On production and abatement time scales in sustainable development. Can we loosen the sustainability screw ?

In this paper we carry out a preliminary exploration of a time scales' conjecture, which postulates that "reasonable" notions of sustainability must include a suitable synchronisation of time scales of both the processes of human development and those of the natural environment. We perform our analysis within a coarse, ?ve variable, model of man-nature interactions expressed as a system of differential equations where production and human capital are coupled with both renewable and non-renewable natural resource. We demonstrate a phenomenon that we name the "sustainability screw" that describes a spiral like trajectory of the three key variables: non-renewable and renewable resources as well as the production capital. Under many plausible scenarios, this spiral tends unacceptably fast to an undesirable equilibrium. However, we also show that by adjusting the ratio of "intensity of production effort" and "intensity of abatement effort", parameters of the relative time scales of production and natural recovery processes can be altered in a manner that produces, arguably, more sustainable trajectories.sustainable optimization systems, viability, multiple time scale

Role of a Conserved Glutamate Residue in the \u3cem\u3eEscherichia coli\u3c/em\u3e SecA ATPase Mechanism

Escherichia coli SecA uses ATP to drive the transport of proteins across cell membranes. Glutamate 210 in the “DEVD” Walker B motif of the SecA ATP-binding site has been proposed as the catalytic base for ATP hydrolysis (Hunt, J. F., Weinkauf, S., Henry, L., Fak, J. J., McNicholas, P., Oliver, D. B., and Deisenhofer, J. (2002) Science 297, 2018–2026). Consistent with this hypothesis, we find that mutation of glutamate 210 to aspartate results in a 90-fold reduction of the ATP hydrolysis rate compared with wild type SecA, 0.3 s–1versus 27 s–1, respectively. SecA-E210D also releases ADP at a slower rate compared with wild type SecA, suggesting that in addition to serving as the catalytic base, glutamate 210 might aid turnover as well. Our results contradict an earlier report that proposed aspartate 133 as the catalytic base (Sato, K., Mori, H., Yoshida, M., and Mizushima, S. (1996) J. Biol. Chem. 271, 17439–17444). Re-evaluation of the SecA-D133N mutant used in that study confirms its loss of ATPase and membrane translocation activities, but surprisingly, the analogous SecA-D133A mutant retains full activity, revealing that this residue does not play a key role in catalysis

Fermentation and Lactic Acid Addition Enhance Iron Bioavailability of Maize

Maize is one of the most important cereal crops for human consumption, yet it is of concern due to its low iron bioavailability. The objective of this study was to determine the effects of processing on iron bioavailability in common maize products and elucidate better processing techniques for enhancing iron bioavailability. Maize products were processed to represent different processing techniques: heating (porridge), fermentation (ogi), nixtamalization (tortillas), and decortication (arepas). Iron and phytate contents were evaluated. Iron bioavailability was assessed using the Caco-2 cell model. Phytate content of maize products was significantly reduced by decortication (25.6%, p ) 0.003) and nixtamalization (15%, p ) 0.03), and iron content was reduced by decortication (29.1%, p ) 0.002). The relative bioavailability (RBA, compared to 100% bioavailability of porridge with FeSO4) of ogi was significantly higher than that of other products when fortified with FeSO4 (p \u3c 0.001) or reduced iron (p \u3c 0.001). Addition of lactic acid (6 mg/g of maize) significantly increased iron solubility and increased bioavailability by about 2-fold (p \u3c 0.01), especially in tortillas. The consumer panel results showed that lactic acid addition does not significantly affect the organoleptic characteristics of tortillas and arepas (p ) 0.166 and 0.831, respectively). The results suggest that fermentation, or the addition of small amounts of lactic acid to unfermented maize products, may significantly improve iron bioavailability. Lactic acid addition may be more feasible than the addition of highly bioavailable but expensive fortificants. This approach may be a novel means to increase the iron bioavailability of maize products to reduce the incidence of iron deficiency anemia

Comparision of Different Classifiers for Prediction of Breast Cancer

The cell formed in the  breast are known as breast cancer. It occurs mainly in women and it may occur rarely in men also. It is considered as the most common ailment that can lead to large number of death in females every year. In spite of the factuality that cancer is treatable and can be relieve if treated at its early stages; many patients are screened for cancer only at a very late stage. Data mining technique such as classifications provides an efficient technique to classify data, where these methods are commonly used for diagnostic decision making. The Machine learning techniques propound various methods such as statistical and probabilistic methods which allow system to learn from past experiences to distinguish and identify patterns from a standard dataset. The research work presents a review of machine learning techniques which can be used in breast cancer disease detection by applying algorithms on breast cancer Wisconsin data set.  Algorithms such as Navies Bayes, Random Forest, Support Vector Machine, Adaboost and Decision Trees were used. The result outcome shows that Random Forest performs better than other techniques

Prevalence of various urogynaecological problems and their subsequent management with outcome amongst women attending a tertiary care hospital of a developing Country

Background: The prevalence of urogynaecological problems may be significantly underestimated since the physicians rarely ask women about these problems and women seldom initiate discussion about these symptoms on their own with the physician.Methods: The present study was conducted from March 2006 to August 2008. All the women between 20 to 80 years of age with varying parity attending gynecological outpatient department were evaluated for urogynaecological and bowel problems, based on a questionnaire incorporating demographic and urogynaecological symptoms (IUGA terminology). The frequency of various urinary problems was correlated with the demographic data, urodynamic studies and cystoscopic findings, whenever appropriate and available. Exclusion criteria: The women with disorders of central nervous system, retention urine and pregnancy were excluded from the study.Results: During this period, 15100 women attended outpatient department of gynecology. Out of these, 376 women had urogynaecological and bowel problems. The prevalence of urogynaecological and bowel problems was 24.9 per 1000 women. The incidence of symptoms was dysuria in 38.5 % women, increased frequency of micturition in 38% women; feeling of something coming out per vaginum in 37% women, nocturia in 27.6 % women and pain lower abdomen in 25 % women.Conclusions: Amongst incontinence, 31.3% women had stress incontinence, 25% women had urge incontinence, 14.6% women had urgency, 8.7% women had continuous urinary incontinence and 2.12% women had anal incontinence

Multilayer Feedforward Neural Network for Internet Traffic Classification

Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard)