bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antibody complementarity-determining region design using AlphaFold2 and DDG predictor
Authors: Ueki Takafumi , Ohue Masahito 
Published: February 2024

AbstractThe constraints imposed by natural antibody affinity maturation often culminate in antibodies with suboptimal binding affinities, thereby limiting their therapeutic efficacy. As such, the augmentation of antibody binding affinity is pivotal for the advancement of efficacious antibody-based therapies. Classical experimental paradigms for antibody engineering are financially and temporally prohibitive due to the extensive combinatorial space of sequence variations in the complementarity-determining regions (CDRs). The advent of computational techniques presents a more expeditious and economical avenue for the systematic design and optimization of antibodies. In this investigation, we assess the performance of AlphaFold2 coupled with the binder hallucination technique for the computational refinement of antibody sequences to elevate the binding affinity of pre-existing antigen-antibody complexes. These methodologies exhibit the capability to predict protein tertiary structures with remarkable fidelity, even in the absence of empirically derived data. Our results intimate that the proposed approach is adept at designing antibodies with improved affinities for antigen-antibody complexes unrepresented in AlphaFold2’s training dataset, underscoring its potential as a robust and scalable strategy for antibody engineering.
Seroprevalence study of brucellosis in wild boar hunted for private consumption in northeast Portugal
Authors: Ruano Zita Martins , Mateus Teresa Letra , Chorense Ana , Santos-Silva Sérgio , Vieira-Pinto Madalena 
Published: February 2024

AbstractBrucellosis is an important infectious disease caused by bacteria of the genus Brucella. In the northeast region of Portugal, infection with Brucella melitensis is endemic in small ruminants, and there are also humans’ cases. However, the epidemiological role of the wild boar in the dynamics of this disease in this region is unknown. In this study, a total of 332 blood samples were collected from wild boar hunted in thirty-six hunting areas during the 2022/2023 hunting season. All were taken by the hunters for private consumption, with no evisceration or examination in the field. Serum samples were tested by indirect ELISA (i-ELISA). It was observed that 88 wild boars were exposed to Brucella spp., pointing to a seroprevalence of 26.5% (95% CI: 21.8 – 31.3%). This high prevalence underlines the importance that wild boar may have in the dynamics of this disease in the region and its potential transmission to other animals, and to humans (for example, during the handling of carcasses). Increased awareness and knowledge of brucellosis in wild boar is essential for the implementation of effective practices and habits and, consequently, for the control and prevention of this important zoonosis.
Machine Learning Applications for Smart Building Energy Utilization: A Survey
Authors: Huotari Matti , Malhi Avleen , Främling Kary 
Published: February 2024

AbstractThe United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field.
Effect of Octamer-Binding Transcription Factor 4 Overexpression on the Neural Induction of Human Dental Pulp Stem Cells
Authors: Gancheva Maria R. , Kremer Karlea , Breen James , Arthur Agnes , Hamilton-Bruce Anne , Thomas Paul , Gronthos Stan , Koblar Simon 
Published: February 2024

AbstractStem cell-based therapy is a potential alternative strategy for brain repair, with neural stem cells (NSC) presenting as the most promising candidates. Obtaining sufficient quantities of NSC for clinical applications is challenging, therefore alternative cell types, such as neural crest-derived dental pulp stem cells (DPSC), may be considered. Human DPSC possess neurogenic potential, exerting positive effects in the damaged brain through paracrine effects. However, a method for conversion of DPSC into NSC has yet to be developed. Here, overexpression of octamer-binding transcription factor 4 (OCT4) in combination with neural inductive conditions was used to reprogram human DPSC along the neural lineage. The reprogrammed DPSC demonstrated a neuronal-like phenotype, with increased expression levels of neural markers, limited capacity for sphere formation, and enhanced neuronal but not glial differentiation. Transcriptomic analysis further highlighted the expression of genes associated with neural and neuronal functions. In vivo analysis using a developmental avian model showed that implanted DPSC survived in the developing central nervous system and respond to endogenous signals, displaying neuronal phenotypes. Therefore, OCT4 enhances the neural potential of DPSC, which exhibited characteristics aligning with neuronal progenitors. This method can be used to standardise DPSC neural induction and provide an alternative source of neural cell types.
Graphical Abstract
Attentional bias to threat: an investigation of psychological predictors beyond trait anxiety
Authors: Williams Monique , Honan Cynthia , Matthews Allison J. 
Published: February 2024

