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ObjectivesOver 100 algorithms have been proposed to estimate respiratory rate (RR) from the electrocardiogram (ECG) and photoplethysmogram (PPG). As they have never been compared systematically it is unclear which algorithm performs the best. Our primary aim was to determine how closely algorithms agreed with a gold standard RR measure when operating under ideal conditions. Secondary aims were: (i) to compare algorithm performance with IP, the clinical standard for continuous respiratory rate measurement in spontaneously breathing patients; (ii) to compare algorithm performance when using ECG and PPG; and (iii) to provide a toolbox of algorithms and data to allow future researchers to conduct reproducible comparisons of algorithms.MethodsAlgorithms were divided into three stages: extraction of respiratory signals, estimation of RR, and fusion of estimates. Several interchangeable techniques were implemented for each stage. Algorithms were assembled using all possible combinations of techniques, many of which were novel. After verification on simulated data, algorithms were tested on data from healthy participants. RRs derived from ECG, PPG and IP were compared to reference RRs obtained using a nasal-oral pressure sensor using the limits of agreement (LOA) technique.Results314 algorithms were assessed. Of these, 270 could operate on either ECG or PPG, and 44 on only ECG. The best algorithm had 95% LOAs of −4.7 to 4.7 bpm and a bias of 0.0 bpm when using the ECG, and −5.1 to 7.2 bpm and 1.0 bpm when using PPG. IP had 95% LOAs of −5.6 to 5.2 bpm and a bias of −0.2 bpm. Four algorithms operating on ECG performed better than IP.ConclusionsThe best ECG-based RR algorithms were found to have similar performance to the current clinical standard for electronic RR measurement. All high-performing algorithms consisted of novel combinations of time domain RR estimation and modulation fusion techniques. Algorithms performed better when using ECG than PPG. The toolbox of algorithms and data used in this study are publicly available at http://peterhcharlton.github.io/RRest ; the full text is in Physiological Measurement, DOI: 10.1088/0967-3334/37/4/610 .

The Challenge on Liver Ultrasound Tracking (CLUST) was held in conjunction with the MICCAI 2014 conference to enable direct comparison of tracking methods for this application. This paper reports the outcome of this challenge, including setup, methods, results and experiences. The database included 54 2D and 3D sequences of the liver of healthy volunteers and tumor patients under free breathing. Participants had to provide the tracking results of 90% of the data (test set) for pre-defined point-landmarks (healthy volunteers) or for tumor segmentations (patient data). In this paper we compare the best six methods which participated in the challenge. Quantitative evaluation was performed by the organizers with respect to manual annotations. Results of all methods showed a mean tracking error ranging between 1.4 mm and 2.1 mm for 2D points, and between 2.6 mm and 4.6 mm for 3D points. Fusing all automatic results by considering the median tracking results, improved the mean error to 1.2 mm (2D) and 2.5 mm (3D). For all methods, the performance is still not comparable to human inter-rater variability, with a mean tracking error of 0.5-0.6 mm (2D) and 1.2-1.8 mm (3D). The segmentation task was fulfilled only by one participant, resulting in a Dice coefficient ranging from 76.7% to 92.3%. The CLUST database continues to be available and the online leader-board will be updated as an ongoing challenge.

Background Electrocardiograms (ECGs) are routinely recorded and stored in a variety of paper or scanned image format. Current ECG recording machines record ECG on graph paper, also it provides digitized ECG signal along with automated cardiovascular diagnosis (CVD). However, such recording machines cannot analyse preserved paper ECG records as it requires input in terms of digitized signal. Therefore, it is important to extract ECG signal from these preserved paper ECG records using digitization method. There are different paper degradations that adversely affect digitization process. The purpose of this work is to perform an image enhancement and digitization of the degraded ECG images to extract continuous ECG signal. Methods In this paper, we propose entropy-based bit plane slicing (EBPS) algorithm in which pre-processing is done using dominant color detection and local bit plane slicing. Maximum entropy based adaptive bit plane selection is applied to the pre-processed image. Discontinuous ECG correction (DECGC) is then done to produce continuous ECG signal. Results The algorithm is tested on 836 different degraded paper ECG records obtained from various diagnostic centers. After analysis with 101 known ground truth ECG signals the accuracy, sensitivity, specificity and overall F-measure of ECG is 99.42%, 99.69%, 99.81% and 99.26% respectively. The RMS error and correlation between the extracted digitized signal and ground truth for 101 cases is 0.040 and 99.89% respectively. Conclusions The EBPS method is able to remove all types of degradation in paper ECG records to generate a uniform digitized signal. Instead of manual measurement and prediction from archived paper ECG records, automated prediction (using already existing cardiovascular diagnosis software) is possible with the help of extracted digitized signal obtained using proposed digitization method, which will also help retrospective cardiovascular analysis.

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

Characterization of the within-host genetic diversity of viral pathogens is required for selection of effective treatment of some important viral infections, e.g. HIV, HBV and HCV. Despite the technical ability of detection, there are conflicting data regarding the clinical significance of low-frequency variants, partially because of the difficulty of their distinguishing from experimental artifacts. The issue of cross-contamination is relevant for all highly sensitive techniques, including deep sequencing: even trace contamination leads to a significant increase of false positives in identified SNVs. Determination of infections by multiple genotypes of some viruses, the incidence of which can be considerable, especially in risk groups, is also clinically significant in some cases. We developed a new viral reference-guided assembler, VirGenA, that can separate mixtures of strains of different intraspecies genetic groups (genotypes, subtypes, clades, etc.) and assemble a separate consensus sequence for each group in a mixture. It produced long assemblies for mixture components of extremely low frequencies (

As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1–3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community’s data analysis tasks.

Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods. All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample.

In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system (T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors (T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host-pathogen interactions and bacterial pathogenesis. Here, we train and compare six machine learning models, namely, Naive Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector machines (SVMs) and multilayer perceptron (MLP), for the identification of T4SEs using 10 types of selected features and 5-fold cross-validation. Our study shows that: (1) including different but complementary features generally enhance the predictive performance of T4SEs; (2) ensemble models, obtained by integrating individual single-feature models, exhibit a significantly improved predictive performance and (3) the 'majority voting strategy' led to a more stable and accurate classification performance when applied to predicting an ensemble learning model with distinct single features. We further developed a new method to effectively predict T4SEs, Bastion4 (Bacterial secretion effector predictor for T4SS), and we show our ensemble classifier clearly outperforms two recent prediction tools. In summary, we developed a state-of-the-art T4SE predictor by conducting a comprehensive performance evaluation of different machine learning algorithms along with a detailed analysis of single- and multi-feature selections.

X-ray crystallography is the main tool for structural determination of proteins. Yet, the underlying crystallization process is costly, has a high attrition rate and involves a series of trial-and-error attempts to obtain diffraction-quality crystals. The Structural Genomics Consortium aims to systematically solve representative structures of major protein-fold classes using primarily high-throughput X-ray crystallography. The attrition rate of these efforts can be improved by selection of proteins that are potentially easier to be crystallized. In this context, bioinformatics approaches have been developed to predict crystallization propensities based on protein sequences. These approaches are used to facilitate prioritization of the most promising target proteins, search for alternative structural orthologues of the target proteins and suggest designs of constructs capable of potentially enhancing the likelihood of successful crystallization. We reviewed and compared nine predictors of protein crystallization propensity. Moreover, we demonstrated that integrating selected outputs from multiple predictors as candidate input features to build the predictive model results in a significantly higher predictive performance when compared to using these predictors individually. Furthermore, we also introduced a new and accurate predictor of protein crystallization propensity, Crysf, which uses functional features extracted from UniProt as inputs. This comprehensive review will assist structural biologists in selecting the most appropriate predictor, and is also beneficial for bioinformaticians to develop a new generation of predictive algorithms.

The approach used to obtain European Union-wide data on the usage and concentration of substances in different food packaging materials is described. Statistics were collected on pack sizes and market shares for the different materials used to package different food groups. The packaging materials covered were plastics (both flexible and rigid), metal containers, light metal packaging, paper and board, as well as the adhesives and inks used on them. An explanation as to how these data are linked in various ways in the FACET exposure modelling tool is given as well as an overview of the software along with examples of the intermediate tables of data. The example of bisphenol A (BPA), used in resins that may be incorporated into some coatings for canned foodstuffs, is used to illustrate how the data in FACET are combined to produce concentration distributions. Such concentration distributions are then linked probabilistically to the amounts of each food item consumed, as recorded in national food consumption survey diaries, in order to estimate exposure to packaging migrants. Estimates of exposure are at the level of the individual consumer and thus can be expressed for various percentiles of different populations and subpopulations covered by the national dietary surveys.

Liver dynamic contrast enhanced (DCE) MRI pharmacokinetic modelling could be useful in the assessment of diffuse liver disease and focal liver lesions, but is compromised by errors in arterial input function (AIF) sampling. In this study, we apply cardiac output correction to arterial input functions (AIFs) for liver DCE MRI and investigate the effect on dual-input single compartment hepatic perfusion parameter estimation and reproducibility. Thirteen healthy volunteers (28.7 ± 1.94 years, seven males) underwent liver DCE MRI and cardiac output measurement using aortic root phase contrast MRI (PCMRI), with reproducibility (n = 9) measured at 7 d. Cardiac output AIF correction was undertaken by constraining the first pass AIF enhancement curve using the indicator-dilution principle. Hepatic perfusion parameters with and without cardiac output AIF correction were compared and 7 d reproducibility assessed. Differences between cardiac output corrected and uncorrected liver DCE MRI portal venous (PV) perfusion (p = 0.066), total liver blood flow (TLBF) (p = 0.101), hepatic arterial (HA) fraction (p = 0.895), mean transit time (MTT) (p = 0.646), distribution volume (DV) (p = 0.890) were not significantly different. Seven day corrected HA fraction reproducibility was improved (mean difference 0.3%, Bland–Altman 95% limits-of-agreement (BA95%LoA) ±27.9%, coefficient of variation (CoV) 61.4% versus 9.3%, ±35.5%, 81.7% respectively without correction). Seven day uncorrected PV perfusion was also improved (mean difference 9.3 ml min−1/100 g, BA95%LoA ±506.1 ml min−1/100 g, CoV 64.1% versus 0.9 ml min−1/100 g, ±562.8 ml min−1/100 g, 65.1% respectively with correction) as was uncorrected TLBF (mean difference 43.8 ml min−1/100 g, BA95%LoA ±586.7 ml min−1/ 100 g, CoV 58.3% versus 13.3 ml min−1/100 g, ±661.5 ml min−1/100 g, 60.9% respectively with correction). Reproducibility of uncorrected MTT was similar (uncorrected mean difference 2.4 s, BA95%LoA ±26.7 s, CoV 60.8% uncorrected versus 3.7 s, ±27.8 s, 62.0% respectively with correction), as was and DV (uncorrected mean difference 14.1%, BA95%LoA ±48.2%, CoV 24.7% versus 10.3%, ±46.0%, 23.9% respectively with correction). Cardiac output AIF correction does not significantly affect the estimation of hepatic perfusion parameters but demonstrates improvements in normal volunteer 7 d HA fraction reproducibility, but deterioration in PV perfusion and TLBF reproducibility. Improved HA fraction reproducibility maybe important as arterialisation of liver perfusion is increased in chronic liver disease and within malignant liver lesions.

This study aimed to develop and to assess an algorithm to facilitate lip print visualization, and to digitally analyze lip prints on different supports, by superimposition. It also aimed to classify lip prints according to sex. A batch image processing algorithm was developed, which facilitated the identification and extraction of information about lip grooves. However, it performed better for lip print images with a uniform background. Paper and glass slab allowed more correct identifications than glass and the both sides of compact disks. There was no significant difference between the type of support and the amount of matching structures located in the middle area of the lower lip. There was no evidence of association between types of lip grooves and sex. Lip groove patterns of type III and type I were the most common for both sexes. The development of systems for lip print analysis is necessary, mainly concerning digital methods.

