Abstract: The invention is related to multivalent immunogenic compositions comprising more than one S. pneumoniae polysaccharide protein conjugates, wherein each of the conjugates comprises a polysaccharide from an S. pneumoniae serotype conjugated to a carrier protein, wherein the serotypes of S. pneumoniae are as defined herein. Also provided are methods for inducing a protective immune response in a human patient comprising administering the multivalent immunogenic compositions of the invention to the patient. The multivalent immunogenic compositions are useful for providing protection against S. pneumoniae infection and diseases caused by S. pneumoniae. The compositions of the invention are also useful as part of treatment regimes that provide complementary protection for patients that have been vaccinated with a multivalent vaccine indicated for the prevention of pneumococcal disease.

Abstract: In an embodiment, the disclosed technologies include receiving a set of digital inputs; where the set of digital inputs includes a candidate entity-member entity pair that includes candidate entity data and member entity data; where the member entity data has been extracted from a node of an online service; where an exact match has not been found between the candidate entity data and the member entity data; in response to the set of digital inputs, outputting models of the candidate entity data and the member entity data, respectively; where the models indicate weight values assigned to text in the candidate entity data and weight values assigned to text in the member entity data, respectively; calculating a similarity score using the models; in response to the similarity score matching a threshold, inputting the candidate entity-member entity pair to a classifier to produce a classification; where the classifier uses a machine learning model that has been trained using features derived from previously-analyz

Abstract: In an embodiment, the disclosed technologies include receiving a set of digital inputs; where the set of digital inputs includes a candidate entity-member entity pair that includes candidate entity data and member entity data; where the member entity data has been extracted from a node of an online service; where an exact match has not been found between the candidate entity data and the member entity data; in response to the set of digital inputs, outputting models of the candidate entity data and the member entity data, respectively; where the models indicate weight values assigned to text in the candidate entity data and weight values assigned to text in the member entity data, respectively; calculating a similarity score using the models; in response to the similarity score matching a threshold, inputting the candidate entity-member entity pair to a classifier to produce a classification; where the classifier uses a machine learning model that has been trained using features derived from previously-analyz

Abstract: Utilizing multiple rock cores samples obtained while drilling a well to determine the mechanical properties of the rock constituting the wellbore and formation zones within the wellbore. A geomechanical model is created from the samples by nanoindentation testing to provide the raw data from which the geomechanical model is then created.