C4Synth: Cross-Caption Cycle-Consistent Text-to-Image Synthesis

Generating an image from its description is a challenging task worth solving because of its numerous practical applications ranging from image editing to virtual reality. All existing methods use one single caption to generate a plausible image. A single caption by itself, can be limited, and may not be able to capture the variety of concepts and behavior that may be present in the image. We propose two deep generative models that generate an image by making use of multiple captions describing it. This is achieved by ensuring 'Cross-Caption Cycle Consistency' between the multiple captions and the generated image(s). We report quantitative and qualitative results on the standard Caltech-UCSD Birds (CUB) and Oxford-102 Flowers datasets to validate the efficacy of the proposed approach.Comment: To appear in the proceedings of IEEE Winter Conference on Applications of Computer Vision, WACV-201

Characterization of Leishmania donovani MCM4: Expression Patterns and Interaction with PCNA

Events leading to origin firing and fork elongation in eukaryotes involve several proteins which are mostly conserved across the various eukaryotic species. Nuclear DNA replication in trypanosomatids has thus far remained a largely uninvestigated area. While several eukaryotic replication protein orthologs have been annotated, many are missing, suggesting that novel replication mechanisms may apply in this group of organisms. Here, we characterize the expression of Leishmania donovani MCM4, and find that while it broadly resembles other eukaryotes, noteworthy differences exist. MCM4 is constitutively nuclear, signifying that, unlike what is seen in S.cerevisiae, varying subcellular localization of MCM4 is not a mode of replication regulation in Leishmania. Overexpression of MCM4 in Leishmania promastigotes causes progress through S phase faster than usual, implicating a role for MCM4 in the modulation of cell cycle progression. We find for the first time in eukaryotes, an interaction between any of the proteins of the MCM2-7 (MCM4) and PCNA. MCM4 colocalizes with PCNA in S phase cells, in keeping with the MCM2-7 complex being involved not only in replication initiation, but fork elongation as well. Analysis of a LdMCM4 mutant indicates that MCM4 interacts with PCNA via the PIP box motif of MCM4 - perhaps as an integral component of the MCM2-7 complex, although we have no direct evidence that MCM4 harboring a PIP box mutation can still functionally associate with the other members of the MCM2-7 complex- and the PIP box motif is important for cell survival and viability. In Leishmania, MCM4 may possibly help in recruiting PCNA to chromatin, a role assigned to MCM10 in other eukaryotes

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Contrasting Social Media Use Between Young Adults With Inflammatory Bowel Disease and Type 1 Diabetes: Cross-sectional Study

BackgroundSocial media is used by young adult patients for social connection and self-identification. ObjectiveThis study aims to compare the social media habits of young adults with inflammatory bowel disease (IBD) and type 1 diabetes (T1D). MethodsThis is a cross-sectional study of subjects from Boston Children’s Hospital outpatient IBD and diabetes clinics. Patients above 18 years of age were invited to complete a brief anonymous survey, which asked about the various ways they use several social media platforms. ResultsResponses were received from 108 patients (92.5% response rate), evenly split across disease type. We found that 83% of participants spent at least 30 minutes per day on social media, most commonly on Instagram and Facebook. Although the content varied based on the platform, patients with IBD posted or shared content related to their disease significantly less than those with T1D (23% vs 38%, P=.02). Among Instagram users, patients with IBD were less likely to engage with support groups (22% vs 56%, P=.04). Among Twitter users, patients with IBD were less likely to seek disease information (77% vs 29%, P=.005). Among Facebook users, patients with IBD were less likely to post about research and clinical trials (31% vs 65%, P=.04) or for information seeking (49% vs 87%, P=.003). Patients with IBD were also less likely to share their diagnosis with friends or family in person. ConclusionsYoung adults with IBD were less willing to share their diagnosis and post about or explore the disease on social media compared to those with T1D. This could lead to a sense of isolation and should be further explored

Emery-Dreifuss muscular dystrophy mutations impair TRC40-mediated targeting of emerin to the inner nuclear membrane.

Emerin is a tail-anchored protein that is found predominantly at the inner nuclear membrane (INM), where it associates with components of the nuclear lamina. Mutations in the emerin gene cause Emery-Dreifuss muscular dystrophy (EDMD), an X-linked recessive disease. Here, we report that the TRC40/GET pathway for post-translational insertion of tail-anchored proteins into membranes is involved in emerin-trafficking. Using proximity ligation assays, we show that emerin interacts with TRC40 in situ. Emerin expressed in bacteria or in a cell-free lysate was inserted into microsomal membranes in an ATP- and TRC40-dependent manner. Dominant-negative fragments of the TRC40-receptor proteins WRB and CAML (also known as CAMLG) inhibited membrane insertion. A rapamycin-based dimerization assay revealed correct transport of wild-type emerin to the INM, whereas TRC40-binding, membrane integration and INM-targeting of emerin mutant proteins that occur in EDMD was disturbed. Our results suggest that the mode of membrane integration contributes to correct targeting of emerin to the INM