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Background: As genomes of many eukaryotic species, especially plants, are large and complex, their de novo sequencing and assembly is still a difficult task despite progress in sequencing technologies. An alternative to genome assembly is the assembly of transcriptome, the set of RNA products of the expressed genes. While a bunch of de novo transcriptome assemblers exists, the challenges of transcriptomes (the existence of isoforms, the uneven expression levels across genes) complicates the generation of high-quality assemblies suitable for downstream analyses. Results: We developed Trans2express – a web-based tool and a pipeline of de novo hybrid transcriptome assembly and postprocessing based on rnaSPAdes with a set of subsequent filtrations. The pipeline was tested on Arabidopsis thaliana cDNA sequencing data obtained using Illumina and Oxford Nanopore Technologies platforms and three non-model plant species. The comparison of structural characteristics of the transcriptome assembly with reference Arabidopsis genome revealed the high quality of assembled transcriptome with 86.1% of Arabidopsis expressed genes assembled as a single contig. We tested the applicability of the transcriptome assembly for gene expression analysis. For both Arabidopsis and non-model species the results showed high congruence of gene expression levels and sets of differentially expressed genes between analyses based on genome and based on the transcriptome assembly. Conclusions: We present Trans2express – a protocol for de novo hybrid transcriptome assembly aimed at recovering of a single transcript per gene. We expect this protocol to promote the characterization of transcriptomes and gene expression analysis in non-model plants and web-based tool to be of use to a wide range of plant biologists. [ABSTRACT FROM AUTHOR]

Type I polyketide synthases (T1PKSs) hold enormous potential as a rational production platform for the biosynthesis of specialty chemicals. However, despite great progress in this field, the heterologous expression of PKSs remains a major challenge. One of the first measures to improve heterologous gene expression can be codon optimization. Although controversial, choosing the wrong codon optimization strategy can have detrimental effects on the protein and product levels. In this study, we analyzed 11 different codon variants of an engineered T1PKS and investigated in a systematic approach their influence on heterologous expression in Corynebacterium glutamicum, Escherichia coli, and Pseudomonas putida. Our best performing codon variants exhibited a minimum 50-fold increase in PKS protein levels, which also enabled the production of an unnatural polyketide in each of these hosts. Furthermore, we developed a free online tool (https://basebuddy.lbl.gov) that offers transparent and highly customizable codon optimization with up-to-date codon usage tables. In this work, we not only highlight the significance of codon optimization but also establish the groundwork for the high-throughput assembly and characterization of PKS pathways in alternative hosts.

Abstract Background As genomes of many eukaryotic species, especially plants, are large and complex, their de novo sequencing and assembly is still a difficult task despite progress in sequencing technologies. An alternative to genome assembly is the assembly of transcriptome, the set of RNA products of the expressed genes. While a bunch of de novo transcriptome assemblers exists, the challenges of transcriptomes (the existence of isoforms, the uneven expression levels across genes) complicates the generation of high-quality assemblies suitable for downstream analyses. Results We developed Trans2express – a web-based tool and a pipeline of de novo hybrid transcriptome assembly and postprocessing based on rnaSPAdes with a set of subsequent filtrations. The pipeline was tested on Arabidopsis thaliana cDNA sequencing data obtained using Illumina and Oxford Nanopore Technologies platforms and three non-model plant species. The comparison of structural characteristics of the transcriptome assembly with reference Arabidopsis genome revealed the high quality of assembled transcriptome with 86.1% of Arabidopsis expressed genes assembled as a single contig. We tested the applicability of the transcriptome assembly for gene expression analysis. For both Arabidopsis and non-model species the results showed high congruence of gene expression levels and sets of differentially expressed genes between analyses based on genome and based on the transcriptome assembly. Conclusions We present Trans2express – a protocol for de novo hybrid transcriptome assembly aimed at recovering of a single transcript per gene. We expect this protocol to promote the characterization of transcriptomes and gene expression analysis in non-model plants and web-based tool to be of use to a wide range of plant biologists.

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

Introduction: Pulsed Field Ablation (PFA) is a novel non-thermal method for cardiac ablation, relying on irreversible electroporation induced by high-energy pulsed electric fields (PEFs) to create localized lesions in the heart atria. A significant challenge in optimizing PFA treatments is determining the lethal electric field threshold (EFT), which governs ablation volume and varies with PEF waveform parameters. However, the proprietary nature of device developer’s waveform characteristics and the lack of standardized nonclinical testing methods have left optimal EFTs for cardiac ablation uncertain. Methods: To address this gap, we introduced a laboratory protocol employing human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in monolayer format to evaluate the impact of a range of clinically relevant biphasic pulse parameters on lethal EFT and adiabatic heating (AH). Cell death areas were assessed using fluorescent dyes and confocal microscopy, while lethal EFTs were quantified through comparison with electric field numerical simulations. Results and conclusion: Our study confirmed a strong correlation between cell death in hiPSC-CMs and the number and duration of pulses in each train, with pulse repetition frequency exerting a comparatively weaker influence. Fitting of these results through machine learning algorithms were used to develop an open-source online calculator. By estimating lethal EFT and associated temperature increases for diverse pulse parameter combinations, this tool, once validated, has the potential to significantly reduce reliance on animal models during early-stage device de-risking and performance assessment. This tool also offers a promising avenue for advancing PFA technology for cardiac ablation medical devices to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

The EUSCREEN project concerns the study of European vision and hearing screening programmes. Part of the project was the development of a cost-effectiveness model to analyse such programmes. We describe the development and usability of an online tool to enable stakeholders to design, analyse or modify a newborn hearing screening (NHS) programme. Data from literature, from existing NHS programmes, and observations by users were used to develop and refine the tool. Required inputs include prevalence of the hearing impairment, test sequence and its timing, attendance, sensitivity, and specificity of each screening step. Outputs include the number of cases detected and the costs of screening and diagnostics. Eleven NHS programmes with reliable data. Three analyses are presented, exploring the effect of low attendance, number of screening steps, testing in the maternity ward, or screening at a later age, on the benefits and costs of the programme. Knowledge of the epidemiology of a staged screening programme is crucial when using the tool. This study presents a tool intended to aid stakeholders to design a new or analyse an existing hearing screening programme in terms of benefits and costs. [ABSTRACT FROM AUTHOR]

Abstract Freshwater ecosystems are complex, diverse, and face multiple imminent threats that have led to changes in both structure and function. It is urgent that we develop and standardize monitoring tools that allow for rapid and comprehensive assessment of freshwater communities to understand their changing dynamics and inform conservation. Environmental DNA surveys offer a means to inventory and monitor aquatic diversity, yet most studies focus on one or a few taxonomic groups because of technical challenges. In this study, we (1) create an eDNA metabarcoding dataset (natural water bodies) with 14 validated primer pairs; (2) create a free online, user‐friendly tool for primer selection that can be used for any metabarcoding data (SNIPe); and (3) using SNIPe, explore our dataset to derive subsets of informative, cost‐effective primer pairs that maximize detection of freshwater diversity. We first evaluated the completeness of public reference sequence databases and the efficiency of 14 primer pairs in silico, in vitro on five mock communities (mix of DNA from tissues of select taxa), in vivo on water samples from aquarium samples with known taxonomic composition, and finally in vivo on water samples from freshwater systems in Eastern Canada. Results from analyses using SNIPe revealed that 13 or 14 primer pairs are necessary to recover 100% of the species in water samples (natural systems), but that four primer pairs are sufficient to recover almost 75% of taxa with little overlap. Our work highlights the power of eDNA metabarcoding for reconstructing freshwater communities, including prey, parasite, pathogen, invasive, and declining species. It also emphasizes the importance of marker choice on species resolution, and primer characteristics and filtering parameters on detection success and accuracy of biodiversity estimates. Together, these results highlight the usefulness of eDNA for freshwater monitoring and should prompt more studies of tools to survey all communities.

