Analytical solutions of velocity profile in flow through submerged vegetation with variable frontal width

Flow within vegetation is one of the main driving forces for material exchange and energy transfer in wetland systems. Impacted by vegetation, the flow velocity profile illustrates distortions to the classic logarithmic velocity profile and has attracted much attention among researchers. Different from analytical models of velocity distribution in literature, which is mainly suitable for vegetation with uniform frontal width, this paper establishes new analytical solutions of the velocity profile for vegetation such as shrub and sedge that have a variable frontal width in the vertical direction. A new shape function is proposed under these conditions in which the frontal width exhibits a gradual increase in the vertical direction from bottom up in the vegetation. Along with different closure models for eddy viscosity in the vegetation layer and surface layer, analytical solutions of the velocity profile are derived from the momentum equations. Good agreement between calculated and measured data shows our analytical model is effective in predicting velocity profiles.Peer ReviewedPostprint (author's final draft

Novel biomass-based polymeric dyes: preparation and performance assessment in the dyeing of biomass-derived aldehyde-tanned leather

High-performance chrome-free leather production is currently one of the most concerning needs to warrant the sustainable development of the leather industry due to the serious chrome pollution. Driven by these research challenges, this work explores using biobased polymeric dyes (BPDs) based on dialdehyde starch and reactive small-molecule dye (reactive red 180, RD-180) as novel dyeing agents for leather tanned using a chrome-free, biomass-derived aldehyde tanning agent (BAT). FTIR, 1H NMR, XPS, and UV-visible spectrometry analyses indicated that a Schiff base structure was generated between the aldehyde group of dialdehyde starch (DST) and the amino group of RD-180, resulting in the successful load of RD-180 on DST to produce BPD. The BPD could first penetrate the BAT-tanned leather efficiently and then be deposited on the leather matrix, thus exhibiting a high uptake ratio. Compared with the crust leathers prepared using a conventional anionic dye (CAD), dyeing, and RD-180 dyeing, the BPD-dyed crust leather not only had better coloring uniformity and fastness but it also showed a higher tensile strength, elongation at break, and fullness. These data suggest that BPD has the potential to be used as a novel sustainable polymeric dye for the high-performance dyeing of organically tanned chrome-free leather, which is paramount to ensuring and promoting the sustainable development of the leather industry

MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery

Analytical solutions of velocity profile in flow through submerged vegetation with variable frontal width

Flow within vegetation is one of the main driving forces for material exchange and energy transfer in wetland systems. Impacted by vegetation, the flow velocity profile illustrates distortions to the classic logarithmic velocity profile and has attracted much attention among researchers. Different from analytical models of velocity distribution in literature, which is mainly suitable for vegetation with uniform frontal width, this paper establishes new analytical solutions of the velocity profile for vegetation such as shrub and sedge that have a variable frontal width in the vertical direction. A new shape function is proposed under these conditions in which the frontal width exhibits a gradual increase in the vertical direction from bottom up in the vegetation. Along with different closure models for eddy viscosity in the vegetation layer and surface layer, analytical solutions of the velocity profile are derived from the momentum equations. Good agreement between calculated and measured data shows our analytical model is effective in predicting velocity profiles.Peer Reviewe

Additional file 11: of Lower serum uric acid level strongly predict short-term poor functional outcome in acute stroke with normoglycaemia: a cohort study in China

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Estudio del potencial agroindustrial y exportador de la Peninsula de Santa Elena y de los recursos necesarios para su implantación "caso plátano

Proyecto de investigación que provee información suficiente a los agentes económicos involucrados en el proceso de desarrollo agrícola de esta área socio-económica del ecuador. ofreciendo a los inversionistas suficiente información para demostrar la viabilidad de invertir en este proyecto agroindustrial en la región de la península de Santa Elena la cual presenta excelentes condiciones de suelo y clima y influencia del proyecto hidráulico acueducto de Santa Elena (phase). el proyecto fue realizado con la colaboración de la Espol, junto con la comisión de estudio para el desarrollo de la cuenca baja del río guayas (CEDEGE), con el apoyo de la universidad de florida y el auspicio financiero del programa de modernización del sector agropecuario (PROMSA) del ministerio de agricultura y ganaderia del Ecuador.GuayaquilEconomista en Gestión empresarial especialización Finanza

Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia

Abstract Background Fibromyalgia (FM) is a multifaceted disease. Along with the genetic, environmental and neuro-hormonal factors, inflammation has been assumed to have role in the pathogenesis of FM. The aim of the present study was to explore the differences in clinical features and pathophysiology of FM patients under different inflammatory status. Methods The peripheral blood gene expression profile of FM patients in the Gene Expression Omnibus database was downloaded. Differentially expressed inflammatory genes were identified, and two molecular subtypes were constructed according to these genes used unsupervised clustering analysis. The clinical characteristics, immune features and pathways activities were compared further between the two subtypes. Then machine learning was used to perform the feature selection and construct a classification model. Results The patients with FM were divided into micro-inflammation and non-inflammation subtypes according to 54 differentially expressed inflammatory genes. The micro-inflammation group was characterized by more major depression (p = 0.049), higher BMI (p = 0.021), more active dendritic cells (p = 0.010) and neutrophils. Functional enrichment analysis showed that innate immune response and antibacterial response were significantly enriched in micro-inflammation subtype (p < 0.050). Then 5 hub genes (MMP8, ENPP3, MAP2K3, HGF, YES1) were screened thought three feature selection algorithms, an accurate classifier based on the 5 hub DEIGs and 2 clinical parameters were constructed using support vector machine model. Model scoring indicators such as AUC (0.945), accuracy (0.936), F1 score (0.941), Brier score (0.079) and Hosmer–Lemeshow goodness-of-fit test (χ2 = 4.274, p = 0.832) proved that this SVM-based classifier was highly reliable. Conclusion Micro-inflammation status in FM was significantly associated with the occurrence of depression and activated innate immune response. Our study calls attention to the pathogenesis of different subtypes of FM

Glycated hemoglobin independently or in combination with fasting plasma glucose versus oral glucose tolerance test to detect abnormal glycometabolism in acute ischemic stroke: a Chinese cross-sectional study

BACKGROUND: The investigation of glycated hemoglobin (HbA1c) as a diagnostic tool for abnormal glycometabolism is lack in acute ischemic stroke patients in China and worldwide. This paper was aimed to determine whether HbA1c, fasting plasma glucose (FPG), or HbA1c combined with FPG, could be used to screen for diabetes mellitus (DM) or prediabetes in acute ischemic stroke patients without previous DM. METHODS: Acute ischemic stroke patients without previous DM (n = 1,316) were selected from the Abnormal gluCose Regulation in Patients with Acute StrOke acrosS China Study (ACROSS-China). Oral glucose tolerance test (OGTT), HbA1c, FPG, and HbA1c combined with FPG were used as the screening methods to categorize the glycometabolic status. OGTT was taken as the golden method. Venn diagrams and the overlap index were used to determine the associations among the three methods of identifying abnormal glycometabolism. The area under the receiver operating characteristic curve (AUROC) and Youden index were used to assess and compare the accuracy in detecting abnormal glycometabolism. Youden analyses were performed to determine the ideal cutoff values of HbA1c in diagnosing abnormal glycometabolism. RESULTS: In acute ischemic stroke patients without previous DM, the overlaps of HbA1c versus OGTT, HbA1c versus FPG, and all the three methods independently, were low for detecting abnormal glycometabolism (all <50%). HbA1c can significantly detect more cases of prediabetes than OGTT (P < 0.001). The combination of HbA1c and FPG significantly raised the sensitivity to over 60.0%, specificity to over 80.0%, and the diagnostic accuracy (Youden index from under 40.0% to 42.4%)for DM. HbA1c of 5.7%-6.4% had a low to moderate concordance with OGTT for identifying prediabetes (AUROC = 0.557, P = 0.001). HbA1c values of 6.3% and 5.9% were found to be the ideal cutoff values for detecting DM and abnormal glycometabolism in our data, respectively. CONCLUSIONS: The combination of HbA1c and FPG increased the diagnostic rate of DM when compared with OGTT, and increased the diagnostic accuracy for DM compared with HbA1c or FPG alone. Our results advocate the use of HbA1c as screening tool for the diagnosis of pre-diabetes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12883-014-0177-0) contains supplementary material, which is available to authorized users

Population parameters and dynamic pool models of commercial fishes in the Beibu Gulf, northern South China Sea

Chinese Ministry of Agriculture [070404]; Social Commonwealth Research National Institute [2009TS08, 2010YD10]Length-frequency data of eight commercial fish species in the Beibu Gulf (Golf of Tonkin), northern South China Sea, were collected during 2006-2007. Length-weight relationships and growth and mortality parameters were analyzed using FiSAT II software. Five species had isometric growth, two species had negative allometric growth, and one species had positive allometric growth. Overall, the exploitation rates of the eight species were lower in 2006-2007 than in 1997-1999: for four species (Saurida tumbil, Saurida undosquamis, Argyrosomus macrocephalus, and Nemipterus virgatus) it was lower in 2006-2007 than in 1997-1999, for two species (Parargyrops edita and Trichiurus haumela) it remained the same, and for the other two species (Trachurus japonicus and Decapterus maruadsi) it was higher in 2006-2007 than in 1997-1999. The exploitation rates might have declined because of the decline in fishing intensity caused by high crude oil prices. The optimum exploitation rate, estimated using Beverton-Holt dynamic pool models, indicated that although fishes in the Beibu Gulf could sustain high exploitation rates, the under-size fishes at first capture resulted in low yields. To increase the yield per recruitment, it is more effective to increase the size at first capture than to control fishing effort