Reciprocal polarization imaging of complex media

The vectorial evolution of polarized light interaction with a medium can reveal its microstructure and anisotropy beyond what can be obtained from scalar light interaction. Anisotropic properties (diattenuation, retardance, and depolarization) of a complex medium can be quantified by polarization imaging by measuring the Mueller matrix. However, polarization imaging in the reflection geometry, ubiquitous and often preferred in diverse applications, has suffered a poor recovery of the medium's anisotropic properties due to the lack of suitable decomposition of the Mueller matrices measured inside a backward geometry. Here, we present reciprocal polarization imaging of complex media after introducing reciprocal polar decomposition for backscattering Mueller matrices. Based on the reciprocity of the optical wave in its forward and backward scattering paths, the anisotropic diattenuation, retardance, and depolarization of a complex medium are determined by measuring the backscattering Mueller matrix. We demonstrate reciprocal polarization imaging in various applications for quantifying complex non-chiral and chiral media (birefringence resolution target, tissue sections, and glucose suspension), uncovering their anisotropic microstructures with remarkable clarity and accuracy. We also highlight types of complex media that Lu-Chipman and differential decompositions of backscattering Mueller matrices lead to erroneous medium polarization properties, whereas reciprocal polar decomposition recovers properly. Reciprocal polarization imaging will be instrumental in imaging complex media from remote sensing to biomedicine and will open new applications of polarization optics in reflection geometry

Rainbow: reliable personally identifiable information retrieval across multi-cloud

Abstract Personally identifiable information (PII) refers to any information that links to an individual. Sharing PII is extremely useful in public affairs yet hard to implement due to the worries about privacy violations. Building a PII retrieval service over multi-cloud, which is a modern strategy to make services stable where multiple servers are deployed, seems to be a promising solution. However, three major technical challenges remain to be solved. The first is the privacy and access control of PII. In fact, each entry in PII can be shared to different users with different access rights. Hence, flexible and fine-grained access control is needed. Second, a reliable user revocation mechanism is required to ensure that users can be revoked efficiently, even if few cloud servers are compromised or collapse, to avoid data leakage. Third, verifying the correctness of received PII and locating a misbehaved server when wrong data are returned is crucial to guarantee user’s privacy, but challenging to realize. In this paper, we propose Rainbow, a secure and practical PII retrieval scheme to solve the above issues. In particular, we design an important cryptographic tool, called Reliable Outsourced Attribute Based Encryption (ROABE) which provides data privacy, flexible and fine-grained access control, reliable immediate user revocation and verification for multiple servers simultaneously, to support Rainbow. Moreover, we present how to build Rainbow with ROABE and several necessary cloud techniques in real world. To evaluate the performance, we deploy Rainbow on multiple mainstream clouds, namely, AWS, GCP and Microsoft Azure, and experiment in browsers on mobile phones and computers. Both theoretical analysis and experimental results indicate that Rainbow is secure and practical

Machine learning prediction of network dynamics with privacy protection

Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion. Often, each client only has the time series data from a partial set of nodes and the client has access to only partial timestamps of the whole time series data and partial structure of the network. Due to privacy concerns or license related issues, the data collected by different clients cannot be shared. To accurately predict the network dynamics while protecting the privacy of different parties is a critical problem in the modern time. Here, we propose a solution based on federated graph neural networks (FGNNs) that enables the training of a global dynamic model for all parties without data sharing. We validate the working of our FGNN framework through two types of simulations to predict a variety of network dynamics (four discrete and three continuous dynamics). As a significant real-world application, we demonstrate successful prediction of State-wise influenza spreading in the USA. Our FGNN scheme represents a general framework to predict diverse network dynamics through collaborative fusing of the data from different parties without disclosing their privacy.Comment: 14 page

Identification of miR-192 target genes in porcine endometrial epithelial cells based on miRNA pull-down

MicroRNAs (miRNAs)—a class of small endogenous non-coding RNAs—are widely involved in post-transcriptional gene regulation of numerous physiological processes. High-throughput sequencing revealed that the miR-192 expression level appeared to be significantly higher in the blood exosomes of sows at early gestation than that in non-pregnant sows. Furthermore, miR-192 was hypothesized to have a regulatory role in embryo implantation; however, the target genes involved in exerting the regulatory function of miR-192 required further elucidation. Methods: In the present study, potential target genes of miR-192 in porcine endometrial epithelial cells (PEECs) were identified through biotin-labeled miRNA pull-down; functional and pathway enrichment analysis was performed via gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment. Bioinformatic analyses were concurrently used to predict the potential target genes associated with sow embryo implantation. In addition, double luciferase reporter vectors, reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR), and Western blot were performed to verify the targeting and regulatory roles of the abovementioned target genes. Results: A total of 1688 differentially expressed mRNAs were identified via miRNA pull-down. Through RT-qPCR, the accuracy of the sequencing data was verified. In the bioinformatics analysis, potential target genes of miR-192 appeared to form a dense inter-regulatory network and regulated multiple signaling pathways, such as metabolic pathways and the PI3K-Akt, MAPKs, and mTOR signaling pathways, that are relevant to the mammalian embryo implantation process. In addition, CSK (C-terminal Src kinase) and YY1 (Yin-Yang-1) were predicted to be potential candidates, and we validated that miR-192 directly targets and suppresses the expression of the CSK and YY1 genes. Conclusion: We screened 1688 potential target genes of miR-192 were screened, and CSK and YY1 were identified as miR-192 target genes. The outcomes of the present study provide novel insights into the regulatory mechanism of porcine embryo implantation and the identification of miRNA target genes

Physical activity attenuates the associations of systemic immune-inflammation index with total and cause-specific mortality among middle-aged and older populations

Abstract Systemic immune-inflammation index (SII) emerged as a biomarker of chronic inflammation and an independent prognostic factor for many cancers. We aimed to investigate the associations of SII level with total and cause-specific mortality risks in the general populations, and the potential modification effects of lifestyle-related factors on the above associations. In this study, we included 30,521 subjects from the Dongfeng-Tongji (DFTJ) cohort and 25,761 subjects from the National Health and Nutrition Examination Survey (NHANES) 1999–2014. Cox proportional hazards regression models were used to estimate the associations of SII with mortality from all-cause, cardiovascular diseases (CVD), cancer and other causes. In the DFTJ cohort, compared to subjects in the low SII subgroup, those within the middle and high SII subgroups had increased risks of total mortality [hazard ratio, HR (95% confidence interval, CI) = 1.12 (1.03–1.22) and 1.26 (1.16–1.36), respectively) and CVD mortality [HR (95%CI) = 1.36 (1.19–1.55) and 1.50 (1.32–1.71), respectively]; those within the high SII subgroup had a higher risk of other causes mortality [HR (95%CI) = 1.28 (1.09–1.49)]. In the NHANES 1999–2014, subjects in the high SII subgroup had higher risks of total, CVD, cancer and other causes mortality [HR (95%CI) = 1.38 (1.27–1.49), 1.33 (1.11–1.59), 1.22 (1.04–1.45) and 1.47 (1.32–1.63), respectively]. For subjects with a high level of SII, physical activity could attenuate a separate 30% and 32% risk of total and CVD mortality in the DFTJ cohort, and a separate 41% and 59% risk of total and CVD mortality in the NHANES 1999–2014. Our study suggested high SII level may increase total and CVD mortality in the general populations and physical activity exerted a beneficial effect on the above associations