Abstract Background The burden of lumbar degenerative diseases (LDD) has increased substantially with the unprecedented aging population. Identifying elderly patients with high risk of postoperative adverse events (AEs) and establishing individualized perioperative management is critical to mitigate added costs and optimize cost-effectiveness to the healthcare system. We aimed to develop a predictive tool for AEs in elderly patients with transforaminal lumbar interbody fusion (TLIF), utilizing multivariate logistic regression, single classification and regression tree (hereafter, “classification tree”), and random forest machine learning algorithms. Methods This study was a retrospective review of a prospective Geriatric Lumbar Disease Database (age ≥ 65). Our outcome measure was postoperative AEs, including prolonged hospital stays, postoperative complications, readmission, and reoperation within 90 days. Patients were grouped as either having at least one adverse event (AEs group) or not (No-AEs group). Three models for predicting postoperative AEs were developed using training dataset and internal validation using testing dataset. Finally, online tool was developed to assess its validity in the clinical setting (external validation). Results The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97 [55.4%] female). The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.72 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. A nomogram based on logistic regression was developed, and the C-index of external validation for AEs was 0.69 (95% CI 0.65–0.76). Conclusion The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our nomogram and online tool ( https://xuanwumodel.shinyapps.io/Model_for_AEs/ ) could inform physicians about elderly patients with a high risk of AEs within the 90 days after TLIF surgery.

Purpose: This study aimed to develop a predictive model for prolonged length of hospital stay (pLOS) in elderly patients undergoing lumbar fusion surgery, utilizing multivariate logistic regression, single classification and regression tree (hereafter, "classification tree") and random forest machine-learning algorithms. Methods: This study was a retrospective review of a prospective Geriatric Lumbar Disease Database. The primary outcome measure was pLOS, which was defined as the LOS greater than the 75th percentile. All patients were grouped as pLOS group and non-pLOS. Three models (including logistic regression, single-classification tree and random forest algorithms) for predicting pLOS were developed using training dataset and internal validation using testing dataset. Finally, online tool based on our model was developed to assess its validity in the clinical setting (external validation). Results: The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97[55.4%] female). Multivariate logistic analyses revealed that older age (odds ratio [OR] 1.06, p < 0.001), higher BMI (OR 1.08, p = 0.002), number of fused segments (OR 1.41, p < 0.001), longer operative time (OR 1.02, p < 0.001), and diabetes (OR 1.05, p = 0.046) were independent risk factors for pLOS in elderly patients undergoing lumbar fusion surgery. The single-classification tree revealed that operative time ≥ 232 min, delayed ambulation, and BMI ≥ 30 kg/m2 as particularly influential predictors for pLOS. A random forest model was developed using the remaining 14 variables. Intraoperative EBL, operative time, delayed ambulation, age, number of fused segments, BMI, and RBC count were the most significant variables in the final model. The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.71 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. The nomogram was developed, and the C-index of external validation for PLOS was 0.69 (95% CI, 0.65–0.76). Conclusion: This investigation produced three predictive models for pLOS in elderly patients undergoing lumbar fusion surgery. The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our predictive model could inform physicians about elderly patients with a high risk of pLOS after surgery. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has demanded more and more attention. Consumers have the power to influence the carbon footprint of goods and services through their purchasing decisions, but to do this they need to learn more. To address this need, it is necessary to develop online questionnaires able to make people aware of which activities have a greater environmental impact in their daily lives. Focusing on this goal, we formulated two tools for quantifying an individual's carbon footprint over a year. The innovativeness of these tools lies in being user-friendly and providing online open access to compilers, as well as using specific emission factors for the reference context. Specifically, we focused on the main emission sources: gas and electricity consumption, mobility, food, and waste. During these last years, the tools have been proposed to Italian students at different levels of education and to employees of Italian and international companies. The responses from 3260 users revealed an average annual direct carbon footprint per capita of about 5600 kg CO2-eq, which, integrated with the estimate of indirect emissions, provides an estimate in good agreement with the value provided by the Italian National Inventory of greenhouse gases. With the developed tools, people are able to observe which sectors have the greatest impact and consequently are stimulated to emit less by adopting more sustainable behaviors. [ABSTRACT FROM AUTHOR]

Abstract—It has been 10 years since CRISPR/Cas technology was applied to edit the genomes of various organisms. Its ability to produce a double-strand break in a DNA region specified by the researcher started a revolution in bioengineering. Later, the Base Editing (BE) method was developed. BE is performed via the formation of single-strand breaks by the mutant form of Cas nuclease (nickase), fused with deaminases and other enzymes. It can be used to promote A G and C T transitions, and a C → G transversion. Just over 3 years ago, a new Prime Editing (PE) variant of CRISPR/Cas was invented. Unlike BE, in PE the nickase is fused with reverse transcriptase, capable of building a new DNA chain using the pegRNA template. The pegRNA consists of an elongated guide RNA with an extra sequence at the 3'-end. Prime editing makes it possible to insert the desired mutations into this extra sequence and to carry out any substitutions and indels of bases without the use of special donor DNA. To date, a number of PE variants have been proposed; they are briefly considered in this review with an emphasis on prime editing of plant genomes. Some attention is also paid to pegRNA design programs, as well as evaluation of the efficiency of the editing. Such a variety of PE techniques is due to the opportunities of high-precision introduction of desired changes with a rather low frequency of off-target mutations in the genomes of various organisms. The relatively low efficiency of prime editing inspires researchers to offer new approaches. There is hope that further development of the technology will improve PE enough to take its rightful place among the genome targeting methods that are suitable for any organisms, and will have a positive impact on the agricultural sector, industrial biotechnologies, and medicine. [ABSTRACT FROM AUTHOR]

