Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With L1-Norm Regularization

Early screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for children with autism spectrum disorder (ASD). Research has shown that electroencephalogram (EEG) signals can reflect abnormal brain function of children with ASD, and screening with EEG signals has the characteristics of good real-time performance and high sensitivity. However, the existing EEG screening algorithms mostly focus on the data analysis in the resting state, and the extracted EEG features have some disadvantages such as weak representation capacity and information redundancy. In this study, we utilized the event-related potential (ERP) technique to acquire the EEG data of the subjects under positive and negative emotional stimulation and proposed an EEG Feature Selection Algorithm based on L1-norm regularization to perform screening of autism. The proposed EEG Feature Selection Algorithm includes the following steps: (1) extracting 20 EEG features from the raw data, (2) classification with support vector machine, (3) selecting appropriate EEG feature with L1-norm regularization according to the classification performance. The experimental results show that the accuracy for screening of children with ASD can reach 93.8% and 87.5% under positive and negative emotional stimulation and the proposed algorithm can effectively eliminate redundant features and improve screening accuracy

Roles of TNF-α gene polymorphisms in the occurrence and progress of SARS-Cov infection: A case-control study

<p>Abstract</p> <p>Background</p> <p>Host genetic factors may play a role in the occurrence and progress of SARS-Cov infection. This study was to investigate the relationship between tumor necrosis factor (TNF)-<it>α </it>gene polymorphisms with the occurrence of SARS-CoV infection and its role in prognosis of patients with lung interstitial fibrosis and femoral head osteonecrosis.</p> <p>Methods</p> <p>The association between genetic polymorphisms of <it>TNF-α </it>gene and susceptibility to severe acute respiratory syndromes (SARS) was conducted in a hospital-based case-control study including 75 SARS patients, 41 health care workers and 92 healthy controls. Relationships of TNF-α gene polymorphisms with interstitial lung fibrosis and femoral head osteonecrosis were carried out in two case-case studies in discharged SARS patients. PCR sequencing based typing (PCR-SBT) method was used to determine the polymorphisms of <it>TNF-α </it>gene in locus of the promoter region and univariate logistic analysis was conducted in analyzing the collected data.</p> <p>Results</p> <p>Compared to TT genotype, the CT genotype at the -204 locus was found associated with a protective effect on SARS with OR(95%<it>CI</it>) of 0.95(0.90–0.99). Also, TT genotype, CT and CC were found associated with a risk effect on femoral head necrosis with ORs(95%<it>CI</it>) of 5.33(1.39–20.45) and 5.67(2.74–11.71), respectively and the glucocorticoid adjusted OR of CT was 5.25(95%CI 1.18–23.46) and the combined (CT and CC) genotype OR was 6.0 (95%<it>CI </it>1.60–22.55) at -1031 site of <it>TNF-α </it>gene. At the same time, the -863 AC genotype was manifested as another risk effect associated with femoral head necrosis with OR(95%<it>CI</it>) of 6.42(1.53–26.88) and the adjusted OR was 8.40(95%CI 1.76–40.02) in cured SARS patients compared to CC genotype.</p> <p>Conclusion</p> <p>SNPs of <it>TNF-α </it>gene of promoter region may not associate with SARS-CoV infection. And these SNPs may not affect interstitial lung fibrosis in cured SARS patients. However, the -1031CT/CC and -863 AC genotypes may be risk factors of femoral head necrosis in discharged SARS patients.</p

Electrically empowered microcomb laser

Optical frequency comb underpins a wide range of applications from communication, metrology, to sensing. Its development on a chip-scale platform -- so called soliton microcomb -- provides a promising path towards system miniaturization and functionality integration via photonic integrated circuit (PIC) technology. Although extensively explored in recent years, challenges remain in key aspects of microcomb such as complex soliton initialization, high threshold, low power efficiency, and limited comb reconfigurability. Here we present an on-chip laser that directly outputs microcomb and resolves all these challenges, with a distinctive mechanism created from synergetic interaction among resonant electro-optic effect, optical Kerr effect, and optical gain inside the laser cavity. Realized with integration between a III-V gain chip and a thin-film lithium niobate (TFLN) PIC, the laser is able to directly emit mode-locked microcomb on demand with robust turnkey operation inherently built in, with individual comb linewidth down to 600 Hz, whole-comb frequency tuning rate exceeding $\rm 2.4\times10^{17}$ Hz/s, and 100% utilization of optical power fully contributing to comb generation. The demonstrated approach unifies architecture and operation simplicity, high-speed reconfigurability, and multifunctional capability enabled by TFLN PIC, opening up a great avenue towards on-demand generation of mode-locked microcomb that is expected to have profound impact on broad applications

ICOSLG-associated immunological landscape and diagnostic value in oral squamous cell carcinoma: a prospective cohort study

