Downregulation of mPGES-1 Expression via

We investigated the mechanism of caffeine in influencing HBx(+) hepatocytes to synthesize PGE2. The inhibitory effect of caffeine on hepatocyte proliferation increased with increasing caffeine concentrations (200–800 μM) and treatment times (1–7 days), which was first observed at the second test time point (caffeine treatment for 4 days). The inhibition of caffeine on the growth of HL7702-HBx and HepG2-HBx cells was most obvious at 800 μM caffeine and at caffeine treatment for 7 days. The PGE2 secretion and the expression of mPGES-1 and EGR1 were downregulated, whereas PPARγ expression was upregulated. The mPGES-1 promoter activity of HBx(+) hepatocytes decreased more significantly than that of HBx(−) hepatocytes. Moreover, the expression of EGR1 and PPARγ changed more significantly in HBx(+) hepatocytes cultured for 12 to 24 hours in the presence of 5 mM caffeine. This limited success may be attributed to caffeine releasing the binding of HBx and PPARγ and furthermore affecting the mPGES-1 expression by EGR1 in HBx(+) hepatocytes. The results indicate that caffeine could effectively reduce PGE2 synthesis in HBx(+) hepatocytes by specifically blocking the PPARγ-EGR1-mPGES-1 pathway, thereby providing a new evidence of molecular biology for the hypothesis that drinking coffee is beneficial to HBV-infected patients

Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels

Deep learning models trained on large-scale data have achieved encouraging performance in many real-world tasks. Meanwhile, publishing those models trained on sensitive datasets, such as medical records, could pose serious privacy concerns. To counter these issues, one of the current state-of-the-art approaches is the Private Aggregation of Teacher Ensembles, or PATE, which achieved promising results in preserving the utility of the model while providing a strong privacy guarantee. PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on. However, the knowledge or voted labels learned by the student are noisy due to private aggregation. Learning directly from noisy labels can significantly impact the accuracy of the student model. In this paper, we propose the PATE++ mechanism, which combines the current advanced noisy label training mechanisms with the original PATE framework to enhance its accuracy. A novel structure of Generative Adversarial Nets (GANs) is developed in order to integrate them effectively. In addition, we develop a novel noisy label detection mechanism for semi-supervised model training to further improve student model performance when training with noisy labels. We evaluate our method on Fashion-MNIST and SVHN to show the improvements on the original PATE on all measures

Patterns of nucleotides that flank substitutions in human orthologous genes

<p>Abstract</p> <p>Background</p> <p>Sequence context is an important aspect of base mutagenesis, and three-base periodicity is an intrinsic property of coding sequences. However, how three-base periodicity is influenced in the vicinity of substitutions is still unclear. The effect of context on mutagenesis should be revealed in the usage of nucleotides that flank substitutions. Relative entropy (also known as Kullback-Leibler divergence) is useful for finding unusual patterns in biological sequences.</p> <p>Results</p> <p>Using relative entropy, we visualized the periodic patterns in the context of substitutions in human orthologous genes. Neighbouring patterns differed both among substitution categories and within a category that occurred at three codon positions. Transition tended to occur in periodic sequences relative to transversion. Periodic signals were stronger in a set of flanking sequences of substitutions that occurred at the third-codon positions than in those that occurred at the first- or second-codon positions. To determine how the three-base periodicity was affected near the substitution sites, we fitted a sine model to the values of the relative entropy. A sine of period equal to 3 is a good approximation for the three-base periodicity at sites not in close vicinity to some substitutions. These periods were interrupted near the substitution site and then reappeared away from substitutions. A comparative analysis between the native and codon-shuffled datasets suggested that the codon usage frequency was not the sole origin of the three-base periodicity, implying that the native order of codons also played an important role in this periodicity. Synonymous codon shuffling revealed that synonymous codon usage bias was one of the factors responsible for the observed three-base periodicity.</p> <p>Conclusions</p> <p>Our results offer an efficient way to illustrate unusual periodic patterns in the context of substitutions and provide further insight into the origin of three-base periodicity. This periodicity is a result of the native codon order in the reading frame. The length of the period equal to 3 is caused by the usage bias of nucleotides in synonymous codons. The periodic features in nucleotides surrounding substitutions aid in further understanding genetic variation and nucleotide mutagenesis.</p

The Ly-6A (Sca-1) GFP transgene is expressed in all adult mouse hematopoietic stem cells

The Sca-1 cell surface glycoprotein is used routinely as a marker of adult hematopoietic stem cells (HSCs), allowing a >100-fold enrichment of these rare cells from the bone marrow of the adult mouse. The Sca-1 protein is encoded by the Ly-6A/E gene, a small 4-exon gene that is tightly controlled in its expression in HSCs and several hematopoietic cell types. For the ability to sort and localize HSCs directly from the mouse, we initiated a transgenic approach in which we created Ly-6A (Sca-1) green fluorescent protein (GFP) transgenic mice. We show here that a 14-kb Ly-6A expression cassette directs the transcription of the GFP marker gene in all functional repopulating HSCs in the adult bone marrow. A >100-fold enrichment of HSCs occurred by sorting for the GFP-expressing cells. Furthermore, as shown by fluorescence-activated cell sorting and histologic analysis of several hematopoietic tissues, the GFP transgene expression pattern generally corresponded to that of Sca-1. Thus, the Ly-6A GFP transgene facilitates the enrichment of HSCs and presents the likelihood of identifying HSCs in situ

A multitask deep learning approach for pulmonary embolism detection and identification

Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold standard for PE diagnoses. Deep learning can enhance the radiologists’workflow by identifying PE using CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients. In this study, we propose a two-phase multitask learning method that can recognize the presence of PE and its properties such as the position, whether acute or chronic, and the corresponding right-to-left ventricle diameter (RV/LV) ratio, thereby reducing false-negative diagnoses. Trained on the RSNA-STR Pulmonary Embolism CT Dataset, our model demonstrates promising PE detection performances on the hold-out test set with the window-level AUROC achieving 0.93 and the sensitivity being 0.86 with a specificity of 0.85, which is competitive with the radiologists’sensitivities ranging from 0.67 to 0.87 with specificities of 0.89–0.99. In addition, our model provides interpretability through attention weight heatmaps and gradient-weighted class activation mapping (Grad-CAM). Our proposed deep learning model could predict PE existence and other properties of existing cases, which could be applied to practical assistance for PE diagnosis

Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis

Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires the exchange of intermediary results which could reveal sensitive patient information. Therefore, the challenge is how to jointly decompose the tensor under rigorous and principled privacy constraints, while still support the model's interpretability. We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR. It embeds advanced privacy-preserving mechanisms with collaborative learning. Hospitals can keep their EHR database private but also collaboratively learn meaningful clinical concepts by sharing differentially private intermediary results. Moreover, DPFact solves the heterogeneous patient population using a structured sparsity term. In our framework, each hospital decomposes its local tensors, and sends the updated intermediary results with output perturbation every several iterations to a semi-trusted server which generates the phenotypes. The evaluation on both real-world and synthetic datasets demonstrated that under strict privacy constraints, our method is more accurate and communication-efficient than state-of-the-art baseline methods