Effect of intestinal microbiota on duck short-beak and dwarf syndrome caused by novel goose parvovirus.

Short-beak and dwarf syndrome (SBDS) is caused by infection with novel goose parvovirus (NGPV), which leads to intestinal dysbiosis, developmental delay, short beak, lameness, and paralysis in ducks and is the cause of skeletal health problems. NGPV infection can cause intestinal microbial disturbances, but it is still unclear whether the intestinal microbiota affects the pathogenicity of NGPV. Here, the effects of intestinal microbiota on NGPV-induced SBDS in Cherry Valley ducks were assessed by establishing a duck model for gut microflora depletion/reestablishment through antibiotics (ABX) treatment/fecal microbiota transplanted (FMT). By measuring body weight, beak length, beak width and tarsal length, we found that SBDS clinical symptoms were alleviated in ducks treated with ABX, but not in FMT ducks. Next, we conducted a comprehensive analysis of bone metabolism, gut barrier integrity, and inflammation levels using quantitative real-time PCR (qPCR), enzyme linked immunosorbent assay (ELISA), biochemical analysis and histological analysis. The results showed that ABX treatment improved bone quality reduced bone resorption, mitigated tissue lesions, protected intestinal barrier integrity, and inhibited systemic inflammation in NGPV-infected ducks. Moreover, cecal microflora composition and short-chain fatty acids (SCFAs) production were examined by bacterial 16S rRNA sequencing and gas chromatography. The results revealed that ABX treatment mitigated the decreased abundance of Firmicutes and Bacteroidota in NGPV-infected ducks, as well as increased SCFAs production. Furthermore, ABX treatment reduced the mucosa-associated lymphoid tissue lymphoma translocation protein 1 (Malt1) and nuclear factor κB (NF-κB) expression, which are correlated with systemic inflammation in SBDS ducks. These findings suggested that intestinal microflora depletion alleviated NGPV-induced SBDS by maintaining intestinal homeostasis, inhibiting inflammatory response and alleviating bone resorption. These results provide evidence for the pivotal role of intestinal microbiota in the process of SBDS and contribute a theoretical basis for the feasibility of microecological preparation as a method to control SBDS.

Bioinspired Lipoproteins of Furoxans-Gemcitabine Preferentially Targets Glioblastoma and Overcomes Radiotherapy Resistance.

Radiotherapy (RT) resistance is an enormous challenge in glioblastoma multiforme (GBM) treatment, which is largely associated with DNA repair, poor distribution of reactive radicals in tumors, and limited delivery of radiosensitizers to the tumor sites. Inspired by the aberrant upregulation of RAD51 (a critical protein of DNA repair), scavenger receptor B type 1 (SR-B1), and C-C motif chemokine ligand 5 (CCL5) in GBM patients, a reduction-sensitive nitric oxide (NO) donor conjugate of gemcitabine (RAD51 inhibitor) (NG) is synthesized as radio-sensitizer and a CCL5 peptide-modified bioinspired lipoprotein system of NG (C-LNG) is rationally designed, aiming to preferentially target the tumor sites and overcome the RT resistance. C-LNG can preferentially accumulate at the orthotopic GBM tumor sites with considerable intratumor permeation, responsively release the gemcitabine and NO, and then generate abundant peroxynitrite (ONOO- ) upon X-ray radiation, thereby producing a 99.64% inhibition of tumor growth and a 71.44% survival rate at 120 days in GL261-induced orthotopic GBM tumor model. Therefore, the rationally designed bioinspired lipoprotein of NG provides an essential strategy to target GBM and overcome RT resistance.

Allomelanin-based biomimetic nanotherapeutics for orthotopic glioblastoma targeted photothermal immunotherapy.

Immune checkpoint blockade (ICB) therapy has shown great potential in the treatment of malignant tumors, but its therapeutic effect on glioblastoma (GBM) is unsatisfactory because of the low immunogenicity and T cell infiltration, as well as the presence of blood-brain barrier (BBB) that blocks most of ICB agents to the GBM tissues. Herein, we developed a biomimetic nanoplatform of AMNP@CLP@CCM for GBM-targeted photothermal therapy (PTT) and ICB synergistic therapy by loading immune checkpoint inhibitor CLP002 into the allomelanin nanoparticles (AMNPs) and followed by coating cancer cell membranes (CCM). The resulting AMNP@CLP@CCM can successfully cross the BBB and deliver CLP002 to GBM tissues due to the homing effect of CCM. As a natural photothermal conversion agent, AMNPs are used for tumor PTT. The increased local temperature by PTT not only enhances BBB penetration but also upregulates the PD-L1 level on GBM cells. Importantly, PTT can effectively stimulate immunogenic cell death to induce tumor-associated antigen exposure and promote T lymphocyte infiltration, which can further amplify the antitumor immune responses of GBM cells to CLP002-mediated ICB therapy, resulting in significant growth inhibition of the orthotopic GBM. Therefore, AMNP@CLP@CCM has great potential for the treatment of orthotopic GBM by PTT and ICB synergistic therapy. STATEMENT OF SIGNIFICANCE: The effect of ICB therapy on GBM is limited by the low immunogenicity and insufficient T-cell infiltration. Here we developed a biomimetic nanoplatform of AMNP@CLP@CCM for GBM-targeted PTT and ICB synergistic therapy. In this nanoplatform, AMNPs are used as both photothermal conversion agents for PTT and nanocarriers for CLP002 delivery. PTT not only enhances BBB penetration but also upregulates the PD-L1 level on GBM cells by increasing local temperature. Additionally, PTT also induces tumor-associated antigen exposure and promotes T lymphocyte infiltration to amplify the antitumor immune responses of GBM cells to CLP002-mediated ICB therapy, resulting in significant growth inhibition of the orthotopic GBM. Thus, this nanoplatform holds great potential for orthotopic GBM treatment.

YouTube and science: models for research impact.

Video communication has been rapidly increasing over the past decade, with YouTube providing a medium where users can post, discover, share, and react to videos. There has also been an increase in the number of videos citing research articles, especially since it has become relatively commonplace for academic conferences to require video submissions. However, the relationship between research articles and YouTube videos is not clear, and the purpose of the present paper is to address this issue. We created new datasets using YouTube videos and mentions of research articles on various online platforms. We found that most of the articles cited in the videos are related to medicine and biochemistry. We analyzed these datasets through statistical techniques and visualization, and built machine learning models to predict (1) whether a research article is cited in videos, (2) whether a research article cited in a video achieves a level of popularity, and (3) whether a video citing a research article becomes popular. The best models achieved F1 scores between 80% and 94%. According to our results, research articles mentioned in more tweets and news coverage have a higher chance of receiving video citations. We also found that video views are important for predicting citations and increasing research articles' popularity and public engagement with science.

The efficacy of temozolomide combined with levetiracetam for glioblastoma (GBM) after surgery: a study protocol for a double-blinded and randomized controlled trial.

BACKGROUND: Temozolomide is applied as the standard chemotherapy agent in patients with glioblastoma (GBM) after surgery. However, the benefit of this treatment for patients is limited by the invasive growth of gliomas and drug resistance. There are indications from fundamental experimental and retrospective studies that levetiracetam has the potential to improve the survival rate of patients with GBM. However, it has yet to be determined whether the combination of temozolomide and levetiracetam is more effective than standard temozolomide chemotherapy. Therefore, we designed a randomized clinical trial to investigate the therapeutic effect of the new combined regime for treating GBM. METHODS/DESIGN: This is a double-blind and randomized clinical trial conducted in a single center. One hundred forty-two patients will be recruited and screened for the inclusion and exclusion criteria. Then, eligible participants will be randomly assigned to an experimental group or a control group in a 1:1 ratio. Based on the administration of radiation therapy (RT), participants in the experimental group will be prescribed levetiracetam plus temozolomide chemotherapy for 34 weeks while participants in the control group will receive placebo tablets plus temozolomide for the same duration. A 3-year follow-up will be conducted on all patients after intervention. Accordingly, the primary outcome will be progression-free survival (PFS). The secondary endpoints include overall survival (OS), the Karnofsky Performance Status (KPS), the objective response rate (ORR), and adverse event incidence. DISCUSSION: It is expected that the results of this trial will provide high-level evidence regarding the clinical benefits of levetiracetam and temozolomide combined in the treatment of GBM. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2100049941 . Registered on 14 August 2021.

Towards Systematic Design Considerations for Visualizing Cross-View Data Relationships.

Due to the scale of data and the complexity of analysis tasks, insight discovery often requires coordinating multiple visualizations (views), with each view displaying different parts of data or the same data from different perspectives. For example, to analyze car sales records, a marketing analyst uses a line chart to visualize the trend of car sales, a scatterplot to inspect the price and horsepower of different cars, and a matrix to compare the transaction amounts in types of deals. To explore related information across multiple views, current visual analysis tools heavily rely on brushing and linking techniques, which may require a significant amount of user effort (e.g., many trial-and-error attempts). There may be other efficient and effective ways of displaying cross-view data relationships to support data analysis with multiple views, but currently there are no guidelines to address this design challenge. In this article, we present systematic design considerations for visualizing cross-view data relationships, which leverages descriptive aspects of relationships and usable visual context of multi-view visualizations. We discuss pros and cons of different designs for showing cross-view data relationships, and provide a set of recommendations for helping practitioners make design decisions.

Understanding Missing Links in Bipartite Networks With MissBiN.

The analysis of bipartite networks is critical in a variety of application domains, such as exploring entity co-occurrences in intelligence analysis and investigating gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of unseen links based on currently observed ones. In this article, we propose a visual analysis system, MissBiN, to involve analysts in the loop for making sense of link prediction results. MissBiN equips a novel method for link prediction in a bipartite network by leveraging the information of bi-cliques in the network. It also provides an interactive visualization for understanding the algorithm outputs. The design of MissBiN is based on three high-level analysis questions (what, why, and how) regarding missing links, which are distilled from the literature and expert interviews. We conducted quantitative experiments to assess the performance of the proposed link prediction algorithm, and interviewed two experts from different domains to demonstrate the effectiveness of MissBiN as a whole. We also provide a comprehensive usage scenario to illustrate the usefulness of the tool in an application of intelligence analysis.

SightBi: Exploring Cross-View Data Relationships with Biclusters.

Multiple-view visualization (MV) has been heavily used in visual analysis tools for sensemaking of data in various domains (e.g., bioinformatics, cybersecurity and text analytics). One common task of visual analysis with multiple views is to relate data across different views. For example, to identify threats, an intelligence analyst needs to link people from a social network graph with locations on a crime-map, and then search for and read relevant documents. Currently, exploring cross-view data relationships heavily relies on view-coordination techniques (e.g., brushing and linking), which may require significant user effort on many trial-and-error attempts, such as repetitiously selecting elements in one view, and then observing and following elements highlighted in other views. To address this, we present SightBi, a visual analytics approach for supporting cross-view data relationship explorations. We discuss the design rationale of SightBi in detail, with identified user tasks regarding the use of cross-view data relationships. SightBi formalizes cross-view data relationships as biclusters, computes them from a dataset, and uses a bi-context design that highlights creating stand-alone relationship-views. This helps preserve existing views and offers an overview of cross-view data relationships to guide user exploration. Moreover, SightBi allows users to interactively manage the layout of multiple views by using newly created relationship-views. With a usage scenario, we demonstrate the usefulness of SightBi for sensemaking of cross-view data relationships.

NFAT as a Biomarker and Therapeutic Target in Non-Small Cell Lung Cancer-Related Brain Metastasis.

