A Research on Dimension Reduction Method of Time Series Based on Trend Division

The characteristics of high dimension, complexity and multi granularity of financial time series make it difficult to deal with effectively. In order to solve the problem that the commonly used dimensionality reduction methods cannot reduce the dimensionality of time series with different granularity at the same time, in this paper, a method for dimensionality reduction of time series based on trend division is proposed. This method extracts the extreme value points of time series, identifies the important points in time series quickly and accurately, and compresses them. Experimental results show that, compared with the discrete Fourier transform and wavelet transform, the proposed method can effectively process data of different granularity and different trends on the basis of fully preserving the original information of time series. Moreover, the time complexity is low, the operation is easy, and the proposed method can provide decision support for high-frequency stock trading at the actual level

Flow prediction meets flow learning : combining different learning strategies for computing the optical flow

Optical flow estimation is an important topic in computer vision. The goal is to computethe inter-frame displacement field between two consecutive frames of an image sequence. In practice, optical flow estimation plays a significant role in multiple application domains including autonomous driving and medical imaging. Different categories of methods exist for solving the optical flow problem. The most common technique is based on a variational framework, where an energy functional is designed and minimized in order to calculate the optical flow. Recently, other approaches like pipeline-based approach and learning-based approach also attract much attention. Despite the great advances achieved by these algorithms, it is still difficult to find an algorithm that can perform well under all the challenges, e.g. lightning changes, large displacements, and occlusions. Hence, it is worth combining different algorithms to create a new approach that can combine their advantages. Inspired by this idea, in this thesis we select two top-performing algorithms PWC-Net and ProFlow as candidate approaches and conduct a combination of these two algorithms. While PWC-Net performs generally well in the estimation of non-occluded areas, ProFlow can especially provide an accurate estimation for the occluded areas. Thereby, we expect that the combination of these two algorithms can yield an algorithm that performs well in both occluded and non-occluded areas. Since ProFlow is a pipeline approach, we first integrate the PWC-Net in the ProFlow pipeline, then evaluate the new created pipeline PWC-ProFlow based on the MPI Sintel and KITTI 2015 benchmarks. Contrary to our expectations, the newly created algorithm does not exceed the candidate methods PWC-Net and ProFlow on either benchmark. Through the analysis of the evaluation results, we explore the problems hidden in the PWC-ProFlow pipeline that can lead to its underperformance, and organize some modification ideas. Based on these ideas, we propose six new pipelines with the purpose of improving the estimation accuracy of PWC-ProFlow. All the new generated pipelines are also evaluated on the Sintel and KITTI benchmarks. The experiment results demonstrate that all the modifications created achieve great improvements on both datasets compared to PWC-ProFlow. Further, all of them also outperform the ProFlow pipeline on both benchmarks. Compared to PWC-Net, one modification exceeds PWC-Net on the KITTI dataset, however, all our modifications achieve a better performance on the Sintel dataset, in particular, one modification presents a significant improvement with a more than 10% lower average endpoint error on the Sintel dataset

LH-moment estimation for statistical analysis on the wave crest distributions of a deepwater spar platform model test

The design of fixed and compliant offshore platforms requires the reliable estimation of extreme values with small probabilities of exceedance based on an appropriate probability distribution. The Weibull distribution is commonly utilised for the statistical analysis of wave crests, including near-field wave run-ups. The parameters are estimated empirically from experimental or onsite measurements. In this paper, the data set of wave crests from a Spar model test was statistically analysed by using the method of LH-moments for parameter estimation of the Weibull distribution. The root-mean-square errors (RMSEs) and the error of LH-kurtosis were used to examine the goodness-of-fit. The results for the first four LH-moments, the estimated parameters, and the probability distributions showed that the level of the LH-moments has a significant influence. At higher levels, the estimation results gave a more focused representation of the upper part of the wave crest distributions, which indicates consistency with the intention of the method of LH-moments. The low tail RMSE values of less than 2.5% demonstrated that a Weibull distribution model estimated by using high-level LH-moments can accurately represent the probability distribution of large extreme wave crests for incident waves, wave run-ups, and moon pool waves. Goodness-of-fit test on the basis of comparison of sampling LH-kurtosis and theoretical LH-kurtosis was recommended as a procedure for selecting an optimum level

OMAE2006-92140 CHARACTERISTICS OF CURRENT GENERATION SYSTEM IN DEEPWATER OFFSHORE BASIN

ABSTRACT The current generation system in deepwater offshore basin is important for the correct modeling of ocean environment. It is generally considered to be a challenge to obtain uniform and stable current flow in the basin. As technical assurance numerical and experimental studies are performed to investigate the characteristics of the deepwater current generation in the basin. Reynolds-Averaged Navier-Stokes (RANS) equations and the standard k-ε turbulence model are adopted to simulate the current generation system numerically. In addition a 1:10 scaled model test is also performed. In both numerical and experimental studies horizontal and vertical current velocity profiles, turbulence levels and pressure losses during the current recirculation etc. are studied. It is concluded that the perforated walls are key components of the current generation system. In addition various vertical current velocity profiles can be realized in the basin

Effect of Ketamine on LTP and NMDAR EPSC in Hippocampus of the Chronic Social Defeat Stress Mice Model of Depression

Depression is a common mental disorder that is associated with memory dysfunction. Ketamine has recently been demonstrated to be a rapid antidepressant. The mechanisms underlying how depression induces memory dysfunction and how ketamine relieves depressive symptoms remain poorly understood. This work compared three groups of male C57BL/6J mice: mice exposed to chronic social defeat stress (CSDS) to induce a depression-like phenotype, depression-like mice treated with ketamine, and control mice that were not exposed to CSDS or treated with ketamine. Spatial working memory and long term memory were assessed by spontaneous alternation Y-maze and fear conditioning tests, respectively. We used western blot to analyze the density of N-methyl-D-aspartate receptor (NMDAR) subunits in the hippocampus. We recorded long term potentiation (LTP) and NMDA receptor-mediated excitatory postsynaptic currents (EPSCs) in hippocampal slices. We observed that compared with control mice, depression-like mice had significant reductions in spatial working memory and contextual fear memory. The level of NR2B, LTP and NMDA receptor-mediated EPSCs of depression-like mice were decreased. Ketamine treatment attenuated the memory impairment, and increased the density of NR2B and the amplitude of LTP and NMDA receptor-mediated EPSCs in the hippocampus of depression-like mice. In conclusion, depression-like mice have deficits in working memory and contextual fear memory. The decrease of NR2B, LTP induction and NMDA receptor-mediated EPSCs in the hippocampus may be involved in this process. Ketamine can improve expression of NR2B, LTP induction and NMDA receptor-mediated EPSCs in the hippocampus of depression-like mice, which might be part of the reason why ketamine can alleviate the memory dysfunction induced by depression

Combination of single-cell and bulk RNA seq reveals the immune infiltration landscape and targeted therapeutic drugs in spinal cord injury

BackgroundIn secondary spinal cord injury (SCI), the immune microenvironment of the injured spinal cord plays an important role in spinal regeneration. Among the immune microenvironment components, macrophages/microglia play a dual role of pro-inflammation and anti-inflammation in the subacute stage of SCI. Therefore, discovering the immune hub genes and targeted therapeutic drugs of macrophages/microglia after SCI has crucial implications in neuroregeneration. This study aimed to identify immune hub genes and targeted therapeutic drugs for the subacute phase of SCI.MethodsBulk RNA sequencing (bulk-RNA seq) datasets (GSE5296 and GSE47681) and single-cell RNA sequencing (scRNA-seq) dataset (GSE189070) were obtained from the Gene Expression Omnibus database. In the bulk RNA-seq, the R package ‘limma,’ ‘WGCNA,’ and ‘CIBERSORT’ were used to jointly screen key immune genes. Subsequently, the R package ‘Seurat’ and the R package ‘celldex’ were used to divide and annotate the cell clusters, respectively. After using the Autodock software to dock immune hub genes and drugs that may be combined, the effectiveness of the drug was verified using an in vivo experiment with the T9 SCI mouse model.ResultsIn the bulk-RNA seq, B2m, Itgb5, and Vav1 were identified as immune hub genes. Ten cell clusters were identified in scRNA-seq, and B2m and Itgb5 were mainly located in the microglia, while Vav1 was mainly located in macrophages. Molecular docking results showed that the proteins corresponding to these immune genes could accurately bind to decitabine. In decitabine-treated mice, the pro-inflammatory factor (TNF-α, IL-1β) levels were decreased while anti-inflammatory factor (IL-4, IL-10) levels were increased at 2 weeks post-SCI, and macrophages/microglia transformed from M1 to M2. At 6 weeks post-SCI, the neurological function score and electromyography of the decitabine treatment group were also improved.ConclusionIn the subacute phase of SCI, B2m, Itgb5, and Vav1 in macrophages/microglia may be key therapeutic targets to promote nerve regeneration. In addition, low-dose decitabine may promote spinal cord regeneration by regulating the polarization state of macrophages/microglia

