Automated Image Capture Research Articles

Open Hardware for Microfluidics: Exploiting Raspberry Pi Singleboard Computer and Camera Systems for Customisable Laboratory Instrumentation.

The integration of Raspberry Pi miniature computer systems with microfluidics has revolutionised the development of low-cost and customizable analytical systems in life science laboratories. This review explores the applications of Raspberry Pi in microfluidics, with a focus on imaging, including microscopy and automated image capture. By leveraging the low cost, flexibility and accessibility of Raspberry Pi components, high-resolution imaging and analysis have been achieved in direct mammalian and bacterial cellular imaging and a plethora of image-based biochemical and molecular assays, from immunoassays, through microbial growth, to nucleic acid methods such as real-time-qPCR. The control of image capture permitted by Raspberry Pi hardware can also be combined with onboard image analysis. Open-source hardware offers an opportunity to develop complex laboratory instrumentation systems at a fraction of the cost of commercial equipment and, importantly, offers an opportunity for complete customisation to meet the users' needs. However, these benefits come with a trade-off: challenges remain for those wishing to incorporate open-source hardware equipment in their own work, including requirements for construction and operator skill, the need for good documentation and the availability of rapid prototyping such as 3D printing plus other components. These advances in open-source hardware have the potential to improve the efficiency, accessibility, and cost-effectiveness of microfluidic-based experiments and applications.

Use of Hough Transform and Homography for the Creation of Image Corpora for Smart Agriculture

In the context of smart agriculture, developing deep learning models demands large and highquality datasets for training. However, the current lack of such datasets for specific crops poses a significant challenge to the progress of this field. This research proposes an automated method to facilitate the creation of training datasets through automated image capture and pre-processing. The method’s efficacy is demonstrated through two study cases conducted in a Cannabis Sativa cultivation setting. By leveraging automated processes, the proposed approach enables to create large-volume and high-quality datasets, significantly reducing human effort. The results indicate that the proposed method not only simplifies dataset creation but also allows researchers to concentrate on other critical tasks, such as refining image labeling and advancing artificial intelligence model creation. This work contributes towards efficient and accurate deep learning applications in smart agriculture.

Mera: A scalable high throughput automated micro-physiological system.

There is an urgent need for scalable Microphysiological Systems (MPS's)1 that can better predict drug efficacy and toxicity at the preclinical screening stage. Here we present Mera, an automated, modular and scalable system for culturing and assaying microtissues with interconnected fluidics, inbuilt environmental control and automated image capture. The system presented has multiple possible fluidics modes. Of these the primary mode is designed so that cells may be matured into a desired microtissue type and in the secondary mode the fluid flow can be re-orientated to create a recirculating circuit composed of inter-connected channels to allow drugging or staining. We present data demonstrating the prototype system Mera using an Acetaminophen/HepG2 liver microtissue toxicity assay with Calcein AM and Ethidium Homodimer (EtHD1) viability assays. We demonstrate the functionality of the automated image capture system. The prototype microtissue culture plate wells are laid out in a 3×3 or 4×10 grid format with viability and toxicity assays demonstrated in both formats. In this paper we set the groundwork for the Mera system as a viable option for scalable microtissue culture and assay development.

A Miniaturized System for Rapid, Isothermal Detection of SARS-CoV-2 in Human and Environmental Samples.

We report a small-footprint cost-effective isothermal rapid DNA amplification system, with integrated microfluidics for automated sample analysis and detection of SARS-CoV-2 in human and environmental samples. Our system measures low-level fluorescent signals in real-time during amplification, while maintaining the desired assay temperature on a low power, portable system footprint. A unique soft microfluidic chip design was implemented to mitigate thermocapillary effects and facilitate optical alignment for automated image capture and signal analysis. The system-on-board prototype, coupled with the LAMP primers designed by BioCoS, was sensitive enough to detect large variations in viral loads of SARS-CoV-2 corresponding to a threshold cycle range of 16 to 39. Furthermore, tested samples consisted of a broad range of viral strains and lineages identified in Canada during 2021-2022. Clinical specimens were collected and tested at the Kingston Health Science Centre using a clinically validated PCR assay, and variants were determined using whole genome sequencing.

