Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.

Beneficial effects of combinatorial micronutrition on body fat and atherosclerosis in mice.

AIMS: More than two billion people worldwide are deficient in key micronutrients. Single micronutrients have been used at high doses to prevent and treat dietary insufficiencies. Yet the impact of combinations of micronutrients in small doses aiming to improve lipid disorders and the corresponding metabolic pathways remains incompletely understood. Thus, we investigated whether a combination of micronutrients would reduce fat accumulation and atherosclerosis in mice. METHODS AND RESULTS: Lipoprotein receptor-null mice fed with an original combination of micronutrients incorporated into the daily chow showed reduced weight gain, body fat, plasma triglycerides, and increased oxygen consumption. These effects were achieved through enhanced lipid utilization and reduced lipid accumulation in metabolic organs and were mediated, in part, by the nuclear receptor PPARα. Moreover, the micronutrients partially prevented atherogenesis when administered early in life to apolipoprotein E-null mice. When the micronutrient treatment was started before conception, the anti-atherosclerotic effect was stronger in the progeny. This finding correlated with decreased post-prandial triglyceridaemia and vascular inflammation, two major atherogenic factors. CONCLUSION: Our data indicate beneficial effects of a combination of micronutritients on body weight gain, hypertriglyceridaemia, liver steatosis, and atherosclerosis in mice, and thus our findings suggest a novel cost-effective combinatorial micronutrient-based strategy worthy of being tested in humans.

The use of positional scanning synthetic combinatorial libraries (PS-SCL) to study T Lympohcyte specificity and degeneracy

Les cellules CD8? T cytolytiques (CTL) sont les principaux effecteurs du système immunitaire adaptatif contre les infections et les tumeurs. La récente identification d?antigènes tumoraux humains reconnus par des cellules T cytolytiques est la base pour le, développement des vaccins antigène spécifiques contre le cancer. Le nombre d?antigènes tumoraux reconnus par des CTL que puisse être utilisé comme cible pour la vaccination des patients atteints du cancer est encore limité. Une nouvelle technique, simple et rapide, vient d?être proposée pour l?identification d?antigènes reconnus par des CTL. Elle se base sur l?utilisation de librairies combinatoriales de peptides arrangées en un format de "scanning" ou balayage par position (PS-SCL). La première partie de cette étude a consisté à valider cette nouvelle technique par une analyse détaillée de la reconnaissance des PS-SCL par différents clones de CTL spécifiques pour des antigènes associés à la tumeur (TAA) connus ainsi que par des clones de spécificité inconnue. Les résultats de ces analyses révèlent que pour tous les clones, la plupart des acides aminés qui composent la séquence du peptide antigénique naturel ont été identifiés par l?utilisation des PS-SCL. Les résultats obtenus ont permis d?identifier des peptides analogues ayant une antigènicité augmentée par rapport au peptide naturel, ainsi que des peptides comportant de multiples modifications de séquence, mais présentant la même réactivité que le peptide naturel. La deuxième partie de cette étude a consisté à effectuer des analyses biométriques des résultats complexes générés par la PS-SCL. Cette approche a permis l?identification des séquences correspondant aux épitopes naturels à partir de bases de données de peptides publiques. Parmi des milliers de peptides, les séquences naturelles se trouvent comprises dans les 30 séquences ayant les scores potentiels de stimulation les plus élevés pour chaque TAA étudié. Mais plus important encore, l?utilisation des PS-SCL avec un clone réactif contre des cellules tumorales mais de spécificité inconnue nous a permis d?identifier I?epitope reconnu par ce clone. Les données présentées ici encouragent l?utilisation des PS-SCL pour l?identification et l?optimisation d?épitopes pour des CTL réactifs anti-tumoraux, ainsi que pour l?étude de la reconnaissance dégénérée d?antigènes par les CTL.<br/><br/>CD8+ cytolytic T lymphocytes (CTL) are the main effector cells of the adaptive immune system against infection and tumors. The recent identification of moleculariy defined human tumor Ags recognized by autologous CTL has opened new opportunities for the development of Ag-specific cancer vaccines. Despite extensive work, however, the number of CTL-defined tumor Ags that are suitable targets for the vaccination of cancer patients is still limited, especially because of the laborious and time consuming nature of the procedures currentiy used for their identification. The use of combinatorial peptide libraries in positionai scanning format (Positional Scanning Synthetic Combinatorial Libraries, PS-SCL)' has recently been proposed as an alternative approach for the identification of these epitopes. To validate this approach, we analyzed in detail the recognition of PS-SCL by tumor-reactive CTL clones specific for multiple well-defined tumor-associated Ags (TAA) as well as by tumor-reactive CTL clones of unknown specificity. The results of these analyses revealed that for all the TAA-specific clones studied most of the amino acids composing the native antigenic peptide sequences could be identified through the use of PS-SCL. Based on the data obtained from the screening of PS-SCL, we could design peptide analogs of increased antigenicity as well as cross-reactive analog peptides containing multiple amino acid substitutions. In addition, the resuits of PS-SCL-screening combined with a recently developed biometric data analysis (PS-SCL-based biometric database analysis) allowed the identification of the native peptides in public protein databases among the 30 most active sequences, and this was the case for all the TAA studied. More importantiy, the screening of PS- SCL with a tumor-reactive CTL clone of unknown specificity resulted in the identification of the actual epitope. Overall, these data encourage the use of PS-SCL not oniy for the identification and optimization of tumor-associated CTL epitopes, but also for the analysis of degeneracy in T lymphocyte receptor (TCR) recognition of tumor Ags.<br/><br/>Les cellules T CD8? cytolytiques font partie des globules blancs du sang et sont les principales responsables de la lutte contre les infections et les tumeurs. Les immunologistes cherchent depuis des années à identifier des molécules exprimées et présentées à la surface des tumeurs qui puissent être reconnues par des cellules T CD8? cytolytiques capables ensuite de tuer ces tumeurs de façon spécifique. Ce type de molécules représente la base pour le développement de vaccins contre le cancer puisqu?elles pourraient être injectées aux patients afin d?induire une réponse anti- tumorale. A présent, il y a très peu de molécules capables de stimuler le système immunitaire contre les tumeurs qui sont connues parce que les techniques développées à ce jour pour leur identification sont complexes et longues. Une nouvelle technique vient d?être proposée pour l?identification de ce type de molécules qui se base sur l?utilisation de librairies de peptides. Ces librairies représentent toutes les combinaisons possibles des composants de base des molécules recherchées. La première partie de cette étude a consisté à valider cette nouvelle technique en utilisant des cellules T CD8? cytolytiques capables de tuer des cellules tumorales en reconnaissant une molécule connue présente à leur surface. On a démontré que l?utilisation des librairies permet d?identifier la plupart des composants de base de la molécule reconnue par les cellules T CD8? cytolytiques utilisées. La deuxième partie de cette étude a consisté à effectuer une recherche des molécules potentiellement actives dans des protéines présentes dans des bases des données en utilisant un programme informatique qui permet de classer les molécules sur la base de leur activité biologique. Parmi des milliers de molécules de la base de données, celles reconnues par nos cellules T CD8? cytolytiques ont été trouvées parmi les plus actives. Plus intéressant encore, la combinaison de ces deux techniques nous a permis d?identifier la molécule reconnue par une population de cellules T CD8? cytolytiques ayant une activité anti-tumorale, mais pour laquelle on ne connaissait pas la spécificité. Nos résultats encouragent l?utilisation des librairies pour trouver et optimiser des molécules reconnues spécifiquement par des cellules T CD8? cytolytiques capables de tuer des tumeurs.

Expert clinical reasoning and pain assessment in mechanically ventilated patients: A descriptive study.

