Abstract: Methods, systems, and computer storage media for providing generative artificial intelligence (AI) output validation using a generative AI output validation engine in an artificial intelligence system. The generative AI output validation engine assesses and determines the quality (e.g., quantified as an output validation score) of generative AI output (e.g., LLM output). In operation, a generative AI output comprising summary data is accessed. Raw data from which summary data is generated is accessed. A plurality of output validation operations associated with a generative AI output validation engine are executed. The generative AI output validation engine comprises multi-categorical analytical models that provide corresponding output validation operations for quantifying quality of generative AI outputs. Using the generative AI output validation engine, generating an output validation score for the summary data. Communicating the output validation score.

Abstract: An exemplary discovery platform includes machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a patient subgroup of interest, comprises inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, clustering the plurality of embeddings to generate one or more clusters of embeddings, identifying one or more patient subgroups corresponding to the one or more clusters of embeddings, and associating each patient subgroup of the one or more patient subgroups with a covariant to identify the patient subgroup of interest.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.

Abstract: A method for calibrating close interval survey (CIS) data with remotely monitored data includes obtaining a plurality of CIS measurements from a plurality of CIS locations along a pipeline, obtaining a first plurality of remotely monitored measurements from a plurality of test stations positioned along the pipeline for a first time period, assigning, for each of the plurality of test stations, one of the plurality of CIS locations as an adjacent CIS location based on location data for the plurality of CIS locations and the plurality of test stations, and, calibrating the CIS data with the remotely monitored data by using the first plurality of remotely monitored measurements for each of the plurality of test stations as a proxy measurement for the adjacent CIS location.

Abstract: A method for calibrating close interval survey (CIS) data with remotely monitored data includes obtaining a plurality of CIS measurements from a plurality of CIS locations along a pipeline, obtaining a first plurality of remotely monitored measurements from a plurality of test stations positioned along the pipeline for a first time period, assigning, for each of the plurality of test stations, one of the plurality of CIS locations as an adjacent CIS location based on location data for the plurality of CIS locations and the plurality of test stations, and, calibrating the CIS data with the remotely monitored data by using the first plurality of remotely monitored measurements for each of the plurality of test stations as a proxy measurement for the adjacent CIS location.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a patient subgroup of interest, comprising: inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space; clustering the plurality of embeddings to generate one or more clusters of embeddings; identifying one or more patient subgroups corresponding to the one or more clusters of embeddings; and associating each patient subgroup of the one or more patient subgroups with a covariant to identify the patient subgroup of interest.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a covariant of interest with respect to a phenotype comprises: receiving covariant information of a covariate class and corresponding phenotypic image data related to the phenotype obtained from a group of clinical subjects; inputting the phenotypic image data into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, each embedding corresponding to a phenotypic state reflected in the phenotypic image data; and determining, based on the covariant information for the group of clinical subjects, the plurality of embeddings, and one or more linear regression models, an association between each candidate covariant of a plurality of candidate covariants and the phenotype state to identify the covariant of interest.

Abstract: The success of anti-tumor immune responses requires effector T cells to infiltrate solid tumors, a process guided by chemokines. Herein, we demonstrate that in vivo post-translational processing of chemokines by dipeptidylpeptidase 4 (DPP4, also known as CD26) limits lymphocyte migration to sites of inflammation and tumors. Inhibition of DPP4 enzymatic activity enhanced tumor rejection by preserving biologically active CXCL10, and increasing trafficking into the tumor by lymphocytes expressing the counter-receptor CXCR3. Furthermore, DPP4 inhibition improved adjuvant-based immunotherapy, adoptive T cell transfer and checkpoint blockade. These findings provide the first direct in vivo evidence for controlling lymphocyte trafficking through CXCL10 cleavage and support the use of DPP4 inhibitors for stabilizing the biologically active form of chemokines as a strategy to enhance tumor immunotherapy.

Abstract: A cryogenic storage system includes a transfer module configured to service one or more cryogenic storage freezers. The transfer module includes a working chamber that maintains a cryogenic environment for the transfer of sample tubes between different sample boxes. One or more freezer ports enable the transfer module to receive a sample box extracted from a respective freezer. An input/output (I/O) port enables external access to samples. A box transport robot operates to transport sample boxes between the freezer ports, the working chamber, and the I/O port. A picker robot operates to transfer sample tubes between sample boxes within the working chamber.

Abstract: The success of anti-tumor immune responses requires effector T cells to infiltrate solid tumors, a process guided by chemokines. Herein, we demonstrate that in vivo post-translational processing of chemokines by dipeptidylpeptidase 4 (DPP4, also known as CD26) limits lymphocyte migration to sites of inflammation and tumors. Inhibition of DPP4 enzymatic activity enhanced tumor rejection by preserving biologically active CXCL10, and increasing trafficking into the tumor by lymphocytes expressing the counter-receptor CXCR3. Furthermore, DPP4 inhibition improved adjuvant-based immunotherapy, adoptive T cell transfer and checkpoint blockade. These findings provide the first direct in vivo evidence for controlling lymphocyte trafficking through CXCL10 cleavage and support the use of DPP4 inhibitors for stabilizing the biologically active form of chemokines as a strategy to enhance tumor immunotherapy.

