Abstract: This disclosure provides, in one aspect, dosing strategies for soluble ?-glucan immunotherapy to optimize acute immunopharmacodynamic responses for the immunotherapy and/or subject. It also provides a method for analyzing a sample from a subject for a biomarker to identify the appropriate dosing strategy for soluble ?-glucan immunotherapy. Generally, the method includes obtaining a biological sample from a subject, analyzing the sample for a biomarker anti-?-glucan antibody level or immunopharmacodynamic response level, classifying the subject into a subgroup based on the biomarker anti-?-glucan antibody level or immunopharmacodynamic response level and identifying the appropriate dosing strategy based on the subgroup classification.

Abstract: This disclosure provides, in one aspect, dosing strategies for soluble ?-glucan immunotherapy to optimize acute immunopharmacodynamic responses for the immunotherapy and/or subject. It also provides a method for analyzing a sample from a subject for a biomarker to identify the appropriate dosing strategy for soluble ?-glucan immunotherapy. Generally, the method includes obtaining a biological sample from a subject, analyzing the sample for a biomarker anti-?-glucan antibody level or immunopharmacodynamic response level, classifying the subject into a subgroup based on the biomarker anti-?-glucan antibody level or immunopharmacodynamic response level and identifying the appropriate dosing strategy based on the subgroup classification.

Abstract: This disclosure provides, in one aspect, dosing strategies for soluble ?-glucan immunotherapy to optimize acute immunopharmacodynamic responses for the immunotherapy and/or subject. It also provides a method for analyzing a sample from a subject for a biomarker to identify the appropriate dosing strategy for soluble ?-glucan immunotherapy. Generally, the method includes obtaining a biological sample from a subject, analyzing the sample for a biomarker anti-?-glucan antibody level or immunopharmacodynamic response level, classifying the subject into a subgroup based on the biomarker anti-?-glucan antibody level or immunopharmacodynamic response level and identifying the appropriate dosing strategy based on the subgroup classification.

Abstract: An object database system is provided. The system may include a plurality of objects. Each object may include a historical attribute-change information system. At least one parameter of the historical attribute-change information system may be determined relative to a transaction-history-identification level. The transaction-history-identification level may be based at least in part on a plurality of database transaction that occurred within the object database system. The transaction-history-identification level may correspond to a date/time value. Each object in the plurality of objects may be stored in a universal file format. The object database system may also include a cache memory and a disk memory. The object database system may be configured to utilize the cache memory in a targeted method. The targeted method may include explicit memory management of the disk memory.

Abstract: A computing system comprises one or more cores. Each core comprises a processor. In some implementations, each processor is coupled to a communication network among the cores. In some implementations, a switch in each core includes switching circuitry to forward data received over data paths from other cores to the processor and to switches of other cores, and to forward data received from the processor to switches of other cores.

Abstract: An active vehicle roll control system is disclosed in which a roll bar (22) has two halves (22a, 22b) which can be rotated relative to each other by a control unit (26) in order to actively control vehicle roll. The control unit (26) receives signals from lateral accelerometers (29, 30). In order to determine accurately when the vehicle is experiencing zero lateral acceleration, the control unit (26) averages the signal from each accelerometer (29, 30) continuously and uses the average signal as an indication of the straight ahead position. The averaging algorithm defines a window of low lateral accelerations to be used, and a gain to be applied to the acceleration signals, each of which is speed dependent so that the averaging process is not affected by abnormal vehicle movements.