Somatic Variants in SVIL in Cerebral Aneurysms

Publisher Copyright: © American Academy of Neurology.Background and ObjectivesWhile somatic mutations have been well-studied in cancer, their roles in other complex traits are much less understood. Our goal is to identify somatic variants that may contribute to the formation of saccular cerebral aneurysms.MethodsWe performed whole-exome sequencing on aneurysm tissues and paired peripheral blood. RNA sequencing and the CRISPR/Cas9 system were then used to perform functional validation of our results.ResultsSomatic variants involved in supervillin (SVIL) or its regulation were found in 17% of aneurysm tissues. In the presence of a mutation in the SVIL gene, the expression level of SVIL was downregulated in the aneurysm tissue compared with normal control vessels. Downstream signaling pathways that were induced by knockdown of SVIL via the CRISPR/Cas9 system in vascular smooth muscle cells (vSMCs) were determined by evaluating changes in gene expression and protein kinase phosphorylation. We found that SVIL regulated the phenotypic modulation of vSMCs to the synthetic phenotype via Krüppel-like factor 4 and platelet-derived growth factor and affected cell migration of vSMCs via the RhoA/ROCK pathway.DiscussionWe propose that somatic variants form a novel mechanism for the development of cerebral aneurysms. Specifically, somatic variants in SVIL result in the phenotypic modulation of vSMCs, which increases the susceptibility to aneurysm formation. This finding suggests a new avenue for the therapeutic intervention and prevention of cerebral aneurysms.Peer reviewe

Effect of Catalyst Preparation on Hydroisomerization of n-Heptane over Pt/Silicalite-1

The isomerization of n-heptane was comparatively investigated over Pt catalysts prepared by deposition (Pt/S-1-DP) and impregnation precipitation (Pt/S-1-IMP) method with a low loading, separately. It was found that Pt/S-1-DP catalyst produced a much higher catalytic conversion compared with the counterpart catalyst prepared by impregnation. Combined with various characterization techniques, the better performance over Pt/S-1-DP was attributed to not only the well improved dispersity and the smaller size of Pt particles but also the faster transfer of reactive species between Pt sites and acid sites. The activity could be improved by increasing the reduction temperature. Moreover, the (de)hydrogenation of Pt over Pt/S-1 catalyst was verified by in situ infrared spectroscopy through H/D isotope exchange experiment

Suture combined with clipping in the treatment of a wide-necked complex intracranial aneurysm

The treatment of wide-necked complex intracranial aneurysms is full of challenges. We report a 72-year-old woman with subarachnoid hemorrhage and three intracranial aneurysms. According to the location of the hemorrhage, we chose the left side Pterional approach to clip the left side aneurysms and remove the hematoma. After clipping the left posterior communicating artery aneurysm, we found a small bifurcation aneurysm associated with dysplastic bulging of the nearby wall which together formed a wide-necked complex aneurysm. We used a novel suture combined with clipping technique to treat this complex aneurysm. Indocyanine green arteriography showed intact vascular patency in distal vessels, and the patient has achieved a good prognosis. It may provide a new idea for treating wide-necked complex intracranial aneurysms

Exploring the Individual Differences in Multidimensional Evolution of Knowledge States of Learners

The key to the effectiveness of Intelligent Tutoring Systems (ITSs) is to fit the uncertainty of each learner’s performance in performing different learning tasks. Throughout the tutoring and learning process, the uncertainty of learners’ performance can reflect their varying knowledge states, which can arise from individual differences in learning characteristics and capacities. In this investigation, we proposed a multidimensional representation of the evolution of knowledge states of learners to better understand individual differences among them. This assumption about this representation is verified using the Tensor Factorization (TF) based method, a modern state-of-the-art model for knowledge tracing. The accuracy of the Tensor-based method is evaluated by comparing it to other knowledge-tracing methods, to gain a deeper insight into individual differences among learners and their learning of diverse contents. The experimental data under focus in our investigation is derived from the AutoTutor lessons that were developed for the Center for the Study of Adult Literacy (CSAL), which employs a trialogue design comprising of a virtual tutor, a virtual companion and a human learner. A broader merit of our proposed approach lies in its capability to capture individual differences more accurately, without requiring any changes in the real-world implementation of ITSs

The Adaptive Features of an Intelligent Tutoring System for Adult Literacy

Adult learners with low literacy skills compose a highly heterogeneous population in terms of demographic variables, educational backgrounds, knowledge and skills in reading, self-efficacy, motivation etc. They also face various difficulties in consistently attending offline literacy programs, such as unstable worktime, transportation difficulties, and childcare issues. AutoTutor for Adult Reading Comprehension (AT-ARC), as an online conversation-based intelligent tutoring system that incorporated a theoretical model of reading comprehension, was developed with great efforts to meet adult learners’ needs and be adaptive to their knowledge, skills, self-efficacy, and motivation. In this paper, we introduced the adaptive features of AT-ARC from four aspects: learning material selection, adaptive branching, trialogues, and interface, as well as the rationale behind these designs. In the end, we suggested further research on improving the adaptivity of AT-ARC

Collecting 3A Data to Enhance HCI in AIS

3A refers to content aware, context aware, and learner aware intelligent tutoring system (ITS) [2]. The idea behind this is that any ITS should be delivering content intelligently by knowing about the state of the user. The state of the user could be emotional or even physical. Almost all the ITS are more or less intelligent to deliver content. But less intelligent to know whether the learner is accepting the content. In addition, the context consists of two components: context of the content and context of the environment. It is easy for an ITS to be aware of the context of the content (e.g., calculus in case of integrals) but very few ITS take into account the context of the environment. For example, a learner is accessing content in a crowded environment from her cell phone and a learner is accessing content inside a library where it is calm. Contexts of these two learners are different. Moreover, the learner awareness includes emotional states as well as physical states of a learner. In this research our focus is to collect data by enabling a 3A learning system in AutoTutor [11]. AutoTutor is a conversation-based ITS that uses an expectation-misconception tailored dialogue to promote learning. Several questions involved in designing 3A enabled AutoTutor. How to collect 3A data without violating learners’ privacy is the most important one. All other design questions revolve around this