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When stimulated by proinflammatory mediators, endothelial cells release ultra-large von Willebrand factor (ULVWF) multimers that are hyperactive in activating and aggregating platelets. These ULVWF multimers can accumulate in the circulation and on the inflamed endothelium because they are insufficiently cleaved by the metalloprotease ADAMTS-13, which becomes moderately deficient under conditions of systemic inflammation. This moderate ADAMTS-13 deficiency may lead to thrombotic complications that contribute to ischemic tissue injury and organ failure that are associated with severe infections. To test this hypothesis, we investigated whether recombinant ADAMTS-13 improves the pathological course of endotoxemia in lipopolysaccharide (LPS)-treated mice. C57BL/J6 mice received a bolus infusion of either 5 µg/mouse of ADAMTS-13 or vehicle control 30 min after LPS challenge and were monitored for seven-day survival. During the monitoring period, platelet counts, VWF antigen, and ADAMTS-13 activity were measured. Thrombosis was also examined by the immunohistochemistry in the liver. We found that ADAMTS-13 reduced mortality from 66% to 34.9%. The improved survival was associated with a greater recovery from thrombocytopenia, higher plasma ADAMTS-13 activity, and less thrombotic vascular occlusion. These results suggest that systemic inflammation could result in deficient ULVWF proteolysis by ADAMTS-13 and that ADAMTS-13 improves the outcomes of endotoxemia-induced inflammation.

A covariance matrix is an important parameter in many computational applications, such as quantitative trading. Recently, a global minimum variance portfolio received great attention due to its performance after the 2007–2008 financial crisis, and this portfolio uses only a covariance matrix to calculate weights for assets. However, the calculation process of that portfolio is sensitive with outliers in the covariance matrix, for example, a sample covariance matrix estimation or linear shrinkage covariance matrix estimations. In this paper, we propose the use of an undersampling technique and ensemble learning to stabilize the covariance matrix by reducing the impacts of outliers on the output of a covariance estimation. Experimenting on an emerging stock market using three performance metrics shows that our approach significantly improves the sample covariance matrix and also a linear shrinkage to the single-index model to a level of two shrinkage estimations, a shrinkage to identity matrix and shrinkage to constant correlation model.

Purpose: This systematic review compares the clinical and radiographic outcomes for patients who received only a corticotomy or periodontal accelerated osteogenic orthodontics (PAOO) with those who received a conventional orthodontic treatment. Methods: An electronic search of four databases and a hand search of peer-reviewed journals for relevant articles published in English between January 1980 and June 2021 were performed. Human clinical trials of ≥10 patients treated with a corticotomy or PAOO with radiographic and/or clinical outcomes were included. Meta-analyses were performed to analyze the weighted mean difference (WMD) and confidence interval (CI) for the recorded variables. Results: Twelve articles were included in the quantitative analysis. The meta-analysis revealed a localized corticotomy distal to the canine can significantly increase canine distalization (WMD = 1.15 mm, 95% CI = 0.18–2.12 mm, p = 0.02) compared to a conventional orthodontic treatment. In addition, PAOO also showed a significant gain of buccal bone thickness (WMD = 0.43 mm, 95% CI = 0.09–0.78 mm, p = 0.01) and an improvement of bone density (WMD = 32.86, 95% CI = 11.83–53.89, p = 0.002) compared to the corticotomy group. Conclusion: Based on the findings of the meta-analyses, the localized use of a corticotomy can significantly increase the amount of canine distalization during orthodontic treatment. Additionally, the use of a corticotomy as a part of a PAOO procedure significantly increases the rate of orthodontic tooth movement and it is accompanied by an increased buccal bone thickness and bone density compared to patients undergoing a conventional orthodontic treatment.

Background: Rural/remote blood collection can cause delays in processing, reducing PBMC number, viability, cell composition and function. To mitigate these impacts, blood was stored at 4 °C prior to processing. Viable cell number, viability, immune phenotype, and Interferon-γ (IFN-γ) release were measured. Furthermore, the lowest protective volume of cryopreservation media and cell concentration was investigated. Methods: Blood from 10 individuals was stored for up to 10 days. Flow cytometry and IFN-γ ELISPOT were used to measure immune phenotype and function on thawed PBMC. Additionally, PBMC were cryopreserved in volumes ranging from 500 µL to 25 µL and concentration from 10 × 106 cells/mL to 1.67 × 106 cells/mL. Results: PBMC viability and viable cell number significantly reduced over time compared with samples processed immediately, except when stored for 24 h at RT. Monocytes and NK cells significantly reduced over time regardless of storage temperature. Samples with >24 h of RT storage had an increased proportion in Low-Density Neutrophils and T cells compared with samples stored at 4 °C. IFN-γ release was reduced after 24 h of storage, however not in samples stored at 4 °C for >24 h. The lowest protective volume identified was 150 µL with the lowest density of 6.67 × 106 cells/mL. Conclusion: A sample delay of 24 h at RT does not impact the viability and total viable cell numbers. When long-term delays exist (>4 d) total viable cell number and cell viability losses are reduced in samples stored at 4 °C. Immune phenotype and function are slightly altered after 24 h of storage, further impacts of storage are reduced in samples stored at 4 °C.

In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.