Automated Software Solution Research Articles

The development of an automated microscope image tracking and analysis system.

Microscopy image analysis plays a crucial role in understanding cellular behavior and uncovering important insights in various biological and medical research domains. Tracking cells within the time-lapse microscopy images is a fundamental technique that enables the study of cell dynamics, interactions, and migration. While manual cell tracking is possible, it is time-consuming and prone to subjective biases that impact results. In order to solve this issue, we sought to create an automated software solution, named cell analyzer, which is able to track cells within microscopy images with minimal input required from the user. The program of cell analyzer was written in Python utilizing the open source computer vision (OpenCV) library and featured a graphical user interface that makes it easy for users to access. The functions of all codes were verified through closeness, area, centroid, contrast, variance, and cell tracking test. Cell analyzer primarily utilizes image preprocessing and edge detection techniques to isolate cell boundaries for detection and analysis. It uniquely recorded the area, displacement, speed, size, and direction of detected cell objects and visualized the data collected automatically for fast analysis. Our cell analyzer provides an easy-to-use tool through a graphical user interface for tracking cell motion and analyzing quantitative cell images.

Statistical approaches to Raman imaging: principal component score mapping.

In this research, Raman imaging was employed to map various samples, and the resulting data were analyzed using a suite of automated tools to extract critical information, including intensity and signal-to-noise ratio. The acquired spectra were further processed to identify similarities and investigate patterns using principal component analysis. The objective of this study was to establish guidelines for investigating Raman imaging results, particularly when dealing with large datasets comprising thousands of relatively low-intensity spectra. The overall quality of the results was assessed, and representative locations were determined based on the main Raman bands. While automated software solutions are insufficient for removing baselines and fitting the data, statistical analysis proved to be a powerful tool for extracting valuable information directly from the raw spectral data. This approach enables the extraction of as much information as possible from large arrays of spectral data, even in complex cases where automated software may fall short. The findings of this study contribute to enhancing the analysis and interpretation of Raman imaging results, providing researchers with a robust methodology for extracting meaningful insights from complex datasets, reducing the amount of effort required during data interpretation and analysis.

Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence.

The shortage of deceased donor organs has prompted the development of alternative liver grafts for transplantation. Living-donor liver transplantation (LDLT) has emerged as a viable option, expanding the donor pool and enabling timely transplantation with favorable graft function and improved long-term outcomes. An accurate evaluation of the donor liver's volumetry (LV) and anatomical study is crucial to ensure adequate future liver remnant, graft volume and precise liver resection. Thus, ensuring donor safety and an appropriate graft-to-recipient weight ratio. Manual LV (MLV) using computed tomography has traditionally been considered the gold standard for assessing liver volume. However, the method has been limited by cost, subjectivity, and variability. Automated LV techniques employing advanced segmentation algorithms offer improved reproducibility, reduced variability, and enhanced efficiency compared to manual measurements. However, the accuracy of automated LV requires further investigation. The study provides a comprehensive review of traditional and emerging LV methods, including semi-automated image processing, automated LV techniques, and machine learning-based approaches. Additionally, the study discusses the respective strengths and weaknesses of each of the aforementioned techniques. The use of artificial intelligence (AI) technologies, including machine learning and deep learning, is expected to become a routine part of surgical planning in the near future. The implementation of AI is expected to enable faster and more accurate image study interpretations, improve workflow efficiency, and enhance the safety, speed, and cost-effectiveness of the procedures. Accurate preoperative assessment of the liver plays a crucial role in ensuring safe donor selection and improved outcomes in LDLT. MLV has inherent limitations that have led to the adoption of semi-automated and automated software solutions. Moreover, AI has tremendous potential for LV and segmentation; however, its widespread use is hindered by cost and availability. Therefore, the integration of multiple specialties is necessary to embrace technology and explore its possibilities, ranging from patient counseling to intraoperative decision-making through automation and AI.

Prognostic Accuracy of CTP Summary Maps in Patients with Large Vessel Occlusive Stroke and Poor Revascularization after Mechanical Thrombectomy-Comparison of Three Automated Perfusion Software Applications.

Background: Innovative automated perfusion software solutions offer support in the management of acute stroke by providing information about the infarct core and penumbra. While the performance of different software solutions has mainly been investigated in patients with successful recanalization, the prognostic accuracy of the hypoperfusion maps in cases of futile recanalization has hardly been validated. Methods: In 39 patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) in the anterior circulation and poor revascularization (thrombolysis in cerebral infarction (TICI) 0-2a) after mechanical thrombectomy (MT), hypoperfusion analysis was performed using three different automated perfusion software solutions (A: RAPID, B: Brainomix e-CTP, C: Syngo.via). The hypoperfusion volumes (HV) as Tmax > 6 s were compared with the final infarct volumes (FIV) on follow-up CT 36–48 h after futile recanalization. Bland–Altman analysis was applied to display the levels of agreement and to evaluate systematic differences. Based on the median hypoperfusion intensity ratio (HIR, volumetric ratio of tissue with a Tmax > 10 s and Tmax > 6 s) patients were dichotomized into high- and low-HIR groups. Subgroup analysis with favorable (<0.6) and unfavorable (≥0.6) HIR was performed with respect to the FIV. HIR was correlated to clinical baseline and outcome parameters using Pearson’s correlation. Results: Overall, there was good correlation without significant differences between the HVs and the FIVs with package A (r = 0.78, p < 0.001) being slightly superior to B and C. However, levels of agreement were very wide for all software applications in Bland-Altman analysis. In cases of large infarcts exceeding 150 mL the performance of the automated software solutions generally decreased. Subgroup analysis revealed the FIV to be generally underestimated in patients with HIR ≥ 0.6 (p < 0.05). In the subgroup with favorable HIR, however, there was a trend towards an overestimation of the FIV. Nevertheless, packages A and B showed good correlation between the HVs and FIVs without significant differences (p > 0.2), while only package C significantly overestimated the FIV (−54.6 ± 56.0 mL, p = 0.001). The rate of modified Rankin Scale (mRS) 0–3 after 3 months was significantly higher in favorable vs. unfavorable HIR (42.1% vs. 13.3%, p = 0.02). Lower HIR was associated with higher Alberta Stroke Program Early CT Score (ASPECTS) at presentation and on follow-up imaging, lower risk of malignant edema, and better outcome (p < 0.05). Conclusion: Overall, the performance of the automated perfusion software solutions to predict the FIV after futile recanalization is good, with decreasing accuracy in large infarcts exceeding 150 mL. However, depending on the HIR, FIV can be significantly over- and underestimated, with Syngo showing the widest range. Our results indicate that the HIR can serve as valuable parameter for outcome predictions and facilitate the decision whether or not to perform MT in delicate cases.

Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram.

Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST's performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds.

Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning

PurposeDuring the emerging COVID-19 pandemic, radiology departments faced a substantial increase in chest CT admissions coupled with the novel demand for quantification of pulmonary opacities. This article describes how our clinic implemented an automated software solution for this purpose into an established software platform in 10 days. The underlying hypothesis was that modern academic centers in radiology are capable of developing and implementing such tools by their own efforts and fast enough to meet the rapidly increasing clinical needs in the wake of a pandemic. MethodDeep convolutional neural network algorithms for lung segmentation and opacity quantification on chest CTs were trained using semi-automatically and manually created ground-truth (Ntotal = 172). The performance of the in-house method was compared to an externally developed algorithm on a separate test subset (N = 66). ResultsThe final algorithm was available at day 10 and achieved human-like performance (Dice coefficient = 0.97). For opacity quantification, a slight underestimation was seen both for the in-house (1.8 %) and for the external algorithm (0.9 %). In contrast to the external reference, the underestimation for the in-house algorithm showed no dependency on total opacity load, making it more suitable for follow-up. ConclusionsThe combination of machine learning and a clinically embedded software development platform enabled time-efficient development, instant deployment, and rapid adoption in clinical routine. The algorithm for fully automated lung segmentation and opacity quantification that we developed in the midst of the COVID-19 pandemic was ready for clinical use within just 10 days and achieved human-level performance even in complex cases.

