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This article presents the results of an analysis made to some process models that have been used to introduce autonomous learning in EFL contexts. The purpose of this analysis was to explore the processes these models followed to reach such a goal. This was done because the authors of this paper considered that knowing the steps that were taken to foster autonomous learning may help to create or adapt a process model to implement this approach to learning in other contexts.

Shadow education has remained a supportive role in many countries, and studies have shown that it has had both positive and negative impacts on students’ academic performance. However, the views so far for private tutors have often been neglected by researchers. Private tutors are one of the important stakeholders in shadow education since they are the knowledge providers and facilitators in the classroom. Their opinions can help to show a more realistic picture of shadow education in Hong Kong. In this study, the focus is on investigating Hong Kong shadow education from private tutors’ perspectives. There were 20 private tutors from local private tutorial centres participating in this study, and they were invited to individual interviews to express their ideas about shadow education in Hong Kong. Thematic analysis was used to organize and analyze the data in this study. The results showed that private tutors felt shadow education in Hong Kong is too ‘materialistic,’ and sometimes they felt lost when teaching because of the result-oriented atmosphere in the Hong Kong education system. Furthermore, social inequalities and washback were reported as well. This has further highlighted some of the negative impacts brought by shadow education.

Abstract: SARS-CoV-2 is a disease that endangers both human life and the economy. There was an 11- month period of relative evolutionary standstill following the appearance of SARS-CoV-2 in late 2019. However, the emergence of clusters of mutations known as' variants of concern 'with variable viral properties such as transmissibility and antigenicity defined the evolution of SARS-CoV-2. Several efforts have been made in recent months to understand the atomic level properties of SARS-CoV-2. A review of the literature on SARS-CoV-2 mutations is offered in this paper. The critical activities performed by different domains of the SARS-CoV-2 genome throughout the virus's entry into the host and overall viral life cycle are discussed in detail. These structural traits may potentially pave the way for the development of a vaccine and medication to combat the SARS-CoV-2 sickness.

Background: Intestinal microbiota is the primary target for the multifunctional nature of berberine. Berberine can reverse the structure and composition of gut microbiota under pathological conditions. This study aimed to investigate the effects of berberine on uric acid (UA) metabolism and gut microbiota in a hyperuricemia rat model established using potassium oxonate. Methods: Sprague-Dawley (SD) male rats were divided into a normal control group (n= 10), a hyperuricemia group (n = 12) and a berberine-treated group (n = 11). The UA level in serum, urine and fecal, blood xanthine oxidase (XOD), and urate transports ABCG2 and Galectin-9 in the liver and colon, were evaluated using ELISA kits. The alterations in gut microbiota were investigated using 16S rRNA sequencing. Results: The UA level in the hyperuricemia group was significantly elevated (p Conclusion: Berberine might be a possible therapeutic candidate in hyperuricemia, which could regulate UA metabolism by affecting XOD, and urate transports and partly by regulating gut microbiota.

Background: Indole and its derivatives have a wide range of pharmacological effects, including analgesic, antimicrobial, antidepressant, anti-diabetic, anti-convulsant, anti-helminthic, and anti-inflammatory properties. They are crucial structural components of many of today's powerful antioxidant medications. Objective: Using the Schotten–Baumann reaction, the indole ring was linked to other key heterocyclic moieties such as morpholine, imidazole, piperidine, and piperazine at the active 3rd position and then tested for antioxidant activity. Method: Synthesis of derivatives was accomplished under appropriate conditions and characterized by IR, NMR (1H and 13C), and mass spectrum. Using the Swiss ADME online application, ADME properties were also determined. The in vitro antioxidant activity was measured using DPPH and Reducing power method. Results: In the DPPH assay, compounds 5a (IC50=1.01±0.22 μg/mL), 5k (IC50=1.21±0.07μg/mL), whereas compounds 5a (EC50=23±1.00 μg/mL), 5h (EC50=26±2.42 μg/mL) in the reducing power assay were most potent as compared with standard Ascorbic acid. Compounds 5a, 5h, and 5k demonstrated maximal potency equivalent to standard. Lipinski's rule was followed in ADME outcomes. Conclusion: The synthesis and evaluation of indole derivatives to investigate their antioxidant action has received a lot of attention. These discoveries could lead to more effective antioxidant candidates being designed and developed.

