Abstract: Image-based machine learning approaches are used to classify audio data, such as speech data as authentic or otherwise. For example, audio data can be obtained and a visual representation of the audio data can be generated. The visual representation can include, for example, an image such as a spectrogram or other visual or electronic representation of the audio data. Before processing the image, the audio data and/or image may undergo various preprocessing techniques. Thereafter, the image representation of the audio data can be analyzed using a trained model to classify the audio data as authentic or otherwise.

Abstract: Disclosed herein are methods of treating cancer in a subject, wherein the subject is known to possess at least one genetic alteration in RET, comprising administering to the subject a therapeutically effective amount of N-[3-[(6,7-dimethoxy-4-quinazolinyl)oxy]phenyl]-N?-[5-(2,2,2-trifluoro-1, 1-dimethylethyl)-3-isoxazolyl]-urea, or a pharmaceutically acceptable salt thereof.

Abstract: Image-based machine learning approaches are used to classify audio data, such as speech data as authentic or otherwise. For example, audio data can be obtained and a visual representation of the audio data can be generated. The visual representation can include, for example, an image such as a spectrogram or other visual or electronic representation of the audio data. Before processing the image, the audio data and/or image may undergo various preprocessing techniques. Thereafter, the image representation of the audio data can be analyzed using a trained model to classify the audio data as authentic or otherwise.

Abstract: Disclosed herein are methods of treating cancer in a subject, wherein the subject is known to possess at least one genetic alteration in RET, comprising administering to the subject a therapeutically effective amount of N-[3-[(6,7-dimethoxy-4-quinazolinyl)oxy]phenyl]-N?-[5-(2,2,2-trifluoro-1, 1-dimethylethyl)-3-isoxazolyl]-urea, or a pharmaceutically acceptable salt thereof.

Abstract: Image-based machine learning approaches are used to classify audio data, such as speech data as authentic or otherwise. For example, audio data can be obtained and a visual representation of the audio data can be generated. The visual representation can include, for example, an image such as a spectrogram or other visual or electronic representation of the audio data. Before processing the image, the audio data and/or image may undergo various preprocessing techniques. Thereafter, the image representation of the audio data can be analyzed using a trained model to classify the audio data as authentic or otherwise.

Abstract: A voice recipient may request a text-to-speech (TTS) voice that corresponds to an age or age range. An existing TTS voice or existing voice data may be used to create a TTS voice corresponding to the requested age by encoding the voice data to voice parameter values, transforming the voice parameter values using a voice-aging model, synthesizing voice data using the transformed parameter values, and then creating a TTS voice using the transformed voice data. The voice-aging model may model how one or more voice parameters of a voice change with age and may be created from voice data stored in a voice bank.

Abstract: A voice recipient may request a text-to-speech (TTS) voice that corresponds to an age or age range. An existing TTS voice or existing voice data may be used to create a TTS voice corresponding to the requested age by encoding the voice data to voice parameter values, transforming the voice parameter values using a voice-aging model, synthesizing voice data using the transformed parameter values, and then creating a TTS voice using the transformed voice data. The voice-aging model may model how one or more voice parameters of a voice change with age and may be created from voice data stored in a voice bank.

Abstract: Voice data may be collected by a plurality of voice donors and stored in a voice bank. A voice donor may authenticate to a voice collection system to start a session to provide voice data. During the voice collection session, the voice donor may be presented with a sequence of prompts to speak and voice data may be transferred to a server. The received voice data may be processed to determine the speech units spoken by the voice donor and a count of speech units received from the voice donor may be updated. Feedback may be provided to the voice donor indicating, for example, a progress of the voice collection, a quality level of the voice data, or information about speech unit counts. The voice bank may be used to create TTS voices for voice recipients, create a model of voice aging, or for other applications.

Abstract: This invention relates to an oramucosal pharmaceutical dosage form in the form of a wafer. The wafer comprises a porous, hydroscopic, muco-adhesive polymeric matrix with at least one desired pharmaceutically active compound added thereto. The polymer is selected from a number of polymers having different dissolution rates and, in use when taken orally, the matrix adheres to an oramucosal surface to dissolve over a predetermined period of time to release the pharmaceutically active compound. The invention also extends to a method of manufacturing an oramucosal pharmaceutical dosage form in the form of a wafer which involves freeze drying or lyophilisation.

Abstract: The present invention provides methods of screening for ligands of target molecules. The methods of the present invention include assays in which a target molecule is subjected to denaturing conditions, and compounds are screened for the ability to alter the susceptibility of the target to unfolding. The methods of the present invention use fluorescence detection to determine that degree of unfolding of a target molecule. In some aspects of the present invention, fluorescence resonance energy transfer (FRET) is detected. In other aspects of the invention, fluorescence polarization (FP) is detected. In preferred embodiments, a target molecule such as a target protein is heated to a temperature, called TATLAS, at which at least a portion of the target molecule unfolds, in the presence of a test compound.