Background: The burden of lumbar degenerative diseases (LDD) has increased substantially with the unprecedented aging population. Identifying elderly patients with high risk of postoperative adverse events (AEs) and establishing individualized perioperative management is critical to mitigate added costs and optimize cost-effectiveness to the healthcare system. We aimed to develop a predictive tool for AEs in elderly patients with transforaminal lumbar interbody fusion (TLIF), utilizing multivariate logistic regression, single classification and regression tree (hereafter, "classification tree"), and random forest machine learning algorithms. Methods: This study was a retrospective review of a prospective Geriatric Lumbar Disease Database (age ≥ 65). Our outcome measure was postoperative AEs, including prolonged hospital stays, postoperative complications, readmission, and reoperation within 90 days. Patients were grouped as either having at least one adverse event (AEs group) or not (No-AEs group). Three models for predicting postoperative AEs were developed using training dataset and internal validation using testing dataset. Finally, online tool was developed to assess its validity in the clinical setting (external validation). Results: The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97 [55.4%] female). The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.72 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. A nomogram based on logistic regression was developed, and the C-index of external validation for AEs was 0.69 (95% CI 0.65–0.76). Conclusion: The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our nomogram and online tool (https://xuanwumodel.shinyapps.io/Model%5ffor%5fAEs/) could inform physicians about elderly patients with a high risk of AEs within the 90 days after TLIF surgery. [ABSTRACT FROM AUTHOR]

As the formation of government coalitions between two or more political parties is a typical procedure in multiparty systems after elections and the parties' decision on what coalition to form heavily influences policy outcomes, electorates, academics, and other stakeholders like journalists or party officials increasingly need digital tools that can determine the parties' most preferred coalitions. This article presents a novel visualization tool for identifying utility-maximizing choices and equilibrium coalitions to provide users with rapid analyzing capabilities revealing parties' most preferred coalitions in the aftermath of parliamentary elections. We demonstrate our tool's analytical leverage taking the 2017 German federal elections as in-depth example. Our tool is available online at www.mytuc.org/mcbz. [ABSTRACT FROM AUTHOR]

In forensic kinship testing and missing person identification, it is a fundamental question to choose the most informative reference relatives, select appropriate genotyping systems, and evaluate the weight of evidence comprehensively. Despite that several useful tools have been developed, they have not addressed these questions satisfactorily. In this paper, we develop a flexible and user-friendly online tool, Easykin, to address the aforementioned issues. It has some promising features: (i) Pedigrees can be constructed easily and presented intuitively with just a few mouse clicks. (ii) System power can be estimated before testing based on certain set of markers and reference relatives. (iii) The pruning function of EasyKin enables users to choose appropriate subsets of available references. (iv) Parameters at a specific LR for a single case may ease evidence interpretation. (v) The user interface (UI) is an HTML-based dashboard, which is friendly to both professional and non-professional users and can be used anytime and anywhere. Here, we presented three common cases as examples to demonstrate how kinship testing and missing person identification can be improved with EasyKin. In conclusion, this tool provides a one-stop solution for forensic use, that is, instructing users to choose appropriate kits and reference relatives before testing, calculating LR in the testing, and providing parameters for data interpretation after testing. EasyKin is freely available at https://forensicsysu.shinyapps.io/EasyKin/. [ABSTRACT FROM AUTHOR]

Variants in the superoxide dismutase (SOD1) gene are among the most common genetic causes of amyotrophic lateral sclerosis. Reflecting the wide spectrum of putatively deleterious variants that have been reported to date, it has become clear that SOD1-linked ALS presents a highly variable age at symptom onset and disease duration. Here we describe an open access web tool for comparative phenotype analysis in ALS: . The tool contains a built-in dataset of clinical information from 1383 people with ALS harboring a SOD1 variant resulting in one of 162 unique amino acid sequence alterations and from a non-SOD1 comparator ALS cohort of 13,469 individuals. We present two examples of analyses possible with this tool, testing how the ALS phenotype relates to SOD1 variants that alter amino acid residue hydrophobicity and to distinct variants at the 94th residue of SOD1, where six are sampled. The tool provides immediate access to the datasets and enables bespoke analysis of phenotypic trends associated with different protein variants, including the option for users to upload their own datasets for integration with the server data. The tool can be used to study SOD1-ALS and provides an analytical framework to study the differences between other user-uploaded ALS groups and our large reference database of SOD1 and non-SOD1 ALS. The tool is designed to be useful for clinicians and researchers, including those without programming expertise, and is highly flexible in the analyses that can be conducted. [ABSTRACT FROM AUTHOR]

• A nomogram for predicting hypoglycemia risk in critically ill patients with sepsis. • An online individualized predictive tool was developed. • Predictive model was validated externally in external validation cohort. Few studies have reported the risk factors or developed a risk predictive model of hypoglycemia patients with sepsis. To develop a predictive model to assess the hypoglycemia risk in critically ill patients with sepsis. For this retrospective study, we collected the data from the Medical Information Mart for Intensive Care III and IV (MIMIC-III and MIMIC-IV). All eligible patients from the MIMIC-III were randomly divided into the training set for development of predictive model and testing set for internal validation of the predictive model at a ratio of 8:2. Patients from the MIMIC-IV database were used as the external validation set. The primary endpoint was the occurrence of hypoglycemia. Univariate and multivariate logistic model was used to screen predictors. Adopted receiver operating characteristics (ROC) and calibration curves to estimate the performance of the nomogram. The median follow-up time was 5.13 (2.61–9.79) days. Diabetes, dyslipidemia, mean arterial pressure, anion gap, hematocrit, albumin, sequential organ failure assessment, vasopressors, mechanical ventilation and insulin were identified as the predictors for hypoglycemia risk in critically ill patients with sepsis. We constructed a nomogram for predicting hypoglycemia risk in critically ill patients with sepsis based on these predictors. An online individualized predictive tool: https://ghongyang.shinyapps.io/DynNomapp/. The established nomogram had a good predictive ability by ROC and calibration curves in the training set, testing set and external validation cohort. A predictive model of hypoglycemia risk was constructed, with a good ability in predicting the risk of hypoglycemia in critically ill patients with sepsis. [ABSTRACT FROM AUTHOR]

Thanks to medical successes and new treatment options, children and adolescents with rare diseases can now attend school more often playing an important role in their recovery as well as improving their social inclusion. For this reason, it is important for teachers to address the issue and acquire skills in dealing with rare diseases. In this context, a multi-professional team at the Salzburg University of Teacher Education organized a blended learning seminar on the topic of rare diseases at schools. Participants were provided with videos, texts, and case studies on a learning platform, which were worked on over a period of three weeks. There were also two online lectures in which questions could be asked. In order to evaluate the tool, 21 participants took part in a quantitative longitudinal study by means of a pretest and a posttest with a four-month interval. The participants completed a questionnaire consisting of a competence screening dealing with rare diseases together with questions to measure general and teacher self-efficacy. As expected, there was a statistically significant increase in both general and teacher self-efficacy with medium effect sizes. In addition, the theoretical and practical skills for supporting affected students at school were also shown to improve in a self-assessment. In view of the positive response from participants, it is recommended to further expand this offering in order to reach a broader population of teachers. In other words, only through raising awareness and increasing the competence of professionals working in schools can an environment be created for affected children and adolescents in which their specific needs are addressed.