Background: Hypoxia-induced pulmonary hypertension (HPH) represents a severe pulmonary disorder with high morbidity and mortality, which necessitates identifying the critical molecular mechanisms underlying HPH pathogenesis. Method: The mRNA expression microarray GSE15197 (containing 8 pulmonary tissues from HPH and 13 normal controls) was downloaded from Gene Expression Omnibus (GEO). Gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were executed by RStudio software. The Protein-Protein Interaction (PPI) network was visualized and established using Cytoscape, and the cytoHubba app from Cytoscape was used to pick out the hub modules. The infiltration of immune cells in HPH was analyzed using the CIBERSORTx. To confirm the potential hub genes, real-time quantitative reverse transcription PCR (qRT-PCR) was conducted using lung tissues of rat HPH models and controls. Results: A total of 852 upregulated and 547 downregulated genes were identified. The top terms in biological processes were apoptosis, proliferation, and regulation of the MAPK cascade, including ERK1/2. Cytoplasm, cytosol, and membrane were enriched in cellular component groups. Molecular functions mainly focus on protein binding, protein serine/threonine kinase activity and identical protein binding. KEGG analysis identified pathways in cancer, regulation of actin cytoskeleton and rap1 signaling pathway. There was significantly different immune cell infiltration between HPH and normal control samples. High proportions of the memory subsets of B cells and CD4 cells, Macrophages M2 subtype, and resting Dendritic cells were found in HPH samples, while high proportions of naive CD4 cells and resting mast cells were found in normal control samples. The qRTPCR results showed that among the ten identified hub modules, FBXL3, FBXL13 and XCL1 mRNA levels were upregulated, while NEDD4L, NPFFR2 and EDN3 were downregulated in HPH rats compared with control rats. Conclusion: Our study revealed the key genes and the involvement of immune cell infiltration in HPH, thus providing new insight into the pathogenesis of HPH and potential treatment targets for patients with HPH.

Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view. They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named tauJOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for “OWL 2 Web Ontology Language” and JSON stands for “JavaScript Object Notation”). tauJOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for tauJOWL , named tauSQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language—Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.

Background: More than 95% patients with acute promyelocytic leukemia (APL) carry the PML-RARα fusion oncoprotein. Arsenic trioxide (ATO) is an efficacious therapeutic agent for APL, and the mechanism involves the binding with PML and degradation of PML-RARα protein. Pyrrolidine dithiocarbamate (PDTC) demonstrates the function of facilitating the cytotoxic effect of ATO. Purpose: To investigate whether PDTC is potential to enhance the cytotoxic effect of ATO to APL cells by acting on PML-RARα oncoproteins. Methods: Inhibitory effects of drugs on cell viability were examined by CCK-8 test, and apoptosis was evaluated by flow cytometry. Western blotting and immunofluorescence assays were used to explore the mechanism Results: PDTC improved the effect of ATO on inhibiting proliferation of NB4 cells in vitro. Further, PDTC-ATO promoted apoptosis and cell cycle arrest in NB4 cells. The expression of caspase- 3 and Bcl-2 was reduced in PDTC-ATO-treated NB4 cells, while cleaved caspase-3 and Bax was up-regulated. Furthermore, less PML-RARα expression were found in PDTC-ATO-treated NB4 cells than that in NB4 cells treated with ATO singly. Laser confocal microscopy showed that protein colocalization of PML and RARα was disrupted more significantly by PDTC-ATO treatment than that with ATO monotherapy. Conclusions: In conclusion, PDTC enhanced the cytotoxic effect of ATO on APL, and the mechanism was, at least in part, related to the promotion of ATO-induced degradation of PML-RARα fusion protein via forming a complex PDTC-ATO.

Abstract: Fatty liver disease (FLD), which includes both non-alcoholic fatty liver disease (NAFLD) and alcoholic fatty liver disease (ALD), is a worldwide health concern. The etiology of ALD is long-term alcohol consumption, while NAFLD is defined as an abnormal amount of lipid present in liver cells, which is not caused by alcohol intake and has recently been identified as a hepatic manifestation of metabolic syndrome (such as type 2 diabetes, obesity, hypertension, and obesity). Inflammation, oxidative stress, and lipid metabolic dysregulation are all known to play a role in FLD progression. Alternative and natural therapies are desperately needed to treat this disease since existing pharmaceuticals are mostly ineffective. The aldose reductase (AR)/polyol pathway has recently been shown to play a role in developing FLD by contributing to inflammation, oxidative stress, apoptosis, and fat accumulation. Herein, we review the effects of plantderived compounds capable of inhibiting AR in FLD models. Natural AR inhibitors have been found to improve FLD in part by suppressing inflammation, oxidative stress, and steatosis via the regulation of several critical pathways, including the peroxisome proliferator-activated receptor (PPAR) pathway, cytochrome P450 2E1 (CYP2E1) pathway, AMP-activated protein kinase (AMPK) pathway, etc. This review revealed that natural compounds with AR inhibitory effects are a promising class of therapeutic agents for FLD.

International audience; This paper is concerned with analytical developments of results firstly introduced by the authors in [1]. These developments are devoted to the optimization of upper bounds of the interval covariance matrices appearing in the Interval Kalman Filter [2]. The proposed study is mainly highlighted through two aspects. Firstly, the optimization is further performed by considering a class of upper bounds and minimizing the traces of these bounds in two stages (in terms of a gain matrix and then with respect to a scalar parameter). Secondly, the paper provides conditions under which the optimal trace value is controlled and hence the proposed Algorithm in [1], namely Optimal Upper Bound Interval Kalman Filter (OUBIKF), is ensured to perform with stability (i.e. without width explosion of the resulting interval estimators). Also under these conditions, the OUBIKF Algorithm, having a similar structure of the Standard Kalman Filter (SKF), is ensured to get a smaller trace upper bound of the covariance matrices in the correction step than the one in the prediction step. Numerical simulations based on anautomotive model is performed to illustrate the developed results.

3D preoperative planning for high tibial osteotomies (HTO) has increasingly replaced 2D planning but is complex, time-consuming and therefore expensive. Several interdependent clinical objectives and constraints have to be considered, which often requires multiple rounds of revisions between surgeons and biomedical engineers. We therefore developed an automated preoperative planning pipeline, which takes imaging data as an input to generate a ready-to-use, patient-specific planning solution. Deep-learning based segmentation and landmark localization was used to enable the fully automated 3D lower limb deformity assessment. A 2D-3D registration algorithm allowed the transformation of the 3D bone models into the weight-bearing state. Finally, an optimization framework was implemented to generate ready-to use preoperative plannings in a fully automated fashion, using a genetic algorithm to solve the multi-objective optimization (MOO) problem based on several clinical requirements and constraints. The entire pipeline was evaluated on a large clinical dataset of 53 patient cases who previously underwent a medial opening-wedge HTO. The pipeline was used to automatically generate preoperative solutions for these patients. Five experts blindly compared the automatically generated solutions to the previously generated manual plannings. The overall mean rating for the algorithm-generated solutions was better than for the manual solutions. In 90% of all comparisons, they were considered to be equally good or better than the manual solution. The combined use of deep learning approaches, registration methods and MOO can reliably produce ready-to-use preoperative solutions that significantly reduce human workload and related health costs., COMPUTER ASSISTED SURGERY, 28 (1)

Angelman Syndrome (AS) is a severe neurodevelopmental disorder, caused by the neuronal absence of the ubiquitin protein ligase E3A (UBE3A). UBE3A promotes ubiquitin-mediated protein degradation and functions as a transcriptional coregulator of nuclear hormone receptors, including the glucocorticoid receptor (GR). Previous studies showed anxiety-like behavior and hippocampal-dependent memory disturbances in AS mouse models. Hippocampal GR is an important regulator of the stress response and memory formation, and we therefore investigated whether the absence of UBE3A in AS mice disrupted GR signaling in the hippocampus. We first established a strong cortisol-dependent interaction between the GR ligand binding domain and a UBE3A nuclear receptor box in a high-throughput interaction screen. In vivo, we found that UBE3A-deficient AS mice displayed significantly more variation in circulating corticosterone levels throughout the day compared to wildtypes (WT), with low to undetectable levels of corticosterone at the trough of the circadian cycle. Additionally, we observed an enhanced transcriptomic response in the AS hippocampus following acute corticosterone treatment. Surprisingly, chronic corticosterone treatment showed less contrast between AS and WT mice in the hippocampus and liver transcriptomic responses. This suggests that UBE3A limits the acute stimulation of GR signaling, likely as a member of the GR transcriptional complex. Altogether, these data indicate that AS mice are more sensitive to acute glucocorticoid exposure in the brain compared to WT mice. This suggests that stress responsiveness is altered in AS which could lead to anxiety symptoms.

Cancer cells may acquire resistance to stress signals and reprogram metabolism to meet the energetic demands to support their high proliferation rate and avoid death. Hence, targeting nutrient dependencies of cancer cells has been suggested as a promising anti-cancer strategy. We explored the possibility of killing breast cancer (BC) cells by modifying nutrient availability. We used in vitro models of BC (MCF7 and MDA-MB-231) that were maintained with a low amount of sulfur amino acids (SAAs) and a high amount of oxidizable polyunsatured fatty acids (PUFAs). Treatment with anti-apoptotic, anti-ferroptotic and antioxidant drugs were used to determine the modality of cell death. We reproduced these conditions in vivo by feeding BC-bearing mice with a diet poor in proteins and SAAs and rich in PUFAs (LSAA/HPUFA). Western blot analysis, qPCR and histological analyses were used to assess the anti-cancer effects and the molecular pathways involved. We found that BC cells underwent oxidative damage to DNA and proteins and both apoptosis and ferroptosis were induced. Along with caspases-mediated PARP1 cleavage, we found a lowering of the GSH-GPX4 system and an increase of lipid peroxides. A LSAA/HPUFA diet reduced tumor mass and its vascularization and immune cell infiltration, and induced apoptosis and ferroptotic hallmarks. Furthermore, mitochondrial mass was found to be increased, and the buffering of mitochondrial reactive oxygen species limited GPX4 reduction and DNA damage. Our results suggest that administration of custom diets, targeting the dependency of cancer cells on certain nutrients, can represent a promising complementary option for anti-cancer therapy.