AbstractAttentional bias to threat (ABT) refers to the tendency to more rapidly orient attention to threatening stimuli, as compared to neutral or pleasant stimuli. Issues with the reliability of this phenomenon have been raised and little is currently known about individual differences that may contribute to ABT. This online study aimed to replicate ABT in trait anxiety and identify psychological predictors of ABT using both dot probe and flanker tasks comprising emotional images. Undergraduate students completed various psychological questionnaires, followed by the ABT tasks. Contrary to expectations, faster responses and enhanced accuracy for positive stimuli were found in a low trait anxiety sub-sample (n = 33) in the dot probe task, while overall slower performance among a high trait anxiety sub-sample (n = 34) was found in the flanker task. Higher ratings of state anxiety and stress and lower ratings of calmness were associated with greater flanker ABT scores in the overall sample (N = 111), while higher scores on facets of trait mindfulness and personality were associated with greater dot probe ABT scores. Trait anxiety was not associated with either ABT score. These findings highlight the difficulties replicating ABT in anxious individuals using behavioural tasks and indicate the importance of measuring state and trait psychological experiences when investigating ABT in experimental settings. Further replication of predictors is required.
Identity, Gender, Age, and Emotion Recognition from Speaker Voice with Multi-task Deep Networks for Cognitive Robotics
Authors: Foggia Pasquale , Greco Antonio , Roberto Antonio , Saggese Alessia , Vento Mario 
Published: February 2024

AbstractThis paper presents a study on the use of multi-task neural networks (MTNs) for voice-based soft biometrics recognition, e.g., gender, age, and emotion, in social robots. MTNs enable efficient analysis of audio signals for various tasks on low-power embedded devices, thus eliminating the need for cloud-based solutions that introduce network latency. However, the strict dataset requirements for training limit the potential of MTNs, which are commonly used to optimize a single reference problem. In this paper, we propose three MTN architectures with varying accuracy-complexity trade-offs for voice-based soft biometrics recognition. In addition, we adopt a learnable voice representation, that allows to adapt the specific cognitive robotics application to the environmental conditions. We evaluate the performance of these models on standard large-scale benchmarks, and our results show that the proposed architectures outperform baseline models for most individual tasks. Furthermore, one of our proposed models achieves state-of-the-art performance on three out of four of the considered benchmarks. The experimental results demonstrate that the proposed MTNs have the potential for being part of effective and efficient voice-based soft biometrics recognition in social robots.
Toward a representative assessment of competencies for a healthy, physically active lifestyle in the German population: development and validation of a short version of the physical activity-related health competence questionnaire (PAHCO_12)
Authors: Carl Johannes , Domanska Olga Maria , Buchmann Maike , Jordan Susanne , Pfeifer Klaus , Sudeck Gorden 
Published: February 2024

AbstractRepresentative studies assessing behavior-oriented determinants for physically active lifestyles in the German adult population are lacking due to the absence of validated assessment instruments. Drawing on the physical activity-related health competence (PAHCO) model, the goal of the present study was to develop and evaluate a short version of PAHCO differentiating between the three sub-competencies ’movement competence’, ’control competence’, and ’self-regulation competence’. In the first step, we reduced the number of items from existing PAHCO-related questionnaires, while adjusting them for a telephone survey mode. In the second step, we validated this 12-item version with N = 3986 adults between 18 and 97 years (57.0 ± 18.2 years; 52.0% male) within the representative survey “German health Update” (GEDA 2023). We also gathered information about participants’ age, gender, education, leisure-time physical activity, and self-reported health. The psychometric analyses revealed satisfactory item statistics. Confirmatory factor analyses suggested a model variant in which two items showed theory-conform parallel loadings on a second sub-competence (comparative fit index [CFI] = 0.924, root mean square error of approximation [RMSEA] = 0.073, standardized root mean square residual [SRMR] = 0.044). The latent sub-competencies were significantly related to physical activity (0.20 ≤ β ≤ 0.27) and health (0.50 ≤ β ≤ 0.65), thus indicating criterion validity. The factor structure turned out to be invariant across age, gender, and educational levels. In summary, this study gained initial insights about the reliability and validity of a 12-item version of the PAHCO questionnaire. We recommend the instrument for use within representative surveys, for initial screening, the reporting of descriptive trends, or secondary outcomes of a trial.
Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease
Authors: Chen Ziman , Wang Yingli , Ying Michael Tin Cheung , Su Zhongzhen 
Published: February 2024

Abstract
Background
Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients.

Methods
A total of 162 patients with CKD who underwent shear wave elastography examinations and renal biopsies at our institution were prospectively enrolled. Four classifiers using machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbor (KNN), which integrated elastosonographic features and clinical characteristics, were established to differentiate moderate-severe renal fibrosis from mild forms. The area under the receiver operating characteristic curve (AUC) and average precision were employed to compare the performance of constructed models, and the SHapley Additive exPlanations (SHAP) strategy was used to visualize and interpret the model output.

Results
The XGBoost model outperformed the other developed machine learning models, demonstrating optimal diagnostic performance in both the primary (AUC = 0.97, 95% confidence level (CI) 0.94–0.99; average precision = 0.97, 95% CI 0.97–0.98) and five-fold cross-validation (AUC = 0.85, 95% CI 0.73–0.98; average precision = 0.90, 95% CI 0.86–0.93) datasets. The SHAP approach provided visual interpretation for XGBoost, highlighting the features’ impact on the diagnostic process, wherein the estimated glomerular filtration rate provided the largest contribution to the model output, followed by the elastic modulus, then renal length, renal resistive index, and hypertension.

Conclusion
This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output.

Graphical Abstract
Combining Pocket Parks with Ecological Rainwater Management Techniques in High-Density Urban Environments
Authors: Bagiouk Stavros , Sotiriadis Dimitrios , Katsifarakis Konstantinos L. 
Published: February 2024

AbstractIn this paper, the benefits of combining pocket parks with Sustainable Drainage Systems (SuDS) in densely built urban areas are demonstrated, through an application proposal in a medium to low-income neighborhood of the historical center of Thessaloniki, Greece. After an outline of the urbanization impact upon rainwater management, the features of the proposed site are presented, in particular those related to rainwater runoff (e.g., rather large ground slope, narrow sidewalks), which result in considerable nuisance, even during medium intensity rainfall events. The choice of the SuDS components, namely a small-scale rain garden and a permeable pavement zone is explained next. Then, the basic design details of the proposed rain garden are given, including the form of the inlet structures and a list of plants, suitable for the climatic conditions of many North Mediterranean regions. The role and the features of the permeable pavement zone are also discussed and some ideas for the urban equipment required to upgrade the park are presented. A preliminary estimate of the cost for the construction of the rainwater management works and for upgrading the available lot to a pocket park is also included. Compared to the cost of sewer pipe network reconstruction, it can be concluded that the proposed solution is cost-efficient, even without considering aesthetic and social benefits. Moreover, it allows for stepwise construction, based on the available funds.
Exploring the Benefits of Vocational Activity for People with Schizophrenia Spectrum Disorder in Norway
Authors: Schjøtt-Pedersen Olivia , Bull Helen Christine , Falkum Erik , Ueland Torill , Gjerdalen Oda Skancke , Haaland Vegard Øksendal , Evensen Stig , Lystad June Ullevoldsæter 
Published: February 2024

AbstractVocational activity holds many benefits for people with severe mental illness. The purpose of the present study is to explore potential benefits of vocational activity on self-esteem and benefits as described by employment specialists. Data from 148 adults with schizophrenia spectrum disorders participating in a comprehensive vocational rehabilitation program was collected as part of the Job Management Program (JUMP) study. Assessments were conducted at inclusion, post intervention (10 months) and 2 years after inclusion. Multiple regressions and mixed between-within subjects analysis of variance were applied to assess potential effects of vocational activity on self-esteem as measured by the Rosenberg Self-Esteem Scale. One hundred and twenty-one reports submitted by the employment specialists post intervention were assessed through a qualitative approach based on thematic analysis. Statistical analyses indicate that vocational activity might be beneficial for self-esteem. Qualitative analysis indicate that vocational activity may provide opportunities for experiencing capability, achievement, and positive social interaction, and be beneficial for well-being, self-confidence, and self-esteem. Vocational activity may be beneficial for well-being and self-esteem among people with schizophrenia spectrum disorders. Statistical analysis indicate that vocational activity may have a positive effect on self-esteem, and there was a non-significant trend indicating that competitive employment may be particularly favourable. However, more research is needed regarding potential differences in effect between types of vocational activity.
Heteroatoms doped iron oxide-based catalyst prepared from zinc slag for efficient selective catalytic reduction of NOx with NH3
Authors: Liang Jiale , Zhang Yaojun , Chen Hao , Liu Licai , He Panyang , Wu Lei 
Published: February 2024

