First systematic chemical profiling of cocaine police seizures in Finland in the framework of an intelligence-led approach

For the first time in Finland, the chemical profiling of cocaine specimens was performed at the National Bureau of Investigation (NBI). The main goals were to determine the chemical composition of cocaine specimens sold in the Finnish market and to study the distribution networks of cocaine in order to provide intelligence related to its trafficking. An analytical methodology enabling through one single GC-MS injection the determination of the added cutting agents (adulterants and diluents), the cocaine purity and the chemical profile (based on the major and minor alkaloids) for each specimen was thus implemented and validated. The methodology was found to be efficient for the discrimination between specimens coming from the same source and specimens coming from different sources. The results highlighted the practical utility of the chemical profiling, especially for supporting the investigation through operational intelligence and improving the knowledge related to the cocaine trafficking through strategic intelligence.

Development and application of computational methodologies in qualitative modeling

La présente thèse s'intitule "Développent et Application des Méthodologies Computationnelles pour la Modélisation Qualitative". Elle comprend tous les différents projets que j'ai entrepris en tant que doctorante. Plutôt qu'une mise en oeuvre systématique d'un cadre défini a priori, cette thèse devrait être considérée comme une exploration des méthodes qui peuvent nous aider à déduire le plan de processus regulatoires et de signalisation. Cette exploration a été mue par des questions biologiques concrètes, plutôt que par des investigations théoriques. Bien que tous les projets aient inclus des systèmes divergents (réseaux régulateurs de gènes du cycle cellulaire, réseaux de signalisation de cellules pulmonaires) ainsi que des organismes (levure à fission, levure bourgeonnante, rat, humain), nos objectifs étaient complémentaires et cohérents. Le projet principal de la thèse est la modélisation du réseau de l'initiation de septation (SIN) du S.pombe. La cytokinèse dans la levure à fission est contrôlée par le SIN, un réseau signalant de protéines kinases qui utilise le corps à pôle-fuseau comme échafaudage. Afin de décrire le comportement qualitatif du système et prédire des comportements mutants inconnus, nous avons décidé d'adopter l'approche de la modélisation booléenne. Dans cette thèse, nous présentons la construction d'un modèle booléen étendu du SIN, comprenant la plupart des composantes et des régulateurs du SIN en tant que noeuds individuels et testable expérimentalement. Ce modèle utilise des niveaux d'activité du CDK comme noeuds de contrôle pour la simulation d'évènements du SIN à différents stades du cycle cellulaire. Ce modèle a été optimisé en utilisant des expériences d'un seul "knock-out" avec des effets phénotypiques connus comme set d'entraînement. Il a permis de prédire correctement un set d'évaluation de "knock-out" doubles. De plus, le modèle a fait des prédictions in silico qui ont été validées in vivo, permettant d'obtenir de nouvelles idées de la régulation et l'organisation hiérarchique du SIN. Un autre projet concernant le cycle cellulaire qui fait partie de cette thèse a été la construction d'un modèle qualitatif et minimal de la réciprocité des cyclines dans la S.cerevisiae. Les protéines Clb dans la levure bourgeonnante présentent une activation et une dégradation caractéristique et séquentielle durant le cycle cellulaire, qu'on appelle communément les vagues des Clbs. Cet évènement est coordonné avec la courbe d'activation inverse du Sic1, qui a un rôle inhibitoire dans le système. Pour l'identification des modèles qualitatifs minimaux qui peuvent expliquer ce phénomène, nous avons sélectionné des expériences bien définies et construit tous les modèles minimaux possibles qui, une fois simulés, reproduisent les résultats attendus. Les modèles ont été filtrés en utilisant des simulations ODE qualitatives et standardisées; seules celles qui reproduisaient le phénotype des vagues ont été gardées. L'ensemble des modèles minimaux peut être utilisé pour suggérer des relations regulatoires entre les molécules participant qui peuvent ensuite être testées expérimentalement. Enfin, durant mon doctorat, j'ai participé au SBV Improver Challenge. Le but était de déduire des réseaux spécifiques à des espèces (humain et rat) en utilisant des données de phosphoprotéines, d'expressions des gènes et des cytokines, ainsi qu'un réseau de référence, qui était mis à disposition comme donnée préalable. Notre solution pour ce concours a pris la troisième place. L'approche utilisée est expliquée en détail dans le dernier chapitre de la thèse. -- The present dissertation is entitled "Development and Application of Computational Methodologies in Qualitative Modeling". It encompasses the diverse projects that were undertaken during my time as a PhD student. Instead of a systematic implementation of a framework defined a priori, this thesis should be considered as an exploration of the methods that can help us infer the blueprint of regulatory and signaling processes. This exploration was driven by concrete biological questions, rather than theoretical investigation. Even though the projects involved divergent systems (gene regulatory networks of cell cycle, signaling networks in lung cells), as well as organisms (fission yeast, budding yeast, rat, human), our goals were complementary and coherent. The main project of the thesis is the modeling of the Septation Initiation Network (SIN) in S.pombe. Cytokinesis in fission yeast is controlled by the SIN, a protein kinase signaling network that uses the spindle pole body as scaffold. In order to describe the qualitative behavior of the system and predict unknown mutant behaviors we decided to adopt a Boolean modeling approach. In this thesis, we report the construction of an extended, Boolean model of the SIN, comprising most SIN components and regulators as individual, experimentally testable nodes. The model uses CDK activity levels as control nodes for the simulation of SIN related events in different stages of the cell cycle. The model was optimized using single knock-out experiments of known phenotypic effect as a training set, and was able to correctly predict a double knock-out test set. Moreover, the model has made in silico predictions that have been validated in vivo, providing new insights into the regulation and hierarchical organization of the SIN. Another cell cycle related project that is part of this thesis was to create a qualitative, minimal model of cyclin interplay in S.cerevisiae. CLB proteins in budding yeast present a characteristic, sequential activation and decay during the cell cycle, commonly referred to as Clb waves. This event is coordinated with the inverse activation curve of Sic1, which has an inhibitory role in the system. To generate minimal qualitative models that can explain this phenomenon, we selected well-defined experiments and constructed all possible minimal models that, when simulated, reproduce the expected results. The models were filtered using standardized qualitative ODE simulations; only the ones reproducing the wave-like phenotype were kept. The set of minimal models can be used to suggest regulatory relations among the participating molecules, which will subsequently be tested experimentally. Finally, during my PhD I participated in the SBV Improver Challenge. The goal was to infer species-specific (human and rat) networks, using phosphoprotein, gene expression and cytokine data and a reference network provided as prior knowledge. Our solution to the challenge was selected as in the final chapter of the thesis.

Can wild ungulate carcasses provide enough biomass to maintain avian scavenger populations? An empirical assessment using a bio-inspired computational model

Background: The reduction in the amount of food available for European avian scavengers as a consequence of restrictive public health policies is a concern for managers and conservationists. Since 2002, the application of several sanitary regulations has limited the availability of feeding resources provided by domestic carcasses, but theoretical studies assessing whether the availability of food resources provided by wild ungulates are enough to cover energetic requirements are lacking. Methodology/Findings: We assessed food provided by a wild ungulate population in two areas of NE Spain inhabited by three vulture species and developed a P System computational model to assess the effects of the carrion resources provided on their population dynamics. We compared the real population trend with to a hypothetical scenario in which only food provided by wild ungulates was available. Simulation testing of the model suggests that wild ungulates constitute an important food resource in the Pyrenees and the vulture population inhabiting this area could grow if only the food provided by wild ungulates would be available. On the contrary, in the Pre-Pyrenees there is insufficient food to cover the energy requirements of avian scavenger guilds, declining sharply if biomass from domestic animals would not be available. Conclusions/Significance: Our results suggest that public health legislation can modify scavenger population trends if a large number of domestic ungulate carcasses disappear from the mountains. In this case, food provided by wild ungulates could be not enough and supplementary feeding could be necessary if other alternative food resources are not available (i.e. the reintroduction of wild ungulates), preferably in European Mediterranean scenarios sharing similar and socio-economic conditions where there are low densities of wild ungulates. Managers should anticipate the conservation actions required by assessing food availability and the possible scenarios in order to make the most suitable decisions.

The impact of balancing on problem hardness in a highly structured domain

Random problem distributions have played a key role in the study and design of algorithms for constraint satisfaction and Boolean satisfiability, as well as in ourunderstanding of problem hardness, beyond standard worst-case complexity. We consider random problem distributions from a highly structured problem domain that generalizes the Quasigroup Completion problem (QCP) and Quasigroup with Holes (QWH), a widely used domain that captures the structure underlying a range of real-world applications. Our problem domain is also a generalization of the well-known Sudoku puz- zle: we consider Sudoku instances of arbitrary order, with the additional generalization that the block regions can have rectangular shape, in addition to the standard square shape. We evaluate the computational hardness of Generalized Sudoku instances, for different parameter settings. Our experimental hardness results show that we can generate instances that are considerably harder than QCP/QWH instances of the same size. More interestingly, we show the impact of different balancing strategies on problem hardness. We also provide insights into backbone variables in Generalized Sudoku instances and how they correlate to problem hardness.

A Technology Intelligence Process for a High Technology Company

Tämä diplomityö määrittelee teknologiaseurantaprosessin, jolla korkean teknologian yritys voi ohjata toimintaansa. Korkean teknologian yrityksille on olennaista seurata teknologian kehitystä. Tällaiset yritykset tarvitsevat hyvin määritellyn järjestelmän, jolla ne voivat seurata ja ennustaa teknologista kehitystä.Työssä esitetään, että teknologiaseuranta ja kilpailuseuranta (competitive intelligence) ovat business intelligencen osa-alueita, jotka täydentävät ja tukevat toisiaan. Tärkeä havainto on, että business intelligence -prosessi on ennen kaikkea organisaation oppimisprosessi. Tästä seuraa, että minkä tahansa BI-prosessin tulisi perustua niihin prosesseihin, joiden avulla organisaatiot oppivat. Työssä esitetään myös, miten business intelligence, tietojohtaminen (knowledge management) ja organisaatioiden oppiminen liittyvät toisiinsa.Teknologiaseuranta on elintärkeä toiminto korkean teknologian yritykselle; sitä tarvitaan monella strategisen johtamisen osa-alueella, ainakin teknologia-, markkinointi- ja henkilöstöjohtamisessa. Teknologiaseurannan havaitaan myös olevan korkean teknologian yritykselle erittäin tärkeä ydinosaamisalue, jota ei voi kokonaan ulkoistaa.Työssä esitellään teknologiaseurantaprosessi, joka perustuu yleiselle business intelligence -prosessille ja siitä johdetulle kilpailuseurantaprosessille. Työssä myös esitetään ehdotus siitä, kuinka teknologiaseuranta voitaisiin järjestää korkean teknologian yrityksessä. Esitetty ratkaisu perustuu Community of practice -käsitteeseen. Community of practice on vapaaehtoisuuteen perustuva tiimi, jonka jäseniä yhdistää kiinnostus johonkin asiaan ja oppimishalu. Esimerkkiyrityksessä on tunnistettu selkeä tarve yhtenäiseen ja koordinoituun teknologiaseurantaan. Työssä esitetään alustava teknologiaseurantaprosessi esimerkkiyritykselle ja tunnistetaan teknologiaseurantaprosessin asiakkaat ja tekijät.

Education and Training in Forensic Intelligence: a New Challenge

From recent calls for positioning forensic scientists within the criminal justice system, but also policing and intelligence missions, this paper emphasizes the need for the development of educational and training programs in the area of forensic intelligence, It is argued that an imbalance exists between perceived and actual understanding of forensic intelligence by police and forensic science managers, and that this imbalance can only be overcome through education. The challenge for forensic intelligence education and training is therefore to devise programs that increase forensic intelligence awareness, firstly for managers to help prevent poor decisions on how to develop information processing. Two recent European courses are presented as examples of education offerings, along with lessons learned and suggested paths forward. It is concluded that the new focus on forensic intelligence could restore a pro-active approach to forensic science, better quantify its efficiency and let it get more involved in investigative and managerial decisions. A new educational challenge is opened to forensic science university programs around the world: to refocus criminal trace analysis on a more holistic security problem solving approach.