Abstract: A cryogenic storage system includes a transfer module configured to service one or more cryogenic storage freezers. The transfer module includes a working chamber that maintains a cryogenic environment for the transfer of sample tubes between different sample boxes. One or more freezer ports enable the transfer module to receive a sample box extracted from a respective freezer. An input/output (I/O) port enables external access to samples. A box transport robot operates to transport sample boxes between the freezer ports, the working chamber, and the I/O port. A picker robot operates to transfer sample tubes between sample boxes within the working chamber.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a covariant of interest with respect to a phenotype comprises: receiving covariant information of a covariate class and corresponding phenotypic image data related to the phenotype obtained from a group of clinical subjects; inputting the phenotypic image data into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, each embedding corresponding to a phenotypic state reflected in the phenotypic image data; and determining, based on the covariant information for the group of clinical subjects, the plurality of embeddings, and one or more linear regression models, an association between each candidate covariant of a plurality of candidate covariants and the phenotype state to identify the covariant of interest.

Abstract: A computer-implemented method of training a classification model includes the steps of obtaining, by at least one computer, a plurality of historical resistance trends from a plurality of installed rectifier sites and rectifier site metadata for each installed rectifier site of the plurality of installed rectifier sites; labelling each historical resistance trend of the plurality of historical resistance trends as one of a plurality of historic resistance trend classifications; and, inputting into a machine learning algorithm the historical resistance trends and the rectifier site metadata of the plurality of installed rectifier sites to train the classification model to output a predicted resistance trend classification in response to rectifier site metadata input into the model.

Abstract: Embodiments described herein relate to a system for positioning indicator lights of a network device. The system may include a circuit board, which may include a circuit board edge positioned behind a front panel of the network device, two surfaces; and a side of the circuit board edge positioned between the two surfaces. The system may also include a hole through the circuit board near the circuit board edge, a cutout extending from a portion of the hole to the side of the circuit board edge, a LED coupled to the surface of the circuit board and adapted to emit light into the hole, and a lightpipe disposed in the hole to receive light emitted from the LED into a first end of the lightpipe. The lightpipe may direct the light from a second end of the lightpipe toward an opening in the front panel of the network device.

Abstract: Embodiments described herein relate to a system for positioning indicator lights of a network device. The system may include a circuit board, which may include a circuit board edge positioned behind a front panel of the network device, two surfaces; and a side of the circuit board edge positioned between the two surfaces. The system may also include a hole through the circuit board near the circuit board edge, a cutout extending from a portion of the hole to the side of the circuit board edge, a LED coupled to the surface of the circuit board and adapted to emit light into the hole, and a lightpipe disposed in the hole to receive light emitted from the LED into a first end of the lightpipe. The lightpipe may direct the light from a second end of the lightpipe toward an opening in the front panel of the network device.

Abstract: A sealing apparatus includes a moveable seal holder with a cavity and includes a seal assembly disposed at least partially in the cavity. The seal assembly includes a resilient seal member including a fluid-facing end with a fluid-facing end surface, and the seal assembly includes an actuator plate configured to reciprocate between a first and a second position relative to the protective plate. The first actuator plate includes a fluid-facing end surface and a first camming surface. The seal assembly further includes a first protective plate disposed between the seal member and the first actuator plate. The actuator plate is configured to compress the seal member and to move the fluid-facing end surface of the seal member outward from a resting position when the actuator plate is in the second position.

Abstract: The success of anti-tumor immune responses requires effector T cells to infiltrate solid tumors, a process guided by chemokines. Herein, we demonstrate that in vivo post-translational processing of chemokines by dipeptidylpeptidase 4 (DPP4, also known as CD26) limits lymphocyte migration to sites of inflammation and tumors. Inhibition of DPP4 enzymatic activity enhanced tumor rejection by preserving biologically active CXCL10, and increasing trafficking into the tumor by lymphocytes expressing the counter-receptor CXCR3. Furthermore, DPP4 inhibition improved adjuvant-based immunotherapy, adoptive T cell transfer and checkpoint blockade. These findings provide the first direct in vivo evidence for controlling lymphocyte trafficking through CXCL10 cleavage and support the use of DPP4 inhibitors for stabilizing the biologically active form of chemokines as a strategy to enhance tumor immunotherapy.

Abstract: The success of anti-tumor immune responses requires effector T cells to infiltrate solid tumors, a process guided by chemokines. Herein, we demonstrate that in vivo post-translational processing of chemokines by dipeptidylpeptidase 4 (DPP4, also known as CD26) limits lymphocyte migration to sites of inflammation and tumors. Inhibition of DPP4 enzymatic activity enhanced tumor rejection by preserving biologically active CXCL10, and increasing trafficking into the tumor by lymphocytes expressing the counter-receptor CXCR3. Furthermore, DPP4 inhibition improved adjuvant-based immunotherapy, adoptive T cell transfer and checkpoint blockade. These findings provide the first direct in vivo evidence for controlling lymphocyte trafficking through CXCL10 cleavage and support the use of DPP4 inhibitors for stabilizing the biologically active form of chemokines as a strategy to enhance tumor immunotherapy.

Abstract: Methods, compositions, processes, and uses related to recombinant RSV soluble F protein polypeptides are provided.