Angiogenic activity of breast cancer patients' monocytes reverted by combined use of systems modeling and experimental approaches.

Angiogenesis plays a key role in tumor growth and cancer progression. TIE-2-expressing monocytes (TEM) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models, but the molecular basis of their pro-angiogenic activity are largely unknown. Moreover, differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date, hindering the identification of specific targets for therapeutic intervention. In this work, we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches. Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM. Secondly, we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa. In silico predicted perturbations were validated experimentally using patient TEM. In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. Finally, the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer.

Conditioning of multiple-point statistics facies simulations to tomographic images

Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment.

Computational modeling of resting-state activity demonstrates markers of normalcy in children with prenatal or perinatal stroke.

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere "take over" their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children.

Business Process Modeling in Software Requirements Engineering for Small and Medium Software Projects

Vaatimusmäärittelyn tavoitteena on luoda halutun järjestelmän kokonaisen, yhtenäisen vaatimusluettelon vaatimusten määrittämiseksi käsitteellisellä tasolla. Liiketoimintaprosessien mallintaminen on varsin hyödyllinen vaatimusmäärittelyn varhaisissa vaiheissa. Tämä työ tutkii liiketoimintaprosessien mallintamista tietojärjestelmien kehittämistä varten. Nykyään on olemassa erilaisia liiketoimintaprosessien mallintamiseen tarkoitettuja tekniikoita. Tämä työ tarkastaa liiketoimintaprosessien mallintamisen periaatteet ja näkökohdat sekä eri mallinnustekniikoita. Uusi menetelmä, joka on suunniteltu erityisesti pienille ja keskisuurille ohjelmistoprojekteille, on kehitetty prosessinäkökohtien ja UML-kaavioiden perusteella.

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

The performance of a hydrologic model depends on the rainfall input data, both spatially and temporally. As the spatial distribution of rainfall exerts a great influence on both runoff volumes and peak flows, the use of a distributed hydrologic model can improve the results in the case of convective rainfall in a basin where the storm area is smaller than the basin area. The aim of this study was to perform a sensitivity analysis of the rainfall time resolution on the results of a distributed hydrologic model in a flash-flood prone basin. Within such a catchment, floods are produced by heavy rainfall events with a large convective component. A second objective of the current paper is the proposal of a methodology that improves the radar rainfall estimation at a higher spatial and temporal resolution. Composite radar data from a network of three C-band radars with 6-min temporal and 2 × 2 km2 spatial resolution were used to feed the RIBS distributed hydrological model. A modification of the Window Probability Matching Method (gauge-adjustment method) was applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation by computing new Z/R relationships for both convective and stratiform reflectivities. An advection correction technique based on the cross-correlation between two consecutive images was introduced to obtain several time resolutions from 1 min to 30 min. The RIBS hydrologic model was calibrated using a probabilistic approach based on a multiobjective methodology for each time resolution. A sensitivity analysis of rainfall time resolution was conducted to find the resolution that best represents the hydrological basin behaviour.

Spectral Color Appearance Modeling

Kuvien laatu on tutkituimpia ja käytetyimpiä aiheita. Tässä työssä tarkastellaan värin laatu ja spektrikuvia. Työssä annetaan yleiskuva olemassa olevista pakattujen ja erillisten kuvien laadunarviointimenetelmistä painottaen näiden menetelmien soveltaminen spektrikuviin. Tässä työssä esitellään spektriväriulkomuotomalli värikuvien laadunarvioinnille. Malli sovelletaan spektrikuvista jäljennettyihin värikuviin. Malli pohjautuu sekä tilastolliseen spektrikuvamalliin, joka muodostaa yhteyden spektrikuvien ja valokuvien parametrien välille, että kuvan yleiseen ulkomuotoon. Värikuvien tilastollisten spektriparametrien ja fyysisten parametrien välinen yhteys on varmennettu tietokone-pohjaisella kuvamallinnuksella. Mallin ominaisuuksien pohjalta on kehitetty koekäyttöön tarkoitettu menetelmä värikuvien laadunarvioinnille. On kehitetty asiantuntija-pohjainen kyselymenetelmä ja sumea päättelyjärjestelmä värikuvien laadunarvioinnille. Tutkimus osoittaa, että spektri-väri –yhteys ja sumea päättelyjärjestelmä soveltuvat tehokkaasti värikuvien laadunarviointiin.

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

Financial Modeling to Support Organizational Control

Tutkimus tarkastelee taloudellisia mallintamismahdollisuuksia metsäteollisuuden liiketoimintayksikössä. Tavoitteena on suunnitella ja luoda taloudellinen malli liiketoimintayksikölle, jonka avulla sen tuloksen analysoiminen ja ennustaminen on mahdollista. Tutkimusta tarkastellaan konstruktiivisen tutkimusmenetelmän avulla. Teoreettinen viitekehys tarkastelee olemassa olevan informaation muotoilemista keskittyen tiedon jalostamisen tarpeisiin, päätöksenteon asettamiin vaatimuksiin sekä mallintamiseen. Toiseksi, teoria esittää informaatiolle asetettavia vaatimuksia organisatorisen ohjauksen näkökulmasta.Empiirinen tieto kerätään osallistuvan havainnoinnin avulla hyödyntäen epävirallisia keskusteluja, tietojärjestelmiä ja laskentatoimen dokumentteja. Tulokset osoittavat, että liikevoiton ennustaminen mallin avulla on vaikeaa, koska taustalla vaikuttavien muuttujien määrä on suuri. Tästä johtuen malli täytyykin rakentaa niin, että se tarkastelee liikevoittoa niin yksityiskohtaisella tasolla kuin mahdollista. Testauksessa mallin tarkkuus osoittautui sitä paremmaksi, mitä tarkemmalla tasolla ennustaminen tapahtui. Lisäksi testaus osoitti, että malli on käyttökelpoinen liiketoiminnan ohjauksessa lyhyellä aikavälillä. Näin se luo myös pohjan pitkän aikavälin ennustamiselle.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling.

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model's prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

A comparison of indices of glucose metabolism in five black populations: data from modeling the epidemiologic transition study (METS).

BACKGROUND: Globally, Africans and African Americans experience a disproportionate burden of type 2 diabetes, compared to other race and ethnic groups. The aim of the study was to examine the association of plasma glucose with indices of glucose metabolism in young adults of African origin from 5 different countries. METHODS: We identified participants from the Modeling the Epidemiologic Transition Study, an international study of weight change and cardiovascular disease (CVD) risk in five populations of African origin: USA (US), Jamaica, Ghana, South Africa, and Seychelles. For the current study, we included 667 participants (34.8 ± 6.3 years), with measures of plasma glucose, insulin, leptin, and adiponectin, as well as moderate and vigorous physical activity (MVPA, minutes/day [min/day]), daily sedentary time (min/day), anthropometrics, and body composition. RESULTS: Among the 282 men, body mass index (BMI) ranged from 22.1 to 29.6 kg/m(2) in men and from 25.8 to 34.8 kg/m(2) in 385 women. MVPA ranged from 26.2 to 47.1 min/day in men, and from 14.3 to 27.3 min/day in women and correlated with adiposity (BMI, waist size, and % body fat) only among US males after controlling for age. Plasma glucose ranged from 4.6 ± 0.8 mmol/L in the South African men to 5.8 mmol/L US men, while the overall prevalence for diabetes was very low, except in the US men and women (6.7 and 12 %, respectively). Using multivariate linear regression, glucose was associated with BMI, age, sex, smoking hypertension, daily sedentary time but not daily MVPA. CONCLUSION: Obesity, metabolic risk, and other potential determinants vary significantly between populations at differing stages of the epidemiologic transition, requiring tailored public health policies to address local population characteristics.

The predictive value of preoperative B-type natriuretic peptide in children undergoing cardiac surgery

