Under the Dim Night Sky

Under the Dim Night Sk

A Qualitative Pilot Study on Text Messaging Intervention for Weight Loss in Adults

Background Overweight and obesity are major risk factors for chronic illnesses such as cancer, diabetes and cardiovascular diseases. Newfoundland and Labrador (NL) has the highest rates of overweight and obesity of all provinces in Canada. Mobile health or mhealth in the form of text messaging is a potential solution to addressing the high overweight and obesity rates of the province. In this study, we explored NL residents’ perceptions of text message programs as an effective intervention for weight loss. Methods This study utilized a descriptive qualitative design through in-person semi-structured interviews with adults with previous or current experience in a weight loss program. Participants were recruited through recurrent postings on a biweekly school newsletter and study posters throughout the medical school at Memorial University. The data were analyzed using deductive thematic analysis. Results This pilot study included two participants, both women. The themes that arose in this study included past positive experiences, past negative experiences, barriers for weight loss, motivation for weight loss, attitudes about text messaging-based weight loss interventions and specific suggestions for future app development. The latter included text message content with reminders and encouragement, group messages, interactive and personal text messages and specific goal-setting in the app. Interpretation There were mixed attitudes towards using a text messaging based intervention. Findings revealed motivating factors of accountability, seeing positive physical bodily changes and goal-setting. Both participants had similar suggestions regarding future app development that involved creating a personalized and interactive experience for the users and to include a sense of community and communication across users of the app

Dynamic reorganization of the genome shapes the recombination landscape in meiotic prophase.

In meiotic prophase, chromosomes are organized into compacted loop arrays to promote homolog pairing and recombination. Here, we probe the architecture of the mouse spermatocyte genome in early and late meiotic prophase using chromosome conformation capture (Hi-C). Our data support the established loop array model of meiotic chromosomes, and infer loops averaging 0.8-1.0 megabase pairs (Mb) in early prophase and extending to 1.5-2.0 Mb in late prophase as chromosomes compact and homologs undergo synapsis. Topologically associating domains (TADs) are lost in meiotic prophase, suggesting that assembly of the meiotic chromosome axis alters the activity of chromosome-associated cohesin complexes. While TADs are lost, physically separated A and B compartments are maintained in meiotic prophase. Moreover, meiotic DNA breaks and interhomolog crossovers preferentially form in the gene-dense A compartment, revealing a role for chromatin organization in meiotic recombination. Finally, direct detection of interhomolog contacts genome-wide reveals the structural basis for homolog alignment and juxtaposition by the synaptonemal complex

DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models

Dataset expansion can effectively alleviate the problem of data scarcity for medical image segmentation, due to privacy concerns and labeling difficulties. However, existing expansion algorithms still face great challenges due to their inability of guaranteeing the diversity of synthesized images with paired segmentation masks. In recent years, Diffusion Probabilistic Models (DPMs) have shown powerful image synthesis performance, even better than Generative Adversarial Networks. Based on this insight, we propose an approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using DPM, which first samples a variety of masks from Gaussian noise to ensure the diversity, and then synthesizes images to ensure the alignment of images and masks. After that, DiffuseExpand chooses high-quality samples to further enhance the effectiveness of data expansion. Our comparison and ablation experiments on COVID-19 and CGMH Pelvis datasets demonstrate the effectiveness of DiffuseExpand. Our code is released at https://anonymous.4open.science/r/DiffuseExpand.Comment: 10 pages, 5 figure

Intersection Complexity and Its Influence on Human Drivers

As mixed traffic between automated vehicles and human drivers in inner city becomes more prevalent in the near future understanding and predicting drivers’ behavior is important. Additionally, there is a wide variety of inner city intersections. They can differ greatly in traffic density, visibility, number of objects and many more aspects. This difference in complexity has an influence on the behavior of human drivers at intersections. To further understand the effect of complexity we conducted a naturalistic driving field study in inner city traffic with 34 participants. We focused on unsignalized intersections because there is a greater range of possibly ambiguous situations at such intersections than compared to e.g. an intersection regulated by traffic lights. Features describing the behavior (commit distance, drop in velocity and the minimal velocity) are extracted from the driven trajectories. Additionally, we define intersection complexity by several features describing an intersection. These features include both the static (street, visible and driveable width, the visibility of the other streets and the number of trees) and the dynamic environment (entry location and turning direction, numbers of vehicles, vehicles with interaction, vehicles with priority, vehicles having to yield and pedestrians). Based on those we show that the entry location and the turning direction have a significant effect on the behavior features. Additionally, we show that the typical behavior of human drivers can be predicted by the features describing an intersection’s complexity. Finally, the feature set is reduced in dimensionality for a more condensed intersection description. For that we test reduced feature sets as well as feature sets from an autoencoder and show that prediction is feasible with them as well

Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Yang, X., Zhu, Z., Qiu, S., Kroeger, K. D., Zhu, Z., & Covington, S. Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series. Remote Sensing of Environment, 276, (2022): 113047, https://doi.org/10.1016/j.rse.2022.113047.Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km2 of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km2 per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.This study was supported by USGS North Atlantic Coast Cooperative Ecosystem Studies Unit (CESU) Program for Detection and Characterization of Coastal Tidal Wetland Change (G19AC00354)

DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically. Different from typical image captioning approaches that generate reports with an encoder and a decoder, DeltaNet applies a conditional generation process. In particular, given a medical image, DeltaNet employs three steps to generate a report: 1) first retrieving related medical reports, i.e., the historical reports from the same or similar patients; 2) then comparing retrieved images and current image to find the differences; 3) finally generating a new report to accommodate identified differences based on the conditional report. We evaluate DeltaNet on a COVID-19 dataset, where DeltaNet outperforms state-of-the-art approaches. Besides COVID-19, the proposed DeltaNet can be applied to other diseases as well. We validate its generalization capabilities on the public IU-Xray and MIMIC-CXR datasets for chest-related diseases. Code is available at \url{https://github.com/LX-doctorAI1/DeltaNet}