Publications by authors named "V Pata"

Purpose: We set out to develop a publicly available tool that could accurately diagnose spinal muscular atrophy (SMA) in exome, genome or panel sequencing datasets aligned to a GRCh37, GRCh38, or T2T reference genome.

Methods: The SMA Finder algorithm detects the most common genetic causes of SMA by evaluating reads that overlap the c.840 position of the SMN1 and SMN2 paralogs.

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This study investigates the challenges and potential of conventional injection molding for producing thick-walled optical components. The research primarily focuses on optimizing process parameters and mold design to enhance product quality. The methods include software simulations and experimental validation using polycarbonate test samples (optical lenses).

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An equibiaxial tension test could be necessary to set up hyperelastic material constants for elastomers exactly. Unfortunately, very often, only uniaxial tension experimental data are available. It is possible to use only uniaxial data to compute hyperelastic constants for a hyperelastic model, but the prediction of behavior in different deformation modes (as is equibiaxial or pure shear) will not work correctly with this model.

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Injection molding technology is widely utilized across various industries for its ability to fabricate complex-shaped components with exceptional dimensional accuracy. However, challenges related to injection quality often arise, necessitating innovative approaches for improvement. This study investigates the influence of surface roughness on the efficiency of conformal cooling channels produced using additive manufacturing technologies, specifically Direct Metal Laser Sintering (DMLS) and Atomic Diffusion Additive Manufacturing (ADAM).

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Spinal muscular atrophy (SMA) is a genetic disorder that causes progressive degeneration of lower motor neurons and the subsequent loss of muscle function throughout the body. It is the second most common recessive disorder in individuals of European descent and is present in all populations. Accurate tools exist for diagnosing SMA from genome sequencing data.

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