Modelling tyre-road noise with data mining techniques

The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and uneven- ness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

Scenario-based assessment of future land use change on butterfly species distributions

Species distribution models (SDMs) are increasingly used to predict environmentally induced range shifts of habitats of plant and animal species. Consequently SDMs are valuable tools for scientifically based conservation decisions. The aims of this paper are (1) to identify important drivers of butterfly species persistence or extinction, and (2) to analyse the responses of endangered butterfly species of dry grasslands and wetlands to likely future landscape changes in Switzerland. Future land use was represented by four scenarios describing: (1) ongoing land use changes as observed at the end of the last century; (2) a liberalisation of the agricultural markets; (3) a slightly lowered agricultural production; and (4) a strongly lowered agricultural production. Two model approaches have been applied. The first (logistic regression with principal components) explains what environmental variables have significant impact on species presence (and absence). The second (predictive SDM) is used to project species distribution under current and likely future land uses. The results of the explanatory analyses reveal that four principal components related to urbanisation, abandonment of open land and intensive agricultural practices as well as two climate parameters are primary drivers of species occurrence (decline). The scenario analyses show that lowered agricultural production is likely to favour dry grassland species due to an increase of non-intensively used land, open canopy forests, and overgrown areas. In the liberalisation scenario dry grassland species show a decrease in abundance due to a strong increase of forested patches. Wetland butterfly species would decrease under all four scenarios as their habitats become overgrown

CARDI Research Brief: Future demand for long-term care in Ireland

With the population across the island of Ireland growing older, the issue of how to provide and pay for care in the home and in residential settings is becoming more urgent. It is important that a strategy for providing long-term care for an ageing population is put in place, and understanding what the demand for care will be is a major part of this. As a result, CARDI funded a research project led by Professor Charles Normand at Trinity College Dublin which aimed to develop a predictive model of future long-term care demand in NI and ROI.This research brief contains information collated by CARDI and a summary of the findings in the full report, Towards the Development of a Predictive Model of Long-Term Care Demand for Northern Ireland and the Republic of Ireland (Wren et al., 2012).

Role of comorbidities in outcome prediction after status epilepticus.

Status epilepticus (SE) is associated with significant mortality and morbidity. A reliable prognosis may help better manage medical resources and treatment strategies. We examined the role of preexisting comorbidities on the outcome of patients with SE, an aspect that has received little attention to date. We prospectively studied incident SE episodes in 280 adults occurring over 55 months in our tertiary care hospital, excluding patients with postanoxic encephalopathy. Different models predicting mortality and return to clinical baseline at hospital discharge were compared, which included demographics, SE etiology, a validated clinical Status Epilepticus Severity Score (STESS), and comorbidities (assessed with the Charlson Comorbidity Index) as independent variables. The overall short-term mortality was 14%, and only half of patients returned to their clinical baseline. On bivariate analyses, age, STESS, potentially fatal etiologies, and number of preexisting comorbidities were all significant predictors of both mortality and return to clinical baseline. As compared with the simplest predictive model (including demographics and deadly etiology), adding SE severity and comorbidities resulted in an improved predictive performance (C statistics 0.84 vs. 0.77 for mortality, and 0.86 vs. 0.82. for return to clinical baseline); comorbidities, however, were not independently related to outcome. Considering comorbidities and clinical presentation, in addition to age and etiology, slightly improves the prediction of SE outcome with respect to both survival and functional status. This analysis also emphasizes the robust predictive role of etiology and age.

La reincidència penitenciària a Catalunya

L’estudi té per objectiu conèixer la reincidència penitenciària de la població excarcerada de les presons catalanes l’any 1997, fent el seguiment durant 5 anys en tot el territori Espanyol. La població total es composa de 3.898 persones, de les que la mostra estudiada ha estat de 1.555 persones. S’ha treballat amb els expedients informàtics dels interns que composen la mostra, identificats amb codis numèrics per garantir la privacitat de les dades. A part d’actualitzar la taxa de reincidència penitenciària (la última datava de l’any 1994 i es referia a les dades de 1987), l’estudi també descriu e les relacions significatives entre determinades variables individuals i d’historial delictiu i penitenciari, i el fet que es reincideixi o no. També identifica el perfil més comú de les persones reincidents front a les que no ho són. I, finalment, es desenvolupa un model predictiu de la reincidència mitjançant l’aplicació de tècniques d’anàlisi multivariat.

Early detection of idiopathic Parkinson's disease based on magnetic resonance imaging

The diagnosis of idiopathic Parkinson's disease (IPD) is entirely clinical. The fact that neuronal damage begins 5-10 years before occurrence of sub-clinical signs, underlines the importance of preclinical diagnosis. A new approach for in-vivo pathophysiological assessment of IPD-related neurodegeneration was implemented based on recently developed neuroimaging methods. It is based on non- invasive magnetic resonance data sensitive to brain tissue property changes that precede macroscopic atrophy in the early stages of IPD. This research aims to determine the brain tissue property changes induced by neurodegeneration that can be linked to clinical phenotypes which will allow us to create a predictive model for early diagnosis in IPD. We hypothesized that the degree of disease progression in IPD patients will have a differential and specific impact on brain tissue properties used to create a predictive model of motor and non-motor impairment in IPD. We studied the potential of in-vivo quantitative imaging sensitive to neurodegeneration- related brain tissue characteristics to detect changes in patients with IPD. We carried out methodological work within the well established SPM8 framework to estimate the sensitivity of tissue probability maps for automated tissue classification for detection of early IPD. We performed whole-brain multi parameter mapping at high resolution followed by voxel-based morphometric (VBM) analysis and voxel-based quantification (VBQ) comparing healthy subjects to IPD patients. We found a trend demonstrating non-significant tissue property changes in the olfactory bulb area using the MT and R1 parameter with p<0.001. Comparing to the IPD patients, the healthy group presented a bilateral higher MT and R1 intensity in this specific functional region. These results did not correlate with age, severity or duration of disease. We failed to demonstrate any changes with the R2* parameter. We interpreted our findings as demyelination of the olfactory tract, which is clinically represented as anosmia. However, the lack of correlation with duration or severity complicates its implications in the creation of a predictive model of impairment in IPD.

Effect of age toxicokinetics among human volunteers exposed to propylene glycol monomethyl ether (PGME)

Rationale: Aging adults represent the fastest growing population segment in many countries. Physiological and metabolic changes in the aging process may alter how aging adults respond to exposures compared to younger workers. Current preventive workplace exposure measures may therefore not be sufficiently protective for the aging workforce. In a controlled human toxicokinetic study (exposure chamber; 12m3), the volunteers (n=11) were men and women over the age of 58 years and exposed to a commonly used, low neurotoxic glycol ether; PGME (CAS no. 107-98- 2) (50 ppm, 6 hours). Oxidative metabolism (Michaelis-Menten) is the major pathway and conjugation the minor in humans. Metabolites, conjugated and free PGME are eliminated through the kidneys, and the elimination kinetics is dose-dependent (0 order). Scope: (1) compare the toxicokinetic profile of PGME obtained in the aging volunteers (58- 62 years) to young volunteers (20-25 years) from a previous study; (2) Test the predictive power of an existing PGME toxicokinetic compartment model for aging persons against urinary PGME concentrations found in volunteers from our experimental study. Experimental procedure: Urine samples were collected before, every 2-hour during exposures for six hours, and ad-lib for additional 20 hours. Urinary analysis of free and total PGME was performed using capillary GC/FID. The toxicokinetic model (Berkley Madonna software) was ageadjusted. Results. Urinary free and total PGME concentration rose rapidly, and did not reach an apparent plateau level during exposure. Less conjugation was observed in the older group. The predictive model developed for the young group predicted well total PGME in the aging group but not free PGME. The age adjusted toxicokinetic model's Vmax1 had to be changed for the aging group, implying slower enzymatic pathway. Conclusion: The toxicokinetic model did not predict well if only the physiological parameters were adjusted for aging adults (existing model); a substance specific metabolic rate parameter was also needed.

Diffusion Spectrum Imaging after stroke shows structural changes in the contra-lateral motor network correlating with functional recovery

There is growing interest in understanding the role of the non-injured contra-lateral hemisphere in stroke recovery. In the experimental field, histological evidence has been reported that structural changes occur in the contra-lateral connectivity and circuits during stroke recovery. In humans, some recent imaging studies indicated that contra-lateral sub-cortical pathways and functional and structural cortical networks are remodeling, after stroke. Structural changes in the contra-lateral networks, however, have never been correlated to clinical recovery in patients. To determine the importance of the contra-lateral structural changes in post-stroke recovery, we selected a population of patients with motor deficits after stroke affecting the motor cortex and/or sub-cortical motor white matter. We explored i) the presence of Generalized Fractional Anisotropy (GFA) changes indicating structural alterations in the motor network of patientsâeuro? contra-lateral hemisphere as well as their longitudinal evolution ii) the correlation of GFA changes with patientsâeuro? clinical scores, stroke size and demographics data iii) and a predictive model.

A simple score for estimating ischemic heart disease in patients with non-traumatic chest pain in primary care

Background: Modelling epidemiological knowledge in validated clinical scores is a practical mean of integrating EBM to usual care. Existing scores about cardiovascular disease have been largely developed in emergency settings, but few in primary care. Such a toll is needed for general practitioners (GP) to evaluate the probability of ischemic heart disease (IHD) in patients with non-traumatic chest pain. Objective: To develop a predictive model to use as a clinical score for detecting IHD in patients with non-traumatic chest-pain in primary care. Methods: A post-hoc secondary analysis on data from an observational study including 672 patients with chest pain of which 85 had IHD diagnosed by their GP during the year following their inclusion. Best subset method was used to select 8 predictive variables from univariate analysis and fitted in a multivariate logistic regression model to define the score. Reliability of the model was assessed using split-group method. Results: Significant predictors were: age (0-3 points), gender (1 point), having at least one cardiovascular risks factor (hypertension, dyslipidemia, diabetes, smoking, family history of CVD; 3 points), personal history of cardiovascular disease (1 point), duration of chest pain from 1 to 60 minutes (2 points), substernal chest pain (1 point), pain increasing with exertion (1 point) and absence of tenderness at palpation (1 point). Area under the ROC curve for the score was of 0.95 (IC95% 0.93; 0.97). Patients were categorised in three groups, low risk of IHD (score under 6; n = 360), moderate risk of IHD (score from 6 to 8; n = 187) and high risk of IHD (score from 9-13; n = 125). Prevalence of IHD in each group was respectively of 0%, 6.7%, 58.5%. Reliability of the model seems satisfactory as the model developed from the derivation set predicted perfectly (p = 0.948) the number of patients in each group in the validation set. Conclusion: This clinical score based only on history and physical exams can be an important tool in the practice of the general physician for the prediction of ischemic heart disease in patients complaining of chest pain. The score below 6 points (in more than half of our population) can avoid demanding complementary exams for selected patients (ECG, laboratory tests) because of the very low risk of IHD. Score above 6 points needs investigation to detect or rule out IHD. Further external validation is required in ambulatory settings.

Agents anti-infectieux et fonction réna!e: vers des posologies sur mesure [Antimicrobial agents and renal elimination: towards individual dosage adjustment?].

The efficacy and safety of anti-infective treatments are associated with the drug blood concentration profile, which is directly correlated with a dosing adjustment to the individual patient's condition. Dosing adjustments to the renal function recommended in reference books are often imprecise and infrequently applied in clinical practice. The recent generalisation of the KDOQI (Kidney Disease Outcome Quality Initiative) staging of chronically impaired renal function represents an opportunity to review and refine the dosing recommendations in patients with renal insufficiency. The literature has been reviewed and compared to a predictive model of the fraction of drug cleared by the kidney based on the Dettli's principle. Revised drug dosing recommendations integrating these predictive parameters are proposed.

A new early and automated MRI-based predictor of motor improvement after stroke.

OBJECTIVES: In this study, we investigated the structural plasticity of the contralesional motor network in ischemic stroke patients using diffusion magnetic resonance imaging (MRI) and explored a model that combines a MRI-based metric of contralesional network integrity and clinical data to predict functional outcome at 6 months after stroke. METHODS: MRI and clinical examinations were performed in 12 patients in the acute phase, at 1 and 6 months after stroke. Twelve age- and gender-matched controls underwent 2 MRIs 1 month apart. Structural remodeling after stroke was assessed using diffusion MRI with an automated measurement of generalized fractional anisotropy (GFA), which was calculated along connections between contralesional cortical motor areas. The predictive model of poststroke functional outcome was computed using a linear regression of acute GFA measures and the clinical assessment. RESULTS: GFA changes in the contralesional motor tracts were found in all patients and differed significantly from controls (0.001 ≤ p < 0.05). GFA changes in intrahemispheric and interhemispheric motor tracts correlated with age (p ≤ 0.01); those in intrahemispheric motor tracts correlated strongly with clinical scores and stroke sizes (p ≤ 0.001). GFA measured in the acute phase together with a routine motor score and age were a strong predictor of motor outcome at 6 months (r(2) = 0.96, p = 0.0002). CONCLUSION: These findings represent a proof of principle that contralesional diffusion MRI measures may provide reliable information for personalized rehabilitation planning after ischemic motor stroke. Neurology® 2012;79:39-46.

Paperikoneen lajinvaihdon säätötapojen vertailu

Työn tavoitteena oli vertailla paperikoneen lajinvaihdon säätötapoja. Vertailun kohteina olivat Metso Automationin IQGradeChange lajinvaihto-ohjelmisto ja operaattoreiden käsin tekemät lajinvaihdot. Kattavan tutkimusaineiston saamiseksi paperikoneen lajinvaihtodataa kerättiin seitsemän kuukauden ajan. Kerätyt lajinvaihdot käytiin läpi Matlab-ympäristössä lajinvaihtoaikojen selvittämiseksi. Lisäksi lajinvaihdoista laskettiin tuotannon muutokset ((t/h)/min) vanhan ja uudenlajin välillä, jotta päästiin selvyyteen lajinvaihdon laajuudesta ja eri lajinvaihtotapojen suorituskyvyistä. Koeajojaksona paperikoneelta kerättiin kaikkiaan 130 lajinvaihdon tiedot. Näistä lajinvaihdoista 58 tehtiin IQGradeChange lajinvaihto-ohjelmistolla ja 72 oli operaattoreiden käsin tekemiä lajinvaihtoja. Kerätyistä 130 lajinvaihdosta 27 kappaletta päättyi ratakatkoon. Yhtenä tehtävänä olikin tutkia katkoon päättyneitä lajinvaihtoja.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity.

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

Empirical models for predicting the production of wild mushrooms in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees

Mushroom picking has become a widespread autumn recreational activity in the Central Pyrenees and other regions of Spain. Predictive models that relate mushroom production or fungal species richness with forest stand and site characteristics are not available. This study used mushroom production data from 24 Scots pine plots over 3 years to develop a predictive model that could facilitate forest management decisions when comparing silvicultural options in terms of mushroom production. Mixed modelling was used to model the dependence of mushroom production on stand and site factors. The results showed that productions were greatest when stand basal area was approximately 20 m2 ha-1. Increasing elevation and northern aspect increased total mushroom production as well as the production of edible and marketed mushrooms. Increasing slope decreased productions. Marketed Lactarius spp., the most important group collected in the region, showed similar relationships. The annual variation in mushroom production correlated with autumn rainfall. Mushroom species richness was highest when the total production was highest.

ASTRAL-R score predicts non-recanalisation after intravenous thrombolysis in acute ischaemic stroke.

Intravenous thrombolysis (IVT) as treatment in acute ischaemic strokes may be insufficient to achieve recanalisation in certain patients. Predicting probability of non-recanalisation after IVT may have the potential to influence patient selection to more aggressive management strategies. We aimed at deriving and internally validating a predictive score for post-thrombolytic non-recanalisation, using clinical and radiological variables. In thrombolysis registries from four Swiss academic stroke centres (Lausanne, Bern, Basel and Geneva), patients were selected with large arterial occlusion on acute imaging and with repeated arterial assessment at 24 hours. Based on a logistic regression analysis, an integer-based score for each covariate of the fitted multivariate model was generated. Performance of integer-based predictive model was assessed by bootstrapping available data and cross validation (delete-d method). In 599 thrombolysed strokes, five variables were identified as independent predictors of absence of recanalisation: Acute glucose > 7 mmol/l (A), significant extracranial vessel STenosis (ST), decreased Range of visual fields (R), large Arterial occlusion (A) and decreased Level of consciousness (L). All variables were weighted 1, except for (L) which obtained 2 points based on β-coefficients on the logistic scale. ASTRAL-R scores 0, 3 and 6 corresponded to non-recanalisation probabilities of 18, 44 and 74 % respectively. Predictive ability showed AUC of 0.66 (95 %CI, 0.61-0.70) when using bootstrap and 0.66 (0.63-0.68) when using delete-d cross validation. In conclusion, the 5-item ASTRAL-R score moderately predicts non-recanalisation at 24 hours in thrombolysed ischaemic strokes. If its performance can be confirmed by external validation and its clinical usefulness can be proven, the score may influence patient selection for more aggressive revascularisation strategies in routine clinical practice.