Infusing Definiteness into Randomness: Rethinking Composition Styles for Deep Image Matting

We study the composition style in deep image matting, a notion that characterizes a data generation flow on how to exploit limited foregrounds and random backgrounds to form a training dataset. Prior art executes this flow in a completely random manner by simply going through the foreground pool or by optionally combining two foregrounds before foreground-background composition. In this work, we first show that naive foreground combination can be problematic and therefore derive an alternative formulation to reasonably combine foregrounds. Our second contribution is an observation that matting performance can benefit from a certain occurrence frequency of combined foregrounds and their associated source foregrounds during training. Inspired by this, we introduce a novel composition style that binds the source and combined foregrounds in a definite triplet. In addition, we also find that different orders of foreground combination lead to different foreground patterns, which further inspires a quadruplet-based composition style. Results under controlled experiments on four matting baselines show that our composition styles outperform existing ones and invite consistent performance improvement on both composited and real-world datasets. Code is available at: https://github.com/coconuthust/composition_stylesComment: Accepted to AAAI 2023; 11 pages, 9 figures; Code is available at https://github.com/coconuthust/composition_style

Identification of closely related species in Aspergillus through Analysis of Whole-Genome

The challenge of discriminating closely related species persists, notably within clinical diagnostic laboratories for invasive aspergillosis (IA)-related species and food contamination microorganisms with toxin-producing potential. We employed Analysis of the whole-GEnome (AGE) to address the challenges of closely related species within the genus Aspergillus and developed a rapid detection method. First, reliable whole genome data for 77 Aspergillus species were downloaded from the database, and through bioinformatic analysis, specific targets for each species were identified. Subsequently, sequencing was employed to validate these specific targets. Additionally, we developed an on-site detection method targeting a specific target using a genome editing system. Our results indicate that AGE has successfully achieved reliable identification of all IA-related species (Aspergillus fumigatus, Aspergillus niger, Aspergillus nidulans, Aspergillus flavus, and Aspergillus terreus) and three well-known species (A. flavus, Aspergillus parasiticus, and Aspergillus oryzae) within the Aspergillus section. Flavi and AGE have provided species-level-specific targets for 77 species within the genus Aspergillus. Based on these reference targets, the sequencing results targeting specific targets substantiate the efficacy of distinguishing the focal species from its closely related species. Notably, the amalgamation of room-temperature amplification and genome editing techniques demonstrates the capacity for rapid and accurate identification of genomic DNA samples at a concentration as low as 0.1 ng/μl within a concise 30-min timeframe. Importantly, this methodology circumvents the reliance on large specialized instrumentation by presenting a singular tube operational modality and allowing for visualized result assessment. These advancements aptly meet the exigencies of on-site detection requirements for the specified species, facilitating prompt diagnosis and food quality monitoring. Moreover, as an identification method based on species-specific genomic sequences, AGE shows promising potential as an effective tool for epidemiological research and species classification

Analysis of Whole-Genome facilitates rapid and precise identification of fungal species

Fungal identification is a cornerstone of fungal research, yet traditional molecular methods struggle with rapid and accurate onsite identification, especially for closely related species. To tackle this challenge, we introduce a universal identification method called Analysis of whole GEnome (AGE). AGE includes two key steps: bioinformatics analysis and experimental practice. Bioinformatics analysis screens candidate target sequences named Targets within the genome of the fungal species and determines specific Targets by comparing them with the genomes of other species. Then, experimental practice using sequencing or non-sequencing technologies would confirm the results of bioinformatics analysis. Accordingly, AGE obtained more than 1,000,000 qualified Targets for each of the 13 fungal species within the phyla Ascomycota and Basidiomycota. Next, the sequencing and genome editing system validated the ultra-specific performance of the specific Targets; especially noteworthy is the first-time demonstration of the identification potential of sequences from unannotated genomic regions. Furthermore, by combining rapid isothermal amplification and phosphorothioate-modified primers with the option of an instrument-free visual fluorescence method, AGE can achieve qualitative species identification within 30 min using a single-tube test. More importantly, AGE holds significant potential for identifying closely related species and differentiating traditional Chinese medicines from their adulterants, especially in the precise detection of contaminants. In summary, AGE opens the door for the development of whole-genome-based fungal species identification while also providing guidance for its application in plant and animal kingdoms