BACKGROUND: Accurate population specific aids are needed for food portion size estimation in diet assessment. OBJECTIVE: To develop and evaluate a food photograph series software for accurate portion size estimation among urban North Indian adults. METHODS: A preliminary food consumption survey was conducted on 60 adults (25–60 years, male = female = 30) selected purposively from urban Delhi. A range of portion sizes of commonly consumed food items were standardized and photographed at 45 degree angle. An online portion size estimation tool was developed comprising data on 368 recipes and photographs for 313 food items. Real time (part A) and recalled estimation after 24 hours (part B) was done for pre-weighed food portions by participants (n = 30, 25–60 years, male = female = 15) using the software. RESULTS: The % mean relative error in estimation of 24 pre-weighed food portions was –5±22.61% and 13.07±53.22% for parts A and B respectively. Significant correlation between actual and estimated food portions was observed (r > 0.90, p < 0.0001). Correct picture was selected by participants in≥60% estimations while correct or adjacent picture was selected in≥89% estimations. CONCLUSIONS: The software is a useful aid for dietitians and other nutrition professionals for accurate portion size estimation among urban North Indian adults. [ABSTRACT FROM AUTHOR]

Purpose: Extrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking. Methods: A total of 3,921 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database, which form the training and internal validation cohorts of the survival prediction model. The endpoint was an overall survival of 0.5-5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation (n = 68) was performed to evaluate the generalization ability of the selected model. Results: Among machine learning algorithms, the random forest model performs best on internal validation, whose area under the curve (AUC) is 0.736-0.800. The net benefit is higher than the TNM classification in decision curve analysis. The AUC of this model on the external validation cohort is 0.739-0.811. This model was then deployed online as a free, publicly available prediction tool of EPSCC (http://42.192.80.13:4399/). Conclusion: This study provides an excellent online survival prediction tool for EPSCC with machine learning and large-scale data. Age, TNM stages, and surgery (including potential performance status information) are the most critical factors for the prediction model. [ABSTRACT FROM AUTHOR]

Modern health care systems need dynamic leaders, empowered with current and evidenced based practices and knowledge. Nurse managers belong to the above leaders. Their role in ward management is crucial. For this reason, nurse managers need to be empowered in order to become acquainted with skills and knowledge that will facilitate them to play their managerial role in a satisfactory way. More specifically, the need to have skills on safe staffing, communication, conflict management and rationing of nursing care. The present article describes the process that was followed for the development of an online educational tool, for the empowerment of nurse managers. The development of the tool was part of the Erasmus+ project Nurse Managers For Safety. Four countries and five partners (Cyprus, Italy, Switzerland and Germany) formed the research consortium (four universities and a technology-expert partner). The four universities, through literature review and focus groups discussion, designed the content of the online tool (scripts, power point presentations, videos), in various languages with additions subtitles. The online tool includes a selection of topics and challenges which nurse managers come across with daily. The pilot implementation of the tool has indicated the importance of such projects and the complexity of the responsibilities nurse managers have. [ABSTRACT FROM AUTHOR]

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Introduction: The global Covid-19 pandemic led to the need for educators to explore online platforms in delivering lessons to students. It has impacted the way things are done in walks of life including nursing education in both developing and developed countries. Methods: A descriptive correlational study was conducted to determine the effectiveness of Google Classroom (GCR) as an online Tool in Teaching and Learning: B.Sc (Nursing) Student's Perceptions using survey approach among 20,000 undergraduate nursing students studying 1st year to final year in more than 208 nursing institutions. The students were selected using purposive sampling technique. After obtaining permission and informed consent from the participants, data was collected using pretested and validated tools such as background variables proforma of nursing students and rating scale on effectiveness of Google classroom in improving the learning outcomes of the students using Google forms through WhatsApp and e-mails. The collected data was analyzed using descriptive and inferential statistics. Results: Most of the students had positive perception with overall mean and standard deviation showing effectiveness of GCR as an online tool m=81.64, SD=10.41. Conclusion: Implementation of online learning in Nursing education has been perceived favorably by majority of the students.It is important for instructors and institutions to consider the needs and preferences of their students when designing and delivering courses, and also to provide adequate support and resources to ensure that students are able to have an innovative learning experience. [ABSTRACT FROM AUTHOR]

Businesses willing to reduce their carbon footprint embrace sustainability and positively impact the progress towards achieving climate neutrality. Well-prepared and presented information to the business customer before purchasing can be a strong driver for better decision-making towards less impactful product alternatives. This study presents the development of a tool for packaging products online marketplace that informs customers about the carbon footprint of packaging products and allows them to evaluate which of the select packaging alternatives is most preferable from an environmental perspective. The tool implements a life cycle analysis (LCA) approach, including the stages of raw material extraction, packaging production, and transportation to the customer. The impact assessment in the tool is performed according to Intergovernmental Panel on Climate Change (IPCC) 2021 methodology for assessing greenhouse gas (GHG) emissions based on information obtained from the Ecoinvent database. The final output of carbon footprint calculation is provided with an indicator marking the carbon footprint performance of customer-defined alternatives in a clear, simple, and consistent way. The tool aims to educate customers, foster informed purchasing decisions, and improve the environmental outcomes of their decisions.

Abstract Background The alertness and practice of health care providers (HCPs) in the correct management of suspected malaria (CMSM) (vigilance) is a central component of malaria surveillance following elimination, and it must be established before malaria elimination certification can be granted. This study was designed to develop and validate a rapid tool, Simulated Malaria Online Tool (SMOT), to evaluate HCPs’ practice in relation to the CMSM. Methods The study was conducted in East Azerbaijan Province, Islamic Republic of Iran, where no malaria transmission has been reported since 2005. An online tool presenting a suspected malaria case for detection of HCPs’ failures in recognition, diagnosis, treatment and reporting was developed based on literature review and expert opinion. A total of 360 HCPs were allocated to two groups. In one group their performance was tested by simulated patient (SP) methodology as gold standard, and one month later by the online tool to allow assessment of its sensitivity. In the other group, they were tested only by the online tool to allow assessment of any possible bias incurred by the exposure to SPs before the tool. Results The sensitivity of the tool was (98.7%; CI 93.6–99.3). The overall agreement and kappa statistics were 96.6% and 85.6%, respectively. In the group tested by both methods, the failure proportion by SP was 86.1% (CI 80.1–90.8) and by tool 87.2% (CI 81.4–91.7). In the other group, the tool found 85.6% (CI 79.5–90.3) failures. There were no significant differences in detecting failures within or between the groups. Conclusion The SMOT tool not only showed high validity for detecting HCPs’ failures in relation to CMSM, but it had high rates of agreement with the real-world situation, where malaria transmission has been interrupted. The tool can be used by program managers to evaluate HCPs’ performance and identify sub-groups, whose malaria vigilance should be strengthened. It could also contribute to the evidence base for certification of malaria elimination, and to strengthening prevention of re-establishment of malaria transmission.

Objective: Gestational trophoblastic diseases (GTD) comprise a group of rare diseases originating from the trophoblast affecting women of childbearing age. Providing optimal information to patients with a rare disease is challenging because of the small number of patients and limited clinical expertise of many healthcare professionals. Both knowledge and lack of knowledge in patients may influence illness perception. We investigated whether a web-based interactive intervention influences illness perception and knowledge in women with GTD.Design: This was a multicenter randomized control trial conducted at general and academic hospitals in the Netherlands, including newly diagnosed GTD patients between 2017 and 2019. Methods: Sixty-nine patients were randomized between direct access or postponed access to an online tool on GTD and received online questionnaires about illness perception, knowledge, and anxiety. The main outcome measures were illness perception (primary outcome measure) and knowledge (secondary outcome measure). Results: Patients using the online tool were satisfied with the information from the tool (92%). Although they had a higher level of knowledge compared to the control group (p = 0.006), illness perception did not change. Also, no differences in levels of anxiety, depression, or distress were observed between the groups. Limitations: Participants had access to other information sources and many searched other websites. It is unknown what kind of websites were visited and when. It is unknown if the increased knowledge levels and low levels of distress will sustain over time as no long term follow-up took place. Healthcare professionals were not interviewed on how they experienced the consultation before and after using the tool by the patients. Conclusions: The online tool did not change illness perception but was shown to be valuable for newly diagnosed GTD patients to gain knowledge. The improvement in knowledge after digital education indicates that this tool can be used as an effective method of supporting GTD patients' informational needs without causing extra distress. Tweetable Abstract: A web-based tool for trophoblastic disease does not change illness perception of patients but is valuable to gain knowledge. [ABSTRACT FROM AUTHOR]

In the search for solutions for Integrated Water Resources Management (IWRM), decision-makers frequently face governance-related challenges such as the fragmentation of institutions, the interconnection with other essential services and overlapping competences. In many cases, solutions to these challenges involve strengthening coordination between different actors and sectors. Today different digital tools and resources are available to support related decision-making processes. These tools can (1) offer a context-specific assessment of the water governance system ("Diagnosis") and (2) provide, in some cases, context-specific recommendations to tackle identified deficits ("Therapy"). In this paper, we aim to advance the understanding of the potential of digital tools for water governance and management assessments. After reviewing relevant tools, we introduce the Diagnostic Water Governance Tool (DWGT) that builds on a clear diagnosis-therapy-logic. After presenting the empirical basis and functional logic of the DWGT, we test the DWGT in two case studies: the Guadalquivir river basin in Spain and the Emscher river basin in Germany. We find that the DWGT provides context-specific, evidence-based instrument recommendations that can enhance water governance and management, albeit accompanying suitability evaluations by governance experts remain critical. We conclude by discussing the limitations of digital tools for water governance and management assessments and sketch out fields of future research. • Most water governance online tools do not allow for case-specific assessments. • Diagnostic Water Governance Tool identifies context-specific coordination deficits. • The Tool gives case-sensitive recommendations for governance instruments. • Fuzzy set Qualitative Comparative Analysis provides sound methodological framework. • The Tool's analysis can supplement but not replace expert evaluations. [ABSTRACT FROM AUTHOR]

In courses in Computing, Digital Technology and related areas, Computer Programming and Algorithms are initial subjects with a high failure rate. Remote teaching adopted as a result of the COVID-19 pandemic has brought new barriers, including the need for online tools. This work aims to identify, test and analyze free online platforms and tools that can help to overcome the adversities faced in Programming and Algorithms disciplines in the context of remote teaching. The methodology adopted was a quantitative study, in which a set of online tools was initially identified, which later had their usability level measured through questionnaires submitted to a group of students from the Technical Course in Computer Science for the Internet. The results identified that, among the analyzed tools, all have a range of positive and negative points, but only one of them presented a greater number of potentialities that facilitate the teaching-learning process in the context of remote teaching. At the end of the research, it was found that there is a wide variety of online tools that can help in the teaching of Programming and Algorithms, however, there are few tools with characteristics that meet all the needs in the context of remote teaching. [ABSTRACT FROM AUTHOR]

This study presents a web-based sentence comprehension test aimed at identifying high school students who are at risk for a language delay. By assessing linguistic skills on a sample of high school students with Italian as an L2 and their monolingual peers, attending a vocational school, we were able to identify a subgroup of L2 students with consistent difficulties in sentence comprehension, though their reading skills were within the average range. The same subgroup revealed to experience a lack of support within the school context, suggesting that poor L2 skills might be a critical variable to consider in order to identify students at risk for school exclusion. Regarding the test, accuracy to the on-line sentence comprehension task was significantly predicted by reading abilities and vocabulary skills, thus indicating that this test might represent a rapid but efficient way to assess linguistic abilities at school. We recommend that establishing a valid and practical procedure for the evaluation of linguistic skills in bilingual students who struggle with their L2 is the first step toward promoting social inclusion in the multilingual classroom, in order to increase their ability to actively participate in school and social activities.

Abstract Background Dizziness and vertigo affect around 15% of adults annually and represent common reasons for contacting health services, accounting for around 3% of all emergency department visits worldwide. Vertigo is also associated with excessive use of diagnostic imaging and emergency care and decreased productivity, primarily because of work absenteeism. Vestibular rehabilitation is an evidence-based treatment for chronic dizziness and supervised group exercise therapy has recently been shown to be effective after vestibular neuritis, a common cause of acute onset vertigo. However, such interventions are not readily available and there is a need for more easily accessible tools. The purpose of this study is to investigate the effects on vestibular symptoms of a 6-week online vestibular rehabilitation tool after acute onset vertigo, with the aim of aiding vestibular rehabilitation by presenting a more accessible tool that can help to reduce recovery time. Methods Three hundred twenty individuals diagnosed with acute vestibular syndrome (AVS) will be recruited from multiple hospitals in Sweden and the effects of an online vestibular rehabilitation tool, YrselTräning, on vestibular symptoms after acute onset vertigo will be compared to standard care (written instructions leaflet) in a two-armed, evaluator-blinded, multicenter randomized controlled trial. The primary outcome will be the Vertigo Symptom Scale Short Form (VSS-SF) score at 6 weeks after symptom onset. Secondary outcomes include effects of the intervention on activities of daily living, mood and anxiety, vestibular function recovery, mobility measures, health economic effects, and the reliability of the Swedish VSS-SF translation. Discussion Participants using the online vestibular rehabilitation tool are expected to recover earlier and to a greater extent from their symptoms as compared to standard care. Since up to 50% of people with AVS without treatment develop persistent symptoms, effective treatment of AVS will likely lead to a higher quality of life and help reduce the societal costs associated with dizziness and vertigo. Trial registration Clinicaltrials.gov NCT05056324 . Registered on September 24, 2021.

