A divide-and-conquer method for 3D capacitance extraction

This thesis describes a divide-and-conquer algorithm to improve the 3D boundary element method (BEM) for capacitance extraction. We divide large interconnect structures into small sections, set new boundary conditions using the borderfor each section, solve each section, and then combine the results to derive the capacitance. The target application is critical nets where 3D accuracy is required. The new algorithm is a significant improvement over the traditional BEMs and their enhancements, such as the "window" method where conductors far away are dropped, and the "shield" method where conductors hidden behind other conductors are dropped. Experimental results show that our algorithm is 25 times faster than the traditional BEM and 5 times faster than the window+shield method, for medium to large structures. The error of the capacitance computed by the new algorithm is within 2% for self capacitance and 7% for coupling capacitance, compared with the results obtained by solving the entire system using BEM. Furthermore, our algorithms gives accurate distributed RC, where none of the previous 3D BEM algorithms and their enhancements can

Comparative Analysis of Fatty Acid Desaturases in Cyanobacterial Genomes

Fatty acid desaturases are enzymes that introduce double bonds into the hydrocarbon chains of fatty acids. The fatty acid desaturases from 37 cyanobacterial genomes were identified and classified based upon their conserved histidine-rich motifs and phylogenetic analysis, which help to determine the amounts and distributions of desaturases in cyanobacterial species. The filamentous or N2-fixing cyanobacteria usually possess more types of fatty acid desaturases than that of unicellular species. The pathway of acyl-lipid desaturation for unicellular marine cyanobacteria Synechococcus and Prochlorococcus differs from that of other cyanobacteria, indicating different phylogenetic histories of the two genera from other cyanobacteria isolated from freshwater, soil, or symbiont. Strain Gloeobacter violaceus PCC 7421 was isolated from calcareous rock and lacks thylakoid membranes. The types and amounts of desaturases of this strain are distinct to those of other cyanobacteria, reflecting the earliest divergence of it from the cyanobacterial line. Three thermophilic unicellular strains, Thermosynechococcus elongatus BP-1 and two Synechococcus Yellowstone species, lack highly unsaturated fatty acids in lipids and contain only one Δ9 desaturase in contrast with mesophilic strains, which is probably due to their thermic habitats. Thus, the amounts and types of fatty acid desaturases are various among different cyanobacterial species, which may result from the adaption to environments in evolution

CMR left ventricular strains beyond global longitudinal strain in differentiating light-chain cardiac amyloidosis from hypertrophic cardiomyopathy

BackgroundThe clinical value of left ventricular (LV) global longitudinal strain (GLS) in the differential diagnosis of light-chain cardiac amyloidosis (AL-CA) and hypertrophic cardiomyopathy (HCM) has been previously reported. In this study, we analyzed the potential clinical value of the LV long-axis strain (LAS) to discriminate AL-CA from HCM. Furthermore, we analyzed the association between all the LV global strain parameters derived from cardiac magnetic resonance (CMR) feature tracking and LAS in both the AL-CA and HCM patients to assess the differential diagnostic efficacies of these global peak systolic strains.Materials and methodsThus, this study enrolled 89 participants who underwent cardiac MRI (CMRI), consisting of 30 AL-CA patients, 30 HCM patients, and 29 healthy controls. The intra- and inter-observer reproducibility of the LV strain parameters including GLS, global circumferential strain (GCS), global radial strain (GRS), and LAS were assessed in all the groups and compared. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic performances of the CMR strain parameters in discriminating AL-CA from HCM.ResultsThe intra- and inter-observer reproducibility of the LV global strains and LAS were excellent (range of interclass correlation coefficients: 0.907–0.965). ROC curve analyses showed that the differential diagnostic performances of the global strains in discriminating AL-CA from HCM were good to excellent (GRS, AUC = 0.921; GCS, AUC = 0.914; GLS, AUC = 0.832). Furthermore, among all the strain parameters analyzed, LAS showed the highest diagnostic efficacy in differentiating between AL-CA and HCM (AUC = 0.962).ConclusionCMRI-derived strain parameters such as GLS, LAS, GRS, and GCS are promising diagnostic indicators that distinguish AL-CA from HCM with high accuracy. LAS showed the highest diagnostic accuracy among all the strain parameters

A new pheromone trail-based genetic algorithm for comparative genome assembly

Gap closing is considered one of the most challenging and time-consuming tasks in bacterial genome sequencing projects, especially with the emergence of new sequencing technologies, such as pyrosequencing, which may result in large amounts of data without the benefit of large insert libraries for contig scaffolding. We propose a novel algorithm to align contigs with more than one reference genome at a time. This approach can successfully overcome the limitations of low degrees of conserved gene order for the reference and target genomes. A pheromone trail-based genetic algorithm (PGA) was used to search globally for the optimal placement for each contig. Extensive testing on simulated and real data sets shows that PGA significantly outperforms previous methods, especially when assembling genomes that are only moderately related. An extended version of PGA can predict additional candidate connections for each contig and can thus increase the likelihood of identifying the correct arrangement of each contig. The software and test data sets can be accessed at http://sourceforge.net/projects/pga4genomics/

Compendium of 5810 genomes of sheep and goat gut microbiomes provides new insights into the glycan and mucin utilization

Background: Ruminant gut microbiota are critical in ecological adaptation, evolution, and nutrition utilization because it regulates energy metabolism, promotes nutrient absorption, and improves immune function. To study the functional roles of key gut microbiota in sheep and goats, it is essential to construct reference microbial gene catalogs and high-quality microbial genomes database. Results A total of 320 fecal samples were collected from 21 different sheep and goat breeds, originating from 32 distinct farms. Metagenomic deep sequencing and binning assembly were utilized to construct a comprehensive microbial genome information database for the gut microbiota. We successfully generated the largest reference gene catalogs for gut microbiota in sheep and goats, containing over 162 million and 82 million nonredundant predicted genes, respectively, with 49 million shared nonredundant predicted genes and 1138 shared species. We found that the rearing environment has a greater impact on microbial composition and function than the host’s species effect. Through subsequent assembly, we obtained 5810 medium- and high-quality metagenome-assembled genomes (MAGs), out of which 2661 were yet unidentified species. Among these MAGs, we identified 91 bacterial taxa that specifically colonize the sheep gut, which encode polysaccharide utilization loci for glycan and mucin degradation. Conclusions By shedding light on the co-symbiotic microbial communities in the gut of small ruminants, our study significantly enhances the understanding of their nutrient degradation and disease susceptibility. Our findings emphasize the vast potential of untapped resources in functional bacterial species within ruminants, further expanding our knowledge of how the ruminant gut microbiota recognizes and processes glycan and mucins

5G Multi-Slices Bi-Level Resource Allocation by Reinforcement Learning

As the centralized unit (CU)—distributed unit (DU) separation in the fifth generation mobile network (5G), the multi-slice and multi-scenario, can be better applied in wireless communication. The development of the 5G network to vertical industries makes its resource allocation also have an obvious hierarchical structure. In this paper, we propose a bi-level resource allocation model. The up-level objective in this model refers to the profit of the 5G operator through the base station allocating resources to slices. The lower-level objective in this model refers to the slices allocating the resource to its users fairly. The resource allocation problem is a complex optimization problem with mixed-discrete variables, so whether a resource allocation algorithm can quickly and accurately give the resource allocation scheme is the key to its practical application. According to the characteristics of the problem, we select the multi-agent twin delayed deep deterministic policy gradient (MATD3) to solve the upper slice resource allocation and the discrete and continuous twin delayed deep deterministic policy gradient (DCTD3) to solve the lower user resource allocation. It is crucial to accurately characterize the state, environment, and reward of reinforcement learning for solving practical problems. Thus, we provide an effective definition of the environment, state, action, and reward of MATD3 and DCTD3 for solving the bi-level resource allocation problem. We conduct some simulation experiments and compare it with the multi-agent deep deterministic policy gradient (MADDPG) algorithm and nested bi-level evolutionary algorithm (NBLEA). The experimental results show that the proposed algorithm can quickly provide a better resource allocation scheme

5G Multi-Slices Bi-Level Resource Allocation by Reinforcement Learning

As the centralized unit (CU)—distributed unit (DU) separation in the fifth generation mobile network (5G), the multi-slice and multi-scenario, can be better applied in wireless communication. The development of the 5G network to vertical industries makes its resource allocation also have an obvious hierarchical structure. In this paper, we propose a bi-level resource allocation model. The up-level objective in this model refers to the profit of the 5G operator through the base station allocating resources to slices. The lower-level objective in this model refers to the slices allocating the resource to its users fairly. The resource allocation problem is a complex optimization problem with mixed-discrete variables, so whether a resource allocation algorithm can quickly and accurately give the resource allocation scheme is the key to its practical application. According to the characteristics of the problem, we select the multi-agent twin delayed deep deterministic policy gradient (MATD3) to solve the upper slice resource allocation and the discrete and continuous twin delayed deep deterministic policy gradient (DCTD3) to solve the lower user resource allocation. It is crucial to accurately characterize the state, environment, and reward of reinforcement learning for solving practical problems. Thus, we provide an effective definition of the environment, state, action, and reward of MATD3 and DCTD3 for solving the bi-level resource allocation problem. We conduct some simulation experiments and compare it with the multi-agent deep deterministic policy gradient (MADDPG) algorithm and nested bi-level evolutionary algorithm (NBLEA). The experimental results show that the proposed algorithm can quickly provide a better resource allocation scheme