Weight management after spinal cord injury:How to monitor and improve body composition & energy expenditure in rehabilitation practice

Chapter 1 presented the background of the thesis and stated this thesis aimed to gain insight into weight management in people with SCI with a specific focus on body composition and resting energy expenditure (REE). Chapter 2 presented the changes in body composition during and up to one year after inpatient rehabilitation in people with SCI. A relatively stable body composition, with among others no significant changes in the percentage of FM (FM%) and FFM (FFM%), was found during inpatient SCI rehabilitation. This was followed by a significant increase in BMI during the first year after discharge. Chapter 3 presented the accuracy of bioelectrical impedance analysis (BIA) and skinfold thickness measurements relative to DXA in estimating body composition. Furthermore, new prediction equations to estimate FFM and FM% in people with SCI were developed. As expected, BIA and skinfold thickness measurements could not be utilized directly to estimate body composition in people with SCI because of their low accuracy. Therefore, new SCI-specific body composition equations were developed using the outcomes of BIA or skinfold thickness measurements. The new equations for predicting FFM and FM% showed good levels of explained variance (R2 = 0.66 – 0.94). Chapter 4 presented the accuracy of two recently developed SCI-specific REE equations and the development of new REE prediction equations in people with SCI. The accuracy of the recently developed SCI-specific REE prediction equations by Chun et al. and by Nightingale and Gorgey may not accurately estimate REE in a general population of SCI. This might be because they were developed in people with motor complete injury under a stricter testing procedure. Using the outcomes of body composition measured by BIA or skinfold thickness, we developed new SCI-specific REE equations in a general population of SCI. However, the accuracy of the new equations did not meet our expectations. Chapter 5 and 6 presented the results that focused on whether using NMES of paralyzed lower-limb muscles increases energy expenditure during daily activities in people with SCI. We found in chapter 5, in people with SCI during sitting, that using the most intense NMES protocol with more muscles activated and the duty cycle with a shorter rest period resulted in a 51% increase in energy expenditure compared to the condition without NMES. Similarly, in chapter 6, we found that the energy expenditure significantly increased by wearing an NMES garment on paralyzed upper-leg muscles during 20-min lying (+29%) and sitting (+22%) but not during wheelchair propulsion (+8%) when participants were asked to perform the activities as they normally would instead of sitting still like in chapter 5. Chapter 7 combined the findings of this thesis and discussed the results from scientific and clinical perspectives. Monitoring body composition at least annually with accurate and feasible measurements is necessary together with ongoing management in daily life by surrogate markers such as BMI and waist circumference. The reliability of BIA and skinfold thickness should be tested, and the newly developed SCI-specific body composition equations need to be cross-validated before implementing in clinical practice. If there are adverse changes in body composition, REE should preferably be measured by clinically feasible indirect calorimetry devices to prescribe necessary interventions. Using NMES of paralyzed lower-limb muscles is a very promising method to increase energy expenditure in people with SCI. Personalizing optimal NMES parameters to achieve a better efficacy and adjusting the NMES garment to make it more suitable for people with SCI will help them achieve a healthy energy balance much easier in daily life and subsequently contribute to reaching their personal goals of weight management

An Approach of Reducing Overall Level of Export Fluctuations of the Export-oriented Countries

Overall level of export fluctuations of the export-oriented countries with rising export volume partly stem from the market failure caused by free choice of export enterprises, some government intervention thus may be necessary. To reduce the level of fluctuations of the export growth rates in these countries, this paper, taking the significant differences of the exports among various markets into account and thus using a new index named relative variance to measure the export volatility risks, proposes a model of merchandise market portfolio, a modified version of Markowitz model, available to provide explicit guidelines for the firms, the industries and even the whole country to optimize the structure of their export markets. An application of this model to the case of China's apple is then discussed. The results show that the market share of China’s apple in 7 sub-markets should be redistributed drastically

Nonparametric approaches for analyzing carbon emission: from statistical and machine learning perspectives

Linear regression models, especially the extended STIRPAT model, are routinely-applied for analyzing carbon emissions data. However, since the relationship between carbon emissions and the influencing factors is complex, fitting a simple parametric model may not be an ideal solution. This paper investigated various nonparametric approaches in statistics and machine learning (ML) for modeling carbon emissions data, including kernel regression, random forest and neural network. We selected data from ten Chinese cities from 2005 to 2019 for modeling studies. We found that neural network had the best performance in both fitting and prediction accuracy, which implies its capability of expressing the complex relationships between carbon emissions and the influencing factors. This study provides a new means for quantitative modeling of carbon emissions research that helps to understand how to characterize urban carbon emissions and to propose policy recommendations for "carbon reduction". In addition, we used the carbon emissions data of Wuhu city as an example to illustrate how to use this new approach

Entity Personalized Talent Search Models with Tree Interaction Features

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models which lack preference personalization in the user-level due to being trained only with globally collected recruiter activity data. In this paper, we propose an entity-personalized Talent Search model which utilizes a combination of generalized linear mixed (GLMix) models and gradient boosted decision tree (GBDT) models, and provides personalized talent recommendations using nonlinear tree interaction features generated by the GBDT. We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems. Finally, we provide offline and online experiment results benchmarking our entity-personalized model with tree interaction features, which demonstrate significant improvements in our precision metrics compared to globally trained non-personalized models.Comment: This paper has been accepted for publication at ACM WWW 201

A Span-Extraction Dataset for Chinese Machine Reading Comprehension

Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. We present several baseline systems as well as anonymous submissions for demonstrating the difficulties in this dataset. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the Chinese machine reading comprehension research. Resources are available: https://github.com/ymcui/cmrc2018Comment: 6 pages, accepted as a conference paper at EMNLP-IJCNLP 2019 (short paper