Background: We previously reported that stroma cells regulate constitutive and inductive PD-L1 (B7-H1) expression and immune escape of oral squamous cell carcinoma. ICOSLG (B7-H2), belongs to the B7 protein family, also participates in regulating T cells activation for tissue homeostasis via binding to ICOS and inducing ICOS+ T cell differentiation as well as stimulate B-cell activation, while it appears to be abnormally expressed during carcinogenesis. Clarifying its heterogeneous clinical expression pattern and its immune landscape is a prerequisite for the maximum response rate of ICOSLG-based immunotherapy in a specific population.Methods: This retrospective study included OSCC tissue samples (n = 105) to analyze the spatial distribution of ICOSLG. Preoperative peripheral blood samples (n = 104) and independent tissue samples (n = 10) of OSCC were collected to analyze the changes of immunocytes (T cells, B cells, NK cells and macrophages) according to ICOSLG level in different cellular contents.Results: ICOSLG is ubiquitous in tumor cells (TCs), cancer-associated fibroblasts (CAFs) and tumor infiltrating lymphocytes (TILs). Patients with high ICOSLGTCs or TILs showed high TNM stage and lymph node metastasis, which predicted a decreased overall or metastasis-free survival. This sub-cohort was featured with diminished CD4+ T cells and increased Foxp3+ cells in invasive Frontier in situ, and increased absolute numbers of CD3+CD4+ and CD8+ T cells in peripheral blood. ICOSLG also positively correlated with other immune checkpoint molecules (PD-L1, CSF1R, CTLA4, IDO1, IL10, PD1).Conclusion: Tumor cell-derived ICOSLG could be an efficient marker of OSCC patient stratification for precision immunotherapy

Impact of 100% measurement data on statistical process control (SPC) in automobile body assembly.

Traditional hard gauge checking fixtures or Coordinate Measuring Machines (CMM) cannot provide large enough samples for effective Statistical Process Control (SPC) in automobile body assembly due to their off-line nature and low speed. With in-line Optical Coordinate Measuring Machines (OCMM), every body assembled can be measured, resulting in 100% measurement. However, manufacturers fail to make efficient use of the data. Conventional control charts, e.g., $\bar{\rm X}$ and R charts, are based on sampled, uncorrelated data, not serially correlated 100% measurement data. This thesis examines the impact of 100% measurement on three aspects of SPC for automobile body assembly: (1) process monitoring, (2) process identification, and (3) process variation reduction. Time series analysis, e.g., Dynamic Data System (DDS), is used in the investigation. Not only prediction errors, but importantly, information contained in the time series models is used. Process monitoring. Autocorrelation in data can result in false alarms when control charts are directly applied to data. The application of Prediction Error Analysis (PEA) can reduce the false alarm rate and also affect the detection speed. The effect of PEA on detection speed is analyzed and presented with examples based on AR(1) and ARMA(2,1) models for a step-function type mean shift. Process parameter identification. Sources of dimensional variation can be identified from the 100% measurement data. Using the autocorrelation in data, process physical characteristics, such as natural frequency, can be estimated. The contribution of each dynamic mode to the total variation can be quantitatively analyzed through decomposition of autocovariance. Cross-correlation can be used to reveal inter-sensor relationships or deformation patterns, such as Side Frame Misalignment or "Match-Boxing". Process variation reduction. "Adaptive quality control" using Forecasting Compensatory Control (FCC) is presented using simulation. However, due to lack of control mechanisms that actuate control instantly, body assembly process can only be adjusted on a batch-to-batch basis. Process control is based on the detection of process faults and human interference. Two successful case studies in variation reduction are presented.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/105205/1/9116203.pdfDescription of 9116203.pdf : Restricted to UM users only

MicroRNAs Regulate Mitochondrial Function in Cerebral Ischemia-Reperfusion Injury

Cerebral ischemia-reperfusion injury involves multiple independently fatal terminal pathways in the mitochondria. These pathways include the reactive oxygen species (ROS) generation caused by changes in mitochondrial membrane potential and calcium overload, resulting in apoptosis via cytochrome c (Cyt c) release. In addition, numerous microRNAs are associated with the overall process. In this review, we first briefly summarize the mitochondrial changes in cerebral ischemia-reperfusion and then describe the possible molecular mechanism of miRNA-regulated mitochondrial function, which likely includes oxidative stress and energy metabolism, as well as apoptosis. On the basis of the preceding analysis, we conclude that studies of microRNAs that regulate mitochondrial function will expedite the development of treatments for cerebral ischemia-reperfusion injury

Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human

DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation

Three-Dimensional Magnetotelluric Inversion for Triaxial Anisotropic Medium in Data Space

The interpretation of three-dimensional (3-D) magnetotelluric (MT) data is usually based on the isotropic assumption of the subsurface structures, and this assumption could lead to erroneous interpretation in the area with considerable electrical anisotropy. Although arbitrary anisotropy is much closer to the ground truth, it is generally more challenging to recover full anisotropy parameters from 3-D inversion. In this paper, we present a 3-D triaxial anisotropic inversion framework using the edge-based finite element method with a tetrahedral mesh. The 3-D inverse problem is solved by the Gauss-Newton (GN) method which shows fast convergence behavior. The computation cost of the data-space method depends on the size of data, which is usually smaller than the size of model; therefore, we transform the inversion algorithm from the model space to the data space for memory efficiency. We validate the effectiveness and applicability of the developed algorithm using several synthetic model studies