(1) Background: Metal-on-metal (MoM) total hip arthroplasty (THA) and hip resurfacingarthroplasty (HRA) was presumed to provide superior functional outcomes compared to metal-onpolyethylene (MoP) THA. (2) Methods: We compared muscle mass, power, step test asymmetry, andpatient-reported outcomes between MoM THA/HRA and MoP THA. A total of 51 MoM THA/HRAsand 23 MoP THAs participated in the cross-sectional study at a mean of 6.5 (2.4–12.5) years postoperatively. Muscle mass was measured by Dual energy X-ray Absorption (DXA) scans and musclepower in a Leg Extensor Power Rig. Step test asymmetry was obtained with an Inertial MeasurementUnit (IMU). The patients completed the Harris Hip Score (HHS) and the Copenhagen Hip and GroinOutcome Score (HAGOS). (3) Results: The MoM THA/HRA group had a greater inter-limb differencein hip muscle mass compared to the MoP THA group (p = 0.02). Other inter-limb differences inmuscle mass and power were similar (p > 0.05). Muscle mass of the thigh and calf area and musclepower in both legs were higher in MoM THA/HRA compared to MoP THA (p < 0.009). Step test timeasymmetry when ascending was lower in MoM THA/HRA compared to MoP THA (p = 0.03). HHSand HAGOS scores were similar between groups (p > 0.05). (4) Conclusion: Overall, we could notverify the hypothesis that MoM THA/HRA contributes to superior functional outcomes compared toMoP THA.

Covalent inhibition has emerged as a promising orthogonal approach for drug discovery, despite the significant challenge in achieving target specificity. To facilitate the structure-based rational design of target-specific covalent modulators, we developed an integrated computational protocol to curate covalent binders from the RCSB Protein Data Bank (PDB). Starting from the macromolecular crystallographic information files (mmCIF) in the PDB archive, covalent bond records, which indicate the side chain modification of amino acid residue by a covalent binder, were collected and cleaned. Then, residue-binder adducts, which are products of chemical reactions between targeted residues and covalent binders, were recovered with the help of the Chemical Component Dictionary in PDB. Finally, several strategies were employed to curate the pre-reaction forms of covalent binders from the adducts. Our curated CovBinderInPDB database contains 7375 covalent modifications in which 2189 unique covalent binders target nine types of amino acid residues (Cys, Lys, Ser, Asp, Glu, His, Met, Thr, and Tyr) from 3555 complex structures of 1170 unique protein chains. This database would set a solid foundation for developing and benchmarking computational strategies for covalent modulator design and is freely accessible at https://yzhang.hpc.nyu.edu/CovBinderInPDB.

Protein-ligand binding free-energy calculations using molecular dynamics (MD) simulations have emerged as a powerful tool for in silico drug design. Here, we present results obtained with the ARROW force field (FF)─a multipolar polarizable and physics-based model with all parameters fitted entirely to high-level ab initio quantum mechanical (QM) calculations. ARROW has already proven its ability to determine solvation free energy of arbitrary neutral compounds with unprecedented accuracy. The ARROW FF parameterization is now extended to include coverage of all amino acids including charged groups, allowing molecular simulations of a series of protein-ligand systems and prediction of their relative binding free energies. We ensure adequate sampling by applying a novel technique that is based on coupling the Hamiltonian Replica exchange (HREX) with a conformation reservoir generated via potential softening and nonequilibrium MD. ARROW provides predictions with near chemical accuracy (mean absolute error of ∼0.5 kcal/mol) for two of the three protein systems studied here (MCL1 and Thrombin). The third protein system (CDK2) reveals the difficulty in accurately describing dimer interaction energies involving polar and charged species. Overall, for all of the three protein systems studied here, ARROW FF predicts relative binding free energies of ligands with a similar accuracy level as leading nonpolarizable force fields.

Rita, P., António, N., & Afonso, A. P. (2023). Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period. Social Network Analysis and Mining, 13(1), 1-16. [46]. https://doi.org/10.1007/s13278-023-01048-1 --- Funding: Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. In a time where social media is fundamental for any political campaign and to share a message with an electoral audience, this study searches for a conclusion of the actual persuasion capacity of social media in the electors when they need to decide whom to vote for as their next government. For this, it compares the sentiment that Social Media users demonstrated during an electoral period with the actual results of those elections. For this analysis, it was used, as a case study, tweets mentioning the two major English parties, Conservative and Labor, their respective candidates for the position of prime minister, and terms that identified their political campaign during the electoral period of the General Elections of the United Kingdom that occurred on December 12, 2019. Data were collected using R. The treatment and analysis were done with R and RapidMiner. Results show that tweets’ sentiment is not a reliable election results predictor. Additionally, results also show that it is impossible to state that social media impacts voting decisions. At least not from the polarity of the sentiment of opinions on social media. publishersversion epub_ahead_of_print

We examine investor behavior on social media platforms related to the GameStop (GME) short squeeze in early 2021. Individual investors stimulated the stock market via Reddit social posts in the presence of institutional investors who bet against GME’s success as short sellers. We analyzed r/WallStreetBets subreddit posts related to GME’s trading patterns. We performed text-based sentiment analysis and compared the social informedness of posting users for GME trading on two social media platforms. The short squeeze occurred due to coordinated trading by individual investors, who discussed trading strategies on the platforms and drove collective social informedness-based trading behavior. Our findings suggest that the valence and number of submissions influenced GME’s intraday transaction volumes and precursors for irrational trading behavior patterns to have emerged. We provide a theoretical interpretation of what occurred and call for tighter monitoring of social news platforms. We also encourage effort to create an in-depth understanding of the observed patterns and the linkages between them and the larger equity markets.

