Psychometric properties of the parent-infant caregiving touch scale

Recent work in animals suggests that the extent of early tactile stimulation by parents of offspring is an important element in early caregiving. We evaluate the psychometric properties of a new parent-report measure designed to assess frequency of tactile stimulation across multiple caregiving domains in infancy. We describe the full item set of the Parent-Infant Caregiving Touch Scale (PICTS) and, using data from a UK longitudinal Child Health and Development Study, the response frequencies and factor structure and whether it was invariant over two time points in early development (5 and 9 weeks). When their infant was 9 weeks old, 838 mothers responded on the PICTS while a stratified subsample of 268 mothers completed PICTS at an earlier 5 week old assessment (229 responded on both occasions). Three PICTS factors were identified reflecting stroking, holding and affective communication. These were moderately to strongly correlated at each of the two time points of interest and were unrelated to, and therefore distinct from, a traditional measure of maternal sensitivity at 7-months. A wholly stable psychometry over 5 and 9-week assessments was not identified which suggests that behavior profiles differ slightly for younger and older infants. Tests of measurement invariance demonstrated that all three factors are characterized by full configural and metric invariance, as well as a moderate degree of evidence of scalar invariance for the stroking factor. We propose the PICTS as a valuable new measure of important aspects of caregiving in infancy.

An assessment of the effects of climate change on horticulture

Horticulture may be defined as the intensive cultivation and harvesting of plants for financial, environmental and social profit. Evidence for the occurrence of climate change more generally and reasons why this process is happening with such rapidity are discussed. These changes are then considered in terms of the effects which might alter the options for worldwide intensive horticultural cultivation of plants and its interactions with other organisms. Potentially changing climates will have considerable impact upon horticultural processes and productivity across the globe . Climate change will alter the growth patterns and capabilities for flowering and fruiting of many perennial and annual horticultural plants. In some regions perennial fruit crops are likely to experience substantial difficulties because of altered seasonal conditions affecting dormancy, acclimation and subsequent flowering and fruiting. Elsewhere these crops may benefit from the effects of climate change as a result of reduced cold damage and increased length of the growing season. There will be considerable effects for aerial and edaphic microbes invertebrate and vertebrate animals which have benign and pathogenic interactions with horticultural plants. Microbial activity and as a consequence soil fertility may alter. New pests and pathogens may become prevalent and damaging in areas where the climate previously excluded their activity. Vital resources such as water and nutrients may become scarce in some regions reducing opportunities for growing horticultural crops. Wind and windiness are significant factors governing the success of horticultural plants and the scale of their impacts may change as climate alters. Damaging winds could limit crop growing in areas where previously it flourished. Forms of macro- and micro-landscaping will change as the spectrum of plants which can be cultivated alters and the availability of resources and their cost changes driven by scarcities brought about by climate change. The horticultural economy of India as it may be affected by climate change is described as an individual example in a detailed study.

The global-scale impacts of climate change: the QUEST-GSI project

This paper introduces the special issue of Climatic Change on the QUEST-GSI project, a global-scale multi-sectoral assessment of the impacts of climate change. The project used multiple climate models to characterise plausible climate futures with consistent baseline climate and socio-economic data and consistent assumptions, together with a suite of global-scale sectoral impacts models. It estimated impacts across sectors under specific SRES emissions scenarios, and also constructed functions relating impact to change in global mean surface temperature. This paper summarises the objectives of the project and its overall methodology, outlines how the project approach has been used in subsequent policy-relevant assessments of future climate change under different emissions futures, and summarises the general lessons learnt in the project about model validation and the presentation of multi-sector, multi-region impact assessments and their associated uncertainties to different audiences.

Spatial patterns and environmental constraints on ecosystem services at a catchment scale

Improved understanding and prediction of the fundamental environmental controls on ecosystem service supply across the landscape will help to inform decisions made by policy makers and land-water managers. To evaluate this issue for a local catchment case study, we explored metrics and spatial patterns of service supply for water quality regulation, agriculture production, carbon storage, and biodiversity for the Macronutrient Conwy catchment. Methods included using ecosystem models such as LUCI and JULES, integration of national scale field survey datasets, earth observation products and plant trait databases, to produce finely resolved maps of species richness and primary production. Analyses were done with both 1x1 km gridded and subcatchment data. A common single gradient characterised catchment scale ecosystem services supply with agricultural production and carbon storage at opposing ends of the gradient as reported for a national-scale assessment. Species diversity was positively related to production due to the below national average productivity levels in the Conwy combined with the unimodal relationship between biodiversity and productivity at the national scale. In contrast to the national scale assessment, a strong reduction in water quality as production increased was observed in these low productive systems. Various soil variables were tested for their predictive power of ecosystem service supply. Soil carbon, nitrogen, their ratio and soil pH all had double the power of rainfall and altitude, each explaining around 45% of variation but soil pH is proposed as a potential metric for ecosystem service supply potential as it is a simple and practical metric which can be carried out in the field with crowd-sourcing technologies now available. The study emphasises the importance of considering multiple ecosystem services together due to the complexity of covariation at local and national scales, and the benefits of exploiting a wide range of metrics for each service to enhance data robustness.

Precipitation diurnal cycle and summer climatology assessment over South America: An evaluation of Regional Climate Model version 3 simulations

Regional Climate Model version 3 (RegCM3) simulations of 17 summers (1988-2004) over part of South America south of 5 degrees S were evaluated to identify model systematic errors. Model results were compared to different rainfall data sets (Climate Research Unit (CRU), Climate Prediction Center (CPC), Global Precipitation Climatology Project (GPCP), and National Centers for Environmental Prediction (NCEP) reanalysis), including the five summers mean (1998-2002) precipitation diurnal cycle observed by the Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR). In spite of regional differences, the RegCM3 simulates the main observed aspects of summer climatology associated with the precipitation (northwest-southeast band of South Atlantic Convergence Zone (SACZ)) and air temperature (warmer air in the central part of the continent and colder in eastern Brazil and the Andes Mountains). At a regional scale, the main RegCM3 failures are the underestimation of the precipitation in the northern branch of the SACZ and some unrealistic intense precipitation around the Andes Mountains. However, the RegCM3 seasonal precipitation is closer to the fine-scale analyses (CPC, CRU, and TRMM-PR) than is the NCEP reanalysis, which presents an incorrect north-south orientation of SACZ and an overestimation of its intensity. The precipitation diurnal cycle observed by TRMM-PR shows pronounced contrasts between Tropics and Extratropics and land and ocean, where most of these features are simulated by RegCM3. The major similarities between the simulation and observation, especially the diurnal cycle phase, are found over the continental tropical and subtropical SACZ regions, which present afternoon maximum (1500-1800 UTC) and morning minimum (0900-1200 UTC). More specifically, over the core of SACZ, the phase and amplitude of the simulated precipitation diurnal cycle are very close to the TRMM-PR observations. Although there are amplitude differences, the RegCM3 simulates the observed nighttime rainfall in the eastern Andes Mountains, over the Atlantic Ocean, and also over northern Argentina. The main simulation deficiencies are found in the Atlantic Ocean and near the Andes Mountains. Over the Atlantic Ocean the convective scheme is not triggered; thus the rainfall arises from the grid-scale scheme and therefore differs from the TRMM-PR. Near the Andes, intense (nighttime and daytime) simulated precipitation could be a response of an incorrect circulation and topographic uplift. Finally, it is important to note that unlike most reported bias of global models, RegCM3 does not trigger the moist convection just after sunrise over the southern part of the Amazon.

PAIN IN FIBROMYALGIA AND DISCRIMINATIVE POWER OF THE INSTRUMENTS: VISUAL ANALOG SCALE, DOLORIMETRY AND THE MCGILL PAIN QUESTIONNAIRE

