A Multi-Center Phase II Study (SAKK 36/06) of Single Agent Everolimus (RAD001) In Patients with Relapsed or Refractory Mantle Cell Lymphoma

Introduction: Mantle cell lymphoma (MCL) accounts for 6% of all B-cell lymphomas and remains incurable for most patients. Those who relapse after first line therapy or hematopoietic stem cell transplantation have a dismal prognosis with short response duration after salvage therapy. On a molecular level, MCL is characterised by the translocation t[11;14] leading to Cyclin D1 overexpression. Cyclin D1 is downstream of the mammalian target of rapamycin (mTOR) kinase and can be effectively blocked by mTOR inhibitors such as temsirolimus. We set out to define the single agent activity of the orally available mTOR inhibitor everolimus (RAD001) in a prospective, multi-centre trial in patients with relapsed or refractory MCL (NCT00516412). The study was performed in collaboration with the EU-MCL network. Methods: Eligible patients with histologically/cytologically confirmed relapsed (not more than 3 prior lines of systemic treatment) or refractory MCL received everolimus 10 mg orally daily on day 1 - 28 of each cycle (4 weeks) for 6 cycles or until disease progression. The primary endpoint was the best objective response with adverse reactions, time to progression (TTP), time to treatment failure, response duration and molecular response as secondary endpoints. A response rate of 10% was considered uninteresting and, conversely, promising if 30%. The required sample size was 35 pts using the Simon's optimal two-stage design with 90% power and 5% significance. Results: A total of 36 patients with 35 evaluable patients from 19 centers were enrolled between August 2007 and January 2010. The median age was 69.4 years (range 40.1 to 84.9 years), with 22 males and 13 females. Thirty patients presented with relapsed and 5 with refractory MCL with a median of two prior therapies. Treatment was generally well tolerated with anemia (11%), thrombocytopenia (11%), neutropenia (8%), diarrhea (3%) and fatigue (3%) being the most frequent complications of CTC grade III or higher. Eighteen patients received 6 or more cycles of everolimus treatment. The objective response rate was 20% (95% CI: 8-37%) with 2 CR, 5 PR, 17 SD, and 11 PD. At a median follow-up of 6 months, TTP was 5.45 months (95% CI: 2.8-8.2 months) for the entire population and 10.6 months for the 18 patients receiving 6 or more cycles of treatment. Conclusion: This study demonstrates that single agent everolimus 10 mg once daily orally is well tolerated. The null hypothesis of inactivity could be rejected indicating a moderate anti-lymphoma activity in relapsed/refractory MCL. Further studies of either everolimus in combination with chemotherapy or as single agent for maintenance treatment are warranted in MCL.

Metastatic tumors to the colon and rectum: a multi-institutional study.

Context.-Unlike the small bowel, the colorectal mucosa is seldom the site of metastatic disease. Objective.-To determine the incidence of truly colorectal metastases, and subsequent clinicopathologic findings, in a substantial colorectal cancer population collected from 7 European centers. Design.-During the last decade, 10 365 patients were identified as having colorectal malignant tumors, other than systemic diseases. Data collected included patient demographics, clinical symptoms, treatment, the presence of metastases in other sites, disease-free interval, follow-up, and overall survival. All secondary tumors resulting from direct invasion from malignant tumors of the contiguous organs were excluded, as well as those resulting from lymph node metastases or peritoneal seeding. Results.-Only 35 patients were included (10 men) with a median age of 59 years. They presented with obstruction, bleeding, abdominal pain, or perforation. The leading source of metastases was the breast, followed by melanoma. Metastases were synchronous in 3 cases. The mean disease-free interval for the remaining cases was 6.61 years. Surgical resection was performed in 28 cases. Follow-up was available for 26 patients; all had died, with a mean survival time of 10.67 months (range, 1-41 months). Conclusions.-Colorectal metastases are exceptional (0.338%) with the breast as a leading source of metastases; they still represent a late stage of disease and reflect a poor prognosis. Therefore, the pathologist should be alert for the possibility of secondary tumors when studying large bowel biopsies. Any therapy is usually palliative, but our results suggest that prolonged survival after surgery and complementary therapy can be obtained in some patients.

