Semi-Parametric Mixture Models Through Log-Concave Density Estimation

This dissertation consists of two parts. The first part considers a semi-parametric two-component mixture model with one component completely known. Assuming the density of the unknown component to be log-concave, which contains a very broad family of densities, we develop a semi-parametric maximum likelihood estimator and propose an EM algorithm to compute it. Our new estimation method finds the mixing proportion and the distribution of the unknown component simultaneously. We establish the identifiability of the proposed semi-parametric mixture model and prove the existence and consistency of the proposed estimators. We further compare our estimator with several existing estimators through simulation studies and apply our method to two real data sets from biological sciences and astronomy.The second part of this dissertation considers the model g(x) = (1 − p)f0(x; θ) + pf(x), where θ represents the unknown parameters of a known distribution f0 , and f represents the distribution of possible outliers. We propose two innovative algorithms to estimate θ nonparametrically. The first method is called Minimum Search, which is based on identifiability of the mixture model. A strong sufficient condition is proposed for the model to be identifiable and a weaker condition is given for the model to be locally identifiable. The second estimator is the maximum likelihood estimator, which is obtained by EM algorithm assuming f is log-concave. Extensive simulation studies show that our methods give very promising performances

Micro ribonucleic acid-448 regulates zinc finger e-box binding homeobox 1 to inhibit the growth of breast cancer cells and increase their sensitivity to chemotherapy

Objective: This study aimed to investigate the effect of Zinc Finger E-box Binding Homeobox 1 (ZEB1) regulation by Micro Ribonucleic acid (miR)-448 on Breast Cancer (BC) cells and their sensitivity to chemotherapy. Methods: miR-448 and ZEB1 mRNA levels in BC and normal tissues were detected by qPCR, and ZEB1 protein was detected by Western Blotting (WB). The correlation between miR-448 and tumor metastasis, clinical staging, and ZEB1 expression was analyzed. MCF-7 cells were transfected or co-transfected with the miR-448 mimic, oe-ZEB1, or their negative controls. Changes in miR-448 and ZEB1 expression were detected by qPCR and WB. Cell proliferation was determined by CCK-8 assays, invasion changes were analyzed by Transwell assays, and apoptosis was detected by flow cytometry. Results: miR-448 expression in BC tissues was lower than that in normal tissues, while ZEB1 expression was increased in the former. ZEB1 expression was lower in BC patients with lymph node metastasis than in those without. In patients with clinical stage I–III BC, miR-448 expression decreased with an increase in tumor stage, which was negatively correlated with ZEB1 expression. Upregulation of miR-448 expression can suppress MCF-7 cell proliferation and invasion and promote apoptosis. Upregulation of ZEB1 expression in cells overexpressing miR-448 can partially reverse the inhibition of BC cell growth induced by miR-448. miR-448 can enhance the sensitivity of cells toward paclitaxel and 5-fluorouracil. Conclusions: miR-448 suppresses cell proliferation and invasion and promotes apoptosis by targeting ZEB1. Moreover, it can increase the sensitivity of cells toward paclitaxel and 5-fluorouracil

Wrinkle-Enabled Highly Stretchable Strain Sensors for Wide-Range Health Monitoring with a Big Data Cloud Platform

Flexible and stretchable strain sensors are vital for emerging fields of wearable and personal electronics, but it is a huge challenge for them to possess both wide-range measurement capability and good sensitivity. In this study, a highly stretchable strain sensor with a wide strain range and a good sensitivity is fabricated based on smart composites of carbon black (CB)/wrinkled Ecoflex. The sensor exhibits a maximum recoverable strain of up to 500% and a high gauge factor of 67.7. It has a low hysteresis, a fast signal response (as short as 120 ms), and a high reproducibility (up to 5000 cycles with a strain of 150%). The sensor is capable of detecting and capturing wide-range human activities, from speech recognition and pulse monitoring to vigorous motions. It is also applicable for real-time monitoring of robot movements and vehicle security crash in an anthropomorphic field. More importantly, the sensor is successfully used to send signals of a volunteer’s breathing data to a local hospital in real time through a big data cloud platform. This research provides the feasibility of using a strain sensor for wearable Internet of things and demonstrates its exciting prospect for healthcare applications