BACKGROUND: Brain metastases (BMs) are associated with poor prognosis and significant mortality, and approximately 25% of patients with non-small cell lung cancer (NSCLC) develop BMs. The present study was aimed to understand the relationships between BM and NSCLC and reveal potential biomarkers and therapeutic targets in NSCLC-related BM. METHODS: The differentially expressed genes (DEGs) expressed during NSCLC and BM development were predicted by bioinformatics analysis, and the expression of the upstream transcription factor nuclear factor of activated T cells (NFAT) was confirmed as a differential gene expressed in both NSCLC and BM. In addition, the expression of proteins encoded by these DEGs was verified by immunohistochemical experiments examining normal lung tissue, lung cancer tissue, and brain metastasis tissue from 30 patients with NSCLC related BM. RESULTS: The co-DEGs interleukin (IL)-11, cadherin 5 (CDH5) and C-C motif chemokine 2 (CCL2) link NSCLC and BM in the Gene Expression Omnibus (GEO) database, and NFAT may target the expression of these co-DEGs. In the GEO database, NFATc1 and NFATc3 were significantly downregulated in NSCLC tissues (P <.05), whereas NFATc1, NFATc2, NFATc3, and NFATc4 were significantly downregulated in BMs (P <.05). Consistent findings were observed in the immunohistochemical analysis. CONCLUSION: NFATc1 and NFATc3 may play important roles in the occurrence of NSCLC and BM by regulating IL-11, CDH5, and CCL2.

The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs.

Exploring coordinated relationships (e.g., shared relationships between two sets of entities) is an important analytics task in a variety of real-world applications, such as discovering similarly behaved genes in bioinformatics, detecting malware collusions in cyber security, and identifying products bundles in marketing analysis. Coordinated relationships can be formalized as biclusters. In order to support visual exploration of biclusters, bipartite graphs based visualizations have been proposed, and edge bundling is used to show biclusters. However, it suffers from edge crossings due to possible overlaps of biclusters, and lacks in-depth understanding of its impact on user exploring biclusters in bipartite graphs. To address these, we propose a novel bicluster-based seriation technique that can reduce edge crossings in bipartite graphs drawing and conducted a user experiment to study the effect of edge bundling and this proposed technique on visualizing biclusters in bipartite graphs. We found that they both had impact on reducing entity visits for users exploring biclusters, and edge bundles helped them find more justified answers. Moreover, we identified four key trade-offs that inform the design of future bicluster visualizations. The study results suggest that edge bundling is critical for exploring biclusters in bipartite graphs, which helps to reduce low-level perceptual problems and support high-level inferences.

Enhancing Web-based Analytics Applications through Provenance.

Visual analytics systems continue to integrate new technologies and leverage modern environments for exploration and collaboration, making tools and techniques available to a wide audience through web browsers. Many of these systems have been developed with rich interactions, offering users the opportunity to examine details and explore hypotheses that have not been directly encoded by a designer. Understanding is enhanced when users can replay and revisit the steps in the sensemaking process, and in collaborative settings, it is especially important to be able to review not only the current state but also what decisions were made along the way. Unfortunately, many web-based systems lack the ability to capture such reasoning, and the path to a result is transient, forgotten when a user moves to a new view. This paper explores the requirements to augment existing client-side web applications with support for capturing, reviewing, sharing, and reusing steps in the reasoning process. Furthermore, it considers situations where decisions are made with streaming data, and the insights gained from revisiting those choices when more data is available. It presents a proof of concept, the Shareable Interactive Manipulation Provenance framework (SIMProv.js), that addresses these requirements in a modern, client-side JavaScript library, and describes how it can be integrated with existing frameworks.

BiDots: Visual Exploration of Weighted Biclusters.

Discovering and analyzing biclusters, i.e., two sets of related entities with close relationships, is a critical task in many real-world applications, such as exploring entity co-occurrences in intelligence analysis, and studying gene expression in bio-informatics. While the output of biclustering techniques can offer some initial low-level insights, visual approaches are required on top of that due to the algorithmic output complexity. This paper proposes a visualization technique, called BiDots, that allows analysts to interactively explore biclusters over multiple domains. BiDots overcomes several limitations of existing bicluster visualizations by encoding biclusters in a more compact and cluster-driven manner. A set of handy interactions is incorporated to support flexible analysis of biclustering results. More importantly, BiDots addresses the cases of weighted biclusters, which has been underexploited in the literature. The design of BiDots is grounded by a set of analytical tasks derived from previous work. We demonstrate its usefulness and effectiveness for exploring computed biclusters with an investigative document analysis task, in which suspicious people and activities are identified from a text corpus.

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking.

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, "in-between", to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.

A Five-Level Design Framework for Bicluster Visualizations.

Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools.