Deciphering the Multi-tiered Regulatory Network That Links Cyclic-di-GMP Signaling to Virulence and Bacterial Behaviors

Bis-(3’-5’)-cyclic dimeric guanosine monophosphate (c-di-GMP) is a bacterial second messenger that regulates multiple cellular behaviors in most major bacterial phyla. C-di-GMP signaling in bacterial often includes enzymes that are responsible for the synthesis and degradation of c-di-GMP, effector proteins or molecules that bind c-di-GMP, and targets that interact with effectors. However, little is known about the specificity of c-di-GMP signaling in controlling virulence and bacterial behaviors. In this work, we have investigated the c-di-GMP signaling network using the model plant pathogen Dickeya dadantii 3937. In Chapter 2, we characterized two PilZ domain proteins that regulate biofilm formation, swimming motility, Type III secretion system (T3SS) gene expression, and pectate lyase production in high c-di-GMP level conditions. YcgR3937 binds c-di-GMP both in vivo and in vitro. Next, we revealed a sophisticated regulatory network that connects the sRNA, c-di-GMP signaling, and flagellar master regulator FlhDC. We proposed FlhDC regulates T3SS through three distinct pathways, including the FlhDC-FliA-YcgR3937 pathway; the FlhDC-EcpC-RpoN-HrpL pathway; and the FlhDC-rsmB-RsmA-HrpL pathway. Genetic analysis showed that EcpC is the most dominant factor for FlhDC to positively regulate T3SS expression. In chapter 3, we constructed a panel of single-deletion mutants, in which each GGDEF and/or EAL domain protein coding gene was individually either deleted or inactivated. Various cellular outputs were investigated using these mutants. We showed that GGDEF domain protein GcpA negatively regulates swimming motility, pectate lyase production, and T3SS gene expression. GcpD and GcpL only negatively regulate the expression of T3SS and swimming motility but not the pectate lyase production

Identification and management of resistant hypertension

Resistant hypertension is defined as blood pressure being higher than the patient's target blood pressure despite the use of three or more different types of antihypertensive drugs at the optimal dose, and one of them should be a diuretic. The evaluation of patients with resistant hypertension should first confirm that they have true resistant hypertension. By eliminating or correcting false resistance factors, such as white coat hypertension, poor blood pressure measurement technique, poor drug compliance, improper dosage or combination of antihypertensive drugs, and white coat effects and clinical inertia. Resistant hypertension therapy includes improved compliance with the use of drugs, secondary hypertension detection and treatment, use of lifestyle measures and treatment of obesity, and other comorbidities. switching to a long-acting diuretic type of thiazide like chlorthalidone could improve the BP from the patients taking hydrochlorothiazide. This review paper illustrates briefly the identification of the underlying causes of resistant hypertension and therapeutic strategies, which may contribute to the proper diagnosis and an improvement of the long term management of resistant hypertension.

Distributing Non-cooperative Object Information in Next Generation Radar Surveillance Systems

Radar surveillance systems, in both airspace and maritime domains, are facing increasing challenges in dealing with objects that cannot be detected by traditional transponder-based radar surveillance technologies. These objects, including birds, weather, Unmanned Aircraft Systems (UAS), hot balloons, are labeled as non-cooperative objects. In order to prevent ambiguity and confusion for human operators using the surveillance data non-cooperative objects are commonly treated as unwanted clutter and removed from the displayed data. However, the omitted information of non-cooperative object can be critical to aircraft safety. With new developments in technology and radar capabilities, it is possible to detect these non-cooperative objects and consider how to distribute relevant information about them to human operators throughout a system. The research goal of this thesis is to identify the human factors challenges in future radar surveillance systems where non-cooperative object information is distributed to both air traffic controllers and pilots. In order to achieve the goal, the thesis first constructed a model of surveillance information distribution in current ATC operations and a model of surveillance information distribution in the expected future operational environment. The expected future surveillance information distribution model was then carefully examined to identify potential human factors challenges in the non-cooperative object information distribution process. Two of the identified challenges (non-equal time delay and information level of details) were studied in depth through conducting human-in-the-loop experiments and online surveys. The results of an asynchronous information (non-equal time delay) static simulation environment experiment showed that while a delay in the non-cooperative object information would lead to observable but not statistically significant longer communication time, it does have a significant effect on number of clarification statements – with an increase of time delay, more clarifications were made. A survey of controller and pilot perceptions of maximum acceptable delay showed no significant differences in the average maximum acceptable delay reported by controller (20.5 seconds) and pilot (13.64 seconds) participants. Future research should consider adopting dynamic simulation environment, subject matter experts and shorter delay intervals to identify an acceptable delay threshold. The survey results also demonstrated that there are more controllers and pilots who have had encounters with UAS in their daily tasks than what was originally expected. The survey also helped identify operational information requirements and availabilities for individual UAS and challenges in sharing non-cooperative object information between controllers and pilots. These findings are quite valuable as they provide guidance on future radar surveillance systems design in supporting the effective distribution of non-cooperative object information. Future work should complete the analysis of the survey and create more dynamic environment for studying information asynchrony

A Supervised STDP-based Training Algorithm for Living Neural Networks

Neural networks have shown great potential in many applications like speech recognition, drug discovery, image classification, and object detection. Neural network models are inspired by biological neural networks, but they are optimized to perform machine learning tasks on digital computers. The proposed work explores the possibilities of using living neural networks in vitro as basic computational elements for machine learning applications. A new supervised STDP-based learning algorithm is proposed in this work, which considers neuron engineering constrains. A 74.7% accuracy is achieved on the MNIST benchmark for handwritten digit recognition.Comment: 5 pages, 3 figures, Accepted by ICASSP 201

RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition

Emotion recognition in conversation (ERC) has received increasing attention from researchers due to its wide range of applications. As conversation has a natural graph structure, numerous approaches used to model ERC based on graph convolutional networks (GCNs) have yielded significant results. However, the aggregation approach of traditional GCNs suffers from the node information redundancy problem, leading to node discriminant information loss. Additionally, single-layer GCNs lack the capacity to capture long-range contextual information from the graph. Furthermore, the majority of approaches are based on textual modality or stitching together different modalities, resulting in a weak ability to capture interactions between modalities. To address these problems, we present the relational bilevel aggregation graph convolutional network (RBA-GCN), which consists of three modules: the graph generation module (GGM), similarity-based cluster building module (SCBM) and bilevel aggregation module (BiAM). First, GGM constructs a novel graph to reduce the redundancy of target node information. Then, SCBM calculates the node similarity in the target node and its structural neighborhood, where noisy information with low similarity is filtered out to preserve the discriminant information of the node. Meanwhile, BiAM is a novel aggregation method that can preserve the information of nodes during the aggregation process. This module can construct the interaction between different modalities and capture long-range contextual information based on similarity clusters. On both the IEMOCAP and MELD datasets, the weighted average F1 score of RBA-GCN has a 2.17$\sim$5.21\% improvement over that of the most advanced method

Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators

Advanced packaging offers a new design paradigm in the post-Moore era, where many small chiplets can be assembled into a large system. Based on heterogeneous integration, a chiplet-based accelerator can be highly specialized for a specific workload, demonstrating extreme efficiency and cost reduction. To fully leverage this potential, it is critical to explore both the architectural design space for individual chiplets and different integration options to assemble these chiplets, which have yet to be fully exploited by existing proposals. This paper proposes Monad, a cost-aware specialization approach for chiplet-based spatial accelerators that explores the tradeoffs between PPA and fabrication costs. To evaluate a specialized system, we introduce a modeling framework considering the non-uniformity in dataflow, pipelining, and communications when executing multiple tensor workloads on different chiplets. We propose to combine the architecture and integration design space by uniformly encoding the design aspects for both spaces and exploring them with a systematic ML-based approach. The experiments demonstrate that Monad can achieve an average of 16% and 30% EDP reduction compared with the state-of-the-art chiplet-based accelerators, Simba and NN-Baton, respectively.Comment: To be published in ICCAD 202

Association between obstructive sleep apnea and resistant hypertension: systematic review and meta-analysis

IntroductionObstructive sleep apnea syndrome (OSAS) is a chronic disorder characterized by recurring episode obstruction and collapse of upper airways during sleep, leading to hypoxia and sleep disruption. OSAS is commonly associated with an increased prevalence of hypertension. The underlying mechanism in OSA with hypertension is related to intermittent hypoxia. This hypoxia induces endothelial dysfunction, overactivity of sympathetic effects, oxidative stress, and systemic inflammation. Hypoxemia triggers the sympathetic process's overactivity, leading to the development of resistant hypertension in OSA. Thus, we hypothesize to evaluate the association between resistant hypertension and OSA.MethodsThe PubMed, ClinicalTrails.gov, CINAHL, Google Scholar, Cochrane Library, and Science Direct databases were searched from 2000 to January 2022 for studies demonstrating the association between resistant hypertension and OSA. The eligible articles underwent quality appraisal, meta-analysis, and heterogeneity assessment.ResultsThis study comprises seven studies, including 2,541 patients ranged from 20 to 70 years. The pooled analysis of six studies demonstrated that OSAS patients with a history of increased age, gender, obesity, and smoking status are at an increased risk for resistant hypertension (OR: 4.16 [3.07, 5.64], I2:0%) than the non-OSAS patients. Similarly, the pooled effect demonstrated that patients with OSAS were at an increased risk of resistant hypertension (OR: 3.34 [2.44, 4.58]; I2:0%) than the non-OSAS patients when all associated risk factors were adjusted using multivariate analysis.ConclusionThis study concludes that OSAS patients with or without related risk factors demonstrated increased risk for resistant hypertension

Adaptive Query Prompting for Multi-Domain Landmark Detection

Medical landmark detection is crucial in various medical imaging modalities and procedures. Although deep learning-based methods have achieve promising performance, they are mostly designed for specific anatomical regions or tasks. In this work, we propose a universal model for multi-domain landmark detection by leveraging transformer architecture and developing a prompting component, named as Adaptive Query Prompting (AQP). Instead of embedding additional modules in the backbone network, we design a separate module to generate prompts that can be effectively extended to any other transformer network. In our proposed AQP, prompts are learnable parameters maintained in a memory space called prompt pool. The central idea is to keep the backbone frozen and then optimize prompts to instruct the model inference process. Furthermore, we employ a lightweight decoder to decode landmarks from the extracted features, namely Light-MLD. Thanks to the lightweight nature of the decoder and AQP, we can handle multiple datasets by sharing the backbone encoder and then only perform partial parameter tuning without incurring much additional cost. It has the potential to be extended to more landmark detection tasks. We conduct experiments on three widely used X-ray datasets for different medical landmark detection tasks. Our proposed Light-MLD coupled with AQP achieves SOTA performance on many metrics even without the use of elaborate structural designs or complex frameworks

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

Top-$k$ frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accuracy for detecting the Top-$k$ items. Despite the memory efficiency concern, the users could suffer from privacy loss if participating in the task without proper protection, since their contributed local data streams may continually leak sensitive individual information. However, most existing works solely focus on addressing either the memory-efficiency problem or the privacy concerns but seldom jointly, which cannot achieve a satisfactory tradeoff between memory efficiency, privacy protection, and detection accuracy. In this paper, we present a novel framework HG-LDP to achieve accurate Top-$k$ item detection at bounded memory expense, while providing rigorous local differential privacy (LDP) protection. Specifically, we identify two key challenges naturally arising in the task, which reveal that directly applying existing LDP techniques will lead to an inferior ``accuracy-privacy-memory efficiency'' tradeoff. Therefore, we instantiate three advanced schemes under the framework by designing novel LDP randomization methods, which address the hurdles caused by the large size of the item domain and by the limited space of the memory. We conduct comprehensive experiments on both synthetic and real-world datasets to show that the proposed advanced schemes achieve a superior ``accuracy-privacy-memory efficiency'' tradeoff, saving $2300\times$ memory over baseline methods when the item domain size is $41,270$. Our code is open-sourced via the link