Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Analysis of Overlapped and Noisy Hydrogen/Deuterium Exchange Mass Spectra

This document is the Accepted Manuscript version of a Published Work that appeared in final form in the Journal of the American Chemical Society, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://doi.org/10.1007/s13361-013-0727-5.Noisy and overlapped mass spectrometry data hinders the sequence coverage that can be obtained from Hydrogen Deuterium exchange analysis, and places a limit on the complexity of the samples that can be studied by this technique. Advances in instrumentation have addressed these limits, but as the complexity of the biological samples under investigation increases, these problems are reencountered. Here we describe the use of binomial distribution fitting with asymmetric linear squares regression for calculating the accurate deuterium content for mass envelopes of low signal or that contain significant overlap. The approach is demonstrated with a test data set of HIV Env gp140 wherein inclusion of the new analysis regime resulted in obtaining exchange data for 42 additional peptides, improving the sequence coverage by 11%. At the same time, the precision of deuterium uptake measurements was improved for nearly every peptide examined. The improved processing algorithms also provide an efficient method for deconvolution of bimodal mass envelopes and EX1 kinetic signatures. All these functions and visualization tools have been implemented in the new version of the freely available software, HX-Express v2

Alternative regression models to assess increase in childhood BMI

<p>Abstract</p> <p>Background</p> <p>Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations.</p> <p>Methods</p> <p>Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity.</p> <p>Results</p> <p>GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models.</p> <p>Conclusion</p> <p>GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.</p

Colorectal Cancer Prognosis Following Obesity Surgery in a Population-Based Cohort Study

Background: Obesity surgery involves mechanical and physiological changes of the gastrointestinal tract that might promote colorectal cancer progression. Thus, we hypothesised that obesity surgery is associated with poorer prognosis in patients with colorectal cancer. Methods: This nationwide population-based cohort study included all patients with an obesity diagnosis who subsequently developed colorectal cancer in Sweden from 1980 to 2012. The exposure was obesity surgery, and the main and secondary outcomes were disease-specific mortality and all-cause mortality, respectively. Cox proportional hazard survival models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs), adjusted for sex, age, calendar year and education level. Results: The exposed and unexposed cohort included 131 obesity surgery and 1332 non-obesity surgery patients with colorectal cancer. There was a statistically significant increased rate of colorectal cancer deaths following obesity surgery (disease-specific HR 1.50, 95% CI 1.00–2.19). When analysed separately, the mortality rate was more than threefold increased in rectal cancer patients with prior obesity surgery (disease-specific HR 3.70, 95% CI 2.00–6.90), while no increased mortality rate was found in colon cancer patients (disease-specific HR 1.10, 85% CI 0.67–1.70). Conclusion: This population-based study among obese individuals found a poorer prognosis in colorectal cancer following obesity surgery, which was primarily driven by the higher mortality rate in rectal cancer

Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients

Terrestrial ecosystems are receiving elevated inputs of nitrogen (N) from anthropogenic sources and understanding how these increases in N availability affect soil microbial communities is critical for predicting the associated effects on belowground ecosystems. We used a suite of approaches to analyze the structure and functional characteristics of soil microbial communities from replicated plots in two long-term N fertilization experiments located in contrasting systems. Pyrosequencing-based analyses of 16S rRNA genes revealed no significant effects of N fertilization on bacterial diversity, but significant effects on community composition at both sites; copiotrophic taxa (including members of the Proteobacteria and Bacteroidetes phyla) typically increased in relative abundance in the high N plots, with oligotrophic taxa (mainly Acidobacteria) exhibiting the opposite pattern. Consistent with the phylogenetic shifts under N fertilization, shotgun metagenomic sequencing revealed increases in the relative abundances of genes associated with DNA/RNA replication, electron transport and protein metabolism, increases that could be resolved even with the shallow shotgun metagenomic sequencing conducted here (average of 75 000 reads per sample). We also observed shifts in the catabolic capabilities of the communities across the N gradients that were significantly correlated with the phylogenetic and metagenomic responses, indicating possible linkages between the structure and functioning of soil microbial communities. Overall, our results suggest that N fertilization may, directly or indirectly, induce a shift in the predominant microbial life-history strategies, favoring a more active, copiotrophic microbial community, a pattern that parallels the often observed replacement of K-selected with r-selected plant species with elevated N

An interactome-centered protein discovery approach reveals novel components involved in mitosome function and homeostasis in giardia lamblia

