Abstract: The disclosure provides methods and systems for predicting an effect of a pharmaceutical agent in a test subject of a first species. Information about the test subject is input into a multi-task model comprising a plurality of parameters. The model applies the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs including a predicted effect of the pharmaceutical agent in the test subject and, for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification. The information about the test subject includes a plurality of abundance values including, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.

Abstract: A method by one or more network devices executing one or more smart contracts stored in a blockchain for anonymizing faces appearing in digital media content. The method includes obtaining, for each of a plurality of users, a facial model associated with that user, obtaining digital media content digital media content, determining whether that detected face matches the face of any of the plurality of users based on applying one or more of the facial models associated with the plurality of users to that detected face, anonymizing that detected face to generate an anonymized face in response to a determination that that detected face matches the face of one of the plurality of users, and providing the anonymized face to the media platform.

Abstract: A method for transferring a dataset-specific nature of a first dataset with sequencing results for a first plurality of specimen to a second dataset with sequencing results for a second plurality of specimen includes receiving a first set of adaptation factors of the first dataset that include two or more eigenvectors, where the sequencing cannot be reconstructed from the first set of adaptation factors without access to the first dataset. The method also includes generating a second set of adaptation factors of the second dataset that include two or more eigenvectors of the second dataset. The method also includes generating an adapted second dataset by adapting the dataset-specific nature of the second dataset to the dataset-specific nature of the second dataset based at least in part on the first and second sets of adaptation factors, and providing the adapted second dataset to the first entity.

Abstract: A method for transferring a dataset-specific nature of a first dataset with sequencing results for a first plurality of specimen to a second dataset with sequencing results for a second plurality of specimen includes receiving a first set of adaptation factors of the first dataset that include two or more eigenvectors, where the sequencing cannot be reconstructed from the first set of adaptation factors without access to the first dataset. The method also includes generating a second set of adaptation factors of the second dataset that include two or more eigenvectors of the second dataset. The method also includes generating an adapted second dataset by adapting the dataset-specific nature of the second dataset to the dataset-specific nature of the second dataset based at least in part on the first and second sets of adaptation factors, and providing the adapted second dataset to the first entity.

Abstract: A method for transferring a dataset-specific nature of a first dataset with sequencing results for a first plurality of specimen to a second dataset with sequencing results for a second plurality of specimen includes receiving a first set of adaptation factors of the first dataset that include two or more eigenvectors, where the sequencing cannot be reconstructed from the first set of adaptation factors without access to the first dataset. The method also includes generating a second set of adaptation factors of the second dataset that include two or more eigenvectors of the second dataset. The method also includes generating an adapted second dataset by adapting the dataset-specific nature of the second dataset to the dataset-specific nature of the second dataset based at least in part on the first and second sets of adaptation factors, and providing the adapted second dataset to the first entity.

Abstract: The transoral repair of choanal atresia is a method for the surgical repair of choanal atresia that uses a transoral approach. The procedure includes the use of a curved drill, a curved shaver, curved grasping forceps, and a suction irrigator. After endotracheal intubation, a retractor is inserted to retract the tongue. A 120° angled telescope is inserted behind the soft palate into the nasopharynx to visualize the posterior choanae. A curved drill is used to drill the atretic plate from the vomer medially to the lateral pterygoid plate laterally. The drilled section of the atretic plate is removed to clear the obstruction.

Abstract: The transoral repair of choanal atresia is a method for the surgical repair of choanal atresia that uses a transoral approach. The procedure includes the use of a curved drill, a curved shaver, curved grasping forceps, and a suction irrigator. After endotracheal intubation, a retractor is inserted to retract the tongue. A 120° angled telescope is inserted behind the soft palate into the nasopharynx to visualize the posterior choanae. A curved drill is used to drill the atretic plate from the vomer medially to the lateral pterygoid plate laterally. The drilled section of the atretic plate is removed to clear the obstruction.