Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training data. To defend against MI attacks, previous work utilizes a unilateral dependency optimization strategy, i.e., minimizing the dependency between inputs (i.e., features) and outputs (i.e., labels) during training the classifier. However, such a minimization process conflicts with minimizing the supervised loss that aims to maximize the dependency between inputs and outputs, causing an explicit trade-off between model robustness against MI attacks and model utility on classification tasks. In this paper, we aim to minimize the dependency between the latent representations and the inputs while maximizing the dependency between latent representations and the outputs, named a bilateral dependency optimization (BiDO) strategy. In particular, we use the dependency constraints as a universally applicable regularizer in addition to commonly used losses for deep neural networks (e.g., cross-entropy), which can be instantiated with appropriate dependency criteria according to different tasks. To verify the efficacy of our strategy, we propose two implementations of BiDO, by using two different dependency measures: BiDO with constrained covariance (BiDO-COCO) and BiDO with Hilbert-Schmidt Independence Criterion (BiDO-HSIC). Experiments show that BiDO achieves the state-of-the-art defense performance for a variety of datasets, classifiers, and MI attacks while suffering a minor classification-accuracy drop compared to the well-trained classifier with no defense, which lights up a novel road to defend against MI attacks.Comment: Accepted to KDD 2022 (Research Track

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer.

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment

Characterization of Shiga toxin-producing Escherichia coli isolated from healthy pigs in China

BACKGROUND: Shiga toxin-producing Escherichia coli (STEC) is recognized as an important human diarrheal pathogen. Swine plays an important role as a carrier of this pathogen. In this study we determined the prevalence and characteristics of STEC from healthy swine collected between May 2011 and August 2012 from 3 cities/provinces in China. RESULTS: A total of 1003 samples, including 326 fecal, 351 small intestinal contents and 326 colon contents samples, was analyzed. Two hundred and fifty five samples were stx-positive by PCR and 93 STEC isolates were recovered from 62 stx-positive samples. Twelve O serogroups and 19 O:H serotypes including 6 serotypes (O100:H20/[H20], O143:H38/[H38], O87:H10, O172:H30/[H30], O159:H16, O9:H30/[H30]) rarely found in swine and ruminants were identified. All 93 STEC isolates harbored stx(2) only, all of which were stx(2e) subtype including 1 isolate being a new variant of stx(2e). 53.76%, 15.05% and 2.15% STEC isolates carried astA, hlyA and ehxA respectively. Four STEC isolates harbored the high-pathogenicity island. Of the 15 adherence-associated genes tested, 13 (eae, efa1, iha, lpfA(O113), lpfA(O157/OI-154), lpfA(O157/OI-141), toxB, saa, F4, F5, F6, F17 or F41) were all absent while 2 (paa and F18) were present in 7 and 4 STEC isolates respectively. The majority of the isolates were resistant to tetracycline (79.57%), nalidixic acid (78.49%), trimethoprim-sulfamethoxazole (73.12%) and kanamycin (55.91%). The STEC isolates were divided into 63 pulsed-field gel electrophoresis patterns and 21 sequence types (STs). Isolates of the same STs generally showed the same or similar drug resistance patterns. A higher proportion of STEC isolates from Chongqing showed multidrug resistance with one ST (ST3628) resistant to 14 antimicrobials. CONCLUSIONS: Our results indicate that swine is a significant reservoir of STEC strains in China. Based on comparison by serotypes and sequence types with human strains and presence of virulence genes, the swine STEC may have a low potential to cause human disease

Efficient W state entanglement concentration using quantum-dot and optical microcavities

We present an entanglement concentration protocols (ECPs) for less-entangled W state with quantum-dot and microcavity coupled system. The present protocol uses the quantum nondemolition measurement on the spin parity to construct the parity check gate. Different from other ECPs, this less-entangled W state with quantum-dot and microcavity coupled system can be concentrated with the help of some single photons. The whole protocol can be repeated to get a higher success probability. It may be useful in current quantum information processing.Comment: 9 pages, 6 figure

The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved