Minimal data requirements for a continuous monitoring of the quality of care using the DRG classification.

The DRG classification provides a useful tool for the evaluation of hospital care. Indicators such as readmissions and mortality rates adjusted for the hospital Casemix could be adopted in Switzerland at the price of minor additions to the hospital discharge record. The additional information required to build patients histories and to identify the deaths occurring after hospital discharge is detailed.

State-space approach to evaluate the relation between soil physical and chemical properties

The state-space approach is used to evaluate the relation between soil physical and chemical properties in an area cultivated with sugarcane. The experiment was carried out on a Rhodic Kandiudalf in Piracicaba, State of São Paulo, Brazil. Sugarcane was planted on an area of 0.21 ha i.e., in 15 rows 100 m long, spaced 1.4 m. Soil water content, soil organic matter, clay content and aggregate stability were sampled along a transect of 84 points, meter by meter. The state-space approach is used to evaluate how the soil water content is affected by itself and by soil organic matter, clay content, and aggregate stability of neighboring locations, in different combinations, aiming to contribute to a better understanding of the relation among these variables in the soil. Results show that soil water contents were successfully estimated by this approach. Best performances were found when the estimate of soil water content at locations i was related to soil water content, clay content and aggregate stability at locations i-1. Results also indicate that this state-space model using all series describes the soil water content better than any equivalent multiple regression equation.

State of Iowa Space Utilization and Building Study, October 9, 2000

Executive Summary Purposes of this Report: • Recommend the most logical and economical options to address state governmental space needs in the Polk County metropolitan area to the year 2010. • Include building size, location, phasing, financing, method of project delivery and estimated cost. • Develop a software tool to compare costs of leasing vs. ownership of space. Methodology: Identify: 1. Current amount and location of owned and leased space, by agency; 2. Types of space and whether best located on or off of the Capitol Complex; 3. Utilization of space, noting over-crowding and under-utilization; 4. Current number of workstations for full and part time employees, Personnel Employment Organization (PEO) workers, contractors, interns, etc.; and, 5. History of staff levels to assist in the prediction of staff growth. Scope: This report focuses on 10 state-owned buildings located on the Capitol Complex and 48 leased spaces in the Polk County metropolitan area. (See Figures 1 and 2.) • Due to a separate space study under way by the Legislature, implications of area and staff for the State Capitol building are included only for the Governor, Lieutenant Governor, Treasurer, Secretary of State, Auditor and the Department of Management. • Because it is largely a museum building that does not have office space available for other agencies, the area and staff of the Historical Building are not fully addressed. • Only the parking implications of the new Judicial Building are included in this study because the building space is under the jurisdiction of the Judicial Branch and not available for other agencies. Several state-owned buildings are not included in the scope of this report, generally because they have highly focused purposes, and their space is not available for assignment to other agencies. Several leased locations are not included for similar reasons, including leases that do not fall within the authority of the Department of General Services.

Bayesian image reconstruction with space-variant noise suppression

In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.

A dynamic model for stem cell homeostasis and patterning in Arabidopsis meristems.

Plants maintain stem cells in their meristems as a source for new undifferentiated cells throughout their life. Meristems are small groups of cells that provide the microenvironment that allows stem cells to prosper. Homeostasis of a stem cell domain within a growing meristem is achieved by signalling between stem cells and surrounding cells. We have here simulated the origin and maintenance of a defined stem cell domain at the tip of Arabidopsis shoot meristems, based on the assumption that meristems are self-organizing systems. The model comprises two coupled feedback regulated genetic systems that control stem cell behaviour. Using a minimal set of spatial parameters, the mathematical model allows to predict the generation, shape and size of the stem cell domain, and the underlying organizing centre. We use the model to explore the parameter space that allows stem cell maintenance, and to simulate the consequences of mutations, gene misexpression and cell ablations.

Potencialidad de la modelización state-space y el filtro de Kalman para el anàlisis regional : una aplicación para el índice de actividad industrial

El déficit existente a nuestro país con respecto a la disponibilidad de indicadores cuantitativos con los que llevar a término un análisis coyuntural de la actividad industrial regional ha abierto un debate centrado en el estudio de cuál es la metodología más adecuada para elaborar indicadores de estas características. Dentro de este marco, en este trabajo se presentan las principales conclusiones obtenidas en anteriores estudios (Clar, et. al., 1997a, 1997b y 1998) sobre la idoneidad de extender las metodologías que actualmente se están aplicando a las regiones españolas para elaborar indicadores de la actividad industrial mediante métodos indirectos. Estas conclusiones llevan a plantear una estrategia distinta a las que actualmente se vienen aplicando. En concreto, se propone (siguiendo a Israilevich y Kuttner, 1993) un modelo de variables latentes para estimar el indicador de la producción industrial regional. Este tipo de modelo puede especificarse en términos de un modelo statespace y estimarse mediante el filtro de Kalman. Para validar la metodología propuesta se estiman unos indicadores de acuerdo con ella para tres de las cuatro regiones españolas que disponen d¿un Índice de Producción Industrial (IPI) elaborado mediante el método directo (Andalucía, Asturias y el País Vasco) y se comparan con los IPIs publicados (oficiales). Los resultados obtenidos muestran el buen comportamiento de l¿estrategia propuesta, abriendo así una línea de trabajo con la que subsanar el déficit al que se hacía referencia anteriormente

Both the Fas ligand and inducible nitric oxide synthase are needed for control of parasite replication within lesions in mice infected with Leishmania major whereas the contribution of tumor necrosis factor is minimal.

Following infection with the protozoan parasite Leishmania major, C57BL/6 mice develop a small lesion that heals spontaneously. Resistance to infection is associated with the development of CD4(+) Th1 cells producing gamma interferon (IFN-gamma) and tumor necrosis factor (TNF), which synergize in activating macrophages to their microbicidal state. We show here that C57BL/6 mice lacking both TNF and Fas ligand (FasL) (gld TNF(-/-) mice) infected with L. major neither resolved their lesions nor controlled Leishmania replication despite the development of a strong Th1 response. Comparable inducible nitric oxide synthase (iNOS) activities were detected in lesions of TNF(-/-), gld TNF(-/-), and gld mice, but only gld and gld TNF(-/-) mice failed to control parasite replication. Parasite numbers were high in gld mice and even more elevated in gld TNF(-/-) mice, suggesting that, in addition to iNOS, the Fas/FasL pathway is required for successful control of parasite replication and that TNF contributes only a small part to this process. Furthermore, FasL was shown to synergize with IFN-gamma for the induction of leishmanicidal activity within macrophages infected with L. major in vitro. Interestingly, TNF(-/-) mice maintained large lesion size throughout infection, despite being able to largely control parasite numbers. Thus, IFN-gamma, FasL, and iNOS appear to be essential for the complete control of parasite replication, while the contribution of TNF is more important in controlling inflammation at the site of parasite inoculation.

Translating from India and the Moving Space of Translation (Illustrated by the Works of Ajñeya)

The inadequacies and obsolescence of Eurocentric theories based on a binaryand static worldview have become a staple topic of postcolonial studies, and tosome extent also of translation studies. Nonetheless, the literary texts that arecalled upon in order to show the dynamism and hybridity of (post)modern worksbelong for the most part to the languages of the former colonial powers, especiallyEnglish, and remain inserted in a system that construes literatures interms of opposition. As a consequence, there is outside India a doubly misleadingunderstanding of Indian literatures other than those written in English:firstly, that translations of works in Hindi and in the Indian bhāṣā seem to belacking, if not inexistent, and secondly, that these "minor" literatures - as theyare regularly termed - are still often viewed as being highly dependent on theidea of "tradition," in opposition to the "postmodern" hybridity of the literatureswritten in the "dominant" languages, such as English or French. Againstthese views and supported by the analysis of Ajñeya's works in Hindi togetherwith their English translations, this paper aims to show: 1) that translationsfrom Hindi, which are not in fact non-existent, are mainly carried out in India,and 2) that Ajñeya's works, while representing a significant instance of the effectivehybridity present in Indian literatures, help to illustrate the moving spaceof translation. This demonstration effectively invalidates the above-mentionedoppositional standpoint.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

Effect of added dead space on sleep disordered breathing at high altitude.

OBJECTIVE: Sleep disordered breathing with central apnea or hypopnea frequently occurs at high altitude and is thought to be caused by a decrease in blood CO(2) level. The aim of this study was to assess the effects of added respiratory dead space on sleep disordered breathing.¦METHODS: Full polysomnographies were performed on 12 unacclimatized swiss mountaineers (11 males, 1 female, mean age 39±12 y.o.) in Leh, Ladakh (3500m). In random order, half of the night was spent with a 500ml increase in dead space through a custom designed full face mask and the other half without it.¦RESULTS: Baseline data revealed two clearly distinct groups: one with severe sleep disordered breathing (n=5, AHI>30) and the other with moderate to no disordered breathing (n=7, AHI<30). DS markedly improved breathing in the first group (baseline vs DS): apnea hypopnea index (AHI) 70.3±25.8 vs 29.4±6.9 (p=0.013), oxygen desaturation index (ODI): 72.9±24.1/h vs 42.5±14.4 (p=0.031), whereas it had no significant effect in the second group or in the total population. Respiratory events were almost exclusively central apnea or hypopnea. Microarousal index, sleep efficiency, and sleep architecture remained unchanged with DS. A minor increase in mean PtcCO(2) (n=3) was observed with DS.¦CONCLUSION: A 500ml increase in dead space through a fitted mask may improve nocturnal breathing in mountaineers with severe altitude-induced sleep disordered breathing.