Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Biological agents for synthesis of nanoparticles and their applications

Green synthesis of ZnO nanoparticles from Syzygium Cumini leaves extract with robust photocatalysis applications

Biological and green remediation of heavy metal contaminated water and soils: A state-of-the-art review.

Kickstarter is a popular crowd-funding platform used by individuals and groups to obtain the seed amount to implement their business ideas. It is essential for the campaign owners to plan their crowd-funding campaign beforehand in order to avoid failure. This paper aims to predict the success or failure of a Kickstarter campaign and help the campaign owners plan better for their campaign by providing trends and analysis based on historical data of campaigns on Kickstarter, ranging from 2014 to February 2019. Various classification and boosting algorithms were applied and it was concluded that Weighted Random Forest along with AdaBoost for the subsampled dataset gives the best accuracy.

Success Prediction using Random Forest, CatBoost, XGBoost and AdaBoost for Kickstarter Campaigns

One of the key reasons for water pollution is the existence of heavy metals in wastewater. Industrial wastewater and domestic sewage are one of the top reasons to cause water pollution. Increasing water pollution is a major concern for humans as it is not only affecting our health but also disturbing the economy and sustainable growth all around the world. Heavy metals affect human health as well as flora and fauna of the region because they are non-biodegradable. Heavy metals induce mutagenesis, cancer, and hereditary genetic disorders because they bind to the same sites in which essential metal ions bind and lead to the destabilization of structures and biomolecules. Conventional methods are well-established for the removal of heavy metals, but they have several drawbacks. Therefore, there is a requirement of alternative methods that can efficiently remove heavy metals. Nanoparticles hold immense potential, and they are used as adsorbents for heavy metal removal. Due to its unique properties like high selectivity and adsorption capacity, they are effective sorbents and extensively used for heavy metal removal from wastewater. This review addresses the significant issue of global wastewater crisis. Various methods of heavy metal remediation (HMR) and wastewater treatment are discussed including the application of microbes, plants, and nanoparticles in HMR. This review also highlights real-time applications and economic aspects of HMR. It has been concluded that the application of nanomaterials both, in the existing technologies and novel methods, will help in increasing efficiency, better operational costs, and enhanced outcomes.

Bioremediation of heavy metals from wastewater using nanomaterials

A matter-dominated model with gravitationally induced matter creation is proposed in the framework of Brans-Dicke theory. We obtain the main cosmological functions such as the scale factor of the Universe, the Hubble expansion rate, and the deceleration parameter analytically. We explore the viability of the model to explain the present accelerated expansion of the Universe. In this scenario, the present cosmic acceleration is supposed to be driven only by the negative creation pressure associated with the matter component. The evolution of such a model is tested by statistical analysis of the latest SNe, OHD, and BAO probes. We study and plot the trajectories of the evolution of the Universe with the best estimated values of the model parameters. It is observed that the expanding Universe begins with a big bang followed by a smooth transition from the decelerated phase to the accelerated phase. The ages of the Universe obtained by $\mathrm{SNe}+\mathrm{OHD}$ and $\mathrm{SNe}+\mathrm{OHD}+\mathrm{BAO}$ in this model are in good agreement with the age predicted by the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model. We analyze the model with a statefinder diagnostic and find that the model is different from the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model but approaches $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ in the future. The model shows quintessence behavior.

Matter creation cosmology in Brans-Dicke theory: Observational tests and thermodynamic analysis

The social media version of fundraising, crowdfunding has, within a very short time, become an important and often critical element of the process that allows budding entrepreneurs to grow in todays market While the underlying principle behind crowdfunding — to get multiple people to financially back an idea and collectively amass enough resources to make it happen — is simple enough in theory, it is much harder to actually implement Kickstarter, the largest and most well-known crowdfunding website internationally, sees more than half of the uploaded projects rejected due to inadequate funds gathered In this study, we take a closer look at the reasons for success and failure of these projects from the project description pages, where the creators attempt to persuade potential backers into supporting their respective creations A dataset of over 26 thousand Kickstarter projects was created and used to understand the factors that most affect the chances of acquiring successful funding In this study, we propose a novel algorithm for the RandomForests learning model in which we assign optimal weights to the individual classifiers and perform weighted majority voting on them using the 13 predictors that were found to be most suitable through implementation logic These predictors will serve to be useful to both, a project creator as well as a potential backer, in order to devote their energy and resources judiciously Finally, we tested our optimized learning model on the freshly acquired dataset and observed 94289% accuracy, 945% precision, 943% recall and 943% F-measure value in success prediction This research achieved the best accuracy and greatest amount of success in this field

Predicting crowdfunding success with optimally weighted random forests

We explore a matter-dominated model with bulk viscosity in Brans-Dicke theory to interpret the observed cosmic accelerating expansion phenomena. We obtain the exact solution of the field equations by taking constant bulk viscous coefficient (which potentially could explain the present accelerated expansion of the Universe) and Brans-Dicke scalar field proportional to some power of the scale factor. The model is studied statistically using the available astronomical data and then compare using the tools taken from information criterion. Using the best-fit values of model parameters we find that the model shows transition from decelerated phase to accelerated phase. The effective equation of state parameter is within the quintessence region ($-1\leq \omega <0$). Thus, the model shows quintessence behavior and approaches to $\varLambda $CDM model in late time. It does not cross the phantom divide line and hence it is free from big-rip singularity. The viscous model also alleviates the age problem. We discuss two diagnostic parameters, namely statefinder and $Om(z)$ and compare the model with the existing models by plotting the trajectories. We also apply Akaike information criterion $(\mathit{AIC})$ and Bayesian information criterion $(\mathit{BIC})$ to discriminate the viscous model based on the penalization associated to the number of parameters. The analyses based on the $\mathit{AIC}$ indicates that there is less support for the viscous model when compared to the $\varLambda $CDM model, while those based on the $\mathit{BIC}$ indicates that there is strong evidence against it in favor of the $\varLambda $CDM model.

Probing bulk viscous matter-dominated model in Brans-Dicke theory

Asymmetric Controlled Quantum Teleportation Via Eight-Qubit Entangled State in a Noisy Environment

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