Pooling control in variable preparative chromatography processes

Preparative chromatographic columns that run at high loads are highly sensitive to batch-to-batch disturbances of the process parameters, placing high demands on the strategy used for pooling of the product fractions. A new approach to pooling control is presented in a proof-of-concept study. A model-based sensitivity analysis was performed identifying the critical process parameters to product purity and optimal cut points. From this, the robust fixed cut points were found and pooling control strategies for variations in the critical parameters were designed. Direct measurements and indirect measurements based on the UV detector signal were used as control signals. The method is demonstrated for two case studies of preparative protein chromatography: hydrophobic interaction and reversed phase chromatography. The yield improved from 88.18 to 92.88% when changing from fixed to variable pooling in hydrophobic interaction chromatography, and from 35.15 to 76.27% in the highly sensitive reversed phase chromatography.     

Pooling Strategies for Screening Blood in Areas with Low Prevalence of HIV

Human immunodeficiency virus (HIV) infection has serious consequences and must be kept out of blood supplies. Screening to ensure the safety of blood supplies is associated with a very high cost. The idea of pooling test samples to obtain significant savings was first suggested in 1943. Recently pooling sera has gained wider interest both as a means to determine the HIV seroprevalence rate in general populations and to weed out all HIV-positive units in blood supplies. We describe a simple method for detecting seropositive samples in mass screening. This method determines the pooling size based on the estimated prevalence rate. Although several repooling stages are allowed, these will be kept to a minimum since the more stages that are required, the greater chance for human and technical errors. The criteria to end pooling are based on both the savings rate and the relative cost between the preparation and the actual test. Two examples illustrate the applications of this method in determining the number of samples to be pooled in successive stages and the resulting savings rate.     

Discovery of rare mutations in populations: TILLING by sequencing

Discovery of rare mutations in populations requires methods, such as TILLING (for Targeting Induced Local Lesions in Genomes), for processing and analyzing many individuals in parallel. Previous TILLING protocols employed enzymatic or physical discrimination of heteroduplexed from homoduplexed target DNA. Using mutant populations of rice (Oryza sativa) and wheat (Triticum durum), we developed a method based on Illumina sequencing of target genes amplified from multidimensionally pooled templates representing 768 individuals per experiment. Parallel processing of sequencing libraries was aided by unique tracer sequences and barcodes allowing flexibility in the number and pooling arrangement of targeted genes, species, and pooling scheme. Sequencing reads were processed and aligned to the reference to identify possible single-nucleotide changes, which were then evaluated for frequency, sequencing quality, intersection pattern in pools, and statistical relevance to produce a Bayesian score with an associated confidence threshold. Discovery was robust both in rice and wheat using either bidimensional or tridimensional pooling schemes. The method compared favorably with other molecular and computational approaches, providing high sensitivity and specificity.     

Case study and application of process analytical technology (PAT) towards bioprocessing: Use of tryptophan fluorescence as at‐line tool for making pooling decisions for process chromatography

Process analytical technology (PAT) has been gaining momentum in the biopharmaceutical community due to the potential for continuous real time quality assurance resulting in improved operational control and compliance. Two imperatives for implementing any PAT tool are that “variability is managed by the process” and “product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.” Recently, we have been examining the feasibility of applying different analytical tools to bioprocessing unit operations. We have previously demonstarted that commercially available online‐high performance liquid chromatography and ultra performance liquid chromatography systems can be used for analysis that can facilitate real‐time decisions for column pooling based on product quality attributes (Rathore et al., 2008 a,b). In this article, we review an at‐line tool that can be used for pooling of process chromatography columns. We have demonstrated that our tryptophan fluorescence method offers a feasible approach and meets the requirements of a PAT application. It is significantly faster than the alternative of fractionation, offline analysis followed by pooling. Although the method as presented here is not an online method, this technique may offer better resolution for certain applications and may be a more optimal approach as it is very conducive to implementation in a manufacturing environment. This technique is also amenable to be used as an online tool via front face fluorescence measurements done concurrently with product concentration determination by UV. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy

Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.     

Novel linkage mapping approach using DNA pooling in human and animal genetics. II. Detection of quantitative traits loci in dairy cattle

Selective DNA pooling is an advanced methodology for linkage mapping of quantitative trait loci (QTL) in farm animals. The principle is based on densitometric estimates of marker allele frequency in pooled DNA samples of phenotypically extreme individuals from half-sib, backcross and F(2) experimental designs in farm animals. This methodology provides a rapid and efficient analysis of a large number of individuals with short tandem repeat markers that are essential to detect QTL through the genome - wide searching approach. Several strategies involving whole genome scanning with a high statistical power have been developed for systematic search to detect the quantitative traits loci and linked loci of complex traits. In recent studies, greater success has been achieved in mapping several QTLs in Israel-Holstein cattle using selective DNA pooling. This paper outlines the currently emerged novel strategies of linkage mapping to identify QTL based on selective DNA pooling with more emphasis on its theoretical pre-requisite to detect linked QTLs, applications, a general theory for experimental half-sib designs, the power of statistics and its feasibility to identify genetic markers linked QTL in dairy cattle. The study reveals that the application of selective DNA pooling in dairy cattle can be best exploited in the genome-wide detection of linked loci with small and large QTL effects and applied to a moderately sized half-sib family of about 500 animals.     

Detection of aptamer-protein interactions using QCM and electrochemical indicator methods

We report novel method of detection thrombin-aptamer interaction based on measurement the charge consumption from the electrode covered by DNA aptamers to an electrochemical indicator methylene blue (MB), that is bounded to a thrombin. The binding of thrombin to an aptamers has been detected also by QCM method in flow measuring cell. We showed that using MB it is possible to detect thrombin with high sensitivity and selectivity.     

A Positive Detecting Code and Its Decoding Algorithm for DNA Library Screening

The study of gene functions requires high-quality DNA libraries. However, a large number of tests and screenings are necessary for compiling such libraries. We describe an algorithm for extracting as much information as possible from pooling experiments for library screening. Collections of clones are called pools, and a pooling experiment is a group test for detecting all positive clones. The probability of positiveness for each clone is estimated according to the outcomes of the pooling experiments. Clones with high chance of positiveness are subjected to confirmatory testing. In this paper, we introduce a new positive clone detecting algorithm, called the Bayesian network pool result decoder (BNPD). The performance of BNPD is compared, by simulation, with that of the Markov chain pool result decoder (MCPD) proposed by Knill et al. in 1996. Moreover, the combinatorial properties of pooling designs suitable for the proposed algorithm are discussed in conjunction with combinatorial designs and dhbox{-}{rm disjunct} matrices. We also show the advantage of utilizing packing designs or BIB designs for the BNPD algorithm.     

Spectrophotometric determination of the quantity of protein in standard biological suspensions

A combined method for spectrophotometric measurement of protein amount for conventionally isolated biological suspensions is suggested. The method is based on the comparison of data on the optical density of a suspension at 280-290 nm with the result of a single colorimetric determination. For the membranes of sarcoplasmic reticulum and mitochondria it has been shown that the combined spectrophotometric method gives possibility to shorten the time of measurement, has high specificity and sensitivity and compares favourably with usual colorimetric methods in relation to accuracy. A calibration chart was obtained for conventionally isolated "Wistar" rat liver mitochondria.     

Considerations for Group Testing: A Practical Approach for the Clinical Laboratory

Group testing, also known as pooled sample testing, was first proposed by Robert Dorfman in 1943. While sample pooling has been widely practiced in blood-banking, it is traditionally seen as anathema for clinical laboratories. However, the ongoing COVID-19 pandemic has re-ignited interest for group testing among clinical laboratories to mitigate supply shortages. We propose five criteria to assess the suitability of an analyte for pooled sample testing in general and outline a practical approach that a clinical laboratory may use to implement pooled testing for SARS-CoV-2 PCR testing. The five criteria we propose are: (1) the analyte concentrations in the diseased persons should be at least one order of magnitude (10 times) higher than in healthy persons; (2) sample dilution should not overly reduce clinical sensitivity; (3) the current prevalence must be sufficiently low for the number of samples pooled for the specific protocol; (4) there is no requirement for a fast turnaround time; and (5) there is an imperative need for resource rationing to maximise public health outcomes. The five key steps we suggest for a successful implementation are: (1) determination of when pooling takes place (pre-pre analytical, pre-analytical, analytical); (2) validation of the pooling protocol; (3) ensuring an adequate infrastructure and archival system; (4) configuration of the laboratory information system; and (5) staff training. While pool testing is not a panacea to overcome reagent shortage, it may allow broader access to testing but at the cost of reduction in sensitivity and increased turnaround time.     

