Thioredoxin1 Binding Metastasis-Associated Lung Adenocarcinoma Transcript 1 Attenuates Inflammation and Apoptosis after Intracerebral Hemorrhage.

Post-transcriptional regulation and RNA-binding proteins (RBPs) play vital roles in the occurrence of secondary injury after intracerebral hemorrhage (ICH). Therefore, we identified RBPs distinctively expressed after ICH by screening and determined thioredoxin1 (Txn1) as one of the most distinctive RBPs. We employed an ICH model and in vitro experiments to investigate the role of Txn1 in ICH. Firstly, we found that Txn1 was mainly expressed in microglia and neurons in the central nervous system, and its expression was significantly reduced in perihematomal tissue. Additionally, adeno-associated virus (AAV) carrying Txn1 was injected into the ICH rat model. Our results showed that overexpression of Txn1 reduced secondary injury and improved outcome in the ICH rat model. Moreover, to understand the therapeutic mechanism of Txn1 after ICH, we performed RNA immunoprecipitation combined with high-throughput sequencing. The results showed that Txn1 binds to inflammation- and apoptosis-related mRNAs and affects gene expression through RNA splicing and translation. Finally, RNA pull-down assays and in vitro experiments confirmed that Txn1 binds to metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), leading to reduced inflammation and apoptosis. Our study suggests that Txn1 is a potential therapeutic target for alleviating ICH-induced brain injury.

Roslingifier: Semi-Automated Storytelling for Animated Scatterplots.

We present Roslingifier, a data-driven storytelling method for animated scatterplots. Like its namesake, Hans Rosling (1948-2017), a professor of public health and a spellbinding public speaker, Roslingifier turns a sequence of entities changing over time-such as countries and continents with their demographic data-into an engaging narrative elling the story of the data. This data-driven storytelling method with an in-person presenter is a new genre of storytelling technique and has never been studied before. In this article, we aim to define a design space for this new genre-data presentation-and provide a semi-automated authoring tool for helping presenters create quality presentations. From an in-depth analysis of video clips of presentations using interactive visualizations, we derive three specific techniques to achieve this: natural language narratives, visual effects that highlight events, and temporal branching that changes playback time of the animation. Our implementation of the Roslingifier method is capable of identifying and clustering significant movements, automatically generating visual highlighting and a narrative for playback, and enabling the user to customize. From two user studies, we show that Roslingifier allows users to effectively create engaging data stories and the system features help both presenters and viewers find diverse insights.

Single-cell RNA sequencing of peripheral blood reveals that monocytes with high cathepsin S expression aggravate cerebral ischemia-reperfusion injury.

BACKGROUND: Stroke is a major cause of morbidity and mortality worldwide. After cerebral ischemia, peripheral immune cells infiltrate the brain and elicit an inflammatory response. However, it is not clear when and how these peripheral immune cells affect the central inflammatory response, and whether interventions that target these processes can alleviate ischemia-reperfusion (I/R) injury. METHODS: Single-cell transcriptomic sequencing and bioinformatics analysis were performed on peripheral blood of mice at different times after I/R to analyze the key molecule of cell subsets. Then, the expression pattern of this molecule was determined through various biological experiments, including quantitative RT-PCR, western blot, ELISA, and in situ hybridization. Next, the function of this molecule was assessed using knockout mice and the corresponding inhibitor. RESULTS: Single-cell transcriptomic sequencing revealed that peripheral monocyte subpopulations increased significantly after I/R. Cathepsin S (Ctss)was identified as a key molecule regulating monocyte activation by pseudotime trajectory analysis and gene function analysis. Next, Cathepsin S was confirmed to be expressed in monocytes with the highest expression level 3 days after I/R. Infarct size (p < 0.05), neurological function scores (p < 0.05), and apoptosis and vascular leakage rates were significantly reduced after Ctss knockout. In addition, CTSS destroyed the blood-brain barrier (BBB) by binding to junctional adhesion molecule (JAM) family proteins to cause their degradation. CONCLUSIONS: Cathepsin S inhibition attenuated cerebral I/R injury; therefore, cathepsin S can be used as a novel target for drug intervention after stroke.

A longitudinal study of topic classification on Twitter.

