Investigating user preferences in utilizing a 2D paper or 3D sketch based interface for creating 3D virtual models

Computer modelling of 2D drawings is becoming increasingly popular in modern design as can be witnessed in the shift of modern computer modelling applications from software requiring specialised training to ones targeted for the general consumer market. Despite this, traditional sketching is still prevalent in design, particularly so in the early design stages. Thus, research trends in computer-aided modelling focus on the the development of sketch based interfaces that are as natural as possible. In this report, we present a hybrid sketch based interface which allows the user to make draw sketches using offline as well as online sketching modalities, displaying the 3D models in an immersive setup, thus linking the object interaction possible through immersive modelling to the flexibility allowed by paper-based sketching. The interface was evaluated in a user study which shows that such a hybrid system can be considered as having pragmatic and hedonic value.peer-reviewe

Addressing Health Literacy in Patient Decision Aids:An Update from the International Patient Decision Aid Standards

BACKGROUND: There is increasing recognition of the importance of addressing health literacy in patient decision aid (PtDA) development. PURPOSE: An updated review as part of IPDAS 2.0 examined the extent to which PtDAs are designed to meet the needs of low health literacy/disadvantaged populations. DATA SOURCES: Reference list of Cochrane review of randomised controlled trials (RCTs) of PtDAs (2014, 2017 and upcoming 2021 versions). STUDY SELECTION: RCTs that assessed the impact of PtDAs on low health literacy or other disadvantaged groups (i.e. ≥50% participants from disadvantaged groups and/or subgroup analysis in disadvantaged group/s). DATA EXTRACTION: Two researchers independently extracted data into a standardized form including PtDA development and evaluation details. We searched online repositories and emailed authors to access PtDAs to verify reading level, understandability and actionability. DATA SYNTHESIS: Twenty-five out of 213 RCTs met inclusion criteria illustrating that only 12% of studies addressed the needs of low health literacy or other disadvantaged groups. Reading age was calculated in 8/25 studies (33%), which is recommended in previous IPDAS guidelines. We accessed and independently assessed 11 PtDAs. None were written at 6(th) grade level or below. Ten PtDAs met the recommended threshold for understandability, but only 5 met the recommended threshold for actionability. We also conducted a post-hoc subgroup meta-analysis and found that knowledge improvements after receiving a PtDA were greater in studies that reported using strategies to reduce cognitive demand in PtDA development compared to studies that did not (Chi(2)=14.11, p=0.0002, I(2)=92.9%). LIMITATIONS: We were unable to access 13 out of 24 PtDAs. CONCLUSIONS: Greater attention to health literacy and disadvantaged populations is needed in the field of PtDAs to ensure equity in decision support

Investigating user response to a hybrid sketch based interface for creating 3D virtual models in an immersive environment

This research was done in collaboration with the Fraunhofer Institute for Production Systems and Design Technology Berlin. It was supported by VISIONAIR, a project funded by the European Commission under grant agreement 262044.Computer modelling of 2D drawings is becoming increasingly popular in modern design as can be witnessed in the shift of modern computer modelling applications from software requiring specialised training to ones targeted for the general consumer market. Despite this, traditional sketching is still prevalent in design, particularly so in the early design stages. Thus, research trends in computer-aided modelling focus on the the development of sketch based interfaces that are as natural as possible. In this paper, we present a hybrid sketch based interface which allows the user to make draw sketches using offline as well as online sketching modalities, displaying the 3D models in an immersive setup, thus linking the object interaction possible through immersive modelling to the flexibility allowed by paper-based sketching. The interface was evaluated in a user study which shows that such a hybrid system can be considered as having pragmatic and hedonic value.peer-reviewe

A Systematic Review and Meta-Analysis of Patient Decision Aids for Socially Disadvantaged Populations:Update from the International Patient Decision Aid Standards (IDPAS)

