Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain

Face recognition technology has been used in many fields due to its high recognition accuracy, including the face unlocking of mobile devices, community access control systems, and city surveillance. As the current high accuracy is guaranteed by very deep network structures, facial images often need to be transmitted to third-party servers with high computational power for inference. However, facial images visually reveal the user's identity information. In this process, both untrusted service providers and malicious users can significantly increase the risk of a personal privacy breach. Current privacy-preserving approaches to face recognition are often accompanied by many side effects, such as a significant increase in inference time or a noticeable decrease in recognition accuracy. This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Due to the utilization of differential privacy, it offers a guarantee of privacy in theory. Meanwhile, the loss of accuracy is very slight. This method first converts the original image to the frequency domain and removes the direct component termed DC. Then a privacy budget allocation method can be learned based on the loss of the back-end face recognition network within the differential privacy framework. Finally, it adds the corresponding noise to the frequency domain features. Our method performs very well with several classical face recognition test sets according to the extensive experiments.Comment: ECCV 2022; Code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/dctd

DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain

With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal privacy breaches. This paper presents DuetFace, a novel privacy-preserving face recognition method that employs collaborative inference in the frequency domain. Starting from a counterintuitive discovery that face recognition can achieve surprisingly good performance with only visually indistinguishable high-frequency channels, this method designs a credible split of frequency channels by their cruciality for visualization and operates the server-side model on non-crucial channels. However, the model degrades in its attention to facial features due to the missing visual information. To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask. The mask is further refined by deriving and overlaying a facial region of interest (ROI). Extensive experiments on multiple datasets validate the effectiveness of the proposed method in protecting face images from undesired visual inspection, reconstruction, and identification while maintaining high task availability and performance. Results show that the proposed method achieves a comparable recognition accuracy and computation cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods. The source code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.Comment: Accepted to ACM Multimedia 202

Privacy-Preserving Face Recognition Using Random Frequency Components

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.Comment: ICCV 202

Transcriptome analysis of the effects of foliar water application on the nap time of mango leaves

Abstract [Objective] In order to reveal the molecular mechanism of photosynthetic midday-depression broken by water spring, the effects of continuous spraying on mango leaves were studied by omics technology. This study can provide basis for further optimizing water management to achieve the best consequences, and will have theoretical significance in enhancing mango yield and quality by aligning with the national policy objectives of reducing fertilizer usage and improving efficiency. [Methods] Mango cultivar “Tainong No. 1” in Yuanjiang was used as material. The mango in the fruiting stage was sprayed by water for three times during the midday depression (12:30-14:10), 20 minutes each time, with no water spraying as the control. The leaves were collected for transcriptome sequencing analysis. GO and KEGG databases were used to analyze the differentially expressed genes (DEGs). Analysis of gene expression was conducted by using DESeq, with |log2αFC| >1 and P <0.05 for DEG screening. [Results] 3 789, 2 885, and 1 667 differentially expressed genes were obtained from three development stages, respectively. GO analysis found that the DEGs were highly correlated with the membrane, intrinsic components of the membrane, components of the membrane and other items in the classification of cell components. KEGG analysis showed that the DEGs were mainly enriched in the pathways of plant hormone signal transduction, MAPK signal pathway, circadian rhythm, metabolism of amino sugar and nucleotide sugar, flavonoid biosynthesis, photosynthetic biological carbon fixation, biosynthesis of cutin, sulfite, and wax, metabolism of porphyrin and chlorophyll, starch and sucrose metabolism, and glyceride metabolism. According to the enrichment results, eight differentially expressed genes were screened. With qRT-PCR verification, the gene expression trend was generally consistent with the results of RNA-seq. [Conclusion] The delayed expression of CAO and POR genes suggests that water spraying can prolong chlorophyll synthesis time. Expression of genes encoding FBP and SBP is slightly decreased, suggesting that water spray may regulate gene expression and ease photosynthetic “midday-depression”, which is beneficial to carbon assimilation and photosynthetic efficiency

m6A Regulator-Based Exosomal Gene Methylation Modification Patterns Identify Distinct Microenvironment Characterization and Predict Immunotherapeutic Responses in Colon Cancer.

peer reviewedRecent studies have highlighted the biological significance of exosomes and m6A modifications in immunity. Nonetheless, it remains unclear whether the m6A modification gene in exosomes of body fluid has potential roles in the tumor microenvironment (TME). Herein, we identified three different m6A-related exosomal gene modification patterns based on 59 m6A-related exosomal genes, which instructed distinguishing characteristics of TME in colon cancer (CC). We demonstrated that these patterns could predict the stage of tumor inflammation, subtypes, genetic variation, and patient prognosis. Furthermore, we developed a scoring mode-m6A-related exosomal gene score (MREGS)-by detecting the level of m6A modification in exosomes to classify immune phenotypes. Low MREGS, characterized by prominent survival and immune activation, was linked to a better response to anti-PDL1 immunotherapy. In contrast, the higher MREGS group displayed remarkable stromal activation, high activity of innate immunocytes, and a lower survival rate. Hence, this work provides a novel approach for evaluating TME cell infiltration in colon cancer and guiding more effective immunotherapy strategies

The m6A Reader IGF2BP2 Regulates Macrophage Phenotypic Activation and Inflammatory Diseases by Stabilizing TSC1 and PPARγ.

peer reviewedPhenotypic polarization of macrophages is regulated by a milieu of cues in the local tissue microenvironment. Currently, little is known about how the intrinsic regulators modulate proinflammatory (M1) versus prohealing (M2) macrophages activation. Here, it is observed that insulin-like growth factor 2 messenger RNA (mRNA)-binding protein 2 (IGF2BP2)-deleted macrophages exhibit enhanced M1 phenotype and promote dextran sulfate sodium induced colitis development. However, the IGF2BP2-/- macrophages are refractory to interleukin-4 (IL-4) induced activation and alleviate cockroach extract induced pulmonary allergic inflammation. Molecular studies indicate that IGF2BP2 switches M1 macrophages to M2 activation by targeting tuberous sclerosis 1 via an N6-methyladenosine (m6A)-dependent manner. Additionally, it is also shown a signal transducer and activators of transcription 6 (STAT6)-high mobility group AT-hook 2-IGF2BP2-peroxisome proliferator activated receptor-γ axis involves in M2 macrophages differentiation. These findings highlight a key role of IGF2BP2 in regulation of macrophages activation and imply a potential therapeutic target of macrophages in the inflammatory diseases

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas