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Online and blended learning have increased drastically during the pandemic, and their popularity has persisted as we emerge from this global crisis. This study aims to adapt and validate the Online Learning Readiness Scale (OLRS) to assess high school students. Secondary school students were recruited (n = 296) for the study. The OLRS scale included five components: Technology Readiness, Learner Control, Online Communication Self-efficacy, Self-directed Learning and Motivation for Learning. Results supported the OLRS scale in terms of reliability and internal construct validity in context of the study by confirmatory factor analysis and Rasch measurement with partial credit analysis. The differential item functioning analysis revealed no bias issues regarding gender, confirming the measurement invariance statistics achieved. The study also found that the majority of students (73.7%) engaged in online learning solely through their mobile phone. ICT familiarity, i.e., interacting with friends regularly, browsing online learning materials, and watching educational videos on YouTube, had a positive association with students’ readiness for online learning. Students’ access to social networks, online forums and online music did not have a significant effect on their readiness for online learning. The scale demonstrated the capacity to function as an assessment instrument for evaluating readiness for online learning in the context of secondary education. Educational implications were considered, including key requirements of supporting technology and pedagogical practice in online and blended learning environments.

Abstract A reliable and practical determination of a chemical species’ solubility in water continues to be examined using empirical observations and exhaustive experimental studies alone. Predictions of chemical solubility in water using data-driven algorithms can allow us to create a rationally designed, efficient, and cost-effective tool for next-generation materials and chemical formulations. We present results from two machine learning (ML) modeling studies to adequately predict various species’ solubility using data for over 8400 compounds. Molecular-descriptors, the most used method in previous studies, and Morgan fingerprint, a circular-based hash of the molecules' structures, were applied to produce water solubility estimates. We trained all models on 80% of the total datasets using the Random Forest (RFs) technique as the regressor and tested the prediction performance using the remaining 20%, resulting in coefficient of determination (R2) test values of 0.88 and 0.81 and root-mean-square deviation (RMSE) test values 0.64 and 0.80 for the descriptors and circular fingerprint methods, respectively. We interpreted the produced ML models and reported the most effective features for aqueous solubility measures using the Shapley Additive exPlanations (SHAP) and thermodynamic analysis. Low error, ability to investigate the molecular-level interactions, and compatibility with thermodynamic quantities made the fingerprint method a distinct model compared to other available computational tools. However, it is worth emphasizing that physicochemical descriptor model outperformed the fingerprint model in achieving better predictive accuracy for the given test set.

Novel treatments for Huntington’s disease (HD), a progressive neurodegenerative disorder, include selective targeting of the mutant allele of the huntingtin gene (mHTT) carrying the abnormally expanded disease-causing cytosine-adenine-guanine (CAG) repeat. WVE-120101 and WVE-120102 are investigational stereopure antisense oligonucleotides that enable selective suppression of mHTT by targeting single-nucleotide polymorphisms (SNPs) that are in haplotype phase with the CAG repeat expansion. Recently developed long-read sequencing technologies can capture CAG expansions and distant SNPs of interest and potentially facilitate haplotype-based identification of patients for clinical trials of oligonucleotide therapies. However, improved methods are needed to phase SNPs with CAG repeat expansions directly and reliably without need for familial genotype/haplotype data. Our haplotype phasing method uses single-molecule real-time sequencing and a custom algorithm to determine with confidence bases at SNPs on mutant alleles, even without familial data. Herein, we summarize this methodology and validate the approach using patient-derived samples with known phasing results. Comparison of experimentally measured CAG repeat lengths, heterozygosity, and phasing with previously determined results showed improved performance. Our methodology enables the haplotype phasing of SNPs of interest and the disease-causing, expanded CAG repeat of the huntingtin gene, enabling accurate identification of patients with HD eligible for allele-selective clinical studies.

Abstract Background Cancer associated copy number variation (CNV) events provide important information for identifying patient subgroups and suggesting treatment strategies. Technical and logistical issues, however, make it challenging to accurately detect abnormal copy number events in a cost-effective manner in clinical studies. Results Here we present CNV Radar, a software tool that utilizes next-generation sequencing read depth information and variant allele frequency patterns, to infer the true copy number status of genes and genomic regions from whole exome sequencing data. Evaluation of CNV Radar in a public multiple myeloma dataset demonstrated that CNV Radar was able to detect a variety of CNVs associated with risk of progression, and we observed > 70% concordance with fluorescence in situ hybridization (FISH) results. Compared to other CNV callers, CNV Radar showed high sensitivity and specificity. Similar results were observed when comparing CNV Radar calls to single nucleotide polymorphism array results from acute myeloid leukemia and prostate cancer datasets available on TCGA. More importantly, CNV Radar demonstrated its utility in the clinical trial setting: in POLLUX and CASTOR, two phase 3 studies in patients with relapsed or refractory multiple myeloma, we observed a high concordance rate with FISH for del17p, a risk defining CNV event (88% in POLLUX and 90% in CASTOR), therefore allowing for efficacy assessments in clinically relevant disease subgroups. Our case studies also showed that CNV Radar is capable of detecting abnormalities such as copy-neutral loss of heterozygosity that elude other approaches. Conclusions We demonstrated that CNV Radar is more sensitive than other CNV detection methods, accurately detects clinically important cytogenetic events, and allows for further interrogation of novel disease biology. Overall, CNV Radar exhibited high concordance with standard methods such as FISH, and its success in the POLLUX and CASTOR clinical trials demonstrated its potential utility for informing clinical and therapeutic decisions.