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To investigate the ability of the ratio of blood urea nitrogen (BUN) to serum albumin ratio (BAR) in patients with sepsis in intensive care units (ICUs) to predict the prognosis of short-and long-term death. Data are from the Marketplace for Intensive Care Medical Information IV (MIMIC-IV v2.0) database for patients with sepsis as defined by SEPSIS-3. The primary outcome was 30-day mortality and the secondary outcome was 360-day mortality. Kaplan–Meier (KM) survival curves were plotted to describe differences in BAR mortality in different subgroups and area under the curve (AUC) analysis was performed to compare the predictive value of sequential organ failure assessment (SOFA), BAR, blood urea nitrogen (BUN) and albumin. Multivariate Cox regression models and subgroup analysis were used to determine the correlation between BAR and 30-day mortality and 360-day mortality. A total of 7656 eligible patients were enrolled in the study with a median BAR of 8.0 mg/g, including 3837 in the ≤ 8.0 group and 3819 in the BAR > 8.0 group, with 30-day mortality rates of 19.1% and 38.2% (P < 0.001) and 360-day mortality rates of 31.1% and 55.6% (P < 0.001). Multivariate Cox regression models showed an increased risk of death for 30-day mortality (HR = 1.219, 95% CI 1.095–1.357; P < 0.001) and 360-day mortality (HR = 1.263, 95% CI 1.159–1.376; P < 0.001) in the high BAR group compared to the low BAR group. For the 30-day outcome, the area under the curve (AUC) was 0.661 for BAR and 0.668 for 360-day BAR. In the subgroup analysis, BAR remained an isolated risk factor for patient death. As a clinically inexpensive and readily available parameter, BAR can be a valuable forecaster of prognosis in patients with sepsis in the intensive care unit. [ABSTRACT FROM AUTHOR]

The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules. [ABSTRACT FROM AUTHOR]

Polymer infiltrated ceramic network (PICN) composites, which combine the flexibility of resin materials with the rigidity of ceramic materials, have become a hot spot for research and development because of their structural and mechanical features that are similar to those of real teeth. In this paper, the porous feldspar ceramic part sintered body is used as the inorganic skeleton to composite with resin, and the preparation processes of porous feldspar ceramic were investigated, including the effects of different sintering temperature and holding time on the pore diameter and porosity of porous feldspar ceramic, and the effects on the properties of resin infiltration ceramic composites. The results show that when the sintering temperature is 750 ℃ and the holding time is 0 min, it has the most suitable porosity and pore size for resin infiltration. The hardness, flexural strength and flexural modulus of PICN composites were 125.5 N/mm2, 162.85 MPa, and 27.01 GPa respectively and the water absorption value and water solubility value are 10.51 μg/mm3 and 2.29 μg/mm3. [ABSTRACT FROM AUTHOR]

ZnMn2O4 anode material with nanosheet lamellar structure was synthesized using microwave method. The bearing of reaction temperature, reaction time, reactant compactness, and pH of the synthesis environment on the discharge–charge performance of samples were systematically studied. It is confirmed that ZnMn2O4 material presents a nanosheet lamellar structure with a uniform distribution. When the reaction temperature is 200℃, the reaction time is 25 min, reactant compactness is 73%, pH is 7, and initial discharge-specific capacity of the samples is 959.6 mAh·g−1. After 40 cycles, the capacity remains at 346.9 mAh·g−1, and the coulomb efficiency is stable at 100%. Spinel ZnMn2O4 anode material was microwave hydrothermally synthesized from zinc nitrate and manganese nitrate as raw materials in an aqueous solution. The material has a nano lamellar structure and shows good electrochemical properties as anode material of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