Correlated Multimodal Imaging in Life Sciences: Expanding the Biomedical Horizon

The frontiers of bioimaging are currently being pushed towards the integration and correlation of several modalities to tackle biomedical research questions holistically and across multiple scales. Correlated Multimodal Imaging (CMI) gathers information about exactly the same specimen with two or more complementary modalities that – combined – create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows to describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, diseases or organisms by untangling their molecular mechanisms within their native environment. The two best-established CMI implementations are hardware-fused platforms in (Pre)clinical Imaging (Hybrid Imaging) and Correlated Light and Electron Microscopy (CLEM) in biological imaging. Although the merits of Hybrid Imaging and CLEM are well established, both approaches would benefit from standardization of protocols, ontologies and data handling, and the development of optimized and advanced implementations. Specifically, CMI pipelines that aim at bridging preclinical and biological imaging beyond CLEM and Hybrid Imaging are rare but bear great potential to substantially advance both bioimaging and biomedical research. CMI faces three main challenges for its routine use in biomedical research: (1) Sample handling and preparation procedures that are compatible across modalities without compromising data quality, (2) soft- and hardware solutions to relocate the same region of interest (ROI) after transfer between imaging platforms including fiducial markers, and (3) automated software solutions to correlate complex, multiscale, multimodal and volumetric image data including reconstruction, segmentation and visualization. This review goes beyond preclinical imaging and puts its accessible information into a broader imaging context. We present a comprehensive overview of the field of CMI from PHI to correlative microscopy, highlight requirements for optimization and standardization, present a synopsis of current solutions to challenges of the field and focus on current efforts to bridge the gap between preclinical and biological imaging. The review is line with major European initiatives, such as COMULIS (CA17121), a COST Action to promote and foster Correlated Multimodal Imaging in Life Sciences.

Rapid, Affordable and Efficient Screening of Multiple Blood Abnormalities Made Possible Using an Automated Tool for MALDI-ToF Spectrometry Analysis

Screening programs for genetic and metabolic diseases such as haemoglobinopathies, thalassemias and diabetes are a worldwide problem that faces economic and technological limitations. This is mainly because genetic and metabolic tests are too expensive and time consuming to be implemented. MALDI-ToF mass spectrometry is a rapid and affordable high throughput technique with diagnostic potential for these diseases but still constrained by the timing and complexity of data analysis. To overcome this technological limitation, we developed a fully automated software solution in our MALDI-ToF instrument towards the detection of haemoglobinopathies, thalassemias and diabetes on one blood card sample. The software was tested for its efficiency and accuracy on 171 blood samples rendering 30-fold faster analysis with less bias and rounding errors in comparison with the manual approach. In this study, we identified the variability associated with the disease biomarkers in healthy individuals and successfully applied predictive models to detect blood abnormalities. Taken together, we demonstrated in this study that population screening of multiple blood disorders is made possible using MALDI-ToF technology in combination with automated software tools.

Adaptation of the attribute dependency model for designing a heterogeneous digital oilfield data warehouse

The development of digital oilfield technologies has led us to the fact that there are virtually no production and management tasks left without any automated software solutions. However, the distribution, implementation, and use of individual IT-solutions often occur outside the rational automation strategy. At the same time, due to the complexity of the management object, due to the nature and composition of the tasks being solved, the lack of integration between the multitude of existing software solutions prevents the enterprise single information space formation. Thus, with the actual availability of critical information, the operational and strategic management tasks solution occurs without considering it. As a solution, the article considers an approach to creating a heterogeneous data warehouse based on an attribute dependency model that describes the relationship and interdependencies of data stored in distributed and heterogeneous sources. The article describes the interim results of a project to develop the architecture and software heterogeneous distributed data warehouse prototype for digital oilfield infrastructure.

Quantifying material loss from the bearing surfaces of retrieved hip replacements: Method validation

Abstract Computational methods used to quantify wear in failed hip arthroplasties are often limited by human and sampling errors. An automated software solution has been developed to overcome these shortcomings. The overarching aim of the current study was to validate this method through a comparison with gravimetric measurements. Seventy-two different wear volumes were quantified to within a mean error of 0.14 mm3 and 0.10 mm3 of gravimetric results for simulated cup and head components, respectively. This approach had an improved accuracy, repeatability and reproducibility over a commonly used, commercially available software solution, which bears many of the common sources of error.

Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden.