Receiver operating characteristic (ROC) analysis is a fundamental tool used for the evaluation and comparison of diagnostic systems that provides estimates of the combinations of sensitivity and specificity that can be achieved with a given technique. Along with critical considerations of practical limitations, such as throughput and time to availability of results, ROC analyses can be applied to provide meaningful assessments and comparisons of available biodosimetry methods. Accordingly, guidance from the Food and Drug Administration to evaluate biodosimetry devices recommends using ROC analysis. However, the existing literature for the numerous biodosimetry methods that have been developed to address the needs for triage either do not contain ROC analyses or present ROC analyses where the dose distributions of the study samples are not representative of the populations to be screened. The use of non-representative sample populations can result in a significant spectrum bias, where estimated performance metrics do not accurately characterize the true performance under real-world conditions. Particularly, in scenarios where a large group of people is screened because they were potentially exposed in a large-scale radiation event, directly measured population data do not exist. However, a number of complex simulations have been performed and reported in the literature that provide estimates of the required dose distributions. Based on these simulations and reported data about the output and uncertainties of biodosimetry assays, we illustrate how ROC curves can be generated that incorporate a realistic representative sample. A technique to generate ROC curves for biodosimetry data is presented along with representative ROC curves, summary statistics and discussion based on published data for triage-ready electron paramagnetic resonance in vivo tooth dosimetry, the dicentric chromosome assay and quantitative polymerase chain reaction assay. We argue that this methodology should be adopted generally to evaluate the performance of radiation biodosimetry screening assays so that they can be compared in the context of their intended use.

The compromise between lateral resolution and usable imaging depth range is a bottleneck for optical coherence tomography (OCT). Existing solutions for optical coherence microscopy (OCM) suffer from either large data size and long acquisition time or a nonideal point spread function. We present volumetric OCM of mouse brain ex vivo with a large depth coverage by leveraging computational adaptive optics (CAO) to significantly reduce the number of OCM volumes that need to be acquired with a Gaussian beam focused at different depths. We demonstrate volumetric reconstruction of ex-vivo mouse brain with lateral resolution of 2.2 μm, axial resolution of 4.7 μm, and depth range of ∼1.2 mm optical path length, using only 11 OCT data volumes acquired on a spectral-domain OCM system. Compared to focus scanning with step size equal to the Rayleigh length of the beam, this is a factor of 4 fewer datasets required for volumetric imaging. Coregistered two-photon microscopy confirmed that CAO-OCM reconstructions can visualize various tissue microstructures in the brain. Our results also highlight the limitations of CAO in highly scattering media, particularly when attempting to reconstruct far from the focal plane or when imaging deep within the sample.

We propose a nonscanning three-dimensional (3-D) fluorescence imaging technique using the transport of intensity equation (TIE) and free-space Fresnel propagation. In this imaging technique, a phase distribution corresponding to defocused fluorescence images with a point-light-source-like shape is retrieved by a TIE-based phase retrieval algorithm. From the obtained phase distribution, and its corresponding amplitude distribution, of the defocused fluorescence image, various images at different distances can be reconstructed at the desired plane after Fresnel propagation of the complex wave function. Through the proposed imaging approach, the 3-D fluorescence imaging can be performed in multiple planes. The fluorescence intensity images are captured with the help of an electrically tunable lens; hence, the imaging technique is free from motion artifacts. We present experimental results corresponding to microbeads and a biological sample to demonstrate the proposed 3-D fluorescence imaging technique.

A coaxial projective imaging (CPI) module acquires surgical scene images from the local site of surgery, transfers them wirelessly to the remote site, and projects instructive annotations to the surgical field. At the remote site, the surgical scene images are displayed, and the instructive annotations from a surgical specialist are wirelessly transferred back to the local site in order to guide the surgical intervention by a less experienced surgeon. The CPI module achieves seamless imaging of the surgical field and accurate projection of the instructive annotations, by a coaxial optical path design that couples the imaging arm with the projection arm and by a color correction algorithm that recovers the true color of the surgical scene. Our benchtop study of tele-guided intervention verifies that the proposed system has a positional accuracy of better than 1 mm at a working distance ranging from 300 to 500 mm. Our in vivo study of cricothyrotomy in a rabbit model proves the concept of tele-mentored surgical navigation. This is the first report of tele-guided surgery based on CPI. The proposed technique can be potentially used for surgical training and for telementored surgery in resource-limited settings.

We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous studies (e.g., optical attenuation and A-line peaks). In addition, we included vascular lumen morphology and three-dimensional (3-D) digital edge and texture features. Classification methods were developed using expansive datasets (∼7000 images), consisting of both clinical in-vivo images and an ex-vivo dataset, which was validated using 3-D cryo-imaging/histology. Conditional random field was used to perform 3-D classification noise cleaning of classification results. We tested various multiclass approaches, classifiers, and feature selection schemes and found that a three-class support vector machine with minimal-redundancy-maximal-relevance feature selection gave the best performance. We found that inclusion of our morphological and 3-D features improved overall classification accuracy. On a held-out test set consisting of >1700 images, we obtained an overall accuracy of 81.58%, with the following (sensitivity/specificity) for each class: other (81.43/89.59), fibrolipidic (94.48/87.32), and fibrocalcific (74.82/95.28). The en-face views of classification results showed that automated classification easily captured the preponderance of a disease segment (e.g., a calcified segment had large regions of fibrocalcific classifications). Finally, we demonstrated proof-of-concept for streamlining A-line classification output with existing fibrolipidic and fibrocalcific boundary segmentation methods, to enable fully automated plaque quantification. The results suggest that our classification approach is a viable step toward fully automated IVOCT plaque classification and segmentation for live-time treatment planning and for offline assessment of drug and biologic therapeutics.

The index of refraction (n) of materials and/or tissues depends on their physical properties and serves as a source of optical contrast in imaging. The variations of the index of refraction have also been investigated for diagnostic purposes in various fields, such as hematology, oncology, etc., since they can signify disease and cell dynamic changes. Optical coherence tomography (OCT) has been used in the past to measure the index ex vivo. However, most methodologies described in the literature are not appropriate for in vivo imaging since they require either a mirror below the sample or a complicated imaging setup and algorithms. We describe a technique that uses two images, obtained at different angles, to estimate the index of refraction and can, thus, also be applied in vivo. The index of refraction is calculated from the path-length difference observed by the OCT beam at the two different angles. When a reflector is not available, the path-length difference can be estimated using image registration and the cross-correlation of adjacent A-scans. The proposed technique was validated experimentally using both clear and scattering samples. The resulting values of the index of refraction were within ∼1% of the expected. The main limitation of this technique is the effect of misalignment on the results, requiring the precision provided by an angular-resolved OCT system. These very promising results provide evidence that the dual-angle method should be further investigated and validated on human tissues so that it can be developed into a clinically useful diagnostic tool in the future.

Dynamic optical coherence elastography (OCE) tracks elastic wave propagation speed within tissue, enabling quantitative three-dimensional imaging of the elastic modulus. We show that propagating mechanical waves are mode converted at interfaces, creating a finite region on the order of an acoustic wavelength where there is not a simple one-to-one correspondence between wave speed and elastic modulus. Depending on the details of a boundary’s geometry and elasticity contrast, highly complex propagating fields produced near the boundary can substantially affect both the spatial resolution and contrast of the elasticity image. We demonstrate boundary effects on Rayleigh waves incident on a vertical boundary between media of different shear moduli. Lateral resolution is defined by the width of the transition zone between two media and is the limit at which a physical inclusion can be detected with full contrast. We experimentally demonstrate results using a spectral-domain OCT system on tissue-mimicking phantoms, which are replicated using numerical simulations. It is shown that the spatial resolution in dynamic OCE is determined by the temporal and spatial characteristics (i.e., bandwidth and spatial pulse width) of the propagating mechanical wave. Thus, mechanical resolution in dynamic OCE inherently differs from the optical resolution of the OCT imaging system.

Subglottic stenosis (SGS) is a challenging disease to diagnose in neonates. Long-range optical coherence tomography (OCT) is an optical imaging modality that has been described to image the subglottis in intubated neonates. A major challenge associated with OCT imaging is the lack of an automated method for image analysis and micrometry of large volumes of data that are acquired with each airway scan (1 to 2 Gb). We developed a tissue segmentation algorithm that identifies, measures, and conducts image analysis on tissue layers within the mucosa and submucosa and compared these automated tissue measurements with manual tracings. We noted small but statistically significant differences in thickness measurements of the mucosa and submucosa layers in the larynx (p 0.001), subglottis (p = 0.015), and trachea (p = 0.012). The automated algorithm was also shown to be over 8 times faster than the manual approach. Moderate Pearson correlations were found between different tissue texture parameters and the patient’s gestational age at birth, age in days, duration of intubation, and differences with age (mean age 17 days). Automated OCT data analysis is necessary in the diagnosis and monitoring of SGS, as it can provide vital information about the airway in real time and aid clinicians in making management decisions for intubated neonates.

Mueller microscopy studies of fixed unstained histological cuts of human skin models were combined with an analysis of experimental data within the framework of differential Mueller matrix (MM) formalism. A custom-built Mueller polarimetric microscope was used in transmission configuration for the optical measurements of skin tissue model adjacent cuts of various nominal thicknesses (5 to 30 mm). The maps of both depolarization and polarization parameters were calculated from the corresponding microscopic MM images by applying a logarithmic Mueller matrix decomposition (LMMD) pixelwise. The parameters derived from LMMD of measured tissue cuts and the intensity of transmitted light were used for an automated segmentation of microscopy images to delineate dermal and epidermal layers. The quadratic dependence of depolarization parameters and linear dependence of polarization parameters on thickness, as predicted by the theory, was confirmed in our measurements. These findings pave the way toward digital histology with polarized light by presenting the combination of optimal optical markers, which allows mitigating the impact of tissue cut thickness fluctuations and increases the contrast of polarimetric images for tissue diagnostics.

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

In conventional retinal region detection methods for optical coherence tomography (OCT) images, many parameters need to be set manually, which is often detrimental to their generalizability. We present a scheme to detect retinal regions based on fully convolutional networks (FCN) for automatic diagnosis of abnormal maculae in OCT images. The FCN model is trained on 900 labeled age-related macular degeneration (AMD), diabetic macular edema (DME) and normal (NOR) OCT images. Its segmentation accuracy is validated and its effectiveness in recognizing abnormal maculae in OCT images is tested and compared with traditional methods, by using the spatial pyramid matching based on sparse coding (ScSPM) classifier and Inception V3 classifier on two datasets: Duke dataset and our clinic dataset. In our clinic dataset, we randomly selected half of the B-scans of each class (300 AMD, 300 DME, and 300 NOR) for training classifier and the rest (300 AMD, 300 DME, and 300 NOR) for testing with 10 repetitions. Average accuracy, sensitivity, and specificity of 98.69%, 98.03%, and 99.01% are obtained by using ScSPM classifier, and those of 99.69%, 99.53%, and 99.77% are obtained by using Inception V3 classifier. These two classification algorithms achieve 100% classification accuracy when directly applied to Duke dataset, where all the 45 OCT volumes are used as test set. Finally, FCN model with or without flattening and cropping and its influence on classification performance are discussed.

High re-excision rates in breast-conserving surgery call for a new intraoperative approach to the lumpectomy margin evaluation problem. The unique intraoperative imaging system, presented here, demonstrated the capability of photoacoustic tomography (PAT) to deliver optical sensitivity and specificity, along with over 2-cm imaging depth, in a clinical setting. The system enabled the evaluation of tumor extent, shape, morphology, and position within lumpectomy specimens measuring up to 11 cm in diameter. The investigation included all major breast cancer-related lesions, such as invasive ductal carcinoma (IDC), multifocal IDC, ductal carcinoma in situ and combinations of these variants. Coregistration with established ultrasound (US) technology, as well as comparison to specimen radiography, validated the performance of PAT, which appeared to facilitate better tumor visualization. Contrary to expected PA contrast mechanisms, PAT images of hemoglobin distribution correlated poorly with US-determined tumor location, while hypointense regions in lipid-weighted PAT images were in better agreement with US.

Optical coherence tomography angiography (OCTA) provides in-vivo images of microvascular perfusion in high resolution. For its application to basic and clinical research, an automatic and robust quantification of the capillary architecture is mandatory. Only this makes it possible to reliably analyze large amounts of image data, to establish biomarkers, and to monitor disease developments. However, due to its optical properties, OCTA images of skin often suffer from a poor signal-to-noise ratio and contain imaging artifacts. Previous work on automatic vessel segmentation in OCTA mostly focuses on retinal and cerebral vasculature. Its applicability to skin and, furthermore, its robustness against imaging artifacts had not been systematically evaluated. We propose a segmentation method that improves the quality of vascular quantification in OCTA images even if corrupted by imaging artifacts. Both the combination of image processing methods and the choice of their parameters are systematically optimized to match the manual labeling of an expert for OCTA images of skin. The efficacy of this optimization-based vessel segmentation is further demonstrated on sample images as well as by a reduced error of derived quantitative vascular network characteristics.

Working memory (WM) plays a crucial role in human brain functions. The application of brain connectivity analysis helps to understand the brain network properties in WM. Combination of functional and effective connectivity can provide new insights for exploring network attributes. Nevertheless, few studies have combined these two modes in WM. Near-infrared spectroscopy was used to investigate the connectivity properties in the prefrontal cortex (PFC) during n-back (0-back and 2-back) tasks by combining functional and effective connectivity analysis. Our results demonstrated that the PFC network showed small-world properties in both WM tasks. The characteristic path length was significantly longer in the 2-back task than in the 0-back task, while there was no obvious difference in the clustering coefficient between two tasks. Regarding the effective connectivity, the Granger causality (GC) was higher for right PFC→left PFC than for left PFC→right PFC in the 2-back task. Compared with the 0-back task, GC of right PFC→left PFC was higher in the 2-back task. Our findings show that, along with memory load increase, long range connections in PFC are enhanced and this enhancement might be associated with the stronger information flow from right PFC to left PFC.

The optical attenuation coefficient (OAC) reflects the optical properties of various tissues or tissues of the same type under different physiological conditions. Quantitative measurement of OAC from optical coherence tomography (OCT) signals can provide additional information and can increase the potential for OCT applications. We present an optimized depth-resolved estimation (ODRE) method that derives a precise mapping between the measured OCT signal and the OAC. In contrast to previous depth-resolved estimation (DRE) methods, the optimized method can estimate the OAC in any depth range and ignore whether the light is completely attenuated. Numerical simulations and phantom experiments are used to verify its validity, and this method is applied to detect cerebral damage. In combination with OCT angiography, real-time observation of the change of blood perfusion and the degree of cerebral damage in mice with focal cerebral ischemia provides important information to help us understand the temporal relationship between brain damage and ischemia.

Oral premalignant lesions (OPLs), such as leukoplakia, are at risk of malignant transformation to oral cancer. Clinicians can elect to biopsy OPLs and assess them for dysplasia, a marker of increased risk. However, it is challenging to decide which OPLs need a biopsy and to select a biopsy site. We developed a multimodal optical imaging system (MMIS) that fully integrates the acquisition, display, and analysis of macroscopic white-light (WL), autofluorescence (AF), and high-resolution microendoscopy (HRME) images to noninvasively evaluate OPLs. WL and AF images identify suspicious regions with high sensitivity, which are explored at higher resolution with the HRME to improve specificity. Key features include a heat map that delineates suspicious regions according to AF images, and real-time image analysis algorithms that predict pathologic diagnosis at imaged sites. Representative examples from ongoing studies of the MMIS demonstrate its ability to identify high-grade dysplasia in OPLs that are not clinically suspicious, and to avoid unnecessary biopsies of benign OPLs that are clinically suspicious. The MMIS successfully integrates optical imaging approaches (WL, AF, and HRME) at multiple scales for the noninvasive evaluation of OPLs.

In the last decades, laparoscopic surgery has become the gold standard in patients with colorectal cancer. To overcome the drawback of reduced tactile feedback, real-time tissue classification could be of great benefit. In this ex vivo study, hyperspectral imaging (HSI) was used to distinguish tumor tissue from healthy surrounding tissue. A sample of fat, healthy colorectal wall, and tumor tissue was collected per patient and imaged using two hyperspectral cameras, covering the wavelength range from 400 to 1700 nm. The data were randomly divided into a training (75%) and test (25%) set. After feature reduction, a quadratic classifier and support vector machine were used to distinguish the three tissue types. Tissue samples of 32 patients were imaged using both hyperspectral cameras. The accuracy to distinguish the three tissue types using both hyperspectral cameras was 0.88 (STD=0.13) on the test dataset. When the accuracy was determined per patient, a mean accuracy of 0.93 (STD=0.12) was obtained on the test dataset. This study shows the potential of using HSI in colorectal cancer surgery for fast tissue classification, which could improve clinical outcome. Future research should be focused on imaging entire colon/rectum specimen and the translation of the technique to an intraoperative setting.

A software for fast rendering the visual appearance of a blood vessel located in human skin was developed based on a numerical solution of the radiative transfer equation. The user can specify geometrical properties, such as the depth and the diameter of the vessel, and physiological properties, such as the oxygen saturation of the vessel or the blood concentration in the skin. From these data, the spatially and spectrally resolved reflectance from the skin containing the blood vessel is calculated via Monte Carlo simulations, by which a two-dimensional image is generated. The short calculation time of about a second is achieved by precalculating and storing the spatially resolved reflectance for a variety of combinations of the optical and geometrical properties. This concept gives the user the opportunity to rapidly explore the influence of the physiological and geometrical properties of the investigated blood vessel on its visual appearance. The correctness of the lookup table was validated by comparison with independent Monte Carlo simulations. Rendering examples of different blood vessels in human skins are given. The current version of the software can be downloaded at https://www.ilm-ulm.de/software.

Sugar-rich diets and poor dental hygiene promote the formation of a biofilm (plaque) that strongly adheres to the dental enamel surface and fosters the evolution of aciduric bacteria. The acid contributes to demineralization of the exterior tooth enamel, which accelerates after the pH drops below a critical value (∼5.5) for extended time periods resulting in the need for restorative procedures. Preventative techniques to alert the dentist and caries-susceptible patients regarding vulnerability to dental decay require a clinical measure of plaque activity. Therefore, there is a need to evaluate the acid production capability of plaque deposits in the pits and fissures of occlusal and interproximal regions. A ratiometric fluorescence pH-sensing device has been developed using an FDA-approved dye and LED excitation. Fluorescein spectral profiles were collected using a spectrometer and analyzed with a spectral unmixing algorithm for calibration over the pH range of 4.5 to 7. An in vivo pilot study on human subjects was performed using a sucrose rinse to accelerate bacterial metabolism and to measure the time-dependent drop in pH. The optical system is relatively immune to confounding factors such as photobleaching, dye concentration, and variation in excitation intensity associated with earlier dye-based pH measurement techniques.

Interferon (IFN)-γ release assays (IGRAs) are probably the most accurate tests for the detection of latent Mycobacterium tuberculosis infection, but IGRAs are labour intensive and the transport of samples over longer distances is difficult. IFN-γ-induced protein (IP)-10 is expressed at 100-fold higher levels than IFN-γ, and IP-10 release assays have comparable performance to IGRAs. The aim of this study was to explore the diagnostic potential of a novel IP-10 release assay based on dried plasma spots (DPS). The presence of IP-10 and IFN-γ was determined in plasma and in DPS by ELISA. Diagnostic algorithms for plasma and DPS tests for IP-10 were developed on a training cohort comprising 60 tuberculosis (TB) patients and 59 healthy controls. Diagnostic accuracy was assessed in a validation cohort comprising 78 TB patients and 98 healthy controls. Plasma was measured in Spain and DPS samples were sent to Denmark using the conventional postal service for analysis. IP-10 was readily detectable in both plasma and DPS, and correlation was excellent (r2 = 0.95). QuantiFERON-TB Gold In-Tube (QFT-TB) and IP-10 in DPS and plasma rendered comparable sensitivity (78%, 82% and 84%, respectively), specificity (100%, 97% and 97%, respectively) and indeterminate rates (p>0.55). The DPS-based IP-10 test has comparable diagnostic accuracy to the QFT-TB and samples can be sent via conventional mail over long distances for analysis without affecting the results.

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.

Delays between contrast agent (CA) arrival at the site of vascular input function (VIF) sampling and the tissue of interest affect dynamic contrast enhanced (DCE) MRI pharmacokinetic modelling. We investigate effects of altering VIF CA bolus arrival delays on liver DCE MRI perfusion parameters, propose an alternative approach to estimating delays and evaluate reproducibility. Thirteen healthy volunteers (28.7 ± 1.9 years, seven males) underwent liver DCE MRI using dual-input single compartment modelling, with reproducibility (n = 9) measured at 7 days. Effects of VIF CA bolus arrival delays were assessed for arterial and portal venous input functions. Delays were pre-estimated using linear regression, with restricted free modelling around the pre-estimated delay. Perfusion parameters and 7 days reproducibility were compared using this method, freely modelled delays and no delays using one-way ANOVA. Reproducibility was assessed using Bland–Altman analysis of agreement. Maximum percent change relative to parameters obtained using zero delays, were −31% for portal venous (PV) perfusion, +43% for total liver blood flow (TLBF), +3247% for hepatic arterial (HA) fraction, +150% for mean transit time and −10% for distribution volume. Differences were demonstrated between the 3 methods for PV perfusion (p = 0.0085) and HA fraction (p

Specialized metabolites (also called natural products or secondary metabolites) derived from bacteria, fungi, marine organisms and plants constitute an important source of antibiotics, anti-cancer agents, insecticides, immunosuppressants and herbicides. Many specialized metabolites in bacteria and fungi are biosynthesized via metabolic pathways whose enzymes are encoded by clustered genes on a chromosome. Metabolic gene clusters comprise a group of physically co-localized genes that together encode enzymes for the biosynthesis of a specific metabolite. Although metabolic gene clusters are generally not known to occur outside of microbes, several plant metabolic gene clusters have been discovered in recent years. The discovery of novel metabolic pathways is being enabled by the increasing availability of high-quality genome sequencing coupled with the development of powerful computational toolkits to identify metabolic gene clusters. To provide a comprehensive overview of various bioinformatics methods for detecting gene clusters, we compare and contrast key aspects of algorithmic logic behind several computational tools, including 'NP.searcher', 'ClustScan', 'CLUSEAN', 'antiSMASH', 'SMURF', 'MIDDAS-M', 'ClusterFinder', 'CASSIS/SMIPS' and 'C-Hunter' among others. We also review additional tools such as 'NRPSpredictor' and 'SBSPKS' that can infer substrate specificity for previously identified gene clusters. The continual development of bioinformatics methods to predict gene clusters will help shed light on how organisms assemble multi-step metabolic pathways for adaptation to various ecological niches.

T-cell specificity is determined by the T-cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination. Massively parallel high-throughput sequencing allows millions of different T-cell receptor genes to be characterized from a single sample of blood or tissue. However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. We outline the major steps in processing of repertoire data, considering low-level processing of raw sequence files and high-level algorithms, which seek to extract biological or pathological information. The latest generation of bioinformatics tools allows millions of DNA sequences to be accurately and rapidly assigned to their respective variable V and J gene segments, and to reconstruct an almost error-free representation of the non-templated additions and deletions that occur. High-level processing can measure the diversity of the repertoire in different samples, quantify V and J usage and identify private and public T-cell receptors. Finally, we discuss the major challenge of linking T-cell receptor sequence to function, and specifically to antigen recognition. Sophisticated machine learning algorithms are being developed that can combine the paradoxical degeneracy and cross-reactivity of individual T-cell receptors with the specificity of the overall T-cell immune response. Computational analysis will provide the key to unlock the potential of the T-cell receptor repertoire to give insight into the fundamental biology of the adaptive immune system and to provide powerful biomarkers of disease.

An important strategy to promote a strong sustainable growth relies on an efficient municipal waste management, and phasing out waste landfilling through waste prevention and recycling emerges as a major target. For this purpose, effective collection schemes are required, in particular those regarding selective waste collection, pursuing a more efficient and high quality recycling of reusable materials. This paper addresses the assessment and benchmarking of selective collection schemes, relevant to guide future operational improvements. In particular, the assessment is based on the monitoring and statistical analysis of a core-set of performance indicators that highlights collection trends, complemented with a performance index that gathers a weighted linear combination of these indicators. This combined analysis underlines a potential tool to support decision makers involved in the process of selecting the collection scheme with best overall performance. The presented approach was applied to a case study conducted in Oporto Municipality, with data gathered from two distinct selective collection schemes.

The amount of municipal solid waste (MSW) has been increasing steadily over the last decade by reason of population rising and waste generation rate. In most of the urban areas, disposal sites are usually located outside of the urban areas due to the scarcity of land. There is no fixed route map for transportation. The current waste collection and transportation are already overloaded arising from the lack of facilities and insufficient resources. In this paper, a model for optimizing municipal solid waste collection will be proposed. Firstly, the optimized plan is developed in a static context, and then it is integrated into a dynamic context using multi-agent based modelling and simulation. A case study related to Hagiang City, Vietnam, is presented to show the efficiency of the proposed model. From the optimized results, it has been found that the cost of the MSW collection is reduced by 11.3%.

The aims of this study were to determine how different background regions of interest (ROIs) around the kidney represent true background activity in over- and underlying tissues in (177)Lu-DOTA-octreatate ((177)Lu-DOTATATE) treatments and to determine the influence of the background positions on the kidney activity concentration estimates by the conjugate view (ConjV) and posterior view (PostV) methods. The analysis was performed in single-photon emission computed tomography (SPECT) images of 20 patients, acquired 24 h post injection of a (177)Lu-DOTATATE treatment, by a computer algorithm that created planar images from the SPECT data. The ratio between the activity concentration in the background and the true background varied from 0.36 to 2.08 [coefficient of variation (CV) = 25-181 %] and from 0.44 to 1.52 (CV = 16-70 %) for the right and left kidneys, respectively. The activity concentration estimate in the kidneys was most accurate with the PostV method using a background ROI surrounding the whole kidney, and this combination might be an alternative planar method for improved kidney dosimetry in the (177)Lu-DOTATATE treatments.

In this work we present a novel treatment planning technique called interactive dose shaping (IDS) to be employed for the optimization of intensity modulated radiation therapy (IMRT). IDS does not rely on a Newton-based optimization algorithm which is driven by an objective function formed of dose volume constraints on pre-segmented volumes of interest (VOIs). Our new planning technique allows for direct, interactive adaptation of localized planning features. This is realized by a dose modification and recovery (DMR) planning engine which implements a two-step approach: firstly, the desired localized plan adaptation is imposed on the current plan (modification) while secondly inevitable, undesired disturbances of the dose pattern elsewhere are compensated for automatically by the recovery module. Together with an ultra-fast dose update calculation method the DMR engine has been implemented in a newly designed 3D therapy planning system Dynaplan enabling true real-time interactive therapy planning. Here we present the underlying strategy and algorithms of the DMR based planning concept. The functionality of the IDS planning approach is demonstrated for a phantom geometry of clinical resolution and size.

In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.

This study aims to determine if light scatter parameters measured with spatial frequency domain imaging (SFDI) can accurately predict stromal, epithelial, and adipose fractions in freshly resected, unstained human breast specimens. An explicit model was developed to predict stromal, epithelial, and adipose fractions as a function of light scattering parameters, which was validated against a quantitative analysis of digitized histology slides for N = 31 specimens using leave-one-out cross-fold validation. Specimen mean stromal, epithelial, and adipose volume fractions predicted from light scattering parameters strongly correlated with those calculated from digitized histology slides (r = 0.90, 0.77, and 0.91, respectively, p-value

The purpose of this study is to construct an approach and a methodology to estimate the future outflows of electronic waste (e-waste) in India. Consequently, the study utilizes a time-series multiple lifespan end-of-life model proposed by Peralta and Fontanos for estimating the current and future quantities of e-waste in India. The model estimates future e-waste generation quantities by modeling their usage and disposal. The present work considers two scenarios for the approximation of e-waste generation based on user preferences to store or to recycle the e-waste. This model will help formal recyclers in India to make strategic decisions in planning for appropriate recycling infrastructure and institutional capacity building. Also an extension of the model proposed by Peralta and Fontanos is developed with the objective of helping decision makers to conduct WEEE estimates under a variety of assumptions to suit their region of study. During 2007-2011, the total WEEE estimates will be around 2.5 million metric tons which include waste from personal computers (PC), television, refrigerators and washing machines. During the said period, the waste from PC will account for 30% of total units of WEEE generated.

Adsorption isotherms of 3 selected paper-based packaging materials, that is, vegetable parchment (VP) paper, Kraft paper, and solid-bleached-sulfate (SBS) paperboard, were determined at 3 different temperatures (25, 40, and 50 degrees C). The GAB isotherm model was found to fit adequately for describing experimental adsorption isotherm data for the paper samples. The monolayer moisture content of the paper samples decreased with increase in temperature, which is in the range of 0.0345 to 0.0246, 0.0301 to 0.0238, and 0.0318 to 0.0243 g water/g solid for the MG paper, the Kraft paper, and the SBS paperboard, respectively. The net isosteric heats of sorption (q(st)) for the paper samples decreased exponentially with increase in moisture content after reaching the maximum values of 18.51, 27.39, and 26.80 kJ/mol for the VP paper, the Kraft paper, and the SBS paperboard, respectively, at low-moisture content. The differential enthalpy and entropy of 3 paper samples showed compensation phenomenon with the isokinetic temperature of 399.7 K indicating that water vapor had been adsorbed onto the paper samples with the same mechanism. Depending on the paper material, tensile strength of paper samples was affected by moisture content.

Studies on avoidance of contaminants by aquatic organisms verified that such behavior may have crucial ecological implications. Yet, avoidance tests have not been considered in ecological risk assessments. This study aimed at developing a short-term test with Daphnia magna Straus based on avoidance of pulp mill effluents and at comparing its sensitivity to the standard 21 d D. magna reproduction test. The avoidance effective dilution values (12 h EDil20 and EDil50) were as sensitive as the 21 d EDil20 and EDil50 values for reproduction. Therefore, this easily standardizable short-term test can be recommended as a valuable complementary tool in ecological risk assessments. http://dx.doi.org/10.1007/s00128-008-9538-y

This paper proposes an image-based painterly rendering algorithm for automatically synthesizing an image with color ink diffusion. We suggest a mathematical model with a physical base to simulate the phenomenon of color colloidal ink diffusing into absorbent paper. Our algorithm contains three main parts: a feature extraction phase, a Kubelka-Munk (KM) color mixing phase, and a color ink diffusion synthesis phase. In the feature extraction phase, the information of the reference image is simplified by luminance division and color segmentation. In the color mixing phase, the KM theory is employed to approximate the result when one pigment is painted upon another pigment layer. Then, in the color ink diffusion synthesis phase, the physically-based model that we propose is employed to simulate the result of color ink diffusion in absorbent paper using a texture synthesis technique. Our image-based ink diffusing rendering (IBCIDR) algorithm eliminates the drawback of conventional Chinese ink simulations, which are limited to the black ink domain, and our approach demonstrates that, without using any strokes, a color image can be automatically converted to the diffused ink style with a visually pleasing appearance.

Quantifying and comparing the scientific output of researchers has become critical for governments, funding agencies and universities. Comparison by reputation and direct assessment of contributions to the field is no longer possible, as the number of scientists increases and traditional definitions about scientific fields become blurred. The h-index is often used for comparing scientists, but has several well-documented shortcomings. In this paper, we introduce a new index for measuring and comparing the publication records of scientists: the pagerank-index (symbolised as π). The index uses a version of pagerank algorithm and the citation networks of papers in its computation, and is fundamentally different from the existing variants of h-index because it considers not only the number of citations but also the actual impact of each citation. We adapt two approaches to demonstrate the utility of the new index. Firstly, we use a simulation model of a community of authors, whereby we create various ‘groups’ of authors which are different from each other in inherent publication habits, to show that the pagerank-index is fairer than the existing indices in three distinct scenarios: (i) when authors try to ‘massage’ their index by publishing papers in low-quality outlets primarily to self-cite other papers (ii) when authors collaborate in large groups in order to obtain more authorships (iii) when authors spend most of their time in producing genuine but low quality publications that would massage their index. Secondly, we undertake two real world case studies: (i) the evolving author community of quantum game theory, as defined by Google Scholar (ii) a snapshot of the high energy physics (HEP) theory research community in arXiv. In both case studies, we find that the list of top authors vary very significantly when h-index and pagerank-index are used for comparison. We show that in both cases, authors who have collaborated in large groups and/or published less impactful papers tend to be comparatively favoured by the h-index, whereas the pagerank-index highlights authors who have made a relatively small number of definitive contributions, or written papers which served to highlight the link between diverse disciplines, or typically worked in smaller groups. Thus, we argue that the pagerank-index is an inherently fairer and more nuanced metric to quantify the publication records of scientists compared to existing measures.

Measurement of illicit drugs on paper currency is of interest for evidentiary purposes in legal cases involving the drug trade. Current methods, primarily based on mass spectrometry, are destructive and prevent re-analysis of the evidence. This article details a method based on Raman microspectroscopy that is able to collect spectra from individual crystals on the surface of paper currency. Mixtures of isoxsuprine and norephedrine, which are non-pharmacologically active drug surrogates, as well as lidocaine and benzocaine, common excipients in street drugs, were doped in small quantities onto US currency. Significant fluorescence interference resulted from the underlying dollar bill. This work presents two methods for reducing the fluorescence background, photobleaching and background subtraction, which both worked well. Finally, a method for determining the percent composition of individual components in heterogeneous mixtures was developed by systematically sampling the surface of the dollar bill. Results were accurate within a few percent, although the method was quite time consuming.

Background: Chronic inflammatory demyelinating polyneuropathy (CIDP) is a heterogeneous disorder and both clinical course and response to treatment vary widely. Because of the propensity for relapse, CIDP requires maintenance therapy after the initial response to treatment. There is no consensus regarding this in the published literature. Present report: A patient with CIDP was treated with oral prednisolone and cyclophosphamide pulse therapy but required repeated plasma exchange and intravenous immunoglobulin (IVIg). Treatment with ciclosporin freed the patient from repeated IVIg administration. Therapeutic responses in 14 subsequent cases including three patients who showed improvement with ciclosporin are also presented along with an algorithm of the authors’ suggested protocol for treatment. Conclusion: Ciclosporin should be considered for patients with intractable CIDP who require repeated IVIg.