Implementation of a quantum algorithm to solve Bernstein-Vazirani's parity problem without entanglement on an ensemble quantum computer

Bernstein and Varizani have given the first quantum algorithm to solve parity problem in which a strong violation of the classical imformation theoritic bound comes about. In this paper, we refine this algorithm with fewer resource and implement a two qubits algorithm in a single query on an ensemble quantum computer for the first time

Methylprednisolone as Adjunct to Endovascular Thrombectomy for Large-Vessel Occlusion Stroke

Importance It is uncertain whether intravenous methylprednisolone improves outcomes for patients with acute ischemic stroke due to large-vessel occlusion (LVO) undergoing endovascular thrombectomy. Objective To assess the efficacy and adverse events of adjunctive intravenous low-dose methylprednisolone to endovascular thrombectomy for acute ischemic stroke secondary to LVO. Design, Setting, and Participants This investigator-initiated, randomized, double-blind, placebo-controlled trial was implemented at 82 hospitals in China, enrolling 1680 patients with stroke and proximal intracranial LVO presenting within 24 hours of time last known to be well. Recruitment took place between February 9, 2022, and June 30, 2023, with a final follow-up on September 30, 2023.InterventionsEligible patients were randomly assigned to intravenous methylprednisolone (n = 839) at 2 mg/kg/d or placebo (n = 841) for 3 days adjunctive to endovascular thrombectomy. Main Outcomes and Measures The primary efficacy outcome was disability level at 90 days as measured by the overall distribution of the modified Rankin Scale scores (range, 0 [no symptoms] to 6 [death]). The primary safety outcomes included mortality at 90 days and the incidence of symptomatic intracranial hemorrhage within 48 hours. Results Among 1680 patients randomized (median age, 69 years; 727 female [43.3%]), 1673 (99.6%) completed the trial. The median 90-day modified Rankin Scale score was 3 (IQR, 1-5) in the methylprednisolone group vs 3 (IQR, 1-6) in the placebo group (adjusted generalized odds ratio for a lower level of disability, 1.10 [95% CI, 0.96-1.25]; P = .17). In the methylprednisolone group, there was a lower mortality rate (23.2% vs 28.5%; adjusted risk ratio, 0.84 [95% CI, 0.71-0.98]; P = .03) and a lower rate of symptomatic intracranial hemorrhage (8.6% vs 11.7%; adjusted risk ratio, 0.74 [95% CI, 0.55-0.99]; P = .04) compared with placebo. Conclusions and Relevance Among patients with acute ischemic stroke due to LVO undergoing endovascular thrombectomy, adjunctive methylprednisolone added to endovascular thrombectomy did not significantly improve the degree of overall disability.Trial RegistrationChiCTR.org.cn Identifier: ChiCTR210005172

Semi-Parametric Mixture Models Through Log-Concave Density Estimation

This dissertation consists of two parts. The first part considers a semi-parametric two-component mixture model with one component completely known. Assuming the density of the unknown component to be log-concave, which contains a very broad family of densities, we develop a semi-parametric maximum likelihood estimator and propose an EM algorithm to compute it. Our new estimation method finds the mixing proportion and the distribution of the unknown component simultaneously. We establish the identifiability of the proposed semi-parametric mixture model and prove the existence and consistency of the proposed estimators. We further compare our estimator with several existing estimators through simulation studies and apply our method to two real data sets from biological sciences and astronomy.The second part of this dissertation considers the model g(x) = (1 − p)f0(x; θ) + pf(x), where θ represents the unknown parameters of a known distribution f0 , and f represents the distribution of possible outliers. We propose two innovative algorithms to estimate θ nonparametrically. The first method is called Minimum Search, which is based on identifiability of the mixture model. A strong sufficient condition is proposed for the model to be identifiable and a weaker condition is given for the model to be locally identifiable. The second estimator is the maximum likelihood estimator, which is obtained by EM algorithm assuming f is log-concave. Extensive simulation studies show that our methods give very promising performances