Protozoan parasites of the genus Giardia are highly prevalent globally, and infect a wide range of vertebrate hosts including humans, with proliferation and pathology restricted to the small intestine. This narrow ecological specialization entailed extensive structural and functional adaptations during host-parasite co-evolution. An example is the streamlined mitosomal proteome with iron-sulphur protein maturation as the only biochemical pathway clearly associated with this organelle. Here, we applied techniques in microscopy and protein biochemistry to investigate the mitosomal membrane proteome in association to mitosome homeostasis. Live cell imaging revealed a highly immobilized array of 30–40 physically distinct mitosome organelles in trophozoites. We provide direct evidence for the single giardial dynamin-related protein as a contributor to mitosomal morphogenesis and homeostasis. To overcome inherent limitations that have hitherto severely hampered the characterization of these unique organelles we applied a novel interaction-based proteome discovery strategy using forward and reverse protein co-immunoprecipitation. This allowed generation of organelle proteome data strictly in a protein-protein interaction context. We built an initial Tom40-centered outer membrane interactome by co-immunoprecipitation experiments, identifying small GTPases, factors with dual mitosome and endoplasmic reticulum (ER) distribution, as well as novel matrix proteins. Through iterative expansion of this protein-protein interaction network, we were able to i) significantly extend this interaction-based mitosomal proteome to include other membrane-associated proteins with possible roles in mitosome morphogenesis and connection to other subcellular compartments, and ii) identify novel matrix proteins which may shed light on mitosome-associated metabolic functions other than Fe-S cluster biogenesis. Functional analysis also revealed conceptual conservation of protein translocation despite the massive divergence and reduction of protein import machinery in Giardia mitosomes

RNA interference-mediated c-MYC inhibition prevents cell growth and decreases sensitivity to radio- and chemotherapy in childhood medulloblastoma cells

BACKGROUND: With current treatment strategies, nearly half of all medulloblastoma (MB) patients die from progressive tumors. Accordingly, the identification of novel therapeutic strategies remains a major goal. Deregulation of c-MYC is evident in numerous human cancers. In MB, over-expression of c-MYC has been shown to cause anaplasia and correlate with unfavorable prognosis. METHODS: To study the role of c-MYC in MB biology, we down-regulated c-MYC expression by using small interfering RNA (siRNA) and investigated changes in cellular proliferation, cell cycle analysis, apoptosis, telomere maintenance, and response to ionizing radiation (IR) and chemotherapeutics in a representative panel of human MB cell lines expressing different levels of c-MYC (DAOY wild-type, DAOY transfected with the empty vector, DAOY transfected with c-MYC, D341, and D425). RESULTS: siRNA-mediated c-MYC down-regulation resulted in an inhibition of cellular proliferation and clonogenic growth, inhibition of G1-S phase cell cycle progression, and a decrease in human telomerase reverse transcriptase (hTERT) expression and telomerase activity. On the other hand, down-regulation of c-MYC reduced apoptosis and decreased the sensitivity of human MB cells to IR, cisplatin, and etoposide. This effect was more pronounced in DAOY cells expressing high levels of c-MYC when compared with DAOY wild-type or DAOY cells transfected with the empty vector. CONCLUSION: In human MB cells, in addition to its roles in growth and proliferation, c-MYC is also a potent inducer of apoptosis. Therefore, targeting c-MYC might be of therapeutic benefit when used sequentially with chemo- and radiotherapy rather than concomitantly

The Proteomic Code: a molecular recognition code for proteins

<p>Abstract</p> <p>Background</p> <p>The Proteomic Code is a set of rules by which information in genetic material is transferred into the physico-chemical properties of amino acids. It determines how individual amino acids interact with each other during folding and in specific protein-protein interactions. The Proteomic Code is part of the redundant Genetic Code.</p> <p>Review</p> <p>The 25-year-old history of this concept is reviewed from the first independent suggestions by Biro and Mekler, through the works of Blalock, Root-Bernstein, Siemion, Miller and others, followed by the discovery of a Common Periodic Table of Codons and Nucleic Acids in 2003 and culminating in the recent conceptualization of partial complementary coding of interacting amino acids as well as the theory of the nucleic acid-assisted protein folding.</p> <p>Methods and conclusions</p> <p>A novel cloning method for the design and production of specific, high-affinity-reacting proteins (SHARP) is presented. This method is based on the concept of proteomic codes and is suitable for large-scale, industrial production of specifically interacting peptides.</p