Improved Test-Time Adaptation for Domain Generalization

The main challenge in domain generalization (DG) is to handle the distribution shift problem that lies between the training and test data. Recent studies suggest that test-time training (TTT), which adapts the learned model with test data, might be a promising solution to the problem. Generally, a TTT strategy hinges its performance on two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase. Both previous arts and our experiments indicate that TTT may not improve but be detrimental to the learned model if those two factors are not properly considered. This work addresses those two factors by proposing an Improved Test-Time Adaptation (ITTA) method. First, instead of heuristically defining an auxiliary objective, we propose a learnable consistency loss for the TTT task, which contains learnable parameters that can be adjusted toward better alignment between our TTT task and the main prediction task. Second, we introduce additional adaptive parameters for the trained model, and we suggest only updating the adaptive parameters during the test phase. Through extensive experiments, we show that the proposed two strategies are beneficial for the learned model (see Figure 1), and ITTA could achieve superior performance to the current state-of-the-art methods on several DG benchmarks. Code is available at https://github.com/liangchen527/ITTA.Comment: Accepted by CVPR 202

Domain Generalization via Rationale Invariance

This paper offers a new perspective to ease the challenge of domain generalization, which involves maintaining robust results even in unseen environments. Our design focuses on the decision-making process in the final classifier layer. Specifically, we propose treating the element-wise contributions to the final results as the rationale for making a decision and representing the rationale for each sample as a matrix. For a well-generalized model, we suggest the rationale matrices for samples belonging to the same category should be similar, indicating the model relies on domain-invariant clues to make decisions, thereby ensuring robust results. To implement this idea, we introduce a rationale invariance loss as a simple regularization technique, requiring only a few lines of code. Our experiments demonstrate that the proposed approach achieves competitive results across various datasets, despite its simplicity. Code is available at \url{https://github.com/liangchen527/RIDG}.Comment: Accepted in ICCV 202

The genetic discrimination observatory : confronting novel issues in genetic discrimination

Genetic discrimination (GD) is the differential or unfair profiling of an individual on the basis of genetic data. This article summarizes the actions of the Genetic Discrimination Observatory (GDO) in addressing GD and recent developments in GD since late 2020. It shows how GD can take many forms in today’s rapidly evolving society.http://www.journals.elsevier.com/trends-in-geneticshj2022Immunolog

Epigenetic Discrimination: Emerging Applications of Epigenetics Pointing to the Limitations of Policies Against Genetic Discrimination

Over more than two decades, various policies have been adopted worldwide to restrict the use of individual genetic information for non-medical reasons by third parties and prevent ‘genetic discrimination’. In this paper, we bring attention to the growing interest for individual epigenetic information by insurers and forensic scientists. We question whether such interest could lead to ‘epigenetic discrimination’ – the differential adverse treatment or abusive profiling of individuals or groups based on their actual or presumed epigenetic characteristics – and argue that we might already be facing the limitations of recently adopted normative approaches against genetic discrimination. First, we highlight some similarities and differences between genetic and epigenetic modifications, and stress potential challenges to regulating epigenetic discrimination. Second, we argue that most existing normative approaches against genetic discrimination fall short in providing oversight into the field of epigenetics. We conclude with a call for discussion on the issue, and the development of comprehensive and forward-looking preventive strategies against epigenetic discrimination

Identification and Analysis of RNA Editing Sites in the Chloroplast Transcripts of Aegilops tauschii L.

RNA editing is an important way to convert cytidine (C) to uridine (U) at specific sites within RNA molecules at a post-transcriptional level in the chloroplasts of higher plants. Although it has been systematically studied in many plants, little is known about RNA editing in the wheat D genome donor Aegilops tauschii L. Here, we investigated the chloroplast RNA editing of Ae. tauschii and compared it with other wheat relatives to trace the evolution of wheat. Through bioinformatics prediction, a total of 34 C-to-U editing sites were identified, 17 of which were validated using RT-PCR product sequencing. Furthermore, 60 sites were found by the RNA-Seq read mapping approach, 24 of which agreed with the prediction and six were validated experimentally. The editing sites were biased toward tCn or nCa trinucleotides and 5′-pyrimidines, which were consistent with the flanking bases of editing sites of other seed plants. Furthermore, the editing events could result in the alteration of the secondary structures and topologies of the corresponding proteins, suggesting that RNA editing might impact the function of target genes. Finally, comparative analysis found some evolutionarily conserved editing sites in wheat and two species-specific sites were also obtained. This study is the first to report on RNA editing in Aegilops tauschii L, which not only sheds light on the evolution of wheat from the point of view of RNA editing, but also lays a foundation for further studies to identify the mechanisms of C-to-U alterations

Alleviation of Plasma Homocysteine Level by Phytoestrogen α-Zearalanol Might Be Related to the Reduction of Cystathionine β-Synthase Nitration

Hyperhomocysteinemia is strongly associated with cardiovascular diseases. Previous studies have shown that phytoestrogen α-zearalanol can protect cardiovascular system from hyperhomocysteinemia and ameliorate the level of plasma total homocysteine; however, the underlying mechanisms remain to be clarified. The aim of this research is to investigate the possible molecular mechanisms involved in ameliorating the level of plasma homocysteine by α-zearalanol. By the successfully established diet-induced hyperhomocysteinemia rat models, we found that, after α-zearalanol treatment, the activity of cystathionine β-synthase, the key enzyme in homocysteine metabolism, was significantly elevated and level of nitrative stress in liver was significantly reduced. In correlation with this, results also showed a decreased nitration level of cystathionine β-synthase in liver. Together data implied that alleviation of plasma homocysteine level by phytoestrogen α-zearalanol might be related to the reduction of cystathionine β-synthase nitration