Abstract: A method for the use of a cytochrome P450 enzyme comprising any of SEQ ID NO: 1-118, or mutants thereof or a variant enzyme having at least 70% identity thereto and having CYP450 activity, for the hydroxylation and or dealkylation of an organic compound.

Abstract: The use of a cytochrome P-450 enzyme comprising SEQ ID NO: 110, or a variant enzyme having at least 70% identity thereto and having CYP-450 activity, for the hydroxylation of an organic compound, wherein the amino acid residue at position 291 is not threonine.

Abstract: The present invention relates to a cytochrome P450 enzyme comprising the amino acid sequence set forth in SEQ ID NO: 3, or a variant thereof having an amino acid sequence having at least 95% identity thereto and having CYP450 activity. The cytochrome P450 enzyme provided herein was isolated from Streptomyces eurythermus NRRL 2539 and has a wide substrate range and high activity, and may be used to oxidate organic compounds.

Abstract: The use of a cytochrome P-450 enzyme comprising SEQ ID NO: 3, or a variant enzyme having at least 70% identity thereto and having CYP-450 activity, for the hydroxylation of an organic compound.

Abstract: A method for the use of a cytochrome P450 enzyme comprising any of SEQ ID NO: 1-118, or mutants thereof or a variant enzyme having at least 70% identity thereto and having CYP450 activity, for the hydroxylation and or dealkylation of an organic compound.

Abstract: The use of a cytochrome P-450 enzyme comprising SEQ ID NO: 110, or a variant enzyme having at least 70% identity thereto and having CYP-450 activity, for the hydroxylation of an organic compound, wherein the amino acid residue at position 291 is not threonine.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: A multi-label ranking method includes receiving, at a processor and from a first set of artificial neural networks (ANNs), multiple signals representing a first set of ANN output pairs for a first label. A signal representing a second set of ANN output pairs for a second label different from the first label is received at the processor from a second set of ANNs different from the first set of ANNs, substantially concurrently with the first set of ANN output pairs. A first activation function is solved based on the first set of ANN output pairs, and a second activation function is solved based on the second set of ANN output pairs. Loss values are calculated based on the solved activations, and a mask is generated based on at least one ground truth label. A signal, including a representation of the mask, is sent from the processor to each of the sets of ANNs.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: The use of a cytochrome P-450 enzyme comprising SEQ ID NO: 3, or a variant enzyme having at least 70% identity thereto and having CYP-450 activity, for the hydroxylation of an organic compound.

Abstract: In an example embodiment, predefined gestures are used to provide an intuitive way to transfer objects between multi-touch devices. A first gesture, such as a pinching motion is used to select an object to transfer from a source device. A second gesture, such as a de-pinching gesture, is used to designate a destination for the object on a second device. Data associated with the initiator of the transfer, such as data representative of a fingerprint scan may be employed to match the source device with the destination device.

Abstract: In an example embodiment, predefined gestures are used to provide an intuitive way to transfer objects between multi-touch devices. A first gesture, such as a pinching motion is used to select an object to transfer from a source device. A second gesture, such as a de-pinching gesture, is used to designate a destination for the object on a second device. Data associated with the initiator of the transfer, such as data representative of a fingerprint scan may be employed to match the source device with the destination device.