Assessment of volumetric bone mineral density of the femoral neck in postmenopausal women with and without vertebral fractures using quantitative multi-slice CT.

OBJECTIVE: To demonstrate the validity and reliability of volumetric quantitative computed tomography (vQCT) with multi-slice computed tomography (MSCT) and dual energy X-ray absorptiometry (DXA) for hip bone mineral density (BMD) measurements, and to compare the differences between the two techniques in discriminating postmenopausal women with osteoporosis-related vertebral fractures from those without. METHODS: Ninety subjects were enrolled and divided into three groups based on the BMD values of the lumbar spine and/or the femoral neck by DXA. Groups 1 and 2 consisted of postmenopausal women with BMD changes <-2SD, with and without radiographically confirmed vertebral fracture (n=11 and 33, respectively). Group 3 comprised normal controls with BMD changes > or =-1SD (n=46). Post-MSCT (GE, LightSpeed16) scan reconstructed images of the abdominal-pelvic region, 1.25 mm thick per slice, were processed by OsteoCAD software to calculate the following parameters: volumetric BMD values of trabecular bone (TRAB), cortical bone (CORT), and integral bone (INTGL) of the left femoral neck, femoral neck axis length (NAL), and minimum cross-section area (mCSA). DXA BMD measurements of the lumbar spine (AP-SPINE) and the left femoral neck (NECK) also were performed for each subject. RESULTS: The values of all seven parameters were significantly lower in subjects of Groups 1 and 2 than in normal postmenopausal women (P<0.05, respectively). Comparing Groups 1 and 2, 3D-TRAB and 3D-INTGL were significantly lower in postmenopausal women with vertebral fracture(s) [(109.8+/-9.61) and (243.3+/-33.0) mg/cm3, respectively] than in those without [(148.9+/-7.47) and (285.4+/-17.8) mg/cm(3), respectively] (P<0.05, respectively), but no significant differences were evident in AP-SPINE or NECK BMD. CONCLUSION: the femoral neck-derived volumetric BMD parameters using vQCT appeared better than the DXA-derived ones in discriminating osteoporotic postmenopausal women with vertebral fractures from those without. vQCT might be useful to evaluate the effect of osteoporotic vertebral fracture status on changes in bone mass in the femoral neck.

Spirituality and religion in outpatients with schizophrenia: A multi-site comparative study of Switzerland, Canada, and the United States

Objective: To assess the importance of spirituality and religious coping among outpatients with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder living in three countries. Method: A total of 276 outpatients (92 from Geneva, Switzerland, 121 from Trois-Rivières, Canada, and 63 from Durham, North Carolina), aged 18-65, were administered a semi-structured interview on the role of spirituality and religiousness in their lives and to cope with their illness. Results: Religion is important for outpatients in each of the three country sites, and religious involvement is higher than in the general population. Religion was helpful (i.e., provided a positive sense of self and positive coping with the illness) among 87% of the participants and harmful (a source of despair and suffering) among 13%. Helpful religion was associated with better social, clinical and psychological status. The opposite was observed for the harmful aspects of religion. In addition, religion sometimes conflicted with psychiatric treatment. Conclusions: These results indicate that outpatients with schizophrenia or schizoaffective disorder often use spirituality and religion to cope with their illness, basically positively, yet sometimes negatively. These results underscore the importance of clinicians taking into account the spiritual and religious lives of patients with schizophrenia.

Evolutionary games on complex networks : the emergence and maintenance of cooperation

Abstract The object of game theory lies in the analysis of situations where different social actors have conflicting requirements and where their individual decisions will all influence the global outcome. In this framework, several games have been invented to capture the essence of various dilemmas encountered in many common important socio-economic situations. Even though these games often succeed in helping us understand human or animal behavior in interactive settings, some experiments have shown that people tend to cooperate with each other in situations for which classical game theory strongly recommends them to do the exact opposite. Several mechanisms have been invoked to try to explain the emergence of this unexpected cooperative attitude. Among them, repeated interaction, reputation, and belonging to a recognizable group have often been mentioned. However, the work of Nowak and May (1992) showed that the simple fact of arranging the players according to a spatial structure and only allowing them to interact with their immediate neighbors is sufficient to sustain a certain amount of cooperation even when the game is played anonymously and without repetition. Nowak and May's study and much of the following work was based on regular structures such as two-dimensional grids. Axelrod et al. (2002) showed that by randomizing the choice of neighbors, i.e. by actually giving up a strictly local geographical structure, cooperation can still emerge, provided that the interaction patterns remain stable in time. This is a first step towards a social network structure. However, following pioneering work by sociologists in the sixties such as that of Milgram (1967), in the last few years it has become apparent that many social and biological interaction networks, and even some technological networks, have particular, and partly unexpected, properties that set them apart from regular or random graphs. Among other things, they usually display broad degree distributions, and show small-world topological structure. Roughly speaking, a small-world graph is a network where any individual is relatively close, in terms of social ties, to any other individual, a property also found in random graphs but not in regular lattices. However, in contrast with random graphs, small-world networks also have a certain amount of local structure, as measured, for instance, by a quantity called the clustering coefficient. In the same vein, many real conflicting situations in economy and sociology are not well described neither by a fixed geographical position of the individuals in a regular lattice, nor by a random graph. Furthermore, it is a known fact that network structure can highly influence dynamical phenomena such as the way diseases spread across a population and ideas or information get transmitted. Therefore, in the last decade, research attention has naturally shifted from random and regular graphs towards better models of social interaction structures. The primary goal of this work is to discover whether or not the underlying graph structure of real social networks could give explanations as to why one finds higher levels of cooperation in populations of human beings or animals than what is prescribed by classical game theory. To meet this objective, I start by thoroughly studying a real scientific coauthorship network and showing how it differs from biological or technological networks using divers statistical measurements. Furthermore, I extract and describe its community structure taking into account the intensity of a collaboration. Finally, I investigate the temporal evolution of the network, from its inception to its state at the time of the study in 2006, suggesting also an effective view of it as opposed to a historical one. Thereafter, I combine evolutionary game theory with several network models along with the studied coauthorship network in order to highlight which specific network properties foster cooperation and shed some light on the various mechanisms responsible for the maintenance of this same cooperation. I point out the fact that, to resist defection, cooperators take advantage, whenever possible, of the degree-heterogeneity of social networks and their underlying community structure. Finally, I show that cooperation level and stability depend not only on the game played, but also on the evolutionary dynamic rules used and the individual payoff calculations. Synopsis Le but de la théorie des jeux réside dans l'analyse de situations dans lesquelles différents acteurs sociaux, avec des objectifs souvent conflictuels, doivent individuellement prendre des décisions qui influenceront toutes le résultat global. Dans ce cadre, plusieurs jeux ont