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Ketamine, an N-methyl-D-aspartate antagonist, reduces pain by decreasing central sensitization and pain windup. However, chronic ketamine use can cause tolerance, dependency, impaired consciousness, urinary symptoms, and abdominal pain. This study aimed to investigate the effects of repeated ketamine injections and ketamine readministration after discontinuation in a rat model of neuropathic pain. To induce neuropathic pain, partial sciatic nerve ligation (PSNL) was performed in 15 male Wistar rats, and these animals were divided into three groups: PSNL (control), PSNL + ketamine 5 mg/kg (K5), and PSNL + ketamine 10 mg/kg (K10; n=5 each). Ketamine was injected intraperitoneally daily for 4 weeks, discontinued for 2 weeks, and then readministered for 1 week. Following PSNL, the mechanical withdrawal threshold was determined weekly using the Von Frey. The K10 group showed a significant increase in the mechanical withdrawal threshold, presented here as the target force (in g), at 21 and 28 days compared to the time point before ketamine injection (mean±SE, 276.0±24.0 vs. 21.6±2.7 and 300.0±0.0 vs. 21.6±2.7, respectively; P2 weeks, but this ketamine effect decreased after drug readministration.

This paper explores Artificial Neural Network (ANN) as a model-free solution for a calibration algorithm of option pricing models. We construct ANNs to calibrate parameters for two well-known GARCH-type option pricing models: Duan's GARCH and the classical tempered stable GARCH that significantly improve upon the limitation of the Black-Scholes model but have suffered from computation complexity. To mitigate this technical difficulty, we train ANNs with a dataset generated by Monte Carlo Simulation (MCS) method and apply them to calibrate optimal parameters. The performance results indicate that the ANN approach consistently outperforms MCS and takes advantage of faster computation times once trained. The Greeks of options are also discussed., 26 pages, 3 figures

This paper proposes analytic forms of portfolio CoVaR and CoCVaR on the normal tempered stable market model. Since CoCVaR captures the relative risk of the portfolio with respect to a benchmark return, we apply it to the relative portfolio optimization. Moreover, we derive analytic forms for the marginal contribution to CoVaR and the marginal contribution to CoCVaR. We discuss the Monte-Carlo simulation method to calculate CoCVaR and the marginal contributions of CoVaR and CoCVaR. As the empirical illustration, we show relative portfolio optimization with thirty stocks under the distress condition of the Dow Jones Industrial Average. Finally, we perform the risk budgeting method to reduce the CoVaR and CoCVaR of the portfolio based on the marginal contributions to CoVaR and CoCVaR.

ObjectivesHigher prevalence of suicidality has been reported in individuals with ASD. This study aimed to (1) Estimate the prevalence of suicidal ideation (SI) in epidemiologically-ascertained, population-based, samples of children with ASD or Autism Spectrum Screening Questionnaire (ASSQ) Screen Positivity (ASP); (2) Determine whether ASD/ASP is an independent risk factor for SI, controlling for known SI risk factors; and, (3) Develop an explanatory model for SI in children with ASD/ASP.MethodsParticipants came from three epidemiologically-ascertained samples of school-aged Korean children (n = 14,423; 3,702; 4,837). ASSQ ≥ 14 was the cutoff for ASP. A subsample (n = 86) was confirmed to have ASD. SI was based on parents' endorsement of items on the Behavioral Assessment System for Children-2-Parent Report Scale-Children. Logistic regressions were used to assess associations between SI and ASD/ASP, controlling for demographics, peer victimization, behavior problems, and depression. To develop an explanatory model for SI within ASD/ASP, the associations between SI and child characteristics (comorbid conditions, ASD symptoms, IQ, adaptive function) were tested.ResultsSI was higher in children with ASD (14%) and ASP (16.6-27.4%) than ASSQ Screen Negative (ASN) peers (3.4-6.9%). ASD/ASP was strongly predictive of SI (ORs: 2.87-5.67), after controlling for known SI risk factors compared to ASN. Within the ASD and ASP groups, anxiety was the strongest predictor of SI.ConclusionsSI prevalence was higher in non-clinical samples of children with ASD and ASP, relative to ASN peers. These results underscore the need for routine screening for SI in children with ASD and social difficulties, particularly those with high anxiety. HighlightsPopulation-based, epidemiologically-ascertained, school-aged childrenASD and ASP are independent risk factors for SI in school-aged childrenAnxiety is an independent risk factor for SI in children with ASD or ASP.