FUS-NLS/Transportin 1 complex structure provides insights into the nuclear targeting mechanism of FUS and the implications in ALS

The C-terminal nuclear localization sequence of FUsed in Sarcoma (FUS-NLS) is critical for its nuclear import mediated by transportin (Trn1). Familial amyotrophic lateral sclerosis (ALS) related mutations are clustered in FUS-NLS. We report here the structural, biochemical and cell biological characterization of the FUS-NLS and its clinical implications. The crystal structure of the FUS-NLS/Trn1 complex shows extensive contacts between the two proteins and a unique α-helical structure in the FUS-NLS. The binding affinity between Trn1 and FUS-NLS (wide-type and 12 ALS-associated mutants) was determined. As compared to the wide-type FUS-NLS (K(D) = 1.7 nM), each ALS-associated mutation caused a decreased affinity and the range of this reduction varied widely from 1.4-fold over 700-fold. The affinity of the mutants correlated with the extent of impaired nuclear localization, and more importantly, with the duration of disease progression in ALS patients. This study provides a comprehensive understanding of the nuclear targeting mechanism of FUS and illustrates the significance of FUS-NLS in ALS

Clinical characteristics and cognitive function in bipolar disorder patients with different onset symptom

BackgroundIn recent years, studies on the clinical features and cognitive impairment of patients with different first-episode types of bipolar disorder have received increasing attention. The patients with bipolar disorder may present with different symptoms at first onset. The aim of this study is to assess the cognitive functions of a patient’s index episode of bipolar disorder, depression or mania, on risk factors of effecting on cognitive functions.MethodOne hundred sixty eight patients with bipolar disorder diagnosed for the first time were enrolled in the study. All patients were divided into two groups according to their index episode of bipolar disorder, either depression or mania. Seventy three patients of the cohort had an index episode mania and 95 patients had initial symptoms of depression. Demographic and clinical disease characteristic data of all enrolled patients were collected. Meanwhile, 75 healthy controls were included. Demographic data of controls were collected. The cognitive functions of all patients and controls were detected by continuous performance test (CPT), digital span test (DST) and Wisconsin card sorting test (WCST). The main cognitive functions data were compared among the mania group, depression group and control group. The relevant risk factors affecting cognitive function were analyzed.Results(1) Most patients with bipolar disorder had an index episode depression (56.55% vs. 43.45%). Compared with the depression group, the mania group had later age of onset [(24.01 ± 4.254) vs. (22.25 ± 6.472), t = 2. 122, p = 0.035]. The education level of patient groups was lower than control group (p < 0.001). (2) The healthy control group’s DST, WCST and CPT scores were better than the patient groups (All p < 0.05). The mania group’s DST (forward, reverse, sum), WCST (total responses, completed classifications, correct responses, incorrect responses, percentage of correct responses, completed the number of responses required for classification, the percentage of conceptualization level, the number of persistent responses, non-persistent errors), CPT (2 digit score, 3 digit score, 4 digit score) was better than the depression group (p < 0.05). (3) In mania group, correlation analysis showed that all CPT parameter, inverse digit span, and the sum of DST was negatively correlated with the education level (All p < 0.05). The CPT-4 digit score was negatively correlated with onset age (p < 0.05). In the WCST, the number of correct responses, the percentage of correct responses and the percentage of conceptualization level were positively correlated with the BRMS score (All p < 0.05). The number of false responses and persistent responses were negatively correlated with the BRMS score (All p < 0.05). The number of persistent errors and percentage of persistent errors was positively correlated with education years (All p < 0.05). In depression group, there was a positive correlation between inverse digit span and the education level (p < 0.05).ConclusionIn our study, there were cognitive impairments in attention, memory, and executive function of patients with different onset syndromes of bipolar disorder. Compared with the mania group, the degree of cognitive impairments in bipolar patients with the depressive episode was more severe. The risk factors affecting cognitive impairments included the age of onset, education level, number of hospitalizations and severity of illness