An integrated custom cross-response sensing array has been developed combining the algorithm module's visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 105 ~ 108 CFU/mL for Escherichia coli, 102 ~ 107 CFU/mL for E. coli-β, 103 ~ 108 CFU/mL for Staphylococcus aureus, 103 ~ 107 CFU/mL for MRSA, 102 ~ 108 CFU/mL for Pseudomonas aeruginosa, 103 ~ 108 CFU/mL for Enterococcus faecalis, 102 ~ 108 CFU/mL for Klebsiella pneumoniae, and 103 ~ 108 CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification. • A molecular response differential profiling (MRDP) was established based on custom cross-response sensor array for rapid and accurate recognition and phenotyping common pathogenic microorganism. • Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array. Each of these sensing elements' performance has competitive reaction with the microorganism. • MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) is the most important food crop in the world, with significant acreage and production across the globe. However, it is affected by low temperatures throughout its growth process, especially during germination. Therefore, it is important to identify more QTLs or genes associated with germination under low-temperature conditions. For the QTL analysis of traits related to low-temperature germination, we used a high-res genetic map of 213 lines of the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, which had 6618 bin markers. We detected 28 QTLs of eight phenotypic characteristics associated with low-temperature germination, while they explained the phenotypic contribution rate of 5.4 ~ 13.34%. Additionally, 14 overlapping QTLs produced six QTL clusters on every chromosome, except for 8 and 10. RNA-Seq found six genes related to low-temperature tolerance in these QTLs, while qRT-PCR analysis demonstrated that the expression trends of the Zm00001d045568 gene in the LT_BvsLT_M group and the CK_BvsCK_M group were highly significantly different at all four-time points (P < 0.01), and encoded the RING zinc finger protein. It was located on qRTL9-2 and qRSVI9-1 and is related to the total length and simple vitality index. These results provided potential candidate genes for further gene cloning and improving the low-temperature tolerance of maize. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China, has led to the rapid development of Coronavirus disease 2019 (COVID-19) pandemic. COVID-19 represents a fatal disease with a great global public health importance. This study aims to develop a three-parameter Weibull mathematical model using continuous functions to represent discrete COVID-19 data. Subsequently, the model was applied to quantitatively analyze the characteristics for the mortality of COVID-19, including the age, sex, the length of symptom time to hospitalization time (SH), hospitalization date to death time (HD) and symptom time to death time time (SD) and others. A three-parameter mathematical model was developed by combining the reported cases in the Data Repository from the Center for Systems Science and Engineering at Johns Hopkins University and applied to estimate and analyze the characteristics for mortality of COVID-19. We found that the scale parameters of males and females were 5.85 and 5.45, respectively. Probability density functions in both males and females were negative skewness. 5% of male patients died under the age of 43.28 (44.37 for females), 50% died under 69.55 (73.25 for females), and 95% died under 86.59 (92.78 for females). The peak age of male death was 67.45 years, while that of female death was 71.10 years. The peak and median values of SH, HD and SD in male death were correspondingly 1.17, 5.18 and 10.30 days, and 4.29, 11.36 and 16.33 days, while those in female death were 1.19, 5.80 and 12.08 days, and 4.60, 12.44 and 17.67 days, respectively. The peak age of probability density in male and female deaths was 69.55 and 73.25 years, while the high point age of their mortality risk was 77.51 and 81.73 years, respectively. The mathematical model can fit and simulate the impact of various factors on IFR. From the simulation results of the model, we can intuitively find the IFR, peak age, average age and other information of each age. In terms of time factors, the mortality rate of the most susceptible population is not the highest, and the distribution of male patients is different from the distribution of females. This means that Self-protection and self-recovery in females against SARS-CoV-2 virus might be better than those of males. Males were more likely to be infected, more likely to be admitted to the ICU and more likely to die of COVID-19. Moreover, the infection fatality ration (IFR) of COVID-19 population was intrinsically linked to the infection age. Public health measures to protect vulnerable sex and age groups might be a simple and effective way to reduce IFR. [ABSTRACT FROM AUTHOR]

