Organic Bulk Heterojunction Infrared Photodiodes for Imaging Out to 1300 nm

This work studies organic bulk heterojunction photodiodes with a wide spectral range capable of imaging out to 1.3 μm in the shortwave infrared. Adjustment of the donor-to-acceptor (polymer:fullerene) ratio shows how blend composition affects the density of states (DOS) which connects materials composition and optoelectronic properties and provides insight into features relevant to understanding dispersive transport and recombination in the narrow bandgap devices. Capacitance spectroscopy and transient photocurrent measurements indicate the main recombination mechanisms arise from deep traps and poor extraction from accumulated space charges. The amount of space charge is reduced with a decreasing acceptor concentration; however, this reduction is offset by an increasing trap DOS. A device with 1:3 donor-to-acceptor ratio shows the lowest density of deep traps and the highest external quantum efficiency among the different blend compositions. The organic photodiodes are used to demonstrate a single-pixel imaging system that leverages compressive sensing algorithms to enable image reconstruction

Motif-All: discovering all phosphorylation motifs

Background: Phosphorylation motifs represent common patterns around the phosphorylation site. The discovery of such kinds of motifs reveals the underlying regulation mechanism and facilitates the prediction of unknown phosphorylation event. To date, people have gathered large amounts of phosphorylation data, making it possible to perform substrate-driven motif discovery using data mining techniques. Results: We describe an algorithm called Motif-All that is able to efficiently identify all statistically significant motifs. The proposed method explores a support constraint to reduce search space and avoid generating random artifacts. As the number of phosphorylated peptides are far less than that of unphosphorylated ones, we divide the mining process into two stages: The first step generates candidates from the set of phosphorylated sequences using only support constraint and the second step tests the statistical significance of each candidate using the odds ratio derived from the whole data set. Experimental results on real data show that Motif-All outperforms current algorithms in terms of both effectiveness and efficiency. Conclusions: Motif-All is a useful tool for discovering statistically significant phosphorylation motifs. Source codes and data sets are available at: http://bioinformatics.ust.hk/MotifAll.rar

Score regularization for peptide identification

<p>Abstract</p> <p>Background</p> <p>Peptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.</p> <p>Results</p> <p>In this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results. This method uses one additional assumption that two peptides belonging to the same protein should be correlated to each other. We formulate an optimization problem that embraces two objectives through regularization: the smoothing consistency among scores of correlated peptides and the fitting consistency between new scores and initial scores. This optimization problem can be solved analytically. The experimental study on several real MS/MS data sets shows that this re-ranking method improves the identification performance.</p> <p>Conclusions</p> <p>The score regularization method can be used as a general post-processing step for improving peptide identifications. Source codes and data sets are available at: <url>http://bioinformatics.ust.hk/SRPI.rar</url>.</p

A hidden two-locus disease association pattern in genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation.</p> <p>Results</p> <p>In this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.</p> <p>Conclusions</p> <p>These newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation.</p> <p>Availability</p> <p>The software is freely available at <url>http://bioinformatics.ust.hk/hidden_pattern_finder.zip</url>.</p

Semi-supervised protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.</p> <p>Results</p> <p>In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions.</p> <p>Conclusion</p> <p>Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.</p

Optical design and system characterization of an imaging microscope at 121.6 nm

We present the optical design and system characterization of an imaging microscope prototype at 121.6 nm. System engineering processes are demonstrated through the construction of a Schwarzschild microscope objective, including tolerance analysis, fabrication, alignment, and testing. Further improvements on the as-built system with a correction phase plate are proposed and analyzed. Finally, the microscope assembly and the imaging properties of the prototype are demonstrated.National Science Foundation (NSF) [1306921]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Elucidating the Detectivity Limits in Shortwave Infrared Organic Photodiodes

p\u3eWhile only few organic photodiodes have photoresponse past 1 µm, novel shortwave infrared (SWIR) polymers are emerging, and a better understanding of the limiting factors in narrow bandgap devices is critically needed to predict and advance performance. Based on state‐of‐the‐art SWIR bulk heterojunction photodiodes, this work demonstrates a model that accounts for the increasing electric‐field dependence of photocurrent in narrow bandgap materials. This physical model offers an expedient method to pinpoint the origins of efficiency losses, by decoupling the exciton dissociation efficiency and charge collection efficiency in photocurrent–voltage measurements. These results from transient photoconductivity measurements indicate that the main loss is due to poor exciton dissociation, particularly significant in photodiodes with low‐energy charge‐transfer states. Direct measurements of the noise components are analyzed to caution against using assumptions that could lead to an overestimation of detectivity. The devices show a peak detectivity of 5 × 1010 Jones with a spectral range up to 1.55 µm. The photodiodes are demonstrated to quantify the ethanol–water content in a mixture within 1% accuracy, conveying the potential of organics to enable economical, scalable detectors for SWIR spectroscopy