Case report: Late in-stent thrombosis in a patient with vertebrobasilar dolichoectasia after stent-assisted coil embolization due to the discontinuation of antiplatelet therapy

Vertebrobasilar dolichoectasia (VBD) is a rare type of cerebrovascular disorder with high rates of morbidity and mortality. Due to the distinct pathological characteristics that fragmented internal elastic lamina and multiple dissections, VBD is difficult to treat and cured. Stent-assisted coil embolization is one of the main treatment modalities for such lesions. However, the duration of healing remained questionable, and there were no effective measures for evaluating endothelial coverage. Before complete endothelial coverage, the discontinuation of antiplatelet therapy may lead to fatal in-stent thrombosis; however, continued antiplatelet therapy could also result in bleeding complications. Thus, we present an autopsy case of late in-stent thrombosis due to the discontinuation of antiplatelet therapy and systematically review the literature to provide a reference for endovascular treatment and antiplatelet regimen of VBD

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson’s correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity

Semaphorin 3A Contributes to Secondary Blood–Brain Barrier Damage After Traumatic Brain Injury

Semaphorin 3A (SEMA3A) is a member of the Semaphorins family, a class of membrane-associated protein that participates in the construction of nerve networks. SEMA3A has been reported to affect vascular permeability previously, but its influence in traumatic brain injury (TBI) is still unknown. To investigate the effects of SEMA3A, we used a mouse TBI model with a controlled cortical impact (CCI) device and a blood–brain barrier (BBB) injury model in vitro with oxygen-glucose deprivation (OGD). We tested post-TBI changes in SEMA3A, and its related receptors (Nrp-1 and plexin-A1) expression and distribution through western blotting and double-immunofluorescence staining, respectively. Neurological outcomes were evaluated by modified neurological severity scores (mNSSs) and beam-walking test. We examined BBB damage through Evans Blue dye extravasation, brain water content, and western blotting for VE-cadherin and p-VE-cadherin in vivo, and we examined the endothelial cell barrier through hopping probe ion conductance microscopy (HPICM), transwell leakage, and western blotting for VE-cadherin and p-VE-cadherin in vitro. Changes in miR-30b-5p were assessed by RT-PCR. Finally, the neuroprotective function of miR-30b-5p is measured by brain water content, mNSSs and beam-walking test. SEMA3A expression varied following TBI and peaked on the third day which expressed approximate fourfold increase compared with sham group, with the protein concentrated at the lesion boundary. SEMA3A contributed to neurological function deficits and secondary BBB damage in vivo. Our results demonstrated that SEMA3A level following OGD injury almost doubled than control group, and the negative effects of OGD injury can be improved by blocking SEMA3A expression. Furthermore, the expression of miR-30b-5p decreased approximate 40% at the third day and 60% at the seventh day post-CCI. OGD injury also exhibited an effect to approximately decrease 50% of miR-30b-5p expression. Additionally, the expression of SEMA3A post-TBI is regulated by miR-30b-5p, and miR-30b-5p could improve neurological outcomes post-TBI efficiently. Our results demonstrate that SEMA3A is a significant factor in secondary BBB damage after TBI and can be abolished by miR-30b-5p, which represents a potential therapeutic target

Online Learning and Control for Tracking Unknown Targets

In the optimal control of unknown systems, offline approaches, such as reinforcement learning or system identification can be helpful in a number of scenarios and have proven themselves over decades. These, however, hinge on a number of crucial assumptions that often fail to hold in practice, most importantly, the availability of a reliable simulator and the possibility of offline learning/identification. When these do not hold or hold partially, the need of online or ’on the-go’ algorithms becomes apparent; these control the system while aiming to stay as close as possible to a performance objective which is revealed only sequentially. The tracking problem of an unknown reference signal is an example of such a problem. It is a challenging task, yet it appears frequently in practice, for example in tracking of a flock of wild animals or in pursuit of malicious agents. To achieve close tracking of an unknown/adversarial target, it is critical for the controller to learn online from collected data during the operation and to adapt to changes fast. We consider the linear quadratic tracking case and propose an online algorithm, RLS MPC, that uses recursive least squares to learn the time-varying dynamic model of the target and solves for the optimal policy under the framework of receding horizon control. We show that its dynamic regret scales with the rate of change of the target dynamics, as opposed to rate of change of target states as in previous works. We prove that for slow-changing target dynamics, like periodic targets, the dynamic regret of RLS-MPC is bounded by O(log T). For general targets, the algorithm achieves a bound of O(1 + √V T), where V is the path length of the target dynamics. We implement the proposed controller on a quadrotor model and validate it both in simulations and on a real mini-quadrotor

Multilinear Singular Integral Operators on Generalized Weighted Morrey Spaces

The purpose of this paper is to discuss the boundedness properties of multilinear Calderón-Zygmund operator and its commutator on the generalized weighted Morrey spaces

Improved logistic regression algorithm based on kernel density estimation for multi-classification with non-equilibrium samples

Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with other methods using non-equilibrium samples, the results show that our approach guarantees sample integrity and achieves superior classification

Presentation_1_Tracing Chinese international students’ psychological and academic adjustments in uncertain times: An exploratory case study in the United Kingdom.pdf

The worldwide spread of COVID-19 has exerted tremendous influences on the wellbeing of international students and the sustainable development of higher education. The current study adopts an 8-month exploratory case study to trace eight Chinese international students’ psychological and academic adjustments in the United Kingdom amid the COVID-19 pandemic. Emerging from the qualitative data constitutive of semi-structured interviews, self-reflection writings, memoing, together with stimulated-recall interviews, findings have demonstrated the three main types of obstruction for such students’ adjustments in the foreign land including COVID-specific challenges (i.e., the threat of infect, reduced access to university facilities and resources); COVID-enhanced challenges (i.e., anxiety exacerbated by parents and social media use, anti-Asian racism and hate incidents); and language barriers and cultural differences as long-standing issues. Students’ previous lockdown experience, individual resilience, development of monocultural friendship patterns, and institutional provision and support are all factors that have contributed to their ability to overcome or at least mitigate the psychological and academic difficulties. The study offers insight into the impacts of COVID-19 on international students, providing implications that could contribute to the sustainable adjustments of international students in times of disruptive events and inform future responses to global health crises from individual and higher education perspectives.</p

Long-term follow-up of methimazole-associated insulin autoimmune syndrome: a rare case report

Insulin autoimmune syndrome (IAS) is a rare cause of hypoglycemia and is characterized by the presence of insulin autoantibodies and fasting or late postprandial hypoglycemia. The number of reports on the association of long-term follow-up of IAS in China is limited. We herein report a case of drug-induced IAS in a 44-year-old Chinese woman. She had been taking methimazole for Graves’ disease and had subsequently presented with recurrent hypoglycemic episodes. Laboratory assessments on admission revealed that her serum insulin level was significantly elevated (>1000 µIU/mL) and that she was positive for serum insulin autoantibody, leading to a diagnosis of IAS. Human leukocyte antigen DNA typing identified *04:06/*09:01:02, an immunogenetic determinant associated with IAS. After treatment with prednisone for 2 months, the hypoglycemic episodes disappeared, her serum insulin level gradually declined, and her insulin antibody levels became negative. Clinicians should be aware of the potential for methimazole to trigger autoimmune hypoglycemia in people with a genetic predisposition

New generation model of word vector representation based on CBOW or skip-gram

Word vector representation is widely used in natural language processing tasks. Most word vectors are generated based on probability model, its bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. Recently, neural-network language models CBOW and Skip-Gram are developed as continuous-space language models for words representation in high dimensional real-valued vectors. These vector representations have recently demonstrated promising results in various NLP tasks because of their superiority in capturing syntactic and contextual regularities in language. In this paper, we propose a new strategy based on optimization in contiguous subset of documents and regression method in combination of vectors, two of new models CBOW-OR and SkipGram-OR for word vector learning are established. Experimental results show that for some words-pair, the cosine distance obtained by the CBOW-OR (or SkipGram-OR) model is generally larger and is more reasonable than CBOW (or Skip-Gram), the vector space for Skip-Gram and SkipGram-OR keep the same structure property in Euclidean distance, and the model SkipGram-OR keeps higher performance for retrieval the relative words-pair as a whole. Both CBOW-OR and SkipGram-OR model are inherent parallel models and can be expected to apply in large-scale information processing

Experimental Studies on Breakup and Fragmentation Behavior of Molten Tin and Coolant Interaction

Jet breakup and fragmentation behavior significantly affect the likelihood (and ultimate strength) of steam explosion, but it is very challenging to assess the potential damage to reactor cavity due to general lack of knowledge regarding jet breakup phenomena. In this study, the METRIC (mechanism study test apparatus for melt-coolant interaction) was launched at Shanghai Jiao Tong University to investigate FCI physics. The first five tests on molten tin and water interactions are analyzed in this paper. Significant breakup and fragmentation were observed without considerable pressure pulse, and intense expansion of droplets in local areas was observed at melt temperature higher than 600°C. The chain interactions of expansion all ceased, however, and there was no energetic steam explosion observed. Quantitative analysis on jet breakup length and debris was studied to investigate the effect of the melt temperature, initial diameter of the jet, and so on. Furthermore, the results of tests were compared with current theories. It is found that melt temperature has strong impact on fragmentation that need to be embodied in advanced fragmentation models