Flotation Characteristics and Particle Size Distribution of Micro-fine Low Rank Coal

AbstractIn this work, attempts to float the micro-fine low rank coal and its particle size distribution in the flotation were made. Then, standard screening, FT-IR, XRD and SEM were adopted to characterize the size distribution and flotation of micro-fine Shendong low rank coal. The results indicated that the size fraction of -0.045mm was the dominant size fraction in raw coal with a yield of 91.65% and ash content of 46.25%. Flotation of Shendong low rank coal required a larger dosage of collector, 50kg/t of diesel oil, to achieve a higher combustible matter recovery (63.25%) and flotation efficiency index (40.70%) accompanied with a significant decrease in ash content (22.44 percentage points) due to the hydrophilicity of coal surface. Under this condition, concentrate contained 83.38% of -0.045mm size fraction (38.04% of total particles in feed) with ash content of 24.98%. In comparison, tailing was almost consisted of -0.045mm fraction (93.63%) with a higher ash content of 60.82%. It seems that the higher ash particles in feed were largely migrated in tailing at a proper collector dosage. The analysis of FT-IR, XRD and SEM would contribute to the understanding of the flotation and size distribution

Systemic Revealing Pharmacological Signalling Pathway Networks in the Hippocampus of Ischaemia-Reperfusion Mice Treated with Baicalin

Background. Baicalin (BA) exhibits ill understood neuroprotective, anti-inflammatory, and antioxidative effects in brain injury. Objective. To identify the differential network pathways associated with BA-related biological effects. Methods. MCAO-induced mice received BA 5 mg/Kg (BA group). Controls received vehicle only. Following ischaemia-reperfusion, ArrayTrack analysed the whole genome microarray of hippocampal genes, and MetaCore analysed differentially expressed genes. Results. Four reversing pathways were common to BA and controls, but only 6 were in the top 10 for BA. Three of the top 5 signalling pathways in controls were not observed in BA. BA treatment made absent 3 pathways of the top 5 signalling pathways from the top 5 in controls. There were 2 reversing pathways between controls and BA that showed altered gene expression. Controls had 6 networks associated with cerebral ischaemia. After BA treatment, 9 networks were associated with cerebral ischaemia. Enrichment analysis identified 10 significant biological processes in BA and controls. Of the 10 most significant molecular functions, 7 were common to BA and controls, and only 3 occurred in BA. BA and controls had 7 significant cellular components. Conclusions. This study showed that the clinical effectiveness of BA was based on the complementary effects of multiple pathways and networks

A novel mutation in exon 11 of COMP gene in a Chinese family with pseudoachondroplasia

Pseudoachondroplasia (PSACH) is a relatively common skeletal dysplasia characterized by disproportionate short stature, joint laxity, early-onset osteoarthrosis, and dysplasia of the spine, epiphysis, and metaphysis. It is known as an autosomal dominant disease which results exclusively from mutations in the gene for Cartilage Oligomeric Matrix Protein (COMP). We have identified a five year old Chinese boy who was diagnosed as pseudoachondroplasia according to clinical manifestations and X-ray symptoms. His mother seems like another effected individual because of the apparent short stature. Genomic DNA was extracted from peripheral blood lymphocytes. DNA sequencing analysis of the COMP gene revealed a heterozygous mutation (c.1219 T > C,p.Cys407Arg) in the patient. His mother was also affected with the same genetic change. Mutations in COMP gene is proved to change the Cartilage Oligomeric Matrix Protein. This missense mutation (c.1219 T > C) has not been reported before and it is not belongs to polymorphism sites. Our results extend the spectrum of mutations in COMP gene leading to pseudoachondroplasia. Keywords: COMP, Novel mutation, Skeletal dysplasia, Pseudoachondroplasia, Therap

Production optimization for water flooding in fractured-vuggy carbonate reservoir - From laboratory physical model to reservoir operation

Conventional physical model experiments of carbonate reservoirs are limited to the investigation of production patterns and mechanisms for a single well or a well group, and cannot effectively optimize the oil production at the reservoir scale. For an actual fractured-vuggy reservoir in Tahe Oilfield, China, seismic and drilling data were first analyzed to obtain the distributions of fractures and vugs, and the dynamic tracer monitoring was used to determine the type of connection between fracture-vug units. Then, according to shape similarity, the real fracture-vug units are simplified into 12 visualized physical models made of transparent acrylic plates with a scale of 300:1. Different physical models were further combined together with the connectivity modes derived from dynamic tracer monitoring, thus embodying the entire complex carbonate reservoir to a laboratory-scale physical model for the first time. Experiments were then carried out with these models and results showed that changing the injection and production parameters and increasing the number of flow channels between injector and producer have little effect on the displacement efficiency, while altering the water flow direction and converting injector into producer achieved better effect, due to the increasing fracture-vug units that can be displaced. In the field production, the output of TK652, TK605CH and TK608 wells increased from 11, 5, 1 m(3)/d to 18, 18, 19 m(3)/d, respectively, by closing well TK659 and converting well TK6100 (two high water-cut producers) to change the direction of water flow. The laboratory physical modeling technique illustrated in this study provides a simplified and unified way for the production optimization of fractured-vuggy carbonate reservoirs

Numerical Investigation of Hydraulic Fracture Extension Based on the Meshless Method

The fracture propagation in hydraulic fracturing is described as a nonlinear problem dynamic boundary. Due to the limitation of mesh refinement, it is difficult to obtain the real crack propagation path using conventional numerical methods. Meshless methods (MMs) are an effective method to eliminate the dependence on the computational grid in the simulation of fracture propagation. In this paper, a hydraulic fracture propagation model is established based on the element-free Galerkin (EFG) method by introducing jump and branch enrichment functions. Based on the proposed method, three types of fracturing technology are investigated. The results reveal that the stress interference between fractures has an important impact on the propagation path. For the codirectional fracturing simultaneously, fractures propagate in a repel direction. However, the new fracture is attracted and eventually trapped by the adjacent fracture in the sequential fracturing case. For the opposite simultaneous fracturing in multiwells, two fractures with a certain lateral spacing will deflect toward each other. The effect of stress shadow should be used rationally in the optimization of construction parameters; for the single well multistage fracturing, the stage spacing should be out of stress inversion area, while for the simultaneous fracturing of multiple wells, stress inversion zones should be used to maximize communication between natural fractures. Overall, this study establishes a novel and effective approach of using MM to simulate the propagation of hydraulic fractures, which can serve as a useful reference for understanding the mechanism of hydraulic fracture propagation under various conditions

Curcumin can influence synaptic dysfunction in APPswe/PS1dE9 mice

Objective: Synaptic loss in the hippocampus in Alzheimer's disease (AD) has been shown to be closely associated with the cognitive impairment. Synaptic dysfunction is a pathological feature that occurs prior to synaptic loss and mainly depends on structural changes and alterations of synaptic proteins. Evidence has suggested that curcumin, obtained from the traditional Chinese medicine—Turmeric (Curcuma longa L.), can ameliorate cognitive impairment, but few studies have focused on the mechanism by which curcumin affects synapses at early stages of AD. Therefore, we performed a study to investigate whether curcumin exerted its effect on synapses at the early stage in AD. Methods: We used 3-month-old APPswe/PS1dE9 mice and wild type (WT) littermates of the APPswe/PS1dE9 mice from the same colony as the normal controls. Seventy-five APPswe/PS1dE9 mice were allocated to the Model group, Rosiglitazone group, and Curcumin groups randomly. The Wild and Model groups were orally administered an equal amount of 0.5% carboxymethyl cellulose (CMC). We observed the ultrastructure of synapses in the CA1 area of hippocampus and analyzed the expression levels of PSD95 and Shank1, two important synapse-associated proteins, in APPswe/PS1dE9 mice by immunohistochemical staining and western blot after gavage for three months. Results: Our findings showed that curcumin treatment not only improved the quantity and ultrastructure of synapses but also increased the expression of PSD95 and Shank1. Conclusion: The results indicate that curcumin improves synaptic dysfunction and the potential mechanism may involve improving the structure of synapses and regulating the synapse related proteins PSD95 and Shank1. Keywords: Alzheimer, Synapse, Ultrastructure, Synaptic protein

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Abstract Background Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Methods Asymptomatic carriers were from Yangzhou Third People’s Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. Results A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Conclusions Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model