BACKGROUND: Pain assessment in mechanically ventilated patients is challenging, because nurses need to decode pain behaviour, interpret pain scores, and make appropriate decisions. This clinical reasoning process is inherent to advanced nursing practice, but is poorly understood. A better understanding of this process could contribute to improved pain assessment and management. OBJECTIVE: This study aimed to describe the indicators that influence expert nurses' clinical reasoning when assessing pain in critically ill nonverbal patients. METHODS: This descriptive observational study was conducted in the adult intensive care unit (ICU) of a tertiary referral hospital in Western Switzerland. A purposive sample of expert nurses, caring for nonverbal ventilated patients who received sedation and analgesia, were invited to participate in the study. Data were collected in "real life" using recorded think-aloud combined with direct non-participant observation and brief interviews. Data were analysed using deductive and inductive content analyses using a theoretical framework related to clinical reasoning and pain. RESULTS: Seven expert nurses with an average of 7.85 (±3.1) years of critical care experience participated in the study. The patients had respiratory distress (n=2), cardiac arrest (n=2), sub-arachnoid bleeding (n=1), and multi-trauma (n=2). A total of 1344 quotes in five categories were identified. Patients' physiological stability was the principal indicator for making decision in relation to pain management. Results also showed that it is a permanent challenge for nurses to discriminate situations requiring sedation from situations requiring analgesia. Expert nurses mainly used working knowledge and patterns to anticipate and prevent pain. CONCLUSIONS: Patient's clinical condition is important for making decision about pain in critically ill nonverbal patients. The concept of pain cannot be assessed in isolation and its assessment should take the patient's clinical stability and sedation into account. Further research is warranted to confirm these results.

Digital evidence, 'absence' of data and ambiguous patterns of reasoning

In this paper we discuss the use of digital data by the Swiss Federal Criminal Court in a recent case of attempted homicide. We use this case to examine drawbacks for the defense when the presentation of scientific evidence is partial, especially when the only perspective mentioned is that of the prosecution. We tackle this discussion at two distinct levels. First, we pursue an essentially non-technical presentation of the topic by drawing parallels between the court's summing up of the case and flawed patterns of reasoning commonly seen in other forensic disciplines, such as DNA and particle traces (e.g., gunshot residues). Then, we propose a formal analysis of the case, using elements of probability and graphical probability models, to justify our main claim that the partial presentation of digital evidence poses a risk to the administration of justice in that it keeps vital information from the defense. We will argue that such practice constitutes a violation of general principles of forensic interpretation as established by forensic science literature and current recommendations by forensic science interest groups (e.g., the European Network of Forensic Science Institutes). Finally, we posit that argument construction and analysis using formal methods can help replace digital evidence appropriately into context and thus support a sound evaluation of the evidence.

Programme de remédiation cognitive pour patients présentant une schizophrénie ou un trouble associé (Recos) : résultats préliminaires [Cognitive remediation program for individuals living with schizophrenia (Recos): preliminary results].

BACKGROUND: Nowadays, cognitive remediation is widely accepted as an effective treatment for patients with schizophrenia. In French-speaking countries, techniques used in cognitive remediation for patients with schizophrenia have been applied from those used for patients with cerebral injury. As cognitive impairment is a core feature of schizophrenia, the Département de psychiatrie du CHUV in Lausanne (DP-CHUV) intended to develop a cognitive remediation program for patients with a schizophrenia spectrum disease (Recos-Vianin, 2007). Numerous studies show that the specific cognitive deficits greatly differ from one patient to another. Consequently, Recos aims at providing individualized cognitive remediation therapy. In this feasibility trial, we measured the benefits of this individualized therapy for patients with schizophrenia. Before treatment, the patients were evaluated with a large battery of cognitive tests in order to determine which of the five specific training modules - Verbal memory, visuospatial memory and attention, working memory, selective attention, reasoning - could provide the best benefit depending on their deficit. OBJECTIVES: The study was designed to evaluate the benefits of the Recos program by comparing cognitive functioning before and after treatment. METHOD: Twenty-eight patients with schizophrenia spectrum disorders (schizophrenia [n=18], schizoaffective disorder [n=5], schizotypal disorder [n=4], schizophreniform disorder [n=1], DSM-IV-TR) participated in between one and three of the cognitive modules. The choice of the training module was based on the results of the cognitive tests obtained during the first evaluation. The patients participated in 20 training sessions per module (one session per week). At the end of the training period, the cognitive functioning of each patient was reevaluated by using the same neuropsychological battery. RESULTS: The results showed a greater improvement in the cognitive functions, which were specifically trained, compared to the cognitive functions, which were not trained. However, an improvement was also observed in both types of cognitive functions, suggesting an indirect cognitive gain. CONCLUSION: In our view, the great heterogeneity of the observed cognitive deficits in schizophrenia necessitates a detailed neuropsychological investigation as well as an individualized cognitive remediation therapy. These preliminary results need to be confirmed with a more extended sample of patients.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

New inhibitors of Helicobacter pylori urease holoenzyme selected from phage-displayed peptide libraries.