Twitter represents a massively distributed information source over topics ranging from social and political events to entertainment and sports news. While recent work has suggested this content can be narrowed down to the personalized interests of individual users by training topic filters using standard classifiers, there remain many open questions about the efficacy of such classification-based filtering approaches. For example, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? In addition, how robust is a topic classifier over the time horizon, e.g., can a model trained in 1 year be used for making predictions in the subsequent year? Furthermore, what features, feature classes, and feature attributes are most critical for long-term classifier performance? To answer these questions, we collected a corpus of over 800 million English Tweets via the Twitter streaming API during 2013 and 2014 and learned topic classifiers for 10 diverse themes ranging from social issues to celebrity deaths to the "Iran nuclear deal". The results of this long-term study of topic classifier performance provide a number of important insights, among them that: (i) such classifiers can indeed generalize to novel topical content with high precision over a year or more after training though performance degrades with time, (ii) the classes of hashtags and simple terms contain the most informative feature instances, (iii) removing tweets containing training hashtags from the validation set allows better generalization, and (iv) the simple volume of tweets by a user correlates more with their informativeness than their follower or friend count. In summary, this work provides a long-term study of topic classifiers on Twitter that further justifies classification-based topical filtering approaches while providing detailed insight into the feature properties most critical for topic classifier performance.

A20/Nrdp1 interaction alters the inflammatory signaling profile by mediating K48- and K63-linked polyubiquitination of effectors MyD88 and TBK1.

A20 is a potent anti-inflammatory protein that mediates both inflammation and ubiquitination in mammals, but the related mechanisms are not clear. In this study, we performed mass spectrometry (MS) screening, gene ontology (GO) analysis, and coimmunoprecipitation (co-IP) in a lipopolysaccharide (LPS)-induced inflammatory cell model to identify novel A20-interacting proteins. We confirmed that the E3 ubiquitin ligase Nrdp1, also known as ring finger protein 41 (RNF41), interacted with A20 in LPS-stimulated cells. Further co-IP analysis demonstrated that when A20 was knocked out, degradation-inducing K48-linked ubiquitination of inflammatory effector MyD88 was decreased, but protein interaction-mediating K63-linked ubiquitination of another inflammatory effector TBK1 was increased. Moreover, western blot experiments showed that A20 inhibition induced an increase in levels of MyD88 and phosphorylation of downstream effector proteins as well as of TBK1 and a downstream effector, while Nrdp1 inhibition induced an increase in MyD88 but a decrease in TBK1 levels. When A20 and Nrdp1 were coinhibited, no further change in MyD88 was observed, but TBK1 levels were significantly decreased compared with those upon A20 inhibition alone. Gain- and loss-of-function analyses revealed that the ZnF4 domain of A20 is required for Nrdp1 polyubiquitination. Upon LPS stimulation, the inhibition of Nrdp1 alone increased the secretion of IL-6 and TNF-α but decreased IFN-ß secretion, as observed in other studies, suggesting that Nrdp1 preferentially promotes the production of IFN-ß. Taken together, these results demonstrated that A20/Nrdp1 interaction is important for A20 anti-inflammation, thus revealing a novel mechanism for the anti-inflammatory effects of A20.

Holo-UNet: hologram-to-hologram neural network restoration for high fidelity low light quantitative phase imaging of live cells.

Intensity shot noise in digital holograms distorts the quality of the phase images after phase retrieval, limiting the usefulness of quantitative phase microscopy (QPM) systems in long term live cell imaging. In this paper, we devise a hologram-to-hologram neural network, Holo-UNet, that restores high quality digital holograms under high shot noise conditions (sub-mW/cm2 intensities) at high acquisition rates (sub-milliseconds). In comparison to current phase recovery methods, Holo-UNet denoises the recorded hologram, and so prevents shot noise from propagating through the phase retrieval step that in turn adversely affects phase and intensity images. Holo-UNet was tested on 2 independent QPM systems without any adjustment to the hardware setting. In both cases, Holo-UNet outperformed existing phase recovery and block-matching techniques by ∼ 1.8 folds in phase fidelity as measured by SSIM. Holo-UNet is immediately applicable to a wide range of other high-speed interferometric phase imaging techniques. The network paves the way towards the expansion of high-speed low light QPM biological imaging with minimal dependence on hardware constraints.

Age-related CCL12 Aggravates Intracerebral Hemorrhage-induced Brain Injury via Recruitment of Macrophages and T Lymphocytes.

