Detection, Activity Measurement and Phylogeny of Ureolytic Bacteria Isolated from Elasmobranch Tissue

Free-ranging marine elasmobranch tissue-associated micro-organisms were cultured from free-ranging Atlantic stingray (Dasyatis sabina) and Atlantic sharpnose sharks (Rhizoprionodon terraenovae). 16S rRNA gene phylogeny indicated bacteria community structure in both elasmobranchs were under phylum Proteobacteria, Firmicutes and Actinobacteria. By conducting split-plot ANOVA, we found the microbial richness is significantly different (P=0.0814) between two superorders of elasmobranch, which may largely due to their preferred habitats and feeding habits. Urease presentence and activity was detected in phylogenetically diverse bacterial strains. Species with high urea-hydrolyzing ability, such as Micrococcus luteus (shark blood isolate: 46.84 mU/mg protein; stingray blood isolate: 24.36 mU/mg protein) and Staphylococcus saprophyticus (could also be xylosus) (66.46 mU/mg protein) were both isolated from blood samples. This study suggests the examination of urease activity to promote the better profile of the virulence of some novel bacteria species. The phylogeny of bacterial 16S rRNA genes and urease-coding ureC genes were analyzed and compared,combined with the examination of urease activity of ureolytic bacteria, we found ureC gene as a potential functional marker. The study of enzymatic (urease) activity and ureC gene-based phylogeny provides a better understanding of ureolytic bacteria for their urea-utilizing potential, enables the further study of urease-positive strains on bioengineering and bioremediating of marine urea eutrophication in a larger scale. To our knowledge, this should be the first study to unveil the urea-hydrolyzing ability of marine elasmobranch tissue-associated ureolytic microbes, and the potential of the ureC gene to be a functional marker

Boundary Green functions of topological insulators and superconductors

Topological insulators and superconductors are characterized by their gapless boundary modes. In this paper, we develop a recursive approach to the boundary Green function which encodes this nontrivial boundary physics. Our approach describes the various topologically trivial and nontrivial phases as fixed points of a recursion and provides direct access to the phase diagram, the localization properties of the edge modes, as well as topological indices. We illustrate our approach in the context of various familiar models such as the Su-Schrieffer-Heeger model, the Kitaev chain, and a model for a Chern insulator. We also show that the method provides an intuitive approach to understand recently introduced topological phases which exhibit gapless corner states.Comment: 18 pages, 3 figures (a new Fig. 3 is added), Accepted by Phys. Rev.

Multimodal Federated Learning via Contrastive Representation Ensemble

With the increasing amount of multimedia data on modern mobile systems and IoT infrastructures, harnessing these rich multimodal data without breaching user privacy becomes a critical issue. Federated learning (FL) serves as a privacy-conscious alternative to centralized machine learning. However, existing FL methods extended to multimodal data all rely on model aggregation on single modality level, which restrains the server and clients to have identical model architecture for each modality. This limits the global model in terms of both model complexity and data capacity, not to mention task diversity. In this work, we propose Contrastive Representation Ensemble and Aggregation for Multimodal FL (CreamFL), a multimodal federated learning framework that enables training larger server models from clients with heterogeneous model architectures and data modalities, while only communicating knowledge on public dataset. To achieve better multimodal representation fusion, we design a global-local cross-modal ensemble strategy to aggregate client representations. To mitigate local model drift caused by two unprecedented heterogeneous factors stemming from multimodal discrepancy (modality gap and task gap), we further propose two inter-modal and intra-modal contrasts to regularize local training, which complements information of the absent modality for uni-modal clients and regularizes local clients to head towards global consensus. Thorough evaluations and ablation studies on image-text retrieval and visual question answering tasks showcase the superiority of CreamFL over state-of-the-art FL methods and its practical value.Comment: ICLR 2023. Code is available at https://github.com/FLAIR-THU/CreamF

Monitoring of the central blood pressure waveform via a conformal ultrasonic device.

Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 μm) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments

Fibroblast Growth Factor Signaling Mediates Pulmonary Endothelial Glycocalyx Reconstitution

The endothelial glycocalyx is a heparan sulfate (HS)-rich endovascular structure critical to endothelial function. Accordingly, endothelial glycocalyx degradation during sepsis contributes to tissue edema and organ injury. We determined the endogenous mechanisms governing pulmonary endothelial glycocalyx reconstitution, and if these reparative mechanisms are impaired during sepsis. We performed intravital microscopy of wild-type and transgenic mice to determine the rapidity of pulmonary endothelial glycocalyx reconstitution after nonseptic (heparinase-III mediated) or septic (cecal ligation and puncture mediated) endothelial glycocalyx degradation. We used mass spectrometry, surface plasmon resonance, and in vitro studies of human and mouse samples to determine the structure of HS fragments released during glycocalyx degradation and their impact on fibroblast growth factor receptor (FGFR) 1 signaling, a mediator of endothelial repair. Homeostatic pulmonary endothelial glycocalyx reconstitution occurred rapidly after nonseptic degradation and was associated with induction of the HS biosynthetic enzyme, exostosin (EXT)-1. In contrast, sepsis was characterized by loss of pulmonary EXT1 expression and delayed glycocalyx reconstitution. Rapid glycocalyx recovery after nonseptic degradation was dependent upon induction of FGFR1 expression and was augmented by FGF-promoting effects of circulating HS fragments released during glycocalyx degradation. Although sepsis-released HS fragments maintained this ability to activate FGFR1, sepsis was associated with the downstream absence of reparative pulmonary endothelial FGFR1 induction. Sepsis may cause vascular injury not only via glycocalyx degradation, but also by impairing FGFR1/EXT1-mediated glycocalyx reconstitution

NICE 2023 Zero-shot Image Captioning Challenge

In this report, we introduce NICE project\footnote{\url{https://nice.lgresearch.ai/}} and share the results and outcomes of NICE challenge 2023. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.Comment: Tech report, project page https://nice.lgresearch.ai

Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM) algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS) 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets

Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation

In this paper, a multiple-input multiple-output (MIMO) communication system with frequency hopping (FH) aided orthogonal frequency division multiplexing differential chaotic shift keying (OFDM-DCSK) modulation is proposed. Our objective is to improve the security of MIMO communication system which is encoded by space time block coding (STBC). In order to combat the eavesdropping or malicious attacks due to the broadcast characteristics of wireless communication system, we propose to use DCSK and FH modules to encrypt the information, and hide the user data in the chaotic sequences, where the initial value of chaotic sequences and the method of generating FH module are only shared among legitimate users. Moreover, we derive the bit error rate (BER) and the secrecy capacity of the scheme in additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. Simulation results show that the proposed scheme can effectively improve the security of MIMO communication system, which can be seen from the BER of eavesdroppers and legitimate users, and the secrecy capacity of the proposed scheme and the benchmark schemes