Out-of-school suspensions and parental involvement in children’s education

Do parents alter their investment in their child’s human capital in response to changes in school inputs? If they do, then ignoring this effect will bias the estimates of school and parental inputs in educational production functions. This paper tries to answer this question by studying out-of-school suspensions and their effect on parental involvement in children’s education. The use of out-of- school suspensions is the novelty of this paper. Out-of-school suspensions are chosen by the teacher or the principal of the school and not by parents, but they are a consequence of student misbehavior. To account for the nature of these out-of-school suspensions, they are instrumented with measures of “principal’s preference toward discipline.” The estimates show that, without controlling for selection, the level of parental involvement is negatively correlated with the number of out-of-school suspensions. Once selection is accounted for, the effect disappears—that is, out-of-school suspensions do not affect parental involvement in children’s education.Education ; School choice

The mismatch between job openings and job seekers

Today's high unemployment rate is often linked to a structural imbalance—a mismatch between the skills and location required to fill vacant jobs and the skills and geographical preferences of the unemployed. But the evidence downplays the role of this mismatch.Unemployment ; Labor market

Local housing crisis is similar to nation's

District OverviewHousing ; Housing - Prices ; Federal Reserve District, 8th

Employment in major cities in the district slumps relative to the rest of the country

Federal Reserve District, 8th ; Employment

Microsatellite diversity of the Nordic type of goats in relation to breed conservation: how relevant is pure ancestry?

In the last decades, several endangered breeds of livestock species have been re-established effectively. However, the successful revival of the Dutch and Danish Landrace goats involved crossing with exotic breeds and the ancestry of the current populations is therefore not clear. We have generated genotypes for 27 FAO-recommended microsatellites of these landraces and three phenotypically similar Nordic-type landraces and compared these breeds with central European, Mediterranean and south-west Asian goats. We found decreasing levels of genetic diversity with increasing distance from the south-west Asian domestication site with a south-east-to-north-west cline that is clearly steeper than the Mediterranean east-to-west cline. In terms of genetic diversity, the Dutch Landrace comes next to the isolated Icelandic breed, which has an extremely low diversity. The Norwegian coastal goat and the Finnish and Icelandic landraces are clearly related. It appears that by a combination of mixed origin and a population bottleneck, the Dutch and Danish Land-races are separated from the other breeds. However, the current Dutch and Danish populations with the multicoloured and long-horned appearance effectively substitute for the original breed, illustrating that for conservation of cultural heritage, the phenotype of a breed is more relevant than pure ancestry and the genetic diversity of the original breed. More in general, we propose that for conservation, the retention of genetic diversity of an original breed and of the visual phenotype by which the breed is recognized and defined needs to be considered separately

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies