Bridging Time Scales in Cellular Decision Making with a Stochastic Bistable Switch

Cellular transformations which involve a significant phenotypical change of the cell's state use bistable biochemical switches as underlying decision systems. In this work, we aim at linking cellular decisions taking place on a time scale of years to decades with the biochemical dynamics in signal transduction and gene regulation, occuring on a time scale of minutes to hours. We show that a stochastic bistable switch forms a viable biochemical mechanism to implement decision processes on long time scales. As a case study, the mechanism is applied to model the initiation of follicle growth in mammalian ovaries, where the physiological time scale of follicle pool depletion is on the order of the organism's lifespan. We construct a simple mathematical model for this process based on experimental evidence for the involved genetic mechanisms. Despite the underlying stochasticity, the proposed mechanism turns out to yield reliable behavior in large populations of cells subject to the considered decision process. Our model explains how the physiological time constant may emerge from the intrinsic stochasticity of the underlying gene regulatory network. Apart from ovarian follicles, the proposed mechanism may also be of relevance for other physiological systems where cells take binary decisions over a long time scale.Comment: 14 pages, 4 figure

Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordLOD 2019: Fifth International Conference on Machine Learning, Optimization, and Data Science, 10-13 September 2019, Siena, ItalyGaussian processes (GPs) belong to a class of probabilistic techniques that have been successfully used in different domains of machine learning and optimization. They are popular because they provide uncertainties in predictions, which sets them apart from other modelling methods providing only point predictions. The uncertainty is particularly useful for decision making as we can gauge how reliable a prediction is. One of the fundamental challenges in using GPs is that the efficacy of a model is conferred by selecting an appropriate kernel and the associated hyperparameter values for a given problem. Furthermore, the training of GPs, that is optimizing the hyperparameters using a data set is traditionally performed using a cost function that is a weighted sum of data fit and model complexity, and the underlying trade-off is completely ignored. Addressing these challenges and shortcomings, in this article, we propose the following automated training scheme. Firstly, we use a weighted product of multiple kernels with a view to relieve the users from choosing an appropriate kernel for the problem at hand without any domain specific knowledge. Secondly, for the first time, we modify GP training by using a multi-objective optimizer to tune the hyperparameters and weights of multiple kernels and extract an approximation of the complete trade-off front between data-fit and model complexity. We then propose to use a novel solution selection strategy based on mean standardized log loss (MSLL) to select a solution from the estimated trade-off front and finalise training of a GP model. The results on three data sets and comparison with the standard approach clearly show the potential benefit of the proposed approach of using multi-objective optimization with multiple kernels.Natural Environment Research Council (NERC

An open, multi-centre, phase II clinical trial to evaluate the efficacy and safety of paclitaxel, UFT, and leucovorin in patients with advanced gastric cancer

The aim of the study was to evaluate the response rate and safety of weekly paclitaxel (Taxol®) combination chemotherapy with UFT (tegafur, an oral 5-fluorouracil prodrug, and uracil at a 1 : 4 molar ratio) and leucovorin (LV) in patients with advanced gastric cancer. Patients with histologically confirmed, locally advanced or recurrent/metastatic gastric cancer were studied. Paclitaxel 1-h infusion at a dose of 100 mg m−2 on days 1 and 8 and oral UFT 300 mg m−2 day−1 plus LV 90 mg day−1 were given starting from day 1 for 14 days, followed by a 7-day period without treatment. Treatment was repeated every 21 days. From February 2003 to October 2004, 55 patients were enrolled. The median age was 62 years (range: 32–82). Among the 48 patients evaluated for tumour response, two achieved a complete response and 22 a partial response, with an overall response rate of 50% (95% confidence interval: 35–65%). All 55 patients were evaluated for survival and toxicities. Median time to progression and overall survival were 4.4 and 9.8 months, respectively. Major grade 3–4 toxicities were neutropenia in 25 patients (45%) and diarrhoea in eight patients (15%). Although treatment was discontinued owing to treatment-related toxicities in nine patients (16%), there was no treatment-related mortality. Weekly paclitaxel plus oral UFT/LV is effective, convenient, and well tolerated in treating patients with advanced gastric cancer

Improved Weighted Random Forest for Classification Problems

Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models. The key to make well-performing ensemble model is in the diversity of the base models. Of the most common solutions for introducing diversity into the decision trees are bagging and random forest. Bagging enhances the diversity by sampling with replacement and generating many training data sets, while random forest adds selecting a random number of features as well. This has made the random forest a winning candidate for many machine learning applications. However, assuming equal weights for all base decision trees does not seem reasonable as the randomization of sampling and input feature selection may lead to different levels of decision-making abilities across base decision trees. Therefore, we propose several algorithms that intend to modify the weighting strategy of regular random forest and consequently make better predictions. The designed weighting frameworks include optimal weighted random forest based on ac-curacy, optimal weighted random forest based on the area under the curve (AUC), performance-based weighted random forest, and several stacking-based weighted random forest models. The numerical results show that the proposed models are able to introduce significant improvements compared to regular random forest

A Study of B0 -> J/psi K(*)0 pi+ pi- Decays with the Collider Detector at Fermilab

We report a study of the decays B0 -> J/psi K(*)0 pi+ pi-, which involve the creation of a u u-bar or d d-bar quark pair in addition to a b-bar -> c-bar(c s-bar) decay. The data sample consists of 110 1/pb of p p-bar collisions at sqrt{s} = 1.8 TeV collected by the CDF detector at the Fermilab Tevatron collider during 1992-1995. We measure the branching ratios to be BR(B0 -> J/psi K*0 pi+ pi-) = (8.0 +- 2.2 +- 1.5) * 10^{-4} and BR(B0 -> J/psi K0 pi+ pi-) = (1.1 +- 0.4 +- 0.2) * 10^{-3}. Contributions to these decays are seen from psi(2S) K(*)0, J/psi K0 rho0, J/psi K*+ pi-, and J/psi K1(1270)

Diffractive Dijet Production at sqrt(s)=630 and 1800 GeV at the Fermilab Tevatron

We report a measurement of the diffractive structure function $F_{jj}^D$ of the antiproton obtained from a study of dijet events produced in association with a leading antiproton in $\bar pp$ collisions at $\sqrt s=630$ GeV at the Fermilab Tevatron. The ratio of $F_{jj}^D$ at $\sqrt s=630$ GeV to $F_{jj}^D$ obtained from a similar measurement at $\sqrt s=1800$ GeV is compared with expectations from QCD factorization and with theoretical predictions. We also report a measurement of the $\xi$ ($x$-Pomeron) and $\beta$ ($x$ of parton in Pomeron) dependence of $F_{jj}^D$ at $\sqrt s=1800$ GeV. In the region $0.035<\xi<0.095$, $|t|<1$ GeV$^2$ and $\beta<0.5$, $F_{jj}^D(\beta,\xi)$ is found to be of the form $\beta^{-1.0\pm 0.1} \xi^{-0.9\pm 0.1}$, which obeys $\beta$-$\xi$ factorization.Comment: LaTeX, 9 pages, Submitted to Phys. Rev. Letter

Search for New Particles Decaying to top-antitop in proton-antiproton collisions at squareroot(s)=1.8 TeV

We use 106 \ipb of data collected with the Collider Detector at Fermilab to search for narrow-width, vector particles decaying to a top and an anti-top quark. Model independent upper limits on the cross section for narrow, vector resonances decaying to \ttbar are presented. At the 95% confidence level, we exclude the existence of a leptophobic \zpr boson in a model of topcolor-assisted technicolor with mass M_{\zpr} $<$ 480 \gev for natural width $\Gamma$ = 0.012 M_{\zpr}, and M_{\zpr} $<$ 780 \gev for $\Gamma$ = 0.04 M_{\zpr}.Comment: The CDF Collaboration, submitted to PRL 25-Feb-200

Double Diffraction Dissociation at the Fermilab Tevatron Collider

We present results from a measurement of double diffraction dissociation in $\bar pp$ collisions at the Fermilab Tevatron collider. The production cross section for events with a central pseudorapidity gap of width $\Delta\eta^0>3$ (overlapping $\eta=0$) is found to be $4.43\pm 0.02{(stat)}{\pm 1.18}{(syst) mb}$ [$3.42\pm 0.01{(stat)}{\pm 1.09}{(syst) mb}$] at $\sqrt{s}=1800$ [630] GeV. Our results are compared with previous measurements and with predictions based on Regge theory and factorization.Comment: 10 pages, 4 figures, using RevTeX. Submitted to Physical Review Letter

Electroluminescent Characteristics of DBPPV–ZnO Nanocomposite Polymer Light Emitting Devices

We have demonstrated that fabrication and characterization of nanocomposite polymer light emitting devices with metal Zinc Oxide (ZnO) nanoparticles and 2,3-dibutoxy-1,4-poly(phenylenevinylene) (DBPPV). The current and luminance characteristics of devices with ZnO nanoparticles are much better than those of device with pure DBPPV. Optimized maximum luminance efficiencies of DBPPV–ZnO (3:1 wt%) before annealing (1.78 cd/A) and after annealing (2.45 cd/A) having a brightness 643 and 776 cd/m2at a current density of 36.16 and 31.67 mA/cm2are observed, respectively. Current density–voltage and brightness–voltage characteristics indicate that addition of ZnO nanoparticles can facilitate electrical injection and charge transport. The thermal annealing is thought to result in the formation of an interfacial layer between emissive polymer film and cathode