A TRIZ-Inspired Conceptual Development of a Roof Tile Transportation and Inspection System

The Indonesian roof tile manufacturing industry relies heavily on manual operations, specifically in transportation and inspection processes, which creates multiple issues, such as fatigue, injuries, human error, and reduced productivity. Various industries in the Indonesian industrial landscape have begun embracing a problem-solving approach known as the theory of inventive problem-solving (TRIZ) to mine solutions for industrial issues. Nevertheless, its application in the Indonesian roof tile manufacturing industry remains unaddressed. The study aims to solve manual handling issues in the roof tile manufacturing industry using TRIZ. Three observations were outlined from manual roof tile transportation and inspection, followed by the formulation of engineering contradictions (ECs). The ECs were linked with system parameters, which were used as indicators within the contradiction matrix to extract inventive principles as solution models for conceptual development. The concept included an automated system with a conveyor belt (#15: dynamics) for effective transportation, automated image capture (#28: mechanics substitution) for effective inspection, and a flipping conveyor (#25: self-service) to eliminate manual contact. Although the study addressed several issues stemming from manual operations, mechanical analysis, prototyping, and usability testing still require improvements.

Field-deployable aptasensor with automated analysis of stain patterns for the detection of chlorpyrifos in water

Organophosphorus compounds such as chlorpyrifos (CPS) are causing serious environmental problems worldwide. Efficient monitoring of the CPS levels in water resources demands portable devices for on-field testing. Here we report the development of a CPS sensor coupled with smartphones for automated reading and data analysis. The sensing mechanism makes use of gold nanoparticles stabilized by a CPS-specific aptamer, where colloidal destabilization occurs in presence of competing CPS molecules. In particular, an innovative readout is proposed: quantitative analysis of the stain patterns left by evaporating drops of the test solutions. We have found that the CPS-induced destabilization suppresses the typical coffee-ring stain of the colloidal gold, and then exploited the phenomenon to quantitatively determine the CPS concentration in water samples. A strong correlation between CPS level in samples and the alteration of the stain patterns was established for a wide range of CPS concentrations (0.048 μM–482 μM). The limit of detection of the sensor was 0.2 μM. The assay was implemented on Whatman filter paper cards that were specially patterned by wax-printing. A smartphone-based Python program was written for fully automated image capture and processing. Notably, as we analyze the spatial configuration of the stains, the reading system is independent of external illumination. The system also enables data management and traceability, which are highly desirable attributes for environmental monitoring.

Scan4CFU: Low-cost, open-source bacterial colony tracking over large areas and extended incubation times

A hallmark of bacterial populations cultured in vitro is their homogeneity of growth, where the majority of cells display identical growth rate, cell size and content. Recent insights, however, have revealed that even cells growing in exponential growth phase can be heterogeneous with respect to variables typically used to measure cell growth. Bacterial heterogeneity has important implications for how bacteria respond to environmental stresses, such as antibiotics. The phenomenon of antimicrobial persistence, for example, has been linked to a small subpopulation of cells that have entered into a state of dormancy where antibiotics are no longer effective. While methods have been developed for identifying individual non-growing cells in bacterial cultures, there has been less attention paid to how these cells may influence growth in colonies on a solid surface. In response, we have developed a low-cost, open-source platform to perform automated image capture and image analysis of bacterial colony growth on multiple nutrient agar plates simultaneously. The descriptions of the hardware and software are included, along with details about the temperature-controlled growth chamber, high-resolution scanner, and graphical interface to extract and plot the colony lag time and growth kinetics. Experiments were conducted using a wild type strain of Escherichia coli K12 to demonstrate the feasibility and operation of our setup. By automated tracking of bacterial growth kinetics in colonies, the system holds the potential to reveal new insights into understanding the impact of microbial heterogeneity on antibiotic resistance and persistence.

The 3D reconstructed skin micronucleus assay using imaging flow cytometry and deep learning: A proof-of-principle investigation

The reconstructed skin micronucleus (RSMN) assay was developed in 2006, as an in vitro alternative for genotoxicity evaluation of dermally applied chemicals or products. In the years since, significant progress has been made in the optimization of the assay, including publication of a standard protocol and extensive validation. However, the diverse morphology of skin cells makes cell preparation and scoring of micronuclei (MN) tedious and subjective, thus requiring a high level of technical expertise for evaluation. This ultimately has a negative impact on throughput and the assay would benefit by the development of an automated method which could reduce scoring subjectivity while also improving the robustness of the assay by increasing the number of cells that can be scored. Imaging flow cytometry (IFC) with the ImageStream®X Mk II can capture high-resolution transmission and fluorescent imagery of cells in suspension. This proof-of-principle study describes protocol modifications that enable such automated measurement in 3D skin cells following exposure to mitomycin C and colchicine. IFC was then used for automated image capture and the Amnis® Artificial Intelligence (AAI) software permitted identification of binucleated (BN) cells with 91% precision. On average, three times as many BN cells from control samples were evaluated using IFC compared to the standard manual analysis. When IFC MNBN cells were visually scored from within the BN cell images, their frequency compared well with manual slide scoring, showing that IFC technology can be applied to the RSMN assay. This method enables faster time to result than microscope-based scoring and the initial studies presented here demonstrate its capability for the detection of statistically significant increases in MNBN frequencies. This work therefore demonstrates the feasibility of combining IFC and AAI to automate scoring for the RSMN assay and to improve its throughput and statistical robustness.

Sharing and performance optimization of reproducible workflows in the cloud

Scientific workflows play a vital role in modern science as they enable scientists to specify, share and reuse computational experiments. To maximizethe benefits, workflows need to support the reproducibility of the experimental methods they capture. Reproducibility enables effective sharing as scientists can re-execute experiments developed by others and quickly derive new or improved results. However, achieving reproducibility in practice is problematic — previous analyses highlight issues due to uncontrolled changes in the input data, configuration parameters, workflow description and the software used to implement the workflow tasks. The resulting problems have become known as workflow decay. In this paper we present a novel framework that addresses workflow decay through the integration of system description, version control, container management and automated deployment techniques. It then introduces a set of performance optimization techniques that significantly reduce the runtime overheads caused by making workflows reproducible. The resulting system significantly improves the performance, repeatability and also the ability to share and re-use workflows by combining a method to uniquely identify task and workflow images with an automated image capture facility and a multi-level cache. The system is evaluated through an extensive set of experiments that validate the approach and highlight the key benefits of the proposed optimizations. This includes methods for reducing the runtime of workflows by up to an order of magnitude in cases where they are enacted concurrently on the same host VM and in different Clouds, and where they share tasks.

Machine Learning Algorithms for automated Image Capture and Identification of Fall Armyworm (FAW) Moths

Automated entomology is one of the field that has received a fair attention from the computer scientists and its support disciplines. This can further be confirmed by the recent attention that the Fall Armyworm (FAW) (Spodoptera frugiperda) has received in Africa particularly the Southern African Development Community (SADC). As the FAW is known for its devastating effects, stakeholders such as the Food and Agriculture Organization (FAO), SADC and University of Zambia (UNZA) have agreed to develop robust early monitoring and warning system. To supplement the stakeholders’ efforts, we choose a branch of artificial intelligence that employs deep neural network architectures known as Google TensorFlow. It is an advanced state-of-the-art machine learning technique that can be used to identify the FAW moths. In this paper, we use Google TensorFlow, an open source deep learning software library for defining, training and deploying machine learning models. We use the transfer learning technique to retrain the Inception v3 model in TensorFlow on the insect dataset, which reduces the training time and improve the accuracy of FAW moth identification. Our retrained model achieves a train accuracy of 57 – 60 %, cross entropy of 65 – 70% and validation accuracy of

163 - Automated quantitative image analysis and precision selection of desired sub-population of iPS cells using cell XTM and ColonyzeTM platform

Induced pluripotent stem (iPS) cells are being developed for a broad range of research and therapeutic applications. However, reprogrammed iPS clones are inevitably heterogeneous. Value judgements must be made regarding which clones to use or exclude. iPS labs currently make decisions by visual inspection and manual selection, which are prone to large variation and lack the standardization needed for clinical translation. Automated methods are needed that enable rapid, quantitative, repeatable and reproducible: i) imaging and quantitative analysis of iPS clones, ii) objective determination of critical quality attributes (CQAs) (e.g. morphology or markers) that predict long term biological performance and iii) precise automated selection of desired cells or removal non-desired cells. This paper describes the development and validation of Cell XTM (Figure 1), a robust robotic system that enables each of these critical aspects of iPS cell processing and analysis. Large field of view images are acquired from standard tissue culture plates (6 to 96 wells) with 1 µ/pixel resolution in 1–8 minutes, using phase-contrast or fluorescence. Integration with ColonyzeTM software enables Automated Multimodal Large field of view Image Analysis (AM-LIA) based on metrics of proliferation, migration, surface markers, or cell and colony morphology, using principles outlined in ASTM Standard F2944-12. This integration enables automated image capture, analysis and “picking” or “removal” of desired or undesired colonies based on defined and reproducible criteria (Figure 2). Cell XTM and ColonyzeTM together eliminated labor-intensive manual processing steps, and provide unprecedented rigor in the analysis and management of heterogeneous iPS cell populations. Figure 2100% picking efficiency is observed (A,B; C,D). Post picking, cells were found to maintain their viabilility and expand during the 4 day follow-up. View Large Image Figure Viewer Download Hi-res image

