Accelerating Community College Graduation Rates: A Benefit–Cost Analysis

This article reports a benefit–cost evaluation of the Accelerated Study in Associate Programs (ASAP) of the City University of New York (CUNY). ASAP was designed to accelerate associate degree completion within 3 years of degree enrollment at CUNY’s community colleges. The program evaluation revealed that the completion rate for the examined cohort increased from 24.1% to 54.9%, and cost per graduate declined considerably (Levin & Garcia, 2012; Linderman & Kolenovic, 2012). The returns on investment to the taxpayer include the benefits from higher tax revenues and lower costs of spending on public health, criminal justice, and public assistance. For each dollar of investment in ASAP by taxpayers, the return was $3 to$4. For each additional graduate, the taxpayer gained an amount equal to a certificate of deposit with a value of $146,000 (net of the costs of the investment). Based on these estimated returns, a cohort of 1,000 students enrolled in ASAP would generate net fiscal benefits for the taxpayer of more than$46 million relative to enrolling in the conventional degree program. ASAP results demonstrate that an effective educational policy can generate returns to the taxpayer that vastly exceed the public investment required

Frequency and type of adverse analytical findings in athletics: Differences among disciplines.

Athletics is a highly diverse sport that contains a set of disciplines grouped into jumps, throws, races of varying distances, and combined events. From a physiological standpoint, the physical capabilities linked to success are quite different among disciplines, with varying involvements of muscle strength, muscle power, and endurance. Thus, the use of banned substances in athletics might be dictated by physical dimensions of each discipline. Thus, the aim of this investigation was to analyse the number and distribution of adverse analytical findings per drug class in athletic disciplines. The data included in this investigation were gathered from the Anti-Doping Testing Figure Report made available by the World Anti-Doping Agency (from 2016 to 2018). Interestingly, there were no differences in the frequency of adverse findings (overall, 0.95%, range from 0.77 to 1.70%) among disciplines despite long distance runners having the highest number of samples analysed per year ( 9812 samples/year). Sprinters and throwers presented abnormally high proportions of adverse analytical findings within the group of anabolic agents (p < 0.01); middle- and long-distance runners presented atypically high proportions of findings related to peptide hormones and growth factors (p < 0.01); racewalkers presented atypically high proportions of banned diuretics and masking agents (p = 0.05). These results suggest that the proportion of athletes that are using banned substances is similar among the different disciplines of athletics. However, there are substantial differences in the class of drugs more commonly used in each discipline. This information can be used to effectively enhance anti-doping testing protocols in athletics.post-print1.911 K

Impact of receptor clustering on ligand binding

<p>Abstract</p> <p>Background</p> <p>Cellular response to changes in the concentration of different chemical species in the extracellular medium is induced by ligand binding to dedicated transmembrane receptors. Receptor density, distribution, and clustering may be key spatial features that influence effective and proper physical and biochemical cellular responses to many regulatory signals. Classical equations describing this kind of binding kinetics assume the distributions of interacting species to be homogeneous, neglecting by doing so the impact of clustering. As there is experimental evidence that receptors tend to group in clusters inside membrane domains, we investigated the effects of receptor clustering on cellular receptor ligand binding.</p> <p>Results</p> <p>We implemented a model of receptor binding using a Monte-Carlo algorithm to simulate ligand diffusion and binding. In some simple cases, analytic solutions for binding equilibrium of ligand on clusters of receptors are provided, and supported by simulation results. Our simulations show that the so-called "apparent" affinity of the ligand for the receptor decreases with clustering although the microscopic affinity remains constant.</p> <p>Conclusions</p> <p>Changing membrane receptors clustering could be a simple mechanism that allows cells to change and adapt its affinity/sensitivity toward a given stimulus.</p

Rescue of a H3N2 Influenza Virus Containing a Deficient Neuraminidase Protein by a Hemagglutinin with a Low Receptor-Binding Affinity

Influenza viruses possess at their surface two glycoproteins, the hemagglutinin and the neuraminidase, of which the antagonistic functions have to be well balanced for the virus to grow efficiently. Ferraris et al. isolated in 2003–2004 viruses lacking both a NA gene and protein (H3NA- viruses) (Ferraris O., 2006, Vaccine, 24(44–46):6656-9). In this study we showed that the hemagglutinins of two of the H3NA- viruses have reduced affinity for SAα2.6Gal receptors, between 49 and 128 times lower than that of the A/Moscow/10/99 (H3N2) virus and no detectable affinity for SAα2.3Gal receptors. We also showed that the low hemagglutinin affinity of the H3NA- viruses compensates for the lack of NA activity and allows the restoration of the growth of an A/Moscow/10/99 virus deficient in neuraminidase. These observations increase our understanding of H3NA- viruses in relation to the balance between the functional activities of the neuraminidase and hemagglutinin

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

Virus Replication Strategies and the Critical CTL Numbers Required for the Control of Infection

