Black Students' Graduation from Elite Colleges: Institutional Characteristics and Between-institution Differences

Among the nation’s elite colleges and universities, black graduation rates vary dramatically from institution to institution. Many sociologists have suggested that this is due not to differences in the student bodies but to institutional factors; however, this “institutional hypothesis” has not been recently examined empirically. We test the institutional hypothesis for a set of elite institutions using College and Beyond, a restricted dataset containing data for the entire 1989 cohort of 27 elite institutions, matched to institution-level data, employing HLM techniques. We ask three questions: Do institutional factors affect black students’ probability of graduation? Do they account for between-institution differences in black graduation? And are institutions where blacks have a high probability of graduation the same as or different from those where whites do? Testing for the effect of eight major institutional factors, we find, surprisingly, that only selectivity has a statistically significant effect. Contrary to common belief, selectivity improves black probabilities of graduation, and helps blacks more than it helps whites. It also accounts for roughly 38% of the between-institution variance in black graduation. Finally, we find that after controls, black and white probabilities of graduation across institutions are highly correlated (.909), such that institutions in which blacks are likely to graduate are those in which whites are likely to graduate, too. Findings suggest that researchers should examine other institutional factors in greater depth, as well as the role of pre-college preparation more seriously.Sociolog

Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors

The food security of smallholder farmers is vulnerable to climate change and climate variability. Cropping intensity, the number of crops planted annually, can be used as a measure of food security for smallholder farmers given that it can greatly affect net production. Current techniques for quantifying cropping intensity may not accurately map smallholder farms where the size of one field is typically smaller than the spatial resolution of readily available satellite data. We evaluated four methods that use multi-scalar datasets and are commonly used in the literature to assess cropping intensity of smallholder farms: 1) the Landsat threshold method, which identifies if a Landsat pixel is cropped or uncropped during each growing season, 2) the MODIS peak method, which determines if there is a phenological peak in the MODIS Enhanced Vegetation Index time series during each growing season, 3) the MODIS temporal mixture analysis, which quantifies the sub-pixel heterogeneity of cropping intensity using phenological MODIS data, and 4) the MODIS hierarchical training method, which quantifies the sub-pixel heterogeneity of cropping intensity using hierarchical training techniques. Each method was assessed using four criteria: 1) data availability, 2) accuracy across different spatial scales (at aggregate scales 250 × 250 m, 1 × 1 km, 5 × 5 km, and 10 × 10 km), 3) ease of implementation, and 4) ability to use the method over large spatial and temporal scales. We applied our methods to two regions in India (Gujarat and southeastern Madhya Pradesh) that represented diversity in crop type, soils, climatology, irrigation access, cropping intensity, and field size. We found that the Landsat threshold method is the most accurate (R2 greater than or equal to 0.71 and RMSE less than or equal to 0.14), particularly at smaller scales of analysis. Yet given the limited availability of Landsat data, we find that the MODIS hierarchical training method meets multiple criteria for mapping cropping intensity over large spatial and temporal scales. Furthermore, the adjusted R2 between predicted and validation data generally increased and the RMSE decreased with spatial aggregation greater than or equal to 5 × 5 km (R2 up to 0.97 and RMSE as low as 0.00). Our model accuracy varied based on the region and season of analysis and was lowest during the summer season in Gujarat when there was high sub-pixel heterogeneity due to sparsely cropped agricultural land-cover. While our results specifically apply to our study regions in India, they most likely also apply to smallholder agriculture in other locations across the globe where the same types of satellite data are readily available

Sensitivity of crop cover to climate variability: Insights from two Indian agro-ecoregions

Crop productivity in India varies greatly with inter-annual climate variability and is highly dependent on monsoon rainfall and temperature. The sensitivity of yields to future climate variability varies with crop type, access to irrigation and other biophysical and socio-economic factors. To better understand sensitivities to future climate, this study focuses on agro-ecological subregions in Central and Western India that span a range of crops, irrigation, biophysical conditions and socioeconomic characteristics. Climate variability is derived from remotely-sensed data products, Tropical Rainfall Measuring Mission (TRMM – precipitation) and Moderate Resolution Imaging Spectroradiometer (MODIS – temperature). We examined green-leaf phenologies as proxy for crop productivity using the MODIS Enhanced Vegetation Index (EVI) from 2000 to 2012. Using both monsoon and winter growing seasons, we assessed phenological sensitivity to inter-annual variability in precipitation and temperature patterns. Inter-annual EVI phenology anomalies ranged from −25% to 25%, with some highly anomalous values up to 200%. Monsoon crop phenology in the Central India site is highly sensitive to climate, especially the timing of the start and end of the monsoon and intensity of precipitation. In the Western India site, monsoon crop phenology is less sensitive to precipitation variability, yet shows considerable fluctuations in monsoon crop productivity across the years. Temperature is critically important for winter productivity across a range of crop and management types, such that irrigation might not provide a sufficient buffer against projected temperature increases. Better access to weather information and usage of climate-resilient crop types would play pivotal role in maintaining future productivity. Effective strategies to adapt to projected climate changes in the coming decades would also need to be tailored to regional biophysical and socio-economic conditions

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties

Activated lymphocyte recruitment into the tumor microenvironment following preoperative sipuleucel-T for localized prostate cancer.

BackgroundSipuleucel-T is a US Food and Drug Administration-approved immunotherapy for asymptomatic or minimally symptomatic metastatic castration-resistant prostate cancer (mCRPC). Its mechanism of action is not fully understood. This prospective trial evaluated the direct immune effects of systemically administered sipuleucel-T on prostatic cancer tissue in the preoperative setting.MethodsPatients with untreated localized prostate cancer were treated on an open-label Phase II study of sipuleucel-T prior to planned radical prostatectomy (RP). Immune infiltrates in RP specimens (posttreatment) and in paired pretreatment biopsies were evaluated by immunohistochemistry (IHC). Correlations between circulating immune response and IHC were assessed using Spearman rank order.ResultsOf the 42 enrolled patients, 37 were evaluable. Adverse events were primarily transient, mild-to-moderate and infusion related. Patients developed T cell proliferation and interferon-γ responses detectable in the blood following treatment. Furthermore, a greater-than-three-fold increase in infiltrating CD3(+), CD4(+) FOXP3(-), and CD8(+) T cells was observed in the RP tissues compared with the pretreatment biopsy (binomial proportions: all P < .001). This level of T cell infiltration was observed at the tumor interface, and was not seen in a control group consisting of 12 concurrent patients who did not receive any neoadjuvant treatment prior to RP. The majority of infiltrating T cells were PD-1(+) and Ki-67(+), consistent with activated T cells. Importantly, the magnitude of the circulating immune response did not directly correlate with T cell infiltration within the prostate based upon Spearman's rank order correlation.ConclusionsThis study is the first to demonstrate a local immune effect from the administration of sipuleucel-T. Neoadjuvant sipuleucel-T elicits both a systemic antigen-specific T cell response and the recruitment of activated effector T cells into the prostate tumor microenvironment

Population dynamics of rhesus macaques and associated foamy virus in Bangladesh.

Foamy viruses are complex retroviruses that have been shown to be transmitted from nonhuman primates to humans. In Bangladesh, infection with simian foamy virus (SFV) is ubiquitous among rhesus macaques, which come into contact with humans in diverse locations and contexts throughout the country. We analyzed microsatellite DNA from 126 macaques at six sites in Bangladesh in order to characterize geographic patterns of macaque population structure. We also included in this study 38 macaques owned by nomadic people who train them to perform for audiences. PCR was used to analyze a portion of the proviral gag gene from all SFV-positive macaques, and multiple clones were sequenced. Phylogenetic analysis was used to infer long-term patterns of viral transmission. Analyses of SFV gag gene sequences indicated that macaque populations from different areas harbor genetically distinct strains of SFV, suggesting that geographic features such as forest cover play a role in determining the dispersal of macaques and SFV. We also found evidence suggesting that humans traveling the region with performing macaques likely play a role in the translocation of macaques and SFV. Our studies found that individual animals can harbor more than one strain of SFV and that presence of more than one SFV strain is more common among older animals. Some macaques are infected with SFV that appears to be recombinant. These findings paint a more detailed picture of how geographic and sociocultural factors influence the spectrum of simian-borne retroviruses

TraCE: Trajectory Counterfactual Explanation Scores

Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier. In this paper, we propose to extend the use of counterfactuals to evaluate progress in sequential decision making tasks. To this end, we introduce a model-agnostic modular framework, TraCE (Trajectory Counterfactual Explanation) scores, which is able to distill and condense progress in highly complex scenarios into a single value. We demonstrate TraCE's utility across domains by showcasing its main properties in two case studies spanning healthcare and climate change.Comment: 7 pages, 4 figures, appendi

The Diverse Properties of the Most Ultraviolet Luminous Galaxies Discovered by the Galaxy Evolution Explorer

We report on the properties of a sample of ultraviolet luminous galaxies (UVLGs) selected by matching the Galaxy Evolution Explorer (GALEX) Surveys with the Sloan Digital Sky Survey Third Data Release. Out of 25362 galaxies between 0.02x10^10 L_solar at 1530 Angstroms (observed wavelength). The properties of this population are well correlated with ultraviolet surface brightness. We find that the galaxies with low UV surface brightness are primarily large spiral systems with a mixture of old and young stellar populations, while the high surface brightness galaxies consist primarily of compact starburst systems. In terms of the behavior of surface brightness with luminosity, size with luminosity, the mass-metallicity relation, and other parameters, the compact UVLGs clearly depart from the trends established by the full sample of galaxies. The subset of compact UVLGs with the highest surface brightness (``supercompact UVLGs'') have characteristics that are remarkably similar to Lyman Break Galaxies at higher redshift. They are much more luminous than typical local ultraviolet-bright starburst galaxies and blue compact dwarf galaxies. They have metallicities that are systematically lower than normal galaxies of the same stellar mass, indicating that they are less chemically evolved. In all these respects, they are the best local analogs for Lyman Break Galaxies.Comment: Fixed error in ObjID column of Table 1. 30 pages, 12 figures. Accepted for the GALEX special issue of ApJS. Abstract abridge