Short SULF1/SULF2 splice variants predominate in mammary tumours with a potential to facilitate receptor tyrosine kinase-mediated cell signalling

The relative roles of SULF1 and SULF2 enzymes in tumour growth are controversial, but short SULF1/SULF2 splice variants predominate in human mammary tumours despite their non-detectable levels in normal mammary tissue. Compared with the normal, the level of receptor tyrosine kinase (RTK) activity was markedly increased in triple-positive mammary tumours during later stages of tumour progression showing increased p-EGFR, p-FGFR1 and p-cMet activity in triple-positive but not in triple-negative tumours. The abundance of catalytically inactive short SULF1/SULF2 variants permits high levels of HS sulphation and thus growth driving RTK cell signalling in primary mammary tumours. Also observed in this study, however, was increased N-sulphation detected by antibody 10E4 indicating that not only 6-O sulphation but also N-sulphation may contribute to increased RTK cell signalling in mammary tumours. The levels of such increases in not only SULF1/SULF2 but also in pEGFR, pFGFR1, p-cMet and Smad1/5/8 signalling were further enhanced following lymph node metastasis. The over-expression of Sulf1 and Sulf2 variants in mammary tumour-derived MDA-MB231 and MCF7 cell lines by transfection further confirms Sulf1-/Sulf2-mediated differential modulation of growth. The short variants of both Sulf1 and Sulf2 promoted FGF2-induced MDA-MB231 and MCF7 in vitro growth while full-length Sulf1 inhibited growth supporting in vivo mammary tumour cell signalling patterns of growth. Since a number of mammary tumours become drug resistant to hormonal therapy, Sulf1/Sulf2 inhibition could be an alternative therapeutic approach to target such tumours by down-regulating RTK-mediated cell signalling

Wheat forecast economics effect study

A model to assess the value of improved information regarding the inventories, productions, exports, and imports of crop on a worldwide basis is discussed. A previously proposed model is interpreted in a stochastic control setting and the underlying assumptions of the model are revealed. In solving the stochastic optimization problem, the Markov programming approach is much more powerful and exact as compared to the dynamic programming-simulation approach of the original model. The convergence of a dual variable Markov programming algorithm is shown to be fast and efficient. A computer program for the general model of multicountry-multiperiod is developed. As an example, the case of one country-two periods is treated and the results are presented in detail. A comparison with the original model results reveals certain interesting aspects of the algorithms and the dependence of the value of information on the incremental cost function

A study of the application of singular perturbation theory

A hierarchical real time algorithm for optimal three dimensional control of aircraft is described. Systematic methods are developed for real time computation of nonlinear feedback controls by means of singular perturbation theory. The results are applied to a six state, three control variable, point mass model of an F-4 aircraft. Nonlinear feedback laws are presented for computing the optimal control of throttle, bank angle, and angle of attack. Real Time capability is assessed on a TI 9900 microcomputer. The breakdown of the singular perturbation approximation near the terminal point is examined Continuation methods are examined to obtain exact optimal trajectories starting from the singular perturbation solutions

A simple sandpile model of active-absorbing state transitions

We study a simple sandpile model of active-absorbing state transitions in which a particle can hop out of a site only if the number of particles at that site is above a certain threshold. We show that the active phase has product measure whereas nontrivial correlations are found numerically in the absorbing phase. It is argued that the system relaxes to the latter phase slower than exponentially. The critical behavior of this model is found to be different from that of the other known universality classes.Comment: Revised version. To appear in Phys. Rev.

Assessment of acid phosphatase enzyme and influence of potassium iodide on its production in the yeast form of Sporothrix schenckii

Background: Sporotrichosis is caused by a dimorphic fungal species, Sporothrix schenckii (S. schenckii). The enzyme acid phosphatase is pervasive among yeast and yeast like fungi. It has been studied in various fungi like Aspergillus oryzae, Candida albicans etc. but in S. schenckii little is known about enzyme acid phosphatase. The present study depicts the in-vitro influence of Potassium Iodide (KI) on the enzyme acid phosphatase produced by the S. schenckii (yeast form).Methods: A master culture was prepared by incorporating the standard strain of S. schenckii in YNB (Yeast Nitrogen Base) medium and was incubated at 37ºC. After preparing the increasing concentrations with KI in YNB medium, 1.0 mL suspension of master culture was inoculated into each bottle and incubated at 37ºC for different time period 6th, 12th, 18th day (early, mid, peak of log period) respectively. After centrifuging, a 5% homogenate was prepared, which was used for acid phosphatase enzyme assay.Results: The mean acid phosphatase level of control specimen was 20.9±2.01, 50.0±2.25, 45.0±5.10 μg and test specimens was ranged from 14.9±4.89 to 20.2±3.49, 10.2±4.19 to 40.0±6.39 and 10.0±1.81 to 34.7±6.08 μg on day 6, 12 and 18 respectively. The mean value was lower significantly for all the test concentrations as compared to control (p<0.05).Conclusions: The low activity of the enzyme acid phosphatase indicates that KI has inhibitory effect on the growth of S. schenckii that has led to decrease in the activity of the enzyme

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF) in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of the risk of SNB, facilitating sound disease management decisions prior to planting of wheat

Strengthening the integration of midwifery in health systems; a leader-to-leader collaboration

Barriers and facilitators for quality midwifery care exist on different levels in the health systems. After decades of challenges and varied degrees of success, a stakeholder leader-to-leader collaboration could provide added value through knowledge sharing on how to integrate the midwifery cadre into an existing health system. Initiated by The Midwifery Society of Nepal, Dalarna University Sweden and MAMTA - Health Institute for Mother and Child India, a research network focusing midwifery has been formed. The background, purpose and activities of this network has been described in this News and Events paper

Porcine brachial artery tortuosity for in vivo evaluation of neuroendovascular devices

We report a novel model of arterial tortuosity in the porcine brachial artery for testing of endovascular devices in the flexed forelimb position. This provides an ideal vascular territory for an in vivo assessment of guidewires, microcatheters, and endovascular implants because it closely mimics the challenging curvature at the carotid siphon

Trapping of a random walk by diffusing traps

We present a systematic analytical approach to the trapping of a random walk by a finite density rho of diffusing traps in arbitrary dimension d. We confirm the phenomenologically predicted e^{-c_d rho t^{d/2}} time decay of the survival probability, and compute the dimension dependent constant c_d to leading order within an eps=2-d expansion.Comment: 16 pages, to appear in J. Phys.