Motivation Species tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree can allow for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging. Results We propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL. Availability wQFM is available in open source form at https://github.com/Mahim1997/wQFM-2020 Supplementary information Supplementary data are available at Bioinformatics online.

Abstract. Carbon nanotube (CNT) is considered as an ideal material for thermal management in electronic packaging because of its extraordinary high thermal conductivity. A series of 2D and 3D CFD simulations have been carried out for CNT based micro-channel cooling architectures based on one and two dimensional fin array in this paper using COMSOL 4.0a software. Micro-channels are generally regarded as an effective method for the heat transfer in electronic products. The influence of various fluids, micro-fin structures, micro-fin array, fluid velocity and heating powers on cooling effects have been simulated and compared in this study. Steady-state thermal stress analyses for the forced convection heat transfer have also been performed to determine maximum allowable stress and deflections for the different types of cooling assembly.

BackgroundR2 elements are a clade of early branching Long Interspersed Elements (LINEs). LINEs are retrotransposable elements whose replication can have profound effects on the genomes in which they reside. No crystal or EM structures exist for the reverse transcriptase (RT) and linker regions of LINEs.ResultsUsing limited proteolysis as a probe for globular domain structure, we show that the protein encoded by the Bombyx mori R2 element has two major globular domains: (1) a small globular domain consisting of the N-terminal zinc finger and Myb motifs, and (2) a large globular domain consisting of the RT, linker, and type II restriction-like endonuclease (RLE). Further digestion of the large globular domain occurred within the RT. Mapping these RT cleavages onto an updated model of the R2Bm RT indicated that the thumb of the RT was largely protected from proteolytic cleavage. The crystal structure of the large globular domain of Prp8, a eukaryotic splicing factor, was a major template used in building the R2Bm RT model, particularly the thumb region. The large fragment of Prp8 consists not only of a RT similar to R2Bm, but also an RLE and a linker connecting the two regions. The linker sequences adjacent to the RLE in LINEs and Prp8 share a set of two important α-helices and a (presumptive) knuckle/ββα structural motif that are closely associated with the thumb. The RLEs of LINEs and Prp8 share a unique catalytic core residue spacing as well as other key residues.ConclusionsThe protein encoded by RLE LINEs consists of two major globular domains. The larger of the two globular domain contains the RT, linker, and RLE and is similar to the large fragment of the spliceosomal protein Prp8. The similarities are suggestive of possible common ancestry.Electronic supplementary materialThe online version of this article (10.1186/s13100-017-0097-9) contains supplementary material, which is available to authorized users.

In this paper, the 2D and 3D CFD simulations have been carried out in liquid-cooled carbon nano tube based (CNT) micro-fin cooling architectures. The effect of heat transfer due to fluid velocity, pressure difference in the micro-fin assembly and thermal expansion due to temperature rise in the cooling assembly have been analyzed in this paper. The analysis indicates that fluid speed is the key factor of heat transfer and 2D carbon nanotube fin array shows greater thermal performance than that of 1D carbon nanotube fin array. The pressure drop between inlet and outlet of the cooling device is an important cause to limit the fluid speed.

Carbon nanotube (CNT) has been proven to be an effective material for the thermal management of MEMS-based devices due to its superior thermal conductivity. At the same time, micro-channel heat-sinks are widely used in electronic products as a high performance heat transfer device because of its simple construction, easy fabrication process and effective heat removal capability. A numerical study has been carried out to investigate the thermal-fluid characteristics of the aligned and staggered MWCNT (multi walled CNT) based micro pin fins having 650 µm long with hydraulic diameter of ~130 µm. Average heat transfer coefficients have been obtained for effective heat fluxes ranging from 50 to 130 W and Reynolds numbers from 14 to 160. The influence of various fluids, fluid velocity; fluid conductivity, fin material, fin conductivity, fin geometry and fin orientations on cooling effects have also been investigated and compared in this study.

Motivation: Species tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree allows for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging. Results: We propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL.

This article addresses the class imbalance issue in a low-resource language called Bengali. As a use-case, we choose one of the most fundamental NLP tasks, i.e., text classification, where we utilize three benchmark text corpora: fake-news dataset, sentiment analysis dataset, and song lyrics dataset. Each of them contains a critical class imbalance. We attempt to tackle the problem by applying several strategies that include data augmentation with synthetic samples via text and embedding generation in order to augment the proportion of the minority samples. Moreover, we apply ensembling of deep learning models by subsetting the majority samples. Additionally, we enforce the focal loss function for class-imbalanced data classification. We also apply the outlier detection technique, data resampling, and hidden feature extraction to improve the minority-f1 score. All of our experimentations are entirely focused on textual content analysis, which results in a more than 90% minority f1 score for each of the three tasks. It is an excellent outcome on such highly class-imbalanced datasets.

No abstract