Bilinear modulation models for seasonal tables of counts

We propose generalized linear models for time or age-time tables of seasonal counts, with the goal of better understanding seasonal patterns in the data. The linear predictor contains a smooth component for the trend and the product of a smooth component (the modulation) and a periodic time series of arbitrary shape (the carrier wave). To model rates, a population offset is added. Two-dimensional trends and modulation are estimated using a tensor product B-spline basis of moderate dimension. Further smoothness is ensured using difference penalties on the rows and columns of the tensor product coefficients. The optimal penalty tuning parameters are chosen based on minimization of a quasi-information criterion. Computationally efficient estimation is achieved using array regression techniques, avoiding excessively large matrices. The model is applied to female death rate in the US due to cerebrovascular diseases and respiratory diseases

Cyclic di-GMP inactivates T6SS and T4SS activity in Agrobacterium tumefaciens

© 2019 The Authors. The Type VI secretion system (T6SS) is a bacterial nanomachine that delivers effector proteins into prokaryotic and eukaryotic preys. This secretion system has emerged as a key player in regulating the microbial diversity in a population. In the plant pathogen Agrobacterium tumefaciens, the signalling cascades regulating the activity of this secretion system are poorly understood. Here, we outline how the universal eubacterial second messenger cyclic di‐GMP impacts the production of T6SS toxins and T6SS structural components. We demonstrate that this has a significant impact on the ability of the phytopathogen to compete with other bacterial species in vitro and in planta. Our results suggest that, as opposed to other bacteria, c‐di‐GMP turns down the T6SS in A. tumefaciens thus impacting its ability to compete with other bacterial species within the rhizosphere. We also demonstrate that elevated levels of c‐di‐GMP within the cell decrease the activity of the Type IV secretion system (T4SS) and subsequently the capacity of A. tumefaciens to transform plant cells. We propose that such peculiar control reflects on c‐di‐GMP being a key second messenger that silences energy‐costing systems during early colonization phase and biofilm formation, while low c‐di‐GMP levels unleash T6SS and T4SS to advance plant colonization.Biotechnology and Biological Sciences Research Council. Grant Numbers: BB/L007959/1, BB/M02735X/1 Ministry of Science and Technology, Taiwan. Grant Number: 104-2311-B-001-025-MY

Visualization of Genomic Changes by Segmented Smoothing Using an L0 Penalty

Copy number variations (CNV) and allelic imbalance in tumor tissue can show strong segmentation. Their graphical presentation can be enhanced by appropriate smoothing. Existing signal and scatterplot smoothers do not respect segmentation well. We present novel algorithms that use a penalty on the norm of differences of neighboring values. Visualization is our main goal, but we compare classification performance to that of VEGA

Two dimensional smoothing via an optimised Whittaker smoother

Background In many applications where moderate to large datasets are used, plotting relationships between pairs of variables can be problematic. A large number of observations will produce a scatter-plot which is difficult to investigate due to a high concentration of points on a simple graph. In this article we review the Whittaker smoother for enhancing scatter-plots and smoothing data in two dimensions. To optimise the behaviour of the smoother an algorithm is introduced, which is easy to programme and computationally efficient. Results The methods are illustrated using a simple dataset and simulations in two dimensions. Additionally, a noisy mammography is analysed. When smoothing scatterplots the Whittaker smoother is a valuable tool that produces enhanced images that are not distorted by the large number of points. The methods is also useful for sharpening patterns or removing noise in distorted images. Conclusion The Whittaker smoother can be a valuable tool in producing better visualisations of big data or filter distorted images. The suggested optimisation method is easy to programme and can be applied with low computational cost

Selection of tuning parameters in bridge regression models via Bayesian information criterion

We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge regression models. The choice of the adjusted parameters can be viewed as a model selection and evaluation problem. We propose a model selection criterion for evaluating bridge regression models in terms of Bayesian approach. This selection criterion enables us to select the adjusted parameters objectively. We investigate the effectiveness of our proposed modeling strategy through some numerical examples.Comment: 20 pages, 5 figure

Restriction of memory B cell differentiation at the germinal center B cell positive selection stage

