Current cloud computing causes serious restrictions to safeguarding users’ data privacy. Since users’ sensitive data is submitted in unencrypted forms to remote machines possessed and operated by untrusted service providers, users’ sensitive data may be leaked by service providers. Program obfuscation shows the unique advantages that it can provide for cloud computing. In this paper, we construct an encrypted threshold signature functionality, which can outsource the threshold signing rights of users to cloud server securely by applying obfuscation, while revealing no more sensitive information. The obfuscator is proven to satisfy the average case virtual black box property and existentially unforgeable under the decisional linear (DLIN) assumption and computational Diffie-Hellman (CDH) assumption in the standard model. Moreover, we implement our scheme using the Java pairing-based cryptography library on a laptop.Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer. The selection of biomarker genes from these profiles is significant and crucial for cancer detection. This paper presents a framework proposing a two-stage multifilter hybrid model of feature selection for colon cancer classification. Colon cancer is being extremely common nowadays among other types of cancer. There is a need to find fast and an accurate method to detect the tissues, and enhance the diagnostic process and the drug discovery. This paper reports on a study whose objective has been to improve the diagnosis of cancer of the colon through a two-stage, multifilter model of feature selection. The model described deals with feature selection using a combination of Information Gain and a Genetic Algorithm. The next stage is to filter and rank the genes identified through this method using the minimum Redundancy Maximum Relevance (mRMR) technique. The final phase is to further analyze the data using correlated machine learning algorithms. This two-stage approach, which involves the selection of genes before classification techniques are used, improves success rates for the identification of cancer cells. It is found that Decision Tree, K-Nearest Neighbor, and Naïve Bayes classifiers had showed promising accurate results using the developed hybrid framework model. It is concluded that the performance of our proposed method has achieved a higher accuracy in comparison with the existing methods reported in the literatures. This study can be used as a clue to enhance treatment and drug discovery for the colon cancer cure.How do attitudes toward vaccination change over the course of a public health crisis? We report results from a longitudinal survey of United States residents during six months (March 16 -August 16, 2020) of the COVID-19 pandemic. Contrary to past research suggesting that the increased salience of a disease threat should improve attitudes toward vaccines, we observed a decrease in intentions of getting a COVID-19 vaccine when one becomes available. We further found a decline in general vaccine attitudes and intentions of getting the influenza vaccine. Analyses of heterogeneity indicated that this decline is driven by participants who identify as Republicans, who showed a negative trend in vaccine attitudes and intentions, whereas Democrats remained largely stable. Consistent with research on risk perception and behavior, those with less favorable attitudes toward a COVID-19 vaccination also perceived the virus to be less threatening. We provide suggestive evidence that differential exposure to media channels and social networks could explain the observed asymmetric polarization between self-identified Democrats and Republicans.Environmental challenges to natural resources have been attributed to human behavior and traditional agricultural production techniques. Natural resource degradation in agriculture has always been a prime concern in agro ecological research and sustainability analysis. Selleck Tefinostat There are many techniques for assessing environmental performance; one of which, ecological footprint (EF), assesses human pressure on the environment and natural resources. The main purpose of this study was calculation of ecological indices including biocapacity (BC) and EF of rural areas of Fars province of Iran. The study was accomplished using survey and structured interviews consisting of three main questionnaires in two different steps. Different agricultural stakeholders, including farmers (for the first step) as well as the policymakers, extension managers and authorities (for the second step) were interviewed. Based on multi-stage stratified random sampling, 50 villages and 423 farmers were selected. Face validity and reliability of tere the main factors affecting the ecological index. Finally, AHP results determined the dominant economic perspectives of agricultural authorities. A paradigm shift toward the comprehensive paradigm of eco-development plus consideration of the results of the ecological indicator calculation as the base of agricultural planning at the local level were recommended.In Appalachia, La Crosse virus (LACV) is a leading pediatric arbovirus and public health concern for children under 16 years. LACV is transmitted via the bite of an infected Aedes mosquito. Thus, it is imperative to understand the dynamics of the local vector population in order to assess risk and transmission. Using entomological data collected from Knox County, Tennessee in 2013, we formulate an environmentally-driven system of ordinary differential equations to model mosquito population dynamics over a single season. Further, we include infected compartments to represent LACV transmission within the mosquito population. Findings suggest that the model, with dependence on degree days and accumulated precipitation, can closely describe field data. This model confirms the need to include these environmental variables when planning control strategies.Liver hepatocellular carcinoma (LIHC) is one of the major causes of cancer-related death worldwide with increasing incidences, however there are very few studies about the underlying mechanisms and pathways in the development of LIHC. We obtained LIHC samples from The Cancer Genome Atlas (TCGA) to screen differentially expressed mRNAs, lncRNAs, miRNAs and driver mutations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses and protein-protein interaction (PPI) network were performed. Moreover, we constructed a competing endogenous lncRNAs-miRNAs-mRNAs network. Finally, cox proportional hazards regression analysis was used to identify important prognostic differentially expressed genes. Total of 1284 mRNAs, 123 lncRNAs, 47 miRNAs were identified within different tissues of LIHC patients. GO analysis indicated that upregulated and downregulated differentially expressed mRNAs (DEmRNAs) were mainly associated with cell division, DNA replication, mitotic sister chromatid segregation and complement activation respectively.