Phylogeographic patterning of mtDNA in the widely distributed harvest mouse (Micromys minutus) suggests dramatic cycles of range contraction and expansion during the mid- to late Pleistocene

We examined sequence variation in the mitochondrial cytochrome b gene (1140 bp, n = 73) and control region (842-851 bp, n = 74) in the Eurasian harvest mouse (Micromys minutus (Pallas, 1771)), with samples drawn from across its range, from Western Europe to Japan. Phylogeographic analyses revealed region-specific haplotype groupings combined with overall low levels of inter-regional genetic divergence. Despite the enormous intervening distance, European and East Asian samples showed a net nucleotide divergence of only 0.36%. Based on an evolutionary rate for the cytochrome b gene of 2.4%(.)(site(.)lineage(.)million years)(-1), the initial divergence time of these populations is estimated at around 80 000 years before present. Our findings are consistent with available fossil evidence that has recorded repeated cycles of extinction and recolonization of Europe by M. minutus through the Quaternary. The molecular data further suggest that recolonization occurred from refugia in the Central to East Asian region. Japanese haplotypes of M. minutus, with the exception of those from Tsushima Is., show limited nucleotide diversity (0.15%) compared with those found on the adjacent Korean Peninsula. This finding suggests recent colonization of the Japanese Archipelago, probably around the last glacial period, followed by rapid population growth.

Fact Sheet: House and Building Fires, 2011

A fire can engulf a structure in a matter of minutes. Understanding the basic characteristics of fire and learning the proper safety practices can be the key to surviving a house or building fire.

Rise in plasma lactate concentrations with psychosocial stress: a possible sign of cerebral energy demand.

OBJECTIVE: It is known that exogenous lactate given as an i.v. energy infusion is able to counteract a neuroglycopenic state that developed during psychosocial stress. It is unknown, however, whether the brain under stressful conditions can induce a rise in plasma lactate to satisfy its increased needs during stress. Since lactate is i) an alternative cerebral energy substrate to glucose and ii) its plasmatic concentration is influenced by the sympathetic nervous system, the present study aimed at investigating whether plasma lactate concentrations increase with psychosocial stress in humans. METHODS: 30 healthy young men participated in two sessions (stress induced by the Trier Social Stress Test and a non-stress control session). Blood samples were frequently taken to assess plasma lactate concentrations and stress hormone profiles. RESULTS: Plasma lactate increased 47% during psychosocial stress (from 0.9 ± 0.05 to 1.4 ± 0.1 mmol/l; interaction time × stress intervention: F = 19.7, p < 0.001). This increase in lactate concentrations during stress was associated with an increase in epinephrine (R(2) = 0.221, p = 0.02) and ACTH concentrations (R(2) = 0.460, p < 0.001). CONCLUSION: Plasma lactate concentrations increase during acute psychosocial stress in humans. This finding suggests the existence of a demand mechanism that functions to allocate an additional source of energy from the body towards the brain, which we refer to as 'cerebral lactate demand'.

Building predictive models of soil particle-size distribution

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Kelle mid'ä pid'äy - kuka tarvitsee mitäkin

Väitöksenalkajaisesitelmä

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Audit report on Mid-Iowa School Improvement Consortium, Carlisle, Iowa for the year ended June 30, 2011

Audit report on Mid-Iowa School Improvement Consortium, Carlisle, Iowa for the year ended June 30, 2011

Data Download, Building Better Programs, Iowa Department of Corrections, August 2011

Beginning in 2006, the Iowa Department of Corrections embarked on a systematic offender program audit at each of the state’s institutions and community-based corrections agencies, the purpose of which was to determine each program’s effectiveness as supported by results and research (evidence-based practices). Those programs demonstrating success were maintained, and all others either modified to comply with evidence-based practices or replaced by programming that did.

Video analysis software increases the interrater reliability of video gait assessments in children with cerebral palsy.

The aim of this study was to determine the effect of using video analysis software on the interrater reliability of visual assessments of gait videos in children with cerebral palsy. Two clinicians viewed the same random selection of 20 sagittal and frontal video recordings of 12 children with cerebral palsy routinely acquired during outpatient rehabilitation clinics. Both observers rated these videos in a random sequence for each lower limb using the Observational Gait Scale, once with standard video software and another with video analysis software (Dartfish(®)) which can perform angle and timing measurements. The video analysis software improved interrater agreement, measured by weighted Cohen's kappas, for the total score (κ 0.778→0.809) and all of the items that required angle and/or timing measurements (knee position mid-stance κ 0.344→0.591; hindfoot position mid-stance κ 0.160→0.346; foot contact mid-stance κ 0.700→0.854; timing of heel rise κ 0.769→0.835). The use of video analysis software is an efficient approach to improve the reliability of visual video assessments.

Building World-Class Schools for Iowa: A Legislative Brief, January 14, 2013

The Iowa Department of Education has joined the Office of the Governor to prepare a set of legislative proposals that will bring Iowa closer to its goal of providing a world-class education to all children, no mater where they live. This legislative brief serves as an overview of the legislation, which I encourage you to read and discuss in greater detail. the goals behind these policies are straightforward: Comprehensive and systematically raise and support the teaching profession while expanding efforts to customize instruction to every student's passion and talents. Iowa's children deserve the best education can provide so they leave our schools with the knowledge and skills necessary for successful and rewarding lives. Iowa has many good schools with hard-working, talented educators who deserves our respect and appreciation. While we honor the past work of generations of Iowans who built a strong foundation, it is our responsibility - and our turn - to make a focused, dedicated effort to improve Iowa's schools. We stand at a pivotal moment in Iowa storied education history, in which we have the opportunity and will as community to make the transition from being "good" to being "great".

Osage-Mitchell County RISE projects dedication, September 27, 1989.

Program from the dedication of the Osage-Mitchell County RISE (Revitalize Iowa's Sound Economy) paving project on Sept. 27, 1989.

Building a Social Accounting Matrix within the ESA95 Framework: Obtaining a Dataset for Applied General Equilibrium Modelling

This research provides a description of the process followed in order to assemble a "Social Accounting Matrix" for Spain corresponding to the year 2000 (SAMSP00). As argued in the paper, this process attempts to reconcile ESA95 conventions with requirements of applied general equilibrium modelling. Particularly, problems related to the level of aggregation of net taxation data, and to the valuation system used for expressing the monetary value of input-output transactions have deserved special attention. Since the adoption of ESA95 conventions, input-output transactions have been preferably valued at basic prices, which impose additional difficulties on modellers interested in computing applied general equilibrium models. This paper addresses these difficulties by developing a procedure that allows SAM-builders to change the valuation system of input-output transactions conveniently. In addition, this procedure produces new data related to net taxation information.