1/f Membrane noise generated by diffusion processes in unstirred solution layers

A mathematical treatment is given for 1/f noise observed in the ion transport through membranes. It is shown that this noise can be generated by current or voltage fluctuations which occur after step changes of the membrane permeability. Due to diffusion polarization in the unstirred solution layers near the membrane these fluctuations exhibit a 1/t time course which produces noise with a 1/f frequency dependence. The spectral density of 1/f noise is calculated for porous membranes with random switches between a finite and zero pore permeability. A wide frequency range and a magnitude of 1/f noise are obtained which are compatible with experimental data of 1/f noise reported for nerve membranes.Supported by Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 38 Membranforschung.     

Assessing the effect of sound file compression and background noise on measures of acoustic signal structure

The study of animal acoustic signals is a central tool for many fields in ecology and evolution, but the diversity of analytical methods and sources of animal sound recordings poses important challenges for carrying out robust acoustic analyses. Sound file compression and background noise may both affect acoustic analysis, although little attention has been paid to their respective effects. We evaluated the effect of these factors by assessing the systematic deviation (i.e. bias) and measurement error (i.e. precision) that they generate on spectrographic parameters and two (dis)similarity methods (dynamic time warping on frequency contours and cross-correlation), which represent the most common methods currently used for quantitative characterization of acoustic signals. Measurements were taken across a wide range of signals from a diverse group of bird species, and compared between uncompressed files and decompressed files obtained from mp3-encoded files generated using the two most common mp3 encoders (Fraunhofer and LAME). Measurements were also compared across a range of synthetically-generated background noise levels. Compression did not significantly bias any of the acoustic or similarity measurements. However, the precision of acoustic parameters representing single extreme values (e.g. peak frequency) as well as dynamic time warping distances, was strongly affected by compression. High background noise biased most energy distribution-related parameters (e.g. spectral entropy) and affected the precision of most acoustic parameters and dynamic time warping. Overall, compression and background noise did have considerable effects on acoustic analyses. We provide recommendations to avoid potential pitfalls and maximize the information that can be reliably obtained.     

High-throughput peptide mass fingerprinting of soybean seed proteins: automated workflow and utility of UniGene expressed sequence tag databases for protein identification

Identification of anonymous proteins from two-dimensional (2-D) gels by peptide mass fingerprinting is one area of proteomics that can greatly benefit from a simple, automated workflow to minimize sample contamination and facilitate high-throughput sample processing. In this investigation we outline a workflow employing robotic automation at each step subsequent to 2-D gel electrophoresis. As proof-of-concept, 96 protein spots from a 2-D gel were analyzed using this approach. Whole protein (1 mg) from mature, dry soybean (Glycine max [L.] Merr.) cv. Jefferson seed was resolved by high resolution 2-D gel electrophoresis. Approximately 150 proteins were observed after staining with Coomassie Blue. The rather low number of detected proteins was due to the fact that the dynamic range of protein expression was greater than 100-fold. The most abundant proteins were seed storage proteins which in total represented over 60% of soybean seed protein. Using peptide mass fingerprinting 44 protein spots were identified. Identification of soybean proteins was greatly aided by the use of annotated, contiguous Expressed Sequence Tag (EST) databases which are available for public access (UniGene, ftp.ncbi.nih.gov/repository/UniGene/). Searches were orders of magnitude faster when compared to searches of unannotated EST databases and resulted in a higher frequency of valid, high-scoring matches. Some abundant, non seed storage proteins identified in this investigation include an isoelectric series of sucrose binding proteins, alcohol dehydrogenase and seed maturation proteins. This survey of anonymous seed proteins will serve as the basis for future comparative analysis of seed-filling in soybean as well as comparisons with other soybean varieties.     

Bsoft: image and molecular processing in electron microscopy

Software for the processing of electron micrographs in structural biology suffers from incompatibility between different packages, poor definition and choice of conventions, and a lack of coherence in software development. The solution lies in adopting a common philosophy of interaction and conventions between the packages. To understand the choices required to have such common interfaces, I am developing a package called "Bsoft." Its foundations lie in the variety of different image file formats used in electron microscopy-a continually frustrating experience to the user and programmer alike. In Bsoft, this problem is greatly diminished by support for many different formats (including MRC, SPIDER, IMAGIC, SUPRIM, and PIF) and by separating algorithmic issues from image format-specific issues. In addition, I implemented a generalized functionality for reading the tag-base STAR (self-defining text archiving and retrieval) parameter file format as a mechanism to exchanging parameters between different packages. Bsoft is written in highly portable code (tested on several Unix systems and under VMS) and offers a continually growing range of image processing functionality, such as Fourier transformation, cross-correlation, and interpolation. Finally, prerequisites for software collaboration are explored, which include agreements on information exchange and conventions, and tests to evaluate compatibility between packages.     

REDHORSE-REcombination and Double crossover detection in Haploid Organisms using next-geneRation SEquencing data

Background

Next-generation sequencing technology provides a means to study genetic exchange at a higher resolution than was possible using earlier technologies. However, this improvement presents challenges as the alignments of next generation sequence data to a reference genome cannot be directly used as input to existing detection algorithms, which instead typically use multiple sequence alignments as input. We therefore designed a software suite called REDHORSE that uses genomic alignments, extracts genetic markers, and generates multiple sequence alignments that can be used as input to existing recombination detection algorithms. In addition, REDHORSE implements a custom recombination detection algorithm that makes use of sequence information and genomic positions to accurately detect crossovers. REDHORSE is a portable and platform independent suite that provides efficient analysis of genetic crosses based on Next-generation sequencing data.

Results

We demonstrated the utility of REDHORSE using simulated data and real Next-generation sequencing data. The simulated dataset mimicked recombination between two known haploid parental strains and allowed comparison of detected break points against known true break points to assess performance of recombination detection algorithms. A newly generated NGS dataset from a genetic cross of Toxoplasma gondii allowed us to demonstrate our pipeline. REDHORSE successfully extracted the relevant genetic markers and was able to transform the read alignments from NGS to the genome to generate multiple sequence alignments. Recombination detection algorithm in REDHORSE was able to detect conventional crossovers and double crossovers typically associated with gene conversions whilst filtering out artifacts that might have been introduced during sequencing or alignment. REDHORSE outperformed other commonly used recombination detection algorithms in finding conventional crossovers. In addition, REDHORSE was the only algorithm that was able to detect double crossovers.

Conclusion

REDHORSE is an efficient analytical pipeline that serves as a bridge between genomic alignments and existing recombination detection algorithms. Moreover, REDHORSE is equipped with a recombination detection algorithm specifically designed for Next-generation sequencing data. REDHORSE is portable, platform independent Java based utility that provides efficient analysis of genetic crosses based on Next-generation sequencing data. REDHORSE is available at http://redhorse.sourceforge.net/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1309-7) contains supplementary material, which is available to authorized users.     

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R(2)(cv)) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n = 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R(2)(val)) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R(2)(cv), RMSECV, R(2)(val) and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (α-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.     

Distributed File System Virtualization Techniques Supporting On-Demand Virtual Machine Environments for Grid Computing

This paper presents a data management solution which allows fast Virtual Machine (VM) instantiation and efficient run-time execution to support VMs as execution environments in Grid computing. It is based on novel distributed file system virtualization techniques and is unique in that: (1) it provides on-demand cross-domain access to VM state for unmodified VM monitors; (2) it enables private file system channels for VM instantiation by secure tunneling and session-key based authentication; (3) it supports user-level and write-back disk caches, per-application caching policies and middleware-driven consistency models; and (4) it leverages application-specific meta-data associated with files to expedite data transfers. The paper reports on its performance in wide-area setups using VMware-based VMs. Results show that the solution delivers performance over 30% better than native NFS and with warm caches it can bring the application-perceived overheads below 10% compared to a local-disk setup. The solution also allows a VM with 1.6 GB virtual disk and 320 MB virtual memory to be cloned within 160 seconds for the first clone and within 25 seconds for subsequent clones. Ming Zhao is a PhD candidate in the department of Electrical and Computer Engineering and a member of the Advance Computing and Information Systems Laboratory, at University of Florida. He received the degrees of BE and ME from Tsinghua University. His research interests are in the areas of computer architecture, operating systems and distributed computing. Jian Zhang is a PhD student in the Department of Electrical and Computer Engineering at University of Florida and a member of the Advance Computing and Information Systems Laboratory (ACIS). Her research interest is in virtual machines and Grid computing. She is a member of the IEEE and the ACM. Renato J. Figueiredo received the B.S. and M.S. degrees in Electrical Engineering from the Universidade de Campinas in 1994 and 1995, respectively, and the Ph.D. degree in Electrical and Computer Engineering from Purdue University in 2001. From 2001 until 2002 he was on the faculty of the School of Electrical and Computer Engineering of Northwestern University at Evanston, Illinois. In 2002 he joined the Department of Electrical and Computer Engineering of the University of Florida as an Assistant Professor. His research interests are in the areas of computer architecture, operating systems, and distributed systems.     

Exploiting redundancy to boost performance in a RAID-10 style cluster-based file system

While aggregating the throughput of existing disks on cluster nodes is a cost-effective approach to alleviate the I/O bottleneck in cluster computing, this approach suffers from potential performance degradations due to contentions for shared resources on the same node between storage data processing and user task computation. This paper proposes to judiciously utilize the storage redundancy in the form of mirroring existed in a RAID-10 style file system to alleviate this performance degradation. More specifically, a heuristic scheduling algorithm is developed, motivated from the observations of a simple cluster configuration, to spatially schedule write operations on the nodes with less load among each mirroring pair. The duplication of modified data to the mirroring nodes is performed asynchronously in the background. The read performance is improved by two techniques: doubling the degree of parallelism and hot-spot skipping. A synthetic benchmark is used to evaluate these algorithms in a real cluster environment and the proposed algorithms are shown to be very effective in performance enhancement. Yifeng Zhu received his B.Sc. degree in Electrical Engineering in 1998 from Huazhong University of Science and Technology, Wuhan, China; the M.S. and Ph.D. degree in Computer Science from University of Nebraska – Lincoln in 2002 and 2005 respectively. He is an assistant professor in the Electrical and Computer Engineering department at University of Maine. His main research interests are cluster computing, grid computing, computer architecture and systems, and parallel I/O storage systems. Dr. Zhu is a Member of ACM, IEEE, the IEEE Computer Society, and the Francis Crowe Society. Hong Jiang received the B.Sc. degree in Computer Engineering in 1982 from Huazhong University of Science and Technology, Wuhan, China; the M.A.Sc. degree in Computer Engineering in 1987 from the University of Toronto, Toronto, Canada; and the PhD degree in Computer Science in 1991 from the Texas A&M University, College Station, Texas, USA. Since August 1991 he has been at the University of Nebraska-Lincoln, Lincoln, Nebraska, USA, where he is Professor and Vice Chair in the Department of Computer Science and Engineering. His present research interests are computer architecture, parallel/distributed computing, cluster and Grid computing, computer storage systems and parallel I/O, performance evaluation, real-time systems, middleware, and distributed systems for distance education. He has over 100 publications in major journals and international Conferences in these areas and his research has been supported by NSF, DOD and the State of Nebraska. Dr. Jiang is a Member of ACM, the IEEE Computer Society, and the ACM SIGARCH. Xiao Qin received the BS and MS degrees in computer science from Huazhong University of Science and Technology in 1992 and 1999, respectively. He received the PhD degree in computer science from the University of Nebraska-Lincoln in 2004. Currently, he is an assistant professor in the department of computer science at the New Mexico Institute of Mining and Technology. He had served as a subject area editor of IEEE Distributed System Online (2000–2001). His research interests are in parallel and distributed systems, storage systems, real-time computing, performance evaluation, and fault-tolerance. He is a member of the IEEE. Dan Feng received the Ph.D degree from Huazhong University of Science and Technology, Wuhan, China, in 1997. She is currently a professor of School of Computer, Huazhong University of Science and Technology, Wuhan, China. She is the principal scientist of the the National Grand Fundamental Research 973 Program of China “Research on the organization and key technologies of the Storage System on the next generation Internet.” Her research interests include computer architecture, storage system, parallel I/O, massive storage and performance evaluation. David Swanson received a Ph.D. in physical (computational) chemistry at the University of Nebraska-Lincoln (UNL) in 1995, after which he worked as an NSF-NATO postdoctoral fellow at the Technical University of Wroclaw, Poland, in 1996, and subsequently as a National Research Council Research Associate at the Naval Research Laboratory in Washington, DC, from 1997–1998. In 1999 he returned to UNL where he directs the Research Computing Facility and currently serves as an Assistant Research Professor in the Department of Computer Science and Engineering. The Office of Naval Research, the National Science Foundation, and the State of Nebraska have supported his research in areas such as large-scale scientific simulation and distributed systems.     

Comprehensive proteome analysis in Cenococcum geophilum Fr. as a tool to discover drought-related proteins

Cenococcum geophilum is a widely distributed ectomycorrhizal fungus potentially playing a significant role in resistance and resilience mechanisms of its tree hosts exposed to drought stress. In this study, we performed a large scale protein analysis in pure cultures of C. geophilum in order to gain first global insights into the proteome assembly of this fungus. Using 1-D gel electrophoresis coupled with ESI-MS/MS, we indentified 638 unique proteins. Most of these proteins were related to the metabolic and cellular processes, and the transport machinery of cells. In a second step, we examined the influence of water deprivation on the proteome of C. geophilum pure cultures at three time points of gradually imposed drought. The results indicated that 12 proteins were differentially abundant in mycelia subjected to drought compared to controls. The induced responses in C. geophilum point towards regulation of osmotic stress, maintainance of cell integrity, and counteracting increased levels of reactive oxygen species formed during water deprivation.