été inventés afin de saisir l'essence de divers dilemmes rencontrés dans d'importantes situations socio-économiques. Bien que ces jeux nous permettent souvent de comprendre le comportement d'êtres humains ou d'animaux en interactions, des expériences ont montré que les individus ont parfois tendance à coopérer dans des situations pour lesquelles la théorie classique des jeux prescrit de faire le contraire. Plusieurs mécanismes ont été invoqués pour tenter d'expliquer l'émergence de ce comportement coopératif inattendu. Parmi ceux-ci, la répétition des interactions, la réputation ou encore l'appartenance à des groupes reconnaissables ont souvent été mentionnés. Toutefois, les travaux de Nowak et May (1992) ont montré que le simple fait de disposer les joueurs selon une structure spatiale en leur permettant d'interagir uniquement avec leurs voisins directs est suffisant pour maintenir un certain niveau de coopération même si le jeu est joué de manière anonyme et sans répétitions. L'étude de Nowak et May, ainsi qu'un nombre substantiel de travaux qui ont suivi, étaient basés sur des structures régulières telles que des grilles à deux dimensions. Axelrod et al. (2002) ont montré qu'en randomisant le choix des voisins, i.e. en abandonnant une localisation géographique stricte, la coopération peut malgré tout émerger, pour autant que les schémas d'interactions restent stables au cours du temps. Ceci est un premier pas en direction d'une structure de réseau social. Toutefois, suite aux travaux précurseurs de sociologues des années soixante, tels que ceux de Milgram (1967), il est devenu clair ces dernières années qu'une grande partie des réseaux d'interactions sociaux et biologiques, et même quelques réseaux technologiques, possèdent des propriétés particulières, et partiellement inattendues, qui les distinguent de graphes réguliers ou aléatoires. Entre autres, ils affichent en général une distribution du degré relativement large ainsi qu'une structure de "petit-monde". Grossièrement parlant, un graphe "petit-monde" est un réseau où tout individu se trouve relativement près de tout autre individu en termes de distance sociale, une propriété également présente dans les graphes aléatoires mais absente des grilles régulières. Par contre, les réseaux "petit-monde" ont, contrairement aux graphes aléatoires, une certaine structure de localité, mesurée par exemple par une quantité appelée le "coefficient de clustering". Dans le même esprit, plusieurs situations réelles de conflit en économie et sociologie ne sont pas bien décrites ni par des positions géographiquement fixes des individus en grilles régulières, ni par des graphes aléatoires. De plus, il est bien connu que la structure même d'un réseau peut passablement influencer des phénomènes dynamiques tels que la manière qu'a une maladie de se répandre à travers une population, ou encore la façon dont des idées ou une information s'y propagent. Ainsi, durant cette dernière décennie, l'attention de la recherche s'est tout naturellement déplacée des graphes aléatoires et réguliers vers de meilleurs modèles de structure d'interactions sociales. L'objectif principal de ce travail est de découvrir si la structure sous-jacente de graphe de vrais réseaux sociaux peut fournir des explications quant aux raisons pour lesquelles on trouve, chez certains groupes d'êtres humains ou d'animaux, des niveaux de coopération supérieurs à ce qui est prescrit par la théorie classique des jeux. Dans l'optique d'atteindre ce but, je commence par étudier un véritable réseau de collaborations scientifiques et, en utilisant diverses mesures statistiques, je mets en évidence la manière dont il diffère de réseaux biologiques ou technologiques. De plus, j'extrais et je décris sa structure de communautés en tenant compte de l'intensité d'une collaboration. Finalement, j'examine l'évolution temporelle du réseau depuis son origine jusqu'à son état en 2006, date à laquelle l'étude a été effectuée, en suggérant également une vue effective du réseau par opposition à une vue historique. Par la suite, je combine la théorie évolutionnaire des jeux avec des réseaux comprenant plusieurs modèles et le réseau de collaboration susmentionné, afin de déterminer les propriétés structurelles utiles à la promotion de la coopération et les mécanismes responsables du maintien de celle-ci. Je mets en évidence le fait que, pour ne pas succomber à la défection, les coopérateurs exploitent dans la mesure du possible l'hétérogénéité des réseaux sociaux en termes de degré ainsi que la structure de communautés sous-jacente de ces mêmes réseaux. Finalement, je montre que le niveau de coopération et sa stabilité dépendent non seulement du jeu joué, mais aussi des règles de la dynamique évolutionnaire utilisées et du calcul du bénéfice d'un individu.

A Multi-Center Phase II Study (SAKK 36/06) of Single Agent Everolimus (RAD001) In Patients with Relapsed or Refractory Mantle Cell Lymphoma

Introduction: Mantle cell lymphoma (MCL) accounts for 6% of all B-cell lymphomas and remains incurable for most patients. Those who relapse after first line therapy or hematopoietic stem cell transplantation have a dismal prognosis with short response duration after salvage therapy. On a molecular level, MCL is characterised by the translocation t[11;14] leading to Cyclin D1 overexpression. Cyclin D1 is downstream of the mammalian target of rapamycin (mTOR) kinase and can be effectively blocked by mTOR inhibitors such as temsirolimus. We set out to define the single agent activity of the orally available mTOR inhibitor everolimus (RAD001) in a prospective, multi-centre trial in patients with relapsed or refractory MCL (NCT00516412). The study was performed in collaboration with the EU-MCL network. Methods: Eligible patients with histologically/cytologically confirmed relapsed (not more than 3 prior lines of systemic treatment) or refractory MCL received everolimus 10 mg orally daily on day 1 - 28 of each cycle (4 weeks) for 6 cycles or until disease progression. The primary endpoint was the best objective response with adverse reactions, time to progression (TTP), time to treatment failure, response duration and molecular response as secondary endpoints. A response rate of ≤ 10% was considered uninteresting and, conversely, promising if ≥ 30%. The required sample size was 35 pts using the Simon's optimal two-stage design with 90% power and 5% significance. Results: A total of 36 patients with 35 evaluable patients from 19 centers were enrolled between August 2007 and January 2010. The median age was 69.4 years (range 40.1 to 84.9 years), with 22 males and 13 females. Thirty patients presented with relapsed and 5 with refractory MCL with a median of two prior therapies. Treatment was generally well tolerated with anemia (11%), thrombocytopenia (11%), neutropenia (8%), diarrhea (3%) and fatigue (3%) being the most frequent complications of CTC grade III or higher. Eighteen patients received 6 or more cycles of everolimus treatment. The objective response rate was 20% (95% CI: 8-37%) with 2 CR, 5 PR, 17 SD, and 11 PD. At a median follow-up of 6 months, TTP was 5.45 months (95% CI: 2.8-8.2 months) for the entire population and 10.6 months for the 18 patients receiving 6 or more cycles of treatment. Conclusion: This study demonstrates that single agent everolimus 10 mg once daily orally is well tolerated. The null hypothesis of inactivity could be rejected indicating a moderate anti-lymphoma activity in relapsed/refractory MCL. Further studies of either everolimus in combination with chemotherapy or as single agent for maintenance treatment are warranted in MCL.

HACEK infective endocarditis: characteristics and outcomes from a large, multi-national cohort.

The HACEK organisms (Haemophilus species, Aggregatibacter species, Cardiobacterium hominis, Eikenella corrodens, and Kingella species) are rare causes of infective endocarditis (IE). The objective of this study is to describe the clinical characteristics and outcomes of patients with HACEK endocarditis (HE) in a large multi-national cohort. Patients hospitalized with definite or possible infective endocarditis by the International Collaboration on Endocarditis Prospective Cohort Study in 64 hospitals from 28 countries were included and characteristics of HE patients compared with IE due to other pathogens. Of 5591 patients enrolled, 77 (1.4%) had HE. HE was associated with a younger age (47 vs. 61 years; p<0.001), a higher prevalence of immunologic/vascular manifestations (32% vs. 20%; p<0.008) and stroke (25% vs. 17% p = 0.05) but a lower prevalence of congestive heart failure (15% vs. 30%; p = 0.004), death in-hospital (4% vs. 18%; p = 0.001) or after 1 year follow-up (6% vs. 20%; p = 0.01) than IE due to other pathogens (n = 5514). On multivariable analysis, stroke was associated with mitral valve vegetations (OR 3.60; CI 1.34-9.65; p<0.01) and younger age (OR 0.62; CI 0.49-0.90; p<0.01). The overall outcome of HE was excellent with the in-hospital mortality (4%) significantly better than for non-HE (18%; p<0.001). Prosthetic valve endocarditis was more common in HE (35%) than non-HE (24%). The outcome of prosthetic valve and native valve HE was excellent whether treated medically or with surgery. Current treatment is very successful for the management of both native valve prosthetic valve HE but further studies are needed to determine why HE has a predilection for younger people and to cause stroke. The small number of patients and observational design limit inferences on treatment strategies. Self selection of study sites limits epidemiological inferences.

Evolutionary branching in deme-structured populations.

Adaptive dynamics shows that a continuous trait under frequency dependent selection may first converge to a singular point followed by spontaneous transition from a unimodal trait distribution into a bimodal one, which is called "evolutionary branching". Here, we study evolutionary branching in a deme-structured population by constructing a quantitative genetic model for the trait variance dynamics, which allows us to obtain an analytic condition for evolutionary branching. This is first shown to agree with previous conditions for branching expressed in terms of relatedness between interacting individuals within demes and obtained from mutant-resident systems. We then show this branching condition can be markedly simplified when the evolving trait affect fecundity and/or survival, as opposed to affecting population structure, which would occur in the case of the evolution of dispersal. As an application of our model, we evaluate the threshold migration rate below which evolutionary branching cannot occur in a pairwise interaction game. This agrees very well with the individual-based simulation results.

Inpatient diabetes care in a Swiss multi-site hospital: is there room for improvement?

Background and objective: Optimal care of diabetic patients (DPs) decreases the risk of complications. Close blood glucose monitoring can improve patient outcomes and shorten hospital stay. The objective of this pilot study was to evaluate the treatment of hospitalized DPs according to the current standards, including their diabetic treatment and drugs to prevent diabetes related complications [=guardian drugs: angiotensin converting enzyme inhibitors (ACEI) or Angiotensin II Receptor Blockers (ARB), antiplatelet drugs, statins]. Guidelines of the American Diabetes Association (ADA) [1] were used as reference as they were the most recent and exhaustive for hospital care. Design: Observational pilot study: analysis of the medical records of all DPs seen by the clinical pharmacists during medical rounds in different hospital units. An assessment was made by assigning points for fulfilling the different criteria according to ADA and then by dividing the total by the maximum achievable points (scale 0-1; 1 = all criteria fulfilled). Setting: Different Internal Medicine and Geriatric Units of the (multi-site) Ho^pital du Valais. Main outcome measures: - Completeness of diabetes-related information: type of diabetes, medical history, weight, albuminuria status, renal function, blood pressure, (recent) lipid profile. - Management of blood glucose: Hb1Ac, glycemic control, plan for treating hyper-/hypoglycaemia. - Presence of guardian drugs if indicated. Results: Medical records of 42 patients in 10 different units were analysed (18 women, 24 men, mean age 75.4 ± 11 years). 41 had type 2 diabetes. - Completeness of diabetes-related information: 0.8 ± 0.1. Information often missing: insulin-dependence (43%) and lipid profile (86%). - Management of blood glucose: 0.5 ± 0.2. 15 patients had suboptimal glycemic balance (target glycaemia 7.2-11.2 mmol/ l, with values[11.2 or\3.8 mmol/l, or Hb1Ac[7%), 10 patients had a deregulated balance (more than 10 values[11.2 mmol/l or \3.8 mmol/l and even values[15 mmol/l). - Presence of guardian drugs if indicated: ACEI/ARB: 19 of 23 patients (82.6%), statin: 16 of 40 patients (40%), antiplatelet drug: 16 of 39 patients (41%). Conclusions: Blood glucose control was insufficient in many DPs and prescription of statins and antiplatelet drugs was often missing. If confirmed by a larger study, these two points need to be optimised. As it is not always possible and appropriate to make those changes during hospital stay, a further project should assess and optimise diabetes care across both inpatient and outpatient settings.

A multi-center study: intra-scan and inter-scan variability of diffusion spectrum imaging.

The objective of this study was to investigate whether it is possible to pool together diffusion spectrum imaging data from four different scanners, located at three different sites. Two of the scanners had identical configuration whereas two did not. To measure the variability, we extracted three scalar maps (ADC, FA and GFA) from the DSI and utilized a region and a tract-based analysis. Additionally, a phantom study was performed to rule out some potential factors arising from the scanner performance in case some systematic bias occurred in the subject study. This work was split into three experiments: intra-scanner reproducibility, reproducibility with twin-scanner settings and reproducibility with other configurations. Overall for the intra-scanner and twin-scanner experiments, the region-based analysis coefficient of variation (CV) was in a range of 1%-4.2% and below 3% for almost every bundle for the tract-based analysis. The uncinate fasciculus showed the worst reproducibility, especially for FA and GFA values (CV 3.7-6%). For the GFA and FA maps, an ICC value of 0.7 and above is observed in almost all the regions/tracts. Looking at the last experiment, it was found that there is a very high similarity of the outcomes from the two scanners with identical setting. However, this was not the case for the two other imagers. Given the fact that the overall variation in our study is low for the imagers with identical settings, our findings support the feasibility of cross-site pooling of DSI data from identical scanners.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization.

