Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

New historical archeointensity data from Brazil: Evidence for a large regional non-dipole field contribution over the past few centuries

We report new archeointensity data obtained from the analyses of baked clay elements (architectural and kiln brick fragments) sampled in Southeast Brazil and historically and/or archeologically dated between the end of the XVIth century and the beginning of the XXth century AD. The results were determined using the classical Thellier and Thellier protocol as modified by Coe, including partial thermoremanent magnetization (pTRM) and pTRM-tail checks, and the Triaxe protocol, which involves continuous high-temperature magnetization measurements. In both protocols, TRM anisotropy and cooling rate TRM dependence effects were taken into account for intensity determinations which were successfully performed for 150 specimens from 43 fragments, with a good agreement between intensity results obtained from the two procedures. Nine site-mean intensity values were derived from three to eight fragments and defined with standard deviations of less than 8%. The site-mean values vary from similar to 25 mu T to similar to 42 mu T and describe in Southeast Brazil a continuous decreasing trend by similar to 5 mu T per century between similar to 1600 AD and similar to 1900 AD. Their comparison with recent archeointensity results obtained from Northeast Brazil and reduced at a same latitude shows that: (1) the geocentric axial dipole approximation is not valid between these southeastern and northeastern regions of Brazil, whose latitudes differ by similar to 10 degrees, and (2) the available global geomagnetic field models (gufm1 models, their recalibrated versions and the CALSK3 models) are not sufficiently precise to reliably reproduce the non-dipole field effects which prevailed in Brazil for at least the 1600-1750 period. The large non-dipole contribution thus highlighted is most probably linked to the evolution of the South Atlantic Magnetic Anomaly (SAMA) during that period. Furthermore, although our dataset is limited, the Brazilian archeointensity data appear to support the view of a rather oscillatory behavior of the axial dipole moment during the past three centuries that would have been marked in particular by a moderate increase between the end of the XVIIIth century and the middle of the XIXth century followed by the well-known decrease from 1840 AD attested by direct measurements. (C) 2011 Elsevier B.V. All rights reserved.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Tokamak TCABR: Acquisition system, data analysis, and remote participation using MDSplus

Each plasma physics laboratory has a proprietary scheme to control and data acquisition system. Usually, it is different from one laboratory to another. It means that each laboratory has its own way to control the experiment and retrieving data from the database. Fusion research relies to a great extent on international collaboration and this private system makes it difficult to follow the work remotely. The TCABR data analysis and acquisition system has been upgraded to support a joint research programme using remote participation technologies. The choice of MDSplus (Model Driven System plus) is proved by the fact that it is widely utilized, and the scientists from different institutions may use the same system in different experiments in different tokamaks without the need to know how each system treats its acquisition system and data analysis. Another important point is the fact that the MDSplus has a library system that allows communication between different types of language (JAVA, Fortran, C, C++, Python) and programs such as MATLAB, IDL, OCTAVE. In the case of tokamak TCABR interfaces (object of this paper) between the system already in use and MDSplus were developed, instead of using the MDSplus at all stages, from the control, and data acquisition to the data analysis. This was done in the way to preserve a complex system already in operation and otherwise it would take a long time to migrate. This implementation also allows add new components using the MDSplus fully at all stages. (c) 2012 Elsevier B.V. All rights reserved.

Effect of partial preferential orientation and distortions in octahedral clusters on the photoluminescence properties of FeWO4 nanocrystals

This communication is a report of our initial research to obtain iron tungstate (FeWO4) nanocrystals by the microwave-hydrothermal method at 170 degrees C for 45 min. X-ray diffraction patterns showed that the FeWO4 nanocrystals prepared with polyethylene glycol-200 have a partial preferential orientation in the (011) plane in relation to other nanocrystals prepared with sodium bis(2-ethylhexyl) sulfosuccinate and water. Rietveld refinement data indicates that all nanocrystals are monophasic with wolframite-type monoclinic structures and exhibit different distortions on octahedral [FeO6]/[WO6] clusters. High resolution transmission electron microcopy revealed an oriented attachment mechanism for the growth of aggregated FeWO4 nanocrystals. Finally, we observed that the photoluminescence properties of these nanocrystals are affected by partial preferential orientation in the (011) plane and distortions on [FeO6]/[WO6] clusters.

Robotic-assisted laparoscopic partial nephrectomy: initial experience in Brazil and a review of the literature

CONTEXT AND PURPOSE: Partial nephrectomy has become the standard of care for renal tumors less than 4 cm in diameter. Controversy still exists, however, regarding the best surgical approach, especially when minimally invasive techniques are taken into account. Robotic-assisted laparoscopic partial nephrectomy (RALPN) has emerged as a promising technique that helps surgeons achieve the standards of open partial nephrectomy care while offering a minimally invasive approach. The objective of the present study was to describe our initial experience with robotic-assisted laparoscopic partial nephrectomy and extensively review the pertinent literature. MATERIALS AND METHODS: Between August 2009 and February 2010, eight consecutive selected patients with contrast enhancing renal masses observed by CT were submitted to RALPN in a private institution. In addition, we collected information on the patients' demographics, preoperative tumor characteristics and detailed operative, postoperative and pathological data. In addition, a PubMed search was performed to provide an extensive review of the robotic-assisted laparoscopic partial nephrectomy literature. RESULTS: Seven patients had RALPN on the left or right sides with no intraoperative complications. One patient was electively converted to a robotic-assisted radical nephrectomy. The operative time ranged from 120 to 300 min, estimated blood loss (EBL) ranged from 75 to 400 mL and, in five cases, the warm ischemia time (WIT) ranged from 18 to 32 min. Two patients did not require any clamping. Overall, no transfusions were necessary, and there were no intraoperative complications or adverse postoperative clinical events. All margins were negative, and all patients were disease-free at the 6-month follow-up. CONCLUSIONS: Robotic-assisted laparoscopic partial nephrectomy is a feasible and safe approach to small renal cortical masses.Further prospective studies are needed to compare open partial nephrectomy with its minimally invasive counterparts.

Seeking tools for image fusion between computed tomography, structural and functional magnetic resonance methods for applications in neurosurgery

OBJECTIVE: To evaluate tools for the fusion of images generated by tomography and structural and functional magnetic resonance imaging. METHODS: Magnetic resonance and functional magnetic resonance imaging were performed while a volunteer who had previously undergone cranial tomography performed motor and somatosensory tasks in a 3-Tesla scanner. Image data were analyzed with different programs, and the results were compared. RESULTS: We constructed a flow chart of computational processes that allowed measurement of the spatial congruence between the methods. There was no single computational tool that contained the entire set of functions necessary to achieve the goal. CONCLUSION: The fusion of the images from the three methods proved to be feasible with the use of four free-access software programs (OsiriX, Register, MRIcro and FSL). Our results may serve as a basis for building software that will be useful as a virtual tool prior to neurosurgery.