A set of DNA methylation markers was detected and evaluated to identify body fluids using the amplification refractory mutation system-PCR (ARMS-PCR) and random forest algorithm. In this study, four multiplex DNA methylation reactions composed of 22 promising methylation markers were used to identify regular forensic body fluids, including venous blood, saliva, semen, menstrual blood, and vaginal fluid. The ARMS-specific primers were used to amplify the candidate markers, and then the methylation values of each CpG site were detected through capillary electrophoresis (CE). The DNA methylation patterns of 22 highly informative methylation markers were consistent with previously reported results to a certain extent. To our knowledge, our study is a new method to apply the ARMS-PCR technique and random forest model to detect DNA methylation patterns and identify the type of body fluids in forensic science, thus providing a new method for forensic body fluid identification. Moreover, we proved that this method is robust, applicable and effective for identifying body fluids using the random forest model. The accuracy to predict all body fluids reached up to 0.9966. We firmly believe that this method will have a great potential in the detection of methylation profiles at the molecular level.The era of Big Data has arrived. Recently, under the environment of intelligent transportation systems (ITS) and connected/automated vehicles (CAV), Big Data has been applied in various fields in transportation including traffic safety. In this study, we review recent research studies that employed Big Data to analyze traffic safety under the environment of ITS and CAV. The particular topics include crash detection or prediction, discovery of contributing factors to crashes, driving behavior analysis, crash hotspot identification, etc. From the reviewed studies, employing advanced analytics for Big Data has a great potential for understanding and enhancing traffic safety. Big Data application in traffic safety integrates and processes massive multi-source data, breaks through the limitations of the traditional data analytics, and discovers and solves the problems, which cannot be solved by the traditional safety analytics. Lastly, suggestions are provided for future Big Data safety analytics under the environment of ITS and CAV.Here we have generated two induced pluripotent stem cell (iPSC) lines, hASC-iPSC-1A and hASC-iPSC-2A, by reprogramming human adipose tissue derived stem cells of 28 and 23 years old healthy donors. Reprogramming was achieved using nonintegrative Sendai viral vector system containing the reprogramming factors Klf4, Oct3/4, Sox2, c-Myc. Though the karyotypes of these cells were normal (46, XX) and (46, XY), their pluripotency potentials were confirmed by the expression of factors of pluripotency markers in vitro and teratoma formation in vivo. These iPSCs differentiated cells can serve as control for disease modeling and drug screening.White brined cheese may serve as an ideal medium for the growth of foodborne pathogens including E. coli O157H7. The objectives of this study were i) to evaluate the inhibitory effects of zinc oxide (ZnO) nanoparticles against E. coli O157H7 at 10 or 37 °C using broth dilution; ii) to address the post-process contamination of white brined cheese with E. coli O157H7 by using chitosan coating with or without ZnO nanoparticles during storage for 28 d at 4 and 10 °C; and iii) to study the physicochemical characteristics of chitosan coating containing ZnO nanoparticles. ZnO nanoparticles at ≥0.0125% inhibited the growth of three E. coli O157H7 strains at both 37 and 10 °C. The chitosan coating with or without ZnO nanoparticles significantly reduced the initial numbers of E. coli O157H7 in white brined cheese by 2.5 and 2.8 log CFU/g, respectively, when stored at 4 °C or by 1.9 and 2.1 log CFU/g, respectively, when stored at 10 °C. The chitosan-ZnO nanoparticle coating was not significantly different (p > 0.05) but was slightly better than chitosan alone as an active, smart packaging material in food applications.Thrombus permeability determines blood flow through the occluding thrombus in acute ischemic stroke (AIS) patients. The quantification of thrombus permeability is challenging since it cannot be directly measured nor derived from radiological imaging data. As a proxy of thrombus permeability, thrombus perviousness has been introduced, which assesses the amount of contrast agent that has penetrated the thrombus on single-phase computed tomography angiography (CTA). We present a method to assess thrombus permeability rather than perviousness. We follow a three-step approach (1) we propose a theoretical channel-like structure model describing the thrombus morphology. Using Darcy's law, we provide an analytical description of the permeability for this model. According to the channel-like model, permeability depends on the number of channels in the thrombus, the radius of the occluded artery, and the void fraction representing the volume available for the blood to flow; (2) we measure intra-thrombus blood flow and velocity on dynamic CTA; and (3) we combine the analytical model with the dynamic CTA measurements to estimate thrombus permeability. Analysis of dynamic CTA data from 49 AIS patients showed that the median blood velocity in the thrombus was 0.58 (IQR 0.26-1.35) cm/s. The median flow within the thrombus was 3.48 · 10-3 (IQR 1.71 · 10-3-9.21 · 10-3) ml/s. Thrombus permeability was of the order of 10-3-10-5 mm2, depending on the number of channels in the thrombus. The channel-like thrombus model offers an intuitive way of modelling thrombus permeability, which can be of interest when studying the effect of thrombolytic drugs.Trait-like tendencies to respond impulsively to emotion, labelled emotion-related impulsivity, are robustly related to aggression. We developed and tested an online intervention to address emotion-related impulsivity and aggression. The 6-session intervention focused on behavioral techniques shown to decrease arousal and aggression, supplemented with implementation intentions and smartphone prompts to facilitate skills transfer into daily life. First, we piloted the intervention in-person with 4 people. Then, 235 participants were randomly assigned to take the online intervention immediately or after a wait-list period; those in the waitlist were then invited to take part in the intervention.  https://www.selleckchem.com/products/ABT-869.html  Participants completed the self-rated Feelings Trigger Action Scale to assess emotion-related impulsivity, the interview-based Modified Overt Aggression Scale and the self-rated Buss Perry Aggression Questionnaire. Participants who took part in the treatment completed daily anger logs. Attrition, as with other online programs, was high; however, treatment completers reported high satisfaction, and outcomes changed more rapidly during treatment than waitlist across all key outcome indices.