A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature.

BACKGROUND: Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis. RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients. CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.

Integration of ground-penetrating radar and microgravimetric methods to map shallow caves

Ground-penetrating radar (GPR) and microgravimetric surveys have been conducted in the southern Jura mountains of western Switzerland in order to map subsurface karstic features. The study site, La Grande Rolaz cave, is an extensive system in which many portions have been mapped. By using small station spacing and careful processing for the geophysical data, and by modeling these data with topographic information from within the cave, accurate interpretations have been achieved. The constraints on the interpreted geologic models are better when combining the geophysical methods than when using only one of the methods, despite the general limitations of two-dimensional (2D) profiling. For example, microgravimetry can complement GPR methods for accurately delineating a shallow cave section approximately 10 X 10 mt in size. Conversely, GPR methods can be complementary in determining cavity depths and in verifying the presence of off-line features and numerous areas of small cavities and fractures, which may be difficult to resolve in microgravimetric data.

Do brief alcohol motivational interventions work like we think they do?

BACKGROUND: Questions remain about how brief motivational interventions (BMIs) for unhealthy alcohol use work, and addressing these questions may be important for improving their efficacy. Therefore, we assessed the effects of various characteristics of BMIs on drinking outcomes across 3 randomized controlled trials (RCTs). METHODS: Audio recordings of 314 BMIs were coded. We used the global rating scales of the Motivational Interviewing Skills Code (MISC) 2.1: counselor's acceptance, empathy, and motivational interviewing (MI) spirit, and patient's self-exploration were rated. MI proficiency was defined as counselor's rating scale scores ≥5. We also used the structure, confrontation, and advice subscale scores of the Therapy Process Rating Scale and the Working Alliance Inventory. We examined these process characteristics in interventions across 1 U.S. RCT of middle-aged medical inpatients with unhealthy alcohol use (n = 124) and 2 Swiss RCTs of young men with binge drinking in a nonclinical setting: Swiss-one (n = 62) and Swiss-two (n = 128). We assessed the associations between these characteristics and drinks/d reported by participants 3 to 6 months after study entry. RESULTS: In all 3 RCTs, mean MISC counselor's rating scales scores were consistent with MI proficiency. In overdispersed Poisson regression models, most BMI characteristics were not significantly associated with drinks/d in follow-up. In the U.S. RCT, confrontation and self-exploration were associated with more drinking. Giving advice was significantly associated with less drinking in the Swiss-one RCT. Contrary to expectations, MI spirit was not consistently associated with drinking across studies. CONCLUSIONS: Across different populations and settings, intervention characteristics viewed as central to efficacious BMIs were neither robust nor consistent predictors of drinking outcome. Although there may be alternative reasons why the level of MI processes was not predictive of outcomes in these studies (limited variability in scores), efforts to understand what makes BMIs efficacious may require attention to factors beyond intervention process characteristics typically examined.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

On the use of IRMS in forensic science: proposals for a methodological approach

The flourishing number of publications on the use of isotope ratio mass spectrometry (IRMS) in forensic science denotes the enthusiasm and the attraction generated by this technology. IRMS has demonstrated its potential to distinguish chemically identical compounds coming from different sources. Despite the numerous applications of IRMS to a wide range of forensic materials, its implementation in a forensic framework is less straightforward than it appears. In addition, each laboratory has developed its own strategy of analysis on calibration, sequence design, standards utilisation and data treatment without a clear consensus.Through the experience acquired from research undertaken in different forensic fields, we propose a methodological framework of the whole process using IRMS methods. We emphasize the importance of considering isotopic results as part of a whole approach, when applying this technology to a particular forensic issue. The process is divided into six different steps, which should be considered for a thoughtful and relevant application. The dissection of this process into fundamental steps, further detailed, enables a better understanding of the essential, though not exhaustive, factors that have to be considered in order to obtain results of quality and sufficiently robust to proceed to retrospective analyses or interlaboratory comparisons.

Cardiovascular risk factor profiles and their social gradient from adolescence to age 74 in a Swiss region.

BACKGROUND: Few European studies have investigated how cardiovascular risk factors (CRF) in adults relate to those observed in younger generations. OBJECTIVE: To explore this issue in a Swiss region using two population health surveys of 3636 adolescents ages 9-19 years and 3299 adults ages 25-74 years. METHODS: Age patterns of continuous CRF were estimated by robust locally weighted regression and those of high-risk groups were calculated using adult criteria with appropriate adjustment for children. RESULTS: Gender differences in height, weight, blood pressure, and HDL cholesterol observed in adults were found to emerge in adolescents. Overweight, affecting 10-12% of adolescents, was increasing steeply in young adults (three times among males and twice among females) in parallel with inactivity. Median age at smoking initiation was decreasing rapidly from 18 to 20 years in young adults to 15 in adolescents. A statistically significant social gradient in disfavor of the lower education level was observed for overweight in all age groups of women above 16 (odds ratios (ORs) 2.4 to 3.3, P < 0.01), for inactivity in adult males (ORs 1.6 to 2.0, P < 0.05), and for regular smoking in older adolescents (OR 1.9 for males, 2.7 for females, P < 0.005), but not for elevated blood pressure. CONCLUSION: Discontinuities in the cross-sectional age patterns of CRF indicated the emergence of a social gradient and the need for preventive actions against the early adoption of persistent unhealthy behaviors, to which low-educated girls and women are particularly exposed.

Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

Analysis of micro- and nano-structures of the corneal surface of Drosophila and its mutants by atomic force microscopy and optical diffraction.

