Johns Hopkins University

Digital Harmonic LLC

The University Of Texas System

Washington University

Sandor Szalay

Edward Givelberg

Eric K Pritchard

Paul Reed Smith

Frederick M Slay

Shane G W Morris

William G Nelson

Jonathan B Coulter

Janis Marie Taube

R Jacob Vogelstein

Gautam Das

Hao Hua Huang

Nan Zhang

Tamas Budavari

Nicholas Samuel Carey

An optical device includes a low-noise illumination source, a support device, an ultra low-noise detector, an analog-to-digital converter, and a controller. The low-noise illumination source is configured to generate a single beam of radiation. The support device is configured to support an object and to pass the single beam of radiation through the object. The object directly blocks, absorbs, or deflects portions of the single beam of radiation, thereby directly modulating the single beam of radiation. The low-noise detector is configured to detect the modulated single beam of radiation and to output an analog signal representative of vibrational spectra of the object. The modulated single beam of radiation is non-interferometric. The analog-to-digital converter is configured to convert the detected analog signal into a digital signal. The controller is configured to analyze and generate vibrational spectra of the object from the digital signal represented as a range of events over time.

System and method for measuring low-noise vibrational spectra

A device may obtain field images of a tissue sample, apply, to the field images, spatial distortion and illumination-based corrections (including corrections for photobleaching of reagents) to derive processed field images, identify, in each processed field image, a primary area including data useful for cell or subcellular component characterization, identify, in the processed field images, areas that overlap with one another, and derive information regarding a spatial mapping of cell(s) and/or sub-cellular components of the tissue sample. Deriving the information may include performing segmentation based on the data included in the primary area of each processed field image, and obtaining flux measurements based on other data included in the overlapping areas. The device may cause the information to be loaded in a data structure to enable statistical analysis of the spatial mapping for identifying factors defining normal tissue structure, associated inflammatory or neoplastic diseases and prognoses thereof, and associated therapeutics.

Characterization platform for scalable, spatially-resolved multispectral analysis of tissue

Disclosed are systems and methods for measuring vibrational spectra of a living cells and tissue that includes a low noise consistent optical source creating a photon beam, a support device, a photon-to-electron converter/detector outputting a streamed analog electrical signal, an analog-to-digital converter, and a digital signal processor with specialized software for measuring and characterizing the signal contained in the photon beam and its subsequent detector's streamed analog converted to digital signal. Motion of the living sample causes modulation to the photon beam as it passes through the living samples by how much of the photon beam is blocked, absorbed or deflected. In addition, specific sub-cellular vibrational features can be segregated utilizing fluorescent markers.

System and method for measuring vibrational spectra in living cells and tissue over time

Systems, computer readable media, and method concern includes generating data renderings for a data set. The data renderings for the data set include one or more of visual renderings of portions of the data set and one or more sonic renderings of portions of the data set. The method further includes providing the data renderings to a user via one or more output devices. The method also includes capturing biofeedback data from the user using one or more human interface devices. The biofeedback data includes biological responses to the one or more data renderings. Further, the method includes continuously generating and providing new data renderings based on the biofeedback data. The new data renderings incorporate features in the data renderings identified from the biofeedback data. The method also includes determining one or more features of interest in the data set based on the biofeedback data and the new data renderings.

Systems and methods for human-machine subconscious data exploration

A system and method for operating a data-intensive computer is provided. The data-intensive computer includes a processing sub-system formed by a plurality of processing node servers and a database sub-system formed by a plurality of database servers configured to form a collective database in excess of a petabyte of storage. The data-intensive computer also includes an operating system sub-system formed by a plurality of operating system servers that extend a unifying operating system environment across the processing sub-system, the database sub-system, and the operating system sub-system to act as components in a single data-intensive computer. The operating system sub-system is configured to coordinate execution of a single application as distributed processes having at least one of the distributed processes executed on the processing sub-system and at least one of the distributed processes executed on the database sub-system.

System and method for program and resource allocation within a data-intensive computer

A data-intensive computer includes a processing sub-system formed by a plurality of processing servers, a database sub-system formed by a plurality of database servers configured to form a collective database, and a unifying operating system environment. The unifying operating system environment extends across the processing sub-system and the database sub-system to coordinate operation of the plurality of processing servers and the plurality of database servers to act as components in a single data-intensive computer and presents the database sub-system to an application running in the data-intensive computer as a layer in a memory hierarchy of the data-intensive computer.

Data-intensive computer architecture

As file systems reach the petabytes scale, users and administrators are increasingly interested in acquiring high-level analytical information for file management and analysis. Two particularly important tasks are the processing of aggregate and top-k queries which, unfortunately, cannot be quickly answered by hierarchical file systems such as ext3 and NTFS. Existing pre-processing based solutions, e.g., file system crawling and index building, consume a significant amount of time and space (for generating and maintaining the indexes) which in many cases cannot be justified by the infrequent usage of such solutions. User interests can often be sufficiently satisfied by approximate (i.e., statistically accurate) answers. A just-in-time sampling-based system can, after consuming a small number of disk accesses, produce extremely accurate answers for a broad class of aggregate and top-k queries over a file system without the requirement of any prior knowledge. The system is efficient, accurate and scalable.

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Baltimore, MD, United States of America

Sandor Szalay