PTEN loss as a predictive biomarker in head and neck squamous cell cancer (HNSCC) patients treated with cetuximab (C).

e17520 Background: No biomarker of C resistance has been identified in HNSCC. PTEN loss is present in approximately 30% of HNSCC. Biomarker analysis of the E5397 study suggested that addition of C to cisplatin in R/M HNSCC improves PFS in PTEN high/PIK3CA wild type patients but not those with PTEN loss or PIK3CA mutation. We hypothesized that PTEN testing may aid patient (pt.) selection for C therapy in HNSCC. Methods: MCC15780 was a phase II randomized trial of C plus sorafenib or C plus placebo in R/M HNSCC. 52/56 pts. in this study received C. PTEN analysis using AQUA as previously described was performed on tumor from 38 pts. Automated image capture was performed with HistoRx PM-2000 using the AQUAsition software. AQUA PTEN cut off determined as 1177 based on the first tertile.Fisher’s exact test used to compare low and high expression group. Event-time distributions estimated by Kaplan-Meier and compared using log-rank. Stratified Cox proportional hazards models used to estimate hazard ratios (HR) and test for significance for OS and PFS. All p-values are two-sided. A level of p < 0.05 is considered statistically significant. Results: 12/37 (32%) tumors were PTEN low. There was statistically significant improvement in PFS in PTEN high tumors compared to PTEN low tumors (HR (high/low) =0.33, 95% CI= (0.14, 0.75), p=0.008); and this remains significant after adjusting for age, sex, race, and ethnicity (HR (high/low) =0.27, 95% CI= (0.11, 0.67), p=0.004). Conclusions: PFS is significantly longer in PTEN high tumors compared to PTEN low tumors (HR=0.33, 95% CI= (0.14-0.75), p=0.008) in patients with R/M HNSCC treated with C. This finding remains significant after adjusting for age, sex, race, and ethnicity (HR=0.27, 95% CI= (0.11-0.67), p=0.004).This warrants PTEN analysis of specimens from larger cetuximab-based RCT. [Table: see text]

AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

BackgroundComputer-based phenotyping of plants has risen in importance in recent years. Whilst much software has been written to aid phenotyping using image analysis, to date the vast majority has been only semi-automatic. However, such interaction is not desirable in high throughput approaches. Here, we present a system designed to analyse plant images in a completely automated manner, allowing genuine high throughput measurement of root traits. To do this we introduce a new set of proxy traits.ResultsWe test the system on a new, automated image capture system, the Microphenotron, which is able to image many 1000s of roots/h. A simple experiment is presented, treating the plants with differing chemical conditions to produce different phenotypes. The automated imaging setup and the new software tool was used to measure proxy traits in each well. A correlation matrix was calculated across automated and manual measures, as a validation. Some particular proxy measures are very highly correlated with the manual measures (e.g. proxy length to manual length, r2 > 0.9). This suggests that while the automated measures are not directly equivalent to classic manual measures, they can be used to indicate phenotypic differences (hence the term, proxy). In addition, the raw discriminative power of the new proxy traits was examined. Principal component analysis was calculated across all proxy measures over two phenotypically-different groups of plants. Many of the proxy traits can be used to separate the data in the two conditions.ConclusionThe new proxy traits proposed tend to correlate well with equivalent manual measures, where these exist. Additionally, the new measures display strong discriminative power. It is suggested that for particular phenotypic differences, different traits will be relevant, and not all will have meaningful manual equivalent measures. However, approaches such as PCA can be used to interrogate the resulting data to identify differences between datasets. Select images can then be carefully manually inspected if the nature of the precise differences is required. We suggest such flexible measurement approaches are necessary for fully automated, high throughput systems such as the Microphenotron.

A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.

In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.

