Abstract: An injectable peptide-based hydrogel is disclosed that incorporates a peptide inhibitor of proprotein convertase subtilisn/kexin type 9. The hydrogel is a polymer composed of the 13-amino-acid protein, Pep2-8, (TVFTSWEEYLDWV) attached to a self-assembling peptide of the ABA block structure (ESLSLSLSLSLSLEG) to generate the repeating multidomain peptide sequence (ESLSLSLSLSLSLEGTVFTSWEEYLDWV).

Abstract: Methods and systems for classifying a multimedia file using interclass data is disclosed. One of the methods can use classification results from one or more engines of different classes to select a different engine for the original classification task. For example, given an audio segment with associated metadata and image data, the disclosed interclass method can use the classification results from a topic classification of metadata and/or an image classification result of the image data as inputs for selecting a new transcription engine to transcribe the audio segment.

Abstract: A lottery ticket is printed on a substrate sheet material having a front surface with lottery game indicia printed thereon and a removable covering material, typically scratch-off material, covering the lottery game indicia which is removable by a player to expose the game indicia for playing the game and other game information printed on the substrate sheet material where at least part of the substrate sheet material is formed of a translucent synthetic material to enable viewing through the part by the player. In order to allow the ticket to be dispensed in a row of the tickets from a dispensing machine with an optical sensor, a patch of an opaque or semi-opaque material is applied onto the substrate at a location to cooperate with the sensor while it is operating to detect ticket location.

Abstract: Provided herein are methods and processes for emulsifying non-polar compounds, such as omega fatty acids. Also provided are compositions that can be prepared according to the methods and processes described herein. The compositions, for example, have a high amount of the non-polar compound, such as a large amount of omega fatty acids. The non-polar compounds are also present in very small droplets within composition. For example, the mean or median particle size of the droplets is less than about 5 ?m. Further, the composition includes a low amount of surfactant, such as less than about 10-15% of the surfactant.

Abstract: Provided herein are methods and processes for emulsifying non-polar compounds, such as omega fatty acids. Also provided are compositions that can be prepared according to the methods and processes described herein. The compositions, for example, have a high amount of the non-polar compound, such as a large amount of omega fatty acids. The non-polar compounds are also present in very small droplets within composition. For example, the mean or median particle size of the droplets is less than about 5 ?m. Further, the composition includes a low amount of surfactant, such as less than about 10-15% of the surfactant.

Abstract: Methods and systems for transcribing a media file using a human intelligence task service and/or reinforcement learning are provided. The disclosed systems and methods provide opportunities for a segment of the input media file to be automatically re-analyzed, re-transcribed, and/or modified for re-transcription using a human intelligence task (HIT) service for verification and/or modification of the transcription results. The segment can also be reanalyzed, reconstructed, and re-transcribed using a reinforcement learning enabled transcription model.

Abstract: Provided herein are embodiments of systems and methods for classifying one or more objects in an image. One of the methods includes: receiving object classification results of the image from one or more classification engines, the object classification results comprise classification of one or more objects and confidence scores associated with the one or more objects; aggregating the object classification results from the one or more classification engines to generate a list of confidence scores associated with each of the one or more objects, the list of confidence scores comprises one or more confidence scores from one or more classification engines; calculating an overall certainty score for each of the one or more objects based at least on the list of confidence scores; and generating a first orchestrated classification result based the overall certainty score for each of the one or more object.

Abstract: A set of cationic amphiphilic self-assembled peptides (CASPs) is presented that employ high-charge density at fiber edges to disrupt bacterial membranes. CASP nanofibers are effective against Pseudomonas biofilms. There is an inherent trade-off between the ability of the peptides to undergo nanofibrous self-assembly and having a high terminal charge density required for effective bactericidal efficacy. The self-assembled peptide hydrogel presented achieves a balance of these opposing factors. Also demonstrated is the applicability of the new composition in an injectable hydrogel formulation. A CASP platform may be useful for topical application and integration into medical coatings, grafts, devices, and prostheses, thereby reducing risk of bacterial infection and related failure.

Abstract: Provided herein are methods and processes for emulsifying non-polar compounds, such as omega fatty acids. Also provided are compositions that can be prepared according to the methods and processes described herein. The compositions, for example, have a high amount of the non-polar compound, such as a large amount of omega fatty acids. The non-polar compounds are also present in very small droplets within composition. For example, the mean or median particle size of the droplets is less than about 5 ?m. Further, the composition includes a low amount of surfactant, such as less than about 10-15% of the surfactant.

Abstract: Methods and systems for training an engine prediction neural network is disclosed. One of the methods can include: extracting image features of a first ground truth image using outputs of one or more layers of an image classification neural network; classifying the first ground truth image using a plurality of candidate neural networks; determining a classification accuracy score of a classification result of the first ground truth image for each candidate neural network of the plurality of candidate neural networks; and training the engine prediction neural network to predict the best candidate engine by associating the image features of the first ground truth image with the classification accuracy score of each candidate neural network.