Urease is an important virulence factor for Helicobacter pylori and is critical for bacterial colonization of the human gastric mucosa. Specific inhibition of urease activity has been proposed as a possible strategy to fight this bacteria which infects billions of individual throughout the world and can lead to severe pathological conditions in a limited number of cases. We have selected peptides which specifically bind and inhibit H. pylori urease from libraries of random peptides displayed on filamentous phage in the context of pIII coat protein. Screening of a highly diverse 25-mer combinatorial library and two newly constructed random 6-mer peptide libraries on solid phase H. pylori urease holoenzyme allowed the identification of two peptides, 24-mer TFLPQPRCSALLRYLSEDGVIVPS and 6-mer YDFYWW that can bind and inhibit the activity of urease purified from H. pylori. These two peptides were chemically synthesized and their inhibition constants (Ki) were found to be 47 microM for the 24-mer and 30 microM for the 6-mer peptide. Both peptides specifically inhibited the activity of H. pylori urease but not that of Bacillus pasteurii.

Epidémiologie clinique: définitions, utilités et raisons pour un développement en Suisse. [Clinical epidemiology: definitions, uses and reasons for development in Switzerland].

Clinical epidemiology is the most currently used name for a comparatively new branch of medicine covering a certain number of activities related to the practice of clinical medicine, but using epidemiological techniques and methods. Clinical epidemiology has only just begun to be known in Europe, whereas units are being increasingly developed and expanded in North America, particularly within the clinical departments of hospitals. The methods it offers are valid for both practicing physicians and hospital doctors (or those being trained in hospitals) and serve the purpose of promoting a better quality medical service, especially where a more adequate evaluation of the effectiveness of diagnostic methods, therapy and prognosis in medicine is concerned. Clinical epidemiology proposes a methodology of medical reasoning and of decision-making, as well as techniques intended to facilitate the indispensable task of keeping up with advances in medical knowledge.

A collaborative approach for incorporating forensic case data into crime investigation using criminal intelligence analysis and visualisation

There is an increasing awareness that the articulation of forensic science and criminal investigation is critical to the resolution of crimes. However, models and methods to support an effective collaboration between these partners are still poorly expressed or even lacking. Three propositions are borrowed from crime intelligence methods in order to bridge this gap: (a) the general intelligence process, (b) the analyses of investigative problems along principal perspectives: entities and their relationships, time and space, quantitative aspects and (c) visualisation methods as a mode of expression of a problem in these dimensions. Indeed, in a collaborative framework, different kinds of visualisations integrating forensic case data can play a central role for supporting decisions. Among them, link-charts are scrutinised for their abilities to structure and ease the analysis of a case by describing how relevant entities are connected. However, designing an informative chart that does not bias the reasoning process is not straightforward. Using visualisation as a catalyser for a collaborative approach integrating forensic data thus calls for better specifications.

Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study.

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

Adénopathies et fièvre chez une patiente VIH positive [Immunocompromised HIV patient with lymphadenopathy and fever].

The case of a immunocompromised HIV patient with fever and lymphadenopathy discussed in an anatomo-pathological round. This complex clinical case was used as an opportunity to discuss the broad differential diagnosis of fever in an immunocompromized individual with multiples lymphadenopathies. Clinical reasoning leading to the probable diagnosis based on clinical, biological and radiological informations is not only a difficult task for the speaker but also a rich source of learning opportunities for our medical community.

The likelihood approach to compare populations : a study on DNA evidence and pitfalls of intuitions

The paper follows on from earlier work [Taroni F and Aitken CGG. Probabilistic reasoning in the law, Part 1: assessment of probabilities and explanation of the value of DNA evidence. Science & Justice 1998; 38: 165-177]. Different explanations of the value of DNA evidence were presented to students from two schools of forensic science and to members of fifteen laboratories all around the world. The responses were divided into two groups; those which came from a school or laboratory identified as Bayesian and those which came from a school or laboratory identified as non-Bayesian. The paper analyses these responses using a likelihood approach. This approach is more consistent with a Bayesian analysis than one based on a frequentist approach, as was reported by Taroni F and Aitken CGG. [Probabilistic reasoning in the law, Part 1: assessment of probabilities and explanation of the value of DNA evidence] in Science & Justice 1998.