Automatic Gradient Threshold Determination for Edge Detection Using a Statistical Model A Description of the Model and Comparison of Algorithms

In the enhancement/thresholding method of edge detection, the gradient values of pixels exceeding a certain threshold are designated as edge pixels. However, selecting a threshold has commonly been performed through ad hoc measures. This paper describes a method for automatically selecting a threshold using a 5-parameter model. The model is based on the weighted sum of two gamma density functions corresponding to edge and ncln-edge pixels. A variety of statistical and fitting methods for finding the model parameters were evaluated by comparing their computed thresholds to perceptual thresholds determined by subjects for 16 different images. The performance of the model was also analyzed under different noise levels. Index Terms: Automatic thresholding, edge detection, gradient, Sobel operator, statistical classification

CATO: The Clone Alignment Tool.

High-throughput cloning efforts produce large numbers of sequences that need to be aligned, edited, compared with reference sequences, and organized as files and selected clones. Different pieces of software are typically required to perform each of these tasks. We have designed a single piece of software, CATO, the Clone Alignment Tool, that allows a user to align, evaluate, edit, and select clone sequences based on comparisons to reference sequences. The input and output are designed to be compatible with standard data formats, and thus suitable for integration into a clone processing pipeline. CATO provides both sequence alignment and visualizations to facilitate the analysis of cloning experiments. The alignment algorithm matches each of the relevant candidate sequences against each reference sequence. The visualization portion displays three levels of matching: 1) a top-level summary of the top candidate sequences aligned to each reference sequence, 2) a focused alignment view with the nucleotides of matched sequences displayed against one reference sequence, and 3) a pair-wise alignment of a single reference and candidate sequence pair. Users can select the minimum matching criteria for valid clones, edit or swap reference sequences, and export the results to a summary file as part of the high-throughput cloning workflow

Change the reference sequence.

<p>Right-clicking a candidate sequence (A) results in an orange check mark in the “Use for Ref Seq” column. After such changes, the alignment needs to be saved and rerun, after which the selected clone sequence will become a reference sequence and the previous reference sequence will become a candidate sequence. (B) The same view as (A) with the new reference sequence now being utilized. Ambiguities are resolved and the original single-base deletion is restored resulting in correction of the reading frame.</p

Main CATO interface.

<p>Results view with 3 panels showing the summary of reference sequences and matched candidate sequences are displayed in panel A. A focused view of a single reference sequence and associated clone candidate sequences is shown in panel B, with the highlighted candidate from the top panel colored brown. The top blue amino acid sequence has motifs highlighted. The blue nucleotide sequence is the reference sequence with restriction sites indicated. Candidate mismatch nucleotides are red, gaps indicated with a dash, and vertical red bars indicate 1 or more nucleotides missing from the reference. A grey background highlights the longest contiguous match with the reference sequence. A single reference-candidate sequence pair-wise alignment is in panel C.</p

Sequences can be edited.

<p>Right clicking on the reference sequence (N) allows the full range of sequence modifications.</p

Global seroprevalence of neutralizing antibodies against adeno-associated virus serotypes used for human gene therapies

Adeno-associated virus (AAV) vectors are promising gene therapy candidates, but pre-existing anti-AAV neutralizing antibodies (NAbs) pose a significant challenge to successful gene delivery. Knowledge of NAb seroprevalence remains limited and inconsistent. We measured activity of NAbs against six clinically relevant AAV serotypes across 10 countries in adults (n = 502) and children (n = 50) using a highly sensitive transduction inhibition assay. NAb prevalence was generally highest for AAV1 and lowest for AAV5. There was considerable variability across countries and geographical regions. NAb prevalence increased with age and was higher in females, participants of Asian ethnicity, and participants in cancer trials. Co-prevalence was most frequently observed between AAV1 and AAV6 and less frequently between AAV5 and other AAVs. Machine learning analyses revealed a unique clustering of AAVs that differed from previous phylogenetic classifications. These results offer insights into the biological relationships between the immunogenicity of AAVs in humans beyond that observed previously using standard clades, which are based on linear capsid sequences. Our findings may inform improved vector design and facilitate the development of AAV vector-mediated clinical gene therapies