protein Concentration Characterization Based on Weak Measurements
Authors: Song Bowen , Chen Binglin , Yu Chen , Yang Hua 
Published: January 2022

AbstractAn optical rotation bio-sensor based on the photonic spin Hall effect was established and applied to detecting the concentration varieties of chiral molecules. The optical rotation, introduced by sample solutions, was exploited to modulate the postselected polarization of a weak measurement system. Much work has been done in the case of glucose and fructose. However, little attention has been paid for biomolecules, such as proteins and amino acids. With this modulation, the optical rotation can be determined through the direction and spin accumulation of light spots, thus mirroring the concentration of solutions. A resolution of 2×10−4 degree was achieved.
Association Between Adverse Childhood Experiences, Resilience and Mental Health in a Hispanic Community
Authors: Dominguez Maribel G. , Brown Louis D. 
Published: January 2022

AbstractThis study explores the relations between adverse childhood experiences (ACEs), mental health and resilience among Hispanic adults living in the United States – Mexico Border region. Numerous studies have investigated the negative impact of ACEs on adult mental health, but the concept of resilience as a protective factor for mental health in the Hispanic communities has limited consideration in ACE treatment interventions. The proposed study addresses this gap in knowledge by investigating relations between ACEs, resilience, and mental health. An online survey was administered to 221 university students to assess the relationship between ACEs, mental distress and resilience. Using hierarchical linear regression, three models were estimated. First, including demographics, second including ACEs and low resilience, followed by the interaction of ACEs and resilience. Analyses indicate that ACEs were associated with mental distress (B = 1.02, 95% CI 0.37 – 1.68, p < 0.01) and low resilience was associated with mental distress (B = 5.37, 95% CI 3.15 – 7.59, p < .01). The interaction between ACEs and low resilience was also related to mental distress (B = 1.32, 95% CI 0.17 – 2.47, p = 0.03), indicating that ACEs had a larger association with mental distress among respondents with low resilience. Findings highlight the importance of the direct association between resilience and mental distress, along with the moderating influence of resilience on the relation between ACEs and mental health. Interventions promoting resilience may be effective in reducing mental distress, especially among individuals with a history of ACEs.
Allosteric modulation of dopamine D2L receptor in complex with Gi1 and Gi2 proteins: the effect of subtle structural and stereochemical ligand modifications
Authors: Żuk Justyna , Bartuzi Damian , Silva Andrea G. , Pitucha Monika , Koszła Oliwia , Wróbel Tomasz M. , Matosiuk Dariusz , Castro Marián , Kaczor Agnieszka A. 
Published: January 2022

Abstract
Background
Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays one of the hot topics in drug discovery. In particular, allosteric modulators of D2 receptor have been proposed as potential modern therapeutics to treat schizophrenia and Parkinson’s disease.

Methods
To address some subtle structural and stereochemical aspects of allosteric modulation of D2 receptor, we performed extensive in silico studies of both enantiomers of two compounds (compound 1 and compound 2), and one of them (compound 2) was synthesized as a racemate in-house and studied in vitro.

Results
Our molecular dynamics simulations confirmed literature reports that the R enantiomer of compound 1 is a positive allosteric modulator of the D2L receptor, while its S enantiomer is a negative allosteric modulator. Moreover, based on the principal component analysis (PCA), we hypothesized that both enantiomers of compound 2 behave as silent allosteric modulators, in line with our in vitro studies. PCA calculations suggest that the most pronounced modulator-induced receptor rearrangements occur at the transmembrane helix 7 (TM7). In particular, TM7 bending at the conserved P7.50 and G7.42 was observed. The latter resides next to the Y7.43, which is a significant part of the orthosteric binding site. Moreover, the W7.40 conformation seems to be affected by the presence of the positive allosteric modulator.

Conclusions
Our work reveals that allosteric modulation of the D2L receptor can be affected by subtle ligand modifications. A change in configuration of a chiral carbon and/or minor structural modulator modifications are solely responsible for the functional outcome of the allosteric modulator.

Graphical abstract
Novel Evidence Showing the Possible Effect of Environmental Variables on COVID‐19 Spread
Authors: Zhang Sixuan , Wang Bingyun , Yin Li , Wang Shigong , Hu Wendong , Song Xueqian , Feng Hongmei 
Published: January 2022

AbstractCoronavirus disease (COVID‐19) remains a serious issue, and the role played by meteorological indicators in the process of virus spread has been a topic of academic discussion. Previous studies reached different conclusions due to inconsistent methods, disparate meteorological indicators, and specific time periods or regions. This manuscript is based on seven daily meteorological indicators in the NCEP reanalysis data set and COVID‐19 data repository of Johns Hopkins University from 22 January 2020 to 1 June 2021. Results showed that worldwide average temperature and precipitable water (PW) had the strongest correlation (ρ > 0.9, p < 0.001) with the confirmed COVID‐19 cases per day from 22 January to 31 August 2020. From 22 January to 31 August 2020, positive correlations were observed between the temperature/PW and confirmed COVID‐19 cases/deaths in the northern hemisphere, whereas negative correlations were recorded in the southern hemisphere. From 1 September to 31 December 2020, the opposite results were observed. Correlations were weak throughout the near full year, and weak negative correlations were detected worldwide (|ρ| < 0.4, p ≤ 0.05); the lag time had no obvious effect. As the latitude increased, the temperature and PW of the maximum confirmed COVID‐19 cases/deaths per day generally showed a decreasing trend; the 2020‐year fitting functions of the response latitude pattern were verified by the 2021 data. Meteorological indicators, although not a decisive factor, may influence the virus spread by affecting the virus survival rates and enthusiasm of human activities. The temperature or PW threshold suitable for the spread of COVID‐19 may increase as the latitude decreases.
Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations
Authors: Gourisaria Mahendra Kumar , Jee Gaurav , Harshvardhan G. M. , Singh Vijander , Singh Pradeep Kumar , Workneh Tewabe Chekole 
Published: January 2022

AbstractOne of the capacious applications of data science could be its use in bioinformatics. With its proper implementation, chronic diseases like diabetes mellitus, responsible for millions of deaths worldwide, could be diagnosed and predicted with high efficacy. But if not attended, could lead to fatal issues such as kidney failures, heart diseases, and even limb amputation. Diabetic cases have only elevated in numbers in the recent past. The authors use various machine learning, deep learning, and data dimensionality reduction techniques to detect diabetes mellitus. The research is principally conducted on two datasets, first from the Frankfurt Hospital, Germany, second from the University of California, Irvine repository. Models such as support vector machines, Naïve Bayes, and Random Forests were implemented to classify diabetic patients from non‐diabetic ones. Subsequently, after hyperparameter tuning, a comparative study on the results was done and the most prominent model was promoted. This process was repeated for the datasets with reduced dimensionality using linear discriminant analysis and principal component analysis. For the Frankfurt, Germany, dataset, K‐nearest neighbours showed the best accuracy of 98.2%, and the Random Forest classifier for the University of California, Irvine, repository showed 99.2%. With such proficiency, the authors thereby propose a statistical approach for the prediction of diabetes in its early stages. They hope to counter the concern of undiagnosed diabetic cases in developing nations where there is a lack of a basic healthcare system.