A web application for follow-up of results from a mobile device test battery for parkinson’s disease patients

Background: A test battery consisting of self-assessments and motor tests (tapping and spiral drawing) was developed for a hand computer with touch screen in a telemedicine setting. Objectives: To develop and evaluate a web-based system that delivers decision support information to the treating clinical staff for assessing PD symptoms in their patients based on the test battery data. Methods: The test battery is currently being used in a clinical trial (DAPHNE, EudraCT No. 2005-002654-21) by sixty five patients with advanced Parkinson’s disease (PD) on 9991 test occasions (four tests per day during in all 362 week-long test periods) at nine clinics around Sweden. Test results are sent continuously from the hand unit over a mobile net to a central computer and processed with statistical methods. They are summarized into scores for different dimensions of the symptom state and an ‘overall test score’ reflecting the overall condition of the patient during a test period. The information in the web application is organized and presented graphically in a way that the general overview of the patient performance per test period is emphasized. Focus is on the overall test score, symptom dimensions and daily summaries. In a recent preliminary user evaluation, the web application was demonstrated to the fifteen study nurses who had used the test battery in the clinical trial. At least one patient per clinic was shown. Results: In general, the responses from nurses were positive. They claimed that the test results shown in the system were consistent with their own clinical observations. They could follow complications, changes and trends within their patients. Discussion: In conclusion, the system is able to summarise the various time series of motor test results and self-assessments during test periods and present them in a useful manner. Its main contribution is a novel and reliable way to capture and easily access symptom information from patients’ home environment. The convenient access to current symptom profile as well as symptom history provides a basis for individualized evaluation and adjustment of treatments.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Effective Data Management Enables Intelligent Utility Management

Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to manage data from smart water meters to the collaboration of data across third party corporations. This paper focuses on practical, utility successes in the water industry where utility managers are leveraging instantaneous access to data from proven, commercial off-the-shelf ODMS solutions to enable better real-time decision making. Successes include saving $650,000 / year in water loss control, safeguarding water quality, saving millions of dollars in energy management and asset management. Immediate opportunities exist to integrate the research being done in academia with these ODMS solutions in the field and to leverage these successes to utilities around the world.

Evaluation Of Reservoir Model Integration With Deterministic And Probabilistic Streamflow Forecasts

Due to the increase in water demand and hydropower energy, it is getting more important to operate hydraulic structures in an efficient manner while sustaining multiple demands. Especially, companies, governmental agencies, consultant offices require effective, practical integrated tools and decision support frameworks to operate reservoirs, cascades of run-of-river plants and related elements such as canals by merging hydrological and reservoir simulation/optimization models with various numerical weather predictions, radar and satellite data. The model performance is highly related with the streamflow forecast, related uncertainty and its consideration in the decision making. While deterministic weather predictions and its corresponding streamflow forecasts directly restrict the manager to single deterministic trajectories, probabilistic forecasts can be a key solution by including uncertainty in flow forecast scenarios for dam operation. The objective of this study is to compare deterministic and probabilistic streamflow forecasts on an earlier developed basin/reservoir model for short term reservoir management. The study is applied to the Yuvacık Reservoir and its upstream basin which is the main water supply of Kocaeli City located in the northwestern part of Turkey. The reservoir represents a typical example by its limited capacity, downstream channel restrictions and high snowmelt potential. Mesoscale Model 5 and Ensemble Prediction System data are used as a main input and the flow forecasts are done for 2012 year using HEC-HMS. Hydrometeorological rule-based reservoir simulation model is accomplished with HEC-ResSim and integrated with forecasts. Since EPS based hydrological model produce a large number of equal probable scenarios, it will indicate how uncertainty spreads in the future. Thus, it will provide risk ranges in terms of spillway discharges and reservoir level for operator when it is compared with deterministic approach. The framework is fully data driven, applicable, useful to the profession and the knowledge can be transferred to other similar reservoir systems.

