A Field Test of Popular Chatbots’ Responses To Questions Concerning Negative Body Image

Background: Chatbots are computer programs, often built upon large artificial intelligence models, that employ dialogue systems to enable online, natural language conversations with users via text, speech, or both. Body image, broadly defined as a combination of thoughts and feelings about one’s physical appearance, has been implicated in many risk behaviors and health problems, especially among adolescents and young adults. Little is known about how chatbots respond to questions about body image. Methods: This study assessed the responses of 14 widely-used chatbots (eight companion and six therapeutic chatbots) to ten body image-related questions developed upon validated instruments. Chatbots’ responses were documented, with qualities systematically assessed by nine pre-determined criteria. Results: The overall quality of the chatbots’ responses was modest (an average score of five out of nine), with substantial variations in the content and quality of responses across chatbots (individual scores ranging from one to eight). Companion and therapeutic chatbots systematically differed in their responses (e.g., focusing on comforting users vs. trying to identify the causes of negative body image and recommending potential remedies). Some therapeutic chatbots recognized potential mental health crises (self-harm) in test users’ messages. Conclusion: Substantial heterogeneities in the responses were present across chatbots and assessment criteria. Adolescents and young adults struggling with body image could be vulnerable to misleading or biased remarks made by chatbots. Still, the technical and supervision challenges to prevent those adverse consequences remain paramount and unsolved

Reinforcement of dry spun polymeric fibers by cellulose nanocrystal

This study presents the development of composite polymeric fibers using cellulose nanocrystals (CNCs) as reinforcements. CNCs are a class of low cost, renewable and biodegradable materials with high mechanical properties and customizable surfaces. In this study, CNCs were successfully integrated into various polymeric fibers using the method of dry spinning in efforts to improve the fibers’ tensile strength and modulus. The effects of CNCs on two different polymer systems (cellulose acetate and polyvinyl alcohol) were studied. The surface morphologies, mechanical properties, and interactions between the CNCs and the polymer matrix within the fibers were investigated. The results of the characterizations show significant improvement in the tensile strength and modulus of both the cellulose acetate and polyvinyl alcohol fibers with low dosage of CNCs. The presence of CNCs increased the crystallinity of the polymer matrix. The effects of the high shear rates associated with dry spinning on the alignment and dispersion of the nanocrystals in the different systems were also studied. A micromechanical model was developed using data from both systems for the prediction of the fiber mechanical properties as a function of the alignment of the CNC rods

Hammerhead-type Fxr Agonists Induce an Enhancer Rna Fincor That Ameliorates Nonalcoholic Steatohepatitis in Mice

The nuclear receptor, farnesoid X receptor (FXR/NR1H4), is increasingly recognized as a promising drug target for metabolic diseases, including nonalcoholic steatohepatitis (NASH). Protein-coding genes regulated by FXR are well known, but whether FXR also acts through regulation of long non-coding RNAs (lncRNAs), which vastly outnumber protein-coding genes, remains unknown. Utilizing RNA-seq and global run-on sequencing (GRO-seq) analyses in mouse liver, we found that FXR activation affects the expression of many RNA transcripts from chromatin regions bearing enhancer features. Among these we discovered a previously unannotated liver-enriched enhancer-derived lncRNA (eRNA), termed FXR-induced non-coding RNA

Impact of the COVID-19 pandemic on emergency department CT for suspected diverticulitis: A natural experiment to explain patients’ and clinicians’ assessment of risk and willingness to undergo CT scanning? [preprint]

Purpose: This study examined the impact of the COVID-19 pandemic on emergency department CT use for acute non-traumatic abdominal pain, to better understand why imaging volume so drastically decreased during the COVID-19 pandemic. Methods: This was a retrospective review of emergency imaging volumes from January 5 to May 30, 2020. Weekly volume data were collected for total imaging studies, abdominopelvic CT, and abdominopelvic CTs positive for common causes of acute non-traumatic abdominal pain. Two emergency radiology attendings scored all diverticulitis cases independently and weekly volume data for uncomplicated and complicated diverticulitis cases was also collected. Volume data prior to and during the COVID-19 pandemic was compared, using 2019 volumes as a control. Results: During the COVID-19 pandemic, overall emergency imaging volume decreased 30% compared to 2019 (p = 0.002). While the number of emergency abdominopelvic CTs positive for appendicitis and small bowel obstruction did not significantly change during the COVID-19 pandemic, the number of cases of diverticulitis decreased significantly compared to 2019 (p = 0.001). This reduction can be specifically attributed to decreased uncomplicated diverticulitis cases, as the number of uncomplicated diverticulitis cases dropped significantly (p = 0.002) while there was no significant difference in the number of complicated diverticulitis cases (p = 0.09). Conclusions: Reduced emergency abdominopelvic CT volume during the COVID-19 pandemic can partially be explained by decreased imaging of lower acuity patients. This data may help formulate future strategies for imaging resource utilization with an improved understanding of the relationship between perceived imaging risk and symptom acuity

SAGE2Splice: Unmapped SAGE Tags Reveal Novel Splice Junctions

Serial analysis of gene expression (SAGE) not only is a method for profiling the global expression of genes, but also offers the opportunity for the discovery of novel transcripts. SAGE tags are mapped to known transcripts to determine the gene of origin. Tags that map neither to a known transcript nor to the genome were hypothesized to span a splice junction, for which the exon combination or exon(s) are unknown. To test this hypothesis, we have developed an algorithm, SAGE2Splice, to efficiently map SAGE tags to potential splice junctions in a genome. The algorithm consists of three search levels. A scoring scheme was designed based on position weight matrices to assess the quality of candidates. Using optimized parameters for SAGE2Splice analysis and two sets of SAGE data, candidate junctions were discovered for 5%–6% of unmapped tags. Candidates were classified into three categories, reflecting the previous annotations of the putative splice junctions. Analysis of predicted tags extracted from EST sequences demonstrated that candidate junctions having the splice junction located closer to the center of the tags are more reliable. Nine of these 12 candidates were validated by RT-PCR and sequencing, and among these, four revealed previously uncharacterized exons. Thus, SAGE2Splice provides a new functionality for the identification of novel transcripts and exons. SAGE2Splice is available online at http://www.cisreg.ca

Visualizing aggregated biological pathway relations

The Genescene development team has constructed an aggregation interface for automatically-extracted biomedical pathway relations that is intended to help researchers identify and process relevant information from the vast digital library of abstracts found in the National Library of Medicine’s PubMed collection. Users view extracted relations at various levels of relational granularity in an interactive and visual node-link interface. Anecdotal feedback reported here suggests that this multi-granular visual paradigm aligns well with various research tasks, helping users find relevant articles and discover new information

Structures of the Ultra-High-Affinity Protein-Protein Complexes of Pyocins S2 and AP41 and Their Cognate Immunity Proteins from Pseudomonas aeruginosa

© 2015 The Authors. Published by Elsevier Ltd. How ultra-high-affinity protein-protein interactions retain high specificity is still poorly understood. The interaction between colicin DNase domains and their inhibitory immunity (Im) proteins is an ultra-high-affinity interaction that is essential for the neutralisation of endogenous DNase catalytic activity and for protection against exogenous DNase bacteriocins. The colicin DNase-Im interaction is a model system for the study of high-affinity protein-protein interactions. However, despite the fact that closely related colicin-like bacteriocins are widely produced by Gram-negative bacteria, this interaction has only been studied using colicins from Escherichia coli. In this work, we present the first crystal structures of two pyocin DNase-Im complexes from Pseudomonas aeruginosa, pyocin S2 DNase-ImS2 and pyocin AP41 DNase-ImAP41. These structures represent divergent DNase-Im subfamilies and are important in extending our understanding of protein-protein interactions for this important class of high-affinity protein complex. A key finding of this work is that mutations within the immunity protein binding energy hotspot, helix III, are tolerated by complementary substitutions at the DNase-Immunity protein binding interface. Im helix III is strictly conserved in colicins where an Asp forms polar interactions with the DNase backbone. ImAP41 contains an Asp-to-Gly substitution in helix III and our structures show the role of a co-evolved substitution where Pro in DNase loop 4 occupies the volume vacated and removes the unfulfilled hydrogen bond. We observe the co-evolved mutations in other DNase-Immunity pairs that appear to underpin the split of this family into two distinct groups

TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation

Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime. Despite success in structured, on-road settings, current end-to-end approaches for scene prediction have yet to be successfully adapted for complex outdoor terrain. To this end, we present TerrainNet, a vision-based terrain perception system for semantic and geometric terrain prediction for aggressive, off-road navigation. The approach relies on several key insights and practical considerations for achieving reliable terrain modeling. The network includes a multi-headed output representation to capture fine- and coarse-grained terrain features necessary for estimating traversability. Accurate depth estimation is achieved using self-supervised depth completion with multi-view RGB and stereo inputs. Requirements for real-time performance and fast inference speeds are met using efficient, learned image feature projections. Furthermore, the model is trained on a large-scale, real-world off-road dataset collected across a variety of diverse outdoor environments. We show how TerrainNet can also be used for costmap prediction and provide a detailed framework for integration into a planning module. We demonstrate the performance of TerrainNet through extensive comparison to current state-of-the-art baselines for camera-only scene prediction. Finally, we showcase the effectiveness of integrating TerrainNet within a complete autonomous-driving stack by conducting a real-world vehicle test in a challenging off-road scenario

Signal to Noise Ratio Estimation of the ASCENDS CarbonHawk Experiment Simulator (ACES) for Atmospheric CO2 Measurement

No abstract availabl