Combining a Fuzzy Matter-Element Model with a Geographic Information System in Eco-Environmental Sensitivity and Distribution of Land Use Planning

Sustainable ecological and environmental development is the basis of regional development. The sensitivity classification of the ecological environment is the premise of its spatial distribution for land use planning. In this paper, a fuzzy matter-element model and factor-overlay method were employed to analyze the ecological sensitivity in Yicheng City. Four ecological indicators, including soil condition,, water condition,, atmospheric conditions and biodiversity were used to classify the ecological sensitivity. The results were categorized into five ranks: insensitive, slightly sensitive, moderately sensitive, highly sensitive and extremely sensitive zones. The spatial distribution map of environmental sensitivity for land use planning was obtained using GIS (Geographical Information System) techniques. The results illustrated that the extremely sensitive and highly sensitive areas accounted for 14.40% and 30.12% of the total area, respectively, while the moderately sensitive and slightly sensitive areas are 25.99% and 29.49%, respectively. The results provide the theoretical foundation for land use planning by categorizing all kinds of land types in Yicheng City

Organic geochemical characterization on a seal excrement sediment core from Fildes Peninsula, Western Antarctica

Organic geochemical analysis was performed on a sediment core HN1 from Fildes Peninsula on King George Island, Western Antarctica. Short-chain n-alkanes were the main components of the aliphatic hydrocarbons present, and they were likely to be from algae and bacteria; n-C23 was likely derived from moss. Fecal sterols and phytol dominated the alcohol composition, and may have come from seal feces and vegetation, respectively. The fluctuations in their concentrations generally have responded to historical changes in the ecosystems near the region. The even-carbon fatty acids, such as n-C16, n-C18 and n-C24, dominated the alkenoic acid composition, which mainly originated from bacteria, moss and zooplankton. The low concentrations of unsaturated fatty acids showed a predominance of C16:1 and C18:1 unsaturated acids, and demonstrated that the sediment was well preserved and had a simple and stable source of organic materials

APE1 controls DICER1 expression in NSCLC through miR-33a and miR-130b

Increasing evidence suggests different, not completely understood roles of microRNA biogenesis in the development and progression of lung cancer. The overexpression of the DNA repair protein apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) is an important cause of poor chemotherapeutic response in lung cancer and its involvement in onco-miRNAs biogenesis has been recently described. Whether APE1 regulates miRNAs acting as prognostic biomarkers of lung cancer has not been investigated, yet. In this study, we analyzed miRNAs differential expression upon APE1 depletion in the A549 lung cancer cell line using high-throughput methods. We defined a signature of 13 miRNAs that strongly correlate with APE1 expression in human lung cancer: miR-1246, miR-4488, miR-24, miR-183, miR-660, miR-130b, miR-543, miR-200c, miR-376c, miR-218, miR-146a, miR-92b and miR-33a. Functional enrichment analysis of this signature revealed its biological relevance in cancer cell proliferation and survival. We validated DICER1 as a direct functional target of the APE1-regulated miRNA-33a-5p and miR-130b-3p. Importantly, IHC analyses of different human tumors confirmed a negative correlation existing between APE1 and Dicer1 protein levels. DICER1 downregulation represents a prognostic marker of cancer development but the mechanisms at the basis of this phenomenon are still completely unknown. Our findings, suggesting that APE1 modulates DICER1 expression via miR-33a and miR-130b, reveal new mechanistic insights on DICER1 regulation, which are of relevance in lung cancer chemoresistance and cancer invasiveness

Baiji genomes reveal low genetic variability and new insights into secondary aquatic adaptations

The baiji, or Yangtze River dolphin (Lipotes vexillifer), is a flagship species for the conservation of aquatic animals and ecosystems in the Yangtze River of China; however, this species has now been recognized as functionally extinct. Here we report a high-quality draft genome and three re-sequenced genomes of L. vexillifer using Illumina short-read sequencing technology. Comparative genomic analyses reveal that cetaceans have a slow molecular clock and molecular adaptations to their aquatic lifestyle. We also find a significantly lower number of heterozygous single nucleotide polymorphisms in the baiji compared to all other mammalian genomes reported thus far. A reconstruction of the demographic history of the baiji indicates that a bottleneck occurred near the end of the last deglaciation, a time coinciding with a rapid decrease in temperature and the rise of eustatic sea level

Psoriasis comorbid with atherosclerosis meets in lipid metabolism

Psoriasis (PSO) is a common skin disease affecting approximately 1%–3% of the population, and the incidence rate is increasing yearly. PSO is associated with a dramatically increased risk of cardiovascular disease, the most common of which is atherosclerosis (AS). In the past, inflammation was considered to be the triggering factor of the two comorbidities, but in recent years, studies have found that lipid metabolism disorders increase the probability of atherosclerosis in patients with psoriasis. In this review, we discuss epidemiological studies, clinical treatment methods, risk factors, and lipid metabolism of psoriasis and atherosclerosis comorbidities

I/O Conformance Test Generation with Colored Petri Nets

This paper explores Input-Output Conformance (IOCO) test generation with Colored Petri Nets (CPN). A test generation oriented CPN model and CPN based IOCO relation is proposed. Feasible test cases are generated by model simulation with the proof of its soundness. The method integrates the merits the IOCO testing theory and the CPN modeling synergistically, and is applied as a nontrivial and competent test case generation approach for practical testing projects. The effectiveness of this test generation approach is demonstrated by a concrete software system. Since the model simulation based test generation process is irrespective with the model size, the effectiveness of the method is enhanced with scalability

A fast deep learning system using GPU

The invention of deep belief network (DBN) provides a powerful tool for data modeling. The key advantage of DBN is that it is driven by training data only, which can alleviate researchers from the routine of devising explicit models or features for data with complicated distributions. However, as the dimensionality and quantity of data increase, the computing load of training a DBN increases rapidly. Prospectively, the remarkable computing power provided by modern GPU devices can reduce the training time of DBN significantly. As highly efficient computational libraries become available, it provides additional support for GPU based parallel computing. Moreover, GPU server is more affordable and accessible compared with computer cluster or supercomputer. In this paper, we implement a variant of the DBNs, called folded-DBN, on NVIDA\u27s Tesla K20 GPU. In our simulations, two sets of database are used to train the folded-DBNs on both CPU and GPU platforms. Comparing execution time of the fine-tuning process, the GPU implementation results 7 to 11 times speedup over the CPU platform. © 2014 IEEE