Direct Observation of the Epitaxial Growth of Bismuth Telluride Topological Insulators from One-Dimensional Heterostructured Nanowires.

As extraordinary topological insulators, 2D bismuth telluride (Bi2Te3) nanosheets have been synthesized and controlled with a few-layer structure by a facile and fast solvothermal process. The detail-oriented growth evolution of 2D Bi2Te3 in an ethylene glycol reducing solution is discovered and recorded for direct observation of the liquid-solid interactions through the use of environmental SEM. At the initial synthesis stage, Te nanowires are rapidly synthesized and observed in solution. In the next stage, Bi nanoclusters slowly adhere to the Te nanowires and react to form hierarchical Te-Bi2Te3 nanostructured materials. Additionally, the Te nanowires shorten in-plane in an orderly manner, while the Bi2Te3 nanosheets exhibit directional out-of-plane epitaxial growth. In the last procedure, Bi2Te3 nanosheets with a clear hexagonal appearance can be largely obtained. Experiments performed under these rigorous conditions require careful consideration of the temperature, time, and alkaline environment for each reaction process. In addition, the yield of a wider and thinner Bi2Te3 nanosheet is synthesized by manipulating the crystal growth with an optimal alkaline concentration, which is found through statistical analysis of the AFM results. In the UV-Vis-NIR spectroscopy results, the main peak in the spectrum tends to redshift, while the other peak in the ultraviolet range decreases during Bi2Te3 nanosheet synthesis, facilitating a rapid understanding of the trends in the morphological evolution of the Bi2Te3 materials in solution. By rationalizing the above observations, we are the first to report the success of environmental SEM, HAADF-STEM, and UV-Vis-NIR spectroscopy for confirming the Bi2Te3 nanosheet formation mechanism and the physical properties in the solvent media. This research promotes the future optimization of promising Bi2Te3 nanomaterials that can be used in the fabrication of thermoelectric and topological components.

NeuroRetriever: Automatic Neuron Segmentation for Connectome Assembly.

Segmenting individual neurons from a large number of noisy raw images is the first step in building a comprehensive map of neuron-to-neuron connections for predicting information flow in the brain. Thousands of fluorescence-labeled brain neurons have been imaged. However, mapping a complete connectome remains challenging because imaged neurons are often entangled and manual segmentation of a large population of single neurons is laborious and prone to bias. In this study, we report an automatic algorithm, NeuroRetriever, for unbiased large-scale segmentation of confocal fluorescence images of single neurons in the adult Drosophila brain. NeuroRetriever uses a high-dynamic-range thresholding method to segment three-dimensional morphology of single neurons based on branch-specific structural features. Applying NeuroRetriever to automatically segment single neurons in 22,037 raw brain images, we successfully retrieved 28,125 individual neurons validated by human segmentation. Thus, automated NeuroRetriever will greatly accelerate 3D reconstruction of the single neurons for constructing the complete connectomes.

Diverse Community Structures in the Neuronal-Level Connectome of the Drosophila Brain.

Drosophila melanogaster is one of the most important model animals in neurobiology owing to its manageable brain size, complex behaviour, and extensive genetic tools. However, without a comprehensive map of the brain-wide neural network, our ability to investigate brain functions at the systems level is seriously limited. In this study, we constructed a neuron-to-neuron network of the Drosophila brain based on the 28,573 fluorescence images of single neurons in the newly released FlyCircuit v1.2 (http://www.flycircuit.tw) database. By performing modularity and centrality analyses, we identified eight communities (right olfaction, left olfaction, olfactory core, auditory, motor, pre-motor, left vision, and right vision) in the brain-wide network. Further investigation on information exchange and structural stability revealed that the communities of different functions dominated different types of centralities, suggesting a correlation between functions and network structures. Except for the two olfaction and the motor communities, the network is characterized by overall small-worldness. A rich club (RC) structure was also found in this network, and most of the innermost RC members innervated the central complex, indicating its role in information integration. We further identified numerous loops with length smaller than seven neurons. The observation suggested unique characteristics in the information processing inside the fruit fly brain.

Self-assembly and ring-opening metathesis polymerization of cyclic conjugated molecules on highly ordered pyrolytic graphite.

