Multi-modal distraction: insights from children's limited attention.

How does the multi-sensory nature of stimuli influence information processing? Cognitive systems with limited selective attention can elucidate these processes. Six-year-olds, 11-year-olds and 20-year-olds engaged in a visual search task that required them to detect a pre-defined coloured shape under conditions of low or high visual perceptual load. On each trial, a peripheral distractor that could be either compatible or incompatible with the current target colour was presented either visually, auditorily or audiovisually. Unlike unimodal distractors, audiovisual distractors elicited reliable compatibility effects across the two levels of load in adults and in the older children, but high visual load significantly reduced distraction for all children, especially the youngest participants. This study provides the first demonstration that multi-sensory distraction has powerful effects on selective attention: Adults and older children alike allocate attention to potentially relevant information across multiple senses. However, poorer attentional resources can, paradoxically, shield the youngest children from the deleterious effects of multi-sensory distraction. Furthermore, we highlight how developmental research can enrich the understanding of distinct mechanisms controlling adult selective attention in multi-sensory environments.

Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people.

The aim of this study was to extract multi-parametric measures characterizing different features of sit-to-stand (Si-St) and stand-to-sit (St-Si) transitions in older persons, using a single inertial sensor attached to the chest. Investigated parameters were transition's duration, range of trunk tilt, smoothness of transition pattern assessed by its fractal dimension, and trunk movement's dynamic described by local wavelet energy. A measurement protocol with a Si-St followed by a St-Si postural transition was performed by two groups of participants: the first group (N=79) included Frail Elderly subjects admitted to a post-acute rehabilitation facility and the second group (N=27) were healthy community-dwelling elderly persons. Subjects were also evaluated with Tinetti's POMA scale. Compared to Healthy Elderly persons, frail group at baseline had significantly longer Si-St (3.85±1.04 vs. 2.60±0.32, p=0.001) and St-Si (4.08±1.21 vs. 2.81±0.36, p=0.001) transition's duration. Frail older persons also had significantly decreased smoothness of Si-St transition pattern (1.36±0.07 vs. 1.21±0.05, p=0.001) and dynamic of trunk movement. Measurements after three weeks of rehabilitation in frail older persons showed that smoothness of transition pattern had the highest improvement effect size (0.4) and discriminative performance. These results demonstrate the potential interest of such parameters to distinguish older subjects with different functional and health conditions.

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.

Approche multidisciptinaire des coûts humains des lombalgies [Human cost of low back pain: a multi-disciplinary approach]

The valuation of human costs is a necessity, but this task poses many problems of method. A team made of a philosopher, a psychologist and a physician has been working with economist researchers in order to look into the meaning that the preferences announced at the time of the inquiries on human costs by QALY methods could assume. These methods are often used to obtain a valuation of the impact of a health attack on people's quality of life. The methods--in the frame of the argument assumed by the economic theory on well-being--hypothesize that people's choices depend mainly on cognitive work. The qualitative interviews show that the psychological construction process for the announced preferences largely overlap this frame. In this paper the authors hastily tackle the factors which have an effect on the preferences. They conclude that the QALY methods don't seem to be able to assess the quality of life nori to valuate the damage that the quality of life could include.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.