Functional characterization of the Ustilago maydis effector genes UMAG_11060 and UMAG_05306

Ustilago maydis causes corn smut and triggers tumor formation in all aerial parts of maize. To adapt to the host plant and promote disease progression, U. maydis uses effector proteins that exhibit organ-specific expression and adaptation during infection. This study focuses on two of these effectors, UMAG_11060 and UMAG_05306. This study characterizes UMAG_11060 (Chapter 2), which encodes the effector protein TOPLESS (TPL) interacting protein 6 (Tip6). The study shows that Tip6 interacts with the N-terminal region of ZmTPL2 through its two EAR (ethylene-responsive element binding factor-associated amphiphilic repression) motifs. These motifs are crucial for virulence function and alter the nuclear distribution pattern of ZmTPL2, disrupting host transcriptional regulation. This disruption leads to the down-regulation of 13 transcription factors in the AP2/ERF B1 subfamily. This study proposes a regulatory mechanism in which Tip6 uses repressive domains to recruit the corepressor ZmTPL2, thereby disrupting the transcriptional networks of the host plant. The second part of the thesis focuses on the characterization of UMAG_05306 (Chapter 3), which exhibits highly specific subcellular localization and appears as thick and twisted filament-like structures. The study shows that UMAG_05306 interacts with four maize dynamin related proteins (DRPs) and is able to interact with both the N- terminal and C-terminal of ZmDRP5. Three DRPs are found to interact with maize tubulin. Furthermore, UMAG_05306 directly interacts with tubulin. These findings shed light on their potential roles in U. maydis infection. In conclusion, this study provides insight into the molecular mechanisms underlying U. maydis infection and reveals the importance of UMAG_11060 and UMAG_05306 effectors for virulence and tumor formation

Online discourse and chronotopic identity work:A longitudinal digital ethnography on WeChat

This article presents a sociolinguistic analysis of online identity construction through the lens of chronotope. Based on a longitudinal digital ethnography spanning 2019–2022, we examined 253 WeChat Moments posts collected from a participant referred to as ‘Green’. Our aim is to understand how individuals with migration experiences tactically draw on multimodal and translingual resources to construct a coherent online persona amid offline mobility, change and discontent. Our research uncovers that Green’s posts normalise an ‘alternative reality’ for her online persona presentation, wherein her offline experiences are reconfigured into ‘invokable histories’ contributing to the creation of ‘chronotopic behavioural scripts’. This reconfiguration of time-spaces yields a ‘collaged’ identity continuity, particularly evident during periods of restricted physical mobility such as pandemic lockdowns. This study not only provides a methodological enhancement to sociolinguistic research on identity but also contributes to ongoing discussions on temporal-spatial reconfigurations in social media communication

SOA pattern effect mitigation by neural network based pre-equalizer for 50G PON

Semiconductor optical amplifier (SOA) is widely used for power amplification in O-band, particularly for passive optical networks (PONs) which can greatly benefit its advantages of simple structure, low power consumption and integrability with photonics circuits. However, the annoying nonlinear pattern effect degrades system performance when the SOA is needed as a pre-amplifier in PONs. Conventional solutions for pattern effect mitigation are either based on optical filtering or gain clamping. They are not simple or sufficiently flexible for practical deployment. Neural network (NN) has been demonstrated for impairment compensation in optical communications thanks to its powerful nonlinear fitting ability. In this paper, for the first time, NN-based equalizer is proposed to mitigate the SOA pattern effect for 50G PON with intensity modulation and direct detection. The experimental results confirm that the NN-based equalizer can effectively mitigate the SOA nonlinear pattern effect and significantly improve the dynamic range of receiver, achieving 29-dB power budget with the FEC limit at 1e-2. Moreover, the well-trained NN model in the receiver side can be directly placed at the transmitter in the optical line terminal to pre-equalize the signal for transmission so as to simplify digital signal processing in the optical network unit

SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection

This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structures of self-augmented compound cross-domain samples to enhance the model's generalization ability. Different from DGOD trained on multiple source domains, Single-DGOD is far more challenging to generalize well to multiple target domains with only one single source domain. Existing methods mostly adopt a similar treatment from DGOD to learn domain-invariant features by decoupling or compressing the semantic space. However, there may have two potential limitations: 1) pseudo attribute-label correlation, due to extremely scarce single-domain data; and 2) the semantic structural information is usually ignored, i.e., we found the affinities of instance-level semantic relations in samples are crucial to model generalization. In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD. Specifically, our SRCD contains two main components, namely, the texture-based self-augmentation (TBSA) module, and the local-global semantic reasoning (LGSR) module. TBSA aims to eliminate the effects of irrelevant attributes associated with labels, such as light, shadow, color, etc., at the image level by a light-yet-efficient self-augmentation. Moreover, LGSR is used to further model the semantic relationships on instance features to uncover and maintain the intrinsic semantic structures. Extensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed SRCD.Comment: 10 pages, 5 figure

The treatment of a Morel-Lavallée lesion of the thigh with incision and drainage along with tissue debridement and a surgically placed drain: A case report and literature review

BackgroundA Morel-Lavallée lesion (MLL) is a rare closed degloving injury that usually occurs around the hips and is associated with pelvic fractures after high-energy trauma, which is commonly overshadowed by other severe post-traumatic manifestations. An isolated MLL, mostly caused by low-energy violence, is even rarer. Thus, the rates of misdiagnosis and missed diagnosis are often high. In this case report and literature review, we review the pathophysiology, clinical manifestations, imaging data, and treatment of this lesion to increase awareness of this rare disease.Case reportWe report the case of an isolated MLL in the right thigh caused by trauma, which happened to be one of missed diagnosis both at the initial visit and at the return visit of the patient, with a significant sign of a mass on MRI. Given the size of the lesion, open debridement and irrigation were adopted to treat the lesion, and the patient recovered well post-operatively.ConclusionYoung surgeons should pay attention to the MLL with sufficient recognization to avoid missed diagnosis and misdiagnosis. Comprehensive physical examination and imaging data play important roles in the diagnosis of MLL. In the early stages of this injury, a detailed history review combined with physical examination and MRI, can reduce the rates of missed diagnosis and misdiagnosis. The choice of the therapeutic scheme depends on the size and severity of the lesion. For an isolated MLL, compared with conservative treatments, we suggest that incision and drainage, along with tissue debridement and a surgically placed drain, will reduce the rates of infection and recurrence