Associations between Body Mass Index and Breast Cancer Markers

Body mass index (BMI) and breast cancer biomarkers (BCBs) such asresistin, leptin adiponectin, monocyte chemoattractant protein-1 (MCP-1)and homeostasis model assessment of insulin resistance (HOMA-IR) arehighly associated with each other. The report has focused the inter-relationship between BMI and BCBs based on probabilistic modeling. It hasbeen shown that mean BMI is directly associated with leptin (P<0.0001)and MCP-1 (P=0.0002), while it is inversely associated with adiponectin(P=0.0003), HOMA-IR (P<0.0001), and it is higher for healthy women(P=0.0116) than breast cancer women. In addition, variance of BMIis inversely associated with resistin (P=0.1450). On the other hand,mean MCP-1 is directly associated with BMI (P<0.0001). Mean resistin is directly associated with the interaction effect of BMI and leptin(BMI*Leptin) (P=0.0415), while its variance is directly associated withBMI (P=0.0942), and it is inversely associated with BMI*Adiponectin(P=0.1518). Leptin is directly associated with BMI (P<0.0001). Alsoadiponectin is inversely associated with BMI (P<0.0001), BMI*Leptin(P=0.1729), while it is directly associated with Age*BMI (P=0.0017)and BMI*Resistin (P=0.0615). It can be concluded that BMI and BCBsare strongly associated with each other. Care should be taken on BMI forbreast cancer women

Studies on Sheet Explosive Formulation Based on Octahydro-1,3,5,7-Tetranitro-1,3,5,7-Tetrazocine and Hydroxyl Terminated Polybutadiene

The effect of replacing hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) by octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) in HTPB-binder on the performance, sensitivity, thermal, and mechanical properties of the sheet explosive formulation has been studied. The maximum loading of HMX was achieved up to 78 per cent in HTPB-binder system. The velocity of detonation (VOD) of HMX-based sheet explosive was observed about 7300 m/s which is marginally higher than existing RDX-based sheet explosive formulation (RDX/HTPB-binder, 80/20). The VOD trends were verified by theoretical calculation by BKW code using FORTRAN executable program. The thermal decomposition kinetics of sheet explosive formulations was investigated by differential scanning calorimetry. The activation energy for sheet explosive formulation HMX/HTPB-binder (78/22) was calculated using Kissinger kinetic method and found to be 170.08 kJ/mol, infer that sheet explosive formulation is thermally stable

Insect pathogens as biological control agents: back to the future

The development and use of entomopathogens as classical, conservation and augmentative biological control agents have included a number of successes and some setbacks in the past 15 years. In this forum paper we present current information on development, use and future directions of insect-specific viruses, bacteria, fungi and nematodes as components of integrated pest management strategies for control of arthropod pests of crops, forests, urban habitats, and insects of medical and veterinary importance. Insect pathogenic viruses are a fruitful source of MCAs, particularly for the control of lepidopteran pests. Most research is focused on the baculoviruses, important pathogens of some globally important pests for which control has become difficult due to either pesticide resistance or pressure to reduce pesticide residues. Baculoviruses are accepted as safe, readily mass produced, highly pathogenic and easily formulated and applied control agents. New baculovirus products are appearing in many countries and gaining an increased market share. However, the absence of a practical in vitro mass production system, generally higher production costs, limited post application persistence, slow rate of kill and high host specificity currently contribute to restricted use in pest control. Overcoming these limitations are key research areas for which progress could open up use of insect viruses to much larger markets. A small number of entomopathogenic bacteria have been commercially developed for control of insect pests. These include several Bacillus thuringiensis sub-species, Lysinibacillus (Bacillus) sphaericus, Paenibacillus spp. and Serratia entomophila. B. thuringiensis sub-species kurstaki is the most widely used for control of pest insects of crops and forests, and B. thuringiensis sub-species israelensis and L. sphaericus are the primary pathogens used for medically important pests including dipteran vectors,. These pathogens combine the advantages of chemical pesticides and microbial control agents (MCAs): they are fast acting, easy to produce at a relatively low cost, easy to formulate, have a long shelf life and allow delivery using conventional application equipment and systemics (i.e. in transgenic plants). Unlike broad spectrum chemical pesticides, B. thuringiensis toxins are selective and negative environmental impact is very limited. Of the several commercially produced MCAs, B. thuringiensis (Bt) has more than 50% of market share. Extensive research, particularly on the molecular mode of action of Bt toxins, has been conducted over the past two decades. The Bt genes used in insect-resistant transgenic crops belong to the Cry and vegetative insecticidal protein families of toxins. Bt has been highly efficacious in pest management of corn and cotton, drastically reducing the amount of broad spectrum chemical insecticides used while being safe for consumers and non-target organisms. Despite successes, the adoption of Bt crops has not been without controversy. Although there is a lack of scientific evidence regarding their detrimental effects, this controversy has created the widespread perception in some quarters that Bt crops are dangerous for the environment. In addition to discovery of more efficacious isolates and toxins, an increase in the use of Bt products and transgenes will rely on innovations in formulation, better delivery systems and ultimately, wider public acceptance of transgenic plants expressing insect-specific Bt toxins. Fungi are ubiquitous natural entomopathogens that often cause epizootics in host insects and possess many desirable traits that favor their development as MCAs. Presently, commercialized microbial pesticides based on entomopathogenic fungi largely occupy niche markets. A variety of molecular tools and technologies have recently allowed reclassification of numerous species based on phylogeny, as well as matching anamorphs (asexual forms) and teleomorphs (sexual forms) of several entomopathogenic taxa in the Phylum Ascomycota. Although these fungi have been traditionally regarded exclusively as pathogens of arthropods, recent studies have demonstrated that they occupy a great diversity of ecological niches. Entomopathogenic fungi are now known to be plant endophytes, plant disease antagonists, rhizosphere colonizers, and plant growth promoters. These newly understood attributes provide possibilities to use fungi in multiple roles. In addition to arthropod pest control, some fungal species could simultaneously suppress plant pathogens and plant parasitic nematodes as well as promote plant growth. A greater understanding of fungal ecology is needed to define their roles in nature and evaluate their limitations in biological control. More efficient mass production, formulation and delivery systems must be devised to supply an ever increasing market. More testing under field conditions is required to identify effects of biotic and abiotic factors on efficacy and persistence. Lastly, greater attention must be paid to their use within integrated pest management programs; in particular, strategies that incorporate fungi in combination with arthropod predators and parasitoids need to be defined to ensure compatibility and maximize efficacy. Entomopathogenic nematodes (EPNs) in the genera Steinernema and Heterorhabditis are potent MCAs. Substantial progress in research and application of EPNs has been made in the past decade. The number of target pests shown to be susceptible to EPNs has continued to increase. Advancements in this regard primarily have been made in soil habitats where EPNs are shielded from environmental extremes, but progress has also been made in use of nematodes in above-ground habitats owing to the development of improved protective formulations. Progress has also resulted from advancements in nematode production technology using both in vivo and in vitro systems; novel application methods such as distribution of infected host cadavers; and nematode strain improvement via enhancement and stabilization of beneficial traits. Innovative research has also yielded insights into the fundamentals of EPN biology including major advances in genomics, nematode-bacterial symbiont interactions, ecological relationships, and foraging behavior. Additional research is needed to leverage these basic findings toward direct improvements in microbial control

Grid-cell based assessment of soil erosion potential for identification of critical erosion prone areas using USLE, GIS and remote sensing: A case study in the Kapgari watershed, India

Estimation of soil erosion is of paramount importance due to its serious environmental and societal concern. Soil erosion would have impact on fertility of agricultural land and quality of water. The major objective of this study was to investigate the spatial heterogeneity of annual soil erosion on the grid-cell basis in a small agricultural watershed of eastern India. The study watershed has a drainage area of 973 ha and is subdivided into three sub-watersheds namely: KGSW1, KGSW2 and KGSW3, based on the land topography and drainage network. Average annual soil erosion was estimated on 100 m×100 m grid-cells by integrating universal soil loss equation (USLE) model with GIS for subsequent identification of critical erosion prone areas. It was found that 82.63% area of the total watershed falls under slight-erosion-class (0–5 t-ha−1-yr−1), 6.87% area lies under the moderate-erosion-class (5–10 t-ha−1-yr−1), 5.96% area is under high-erosion-class (10–20 t-ha−1-yr−1), 3.3% area of watershed lies under the very-high-erosion-class (20–40 t-ha−1-yr−1) and 1.24% area falls under “severe-erosion-class” (40–80 t-ha−1-yr−1). The study revealed that the sub-watershed KGSW3 is critical due to the presence of the highest number of critical erosion prone grid-cells. The sediment delivery ratio (SDR) was also estimated to analyze the contribution of sediment yield at the sub-watershed level. Lowest SDR for the whole watershed as compared to sub-watersheds indicates that most of the eroded soil got deposited in rice crop check-basins before reaching the outlet. The reported results can be used for prioritizing critical erosion prone areas and for determining appropriate soil erosion prevention and control measures

Feature Selection of Gene Expression Data for Cancer Classification: A Review

AbstractThe DNA microarray technology has capability to determine the levels of thousands of gene simultaneously in a single experiment. Analysis of gene expression is important in many fields of biological research in order to retrieve the required information. As time progresses, the illness in general and cancer in particular have become more and more complex and complicated, in detecting, analyzing and curing. We know cancer is deadly disease. Cancer research is one of the major area of research in medical field. Predicting precisely of different tumor types is a great challenge and providing accurate prediction will have great value in providing better treatment to the patients. To achieve this, data mining algorithms are important tools and the most extensively used approach to achieve important feature of gene expression data and plays an important role for gene classification. One of major challenges is to discover how to extract useful information from huge datasets. This paper presents recent advances in the machine learning based gene expression data analysis with different feature selection algorithms.Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. But compared to the number of genes involved, available training data sets generally have a fairly small sample size for classification. These training data limitations constitute a challenge to certain classification methodologies. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the un wanted noisy and redundant genes This paper presents a review of feature selection techniques that have been employed in micro array data based cancer classification and also the predominant role of SVM for cancer classification