Detection of Breast Cancer Based on Fuzzy Frequent Itemsets Mining

Background: Breast cancer, a type of malignant tumor, affects women more than men. About one third of women with breast cancer die of this disease. Hence, it is imperative to find a tool for the proper identification and early treatment of breast cancer. Unlike the conventional data mining algorithms, fuzzy logic based approaches help in the mining of association rules from quantitative transactions.Methods: In this study a novel fuzzy methodology IFFP (Improved Fuzzy Frequent Pattern Mining), based on a fuzzy association rule mining for biological knowledge extraction, is introduced to analyze the dataset in order to find the core factors that cause breast cancer. This method consists of two phases. During the first phase, fuzzy frequent itemsets are mined using the proposed algorithm IFFP. Fuzzy association rules are formed during the second phase, indicating whether a person belongs to benign or malignant. This algorithm is applied on WBCD (Wisconsin Breast Cancer Database) to detect the presence of breast cancer.Results: It is determined that the factor, Mitoses has low range of values on both malignant and benign and hence it does not contribute to the detection of breast cancer. On the other hand, the high range of Bare Nuclei shows more chances for the presence of breast cancer.Conclusion: Experimental evaluations on real datasets show that our proposed method outperforms recently proposed state-of-the-art algorithms in terms of runtime and memory usage.     

Vegetation and Soil Development in Compost‐Amended Iron Oxide Precipitates at a 50‐Year‐Old Acid Mine Drainage Barrens

Acid mine drainage (AMD) barrens result from destruction of vegetation within AMD flow paths. When exposed to air, soluble iron in AMD undergoes oxidation and hydrolysis to form ferric iron (oxyhydr)oxides which accumulate on soil surfaces. A restoration experiment was conducted at a 50‐year‐old AMD barrens created by discharge from an abandoned underground coal mine. The objective was to determine whether vegetation could be established by altering rather than removing surface layers of acidic precipitates at a site representative of other mining‐degraded areas. Three zones in the barrens were identified based on moisture content, pH (2.7–3.3), and thickness of precipitates (0–35 cm). Our hypothesis was that application of the same reclamation method to all zones would fail to sustain >70% vegetative cover in each zone after four growing seasons. The method consisted of applying 11 t/ha lime and 27 or 54 t/ha compost before rototilling (top 15 cm) and mulching with oat straw containing viable seeds for a nurse crop. Lime‐only plots were included for comparison, and all amended plots were sown with a mine reclamation seed mix. Oats, sown species, and indigenous species dominated cover in the first, second, and fourth growing seasons, respectively. In the fourth year following reclamation, compost‐amended plots had >70% cover and improved soil properties in all three zones, providing evidence to reject our hypothesis. Vegetative restoration of AMD barrens did not require removal of highly acidic precipitates, since they could be transformed at low‐cost into a medium that supports indigenous plants.     

Electrocardiogram data mining based on frame classification by dynamic time warping matching

This paper presents an electrocardiogram (ECG) data mining scheme based on the ECG frame classification realised by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar non-stationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DTW matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-min ECG recording.     

Risk Assessment of the Abandoned Jukjeon Metal Mine in South Korea Following the Korean Guidelines

Toxic metal contamination in the vicinity of Korean abandoned metal mines has been reported. A risk assessment for these metals was performed for the inhabitants in the area of the abandoned Jukjeon metal mine. Soil, groundwater, and crop samples were collected around the mine. After pretreatment of these samples, metal concentrations were measured and then a risk assessment was performed using the Korean soil-contamination risk assessment guidelines. Phytoaccumulation of metals in crops was observed in soybeans (As and Zn), red peppers (Zn), sweet potatoes (As and Zn), and cabbage (Cu), which had higher metal concentrations than soils in the area. The metal intake rate was highest for inhalation of soil. Cancer risk was highest from ingestion of As-contaminated crops. The sum of carcinogenic risks was 6.29 × 10^–3. The non-carcinogenic risk was highest for ingestion of As-contaminated crops (8.17). Most of the risks were attributable to As, Pb, and Hg contamination, therefore these three metals must be considered as the principal metals toxic to human health in the sampled area. In particular, the inhalation of metal-contaminated soil should be considered for risk assessment along with ingestion of water and crops in abandoned mine areas.     

Identification and characterization of candidates involved in production of OMEGAs in microalgae: a gene mining and phylogenomic approach

Abstract

Optimizing the production of the high-value renewables such as OMEGAs through pathway engineering requires an in-depth understanding of the structure–function relationship of genes involved in the OMEGA biosynthetic pathways. In this preliminary study, our rationale is to identify and characterize the ～221 putative genes involved in production of OMEGAs using bioinformatic analysis from the Streptophyte (plants), Chlorophyte (green algae), Rhodophyta (red algae), and Bacillariophyta (diatoms) lineages based on their phylogenomic profiling, conserved motif/domain organization and physico-chemical properties. The MEME suite predicted 12 distinct protein domains, which are conserved among these putative genes. The phylogenomic analysis of the putative candidate genes [such as FAD2 (delta-12 desaturase); ECR (enoyl-CoA reductase); FAD2 (delta-12 desaturase); ACOT (acyl CoA thioesterase); ECH (enoyl-CoA hydratase); and ACAT (acetyl-CoA acyltransferase)] with similar domains and motif patterns were remarkably well conserved. Furthermore, the subcellular network prediction of OMEGA biosynthetic pathway genes revealed a unique interaction between the light-dependent chlorophyll biosynthesis and glycerol-3-phosphate dehydrogenase, which predicts a major cross-talk between the key essential pathways. Such bioinformatic analysis will provide insights in finding the key regulatory genes to optimize the productivity of OMEGAs in microalgal cell factories.     

Crop seed oil bodies: From challenges in protein identification to an emerging picture of the oil body proteome

Oleaginous seeds store lipids in specialized structures called oil bodies (OBs). These organelles consist of a core of neutral lipids bound by proteins embedded in a phospholipid monolayer. OB proteins are well conserved in plants and have long been grouped into only two categories: structural proteins or enzymes. Recent work, however, which identified other classes of proteins associated with OBs, clearly shows that this classification is obsolete. Proteomics‐mediated OB protein identification is facilitated in plants for which the genome is sequenced and annotated. However, it is not clear whether this knowledge can be dependably transposed to less well‐characterized plants, including the well‐established commercial sources of seed oil as well as the many others being proposed as novel sources for biodiesel, especially in Africa and Asia. Toward an update of the current data available on OB proteins this review discusses (i) the specific difficulties for proteomic studies of organelles; (ii) a 2012 census of the proteins found in seed OBs from various crops; (iii) the oleosin composition of OBs and their role in organelle stability; (iv) PTM of OB proteins as an emerging field of investigation; and finally we describe the emerging model of the OB proteome from oilseed crops.     

PTM MarkerFinder,a software tool to detect and validate spectra from peptides carrying post‐translational modifications

Mass spectrometry (MS) analysis of peptides carrying post‐translational modifications is challenging due to the instability of some modifications during MS analysis. However, glycopeptides as well as acetylated, methylated and other modified peptides release specific fragment ions during CID (collision‐induced dissociation) and HCD (higher energy collisional dissociation) fragmentation. These fragment ions can be used to validate the presence of the PTM on the peptide. Here, we present PTM MarkerFinder, a software tool that takes advantage of such marker ions. PTM MarkerFinder screens the MS/MS spectra in the output of a database search (i.e., Mascot) for marker ions specific for selected PTMs. Moreover, it reports and annotates the HCD and the corresponding electron transfer dissociation (ETD) spectrum (when present), and summarizes information on the type, number, and ratios of marker ions found in the data set. In the present work, a sample containing enriched N‐acetylhexosamine (HexNAc) glycopeptides from yeast has been analyzed by liquid chromatography‐mass spectrometry on an LTQ Orbitrap Velos using both HCD and ETD fragmentation techniques. The identification result (Mascot .dat file) was submitted as input to PTM MarkerFinder and screened for HexNAc oxonium ions. The software output has been used for high‐throughput validation of the identification results.     

Liposomes Enhance Bioremediation of Oil-Contaminated Soil

Abstract

Liposomes (composed of soy phosphatides) in the form of small unilamellar vesicles (SUV), when added to soil contaminated by crude oil, accelerate bioremediation. After three weeks incubation at 30°C, using soil experimentally contaminated (with 10,000 ppm crude oil), level of bioremediation increased from 40% without SUV to 75% with SUV (0.1 wt% phospholipids per dry weight soil). Similarly, for accidentally contaminated soil (with ～17,000 ppm crude oil), addition of 0.1 wt% SUV to the soil increased the bioremediation level from 55 to 80%. The enhancing effect of liposomes is explained by two interrelated phenomena: a large increase both in total bacteria number and in diversity of bacterial species in the soil. Comparison after four weeks revealed 21 bacterial species in the presence of liposomes (many being oil-degrading bacterial species) and only nine species in the absence of liposomes. Both effects may be related to the physical effects of liposome phospholipids, which modify the crude oil by wetting it, thereby making it more accessible to the microorganisms. In addition, liposome phospholipids serve as phosphate and nitrogen sources for the bacteria.     

Genomics-based precision breeding approaches to improve drought tolerance in rice

Rice (Oryza sativa L.), the major staple food crop of the world, faces a severe threat from widespread drought. The development of drought-tolerant rice varieties is considered a feasible option to counteract drought stress. The screening of rice germplasm under drought and its characterization at the morphological, genetic, and molecular levels revealed the existence of genetic variation for drought tolerance within the rice gene pool. The improvements made in managed drought screening and selection for grain yield under drought have significantly contributed to progress in drought breeding programs. The availability of rice genome sequence information, genome-wide molecular markers, and low-cost genotyping platforms now makes it possible to routinely apply marker-assisted breeding approaches to improve grain yield under drought. Grain yield QTLs with a large and consistent effect under drought have been indentified and successfully pyramided in popular rice mega-varieties. Various rice functional genomics resources, databases, tools, and recent advances in “-omics” are facilitating the characterization of genes and pathways involved in drought tolerance, providing the basis for candidate gene identification and allele mining. The transgenic approach is successful in generating drought tolerance in rice under controlled conditions, but field-level testing is necessary. Genomics-assisted drought breeding approaches hold great promise, but a well-planned integration with standardized phenotyping is highly essential to exploit their full potential.     

A layover stop in the African American great migration: identity,ruination, and memory

Gone Home challenges dominant representations of Appalachia as white, placing in the foreground the life histories of African Americans who hail from the coalfields of eastern Kentucky. The book reveals how mass migration shapes subjectivities, examining two generations of African Americans who migrated into and out of the coalfields between 1910 and 1970. The coalfields were a layover stop in an intergenerational migration from the rural South to urban centres in the North, Midwest, and West. This review reflects on the significance of the layover stop, both empirically and conceptually. One of the key contributions of Gone Home is to show how the layover stop was important not only in terms of time and place, but also in terms of collective identity. The review concludes by reflecting on interconnected themes of industrial ruination and intergenerational memory.