Study on the activation of calcined kaolin

Calcined temperature is a key factor to the activity of metakaolin. Structure characteristics and alkali activation of kaolin and its calcined products at different temperatures were analyzed by X-ray diffraction (XRD), nuclear magnetic resonance (NMR), infrared spectrometry (IR) and isothermal calorimetry. The results show that the activity of kaolin calcined at 900°C is best. The characteristic absorption peak of kaolin disappears, a large amount of Al atoms convert from 6-coordination to 5-coordination; some characteristic vibration peaks of kaolin disappear while characteristic absorption peaks of metakaolin appear; There is much heat evolution after mixing it with alkali and the compressive strength is the highest. The strength of samples cured at 80°C for 3 days and 7 days reaches 33.8 and 35.3 MPa respectively

LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening

In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency due to overlooking the task-specific supervision provided by slide labels. Here we propose a weakly-supervised Label-Efficient WSI Screening method, dubbed LESS, for cytological WSI analysis with only slide-level labels, which can be effectively applied to small datasets. First, we suggest using variational positive-unlabeled (VPU) learning to uncover hidden labels of both benign and malignant patches. We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features. Next, we take into account the sparse and random arrangement of cells in cytological WSIs. To address this, we propose a strategy to crop patches at multiple scales and utilize a cross-attention vision transformer (CrossViT) to combine information from different scales for WSI classification. The combination of our two steps achieves task-alignment, improving effectiveness and efficiency. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and FNAC 2019 dataset with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on a urine cytology WSI dataset, and 96.88%, 96.86%, 98.95%, 97.06% on FNAC 2019 dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art MIL methods on pathology WSIs and realizes automatic cytological WSI cancer screening.Comment: This paper was submitted to Medical Image Analysis. It is under revie

The Emerging Role of Bone-Derived Hormones in Diabetes Mellitus and Diabetic Kidney Disease

Diabetic kidney disease (DKD) causes the greatest proportion of end-stage renal disease (ESRD)–related mortality and has become a high concern in patients with diabetes mellitus (DM). Bone is considered an endocrine organ, playing an emerging role in regulating glucose and energy metabolism. Accumulating research has proven that bone-derived hormones are involved in glucose metabolism and the pathogenesis of DM complications, especially DKD. Furthermore, these hormones are considered to be promising predictors and prospective treatment targets for DM and DKD. In this review, we focused on bone-derived hormones, including fibroblast growth factor 23, osteocalcin, sclerostin, and lipocalin 2, and summarized their role in regulating glucose metabolism and DKD

Gut Microbial Compositions in Four Age Groups of Tibetan Minipigs

In this study, the gut microbiota was characterized in four age strata of Tibetan minipigs. Results indicated that the fecal bacteria of 7-, 28-, 56-, and 180-day-old minipigs did not significantly differ in terms of phylogenetic diversity (i.e., PD whole tree) or the Shannon index (both, p > 0.05). Findings of a principal coordinate analysis demonstrated that fecal bacteria of 180-day-old minipigs were discernable from those of the other three age groups. From ages seven to 56 days, the abundance of Bacteroidetes or Firmicutes appeared to vary. Regarding genera, the populations of Bacteroides and Akkermansia decreased with increasing age

Toward an indexing approach to evaluate fly ashes for geopolymer manufacture

Variations between fly ashes can lead to significant differences in the geopolymers derived from them, in both microstructural and mechanical properties. This study assesses the effect of physical, crystallographic and chemical characteristics of fly ash on geopolymerisation performance and the strength of the resulting binders. Physical and glass chemistry factors are combined to develop a comprehensive index to evaluate the suitability of fly ashes for the production of high-strength geopolymers. An equation for this index is proposed, developed using five typical low-calcium fly ashes and then validated against a further eight literature datasets, showing a good relationship between the ranking order of the calculated index and the compressive strengths of geopolymer pastes produced with comparable activator and paste workability. This index can be used to screen the source materials, which is of significant value in moving alkali activated cements towards acceptance in practice

A COVID-19 Risk Score Combining Chest CT Radiomics and Clinical Characteristics to Differentiate COVID-19 Pneumonia From Other Viral Pneumonias

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective

Spatial-temporal clustering of an outbreak of SARS-CoV-2 Delta VOC in Guangzhou, China in 2021

BackgroundIn May 2021, the SARS-CoV-2 Delta variant led to the first local outbreak in China in Guangzhou City. We explored the epidemiological characteristics and spatial-temporal clustering of this outbreak.MethodsBased on the 153 cases in the SARS-CoV-2 Delta variant outbreak, the Knox test was used to analyze the spatial-temporal clustering of the outbreak. We further explored the spatial-temporal clustering by gender and age groups, as well as compared the changes of clustering strength (S) value between the two outbreaks in Guangzhou.ResultsThe result of the Knox analysis showed that the areas at short distances and brief periods presented a relatively high risk. The strength of clustering of male-male pairs was higher. Age groups showed that clustering was concentrated in cases aged ≤ 18 years matched to 18–59 years and cases aged 60+ years. The strength of clustering of the outbreak declined after the implementation of public health measures. The change of strength of clustering at time intervals of 1–5 days decreased greater in 2021 (S = 129.19, change rate 38.87%) than that in 2020 (S = 83.81, change rate 30.02%).ConclusionsThe outbreak of SARS-CoV-2 Delta VOC in Guangzhou has obvious spatial-temporal clustering. The timely intervention measures are essential role to contain this outbreak of high transmission

Triple-Mode Model Predictive Control Using Future Target Information

In this paper, we propose a triple-mode model predictive control (MPC) algorithm that uses future target information to improve tracking performance. To explicitly take into account the future target information in the MPC optimization, the proposed triple-mode control law encompasses three parts: (i) the future target information feedforward, (ii) the output feedback, and (iii) the extra degrees of freedom for constraint satisfaction. The first two parts of the control law are off-line designed through unconstrained MPC, and the optimal future trajectory horizon is obtained by golden section search based on the integral of squared error (ISE) criterion. The final part is calculated by the on-line MPC algorithm aiming to satisfy constraints. Furthermore, we analyze the feasibility and convergence properties of the proposed algorithm. The method is demonstrated by the simulation of the shell fundamental control problem and also tested on the coordinated control problem in the power plant. The test results show that the proposed algorithm can increase tracking performance dramatically due to the proper selection of future trajectory horizon