GLIS2 Prevents Hepatic Fibrosis by Competitively Binding HDAC3 to Inhibit Hepatic Stellate Cell ActivationSummary

Background: The role of GLIS2 in fibrotic diseases is controversial. GLIS2 deficiency has been reported to contribute to renal fibrosis in mice and has also been reported to prevent high lipid-induced mice hepatic fibrosis. Methods: Hepatic fibrosis in mice was induced by CCl4. Hematoxylin and eosin, Masson, Sirius red, and enzyme-linked immunosorbent assay were used to detect and evaluate the stage of hepatic fibrosis in humans or mice. A study model of tetracycline-responsive GLIS2 knockout hepatic stellate cells (HSCs) was constructed and named GLIS2-SG-Dox. By adding transforming growth factor β1 to stimulate the transdifferentiation of HSCs, the activation status of HSCs was comprehensively evaluated from the aspects of cell proliferation, migration, and the amount of lipid droplets. In mechanistic studies, dual-luciferase, coimmunoprecipitation, yeast two-hybrid system, chromatin immunoprecipitation, and DNA pulldown were performed to investigate or to prove the molecular mechanism that GLIS2 was involved in regulating liver fibrosis. Throughout the study, real-time fluorescence polymerase chain reaction (quantitative reverse-transcription polymerase chain reaction) was used to detect the relative abundance of messenger RNA expression of each target gene, Western blot was used to detect the relative abundance of protein, and immunohistochemistry or immunofluorescence was used to observe the subcellular localization of the target protein. Results: The expression of GLIS2 was significantly decreased in human liver fibrosis tissues and CCL4-induced mouse liver fibrosis tissues, especially in HSCs. In the GLIS2-SG-Dox cells, the peroxisome proliferator-activated receptor γ (PPAR-γ) pathway was inactive and cells underwent myofibroblastic transdifferentiation transformation. Overexpression of GLIS2 can increase the acetylation level of PPAR-γ and alleviate CCL4-induced liver fibrosis in mice. Mechanically, relatively abundant GLIS2 and histone deacetylase 3 (HDAC3) form chelates to avoid the deacetylation of PPAR-γ, so as to maintain the activation level of PPAR-γ signaling pathway in HSCs cells. In this process, HDAC3 acts as a medium for GLIS2 to influence PPAR-γ signaling. Nonetheless, when GLIS2 is absent, HDAC3 deacetylates PPAR-γ, activates HSCs, and leads to liver fibrosis. Conclusions: GLIS2 deficiency promotes myofibroblastic transdifferentiation and activation of HSCs. Mechanically, GLIS2 regulates the acetylation of PPAR-γ by competitively binding to HDAC3 in HSCs

Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value

Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma

MicroRNA-132 Interact with p250GAP/Cdc42 Pathway in the Hippocampal Neuronal Culture Model of Acquired Epilepsy and Associated with Epileptogenesis Process

Increasing evidence suggests that epilepsy is the result of synaptic reorganization and pathological excitatory loop formation in the central nervous system; however, the mechanisms that regulate this process are not well understood. We proposed that microRNA-132 (miR-132) and p250GAP might play important roles in this process by activating the downstream Rho GTPase family. We tested this hypothesis using a magnesium-free medium-induced epileptic model of cultured hippocampal neurons. We investigated whether miR-132 regulates GTPase activity through p250GAP and found that Cdc42 was significantly activated in our experimental model. Silencing miR-132 inhibited the electrical excitability level of cultured epileptic neurons, whereas silencing p250GAP had an opposite effect. In addition, we verified the effect of miR-132 in vivo and found that silencing miR-132 inhibited the aberrant formation of dendritic spines and chronic spontaneous seizure in a lithium-pilocarpine-induced epileptic mouse model. Finally, we confirmed that silencing miR-132 has a neuroprotective effect on cultured epileptic neurons; however, this effect did not occur through the p250GAP pathway. Generally, silencing miR-132 may suppress spontaneous seizure activity through the miR-132/p250GAP/Cdc42 pathway by regulating the morphology and electrophysiology of dendritic spines; therefore, miR-132 may serve as a potential target for the development of antiepileptic drugs