ICARE improves antinuclear antibody detection by overcoming the barriers preventing accreditation

BackgroundAntinuclear antibodies (ANA) are useful biomarkers for the diagnosis and the monitoring of rheumatic diseases. The American College of Rheumatology has stated that indirect immunofluorescence (IIF) analysis remains the gold standard for ANA screening. However, IIF is time consuming, subjective, not fully standardized and presents several issues for accreditation which is the process leading to ISO 15189 certification for medical laboratories. We propose an innovative tool for accreditation by using the quantitative evaluation of the automated image capture and analysis “ICARE” (Immunofluorescence for Computed Antinuclear antibody Rational Evaluation). MethodsWe established the optimal screening dilution (1:160) and a fluorescence index (FI) cutoff for ICARE on a cohort of 91 healthy blood donors. Then, we evaluated performance of ICARE on a routine cohort of 236 patients. Precision parameters of ANA detection by IIF were evaluated according to ISO 15189. ResultsICARE showed an excellent concordance with visual evaluation (88%, Kappa=0.76) and significantly discriminated between weak to moderate (1:160–1:320 titers) and high (>1:320 titers) ANA levels. A significant correlation was found between FI and ANA titers (Spearman's ρ=0.67; P<0.0001). Using ICARE, we reported precision parameters such as repeatability (CV<13.8%) and reproducibility (CV<13.1%) as well as absence of inter-sample contamination for ANA detection by IIF according to ISO 15189 standards. ConclusionsICARE offers a precious help for the accreditation of IIF qualitative methods. This innovative quantitative approach is in adequacy with the process of continuous improvement of the quality of clinical laboratories.

High-throughput cell-cycle imaging opens new doors for discovery.

During the life of a cell, numerous essential cellular processes must be coordinated both spatially and temporally, from DNA replication and chromosome segregation to gene expression and cytokinesis. In order to analyze these inherently dynamic and cell-cycle-dependent processes, it is essential to observe the dynamic localization of the cellular machinery throughout the entire cell cycle. Although some coarse features of cell-cycle dynamics can be captured in snapshot imaging, where cellular size or morphology can be used as a proxy for cell-cycle phase, the inherently stochastic nature of ultrastructures in the cell makes the direct visualization of subcellular dynamics an essential tool to differentiate between structural differences that are the result of biologically relevant dynamics versus cell-to-cell variation. With these goals in mind, we have developed a unique high-throughput imaging approach, and have recently applied this to characterize the cell-cycle localization of nearly every protein in the bacterial cell (Kuwada in Mol Microbiol, 95(1), 64-79, 2015). This approach combines large-format sample preparation with automated image capture, processing, and analysis to quantitatively characterize proteome localization of tens of thousands of complete cell cycles.

Particle size distributions by transmission electron microscopy: an interlaboratory comparison case study

This paper reports an interlaboratory comparison that evaluated a protocol for measuring and analysing the particle size distribution of discrete, metallic, spheroidal nanoparticles using transmission electron microscopy (TEM). The study was focused on automated image capture and automated particle analysis. NIST RM8012 gold nanoparticles (30 nm nominal diameter) were measured for area-equivalent diameter distributions by eight laboratories. Statistical analysis was used to (1) assess the data quality without using size distribution reference models, (2) determine reference model parameters for different size distribution reference models and non-linear regression fitting methods and (3) assess the measurement uncertainty of a size distribution parameter by using its coefficient of variation. The interlaboratory area-equivalent diameter mean, 27.6 nm ± 2.4 nm (computed based on a normal distribution), was quite similar to the area-equivalent diameter, 27.6 nm, assigned to NIST RM8012. The lognormal reference model was the preferred choice for these particle size distributions as, for all laboratories, its parameters had lower relative standard errors (RSEs) than the other size distribution reference models tested (normal, Weibull and Rosin–Rammler–Bennett). The RSEs for the fitted standard deviations were two orders of magnitude higher than those for the fitted means, suggesting that most of the parameter estimate errors were associated with estimating the breadth of the distributions. The coefficients of variation for the interlaboratory statistics also confirmed the lognormal reference model as the preferred choice. From quasi-linear plots, the typical range for good fits between the model and cumulative number-based distributions was 1.9 fitted standard deviations less than the mean to 2.3 fitted standard deviations above the mean. Automated image capture, automated particle analysis and statistical evaluation of the data and fitting coefficients provide a framework for assessing nanoparticle size distributions using TEM for image acquisition.

