Set-Stat-Map: Visualizing Spatial Data with Mixed Numeric and Categorical Attributes

Multi-attribute datasets are common and appear in many important scenarios for data analytics. Such data can be complex and thus difficult to understand directly without using visualization techniques. Existing visualizations for multi-attribute datasets are often designed based on attribute types, i.e., whether the attributes are categorical or numerical. Parallel Coordinates and Parallel Sets are two well-known techniques to visualize numerical and categorical data, respectively. However, visualization for mixed data types appears to be challenging. A common strategy to visualize mixed data is to use multiple information-linked views, e.g., Parallel Coordinates are often augmented with maps to explore spatial data with numeric attributes. In this paper, we design visualizations for mixed data types, where the dataset may include numerical, categorical, and spatial attributes. The proposed solution Set-Stat-Map is a harmonious combination of three interactive components: Parallel Sets (visualizes sets determined by the combination of categories or numeric ranges), statistics columns (visualizes numerical summaries of the sets), and a dataset-specified map view (geospatial map view for spatial information, heatmap for pairwise information, etc.). We also augment the Parallel Sets view in two main ways: First, we impose textures on top of colors, which are spread into the other views, to enhance users' capability of analyzing distributions of pairs of attribute combinations. Second, we limit the number of sets for each axis to a small number by merging some of them into one and limit the sizes of the merged sets to improve the rendering performance as well as to reduce users' cognitive loads. We demonstrate the use of Set-Stat-Map using different types of datasets: a meteorological dataset (CFSR), an online vacation rental dataset (Airbnb), and a software developer community dataset (StackOverflow). We provide design guidelines based on the results of the analysis of the performance from both visual analytics and scalability aspects. To examine the usability of the system, we collaborated with meteorologists, which reveals both challenges and opportunities for Set-Stat-Map to be used for real-life visual analytics

The plastidial retrograde signal methyl erythritol cyclopyrophosphate is a regulator of salicylic acid and jasmonic acid crosstalk.

The exquisite harmony between hormones and their corresponding signaling pathways is central to prioritizing plant responses to simultaneous and/or successive environmental trepidations. The crosstalk between jasmonic acid (JA) and salicylic acid (SA) is an established effective mechanism that optimizes and tailors plant adaptive responses. However, the underlying regulatory modules of this crosstalk are largely unknown. Global transcriptomic analyses of mutant plants (ceh1) with elevated levels of the stress-induced plastidial retrograde signaling metabolite 2-C-methyl-D-erythritol cyclopyrophosphate (MEcPP) revealed robustly induced JA marker genes, expected to be suppressed by the presence of constitutively high SA levels in the mutant background. Analyses of a range of genotypes with varying SA and MEcPP levels established the selective role of MEcPP-mediated signal(s) in induction of JA-responsive genes in the presence of elevated SA. Metabolic profiling revealed the presence of high levels of the JA precursor 12-oxo-phytodienoic acid (OPDA), but near wild type levels of JA in the ceh1 mutant plants. Analyses of coronatine-insensitive 1 (coi1)/ceh1 double mutant plants confirmed that the MEcPP-mediated induction is JA receptor COI1 dependent, potentially through elevated OPDA. These findings identify MEcPP as a previously unrecognized central regulatory module that induces JA-responsive genes in the presence of high SA, thereby staging a multifaceted plant response within the environmental context

Élaboration d’un tableau de bord pour la formation professorale dans les programmes de formation axés sur les compétences : projet de recherche orientée par la conception

Background: Canadian specialist residency training programs are implementing a form of competency-based medical education (CBME) that requires frequent assessments of entrustable professional activities (EPAs). Faculty struggle to provide helpful feedback and assign appropriate entrustment scores. CBME faculty development initiatives rarely incorporate teaching metrics. Dashboards could be used to visualize faculty assessment data to support faculty development. Methods: Using a design-based research process, we identified faculty development needs related to CBME assessments and designed a dashboard containing elements (data, analytics, and visualizations) meeting these needs. Data was collected within the emergency medicine residency program at the University of Saskatchewan through interviews with program leaders, faculty development experts, and faculty participating in development sessions. Two investigators thematically analyzed interview transcripts to identify faculty needs that were audited by a third investigator. The needs were described using representative quotes and the dashboard elements designed to address them. Results: Between July 1, 2019 and December 11, 2020 we conducted 15 interviews with nine participants (two program leaders, three faculty development experts, and four faculty members). Three needs emerged as themes from the analysis: analysis of assessments, contextualization of assessments, and accessible reporting. We addressed these needs by designing an accessible dashboard to present contextualized quantitative and narrative assessment data for each faculty member. Conclusions: We identified faculty development needs related to EPA assessments and designed dashboard elements to meet them. The resulting dashboard was used for faculty development sessions. This work will inform the development of CBME assessment dashboards for faculty.Contexte : Les programmes de résidence de spécialité au Canada mettent en œuvre une forme d’éducation axée sur les compétences (EASC) qui exige des évaluations formatives fréquentes des activités professionnelles confiables (APC). Les enseignants ont du mal à fournir une rétroaction utile et à attribuer des notes appropriées au niveau de confiance. Les initiatives de formation professorale des enseignants qui interviennent dans la EASC intègrent rarement leurs données psychométriques. Des tableaux de bord pourraient être utilisés pour visualiser les données d’évaluation du corps professoral afin de soutenir leur perfectionnement. Méthodes : En utilisant un processus de recherche orientée par la conception, nous avons déterminé les besoins de formation professorale liés aux évaluations dans la EASC et nous avons conçu un tableau de bord contenant des éléments (données, analyses et éléments visuels) pour répondre à ces besoins. Les données ont été recueillies dans le cadre du programme de résidence en médecine d’urgence de l’Université de Saskatchewan par le biais d’entretiens avec les responsables du programme, des experts en formation professorale et les enseignants participant aux séances de formation. Deux chercheurs ont procédé à une analyse thématique des transcriptions d’entrevues afin d’identifier les besoins des enseignants, et un troisième chercheur les a vérifiées. Les besoins ont été décrits à l’aide de citations représentatives et des éléments du tableau de bord conçus pour y répondre. Résultats : Entre le 1er juillet 2019 et le 11 décembre 2020, nous avons mené 15 entretiens avec neuf participants (deux responsables de programme, trois experts en formation professorale et quatre membres du corps professoral). Trois besoins sont ressortis comme thèmes de l’analyse : l’analyse des évaluations formatives, la contextualisation des évaluations formatives et l’accessibilité des rapports. Pour répondre à ces besoins, nous avons conçu un tableau de bord accessible présentant des données d’évaluation quantitatives et narratives contextualisées pour chaque membre du corps professoral. Conclusions : Nous avons identifié les besoins de formation professorale liés aux évaluations des APC et conçu les éléments d’un tableau de bord permettant d’y répondre. Le tableau de bord a été utilisé dans des séances de formation professorale. Ce travail orientera la réalisation de tableaux de bord afin de faciliter l’évaluation pour les enseignants dans le cadre de la EASC

