Abstract: Aspects of this disclosure relate to methods and systems for assessing multifactorial balance health and implementation of a risk alert system. The fall risk information can be used to notify the person and/or a third-party monitoring person (e.g., doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors, or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling.

Abstract: Disclosed are methods for detecting spatial proximity relationships between nucleic acid sequences in a cell. The methods include: providing a sample of one or more cells comprising nucleic acids; fragmenting the nucleic acids present in the cells, wherein the fragmented nucleic acids have ends capable of joining to other fragmented nucleic acids; joining ends of fragmented nucleic acids to other ends fragmented nucleic acid to create at least one nucleic acid concatemer having at least one junction between the joined fragmented nucleic acids, and wherein the at least one nucleic acid concatemer encodes the information about the proximity of the DNA sequences in the cell; and determining the sequence at least one junction of the at least one nucleic acid concatemer, thereby detecting spatial proximity relationships between nucleic acid sequences in a cell.

Abstract: Disclosed is an in situ method for detecting spatial proximity relationships between nucleic acid sequences, such as DNA, in a cell. The method includes: providing a sample of one or more cells comprising nucleic acids; fragmenting the nucleic acids present in the cells that leaves 5? overhanging ends; filling in the overhanging ends with at least one labeled nucleotide; joining the filled in end of the fragmented nucleic acids that are in close physical proximity to create one or more end joined nucleic acid fragments having a junction; isolating the one or more end joined nucleic acid fragments using the labeled nucleotide; and determining the sequence at the junction of the one or more end joined nucleic acid fragments.

Abstract: Chromatin 3D structure modulating agents in the context of the present invention are intended to interfere or manipulate the function of loop anchor motifs, such as CTCF motifs. In certain example embodiments, the present invention may block formation of an loop anchor or chromatin domain or induce formation of a loop anchor or chromatin domain at a targeted genomic location. For instance, a loop anchor motif can be altered, such as by mutating (including inverting) a binding motif so as to remove such a motif, or by adding new binding motifs in new locations within a loop domain, so as to reduce the size of an existing loop, so as to modify the size of an existing loop, or combinations thereof. Alternatively, the chromatin 3D structure modulating agent may bind a target region and mask a loop anchor motif, thereby preventing a loop anchor or chromatin domain from forming. The chromatin 3D structure modulating agent may bind a target region and cause a loop anchor of chromatin domain to form.

Abstract: Disclosed is a method for detecting spatial proximity relationships between RNA and DNA molecules in a cell. The method includes: providing a sample of RNA and DNA wherein the RNA and DNA have ends capable ofjoining to other DNA and RNA, respectively; joining at least one end of the fragmented RNA to the end of at least one fragmented DNA, to create at least one joined RNA-DNA hybrid molecule, wherein the join encodes the information about the proximity of the RNA and DNA in the cell; reverse transcribing the at least one joined rRNA-DNA hybrid molecule to create least one target join DNA molecule that retains the information of the join, and determining the sequence of the target join thereby detecting spatial proximity relationships between RNA and DNA molecules in a cell.

Abstract: Disclosed is an in situ method for detecting spatial proximity relationships between nucleic acid sequences, such as DNA, in a cell. The method includes: providing a sample of one or more cells comprising nucleic acids; fragmenting the nucleic acids present in the cells that leaves 5? overhanging ends; filling in the overhanging ends with at least one labeled nucleotide; joining the filled in end of the fragmented nucleic acids that are in close physical proximity to create one or more end joined nucleic acid fragments having a junction; isolating the one or more end joined nucleic acid fragments using the labeled nucleotide; and determining the sequence at the junction of the one or more end joined nucleic acid fragments.

Abstract: Chromatin 3D structure modulating agents in the context of the present invention are intended to interfere or manipulate the function of loop anchor motifs, such as CTCF motifs. In certain example embodiments, the present invention may block formation of an loop anchor or chromatin domain or induce formation of a loop anchor or chromatin domain at a targeted genomic location. For instance, a loop anchor motif can be altered, such as by mutating (including inverting) a binding motif so as to remove such a motif, or by adding new binding motifs in new locations within a loop domain, so as to reduce the size of an existing loop, so as to modify the size of an existing loop, or combinations thereof. Alternatively, the chromatin 3D structure modulating agent may bind a target region and mask a loop anchor motif, thereby preventing a loop anchor or chromatin domain from forming. The chromatin 3D structure modulating agent may bind a target region and cause a loop anchor of chromatin domain to form.

Abstract: The disclosed Hi-C protocol can identify genomic loci that are spatially co-located in vivo. These spatial co-locations may include, but are not limited to, intrachromosomal interactions and/or interchromosomal interactions. Hi-C techniques may be applied to many different scales of interest. For example, on a large scale, Hi-C techniques can be used to identify long-range interactions between distant genomic loci.

