A Discrete Time Model for the Analysis of Medium-Throughput C. elegans Growth Data

Lidar(Light Detection And Ranging) is a remote sensing technology that measures distance by illuminating a target with a laser and analyzing the reflected light . The pulsed laser ranging precision depends on receiving channel bandwidth, SNR (Signal-to-Noise Ratio), time discrimination precision and TOF (Time of Flight) measurement precision. However, the ranging precision is mainly determined by the TOF measurement precision. Various methods are developed to realize the TOF measurement and can be classified into coarse counting method, analog method, and interpolator method. The interpolator method combined with the advantage of the former two methods is proved be effective, but its realization principle is complex and difficult to realize in hardware. Here, Similar to the interpolation method, a new TDC method based on a reference sine signal is introduced to measure the TOF and can attain high precision and wide measurement range. At first, in principle how to use a reference sine signal to realize TOF measurement is analyzed and proved in detail. The TOF measurement is converted to measure the phase shift of reference sine signal. Then the factors that affect the measurement precision are quantitatively analyzed, which indicates that the frequency and the noise of reference sine signal are the main factors which affect the TOF measurement precision. Finally, numerical simulation and confirmatory experiment are presented, both showing that the proposed method is feasible to realize high precision by using low frequency reference sine signal. This proposed TOF measurement method can be applied in pulsed laser ranging and Three-dimensional imaging system.

BackgroundThe nematode Caenorhabditis elegans is being assessed as an alternative model organism as part of an interagency effort to develop better means to test potentially toxic substances. As part of this effort, assays that use the COPAS Biosort flow sorting technology to record optical measurements (time of flight (TOF) and extinction (EXT)) of individual nematodes under various chemical exposure conditions are being developed. A mathematical model has been created that uses Biosort data to quantitatively and qualitatively describe C. elegans growth, and link changes in growth rates to biological events. Chlorpyrifos, an organophosphate pesticide known to cause developmental delays and malformations in mammals, was used as a model toxicant to test the applicability of the growth model for in vivo toxicological testing.Methodology/Principal FindingsL1 larval nematodes were exposed to a range of sub-lethal chlorpyrifos concentrations (0–75 µM) and measured every 12 h. In the absence of toxicant, C. elegans matured from L1s to gravid adults by 60 h. A mathematical model was used to estimate nematode size distributions at various times. Mathematical modeling of the distributions allowed the number of measured nematodes and log(EXT) and log(TOF) growth rates to be estimated. The model revealed three distinct growth phases. The points at which estimated growth rates changed (change points) were constant across the ten chlorpyrifos concentrations. Concentration response curves with respect to several model-estimated quantities (numbers of measured nematodes, mean log(TOF) and log(EXT), growth rates, and time to reach change points) showed a significant decrease in C. elegans growth with increasing chlorpyrifos concentration.ConclusionsEffects of chlorpyrifos on C. elegans growth and development were mathematically modeled. Statistical tests confirmed a significant concentration effect on several model endpoints. This confirmed that chlorpyrifos affects C. elegans development in a concentration dependent manner. The most noticeable effect on growth occurred during early larval stages: L2 and L3. This study supports the utility of the C. elegans growth assay and mathematical modeling in determining the effects of potentially toxic substances in an alternative model organism using high-throughput technologies.

In recent years, multi-pixel photon counters (MPPCs) have been actively studied for use in a module for such next-generation PET systems as MRI-PET, DoI-PET, and ToF-PET scanners. In particular, Time of Flight (ToF) measurement is a challenging approach to identify the position of a γ-ray source, according to differences in the arrival times of annihilation γ rays after positron decay. Several simulations suggest a substantial improvement in the signal-to-noise ratio of PET images when using ToF information. However, ToF-PET performance is determined by the time resolution of a γ-ray sensor (including scintillators, photo-sensors and readout electronics) as a whole, thus making it often difficult to achieve ToF resolution as good as 500 ps (FWHM) in actual PET systems. This paper describes our development of a new method of ToF measurement using MPPC-based scintillation detectors. We showed that our method effectively reduces the contamination of dark noise, and minimizes the effects of time walk and timing jitter. The best ToF resolution of 213 ps (FWHM) was achieved by coupling 3×3×10 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Ce:LYSO crystal with a 3×3 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> pixel detector. We conclude by commenting on our ultra-fast ASIC for 16-ch MPPC readout as pertaining to future applications in MPPC-PET scanners with ToF measurement capability.

Time of Flight (TOF) measurement technique of adjacent pulses with sub-nano second interval is presented. In the conventional TOF measurement, the measurement of adjacent pulses is restricted to dead time or recovery time of a single TDC circuit. In the proposed TOF measurement technique, adjacent pulses are distributed to multiple TDCs in the order of arriving sequences in the distribution system. Thus measurement time is not restricted to the dead time or recovery time of a single TDC circuit. In the distribution system, programmable delay circuits are used to optimize the timing of the system so that minimum interval of measurable adjacent pulses is optimized. In this paper, the configuration and modules for measuring adjacent pulses are explained in section I, and various topologies for distribution system are explained in section II. The hardware implementation is considered in section III. And finally the conclusion and future works are presented in section IV.

BackgroundThe increasing access to autonomously-operated technologies offer vast opportunities to sample large volumes of biological data. However, these technologies also impose novel demands on ecologists who need to apply tools for data management and processing that are efficient, publicly available and easy to use. Such tools are starting to be developed for a wider community and here we present an approach to combine essential analytical functions for analysing large volumes of image data in marine ecological research.New informationThis paper describes the Koster Seafloor Observatory, an open-source approach to analysing large amounts of subsea movie data for marine ecological research. The approach incorporates three distinct modules to: manage and archive the subsea movies, involve citizen scientists to accurately classify the footage and, finally, train and test machine learning algorithms for detection of biological objects. This modular approach is based on open-source code and allows researchers to customise and further develop the presented functionalities to various types of data and questions related to analysis of marine imagery. We tested our approach for monitoring cold water corals in a Marine Protected Area in Sweden using videos from remotely-operated vehicles (ROVs). Our study resulted in a machine learning model with an adequate performance, which was entirely trained with classifications provided by citizen scientists. We illustrate the application of machine learning models for automated inventories and monitoring of cold water corals. Our approach shows how citizen science can be used to effectively extract occurrence and abundance data for key ecological species and habitats from underwater footage. We conclude that the combination of open-source tools, citizen science systems, machine learning and high performance computational resources are key to successfully analyse large amounts of underwater imagery in the future.

We have measured the time of flight (TOF) through a 4 silicon wafer with and without a 1.8 /spl mu/m coating of Shipley 1813 positive photoresist. The TOF change from bare to coated wafer was measured three times on a single wafer; the average of the three experiments was a TOF of 14.3 ns (+/-2.5 ns), consistent with the calculated expected results. In order to increase the precision of the TOF measurement to the level needed for prebake monitoring, we have applied a least squares algorithm to estimate the transfer function between the echo and received waveforms. Results of pulsed data studies indicate that we can get a decrease in the standard deviation of the TOF measurement from 2.93 ns to 2.53 ns by determining the TOF from the estimated transfer function.

Local sequence alignment (LSA) is an essential part of DNA sequencing. LSA helps to identify the facts in biological identity, criminal investigations, disease identification, drug design and research. Large volume of biological data makes difficulties to the performance of efficient analysis and proper management of data in small space has become a serious issue. We have subdivided the data sets into various segments to reduce the data sets as well as for efficient memory use. The integration of dynamic programming (DP) and Chapman–Kolmogorov equations (CKE) makes the analysis faster. The subdivision process is named data reducing process (DRP). DRP is imposed before DP and CKE. This approach needs less space compared with other methods and the time requirement is also improved.

