Butterfly species diversity and their floral preferences in the Rupa Wetland of Nepal

Floral attributes often influence the foraging choices of nectar-feeding butterflies, given the close association between plants and these butterfly pollinators. The diversity of butterflies is known to a large extent in Nepal, but little information is available on the feeding habits of butterflies. This study was conducted along the periphery of Rupa Wetland from January to December 2019 to assess butterfly species diversity and to identify the factors influencing their foraging choices. In total, we recorded 1535 individuals of 138 species representing all six families. For our examination of butterfly-nectar plant interactions, we recorded a total of 298 individuals belonging to 31 species of butterfly visiting a total of 28 nectar plant species. Overall, total butterfly visitation was found to be significantly influenced by plant category (herbaceous preferred over woody), floral color (yellow white and purple preferred over pink), and corolla type (tubular preferred over nontubular). Moreover, there was a significant positive correlation between the proboscis length of butterflies and the corolla tube length of flowers. Examining each butterfly family separately revealed that, for four of the families (Lycaenidae, Nymphalidae, Papilionidae, and Pieridae), none of the tested factors (flower color, plant category, and corolla type) were shown to significantly influence butterfly abundance at flowers. However, Hesperidae abundance was found to be significantly influenced by both flower color (with more butterflies observed at yellow flowers than purple) and flower type (with more butterflies observed at tubular flowers than nontubular flowers). Our results reveal that Rupa Lake is a suitable habitat for butterflies, providing valuable floral resources. Hence, further detailed studies encompassing all seasons, a greater variety of plants, and other influential factors in different ecological regions are fundamental for creating favorable environments to sustain important butterfly pollinators and help create balanced wetland ecosystems.Peer reviewe

Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts are affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-Net, a neural transfer learning architecture which allows us to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it should emphasize learning from COVID-related signals and when it should learn from the historical model. Thus, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.Comment: Appears in AAAI-2

C-H bond functionalization under electrochemical flow conditions

Electrochemical C−H functionalization is a rapidly growing area of interest in organic synthesis. To achieve maximum atom economy, the flow electrolysis process is more sustainable. This allows shorter reaction times, safer working environments, and better selectivities. Using this technology, the problem of overoxidation can be reduced and less emergence of side products or no side products are possible. Flow electro-reactors provide high surface-to-volume ratios and contain electrodes that are closely spaced where the diffusion layers overlap to give the desired product, electrochemical processes can now be managed without the need for a deliberately added supporting electrolyte. Considering the importance of flow electrochemical C−H functionalization, a comprehensive review is presented. Herein, we summarize flow electrolysis for the construction of C−C and C−X (X=O, N, S, and I) bonds formation. Also, benzylic oxidation and access to biologically active molecules are discussed

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