Protecting Geolocation Privacy of Photo Collections

People increasingly share personal information, including their photos and photo collections, on social media. This information, however, can compromise individual privacy, particularly as social media platforms use it to infer detailed models of user behavior, including tracking their location. We consider the specific issue of location privacy as potentially revealed by posting photo collections, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. One means to limit associated inadvertent geolocation privacy disclosure is by carefully pruning select photos from photo collections before these are posted publicly. We study this problem formally as a combinatorial optimization problem in the context of geolocation prediction facilitated by deep learning. We first demonstrate the complexity both by showing that a natural greedy algorithm can be arbitrarily bad and by proving that the problem is NP-Hard. We then exhibit an important tractable special case, as well as a more general approach based on mixed-integer linear programming. Through extensive experiments on real photo collections, we demonstrate that our approaches are indeed highly effective at preserving geolocation privacy.Comment: AAAI 2

Relabeling Minimal Training Subset to Flip a Prediction

When facing an unsatisfactory prediction from a machine learning model, it is crucial to investigate the underlying reasons and explore the potential for reversing the outcome. We ask: can we result in the flipping of a test prediction $x_t$ by relabeling the smallest subset $\mathcal{S}_t$ of the training data before the model is trained? We propose an efficient procedure to identify and relabel such a subset via an extended influence function. We find that relabeling fewer than 1% of the training points can often flip the model's prediction. This mechanism can serve multiple purposes: (1) providing an approach to challenge a model prediction by recovering influential training subsets; (2) evaluating model robustness with the cardinality of the subset (i.e., $|\mathcal{S}_t|$); we show that $|\mathcal{S}_t|$ is highly related to the noise ratio in the training set and $|\mathcal{S}_t|$ is correlated with but complementary to predicted probabilities; (3) revealing training points lead to group attribution bias. To the best of our knowledge, we are the first to investigate identifying and relabeling the minimal training subset required to flip a given prediction.Comment: Under revie

Model Sparsification Can Simplify Machine Unlearning

Recent data regulations necessitate machine unlearning (MU): The removal of the effect of specific examples from the model. While exact unlearning is possible by conducting a model retraining with the remaining data from scratch, its computational cost has led to the development of approximate but efficient unlearning schemes. Beyond data-centric MU solutions, we advance MU through a novel model-based viewpoint: sparsification via weight pruning. Our results in both theory and practice indicate that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient. With this insight, we develop two new sparsity-aware unlearning meta-schemes, termed `prune first, then unlearn' and `sparsity-aware unlearning'. Extensive experiments show that our findings and proposals consistently benefit MU in various scenarios, including class-wise data scrubbing, random data scrubbing, and backdoor data forgetting. One highlight is the 77% unlearning efficacy gain of fine-tuning (one of the simplest approximate unlearning methods) in the proposed sparsity-aware unlearning paradigm. Codes are available at https://github.com/OPTML-Group/Unlearn-Sparse

An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases

Dermatological diseases are among the most common disorders worldwide. This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled samples from minority classes have a higher probability at each iteration of class-rebalancing self-training, thereby promoting the utilization of unlabeled samples to solve the class imbalance problem. Our ISDL achieved a promising performance with an accuracy of 0.979, sensitivity of 0.975, specificity of 0.973, macro-F1 score of 0.974 and area under the receiver operating characteristic curve (AUC) of 0.999 for multi-label skin disease classification. The Shapley Additive explanation (SHAP) method is combined with our ISDL to explain how the deep learning model makes predictions. This finding is consistent with the clinical diagnosis. We also proposed a sampling distribution optimisation strategy to select pseudo-labelled samples in a more effective manner using ISDLplus. Furthermore, it has the potential to relieve the pressure placed on professional doctors, as well as help with practical issues associated with a shortage of such doctors in rural areas

Liquid biopsy biomarkers to guide immunotherapy in breast cancer

Immune checkpoint inhibitors (ICIs) therapy has emerged as a promising treatment strategy for breast cancer (BC). However, current reliance on immunohistochemical (IHC) detection of PD-L1 expression alone has limited predictive capability, resulting in suboptimal efficacy of ICIs for some BC patients. Hence, developing novel predictive biomarkers is indispensable to enhance patient selection for immunotherapy. In this context, utilizing liquid biopsy (LB) can provide supplementary or alternative value to PD-L1 IHC testing for identifying patients most likely to benefit from immunotherapy and exhibit favorable responses. This review discusses the predictive and prognostic value of LB in breast cancer immunotherapy, as well as its limitations and future directions. We aim to promote the individualization and precision of immunotherapy in BC by elucidating the role of LB in clinical practice

Isochorismatase domain-containing protein 1 (ISOC1) participates in DNA damage repair and inflammation-related pathways to promote lung cancer development

Background: The advent of novel molecular targets has dramatically changed the treatment landscape of lung cancer in recent years. Isochorismatase domain-containing protein 1 (ISOC1) has been reported as a potential biomarker in gastrointestinal cancer, while its function in lung cancer has not been determined.Methods: The expression levels and prognostic significance of ISOC1 were assessed using bioinformatic analysis. Overexpression of ISOC1 and miR-4633, and knockdown of ISOC1 in non-small cell lung cancer (NSCLC) cell lines were generated by lentiviral infection with overexpressed or shRNA plasmids. CRISPR/Cas9 system was applied to knockout ISOC1 in A549 cells. The functions of ISOC1 and miR-4633 in lung cancer development were investigated using cell proliferation, migration, and invasion assays. Xenograft tumor growth assays in nude mice were further assessed the effect of ISOC1 in the tumorigenesis of NSCLC in vivo. Cell cycle distribution analysis was performed to uncover the underlying mechanism of ISOC1 and miR-4633 in promoting NSCLC cell proliferation. Co-immunoprecipitation combined with mass spectrometry and RNA sequencing were performed to uncover the potential mechanism of ISOC1 in lung cancer development.Results: Our results found that ISOC1 expression was upregulated in NSCLC tissues and that increased expression of ISOC1 was significantly associated with worse disease-free survival in NSCLC patients. Overexpression of ISOC1 could increase the proliferation, viability, migration, and invasion of NSCLC cells. Furthermore, miR-4633, located in the first intron of ISOC1, could also promote tumor cell progression and metastasis. Mice xenograft tumor assay showed that knockout of ISOC1 could significantly inhibit tumor growth in vivo. Besides, co-immunoprecipitation combined with mass spectrometry assay revealed that ISOC1 interacted with the proteins of DNA damage repair pathways and that upregulated ISOC1 expression could significantly increase the number of DNA damage lesions. RNA sequencing analysis showed that the downstream signaling pathways mediated by ISOC1 were mainly inflammation-related.Conclusions: We demonstrated that ISOC1 and its intronic miR-4633, both of them could promote NSCLC cell proliferation, migration, invasion, and cell cycle progression. ISOC1 participates in DNA damage repair and inflammation to promote lung cancer development

Genetically predicted high IGF-1 levels showed protective effects on COVID-19 susceptibility and hospitalization:a Mendelian randomisation study with data from 60 studies across 25 countries

Background: Epidemiological studies observed gender differences in COVID-19 outcomes, however, whether sex hormone plays a causal in COVID-19 risk remains unclear. This study aimed to examine associations of sex hormone, sex hormones-binding globulin (SHBG), insulin-like growth factor-1 (IGF-1), and COVID-19 risk. Methods: Two-sample Mendelian randomization (TSMR) study was performed to explore the causal associations between testosterone, estrogen, SHBG, IGF-1, and the risk of COVID-19 (susceptibility, hospitalization, and severity) using genome-wide association study (GWAS) summary level data from the COVID-19 Host Genetics Initiative (N=1,348,701). Random-effects inverse variance weighted (IVW) MR approach was used as the primary MR method and the weighted median, MR-Egger, and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test were conducted as sensitivity analyses. Results: Higher genetically predicted IGF-1 levels have nominally significant association with reduced risk of COVID-19 susceptibility and hospitalization. For one standard deviation increase in genetically predicted IGF-1 levels, the odds ratio was 0.77 (95% confidence interval [CI], 0.61-0.97, p=0.027) for COVID-19 susceptibility, 0.62 (95% CI: 0.25-0.51, p=0.018) for COVID-19 hospitalization, and 0.85 (95% CI: 0.52-1.38, p=0.513) for COVID-19 severity. There was no evidence that testosterone, estrogen, and SHBG are associated with the risk of COVID-19 susceptibility, hospitalization, and severity in either overall or sex-stratified TSMR analysis. Conclusions: Our study indicated that genetically predicted high IGF-1 levels were associated with decrease the risk of COVID-19 susceptibility and hospitalization, but these associations did not survive the Bonferroni correction of multiple testing. Further studies are needed to validate the findings and explore whether IGF-1 could be a potential intervention target to reduce COVID-19 risk

Boosting with an aerosolized Ad5-nCoV elicited robust immune responses in inactivated COVID-19 vaccines recipients

IntroductionThe SARS-CoV-2 Omicron variant has become the dominant SARS-CoV-2 variant and exhibits immune escape to current COVID-19 vaccines, the further boosting strategies are required.MethodsWe have conducted a non-randomized, open-label and parallel-controlled phase 4 trial to evaluate the magnitude and longevity of immune responses to booster vaccination with intramuscular adenovirus vectored vaccine (Ad5-nCoV), aerosolized Ad5-nCoV, a recombinant protein subunit vaccine (ZF2001) or homologous inactivated vaccine (CoronaVac) in those who received two doses of inactivated COVID-19 vaccines. ResultsThe aerosolized Ad5-nCoV induced the most robust and long-lasting neutralizing activity against Omicron variant and IFNg T-cell response among all the boosters, with a distinct mucosal immune response. SARS-CoV-2-specific mucosal IgA response was substantially generated in subjects boosted with the aerosolized Ad5-nCoV at day 14 post-vaccination. At month 6, participants boosted with the aerosolized Ad5-nCoV had remarkably higher median titer and seroconversion of the Omicron BA.4/5-specific neutralizing antibody than those who received other boosters. DiscussionOur findings suggest that aerosolized Ad5-nCoV may provide an efficient alternative in response to the spread of the Omicron BA.4/5 variant.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=152729, identifier ChiCTR2200057278