The hAT family hopper transposon exists as highly similar yet discontinuous elements in the Bactrocera tephritid fly genus.

The hAT family transposable element, hopper, was originally discovered as a defective 3120-bp full-length element in a wild-type strain of the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), and subsequently a functional 3131-bp element, hopper, was isolated from a white eye mutant strain. The latter study showed that closely related elements exist in melonfly, Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae), a closely related subgenus, suggesting that hopper could have a widespread presence in the Bactrocera genus. To further understand the distribution of hopper within and beyond the B.

Clinical spotlight intervention to accelerate translation of evidence-based practices in primary care.

Evidence-based medical practice is often slow to diffuse into widespread clinical practice. To accelerate translation of updated best practices into clinical care, we developed a quality improvement intervention called the 'Clinical Spotlight'. This programme was based on a knowledge translation model of four steps: education on evidence-based practices, using Lean for incorporation into patient care flow, support of implementation and sustainability, and measurement of outcomes.

Gene editing of the ABC Transporter/White locus using CRISPR/Cas9-mediated mutagenesis in the Indian Meal Moth.

ATP binding cassette (ABC) proteins are involved in transport of substrates across membranes including eye pigments. Mutations of ABC transporter white, brown and scarlet genes of Drosophila and other insects result in visible eye color phenotypes. White locus was identified in a genome assembly of Plodia interpunctella and was found to extend for 16,670 bp comprising 13 exons.

Deep Learning and Entropy-Based Texture Features for Color Image Classification.

In the domain of computer vision, entropy-defined as a measure of irregularity-has been proposed as an effective method for analyzing the texture of images. Several studies have shown that, with specific parameter tuning, entropy-based approaches achieve high accuracy in terms of classification results for texture images, when associated with machine learning classifiers. However, few entropy measures have been extended to studying color images.

Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification.

Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly.