Noise characterization of an atomic magnetometer at sub-millihertz frequencies

Noise measurements have been carried out in the LISA bandwidth (0.1 mHz to 100 mHz) to characterize an all-optical atomic magnetometer based on nonlinear magneto-optical rotation. This was done in order to assess if the technology can be used for space missions with demanding low-frequency requirements like the LISA concept. Magnetometry for low-frequency applications is usually limited by $1/f$ noise and thermal drifts, which become the dominant contributions at sub-millihertz frequencies. Magnetic field measurements with atomic magnetometers are not immune to low-frequency fluctuations and significant excess noise may arise due to external elements, such as temperature fluctuations or intrinsic noise in the electronics. In addition, low-frequency drifts in the applied magnetic field have been identified in order to distinguish their noise contribution from that of the sensor. We have found the technology suitable for LISA in terms of sensitivity, although further work must be done to characterize the low-frequency noise in a miniaturized setup suitable for space missions.Comment: 11 pages, 12 figure

Interpolation of the magnetic field at the test masses in eLISA

A feasible design for a magnetic diagnostics subsystem for eLISA will be based on that of its precursor mission, LISA Pathfinder. Previous experience indicates that magnetic field estimation at the positions of the test masses has certain complications. This is due to two reasons. The first is that magnetometers usually back-act due to their measurement principles (i.e., they also create their own magnetic fields), while the second is that the sensors selected for LISA Pathfinder have a large size, which conflicts with space resolution and with the possibility of having a sufficient number of them to properly map the magnetic field around the test masses. However, high-sensitivity and small-sized sensors that significantly mitigate the two aforementioned limitations exist, and have been proposed to overcome these problems. Thus, these sensors will be likely selected for the magnetic diagnostics subsystem of eLISA. Here we perform a quantitative analysis of the new magnetic subsystem, as it is currently conceived, and assess the feasibility of selecting these sensors in the final configuration of the magnetic diagnostic subsystem.Peer ReviewedPostprint (author's final draft

Towards a FPGA-controlled deep phase modulation interferometer

Deep phase modulation interferometry was proposed as a method to enhance homodyne interferometers to work over many fringes. In this scheme, a sinusoidal phase modulation is applied in one arm while the demodulation takes place as a post-processing step. In this contribution we report on the development to implement this scheme in a fiber coupled interferometer controlled by means of a FPGA, which includes a LEON3 soft-core processor. The latter acts as a CPU and executes a custom made application to communicate with a host PC. In contrast to usual FPGA-based designs, this implementation allows a real-time fine tuning of the parameters involved in the setup, from the control to the post-processing parameters.Comment: Proceedings of the X LISA Symposium, Gainesville, May 18-23, 201

Metal substrate catalysis in the confined space for platinum drug delivery

[EN] Catalysis-based approaches for the activation of anticancer agents hold considerable promise. These principally rely on the use of metal catalysts capable of deprotecting inactive precursors of organic drugs or transforming key biomolecules available in the cellular environment. Nevertheless, the efficiency of most of the schemes described so far is rather low, limiting the benefits of catalytic amplification as strategy for controlling the therapeutic effects of anticancer compounds. In the work presented here, we show that flavin reactivity within a hydrogel matrix provides a viable solution for the efficient catalytic activation and delivery of cisplatin, a worldwide clinically-approved inorganic chemotherapy agent. This is achieved by ionically adsorbing a flavin catalyst and a Pt(iv) prodrug as substrate into porous amino-functionalized agarose beads. The hydrogel chassis supplies high local concentrations of electron donating groups/molecules in the surrounding of the catalyst, ultimately boosting substrate conversion rates (TOF >200 min(-1)) and enabling controlled liberation of the drug by light or chemical stimuli. Overall, this approach can afford platforms for the efficient delivery of platinum drugs as demonstrated herein by using a transdermal diffusion model simulating the human skin.We acknowledge financial support from the Spanish State Research Agency (grants CTQ2016-80844-R, PID2019-109111RBI00, RTI2018-094398-B-I00, BIO2014-61838-EXP) and the Basque Government (Eusko Jaurlaritza, grant PIBA_2021_1_0034). S. V. L. thanks the Mexican Council of Science and Technology (CONACyT) for the postdoctoral fellowship she received (ref. CVU-267390). C. S. C. thanks Gipuzkoa Foru Aldundia (Gipuzkoa Fellows program; grant number 2019-FELL-000018-01/62/2019) for.nancial support. L. S. thanks the Spanish MultiMetDrugs network (RED2018-102471-T) for fruitful discussion. FLG thanks the Spanish Biocatalysis network (RED2018-102403T) and the European Research Council (ERC-Co-2018 818089). This work was performed under the Maria de Maeztu and Severo Ochoa Centres of Excellence Programme run by the Spanish State Research Agency, Grant No. MDM-2017-0720 (CIC biomaGUNE) and CEX2018-000867-S (DIPC)

Mixture of Probabilistic Principal Component Analyzers for Shapes from Point Sets

