A method for treating cancer by treating target cancer cells with cold atmospheric plasma to induce apoptosis of the target cancer cells through the degradation of Histone RNA during S-phase induced by oxidation of RNA.

A method evolves generic computational building blocks. The method initializes a parent population with randomly generated programs or programs evolved by a genetic programming instance that uses randomized targets. The method also obtains a list of randomly generated test inputs. The method generates a target dataset that includes input-output pairs of randomly generated binary strings. The method also applies a fitness function to assign a fitness score to each program, based on the target dataset. The method grows a seed list by applying genetic operators, and selecting offspring that satisfy a novelty condition. The novelty condition is representative of an ability of a program to produce unique output for the list of randomly generated test inputs. The method iterates until a terminating condition has been satisfied. The terminating condition is representative of an ability of programs in the seed list to solve one or more genetic programming instances.

A method evolves generic computational building blocks. The method initializes a parent population with randomly generated programs or programs evolved by a genetic programming instance that uses randomized targets. The method also obtains a list of randomly generated test inputs. The method generates a target dataset that includes input-output pairs of randomly generated binary strings. The method also applies a fitness function to assign a fitness score to each program, based on the target dataset. The method grows a seed list by applying genetic operators, and selecting offspring that satisfy a novelty condition. The novelty condition is representative of an ability of a program to produce unique output for the list of randomly generated test inputs. The method iterates until a terminating condition has been satisfied. The terminating condition is representative of an ability of programs in the seed list to solve one or more genetic programming instances.

The present invention is in the technical field of bioinformatics, and the implementation of bioinformatics. Advances in technology have led to a large increase in the rate at which data, in particular in the medical domain, can be generated (from patient sources, clinical trials, and research campaigns). The researcher is thus confronted with a large amount of information, and it is difficult to discover connections in the data, and thus to improve medical knowledge, even in spite of the amount of data available. The present application proposes to process and to structure medical data using a computer-implemented semantic network, enabling undiscovered connections between experiments and data sources to be made, and to continually add new data to the semantic network. In summary, it is proposed to provide a computer-implemented method and associated system which are able to automatically provide neurological knowledge model data by annotating neural connectivity data with further data sources.

The present invention is in the technical field of bioinformatics, and the implementation of bioinformatics. Advances in technology have led to a large increase in the rate at which data, in particular in the medical domain, can be generated (from patient sources, clinical trials, and research campaigns). The researcher is thus confronted with a large amount of information, and it is difficult to discover connections in the data, and thus to improve medical knowledge, even in spite of the amount of data available. The present application proposes to process and to structure medical data using a computer-implemented semantic network, enabling undiscovered connections between experiments and data sources to be made, and to continually add new data to the semantic network. In summary, it is proposed to provide a computer-implemented method and associated system which are able to automatically provide neurological knowledge model data by annotating neural connectivity data with further data sources.

Localized detection of RNA in a tissue sample that includes cells is accomplished on an array. The array include a number of features on a substrate. Each feature includes a different capture probe immobilized such that the capture probe has a free 3′ end. Each feature occupies a distinct position on the array and has an area of less than about 1 mm.sup.2. Each capture probe is a nucleic acid molecule, which includes a positional domain including a nucleotide sequence unique to a particular feature, and a capture domain including a nucleotide sequence complementary to the RNA to be detected. The capture domain can be at a position 3′ of the positional domain.

Localized detection of RNA in a tissue sample that includes cells is accomplished on an array. The array include a number of features on a substrate. Each feature includes a different capture probe immobilized such that the capture probe has a free 3′ end. Each feature occupies a distinct position on the array and has an area of less than about 1 mm.sup.2. Each capture probe is a nucleic acid molecule, which includes a positional domain including a nucleotide sequence unique to a particular feature, and a capture domain including a nucleotide sequence complementary to the RNA to be detected. The capture domain can be at a position 3′ of the positional domain.

Various techniques for accelerating Smith-Waterman sequence alignments are provided. For example, threads in a group of threads are employed to use an interleaved cell layout to store relevant data in registers while computing sub-alignment data for one or more local alignment problems. In another example, specialized instructions that reduce the number of cycles required to compute each sub-alignment score are utilized. In another example, threads are employed to compute sub-alignment data for a subset of columns of one or more local alignment problems while other threads begin computing sub-alignment data based on partial result data received from the preceding threads. After computing a maximum sub-alignment score, a thread stores the maximum sub-alignment score and the corresponding position in global memory.

Various techniques for accelerating Smith-Waterman sequence alignments are provided. For example, threads in a group of threads are employed to use an interleaved cell layout to store relevant data in registers while computing sub-alignment data for one or more local alignment problems. In another example, specialized instructions that reduce the number of cycles required to compute each sub-alignment score are utilized. In another example, threads are employed to compute sub-alignment data for a subset of columns of one or more local alignment problems while other threads begin computing sub-alignment data based on partial result data received from the preceding threads. After computing a maximum sub-alignment score, a thread stores the maximum sub-alignment score and the corresponding position in global memory.

Various techniques for accelerating Smith-Waterman sequence alignments are provided. For example, threads in a group of threads are employed to use an interleaved cell layout to store relevant data in registers while computing sub-alignment data for one or more local alignment problems. In another example, specialized instructions that reduce the number of cycles required to compute each sub-alignment score are utilized. In another example, threads are employed to compute sub-alignment data for a subset of columns of one or more local alignment problems while other threads begin computing sub-alignment data based on partial result data received from the preceding threads. After computing a maximum sub-alignment score, a thread stores the maximum sub-alignment score and the corresponding position in global memory.