A Kinematic, Flexure-based Mechanism for Precise, Parallel Motion for the Hertz Variable-delay Polarization Modulator (VPM)

We describe the design of the linear motion stage for a Variable-delay Polarization Modulator (VPM) and of a grid flattener that has been built and integrated into the Hertz ground-based, submillimeter polarimeter. VPMs allow the modulation of a polarized source by controlling the phase difference between two linear, orthogonal polarizations. The size of the gap between a mirror and a very flat polarizing grid determines the amount of the phase difference. This gap must be parallel to better than 1% of the wavelength. A novel, kinematic, flexure-based mechanism is described that passively maintains the parallelism of the mirror and the grid to 1.5 pm over a 150 mm diameter, with a 400 pm throw. A single piezoceramic actuator is used to modulate the gap, and a capacitive sensor provides position feedback for closed-loop control. A simple device that ensures the planarity of the polarizing grid is also described. Engineering results from the deployment of this device in the Hertz instrument April 2006 at the Submillimeter Telescope Observatory (SMTO) in Arizona are presented

Combining Multiplexed Ion Beam Imaging (MIBI) with Convolutional Neural Networks to accurately segment cells in human tissue

Background: Multiplexed imaging is a rapidly growing field that promises to substantially increase the number of proteins that can be imaged simultaneously. We have developed Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF), which uses elemental reporters conjugated to primary antibodies that are then quantified using a time of flight mass-spectrometer. This technique allows for more than 40 distinct proteins to visualized at once in the same clinical samples. This has already yielded significant insights into the interactions and relationships between the many different immune cell populations present in the tumor microenvironment. However, one of the remaining challenges in analyzing such data is accurately determining target protein expression values for each cell in the image. This requires the precise delineation of boundaries between cells that are often tightly packed next to one another. Current methods to address this challenge largely rely on DNA intensity to make these splits, and are thus mostly limited to nuclear segmentation. Methods: We have developed a novel convolutional neural network to perform whole-cell segmentation from multiplexed imaging data. Rather than relying only on DNA signal, we use a panel of morphological markers. Our method integrates the information from these distinct proteins, allowing it to segment large cancer cells, small lymphocytes, and normal epithelium at the same time without requiring fine-tuning or manual adjustment. Results: By combining our novel imaging platform with new computational tools, we are able to achieve extremely accurate segmentation of whole cells in tissue. Our approach compares favorably with many of the currently used tools for segmentation. We show that our improvements in accuracy come both from our novel imaging approach as well as algorithmic advances. We perform significantly better than traditional machine learning algorithms trained on the same dataset. Additionally, we show that our algorithm can be trained to identify cells across a range of cancer histologies and disease grades. Conclusions: We have developed a robust and accurate approach to whole-cell segmentation in human tissues. We show the superiority over this method over current state of the art algorithms. The accurate segmentation generated by our approach will enable the analysis of complex tissue architectures with highly overlapping cell types, and will help to advance our understanding of the interactions between cell types in the diseased state

SemEHR:A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research

OBJECTIVE: Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical insights, hospital management, and trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search and analytics tool for EHRs. METHODS: SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualized mentions of a wide range of biomedical concepts within EHRs. Natural language processing annotations are further assembled at the patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data are serviced via ontology-based search and analytics interfaces. RESULTS: SemEHR has been deployed at a number of UK hospitals, including the Clinical Record Interactive Search, an anonymized replica of the EHR of the UK South London and Maudsley National Health Service Foundation Trust, one of Europe's largest providers of mental health services. In 2 Clinical Record Interactive Search-based studies, SemEHR achieved 93% (hepatitis C) and 99% (HIV) F-measure results in identifying true positive patients. At King's College Hospital in London, as part of the CogStack program (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100 000 Genomes Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast at searching phenotypes; time for recruitment criteria checking was reduced from days to minutes. Validated on open intensive care EHR data, Medical Information Mart for Intensive Care III, the vital signs extracted by SemEHR can achieve around 97% accuracy. CONCLUSION: Results from the multiple case studies demonstrate SemEHR's efficiency: weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of patients, bringing in more and unexpected insight compared to study-oriented bespoke IE systems. SemEHR is open source, available at https://github.com/CogStack/SemEHR

