Abstract: Artificial expression constructs for modulating gene expression in GABAergic neurons and astrocytes are described. The artificial expression constructs can be used to express SLC6A1 for the treatment of SLC6A1-associated disorders, among other uses.

Abstract: Electrostimulating waveforms offering simultaneous and controllable cell-type-specific entrainment are described. The waveforms selectively entrain excitatory versus inhibitory cortical and hippocampal neurons including pyramidal neurons, parvalbumin neurons, and somatostatin neurons. The embodiment provides targeted electrical stimulation (ES) entrainment of excitatory versus inhibitory neurons. For example, the current disclosure provides methods of selectively entraining excitatory neurons with ES frequencies below 30 Hertz (Hz) and in particular embodiments to frequencies below 15 Hz, such as 8 Hz and 4 Hz. The current disclosure also provides methods of selectively entraining inhibitory neurons to ES frequencies of at least 30 Hz and depending on the type of inhibitory neuron, utilizing a frequency that is 30-60 Hz or a frequency that is greater than 100 Hz.

Abstract: A system, apparatus and methods for generating database entries and tools for accessing and searching a database from an Ontology. Starting with an Ontology used to represent data and relationships between data, the system and methods described enable that data to be stored in a desired type of database and accessed using an API and search query generated from the Ontology. Embodiments provide a structure and process to implement a data access system or framework that can be used to unify and better understand information across an organization's entire set of data. Such a framework can help enable and improve the organization and discovery of knowledge, increase the value of existing data, and reduce complexity when developing next-generation applications.

Abstract: Artificial expression constructs for modulating gene expression in targeted central nervous system cell types are described. The artificial expression constructs can be used to express synthetic genes or modify gene expression in chandelier cells. Chandelier cells are a subtype of GABAergic interneurons that that have been implicated in disorders such as epilepsy and schizophrenia.

Abstract: Artificial expression constructs for modulating gene expression in striatal neurons are described. The artificial expression constructs can be used to express heterologous genes in striatal neurons including in striatal medium spiny neuron-pan, striatal medium spiny neuron-indirect pathway, striatal medium spiny neuron-direct pathway, striatal interneuron-cholinergic, and Drd3+ medium spiny neurons in olfactory tubercle. The artificial expression constructs can be used for many purposes, including to research and treat movement disorders such as Parkinson's disease and Huntington's disease.

Abstract: Artificial expression constructs for selectively modulating gene expression in selected central nervous system cell types are described. The artificial expression constructs can be used to selectively express synthetic genes or modify gene expression in inhibitory neocortical GABAergic neurons including somatostatin GABAergic neurons, parvalbumin GABAergic neurons, vasointestinal peptide GABAergic neurons, Lamp5 GABAergic neurons, and in some instances astrocytes.

Abstract: Artificial expression constructs for selectively modulating gene expression in selected central nervous system cell types are described. Particularly, the artificial expression constructs can be used to selectively express synthetic genes and/or modify gene expression in neocortical glutamatergic layer 5 neurons, such as glutamatergic layer 5 extratelencephalic-projecting (L5 ET) neurons.

Abstract: Artificial expression constructs for selectively modulating gene expression in selected central nervous system cell types are described. The artificial expression constructs can be used to selectively express synthetic genes or modify gene expression in astrocytes, oligodendrocytes, microglia, pericytes, SMC, or endothelial cells.

Abstract: A computing device, method, system, and instructions in a non-transitory computer-readable medium for performing image analysis on 3D microscopy images to predict localization and/or labeling of various structures or objects of interest, by predicting the location in such images at which a dye or other marker associated with such structures would appear. The computing device, method, and system receives sets of 3D images that include unlabeled images, such as transmitted light images or electron microscope images, and labeled images, such as images captured with fluorescence tagging. The computing device trains a statistical model to associate structures in the labeled images with the same structures in the unlabeled light images. The processor further applies the statistical model to a new unlabeled image to generate a predictive labeled image that predicts the location of a structure of interest in the new image.

Abstract: A facility for identifying the boundaries of 3-dimensional structures in 3-dimensional images is described. For each of multiple 3-dimensional images, the facility receives results of a first attempt to identify boundaries of structures in the 3-dimensional image, and causes the results of the first attempt to be presented to a person. For each of a number of 3-dimensional images, the facility receives input generated by the person providing feedback on the results of the first attempt. The facility then uses the following to train a deep-learning network to identify boundaries of 3-dimensional structures in 3-dimensional images: at least a portion of the plurality of 3-dimensional images, at least a portion of the received results, and at least a portion of provided feedback.

Abstract: Systems, apparatuses, and methods for efficiently and accurately processing an image in order to detect and identify one or more objects contained in the image, and methods that may be implemented on mobile or other resource constrained devices. Embodiments of the invention introduce simple, efficient, and accurate approximations to the functions performed by a convolutional neural network (CNN); this is achieved by binarization (i.e., converting one form of data to binary values) of the weights and of the intermediate representations of data in a convolutional neural network. The inventive binarization methods include optimization processes that determine the best approximations of the convolution operations that are part of implementing a CNN using binary operations.

