Abstract: This invention, in some embodiments thereof, provides a core-shell nanoparticle including a lipid-PSMA conjugate, encapsulating a liquid oil-based core. The invention further provides aqueous compositions including the core-shell nanoparticles, and methods for using same, such as for the treatment of proliferative diseases associated with PSMA upregulated expression.

Abstract: A machine-learning (ML) error-correcting code (ECC) controller may include a hard-decision (HD) ECC decoder optimized for high-speed data throughput, a soft-decision (SD) ECC decoder optimized for high-correctability data throughput, and a machine-learning equalizer (MLE) configured to variably select one of the HD ECC decoder or the SD ECC decoder for data throughput. An embodiment of the ML ECC controller may provide speed-optimized HD throughput based on a linear ECC. The linear ECC may be a soft Hamming permutation code (SHPC).

Abstract: A method for analyzing a spectral signature of a compound specimen is provided. The method includes illuminating a compound specimen with a light in a predetermined spectrum; obtaining a spectral signature of the compound specimen, the spectral signature including at least one light property of a plurality of wavelengths in the spectrum transmitted through the compound specimen; extracting characterizing features of the spectral signature, the characterizing features being light properties of predetermined wavelengths within the spectrum; and comparing the characterizing features with corresponding features stored in a database, the corresponding features are corresponding properties of an expected spectral signature of a specimen including an examined substance.

Abstract: A machine-learning (ML) error-correcting code (ECC) controller may include a hard-decision (HD) ECC decoder optimized for high-speed data throughput, a soft-decision (SD) ECC decoder optimized for high-correctability data throughput, and a machine-learning equalizer (MLE) configured to variably select one of the HD ECC decoder or the SD ECC decoder for data throughput. An embodiment of the ML ECC controller may provide speed-optimized HD throughput based on a linear ECC. The linear ECC may be a soft Hamming permutation code (SHPC).

Abstract: A device for spectral analysis including a seat for holding therein a compound specimen; a light source for illuminating the compound specimen with a spectrum of light; and a detector configured for detecting light transmitted through or reflected from the biological sample, the detector including a pixel array having a plurality of pixels each of which being configured to detect intensity of one wavelength within the spectrum such that the pixel array obtains a spectral signature of the biological sample including intensities of wavelengths within the spectrum.

Abstract: A machine-learning (ML) error-correcting code (ECC) controller may include a hard-decision (HD) ECC decoder optimized for high-speed data throughput, a soft-decision (SD) ECC decoder optimized for high-correctability data throughput, and a machine-learning equalizer (MLE) configured to variably select one of the HD ECC decoder or the SD ECC decoder for data throughput. An embodiment of the ML ECC controller may provide speed-optimized HD throughput based on a linear ECC. The linear ECC may be a soft Hamming permutation code (SHPC).