Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification

Deep learning models were frequently reported to learn from shortcuts like dataset biases. As deep learning is playing an increasingly important role in the modern healthcare system, it is of great need to combat shortcut learning in medical data as well as develop unbiased and trustworthy models. In this paper, we study the problem of developing debiased chest X-ray diagnosis models from the biased training data without knowing exactly the bias labels. We start with the observations that the imbalance of bias distribution is one of the key reasons causing shortcut learning, and the dataset biases are preferred by the model if they were easier to be learned than the intended features. Based on these observations, we proposed a novel algorithm, pseudo bias-balanced learning, which first captures and predicts per-sample bias labels via generalized cross entropy loss and then trains a debiased model using pseudo bias labels and bias-balanced softmax function. We constructed several chest X-ray datasets with various dataset bias situations and demonstrated with extensive experiments that our proposed method achieved consistent improvements over other state-of-the-art approaches.Comment: To appear in MICCAI 2022. Code available at https://github.com/LLYXC/PBB

Thrombectomy combined with indwelling-catheter thrombolysis is more effective than pure thrombectomy for the treatment of lower extremity deep venous thrombosis

This study was a retrospective analysis of the efficacy of thrombectomy plus local catheter-directed thrombolysis (CDT) for the treatment of lower extremity deep venous thrombosis (LDVT)

Summary of the major clades recovered by different datasets and analytical approaches.

<p>Summary of the major clades recovered by different datasets and analytical approaches.</p

Phylogenetic analysis of the mitochondrial genomes in bees (Hymenoptera: Apoidea: Anthophila)

<div><p>In this study, the first complete mitogenome of Andrenidae, namely <i>Andrena camellia</i>, is newly sequenced. It includes 13 protein-coding (PCG) genes, 22 transfer RNA (rRNA) genes, two ribosomal RNA (tRNA) genes, and a control region. Among PCGs, high conservation is observed in cytochrome oxidase genes with <i>cox1</i> exhibits the highest conservation. Conversely, NADH dehydrogenase and ATPase subunit genes are more variable with <i>atp8</i> presents the maximal variation. Comparison of the gene order indicates complex rearrangement in bees. Most of the rearranged events are located in the tRNA clusters of <i>trnI</i>-<i>trnQ</i>-<i>trnM</i>, <i>trnW</i>-<i>trnC</i>-<i>trnY</i>, and <i>trnA-trnR-trnN-trnS1-trnE-trnF</i>. Furthermore, we present the most comprehensive mitochondrial phylogeny of bee families. The monophyly of each family and the long-tongued bees is highly supported. However, short-tongued bees are inferred as paraphyletic relative to the sister relationship between Melittidae and other bee families. Furthermore, to improve the resolution of phylogeny, various datasets and analytical approaches are performed. It is indicated that datasets including third codons of PCGs facilitate to produce identical topology and higher nodal support. The tRNA genes that have typical cloverleaf secondary structures also exhibit similar positive effects. However, rRNAs present poor sequence alignment and distinct substitution saturation, which result in negative effects on both tree topology and nodal support. In addition, Gblocks treatment can increase the congruence of topologies, but has opposite effects on nodal support between the two inference methods of maximum likelihood and Bayesian inference.</p></div

Gene arrangement of the mitogenomes of bees.

<p>PCGs, rRNAs, tRNAs, and the control region are marked with yellow, pink, green, and grey, respectively. Gene with underscore indicates that it is encoded in the N strand.</p

The phylogenetic relationships of bees inferred from the mitogenome dataset of P123T_G_BI.

<p>Numbers on branches are Bayesian posterior probabilities.</p

Saturation substitution tests for PCGs, rRNAs, and tRNAs of mitogenomes of bees.

<p>Saturation substitution tests for PCGs, rRNAs, and tRNAs of mitogenomes of bees.</p

Circular map of the mitogenomes of bees.

<p>Gene identity is obtained by BLAST searches, with the reference genome of <i>A</i>. <i>camellia</i>. The sequences are arranged in an order that the most similar mitogenome is closest to the outer edge of the map.</p

Lipidomic Profiling Reveals Distinct Differences in Sphingolipids Metabolic Pathway between Healthy Apis cerana cerana larvae and Chinese Sacbrood Disease

Chinese sacbrood disease (CSD), which is caused by Chinese sacbrood virus (CSBV), is a major viral disease in Apis cerana cerana larvae. Analysis of lipid composition is critical to the study of CSBV replication. The host lipidome profiling during CSBV infection has not been conducted. This paper identified the lipidome of the CSBV–larvae interaction through high-resolution mass spectrometry. A total of 2164 lipids were detected and divided into 20 categories. Comparison of lipidome between healthy and CSBV infected-larvae showed that 266 lipid species were altered by CSBV infection. Furthermore, qRT-PCR showed that various sphingolipid enzymes and the contents of sphingolipids in the larvae were increased, indicating that sphingolipids may be important for CSBV infection. Importantly, Cer (d14:1 + hO/21:0 + O), DG (41:0e), PE (18:0e/18:3), SM (d20:0/19:1), SM (d37:1), TG (16:0/18:1/18:3), TG (18:1/20:4/21:0) and TG (43:7) were significantly altered in both CSBV_24 h vs. CK_24 h and CSBV_48 h vs. CK_48 h. Moreover, TG (39:6), which was increased by more than 10-fold, could be used as a biomarker for the early detection of CSD. This study provides evidence that global lipidome homeostasis in A. c. cerana larvae is remodeled after CSBV infection. Detailed studies in the future may improve the understanding of the relationship between the sphingolipid pathway and CSBV replication