Circulating factors associated with aging have been shown to be involved in the development of age-related chronic and acute brain diseases. Here, we aimed to investigate the roles and mechanisms of CCL12, a circulating factor that is highly expressed in the plasma of aged rodents after intracerebral hemorrhage (ICH) using parabiosis and ICH models. Neurological deficit score (NDS), mortality rate, brain water content (BWC), and levels of inflammatory factors were determined to assess the degree of ICH-induced brain injury. Peripheral inflammatory cell infiltration was examined using immunofluorescence and flow cytometry. After confirming that acute brain injury after ICH was aggravated with age, we found that brain and plasma CCL12 levels were markedly higher in old mice than in young mice after ICH, and that plasma CCL12 was able to enter the brain. Using CCL12-/- mice, we showed that the degree of damage in the brain-as determined by NDS, mortality rate, BWC, levels of inflammatory factors, and numbers of degenerative and apoptotic neural cells and surviving neurons was significantly attenuated compared to that observed in old wild-type (WT) mice. These effects were reversed in CCL12-treated old mice. The detrimental effects caused by CCL12 may involve its ability to recruit macrophages and T cells. Finally, the administration of an anti-CCL12 antibody markedly improved the outcomes of ICH mice. Our results are the first to indicate that elevated peripheral CCL12 levels in old mice aggravates ICH-induced brain injury by recruiting macrophages and T cells. Thus, CCL12 may be a new target for ICH treatment.

Prdx1 Reduces Intracerebral Hemorrhage-Induced Brain Injury via Targeting Inflammation- and Apoptosis-Related mRNA Stability.

RNA-binding proteins (RBPs) have been shown to be involved in posttranscriptional regulation, which plays an important role in the pathophysiology of intracerebral hemorrhage (ICH). Peroxiredoxin 1 (Prdx1), an RBP, plays an important role in regulating inflammation and apoptosis. On the basis that inflammation and apoptosis may contribute to ICH-induced brain injury, in this study, we used ICH models coupled with in vitro experiments, to investigate the role and mechanism of Prdx1 in regulating inflammation and apoptosis by acting as an RBP after ICH. We first found that Prdx1 was significantly up-regulated in response to ICH-induced brain injury and was mainly expressed in astrocytes and microglia in ICH rat brains. After overexpressing Prdx1 by injecting adeno-associated virus (AAV) into the striatum of rats at 3 weeks, we constructed ICH models and found that Prdx1 overexpression markedly reduced inflammation and apoptosis after ICH. Furthermore, RNA immunoprecipitation combined with high-throughput sequencing (RIP-seq) in vitro revealed that Prdx1 affects the stability of inflammation- and apoptosis-related mRNA, resulting in the inhibition of inflammation and apoptosis. Finally, overexpression of Prdx1 significantly alleviated the symptoms and mortality of rats subjected to ICH. Our results show that Prdx1 reduces ICH-induced brain injury by targeting inflammation- and apoptosis-related mRNA stability. Prdx1 may be an improved therapeutic target for alleviating the brain injury caused by ICH.

Differential Topic Models.

In applications we may want to compare different document collections: they could have shared content but also different and unique aspects in particular collections. This task has been called comparative text mining or cross-collection modeling. We present a differential topic model for this application that models both topic differences and similarities. For this we use hierarchical Bayesian nonparametric models. Moreover, we found it was important to properly model power-law phenomena in topic-word distributions and thus we used the full Pitman-Yor process rather than just a Dirichlet process. Furthermore, we propose the transformed Pitman-Yor process (TPYP) to incorporate prior knowledge such as vocabulary variations in different collections into the model. To deal with the non-conjugate issue between model prior and likelihood in the TPYP, we thus propose an efficient sampling algorithm using a data augmentation technique based on the multinomial theorem. Experimental results show the model discovers interesting aspects of different collections. We also show the proposed MCMC based algorithm achieves a dramatically reduced test perplexity compared to some existing topic models. Finally, we show our model outperforms the state-of-the-art for document classification/ideology prediction on a number of text collections.

Optimal-scaling-factor assignment for patch-wise image retargeting.

Image retargeting adjusts images to arbitrary sizes such that they can be viewed on different displays. Content-aware image retargeting has been receiving increased attention. In particular, researchers have improved a patch-wise scaling method for image retargeting at the object level. The scaling partitions the image into rectangular patches of adaptive sizes, which are comparable to the sizes of the salient objects in the image. This partitioning is based on a visual-saliency map; accordingly, the method labels the patches as important or unimportant. Then, the method scales the important patches as uniformly as possible and stretches or squeezes the unimportant patches to fit the target size. A patch-based image-similarity measure finds the optimal set of scaling factors. In experiments, the improved method performed well for three image types: lines and edges, foreground objects, and geometric structures.

ImageHive: interactive content-aware image summarization.

ImageHive communicates information about an image collection by generating a summary image that preserves the relationships between images and avoids occluding their salient parts. It uses a constrained graph-layout algorithm first, to preserve image similarities and keep important parts visible, and then a constrained Voronoi tessellation algorithm to locally refine the layout and tile the image plane.