Cyclic conjugated monomers comprising cyclopentadithiophene-vinylene trimers and their polymers on HOPG are observed using STM and AFM. ROMP of the monomers is performed using a Grubbs catalyst. Their STM images exhibit single chains of planar polymers, whereas their AFM images show elongation of the polymer chains on HOPG.

A Single-Cell Level and Connectome-Derived Computational Model of the Drosophila Brain.

Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which (1) identifies the polarity of each neuron arbor, (2) predicts connections between neurons, (3) translates morphology data from the database into physiology parameters for computational modeling, (4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and (5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment. Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.

Connectomics-based analysis of information flow in the Drosophila brain.

Understanding the overall patterns of information flow within the brain has become a major goal of neuroscience. In the current study, we produced a first draft of the Drosophila connectome at the mesoscopic scale, reconstructed from 12,995 images of neuron projections collected in FlyCircuit (version 1.1). Neuron polarities were predicted according to morphological criteria, with nodes of the network corresponding to brain regions designated as local processing units (LPUs). The weight of each directed edge linking a pair of LPUs was determined by the number of neuron terminals that connected one LPU to the other. The resulting network showed hierarchical structure and small-world characteristics and consisted of five functional modules that corresponded to sensory modalities (olfactory, mechanoauditory, and two visual) and the pre-motor center. Rich-club organization was present in this network and involved LPUs in all sensory centers, and rich-club members formed a putative motor center of the brain. Major intra- and inter-modular loops were also identified that could play important roles for recurrent and reverberant information flow. The present analysis revealed whole-brain patterns of network structure and information flow. Additionally, we propose that the overall organizational scheme showed fundamental similarities to the network structure of the mammalian brain.

HaplotypeCN: copy number haplotype inference with Hidden Markov Model and localized haplotype clustering.

Copy number variation (CNV) has been reported to be associated with disease and various cancers. Hence, identifying the accurate position and the type of CNV is currently a critical issue. There are many tools targeting on detecting CNV regions, constructing haplotype phases on CNV regions, or estimating the numerical copy numbers. However, none of them can do all of the three tasks at the same time. This paper presents a method based on Hidden Markov Model to detect parent specific copy number change on both chromosomes with signals from SNP arrays. A haplotype tree is constructed with dynamic branch merging to model the transition of the copy number status of the two alleles assessed at each SNP locus. The emission models are constructed for the genotypes formed with the two haplotypes. The proposed method can provide the segmentation points of the CNV regions as well as the haplotype phasing for the allelic status on each chromosome. The estimated copy numbers are provided as fractional numbers, which can accommodate the somatic mutation in cancer specimens that usually consist of heterogeneous cell populations. The algorithm is evaluated on simulated data and the previously published regions of CNV of the 270 HapMap individuals. The results were compared with five popular methods: PennCNV, genoCN, COKGEN, QuantiSNP and cnvHap. The application on oral cancer samples demonstrates how the proposed method can facilitate clinical association studies. The proposed algorithm exhibits comparable sensitivity of the CNV regions to the best algorithm in our genome-wide study and demonstrates the highest detection rate in SNP dense regions. In addition, we provide better haplotype phasing accuracy than similar approaches. The clinical association carried out with our fractional estimate of copy numbers in the cancer samples provides better detection power than that with integer copy number states.

Enabling large-scale biomedical analysis in the cloud.

Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable.

Simpute: an efficient solution for dense genotypic data.

Single nucleotide polymorphism (SNP) data derived from array-based technology or massive parallel sequencing are often flawed with missing data. Missing SNPs can bias the results of association analyses. To maximize information usage, imputation is often adopted to compensate for the missing data by filling in the most probable values. To better understand the available tools for this purpose, we compare the imputation performances among BEAGLE, IMPUTE, BIMBAM, SNPMStat, MACH, and PLINK with data generated by randomly masking the genotype data from the International HapMap Phase III project. In addition, we propose a new algorithm called simple imputation (Simpute) that benefits from the high resolution of the SNPs in the array platform. Simpute does not require any reference data. The best feature of Simpute is its computational efficiency with complexity of order (mw + n), where n is the number of missing SNPs, w is the number of the positions of the missing SNPs, and m is the number of people considered. Simpute is suitable for regular screening of the large-scale SNP genotyping particularly when the sample size is large, and efficiency is a major concern in the analysis.

PreZon: prediction by zone and its application to egg productivity in chickens.

Taiwan red-feathered country chickens (TRFCCs) are one of the main meat resources in Taiwan. Due to the lack of any systematic breeding programs to improve egg productivity, the egg production rate of this breed has gradually decreased. The prediction by zone (PreZone) program was developed to select the chickens with low egg productivity so as to improve the egg productivity of TRFCCs before they reach maturity. Three groups (A, B, and C) of chickens were used in this study. Two approaches were used to identify chickens with low egg productivity. The first approach used predictions based on a single dataset, and the second approach used predictions based on the union of two datasets. The levels of four serum proteins, including apolipoprotein A-I, vitellogenin, X protein (an IGF-I-like protein), and apo VLDL-II, were measured in chickens that were 8, 14, 22, or 24 weeks old. Total egg numbers were recorded for each individual bird during the egg production period. PreZone analysis was performed using the four serum protein levels as selection parameters, and the results were compared to those obtained using a first-order multiple linear regression method with the same parameters. The PreZone program provides another prediction method that can be used to validate datasets with a low correlation between response and predictors. It can be used to find low and improve egg productivity in TRFCCs by selecting the best chickens before they reach maturity.

The essential role of Oct-2 in LPS-induced expression of iNOS in RAW 264.7 macrophages and its regulation by trichostatin A.

This article reports on a study of the effect of trichostatin A (TSA), an inhibitor of histone deacetylase, on lipopolysaccharide (LPS)-induced expression of inducible nitric oxide synthase (iNOS) in RAW 264.7 macrophages and its underlying mechanisms. TSA pretreatment potently diminishes LPS-stimulated nitric oxide (NO) release and both mRNA and protein levels of iNOS in macrophages. The effects of TSA and LPS on transcription factors binding to two LPS-responsive elements within the iNOS promoter, one binding the NF-kappaB site and the other the octamer element, were investigated. Results show that TSA did not alter the LPS-activated NF-kappaB activity demonstrated by the nuclear translocation of p50 and p65 and by a NF-kappaB-driven reporter gene expression system. In addition, neither TSA nor LPS changed the expression of Oct-1, a ubiquitously expressed octamer binding protein. However, TSA suppressed the LPS-induced expression of Oct-2, another octamer binding protein, at both mRNA and protein levels. Chromatin immunoprecipitation assays revealed that binding of Oct-2 to the iNOS promoter was enhanced by LPS treatment; however, pretreatment with TSA resulted in loss of this binding. Moreover, forced expression of Oct-2 by transfection of pCG-Oct-2 plasmid restored the TSA-suppressed iNOS expression elevated by LPS stimulation, further indicating that Oct-2 activation is a crucial step for transcriptional activation of the iNOS gene in response to LPS stimulation in macrophages. This study demonstrates that TSA diminishes iNOS expression in LPS-treated macrophages by inhibiting Oct-2 expression and thus reducing the production of NO.

Identification of degenerate motifs using position restricted selection and hybrid ranking combination.

The identification of regulatory elements recognized by transcription factors and chromatin remodeling factors is essential to studying the regulation of gene expression. When no auxiliary data, such as orthologous sequences or expression profiles, are used, the accuracy of most tools for motif discovery is strongly influenced by the motif degeneracy and the lengths of sequence. Since suitable auxiliary data may not always be available, more work must be conducted to enhance tool performance to identify transcription elements in the metazoan. A non-alignment-based algorithm, MotifSeeker, is proposed to enhance the accuracy of discovering degenerate motifs. MotifSeeker utilizes the property that variable sites of transcription elements are usually position-specific to reduce exposure to noise. Consequently, the efficiency and accuracy of motif identification are improved. Using data fusion, the ranking process integrates two measures of motif significance, resulting in a more robust significance measure. Testing results for the synthetic data reveal that the accuracy of MotifSeeker is less sensitive to the motif degeneracy and the length of input sequences. Furthermore, MotifSeeker has been tested on a well-known benchmark [M. Tompa, N. Li, T.L. Bailey, G.M. Church, B. De Moor, E. Eskin, A.V. Favorov, M.C. Frith, Y. Fu, W.J. Kent, et al. (2005) Nat. Biotechnol., 23, 137-144], yielding a correlation coefficient of 0.262, which compares favorably with those of other tools. The high applicability of MotifSeeker to biological data is further demonstrated experimentally on regulons of Saccharomyces cerevisiae and liver-specific genes with experimentally verified regulatory elements.