Memory B cells (MBCs) are key for protection from reinfection. However, it is mechanistically unclear how germinal center (GC) B cells differentiate into MBCs. MYC is transiently induced in cells fated for GC expansion and plasma cell (PC) formation, so-called positively selected GC B cells. We found that these cells coexpressed MYC and MIZ1 (MYC-interacting zinc-finger protein 1 [ZBTB17]). MYC and MIZ1 are transcriptional activators; however, they form a transcriptional repressor complex that represses MIZ1 target genes. Mice lacking MYC-MIZ1 complexes displayed impaired cell cycle entry of positively selected GC B cells and reduced GC B cell expansion and PC formation. Notably, absence of MYC-MIZ1 complexes in positively selected GC B cells led to a gene expression profile alike that of MBCs and increased MBC differentiation. Thus, at the GC positive selection stage, MYC-MIZ1 complexes are required for effective GC expansion and PC formation and to restrict MBC differentiation. We propose that MYC and MIZ1 form a module that regulates GC B cell fate

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

Measuring the density fields around bright quasars at z ∼6 with XQR-30 Spectra

Measuring the density of the intergalactic medium using quasar sight lines in the epoch of reionization is challenging due to the saturation of Lyα absorption. Near a luminous quasar, however, the enhanced radiation creates a proximity zone observable in the quasar spectra where the Lyα absorption is not saturated. In this study, we use 10 high-resolution (R â 3 10,000) z ∼6 quasar spectra from the extended XQR-30 sample to measure the density field in the quasar proximity zones. We find a variety of environments within 3 pMpc distance from the quasars. We compare the observed density cumulative distribution function (CDF) with models from the Cosmic Reionization on Computers simulation and find a good agreement between 1.5 and 3 pMpc from the quasar. This region is far away from the quasar hosts and hence approaching the mean density of the universe, which allows us to use the CDF to set constraints on the cosmological parameter σ 8 = 0.6 ± 0.3. The uncertainty is mainly due to the limited number of high-quality quasar sight lines currently available. Utilizing the more than 200 known quasars at z â 3 6, this method will allow us to tighten the constraint on σ 8 to the percent level in the future. In the region closer to the quasar within 1.5 pMpc, we find that the density is higher than predicted in the simulation by 1.23 ± 0.17, suggesting that the typical host dark matter halo mass of a bright quasar (M 1450 <-26.5) at z ∼6 is log10(Mh/M⊠)=12.5-0.7+0.4

Examining the Decline in the C~IV Content of the Universe over 4.3 ≲ z ≲ 6.3 using the E-XQR-30 Sample

Intervening C iv absorbers are key tracers of metal-enriched gas in galaxy haloes over cosmic time. Previous studies suggest that the C iv cosmic mass density (⁠[Math Processing Error]⁠) decreases slowly over 1.5 [Math Processing Error] 5 before declining rapidly at z ≳ 5, but the cause of this downturn is poorly understood. We characterize the [Math Processing Error] evolution over 4.3 ≲ z ≲ 6.3 using 260 absorbers found in 42 XSHOOTER spectra of z ∼ 6 quasars, of which 30 come from the ESO Large Program XQR-30. The large sample enables us to robustly constrain the rate and timing of the downturn. We find that [Math Processing Error] decreases by a factor of 4.8 ± 2.0 over the ∼300 Myr interval between z ∼ 4.7 and ∼5.8. The slope of the column density (log N) distribution function does not change, suggesting that C iv absorption is suppressed approximately uniformly across 13.2 ≤ log N/cm−2 < 15.0. Assuming that the carbon content of galaxy haloes evolves as the integral of the cosmic star formation rate density (with some delay due to stellar lifetimes and outflow travel times), we show that chemical evolution alone could plausibly explain the fast decline in [Math Processing Error] over 4.3 ≲ z ≲ 6.3. However, the C iv/C ii ratio decreases at the highest redshifts, so the accelerated decline in [Math Processing Error] at z ≳ 5 may be more naturally explained by rapid changes in the gas ionization state driven by evolution of the UV background towards the end of hydrogen reionization