Undiagnosed diabetic retinopathy in Northeast China: prevalence and determinants

ObjectiveTo report the prevalence and contributing factors of undiagnosed diabetic retinopathy (DR) in a population from Northeastern China.Subjects/MethodsA total of 800 subjects from the Fushun Diabetic Retinopathy Cohort Study were enrolled. A questionnaire assessing incentives and barriers to diagnosis of DR was administered. Logistic regression was used to identify clinical and sociodemographic factors associated with undiagnosed DR. In a prespecified subgroup analysis, we divided patients into vision-threatening diabetic retinopathy (VTDR) and non-VTDR (NVTDR) subgroups.ResultsAmong 800 participants with DR, 712 (89.0%) were undiagnosed. Among 601 with NVTDR, 566 (94.2%) were undiagnosed. Among 199 with VTDR, 146 (73.4%) were undiagnosed. The risk factors affecting the timely diagnosis of NVTDR and VTDR exhibit significant disparities. In multivariate models, factors associated with undiagnosed VTDR were age over 60 years (OR = 2.966; 95% CI = 1.205-7.299; P = 0.018), duration of diabetes over 10 years (OR = 0.299; 95% CI = 0.118-0753; P = 0.010), visual impairment or blindness (OR = 0.310; 95% CI = 0.117-0.820; P = 0.018), receiving a reminder to schedule an eye examination (OR = 0.380; 95% CI = 0.163-0.883; P = 0.025), and the belief that “people with diabetes are unlikely to develop an eye disease” (OR = 4.691; 95% CI = 1.116-19.724; P = 0.035). However, none of the factors were associated with undiagnosed NVTDR (all P ≥ 0.145).ConclusionOur research has uncovered a disconcerting trend of underdiagnosis in cases of DR within our population. Addressing determinants of undiagnosed DR may facilitate early detection

Biological control of potato common scab and growth promotion of potato by Bacillus velezensis Y6

Potato common scab, caused mainly by Streptomyces scabies, causes surface necrosis and reduces the economic value of potato tubers, but effective chemical control is still lacking. In this study, an attempt was made to control potato common scab by inoculating potatoes with Bacillus velezensis (B. velezensis) and to further investigate the mechanism of biological control. The results showed that B. velezensis Y6 could reduce the disease severity of potato common scab from 49.92 ± 25.74% [inoculated with Streptomyces scabies (S. scabies) only] to 5.56 ± 1.89% (inoculated with S. scabies and Y6 on the same day) and increase the potato yield by 37.32% compared with the control under pot experiment in this study. Moreover, in the field trial, it was found that Y6 could also significantly reduce disease severity from 13.20 ± 1.00% to 4.00 ± 0.70% and increase the potato yield from 2.07 ± 0.10 ton/mu to 2.87 ± 0.28 ton/mu (p < 0.01; Tukey’s test). Furthermore, RNA-seq analysis indicated that 256 potato genes were upregulated and 183 potato genes were downregulated in response to B. velezensis Y6 inoculation. In addition, strain Y6 was found to induce the expression of plant growth-related genes in potato, including cell wall organization, biogenesis, brassinosteroid biosynthesis, and plant hormone transduction genes, by 1.01–4.29 times. As well as up-regulate hydroquinone metabolism-related genes and several transcription factors (bHLH, MYB, and NAC) by 1.13–4.21 times. In summary, our study will help to understand the molecular mechanism of biological control of potato common scab and improve potato yield

Can Large Language Models Understand Real-World Complex Instructions?

Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or complex input that contains long context, noise, heterogeneous information and multi-turn format. Due to these features, LLMs often ignore semantic constraints from task descriptions, generate incorrect formats, violate length or sample count constraints, and be unfaithful to the input text. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions, as they are close-ended and simple. To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically. We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios. We also establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained. We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments. Resources of CELLO are publicly available at https://github.com/Abbey4799/CELLO