Introduction and hypothesis: The aim was to develop and validate (internally and externally) a prediction model for the presence and diagnosis of pelvic floor dysfunction (PFD) in women, including pelvic organ prolapse, stress urinary incontinence and/or overactive bladder via a patient-completed online tool. Methods: Using a retrospective cohort of women aged >18 years, from multiple tertiary gynaecology units within Queensland, Australia (2014–2018), the prediction model was developed via penalized logistic regression with internal and external validation utilizing multiple clinical predictors (42 questions from the Australian Pelvic Floor Questionnaire and demographics: age, body mass index, parity and mode of delivery). The main outcome measures were the accuracy of the model in predicting a diagnosis of pelvic floor dysfunction and its specific conditions of prolapse and incontinence. Results: A total of 3,501 women were utilized for model development and internal validation and 449 for external validation. On internal validation the model correctly identified those with PFD with 97% sensitivity, 74% specificity and a concordance index (C-index) of 0.96. Predictions of pelvic organ prolapse were also accurate, with 86% sensitivity, 83% specificity, C-index 0.83, as was stress urinary incontinence, 84% sensitivity, 87% specificity, C-index 0.87, and overactive bladder, 76% sensitivity, 77% specificity, C-index 0.77. External validation confirmed the model's accuracy with a similar C-index in all parameters. Conclusions: This model provides an accurate online tool to differentiate between those with and without PFD and diagnoses of common pelvic floor disorders. It serves as a valuable self-assessment for women and primary care providers. [ABSTRACT FROM AUTHOR]

Background: The alertness and practice of health care providers (HCPs) in the correct management of suspected malaria (CMSM) (vigilance) is a central component of malaria surveillance following elimination, and it must be established before malaria elimination certification can be granted. This study was designed to develop and validate a rapid tool, Simulated Malaria Online Tool (SMOT), to evaluate HCPs' practice in relation to the CMSM. Methods: The study was conducted in East Azerbaijan Province, Islamic Republic of Iran, where no malaria transmission has been reported since 2005. An online tool presenting a suspected malaria case for detection of HCPs' failures in recognition, diagnosis, treatment and reporting was developed based on literature review and expert opinion. A total of 360 HCPs were allocated to two groups. In one group their performance was tested by simulated patient (SP) methodology as gold standard, and one month later by the online tool to allow assessment of its sensitivity. In the other group, they were tested only by the online tool to allow assessment of any possible bias incurred by the exposure to SPs before the tool. Results: The sensitivity of the tool was (98.7%; CI 93.6–99.3). The overall agreement and kappa statistics were 96.6% and 85.6%, respectively. In the group tested by both methods, the failure proportion by SP was 86.1% (CI 80.1–90.8) and by tool 87.2% (CI 81.4–91.7). In the other group, the tool found 85.6% (CI 79.5–90.3) failures. There were no significant differences in detecting failures within or between the groups. Conclusion: The SMOT tool not only showed high validity for detecting HCPs' failures in relation to CMSM, but it had high rates of agreement with the real-world situation, where malaria transmission has been interrupted. The tool can be used by program managers to evaluate HCPs' performance and identify sub-groups, whose malaria vigilance should be strengthened. It could also contribute to the evidence base for certification of malaria elimination, and to strengthening prevention of re-establishment of malaria transmission. [ABSTRACT FROM AUTHOR]

Power calculation is a necessary step when planning genome-wide association studies (GWAS) to ensure meaningful findings. Statistical power of GWAS depends on the genetic architecture of phenotype, sample size, and study design. While several computer programs have been developed to perform power calculation for single SNP association testing, it might be more appropriate for GWAS power calculation to address the probability of detecting any number of associated SNPs. In this paper, we derive the statistical power distribution across causal SNPs under the assumption of a point-normal effect size distribution. We demonstrate how key outcome indices of GWAS are related to the genetic architecture (heritability and polygenicity) of the phenotype through the power distribution. We also provide a fast, flexible and interactive power calculation tool which generates predictions for key GWAS outcomes including the number of independent significant SNPs, the phenotypic variance explained by these SNPs, and the predictive accuracy of resulting polygenic scores. These results could also be used to explore the future behaviour of GWAS as sample sizes increase further. Moreover, we present results from simulation studies to validate our derivation and evaluate the agreement between our predictions and reported GWAS results. [ABSTRACT FROM AUTHOR]

This case study assesses how an Australian University used an online tool (WeChat) to support commencing online Chinese articulation students transition in their studies in response to COVID-19 border restrictions. It investigates how effectively the engagement initiatives met the students' needs and identify areas of improvement. The usage of an online tool allows staff and students to build a relationship and provided students with key information to manage their expectations. This case study demonstrates an opportunity for further research on the effect of online tools to help international students transition. [ABSTRACT FROM AUTHOR]

Many retrofit projects went wrong in the UK principally because of the application of inappropriate retrofit solutions, which resulted in damp issues, with some leaving houses in worse conditions than pre-retrofit. Various online tools were developed to inform homeowners about the benefits of retrofitting. Prior to this study, little was known about users' evaluation of these tools and the effects of calculator use. Furthermore, no retrofit tool aims to raise the awareness of homeowners about moisture risks in a retrofit project. The originality of this study is to develop and evaluate an online moisture-safe retrofit decision-making tool for homeowners. The adopted methodology consisted of two phases. Phase one aimed to develop the tool. In phase two, semi-structured interviews were conducted to evaluate the tool. The results indicate that the tool has been well received by homeowners. The tool significantly increased participants' awareness of moisture risks related to a retrofit project. Most participants considered the tool an eye-opener, while few of them found it scary. However, the tool did not result in an increased willingness to invest in energy efficiency measures. The discouragement was related to high investment costs and long payback periods of some retrofit measures. Based on our findings, we formulate a set of design recommendations to improve the proposed tool and help retrofit calculators, in general, overcome challenges. [ABSTRACT FROM AUTHOR]

CRISPR-based genome-editing tools have emerged as an efficient tool for functional genomics studies. Online tools and databases have been developed to facilitate the design and selection of CRISPR single guide RNA (sgRNA) for gene modifications. However, to the best of our knowledge, none of these tools or database are designated to cell surface proteins. In a previous study, we described the development and application of surfaceome CRISPR libraries targeting to cell surface proteins on human cells. Here, we present the design and construction of an online tool and database (https://crispr-surfaceome.siais.shanghaitech.edu.cn/home), named CRISPR-Surfaceome, for the design of highly efficient sgRNA targeting to the surface proteins on human cells. To show case and validate the efficiencies of sgRNAs designed by this online tool, we chose ICAM-1 gene for knockout studies and found that all the 10 designed ICAM-1 sgRNAs could efficiently generate knockout cells, with more than 80% gene disruption rates. These ICAM-1 knockout cells were found to be resistant to the infection of rhinovirus (RV), which utilizes ICAM-1 as the receptor. Therefore, CRISPR-Surfaceome can serve the research community for the functional genomics studies on cell surface proteins, such as identification of pathogen receptors and discovery of drug targets.

