Cyclopeptide Inhibitors Target the N-Terminal Tail of STING and Alleviate Autoinflammation.

Cyclic GMP-AMP synthase (cGAS)- stimulator of interferon genes (STING) signaling pathway is a crucial component of innate immunity that plays a vital role in protecting against pathogen infections and cellular stress. However, aberrant activation of cGAS-STING pathway is associated with inflammatory and autoimmune diseases. Here, we developed cyclopeptide STING inhibitors by cyclizing the N-terminal tail (NTT) of STING. These cyclopeptides selectively inhibited the activation of STING pathway in human or murine cell lines. Mechanistically, the inhibitors directly bound to STING, and subsequently blocked the aggregation and activation of STING. In addition, the optimal inhibitor STi-2 significantly suppressed the elevated levels of type I interferon and proinflammatory cytokines in primary macrophages derived from Trex1-/- mice and systemic inflammation in Trex1-/- mice. Overall, our work facilitates the development of specific inhibitors of STING as potential therapies in the treatment of cGAS-STING associated autoinflammatory diseases.

Action-Evaluator: A Visualization Approach for Player Action Evaluation in Soccer.

In soccer, player action evaluation provides a fine-grained method to analyze player performance and plays an important role in improving winning chances in future matches. However, previous studies on action evaluation only provide a score for each action, and hardly support inspecting and comparing player actions integrated with complex match context information such as team tactics and player locations. In this work, we collaborate with soccer analysts and coaches to characterize the domain problems of evaluating player performance based on action scores. We design a tailored visualization of soccer player actions that places the action choice together with the tactic it belongs to as well as the player locations in the same view. Based on the design, we introduce a visual analytics system, Action-Evaluator, to facilitate a comprehensive player action evaluation through player navigation, action investigation, and action explanation. With the system, analysts can find players to be analyzed efficiently, learn how they performed under various match situations, and obtain valuable insights to improve their action choices. The usefulness and effectiveness of this work are demonstrated by two case studies on a real-world dataset and an expert interview.

Effect Predictor of Driver Synonymous Mutations Based on Multi-Feature Fusion and Iterative Feature Representation Learning.

Accurate identification of driver mutations is crucial in genetic studies of human cancers. While numerous cancer driver missense mutations have been identified, research into potential cancer drivers for synonymous mutations has shown limited success to date. Here, we developed a novel machine learning framework, epSMic, for predicting cancer driver synonymous mutations. epSMic employs an iterative feature representation scheme that facilitates the learning of discriminative features from various sequential models in a supervised iterative mode. We constructed the benchmark datasets and encoded the embedding sequence, physicochemical property, and basic information such as conservation and splicing feature. The evaluation results on benchmark test datasets demonstrate that epSMic outperforms existing methods, making it a valuable tool for researchers in identifying functional synonymous mutations in cancer. We hope epSMic can enable researchers to concentrate on synonymous mutations that have a functional impact on cancer.

Distribution, connection and function of ALDH1A1+/TH+ neurons in substantia nigra pars reticulata of mouse.

The massive cell death of dopaminergic neurons (DANs) in substantia nigra pars compacta (SNC) is associated with motor diseases, such as Parkinson's disease. Moreover, as a subtype of DANs in SNC, ALDH1A1+ neurons show better resistance to PD related neurotoxin. DANs can also be found in the substantia nigra pars reticulata (SNR), however, whether they are ALDH1A1+ neurons are rarely reported, as well as their projection, function, and reaction in the PD pathology. We studied the distribution of ALDH1A1+ neurons and track their projection by injecting pAAV. We figured out that, in SNR, 87 % neurons are ALDH1A1+/TH+ in ALDH1A1+ cluster averagely, while ALDH1A1+/TH+: TH+ is 52 % averagely. There are two enrichment regions of ALDH1A1+/TH+ neurons at brgma -3.40 mm and brgma -3.70 mm in the SNR of the nTg mice. Nevertheless, in one type of PD-liked mice model, the proportion of ALDH1A1+/TH+: ALDH1A1+ neurons are 98 % averagely, while ALHD1A1+/TH+: TH+ is 57 %. Intriguingly, neuro-tracing discovered that there may be a previously unreported connection between SNR and anterior dorsal thalamus (ADT). The mouse received MPTP stereotactic injection to destroy TH+ neurons in SNR showed depression behavior, indicated the DANs death in SNR may contribute to depression behavior.

Development of cyclopeptide inhibitors of cGAS targeting protein-DNA interaction and phase separation.

Cyclic GMP-AMP synthase (cGAS) is an essential sensor of aberrant cytosolic DNA for initiating innate immunity upon invading pathogens and cellular stress, which is considered as a potential drug target for autoimmune and autoinflammatory diseases. Here, we report the discovery of a class of cyclopeptide inhibitors of cGAS identified by an in vitro screening assay from a focused library of cyclic peptides. These cyclopeptides specifically bind to the DNA binding site of cGAS and block the binding of dsDNA with cGAS, subsequently inhibit dsDNA-induced liquid phase condensation and activation of cGAS. The specificity and potency of one optimal lead XQ2B were characterized in cellular assays. Concordantly, XQ2B inhibited herpes simplex virus-1 (HSV-1)-induced antiviral immune responses and enhanced HSV-1 infection in vitro and in vivo. Furthermore, XQ2B significantly suppressed the elevated levels of type I interferon and proinflammatory cytokines in primary macrophages from Trex1-/- mice and systemic inflammation in Trex1-/- mice. XQ2B represents the specific cGAS inhibitor targeting protein-DNA interaction and phase separation and serves as a scaffold for the development of therapies in the treatment of cGAS-dependent inflammatory diseases.

Campaign-style governance of air pollution in China? A comprehensive analysis of the central environmental protection inspection.

Central environmental protection inspection (CEPI) is a major institutional innovation in China's environmental governance, but its effectiveness in improving air quality is still unclear. However, the effectiveness of CEPI is of great significance and can be regarded as an important reference for deepening the reform of environmental governance system in China. This article takes the CEPI as a quasi-natural experiment and uses the regression discontinuity design (RDD) and the difference-in-differences (DID) methods to examine the effectiveness of this policy. The study found that the first round of CEPI reduced the air pollution of cities in the inspected provinces in a short time. Moreover, this positive policy effect persisted in the aftermath of the inspection, but this long-term effect is mainly reflected in PM10 and SO2. Heterogeneity analysis showed that CEPI was only effective in reducing air pollutants of industry-oriented cities, cities in Central and Eastern China, and cities with large or small population size. The moderating effect analysis indicated that a healthy relationship (close and clean) between the local governments and businesses was conducive to reducing air pollution. The research confirmed the presence of "selective" reduction of air pollutants in the long run caused by CEPI, thereby providing new inspiration for the improvement of campaign-style environmental governance and the follow-up CEPI work.

Team-Builder: Toward More Effective Lineup Selection in Soccer.

Lineup selection is an essential and important task in soccer matches. To win a match, coaches must consider various factors and select appropriate players for a planned formation. Computation-based tools have been proposed to help coaches on this complex task, but they are usually based on over-simplified models on player performances, do not support interactive analysis, and overlook the inputs by coaches. In this article, we propose a method for visual analytics of soccer lineup selection by tackling two challenges: characterizing essential factors involved in generating optimal lineup, and supporting coach-driven visual analytics of lineup selection. We develop a lineup selection model that integrates such important factors, such as spatial regions of player actions and defensive interactions with opponent players. A visualization system, Team-Builder, is developed to help coaches control the process of lineup generation, explanation, and comparison through multiple coordinated views. The usefulness and effectiveness of our system are demonstrated by two case studies on a real-world soccer event dataset.

Identification of DNA aptamers that specifically targets EBV+ nasopharyngeal carcinoma via binding with EphA2/CD98hc complex.

Nasopharyngeal carcinoma (NPC) is one of the Epstein-Barr virus (EBV)-associated malignancies and has a distinct geographical distribution. The high mortality rates of NPC patients with advanced and recurrent disease highlight the urgent need for biomarkers for early diagnosis and effective treatments. In this study, we developed DNA aptamers that specifically bind to EBV positive NPC cells by the Cell-SELEX procedure. We further identified the EphA2 (ephrin type-A receptor 2)/CD98hc (CD98 heavy chain) complex as the potential target of the aptamer EA-3 by combining aptamer-based separation and mass spectrometry analysis. Our results revealed for the first time that EphA2 colocalized with CD98hc at the plasma membrane and EphA2 coimmunoprecipitated with CD98hc, which may serve as a starting point for exploring the potential functions of the complex of EphA2 and CD98hc in NPCs. Here, we demonstrated that aptamers can be useful for the identification of protein complexes on the surface of cancer cells.

Tac-Miner: Visual Tactic Mining for Multiple Table Tennis Matches.

In table tennis, tactics specified by three consecutive strokes represent the high-level competition strategies in matches. Effective detection and analysis of tactics can reveal the playing styles of players, as well as their strengths and weaknesses. However, tactical analysis in table tennis is challenging as the analysts can often be overwhelmed by the large quantity and high dimension of the data. Statistical charts have been extensively used by researchers to explore and visualize table tennis data. However, these charts cannot support efficient comparative and correlation analysis of complicated tactic attributes. Besides, existing studies are limited to the analysis of one match. However, one player's strategy can change along with his/her opponents in different matches. Therefore, the data of multiple matches can support a more comprehensive tactical analysis. To address these issues, we introduced a visual analytics system called Tac-Miner to allow analysts to effectively analyze, explore, and compare tactics of multiple matches based on the advanced embedding and dimension reduction algorithms along with an interactive glyph. We evaluate our glyph's usability through a user study and demonstrate the system's usefulness through a case study with insights approved by coaches and domain experts.

Tac-Simur: Tactic-based Simulative Visual Analytics of Table Tennis.

Simulative analysis in competitive sports can provide prospective insights, which can help improve the performance of players in future matches. However, adequately simulating the complex competition process and effectively explaining the simulation result to domain experts are typically challenging. This work presents a design study to address these challenges in table tennis. We propose a well-established hybrid second-order Markov chain model to characterize and simulate the competition process in table tennis. Compared with existing methods, our approach is the first to support the effective simulation of tactics, which represent high-level competition strategies in table tennis. Furthermore, we introduce a visual analytics system called Tac-Simur based on the proposed model for simulative visual analytics. Tac-Simur enables users to easily navigate different players and their tactics based on their respective performance in matches to identify the player and the tactics of interest for further analysis. Then, users can utilize the system to interactively explore diverse simulation tasks and visually explain the simulation results. The effectiveness and usefulness of this work are demonstrated by two case studies, in which domain experts utilize Tac-Simur to find interesting and valuable insights. The domain experts also provide positive feedback on the usability of Tac-Simur. Our work can be extended to other similar sports such as tennis and badminton.