Visual-Based error diffusion for printers

An approach for halftoning is presented that incorporates a printer model and also explicitly uses the human visual model. Conventional methods, such as clustered-dot screening or dispersed-dot screening, do not solve the gray-level distortion of printers and just implicitly use the eye as a lowpass filter. Error diffusion accounts for errors when processing subsequent pixels to minimize the overall mean-square errors. Recently developed model-based halftoning technique eliminates the effect of printer luminance distortion, but this method does not consider the filtering action of the eye, that is, some artifacts of standard error diffusion still exist when the printing resolution and view distance change. Another visual error diffusion method incorporates the human visual filter into error diffusion and results in improved noise characteristics and better resolution for structured image regions, but gray levels are still distorted. Experiments prove that human viewers judge the quality of a halftoning image based mainly on the region which exhibits the worst local error, and low-frequency distortions introduced by the halftoning process are responsible for more visually annoying artifacts in the halftone image than high-frequency distortion. Consequently, we adjust the correction factors of the feedback filter by local characteristics and adjust the dot patterns for some gray levels to minimize the visual blurred local error. Based on the human visual model, we obtain the visual-based error diffusion algorithm, and further we will also incorporate the printer model to correct the printing distortion. The artifacts connected with standard error diffusion are expected to be eliminated or decreased and therefore better print quality should be achieved. In addition to qualitative analysis, we also introduce a subjective evaluation of algorithms. The tests show that the algorithms developed here have improved the performance of error diffusion for printers

Ion association in low-polarity solvents: comparisons between theory, simulation, and experiment

The association of ions in electrolyte solutions at very low concentration and low temperature is studied using computer simulations and quasi-chemical ion-pairing theory. The specific case of the restricted primitive model (charged hard spheres) is considered. Specialised simulation techniques are employed that lead to efficient sampling of the arrangements and distributions of clusters and free ions, even at conditions corresponding to nanomolar solutions of simple salts in solvents with dielectric constants in the range 5-10, as used in recent experimental work on charged-colloid sus- pensions. A direct comparison is effected between theory and simulation using a variety of clustering criteria and theoretical approximations. It is shown that conventional distance-based cluster criteria can give erroneous results. A reliable set of theoretical and simulation estimators for the degree of association is proposed. The ion-pairing theory is then compared to experimental results for salt solutions in low-polarity solvents. The agreement is excellent, and on this basis some calculations are made for the screening lengths which will figure in the treatment of colloid-colloid interactions in such solutions. The accord with available experimental results is complete

The Application of CRISPR Technology to High Content Screening in Primary Neurons

Axon growth is coordinated by multiple interacting proteins that remain incompletely characterized. High content screening (HCS), in which manipulation of candidate genes is combined with rapid image analysis of phenotypic effects, has emerged as a powerful technique to identify key regulators of axon outgrowth. Here we explore the utility of a genome editingapproach referred to as CRISPR (Clustered Regularly Interspersed Palindromic Repeats) for knockout screening in primary neurons. In the CRISPR approach a DNA-cleaving Cas enzyme is guided to genomic target sequences by user-created guide RNA (sgRNA), where it initiates a double-stranded break that ultimately results in frameshift mutation and loss of protein production. Using electroporation of plasmid DNA that co-expresses Cas9enzyme and sgRNA, we first verified the ability of CRISPR targeting to achieve protein-level knockdown in cultured postnatal cortical neurons. Targeted proteins included NeuN (RbFox3) and PTEN, a well-studied regulator of axon growth. Effective knockdown lagged at least four days behind transfection, but targeted proteins were eventually undetectable by immunohistochemistry in \u3e 80% of transfected cells. Consistent with this, anti-PTEN sgRNA produced no changes in neurite outgrowth when assessed three days post-transfection. When week-long cultures were replated, however, PTEN knockdown consistently increased neurite lengths. These CRISPR-mediated PTEN effects were achieved using multi-well transfection and automated phenotypic analysis, indicating the suitability of PTEN as a positive control for future CRISPR-based screening efforts. Combined, these data establish an example of CRISPR-mediated protein knockdown in primary cortical neurons and its compatibility with HCS workflows

Dynamic Inefficiencies in Employment-Based Health Insurance System Theory and Evidence

We investigate how the employment-based health insurance system in the U.S. affects individuals' life-cycle health-care decisions. We take the viewpoint that health is a form of human capital that affects workers' productivities on the job, and derive implications of employees' turnover on the incentives to undertake health investment. Our model suggests that employee turnovers lead to dynamic inefficiencies in health investment, and particularly, it suggests that employment-based health insurance system in the U.S. might lead to an inefficient low level of individual health during individuals' working ages. Moreover, we show that under-investment in health is positively related to the turnover rate of the workers' industry and increases medical expenditure in retirement. We provide empirical evidence for the predictions of the model using two data sets, the Medical Expenditure Panel Survey (MEPS) and the Health and Retirement Study (HRS). In MEPS, we find that employers in industries with high turnover rates are much less likely to offer health insurance to their workers. When employers offer health insurance, the contracts have higher deductibles and employers' contribution to the insurance premium is lower in high turnover industries. Moreover, workers in high turnover industries have lower medical expenditure and undertake less preventive care. In HRS, instead we find that individuals who were employed in high turnover industries have higher medical expenditure when retired. The magnitude of our estimates suggests significant degree of intertemporal inefficiencies in health investment in the U.S. as a result of the employment-based health insurance system. We also evaluate and cast doubt on alternative explanations.

Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Programmed cell death 6 interacting protein (PDCD6IP) and Rabenosyn-5 (ZFYVE20) are potential urinary biomarkers for upper gastrointestinal cancer

PURPOSE: Cancer of the upper digestive tract (uGI) is a major contributor to cancer-related death worldwide. Due to a rise in occurrence, together with poor survival rates and a lack of diagnostic or prognostic clinical assays, there is a clear need to establish molecular biomarkers. EXPERIMENTAL DESIGN: Initial assessment was performed on urine samples from 60 control and 60 uGI cancer patients using MS to establish a peak pattern or fingerprint model, which was validated by a further set of 59 samples. RESULTS: We detected 86 cluster peaks by MS above frequency and detection thresholds. Statistical testing and model building resulted in a peak profiling model of five relevant peaks with 88% overall sensitivity and 91% specificity, and overall correctness of 90%. High-resolution MS of 40 samples in the 2-10 kDa range resulted in 646 identified proteins, and pattern matching identified four of the five model peaks within significant parameters, namely programmed cell death 6 interacting protein (PDCD6IP/Alix/AIP1), Rabenosyn-5 (ZFYVE20), protein S100A8, and protein S100A9, of which the first two were validated by Western blotting. CONCLUSIONS AND CLINICAL RELEVANCE: We demonstrate that MS analysis of human urine can identify lead biomarker candidates in uGI cancers, which makes this technique potentially useful in defining and consolidating biomarker patterns for uGI cancer screening

Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace

IL-4Rα Blockade by Dupilumab Decreases Staphylococcus aureus Colonization and Increases Microbial Diversity in Atopic Dermatitis.

Dupilumab is a fully human antibody to interleukin-4 receptor α that improves the signs and symptoms of moderate to severe atopic dermatitis (AD). To determine the effects of dupilumab on Staphylococcus aureus colonization and microbial diversity on the skin, bacterial DNA was analyzed from swabs collected from lesional and nonlesional skin in a double-blind, placebo-controlled study of 54 patients with moderate to severe AD randomized (1:1) and treated with either dupilumab (200 mg weekly) or placebo for 16 weeks. Microbial diversity and relative abundance of Staphylococcus were assessed by DNA sequencing of 16S ribosomal RNA, and absolute S. aureus abundance was measured by quantitative PCR. Before treatment, lesional skin had lower microbial diversity and higher overall abundance of S. aureus than nonlesional skin. During dupilumab treatment, microbial diversity increased and the abundance of S. aureus decreased. Pronounced changes were seen in nonlesional and lesional skin. Decreased S. aureus abundance during dupilumab treatment correlated with clinical improvement of AD and biomarkers of type 2 immunity. We conclude that clinical improvement of AD that is mediated by interleukin-4 receptor α inhibition and the subsequent suppression of type 2 inflammation is correlated with increased microbial diversity and reduced abundance of S. aureus