Activity

Moreover, the results are also useful to government agencies to improve GIs registration systems and promotion strategies.

The online version contains supplementary material available at 10.1007/s00217-021-03782-2.

The online version contains supplementary material available at 10.1007/s00217-021-03782-2.Computer and Information Security (CIS) is usually approached adopting a technology-centric viewpoint, where the human components of sociotechnical systems are generally considered as their weakest part, with little consideration for the end users’ cognitive characteristics, needs and motivations. This paper presents a holistic/Human Factors (HF) approach, where the individual, organisational and technological factors are investigated in pilot healthcare organisations to show how HF vulnerabilities may impact on cybersecurity risks. An overview of current challenges in relation to cybersecurity is first provided, followed by the presentation of an integrated top-down and bottom-up methodology using qualitative and quantitative research methods to assess the level of maturity of the pilot organisations with respect to their capability to face and tackle cyber threats and attacks. This approach adopts a user-centred perspective, involving both the organisations’ management and employees, The results show that a better cyber-security culture does not always correspond with more rule compliant behaviour. In addition, conflicts among cybersecurity rules and procedures may trigger human vulnerabilities. In conclusion, the integration of traditional technical solutions with guidelines to enhance CIS systems by leveraging HF in cybersecurity may lead to the adoption of non-technical countermeasures (such as user awareness) for a comprehensive and holistic way to manage cyber security in organisations.Tumor vaccine has shown outstanding advantages and good therapeutic effects in tumor immunotherapy. However, antigens in tumor vaccines can be easily cleared by the reticuloendothelium system in advance, which leads to poor therapeutic effect of tumor vaccines. Moreover, it was still hard to monitor the fate and distribution of antigens. To address these limitations, we synthesized a traceable nanovaccine based on gold nanocluster-labeled antigens and upconversion nanoparticles (UCNPs) for the treatment of melanoma in this study. PH-sensitive Schiff base bond is introduced between UCNPs and gold nanocluster-labeled ovalbumin antigens for monitoring antigens release. Our studies demonstrated that UCNPs conjugated metallic antigen showed excellent biocompatibility, pH-sensitive and therapeutic effect.Marital status is recognized as an important social determinant of health, income, and social support, but is rarely available in administrative data. GSK-4362676 clinical trial We assessed the feasibility of using exact address data and zip code history to identify cohabiting couples using the 2018 Medicare Vital Status file and ZIP codes in the 2011-2014 Master Beneficiary Summary Files. Medicare beneficiaries meeting our algorithm displayed characteristics consistent with assortative mating and resembled known married couples in the Health and Retirement Study linked to Medicare claims. Address information represents a promising strategy for identifying cohabiting couples in administrative data including healthcare claims and other data types.As the use of electronic health records (EHR) to estimate treatment effects has become widespread, concern about bias introduced by error in EHR-derived covariates has also grown. While methods exist to address measurement error in individual covariates, little prior research has investigated the implications of using propensity scores for confounder control when the propensity scores are constructed from a combination of accurate and error-prone covariates. We reviewed approaches to account for error in propensity scores and used simulation studies to compare their performance. These comparisons were conducted across a range of scenarios featuring variation in outcome type, validation sample size, main sample size, strength of confounding, and structure of the error in the mismeasured covariate. We then applied these approaches to a real-world EHR-based comparative effectiveness study of alternative treatments for metastatic bladder cancer. This head-to-head comparison of measurement error correction methods in the context of a propensity score-adjusted analysis demonstrated that multiple imputation for propensity scores performs best when the outcome is continuous and regression calibration-based methods perform best when the outcome is binary.Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason, that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause privacy leakage. To solve this problem, we adopt the Federated Learning (FL) framework, a new technique being used to protect data privacy. Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of the training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia). A large number of truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.