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The T2K collaboration has recently reported their results, which gives the best-fit values of the atmospheric mixing angle [Formula omitted] and the Dirac CP-violating phase [Formula omitted] for normal neutrino mass ordering. We give a possible theoretical origin of such values based on the [Formula omitted]- [Formula omitted] reflection symmetry. It has been found that the breaking of such symmetry using one-loop renormalization-group equations (RGEs) in the framework of minimal supersymmetric standard model can fit well with the latest T2K results and the recent global fits. To make a quantitative analysis, we have included for the first time the complete dataset of oscillation, beta decay, neutrinoless double-beta decay and cosmological observations in comparing the theory to experiments. We also further examine the importance of such breaking patterns for neutrinoless double-beta decay experiments.

ObjectivesTo explore the relationship between the imaging manifestations and clinical classification of COVID-19.MethodsWe conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%), or 4 (76–100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type.ResultsThis study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severe-critical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962–0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity.ConclusionsThe proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19.Key Points• CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe-critical type group was significantly higher than common type (p < 0.001).• ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843–0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity.• The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality

The reflective thinking ability is one of the higher order thinking skills (HOTS), and mathematics reflective thinking is one of the abilities needed in learning mathematics. This study aims to reveal the relation between prior knowledge and mathematics reflective thinking ability which studied by quantitative method. Data collection used instruments of mathematics reflective thinking test. Samples in this study were students of class VIII at junior high school. The results showed that there was a positive relation between prior knowledge and students' mathematics reflective thinking ability with the correlation coefficient 0.49. The regression equation shows that each addition of one prior knowledge score was followed by change in increase in students' mathematics reflective thinking ability by 0.5539. It can be concluded that the prerequisite material as a prior knowledge must be thoroughly learned so as to have a large contribution to the success of learning further material.

Nucleophilic amino acids make important contributions to protein function, including performing key roles in catalysis and serving as sites for post-translational modification. Electrophilic groups that target amino-acid nucleophiles have been used to create covalent ligands and drugs, but have, so far, been mainly limited to cysteine and serine. Here, we report a chemical proteomic platform for the global and quantitative analysis of lysine residues in native biological systems. We have quantified, in total, more than 9,000 lysines in human cell proteomes and have identified several hundred residues with heightened reactivity that are enriched at protein functional sites and can frequently be targeted by electrophilic small molecules. We have also discovered lysine-reactive fragment electrophiles that inhibit enzymes by active site and allosteric mechanisms, as well as disrupt protein–protein interactions in transcriptional regulatory complexes, emphasizing the broad potential and diverse functional consequences of liganding lysine residues throughout the human proteome. A chemical proteomic strategy has now been reported for the global profiling of lysine reactivity and ligandability. Using this approach, >9000 lysines in the human proteome were evaluated, leading to the discovery of hyper-reactive lysines, and lysines that can be targeted by electrophilic small molecules to perturb enzyme function and protein–protein interactions.

Metabolic flux analysis in mice reveals that lactate often acts as the primary carbon source for the tricarboxylic acid cycle both in normal tissues and in tumour microenvironments. Lactate fuels the citric acid cycle Glucose is thought to be the primary source of fuel for the tricarboxylic acid (TCA) cycle, also known as the citric acid cycle, which produces important metabolites and energy. Sheng Hui et al . now perform whole-body metabolite analysis in mice. They find that circulating lactate rather than glucose can be a major source of carbon and hence fuel for TCA metabolism in both fed and fasting mice. They furthermore show this to be the case in tumour tissue. Mammalian tissues are fuelled by circulating nutrients, including glucose, amino acids, and various intermediary metabolites. Under aerobic conditions, glucose is generally assumed to be burned fully by tissues via the tricarboxylic acid cycle (TCA cycle) to carbon dioxide. Alternatively, glucose can be catabolized anaerobically via glycolysis to lactate, which is itself also a potential nutrient for tissues 1 and tumours 2 , 3 , 4 , 5 . The quantitative relevance of circulating lactate or other metabolic intermediates as fuels remains unclear. Here we systematically examine the fluxes of circulating metabolites in mice, and find that lactate can be a primary source of carbon for the TCA cycle and thus of energy. Intravenous infusions of 13 C-labelled nutrients reveal that, on a molar basis, the circulatory turnover flux of lactate is the highest of all metabolites and exceeds that of glucose by 1.1-fold in fed mice and 2.5-fold in fasting mice; lactate is made primarily from glucose but also from other sources. In both fed and fasted mice, 13 C-lactate extensively labels TCA cycle intermediates in all tissues. Quantitative analysis reveals that during the fasted state, the contribution of glucose to tissue TCA metabolism is primarily indirect (via circulating lactate) in all tissues except the brain. In genetically engineered lung and pancreatic cancer tumours in fasted mice, the contribution of circulating lactate to TCA cycle intermediates exceeds that of glucose, with glutamine making a larger contribution than lactate in pancreatic cancer. Thus, glycolysis and the TCA cycle are uncoupled at the level of lactate, which is a primary circulating TCA substrate in most tissues and tumours.

