Disparity in Populace Phenotype Impels Predisposition to Variations in Health Status

Background: The disparity in populace phenotype, embracing stature and pigmentation, drives of internal physiological constituent and adaptation to the external environment, impel variations in populace health status. The study tends to explore how significantly populaces phenotype influenced by internal or external exposures, and how effectually it prognosticates predisposition to variant health states. Methods: Ecological study encompassing twenty states residing on antipodes (North and South) endures distinct exposures, exhibit disparate phenotypes, execute different health status. Each State selected employing non-proportion quota sampling, standing extreme on either stature or pigmentation, residing in cold or hot region, contrasted on considered variables embracing UV-index, malnutrition, healthy life expectancy, fertility rate, natural increase, and top five causes of mortality. All computation, analysis, and interpretation perform employing MS-EXCEL. The complete compilation phase last from March to June 2019. Results: Findings reveal a strong association between exposures and phenotypes, UV-index and pigmentation (r = 0.96), malnutrition and stature (r = -0.81). Stature descends and pigmentation ascends along the latitude (North to South). Populace light pigmentation tall stature executes healthy prolong life span contrast to deep pigmentation standing short. Cardiac and cancer diseases significantly reported among statured nations. While high susceptibility to Influenza/ Pneumonia, and HIV/AIDS observe in pigmented nations. Conclusion: Decisively homo sapiens phenotypes inordinately servile to internal and external exposures effectually prognosticate predisposition to distinct health states. Enforcement of measures mitigating populace internal and external exposure can elicit desire output in phenotype and outcomes in health status.

Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.

ENC1 Facilitates Colorectal Carcinoma Tumorigenesis and Metastasis via JAK2/STAT5/AKT Axis-Mediated Epithelial Mesenchymal Transition and Stemness

Ectodermal neural cortex 1 (ENC1) is an actin-binding protein and has been known to be upregulated in several cancers, but the molecular mechanisms through which it contributes to the pathology of CRC have largely been elusive. We utilized data mining and validated the aberrant expression of ENC1, following which phenotypic traits of malignancy were assessed in vitro. Ruxolitinib was used as a surrogate to compare the effects of ENC1 expression and silencing on the JAK-STAT-AKT pathway. In vivo models were employed to confirm the in vitro observations. Computation analysis, strengthened by in situ and in vitro data, confirmed the overexpression of ENC1 in CRC and predicted a poor prognosis, while enhanced cell proliferation, invasion, migration, EMT, and stemness were associated with ENC1 overexpression. Silencing of ENC1 downregulated the phenotypes. Additionally, silencing of ENC1 significantly reduced the activation of JAK2 and consequent activation of STAT5 and AKT comparable to ruxolitinib inhibition of JAK2. Silencing of ENC1 resulted in lesser tumor volumes and fewer numbers of tumors, in vivo. These data suggest that ENC1 induces CRC through the JAK2-STAT5-AKT axis. ENC1 is a suitable diagnostic marker for CRC detection, and ENC1 targeting therapies may suppress CRC progression.

A White-Box Masking Scheme Resisting Computational and Algebraic Attacks

White-box cryptography attempts to protect cryptographic secrets in pure software implementations. Due to their high utility, white-box cryptosystems (WBC) are deployed by the industry even though the security of these constructions is not well defined. A major breakthrough in generic cryptanalysis of WBC was Differential Computation Analysis (DCA), which requires minimal knowledge of the underlying white-box protection and also thwarts many obfuscation methods. To avert DCA, classic masking countermeasures originally intended to protect against highly related side-channel attacks have been proposed for use in WBC. However, due to the controlled environment of WBCs, new algebraic attacks against classic masking schemes have quickly been found. These algebraic DCA attacks break all classic masking countermeasures efficiently, as they are independent of the masking order.In this work, we propose a novel generic masking scheme that can resist both DCA and algebraic DCA attacks. The proposed scheme extends the seminal work by Ishai et al. which is probing secure and thus resists DCA, to also resist algebraic attacks. To prove the security of our scheme, we demonstrate the connection between two main security notions in white-box cryptography: probing security and prediction security. Resistance of our masking scheme to DCA is proven for an arbitrary order of protection, using the well-known strong non-interference notion by Barthe et al. Our masking scheme also resists algebraic attacks, which we show concretely for first and second-order algebraic protection. Moreover, we present an extensive performance analysis and quantify the overhead of our scheme, for a proof-of-concept protection of an AES implementation.