3,293 research outputs found
Template-free synthesis of Nd0.1Bi0.9FeO3 nanotubes with large inner diameter and wasp-waisted hysteresis loop
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Inference of gene-phenotype associations via protein-protein interaction and orthology
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The abnormal electrical and optical properties in Na and Ni codoped BiFeO3 nanoparticles
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Cloning, expression, and chromosomal localization to 11p12-13 of a human LIM/HOMEOBOX gene, hLim-1
We have identified a putative transcription factor, designated hLim-1, from human fetal brain using degenerate polymerase chain reaction (PCR) and cDNA library screening. The deduced open reading frame, derived from sequencing a 3.0-kb hLim-1 cDNA, encodes a protein of 384 amino acids with two cysteine-rich LIM domains and one homeobox (HOX) DNA-binding domain. The nucleotide sequence of hLim-1 cDNA is 87% identical to mouse Lim-1 and the predicted amino acid sequence is greater than 97% conserved. Expression patterns of hLim-1 were evaluated by Northern analysis and reverse transcription (RT)-PCR coupled with Southern blotting. HLim-1 expression was observed in human brain, thymus, and tonsillar tissue. Expression of hLim-1 was also observed in 58% of acute myelogenous leukemia (AML) cell lines and in four of five primary samples from patients with chronic myeloid leukemia (CML) in myeloid blast transformation. The gene encoding hLim-1 was mapped using fluorescence in situ hybridization (FISH) to human chromosome 11p12-13. The expression pattern and structural characteristics of the hLim-1 gene suggest that it encodes a transcriptional regulatory protein involved in the control of differentiation and development of neural and lymphoid cells. Its expression in CML in blast crisis suggests that it may be involved with progression in this disease; a prospective study is required to confirm this.published_or_final_versio
Battery management system and control strategy for hybrid and electric vehicle
Author name used in this publication: K. W. E. ChengAuthor name used in this publication: K. DingAuthor name used in this publication: W. TingVersion of RecordPublishe
Abnormal variation of band gap in Zn doped Bi0.9La0.1FeO3 nanoparticles: Role of Fe-O-Fe bond angle and Fe-O bond anisotropy
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Distribution of Introns in Fungal Histone Genes
Saccharomycotina and Taphrinomycotina lack intron in their histone genes, except for an intron in one of histone H4 genes of Yarrowia lipolytica. On the other hand, Basidiomycota and Perizomycotina have introns in their histone genes. We compared the distributions of 81, 47, 79, and 98 introns in the fungal histone H2A, H2B, H3, and H4 genes, respectively. Based on the multiple alignments of the amino acid sequences of histones, we identified 19, 13, 31, and 22 intron insertion sites in the histone H2A, H2B, H3, and H4 genes, respectively. Surprisingly only one hot spot of introns in the histone H2A gene is shared between Basidiomycota and Perizomycotina, suggesting that most of introns of Basidiomycota and Perizomycotina were acquired independently. Our findings suggest that the common ancestor of Ascomycota and Basidiomycota maybe had a few introns in the histone genes. In the course of fungal evolution, Saccharomycotina and Taphrinomycotina lost the histone introns; Basidiomycota and Perizomycotina acquired other introns independently. In addition, most of the introns have sequence similarity among introns of phylogenetically close species, strongly suggesting that horizontal intron transfer events between phylogenetically distant species have not occurred recently in the fungal histone genes
Sequence and Phylogenetic Analysis of SSU rRNA Gene of Five Microsporidia
The complete small subunit rRNA (SSU rRNA) gene sequences of five microsporidia including Nosemaheliothidis, and four novel microsporidia isolated from Pieris rapae, Phyllobrotica armta, Hemerophila atrilineata, and Bombyx mori, respectively, were obtained by PCR amplification, cloning, and sequencing. Two phylogenetic trees based on SSU rRNA sequences had been constructed by using Neighbor-Joining of Phylip software and UPGMA of MEGA4.0 software. The taxonomic status of four novel microsporidia was determined by analysis of phylogenetic relationship, length, G+C content, identity, and divergence of the SSU rRNA sequences. The results showed that the microsporidia isolated from Pieris rapae, Phyllobrotica armta, and Hemerophila atrilineata have close phylogenetic relationship with the Nosema, while another microsporidium isolated from Bombyx mori is closely related to the Endoreticulatus. So, we temporarily classify three novel species of microsporidia to genus Nosema, as Nosema sp. PR, Nosema sp. PA, Nosema sp. HA. Another is temporarily classified into genus Endoreticulatus, as Endoreticulatus sp. Zhenjiang. The result indicated as well that it is feasible and valuable to elucidate phylogenetic relationships and taxonomic status of microsporidian species by analyzing information from SSU rRNA sequences of microsporidia
A Taxonomy of Explainable Bayesian Networks
Artificial Intelligence (AI), and in particular, the explainability thereof,
has gained phenomenal attention over the last few years. Whilst we usually do
not question the decision-making process of these systems in situations where
only the outcome is of interest, we do however pay close attention when these
systems are applied in areas where the decisions directly influence the lives
of humans. It is especially noisy and uncertain observations close to the
decision boundary which results in predictions which cannot necessarily be
explained that may foster mistrust among end-users. This drew attention to AI
methods for which the outcomes can be explained. Bayesian networks are
probabilistic graphical models that can be used as a tool to manage
uncertainty. The probabilistic framework of a Bayesian network allows for
explainability in the model, reasoning and evidence. The use of these methods
is mostly ad hoc and not as well organised as explainability methods in the
wider AI research field. As such, we introduce a taxonomy of explainability in
Bayesian networks. We extend the existing categorisation of explainability in
the model, reasoning or evidence to include explanation of decisions. The
explanations obtained from the explainability methods are illustrated by means
of a simple medical diagnostic scenario. The taxonomy introduced in this paper
has the potential not only to encourage end-users to efficiently communicate
outcomes obtained, but also support their understanding of how and, more
importantly, why certain predictions were made
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