The present investigation demonstrated pretreatment of lignocellulosic biomass rice straw using natural deep eutectic solvents (NADESs), and separation of high-quality lignin and holocellulose in a single step. Qualitative analysis of the NADES extract showed that the extracted lignin was of high purity (>90 %), and quantitative analysis showed that nearly 60 ± 5 % ( w / w ) of total lignin was separated from the lignocellulosic biomass. Addition of 5.0 % ( v / v ) water during pretreatment significantly enhanced the total lignin extraction, and nearly 22 ± 3 % more lignin was released from the residual biomass into the NADES extract. X-ray diffraction studies of the untreated and pretreated rice straw biomass showed that the crystallinity index ratio was marginally decreased from 46.4 to 44.3 %, indicating subtle structural alterations in the crystalline and amorphous regions of the cellulosic fractions. Thermogravimetric analysis of the pretreated biomass residue revealed a slightly higher T dcp (295 °C) compared to the T dcp (285 °C) of untreated biomass. Among the tested NADES reagents, lactic acid/choline chloride at molar ratio of 5:1 extracted maximum lignin of 68 ± 4 mg g −1 from the rice straw biomass, and subsequent enzymatic hydrolysis of the residual holocellulose enriched biomass showed maximum reducing sugars of 333 ± 11 mg g −1 with a saccharification efficiency of 36.0 ± 3.2 % in 24 h at 10 % solids loading.

Biological materials, such as bones, teeth and mollusc shells, are well known for their excellent strength, modulus and toughness 1 – 3 . Such properties are attributed to the elaborate layered microstructure of inorganic reinforcing nanofillers, especially two-dimensional nanosheets or nanoplatelets, within a ductile organic matrix 4 – 6 . Inspired by these biological structures, several assembly strategies—including layer-by-layer 4 , 7 , 8 , casting 9 , 10 , vacuum filtration 11 – 13 and use of magnetic fields 14 , 15 —have been used to develop layered nanocomposites. However, how to produce ultrastrong layered nanocomposites in a universal, viable and scalable manner remains an open issue. Here we present a strategy to produce nanocomposites with highly ordered layered structures using shear-flow-induced alignment of two-dimensional nanosheets at an immiscible hydrogel/oil interface. For example, nanocomposites based on nanosheets of graphene oxide and clay exhibit a tensile strength of up to 1,215 ± 80 megapascals and a Young’s modulus of 198.8 ± 6.5 gigapascals, which are 9.0 and 2.8 times higher, respectively, than those of natural nacre (mother of pearl). When nanosheets of clay are used, the toughness of the resulting nanocomposite can reach 36.7 ± 3.0 megajoules per cubic metre, which is 20.4 times higher than that of natural nacre; meanwhile, the tensile strength is 1,195 ± 60 megapascals. Quantitative analysis indicates that the well aligned nanosheets form a critical interphase, and this results in the observed mechanical properties. We consider that our strategy, which could be readily extended to align a variety of two-dimensional nanofillers, could be applied to a wide range of structural composites and lead to the development of high-performance composites. Layered nanocomposites fabricated using a continuous and scalable process achieve properties exceeding those of natural nacre, the result of stiffened matrix polymer chains confined between highly aligned nanosheets.

Plastic in the marine environment is a growing environmental issue. Sea turtles are at significant risk of ingesting plastic debris at all stages of their lifecycle with potentially lethal consequences. We tested the relationship between the amount of plastic a turtle has ingested and the likelihood of death, treating animals that died of known causes unrelated to plastic ingestion as a statistical control group. We utilized two datasets; one based on necropsies of 246 sea turtles and a second using 706 records extracted from a national strandings database. Animals dying of known causes unrelated to plastic ingestion had less plastic in their gut than those that died of either indeterminate causes or due to plastic ingestion directly (e.g. via gut impaction and perforation). We found a 50% probability of mortality once an animal had 14 pieces of plastic in its gut. Our results provide the critical link between recent estimates of plastic ingestion and the population effects of this environmental threat.

Rice disease recognition is crucial in automated rice disease diagnosis systems. At present, deep convolutional neural network (CNN) is generally considered the state-of-the-art solution in image recognition. In this paper, we propose a novel rice blast recognition method based on CNN. A dataset of 2906 positive samples and 2902 negative samples is established for training and testing the CNN model. In addition, we conduct comparative experiments for qualitative and quantitatively analysis in our evaluation of the effectiveness of the proposed method. The evaluation results show that the high-level features extracted by CNN are more discriminative and effective than traditional hand-crafted features including local binary patterns histograms (LBPH) and Haar-WT (Wavelet Transform). Moreover, quantitative evaluation results indicate that CNN with Softmax and CNN with support vector machine (SVM) have similar performances, with higher accuracy, larger area under curve (AUC), and better receiver operating characteristic (ROC) curves than both LBPH plus an SVM as the classifier and Haar-WT plus an SVM as the classifier. Therefore, our CNN model is a top performing method for rice blast disease recognition and can be potentially employed in practical applications.

Analysis of the pattern of proteins or messenger RNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call \"spatial transcriptomics,\" that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.