Development and validation of a nomogram for predicting carbapenem-resistant organism infection and prognostic risk factors analysis in elderly hospitalized patients

Background: With the extensive use of carbapenem-resistant organism (CRO), CRO infection is constantly detected clinically which has limited available drugs. There have many mechanisms of CRO resistance and rapid horizontal transmission, while the elderly with low resistance is more likely to acquire nosocomial infection. We aimed to screen elderly patients for nosocomial CRO infection and potential risk factors for prognosis. Methods: A total of 177 patients with CRO and carbapenem-sensitive organism (CSO) infection were included in this study. A least absolute shrinkage and selection operator (LASSO) analysis was used to select variables. The nomogram was constructed with multivariable logistic regression. The performance of the model was assessed by the receiver operating characteristic(ROC) curve, calibration, decision curve , and clinical influence curve. Using X-tile to stratify the prognosis risk, Kaplan-Meier curve for risk assessment. Results: Respiratory diseases, mechanical ventilation, indwelling urinary catheters , and APACHE II over 20 were selected as predictors of the CRO infection model. The model showed good discrimination and consistency in the training set and the validation set. The area under the ROC was 0.840 (95% CI: 0.773-0.900)in the training set and 0.822 (95% CI: 0.678-0.936) in the validation set. Decision analysis and influence curve showed that the model was clinically useful. Hepatobiliary diseases, indwelling urinary catheters, and hospital stays longer than 20 days were used as prognostic predictors. After analysis, the prognostic model demonstrated good discrimination of 0.817 (95% CI: 0.729-0.893) and consistency. Risk stratification showed the high-risk group had a poorer prognosis. Conclusion: Predicting clinically relevant risk factors for CRO nosocomial infection and prognosis in elderly patients. This may help the treatment of clinical drug-resistant infections.

City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China

Based on the panel data of 106 cities in the Yangtze River Economic Belt of China from 2007 to 2016, this paper explores the impact of city centrality on the green innovation efficiency and proves the mediation effect of migrants by using spatial econometric model. The results show that there are more and more innovation contacts between cities, and the innovation network is becoming more and more dense. The core cities of the downstream innovation network are mainly Yangzhou, Zhenjiang, Wuxi, Changzhou, Suzhou and Hangzhou; the core cities in the midstream are mainly Wuhan, Changsha and Yichun; the core cities in the upstream are Chengdu and Bazhong. There is an inverted U-shaped relationship between city centrality and green innovation efficiency. In addition, the influence curve of city centrality on the green innovation efficiency of surrounding cities is also inverted U-shaped. Cities with high city centrality attract a large number of migrants that come from cities with lower centrality to improve the green innovation efficiency, but the green innovation efficiency of cities with low city centrality will decline due to lack of talents.

Complex Contagions in Charitable Donations

Cascades over social networks can spread information, beliefs, diseases, technologies, and behaviors. Simple cascades spread from mere contact and produce submodular influence curves. Complex cascades assume agents with thresholding behavior and may produce non-submodular influence curves. In this study, we run three experiments that request charitable donations from human participants and experimentally manipulate whether and where their peers donate. We find evidence that we can (1) direct donations to an otherwise unpopular charity and (2) elicit complex contagion as evidenced by a non-submodular influence curve. The findings represent the most straightforward evidence to date of treatment-induced complex contagion - explicitly and formally defined - in human decision-making.

One-Step Targeted Minimum Loss-based Estimation Based on Universal Least Favorable One-Dimensional Submodels

Consider a study in which one observesnindependent and identically distributed random variables whose probability distribution is known to be an element of a particular statistical model, and one is concerned with estimation of a particular real valued pathwise differentiable target parameter of this data probability distribution. The targeted maximum likelihood estimator (TMLE) is an asymptotically efficient substitution estimator obtained by constructing a so called least favorable parametric submodel through an initial estimator with score, at zero fluctuation of the initial estimator, that spans the efficient influence curve, and iteratively maximizing the corresponding parametric likelihood till no more updates occur, at which point the updated initial estimator solves the so called efficient influence curve equation. In this article we construct a one-dimensional universal least favorable submodel for which the TMLE only takes one step, and thereby requires minimal extra data fitting to achieve its goal of solving the efficient influence curve equation. We generalize these to universal least favorable submodels through the relevant part of the data distribution as required for targeted minimum loss-based estimation. Finally, remarkably, given a multidimensional target parameter, we develop a universal canonical one-dimensional submodel such that the one-step TMLE, only maximizing the log-likelihood over a univariate parameter, solves the multivariate efficient influence curve equation. This allows us to construct a one-step TMLE based on a one-dimensional parametric submodel through the initial estimator, that solves any multivariate desired set of estimating equations.

Study on Influence of Paper Fillers on Ink Transfer

This paper uses the culture paper commonly-used filler PCC (precipitated calcium carbonate) and makes a good ratio of other raw materials and dosage of various other chemicals. Under the certain papermaking craft condition, with the certain quantum, the experiment changes the filler proportion of the paper to make paper samples and test the samples’ properties and the printing ink transfer.The experiment establishes an influence curve relationship, through that we analysis and draw the conclusion when the proportion of PCC is in the range 20% to 30%, it will get better ink transfer effect, which will provide reference for actual production.

The Influence of High-Speed Cutting Technics Parameter No Materials Removal in Turning High Alloy Antifriction Cast Iron

Though the high-speed cutting experimentation of high alloy antifriction cast iron, the materials performance and request on tools in cutting were analyzed. Though single-factor experimentation, the factors of affecting on cutting efficiency were discussed, such as tools materals, cutting speed, feed speed and cutting depth. The results show that the durability of PCBN tools is higher, but the durability of ceramics and tungsten-cobalt carbide tipped tools is low. The influence curve of cutting speed to machining efficiency is a part of parabola. And the influence curve of cutting depth to machining efficiency can be divided two parts of materials removal and tools wear. In the ensuring of technical requirements of work-piece machining. The high cutting efficiency can be obtained, when cutting speed vc=75~100m/min, feed speed f≤8mm and cutting depth ap =0.1~0.3mm.