Hunterjoyner1211

In future works, we will be able to propose meteorosensitive envelope responses based on these results.Ovarian cancer is one of the leading causes of cancer related deaths affecting United States women. Early-stage detection of ovarian cancer has been linked to increased survival, however, current screening methods, such as biomarker testing, have proven to be ineffective in doing so. Therefore, further developments are necessary to be able to achieve positive patient prognosis. Ongoing efforts are being made in biomarker discovery towards clinical applications in screening for early-stage ovarian cancer. In this perspective, we discuss and provide examples for several workflows employing mass spectrometry-based proteomics towards protein biomarker discovery and characterization in the context of ovarian cancer; workflows include protein identification and characterization as well as intact protein profiling. We also discuss the opportunities to merge these workflows for a multiplexed approach for biomarkers. Lastly, we provide our insight as to future developments that may serve to enhance biomarker discovery workflows while also considering translational potential.Veterans suffer disproportionate health impacts from the opioid epidemic, including overdose, suicide, and death. Prediction models based on electronic medical record data can be powerful tools for identifying patients at greatest risk of such outcomes. The Veterans Health Administration implemented the Stratification Tool for Opioid Risk Mitigation (STORM) in 2018. In this study we propose changes to the original STORM model and propose alternative models that improve risk prediction performance. The best of these proposed models uses a multivariate generalized linear mixed modeling (mGLMM) approach to produce separate predictions for overdose and suicide-related events (SRE) rather than a single prediction for combined outcomes. Further improvements include incorporation of additional data sources and new predictor variables in a longitudinal setting. Compared to a modified version of the STORM model with the same outcome, predictor and interaction terms, our proposed model has a significantly better prediction performance in terms of AUC (84% vs. 77%) and sensitivity (71% vs. 66%). The mGLMM performed particularly well in identifying patients at risk for SREs, where 72% of actual events were accurately predicted among patients with the 100,000 highest risk scores compared with 49.7% for the modified STORM model. The mGLMM's strong performance in identifying true cases (sensitivity) among this highest risk group was the most important improvement given the model's primary purpose for accurately identifying patients at most risk for adverse outcomes such that they are prioritized to receive risk mitigation interventions. Some predictors in the proposed model have markedly different associations with overdose and suicide risks, which will allow clinicians to better target interventions to the most relevant risks.

The online version contains supplementary material available at 10.1007/s10742-021-00263-7.

The online version contains supplementary material available at 10.1007/s10742-021-00263-7.Blockchain was at the top of the 2016 Gartner hype cycle and has been integrated into business profiles by numerous start-ups. Since the emergence of blockchain through Bitcoin, studies have been conducted to increase blockchain applications for nonfinancial uses. A supply chain is a sector where blockchain is anticipated to have crucial applications. In a traditional supply chain, maintaining traceability and ownership remains a serious issue. In the supply chain, blockchain can increase trust, improve traceability, and eliminate the middle man. It makes the supply chain more transparent though, raising the privacy issue. In this paper, a new approach for transaction privacy is proposed by considering ownership and traceability. The proposed system retains the advantages of blockchain and centralised database server. Its novelty lies in achieving privacy by generating symmetric keys, employing product codes and current timestamps, and it uses asymmetric key elliptic curve cryptography for transaction validation and user identification. The proposed system allows product owners to trace the product and enables its transfer. It protects the supply chain from counterfeit products. The Hyperledger Sawtooth blockchain was used for experiments. Security and privacy analysis show that the proposed system can afford privacy without impinging on traceability and ownership. The results estimate that privacy incorporation introduces an overhead of 4.4%. In the experiment, the performance of the proposed system bettered the results of the existing techniques such as POMS and b_verify.Blockchain technology is an undeniable ledger technology that stores transactions in high-security chains of blocks. Blockchain can solve security and privacy issues in a variety of domains. With the rapid development of smart environments and complicated contracts between users and intelligent devices, federated learning (FL) is a new paradigm to improve accuracy and precision factors of data mining by supporting information privacy and security. Much sensitive information such as patient health records, safety industrial information, and banking personal information in various domains of the Internet of Things (IoT) including smart city, smart healthcare, and smart industry should be collected and gathered to train and test with high potential privacy and secured manner. Using blockchain technology to the adaption of intelligent learning can influence maintaining and sustaining information security and privacy. Finally, blockchain-based FL mechanisms are very hot topics and cut of scientific edge in data science and artificial intelligence. This research proposes a systematic study on the discussion of privacy and security in the field of blockchain-based FL methodologies on the scientific databases to provide an objective road map of the status of this issue. According to the analytical results of this research, blockchain-based FL has been grown significantly during these 5 years and blockchain technology has been used more to solve problems related to patient healthcare records, image retrieval, cancer datasets, industrial equipment, and economical information in the field of IoT applications and smart environments.Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p 

The online version contains supplementary material available at 10.1007/s11119-021-09810-1.

The online version contains supplementary material available at 10.1007/s11119-021-09810-1.Facemasks have become important tools to fight virus spread during the recent COVID-19 pandemic, but their effectiveness is still under debate. We present a computational model to predict the filtering efficiency of an N95-facemask, consisting of three non-woven fiber layers with different particle capturing mechanisms. Parameters such as fiber layer thickness, diameter distribution, and packing density are used to construct two-dimensional cross-sectional geometries. https://www.selleckchem.com/products/Fedratinib-SAR302503-TG101348.html An essential and novel element is that the polydisperse fibers are positioned randomly within a simulation domain, and that the simulation is repeated with different random configurations. This strategy is thought to give a more realistic view of practical facemasks compared to existing analytical models that mostly assume homogeneous fiber beds of monodisperse fibers. The incompressible Navier-Stokes and continuity equations are used to solve the velocity field for various droplet-laden air inflow velocities. Droplet diameters are ranging from 10 nm to 1.0 µm, which covers the size range from the SARS-CoV-2 virus to the large virus-laden airborne droplets. Air inflow velocities varying between 0.1 m·s-1 to 10 m·s-1 are considered, which are typically encountered during expiratory events like breathing, talking, and coughing. The presented model elucidates the different capturing efficiencies (i.e., mechanical and electrostatic filtering) of droplets as a function of their diameter and air inflow velocity. Simulation results are compared to analytical models and particularly compare well with experimental results from literature. Our numerical approach will be helpful in finding new directions for anti-viral facemask optimization.Most changes during software maintenance and evolution are not atomic changes, but rather the result of several related changes affecting different parts of the code. It may happen that developers omit needed changes, thus leaving a task partially unfinished, introducing technical debt or injecting bugs. We present a study investigating "quick remedy commits" performed by developers to implement changes omitted in previous commits. With quick remedy commits we refer to commits that (i) quickly follow a commit performed by the same developer, and (ii) aim at remedying issues introduced as the result of code changes omitted in the previous commit (e.g., fix references to code components that have been broken as a consequence of a rename refactoring) or simply improve the previously committed change (e.g., improve the name of a newly introduced variable). Through a manual analysis of 500 quick remedy commits, we define a taxonomy categorizing the types of changes that developers tend to omit. The taxonomy can (i) guide the development of tools aimed at detecting omitted changes and (ii) help researchers in identifying corner cases that must be properly handled. For example, one of the categories in our taxonomy groups the reverted commits, meaning changes that are undone in a subsequent commit. We show that not accounting for such commits when mining software repositories can undermine one's findings. In particular, our results show that considering completely reverted commits when mining software repositories accounts, on average, for 0.07 and 0.27 noisy data points when dealing with two typical MSR data collection tasks (i.e., bug-fixing commits identification and refactoring operations mining, respectively).The study evaluated perceived reactions and counter-actions of Himalayan communities to climate change. The evaluation was conducted through identification and characterization of 62 socio-environment-specific indicators in three altitude zones ( 1800 m asl (zone C)) in Pauri district, Uttarakhand, India, using a bottom-up, indicator-based approach. Indicators with higher significance for the local economy, livelihoods, or conservation were selected and assimilated into dimensions of vulnerability and resilience. Finally, these were integrated into a sustainable livelihood framework in an approach intended to calculate vulnerability and resilience jointly. The results indicated that the vulnerability and resilience of the mountain communities studied varied widely along the altitude gradient, due to variations in socioeconomic profile, livelihood requirements, resource availability, accessibility, and utilization pattern, and climate risk. The overall values for vulnerability (exposure + sensitivity-adaptive capacity) and resilience (exposure + sensitivity-restorative capacity) were, respectively, 0.