Abstract: A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.

Abstract: The disclosed Hi-C protocol can identify genomic loci that are spatially co-located in vivo. These spatial co-locations may include, but are not limited to, intrachromosomal interactions and/or interchromosomal interactions. Hi-C techniques may be applied to many different scales of interest. For example, on a large scale, Hi-C techniques can be used to identify long-range interactions between distant genomic loci.

Abstract: Chromatin 3D structure modulating agents in the context of the present invention are intended to interfere or manipulate the function of loop anchor motifs, such as CTCF motifs. In certain example embodiments, the present invention may block formation of all or essentially all loop anchor or chromatin domains or block formation of a loop anchor or chromatin domain at a targeted genomic location. For instance, the chromatin 3D structure modulating agent may bind a target region and mask a loop anchor motif, thereby preventing a loop anchor or chromatin domain from forming. The chromatin 3D structure modulating agent may bind a target region and cause a loop anchor of chromatin domain to form.

Abstract: A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.

Abstract: A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.

Abstract: A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.

Abstract: A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.

Abstract: A system may produce images including narrow-bandwidth colors. One or more sets of the narrow-bandwidth colors may be selected to be interpreted as substantially a same color by a user. The system may include a light source configured to produce the narrow-bandwidth colors, and/or narrow-passband filters may create narrow-bandwidth colors from light emitted by broad-spectrum light sources or color sources. Spatial and/or time multiplexing may be used to create separate narrow-bandwidth colors interpreted as substantially a same color by the user. For example, the light source and/or the narrow-passband filter elements may be adjustable and may alternate between emission of two or more narrow-bandwidth colors. A viewing device may include filters allowing the user to selectively filter the narrow-bandwidth colors. The user may filter the narrow-bandwidth colors to separate a stereoscopic image pair, to view a user-specific image, to view desired content obfuscated by an obfuscating image, and/or the like.

Abstract: A system may produce images including narrow-bandwidth colors. One or more sets of the narrow-bandwidth colors may be selected to be interpreted as substantially a same color by a user. The system may include a light source configured to produce the narrow-bandwidth colors, and/or narrow-passband filters may create narrow-bandwidth colors from light emitted by broad-spectrum light sources or color sources. Spatial and/or time multiplexing may be used to create separate narrow-bandwidth colors interpreted as substantially a same color by the user. The light source and/or the narrow-passband filter elements may be adjustable and may alternate between emission of two or more narrow-bandwidth colors. A viewing device may include filters configured to selectively filter the narrow-bandwidth colors. The user may filter the narrow-bandwidth colors to separate a stereoscopic image pair, to view an image specific to a user, to view desired content obfuscated by an obfuscating image, and/or the like.

Abstract: Chromatin 3D structure modulating agents in the context of the present invention are intended to interfere or manipulate the function of loop anchor motifs, such as CTCF motifs. In certain example embodiments, the present invention may block formation of an loop anchor or chromatin domain or induce formation of a loop anchor or chromatin domain at a targeted genomic location. For instance, a loop anchor motif can be altered, such as by mutating (including inverting) a binding motif so as to remove such a motif, or by adding new binding motifs in new locations within a loop domain, so as to reduce the size of an existing loop, so as to modify the size of an existing loop, or combinations thereof. Alternatively, the chromatin 3D structure modulating agent may bind a target region and mask a loop anchor motif, thereby preventing a loop anchor or chromatin domain from forming. The chromatin 3D structure modulating agent may bind a target region and cause a loop anchor of chromatin domain to form.

Abstract: Devices and methods for personalized real time optical remapping of streamed content are provided. The personalized optical remapping can correct for a wide variety of visual deficiencies or preferences by manipulating visual and spatial elements of streamed content. A device for presenting a personalized visual feed to a user includes a sensor module for detecting a visual input and providing a visual feed, a transformation module performing a computational transformation, selected according to a visual deficit or personal preference of the user, on at least a portion of the visual feed producing a personalized visual feed, and a visual display presenting the personalized visual feed to the user.

Abstract: Described embodiments include a system, method, and computer program product. In a described system, a receiver circuit receives at least two reference images of a patient body part. Each reference image includes a respective landmark subsurface feature of the patient body part, and each imaged landmark subsurface feature has a respective spatial relationship to a respective region of a surface of the patient body part imaged during a medical examination. A feature matching circuit determines a correspondence between (x) each atlas landmark subsurface feature of the patient body part included in a landmark subsurface feature atlas and (y) each respective imaged landmark subsurface feature. A reporting circuit generates informational data reporting a depiction of an area of the surface of the patient body part by at least two adjacent imaged regions of the surface of the patient body part. A communication circuit outputs the informational data.