Modeling and identification of gene regulatory networks: A Granger causality approach

It is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from time-series genomic data, i.e., to explore the interactions among a large number of genes and gene products over time. Currently, one common approach is based on Granger causality, which models the time-series genomic data as a vector autoregressive (VAR) process and estimates the GRNs from the VAR coefficient matrix. The main challenge for identification of VAR models is the high dimensionality of genes and limited number of time points, which results in statistically inefficient solution and high computational complexity. Therefore, fast and efficient variable selection techniques are highly desirable. In this paper, an introductory review of identification methods and variable selection techniques for VAR models in learning the GRNs will be presented. Furthermore, a dynamic VAR (DVAR) model, which accounts for dynamic GRNs changing with time during the experimental cycle, and its identification methods are introduced. © 2010 IEEE.published_or_final_versionThe 9th International Conference on Machine Learning and Cybernetics (ICMLC 2010), Qingdao, China, 11-14 July 2010. In Proceedings of the 9th ICMLC, 2010, v. 6, p. 3073-307

Representation of temporal memory retrieval in the human precuneus

Shared neural ensembles link distinct memories encoded close in time, thus events encoded within close temporal distance (TD) are more likely to be co-recalled than events encoded across more distant TD: here we identified the multivoxel response pattern reflecting this effect in human parietal cortex

Efficient Implementation and Design of A New Single-Channel Electrooculography-based Human-Machine Interface System

published_or_final_versio

Depletion of Arabidopsis ACYL-COA-BINDING PROTEIN3 affects fatty acid composition in the phloem

published_or_final_versio

Assessing the decarbonization of electricity generation in major emitting countries by 2030 and 2050: Transition to a high share renewable energy mix

The urgent need to mitigate the severe environmental impacts of climate change necessitates a transition to a low-carbon energy infrastructure, crucial for decarbonization and achieving global sustainability goals. This study investigates the decarbonization trajectories of five major economies and significant carbon emitters: the United States of America (USA), China, Japan, Germany, and India. We focus on evaluating two decarbonization scenarios for power generation. Scenario 1 explores the use of a generic storage system for reducing critical excess electricity production (CEEP), maintaining the same thermal power plant capacity as in the reference year 2021. In contrast, Scenario 2 models thermal power plants to meet the exact electricity demand without introducing a new electricity storage system. The primary aim is to assess the feasibility and implications of achieving a 100% share of renewable and nuclear energy by 2030 and 2050 in these countries. EnergyPLAN software was utilized to model and simulate the electricity systems of these countries. The two scenarios represent different degrees of renewable energy integration, demonstrating possible transitional pathways towards an environmentally friendly electricity generation system. The study provides a comparative analysis of the outcomes for each country, focusing on carbon emissions reduction and the impact on annual total costs in 2030 and 2050. Results show that by 2030, China could reduce its emissions by 88.5% and 85.14% in Scenarios 1 and 2, relative to 2021 levels. From the two scenarios considered in all the countries, India records the highest percentage reduction while Germany has the least percentage emission in reference to 2021, with a potential decrease of 90.63% and 52.42% respectively. By 2050, carbon emissions in the USA will be reduced by 83% and 79.8% using Scenario 1 and Scenario 2 decarbonization pathways. This research significantly contributes to understanding the decarbonization potential of global electricity generation. It provides vital data for policymakers, energy planners, and stakeholders involved in developing sustainable energy policies

Accuracy of Transperineal Targeted Prostate Biopsies, Visual Estimation and Image Fusion in Men Needing Repeat Biopsy in the PICTURE Trial

PURPOSE: To evaluate detection of clinically significant prostate cancer (csPCa) using MRI-targeted biopsies, and compare visual-estimation to image-fusion targeting, in patients requiring repeat prostate biopsies. MATERIALS AND METHODS: Prospective, ethics-committee approved, registered PICTURE trial enrolling 249 consecutive patients (11th/January/2012-29th/January/2014). Men underwent an mpMRI and were blinded to its results. All underwent transperineal template prostate mapping (TTPM) biopsies. In 200 with a lesion, this was preceded by visual-estimation and image-fusion targeted biopsies. For the primary endpoint, csPCa was defined as Gleason >/=4+3 and/or any grade of cancer length >/=6mm. Other definitions of csPCa were also evaluated. RESULTS: Mean (SD) age was 62.6 (7) years, median (IQR) PSA 7.17ng/ml (5.25, 10.09), mean primary lesion size 0.37cc (SD1.52), with mean 4.3 (SD2.3) targeted cores per lesion (visual-estimation and image-fusion combined) and mean 48.7 (SD12.3) TTPM-biopsy cores. TTPM-biopsies detected 97 (48.5%) cases of csPCa and 85 (42.5%) insignificant cancers. Overall, mpMRI-targeted biopsies detected 81 (40.5%) csPCa and 63 (31.5%) insignificant cancers. Eighteen (9%) with csPCa on MRI-targeted biopsies were benign or clinically insignificant on TTPM-biopsy. Thirty-four (17%) had csPCa detected on TTPM-biopsy but not on MRI-targeted biopsies; approximately half of these were present in non-targeted areas. csPCa was found with visual-estimation and image-fusion in 53/169 (31.3%) and 48/169 (28.4%) (McNemar's test, p=0.5322). Visual-estimation missed 23 (13.6%) csPCa detected by image-fusion; image-fusion missed 18 (10.8%) csPCa that visual-estimation detected. CONCLUSIONS: MRI-targeted biopsies are accurate at detection of csPCa and reducing over-diagnosis of insignificant cancers. To maximise detection both visual-estimation and image-fusion targeted biopsies are required