Adaptive and Non-Adaptive Evolution of the Control of Gene Expression

Non-adaptive evolution refers to evolutionary processes that are primarily driven not by natural selection, but by factors such as a bias towards generating certain mutations over others. Although non-adaptive evolution is supported by abundant data, it is obscure outside the field of evolutionary biology, potentially for historical reasons. Considering non-adaptive evolution helps us to understand the origins and roles of traits at molecular and cellular levels, where research is often dominated by adaptationist assumptions. To demonstrate that a balanced view on evolution is necessary, my thesis research asks how adaptive and non-adaptive evolution shape the control of gene expression. I start by simulating the evolution of mechanisms for quality control of gene expression. I show that the error rate associated with gene expression is determined by both the mutational bias that tends to increase the error rate and by the effective population size of the species, which determines the strength of natural selection on the error rate. This offers an explanation for the observed non-monotonic relationship between transcriptional error rate and effective population size. I next study the evolution of transcriptional regulatory networks (TRNs). The adaptationist view hypothesizes that the enrichment of a subnetwork called coherent type 1 feed-forward loops (C1-FFLs) in TRNs is an adaptation for filtering out short spurious signals, but this and similar hypotheses about other enriched subnetworks are widely questioned by evolutionary biologists, because the adaptive hypothesis fails to consider network topologies that evolve non-adaptively. To help resolve this debate, I developed a highly mechanistic computational model that captures non-adaptive factors that can shape the topology of TRNs. I show that functional C1-FFLs evolve readily under selection for filtering out a spurious signal, but not under control selection conditions. While this result supports the adaptive origin of C1-FFLs, I show that non-adaptive subnetworks can also be enriched in TRNs evolved for filtering out a spurious signal, suggesting that inferring functions of TRNs from topology alone can be problematic. A further complication comes from the fact that a subnetwork that is topologically different from C1-FFLs also evolves to filter out spurious signals. In conclusion, I argue that non-adaptive evolution can explain the origins and roles of traits that are difficult to understand under adaptationism, and that considering non-adaptive evolution is necessary to carry out scientific research in all fields of biology. Molecular and cellular biologists should actively consider non-adaptive evolution in their research

Universal R-matrix Of The Super Yangian Double DY(gl(1|1))

Based on Drinfeld realization of super Yangian Double DY(gl(1|1)), its pairing relations and universal R-matrix are given. By taking evaluation representation of universal R-matrix, another realization $L^{\pm}(u)$ of DY(gl(1|1)) is obtained. These two realizations of DY(gl(1|1)) are related by the supersymmetric extension of Ding-Frenkel map.Comment: 6 pages, latex, no figure

Detecting Textual Adversarial Examples through Randomized Substitution and Vote

A line of work has shown that natural text processing models are vulnerable to adversarial examples. Correspondingly, various defense methods are proposed to mitigate the threat of textual adversarial examples, eg, adversarial training, input transformations, detection, etc. In this work, we treat the optimization process for synonym substitution based textual adversarial attacks as a specific sequence of word replacement, in which each word mutually influences other words. We identify that we could destroy such mutual interaction and eliminate the adversarial perturbation by randomly substituting a word with its synonyms. Based on this observation, we propose a novel textual adversarial example detection method, termed Randomized Substitution and Vote (RS&V), which votes the prediction label by accumulating the logits of k samples generated by randomly substituting the words in the input text with synonyms. The proposed RS&V is generally applicable to any existing neural networks without modification on the architecture or extra training, and it is orthogonal to prior work on making the classification network itself more robust. Empirical evaluations on three benchmark datasets demonstrate that our RS&V could detect the textual adversarial examples more successfully than the existing detection methods while maintaining the high classification accuracy on benign samples.Comment: Accepted by UAI 2022, code is avaliable at https://github.com/JHL-HUST/RS

Cimiracemate A confers protection on arthritic neonatal rats via regulation of iNOS/NF-κB/TLR-4 pathway

Purpose: To investigate the protective effect of cimiracemate A on Freund’s adjuvant-induced rheumatoid arthritis (RA) in neonatal rats, and the underlying mechanism. Methods: Rheumatoid arthritis was induced in rat pups using Complete Freund’s adjuvant (100 µg/100 µL/body weight) which was intra-dermally injected at the tail region. After 21 days of establishment of RA, the rats were randomly assigned to four groups of ten rats each: control group, RA group, 5 mg/kg cimiracemate A group, and 10 mg/kg cimiracemate A group. Cimiracemate A was orally administered for 45 days. The effect of cimiracemate A on oxidative stress biomarkers, superoxide dismutase (SOD), malondialdehyde (MDA) and reduced glutathione (GSH) were determined using standard methods. Plasma levels of the inflammatory cytokines interleukin 1β (IL-1β) and tumor necrosis factor-α (TNF-α), and prostaglandin E2 (PGE-2) and matrix metalloproteinase-3 (MMP-3) were determined using enzyme-linked immunosorbent assay (ELISA). Western blotting was used to determine the levels of protein expressions of iNOS, NF-κB and TLR-4. Results: The level of MDA significantly increased and the level of GSH significantly decreased in RA group relative to control group (p < 0.05) following treatment with cimiracemate A. SOD activity was significantly reduced in RA group, when compared with control group (p < 0.05). However, treatment with cimiracemate A significantly and dose-dependently reversed the altered levels of MDA and GSH and SOD activity, when compared with RA group (p < 0.05). Plasma levels of IL-1β, TNF-α, PGE-2 and MMP-3 were significantly higher in RA group than in control group, but were significantly and dosedependently reduced after treatment with cimiracemate A (p < 0.05). There were significant increases in the levels of expression of iNOS, NF-κB and TLR-4 proteins in the chondrocytes of RA group, relative to control group (p < 0.05). However, treatment with cimiracemate A significantly and dose-dependently down-regulated the expressions of these proteins, when compared with RA group (p < 0.05). Conclusion: The results of this study indicate that cimiracemate A confers some degree of protection on arthritic neonatal rats via a mechanism that involves regulation of iNOS/NF-κB/TLR-4 pathway