Abstract: Artificial expression constructs for selectively modulating gene expression in selected central nervous system cell types are described. The artificial expression constructs can be used to selectively express synthetic genes or modify gene expression in GABAergic neurons generally; and/or GABAergic neuron cell types such as lysosomal associated membrane protein 5 (Lamp5) neurons; vasoactive intestinal polypeptide-expressing (Vip) neurons; somatostatin (Sst) neurons; and/or parvalbumin (Pvalb) neuron cell types. Certain artificial expression constructs additionally drive selective gene expression in Layer 4 and/or layer 5 intratelencephalic (IT) neurons, deep cerebellar nuclear neurons or cerebellar Purkinje cells.

Abstract: A system includes a light source to generate an optical signal having a set of pulses at a first repetition rate. The system also includes a multiplexer circuit to generate a multiplexed optical signal from the optical signal n sets of pulses at a second repetition rate, where the n sets of pulses have different polarization states and are at the first repetition rate. The system also includes a focusing unit to split the multiplexed optical signal into n excitation signals to excite a sample. The system also includes an objective to receive the n excitation signals and to illuminate the sample. The objective and the focusing unit collectively focus each excitation signal of the n excitation signals on a different focal plane of the sample to generate a response signal. The system also includes a demultiplexer circuit to generate n emission signals based on the response signal.

Abstract: Artificial expression constructs for selectively modulating gene expression in selected central nervous system cell types are described. The artificial expression constructs can be used to selectively express synthetic genes or modify gene expression in excitatory cortical neurons, such as primarily within cortical layers 2/3, 4, 5, and 6 and including those with extratelencephalic (ET) projections, intratelencephalic (IT) projections, and pyramidal tract (PT) projections, among others.

Abstract: Artificial expression constructs for selectively modulating gene expression in selected central nervous system cell types are described. The artificial expression constructs can be used to selectively express synthetic genes or modify gene expression in GABAergic interneurons.

Abstract: Systems, apparatuses, and methods for representing words or phrases, and using the representation to perform NLP and NLU tasks, where these tasks include sentiment analysis, question answering, and conference resolution. Embodiments introduce a type of deep contextualized word representation that models both complex characteristics of word use, and how these uses vary across linguistic contexts. The word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. These representations can be added to existing task models and significantly improve the state of the art across challenging NLP problems, including question answering, textual entailment and sentiment analysis.

Abstract: A computing device, method, system, and instructions in a non-transitory computer-readable medium for performing image analysis on 3D microscopy images to predict localization and/or labeling of various structures or objects of interest, by predicting the location in such images at which a dye or other marker associated with such structures would appear. The computing device, method, and system receives sets of 3D images that include unlabeled images, such as transmitted light images or electron microscope images, and labeled images, such as images captured with fluorescence tagging. The computing device trains a statistical model to associate structures in the labeled images with the same structures in the unlabeled light images. The processor further applies the statistical model to a new unlabeled image to generate a predictive labeled image that predicts the location of a structure of interest in the new image.

Abstract: Selectively providing voltage-gated sodium channel function sufficient to rescue impaired Nav1.1 function to inhibitory neurons is described. Provided voltage-gated sodium channel function sufficient to rescue impaired Nav1.1 function in inhibitory neurons can be used to treat disorders such as epilepsy, and more particularly, Dravet Syndrome.

Abstract: A system includes a light source to generate an optical signal having a set of pulses at a first repetition rate. The system also includes a multiplexer circuit to generate a multiplexed optical signal from the optical signal n sets of pulses at a second repetition rate, where the n sets of pulses have different polarization states and are at the first repetition rate. The system also includes a focusing unit to split the multiplexed optical signal into n excitation signals to excite a sample. The system also includes an objective to receive the n excitation signals and to illuminate the sample. The objective and the focusing unit collectively focus each excitation signal of the n excitation signals on a different focal plane of the sample to generate a response signal. The system also includes a demultiplexer circuit to generate n emission signals based on the response signal.

Abstract: A computing device, method, system, and instructions in a non-transitory computer-readable medium for performing image analysis on 3D microscopy images to predict localization and/or labeling of various structures or objects of interest, by predicting the location in such images at which a dye or other marker associated with such structures would appear. The computing device, method, and system receives sets of 3D images that include unlabeled images, such as transmitted light images or electron microscope images, and labeled images, such as images captured with fluorescence tagging. The computing device trains a statistical model to associate structures in the labeled images with the same structures in the unlabeled light images. The processor further applies the statistical model to a new unlabeled image to generate a predictive labeled image that predicts the location of a structure of interest in the new image.