International audienceBackground. The effectiveness of patient decision aids (PtDAs) and other shared decision-making (SDM) interventions for socially disadvantaged populations has not been well studied. Purpose. To assess whether PtDAs and other SDM interventions improve outcomes or decrease health inequalities among socially disadvantaged populations and determine the critical features of successful interventions. Data Sources. MEDLINE, CINAHL, Cochrane, Psy-cINFO, and Web of Science from inception to October 2019. Cochrane systematic reviews on PtDAs. Study Selection. Randomized controlled trials of PtDAs and SDM interventions that included socially disadvantaged populations. Data Extraction. Independent double data extraction using a standardized form and the Template for Intervention Description and Replication checklist. Data Synthesis. Twenty-five PtDA and 13 other SDM intervention trials met our inclusion criteria. Compared with usual care, PtDAs improved knowledge (mean difference = 13.91, 95% confidence interval [CI] 9.01, 18.82 [I 2 = 96%]) and patient-clinician communication (relative risk = 1.62, 95% CI 1.42, 1.84 [I 2 = 0%]). PtDAs reduced decisional conflict (mean difference = 29.59; 95% CI 218.94, 20.24 [I 2 = 84%]) and the proportion undecided (relative risk = 0.39; 95% CI 0.28, 0.53 [I 2 = 75%]). PtDAs did not affect anxiety (standardized mean difference = 0.02, 95% CI 20.22, 0.26 [I 2 = 70%]). Only 1 trial looked at clinical outcomes (hemoglobin A1C). Five of the 12 PtDA studies that compared outcomes by disadvantaged standing found that outcomes improved more for socially disadvantaged participants. No evidence indicated which intervention characteristics were most effective. Results were similar for SDM intervention trials. Limitations. Sixteen PtDA studies had an overall unclear risk of bias. Heterogeneity was high for most outcomes. Most studies only had short-term follow-up. Conclusions. PtDAs led to better outcomes among socially disadvantaged populations but did not reduce health inequalities. We could not determine which intervention features were most effective

Association between haplotypes of manganese superoxide dismutase (SOD2), smoking, and lung cancer risk

Tobacco smoke contains high concentrations of reactive oxygen species (ROS) that can damage DNA, proteins, and lipids. Manganese superoxide dismutase (SOD2) catalyzes the dismutation of superoxide radicals into hydrogen peroxide and protects against oxidative stress in lung tissues. Three tagSNPs were identified in one block of high linkage disequilibrium that spans the entire SOD2 gene and 5-kb promoter region. These tagSNPs, representing four haplotypes (TAA, TCA, TCG, CCG), were genotyped in 372 lung cancer cases and 605 controls. There was no association between the haplotype frequencies and the overall lung cancer risk. The TCG haplotype (6% in controls) was significantly associated with a lower risk of lung cancer in light smokers (< median pack-years; P value = 0.02) but not in heavy smokers. In histologic-specific analysis, the TCG haplotype was significantly associated with a reduced risk of lung adenocarcinoma (odds ratio = 0.39, 95% CI 0.17–0.88), but an inverse association with squamous cell carcinoma was not significant. The association with adenocarcinoma was most apparent in light smokers (haplotype-specific P value = 0.005); none of the 61 case subjects with adenocarcinoma had the TCG allele. This study suggests that subjects with the SOD2 TCG haplotype may be at decreased risk for lung adenocarcinoma and that this association may depend on smoking amount

Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.0)

The PARSEME shared task aims at identifying verbal MWEs in running texts. Verbal MWEs include idioms (let the cat out of the bag), light verb constructions (make a decision), verb-particle constructions (give up), and inherently reflexive verbs (se suicider 'to suicide' in French). VMWEs were annotated according to the universal guidelines in 18 languages. The corpora are provided in the parsemetsv format, inspired by the CONLL-U format. For most languages, paired files in the CONLL-U format - not necessarily using UD tagsets - containing parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). This item contains training and test data, tools and the universal guidelines file

PARSEME corpora annotated for verbal multiword expressions (version 1.3)

This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). This is the first release of the corpora without an associated shared task. Previous version (1.2) was associated with the PARSEME Shared Task on semi-supervised Identification of Verbal MWEs (2020). The data covers 26 languages corresponding to the combination of the corpora for all previous three editions (1.0, 1.1 and 1.2) of the corpora. VMWEs were annotated according to the universal guidelines. The corpora are provided in the cupt format, inspired by the CONLL-U format. Morphological and syntactic information, ­­­­including parts of speech, lemmas, morphological features and/or syntactic dependencies, are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). All corpora are split into training, development and test data, following the splitting strategy adopted for the PARSEME Shared Task 1.2. The annotation guidelines are available online: https://parsemefr.lis-lab.fr/parseme-st-guidelines/1.3 The .cupt format is detailed here: https://multiword.sourceforge.net/cupt-format

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.