Abstract Genetic map is a linear arrangement of the relative positions of sites in the chromosome or genome based on the recombination frequency between genetic markers. It is the important basis for genetic analysis. Several kinds of software have been designed for genetic mapping, but all these tools require users to write or edit code, making it time-costing and difficult for researchers without programming skills to handle with. Here, MG2C, a new online tool was designed, based on PERL and SVG languages. Users can get a standard genetic map, only by providing the location of genes (or quantitative trait loci) and the length of the chromosome, without writing additional code. The operation interface of MG2C contains three sections: data input, data output and parameters. There are 33 attribute parameters in MG2C, which are further divided into 8 modules. Values of the parameters can be changed according to the users’ requirements. The information submitted by users will be transformed into the genetic map in SVG file, which can be further modified by other image processing tools. MG2C is a user-friendly and time-saving online tool for drawing genetic maps, especially for those without programming skills. The tool has been running smoothly since 2015, and updated to version 2.1. It significantly lowers the technical barriers for the users, and provides great convenience for the researchers.

Computer science students often evaluate the behavior of the code they write by running it on specific inputs and studying the outputs, and then apply their comprehension to a more general understanding of the code. While this is a good starting point in the student’s career, successful graduates must be able to reason analytically about the code they create or encounter. They must be able to reason about the behavior of the code on arbitrary inputs, without running the code. Abstraction is central for such reasoning. In our quest to help students learn to reason abstractly and develop logically correct code, we have developed tools that rely on a verification engine. Code involves assignment, conditional, and loop statements, along with objects and operations. Reasoning activities involve symbolic reasoning with simple assertions and design-by-contract assertions such as pre-and post-conditions as well as loop invariants with data abstractions. Students progress from tracing and reading code to the design and implementation of code, all relying on abstraction for verification. This paper reports some key results and findings from associated studies spanning several years.

This cross-sectional study aims to explore the prevalence of protein-energy wasting (PEW) in dialysis patients in Catalonia, Spain, using a new and practical online tool which enables rapid calculation and comparison with other nutritional scores. Methods: A web tool (Nutrendial) was created to introduce different variables and automatically calculate PEW, Malnutrition inflammation Score (MIS) and Subjective Global Assessment (SGA) in 1389 patients (88% in haemodialysis (HD)), 12% in peritoneal dialysis (PD) from different regions of Catalonia. Results: A prevalence of 23.3% (26% HD, 10.2% PD) of PEW was found, with a mean MIS score of 6 and SGA score of C in 7% of the patients. ROC analysis showed MIS as the best nutritional score to diagnose PEW (AUC 0.85). Albumin delivered lower diagnostic precision (AUC 0.77) and sensitivity (66%). A cut off point of 7 (86% sensitivity and 75% specificity) for MIS and 3.7 mg/dL for albumin were found to predict the appearance of PEW in this population. SGA B or C showed an 87% sensitivity and 55% specificity to diagnose PEW. Very low nutritional intervention (14%) was recorded with this tool in patients with PEW. Conclusions: This new online tool facilitated the calculation of PEW, enabling different professionals—including nephrologists, dieticians and nurses—to efficiently obtain insights into the nutritional status of the Catalonian dialysis population and implement the required nutritional interventions. MIS is the score with more sensitivity to diagnose PEW. [ABSTRACT FROM AUTHOR]

Background: It is clear that individual outcomes of spine surgery can be quite heterogeneous. When consenting a patient for surgery, it is important to be able to offer an individualized prediction regarding the likely outcome. This study used a comprehensive set of data collected over 12 years in an in-house registry to develop a parsimonious model to predict the multidimensional outcome of patients undergoing surgery for degenerative pathologies of the thoracic, lumbar or cervical spine.Methods: Data from 8374 patients (mean age 63.9 (14.9-96.3) y, 53.4% female) were used to develop a model to predict the 12-month scores for the Core Outcome Measures Index (COMI) and its subdomain scores. The data were split 80:20 into a training and test set. The top predictors were selected by applying recursive feature elimination based on LASSO cross validation models. Based on the 111 top predictors (contained within 20 variables), Ridge cross validation models were trained, validated, and tested for each of 9 outcome domains, for patients with either "Back" (thoracic/lumbar spine) or "Neck" (cervical spine) problems (total 18 models).Results: Among the strongest outcome predictors in most models were: preoperative scores for almost all COMI items (especially axial pain (back or neck) and peripheral pain (leg/buttock or arm/shoulder)), catastrophizing, fear avoidance beliefs, comorbidity, age, BMI, nationality, previous spine surgery, type and spinal level of intervention, number of affected levels, and surgeon seniority. The R2 of the models on the validation/test sets averaged 0.16/0.13. A preliminary online tool was programmed to present the predicted outcomes for individual patients, based on their presenting characteristics. https://linkup.kws.ch/prognostictool .Conclusion: The models provided estimates to enable a bespoke prediction of the outcome of surgery for individual patients with varying degenerative pathologies and baseline characteristics. The models form the basis of a simple, freely-available online prognostic tool developed to improve access to and usability of prognostic information in clinical practice. It is hoped that, following confirmation of its validity and practical utility, the tool will ultimately serve to facilitate decision-making and the management of patients' expectations. [ABSTRACT FROM AUTHOR]

Purpose: To establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle. Methods: We have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with <5 oocytes retrieved during assisted reproductive technology treatment cycles. Least Absolute Shrinkage and Selection Operator logistic regression with 5-fold cross validation methods was applied to construct the model, and that with the lowest scaled log-likelihood was selected as the final one. Results: The areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed. Conclusions: Based on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis., Objectives: Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models., Results: ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57-24.88, P = 3.06 × 10 -29 ). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC 3-year  = 0.844 (95% CI: 0.805-0.883), AUC 5-year  = 0.816 (95% CI: 0.775-0.857), and C-index = 0.803 (95% CI: 0.776-0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038-1.083) and 1.090 (95% CI: 1.057-1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (P t  = 0.4: NB 3-year  = 19‰, NB 5-year  = 62‰; NR 3-year  = 69.21%, NR 5-year  = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/., Conclusion: ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Production and hosting by Elsevier B.V.)

