Structure and stereochemistry of the base excision repair glycosylase MutY reveal a mechanism similar to retaining glycosidases.

MutY adenine glycosylases prevent DNA mutations by excising adenine from promutagenic 8-oxo-7,8-dihydroguanine (OG):A mismatches. Here, we describe structural features of the MutY active site bound to an azaribose transition state analog which indicate a catalytic role for Tyr126 and approach of the water nucleophile on the same side as the departing adenine base. The idea that Tyr126 participates in catalysis, recently predicted by modeling calculations, is strongly supported by mutagenesis and by seeing close contact between the hydroxyl group of this residue and the azaribose moiety of the transition state analog. NMR analysis of MutY methanolysis products corroborates a mechanism for adenine removal with retention of stereochemistry. Based on these results, we propose a revised mechanism for MutY that involves two nucleophilic displacement steps akin to the mechanisms accepted for 'retaining' O-glycosidases. This new-for-MutY yet familiar mechanism may also be operative in related base excision repair glycosylases and provides a critical framework for analysis of human MutY (MUTYH) variants associated with inherited colorectal cancer

Study on the triphenyl tetrazolium chloride– dehydrogenase activity (TTC-DHA) method in determination of bioactivity for treating tomato paste wastewater

A quick analysis of the sludge activity method based on triphenyltetrazolium chloride-dehydrogenase activity (TTC-DHA) was developed to change the rule and status of the biological activity of the activated sludge in tomato paste wastewater treatment. The results indicate that dehydrogenase activity (DHA) can effectively facilitate the biochemical reaction of tomato paste wastewater treatment upon analysis of the influences of various DHA and kinetic factors. The biological activity of the activated sludge by TTC-DHA was changed to become applicable to aeration and wastewater treatment operation and management.Key words: Tomato paste wastewater, TTC-DHA, bioactivity, active sludge

Sequential recommender systems: Challenges, progress and prospects

© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users' preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area

Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender

Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The latent embedding space of those techniques makes adversarial attacks challenging to detect at an early stage. Recent advance in causality shows that counterfactual can also be considered one of the ways to generate the adversarial samples drawn from different distribution as the training samples. We propose to explore adversarial examples and attack agnostic detection on reinforcement learning (RL)-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our white-box detector trained with one crafting method has the generalization ability over several other crafting methods

Dimensional reduction, quantum Hall effect and layer parity in graphite films

The quantum Hall effect (QHE) originates from discrete Landau levels forming in a two-dimensional (2D) electron system in a magnetic field. In three dimensions (3D), the QHE is forbidden because the third dimension spreads Landau levels into multiple overlapping bands, destroying the quantisation. Here we report the QHE in graphite crystals that are up to hundreds of atomic layers thick - thickness at which graphite was believed to behave as a 3D bulk semimetal. We attribute the observation to a dimensional reduction of electron dynamics in high magnetic fields, such that the electron spectrum remains continuous only in the direction of the magnetic field, and only the last two quasi-one-dimensional (1D) Landau bands cross the Fermi level. In sufficiently thin graphite films, the formation of standing waves breaks these 1D bands into a discrete spectrum, giving rise to a multitude of quantum Hall plateaux. Despite a large number of layers, we observe a profound difference between films with even and odd numbers of graphene layers. For odd numbers, the absence of inversion symmetry causes valley polarisation of the standing-wave states within 1D Landau bands. This reduces QHE gaps, as compared to films of similar thicknesses but with even layer numbers because the latter retain the inversion symmetry characteristic of bilayer graphene. High-quality graphite films present a novel QHE system with a parity-controlled valley polarisation and intricate interplay between orbital, spin and valley states, and clear signatures of electron-electron interactions including the fractional QHE below 0.5 K

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Super-reflection of light from a random amplifying medium with disorder in the complex refractive index : Statistics of fluctuations

The probability distribution of the reflection coefficient for light reflected from a one-dimensional random amplifying medium with {\it cross-correlated} spatial disorder in the real and the imaginary parts of the refractive index is derived using the method of invariant imbedding. The statistics of fluctuations have been obtained for both the correlated telegraph noise and the Gaussian white-noise models for the disorder. In both cases, an enhanced backscattering (super-reflection with reflection coefficient greater than unity) results because of coherent feedback due to Anderson localization and coherent amplification in the medium. The results show that the effect of randomness in the imaginary part of the refractive index on localization and super-reflection is qualitatively different.Comment: RevTex 6 pages, 3 figures in ps file