Increased tumor burden is associated with inferior outcomes in many lymphoma subtypes. Surrogates of tumor burden that are easy to measure, such as the maximum tumor dimension of the bulkiest lesion on CT, have been used as prognostic indices for many years. Recently, total metabolic tumor volume (MTV) and tumor lesion glycolysis have emerged as promising and robust biomarkers of outcome in various lymphomas. The median MTV and the optimal cutoffs to separate patients into risk groups in a study population are, however, highly dependent on the population characteristics and the delineation method used to outline tumor on the PET image. This issue has precluded the use of MTV for risk stratification in trials and clinical practice. Standardization of the methodology is timely to allow the potential for risk adaptation to be explored in addition to response adaptation using PET. Meetings between representatives from research groups active in the field were held under the auspices of the PET International Lymphoma and Myeloma Workshop. A summary of those discussions, which included a review of the literature and a practical assessment of methods used for outlining, including various software options, is presented. Finally, a proposal is made to perform a technical validation of MTV measurement enabling benchmark reference ranges to be derived for published delineation approaches used for outlining with various software. This process would require collation of representative imaging data sets of the most common lymphoma subtypes; agreement on pragmatic criteria for the selection of lesions; generation of a range of MTVs, with consensus to be reached on final contours in a training set; and development of automated software solutions with a set of minimum functionalities to reduce measurement variability. Methods developed in the above training exercise could then be applied to another data set, with a final set of contours and values generated. This final data set would provide a benchmark against which end-users could test their ability to measure MTVs that are consistent with expected values. The data set and automated software solutions could be shared with manufacturers with the aim of including these in standard workflows to allow standardization of MTV measurement across the world.

D2ome, Software for in Vivo Protein Turnover Analysis Using Heavy Water Labeling and LC-MS, Reveals Alterations of Hepatic Proteome Dynamics in a Mouse Model of NAFLD.

Metabolic labeling with heavy water followed by LC-MS is a high throughput approach to study proteostasis in vivo. Advances in mass spectrometry and sample processing have allowed consistent detection of thousands of proteins at multiple time points. However, freely available automated bioinformatics tools to analyze and extract protein decay rate constants are lacking. Here, we describe d2ome-a robust, automated software solution for in vivo protein turnover analysis. d2ome is highly scalable, uses innovative approaches to nonlinear fitting, implements Grubbs' outlier detection and removal, uses weighted-averaging of replicates, applies a data dependent elution time windowing, and uses mass accuracy in peak detection. Here, we discuss the application of d2ome in a comparative study of protein turnover in the livers of normal vs Western diet-fed LDLR-/- mice (mouse model of nonalcoholic fatty liver disease), which contained 256 LC-MS experiments. The study revealed reduced stability of 40S ribosomal protein subunits in the Western diet-fed mice.

An efficient human whole blood workflow using CyTOF technology: a lyophilized 30-plex antibody panel coupled with automated data analysis

Abstract CyTOF® mass cytometry is a highly multiplexed, single-cell analysis platform that uses metal-tagged antibodies. A significant advantage of this technology is its ability to resolve more than 30 markers in a single panel without the need for compensation. Nonetheless, a highly multiplexed panel requires optimization of antibody titers, full panel verification, sample preparation, data acquisition, and analysis. Preparation of a staining cocktail from 30 individual tubes may be prone to errors. Not only is data analysis time-consuming for complex panels, it is difficult to interpret without expertise in immunology. We have developed a lyophilized 30-plex immunphenotyping panel contained in a single tube, an efficient workflow, and an automated software solution for human whole blood analysis using mass cytometry. The panel focuses on T cell lineage while also capturing other relevant immune populations. Blood is added directly to the lyophilized antibody tube, followed by RBC lysis, wash, and fixation steps and finally data acquisition of stained samples on the Helios™ system. The dedicated software tool accepts FCS3.0 files and in under 10 minutes automatically generates reports on the number of live cells, percentage of specific cell populations, staining intensities, histograms, 2D dot plots, and tSNE graphs. The auto-calculated frequencies of populations are comparable to manual gating, with a validated correlation coefficient over 0.9. The panel and software tool enable researchers to streamline immunophenotyping of whole blood while accurately and reproducibly monitoring changes in immune cell subsets in patient samples.

Image guided adaptive external beam radiation therapy for cervix cancer: Evaluation of a clinically implemented plan-of-the-day technique

BackgroundRadiotherapy for cervix cancer is challenging in patients exhibiting large daily changes in the pelvic anatomy, therefore adaptive treatments (ART) have been proposed. The aim of this study was the clinical implementation and subsequent evaluation of plan-of-the-day (POTD)-ART for cervix cancer in supine positioning. The described workflow was based on standard commercial equipment and current quality assurance (QA) methods. Materials and methodsA POTD strategy, which employs a VMAT plan library consisting of an empty bladder plan, a full bladder plan and a motion robust backup plan, was developed. Daily adaption was guided by cone beam computed tomography (CBCT) imaging after which the best plan from the library was selected. Sixteen patients were recruited in a clinical study on ART, for nine POTD was applied due to their large organ motion derived from two computed tomography (CT) scans with variable bladder filling. All patients were treated to 45Gy in 25 fractions. Plan selection frequencies over the treatment course were analyzed. Daily doses in the rectum, bladder and cervix-uterus target (CTV-T) were derived and compared to a simulated non-adapted treatment (non-ART), which employed the robust plan for each fraction. Additionally, the adaption consistency was determined by repeating the plan selection procedure one month after treatment by a group of experts. ART-specific QA methods are presented. Results225 ART fractions with CBCTs were analyzed. The empty bladder plan was delivered in 49% of the fractions in the first treatment week and this number increased to 78% in the fifth week. The daily coverage of the CTV-T was equivalent between ART and the non-ART simulation, while the daily total irradiated volume V42.75Gy (95% of prescription dose) was reduced by a median of 87cm3. The median delivered V42.75Gy was 1782cm3. Daily delivered doses (V42.75Gy, V40Gy, V30G) to the organs at risk were statistically significantly reduced by ART, with a median difference in daily V42.75Gy in rectum and bladder of 3.2% and 1.1%, respectively. The daily bladder V42.75Gy and V40Gy were decreased by more than 10 percent points in 30% and 24% of all fractions, respectively, through ART. The agreement between delivered plans and retrospective expert-group plan selections was 84%. ConclusionA POTD-ART technique for cervix cancer was successfully and safely implemented in the clinic and evaluated. Improved normal tissue sparing compared to a simulated non-ART treatment could be demonstrated. Future developments should focus on commercial automated software solutions to allow for a more widespread adoption and to keep the increased workload manageable.

Concept for estimating mitochondrial DNA haplogroups using a maximum likelihood approach (EMMA)

The assignment of haplogroups to mitochondrial DNA haplotypes contributes substantial value for quality control, not only in forensic genetics but also in population and medical genetics. The availability of Phylotree, a widely accepted phylogenetic tree of human mitochondrial DNA lineages, led to the development of several (semi-)automated software solutions for haplogrouping. However, currently existing haplogrouping tools only make use of haplogroup-defining mutations, whereas private mutations (beyond the haplogroup level) can be additionally informative allowing for enhanced haplogroup assignment. This is especially relevant in the case of (partial) control region sequences, which are mainly used in forensics. The present study makes three major contributions toward a more reliable, semi-automated estimation of mitochondrial haplogroups. First, a quality-controlled database consisting of 14,990 full mtGenomes downloaded from GenBank was compiled. Together with Phylotree, these mtGenomes serve as a reference database for haplogroup estimates. Second, the concept of fluctuation rates, i.e. a maximum likelihood estimation of the stability of mutations based on 19,171 full control region haplotypes for which raw lane data is available, is presented. Finally, an algorithm for estimating the haplogroup of an mtDNA sequence based on the combined database of full mtGenomes and Phylotree, which also incorporates the empirically determined fluctuation rates, is brought forward. On the basis of examples from the literature and EMPOP, the algorithm is not only validated, but both the strength of this approach and its utility for quality control of mitochondrial haplotypes is also demonstrated.

Apparent Diffusion Coefficient Threshold for Delineation of Ischemic Core

MRI-based selection of patients for acute stroke interventions requires rapid accurate estimation of the infarct core on diffusion-weighted MRI. Typically used manual methods to delineate restricted diffusion lesions are subjective and time consuming. These limitations would be overcome by a fully automated method that can rapidly and objectively delineate the ischemic core. An automated method would require predefined criteria to identify the ischemic core. The aim of this study is to determine apparent diffusion coefficient-based criteria that can be implemented in a fully automated software solution for identification of the ischemic core. Imaging data from patients enrolled in the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) study who had early revascularization following intravenous thrombolysis were included. The patients' baseline restricted diffusion and 30-day T2 -weighted fluid-attenuated inversion recovery lesions were manually delineated after coregistration. Parts of the restricted diffusion lesion that corresponded with 30-day infarct were considered ischemic core, whereas parts that corresponded with normal brain parenchyma at 30 days were considered noncore. The optimal apparent diffusion coefficient threshold to discriminate core from noncore voxels was determined by voxel-based receiver operating characteristics analysis using the Youden index. 51,045 diffusion positive voxels from 14 patients who met eligibility criteria were analyzed. The mean DWI lesion volume was 24 (± 23) ml. Of this, 18 (± 22) ml was ischemic core and 3 (± 5) ml was noncore. The remainder corresponded to preexisting gliosis, cerebrospinal fluid, or was lost to postinfarct atrophy. The apparent diffusion coefficient of core was lower than that of noncore voxels (P < 0.0001). The optimal threshold for identification of ischemic core was an apparent diffusion coefficient ≤ 620 × 10(-6) mm(2) /s (sensitivity 69% and specificity 78%). Our data suggest that the ischemic core can be identified with an absolute apparent diffusion coefficient threshold. This threshold can be implemented in image analysis software for fully automated segmentation of the ischemic core.

Numerical simulation of process forces during re-contouring of welded parts considering different material properties

Repair techniques for high-value components offer a huge economical and ecologic potential, but are rarely investigated in science, yet. One main step of the process chain for regeneration of damaged components is the removal of excess weld material, called re-contouring, which is often carried out manually nowadays. Although there are special automated software solutions using milling machines for re-contouring, changing material properties and microstructure as well as changing engagement conditions due to welded or brazed areas are not considered in tool path planning. This paper presents a research approach in which the influence of the material inhomogeneity and discontinuous shape of weld on the machining processes for re-contouring is investigated. A numerical simulation is used to calculate the process forces accurately per time step with consideration of changing material properties. The experimental and numerical investigations prove a much higher influence on the changing engagement conditions due to geometrical variations of the weld compared to the influence of material inhomogeneity due to welding.

Automating Data Analysis and Acquisition Setup in a Silicon Debug Environment

With the growing size of modern designs and more strict time-to-market constraints, design errors can unavoidably escape pre-silicon verification and reside in silicon prototypes. Due to those errors and faults in the fabrication process, silicon debug has become a necessary step in the digital integrated circuit design flow. Embedded hardware blocks, such as scan chains and trace buffers, provide a means to acquire data of internal signals in real time for debugging. However, the amount of the data is limited compared to pre-silicon debugging. This paper presents an automated software solution to analyze this sparse data to detect suspects of the failure in both the spatial and temporal domain. It also introduces a technique to automate the configuration process for trace-buffer-based hardware in order to acquire helpful information for debugging the failure. The technique takes the hardware constraints into account and identifies alternatives for signals not part of the traceable set so that their values can be restored by implications. The experiments demonstrate the effectiveness of the proposed software solution in terms of run-time and resolution.

Fully automated software solution for protein quantitation by global metabolic labeling with stable isotopes

Metabolic stable isotope labeling is increasingly employed for accurate protein (and metabolite) quantitation using mass spectrometry (MS). It provides sample-specific isotopologues that can be used to facilitate comparative analysis of two or more samples. Stable Isotope Labeling by Amino acids in Cell culture (SILAC) has been used for almost a decade in proteomic research and analytical software solutions have been established that provide an easy and integrated workflow for elucidating sample abundance ratios for most MS data formats. While SILAC is a discrete labeling method using specific amino acids, global metabolic stable isotope labeling using isotopes such as (15)N labels the entire element content of the sample, i.e. for (15)N the entire peptide backbone in addition to all nitrogen-containing side chains. Although global metabolic labeling can deliver advantages with regard to isotope incorporation and costs, the requirements for data analysis are more demanding because, for instance for polypeptides, the mass difference introduced by the label depends on the amino acid composition. Consequently, there has been less progress on the automation of the data processing and mining steps for this type of protein quantitation. Here, we present a new integrated software solution for the quantitative analysis of protein expression in differential samples and show the benefits of high-resolution MS data in quantitative proteomic analyses.

LC–high resolution MS in environmental analysis: from target screening to the identification of unknowns

This article provides an overview of the state-of-the-art and future trends of the application of LC-high resolution mass spectrometry to the environmental analysis of polar micropollutants. Highly resolved and accurate hybrid tandem mass spectrometry such as quadrupole/time-of-flight and linear ion trap/orbitrap technology allows for a more reliable target analysis with reference standards, a screening for suspected analytes without reference standards, and a screening for unknowns. A reliable identification requires both high resolving power and high mass spectral accuracy to increase selectivity against the matrix background and for a correct molecular formula assignment to unknown compounds. For the identification and structure elucidation of unknown compounds within a reasonable time frame and with a reasonable soundness, advanced automated software solutions as well as improved prediction systems for theoretical fragmentation patterns, retention times, and ionization behavior are needed.