Payments for Ecosystem Services: Past, Present and Future

While we don’t tend to think about it, healthy ecosystems provide a variety of critical benefits. Ecosystem goods, the physical items an ecosystem provides, are obvious. Forests provide timber; coastal marshes provide shellfish. While less visible and generally taken for granted, the services underpinning these goods are equally important. Created by the interactions of living organisms with their environment, ecosystem services provide the conditions and processes that sustain human life.1 If you doubt this, consider how to grow an apple without pollination, pest control, or soil fertility. Once one realizes the importance of ecosystem services, three points quickly emerge: (1) landscapes provide a stream of services ranging from water quality and flood control to climate stability—the economic value of which can be significant; (2) the vast majority of these services are public goods and not exchanged in markets, so landowners have little incentive to provide these positive externalities; and (3) we, therefore, need to think creatively about creating markets for these services so they are not under-provided. This is the basis of the policy approach known as Payments for Ecosystem Services (“PES”)

A simple technique for quantifying apoptosis in 96-well plates

BACKGROUND: Analyzing apoptosis has been an integral component of many biological studies. However, currently available methods for quantifying apoptosis have various limitations including multiple, sometimes cell-damaging steps, the inability to quantify live, necrotic and apoptotic cells at the same time, and non-specific detection (i.e. "false positive"). To overcome the shortcomings of current methods that quantify apoptosis in vitro and to take advantage of the 96-well plate format, we present here a modified ethidium bromide and acridine orange (EB/AO) staining assay, which may be performed entirely in a 96-well plate. Our method combines the advantages of the 96-well format and the conventional EB/AO method for apoptotic quantification. RESULTS: We compared our method and the conventional EB/AO method for quantifying apoptosis of suspension cells (Jurkat) and adherent cells (A375) under normal growth and apoptosis-inducing conditions. We found that our new EB/AO method achieved quantification results comparable to those produced using the conventional EB/AO method for both suspension and adherent cells. CONCLUSION: By eliminating the detaching and washing steps, our method drastically reduces the time needed to perform the test, minimizes damage to adherent cells, and decreases the possibility of losing floating cells. Overall, our method is an improvement over the currently available techniques especially for adherent cells

Leveraging U.S. Army Administrative Data for Individual and Team Performance

The Army possesses vast amounts of administrative (archival) data about Soldiers. These data sources include screening tests, personnel action codes, training scores, global assessments, physical fitness scores, and more. However, the Army has yet to integrate these data to create a holistic operating picture. Our research focuses on repurposing Army administrative data to (1) operationalize social constructs of interest to the Army (e.g., Army Values, Warrior Ethos) and (2) model the predictive relationship between these constructs and individual (i.e., Soldier) and team (i.e., unit) performance and readiness. The goal of the project is to provide people analytics models to Army leadership for the purposes of optimizing human capital management decisions. Our talk will describe the theoretical underpinnings of our human performance model, drawing on disciplines such as social and industrial/organizational psychology, as well as our experience gaining access to and working with Army administrative data sources. Access to the archival administrative data is provided through the Army Analytics Group (AAG), Person-event Data Environment (PDE). The PDE is a business intelligence platform that has two central functions: (1) to provide a secure repository for data sources on U.S. military personnel; and (2) to provide a secure collaborative work environment where researchers can access unclassified but sensitive military data

Leveraging U.S. Army Administrative Data for Individual and Team Performance

The Army possesses vast amounts of administrative (archival) data about Soldiers. These data sources include screening tests, personnel action codes, training scores, global assessments,  physical fitness scores, and more. However, the Army has yet to integrate these data to create a holistic operating picture. Our research focuses on repurposing Army administrative data to (1) operationalize social constructs of interest to the Army (e.g., Army Values, Warrior Ethos) and (2) model the predictive relationship between these constructs and individual (i.e., Soldier) and team (i.e., unit) performance and readiness. The goal of the project is to provide people analytics models to Army leadership for the purposes of optimizing human capital management decisions. Our talk will describe the theoretical underpinnings of our human performance model, drawing on disciplines such as social and industrial/organizational psychology, as well as our experience gaining access to and working with Army administrative data sources. Access to the archival administrative data is provided through the Army Analytics Group (AAG), Person-event Data Environment (PDE). The PDE is a business intelligence platform that has two central functions: (1) to provide a secure repository for data sources on U.S. military personnel; and (2) to provide a secure collaborative work environment where researchers can access unclassified but sensitive military data

MicroRNA-26a Is Strongly Downregulated in Melanoma and Induces Cell Death through Repression of Silencer of Death Domains (SODD)

Melanoma is an aggressive cancer that metastasizes rapidly and is refractory to conventional chemotherapies. Identifying microRNAs (miRNAs) that are responsible for this pathogenesis is therefore a promising means of developing new therapies. We identified miR-26a through microarray and quantitative reverse-transcription–PCR (qRT-PCR) experiments as an miRNA that is strongly downregulated in melanoma cell lines as compared with primary melanocytes. Treatment of cell lines with miR-26a mimic caused significant and rapid cell death compared with a negative control in most melanoma cell lines tested. In surveying targets of miR-26a, we found that protein levels of SMAD1 (mothers against decapentaplegic homolog 1) and BAG-4/SODD were strongly decreased in sensitive cells treated with miR-26a mimic as compared with the control. The luciferase reporter assays further demonstrated that miR-26a can repress gene expression through the binding site in the 3′ untranslated region (3′UTR) of SODD (silencer of death domains). Knockdown of these proteins with small interfering RNA (siRNA) showed that SODD has an important role in protecting melanoma cells from apoptosis in most cell lines sensitive to miR-26a, whereas SMAD1 may have a minor role. Furthermore, transfecting cells with a miR-26a inhibitor increased SODD expression. Our findings indicate that miR-26a replacement is a potential therapeutic strategy for metastatic melanoma, and that SODD, in particular, is a potentially useful therapeutic target

A dynamic risk score for early prediction of cardiogenic shock using machine learning

Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US. The morbidity and mortality are highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock is critical. Prompt implementation of treatment measures can prevent the deleterious spiral of ischemia, low blood pressure, and reduced cardiac output due to cardiogenic shock. However, early identification of cardiogenic shock has been challenging due to human providers' inability to process the enormous amount of data in the cardiac intensive care unit (ICU) and lack of an effective risk stratification tool. We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock. To develop and validate CShock, we annotated cardiac ICU datasets with physician adjudicated outcomes. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.820, which substantially outperformed CardShock (AUROC 0.519), a well-established risk score for cardiogenic shock prognosis. CShock was externally validated in an independent patient cohort and achieved an AUROC of 0.800, demonstrating its generalizability in other cardiac ICUs