With the advancements in high-throughput sequencing technologies such as Illumina, PacBio, and 10X Genomics platforms, and gas/liquid chromatography-mass spectrometry, large volumes of biological data in multiple formats can now be obtained through multi-omics analysis. Bioinformatics is constantly evolving and seeking breakthroughs to solve multi-omics problems; however, it is challenging for most experimental biologists to analyse data using command-line interfaces, coding, and scripting. Based on experience with multi-omics, we have developed OmicsSuite, a desktop suite that comprehensively integrates statistics and multi-omics analysis and visualization. The suite has 175 sub-applications in 12 categories, including Sequence, Statistics, Algorithm, Genomics, Transcriptomics, Enrichment, Proteomics, Metabolomics, Clinical, Microorganism, Single Cell, and Table Operation. We created the user interface with Sequence View, Table View, and intelligent components based on JavaFX and the popular Shiny framework. The multi-omics analysis functions were developed based on BioJava and 300+ packages provided by the R CRAN and Bioconductor communities, and it encompasses over 3000 adjustable parameter interfaces. OmicsSuite can directly read multi-omics raw data in FastA, FastQ, Mutation Annotation Format, mzML, Matrix, and HDF5 formats, and the programs emphasize data transfer directions and pipeline analysis functions. OmicsSuite can produce pre-publication images and tables, allowing users to focus on biological aspects. OmicsSuite offers multi-omics step-by-step workflows that can be easily applied to horticultural plant breeding and molecular mechanism studies in plants. It enables researchers to freely explore the molecular information contained in multi-omics big data (Source: https://github.com/OmicsSuite/, Website: https://omicssuite.github.io, v1.3.9).

Bioinformatics has provided tremendous breakthroughs in the field of molecular biology. All this evolution has generated a large volume of biological data that increasingly require the use of computing for analysis and storage of this information. The identification of the human leukocyte antigen (HLA) genotypes is critical to the success of organ transplants in humans. HLA typing involves not only laboratory tests but also DNA sequencing, with the participation of several professionals responsible for different stages of the process. Thus, the objective of this paper is to map the main steps in HLA typing in a laboratory specialized in performing such procedures, analyzing each process and proposing solutions to speed up the these steps, avoiding mistakes.

Familiar sciences of biology, physics, chemistry, cybernetics, and computational methods for dealing with vast new data sets of information at molecular and sub-molecular levels are morphing into new sciences. Some exist beneath our line of sight where laws of nature hover between Newtonian and quantum mechanics. New fields of cyber-, bio-, nanotechnology and systems biology raise arcane new concepts. The completed human genome has led to an explosion of interest in genetics and molecular biology. The view of the genome as a network of interacting computational components is well established and here writers explore it in new ways. These systemic approaches are timely in light of the availability of an increasing number of genomic sequences, and the generation of large volumes of biological data by high-throughput methods. Suitable for two-semesters of study, the works surveys genomics principles in the 13 chapters of Vol I, and networks and models in the 14 chapters of Vol II. Both, as a two-book set, will serve as core foundation titles for Dennis Shasha's Series in Systems Biology, establishing the principles and challenges for this emerging field of study. In each chapter world-renowned experts trail-blazing in their respective fields will review corresponding topics as well as current and planned research. Chapters will treat the integrated study and analysis of biological systems by use of data and information about the system components in their entirety, as opposed to the study of individual components in isolation. Systems Biology courses are popping up all over the place and biology, computer science, and bioinformatics programs are the primary potential takers. The editors plan books for a very wide audience, at the same time providing a comprehensive repository of up-to-date overviews and predictions for a number of inter-related sub-fields within this hierarchy. Intended readers include graduate students plus academic and professional researchers of genomics, bioinformatics, molecular biology, biochemistry, bioengineering, and computer systemic approaches to those fields. By comparison, Shasha's first Systems Biology Series title, Amos's Cellular Biology, is a book for technologists using biology as a vehicle to do something else, whereas this is a book about systems and related technologies in service to biologists. The volume editors plan to review or have reviewed, and to edit the invited chapters for content and consistent conceptual level, each chapter contributing uniquely to the key aspects of the Systems Biology hierarchy. A few chapter contents may date after two years, but the majority will endure for longer-term reference use because they treat methodologies and provide sample applications.

The post-genomic era is characterized by the rapid data accumulation leading to unwieldy and large volumes of biological data. The proteomics results (large sets of identified proteins or peptides) that originate from several workflow steps, play an important role in the analysis of a proteomics experiment. As a result, the area of high-through-put proteomics created new visualization challenges in interpreting large-scale datasets. We present VIP, an interactive visualization tool for proteomics data, which integrates protein or peptide features emanating at every step of the proteomics analysis and combines them visually in synthetic maps. Our novel tool offers the flexibility to choose any desired features according to the analysis objectives, examine simultaneously more than one maps and interact with the visualization by querying and filtering the results. The synthetic maps visualization aims not only at summarizing proteomics experiments in a unified manner for both 2DE-MS and LC-MS based analyses, but also at providing a quick and combined overview of protein/peptide features, thus facilitating the data analysis and interpretation.

The development of new technologies for the design of DNA microarrays has boosted the generation of large volumes of biological data, which requires the development of efficient computational methods for their analysis and annotation. Among these methods, biclusters generation algorithms attempt to identify coherent associations of genes and experimental conditions. In this paper, we introduce an improved version of a multi-objective genetic algorithm to find large biclusters that are, at the same time, highly homogeneous. The proposed improvement uses a group based representation for the genes-conditions associations rather than long binary strings. To assess the proposal performance the algorithm is applied to generate biclusters for two real gene expression data: Saccharomyces Cerevisiae with 2884 genes and 17 conditions, and the human B cells Lymphoma with 4026 genes and 96 conditions. The results of computational experiments show that the proposed approach outperforms current state-of-the-art algorithms on these data sets.

Reconstruction of biological and biochemical networks is a crucial step in extracting information from a large volume of biological data. There are several methods developed recently to reconstruct biological networks using dynamic data, each with specific benefits and some drawbacks. Here, we have developed a new method called Doubly Penalized Linear Absolute Shrinkage and Selection Operator (DPLASSO) for reconstruction of dynamic biological networks. In this approach, we have integrated two distinct methods viz., statistical significance testing of model coefficients and penalized/constrained optimization. Principal component analysis with statistical significance testing acts as a supervisory-level filter to extract the most informative components of the network from a dataset (Layer 1). In the lower level (Layer 2), LASSO with extra weights on the smaller parameters obtained in the first layer is employed to retain the main predictors and to set the small coefficients to zero. Two case studies are used to compare the relative performance of DPLASSO and LASSO in terms of several metrics, such as sensitivity, specificity, accuracy and fractional-error in the estimates of the coefficients. In the first case study, with a synthetic data set, our simulation results show substantial improvements over LASSO for the reconstruction of the network in terms of accuracy and specificity. The second case study relies on experimental datasets for cell division cycle of fission yeast. This case study illustrates that DPLASSO performs better than LASSO in terms of sensitivity, specificity and accuracy in reconstructing networks.

We are living the era where information may no longer be a bottleneck in the path to understand the complex biological systems. The namely post-genomic era brought with it the availability of automatic miniaturized assays able to generate omics data types, from complete genomes to proteomes, transcriptome, and metabolome data of different organisms from various taxa. As the output to this approach, different computational and in silico tools have been developed to process and mine knowledge from the large volume of biological data generated, among which the systems-scale knowledge mining concept has recently become the focus of many post-genomic researches. Actinobacterial omics data are being increasingly produced and explored in order to derive the omics-scale knowledge required for optimizing biotechnological potentials and productions as well as uncovering the pathogenicity mechanisms of these bacteria for therapeutic approaches. Accordingly, this review highlights the current status of actinobacterial omics data and systems biological research with the main focus being on the optimization of biotechnological potentials of this important bacterial cell factory.

Data-driven reconstruction of biological networks is a crucial step towards making sense of large volumes of biological data. Although several methods have been developed recently to reconstruct biological networks, there are few systematic and comprehensive studies that compare different methods in terms of their ability to handle incomplete datasets, high data dimensions and noisy data. The authors use experimentally measured and synthetic datasets to compare three popular methods - principal component regression (PCR), linear matrix inequalities (LMI) and least absolute shrinkage and selection operator (LASSO) - in terms of root-mean-squared error (RMSE), average fractional error in the value of the coefficients, accuracy, sensitivity, specificity and the geometric mean of sensitivity and specificity. This comparison enables the authors to establish criteria for selection of an appropriate approach for network reconstruction based on a priori properties of experimental data. For instance, although PCR is the fastest method, LASSO and LMI perform better in terms of accuracy, sensitivity and specificity. Both PCR and LASSO are better than LMI in terms of fractional error in the values of the computed coefficients. Trade-offs such as these suggest that more than one aspect of each method needs to be taken into account when designing strategies for network reconstruction.