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Objective: Cognitive impairments contribute significantly to inadequate functional recovery following illness episodes in bipolar disorder, yet data on treatment interventions are sparse. We assessed the cognitive effects of a standardized extract of the medicinal herb Withania somnifera (WSE) in bipolar disorder. Method: Sixty euthymic subjects with DSM-IV bipolar disorder were enrolled in an 8-week, double-blind, placebo-controlled, randomized study of WSE (500 mg/d) as a procognitive agent added adjunctively to the medications being used as maintenance treatment for bipolar disorder. Study enrollment and data analyses were completed between December 2008 and September 2012. Cognitive testing at baseline and 8 weeks assessed primary efficacy outcomes. Psychopathology and adverse events were monitored at scheduled visits. Results: Fifty-three patients completed the study (WSE, n = 24; placebo, n = 29), and the 2 groups were matched in terms of demographic, illness, and treatment characteristics. Compared to placebo, WSE provided significant benefits for 3 cognitive tasks: digit span backward (P = .035), Flanker neutral response time (P = .033), and the social cognition response rating of the Penn Emotional Acuity Test (P = .045). The size of the WSE treatment effect for digit span backward was in the medium range (Cohen d = 0.51; 95% CI, 0.25–0.77). None of the other cognitive tasks showed significant betweengroup differences. Mood and anxiety scale scores remained stable, and adverse events were minor. Conclusions: Although results are preliminary, WSE appears to improve auditory-verbal working memory (digit span backward), a measure of reaction time, and a measure of social cognition in bipolar disorder. Given the paucity of data for improving cognitive capacity in bipolar disorder, WSE offers promise, appears to have a benign side-effects profile, and merits further study. Trial Registration: ClinicalTrials.gov identifier: NCT00761761

This pilot, double-blind, placebo-controlled, randomized, escalating dose trial explored the safety and efficacy of tideglusib, an inhibitor of glycogen synthase kinase-3, in Alzheimer's disease (AD) patients. Thirty mild-moderate AD patients on cholinesterase inhibitor treatment were administered escalating doses (400, 600, 800, 1,000 mg) of tideglusib or placebo (ratio 2 : 1) for 4, 4, 6, and 6 weeks, respectively. The primary objective was to evaluate the safety and tolerability of tideglusib with strict criteria for drug escalation or withdrawal. Mini-Mental Status Examination (MMSE), Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog+), word fluency, Geriatric Depression Scale (GDS), and a final Global Clinical Assessment (GCA) were assessed as secondary objectives. Treatment was well tolerated. Adverse events were as frequent in active and placebo groups, except for some moderate, asymptomatic, and fully reversible increases (>2.5 × ULN) of serum transaminases in 6 active cases (p = 0.001). Tideglusib produced positive trends in MMSE, ADAS-cog, GDS, and GCA without statistical significance in this small sample. Responders in MMSE were significantly higher in the active group (p = 0.05). Patients escalated up to 1000 mg/day had a benefit of 1.68 points in the MMSE and 4.72 points in the ADAS-cog+ when compared to placebo. This small pilot study provides valuable safety and efficacy estimates for the treatment of AD patients with tideglusib, currently being confirmed in a larger clinical trial. Due to escalating doses and the small sample size, this trial provides insufficient evidence to support or reject a benefit of tideglusib in AD.

Background Targeting glutamatergic dysfunction provides an exciting opportunity to improve cognitive impairment in schizophrenia. One treatment approach has targeted inadequate antioxidant defenses at glutamatergic synapses. Animal and human data suggest NMDA antagonists worsen executive cognitive controls — e.g. increase perseverative responses and impair set-shifting. We conducted a preliminary study to test the hypothesis that l -carnosine, an antioxidant and anti-glycation agent which is co-localized and released with glutamate would improve executive dysfunction, a cognitive domain associated with glutamate. Methods Seventy-five symptomatically stable adults with chronic schizophrenia were randomly assigned to l -carnosine as adjunctive treatment (2 g/day) or a matched placebo in a double-blind manner for 3 months. Cognitive domains (executive dysfunction, memory, attention and motor speed) were assessed using a computerized battery at baseline, 4 and 12 weeks, along with psychopathology ratings and safety parameters. Results The l -carnosine group performed significantly faster on non-reversal condition trials of the set-shifting test compared with placebo but reversal reaction times and errors were not significantly different between treatments. On the strategic target detection test, the l -carnosine group displayed significantly improved strategic efficiency and made fewer perseverative errors compared with placebo. Other cognitive tests showed no significant differences between treatments. Psychopathology scores remained stable. The carnosine group reported more adverse events (30%) compared with the placebo group (14%). Laboratory indices remained within acceptable ranges. Conclusions These preliminary findings suggest that l -carnosine merits further consideration as adjunctive treatment to improve executive dysfunction in persons with schizophrenia.

Background/Aims: The aim of this single-center, open-label study was to assess the absolute bioavailability of an oral (tablet) versus intravenous formulation of almorexant in healthy subjects. Methods: A pilot phase in 3 healthy male subjects, which preceded the main study, consisted of a single 30-min intravenous infusion of 10 mg almorexant. Its objectives were to ensure the tolerability of the intravenous formulation and to select the intravenous dose for the main study. The main study was a randomized, two-way crossover study in 20 healthy subjects (10 males and 10 females). Subjects received a single oral dose of 200 mg almorexant and a single 30-min intravenous infusion of 20 mg almorexant. Results: All 23 subjects completed the study as planned. Almorexant was well tolerated; the main observed adverse events were somnolence and fatigue. A geometric mean total body clearance of 43 l/h (95% CI 39–47) and a volume of distribution of 683 liters (95% CI 552–845) were determined. The absolute oral bioavailability of almorexant was 11.2% (90% CI 9.6–13.1). Conclusion: Almorexant seems to possess a pronounced first-pass effect and metabolism.

An important factor in the universal failure in phase III trials in mild to moderate Alzheimer’s disease in the past decade is the lack of phase II clinical data prior to entering phase III, with common reliance on biomarker results alone. Conduction of two learn-confirm cycles according to the Sheiner model would allow go/no-go decision making to include reliable clinical efficacy data prior to conducting phase III and would likely bring the rate of late stage failure more into line with that of other neurological indications. In studies in earlier disease stages, combined phase IIB/III adaptive approaches merit consideration in view of the long timelines of each study, though advantages and disadvantages of this approach versus the classical development pathway still need careful assessment.

This report was developed to assist airports of all types and sizes and their stakeholders in gaining an understanding of Unmanned Aircraft Systems (UAS) and their potential use and impact on airports. Information in the primer includes a glossary of key terms and a background on the current state of UAS operations. The primer addresses costs and benefits to airports, regulatory and community considerations, UAS infrastructure and operational considerations, and UAS safety and security among other issues.

International audience; This paper describes a probabilistic generative model and its associated algorithm to jointly register multiple point sets. The vast majority of state-of-the-art registration techniques select one of the sets as the ''model" and perform pairwise alignments between the other sets and this set. The main drawback of this mode of operation is that there is no guarantee that the model-set is free of noise and outliers, which contaminates the estimation of the registration parameters. Unlike previous work, the proposed method treats all the point sets on an equal footing: they are realizations of a Gaussian mixture (GMM) and the registration is cast into a clustering problem. We formally derive an EM algorithm that estimates both the GMM parameters and the rotations and translations that map each individual set onto the ''central" model. The mixture means play the role of the registered set of points while the variances provide rich information about the quality of the registration. We thoroughly validate the proposed method with challenging datasets, we compare it with several state-of-the-art methods, and we show its potential for fusing real depth data.