Optimized Water Demand Management Through Intelligent Sensing And Analytics: The WISDOM Approach

New business and technology platforms are required to sustainably manage urban water resources [1,2]. However, any proposed solutions must be cognisant of security, privacy and other factors that may inhibit adoption and hence impact. The FP7 WISDOM project (funded by the European Commission - GA 619795) aims to achieve a step change in water and energy savings via the integration of innovative Information and Communication Technologies (ICT) frameworks to optimize water distribution networks and to enable change in consumer behavior through innovative demand management and adaptive pricing schemes [1,2,3]. The WISDOM concept centres on the integration of water distribution, sensor monitoring and communication systems coupled with semantic modelling (using ontologies, potentially connected to BIM, to serve as intelligent linkages throughout the entire framework) and control capabilities to provide for near real-time management of urban water resources. Fundamental to this framework are the needs and operational requirements of users and stakeholders at domestic, corporate and city levels and this requires the interoperability of a number of demand and operational models, fed with data from diverse sources such as sensor networks and crowsourced information. This has implications regarding the provenance and trustworthiness of such data and how it can be used in not only the understanding of system and user behaviours, but more importantly in the real-time control of such systems. Adaptive and intelligent analytics will be used to produce decision support systems that will drive the ability to increase the variability of both supply and consumption [3]. This in turn paves the way for adaptive pricing incentives and a greater understanding of the water-energy nexus. This integration is complex and uncertain yet being typical of a cyber-physical system, and its relevance transcends the water resource management domain. The WISDOM framework will be modeled and simulated with initial testing at an experimental facility in France (AQUASIM – a full-scale test-bed facility to study sustainable water management), then deployed and evaluated in in two pilots in Cardiff (UK) and La Spezia (Italy). These demonstrators will evaluate the integrated concept providing insight for wider adoption.

Sistema fuzzy para estimativa do bem-estar de matrizes pesadas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Spatial choice is biased by chemical cues from conspecifics in the speckeled worm eel Myrophis punctatus

A enguia mirongo-mirim Myrophis punctatus vive em agrupamentos de alta densidade populacional e comumente se enterra ou permanece sob o substrato. Esses comportamentos podem levar a marcas químicas no subtrato e podem, portanto, modular o uso do espaço nessa espécie. Neste estudo, testamos a hipótese de que a preferência espacial da enguia mirongo-mirim é influenciada pela presença de odor do animal coespecífico no subtrato. Mostramos que as enguias evitam a área que contém tal odor, indicando que as decisões de ocupação espacial podem ser influenciadas por pistas químicas de coespecíficos. As enguias claramente detectaram o odor de um animal coespecífico e essa percepção poderia ser um indicativo da presença de um coespecífico enterrado no substrato. Visto que elas evitam uma área contendo tal odor, sugerimos que isso poderia ser uma resposta para evitar invadir o território de um animal residente.

Oscilações Teta no hipocampo do rato durante tarefa de tomada de decisão espacial

The processing of spatial and episodic information during memory tasks depends on hippocampal theta oscillations. In the present study, I investigated the relationship between theta power and choice selection during spatial decision-making. I recorded local field potentials from the CA1 region of rats retrieving reward locations in a 4-arm maze. In trained but not in naïve animals, I observed a significant increase in theta power during decision-making, which could not be explained by changes in locomotion speed. Furthermore, a Bayesian decoder based on theta power predicted choice outcomes in speed-matched trials. The decoding time course revealed that performance increased above chance before the decision moment exclusively for theta power, remaining flat for other frequency bands. These results occurred for trained animals, but no significant prediction could be made for naïve animals. Altogether, the data support a mnemonic function of theta rhythm during spatial decision-making, indicating that these oscillations correlate with the retrieval of memories required for successful decisions

Social support and infant malnutrition: a case-control study in an urban area of Southeastern Brazil

The relationship between malnutrition and social support was first suggested in the mid-1990s. Despite its plausibility, no empirical studies aimed at obtaining evidence of this association could be located. The goal of the present study was to investigate such evidence. A case-control study was carried out including 101 malnourished children (weight-for-age National Center for Health Statistics/WHO 5th percentile) aged 12-23 months, who were compared with 200 well-nourished children with regard to exposure to a series of factors related to their social support system. Univariate and multiple logistic regressions were carried out, odds ratios being adjusted for per capita family income, mother's schooling, and number of children. The presence of an interaction between income and social support variables was also tested. Absence of a partner living with the mother increased risk of malnutrition (odds ratio 2.4 (95 % CI 1.19, 4.89)), even after adjustment for per capita family income, mother's schooling, and number of children. The lack of economic support during adverse situations accounted for a very high risk of malnutrition (odds ratio 10.1 (95 % CI 3.48, 29.13)) among low-income children, but had no effect on children of higher-income families. Results indicate that receiving economic support is an efficient risk modulator for malnutrition among low-income children. In addition, it was shown that the absence of a partner living with the mother is an important risk factor for malnutrition, with an effect independent from per capita family income, mother's schooling, and number of children.

Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica

This work combines symbolic machine learning and multiscale fractal techniques to generate models that characterize cellular rejection in myocardial biopsies and that can base a diagnosis support system. The models express the knowledge by the features threshold, fractal dimension, lacunarity, number of clusters, spatial percolation and percolation probability, all obtained with myocardial biopsies processing. Models were evaluated and the most significant was the one generated by the C4.5 algorithm for the features spatial percolation and number of clusters. The result is relevant and contributes to the specialized literature since it determines a standard diagnosis protocol. © 2013 Springer.

Interrelações da produtividade de cana-de-açúcar com atributos químicos de um argissolo vermelho eutrófico paulista

Pós-graduação em Agronomia - FEIS

Conflitos por múltiplos usos da água no reservatório da UHE de Tucuruí

Os conflitos por múltiplos usos da água, no reservatório da UHE Tucuruí, surgem com o anúncio da construção da mega hidrelétrica na região, e os conflitos socioambientais subseqüentes. Aliada a elevação do nível de percepção social em relação aos problemas ambientais cresce também a busca por eficientes processos de gestão e gerenciamento de recursos hídricos. Este estudo objetiva analisar e tipificar os conflitos por múltiplos usos da água no reservatório da UHE Tucuruí, utilizando-se como ferramenta de apoio à decisão o software de modelagem qualitativa NVivo 8, e dessa forma verificar as melhores alternativas a serem adotadas para a conciliação dos usos múltiplos no reservatório. As tipificações realizadas basearam-se na análise dos conflitos, seus componentes, elementos e aspectos, tipo, natureza e origem. Sendo assim, identificaram-se três principais tipos de conflitos no reservatório da UHE Tucuruí: conflitos entre distintos grupos de usuários da água, conflitos por obras hidráulicas e conflitos decorrentes de poluição ambiental. Para este estudo adotou - se uma abordagem qualitativa, através do método de mapeamento cognitivo. Este tipo de mapeamento possibilitou a construção de um modelo cognitivo para a gestão dos conflitos no reservatório de Tucuruí. Sendo assim, o software NVivo 8 possibilitou, além da análise dos dados obtidos nas entrevistas e no levantamento bibliográfico, a construção do modelo gráfico de apoio à gestão de conflitos por múltiplos usos da água. Verificou-se que uma das formas de solução dos conflitos é através da análise destes, em vistas de se investigar os mecanismos adequados para sua resolução, e posterior proposição de medidas estruturais e/ou nãoestruturais para a gestão de recursos hídricos. As principais ações para a solução dos conflitos estão enquadradas nos métodos de resolução institucional de longo prazo. O modelo pode funcionar como suporte ao planejamento e tomada de decisão, tendo em vista os problemas ambientais, a participação dos usuários da água no sistema hídrico, as políticas públicas, e também a gestão integrada dos recursos hídricos. Concluiu- se, então, que a exploração dos recursos hídricos deve proporcionar os múltiplos usos da água em atendimento aos princípios da sustentabilidade ambiental, inseridos num processo de gestão dos conflitos por múltiplos usos da água e gerenciamento integrado dos recursos hídricos.

Sistema de informação sobre violência urbana (SiViU) como apoio à tomada de decisão em políticas públicas de cidades médias

Pós-graduação em Geografia - IGCE

Estudo exploratório da integração de processos em uma instituição pública de ensino superior a partir das funcionalidades de um sistema de gestão de processos de negócio

Pós-graduação em Engenharia de Produção - FEB

Landwirtschaftliche Schaderregerprognose mit Hilfe von geographischen Informationssystemen

In der hier vorliegenden Arbeit wurde am Beispiel der Kraut- und Knollenfäule an Kartoffeln Phytophthora infestans und des Kartoffelkäfers Leptinotarsa decemlineata untersucht, ob durch den Einsatz von Geographischen Informationssystemen (GIS) landwirtschaftliche Schader¬reger¬prognosen für jeden beliebigen Kartoffelschlag in Deutschland erstellt werden können. Um dieses Ziel zu erreichen, wurden die Eingangsparameter (Temperatur und relative Luftfeuchte) der Prognosemodelle für die beiden Schaderreger (SIMLEP1, SIMPHYT1, SIMPHYT3 and SIMBLIGHT1) so aufbereitet, dass Wetterdaten flächendeckend für Deutschland zur Verfügung standen. Bevor jedoch interpoliert werden konnte, wurde eine Regionalisierung von Deutschland in Interpolationszonen durchgeführt und somit Naturräume geschaffen, die einen Vergleich und eine Bewertung der in ihnen liegenden Wetterstationen zulassen. Hierzu wurden die Boden-Klima-Regionen von SCHULZKE und KAULE (2000) modifiziert, an das Wetterstationsnetz angepasst und mit 5 bis 10 km breiten Pufferzonen an der Grenze der Interpolationszonen versehen, um die Wetterstationen so häufig wie möglich verwenden zu können. Für die Interpolation der Wetterdaten wurde das Verfahren der multiplen Regression gewählt, weil dieses im Vergleich zu anderen Verfahren die geringsten Abweichungen zwischen interpolierten und gemessenen Daten aufwies und den technischen Anforderungen am besten entsprach. Für 99 % aller Werte konnten bei der Temperaturberechnung Abweichungen in einem Bereich zwischen -2,5 und 2,5 °C erzielt werden. Bei der Berechnung der relativen Luftfeuchte wurden Abweichungen zwischen -12 und 10 % relativer Luftfeuchte erreicht. Die Mittelwerte der Abweichungen lagen bei der Temperatur bei 0,1 °C und bei der relativen Luftfeuchte bei -1,8 %. Zur Überprüfung der Trefferquoten der Modelle beim Betrieb mit interpolierten Wetterdaten wurden Felderhebungsdaten aus den Jahren 2000 bis 2007 zum Erstauftreten der Kraut- und Knollenfäule sowie des Kartoffelkäfers verwendet. Dabei konnten mit interpolierten Wetterdaten die gleichen und auch höhere Trefferquoten erreicht werden, als mit der bisherigen Berechnungsmethode. Beispielsweise erzielte die Berechnung des Erstauftretens von P. infestans durch das Modell SIMBLIGHT1 mit interpolierten Wetterdaten im Schnitt drei Tage geringere Abweichungen im Vergleich zu den Berechnungen ohne GIS. Um die Auswirkungen interpretieren zu können, die durch Abweichungen der Temperatur und der relativen Luftfeuchte entstanden wurde zusätzlich eine Sensitivitätsanalyse zur Temperatur und relativen Luftfeuchte der verwendeten Prognosemodelle durchgeführt. Die Temperatur hatte bei allen Modellen nur einen geringen Einfluss auf das Prognoseergebnis. Veränderungen der relativen Luftfeuchte haben sich dagegen deutlich stärker ausgewirkt. So lag bei SIMBLIGHT1 die Abweichung durch eine stündliche Veränderung der relativen Luftfeuchte (± 6 %) bei maximal 27 Tagen, wogegen stündliche Veränderungen der Temperatur (± 2 °C) eine Abweichung von maximal 10 Tagen ausmachten. Die Ergebnisse dieser Arbeit zeigen, dass durch die Verwendung von GIS mindestens die gleichen und auch höhere Trefferquoten bei Schaderregerprognosen erzielt werden als mit der bisherigen Verwendung von Daten einer nahegelegenen Wetterstation. Die Ergebnisse stellen einen wesentlichen Fortschritt für die landwirtschaftlichen Schaderregerprognosen dar. Erstmals ist es möglich, bundesweite Prognosen für jeden beliebigen Kartoffelschlag zur Bekämpfung von Schädlingen in der Landwirtschaft bereit zu stellen.