Detection of Avian bornavirus 5 RNA in Eclectus roratus with feather picking disorder

Avian bornavirus (ABV) was discovered recently in parrots with proventricular dilatation disease (PDD), a fatal neurological disease. Although ABV has been shown to be a causative agent of PDD, its virological characteristics are largely unknown. Here we report the detection of ABV genotype 5 RNA in an Eclectus roratus with feather picking disorder (FPD). Interestingly, although the bird was persistently infected with ABV5 for at least 8 months, it had no clinical signs of PDD. Although it remains unclear whether ABV5 is associated with FPD, these findings raise the importance of epidemiological studies of birds with diseases other than PDD.     

Single-cell microliter-droplet screening system (MISS Cell): An integrated platform for automated high-throughput microbial monoclonal cultivation and picking

Solid plates have been used for microbial monoclonal isolation, cultivation, and colony picking since 1881. However, the process is labor- and resource-intensive for high-throughput requirements. Currently, several instruments have been integrated for automated and high-throughput picking, but complicated and expensive. To address these issues, we report a novel integrated platform, the single-cell microliter-droplet screening system (MISS Cell), for automated, high-throughput microbial monoclonal colony cultivation and picking. We verified the monoclonality of droplet cultures in the MISS Cell and characterized culture performance. Compared with solid plates, the MISS Cell generated a larger number of monoclonal colonies with higher initial growth rates using fewer resources. Finally, we established a workflow for automated high-throughput screening of Corynebacterium glutamicum using the MISS Cell and identified high glutamate-producing strains. The MISS Cell can serve as a universal platform to efficiently produce monoclonal colonies in high-throughput applications, overcoming the limitations of solid plates to promote rapid development in biotechnology.     

The program XEASY for computer-supported NMR spectral analysis of biological macromolecules

Summary A new program package, XEASY, was written for interactive computer support of the analysis of NMR spectra for three-dimensional structure determination of biological macromolecules. XEASY was developed for work with 2D, 3D and 4D NMR data sets. It includes all the functions performed by the precursor program EASY, which was designed for the analysis of 2D NMR spectra, i.e., peak picking and support of sequence-specific resonance assignments, cross-peak assignments, cross-peak integration and rate constant determination for dynamic processes. Since the program utilizes the X-window system and the Motif widget set, it is portable on a wide range of UNIX workstations. The design objective was to provide maximal computer support for the analysis of spectra, while providing the user with complete control over the final resonance assignments. Technically important features of XEASY are the use and flexible visual display of strips, i.e., two-dimensional spectral regions that contain the relevant parts of 3D or 4D NMR spectra, automated sorting routines to narrow down the selection of strips that need to be interactively considered in a particular assignment step, a protocol of resonance assignments that can be used for reliable bookkeeping, independent of the assignment strategy used, and capabilities for proper treatment of spectral folding and efficient transfer of resonance assignments between spectra of different types and different dimensionality, including projected, reduced-dimensionality triple-resonance experiments.Abbreviations 1D, 2D, 3D, 4D one-, two-, three-, four-dimensional - NOE nuclear Overhauser enhancement - NOESY nuclear Overhauser enhancement spectroscopy - TOCSY total correlation spectroscopy - COSY correlation spectroscopy - TPPI time-proportional phase incrementation     

Bayesian Peak Picking for NMR Spectra

Protein structure determination is a very important topic in structural genomics,which helps people to understand varieties of biological functions such as protein-protein interactions,protein–DNA interactions and so on.Nowadays,nuclear magnetic resonance(NMR) has often been used to determine the three-dimensional structures of protein in vivo.This study aims to automate the peak picking step,the most important and tricky step in NMR structure determination.We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem.Under the Bayesian framework,the peak picking problem is casted as a variable selection problem.The proposed method can automatically distinguish true peaks from false ones without preprocessing the data.To the best of our knowledge,this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.     

夏枯草采收时期对果穗与种子质量影响的研究

目的:研究夏枯草果穗不同采收时间对其熊果酸含量、种子千粒重和发芽率的影响,以确定适宜的采收期。方法:以果穗成熟度(发黄程度)为采收指标,分6个采收期,分别测定其果穗熊果酸含量、种子千粒重与发芽率,并进行对比。结果:采收期对夏枯草果穗与种子质量存在明显的影响,熊果酸含量、种子千粒重和发芽率均随果穗成熟度的增加而提高,至果穗全黄时达最大值。果穗全黄7 d后采收,熊果酸含量下降明显;千粒重与发芽率无明显变化。结论:夏枯草果穗应在果穗全黄后及时采收。     

不同浓度复合肥对松材线虫病抗性马尾松采穗圃萌条和穗条扦插的影响

以2.5 g·L^-1、5.0 g·L^-1、10.0 g·L^-1三个浓度复合肥对采穗圃进行施肥试验,研究不同浓度复合肥对松材线虫病抗性马尾松采穗圃的产穗和穗条扦插的影响。结果表明,与空白对照相比,施用复合肥使萌条数量,穗条扦插的生根率、不定根数量、最长不定根、总根长和侧根数增加。不同浓度复合肥对性状的影响不同。复合肥2.5 g·L^-1最有利于萌条数量增加,较对照增加106%,差异达显著水平;5.0 g·L^-1最有利于提高不定根数、总根长和侧根数,较对照分别提高100.0%、189.1%和186.4%,其中总根长差异达极显著水平,侧根数达显著水平;10.0 g·L^-1最有利于提高生根率和最长不定根长度,较对照分别上升23.2%和134.7%,其中最长不定根差异达极显著水平。综合评价10项萌条和生根性状,复合肥10.0 g·L^-1综合目标值最高,是三个浓度中最佳施肥浓度。     

APSY-NMR with proteins: practical aspects and backbone assignment

Automated projection spectroscopy (APSY) is an NMR technique for the recording of discrete sets of projection spectra from higher-dimensional NMR experiments, with automatic identification of the multidimensional chemical shift correlations by the dedicated algorithm GAPRO. This paper presents technical details for optimizing the set-up and the analysis of APSY-NMR experiments with proteins. Since experience so far indicates that the sensitivity for signal detection may become the principal limiting factor for applications with larger proteins or more dilute samples, we performed an APSY-NMR experiment at the limit of sensitivity, and then investigated the effects of varying selected experimental parameters. To obtain the desired reference data, a 4D APSY-HNCOCA experiment with a 12-kDa protein was recorded in 13 min. Based on the analysis of this data set and on general considerations, expressions for the sensitivity of APSY-NMR experiments have been generated to guide the selection of the projection angles, the calculation of the sweep widths, and the choice of other acquisition and processing parameters. In addition, a new peak picking routine and a new validation tool for the final result of the GAPRO spectral analysis are introduced. In continuation of previous reports on the use of APSY-NMR for sequence-specific resonance assignment of proteins, we present the results of a systematic search for suitable combinations of a minimal number of four- and five-dimensional APSY-NMR experiments that can provide the input for algorithms that generate automated protein backbone assignments.     

An in situ hybridization screen for the rapid isolation of differentially expressed genes

To rapidly isolate genes specifically expressed during medaka development we generated a cDNA library enriched for genes expressed in the head region of the developing embryo. Clones were spotted on filters automatically and preselected for abundantly expressed genes by hybridizing them with a probe derived from RNA of undifferentiated totipotent cells. Of the nonhybridizing clones 153 were chosen randomly and further analyzed by whole-mount in situ hybridization. There were 67 selected clones differentially expressed in the developing embryos, and 48 of these were expressed in the developing head. Differentially expressed genes were either of novel type or showed homology to known genes containing DNA binding motifs or to putative housekeeping genes. Received: 1 December 1998 / Accepted: 17 May 1999     

High resolution electron tomography and segmentation‐by‐modeling interpretation in Bsoft

Bsoft offers many tools for the processing of tomographic tilt series and the interpretation of tomograms. Since I introduced tomography into Bsoft almost two decades ago, the field has advanced significantly, requiring refinement of old algorithms and development of new ones. The current direct detectors allow us to collect data more efficiently and with better quality, progressing towards automation. The goal is then to also automate alignment of tilt series and reconstruction. I added an estimation of the specimen thickness as well as fiducialless alignment, to augment the existing fiducial‐based alignment. High‐resolution work requires correction for the contrast transfer function, in tomography complicated by the tilted specimen. For this, I developed a method to generate a power spectrum using the whole micrograph, compensating for tilting. This is followed by routine determination of the contrast transfer function, and correction for it during reconstruction. The next steps involve interpretation of the tomogram, either by subtomogram averaging where possible, or by segmentation and modeling otherwise. Such interpretation actually constitutes the main time‐consuming part of tomography and is less amenable to automation compared to the initial reconstruction.     

Automatic particle selection from electron micrographs using machine learning techniques

The 3D reconstruction of biological specimens using Electron Microscopy is currently capable of achieving subnanometer resolution. Unfortunately, this goal requires gathering tens of thousands of projection images that are frequently selected manually from micrographs. In this paper we introduce a new automatic particle selection that learns from the user which particles are of interest. The training phase is semi-supervised so that the user can correct the algorithm during picking and specifically identify incorrectly picked particles. By treating such errors specially, the algorithm attempts to minimize the number of false positives. We show that our algorithm is able to produce datasets with fewer wrongly selected particles than previously reported methods. Another advantage is that we avoid the need for an initial reference volume from which to generate picking projections by instead learning which particles to pick from the user. This package has been made publicly available in the open-source package Xmipp.