COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

The complete chloroplast genome sequence of traditional Chinese medicine Uncaria macrophylla (Rubiaceae).

Uncaria macrophylla (Rubiaceae) is a medicinal vine plant of the Rubiaceae family that was distributed in East Asia and Southeast Asia. The first complete chloroplast genome of Uncaria macrophylla was sequenced and assembled in this study. The genome is 155,138 bp in length and contained 129 encoded genes in total, including 79 protein-coding genes, eight ribosomal RNA genes, and 37 transfer RNA genes. The phylogenomic analysis showed that U. macrophylla was closely related to Uncaria rhynchophylla according to the current sampling extent.

Quantitative determination of cucumber mosaic virus genome RNAs in virions by real-time reverse transcription-polymerase chain reaction.

A real-time RT-PCR procedure using the green fluorescent dye SYBR Green I was developed for determining the absolute and relative copies of cucumber mosaic virus (CMV) genomic RNAs contained in purified virions. Primers specific to each CMV ORF were designed and selected. Sequences were then amplified with length varying from 61 to 153 bp. Using dilution series of CMV genome RNAs prepared by in vitro transcription as the standard samples, a good linear correlation was observed between their threshold cycle (Ct) values and the logarithms of the initial template amounts. The copies of genomic RNA 1, RNA 2, RNA 3 and the subgenomic RNA 4 in CMV virions were quantified by this method, and the ratios were about 1.00:1.17:3.58:5.81. These results were confirmed by Lab-on-a-chip and northern blot hybridization assays. Our work is the first report concerning the relative amounts of different RNA fragments in CMV virions as a virus with tripartite genome.