Noninvasive Method for Simultaneously Measuring the Thermophysical Properties and Blood Perfusion in Cylindrically Shaped Living Tissues

An easy-to-use noninvasive method was developed to simultaneously measure the thermophysical parameters and blood perfusion in cylindrically shaped living tissues. This method is based on a two-dimensional mathematical model which requires temperature measurements at only three separate points along the axial direction on the cylinder surface. A sensitivity analysis has shown that the key thermophysical parameters, such as the thermal conductivity, volumetric heat capacity, and blood perfusion can be estimated simultaneously with high accuracy. Genetic algorithm (GA) selection, crossover, and mutation operators were developed to solve this multi-parameter optimization problem. This three-point method was validated by measuring the properties of a dynamic tissue-equivalent phantom with known thermal parameters. The method has also been applied to measure the thermophysical parameters and blood perfusion in human forearms with measured results agreeing well with the literature values.     

A class of edit kernels for SVMs to predict translation initiation sites in eukaryotic mRNAs.

The prediction of translation initiation sites (TISs) in eukaryotic mRNAs has been a challenging problem in computational molecular biology. In this paper, we present a new algorithm to recognize TISs with a very high accuracy. Our algorithm includes two novel ideas. First, we introduce a class of new sequence-similarity kernels based on string editing, called edit kernels, for use with support vector machines (SVMs) in a discriminative approach to predict TISs. The edit kernels are simple and have significant biological and probabilistic interpretations. Although the edit kernels are not positive definite, it is easy to make the kernel matrix positive definite by adjusting the parameters. Second, we convert the region of an input mRNA sequence downstream to a putative TIS into an amino acid sequence before applying SVMs to avoid the high redundancy in the genetic code. The algorithm has been implemented and tested on previously published data. Our experimental results on real mRNA data show that both ideas improve the prediction accuracy greatly and that our method performs significantly better than those based on neural networks and SVMs with polynomial kernels or Salzberg kernels.     

Bayesian inference for prevalence and diagnostic test accuracy based on dual-pooled screening

We propose a useful protocol for the problem of screening populations for low-prevalence characteristics such as HIV or drugs. Current HIV screening of blood that has been donated for transfusion involves the testing of individual blood units with an inexpensive enzyme-linked immunosorbent assay test and follow-up with a more accurate and more expensive western blot test for only those units that tested positive. Our cost-effective pooling strategy would enhance current methods by making it possible to accurately estimate the sensitivity and specificity of the initial screening test, and the proportion of defective units that have passed through the system. We also provide a method of estimating the distribution of prevalences for the characteristic throughout the population or subpopulations of interest.     

Bacterial classification with convolutional neural networks based on different data reduction layers

Abstract

For high accuracy classification of DNA sequences through Convolutional Neural Networks (CNNs), it is essential to use an efficient sequence representation that can accelerate similarity comparison between DNA sequences. In addition, CNN networks can be improved by avoiding the dimensionality problem associated with multi-layer CNN features. This paper presents a new approach for classification of bacterial DNA sequences based on a custom layer. A CNN is used with Frequency Chaos Game Representation (FCGR) of DNA. The FCGR is adopted as a sequence representation method with a suitable choice of the frequency k-lengthen words occurrence in DNA sequences. The DNA sequence is mapped using FCGR that produces an image of a gene sequence. This sequence displays both local and global patterns. A pre-trained CNN is built for image classification. First, the image is converted to feature maps through convolutional layers. This is sometimes followed by a down-sampling operation that reduces the spatial size of the feature map and removes redundant spatial information using the pooling layers. The Random Projection (RP) with an activation function, which carries data with a decent variety with some randomness, is suggested instead of the pooling layers. The feature reduction is achieved while keeping the high accuracy for classifying bacteria into taxonomic levels. The simulation results show that the proposed CNN based on RP has a trade-off between accuracy score and processing time.     

RAP: a new computer program for de novo identification of repeated sequences in whole genomes

MOTIVATION: DNA repeats are a common feature of most genomic sequences. Their de novo identification is still difficult despite being a crucial step in genomic analysis and oligonucleotides design. Several efficient algorithms based on word counting are available, but too short words decrease specificity while long words decrease sensitivity, particularly in degenerated repeats. RESULTS: The Repeat Analysis Program (RAP) is based on a new word-counting algorithm optimized for high resolution repeat identification using gapped words. Many different overlapping gapped words can be counted at the same genomic position, thus producing a better signal than the single ungapped word. This results in better specificity both in terms of low-frequency detection, being able to identify sequences repeated only once, and highly divergent detection, producing a generally high score in most intron sequences. AVAILABILITY: The program is freely available for non-profit organizations, upon request to the authors. CONTACT: giorgio.valle@unipd.it SUPPLEMENTARY INFORMATION: The program has been tested on the Caenorhabditis elegans genome using word lengths of 12, 14 and 16 bases. The full analysis has been implemented in the UCSC Genome Browser and is accessible at http://genome.cribi.unipd.it.     

Double-Exposure Optical Sectioning Structured Illumination Microscopy Based on Hilbert Transform Reconstruction

Structured illumination microscopy (SIM) with axially optical sectioning capability has found widespread applications in three-dimensional live cell imaging in recent years, since it combines high sensitivity, short image acquisition time, and high spatial resolution. To obtain one sectioned slice, three raw images with a fixed phase-shift, normally 2π/3, are generally required. In this paper, we report a data processing algorithm based on the one-dimensional Hilbert transform, which needs only two raw images with arbitrary phase-shift for each single slice. The proposed algorithm is different from the previous two-dimensional Hilbert spiral transform algorithm in theory. The presented algorithm has the advantages of simpler data processing procedure, faster computation speed and better reconstructed image quality. The validity of the scheme is verified by imaging biological samples in our developed DMD-based LED-illumination SIM system.     

Deconvoluting BAC-gene relationships using a physical map

Deconvolution of relationships between bacterial artificial chromosome (BAC) clones and genes is a crucial step in the selective sequencing of regions of interest in a genome. It often includes combinatorial pooling of unique probes obtained from the genes (unigenes), and screening of the BAC library using the pools in a hybridization experiment. Since several probes can hybridize to the same BAC, in order for the deconvolution to be achievable the pooling design has to be able to handle a large number of positives. As a consequence, smaller pools need to be designed, which in turn increases the number of hybridization experiments, possibly making the entire protocol unfeasible. We propose a new algorithm that is capable of producing high-accuracy deconvolution even in the presence of a weak pooling design, i.e. when pools are rather large. The algorithm compensates for the decrease of information in the hybridization data by taking advantage of a physical map of the BAC clones. We show that the right combination of combinatorial pooling and our algorithm not only dramatically reduces the number of pools required, but also successfully deconvolutes the BAC-gene relationships with almost perfect accuracy. Software is available on request from the first author.     

Measuring intracardiac impedance for determining sympathetic nerve activity in frequency-adjustment electrostimulation--1: Biomedical technical principles]

Modern Pacemaker technology makes it possible to adapt the pacing rate to hemodynamic requirements. The most ambitious approach aims at restoring the physiological closed-loop system by utilizing the information supplied by the Autonomic Nervous System and extracted from myocardial contractile performance. Measurement is accomplished by the impedance method using the stimulating electrode as the measuring electrode. The Ventricular Inotropic Parameter (VIP) has been identified as an ANS-dependent parameter. A special detection algorithm, the Regional Effective Slope Quantity (RQ), with a high ANS sensitivity has been developed specially for the purpose. Rate adaptation is achieved by using an individually-adjustable Inotropic Index (II). The concept has been evaluated in a multicenter study employing a standardized exercise protocol. The clinical results will be presented in Part 2 of this paper.     

Statistical models for discerning protein structures containing the DNA-binding helix-turn-helix motif

A method for discerning protein structures containing the DNA-binding helix-turn-helix (HTH) motif has been developed. The method uses statistical models based on geometrical measurements of the motif. With a decision tree model, key structural features required for DNA binding were identified. These include a high average solvent-accessibility of residues within the recognition helix and a conserved hydrophobic interaction between the recognition helix and the second alpha helix preceding it. The Protein Data Bank was searched using a more accurate model of the motif created using the Adaboost algorithm to identify structures that have a high probability of containing the motif, including those that had not been reported previously.