Adaptively subtracting multiple model from the initial data is an essential assignment for the successful elimination of seismic surface-related multiples. Conventional expanded multichannel linear regression (EMLR) method has been proposed to address this challenge by utilizing multi-pattern multiple model. These patterns include the multiple model itself and its first derivative, its Hilbert transform and its first derivative of the Hilbert transform, which are matched with the initial data in the EMLR method. It may lead to inaccurate primary preservation or give rise to residual multiples by using the LR model. The existing U-Net method effectively mitigates complex disparities between the multiple model and actual multiples through integrating adaptive subtraction into the non-LR architecture. Nevertheless, residual multiples are produced by this method using the multiple model itself, especially in complex media contexts. In order to improve surface-related multiple removal’s accuracy, we propose the expanded multichannel U-Net (EMUN) method with multi-pattern multiple model. In the proposed method, the initial data is matched with the multi-pattern multiple model through U-Net in the way of self-supervised training without true primaries as labels. The proposed method incorporates rich information from expanded multichannel of U-Net, enabling better U-Net training for adaptive subtraction. In contrast to the EMLR method and the existing U-Net method, the proposed EMUN method exhibits exceptional efficacy in protecting primaries and eliminating surface-related multiples, as evidenced by its outstanding performance in both synthetic and field data tests.

Effectively suppressing seismic multiples relies heavily on the crucial task of adaptively subtracting the simulated multiples from the initial recorded data. By executing adaptive subtraction within the non-linear regression (non-LR) framework the U-net method has shown superior capability in mitigating the intricate disparities between the simulated and actual multiples when compared to the LR method. The low, medium and high frequency-bands of simulated multiples have been employed to effectively address frequency-dependent inconsistencies in the LR method. To further improve multiple suppression accuracy three frequency-bands of simulated multiples are employed as three channels of the U-net input, which are matched with the initial recorded data during self-supervised training in this letter. Compared to the LR method inputting simulated multiples alone, the LR method inputting three frequency-bands of simulated multiples and the U-net method inputting simulated multiples alone, the proposed U-net method inputting three frequency-bands of simulated multiples improves the signal-to-noise ratio (SNR) by 4.41, 2.07 and 1.99 in the synthetic data example, and demonstrates superior improvement in preserving primaries and eliminating residual multiples in the field data example.

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design.

MOTIVATION: Gene regulatory networks (GRNs) are stochastic, thus, do not have attractors, but can remain in confined regions of the state space, i.e. the 'noisy attractors', which define the cell type and phenotype. RESULTS: We propose a gamma-Bernoulli mixture model clustering algorithm (GammaBMM), tailored for quantizing states from gamma and Bernoulli distributed data, to determine the noisy attractors of stochastic GRN. GammaBMM uses multiple data sources, naturally selects the number of states and can be extended to other parametric distributions according to the number and type of data sources available. We apply it to protein and RNA levels, and promoter occupancy state of a toggle switch and show that it can be bistable, tristable or monostable depending on its internal noise level. We show that these results are in agreement with the patterns of differentiation of model cells whose pathway choice is driven by the switch. We further apply GammaBMM to a model of the MeKS module of Bacillus subtilis, and the results match experimental data, demonstrating the usability of GammaBMM. AVAILABILITY: Implementation software is available upon request.

BACKGROUND: Cluster analysis has become a standard computational method for gene function discovery as well as for more general explanatory data analysis. A number of different approaches have been proposed for that purpose, out of which different mixture models provide a principled probabilistic framework. Cluster analysis is increasingly often supplemented with multiple data sources nowadays, and these heterogeneous information sources should be made as efficient use of as possible. RESULTS: This paper presents a novel Beta-Gaussian mixture model (BGMM) for clustering genes based on Gaussian distributed and beta distributed data. The proposed BGMM can be viewed as a natural extension of the beta mixture model (BMM) and the Gaussian mixture model (GMM). The proposed BGMM method differs from other mixture model based methods in its integration of two different data types into a single and unified probabilistic modeling framework, which provides a more efficient use of multiple data sources than methods that analyze different data sources separately. Moreover, BGMM provides an exceedingly flexible modeling framework since many data sources can be modeled as Gaussian or beta distributed random variables, and it can also be extended to integrate data that have other parametric distributions as well, which adds even more flexibility to this model-based clustering framework. We developed three types of estimation algorithms for BGMM, the standard expectation maximization (EM) algorithm, an approximated EM and a hybrid EM, and propose to tackle the model selection problem by well-known model selection criteria, for which we test the Akaike information criterion (AIC), a modified AIC (AIC3), the Bayesian information criterion (BIC), and the integrated classification likelihood-BIC (ICL-BIC). CONCLUSION: Performance tests with simulated data show that combining two different data sources into a single mixture joint model greatly improves the clustering accuracy compared with either of its two extreme cases, GMM or BMM. Applications with real mouse gene expression data (modeled as Gaussian distribution) and protein-DNA binding probabilities (modeled as beta distribution) also demonstrate that BGMM can yield more biologically reasonable results compared with either of its two extreme cases. One of our applications has found three groups of genes that are likely to be involved in Myd88-dependent Toll-like receptor 3/4 (TLR-3/4) signaling cascades, which might be useful to better understand the TLR-3/4 signal transduction.

Motion-detection only games have inherent limitations on game experience in that the systems cannot identify the player's existence and identity. A way of improvement is by introducing information such as a player's face or head into the system. We designed and implemented two game prototypes that apply real-time face position information as intrinsic elements of gameplay to enhance game experience. The first prototype augmented a typical motion-detection-based game. Face information was designed to enhance the sense of presence and role-playing. In the second prototype, face tracking is applied as a new axis of control in a First Person Shooter (FPS) game.Although Face detection and tracking technology has started utilizing in game scenarios, there was little systematic research on how user experience is leveraged by applying face information to video games. The results of our user tests on comparing camera-based video games with and without face tracking demonstrated that using face position information can effectively enhance presence and role-playing. In addition, an intuitive control that augmented by face-tracking in the FPS game also got positive feedbacks from the test.