Abstract: Provided herein, in one aspect, is a composition for in vitro transcription and translation, comprising: a treated cell lysate derived from one or more host cells such as bacteria, archaea, plant or animal; a plurality of supplements for gene transcription and translation; an energy recycling system for providing and recycling adenosine triphosphate (ATP); and one or more exogenous additives. Methods for making and using the same are also provided.

Abstract: A medical device comprising a shaft, a handle including a proximal portion and a distal portion, wherein the distal portion is fixed to a proximal portion of the shaft, and a channel extending from the handle into the shaft, wherein the channel includes a proximal channel and a distal channel, the proximal channel being rotatable relative to the distal channel and aligned with the distal channel throughout rotation of the proximal channel, wherein the proximal portion includes the proximal channel and the distal portion includes the distal channel, wherein the distal portion is rotatable relative to the proximal portion of the handle, and the shaft is configured to rotate with the distal portion of the handle.

Abstract: Provided herein, in one aspect, is a composition for in vitro transcription and translation, comprising: a treated cell lysate derived from one or more organisms such as bacteria, archaea, plant or animal; a plurality of supplements for gene expression; an energy recycling system for providing adenosine triphosphate and recycling adenosine diphosphate; and an engineered propeptide operably linked to a stabilizing domain. Methods for making and using the same are also provided.

Abstract: A medical device comprising a shaft, a handle including a proximal portion and a distal portion, wherein the distal portion is fixed to a proximal portion of the shaft, and a channel extending from the handle into the shaft, wherein the channel includes a proximal channel and a distal channel, the proximal channel being rotatable relative to the distal channel and aligned with the distal channel throughout rotation of the proximal channel, wherein the proximal portion includes the proximal channel and the distal portion includes the distal channel, wherein the distal portion is rotatable relative to the proximal portion of the handle, and the shaft is configured to rotate with the distal portion of the handle.

Abstract: An apparatus for independent readiness determination for AED deployment includes a controller configured to detect an AED enclosure event corresponding to one or more AEDs registered to be disposed within one or more corresponding AED enclosures, where the controller is configured to communicate data for classifying an AED enclosure event into a predetermined event category, and further includes a communication interface configured to communicate a response action in response to data of the AED enclosure event satisfying one or more predetermined criteria for the predetermined event category. A method and a computer program product also perform the functions of the apparatus.

Abstract: A medical device comprising a shaft, a handle including a proximal portion and a distal portion, wherein the distal portion is fixed to a proximal portion of the shaft, and a channel extending from the handle into the shaft, wherein the channel includes a proximal channel and a distal channel, the proximal channel being rotatable relative to the distal channel and aligned with the distal channel throughout rotation of the proximal channel, wherein the proximal portion includes the proximal channel and the distal portion includes the distal channel, wherein the distal portion is rotatable relative to the proximal portion of the handle, and the shaft is configured to rotate with the distal portion of the handle.

Abstract: An apparatus for independent readiness determination for AED deployment includes a controller configured to detect an AED enclosure event corresponding to one or more AEDs registered to be disposed within one or more corresponding AED enclosures, where the controller is configured to communicate data for classifying an AED enclosure event into a predetermined event category, and further includes a communication interface configured to communicate a response action in response to data of the AED enclosure event satisfying one or more predetermined criteria for the predetermined event category. A method and a computer program product also perform the functions of the apparatus.

Abstract: An apparatus for independent readiness determination for AED deployment includes a controller configured to detect an AED enclosure event corresponding to an AED registered to be disposed within an AED enclosure, where the controller is configured to communicate data for classifying an AED enclosure event into a predetermined event category, and further includes a communication interface configured to communicate a response action in response to data of the AED enclosure event satisfying a predetermined criteria for the predetermined event category. A method and a computer program product also perform the functions of the apparatus.

Abstract: Provided herein, in one aspect, is a composition for in vitro transcription and translation, comprising: a treated cell lysate derived from one or more host cells such as bacteria, archaea, plant or animal; a plurality of supplements for gene transcription and translation; an energy recycling system for providing and recycling adenosine triphosphate (ATP); and one or more exogenous additives. Methods for making and using the same are also provided.

Abstract: Provided herein, in one aspect, is a composition for in vitro transcription and translation, comprising: a treated cell lysate derived from one or more organisms such as bacteria, archaea, plant or animal; a plurality of supplements for gene expression; an energy recycling system for providing adenosine triphosphate and recycling adenosine diphosphate; and an engineered propeptide operably linked to a stabilizing domain. Methods for making and using the same are also provided.

Abstract: An LED illumination device comprising a lens assembly, a circuit board having at least one LED and a housing to retain said circuit board and said lens assembly, and wherein said circuit board comprises a plurality of pores adjacent said at least one LED to facilitate airflow over a large surface area of said circuit board in the vicinity of said at least one LED.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. Historical multi-dimensional data is received representing multiple variables to be used as an input to a predictive model of a commercial system variables are pruned for which the data is sparse or missing, and the population of variables is adjusted to represent main effects exhibited by the data and interaction and non-linear effects exhibited by the data.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. In connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is enabled to validate the model development process with cross-validation between at least two subsets of the historical data; the validated model development process is enabled to be reapplied.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. In connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is enabled to validate the model development process with cross-validation between at least two subsets of the historical data; the validated model development process is enabled to be reapplied.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. Historical multi-dimensional data is received representing multiple variables to be used as an input to a predictive model of a commercial system variables are pruned for which the data is sparse or missing, and the population of variables is adjusted to represent main effects exhibited by the data and interaction and non-linear effects exhibited by the data.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. Historical multi-dimensional data is received representing multiple source variables to be used as an input to a predictive model of a commercial system and applying transformations to the data that are selected based on the strength of measurement represented by a variable; variables are transformed into new more predictive variables, including the Bayesian renormalization of sparsely sampled variable and including the imputation of missing values for categorical or continuous variables.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. In connection with a process in which a user generates a collection of predictive models or an aggregate predictive model based on historical data about a system being modeled, profiles of aggregate targets are generated based on key contributory variables.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. A user generates a predictive model based on historical data about a system being modeled. Structured project information is automatically stored that captures a state of the project at successive steps in generating the model.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. A graphical user interface enables a user of a model generation tool to view and manage subgroups of variables associated with generation of a predictive model including original source variables and derived variables; variable response functions are displayed; variables are edited and transformed into new more predictive variables.

Abstract: Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. In connection with a project in which a user generates a predictive model based on historical data about a system being modeled, and in which the project includes a series of user choice points and actions or parameter settings that govern the generation of the model based on rules, which direct the user to select and apply an optimal model.