Nucleophosmin expression in ovarian cancer.

e15582 Background: The nucleolar protein, nucleophosmin (NPM1) is implicated multiple cellular processes, including proliferation, duplication of centrosomes, ARF-HDM2-p53 signaling. NPM1 is associated with sites of double strand DNA breaks, with persistence of its expression indicative of impaired DNA repair. Data from the TCGA data have emphasized that genomic instability through impaired DNA repair processes is a characteristic feature of many ovarian cancers. Our aim was to determine the expression of NPM1 in ovarian cancers and to determine the relationship between NPM1 expression and clinical outcomes including overall survival (OS), progression-free survival (PFS) and chemotherapy response. Methods: Tissue microarrays were created from 209 patients treated for ovarian cancer at a single institution from 1994-2004. Expression levels of NPM1 were examined by immunohistochemical staining. Slides were scored using the an automated image capture and analysis system. Positive nuclear staining was used to stratify tumors into high (&gt;50%) and low (&lt;50%) groups, and the results were related to overall survival (OS) and progression-free survival (PFS) via Kaplan-Meier analysis. The relationship between ki67, a proliferation marker and pH2AX, a mark of double strand DNA breaks was measured with Spearman rank correlation coefficient analyses. Results: The majority of tumors, 140/209 (69%) were of serous histology, advanced stage 146/209 (70%) and high grade 158/209 (76%). There was &gt;50% NPM1 expression in 83/209 (40%) and &lt;50% in 126/209 (60%) of the cases. Expression of NPM1 was higher in high grade tumors, and its expression alone was a significant predictor of PFS (p=0.022) but not OS (p=0.053). When adjusting for other predictors, NPM1 expression was predictive of PFS (p=0.047), but not OS (p=0.054). No relationship between NPM1 expression and response to platinum chemotherapy was observed. However, NPM1 expression correlated with Ki67 (r=0.43, p&lt;0.0001) and pH2AX (r=0.22, p=0.0014). Conclusions: NPM1 expression is a mark of poor prognosis in ovarian cancer. Whether these observations reflect increased proliferation and/or genomic instability in ovarian cancer cells will be the focus of future investigation.

A Rapid Method for Determining the Concentration of Recombinant Protein Secreted from Pichia pastoris

Pichia secretive expression system is one of powerful eukaryotic expression systems in genetic engineering, which is especially suitable for industrial utilization. Because of the low concentration of the target protein in initial experiment, the methods and conditions for expression of the target protein should be optimized according to the protein yield repetitively. It is necessary to set up a rapid, simple and convenient analysis method for protein expression levels instead of the generally used method such as ultrafiltration, purification, dialysis, lyophilization and so on. In this paper, acetone precipitation method was chosen to concentrate the recombinant protein firstly after comparing with four different protein precipitation methods systematically, and then the protein was analyzed by SDS-Polyacrylamide Gel Electrophoresis. The recombinant protein was determined with the feature of protein band by the Automated Image Capture and 1-D Analysis Software directly. With this method, the optimized expression conditions of basic fibroblast growth factor secreted from pichia were obtained, which is as the same as using traditional methods. Hence, a convenient tool to determine the optimized conditions for the expression of recombinant proteins in Pichia was established.

QTL analysis of seed germination and pre-emergence growth at extreme temperatures in Medicago truncatula

Enhancing the knowledge on the genetic basis of germination and heterotrophic growth at extreme temperatures is of major importance for improving crop establishment. A quantitative trait loci (QTL) analysis was carried out at sub- and supra-optimal temperatures at these early stages in the model Legume Medicago truncatula. On the basis of an ecophysiological model framework, two populations of recombinant inbred lines were chosen for the contrasting behaviours of parental lines: LR5 at sub-optimal temperatures (5 or 10°C) and LR4 at a supra-optimal temperature (20°C). Seed masses were measured in all lines. For LR5, germination rates and hypocotyl growth were measured by hand, whereas for LR4, imbibition and germination rates as well as early embryonic axis growth were measured using an automated image capture and analysis device. QTLs were found for all traits. The phenotyping framework we defined for measuring variables, distinguished stages and enabled identification of distinct QTLs for seed mass (chromosomes 1, 5, 7 and 8), imbibition (chromosome 4), germination (chromosomes 3, 5, 7 and 8) and heterotrophic growth (chromosomes 1, 2, 3 and 8). The three QTL identified for hypocotyl length at sub-optimal temperature explained the largest part of the phenotypic variation (60% together). One digenic interaction was found for hypocotyl width at sub-optimal temperature and the loci involved were linked to additive QTLs for hypocotyl elongation at low temperature. Together with working on a model plant, this approach facilitated the identification of genes specific to each stage that could provide reliable markers for assisting selection and improving crop establishment. With this aim in view, an initial set of putative candidate genes was identified in the light of the role of abscissic acid/gibberellin balance in regulating germination at high temperatures (e.g. ABI4, ABI5), the molecular cascade in response to cold stress (e.g. CBF1, ICE1) and hypotheses on changes in cell elongation (e.g. GASA1, AtEXPA11) with changes in temperatures based on studies at the whole plant scale.