Cryo-EM structure of the mature dengue virus at 3.5-Å resolution.

Regulated by pH, membrane-anchored proteins E and M function during dengue virus maturation and membrane fusion. Our atomic model of the whole virion from cryo-electron microscopy at 3.5-Å resolution reveals that in the mature virus at neutral extracellular pH, the N-terminal 20-amino-acid segment of M (involving three pH-sensing histidines) latches and thereby prevents spring-loaded E fusion protein from prematurely exposing its fusion peptide. This M latch is fastened at an earlier stage, during maturation at acidic pH in the trans-Golgi network. At a later stage, to initiate infection in response to acidic pH in the late endosome, M releases the latch and exposes the fusion peptide. Thus, M serves as a multistep chaperone of E to control the conformational changes accompanying maturation and infection. These pH-sensitive interactions could serve as targets for drug discovery

SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction

Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downstream segmentation tasks under different scenarios remains a challenge, as the varied characteristics of different scenarios naturally requires diverse model parameter spaces. Most existing fine-tuning methods attempt to bridge the gaps among different scenarios by introducing a set of new parameters to modify SAM's original parameter space. Unlike these works, in this paper, we propose fine-tuning SAM efficiently by parameter space reconstruction (SAM-PARSER), which introduce nearly zero trainable parameters during fine-tuning. In SAM-PARSER, we assume that SAM's original parameter space is relatively complete, so that its bases are able to reconstruct the parameter space of a new scenario. We obtain the bases by matrix decomposition, and fine-tuning the coefficients to reconstruct the parameter space tailored to the new scenario by an optimal linear combination of the bases. Experimental results show that SAM-PARSER exhibits superior segmentation performance across various scenarios, while reducing the number of trainable parameters by $\approx 290$ times compared with current parameter-efficient fine-tuning methods

Community Detection Using Revised Medoid-Shift Based on KNN

Community detection becomes an important problem with the booming of social networks. As an excellent clustering algorithm, Mean-Shift can not be applied directly to community detection, since Mean-Shift can only handle data with coordinates, while the data in the community detection problem is mostly represented by a graph that can be treated as data with a distance matrix (or similarity matrix). Fortunately, a new clustering algorithm called Medoid-Shift is proposed. The Medoid-Shift algorithm preserves the benefits of Mean-Shift and can be applied to problems based on distance matrix, such as community detection. One drawback of the Medoid-Shift algorithm is that there may be no data points within the neighborhood region defined by a distance parameter. To deal with the community detection problem better, a new algorithm called Revised Medoid-Shift (RMS) in this work is thus proposed. During the process of finding the next medoid, the RMS algorithm is based on a neighborhood defined by KNN, while the original Medoid-Shift is based on a neighborhood defined by a distance parameter. Since the neighborhood defined by KNN is more stable than the one defined by the distance parameter in terms of the number of data points within the neighborhood, the RMS algorithm may converge more smoothly. In the RMS method, each of the data points is shifted towards a medoid within the neighborhood defined by KNN. After the iterative process of shifting, each of the data point converges into a cluster center, and the data points converging into the same center are grouped into the same cluster

COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, benefits seeking consultants, collaborators, and candidate qualifications. However, its quality is suffered from a single source of support information for experts. This paper employs AMiner, a free online academic search and mining system, having collected more than over 100 million researcher profiles together with 200 million papers from multiple publication databases, as the basis for investigating the problem of expert linking, which aims at linking any external information of persons to experts in AMiner. A critical challenge is how to perform zero shot expert linking without any labeled linkages from the external information to AMiner experts, as it is infeasible to acquire sufficient labels for arbitrary external sources. Inspired by the success of self supervised learning in computer vision and natural language processing, we propose to train a self supervised expert linking model, which is first pretrained by contrastive learning on AMiner data to capture the common representation and matching patterns of experts across AMiner and external sources, and is then fine-tuned by adversarial learning on AMiner and the unlabeled external sources to improve the model transferability. Experimental results demonstrate that COAD significantly outperforms various baselines without contrastive learning of experts on two widely studied downstream tasks: author identification (improving up to 32.1% in HitRatio@1) and paper clustering (improving up to 14.8% in Pairwise-F1). Expert linking on two genres of external sources also indicates the superiority of the proposed adversarial fine-tuning method compared with other domain adaptation ways (improving up to 2.3% in HitRatio@1).Comment: TKDE under revie

A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction

The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based image segmentation algorithms. This paper offers a comprehensive review on label-efficient image segmentation methods. To this end, we first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels (including no supervision, inexact supervision, incomplete supervision and inaccurate supervision) and supplemented by the types of segmentation problems (including semantic segmentation, instance segmentation and panoptic segmentation). Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction -- the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation. Finally, we share our opinions about the future research directions for label-efficient deep image segmentation.Comment: Accepted to IEEE TPAM

Global trends in the incidence rates of MDR and XDR tuberculosis: Findings from the global burden of disease study 2019

Purpose: The study aimed to quantify the global trends of the incidence rates of multidrug-resistant (MDR) tuberculosis (MDR-TB) and extensively drug-resistant (XDR) tuberculosis (XDR-TB).Methods: Cases, age-standardized rates (ASRs), and incidence rates of MDR-TB and XDR-TB during 2010–2019 were obtained from the Global Burden of Disease Study 2019. The incidence trends of MDR-TB and XDR-TB were evaluated using the estimated annual percentage changes (EAPCs) in ASRs. The relationships among the ASRs of MDR-TB and XDR-TB, the MDR rate, the XDR rate, and socio-demographic index (SDI) were assessed using locally weighted regression and Pearson’s correlation coefficient.Results: The global ASR of MDR-TB on average decreased by 1.36% (EAPC = −1.36, 95% confidence interval [CI] = −2.19 to −0.52) per year whereas that of XDR-TB was stable (EAPC = 0.69, 95% CI = −0.15–1.54) during 2010–2019. The incidence trends of MDR-TB in most regions and countries were decreasing, but those of XDR-TB were increasing. People aged 35–44 and 55–64 years had the highest incidence rates for MDR-TB and XDR-TB. The MDR and XDR rates both peaked in those aged 35–44 years. Areas with higher SDI tended to have lower ASRs of MDR-TB (p < 0.001, ρ = −0.43).Conclusion: The current achievements for the incidence trends of MDR-TB and XDR-TB are insufficient. More strategies and tools need to be developed to further curb MDR-TB and XDR-TB, especially in high-risk areas and age groups, and in low SDI regions

Interactions of Oxide Surfaces with Water Revealed with Solid-State NMR Spectroscopy.

Hydrous materials are ubiquitous in the natural environment and efforts have previously been made to investigate the structures and dynamics of hydrated surfaces for their key roles in various chemical and physical applications, with the help of theoretical modeling and microscopy techniques. However, an overall atomic-scale understanding of the water-solid interface, including the effect of water on surface ions, is still lacking. Herein, we employ ceria nanorods with different amounts of water as an example and demonstrate a new approach to explore the water-surface interactions by using solid-state NMR in combination with density functional theory. NMR shifts and relaxation time analysis provide detailed information on the local structure of oxygen ions and the nature of water motion on the surface: the amount of molecularly adsorbed water decreases rapidly with increasing temperature (from room temperature to 150 °C), whereas hydroxyl groups are stable up to 150 °C, and dynamic water molecules are found to instantaneously coordinate to the surface oxygen ions. The applicability of dynamic nuclear polarization for selective detection of surface oxygen species is also compared to conventional NMR with surface selective isotopic-labeling: the optimal method depends on the feasibility of enrichment and the concentration of protons in the sample. These results provide new insight into the interfacial structure of hydrated oxide nanostructures, which is important to improve performance for various applications

Eightfold Fermionic Excitation in a Charge Density Wave Compound

Unconventional quasiparticle excitations in condensed matter systems have become one of the most important research frontiers. Beyond two- and fourfold degenerate Weyl and Dirac fermions, three-, six- and eightfold symmetry protected degeneracies have been predicted however remain challenging to realize in solid state materials. Here, charge density wave compound TaTe4 is proposed to hold eightfold fermionic excitation and Dirac point in energy bands. High quality TaTe4 single crystals are prepared, where the charge density wave is revealed by directly imaging the atomic structure and a pseudogap of about 45 meV on the surface. Shubnikov de-Haas oscillations of TaTe4 are consistent with band structure calculation. Scanning tunneling microscopy reveals atomic step edge states on the surface of TaTe4. This work uncovers that charge density wave is able to induce new topological phases and sheds new light on the novel excitations in condensed matter materials.Comment: Accepted by PRB: https://journals.aps.org/prb/accepted/7907cK4eW0b1ee0b93fd67c1b42942bbb08eafc3

RTA-408 Protects Kidney from Ischemia-Reperfusion Injury in Mice via Activating Nrf2 and Downstream GSH Biosynthesis Gene

Acute kidney injury (AKI) induced by ischemia-reperfusion is a critical conundrum in many clinical settings. Here, this study aimed to determine whether and how RTA-408, a novel oleanane triterpenoid, could confer protection against renal ischemia-reperfusion injury (IRI) in male mice. Mice treated with RTA-408 undergoing unilateral ischemia followed by contralateral nephrectomy had improved renal function and histological outcome, as well as decreased apoptosis, ROS production, and oxidative injury marker compared with vehicle-treated mice. Also, we had found that RTA-408 could strengthen the total antioxidant capacity by increasing Nrf2 nuclear translocation and subsequently increased Nrf2 downstream GSH-related antioxidant gene expression and activity. In vitro study demonstrated that GSH biosynthesis enzyme GCLc could be an important target of RTA-408. Furthermore, Nrf2-deficient mice treated with RTA-408 had no significant improvement in renal function, histology, ROS production, and GSH-related gene expression. Thus, by upregulating Nrf2 and its downstream antioxidant genes, RTA-408 presents a novel and potential approach to renal IRI prevention and therapy