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ing room. With added open-ended questions, the SAQ-NL appears to be an assessment tool that allows for developing strategies that are instrumental in improving quality of care. ©Alexander D Hilt, Ad A Kaptein, Martin J Schalij, Jan van Schaik. Originally published in JMIR Human Factors (http//humanfactors.jmir.org), 08.04.2020.BACKGROUND Bone marrow aspiration and biopsy remain the gold standard for the diagnosis of hematological diseases despite the development of flow cytometry (FCM) and molecular and gene analyses. However, the interpretation of the results is laborious and operator dependent. Furthermore, the obtained results exhibit inter- and intravariations among specialists. Therefore, it is important to develop a more objective and automated analysis system. Several deep learning models have been developed and applied in medical image analysis but not in the field of hematological histology, especially for bone marrow smear applications. OBJECTIVE The aim of this study was to develop a deep learning model (BMSNet) for assisting hematologists in the interpretation of bone marrow smears for faster diagnosis and disease monitoring. METHODS From January 1, 2016, to December 31, 2018, 122 bone marrow smears were photographed and divided into a development cohort (N=42), a validation cohort (N=70), and a competition cohort (N=10ally published in JMIR Medical Informatics (http//medinform.jmir.org), 08.04.2020.BACKGROUND In long-term residential care (LTRC), caregivers’ attempts to provide person-centered care can be challenging when assisting residents living with a communication disorder (eg, aphasia) and/or a language-cultural barrier. Mobile communication technology, which includes smartphones and tablets and their software apps, offers an innovative solution for preventing and overcoming communication breakdowns during activities of daily living. There is a need to better understand the availability, relevance, and stability of commercially available communication apps (cApps) that could support person-centered care in the LTRC setting. OBJECTIVE This study aimed to (1) systematically identify and evaluate commercially available cApps that could support person-centered communication (PCC) in LTRC and (2) examine the stability of cApps over 2 years. METHODS We conducted systematic searches of the Canadian App Store (iPhone Operating System platform) in 2015 and 2017 using predefined search terms. cApps that metcial participation and person-centered care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/10.2196/17136. ©Rozanne Wilson, Diana Cochrane, Alex Mihailidis, Jeff Small. Originally published in JMIR Aging (http//aging.jmir.org), 08.04.2020.BACKGROUND Interprofessional team training is needed to improve nurse-physician communication skills that are lacking in clinical practice. Using simulations has proven to be an effective learning approach for team training. Yet, it has logistical constraints that call for the exploration of virtual environments in delivering team training. OBJECTIVE This study aimed to evaluate a team training program using virtual reality vs conventional live simulations on medical and nursing students’ communication skill performances and teamwork attitudes. METHODS In June 2018, the authors implemented nurse-physician communication team training using communication tools. A randomized controlled trial study was conducted with 120 undergraduate medical and nursing students who were randomly assigned to undertake team training using virtual reality or live simulations. The participants from both groups were tested on their communication performances through team-based simulation assessments. Their teamwork attitudes were evion. TRIAL REGISTRATION ClinicalTrials.gov NCT04330924; https//clinicaltrials.gov/ct2/show/NCT04330924. ©Sok Ying Liaw, Sim Win Ooi, Khairul Dzakirin Bin Rusli, Tang Ching Lau, Wilson Wai San Tam, Wei Ling Chua. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 08.04.2020.Human epidermal growth factor receptors (HERs) are the primary targets of many directed cancer therapies. However, the reason a specific dimer of HERs generates a stronger proliferative signal than other permutations remains unclear. Here, we used single-molecule immunoprecipitation to develop a biochemical assay for endogenously-formed, entire HER2-HER3 heterodimers. We observed unexpected, large conformational fluctuations in juxta-membrane and kinase domains of the HER2-HER3 heterodimer. Nevertheless, the individual HER2-HER3 heterodimers catalyze tyrosine phosphorylation at an unusually high rate, while simultaneously interacting with multiple copies of downstream signaling effectors. Our results suggest that the high catalytic rate and multi-tasking capability make a concerted contribution to the strong signaling potency of the HER2-HER3 heterodimers. © 2020, Choi et al.Hormonal signalling in animals often involves direct transcription factor-hormone interactions that modulate gene expression. In contrast, plant hormone signalling is most commonly based on de-repression via the degradation of transcriptional repressors. Recently, we uncovered a non-canonical signalling mechanism for the plant hormone auxin whereby auxin directly affects the activity of the atypical auxin response factor (ARF), ETTIN towards target genes without the requirement for protein degradation. Here we show that ETTIN directly binds auxin, leading to dissociation from co-repressor proteins of the TOPLESS/TOPLESS-RELATED family followed by histone acetylation and induction of gene expression. this website This mechanism is reminiscent of animal hormone signalling as it affects the activity towards regulation of target genes and provides the first example of a DNA-bound hormone receptor in plants. Whilst auxin affects canonical ARFs indirectly by facilitating degradation of Aux/IAA repressors, direct ETTIN-auxin interactions allow switching between repressive and de-repressive chromatin states in an instantly-reversible manner. © 2020, Kuhn et al.Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large datasets, existing analysis tools are not suitable to process the large volume of data obtained in high-throughput paradigms. Here, we present a new analysis platform (DISC) that accelerates unsupervised analysis of single-molecule trajectories. By merging model-free statistical learning with the Viterbi algorithm, DISC idealizes single-molecule trajectories up to three orders of magnitude faster with improved accuracy compared to other commonly used algorithms. Further, we demonstrate the utility of DISC algorithm to probe cooperativity between multiple binding events in the cyclic nucleotide binding domains of HCN pacemaker channel. Given the flexible and efficient nature of DISC, we anticipate it will be a powerful tool for unsupervised processing of high-throughput data across a range of single-molecule experiments.