Flood Risk Analysis Based on Information Diffusion Theory

Due to the fact that the flood data series of small drainage basins is relatively short, available data are often not sufficient for flood risk analysis. This presents the problem of risk analysis using very small data samples. One method that can be applied is to regard the available small samples as fuzzy information and optimize them using information diffusion technology to yield analytical results with greater reliability. In this article a risk analysis method based on information diffusion theory is applied to create a new flood risk analysis model. Application of the model is illustrated taking the Jinhuajiang and Qujiang drainage basins as examples. This is a new attempt at applying information diffusion theory in flood risk analysis. Computations based on this analytical flood risk model can yield an estimated flood damage value that is relatively accurate. This study indicates that the aforementioned model exhibits fairly stable analytical results, even when using a small set of sample data. The results also indicate that information diffusion technology is highly capable of extracting useful information and therefore improves system recognition accuracy. This method can be easily applied and the analytical results produced are easy to understand. Results are accurate enough to act as a guide in disaster situations.     

Repetitive changes in Early Pliocene vegetation revealed by high-resolution pollen analysis: revised cyclostratigraphy of southwestern Romania

A high-resolution pollen analysis has been carried out on the Lupoaia section (SW Romania) in order to check whether the repetitive clay–lignite alternations correspond to cyclic changes in climate. Increases in altitudinal tree pollen content appear to have been caused by drops in temperature, while developments of thermophilous elements correspond to rises in temperature, still under humid conditions. Such repeated changes in vegetation, on the whole consistent with the clay–lignite alternations, have been forced by cycles in eccentricity. On the basis of a comparison between the Lupoaia pollen record and (1) European climatostratigraphy (based on reference pollen diagrams documenting global changes), and (2) global climatic curves (eccentricity, δ¹⁸O), the age of the section has been reconsidered. The Lupoaia section (i.e. from lignite IV to lignite XIII) starts just before the C3n.3n Chron and probably ends just before the C3n.1n Chron. The section represents a time span of about 600 kyr, i.e. from about 4.90 Ma to about 4.30 Ma.     

Historical and recent evidence for close relationships among Rickettsia parkeri,R. conorii,R. africae,and R. sibirica: Implications for rickettsial taxonomy

Rickettsia parkeri, a member of the spotted fever group rickettsias, was first described in 1939 and was thought to be non‐pathogenic until recently, when it was found to cause a spotted fever‐like illness in humans and areas of necrosis (eschars) at the sites of tick bites. Accordingly, there is currently much interest in this emerging pathogen. In this study, all published articles concerning R. parkeri were reviewed and analyzed for evidence of relatedness among this agent and other spotted fever group (SFG) rickettsiae which also produce similar clinical syndromes and/or eschars, including R. conorii, R. africae, and R. sibirica. A synthesis of the historical (antigenic) and recent (molecular) data supporting a phylogenetic sub‐grouping of these SFG organisms is presented and comments are offered about the taxonomy of rickettsial organisms in general, and R. parkeri in particular.     

Can minimum convex polygon home ranges be used to draw biologically meaningful conclusions?

Many conclusions about mammalian ranging behaviour have been drawn based on minimum convex polygon (MCP) estimates of home range size, although several studies have revealed its unpredictable nature compared to that of the kernel density estimator. We investigated to what extent the choice of home range estimator affected the biological interpretation in comparative studies. We found no discrepancy when the question asked covered a wide range of taxa, as real and very large differences in range size were likely to have masked smaller differences due to the choice of home range estimator. However, when the question asked concerned within-species characteristics, the choice of home range estimator explained as much of the variation in range size as did the ecological variable in question. The implications for macro-ecological and intraspecific studies are discussed.     

