Eco-Sponge Elasticity and its Indices Developed to Assess the Performance of Infrastructure in Sponge Cities: A Case Study in Xiamen, China

In recent years techniques for assessing the “sponge city” concept in practice have been developed and diversified at a rapid pace, therefore a unified assessment framework based on sponge techniques is becoming more and more important for comparing and analysing the performance of different techniques across different sponge city projects. However, previous work has mainly focused on enhancing or developing a certain single sponge technique. This research tries to establish a framework through integrating the resilience of the natural ecosystem with that of engineered infrastructure of sponge cities, forming a new concept of ‘Eco-sponge resilience\u27, and quantifying \u27Eco sponge Elasticity\u27. In particular, a set of elasticities with a unified dimension are developed. The eco-sponge elasticity mainly consists of five types of sponge elasticity and two types of ecological elasticity, including factors such as infiltration, storage, detention, transportation and decontamination, ecological vegetation and natural ecological water elasticities, with which the value of eco-sponge elasticity of a sponge city project can be easily estimated. This research also considers a case study to interpret how to assess the eco sponge elasticities of six pilot sites of sponge city projects in Xiamen. The result shows that the presented evaluation method is feasible and helpful for assessing and enhancing the performance of sponge cities considering four aspects: the water environment, water resources, water security and water ecosystem of the urban system

Ethyl 4-(4-cyano­phen­yl)-6-methyl-2-thioxo-1,2,3,4-tetra­hydro­pyrimidine-5-carboxyl­ate

The asymmetric unit of the title compound, C15H15N3O2S, contains two independent mol­ecules corresponding to the R and S enanti­omers. The dihydro­pyrimidinone rings adopt a flattened boat conformation. One of the ethyl groups is disordered over two orientations with occupancy factors of 0.700 (7) and 0.300 (7). In the crystal structure, mol­ecules are linked by inter­molecular N—H⋯O hydrogen-bonding inter­actions into one-dimensional chains along the c-axis direction. The chains are further connected by N—H⋯S hydrogen bonds, forming a three-dimensional network

3-Nitro­benzaldehyde thio­semicarbazone

The mol­ecule of the title compound, C8H8N4O2S, adopts an E configuration about both the C—N bonds. In the crystal structure, adjacent mol­ecules are linked by inter­molecular N—H⋯S hydrogen-bonding inter­actions, forming chains running parallel to the b axis

A case of advanced mycosis fungoides with comprehensive skin and visceral organs metastasis: sensitive to chemical and biological therapy

AbstractMycosis fungoides is a common cutaneous T-cell lymphoma, which is usually characterized by chronic, indolence progression, with absence of typical symptoms in early stage, metastasis to lymph nodes, bone marrow and visceral organs in later stage and ultimately progression to systemic lymphoma. It can result in secondary skin infection which is a frequent cause of death. At present, no curative therapy existed. Therapeutic purpose is to induce remission, reduce tumor burden and protect immune function of patients. A case of patient with advanced severe mycosis fungoides receiving CHOP plus interferon α-2a was reported here, with disease-free survival of 7 months and overall survival of over 17.0 months, and current status as well as developments of mycosis fungoides were briefly introduced

From the Lab to the Classroom: Research at the Interface Between Cognitive Science and Education

Presented at the 29th Association for Psychological Science (APS) Annual Convention in Boston, MA

A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

<p>Abstract</p> <p>Background</p> <p>Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design.</p> <p>Results</p> <p>In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in <it>S. cerevisiae </it>to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in <it>S. cerevisiae </it>(with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets.</p> <p>Conclusions</p> <p>We developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: <url>http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm</url></p

Financial Vulnerability of Midwest Grain Farms: Implications of Price, Yield, and Cost Shocks

Recent years have witnessed increasing volatility in crop prices and yields, fertilizer prices, and farm asset values. In this study, the financial performance of illustrative Midwest grain farms with different scales, tenure status, and capital structures was examined under the shocks of volatile crop prices, yields, fertilizer prices, farmland value, and cash rent. Illustrative farms of 550, 1,200, and 2,500 acres were constructed reflecting the production activity for these farms with three different farmland ownership structures (15%, 50%, and 85% of land owned) and two capital structures measured by debt-to- asset ratio (25% and 50%). Absolute measures and financial ratios were used to evaluate the income, cash flow, debt servicing, and equity position of these illustrative farms. The “stress test” results suggest that farms with modest size (i.e., 550 acres) and a large proportion of their land rented are very vulnerable irrespective of their leverage positions. Large-size farms with modest leverage (25% debt-to- asset ratio) that combine rental and ownership of the land they operated have strong financial performance and limited vulnerability to price, cost, yield, and asset value shocks. And these farms can increase their leverage positions significantly (from 25% to 50% in this study) with only modest deterioration in their financial performance and a slight increase in their vulnerability. These results suggest that the perspective that farmers are resilient to price, cost, yield, and asset value shocks because of the current low use of debt in the industry (an average of about 13% debt-to- asset ratio for the farming sector) does not adequately recognize the financial vulnerable of many typical family farms to those shocks