Structure-function analysis of Cmu1, the secreted chorismate mutase from Ustilago maydis

The basidiomycete fungus Ustilago maydis is the causative agent for smut disease of maize (Zea mays). More than 400 putative secreted proteins are encoded in the genome of U. maydis. The secreted chorismate mutase Cmu1 of U. maydis is such a translocated virulence promoting effector. The chorismate mutase activity of Cmu1 in the cytosol is proposed to lower the chorismate levels in the chloroplast where it would serve as precursor for the biosynthesis of the plant defense hormone salicylic acid (SA). The crystal structure of Cmu1 revealed several unique features in comparison to the cytoplasmic chorismate mutase Aro7p of Saccharomyces cerevisiae, including a surface exposed acidic patch, a disulfide bond, a putative fatty acid binding site and a loop region. This thesis shows, that site-directed mutagenesis affecting the acidic patch, the disulfide bond and the fatty acid binding site results in functional mutant proteins that can complement the virulence phenotype of CL13Δcmu1 strains. Wildtype Cmu1 protein purified after heterologous expression in E. coli followed a Michaelis-Menten kinetic in a chorismase mutase activity assay. Mutations in the fatty acid binding site did not alter the observed kinetic. A U. maydis triple mutant of cmu1, the isochorismatase coding gene um12021 and shy1 encoding a salicylate hydroxylase was reduced in virulence compared to any single or double mutants, suggesting an interplay of three U. maydis enzymes in suppressing SA pathway. By performing immunoprecipitation (IP) of Cmu1 from infected leave tissues followed by mass spectrometry, the maize protein Cmi1 (Cmu1 interactor 1) could be identified as an interactor. In vitro pull-down experiments confirmed the interaction between Cmu1 and Cmi1. Recombinant Cmi1 inhibited the chorismate mutase activity of Cmu1. The expression of cmi1 is strongly induced upon the infection of U. maydis, indicating that it is likely a pathogenesis related (PR) protein. Hydrogen-Deuterium exchange mass spectrometry (HDX/MS) mapped the interaction interface between Cmu1 and Cmi1, which involved the loop region of Cmu1. Truncation of the loop in Cmu1, which abolished the interaction of Cmu1 with Cmi1, showed only partial complementation of CL13Δcmu1 mutants, suggesting that the interaction between Cmu1 and Cmi1 may be relevant for the virulence of U. maydis

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data nonstationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and out-perform baselines.Comment: Accepted at the 22nd IEEE International Conference on Data Mining (ICDM 2022) Full Pape

Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff

Spiking neural networks (SNNs), a variant of artificial neural networks (ANNs) with the benefit of energy efficiency, have achieved the accuracy close to its ANN counterparts, on benchmark datasets such as CIFAR10/100 and ImageNet. However, comparing with frame-based input (e.g., images), event-based inputs from e.g., Dynamic Vision Sensor (DVS) can make a better use of SNNs thanks to the SNNs' asynchronous working mechanism. In this paper, we strengthen the marriage between SNNs and event-based inputs with a proposal to consider anytime optimal inference SNNs, or AOI-SNNs, which can terminate anytime during the inference to achieve optimal inference result. Two novel optimisation techniques are presented to achieve AOI-SNNs: a regularisation and a cutoff. The regularisation enables the training and construction of SNNs with optimised performance, and the cutoff technique optimises the inference of SNNs on event-driven inputs. We conduct an extensive set of experiments on multiple benchmark event-based datasets, including CIFAR10-DVS, N-Caltech101 and DVS128 Gesture. The experimental results demonstrate that our techniques are superior to the state-of-the-art with respect to the accuracy and latency

Differences in Intrinsic Brain Abnormalities Between Patients With Left- and Right-Sided Long-Term Hearing Impairment

Unilateral hearing impairment is characterized by asymmetric hearing input, which causes bilateral unbalanced auditory afferents and tinnitus of varying degrees. Long-term hearing imbalance can cause functional reorganization in the brain. However, differences between intrinsic functional changes in the brains of patients with left- and those with right-sided long-term hearing impairments are incompletely understood. This study included 67 patients with unilateral hearing impairments (left-sided, 33 patients; right-sided, 34 patients) and 32 healthy controls. All study participants underwent blood oxygenation level dependent resting-state functional magnetic resonance imaging and T1-weighted imaging with three-dimensional fast spoiled gradient-echo sequences. After data preprocessing, fractional amplitude of low frequency (fALFF) and functional connectivity (FC) analyses were used to evaluate differences between patients and healthy controls. When compared with the right-sided hearing impairment group, the left-sided hearing impairment group showed significantly higher fALFF values in the left superior parietal gyrus, right inferior parietal lobule, and right superior frontal gyrus, whereas it showed significantly lower fALFF values in the left Heschl’s gyrus, right supramarginal gyrus, and left superior frontal gyrus. In the left-sided hearing impairment group, paired brain regions with enhanced FC were the left Heschl’s gyrus and right supramarginal gyrus, left Heschl’s gyrus and left superior parietal gyrus, left superior parietal gyrus and right inferior parietal lobule, right inferior parietal lobule and right superior frontal gyrus, and left and right superior frontal gyri. In the left-sided hearing impairment group, the FC of the paired brain regions correlated negatively with the duration and pure tone audiometry were in the left Heschl’s gyrus and right supramarginal gyrus. In the right-sided hearing impairment group, the FC of the paired brain regions correlated negatively with the duration was in the left Heschl’s gyrus and superior parietal gyrus, and with pure tone audiometry was right inferior parietal lobule and superior frontal gyrus. The intrinsic reintegration mechanisms of the brain appeared to differ between patients with left-sided hearing impairment and those with right-sided hearing impairment, and the severity of hearing impairment was associated with differences in functional integration in certain brain regions

Unified Classification of Nominal Classifiers and Formalization of Classifier-noun Phrases

One of the main problems that affect the quality of machine translation is how to express the knowledge of language in precision. Based on the theory of Semantic Element (SE) in Unified Linguistics, a new unified classification of English and Chinese nominal classifiers is proposed from the perspective of C-E and E-C translation. Different Semantic Element Representations (SER) of classifiers in English and Chinese have the same semantic type of classifiers. The English and Chinese noun-classifier phrases are formalized into English and Chinese SER respectively. Key words: Chinese Classifier-Noun Phrase; Classification of Nominal Classifiers; Formalization; SE; SE

SHARING THE MAJORITY OF A SCREEN BASED ON OBJECT DETECTION

Users typically do not hesitate to click on a screen sharing button. However, their personal information (such as website bookmarks, notifications, and desktop files) may be revealed unwillingly through such a share. Accordingly, techniques are presented herein that protect a users\u27 private information when they share their entire desktop or separate windows. Aspects of the presented techniques adopt a state of the art (SOTA) object detection model and achieve real-time inference. Further, the incorporated algorithm is lightweight but useful in that it will not impact performance, but it will provide a positive user experience

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer. Additionally, we optimize the model's reasoning path selection using the Proximal Policy Optimization (PPO) algorithm, further enhancing its reasoning capabilities. Our method offers several advantages compared to previous approaches. Firstly, we transform the process of solving complex reasoning questions by breaking them down into a series of simpler sub-questions, significantly reducing the task difficulty and making it more suitable for SLMs. Secondly, we optimize the model's reasoning path selection through the PPO algorithm. We conduct comprehensive experiments on four arithmetic reasoning datasets, demonstrating that our method achieves significant performance improvements compared to state-of-the-art competitors.Comment: Accepted to EMNLP 202