Drosophila melanogaster is a model organism instrumental for numerous biological studies. The compound eye of this insect consists of some eight hundred individual ommatidia or facets, ca. 15 µm in cross-section. Each ommatidium contains eighteen cells including four cone cells secreting the lens material (cornea). High-resolution imaging of the cornea of different insects has demonstrated that each lens is covered by the nipple arrays--small outgrowths of ca. 200 nm in diameter. Here we for the first time utilize atomic force microscopy (AFM) to investigate nipple arrays of the Drosophila lens, achieving an unprecedented visualization of the architecture of these nanostructures. We find by Fourier analysis that the nipple arrays of Drosophila are disordered, and that the seemingly ordered appearance is a consequence of dense packing of the nipples. In contrast, Fourier analysis confirms the visibly ordered nature of the eye microstructures--the individual lenses. This is different in the frizzled mutants of Drosophila, where both Fourier analysis and optical imaging detect disorder in lens packing. AFM reveals intercalations of the lens material between individual lenses in frizzled mutants, providing explanation for this disorder. In contrast, nanostructures of the mutant lens show the same organization as in wild-type flies. Thus, frizzled mutants display abnormal organization of the corneal micro-, but not nano-structures. At the same time, nipples of the mutant flies are shorter than those of the wild-type. We also analyze corneal surface of glossy-appearing eyes overexpressing Wingless--the lipoprotein ligand of Frizzled receptors, and find the catastrophic aberration in nipple arrays, providing experimental evidence in favor of the major anti-reflective function of these insect eye nanostructures. The combination of the easily tractable genetic model organism and robust AFM analysis represents a novel methodology to analyze development and architecture of these surface formations.

Selection methods regulate evolution of cooperation in digital evolution.

A key, yet often neglected, component of digital evolution and evolutionary models is the 'selection method' which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with formal analysis and numerical experiments the evolution of cooperation under the five most common selection methods (proportionate, rank, truncation-proportionate, truncation-uniform and tournament). We consider related individuals engaging in a Prisoner's Dilemma game where individuals can either cooperate or defect. A cooperator pays a cost, whereas its partner receives a benefit, which affect their performance scores. These performance scores are translated into fitness by one of the five selection methods. We show that cooperation is positively associated with the relatedness between individuals under all selection methods. By contrast, the change in the performance benefit of cooperation affects the populations' average level of cooperation only under the proportionate methods. We also demonstrate that the truncation and tournament methods may introduce negative frequency-dependence and lead to the evolution of polymorphic populations. Using the example of the evolution of cooperation, we show that the choice of selection method, though it is often marginalized, can considerably affect the evolutionary dynamics.

The spared nerve injury model of neuropathic pain.

The spared nerve injury (SNI) model mimics human neuropathic pain related to peripheral nerve injury and is based upon an invasive but simple surgical procedure. Since its first description in 2000, it has displayed a remarkable development. It produces a robust, reliable and long-lasting neuropathic pain-like behaviour (allodynia and hyperalgesia) as well as the possibility of studying both injured and non-injured neuronal populations in the same spinal ganglion. Besides, variants of the SNI model have been developed in rats, mice and neonatal/young rodents, resulting in several possible angles of analysis. Therefore, the purpose of this chapter is to provide a detailed guidance regarding the SNI model and its variants, highlighting its surgical and behavioural testing specificities.

Cross validation of experts versus registration methods for target localization in deep brain stimulation.

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatent of Parkinson's disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.

A regulatory network for the efficient control of transgene expression.

BACKGROUND: Expression of heterologous genes in mammalian cells or organisms for therapeutic or experimental purposes often requires tight control of transgene expression. Specifically, the following criteria should be met: no background gene activity in the off-state, high gene expression in the on-state, regulated expression over an extended period, and multiple switching between on- and off-states. METHODS: Here, we describe a genetic switch system for controlled transgene transcription using chimeric repressor and activator proteins functioning in a novel regulatory network. In the off-state, the target transgene is actively silenced by a chimeric protein consisting of multimerized eukaryotic transcriptional repression domains fused to the DNA-binding tetracycline repressor. In the on-state, the inducer drug doxycycline affects both the derepression of the target gene promoter and activation by the GAL4-VP16 transactivator, which in turn is under the control of an autoregulatory feedback loop. RESULTS: The hallmark of this new system is the efficient transgene silencing in the off-state, as demonstrated by the tightly controlled expression of the highly cytotoxic diphtheria toxin A gene. Addition of the inducer drug allows robust activation of transgene expression. In stably transfected cells, this control is still observed after months of repeated cycling between the repressed and activated states of the target genes. CONCLUSIONS: This system permits tight long-term regulation when stably introduced into cell lines. The underlying principles of this network system should have general applications in biotechnology and gene therapy.

Nouvelles méthodes pour le diagnostic des infections liées aux implants [New methods for the diagnosis of implant-associated infections]

For successful treatment of prosthetic joint infection, the identification of the infecting microorganism is crucial. Cultures of synovial fluid and intraoperative periprosthetic tissue represent the standard method for diagnosing prosthetic joint infection. Rapid and accurate diagnostic tools which can detect a broad range of causing microorganisms and their antimicrobial resistance are increasingly needed. With newer diagnostic techniques, such as sonication of removed implants, microcalorimetry, molecular methods and mass spectrometry, the sensitivity has been significantly increased. In this article, we describe the conventional and newer diagnostic techniques with their advantages and potential future applications.