Investor Sentiment and the Fragility of Liquidity

Ph.DDOCTOR OF PHILOSOPH

Mutual Funds and Information Diffusion: The Role of Country-Level Governance

__Abstract__ We hypothesize that poor country-level governance, which makes public information less reliable, induces fund managers to increase their use of semi-public information. Utilizing data from international mutual funds and stocks over the 2000-2009 period, we find that semi-public information-related stock rebalancing can be five times higher in countries with the worst quality of governance than in countries with the best. The use of semi-public information increases price informativeness but also increases information asymmetry and reduces stock liquidity. It also intensifies the price impact and liquidity crunch during the recent global financial crisis

Serum proteomic, peptidomic and metabolomic profiles in myasthenia gravis patients during treatment with Qiangji Jianli Fang

BACKGROUND: Qiangji Jianli Fang (QJF) has been used for treatment of myasthenia gravis (MG) in China. However, our understanding of the effects of QJF against MG at the molecular level is limited. This study aims to investigate the effects of QJF treatment of MG patients on the protein, peptide and metabolite levels in serum. METHODS: High-throughput proteomic, peptidomic and metabolomic techniques were applied to investigate serum samples from 21 healthy individuals and 47 MG patients before and after QJF treatment via two-dimensional gel electrophoresis, matrix-assisted laser desorption/ionization time of flight mass spectrometry and liquid chromatography Fourier transform mass spectrometry, respectively. RESULTS: After QJF treatment, the expression levels of peptides m/z 1865.019, 2021.128 and 1211.668 of complement C3f increased (P = 0.004, P = 0.001 and P = 0.043, respectively), while that of peptide m/z 1739.931 of component C4b decreased (P = 0.043), in the serum of MG patients. The levels of γ-aminobutyric acid (P = 0.000) and coenzyme Q4 (P = 0.000) resumed their normal states. CONCLUSION: QJF could inhibit the activity of the complement system and restore the normal levels of metabolites

Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning

BackgroundThe aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation.MethodsThe clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others.ResultsThe heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance.ConclusionOur study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery

Development, validation, and visualization of a web-based nomogram to predict the effect of tubular microdiscectomy for lumbar disc herniation

ObjectiveThe purpose of this study was to retrospectively collect the relevant clinical data of lumbar disc herniation (LDH) patients treated with the tubular microdiscectomy (TMD) technique, and to develop and validate a prediction model for predicting the treatment improvement rate of TMD in LDH patients at 1 year after surgery.MethodsRelevant clinical data of LDH patients treated with the TMD technology were retrospectively collected. The follow-up period was 1 year after surgery. A total of 43 possible predictors were included, and the treatment improvement rate of the Japanese Orthopedic Association (JOA) score of the lumbar spine at 1 year after TMD was used as an outcome measure. The least absolute shrinkage and selection operator (LASSO) method was used to screen out the most important predictors affecting the outcome indicators. In addition, logistic regression was used to construct the model, and a nomogram of the prediction model was drawn.ResultsA total of 273 patients with LDH were included in this study. Age, occupational factors, osteoporosis, Pfirrmann classification of intervertebral disc degeneration, and preoperative Oswestry Disability Index (ODI) were screened out from the 43 possible predictors based on LASSO regression. A total of 5 predictors were included while drawing a nomogram of the model. The area under the ROC curve (AUC) value of the model was 0.795.ConclusionsIn this study, we successfully developed a good clinical prediction model that can predict the effect of TMD for LDH. A web calculator was designed on the basis of the model (https://fabinlin.shinyapps.io/DynNomapp/)

Clinical features and prognosis of lung cancer in patients with connective tissue diseases: a retrospective cohort study

BackgroundStudies have demonstrated a close association between connective tissue diseases (CTDs) and lung cancer (LC). Evidence supports that poor survival may be associated with the presence of CTDs in patients with LC.MethodsThis retrospective cohort study investigated 29 patients with LC with CTDs, and 116 patients with LC without CTDs were enrolled as case-matched control cohorts. Medical records, therapeutic efficacy of cancer, and outcomes were analyzed.ResultsThe median duration from the diagnosis of CTDs to LC was 17 years. The Eastern Cooperative Oncology Group (ECOG) performance score for LC-CTD patients was worse than that for matched non-CTD LC patients. The median progression-free survival (mPFS) and overall survival (mOS) of first-line chemotherapy did not differ between patients with lung adenocarcinoma (AC) with and without CTDs. A significant difference was observed in mPFS [4 months vs. 17 months; hazard ratio (HR), 9.987; p = 0.004] and mOS (6 months vs. 35 months; HR, 26.009; p < 0.001) of first-line epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment between patients with AC with and without CTDs. The presence of CTD, sex, ECOG performance status, and tumor-node-metastasis clinical stage were the independent prognostic factors in all patients with non–small cell LC (NSCLC). ECOG performance status was determined to be an independent prognostic factor in patients with LC-CTD. In patients with NSCLC with CTD (n = 26), sex (male) and worse ECOG score were the independent poor prognostic factors.ConclusionsCTDs were associated with poor survival in patients with LC. The therapeutic efficacy of first-line EGFR-TKI therapy was significantly worse in patients with lung AC with CTDs than in those without CTDs. ECOG performance status was determined as an independent prognostic factor for patients with LC and CTDs

Petrographic characterization to build an accurate rock model using micro-CT: Case study on low-permeable to tight turbidite sandstone from Eocene Shahejie Formation

Pore scale flow simulations heavily depend on petrographic characterizing and modeling of reservoir rocks. Mineral phase segmentation and pore network modeling are crucial stages in micro-CT based rock modeling. The success of the pore network model (PNM) to predict petrophysical properties relies on image segmentation, image resolution and most importantly nature of rock (homogenous, complex or microporous). The pore network modeling has experienced extensive research and development during last decade, however the application of these models to a variety of naturally heterogenous reservoir rock is still a challenge. In this paper, four samples from a low permeable to tight sandstone reservoir were used to characterize their petrographic and petrophysical properties using high-resolution micro-CT imaging. The phase segmentation analysis from micro-CT images shows that 5-6% microporous regions are present in kaolinite rich sandstone (E3 and E4), while 1.7-1.8% are present in illite rich sandstone (El and E2). The pore system percolates without micropores in El and E2 while it does not percolate without micropores in E3 and E4. In El and E2, total MICP porosity is equal to the volume percent of macrospores determined from micro-CT images, which indicate that the macropores are well connected and microspores do not play any role in non-wetting fluid (mercury) displacement process. Whereas in E3 and E4 sandstones, the volume percent of micropores is far less (almost 50%) than the total MICP porosity which means that almost half of the pore space was not detected by the micro-CT scan. PNM behaved well in El and E2 where better agreement exists in PNM and MICP measurements. While E3 and E4 exhibit multiscale pore space which cannot be addressed with single scale PNM method, a multiscale approach is needed to characterize such complex rocks. This study provides helpful insights towards the application of existing micro-CT based petrographic characterization methodology to naturally complex petroleum reservoir rocks

Intra- and Inter-reasoning Graph Convolutional Network for Saliency Prediction on 360° Images

Cubic projection can be utilized to divide 360° images into multiple rectilinear images, with little distortion. However, the existing saliency prediction models fail to integrate semantic information of these images. In this paper, we address this by proposing an intra- and inter-reasoning graph convolutional network for saliency prediction on 360 ° images (SalReGCN360). The whole framework contains six sub-networks, each of which contains two branches. In the training phase, after utilizing Multiple Cubic Projection (MCP), six rectilinear images are simultaneously put into corresponding sub-networks. In one of the branches, the global features of a single rectilinear image are extracted by the intra-graph inference module to finely predict local saliency of 360 ° images. In the other branch, the contextual features are extracted by the inter-graph inference module to effectively integrate semantic information of six rectilinear images. Finally, the feature maps are generated by the two branches fusion, and six corresponding rectilinear saliency maps are predicted. Extensive experiments on two popular saliency datasets illustrate the superiority of the proposed model, especially the improvement in KLD metric

Statins and Thyroid Carcinoma: a Meta-Analysis

Background/Aims: Experimental studies have reported the antineoplastic effects of statins in thyroid carcinoma; however, observational studies suggested that statins might increase the risk of thyroid carcinoma. Therefore, this study evaluated the antineoplastic effects of statins in both in vitro studies and animal models, as well as the epidemiological evidence. Methods: Databases—PubMed, Cochrane Library, SinoMed, CNKI, Wanfang, and clinical trial registries— were searched. A meta-analysis was performed with sufficiently homogeneous studies. Eighteen articles were involved. Results: In in vitro studies, statins showed a concentration-dependent inhibition of cell line growth (weighted mean difference –34.68, 95% confidence interval –36.53 to –32.83). A significant efficacy of statin-induced apoptosis was observed (weighted mean difference [95% confidence interval]: 24 h, 57.50 [55.98–59.03]; 48 h, 23.43 [22.19–24.66]; 72 h, 51.29 [47.52–55.07]). Early apoptosis was increased in a dose- and time-dependent manner. In in vivo antitumor studies, lovastatin inhibited tumor growth, as shown by a reduction in tumor volume. However, two clinical studies showed discordant results from the experimental studies. Conclusion: Experimental studies revealed the antineoplastic efficacy of statins but statins were associated with thyroid carcinoma in clinical studies. This discrepancy may be due to the different concentrations of statins used and the effects of hyperlipidemia interventions, and thus further study is required