Abstract: Methods and systems for classifying a multimedia file using interclass data is disclosed. One of the methods can use classification results from one or more engines of different classes to select a different engine for the original classification task. For example, given an audio segment with associated metadata and image data, the disclosed interclass method can use the classification results from a topic classification of metadata and/or an image classification result of the image data as inputs for selecting a new transcription engine to transcribe the audio segment.

Abstract: Methods and systems for classifying a multimedia file using interclass data is disclosed. One of the methods includes receiving, from a first transcription engine, one or more transcription results of one or more audio segments of the multimedia file; identifying a first transcription result for a first audio segment having a low confidence of accuracy; identifying a first image data of the multimedia file corresponding to the first segment; receiving, from an image classification engine trained to classify image data, an image classification result of one or more portions of the first image data in response to requesting the image classification engine to classify the first image data; and selecting, based at least on the image classification result of the one or more portions of the first image data, a second transcription engine to re-classify the first audio segment.

Abstract: Methods and systems for training one or more neural networks for transcription and for transcribing a media file using the trained one or more neural networks are provided. One of the methods includes: segmenting the media file into a plurality of segments; inputting each segment, one segment at a time, of the plurality of segments into a first neural network trained to perform speech recognition; extracting outputs, one segment at a time, from one or more layers of the first neural network; and training a second neural network to generate a predicted-WER (word error rate) of a plurality of transcription engines for each segment based at least on outputs from the one or more layers of the first neural network.

Abstract: Rather than randomly selecting neural networks to classify a media file, the conductor can determine which neural network engines (from the conductor ecosystem of neural networks) are the best candidates to classify a particular portion/segment of the media file (e.g., audio file, image file, video files). The best candidate neural network engine(s) can depend on the nature of the input media and the characteristics of the neural network engines. In object recognition and identification, certain neural networks can classify vehicles better than others, while another group of neural networks can classify structures better. The conductor can take out the guess work and construct in real-time an inter-class neural network using one or more layers selected from one or more pre-trained neural network, based on attribute(s) of the media file, to classify the media file.

Abstract: Rather than randomly selecting neural networks to classify a media file, the conductor can determine which neural network engines (from the conductor ecosystem of neural networks) are the best candidates to classify a particular portion/segment of the media file (e.g., audio file, image file, video files). The best candidate neural network engine(s) can depend on the nature of the input media and the characteristics of the neural network engines. In object recognition and identification, certain neural networks can classify vehicles better than others, while another group of neural networks can classify structures better. The conductor can take out the guess work and construct in real-time an inter-classifier neural network using one or more layers selected from one or more pre-trained neural network, based on attribute(s) of the media file, to classify the media file.

Abstract: Methods and systems for training one or more neural networks for transcription and for transcribing a media file using the trained one or more neural networks are provided. One of the methods includes: segmenting the media file into a plurality of segments; extracting, using a first neural network, audio features of a first and second segment of the plurality of segments; and identifying, using a second neural network, a best-candidate engine for each of the first and second segments based at least on audio features of the first and second segments. A best-candidate engine is a neural network having a highest predicted transcription accuracy among a collection of neural networks.

Abstract: A set of cationic amphiphilic self-assembled peptides (CASPs) is presented that employ high-charge density at fiber edges to disrupt bacterial membranes. CASP nanofibers are effective against Pseudomonas biofilms. There is an inherent trade-off between the ability of the peptides to undergo nanofibrous self-assembly and having a high terminal charge density required for effective bactericidal efficacy. The self-assembled peptide hydrogel presented achieves a balance of these opposing factors. Also demonstrated is the applicability of the new composition in an injectable hydrogel formulation. A CASP platform may be useful for topical application and integration into medical coatings, grafts, devices, and prostheses, thereby reducing risk of bacterial infection and related failure.

Abstract: Methods and systems for transcribing a media file using reinforcement learning are provided. In one aspect, the method includes: identifying a low confidence of accuracy portion from a transcription result of the media file; constructing a phoneme sequence that includes an audio segment corresponding to the identified low confidence of accuracy portion, based on at least on a reward function; creating a new audio waveform based at least on the constructed phoneme sequence; and generating a new transcription using a transcription engine based on the new audio waveform.

Abstract: A mimic of an anti-angiogenic peptide is combined with a self-assembling peptide hydrogel to provide improved treatment for pathological neovascularization management. Pathological neovascularization may cause or worsen intraocular posterior segment diseases, such as diabetic retinopathy (DR) and wet age-related macular degeneration (wet AMD). The attachment of a therapeutic anti-angiogenic motif to a fibrillizing peptide backbone that undergoes nanofibrous self-assembly into an injectable hydrogel was found beneficial for the treatment of aberrant neovascularization. The peptide persists for extended periods in a target site for prolonging the therapeutic timeframe. This injectable hydrogel therapy may unlock potential clinical routes for treating many neovascular diseases.