Background: Dizziness and vertigo affect around 15% of adults annually and represent common reasons for contacting health services, accounting for around 3% of all emergency department visits worldwide. Vertigo is also associated with excessive use of diagnostic imaging and emergency care and decreased productivity, primarily because of work absenteeism. Vestibular rehabilitation is an evidence-based treatment for chronic dizziness and supervised group exercise therapy has recently been shown to be effective after vestibular neuritis, a common cause of acute onset vertigo. However, such interventions are not readily available and there is a need for more easily accessible tools. The purpose of this study is to investigate the effects on vestibular symptoms of a 6-week online vestibular rehabilitation tool after acute onset vertigo, with the aim of aiding vestibular rehabilitation by presenting a more accessible tool that can help to reduce recovery time.Methods: Three hundred twenty individuals diagnosed with acute vestibular syndrome (AVS) will be recruited from multiple hospitals in Sweden and the effects of an online vestibular rehabilitation tool, YrselTräning, on vestibular symptoms after acute onset vertigo will be compared to standard care (written instructions leaflet) in a two-armed, evaluator-blinded, multicenter randomized controlled trial. The primary outcome will be the Vertigo Symptom Scale Short Form (VSS-SF) score at 6 weeks after symptom onset. Secondary outcomes include effects of the intervention on activities of daily living, mood and anxiety, vestibular function recovery, mobility measures, health economic effects, and the reliability of the Swedish VSS-SF translation.Discussion: Participants using the online vestibular rehabilitation tool are expected to recover earlier and to a greater extent from their symptoms as compared to standard care. Since up to 50% of people with AVS without treatment develop persistent symptoms, effective treatment of AVS will likely lead to a higher quality of life and help reduce the societal costs associated with dizziness and vertigo.Trial Registration: Clinicaltrials.gov NCT05056324 . Registered on September 24, 2021. [ABSTRACT FROM AUTHOR]

Background: Predicting the number of oocytes retrieved (NOR) following controlled ovarian stimulation (COS) is the only way to ensure effective and safe treatment in assisted reproductive technology (ART). To date, there have been limited studies about predicting specific NOR, which hinders the development of individualized treatment in ART. Objective: To establish an online tool for predicting NOR. Materials and Methods: In total, 621 prospective routine gonadotropin releasing hormone (GnRH) antagonist COS cycles were studied. Independent variables included age, body mass index, antral follicle counts, basal FSH, basal and increment of anti-mullerian hormone, Luteinizing hormon, estradiol, testosterone, androstenedione, and inhibin B. The outcome variable was NOR. The independent variables underwent appropriate transformation to achieve a better fit for a linear relationship with NOR. Pruned forward selection with holdback validation was then used to establish predictive models. Corrected Akaike's information criterion, Schwarz–Bayesian information criterion, scaled – log [likelihood], and the generalized coefficient of determination (R 2) were used for model evaluation. Results: A multiple negative binomial regression model was used for predicting NOR because it fitted a negative binomial distribution. We established Model 1, using basal ovarian reserve markers, and Model 2, using both basal and early dynamic markers for predicting NOR following COS. The generalized R 2 values were 0.54 and 0.51 for Model 1 and 0.64 and 0.62 for Model 2 in the training and validation sets, respectively. Conclusion: Models 1 and 2 could be applied to different scenarios. For directing the starting dose of recombinant follicle stimulation hormone (rFSH), Model 1 using basic predictors could be used prior to COS. Model 2 could be used for directing the adjustment of rFSH dosages during COS. An online tool (http://121.43.113.123:8002/) based on these two models is also developed. We anticipate that the clinical application of this tool could help the ART clinics to reduce iatrogenic ovarian under- or over-responses, and could reduce costs during COS for ART. [ABSTRACT FROM AUTHOR]

Digital circuits are efficiently designed with abstract models in hardware description languages. Digital design which requires understanding the modeling language, design-flow and tools is considered difficult to the entry-level students. To boost learning, we propose a small hardware description language (SHDL) and an online tool for modeling, simulation and transformation to a standard language. The paper presents the SHDL structure and a novel tool for data-flow visualisation and circut area estimation. Early estimation of the synthesized circuit structure helps students at their taking circuit modeling design decisions. [ABSTRACT FROM AUTHOR]

Summary: Background: Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions. Methods: Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review. Findings: APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10−69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness. Interpretation: APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO). Funding: National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

BackgroundPredicting the number of oocytes retrieved (NOR) following controlled ovarian stimulation (COS) is the only way to ensure effective and safe treatment in assisted reproductive technology (ART). To date, there have been limited studies about predicting specific NOR, which hinders the development of individualized treatment in ART.ObjectiveTo establish an online tool for predicting NOR.Materials and MethodsIn total, 621 prospective routine gonadotropin releasing hormone (GnRH) antagonist COS cycles were studied. Independent variables included age, body mass index, antral follicle counts, basal FSH, basal and increment of anti-mullerian hormone, Luteinizing hormon, estradiol, testosterone, androstenedione, and inhibin B. The outcome variable was NOR. The independent variables underwent appropriate transformation to achieve a better fit for a linear relationship with NOR. Pruned forward selection with holdback validation was then used to establish predictive models. Corrected Akaike’s information criterion, Schwarz–Bayesian information criterion, scaled –log[likelihood], and the generalized coefficient of determination (R2) were used for model evaluation.ResultsA multiple negative binomial regression model was used for predicting NOR because it fitted a negative binomial distribution. We established Model 1, using basal ovarian reserve markers, and Model 2, using both basal and early dynamic markers for predicting NOR following COS. The generalized R2 values were 0.54 and 0.51 for Model 1 and 0.64 and 0.62 for Model 2 in the training and validation sets, respectively.ConclusionModels 1 and 2 could be applied to different scenarios. For directing the starting dose of recombinant follicle stimulation hormone (rFSH), Model 1 using basic predictors could be used prior to COS. Model 2 could be used for directing the adjustment of rFSH dosages during COS. An online tool (http://121.43.113.123:8002/) based on these two models is also developed. We anticipate that the clinical application of this tool could help the ART clinics to reduce iatrogenic ovarian under- or over-responses, and could reduce costs during COS for ART.

Practicum is an essential and meaningful part of teacher education but due to the great diversity of educational programmes worldwide, their nature and quality depend too much on each particular situation. There is an urgent need to unify and upgrade practicum formats. This paper reports a proposal for technology enhancement and structure reconfiguration of the practicum within the wider curricula. Added to that, we present an Interactive Digital Notepad (IDN) prototype, based on Kanban and powered up by Trello, as an optimal solution to the challenges posed in practicum management in foreign language teacher education programmes. Circumscribed to the Spanish educational context, this IDN is an online tool designed to improve the monitoring of the learning process in order to foster interactive and effective communication among the participants, and to promote critical thinking and autonomy. Results are promising and further steps for further improvement and implementation are outlined.

Computer science students often evaluate the behavior of the code they write by running it on specific inputs and studying the outputs, and then apply their comprehension to a more general understanding of the code. While this is a good starting point in the student's career, successful graduates must be able to reason analytically about the code they create or encounter. They must be able to reason about the behavior of the code on arbitrary inputs, without running the code. Abstraction is central for such reasoning. In our quest to help students learn to reason abstractly and develop logically correct code, we have developed tools that rely on a verification engine. Code involves assignment, conditional, and loop statements, along with objects and operations. Reasoning activities involve symbolic reasoning with simple assertions and design-by-contract assertions such as pre-and post-conditions as well as loop invariants with data abstractions. Students progress from tracing and reading code to the design and implementation of code, all relying on abstraction for verification. This paper reports some key results and findings from associated studies spanning several years. [ABSTRACT FROM AUTHOR]