Service Learning in Preschool: An Intergenerational Project Involving Five-Y ear-Olds, Fifth Graders, and Senior Citizens

Service Learning is a powerful form of experiential pedagogy that is gaining popularity in classrooms from preprimary settings through graduate school. It involves students in activities that explicitly and intentionally integrate community involvement with appropriate academic objectives. This article describes an intergenerational service learning project that brought together preschoolers, golden-agers, and at-risk elementary-aged students. Lunch Time Book Buddies-Pass It On included both direct service and indirect service and made valuable contributions to young children\u27s developing literacy, social-emotional, physical, and cognitive abilities

Gemfibrozil Inhibits Legionella pneumophila and Mycobacterium tuberculosis Enoyl Coenzyme A Reductases and Blocks Intracellular Growth of These Bacteria in Macrophages

We report here that gemfibrozil (GFZ) inhibits axenic and intracellular growth of Legionella pneumophila and of 27 strains of wild-type and multidrug-resistant Mycobacterium tuberculosis in bacteriological medium and in human and mouse macrophages, respectively. At a concentration of 0.4 mM, GFZ completely inhibited L. pneumophila fatty acid synthesis, while at 0.12 mM it promoted cytoplasmic accumulation of polyhydroxybutyrate. To assess the mechanism(s) of these effects, we cloned an L. pneumophila FabI enoyl reductase homolog that complemented for growth an Escherichia coli strain carrying a temperature-sensitive enoyl reductase and rendered the complemented E. coli strain sensitive to GFZ at the nonpermissive temperature. GFZ noncompetitively inhibited this L. pneumophila FabI homolog, as well as M. tuberculosis InhA and E. coli FabI

Weak effects of common genetic variation in oxytocin and vasopressin receptor genes on rhesus macaque social behavior

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordThe neuropeptides oxytocin (OT) and arginine vasopressin (AVP) influence pair bonding, attachment, and sociality, as well as anxiety and stress responses in humans and other mammals. The effects of these peptides are mediated by genetic variability in their associated receptors, OXTR and the AVPR gene family. However, the role of these genes in regulating social behaviors in non‐human primates is not well understood. To address this question, we examined whether genetic variation in the OT receptor gene OXTR and the AVP receptor genes AVPR1A and AVPR1B influence naturally‐occurring social behavior in free‐ranging rhesus macaques—gregarious primates that share many features of their biology and social behavior with humans. We assessed rates of social behavior across 3,250 hr of observational behavioral data from 201 free‐ranging rhesus macaques on Cayo Santiago island in Puerto Rico, and used genetic sequence data to identify 25 OXTR, AVPR1A, and AVPR1B single‐nucleotide variants (SNVs) in the population. We used an animal model to estimate the effects of 12 SNVs (n = 3 OXTR; n = 5 AVPR1A; n = 4 AVPR1B) on rates of grooming, approaches, passive contact, contact aggression, and non‐contact aggression, given and received. Though we found evidence for modest heritability of these behaviors, estimates of effect sizes of the selected SNVs were close to zero, indicating that common OXTR and AVPR variation contributed little to social behavior in these animals. Our results are consistent with recent findings in human genetics that the effects of individual common genetic variants on complex phenotypes are generally small.This research supported by NIH grant 5R01‐MH096875‐02. The CPRC is supported by grant 8‐P40 OD012217‐25 from the National Center for Research Resources and the Office of Research Infrastructure Programs of the National Institutes of Health

Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies

Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibratio

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC0000

NCI60 Cancer Cell Line Panel Data and RNAi Analysis Help Identify EAF2 as a Modulator of Simvastatin and Lovastatin Response in HCT-116 Cells

Simvastatin and lovastatin are statins traditionally used for lowering serum cholesterol levels. However, there exists evidence indicating their potential chemotherapeutic characteristics in cancer. In this study, we used bioinformatic analysis of publicly available data in order to systematically identify the genes involved in resistance to cytotoxic effects of these two drugs in the NCI60 cell line panel. We used the pharmacological data available for all the NCI60 cell lines to classify simvastatin or lovastatin resistant and sensitive cell lines, respectively. Next, we performed whole-genome single marker case-control association tests for the lovastatin and simvastatin resistant and sensitive cells using their publicly available Affymetrix 125K SNP genomic data. The results were then evaluated using RNAi methodology. After correction of the p-values for multiple testing using False Discovery Rate, our results identified three genes (NRP1, COL13A1, MRPS31) and six genes (EAF2, ANK2, AKAP7, STEAP2, LPIN2, PARVB) associated with resistance to simvastatin and lovastatin, respectively. Functional validation using RNAi confirmed that silencing of EAF2 expression modulated the response of HCT-116 colon cancer cells to both statins. In summary, we have successfully utilized the publicly available data on the NCI60 cell lines to perform whole-genome association studies for simvastatin and lovastatin. Our results indicated genes involved in the cellular response to these statins and siRNA studies confirmed the role of the EAF2 in response to these drugs in HCT-116 colon cancer cells

A narrative review on the similarities and dissimilarities between myalgic encephalomyelitis/chronic fatigue syndrome (me/cfs) and sickness behavior

It is of importance whether myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a variant of sickness behavior. The latter is induced by acute infections/injury being principally mediated through proinflammatory cytokines. Sickness is a beneficial behavioral response that serves to enhance recovery, conserves energy and plays a role in the resolution of inflammation. There are behavioral/symptomatic similarities (for example, fatigue, malaise, hyperalgesia) and dissimilarities (gastrointestinal symptoms, anorexia and weight loss) between sickness and ME/CFS. While sickness is an adaptive response induced by proinflammatory cytokines, ME/CFS is a chronic, disabling disorder, where the pathophysiology is related to activation of immunoinflammatory and oxidative pathways and autoimmune responses. While sickness behavior is a state of energy conservation, which plays a role in combating pathogens, ME/CFS is a chronic disease underpinned by a state of energy depletion. While sickness is an acute response to infection/injury, the trigger factors in ME/CFS are less well defined and encompass acute and chronic infections, as well as inflammatory or autoimmune diseases. It is concluded that sickness behavior and ME/CFS are two different conditions

The Fishery Performance Indicators: A Management Tool for Triple Bottom Line Outcomes

Pursuit of the triple bottom line of economic, community and ecological sustainability has increased the complexity of fishery management; fisheries assessments require new types of data and analysis to guide science-based policy in addition to traditional biological information and modeling. We introduce the Fishery Performance Indicators (FPIs), a broadly applicable and flexible tool for assessing performance in individual fisheries, and for establishing cross-sectional links between enabling conditions, management strategies and triple bottom line outcomes. Conceptually separating measures of performance, the FPIs use 68 individual outcome metrics—coded on a 1 to 5 scale based on expert assessment to facilitate application to data poor fisheries and sectors—that can be partitioned into sector-based or triple-bottom-line sustainability-based interpretative indicators. Variation among outcomes is explained with 54 similarly structured metrics of inputs, management approaches and enabling conditions. Using 61 initial fishery case studies drawn from industrial and developing countries around the world, we demonstrate the inferential importance of tracking economic and community outcomes, in addition to resource status