TCR ENGINEERING WITH DEEP REINFORCEMENT LEARNING FOR INCREASING EFFICACY AND SAFETY OF TCR-T IMMUNOTHERAPY

    公开(公告)号:US20230304189A1

    公开(公告)日:2023-09-28

    申请号:US18174799

    申请日:2023-02-27

    CPC classification number: C40B30/04 G06N3/092 G06N3/0442 C40B20/04 G16B40/00

    Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs for immunotherapy includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from patients, predicting interaction scores between the extracted peptides and the TCRs from the patients, developing a deep reinforcement learning framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions, outputting mutated TCRs, ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells, and for each top-ranked TCR candidate, repeatedly identifying a set of self-peptides that the top-ranked TCR candidate binds to and further optimizing it greedily by maximizing a sum of its interaction scores with a given set of peptide antigens while minimizing a sum of its interaction scores with the set of self-peptides until stopping criteria of efficacy and safety are met.

    HISTOGRAM MODEL FOR CATEGORICAL ANOMALY DETECTION

    公开(公告)号:US20230280739A1

    公开(公告)日:2023-09-07

    申请号:US18173452

    申请日:2023-02-23

    CPC classification number: G05B23/0283

    Abstract: Methods and systems for anomaly detection include training an anomaly detection histogram model using historical categorical value data. Training the anomaly detection histogram model includes generating a histogram template based on historical categorical data, converting the historical categorical data to a histogram using the histogram template, and determining a normal range and anomaly threshold for the categorical data using the histogram.

    Active microphone for increased DAS acoustic sensing capability

    公开(公告)号:US11736867B2

    公开(公告)日:2023-08-22

    申请号:US17579516

    申请日:2022-01-19

    Abstract: Aspects of the present disclosure describe DFOS/DAS systems, methods, and structures that employ active microphones to enhance DAS operational capabilities by using an active circuit to amplify acoustic signals including voice(s). The circuit includes a microphone to collect acoustic signal(s) resulting from voice signals in the environment, and a speaker or a vibration device driven by an amplifier. The circuit can be clipped onto the fiber, with direct contact through the speaker or vibration device. A microcontroller may advantageously be employed to control the circuit for reduced power consumption, by detecting activities locally and only enabling the speaker when needed. The microcontroller may also send other information such as battery status to the DFOS interrogator through vibration codes.

    T-CELL RECEPTOR OPTIMIZATION WITH REINFORCEMENT LEARNING AND MUTATION POLICIES FOR PRECISION IMMUNOTHERAPY

    公开(公告)号:US20230253068A1

    公开(公告)日:2023-08-10

    申请号:US18151686

    申请日:2023-01-09

    CPC classification number: G16B15/30 G06N20/00 G16B20/50 G16B40/20

    Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.

    EFFICIENT TRANSFORMER FOR CONTENT-AWARE ANOMALY DETECTION IN EVENT SEQUENCES

    公开(公告)号:US20230252139A1

    公开(公告)日:2023-08-10

    申请号:US18157180

    申请日:2023-01-20

    CPC classification number: G06F21/554

    Abstract: A method for implementing a self-attentive encoder-decoder transformer framework for anomaly detection in event sequences is presented. The method includes feeding event content information into a content-awareness layer to generate event representations, inputting, into an encoder, event sequences of two hierarchies to capture long-term and short-term patterns and to generate feature maps, adding, in the decoder, a special sequence token at a beginning of an input sequence under detection, during a training stage, applying a one-class objective to bound the decoded special sequence token with a reconstruction loss for sequence forecasting using the generated feature maps from the encoder, and during a testing stage, labeling any event representation whose decoded special sequence token lies outside a hypersphere as an anomaly.

    Polarization diversity combining method in coherent DAS maintaining phase continuity

    公开(公告)号:US11692867B2

    公开(公告)日:2023-07-04

    申请号:US17506471

    申请日:2021-10-20

    CPC classification number: G01H9/004 G02B6/4212

    Abstract: A distributed optical fiber sensing (DOFS)/distributed acoustic sensing (DAS) method employing polarization diversity combining and spatial diversity combining for a DOFS/DAS system wherein the polarization diversity combining determines a temporal average product for each beating product, determines one having a max average power, rotates that one having max average power for its phase shift to produce a reference, determines a phase difference for each beating product as compared to the reference, compensates any phase difference such that all beating products exhibit a well-aligned phase; and combining the beating products; and wherein the spatial diversity combining uses the combined beating products for each location, determines a temporal average power, determines a location having a greatest average power; and combines the results and provides an indicia of the combined result(s).

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