Introduction: B-type natriuretic peptide (BNP) is a biomarker of myocardial stress. In children, the value of preoperative BNP on postoperative outcome is unclear. The aim of this study was to determine the predictive value of preoperative NT-proBNP on postoperative outcome in children after congenital heart surgery. Results: Ninety-seven patients were included in the study with a median age of 3.3 years [0.7-5.2]. Preoperative median NT-proBNP was 412 pg/ml [164-1309]. NT-proBNP was above the P95 reference value for age in 56 patients (58%). Preoperative NT-proBNP was significantly higher in patients who had mechanical ventilation duration of more than 2 days (1156 pg/ml [281-1951] vs. 267 pg/ml [136-790], p=0.003) and who stayed more than 6 days in the pediatric intensive care unit (727 pg/ml [203-1951] vs. 256 pg/ml [136-790], p=0.007). However, preoperative NT-proBNP was not significantly higher in patients with an increased inotropic score, a prolonged cardiopulmonary bypass time or an increased surgical risk category. Conclusions: An elevated preoperative NT-proBNP reflects hemodynamic status and cardiac dysfunction, and therefore is a valuable adjunct in predicting a complicated postoperative course. ___________________________________ Introduction: Le peptide natriurétique type B (BNP) est un marqueur reflétant le stress myocardique. Dans la population pédiatrique, la signification des valeurs préopératoire de BNP, en particulier sur l'évolution postopératoire, n'est pas clairement établie. Le but de l'étude est de déterminer la valeur prédictive de la partie NT sérique du BNP (NT-proBNP) sur l'évolution post opératoire d'enfants porteur d'une cardiopathie congénitale et ayant eu une chirurgie cardiaque. Résultats: Nonante-sept enfants ont été inclus dans l'étude, avec un âge médian de 3.3 ans [0.7-5.2]. La valeur médiane du NT-proBNP préopératoire était de 412 pg/ml [164-1309]. Le NT-proBNP préopératoire était supérieur au P95 des valeurs de référence pour l'âge chez 56 patients (58%). Le NT-proBNP préopératoire était significativement plus élevé chez les patients ayant eu plus de deux jours de ventilation mécanique dans la période postopératoire (1156 pg/ml [281-1951] vs. 267 pg/ml [136-790], p=0.003) et ayant été hospitalisés plus de 6 jours dans l'unité de soins intensifs pédiatrique (727 pg/ml [203-1951] vs. 256 pg/ml [136-790], p=0.007). Par contre, le NT-proBNP préopératoire n'était pas significativement plus élevé chez les patients ayant eu un score d'inotrope élevé pendant leur hospitalisation aux soins intensifs, un temps de circulation extracorporelle prolongé ou ayant subi une chirurgie avec un risque chirurgical élevé. Conclusions: Un NT-proBNP sérique élevé en préopératoire reflète l'importance du stress myocardique induit par l'hémodynamique et la dysfonction myocardique, il est un marqueur qui permet d'améliorer l'identification des patients à risque d'avoir une évolution post opératoire compliquée.

Predictive ability of heel quantitative ultrasound for incident fractures: an individual-level meta-analysis.

UNLABELLED: The relationship between bone quantitative ultrasound (QUS) and fracture risk was estimated in an individual level data meta-analysis of 9 prospective studies of 46,124 individuals and 3018 incident fractures. Low QUS is associated with an increase in fracture risk, including hip fracture. The association with osteoporotic fracture decreases with time. INTRODUCTION: The aim of this meta-analysis was to investigate the association between parameters of QUS and risk of fracture. METHODS: In an individual-level analysis, we studied participants in nine prospective cohorts from Asia, Europe and North America. Heel broadband ultrasonic attenuation (BUA dB/MHz) and speed of sound (SOS m/s) were measured at baseline. Fractures during follow-up were collected by self-report and in some cohorts confirmed by radiography. An extension of Poisson regression was used to examine the gradient of risk (GR, hazard ratio per 1 SD decrease) between QUS and fracture risk adjusted for age and time since baseline in each cohort. Interactions between QUS and age and time since baseline were explored. RESULTS: Baseline measurements were available in 46,124 men and women, mean age 70 years (range 20-100). Three thousand and eighteen osteoporotic fractures (787 hip fractures) occurred during follow-up of 214,000 person-years. The summary GR for osteoporotic fracture was similar for both BUA (1.45, 95 % confidence intervals (CI) 1.40-1.51) and SOS (1.42, 95 % CI 1.36-1.47). For hip fracture, the respective GRs were 1.69 (95 % CI, 1.56-1.82) and 1.60 (95 % CI, 1.48-1.72). However, the GR was significantly higher for both fracture outcomes at lower baseline BUA and SOS (p < 0.001). The predictive value of QUS was the same for men and women and for all ages (p > 0.20), but the predictive value of both BUA and SOS for osteoporotic fracture decreased with time (p = 0.018 and p = 0.010, respectively). For example, the GR of BUA for osteoporotic fracture, adjusted for age, was 1.51 (95 % CI 1.42-1.61) at 1 year after baseline, but at 5 years, it was 1.36 (95 % CI 1.27-1.46). CONCLUSIONS: Our results confirm that quantitative ultrasound is an independent predictor of fracture for men and women particularly at low QUS values.

Toward a rational design of combination therapy in cancer.

By merging computational systems modeling and experimental approaches, we have uncovered treatments reprogramming pro-angiogenic monocytes present in breast tumor into immunologically potent cells capable of mediating an anti-tumor immune response. The unraveled pathways and ligands which underlie monocyte pro-angiogenic activity have a strong predictive value for breast cancer patient relapse - free survival.