Through this meta-analysis, we sought to examine the prevalence of, risks for, and factors associated with bullying involvement (victimization, perpetration, perpetration-victimization) among students with autism spectrum disorder (ASD). Additionally, we attempted to examine sources of variance in the prevalence and effect sizes of bullying in students with ASD across studies. Systematic database and literature review identified 34 relevant studies (31 for Western countries, three for Eastern countries). Pooled prevalence estimates for victimization, perpetration, and perpetration-victimization in general were 67%, 29%, and 14%, respectively. The risk of victimization in students with ASD was significantly higher than that in typically developing students and students with other disabilities. Further, deficits in social interaction and communication, externalizing symptoms, internalizing symptoms, and integrated inclusive school settings were related to higher victimization, and externalizing symptoms were related to higher perpetration. Finally, moderation analyses revealed significant variations in the pooled prevalences thereof depending on culture, age, school settings, and methodological quality and in the pooled effect sizes according to publication year and methodological quality. Our results highlight needs for bullying intervention for students with ASD, especially those who are younger, are in an inclusive school setting, and have higher social difficulties and externalizing/internalizing symptoms; for intensive research of bullying experiences among students with ASD in Eastern countries; and for efforts to improve the methodological quality of such research.

Smartphones are contributing to the improvement of healthcare information and services with the help of mHealth apps. Commercially available mHealth apps have drawn prominent public attention by providing improved medication adherence and efficient results, but some of the studies exist to support their use. Usability has become the main factor for the success or adoption of smartphone apps since it helps to organize the consistency for the users to achieve their goal in an easy and efficient way. This study aims to investigate the usability evaluation process of mHealth applications with the help of a Systematic Literature Review (SLR). Our findings show that the usability evaluation process can be more reliable and satisfactory by applying the mix-method approach. This study will encourage developers and researchers to design more effortless and usable applications for users, especially for older adults and novice users.

Additional file 1: Supplemental Data 1. The 18-month and 36-month developmental checkups in Japan. Supplemental Data 2. Screening tools. Supplemental Data 3. Screening criteria in the Hirosaki Five-year-old Study. Supplemental Data 4. Detailed demographic information about study participants. Supplemental Data 5. Statistical methods for computing the adjusted prevalence for ASD. Supplemental Data 6a. Trend of 5-year cumulative incidence of ASD from the year 2013 - the year 2016. Supplemental Data 6b. Trend in Cumulative Incidence of ASD from 2013-2016. Supplemental Data 7. The census data reporting the number of children who moved out of Hirosaki city from 2015 – 2019.

This paper uses simulation-based portfolio optimization to mitigate the left tail risk of the portfolio. The contribution is twofold. (i) We propose the Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. The volatility of each asset independently follows the regime-switch GARCH model, while the correlation of joint innovation of the GARCH models follows the Hidden Markov Model. (ii) We use tail risk measures, namely conditional value-at-risk (CVaR) and conditional drawdown-at-risk (CDaR), in the portfolio optimization. The optimization is performed with the sample paths simulated by the MRS-MNTS-GARCH model. We conduct an empirical study on the performance of optimal portfolios. Out-of-sample tests show that the optimal portfolios with tail measures outperform the optimal portfolio with standard deviation measure and the equally weighted portfolio in various performance measures. The out-of-sample performance of the optimal portfolios is also more robust to suboptimality on the efficient frontier.

In this paper, we will discuss an approximation of the characteristic function of the first passage time for a Levy process using the martingale approach. The characteristic function of the first passage time of the tempered stable process is provided explicitly or by an indirect numerical method. This will be applied to the perpetual American option pricing and the barrier option pricing. Numerical illustrations are provided for the calibrated parameters using the market call and put prices.

While a growing number of studies indicate associations between experiences of bullying and autism spectrum disorder (ASD), it is not clear what roles comorbid behavioral problems may play. We investigated the experiences of children with ASD as victims and/or perpetrators of bullying. Children with ASD epidemiologically ascertained participated in a cross-sectional study. Although children with ASD showed significantly increased risk for bullying involvement compared to community children, after controlling for comorbid psychopathology and other demographic factors, increased risks for being perpetrators or victim-perpetrators disappeared while risk for being bullied/teased continued to be significantly elevated. This finding will help guide medical, educational and community personnel to effectively identify children with ASD at risk for school bullying and develop interventions.