Objective: The aim of this study was to verify the discriminative power of the most widely used pain assessment instruments. Methods: The sample consisted of 279 subjects divided into Fibromyalgia Group (FM- 205 patients with fibromyalgia) and Control Group (CG-74 healthy subjects), mean age 49.29 +/- 10.76 years. Only 9 subjects were male, 6 in FM and 3 in CG. FM were outpatients from the Rheumatology Clinic of the University of Sao Paulo - Hospital das Clinicas (HCFMUSP); the CG included people accompanying patients and hospital staff with similar socio-demographic characteristics. Three instruments were used to assess pain: the McGill Pain Questionnaire (MPQ), the Visual Analog Scale (VAS), and the Dolorimetry, to measure pain threshold on tender points (generating the TP index). In order to assess the discriminative power of the instruments, the measurements obtained were submitted to descriptive analysis and inferential analysis using ROC Curve - sensibility (S), specificity (S I) and area under the curve (AUC) - and Contingence tables with Chi-square Test and odds ratio. Significance level was 0.05. Results: Higher sensibility, specificity and area under the curve was obtained by VAS (80%, 80% and 0.864, respectively), followed by Dolorimetry (S 77%, S177% and AUC 0.851), McGill Sensory (S 72%, S167% and AUC 0.765) and McGill Affective (S 69%, S1 67% and AUC 0.753). Conclusions: VAS presented the higher sensibility, specificity and AUC, showing the greatest discriminative power among the instruments. However, these values are considerably similar to those of Dolorimetry.

Getting Them There: A Small-Scale Usability Test of a University Library Website

This presentation was offered as part of the CUNY Library Assessment Conference, Reinventing Libraries: Reinventing Assessment, held at the City University of New York in June 2014.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Automatic and objective assessment of alternating tapping performance in Parkinson’s disease

This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson’s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions (‘speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regression classifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson’s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance of PD patients and can be included in telemedicine tools for remote monitoring of tapping.

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.

Assessment of PD Speech Anomalies @ Home

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip. Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed. Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3. Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.

Newly Automated System For Integrated Assessment Of The Conditions Of Underwater Gas Pipelines

An underwater gas pipeline is the portion of the pipeline that crosses a river beneath its bottom. Underwater gas pipelines are subject to increasing dangers as time goes by. An accident at an underwater gas pipeline can lead to technological and environmental disaster on the scale of an entire region. Therefore, timely troubleshooting of all underwater gas pipelines in order to prevent any potential accidents will remain a pressing task for the industry. The most important aspect of resolving this challenge is the quality of the automated system in question. Now the industry doesn't have any automated system that fully meets the needs of the experts working in the field maintaining underwater gas pipelines. Principle Aim of this Research: This work aims to develop a new system of automated monitoring which would simplify the process of evaluating the technical condition and decision making on planning and preventive maintenance and repair work on the underwater gas pipeline. Objectives: Creation a shared model for a new, automated system via IDEF3; Development of a new database system which would store all information about underwater gas pipelines; Development a new application that works with database servers, and provides an explanation of the results obtained from the server; Calculation of the values MTBF for specified pipelines based on quantitative data obtained from tests of this system. Conclusion: The new, automated system PodvodGazExpert has been developed for timely and qualitative determination of the physical conditions of underwater gas pipeline; The basis of the mathematical analysis of this new, automated system uses principal component analysis method; The process of determining the physical condition of an underwater gas pipeline with this new, automated system increases the MTBF by a factor of 8.18 above the existing system used today in the industry.

Cabinet composition and assessment of a multiparty presidential system

This doctoral dissertation provides a detailed analysis of the Brazilian cabinet according to the concepts of a multiparty presidential system. Appointing politicians as ministers is one of the most important coalition-building tools and has been widely used by minority presidents. This dissertation will therefore analyze the high-level Brazilian national bureaucracy between 1995 and 2014. It argues that the ministries – or departments – are not equal, and that allied parties therefore take into account the different characteristics of a ministry when demanding positions as a patronage strategy or for use as other kinds of political assets. After reviewing the literature on the theme, followed by a comparative analysis of the Brazilian, Chilean, Mexican, and Guatemalan cabinets, all the Brazilian ministries will be weighed and ranked on a scale that is able to measure their political importance and attractiveness. This rank takes into account variables such as the budgetary power, the ability to spend money according the ministers’ will, the ability to hire new employees, the ministries’ influence over other governmental agents such as companies, agencies, and so on, the ministers’ tenure in office. Finally, a proxy is provided that seeks to identify the normative power a department may hold. All of these characteristics will then be taken into account in considering the representatives’ opinion, thus helping to ascertain whether the cabinet appointment has been coalescent among the several parties that belong to the president’s coalition.

Assessment of cumulative damage by using ultrasonic C-scan on carbon fiber/epoxy composites under thermal cycling

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)