AbstractExcessive emissions of nitrogen oxides from flue gas have imposed various detrimental impacts on environment, and the development of deNOx catalysts with low-cost and high performance is an urgent requirement. Iron oxide-based material has been explored for promising deNOx catalysts. However, the unsatisfactory low-temperature activity limits their practical applications. In this study, a series of excellent low-temperature denitrification catalysts (Ha-FeOx/yZS) were prepared by acid treatment of zinc slag, and the mass ratios of Fe to impure ions was regulated by adjusting the acid concentrations. Ha-FeOx/yZS showed high denitrification performance (> 90%) in the range of 180–300 °C, and the optimal NO conversion and N2 selectivity were higher than 95% at 250 °C. Among them, the Ha-FeOx/2ZS synthesized with 2 mol/L HNO3 exhibited the widest temperature window (175–350 °C). The excellent denitrification performance of Ha-FeOx/yZS was mainly attributed to the strong interaction between Fe and impurity ions to inhibit the growth of crystals, making Ha-FeOx/yZS with amorphous structure, nice fine particles, large specific surface area, more surface acid sites and high chemisorbed oxygen. The in-situ DRIFT experiments confirmed that the SCR reaction on the Ha-FeOx/yZS followed both Langmuir-Hinshelwood (L-H) mechanism and Eley-Rideal (E-R) mechanism. The present work proposed a high value-added method for the preparation of cost-effective catalysts from zinc slag, which showed a promising application prospect in NOx removal by selective catalytic reduction with ammonia.
Acquisition activity: do firm age and family control matter?
Authors: López-Delgado Pilar , Diéguez-Soto Julio , Martínez-Romero María J. , Mariño-Garrido Teresa 
Published: February 2024

AbstractThis article examines the relationship between firm age and acquisition activity and how family and non-family firms differ in the number of acquisitions they undertake. Inspired by previous research requiring firm age as a focal aspect and literature studying the antecedents of acquisitions, we draw on the SEW perspective to test our hypotheses based on the analysis of the acquisition activity of Asia-Pacific public firms. Our empirical findings support a U-shaped relationship between firm age and acquisition activity. Moreover, the findings reveal that family firms engage in fewer acquisitions than non-family firms irrespective of the age of the firm.
The ‘Gospel’ according to Vygotsky? Reflections on the role of symbolic mediation in religious education
Authors: Vestøl Jon Magne 
Published: February 2024

AbstractAs perspectives from the Russian psychologist Lev Vygotsky have made a substantial impact in the field of educational sciences, they have generated interest even in the field of religious education. To address some possible implications of Vygotskyan perspectives for religious education, this study focuses on Vygotsky’s notion of symbolic mediation, compared with perspectives from theological publications, interviews with religiously affiliated students as well as curricula and textbooks. Based on a comparison and discussion of these sources, the study argues that Vygotsky’s psychological perspective on symbolic mediation as key to human self-determination is not in conflict with theological and empirical approaches to religious symbols but that the latter display a wider variety of perspectives. It is argued that while religious education may benefit from Vygotskyan perspectives, a reflection is also needed on how to deal with the framing of religious symbols by varying theological positions and conflicting worldviews in different religious education settings.
Genetic characterization of historic Norwegian Erwinia amylovora isolates by SSR-genotyping
Authors: Brurberg May Bente , Elameen Abdelhameed , Sletten Arild , Rossmann Simeon Lim 
Published: February 2024

AbstractErwinia amylovora, the causative agent of fire blight of pome fruits and other rosaceous plants belongs to the group of regulated quarantine pests. The aim of this work was to characterize the populations of E. amylovora in Norway and their geographical distribution. A total of 238 E. amylovora isolates recovered from symptomatic host plants in Norway between 1986 and 2004 were genotyped by means of a short sequence repeat (SSR) marker (ATTACAGA) on plasmid pEa29. The SSR region was amplified and amplicon size determined using fluorescent labelling and rapid, automated capillary gel electrophoresis. All isolates contained the pEa29 plasmid harbouring the investigated marker. In total, ten genotypes were identified, of which two were detected only once. The number of repeats varied from 3 to 13, with 43% of the isolates containing five repeats. Of 17 isolates collected between 1986 and 1991, all but one contained five repeats, whereas more variation was observed in isolates from the period 2000 to 2004. Most of the isolates (80%) originated from Cotoneaster bullatus, hence no relationship between genotype of the isolate and host species that it was isolated from could be detected. This historic data suggests multiple introductions of E. amylovora to Norway.