PEAR: PEriodic And fixed Rank separation for fast fMRI

In functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial-temporal resolution trade-off and increase statistical robustness through increased degrees-of-freedom. High quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements. We decompose the fMRI signal into a component which a has fixed rank and a component consisting of a sum of periodic signals which is sparse in the temporal Fourier domain. Data reconstruction is performed by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI. Experimental results include purely synthetic simulation, a simulation with real timecourses and retrospective undersampling of a real fMRI dataset. Evaluation was performed both quantitatively and visually versus ground truth, comparing PEAR to two additional recent methods for fMRI reconstruction from undersampled measurements. Results demonstrate PEAR's improvement in estimating the timecourses and activation maps versus the methods compared against at acceleration ratios of R=8,16 (for simulated data) and R=6.66,10 (for real data). PEAR results in reconstruction with higher fidelity than when using a fixed-rank based model or a conventional Low-rank+Sparse algorithm. We have shown that splitting the functional information between the components leads to better modeling of fMRI, over state-of-the-art methods

The earliest evidence for Upper Paleolithic occupation in the Armenian Highlands at Aghitu-3 Cave

With its well-preserved archaeological and environmental records, Aghitu-3 Cave permits us to examine the settlement patterns of the Upper Paleolithic (UP) people who inhabited the Armenian Highlands. We also test whether settlement of the region between ∼39–24,000 cal BP relates to environmental variability. The earliest evidence occurs in archaeological horizon (AH) VII from ∼39–36,000 cal BP during a mild, moist climatic phase. AH VI shows periodic occupation as warm, humid conditions prevailed from ∼36–32,000 cal BP. As the climate becomes cooler and drier at ∼32– 29,000 cal BP (AH V-IV), evidence for occupation is minimal. However, as cooling continues, the deposits of AH III demonstrate that people used the site more intensively from ∼29–24,000 cal BP, leaving behind numerous stone artifacts, faunal remains, and complex combustion features. Despite the climatic fluctuations seen across this 15,000-year sequence, lithic technology remains attuned to one pattern: unidirectional reduction of small cores geared towards the production of bladelets for tool manufacture. Subsistence patterns also remain stable, focused on medium-sized prey such as ovids and caprids, as well as equids. AH III demonstrates an expansion of social networks to the northwest and southwest, as the transport distance of obsidian used to make stone artifacts increases. We also observe the addition of bone tools, including an eyed needle, and shell beads brought from the east, suggesting that these people manufactured complex clothing and wore ornaments. Remains of micromammals, birds, charcoal, pollen, and tephra relate the story of environmental variability. We hypothesize that UP behavior was linked to shifts in demographic pressures and climatic changes. Thus, by combining archaeological and environmental data, we gain a clearer picture about the first UP inhabitants of the Armenian Highlands

Fast Statistical Alignment

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/

Science Opportunities offered by Mercury's Ice-Bearing Polar Deposits

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SOSORT consensus paper: school screening for scoliosis. Where are we today?

This report is the SOSORT Consensus Paper on School Screening for Scoliosis discussed at the 4th International Conference on Conservative Management of Spinal Deformities, presented by SOSORT, on May 2007. The objectives were numerous, 1) the inclusion of the existing information on the issue, 2) the analysis and discussion of the responses by the meeting attendees to the twenty six questions of the questionnaire, 3) the impact of screening on frequency of surgical treatment and of its discontinuation, 4) the reasons why these programs must be continued, 5) the evolving aim of School Screening for Scoliosis and 6) recommendations for improvement of the procedure