Scalable Image Self-Embedding Based on Dual-Rate SPIHT-LDPC Reference Generation Scheme

Image Self-Embedding is a method of embedding two sets of data into the original image, authentication data for tamper detection and reference data for image recovery. In this paper, a scalable self-embedding method is proposed based on dual-rate source-channel coding for reference data generation. The proposed method uses Set Partitioning in Hierarchical Tree (SPIHT) algorithm for source coding and Low-Density Parity Check (LDPC) for channel coding. Accordingly, the proposed recovery system provides higher reconstruction quality at low tampering rates, while it can handle higher tampering rates with less reconstruction quality. Therefore, the proposed method has the ability of both preserving the image quality and recovering higher tampering rates. Simulation results show noticeable improvements compared with the related self-embedding methods in the literature

A novel mutation in the ALS2 gene in an iranian kurdish family with juvenile amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a rare disorder that affects both upper and lower motor neurons. Mutations in Alsin Rho Guanine Nucleotide Exchange Factor (ALS2) correlates with three similar but distinctive syndromes, including the juvenile form of ALS. An Iranian Kurdish family was involved in this study and all members were evaluated with relevant clinical guidelines. Whole exome sequencing and sanger sequencing were applied to all family members to undermine the possible genetic factors. A substitution c. 2110 C>T (p. Arg704X) identified in the ALS2 gene. Bioinformatics analysis indicated the mutation is located in the well-conserved and functional domain of the protein. This study recognized a novel mutation in the ALS2 gene in a proband with the juvenile form of ALS. To our knowledge, this is the first identified ALS2 mutation among the Iranian population. © 2022 World Federation of Neurology on behalf of the Research Group on Motor Neuron Diseases