The buds of I Z. striolatum i were collected in August of 2019 from Guizhou Province of P. R. China and were authenticated by Dr. Daopeng Tan (Pharmacognosy), an associate professor of Zunyi Medical University. In the present study, our further chemical investigation into the constituents of the buds of I Z. striolatum, i nine compounds were isolated and identified previously from this plant. The EtOAc extract (60 g) was subjected to a series of chromatographic methods, such as column chromatography using silica gel (200-300 or 300-400 mesh) and Sephadex LH-20, and yielded compounds B 1-9 b . The buds of I Z. striolatum i contain abundant protein, polysaccharides, amino acids, and trace elements, and serve as a delicious vegetable with high nutritional value [[3]]. [Extracted from the article]

Nasopharyngeal carcinoma (NPC) is a rare malignancy, with the unique geographical and ethnically characteristics of distribution. Gene chip and bioinformatics have been employed to reveal regulatory mechanisms in current functional genomics. However, a practical solution addressing the unresolved aspects of microarray data processing and analysis have been long pursuit. This study developed a new method to improve the accuracy of identifying key biomarkers, namely Unit Gamma Measurement (UGM), accounting for multiple hypotheses test statistics distribution, which could reduce the dependency problem. Three mRNA expression profile of NPC were selected to feed UGM. Differentially expressed genes (DEGs) were identified with UGM and hub genes were derived from them to explore their association with NPC using functional enrichment and pathway analysis. 47 potential DEGs were identified by UGM from the 3 selected datasets, and affluent in cysteine-type endopeptidase inhibitor activity, cilium movement, extracellular exosome etc. also participate in ECM-receptor interaction, chemical carcinogenesis, TNF signaling pathway, small cell lung cancer and mismatch repair pathway. Down-regulation of CAPS and WFDC2 can prolongation of the overall survival periods in the patients. ARMC4, SERPINB3, MUC4 etc. have a close relationship with NPC. The UGM is a practical method to identify NPC-associated genes and biomarkers. [ABSTRACT FROM AUTHOR]

In this paper, a trench-assisted large-mode-field-area multi-core fiber with an air-hole structure composed of 21 conventional fiber cores and 16 air holes is proposed, and it can realize dual-mode transmission. By adjusting the structural parameters, the effective mode field area of fundamental mode can be up to 1916.042 µm2 at 1550 nm. In addition, the fiber has good bending characteristics by adding a trench with a lower refractive index than the cladding. When the bending radius is 1.4 cm, the bending loss is low to 2.96 × 10–3 dB/m. This fiber is of great significance for the design of bending insensitive fiber. [ABSTRACT FROM AUTHOR]

The state of the art of modeling fluid flow in shale reservoirs is dominated by dual-porosity models which divide the reservoirs into matrix blocks that significantly contribute to fluid storage and fracture networks which principally control flow capacity. However, recent extensive microscopic studies reveal that there exist massive micro- and nano-pore systems in shale matrices. Because of this, the actual flow mechanisms in shale reservoirs are considerably more complex than can be simulated by the conventional dual-porosity models and Darcy's law. Therefore, a model capturing multiple pore scales and flow can provide a better understanding of the complex flow mechanisms occurring in these reservoirs. This paper presents a micro-scale multiple-porosity model for fluid flow in shale reservoirs by capturing the dynamics occurring in three porosity systems: inorganic matter, organic matter (mainly kerogen), and natural fractures. Inorganic and organic portions of shale matrix are treated as sub-blocks with different attributes, such as wettability and pore structures. In kerogen, gas desorption and diffusion are the dominant physics. Since the flow regimes are sensitive to pore size, the effects of nano-pores and micro-pores in kerogen are incorporated into the simulator. The multiple-porosity model is built upon a unique tool for simulating general multiple-porosity systems in which several porosity systems may be tied to each other through arbitrary connectivities. This new model allows us to better understand complex flow mechanisms and eventually is extended into the reservoir scale through upscaling techniques. Sensitivity studies on the contributions of the different flow mechanisms and kerogen properties give some insight as to their importance. Results also include a comparison of the conventional dual-porosity treatment and show that significant differences in fluid distributions and dynamics are obtained with the improved multiple-porosity simulation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to report the first preliminary study of the recently introduced combinatorial multilevel (CML) method for solver preconditioning in large-scale reservoir simulation with coupled geomechanics. The CML method is a variant of the popular algebraic multigrid (AMG) method yet with essential differences. The basic idea of this new approach is to construct a hierarchy of matrices using the discrete geometry of the graph, based on support theory for preconditioners. In this way, the CML method combines the merits of both geometric and algebraic multigrid methods. The resulting hybrid approach not only provides a simpler and faster setup phase compared to AMG, but the method can be proven to exhibit strong convergence guarantees for arbitrary symmetric diagonally dominant matrices. In addition, the underlying theoretical soundness of the CML method contrasts to the heuristic AMG approach, which often can show slow convergence for difficult problems. This new approach is implemented in a reservoir simulator for both pressure and poroelastic displacement preconditioners in the multistage preconditioning technique. We present results based on several known benchmark problems and provide a comparison of performance and complexity with the widespread preconditioning schemes used in large-scale reservoir simulation. An adaptation of CML for non-symmetric matrices is shown to exhibit excellent convergence properties for realistic cases. [ABSTRACT FROM AUTHOR]

