Security Strategies for Hosting Sensitive Information in the Commercial Cloud

IT experts often struggle to find strategies to secure data on the cloud. Although current security standards might provide cloud compliance, they fail to offer guarantees of security assurance. The purpose of this qualitative case study was to explore the strategies used by IT security managers to host sensitive information in the commercial cloud. The study\u27s population consisted of information security managers from a government agency in the eastern region of the United States. The routine active theory, developed by Cohen and Felson, was used as the conceptual framework for the study. The data collection process included IT security manager interviews (n = 7), organizational documents and procedures (n = 14), and direct observation of a training meeting (n = 35). Data collection from organizational data and observational data were summarized. Coding from the interviews and member checking were triangulated with organizational documents and observational data/field notes to produce major and minor themes. Through methodological triangulation, 5 major themes emerged from the data analysis: avoiding social engineering vulnerabilities, avoiding weak encryption, maintaining customer trust, training to create a cloud security culture, and developing sufficient policies. The findings of this study may benefit information security managers by enhancing their information security practices to better protect their organization\u27s information that is stored in the commercial cloud. Improved information security practices may contribute to social change by providing by proving customers a lesser amount of risk of having their identity or data stolen from internal and external thieve

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-invasive PD-L1 quantification using [18F]DK222-PET imaging in cancer immunotherapy

Background Combination therapies that aim to improve the clinical efficacy to immune checkpoint inhibitors have led to the need for non-invasive and early pharmacodynamic biomarkers. Positron emission tomography (PET) is a promising non-invasive approach to monitoring target dynamics, and programmed death-ligand 1 (PD-L1) expression is a central component in cancer immunotherapy strategies. [18F]DK222, a peptide-based PD-L1 imaging agent, was investigated in this study using humanized mouse models to explore the relationship between PD-L1 expression and therapy-induced changes in cancer.Methods Cell lines and xenografts derived from three non-small cell lung cancers (NSCLCs) and three urothelial carcinomas (UCs) were used to validate the specificity of [18F]DK222 for PD-L1. PET was used to quantify anti-programmed cell death protein-1 (PD-1) therapy-induced changes in PD-L1 expression in tumors with and without microsatellite instability (MSI) in humanized mice. Furthermore, [18F]DK222-PET was used to validate PD-L1 pharmacodynamics in the context of monotherapy and combination immunotherapy in humanized mice bearing A375 melanoma xenografts. PET measures of PD-L1 expression were used to establish a relationship between pathological and immunological changes. Lastly, spatial distribution analysis of [18F]DK222-PET was developed to assess the effects of different immunotherapy regimens on tumor heterogeneity.Results [18F]DK222-PET and biodistribution studies in mice with NSCLC and UC xenografts revealed high but variable tumor uptake at 60 min that correlated with PD-L1 expression. In MSI tumors treated with anti-PD-1, [18F]DK222 uptake was higher than in control tumors. Moreover, [18F]DK222 uptake was higher in A375 tumors treated with combination therapy compared with monotherapy, and negatively correlated with final tumor volumes. In addition, a higher number of PD-L1+ cells and higher CD8+-to-CD4+ cell ratio was observed with combination therapy compared with monotherapy, and positively correlated with PET. Furthermore, spatial distribution analysis showed higher [18F]DK222 uptake towards the core of the tumors in combination therapy, indicating a more robust and distinct pattern of immune cell infiltration.Conclusion [18F]DK222-PET has potential as a non-invasive tool for monitoring the effects of immunotherapy on tumors. It was able to detect variable PD-L1 expression in tumors of different cancer types and quantify therapy-induced changes in tumors. Moreover, [18F]DK222-PET was able to differentiate the impact of different therapies on tumors

Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of lung cancer and mesothelioma

Immunotherapy has transformed lung cancer care in recent years. In addition to providing durable responses and prolonged survival outcomes for a subset of patients with heavily pretreated non-small cell lung cancer (NSCLC), immune checkpoint inhibitors (ICIs)- either as monotherapy or in combination with other ICIs or chemotherapy-have demonstrated benefits in first-line therapy for advanced disease, the neoadjuvant and adjuvant settings, as well as in additional thoracic malignancies such as small-cell lung cancer (SCLC) and mesothelioma. Challenging questions remain, however, on topics including therapy selection, appropriate biomarker-based identification of patients who may derive benefit, the use of immunotherapy in special populations such as people with autoimmune disorders, and toxicity management. Patient and caregiver education and support for quality of life (QOL) is also important to attain maximal benefit with immunotherapy. To provide guidance to the oncology community on these and other important concerns, the Society for Immunotherapy of Cancer (SITC) convened a multidisciplinary panel of experts to develop a clinical practice guideline (CPG). This CPG represents an update to SITC\u27s 2018 publication on immunotherapy for the treatment of NSCLC, and is expanded to include recommendations on SCLC and mesothelioma. The Expert Panel drew on the published literature as well as their clinical experience to develop recommendations for healthcare professionals on these important aspects of immunotherapeutic treatment for lung cancer and mesothelioma, including diagnostic testing, treatment planning, immune-related adverse events, and patient QOL considerations. The evidence- and consensus-based recommendations in this CPG are intended to give guidance to cancer care providers using immunotherapy to treat patients with lung cancer or mesothelioma

Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of lung cancer and mesothelioma

Immunotherapy has transformed lung cancer care in recent years. In addition to providing durable responses and prolonged survival outcomes for a subset of patients with heavily pretreated non-small cell lung cancer (NSCLC), immune checkpoint inhibitors (ICIs)- either as monotherapy or in combination with other ICIs or chemotherapy-have demonstrated benefits in first-line therapy for advanced disease, the neoadjuvant and adjuvant settings, as well as in additional thoracic malignancies such as small-cell lung cancer (SCLC) and mesothelioma. Challenging questions remain, however, on topics including therapy selection, appropriate biomarker-based identification of patients who may derive benefit, the use of immunotherapy in special populations such as people with autoimmune disorders, and toxicity management. Patient and caregiver education and support for quality of life (QOL) is also important to attain maximal benefit with immunotherapy. To provide guidance to the oncology community on these and other important concerns, the Society for Immunotherapy of Cancer (SITC) convened a multidisciplinary panel of experts to develop a clinical practice guideline (CPG). This CPG represents an update to SITC\u27s 2018 publication on immunotherapy for the treatment of NSCLC, and is expanded to include recommendations on SCLC and mesothelioma. The Expert Panel drew on the published literature as well as their clinical experience to develop recommendations for healthcare professionals on these important aspects of immunotherapeutic treatment for lung cancer and mesothelioma, including diagnostic testing, treatment planning, immune-related adverse events, and patient QOL considerations. The evidence- and consensus-based recommendations in this CPG are intended to give guidance to cancer care providers using immunotherapy to treat patients with lung cancer or mesothelioma

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants