The charging of neutral dimethylamine and dimethylamine-sulfuric acid clusters using protonated acetone

Sulfuric acid is generally considered one of the most important substances taking part in atmospheric particle formation. However, in typical atmospheric conditions in the lower troposphere, sulfuric acid and water alone are unable to form particles. It has been suggested that strong bases may stabilize sulfuric acid clusters so that particle formation may occur. More to the point, amines - strong organic bases - have become the subject of interest as possible cause for such stabilization. To probe whether amines play a role in atmospheric nucleation, we need to be able to measure accurately the gas-phase amine vapour concentration. Such measurements often include charging the neutral molecules and molecular clusters in the sample. Since amines are bases, the charging process should introduce a positive charge. This can be achieved by, for example, using chemical ionization with a positively charged reagent with a suitable proton affinity. In our study, we have used quantum chemical methods combined with a cluster dynamics code to study the use of acetone as a reagent ion in chemical ionization and compared the results with measurements performed with a chemical ionization atmospheric pressure interface time-of-flight mass spectrometer (CI-APi-TOF). The computational results indicate that protonated acetone is an effective reagent in chemical ionization. However, in the experiments the reagent ions were not depleted at the predicted dimethylamine concentrations, indicating that either the modelling scheme or the experimental results - or both - contain unidentified sources of error.Peer reviewe

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Bright Field Microscopy as an Alternative to Whole Cell Fluorescence in Automated Analysis of Macrophage Images

Fluorescence microscopy is the standard tool for detection and analysis of cellular phenomena. This technique, however, has a number of drawbacks such as the limited number of available fluorescent channels in microscopes, overlapping excitation and emission spectra of the stains, and phototoxicity.We here present and validate a method to automatically detect cell population outlines directly from bright field images. By imaging samples with several focus levels forming a bright field -stack, and by measuring the intensity variations of this stack over the -dimension, we construct a new two dimensional projection image of increased contrast. With additional information for locations of each cell, such as stained nuclei, this bright field projection image can be used instead of whole cell fluorescence to locate borders of individual cells, separating touching cells, and enabling single cell analysis. Using the popular CellProfiler freeware cell image analysis software mainly targeted for fluorescence microscopy, we validate our method by automatically segmenting low contrast and rather complex shaped murine macrophage cells.The proposed approach frees up a fluorescence channel, which can be used for subcellular studies. It also facilitates cell shape measurement in experiments where whole cell fluorescent staining is either not available, or is dependent on a particular experimental condition. We show that whole cell area detection results using our projected bright field images match closely to the standard approach where cell areas are localized using fluorescence, and conclude that the high contrast bright field projection image can directly replace one fluorescent channel in whole cell quantification. Matlab code for calculating the projections can be downloaded from the supplementary site: http://sites.google.com/site/brightfieldorstaining

Medical communication and technology: a video-based process study of the use of decision aids in primary care

Background: much of the research on decision-making in health care has focused on consultation outcomes. Less is known about the process by which clinicians and patients come to a treatment decision. This study aimed to quantitatively describe the behaviour shown by doctors and patients during primary care consultations when three types of decision aids were used to promote treatment decision-making in a randomised controlled trial.Methods: a video-based study set in an efficacy trial which compared the use of paper-based guidelines (control) with two forms of computer-based decision aids (implicit and explicit versions of DARTS II). Treatment decision concerned warfarin anti-coagulation to reduce the risk of stroke in older patients with atrial fibrillation. Twenty nine consultations were video-recorded. A ten-minute 'slice' of the consultation was sampled for detailed content analysis using existing interaction analysis protocols for verbal behaviour and ethological techniques for non-verbal behaviour.Results: median consultation times (quartiles) differed significantly depending on the technology used. Paper-based guidelines took 21 (19–26) minutes to work through compared to 31 (16–41) minutes for the implicit tool; and 44 (39–55) minutes for the explicit tool. In the ten minutes immediately preceding the decision point, GPs dominated the conversation, accounting for 64% (58–66%) of all utterances and this trend was similar across all three arms of the trial. Information-giving was the most frequent activity for both GPs and patients, although GPs did this at twice the rate compared to patients and at higher rates in consultations involving computerised decision aids. GPs' language was highly technically focused and just 7% of their conversation was socio-emotional in content; this was half the socio-emotional content shown by patients (15%). However, frequent head nodding and a close mirroring in the direction of eye-gaze suggested that both parties were active participants in the conversationConclusion: irrespective of the arm of the trial, both patients' and GPs' behaviour showed that they were reciprocally engaged in these consultations. However, even in consultations aimed at promoting shared decision-making, GPs' were verbally dominant, and they worked primarily as information providers for patients. In addition, computer-based decision aids significantly prolonged the consultations, particularly the later phases. These data suggest that decision aids may not lead to more 'sharing' in treatment decision-making and that, in their current form, they may take too long to negotiate for use in routine primary car

Reconstruction and Validation of RefRec: A Global Model for the Yeast Molecular Interaction Network

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is ∼67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in ∼590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format

Efficacy of Synaptic Inhibition Depends on Multiple, Dynamically Interacting Mechanisms Implicated in Chloride Homeostasis

Chloride homeostasis is a critical determinant of the strength and robustness of inhibition mediated by GABAA receptors (GABAARs). The impact of changes in steady state Cl− gradient is relatively straightforward to understand, but how dynamic interplay between Cl− influx, diffusion, extrusion and interaction with other ion species affects synaptic signaling remains uncertain. Here we used electrodiffusion modeling to investigate the nonlinear interactions between these processes. Results demonstrate that diffusion is crucial for redistributing intracellular Cl− load on a fast time scale, whereas Cl−extrusion controls steady state levels. Interaction between diffusion and extrusion can result in a somato-dendritic Cl− gradient even when KCC2 is distributed uniformly across the cell. Reducing KCC2 activity led to decreased efficacy of GABAAR-mediated inhibition, but increasing GABAAR input failed to fully compensate for this form of disinhibition because of activity-dependent accumulation of Cl−. Furthermore, if spiking persisted despite the presence of GABAAR input, Cl− accumulation became accelerated because of the large Cl− driving force that occurs during spikes. The resulting positive feedback loop caused catastrophic failure of inhibition. Simulations also revealed other feedback loops, such as competition between Cl− and pH regulation. Several model predictions were tested and confirmed by [Cl−]i imaging experiments. Our study has thus uncovered how Cl− regulation depends on a multiplicity of dynamically interacting mechanisms. Furthermore, the model revealed that enhancing KCC2 activity beyond normal levels did not negatively impact firing frequency or cause overt extracellular K− accumulation, demonstrating that enhancing KCC2 activity is a valid strategy for therapeutic intervention

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology.Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials

Multi-scale Gaussian representation and outline-learning based cell image segmentation

BACKGROUND: High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. METHODS: We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. RESULTS AND CONCLUSIONS: We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks

Echovirus 1 internalization negatively regulates epidermal growth factor receptor downregulation

We have demonstrated previously that the human picornavirus Echovirus 1 (EV1) triggers an infectious internalization pathway that follows closely, but seems to stay separate, from the epidermal growth factor receptor (EGFR) pathway triggered by epidermal growth factor (EGF). Here, we confirmed by using live and confocal microscopy that EGFR and EV1 vesicles are following intimately each other but are distinct entities with different degradation kinetics. We show here that despite being sorted to different pathways and located in distinct endosomes, EV1 inhibits EGFR downregulation. Simultaneous treatment with EV1 and EGF led to an accumulation of EGFR in cytoplasmic endosomes, which was evident already 15 min p.i. and more pronounced after 2 hr p.i. EV1 treatment led to reduced downregulation, which was proven by increased total cellular amount of EGFR. Confocal microscopy studies revealed that EGFR accumulated in large endosomes, presumably macropinosomes, which were not positive for markers of the early, recycling, or late endosomes/lysosomes. Interestingly, EV1 did not have a similar blocking effect on bulk endosomal trafficking or transferrin recycling along the clathrin pathway suggesting that EV1 did not have a general effect on cellular trafficking pathways. Importantly, EGF treatment increased EV1 infection and increased cell viability during infection. Simultaneous EV1 and EGF treatment seemed to moderately enhance phosphorylation of protein kinase C α. Furthermore, similar phenotype of EGFR trafficking could be produced by phorbol 12‐myristate 13‐acetate treatment, further suggesting that activated protein kinase C α could be contributing to EGFR phenotype. These results altogether demonstrate that EV1 specifically affects EGFR trafficking, leading to EGFR downregulation, which is beneficial to EV1 infection.peerReviewe