The production evaluation of high-temperature and high-pressure gas condensate remains unsatisfactory in terms of precision due to the inadequate knowledge of viscosity variation and dynamic relative permeability. Here, we first conduct phase behavior experiments to clarify the mechanisms of viscosity variation followed by core flooding experiments to reveal the dynamic relative permeability. The viscosity variation responses in different regimes to pressure and temperature increases. Besides, the carbon number of the gas condensate exhibits three influencing scenarios on viscosity variation at different pressure and temperature conditions. Moreover, the variation of relative permeability is limited within 5% as temperature decreases together with pressure decreases, but the decline rate of pressure is expected to be higher than that of temperatures. We then theoretically model the production of the gas condensate reservoir by integrating the viscosity variation and dynamic relative permeability mechanisms obtained from the experiments. Modeling results well fit the field data. We adopt the model to analyze and elucidate the effects of temperature, pressure and fracture size on water flooding recovery of ultra-deep gas condensate reservoirs. We figure out that at high temperature, the gas condensate recovery can be enhanced by a rapid pressure decline which can further accelerate the oil thinning and gas condensate production. A high gas condensate production is obtained in the early stage at high pressure, but the oil with high viscosity consumes vast amounts of reservoir energy, leading to a rapid pressure drop in the early development stage. This work pioneers in revealing the interactive mechanisms of viscosity variation and dynamic permeability of ultra-deep reservoir during water flooding development, and brings insights into more adequate gas condensate recovery evaluation. [ABSTRACT FROM AUTHOR]

Ethyl 2-methylcyclopent-2-ene-1-carboxylate (3) is an important building block in the synthesis of some natural products. In this paper, 2GGC was used for the RCM reaction to prepare this scaffold instead of reported 1GGC. This scaffold (3) was obtained in a moderate yield and its structure was determined by 1H NMR and 13C NMR. A contra-ring closing metathesis mechanism was also put forward to explain the formation of the product. [ABSTRACT FROM AUTHOR]

In this cohort of 217 bladder cancer patients and 484 healthy controls, we explored the association between CYP24A1 variants (rs2762934, rs1570669, rs6068816, rs2296241) and bladder cancer risk in the Chinese Han population. Utilizing the Agena MassARRAY system, we genotyped four selected CYP24A1 polymorphisms. Logistic regression revealed a significant association of rs2762934 and rs1570669 with elevated bladder cancer risk, while rs6068816 exhibited a protective effect. Bioinformatics analysis of CYP24A1 expression in normal and cancerous bladder tissues indicated higher expression in normal tissue. In conclusion, our findings highlight the potential role of CYP24A1 variants in bladder cancer susceptibility. [ABSTRACT FROM AUTHOR]