Robust prediction of clinical outcomes using cytometry data.

MotivationFlow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction.ResultsWe propose a novel strategy (CytoDx) to predict clinical outcomes using cytometry data without cell gating. Applying CytoDx on real-world datasets allow us to predict multiple types of clinical features. In particular, CytoDx is able to predict the response to influenza vaccine using highly heterogeneous datasets, demonstrating that it is not only accurate but also robust to batch effects and cytometry platforms.Availability and implementationCytoDx is available as an R package on Bioconductor (bioconductor.org/packages/CytoDx). Data and scripts for reproducing the results are available on bitbucket.org/zichenghu_ucsf/cytodx_study_code/downloads.Supplementary informationSupplementary data are available at Bioinformatics online

Learning Structured Inference Neural Networks with Label Relations

Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with fine-grained labels that describe major components, coarse-grained labels that depict high level abstraction or a set of labels that reveal attributes. Such categorization at different concept layers can be modeled with label graphs encoding label information. In this paper, we exploit this rich information with a state-of-art deep learning framework, and propose a generic structured model that leverages diverse label relations to improve image classification performance. Our approach employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. We evaluate our method on benchmark image datasets, and empirical results illustrate the efficacy of our model.Comment: Conference on Computer Vision and Pattern Recognition(CVPR) 201

The Solar-Heat Pump Combined Drying Characteristics and Dynamic Model of Kelp Knots

For controlling the entire drying process of a material, it is crucial to understand the moisture ratio of the material in the drying process. In order to ascertain the moisture change rules of kelp knots in the solar-heat pump combined drying process, an analysis was made on the impacts of different drying temperatures, wind speeds and loading capacities on the drying rate in this research; meanwhile, three common drying dynamic models were selected and compared to know their applicability to the solar-heat pump combined drying of kelp knots. Further, the model coefficient was determined and the optimal model was obtained. The results reveal as follows: drying temperature, wind speed and loading capacity have significant impact on and significant correlation (P<0.05) with the drying rate of kelp knots; under different drying conditions, the drying rate is always high in the early stage, lowered and gradually moderate in the later stage. After fitting the drying dynamic model, it is found that among the experimental data, regression coefficient (R2) is the largest in the Verma model, and the sum of squares for error (SSE) and root mean square error (RMSE) are low. This indicates that the Verma model can be used to accurately express and predict the change rules of moisture in kelp knots during the solar-heat pump combined drying. According to Fick's second diffusion law, the effective diffusion coefficient Deff increases with the increase in drying temperature and wind speed, and decreases with the increase in loading capacity

VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning

It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps). In this challenge, no additional image-caption training data, other thanCOCO Captions, is allowed for model training. Thus, conventional Vision-Language Pre-training (VLP) methods cannot be applied. This paper presents VIsual VOcabulary pretraining (VIVO) that performs pre-training in the absence of caption annotations. By breaking the dependency of paired image-caption training data in VLP, VIVO can leverage large amounts of paired image-tag data to learn a visual vocabulary. This is done by pre-training a multi-layer Transformer model that learns to align image-level tags with their corresponding image region features. To address the unordered nature of image tags, VIVO uses a Hungarian matching loss with masked tag prediction to conduct pre-training. We validate the effectiveness of VIVO by fine-tuning the pre-trained model for image captioning. In addition, we perform an analysis of the visual-text alignment inferred by our model. The results show that our model can not only generate fluent image captions that describe novel objects, but also identify the locations of these objects. Our single model has achieved new state-of-the-art results on nocaps and surpassed the human CIDEr score.Comment: AAAI 202

ImmPort, toward repurposing of open access immunological assay data for translational and clinical research

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the ImmPort Shared Data portal , which allows research data to be repurposed to accelerate the translation of new insights into discoveries

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups

Detecting calcium flux in T cells using a Bayesian model

textUpon antigen recognition, T cells are activated to carry out its effector functions. A hallmark of T cell activation is the dramatic increase of the intracellular calcium concentration (calcium influx). Indo-1 is a calcium indicator dye widely used to detect T cell activation events in in vitro assays. The use of Indo-1 to detect T cell activation events in live tissues remains a challenge, due to the high noise to signal ratio data generated. Here, we developed a Bayesian probabilistic model to identify T cell activation events from noisy Indo-1 data. The model was able to detect T cell activation events accurately from simulated data, as well as real biological data in which the time of T cell activation events are known. We then used the model to detect OTII T cells that are activated by dendritic cells in thymic medulla in Rip-OVAhi transgenic mouse. We found that dendritic cells contribute 60% of all T cell activations in the mouse model.Statistic