Biochemical indicators for neuronal regeneration during intrathecal triamcinolone application in multiple sclerosis

Multiple sclerosis （MS） is a chronic inflammatory disease of the central nervous system （CNS）. Essential charac- teristics are demyelination, inflammation and neurode- generation. This process affects the white and grey matter in the CNS. MS patients experience various progression subtypes in association with the cerebral or spinal, acute inflammatory or glial sclerotic lesions （Mtiller, 2009）. Most patients end up in a progressive, smouldering, chronic inflammatory process （MUller, 2009）. Current predominantly used 1.5 respectively 3 Tesla MRI with Gadolinium~ application visualize the various old and acute lesions. They serve as a biological marker in com- bination with standardised assessment of brain atrophy, black holes, etc. However, MRI with a stronger magnetic 7 Tesla field with better sensitivity gave hints on an on- going, acute inflammatory, smouldering process even with Gadolinium~ enhancing acute lesions in the brain and the spinal cord in progressive, so-called relapse free MS patients （Mtiller, 2009; Sinnecker et al., 2012）. Ad- ditionally, progress of MS is determined with subjective standardised clinical ratings （Sinnecker et al., 2012）. Both methods are used for the evaluation of the efficacy of relapse rate reducing drugs. These compounds, i.e., in- terferons, teriflunamide, glatiramer acetate, fingolimod, fumarate or monoclonal antibodies, preponderantly weaken the malfunction of the peripheral immune system in relapse remitting MS patients. These MS drugs share one common disadvantage. They do not stop progression or improve MS within a framework of a regenerative process. They do not enable reversal of symptoms, for in- stance functional deficits or spasticity （Mtiller, 2009）.

Risk reduction in Sechahun iron ore deposit by geological boundary modification using multiple indicator Kriging

Uncertainty on the geological contacts and the block volumes of the models along boundaries is often a major part of the global uncertainty of reserve estimation.This work introduces a geostatistical technique that has been developed and tested in an iron ore deposit at Bafq mining district,in central Iran,and that,based on a probability criterion,helps to objectively model the geometry of this iron ore deposit.The main problem in reserve estimation of this ore body is its geometrical modeling and uncertainty in geological boundaries.This work deals with the geostatistical method of multiple indicator kriging,which is used to determine the real boundaries of ore body in different categories.This approach has potential to improve project performance and decrease operational risk.For this purpose,the ore body is separated into two categories including rich iron zone(w(Fe)>45%)and poor iron zone(20%<w(Fe)<45%).It significantly benefits to decrease the risk of reserve evaluation in the deposit.This case study also highlights the value of multiple indicator kriging as a tool for estimates the position of grade boundaries within the deposit.Comparison of the resultant probability maps with the real ore/waste contacts on the extracted levels shows that the first indicator model could separate the whole ore body(poor plus rich)from the waste zone by probability of more than 0.35,which concludes the total reserve of 53 million tons.The second indicator model applied to separate the rich and poor domains and the results show that the blocks with the estimated probability of equal to or more than 0.4 lay within the rich ore zone consisting of 15.8 million tons reserve.

Modeling and gender difference analysis of acceptance of autonomous driving technology

In order to deeply analyze the differences in the acceptance of autonomous driving technology among different gender groups,a multiple indicators and multiple causes model was constructed by integrating a technology acceptance model and theory of planned behavior to comprehensively reveal the gender differences in the influence mechanisms of subjective and objective factors.The analysis is based on data collected from Chinese urban residents.Among objective factors,age has a significant negative impact on women's perceived behavior control and a significant positive impact on perceived ease of use.Education has a significant positive impact on men's perceived behavior control,and has a strong positive impact on women's perceived usefulness(PU).For men,income and education are found to have strong positive impacts on perceived behavior control.Among subjective factors,perceived ease of use(PEU)has the greatest influence on women's behavior intention,and it is the only influential factor for women's intention to use autonomous driving technology,with an influence coefficient of 0.72.The influencing path of men's intention to use autonomous driving technology is more complex.It is not only directly affected by the significant and positive joint effects of attitude and PU,but also indirectly affected by perceived behavior controls,subjective norms,and PEU.

Comprehensive Evaluation of Electric Power Prediction Models Based on D-S Evidence Theory Combined with Multiple Accuracy Indicators

A comprehensive evaluation method of electric power prediction models using multiple accuracy indicators is proposed.To obtain the preferred models,this paper selects a number of accuracy indicators that can reflect the accuracy of single-point prediction and the correlation of predicted data,and carries out a comprehensive evaluation.First,according to Dempster-Shafer(D-S)evidence theory,a new accuracy indicator based on the relative error(RE)is proposed to solve the problem that RE is inconsistent with other indicators in the quantity of evaluation values and cannot be adopted at the same time.Next,a new dimensionless method is proposed,which combines the efficiency coefficient method with the extreme value method to unify the accuracy indicator into a dimensionless positive indicator,to avoid the conflict between pieces of evidence caused by the minimum value of zero.On this basis,the evidence fusion is used to obtain the comprehensive evaluation value of each model.Then,the principle and the process of consistency checking of the proposed method using the entropy method and the linear combination formula are described.Finally,the effectiveness and the superiority of the proposed method are validated by an illustrative instance.

Multiple analyses indicate the specific association of NR1I3,C6 and TNN with low hip BMD risk

Low hip bone mineral density(BMD)is an important index for osteoporosis and is associated with hip fracture,which leads to more cases of disability and mortality than all other kinds of fractures(Kanis et al.,2007).BMD’s heritability is more than 60%(Arden et al.,1996).A number of candidate loci for BMD have been previously identified by Genome Wide Association Studies(GWAS)(Xiong et al.,2009;Karasik et al.,2010;Zhang et al.,2013).Nevertheless,many significant signals based on GWAS are

Examining unobserved factors associated with red light running in Vietnam:A latent class model analysis

Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.