Developing and Applying Generative Market Intelligence within Multinational Companies

Tutkielman tarkoitus on kehittää monikansallisille yrityksille tuottavan markkinaälyn malli, jonka avulla yritykset pystyvät käsittelemään muuttuvasta ja globalisoituvasta markkinaympäristöstä aiheutuvaa epävarmuutta. Malli koostuu pääosin kolmesta käsitteestä: markkinainformaation prosessoinnista, markkinasuuntautuneisuudesta ja organisationaalisesta oppimisesta. Tutkimuksessa osoitetaan, kuinka näiden samanaikainen soveltaminen johtaa synergiaetuihin. Lähdeaineistona käytettiin alan kirjallisuutta. Lisäksi haastateltiin neljää johtajaa monikansallisista yrityksistä. Käytännössä markkinaälyn soveltamisen haasteet liittyvät lähinnä markkinainformaation prosessoinnin asenteellisiin ja psykologisiin aspekteihin. Ihmisten tulisi ymmärtää, että koko yritys hyötyy heidän halukkuudestaan tiedon tuottamiseen ja jakamiseen. Lisäksi tietoa itsessään voimavarana tulisi kunnioittaa

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.

Emotional intelligence compensates for low IQ and boosts low emotionality individuals in a self-presentation task

The research on emotional intelligence (EI) has focused mainly on testing the incremental validity of EI with respect to general intelligence and personality; less attention has been devoted to investigating the potential interaction effects. In a self-presentation task that required participants to obtain positive evaluations from others, individuals low in IQ but high in EI performed as well as the high IQ individuals. In addition, the low emotionality individuals performed significantly higher when also high in EI. The results extend the previous findings on the compensatory effect of EI on low IQ to the domain of interpersonal effectiveness and shed light on the effective functioning of personality traits when interpreted with the interaction of EI. Overall this study suggests that the role of EI in predicting performance might have been overlooked by checking solely for main effects and illustrates new venues for understanding the contribution of EI in explaining emotion-laden performance.

Computational toolbox towards evolutionary domain mapping of membrane proteins

Membrane proteins account for about 20% to 30% of all proteins encoded in a typical genome. They play central roles in multiple cellular processes mediating the interaction of the cell with its surrounding. Over 60% of all drug targets contain a membrane domain. The experimental difficulties of obtaining a crystal structural severely limits our ability or understanding of membrane protein function. Computational evolutionary studies of proteins are crucial for the prediction of 3D structures. In this project, we construct a tool able to quantify the evolutionary positive selective pressure on each residue of membrane proteins through maximum likelihood phylogeny reconstruction. The conservation plot combined with a structural homology model is also a potent tool to predict those residues that have essentials roles in the structure and function of a membrane protein and can be very useful in the design of validation experiments.

Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology.

PURPOSE OF REVIEW: Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI). RECENT FINDINGS: qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function. SUMMARY: Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.

Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior

Understanding the basis on which recruiters form hirability impressions for a job applicant is a key issue in organizational psychology and can be addressed as a social computing problem. We approach the problem from a face-to-face, nonverbal perspective where behavioral feature extraction and inference are automated. This paper presents a computational framework for the automatic prediction of hirability. To this end, we collected an audio-visual dataset of real job interviews where candidates were applying for a marketing job. We automatically extracted audio and visual behavioral cues related to both the applicant and the interviewer. We then evaluated several regression methods for the prediction of hirability scores and showed the feasibility of conducting such a task, with ridge regression explaining 36.2% of the variance. Feature groups were analyzed, and two main groups of behavioral cues were predictive of hirability: applicant audio features and interviewer visual cues, showing the predictive validity of cues related not only to the applicant, but also to the interviewer. As a last step, we analyzed the predictive validity of psychometric questionnaires often used in the personnel selection process, and found that these questionnaires were unable to predict hirability, suggesting that hirability impressions were formed based on the interaction during the interview rather than on questionnaire data.