In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Evolutionary and dispersal history of Eurasian house mice Mus musculus clarified by more extensive geographic sampling of mitochondrial DNA.

We examined the sequence variation of mitochondrial DNA control region and cytochrome b gene of the house mouse (Mus musculus sensu lato) drawn from ca. 200 localities, with 286 new samples drawn primarily from previously unsampled portions of their Eurasian distribution and with the objective of further clarifying evolutionary episodes of this species before and after the onset of human-mediated long-distance dispersals. Phylogenetic analysis of the expanded data detected five equally distinct clades, with geographic ranges of northern Eurasia (musculus, MUS), India and Southeast Asia (castaneus, CAS), Nepal (unspecified, NEP), western Europe (domesticus, DOM) and Yemen (gentilulus). Our results confirm previous suggestions of Southwestern Asia as the likely place of origin of M. musculus and the region of Iran, Afghanistan, Pakistan, and northern India, specifically as the ancestral homeland of CAS. The divergence of the subspecies lineages and of internal sublineage differentiation within CAS were estimated to be 0.37-0.47 and 0.14-0.23 million years ago (mya), respectively, assuming a split of M. musculus and Mus spretus at 1.7 mya. Of the four CAS sublineages detected, only one extends to eastern parts of India, Southeast Asia, Indonesia, Philippines, South China, Northeast China, Primorye, Sakhalin and Japan, implying a dramatic range expansion of CAS out of its homeland during an evolutionary short time, perhaps associated with the spread of agricultural practices. Multiple and non-coincident eastward dispersal events of MUS sublineages to distant geographic areas, such as northern China, Russia and Korea, are inferred, with the possibility of several different routes.

Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon.

BACKGROUND: Elucidating disease and developmental dysfunction requires understanding variation in phenotype. Single-species model organism anatomy ontologies (ssAOs) have been established to represent this variation. Multi-species anatomy ontologies (msAOs; vertebrate skeletal, vertebrate homologous, teleost, amphibian AOs) have been developed to represent 'natural' phenotypic variation across species. Our aim has been to integrate ssAOs and msAOs for various purposes, including establishing links between phenotypic variation and candidate genes. RESULTS: Previously, msAOs contained a mixture of unique and overlapping content. This hampered integration and coordination due to the need to maintain cross-references or inter-ontology equivalence axioms to the ssAOs, or to perform large-scale obsolescence and modular import. Here we present the unification of anatomy ontologies into Uberon, a single ontology resource that enables interoperability among disparate data and research groups. As a consequence, independent development of TAO, VSAO, AAO, and vHOG has been discontinued. CONCLUSIONS: The newly broadened Uberon ontology is a unified cross-taxon resource for metazoans (animals) that has been substantially expanded to include a broad diversity of vertebrate anatomical structures, permitting reasoning across anatomical variation in extinct and extant taxa. Uberon is a core resource that supports single- and cross-species queries for candidate genes using annotations for phenotypes from the systematics, biodiversity, medical, and model organism communities, while also providing entities for logical definitions in the Cell and Gene Ontologies. THE ONTOLOGY RELEASE FILES ASSOCIATED WITH THE ONTOLOGY MERGE DESCRIBED IN THIS MANUSCRIPT ARE AVAILABLE AT: http://purl.obolibrary.org/obo/uberon/releases/2013-02-21/ CURRENT ONTOLOGY RELEASE FILES ARE AVAILABLE ALWAYS AVAILABLE AT: http://purl.obolibrary.org/obo/uberon/releases/