International audience; Inspired by the great success of sparse coding for vector valued data, our goal is to represent symmetric positive definite (SPD) data matrices as sparse linear combinations of atoms from a dictionary, where each atom itself is an SPD matrix. Since SPD matrices follow a non-Euclidean (in fact a Riemannian) geometry, existing sparse coding techniques for Euclidean data cannot be directly extended. Prior works have approached this problem by defining a sparse coding loss function using either extrinsic similarity measures (such as the log-Euclidean distance) or kernelized variants of statistical measures (such as the Stein divergence, Jeffrey's divergence, etc.). In contrast, we propose to use the intrinsic Riemannian distance on the manifold of SPD matrices. Our main contribution is a novel mathematical model for sparse coding of SPD matrices; we also present a computationally simple algorithm for optimizing our model. Experiments on several computer vision datasets showcase superior classification and retrieval performance compared with state-of-the-art approaches.

International audience; We present a novel methodology for the analysis of complex object shapes in motion observed by multiple video cameras. In particular, we propose to learn local surface rigidity probabilities (i.e., deformations), and to estimate a mean pose over a temporal sequence. Local deformations can be used for rigidity-based dynamic surface segmentation, while a mean pose can be used as a sequence keyframe or a cluster prototype and has therefore numerous applications, such as motion synthesis or sequential alignment for compression or morphing. We take advantage of recent advances in surface tracking techniques to formulate a generative model of 3D temporal sequences using a probabilistic framework, which conditions shape fitting over all frames to a simple set of intrinsic surface rigidity properties. Surface tracking and rigidity variable estimation can then be formulated as an Expectation-Maximization inference problem and solved by alternatively minimizing two nested fixed point iterations. We show that this framework provides a new fundamental building block for various applications of shape analysis, and achieves comparable tracking performance to state of the art surface tracking techniques on real datasets, even compared to approaches using strong kinematic priors such as rigid skeletons.

International audience; In large video collections with clusters of typical categories, such as ''birthday party'' or ''flash-mob'', category-specific video summarization can produce higher quality video summaries than unsupervised approaches that are blind to the video category. Given a video from a known category, our approach first efficiently performs a temporal segmentation into semantically-consistent segments, delimited not only by shot boundaries but also general change points. Then, equipped with an SVM classifier, our approach assigns importance scores to each segment. The resulting video assembles the sequence of segments with the highest scores. The obtained video summary is therefore both short and highly informative. Experimental results on videos from the multimedia event detection (MED) dataset of TRECVID'11 show that our approach produces video summaries with higher relevance than the state of the art.

We are given a set of video clips, each one annotated with an {\em ordered} list of actions, such as "walk" then "sit" then "answer phone" extracted from, for example, the associated text script. We seek to temporally localize the individual actions in each clip as well as to learn a discriminative classifier for each action. We formulate the problem as a weakly supervised temporal assignment with ordering constraints. Each video clip is divided into small time intervals and each time interval of each video clip is assigned one action label, while respecting the order in which the action labels appear in the given annotations. We show that the action label assignment can be determined together with learning a classifier for each action in a discriminative manner. We evaluate the proposed model on a new and challenging dataset of 937 video clips with a total of 787720 frames containing sequences of 16 different actions from 69 Hollywood movies., 17 pages, completed version of a ECCV2014 conference paper

International audience; Object tracking is a reoccurring problem in computer vision. Tracking-by-detection approaches, in particular Struck (Hare et al., 2011), have shown to be competitive in recent evaluations. However, such approaches fail in the presence of long-term occlusions as well as severe viewpoint changes of the object. In this paper we propose a principled way to combine occlusion and motion reasoning with a tracking-by-detection approach. Occlusion and motion reasoning is based on state-of-the-art long-term trajectories which are labeled as object or background tracks with an energy-based formulation. The overlap between labeled tracks and detected regions allows to identify occlusions. The motion changes of the object between consecutive frames can be estimated robustly from the geometric relation between object trajectories. If this geometric change is significant, an additional detector is trained. Experimental results show that our tracker obtains state-of-the-art results and handles occlusion and viewpoints changes better than competing tracking methods.