MEMS Microshutter Array System for James Webb Space Telescope

A complex MEMS microshutter array system has been developed at NASA Goddard Space Flight Center (GSFC) for use as a multi-object aperture array for a Near-Infrared Spectrometer (NIRSpec). The NIRSpec is one of the four major instruments carried by the James Webb Space Telescope (JWST), the next generation of space telescope after the Hubble Space Telescope retires. The microshutter arrays (MSAs) are designed for the selective transmission of light with high efficiency and high contrast. It is demonstrated in Figure 1 how a MSA is used as a multiple object selector in deep space. The MSAs empower the NIRSpec instrument simultaneously collect spectra from more than 100 targets therefore increases the instrument efficiency 100 times or more. The MSA assembly is one of three major innovations on JWST and the first major MEMS devices serving observation missions in space. The MSA system developed at NASA GSFC is assembled with four quadrant fully addressable 365x171 shutter arrays that are actuated magnetically, latched and addressed electrostatically. As shown in Figure 2, each MSA is fabricated out of a 4' silicon-on-insulator (SOI) wafer using MEMS bulk-micromachining technology. Individual shutters are close-packed silicon nitride membranes with a pixel size close to 100x200 pm (Figure 3). Shutters are patterned with a torsion flexure permitting shutters to open 90 degrees with a minimized mechanical stress concentration. In order to prevent light leak, light shields are made on to the surrounding frame of each shutter to cover the gaps between the shutters and the Game (Figure 4). Micro-ribs and sub-micron bumps are tailored on hack walls and light shields, respectively, to prevent sticktion, shown in Figures 4 and 5. JWST instruments are required to operate at cryogenic temperatures as low as 35K, though they are to be subjected to various levels of ground tests at room temperature. The shutters should therefore maintain nearly flat in the entire temperature range between 35K and 300K. Through intensive numerical simulations and experimental studies, an optically opaque and electrically conductive metal-nitride thin film was selected as a coating material deposited on the shutters with the best thermal-expansion match to silicon nitride - the shutter blade thin film material. A shutter image shown in Figure 6 was taken at room temperature, presenting shutters slightly bowing down as expected. Shutters become flat when the temperature decreases to 35K. The MSAs are then bonded to silicon substrates that are fabricated out of 6" single-silicon wafers in the thickness of 2mm. The bonding is conducted using a novel single-sided indium flip-chip bonding technology. Indium bumps fabricated on a substrate are shown in Figure 7. There are 180,000 indium bumps for bonding a flight format MSA array to its substrate. Besides a MSA, each substrate houses five customer-designed ASIC (Application Specific Integrated Circuit) multiplexer/address chips for 2-dimensional addressing, twenty capacitors, two temperature sensors, numbers of resistors and all necessary interconnects, as shown in Figure 8. Complete MSA quadrant assemblies have been successfully manufactured and fully functionally tested. The assemblies have passed a series of critical reviews required by JWST in satisfying all the design specifications. The qualification tests cover programmable 2-D addressing, life tests, optical contrast tests, and environmental tests including radiation, vibration, and acoustic tests. A 2-D addressing pattern with 'ESA' letters programmed in a MSA is shown in Figure 9. The MSAs passed 1 million cycle life tests and achieved high optical contrast over 10,000. MSA teams are now making progress in final fabrication, testing and assembly (Figure 10). The delivery of flight-format MSA system is scheduled at the end of 2008 for being integrated to the focal plane of the NIRSpec detectors

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

Feasibility of a mental practice intervention in stroke patients in nursing homes; a process evaluation

<p>Abstract</p> <p>Background</p> <p>Within a multi-centre randomised controlled trial in three nursing homes, a process evaluation of a mental practice intervention was conducted. The main aims were to determine if the intervention was performed according to the framework and to describe the therapists' and participants' experiences with and opinions on the intervention.</p> <p>Methods</p> <p>The six week mental practice intervention was given by physiotherapists and occupational therapists in the rehabilitation teams and consisted of four phases: explanation of imagery, teaching patients how to use imagery, using imagery as part of therapy, and facilitating the patient in using it alone and for new tasks. It had a mandatory and an optional part. Data were collected by means of registration forms, pre structured patient files, patient logs and self-administered questionnaires.</p> <p>Results</p> <p>A total of 14 therapists and 18 patients with stroke in the sub acute phase of recovery were involved. Response rates differed per assessment (range 57-93%). Two patients dropped out of the study (total n = 16). The mandatory part of the intervention was given to 11 of 16 patients: 13 received the prescribed amount of mental practice and 12 practiced unguided outside of therapy. The facilitating techniques of the optional part of the framework were partly used. Therapists were moderately positive about the use of imagery in this specific sample. Although it was more difficult for some patients to generate images than others, all patients were positive about the intervention and reported perceived short term benefits from mental practice.</p> <p>Conclusions</p> <p>The intervention was less feasible than we hoped. Implementing a complex therapy delivered by existing multi-professional teams to a vulnerable population with a complex pathology poses many challenges.</p