Background To evaluate the effects of a shared decision making (SDM) intervention for older adults with multiple chronic conditions (MCCs). Methods A pragmatic trial evaluated the effects of the SDMMCC intervention, existing of SDM training for nine geriatricians in two hospitals and a preparatory tool for patients. A prospective pre-intervention post-intervention multi-center clinical study was conducted in which an usual care group of older patients with MCC and their informal caregivers was included before the implementation of the intervention and a new cohort of patients and informal caregivers after the implementation of the intervention. SDM was observed using the OPTIONMCC during video-recorded consultations. Patient- and caregivers reported outcomes regarding their role in SDM, involvement, perceived SDM and decisional conflict were measured. The differences between groups regarding the level of observed SDM (OPTIONMCC) were analyzed with a mixed model analysis. Dichotomous patient-reported outcomes were analyzed with a logistic mixed model. Results From two outpatient geriatric clinics 216 patients with MCCs participated. The mean age was 77.3 years, and 56.3% of patients were female. No significant difference was found in the overall level of SDM as measured with the OPTIONMCC or in patient-reported outcomes. However, at item level the items discussing ‘goals’, ‘options’, and ‘decision making’ significantly improved after the intervention. The items discussing ‘partnership’ and ‘evaluating the decision-making process’ showed a significant decrease. Fifty-two percent of the patients completed the preparatory tool, but the results were only discussed in 12% of the consultations. Conclusion This study provides scope for improvement of SDM in geriatrics. Engaging older adults with MCCs and informal caregivers in the decision making process should be an essential part of SDM training for geriatricians, beyond the SDM steps of explaining options, benefits and harms. More attention should be paid to the integration of preparatory work in the consultation.

This study sought to develop and evaluate an online module based on inquiry learning with digital laboratories, which was intended to address students’ misconceptions in a science domain. In a quasi-experimental design, 171 first-year students in a higher education introductory physics course on circular motion were as their existing groups assigned to an experimental (n = 100) or a control (n = 71) condition. The experimental condition was developed by arranging online inquiry activities that would encourage students to probe five identified misconceptions. The control condition required students to engage in online inquiry following the traditional syllabus outline. Students in both conditions used the same type of digital laboratory setup. The participants learned about the topic of circular motion and their knowledge was assessed. Results of the knowledge test revealed that the experimental condition geared towards addressing students’ misconceptions facilitated conceptual change more than the control condition.

The original version of this Article contained an error in the spelling of the author Krzysztof Hanusz, which was incorrectly given as Hanusz Krzysztof. This has now been corrected in both the PDF and HTML versions of the Article.

Background The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases. Models tailored towards such cases can help shorten the gap of missing diagnoses and can aid researchers in dealing with the high complexity of the derived data. The predictor VarCoPP (Variant Combinations Pathogenicity Predictor) that was published in 2019 and identified potentially pathogenic variant combinations in gene pairs (bilocus variant combinations), was the first important step in this direction. Despite its usefulness and applicability, several issues still remained that hindered a better performance, such as its False Positive (FP) rate, the quality of its training set and its complex architecture. Results We present VarCoPP2.0: the successor of VarCoPP that is a simplified, faster and more accurate predictive model identifying potentially pathogenic bilocus variant combinations. Results from cross-validation and on independent data sets reveal that VarCoPP2.0 has improved in terms of both sensitivity (95% in cross-validation and 98% during testing) and specificity (5% FP rate). At the same time, its running time shows a significant 150-fold decrease due to the selection of a simpler Balanced Random Forest model. Its positive training set now consists of variant combinations that are more confidently linked with evidence of pathogenicity, based on the confidence scores present in OLIDA, the Oligogenic Diseases Database (https://olida.ibsquare.be). The improvement of its performance is also attributed to a more careful selection of up-to-date features identified via an original wrapper method. We show that the combination of different variant and gene pair features together is important for predictions, highlighting the usefulness of integrating biological information at different levels. Conclusions Through its improved performance and faster execution time, VarCoPP2.0 enables a more accurate analysis of larger data sets linked to oligogenic diseases. Users can access the ORVAL platform (https://orval.ibsquare.be) to apply VarCoPP2.0 on their data.

Introduction The Semantic Web community provides a common Resource Description Framework (RDF) that allows representation of resources such that they can be linked. To maximize the potential of linked data - machine-actionable interlinked resources on the Web - a certain level of quality of RDF resources should be established, particularly in the biomedical domain in which concepts are complex and high-quality biomedical ontologies are in high demand. However, it is unclear which quality metrics for RDF resources exist that can be automated, which is required given the multitude of RDF resources. Therefore, we aim to determine these metrics and demonstrate an automated approach to assess such metrics of RDF resources. Methods An initial set of metrics are identified through literature, standards, and existing tooling. Of these, metrics are selected that fulfil these criteria: (1) objective; (2) automatable; and (3) foundational. Selected metrics are represented in RDF and semantically aligned to existing standards. These metrics are then implemented in an open-source tool. To demonstrate the tool, eight commonly used RDF resources were assessed, including data models in the healthcare domain (HL7 RIM, HL7 FHIR, CDISC CDASH), ontologies (DCT, SIO, FOAF, ORDO), and a metadata profile (GRDDL). Results Six objective metrics are identified in 3 categories: Resolvability (1), Parsability (1), and Consistency (4), and represented in RDF. The tool demonstrates that these metrics can be automated, and application in the healthcare domain shows non-resolvable URIs (ranging from 0.3% to 97%) among all eight resources and undefined URIs in HL7 RIM, and FHIR. In the tested resources no errors were found for parsability and the other three consistency metrics for correct usage of classes and properties. Conclusion We extracted six objective and automatable metrics from literature, as the foundational quality requirements of RDF resources to maximize the potential of linked data. Automated tooling to assess resources has shown to be effective to identify quality issues that must be avoided. This approach can be expanded to incorporate more automatable metrics so as to reflect additional quality dimensions with the assessment tool implementing more metrics.

Funder: Amazon Web Services (AWS) Imagine Grant, Funder: Kingston University (Kingston University, London); doi: https://doi.org/10.13039/100010049, Funder: Association Nationale de la Recherche et de la Technologie (National Association for Research and Technology); doi: https://doi.org/10.13039/501100003032, Funder: Association Nationale de la Recherche Scientifique and Pacifica (CIFRE grant 2017/0245), Funder: PRIMUS/20/HUM/009, Funder: UID/PSI/03125/2019 from the Portuguese National Foundation for Science and Technology (FCT)., Funder: PSA research grant ($285.59) for the PSACR projects data collection, Funder: The work of Dmitrii Dubrov was supported within the framework of the Basic Research Program at HSE University, RF, Funder: Agentúra na podporu výskumu a vývoja (Slovak Research and Development Agency) - APVV-17-0596, Funder: Progres Q18, Charles University, Funder: JSPS KAKENHI Grant Numbers JP18K12015 and JP20H04581, Funder: National Science Centre, Poland (2019/35/B/HS6/00528), Funder: Slovak Research and Development Agency - APVV-17-0596, Funder: Huo Family Foundation, Funder: The Japan Society for the Promotion of Science KAKENHI [19K14370], Funder: The Institute of Psychology, Jagiellonian University, Funder: Program FUTURE LEADER of Lorraine Université d’Excellence within the program Investissements Avenir (ANR-15-IDEX-04-LUE) operated by the French National Research Agency, Funder: Rubicon grant (019.183SG.007) from the Netherlands Organization for Scientific Research (NWO), Funder: Australian Research Council (DP180102384), Funder: HSE University Basic Research Program, Funder: Horizon 2020 grant 964728 (JITSUVAX) from the European Commission and was supported by a United Kingdom Research and Innovation (UKRI) Research Fellowship grant ES/V011901/1, Funder: Social Science and Humanities Research Council of Canada, Funder: Dominican University Faculty Support Grant, Funder: FONDECYT 1221538, Funder: Vicerrectoria de Investigaciones, Uniandes, Funder: Statutory funds of the Institute of Psychology, University of Wroclaw, Funder: University of Desarrollo, Faculty of Psychology, Funder: IDN Being Human Lab (University of Wrocław), Funder: ANID - Fondecyt 1201513, In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data.

Funding Information: This work is supported by National Medical Research Council Individual Research Grant (NMRC/0975/2005), National Medical Research Council Center Grant (NMRC/CG/C010A/2017_SERI) and Nurturing Clinician Researcher Scheme Program Grant Award (05/FY2021/P2/11-A92). Publisher Copyright: © 2023, The Author(s). Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6–12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms – image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ −6.00 diopter) during teenage years (5 years later, age 11–17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93–0.95; Test dataset 0.91–0.93), clinical models (Primary dataset AUC 0.90–0.97; Test dataset 0.93–0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97–0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify “at-risk” children for early intervention. publishersversion published

Grisaille was the first paint applied on stained-glass panels, used in Europe since the twelfth century. Historical written sources described the use of iron and copper together with a high lead-silica base glass in the grisailles production. This project aims to study the evolution of the grisaille paint composition throughout time and the changes in the raw materials used in their production using non-destructive and non-invasive techniques. To achieve this objective, 23 grisaille samples dated from the 13th to the twentieth centuries from nine different countries (Portugal, Poland, United Kingdom, Sweden, Norway, Belgium, Low Countries, Germany, and France) were studied by means of micro particle-induced X-ray emission (μ-PIXE), micro energy dispersive X-ray fluorescence (μ-EDXRF), laser-induced breakdown spectroscopy (LIBS), laser-induced fluorescence (LIF), non-linear optical microscopy (NLOM) in the modality of multiphoton excitation fluorescence (MPEF) and optical microscopy (OM). The results showed that it was possible to identify compositional differences and patterns throughout the samples when compared with literature results. The preference for using copper in central and south-central European countries and the addition of new compounds (CoO, Cr2O3, MnO) as colouring agents since the nineteenth century was verified. The LIBS analyses allow the identification of boron on two samples, confirming the change of base glass components since the seventeenth century. The NLOM-MPEF showed the capability of this technique to measure the grisaille paint layers’ thickness. This non-invasive multi-analytical and complementary approach proves itself efficient in identifying changes in the grisaille’s composition throughout time, which can be interpreted as changes in the raw materials and manufacture used in the production of these paint materials., This research has been funded by the H2020 European project IPERION HS (Integrated Platform for the European Research Infrastructure ON Heritage Science, GA 871034), by the Spanish State Research Agency (AEI) through project PID2019-104124RB-I00/AEI/, by the Fundação de Ciência e Tecnologia de Portugal (project ref. UIDB/EAT/00729/2020, UIDP/00729/2020, LA/P/0008/2020, UIDB/ 04349/2020 and PTDC/ART-PER/1702/2021, researcher grant CEECIND/02249/2021 and doctoral grant ref. PD/BD/136673/2018) and by project TOP Heritage-CM (S2018/NMT-4372) from Community of Madrid.

International audience

Explainable artificial intelligence (XAI) is an important advance in the field of machine learning to shed light on black box algorithms and thus a promising approach to improving artificial intelligence (AI) adoption. While previous literature has already addressed the technological benefits of XAI, there has been little research on XAI from the user’s perspective. Building upon the theory of trust, we propose a model that hypothesizes that post hoc explainability (using Shapley Additive Explanations) has a significant impact on use-related variables in this context. To test our model, we designed an experiment using a randomized controlled trial design where participants compare signatures and detect forged signatures. Surprisingly, our study shows that XAI only has a small but significant impact on perceived explainability. Nevertheless, we demonstrate that a high level of perceived explainability has a strong impact on important constructs including trust and perceived usefulness. A post hoc analysis shows that hedonic factors are significantly related to perceived explainability and require more attention in future research. We conclude with important directions for academia and for organizations.

Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner—a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods’ accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models’ predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients’ groups based on RNA-seq data.

Funding Information: This research was funded by the Portuguese Science Foundation [Fundação para a Ciência e Tecnologia, Ministério da Educação e da Ciência (FCT/MCTES)], through doctoral programme CORES-PD/00253/2012, and PhD Grants awarded to Márcia Vieira [SFRH/BD/148729/2019] and Hermine Grigoryan [PD/BD/142886/2018]; Project STEMMA (“From singing to writing—survey on material production and routes of Galician-Portuguese Lyric”, PTDC/LLT-EGL/30984/2017); Polyphenols in Art—Chemistry and biology hand in hand with conservation of cultural heritage, PTDC/QUI-OUT/29925/2017, as well as "Pruevalo e veras ques çierto. Recetas y conocimientos de la sociedad medieval para el siglo XXI | "Try it and you will see thats true. Recipes and konwledges from medieval society to 21th century", PID2019-108736 GB-I00, Ministerio de Ciencia e Innovación. N. Teixeira and V. Otero acknowledge FCT for CEECIND/00025/2018/CP1545/CT0009 and 2020.00647.CEECIND, respectively; R. J. Díaz Hidalgo, posdoctoral UCO 2020, La producción documental y libraría al Ándalus siglos XIII XV, Plan Propio de Investigación de la Universidad de Córdoba; Associate Laboratory for Green Chemistry- LAQV financed by FCT/MCTES (UID/QUI/50006/2019 and UIDB/50006/2020) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01–0145-FEDER-007265). Publisher Copyright: © 2023, The Author(s). For the first time, this paper systematises the medieval preparation of black writing inks found in the important thirteenth century Andalusian technical treatise written by Muhammad ibn Idrīs ibn al-Qalalūsī (1210–1308). We present the Arabic version of this extraordinary text (‘The gifts of the wise men on the curiosities of the substances’), and its first English translation, as well as discuss key aspects of the processes that remain missing or are unclear indications. In this work, we studied the iron gall inks based on galls, where no other phenolic source is present. In this pedagogical treatise, the recipes for these black iron-gall inks are organised and classified by the gallnuts extraction method used: boiling (decoction), squeezing and infusion, with water being the only solvent used. The inks selected were reproduced and characterised through a multi-analytical approach. Quantification was performed by HPLC–DAD (high performance liquid chromatography with diode array detectors in the UV–VIS), showing that gallic acid is a minor compound in the gall extracts prepared following al-Qalalūsī instructions. In all the recipes, the higher concentration compounds in the gall extracts are the gallotannins pentagalloylglucose and hexagalloylglucose, ranging from 79 to 50% of the phenolic compounds. This supports the results of Raman and infrared spectroscopies. A comparison with medieval Iberian recipes was also done, which served to reinforce our previous results that show water as the sole solvent extracts with much lower yields than mixed solvents (water plus white wine or vinegar). publishersversion published

The increased popularity of flipped classroom in higher education warrants more thorough investigation of the pedagogical format’s effects on student learning. This paper utilizes two iterations of a randomized field experiment to study the effects of flipped classroom on student learning specifically focusing on heterogeneous treatment effects across the important classroom-level factor of teachers. The empirical setting is an undergraduate macroeconomics course with 933 students and 11 teachers. Our findings show a positive yet insignificant average effect of flipped classroom on both pass rate and final exam grades. We further find substantial shifts in the ranking of the participating teachers’ effectiveness when comparing traditional and flipped classroom conditions, which suggests that the most successful teacher in a traditional teaching environment is not necessarily the most successful teacher in a flipped classroom environment.

Graph matching, or the determination of the vertex correspondences between a pair of graphs, is a crucial task in various problems in different science and engineering disciplines. This article aims to propose a distributed optimization approach for graph matching (GM) between two isomorphic graphs over multiagent networks. For this, we first show that for a class of asymmetric graphs, GM of two isomorphic graphs is equivalent to a convex relaxation where the set of permutation matrices is replaced by the set of pseudostochastic matrices. Then, we formulate GM as a distributed convex optimization problem with equality constraints and a set constraint, over a network of multiple agents. For arbitrary labelings of the vertices, each agent only has information about just one vertex and its neighborhood, and can exchange information with its neighbors. A projected primal-dual gradient method is developed to solve the constrained optimization problem, and globally exponential convergence of the agents' states to the optimal permutation is achieved. Finally, we illustrate the effectiveness of the algorithm through simulation examples.

Abstract: One of the most dynamic approaches in biotechnology is reverse vaccinology, which plays a huge role in today’s developing vaccines. It has the capability of exploring and identifying the most potent vaccine candidate in a limited period of time. The first successful novel approach of reverse vaccinology was observed in Neisseria meningitidis serogroup B, which has revolutionised the whole field of computational biology. In this review, we have summarized the application of reverse vaccinology for different infectious diseases, discussed epitope prediction and various available bioinformatic tools, and explored the advantages, limitations and necessary elements of this approach. Some of the modifications in the reverse vaccinology approach, like pan-genome and comparative reverse vaccinology, are also outlined. Vaccines for illnesses like AIDS and hepatitis C have not yet been developed. Computer Aided Reverse vaccinology has the potential to be a game-changer in this area. The use of computational tools, pipelines and advanced soft-computing methods, such as artificial intelligence and deep learning, and exploitation of available omics data in integration have paved the way for speedy and effective vaccine designing. Is reverse vaccinology a viable option for developing vaccines against such infections, or is it a myth? Vaccine development gained momentum after the spread of various infections, resulting in numerous deaths; these vaccines are developed using the traditional technique, which includes inactivated microorganisms. As a result, reverse vaccinology may be a far superior technique for creating an effective vaccine.

Background: Abnormal epigenetic alterations influenced by external factors and affecting DNA expression contribute to the development of asthma. However, the role of nasal epithelium in airway inflammation remains unknown. Objective: The objective of this study is to identify novel DNA promoter hypermethylation, which completely suppresses mRNA expression in nasal epithelial of asthma. Methods: Microarray datasets were downloaded from the Gene Expression Omnibus [GEO] database. Gene expression and DNA promoter methylation sites in key correlated modules between asthma and normal were identified by weighted gene co-expression network analysis [WGCNA]. Gene Ontology and KEGG were conducted to analyse the function of genes. Further validation was performed in human BEAS-2B cells challenged by IL-4 or IL-13. Results: Lightcyan, lightgreen, midnightblue, cyan and tan modules in mRNA expression dataset showed a close relationship with asthma, in which genes were enriched in TNF, IL-17, ErbB, MAPK and Estrogen signalling pathways. Blue and turquoise modules in methylation profiling dataset were associated with asthma. 49 lowly expressed genes were identified to be correlated with aberrant DNA hypermethylation of promoters. Among these genes, the mRNA levels of BCL10, GADD45B, LSR and SQSTM1 were downregulated in BEAS-2B cells challenged with IL-4 or IL-13. Conclusion: Four potential genes in nasal epithelium, by hypermethylating their own DNA promoter, might mediate the inflammatory response in the pathogenesis of asthma. Analyzing epigenomic data by integrated bioinformatics helps to understand the role of DNA methylation in asthma, with the goal of providing new perspectives for diagnosis and therapy.

Background: Intervertebral Disc Degeneration (IDD) is a heterogeneous spinal disease whose underlying molecular mechanism is unclear. Objectives: This study aimed to identify, profile, and analyze microRNAs (miRNAs) related to IDD. Method: Microarray Gene Expression IDD data (GSE63492) were downloaded from Gene Expression Omnibus datasets. We employed Weighted Gene Co-Expression Network Analysis (WGCNA) to construct a miRNA co-expression network, and the miRNAs related to the IDD stage were detected. The number of differentially expressed miRNAs between normal and degenerated nucleus pulposus tissues was calculated. Twenty-three clinical specimens were used to validate the expression of miRNAs using qRT-PCR. Results: WGCNA identified 48 miRNAs significantly related to the IDD stage, and 94 miRNAs that were significantly different between normal and degenerated nucleus pulposus tissues. We selected 32 overlapping miRNAs and identified 347 corresponding target genes. The integrative analysis revealed the biological function and pathways of these targeted genes. Analysis of clinical specimens validated that hsa-miR-4534 was upregulated in IDD, whereas hsa-miR-1827 and hsa-miR- 185-5p were downregulated in IDD. Conclusion: This study has identified a subset of miRNAs that are related to IDD pathogenesis and hub miRNAs that are keys to the IDD co-expression network, which may potentially be utilized as indicators for treatment.