Abstract: Predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is described. A training data set is constructed from a series of libraries, including at least one library comprising genomic, proteomic, and research data specific to non-humans. A predictive machine learning model is developed based on the constructed training data set and utilized to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. A subset of sequences is selected for evaluation from the plurality of sequences based on the predicted quality or performance and candidate samples are designed derived from the non-human donor using the selected subset of sequences.

Abstract: A method for predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is provided. The method includes constructing a training data set from a series of libraries, wherein at least one library in the series of libraries comprises genomic, proteomic, and research data specific to non-humans. The method includes developing a predictive machine learning model based on the constructed training data set. The method includes utilizing the predictive machine learning model to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. The method includes selecting a subset of sequences for evaluation from the plurality of sequences based on the predicted quality or performance.

Abstract: A method for predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is provided. The method includes constructing a training data set from a series of libraries, wherein at least one library in the series of libraries comprises genomic, proteomic, and research data specific to non-humans. The method includes developing a predictive machine learning model based on the constructed training data set. The method includes utilizing the predictive machine learning model to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. The method includes selecting a subset of sequences for evaluation from the plurality of sequences based on the predicted quality or performance.

Abstract: A block chain data lake system comprises a data lake of a plurality of data files and software applications interfacing the data files. The system further comprises a block chain and block chain controller therefor. A transaction controller monitors transactions performed on the data files by functions of the software applications to add hashes to blocks of the block chain using the data files. A verification controller may verify data files by searching the block chain for hashes matching the data files. An authentication controller may issue cryptographic keys for software application, including specific functions thereof. A data migration system may migrate data from a legacy data system to the block chain data lake system. A synchronisation controller may synchronise data updated by software applications of the legacy data system in substantial real-time.

Abstract: A method for predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is provided. The method includes constructing a training data set from a series of libraries, wherein at least one library in the series of libraries comprises genomic, proteomic, and research data specific to non-humans. The method includes developing a predictive machine learning model based on the constructed training data set. The method includes utilizing the predictive machine learning model to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. The method includes selecting a subset of sequences for evaluation from the plurality of sequences based on the predicted quality or performance.

Abstract: A block chain-based system for multi-party, multistage process verification has a server in operable communication with a plurality of electronic client terminals. The server has a database which may have user data and implement a process verification controller. The system further has a block chain ledger. Fora multistage process involving multiple users, for each stage, the system is configured for receiving a function ID selected from a set of available functions via user interfaces of respective client terminals, generating a function hash comprising the function ID and associated function data and creating a function block chain transaction comprising the function hash which is added to the block chain ledger.

Abstract: This disclosure is directed to an apparatus, system and method for retrieving a target material from a sample. A fraction-density-altering solution may be added to a vessel that contains the sample to change the density of a first fraction of the sample without changing the density of the target material or the density of any other sample fraction. A collector may be inserted into the vessel to funnel the target material from the sample into the collector or into a processing receptacle included in the collector. In one implementation, the collector may include a cannula which extends from a funnel into a chamber to hold the processing receptacle. In another implementation, the processing receptacle may be inserted into a bore within the collector.

Abstract: This disclosure is directed to an apparatus, system and method for retrieving a target material from a sample. A fraction-density-altering solution may be added to a vessel that contains the sample to change the density of a first fraction of the sample without changing the density of the target material or the density of any other sample fraction. A collector may be inserted into the vessel to funnel the target material from the sample into the collector or into a processing receptacle included in the collector. In one implementation, the collector may include a cannula which extends into a chamber to hold the processing receptacle at a first end of the collector and a funnel at a second end that that is in fluid communication with the cannula. In another implementation, the processing receptacle may be inserted into a bore within the collector.