Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework -- deep verifier networks (DVN) to verify the inputs and outputs of deep discriminative models with deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints. We give both intuitive and theoretical justifications of the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.Comment: Accepted to AAAI 202

A recombinant avian antibody against VP2 of infectious bursal disease virus protects chicken from viral infection

【Abstract】A stable cell-line was established that expressed the recombinant avian antibody (rAb) against the infectious bursal disease virus (IBDV). rAb exhibited neutralization activity to IBDV-B87 strain in DF1 cells. The minimum rAb concentration required for inhibition of the cytopathic effect (CPE) was 1.563 μg/mL. To test the efficacy of rAb, a 168-h cohabitation challenge experiment was performed to transmit the disease from the chickens challenged with vvIBDV (HLJ0504 strain) to three test groups of chickens, i.e. (1) chickens treated with rAb, (2) chickens treated with yolk antibody, and (3) non-treatment chickens. The survival rates of chickens treated with rAb, yolk antibody and without treatment were 73%, 67% and 20%, respectively. Another batch of chickens was challenged with IBDV (BC6/85 strain) and then injected with rAb (1.0 mg/kg) 6, 24 and 36 h post-challenge. Non-treatment chickens had 100% morbidity, whereas those administered with rAb exhibited only 20% morbidity. Morbidity was evaluated using clinical indicators and bursal histopathological section. This study provides a new approach to treating IBDV and the rAb represents a promising candidate for this IBDV therapy.This research was supported by Heilongjiang province project of applied technology research and development (2013GC13C105) and The National Natural Science Fund biologic science base improve program of research training and capacity (J1210069/J0124)

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification tasks, most of them overlook the scarcity of labeled graph data in many applications. For example, in bioinformatics, obtaining protein graph labels usually needs laborious experiments. Recently, few-shot learning has been explored to alleviate this problem with only given a few labeled graph samples of test classes. The shared sub-structures between training classes and test classes are essential in few-shot graph classification. Exiting methods assume that the test classes belong to the same set of super-classes clustered from training classes. However, according to our observations, the label spaces of training classes and test classes usually do not overlap in real-world scenario. As a result, the existing methods don't well capture the local structures of unseen test classes. To overcome the limitation, in this paper, we propose a direct method to capture the sub-structures with well initialized meta-learner within a few adaptation steps. More specifically, (1) we propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data, and a step controller for the robustness and generalization of meta-learner; (2) we provide quantitative analysis for the framework and give a graph-dependent upper bound of the generalization error based on our framework; (3) the extensive experiments on real-world datasets demonstrate that our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines

In vitro activity and In vivo efficacy of Isoliquiritigenin against Staphylococcus xylosus ATCC 700404 by IGPD target.

Staphylococcus xylosus (S. xylosus) is a type of coagulase-negative Staphylococcus, which was previously considered as non-pathogenic. However, recent studies have linked it with cases of mastitis in cows. Isoliquiritigenin (ISL) is a bioactive compound with pharmacological functions including antibacterial activity. In this study, we evaluated the effect of ISL on S. xylosus in vitro and in vivo. The MIC of ISL against S. xylosus was 80 μg/mL. It was observed that sub-MICs of ISL (1/2MIC, 1/4MIC, 1/8MIC) significantly inhibited the formation of S. xylosus biofilm in vitro. Previous studies have observed that inhibiting imidazole glycerol phosphate dehydratase (IGPD) concomitantly inhibited biofilm formation in S. xylosus. So, we designed experiments to target the formation of IGPD or inhibits its activities in S. xylosus ATCC 700404. The results indicated that the activity of IGPD and its histidine content decreased significantly under 1/2 MIC (40 μg/mL) ISL, and the expression of IGPD gene (hisB) and IGPD protein was significantly down-regulated. Furthermore, Bio-layer interferometry experiments showed that ISL directly interacted with IGPD protein (with strong affinity; KD = 234 μM). In addition, molecular docking was used to predict the binding mode of ISL and IGPD. In vivo tests revealed that, ISL significantly reduced TNF-α and IL-6 levels, mitigated the destruction of the mammary glands and reversed the production of inflammatory cells in mice. The results of the study suggest that, ISL may inhibit S. xylosus growth by acting on IGPD, which can be used as a target protein to treat infections caused by S. xylosus

Scheme of the different rNDVs expressing EGFP (A) or IL2 (B) proteins.

<p>The small cassette which includes GS, GE, Kozak sequence, and EGFP or IL2 gene was inserted in different intergenic regions in the rNDV genome. The effects of inserted foreign genes on the proliferation of rNDV-EGFPs (C) and rNDV-IL2s (D). Ten-day-old embryonated eggs were inoculated with 100 PFU of the indicated rNDV, and llantoic fluids were harvested at 96 hpi. rNDV titers on DF-1 cells were determined by measuring TCID₅₀ and expressed as log10 TCID₅₀/mL from three independent experiments (NS: nonsignificant).</p

Identification of Optimal Insertion Site in Recombinant Newcastle Disease Virus (rNDV) Vector Expressing Foreign Gene to Enhance Its Anti-Tumor Effect - Fig 4

<p>(A) rNDV/IL2s effectively suppressed tumor growth. H22 oxter tumor-bearing mice model was treated with rNDV/IL2s, rNDV and PBS, and tumor volume was measured every other day using digital calipers in two dimensions. (B) Survival of H22 models animal in 120 days period after treatment with the rNDV-IL2s, rNDV, and PBS. The tumor-bearing mice were sacrificed when the tumor volume grew to a significant size (diameter > 18 mm). All the values are the mean and SEM of 10 samples. The log-rank test reveals a significant effect (NS: nonsignificant; * <i>P</i> < 0.05; ** <i>P</i> < 0.01).</p

Percentage of the CD4⁺ T and CD8⁺ T cells in spleen from H22 models mice treated with rNDV-IL2s.

<p>The CD4⁺ T and CD8⁺ T cells isolated from spleen of H22 models mice mock-treated or treated with rNDV-IL2s or rNDV were analyzed by flow cytometry (* <i>P</i> < 0.05; ** <i>P</i> < 0.01).</p

Premier sequences used for amplification of EGFP and IL2 gene with PCR.

<p>Premier sequences used for amplification of EGFP and IL2 gene with PCR.</p

Premier sequences used for detecting transcriptional levels of EGFP and IL2 genes with qRT-PCR.

<p>Premier sequences used for detecting transcriptional levels of EGFP and IL2 genes with qRT-PCR.</p