Cactus pear (Opuntia ficus-indica) productivity, proximal composition and soil parameters as affected by planting time and agronomic management in a semi-arid region of india

Study of appropriate planting time and response to agronomic management practices is imperative for the newly introduced cactus pear (Opuntia ficus-indica (L.) Mill.) into a semi-arid region of India. Responses of cactus pear to agronomic practices (planting time and irrigation and fertilizer application) were evaluated to determine the potential for fodder production and livestock feed in a semi-arid environment of India. We assessed four planting times (February, March, July and October) and two agronomic managements (with and without irrigation and fertilizer application) during 2016–2020 at Jhansi, India. Cactus pear establishment and growth improved with planting time in July and October due to favorable soil moisture and congenial temperature. However, plant height (19 cm) and cladode weight (118 g) were greater in July than in October planting. Nutrient uptake and crude protein contents, however, were higher for the earlier plantings of February and April compared to June and October. Irrigation and nutrients application had little effect on the cactus pear plant growth, except on plant width and cladode length and width. Cactus pear can be planted during July in moderately fertile soils without any agronomic intervention in semi-arid situations of India and has potential as an effective alternative source of forage for livestock during the summer months

Machine learning for the Zwicky transient facility

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

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

Machine Learning for the Zwicky Transient Facility

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective