Cherry Orchard Community Garden

Community garden project and allotments aims to increase community involvement, participation and activity in the locality.  We have a number of family groups (parents and children) working together. It is a lot of fun and a great way to meet new people from the area, get out of the house a few hours a week and be part of a really nice community group. We see our garden somewhat like a Co-op. Everyone is welcome to join us, we meet up at least three times a week and do the jobs around the garden together – mainly around vegetables and some fruit trees but we are interested in flowers and other things as well. We have a garden sale every Thursday between 12pm-2pm. We sell all organic produce grown in the garden and make our own jams to sell weekly also. At the end of the season we all share the crops, vegetables and fruits if there is any left after the sale on Thursday. Contact: Kevin Dunne, 087 272 4537. Email: communitygardens@ballyfermotpartnership.ie   Dublin City Council via Cherry Orchard Regeneration Board Initiative Type Community Food Growing Projects Location Dublin 10 Target Groups Children ( 4-12 years) Children (0-4 years) Children (13-18 years) Lone parents Men Older people Unemployed Funding Dublin City Council via Cherry Orchard Regeneration Board Website https://www.youtube.com/channel/UCxED179ZBWiRa251UQh4l1A Connect with this Initiative Facebook

The contributions of the Genome Project to the study of schistosomiasis

In this paper we review the impact that the availability of the Schistosoma mansoni genome sequence and annotation has had on schistosomiasis research. Easy access to the genomic information is important and several types of data are currently being integrated, such as proteomics, microarray and polymorphic loci. Access to the genome annotation and powerful means of extracting information are major resources to the research community.

Sequencing and characterizing the genome of Estrella lausannensis as an undergraduate project: training students and biological insights.

With the widespread availability of high-throughput sequencing technologies, sequencing projects have become pervasive in the molecular life sciences. The huge bulk of data generated daily must be analyzed further by biologists with skills in bioinformatics and by "embedded bioinformaticians," i.e., bioinformaticians integrated in wet lab research groups. Thus, students interested in molecular life sciences must be trained in the main steps of genomics: sequencing, assembly, annotation and analysis. To reach that goal, a practical course has been set up for master students at the University of Lausanne: the "Sequence a genome" class. At the beginning of the academic year, a few bacterial species whose genome is unknown are provided to the students, who sequence and assemble the genome(s) and perform manual annotation. Here, we report the progress of the first class from September 2010 to June 2011 and the results obtained by seven master students who specifically assembled and annotated the genome of Estrella lausannensis, an obligate intracellular bacterium related to Chlamydia. The draft genome of Estrella is composed of 29 scaffolds encompassing 2,819,825 bp that encode for 2233 putative proteins. Estrella also possesses a 9136 bp plasmid that encodes for 14 genes, among which we found an integrase and a toxin/antitoxin module. Like all other members of the Chlamydiales order, Estrella possesses a highly conserved type III secretion system, considered as a key virulence factor. The annotation of the Estrella genome also allowed the characterization of the metabolic abilities of this strictly intracellular bacterium. Altogether, the students provided the scientific community with the Estrella genome sequence and a preliminary understanding of the biology of this recently-discovered bacterial genus, while learning to use cutting-edge technologies for sequencing and to perform bioinformatics analyses.

EGASP: the human ENCODE Genome Annotation Assessment Project

Background: We present the results of EGASP, a community experiment to assess the state-ofthe-art in genome annotation within the ENCODE regions, which span 1% of the human genomesequence. The experiment had two major goals: the assessment of the accuracy of computationalmethods to predict protein coding genes; and the overall assessment of the completeness of thecurrent human genome annotations as represented in the ENCODE regions. For thecomputational prediction assessment, eighteen groups contributed gene predictions. Weevaluated these submissions against each other based on a ‘reference set’ of annotationsgenerated as part of the GENCODE project. These annotations were not available to theprediction groups prior to the submission deadline, so that their predictions were blind and anexternal advisory committee could perform a fair assessment.Results: The best methods had at least one gene transcript correctly predicted for close to 70%of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into accountalternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotidelevel, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programsrelying on mRNA and protein sequences were the most accurate in reproducing the manuallycurated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could beverified.Conclusions: This is the first such experiment in human DNA, and we have followed thestandards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe theresults presented here contribute to the value of ongoing large-scale annotation projects and shouldguide further experimental methods when being scaled up to the entire human genome sequence.

New Iowans Pilot Project, 2001

Governor Vilsack and Lt. Governor Pederson are encouraging the development of a strategic community plan to encourage a smooth transition for bringing skilled workers and their families to Iowa from other countries. Skilled workers from other countries can provide a valuable resource to Iowa employers and can add cultural richness and diversity to Iowa's communities. New Iowans can help Iowa's communities, businesses, and economy grow

Cedar Lake Watershed Project, 2004

For years, Cedar Lake has been there for the Winterset community, providing drinking water, offering recreational opportunities, teaching students outdoors lessons and giving wildlife a home. With water quality a serious concern, it is now our turn to be there for the lake.

The Alps Vegetation Database - a geo-referenced community-level archive of all terrestrial plants occurring in the Alps.

Mountain ranges are biodiversity hotspots worldwide and provide refuge to many organisms under contemporary climate change. Gathering field information on mountain biodiversity over time is of primary importance to understand the response of biotic communities to climate changes. For plants, several long-term observation sites and networks of mountain biodiversity are emerging worldwide to gather field data and monitor altitudinal range shifts and community composition changes under contemporary climate change. Most of these monitoring sites, however, focus on alpine ecosystems and mountain summits, such as the global observation research initiative in alpine environments (GLORIA). Here we describe the Alps Vegetation Database, a comprehensive community level archive (GIVD ID EU-00-014) which aims at compiling all available geo-referenced vegetation plots from lowland forests to alpine grasslands across the greatest mountain range in Europe: the Alps. This research initiative was funded between 2008 and 2011 by the Danish Council for Independent Research and was part of a larger project to compare cross-scale plant community structure between the Alps and the Scandes. The Alps Vegetation Database currently harbours 35,731 geo-referenced vegetation plots and 5,023 valid taxa across Mediterranean, temperate and alpine environments. The data are mainly used by the main contributors of the Alps Vegetation Database in an ecoinformatics approach to test hypotheses related to plant macroecology and biogeography, but external proposals for joint collaborations are welcome.

Iowa's Rural Alzheimer's Demonstration Project, July 1, 2004 - June 31, 2007

The Iowa Department of Elder Affairs, in collaboration with the Iowa Department of Elder Affairs (IDEA) and the University of Iowa College of Nursing (UI CON), has been engaged in developing and evaluating community based services for persons with dementia in the state of Iowa over the past 7 years under two grants form the Administration on Aging. In the current grant period, the involved agencies have completed a collaborative effort aimed to increase the capacity of Adult Day Health and Respite (ADR) providers in serving persons with dementia. Adult day services and respite care were identified by participants in the initial grant through various processes and service providers as important components of caring for persons with dementia and that there was a gap of these services in the state. Therefore, adult day and respite services were chosen as a target for the second AoA grant. The focus, in particular, was to enhance capacity to care for persons with later stages of the disease and those in rural settings as well as to begin to develop services that are more responsive to emerging minority populations. The process of the grant provided the state with a rich amount of information about the status of Iowa’s Adult Day Service providers in general and in regard to provision of dementia specific services, as well as valuable insights into the capability of rural communities to serve persons with dementia and their caregivers at home. Final Performance Report

EGASP: the human ENCODE Genome Annotation Assessment Project.

BACKGROUND: We present the results of EGASP, a community experiment to assess the state-of-the-art in genome annotation within the ENCODE regions, which span 1% of the human genome sequence. The experiment had two major goals: the assessment of the accuracy of computational methods to predict protein coding genes; and the overall assessment of the completeness of the current human genome annotations as represented in the ENCODE regions. For the computational prediction assessment, eighteen groups contributed gene predictions. We evaluated these submissions against each other based on a 'reference set' of annotations generated as part of the GENCODE project. These annotations were not available to the prediction groups prior to the submission deadline, so that their predictions were blind and an external advisory committee could perform a fair assessment. RESULTS: The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into account alternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programs relying on mRNA and protein sequences were the most accurate in reproducing the manually curated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could be verified. CONCLUSION: This is the first such experiment in human DNA, and we have followed the standards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe the results presented here contribute to the value of ongoing large-scale annotation projects and should guide further experimental methods when being scaled up to the entire human genome sequence.

Le Soutien psychiatrique Intensif dans le Milieu (SIM) à Lausanne: un projet pilote [Intensive Psychiatric Support at the Setting in Lausanne: a pilot project]

While the hospital remains an important element of the psychiatric health-care system, the fact that it is always the best place to treat acute psychotic episodes is still debated. After a brief review of the literature relative to the main existing community care models, the authors describe the development in the Department Universitaire de Psychiatrie Adulte (DUPA), of an alternative to hospitalisation for patient going through a severe acute psychiatric episode. They present three clinical situations and the aims of the research project, which will follow this pilot phase.

Community Policing in Switzerland's Major Urban Areas: An Observational Study of the Implementation and Impact Using Geospatial Data Mining

La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.

Project Newsletter Iowa Public Health Tracking, July 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, August 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, September 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, October 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/