A comparative Proteomics Analysis Identified Differentially Expressed Proteins in Pancreatic Cancer–Associated Stellate Cell Small Extracellular Vesicles

Human pancreatic stellate cells (HPSCs) are an essential stromal component and mediators of pancreatic ductal adenocarcinoma (PDAC) progression. Small extracellular vesicles (sEVs) are membrane-enclosed nanoparticles involved in cell-to-cell communications and are released from stromal cells within PDAC. A detailed comparison of sEVs from normal pancreatic stellate cells (HPaStec) and from PDAC-associated stellate cells (HPSCs) remains a gap in our current knowledge regarding stellate cells and PDAC. We hypothesized there would be differences in sEVs secretion and protein expression that might contribute to PDAC biology. To test this hypothesis, we isolated sEVs using ultracentrifugation followed by characterization by electron microscopy and Nanoparticle Tracking Analysis. We report here our initial observations. First, HPSC cells derived from PDAC tumors secrete a higher volume of sEVs when compared to normal pancreatic stellate cells (HPaStec). Although our data revealed that both normal and tumor-derived sEVs demonstrated no significant biological effect on cancer cells, we observed efficient uptake of sEVs by both normal and cancer epithelial cells. Additionally, intact membrane-associated proteins on sEVs were essential for efficient uptake. We then compared sEV proteins isolated from HPSCs and HPaStecs cells using liquid chromatography–tandem mass spectrometry. Most of the 1481 protein groups identified were shared with the exosome database, ExoCarta. Eighty-seven protein groups were differentially expressed (selected by 2-fold difference and adjusted p value ≤0.05) between HPSC and HPaStec sEVs. Of note, HPSC sEVs contained dramatically more CSE1L (chromosome segregation 1–like protein), a described marker of poor prognosis in patients with pancreatic cancer. Based on our results, we have demonstrated unique populations of sEVs originating from stromal cells with PDAC and suggest that these are significant to cancer biology. Further studies should be undertaken to gain a deeper understanding that could drive novel therapy.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Simple But Efficacious Enrichment of Integral Membrane Proteins and Their Interactions for In-Depth Membrane Proteomics

Membrane proteins play essential roles in various cellular processes, such as nutrient transport, bioenergetic processes, cell adhesion, and signal transduction. Proteomics is one of the key approaches to exploring membrane proteins comprehensively. Bottom–up proteomics using LC–MS/MS has been widely used in membrane proteomics. However, the low abundance and hydrophobic features of membrane proteins, especially integral membrane proteins, make it difficult to handle the proteins and are the bottleneck for identification by LC–MS/MS. Herein, to improve the identification and quantification of membrane proteins, we have stepwisely evaluated methods of membrane enrichment for the sample preparation. The enrichment methods of membranes consisted of precipitation by ultracentrifugation and treatment by urea or alkaline solutions. The best enrichment method in the study, washing with urea after isolation of the membranes, resulted in the identification of almost twice as many membrane proteins compared with samples without the enrichment. Notably, the method significantly enhances the identified numbers of multispanning transmembrane proteins, such as solute carrier transporters, ABC transporters, and G-protein–coupled receptors, by almost sixfold. Using this method, we revealed the profiles of amino acid transport systems with the validation by functional assays and found more protein–protein interactions, including membrane protein complexes and clusters. Our protocol uses standard procedures in biochemistry, but the method was efficient for the in-depth analysis of membrane proteome in a wide range of samples.     

Validation of density–elasticity relationships for finite element modeling of human pelvic bone by modal analysis

In total hip arthroplasty and particularly in revision surgery, computer assisted pre-operative prediction of the best possible anchorage strategy for implant fixation would be a great help to the surgeon. Computer simulation relies on validated numerical models. In the current study, three density–elasticity relationships (No. 1–3) from the literature for inhomogeneous material parameter assignment from CT data in automated finite element (FE) modeling of long bones were evaluated for their suitability for FE modeling of human pelvic bone. Numerical modal analysis was conducted on 10 FE models of hemipelvic bone specimens and compared to the gold standard provided by experimental modal analysis results from a previous in-vitro study on the same specimens. Overall, calculated resonance frequencies came out lower than measured values. Magnitude of mean relative deviation of numerical resonance frequencies with regard to measured values is lowest for the density–elasticity relationship No. 3 (−15.9%) and considerably higher for both density–elasticity relationships No. 1 (−41.1%) and No. 2 (−45.0%). Mean MAC values over all specimens amount to 77.8% (No. 1), 78.5% (No. 2), and 83.0% (No. 3). MAC results show, that mode shapes are only slightly influenced by material distribution. Calculated resonance frequencies are generally lower than measured values, which indicates, that numerical models lack stiffness. Even when using the best suited (No. 3) out of three investigated density–elasticity relationships, in FE modeling of pelvic bone a considerable underestimation of model stiffness has to be taken into account.