ObjectivesTo evaluate whether Asian (Korean children) populations can be validly diagnosed with autism spectrum disorder (ASD) using Western-based diagnostic instruments and criteria based on Diagnostic and Statistical Manual on Mental Disorders, 5th edition (DSM-5).MethodsParticipants included an epidemiologically ascertained 7-14-year-old (N = 292) South Korean cohort from a larger prevalence study (N = 55,266). Main outcomes were based on Western-based diagnostic methods for Korean children using gold standard instruments, Autism Diagnostic Interview-Revised, and Autism Diagnostic Observation Schedule. Factor analysis and ANOVAs were performed to examine factor structure of autism symptoms and identify phenotypic differences between Korean children with ASD and non-ASD diagnoses.ResultsUsing Western-based diagnostic methods, Korean children with ASD were successfully identified with moderate-to-high diagnostic validity (sensitivities/specificities ranging 64%-93%), strong internal consistency, and convergent/concurrent validity. The patterns of autism phenotypes in a Korean population were similar to those observed in a Western population with two symptom domains (social communication and restricted and repetitive behavior factors). Statistically significant differences in the use of socially acceptable communicative behaviors (e.g., direct gaze, range of facial expressions) emerged between ASD versus non-ASD cases (mostly p

The Social Responsiveness Scale (SRS) is an autism rating scales in widespread use, with over 20 official foreign language translations. It has proven highly feasible for quantitative ascertainment of autistic social impairment in public health settings, however, little is known about the validity of the reinforcement in Asia populations or in references to DSM5. The current study aims to evaluate psychometric properties and cross-cultural aspects of the SRS-Korean version (K-SRS).The study subjects were ascertained from three samples: a general sample from 3 regular education elementary schools (n=790), a clinical sample (n=154) of 6-12-year-olds from four psychiatric clinics, and an epidemiological sample of children with ASD, diagnosed using both DSM IV PDD, DSM5 ASD and SCD criteria (n=151). Their parents completed the K-SRS and the Autism Spectrum Screening Questionnaire(ASSQ). Descriptive statistics, correlation analyses and principal components analysis (PCA) were performed on the total population. Mean total scores on the K-SRS differed significantly between the three samples. ASSQ scores were significantly correlated with the K-SRS T-scores. PCA suggested a one-factor solution for the total population.Our results indicate that the K-SRS exhibits adequate reliability and validity for measuring ASD symptoms in Korean children with DSM IV PDD and DSM5 ASD. Our findings further suggest that it is difficult to distinguish SCD from other child psychiatric conditions using the K-SRS.This is the first study to examine the relationship between the SRS subscales and DSM5-based clinical diagnoses. This study provides cross-cultural confirmation of the factor structure for ASD symptoms and traits measured by the SRS. Autism Res 2016, 9: 970-980. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

In this paper we will introduce a hybrid option pricing model that combines the classical tempered stable model and regime switching by a hidden Markov chain. This model allows the description of some stylized phenomena about asset return distributions that are well documented in financial markets such as time-varying volatility, skewness, and heavy tails.We will derive the option pricing formula under the this model by means of Fourier transform method. In order to demonstrate the superior accuracy and the capacity of capturing dynamics using the proposed model, we will empirically test the model using call option prices where the underlying is the S&P 500 Index.

In this paper, we introduce a new GARCH model with an infinitely divisible distributed innovation, referred to as the rapidly decreasing tempered stable (RDTS) GARCH model. This model allows the description of some stylized empirical facts observed for stock and index returns, such as volatility clustering, the non-zero skewness and excess kurtosis for the residual distribution. Furthermore, we review the classical tempered stable (CTS) GARCH model, which has similar statistical properties. By considering a proper density transformation between infinitely divisible random variables, these GARCH models allow to find the risk-neutral price process, and hence they can be applied to option pricing. We propose algorithms to generate scenario based on GARCH models with CTS and RDTS innovation. To investigate the performance of these GARCH models, we report a parameters estimation for Dow Jones Industrial Average (DJIA) index and stocks included in this index, and furthermore to demonstrate their advantages, we calculate option prices based on these models. It should be noted that only historical data on the underlying asset and on the riskfree rate are taken into account to evaluate option prices.