Vaccines that elicit protective cytotoxic T lymphocytes (CTL) may improve on or augment those designed primarily to elicit antibody responses. However, we have little basis for estimating the numbers of CTL required for sterilising immunity at an infection site. To address this we begin with a theoretical estimate obtained from measurements of CTL surveillance rates and the growth rate of a virus. We show how this estimate needs to be modified to account for (i) the dynamics of CTL-infected cell conjugates, and (ii) features of the virus lifecycle in infected cells. We show that provided the inoculum size of the virus is low, the dynamics of CTL-infected cell conjugates can be ignored, but knowledge of virus life-histories is required for estimating critical thresholds of CTL densities. We show that accounting for virus replication strategies increases estimates of the minimum density of CTL required for immunity over those obtained with the canonical model of virus dynamics, and demonstrate that this modeling framework allows us to predict and compare the ability of CTL to control viruses with different life history strategies. As an example we predict that lytic viruses are more difficult to control than budding viruses when net reproduction rates and infected cell lifetimes are controlled for. Further, we use data from acute SIV infection in rhesus macaques to calculate a lower bound on the density of CTL that a vaccine must generate to control infection at the entry site. We propose that critical CTL densities can be better estimated either using quantitative models incorporating virus life histories or with in vivo assays using virus-infected cells rather than peptide-pulsed targets

Impacts of past abrupt land change on local biodiversity globally

Abrupt land change, such as deforestation or agricultural intensification, is a key driver of biodiversity change. Following abrupt land change, local biodiversity often continues to be influenced through biotic lag effects. However, current understanding of how terrestrial biodiversity is impacted by past abrupt land changes is incomplete. Here we show that abrupt land change in the past continues to influence present species assemblages globally. We combine geographically and taxonomically broad data on local biodiversity with quantitative estimates of abrupt land change detected within time series of satellite imagery from 1982 to 2015. Species richness and abundance were 4.2% and 2% lower, respectively, and assemblage composition was altered at sites with an abrupt land change compared to unchanged sites, although impacts differed among taxonomic groups. Biodiversity recovered to levels comparable to unchanged sites after >10 years. Ignoring delayed impacts of abrupt land changes likely results in incomplete assessments of biodiversity change

The interactive effects of arbuscular mycorrhiza and plant growth-promoting rhizobacteria synergistically enhance host plant defences against pathogens

Belowground interactions between plant roots, mycorrhizal fungi and plant growth-promoting rhizobacteria (PGPR) can improve plant health via enhanced nutrient acquisition and priming of the plant immune system. Two wheat cultivars differing in their ability to form mycorrhiza were (co)inoculated with the mycorrhizal fungus Rhizophagus irregularis and the rhizobacterial strain Pseudomonas putida KT2440. The cultivar with high mycorrhizal compatibility supported higher levels of rhizobacterial colonization than the low compatibility cultivar. Those levels were augmented by mycorrhizal infection. Conversely, rhizobacterial colonization of the low compatibility cultivar was reduced by mycorrhizal arbuscule formation. Single inoculations with R. irregularis or P. putida had differential growth effects on both cultivars. Furthermore, while both cultivars developed systemic priming of chitosan-induced callose after single inoculations with R. irregularis or P. putida, only the cultivar with high mycorrhizal compatibility showed a synergistic increase in callose responsiveness following co-inoculation with both microbes. Our results show that multilateral interactions between roots, mycorrhizal fungi and PGPR can have synergistic effects on growth and systemic priming of wheat

Benzoxazinoids in Root Exudates of Maize Attract Pseudomonas putida to the Rhizosphere

Benzoxazinoids, such as 2,4-dihydroxy-7-methoxy-2H-1,4-benzoxazin-3(4H)-one (DIMBOA), are secondary metabolites in grasses. In addition to their function in plant defence against pests and diseases above-ground, benzoxazinoids (BXs) have also been implicated in defence below-ground, where they can exert allelochemical or antimicrobial activities. We have studied the impact of BXs on the interaction between maize and Pseudomonas putida KT2440, a competitive coloniser of the maize rhizosphere with plant-beneficial traits. Chromatographic analyses revealed that DIMBOA is the main BX compound in root exudates of maize. In vitro analysis of DIMBOA stability indicated that KT2440 tolerance of DIMBOA is based on metabolism-dependent breakdown of this BX compound. Transcriptome analysis of DIMBOA-exposed P. putida identified increased transcription of genes controlling benzoate catabolism and chemotaxis. Chemotaxis assays confirmed motility of P. putida towards DIMBOA. Moreover, colonisation essays in soil with Green Fluorescent Protein (GFP)-expressing P. putida showed that DIMBOA-producing roots of wild-type maize attract significantly higher numbers of P. putida cells than roots of the DIMBOA-deficient bx1 mutant. Our results demonstrate a central role for DIMBOA as a below-ground semiochemical for recruitment of plant-beneficial rhizobacteria during the relatively young and vulnerable growth stages of maize