Inferring a probability density function (pdf) for shape from a population of point sets is a challenging problem. The lack of point-to-point correspondences and the non-linearity of the shape spaces undermine the linear models. Methods based on manifolds model the shape variations naturally, however, statistics are often limited to a single geodesic mean and an arbitrary number of variation modes. We relax the manifold assumption and consider a piece-wise linear form, implementing a mixture of distinctive shape classes. The pdf for point sets is defined hierarchically, modeling a mixture of Probabilistic Principal Component Analyzers (PPCA) in higher dimension. A Variational Bayesian approach is designed for unsupervised learning of the posteriors of point set labels, local variation modes, and point correspondences. By maximizing the model evidence, the numbers of clusters, modes of variations, and points on the mean models are automatically selected. Using the predictive distribution, we project a test shape to the spaces spanned by the local PPCA's. The method is applied to point sets from: i) synthetic data, ii) healthy versus pathological heart morphologies, and iii) lumbar vertebrae. The proposed method selects models with expected numbers of clusters and variation modes, achieving lower generalization-specificity errors compared to state-of-the-art

Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images

Purpose: The primary goal of this article is to achieve an automatic and objective method to compute the Pfirrmann’s degeneration grade of intervertebral discs (IVD) from MRI. This grading system is used in the diagnosis and management of patients with low back pain (LBP). In addition, biomechanical models, which are employed to assess the treatment on patients with LBP, require this grading value to compute proper material properties. Materials and methods: T2-weighted MR images of 48 patients were employed in this work. The 240 lumbar IVDs were divided into a training set (140) and a testing set (100). Three experts manually classified the whole set of IVDs using the Pfirrmann’s grading system and the ground truth was selected as the most voted value among them. The developed method employs active contour models to delineate the boundaries of the IVD. Subsequently, the classification is achieved using a trained Neural Network (NN) with eight designed features that contain shape and intensity information of the IVDs. Results: The classification method was evaluated using the testing set, resulting in a mean specificity (95.5 %) and sensitivity (87.3 %) comparable to those of every expert with respect to the ground truth. Conclusions: Our results show that the automatic method and humans perform equally well in terms of the classification accuracy. However, human annotations have inherent inter- and intra-observer variabilities, which lead to inconsistent assessments. In contrast, the proposed automatic method is objective, being only dependent on the input MRI

Accurate Segmentation of Vertebral Bodies and Processes Using Statistical Shape Decomposition and Conditional Models

Detailed segmentation of the vertebrae is an important pre-requisite in various applications of image-based spine assessment, surgery and biomechanical modeling. In particular, accurate segmentation of the processes is required for image-guided interventions, for example for optimal placement of bone grafts between the transverse processes. Furthermore, the geometry of the processes is now required in musculoskeletal models due to their interaction with the muscles and ligaments. In this paper, we present a new method for detailed segmentation of both the vertebral bodies and processes based on statistical shape decomposition and conditional models. The proposed technique is specifically developed with the aim to handle the complex geometry of the processes and the large variability between individuals. The key technical novelty in this work is the introduction of a part-based statistical decomposition of the vertebrae, such that the complexity of the subparts is effectively reduced, and model specificity is increased. Subsequently, in order to maintain the statistical and anatomic coherence of the ensemble, conditional models are used to model the statistical inter-relationships between the different subparts. For shape reconstruction and segmentation, a robust model fitting procedure is used to exclude improbable inter-part relationships in the estimation of the shape parameters. Segmentation results based on a dataset of 30 healthy CT scans and a dataset of 10 pathological scans show a point-to-surface error improvement of 20% and 17% respectively, and the potential of the proposed technique for detailed vertebral modeling

The LISA PathFinder DMU and Radiation Monitor

The LISA PathFinder DMU (Data Management Unit) flight model was formally accepted by ESA and ASD on 11 February 2010, after all hardware and software tests had been successfully completed. The diagnostics items are scheduled to be delivered by the end of 2010. In this paper we review the requirements and performance of this instrumentation, specially focusing on the Radiation Monitor and the DMU, as well as the status of their programmed use during mission operations, on which work is ongoing at the time of writing.Comment: 11 pages, 7 figures, prepared for the Proceedings of the 8th International LISA Symposium, Classical and Quantum Gravit

The diagnostics subsystem on board LISA PathFinder and LISA

The Data and Diagnostics Subsystem of the LTP hardware and software are at present essentially ready for delivery. In this presentation we intend to describe the scientific and technical aspects of this subsystem, which includes thermal diagnostics, magnetic diagnostics and a Radiation Monitor, as well as the prospects for their integration within the rest of the LTP. We also sketch a few lines of progress recently opened up towards the more demanding diagnostics requirements which will be needed for LISA.Comment: 11 pages, 5 figures, pdflatex, prepared for the Proceedings of the 7th International LISA Symposium (Barcelona, Spain, 16-20 June-2008), submitted to Classical and Quantum Gravit

A multi-center milestone study of clinical vertebral CT segmentation

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention