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The authors would like to thank Luneau Technology Portugal for lending the Visionix VX120., Purpose: To study the effect of near-vision reading task on optical quality of the eye when performed on a computer monitor and on printed paper, and to identify which of the two results in greater changes. Methods: Two groups of subjects performed a 30-min reading task in two different conditions: on a computer monitor and on printed paper. Ocular, corneal, and internal wavefront aberrations (Zernike coefficients up to 6th order), root-mean-square of low- and high-order aberrations, spherical equivalent, vectoral components of ocular astigmatism ( J45 and J0), and the compensation factor between internal and corneal aberrations were measured before and after the tasks. Their changes were analyzed in each group and between groups. Results: Statistically significant changes in wavefront aberrations and in root mean square of low- and high-order aberrations were observed in both groups which was significantly greater when the task was performed on printed paper. Partial loss of compensation mechanism and variation in spherical equivalent in a negative direction occurred after both reading tasks; however, it was statistically significant only with printed paper reading task. The vectoral components of ocular astigmatism did not show statistically significant changes in either groups. Conclusion: Near-vision reading tasks can change the optical quality of the eye, especially when the task is performed on printed paper., This work was supported by the Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UID/FIS/04650/2019 and by the project PTDC/FIS-OTI/ 31486/2017.

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.

Adverse outcome pathways have shown themselves to be useful ways of understanding and expressing knowledge about sequences of events that lead to adverse outcomes (AOs) such as toxicity. In this paper we use the building blocks of adverse outcome pathways—namely key events (KEs) and key event relationships—to construct networks which can be used to make predictions of the likelihood of AOs. The networks of KEs are augmented by data from and knowledge about assays as well as by structure activity relationship predictions linking chemical classes to the observation of KEs. These inputs are combined within a reasoning framework to produce an information-rich display of the relevant knowledge and data and predictions of AOs both in the abstract case and for individual chemicals. Illustrative examples are given for skin sensitization, reprotoxicity and non-genotoxic carcinogenicity.

Neurotoxicity is one of the main causes of drug withdrawal, and the biological experimental methods of detecting neurotoxic toxicity are time-consuming and laborious. In addition, the existing computational prediction models of neurotoxicity still have some shortcomings. In response to these shortcomings, we collected a large number of data set of neurotoxicity and used PyBioMed molecular descriptors and eight machine learning algorithms to construct regression prediction models of chemical neurotoxicity. Through the cross-validation and test set validation of the models, it was found that the extra-trees regressor model had the best predictive effect on neurotoxicity (${q}_{\mathrm{test}}^2$ = 0.784). In addition, we get the applicability domain of the models by calculating the standard deviation distance and the lever distance of the training set. We also found that some molecular descriptors are closely related to neurotoxicity by calculating the contribution of the molecular descriptors to the models. Considering the accuracy of the regression models, we recommend using the extra-trees regressor model to predict the chemical autonomic neurotoxicity.

A developmental validation was performed to demonstrate reliability, reproducibility, and robustness of the ANDE Rapid DNA Identification System for processing of crime scene and disaster victim identification (DVI) samples. A total of 1705 samples were evaluated, including blood, oral epithelial samples from drinking containers, samples on FTA and untreated paper, semen, bone, and soft tissues. This study was conducted to address the FBI’s Quality Assurance Standards on developmental validation and to accumulate data from a sufficient number of unique donors and sample types to meet NDIS submission requirements for acceptance of the ANDE Expert System for casework use. To date, no Expert System has been approved for such samples, but the results of this study demonstrated that the automated Expert System performs similarly to conventional laboratory data analysis. Furthermore, Rapid DNA analysis demonstrated accuracy, precision, resolution, concordance, and reproducibility that were comparable to conventional processing along with appropriate species specificity, limit of detection, performance in the presence of inhibitors. No lane‐to‐lane or run‐to‐run contamination was observed, and the system correctly identified the presence of mixtures. Taken together, the ANDE instrument, I‐Chip consumable, FlexPlex chemistry (a 27‐locus STR assay compatible with all widely used global loci, including the CODIS core 20 loci), and automated Expert System successfully processed and interpreted more than 1200 unique samples with over 99.99% concordant CODIS alleles. This extensive developmental validation data provides support for broad use of the system by agencies and accredited forensic laboratories in single‐source suspect‐evidence comparisons, local database searches, and DVI.

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles.

In the present study, iodide-mediated 3,3′,5,5′-tetramethylbenzidine (TMB)-H2O2 reaction system was applied to a microfluidic paper-based analytical device (μPAD) for non-enzymatic colorimetric determination of H2O2. The proposed system is portable and incorporates a μPAD with a machine learning-based smartphone app. A smartphone app called “Hi-perox Sens” capable of image capture, cropping and processing was developed to make the system simple and user-friendly. Briefly, circular μPADs were designed and tested with varying concentrations of H2O2. Following the color change, the images of the μPADs were taken with four different smartphones under seven different illumination conditions. In order to make the system more robust and adaptive against illumination variation and camera optics, the images were first processed for feature extraction and then used to train machine learning classifiers. According to the results, TMB + KI showed the highest classification accuracy (97.8%) with inter-phone repeatability at t = 30 s under versatile illumination and maintained its accuracy for 10 minutes. In addition, the performance of the system was also comparable to two different commercially available H2O2 kits in real samples.

Significance: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification. Aim: To reduce the class bias caused by data imbalance. Approach: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings. Results: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of “premalignancy” class is also increased, which is ideal for screening applications. Conclusions: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Significance: Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications. Aim: Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes. Approach: A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue. Results: The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes. Conclusion: These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Significance: Screening and early detection of oral potentially malignant lesions (OPMLs) are of great significance in reducing the mortality rates associated with head and neck malignancies. Intra-oral multispectral optical imaging of tissues in conjunction with cloud-based machine learning (CBML) can be used to detect oral precancers at the point-of-care (POC) and guide the clinician to the most malignant site for biopsy. Aim: Develop a bimodal multispectral imaging system (BMIS) combining tissue autofluorescence and diffuse reflectance (DR) for mapping changes in oxygenated hemoglobin (HbO2) absorption in the oral mucosa, quantifying tissue abnormalities, and guiding biopsies. Approach: The hand-held widefield BMIS consisting of LEDs emitting at 405, 545, 575, and 610 nm, 5MPx monochrome camera, and proprietary Windows-based software was developed for image capture, processing, and analytics. The DR image ratio (R610/R545) was compared with pathologic classification to develop a CBML algorithm for real-time assessment of tissue status at the POC. Results: Sensitivity of 97.5% and specificity of 92.5% were achieved for discrimination of OPML from patient normal in 40 sites, whereas 82% sensitivity and 96.6% specificity were obtained for discrimination of abnormal (OPML + SCC) in 89 sites. Site-specific algorithms derived for buccal mucosa (27 sites) showed improved sensitivity and specificity of 96.3% for discrimination of OPML from normal. Conclusions: Assessment of oral cancer risk is possible by mapping of HbO2 absorption in tissues, and the BMIS system developed appears to be suitable for biopsy guidance and early detection of oral cancers.

The clinical importance of blood group (BG) antigens is related to their ability to induce immune antibodies that can cause hemolysis. Yet, ABO and D (Rh) are still considered to be the key antigens for healthy blood transfusion and secondary antigens are the next priority. Serological typing is the most widely used typing method. Rapid and accurate blood grouping plays an important role in some clinical conditions, rather than conventional techniques. Hence, developing a simple and economical model for rapid blood grouping would facilitate these tests. In recent decades, paper-based microfluidics such as μPADs has gained much interest in wide application areas such as point-of-care diagnostic. In this study, we evaluated μPADs that are performed for blood grouping and its recent progress. A comprehensive literature search was performed using databases including PUBMED, SCOPUS, Web of Science and Google Scholar. Keywords were blood grouping or typing, paper analytical device, rapid test, etc. After investigation of search results, 16 papers from 2010 to 2020 were included. Further information in detail was classified in Table 1. Generally, two principles for blood typing μPADs are introduced. The lateral chromatographic flow method and the vertical flow-through method that detects BG in a visual-based manner. To detect results with acceptable clarity many factors and challenges like paper, blood sample, buffer, Ab and RBC interaction and also μPADs stability need to be considered, which are discussed. In conclusion, the simplicity, stability, cheapness, portability and biocompatibility of μPADs for blood grouping confirming its utility and also they have the capability to robust, universal blood-grouping platform. Table 1 Summary of blood grouping tests using paper-based analytical devices Antigens Type of diagnosis Validation method Sample No Accuracy Action time Paper type Stability Sample dilution Buffer Ref A, B, Rh Forward volunteers records 5 - - Whatman No. 4 - 1/2 PBS* (Khan et al. 2010) A, B, Rh Forward gel assay test and conventional slide test 100 100% 1 min Whatman No. 4 and Kleeenex paper towel 7 Days in 4 °C 1/1 NSS (Al-Tamimi et al. 2012) A, B, Rh Forward gel card assay 99 100% 20 Sec + Washing Kleeenex paper towel - 1/1 NSS (Li et al. 2012) A, B, Rh Forward - - - - Kleeenex paper towel - 45/100 PSS (Li et al. 2013) A, B, Rh Forward gel card assay 98 100% 1.5 min Kleeenex paper towel - 85/100 PBS (Guan et al. 2014b) C, E, c, e, K, Jka, Jkb, M, N, S, P1, and Lea Forward gel card assay 266 100% - Kleeenex paper towel - 1/1 NSS (Li et al. 2014b) A, B, Rh Forward and Reverse conventional slide test 96 ≈ 91% 10 min Whatman No. 1 21 Days in 4 °C 1/2 NSS (Noiphung et al. 2015) C, c, E, e, K, k, Fya, Fyb, Jka, Jkb, M, N, S and s, P1, Lea and Leb Forward - 478 - - Kleeenex paper towel - 1/1 NSS, PBS (Then et al. 2015) A, B Forward and Reverse conventional slide test 76 100% 5-8 min Whatman No. 4 38 Days in 4 °C 1/4, 1/1 NSS (Songjaroen and Laiwattanapaisal 2016) D, K Forward volunteers records 210 - 7.5 min Kleenex paper towel - 1/1 NSS (Yeow et al. 2016) A, B, c, e, D, C, E, M, N, S, s, P1, Jka, Jkb, Lea, Leb, Fya, and Fyb Forward and Reverse gel card assay 3550 ≈100% 30 s Fiber glass and cotton linter 180 Days in 25 °C 45/100, 1/1 PBS (Zhang et al. 2017) A, B Forward conventional slide test 598 100% 3 min Whatman No. 113 14 Day in 4 °C 1/1 NSS (Songjaroen et al. 2018) A, B, Rh Forward conventional slide test - - 30 Sec + Washing Unrefined sisal paper - 1/2 NSS (Casals-Terré et al. 2019) A, B, Rh Forward - - - - Whatman No.1 - 1/1 NSS (Ansari et al. 2020) ABORh Forward and Reverse conventional slide test - 100% Unrefined Eucalyptus papers - 1/2 NSS, PBS (Casals-Terré et al. 2020) A, B, Rh Forward - - - 30 Sec + Washing Whatman No. 4 modified with chitosan ≥ 100 days in 25 °C 1/1 NSS (Parween et al. 2020)

Significance: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. Aim: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. Approach: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. Results: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. Conclusions: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e. spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95 ± 0.05, 0.85 ± 0.04, 0.96 ± 0.01, 0.91 ± 0.03, 0.83 ± 0.06 and 0.65 ± 0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy.

SignificanceAutomated understanding of human embryonic stem cell (hESC) videos is essential for the quantified analysis and classification of various states of hESCs and their health for diverse applications in regenerative medicine.AimThis paper aims to develop an ensemble method and bagging of deep learning classifiers as a model for hESC classification on a video dataset collected using a phase contrast microscope.ApproachThe paper describes a deep learning-based random network (RandNet) with an autoencoded feature extractor for the classification of hESCs into six different classes, namely, (1)cell clusters, (2)debris, (3)unattached cells, (4)attached cells, (5)dynamically blebbing cells, and (6)apoptotically blebbing cells. The approach uses unlabeled data to pre-train the autoencoder network and fine-tunes it using the available annotated data.ResultsThe proposed approach achieves a classification accuracy of 97.23 ± 0.94 % and outperforms the state-of-the-art methods. Additionally, the approach has a very low training cost compared with the other deep-learning-based approaches, and it can be used as a tool for annotating new videos, saving enormous hours of manual labor.ConclusionsRandNet is an efficient and effective method that uses a combination of subnetworks trained using both labeled and unlabeled data to classify hESC images.

Thermal papers, commonly used for printed receipts or lottery tickets, are omnipresent in our everyday life. They are regarded as semi-porous substrates, and yet can be critical to analyze when looking for latent fingermarks due to their thermosensibility. The aim of this study was to investigate a development sequence that would better combine the adequate detection techniques in order to maximize the chances to develop latent fingermarks left on these substrates. Different methods of development have been compared on test substrates: black magnetic powder, Lumicyano™, thermal development, ninhydrin and 1,2-indanedione/ZnCl2. Whitening stages and thermal development have been focused on, tested and optimized. The results of these preliminary tests enabled the study of three development sequences. They have subsequently been compared to the one currently used in the Gendarmerie's laboratories and the best results have been provided during pseudo-operational comparative trials by one of these sequences, consisting in 6 stages.

Significance: Physiological parameters extracted from diffuse reflectance spectroscopy (DRS) provide clinicians quantitative information about tissue that helps aid in diagnosis. There is a great need for an accurate and cost-effective method for extracting parameters from DRS measurements. Aim: The aim is to explore the accuracy and speed of physiological parameter extraction using machine learning models compared to that of the widely used Monte Carlo lookup table (MCLUT) inverse model. Approach: Diffuse reflectance spectra were simulated using a light transport model based on Monte Carlo simulations and weighted to six wavelengths. Deep learning (DL), random forest (RF), gradient boosting machine (GBM), and generalized linear model (GLM) machine learning models were built using a training set of 10,000 spectra from the simulated data. The MCLUT and machine learning models were used to predict physiological parameters from a separate test set of 30,000 simulated spectra. Mean absolute errors were calculated to evaluate the accuracy and compare it among MCLUT and machine learning models. In addition, the computational time to predict parameters from the test set was recorded to compare the speed among MCLUT and machine learning models. Results: The DL, RF, GBM, and GLM models all had significantly lower errors than the MCLUT inverse method for six wavelengths. The DL model proved to have the lowest errors, with all absolute percent errors under 10%. The DL model had much faster runtimes than the MCLUT. Conclusions: Machine learning is promising for extracting physiological parameters from six-wavelength DRS data, with both lower errors and a faster runtime than the widely used MCLUT model.

Significance: Raman spectroscopy has been developed for surgical guidance applications interrogating live tissue during tumor resection procedures to detect molecular contrast consistent with cancer pathophysiological changes. To date, the vibrational spectroscopy systems developed for medical applications include single-point measurement probes and intraoperative microscopes. There is a need to develop systems with larger fields of view (FOVs) for rapid intraoperative cancer margin detection during surgery. Aim: We design a handheld macroscopic Raman imaging system for in vivo tissue margin characterization and test its performance in a model system. Approach: The system is made of a sterilizable line scanner employing a coherent fiber bundle for relaying excitation light from a 785-nm laser to the tissue. A second coherent fiber bundle is used for hyperspectral detection of the fingerprint Raman signal over an area of 1 cm2. Machine learning classifiers were trained and validated on porcine adipose and muscle tissue. Results: Porcine adipose versus muscle margin detection was validated ex vivo with an accuracy of 99% over the FOV of 95 mm2 in ∼3 min using a support vector machine. Conclusions: This system is the first large FOV Raman imaging system designed to be integrated in the workflow of surgical cancer resection. It will be further improved with the aim of discriminating brain cancer in a clinically acceptable timeframe during glioma surgery.

Purpose: Mesenchymal stem cells (MSCs) have demonstrated clinically relevant therapeutic effects for treatment of trauma and chronic diseases. The proliferative potential, immunomodulatory characteristics, and multipotentiality of MSCs in monolayer culture is reflected by their morphological phenotype. Standard techniques to evaluate culture viability are subjective, destructive, or time-consuming. We present an image analysis approach to objectively determine morphological phenotype of MSCs for prediction of culture efficacy. Approach: The algorithm was trained using phase-contrast micrographs acquired during the early and mid-logarithmic stages of MSC expansion. Cell regions are localized using edge detection, thresholding, and morphological operations, followed by cell marker identification using H-minima transform within each region to differentiate individual cells from cell clusters. Clusters are segmented using marker-controlled watershed to obtain single cells. Morphometric and textural features are extracted to classify cells based on phenotype using machine learning. Results: Algorithm performance was validated using an independent test dataset of 186 MSCs in 36 culture images. Results show 88% sensitivity and 86% precision for overall cell detection and a mean Sorensen–Dice coefficient of 0.849 ± 0.106 for segmentation per image. The algorithm exhibited an area under the curve of 0.816 (CI95 = 0.769 to 0.886) and 0.787 (CI95 = 0.716 to 0.851) for classifying MSCs according to their phenotype at early and mid-logarithmic expansion, respectively. Conclusions: The proposed method shows potential to segment and classify low and moderately dense MSCs based on phenotype with high accuracy and robustness. It enables quantifiable and consistent morphology-based quality assessment for various culture protocols to facilitate cytotherapy development.

Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.

Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding. Aim: We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities. Approach: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI. Results: When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis. Conclusion: DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

Purpose: The goal of this research is to develop innovative methods of acquiring simultaneous multidimensional molecular images of several different physiological random processes (PRPs) that might all be active in a particular disease such as COVID-19. Approach: Our study is part of an ongoing effort at the University of Arizona to derive biologically accurate yet mathematically tractable models of the objects of interest in molecular imaging and of the images they produce. In both cases, the models are fully stochastic, in the sense that they provide ways to estimate any estimable property of the object or image. The mathematical tool we use for images is the characteristic function, which can be calculated if the multivariate probability density function for the image data is known. For objects, which are functions of continuous variables rather than discrete pixels or voxels, the characteristic function becomes infinite dimensional, and we refer to it as the characteristic functional. Results: Several innovative mathematical results are derived, in particular for simultaneous imaging of multiple PRPs. Then the application of these methods to cancers that disrupt the mammalian target of rapamycin signaling pathway and to COVID-19 are discussed qualitatively. One reason for choosing these two problems is that they both involve lipid rafts. Conclusions: We found that it was necessary to employ a new algorithm for energy estimation to do simultaneous single-photon emission computerized tomography imaging of a large number of different tracers. With this caveat, however, we expect to be able to acquire and analyze an unprecedented amount of molecular imaging data for an individual COVID patient.

Significance: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics. Aim: We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration. Approach: Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves. Results: Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs. Conclusions: From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.

Significance: The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures. Aim: To determine whether a new light-scattering spectroscopy (LSS) system that analyzes spectra via neural networks is capable of predicting the nuclear densities (NDs) of ventricular tissues. Approach: We developed an LSS system with a fiber-optics probe and applied it for measurements on cardiac tissues from an ovine model. We quantified the ND in the cardiac tissues using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks (CNNs) to assess the feasibility of characterizing the ND of tissue via LSS. Results: Spectral clustering revealed distinct groups of spectra correlated to ranges of ND. CNNs classified three groups of spectra with low, medium, or high ND with an accuracy of 95.00±11.77% (mean and standard deviation). Our analyses revealed the sensitivity of the classification accuracy to wavelength range and subsampling of spectra. Conclusions: LSS and machine learning are capable of assessing ND in cardiac tissues. We suggest that the approach is useful for the diagnosis of cardiac diseases associated with changes of ND, such as hypertrophy and fibrosis.

Significance: Researchers have made great progress in single-image super-resolution (SISR) using deep convolutional neural networks. However, in the field of leukocyte imaging, the performance of existing SISR methods is still limited as it fails to thoroughly explore the geometry and structural consistency of leukocytes. The inaccurate super-resolution (SR) results will hinder the pathological study of leukocytes, since the structure and cell lineage determine the types of leukocyte and will significantly affect the subsequent inspection. Aim: We propose a deep network that takes full use of the geometry prior and structural consistency of the leukocyte images. We establish and annotate a leukocyte dataset, which contains five main types of leukocytes (basophil, eosinophil, monocyte, lymphocyte, and neutrophil), for learning the structure and geometry information. Approach: Our model is composed of two modules: prior network and SR network. The prior network estimates the parsing map of the low-resolution (LR) image, and then the SR network takes both the estimated parsing map and LR image as input to predict the final high-resolution image. Result: Experiments show that the geometry prior and structural consistency in use obviously improves the SR performance of leukocyte images, enhancing the peak-signal-to-noise ratio (PSNR) by about 0.4 dB in our benchmark. Conclusion: As proved by our leukocyte SR benchmark, the proposed method significantly outperforms state-of-the-art SR methods. Our method not only improves the PSNR and structural similarity indices, but also accurately preserves the structural details of leukocytes. The proposed method is believed to have potential use in the wide-field cell prescreening by simply using a low-power objective.

A previous paper published in this journal proposed a model for evaluating the location of fingermarks on two-dimensional items (de Ronde, van Aken, de Puit and de Poot (2019)). In this paper, we apply the proposed model to a dataset consisting of letters to test whether the activity of writing a letter can be distinguished from the alternative activity of reading a letter based on the location of the fingermarks on the letters. An experiment was conducted in which participants were asked to read a letter and write a letter as separate activities on A4- and A5-sized papers. The fingermarks on the letters were visualized, and the resulting images were transformed into grid representations. A binary classification model was used to classify the letters into the activities of reading and writing based on the location of the fingermarks in the grid representations. Furthermore, the limitations of the model were studied by testing the influence of the length of the letter, the right- or left-handedness of the donor and the size of the paper with an additional activity of folding the paper. The results show that the model can predict the activities of reading or writing a letter based on the fingermark locations on A4-sized letters of right-handed donors with 98 % accuracy. Additionally, the length of the written letter and the handedness of the donor did not influence the performance of the classification model. Changing the size of the letters and adding an activity of folding the paper after writing on it decreased the model's accuracy. Expanding the training set with part of this new set had a positive influence on the model's accuracy. The results demonstrate that the model proposed by de Ronde, van Aken, de Puit and de Poot (2019) can indeed be applied to other two-dimensional items on which the disputed activities would be expected to lead to different fingermark locations. Moreover, we show that the location of fingermarks on letters provides valuable information about the activity that is carried out.

Significance: Accurate and objective identification of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) is of major clinical importance due to the current lack of low-cost and noninvasive diagnostic tools to differentiate between the two. Developing an approach for such identification can have a great impact in the field of dementia diseases as it would offer physicians a routine objective test to support their diagnoses. The problem is especially acute because these two dementias have some common symptoms and characteristics, which can lead to misdiagnosis of DLB as AD and vice versa, mainly at their early stages. Aim: The aim is to evaluate the potential of mid-infrared (IR) spectroscopy in tandem with machine learning algorithms as a sensitive method to detect minor changes in the biochemical structures that accompany the development of AD and DLB based on a simple peripheral blood test, thus improving the diagnostic accuracy of differentiation between DLB and AD. Approach: IR microspectroscopy was used to examine white blood cells and plasma isolated from 56 individuals: 26 controls, 20 AD patients, and 10 DLB patients. The measured spectra were analyzed via machine learning. Results: Our encouraging results show that it is possible to differentiate between dementia (AD and DLB) and controls with an ∼86% success rate and between DLB and AD patients with a success rate of better than 93%. Conclusions: The success of this method makes it possible to suggest a new, simple, and powerful tool for the mental health professional, with the potential to improve the reliability and objectivity of diagnoses of both AD and DLB.

Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells’ morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial–mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

Noninvasive in vivo imaging of microcirculatory blood flow or perfusion (average speed times concentration of moving red blood cells) is of interest in several clinical applications, including monitoring of burn wounds1,2 and investigation of peripheral arterial disease,3 as well as several others.4 One technique that has gained a lot of focus in the last decades is laser speckle contrast imaging [LSCI; sometimes laser speckle contrast analysis (LASCA)] in which tissue is illuminated with a laser and the resulting speckle pattern is detected with a camera. Light that scatters when interacting with moving particles in the tissue will obtain a Doppler frequency shift depending on the velocity of the particles. The interference of light with different frequencies will give rise to fluctuations in the speckle pattern formed on the imaging sensor. The movement of the speckles on the sensor will cause an image blur that increases with exposure time. The local spatial statistics of this blur can be related to the movement of particles in the tissue, as is done in LSCI.5 LSCI has been increasingly used over laser Doppler imaging mainly due to its simple setup and fast acquisition and data processing. However, conventional LSCI has drawbacks such as a nonlinear response to perfusion, dependency to static scattering contrast, and a high variability in the presence of noise.5,6 Recent work has gone into calibrating and correcting for these issues to make LSCI a more accurate technique.7 Despite this, while it is generally accepted that LSCI is related to microcirculatory perfusion, the relationship is complex and a direct mapping is still not known.4,8 For the older laser Doppler flowmetry (LDF) technique, the relation to microcircular perfusion is better understood, where it is, for example, possible to theoretically show that the perfusion estimate is linearly related to the flow speed.9,10 To address the nonlinearity of LSCI to perfusion, Parthasarathy et al.11 proposed a new setup using multiple exposure times. The technique, called multi-exposure laser speckle contrast imaging (MELSCI, sometimes MESI), obtains information about the speckle motion blur at various exposures, enabling more advanced models to be used when estimating perfusion from the contrast.5,12 Several approaches to capture multi-exposure images have been proposed. The initial approach by Parthasarathy et al.11 was to use a time-modulated laser to achieve multi-exposure images. By capturing all images with a fixed exposure time but only illuminating the tissue during parts of that time, multi-exposures can be generated. However, a problem with this approach is that images with different exposure times are separated in time since they are captured sequentially. Dragojevic et al.13 suggested another approach that addressed this drawback. This was based on a high-speed camera, only capturing images at the shortest exposure time required, and in postprocessing adding successive images together to create longer exposure times. These synthetic exposure times are valid if the interframe delay in the camera is negligible. This method is faster and more accurate than previous methods due to all exposure times essentially being captured simultaneously, but it produces immense amounts of data that must be transferred and processed. At the time, this was, therefore, limited to an offline technique. Hence, MELSCI has so far been held back by technical limitations in both the imaging setup and the computation time of the models. To address this problem of a data and processing bottleneck, we have previously presented a system for synthetic MELSCI,14 based on a high-speed 1000-frames per second (fps) 1-megapixel camera directly interfaced to a field programmable gate array (FPGA) performing the multi-exposure contrast algorithm outlined by Dragojevic13 in real-time. Processed contrast images were sent to a computer, massively reducing the data throughput requirements. This system could produce multi-exposure contrast images at 15.6 fps, using all available frames from the 1000-fps camera, without any loss of data. However, due to insufficient processing power, the system was not able to transfer the processed images to the computer fast enough, and thus could not provide a real-time video-rate output. Furthermore, while the calculation of multi-exposure contrast images was fast enough, at the time there was no multi-exposure perfusion algorithm fast enough to keep up with the continuous stream of contrast images. The model proposed by Parthasarathy et al.11 and later refined by Kazmi et al.15 requires nonlinear fitting to extract perfusion-related parameters from the multi-exposure contrast. This process must be performed individually in each pixel for full field perfusion images, which is not feasible in real-time with satisfactory frame rates.6 We have previously presented a method using machine learning and simulated data from thousands of Monte Carlo tissue models from which we train an artificial neural network (ANN) to translate multi-exposure contrast to laser Doppler perfusion. We showed that this technique not only achieves a model-inference time of

Biomedical photoacoustic tomography, which can provide high resolution 3D soft tissue images based on the optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of Deep Learning, or deep neural networks, to this problem has received a great deal of attention. This paper reviews the literature on learned image reconstruction, summarising the current trends, and explains how these new approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these new techniques can be understood from a Bayesian perspective, providing useful insights. The paper also provides a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications - where data may be sparse, fast imaging critical and priors difficult to construct by hand - that Deep Learning will have the most impact. With this in mind, the paper concludes with some indications of possible future research directions.

Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification. Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes. Approach: The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images. Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes. Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

As photoacoustic imaging (PAI) technology matures, computational modeling will increasingly represent a critical tool for facilitating clinical translation through predictive simulation of real-world performance under a wide range of device and biological conditions. While modeling currently offers a rapid, inexpensive tool for device development and prediction of fundamental image quality metrics (e.g., spatial resolution and contrast ratio), rigorous verification and validation will be required of models used to provide regulatory-grade data that effectively complements and/or replaces in vivo testing. To address methods for establishing model credibility, we developed an integrated computational model of PAI by coupling a previously developed three-dimensional Monte Carlo model of tissue light transport with a two-dimensional (2D) acoustic wave propagation model implemented in the well-known k-Wave toolbox. We then evaluated ability of the model to predict basic image quality metrics by applying standardized verification and validation principles for computational models. The model was verified against published simulation data and validated against phantom experiments using a custom PAI system. Furthermore, we used the model to conduct a parametric study of optical and acoustic design parameters. Results suggest that computationally economical 2D acoustic models can adequately predict spatial resolution, but metrics such as signal-to-noise ratio and penetration depth were difficult to replicate due to challenges in modeling strong clutter observed in experimental images. Parametric studies provided quantitative insight into complex relationships between transducer characteristics and image quality as well as optimal selection of optical beam geometry to ensure adequate image uniformity. Multidomain PAI simulation tools provide high-quality tools to aid device development and prediction of real-world performance, but further work is needed to improve model fidelity, especially in reproducing image noise and clutter.

Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances on different species. Moreover, the majority of tools cannot be re-trained or tailored by users and neither can the features be customized or integrated to meet researchers’ requirements. In this study, features extracted from sequence-intrinsic composition, secondary structure and physicochemical property are comprehensively reviewed and evaluated. An integrated platform named LncFinder is also developed to enhance the performance and promote the research of lncRNA identification. LncFinder includes a novel lncRNA predictor using the heterologous features we designed. Experimental results show that our method outperforms several state-of-the-art tools on multiple species with more robust and satisfactory results. Researchers can additionally employ LncFinder to extract various classic features, build classifier with numerous machine learning algorithms and evaluate classifier performance effectively and efficiently. LncFinder can reveal the properties of lncRNA and mRNA from various perspectives and further inspire lncRNA–protein interaction prediction and lncRNA evolution analysis. It is anticipated that LncFinder can significantly facilitate lncRNA-related research, especially for the poorly explored species. LncFinder is released as R package (https://CRAN.R-project.org/package=LncFinder). A web server (http://bmbl.sdstate.edu/lncfinder/) is also developed to maximize its availability.

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them. In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.

The bipartite network representation of the drug–target interactions (DTIs) in a biosystem enhances understanding of the drugs’ multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared—using standard and innovative validation frameworks—with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory—initially detected in brain-network topological self-organization and afterwards generalized to any complex network—is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug–target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.

The German Network for Bioinformatics Infrastructure (de.NBI) is a national initiative funded by the German Federal Ministry of Education and Research (BMBF). The mission of de.NBI is (i) to provide high-quality bioinformatics services to users in basic and applied life sciences research from academia, industry and biomedicine; (ii) to offer bioinformatics training to users in Germany and Europe through a wide range of workshops and courses; and (iii) to foster the cooperation of the German bioinformatics community with international network structures such as the European life-sciences Infrastructure for biological Information (ELIXIR). The network was launched by the BMBF in March 2015 and now includes 40 service projects operated by 30 project partners that are organized in eight service centers. The de.NBI staff develops further and maintains almost 100 bioinformatics services for the human, plant and microbial research fields and provides comprehensive training courses to support users with different expertise levels in bioinformatics. In the future, de.NBI will expand its activities to the European level, as the de.NBI consortium was assigned by the BMBF to establish and run the German node of ELIXIR. The Author 2017. Published by Oxford University Press.

Photoacoustic microscopy (PAM) is a fast-growing biomedical imaging technique that provides high-resolution in vivo imaging beyond the optical diffusion limit. Depending on the scalable lateral resolution and achievable penetration depth, PAM can be classified into optical resolution PAM (OR-PAM) and acoustic resolution PAM (AR-PAM). The use of a microelectromechanical systems (MEMS) scanner has improved OR-PAM imaging speed significantly and is highly beneficial in the development of miniaturized handheld devices. The shallow penetration depth of OR-PAM limits the use of such devices for a wide range of clinical applications. We report the use of a high-speed MEMS scanner for both OR-PAM and AR-PAM. A high-speed, wide-area scanning integrated OR-AR-PAM system combining MEMS scanner and raster mechanical movement was developed. A lateral resolution of 5 μm and penetration depth ∼0.9-mm in vivo was achieved using OR-PAM at 586 nm, whereas a lateral resolution of 84 μm and penetration depth of ∼2-mm in vivo was achieved using AR-PAM at 532 nm.

Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization.

Digital screening and diagnosis from cytology slides can be aided by capturing multiple focal planes. However, using conventional methods, the large file sizes of high-resolution whole-slide images increase linearly with the number of focal planes acquired, leading to significant data storage and bandwidth requirements for the efficient storage and transfer of cytology virtual slides. We investigated whether a sequence of focal planes contained sufficient redundancy to efficiently compress virtual slides across focal planes by applying a commonly available video compression standard, high-efficiency video coding (HEVC). By developing an adaptive algorithm that applied compression to achieve a target image quality, we found that the compression ratio of HEVC exceeded that obtained using JPEG and JPEG2000 compression while maintaining a comparable level of image quality. These results suggest an alternative method for the efficient storage and transfer of whole-slide images that contain multiple focal planes, expanding the utility of this rapidly evolving imaging technology into cytology.

Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn management, increasing the risk of scarring and infection. To this end, numerous imaging techniques have been used to examine tissue properties to infer burn severity. Spatial frequency-domain imaging (SFDI) has also been used to characterize burns based on the relationships between histologic observations and changes in tissue properties. Recently, machine learning has been used to classify burns by combining optical features from multispectral or hyperspectral imaging. Rather than employ models of light propagation to deduce tissue optical properties, we investigated the feasibility of using SFDI reflectance data at multiple spatial frequencies, with a support vector machine (SVM) classifier, to predict severity in a porcine model of graded burns. Calibrated reflectance images were collected using SFDI at eight wavelengths (471 to 851 nm) and five spatial frequencies (0 to 0.2 mm−1). Three models were built from subsets of this initial dataset. The first subset included data taken at all wavelengths with the planar (0 mm−1) spatial frequency, the second comprised data at all wavelengths and spatial frequencies, and the third used all collected data at values relative to unburned tissue. These data subsets were used to train and test cubic SVM models, and compared against burn status 28 days after injury. Model accuracy was established through leave-one-out cross-validation testing. The model based on images obtained at all wavelengths and spatial frequencies predicted burn severity at 24 h with 92.5% accuracy. The model composed of all values relative to unburned skin was 94.4% accurate. By comparison, the model that employed only planar illumination was 88.8% accurate. This investigation suggests that the combination of SFDI with machine learning has potential for accurately predicting burn severity.

We propose a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on computed tomography (CT). We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. We generate a spline from the centerline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analyzing different radiation doses, voxel sizes, and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effect of airway bifurcations. Using 74 airways from 10 CT scans, we show a statistical difference, p = 3.4 × 10 − 4, in tapering between healthy airways ( n = 35) and those affected by bronchiectasis ( n = 39). The difference between the mean of the two populations is 0.011 mm − 1, and the difference between the medians of the two populations was 0.006 mm − 1. The tapering measurement retained a 95% confidence interval of ±0.005 mm − 1 in a simulated 25 mAs scan and retained a 95% confidence of ±0.005 mm − 1 on simulated CTs up to 1.5 times the original voxel size. We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of the simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose, and reconstruction algorithm that are to be used in any quantitative studies.

Significance: An accurate, automated, and unbiased cell counting procedure is needed for tissue selection for corneal transplantation. Aim: To improve accuracy and reduce bias in endothelial cell density (ECD) quantification by combining Gabor-domain optical coherence microscopy (GDOCM) for three-dimensional, wide field-of-view (1 mm2) corneal imaging and machine learning for automatic delineation of endothelial cell boundaries. Approach: Human corneas stored in viewing chambers were imaged over a wide field-of-view with GDOCM without contacting the specimens. Numerical methods were applied to compensate for the natural curvature of the cornea and produce an image of the flattened endothelium. A convolutional neural network (CNN) was trained to automatically delineate the cell boundaries using 180 manually annotated images from six corneas. Ten additional corneas were imaged with GDOCM and compared with specular microscopy (SM) to determine performance of the combined GDOCM and CNN to achieve automated endothelial counts relative to current procedural standards. Results: Cells could be imaged over a larger area with GDOCM than SM, and more cells could be delineated via automatic cell segmentation than via manual methods. ECD obtained from automatic cell segmentation of GDOCM images yielded a correlation of 0.94 (p

Significance: Functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brains. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy. Aim: We aim to highlight the importance of accurate anatomical modeling in the context of fNIRS and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis. Approach: We have developed a surface-based brain meshing pipeline that can produce significantly better brain mesh models, compared to conventional meshing techniques. It can convert segmented volumetric brain scans into multilayered surfaces and tetrahedral mesh models, with typical processing times of only a few minutes and broad utilities, such as in Monte Carlo or finite-element-based photon simulations for fNIRS studies. Results: A variety of high-quality brain mesh models have been successfully generated by processing publicly available brain atlases. In addition, we compare three brain anatomical models—the voxel-based brain segmentation, tetrahedral brain mesh, and layered-slab brain model—and demonstrate noticeable discrepancies in brain partial pathlengths when using approximated brain anatomies, ranging between −1.5% to 23% with the voxelated brain and 36% to 166% with the layered-slab brain. Conclusion: The generation and utility of high-quality brain meshes can lead to more accurate brain quantification in fNIRS studies. Our open-source meshing toolboxes “Brain2Mesh” and “Iso2Mesh” are freely available at http://mcx.space/brain2mesh.

The scientific, clinical, and pedagogical significance of devising methodologies to train nonprofessional subjects to recognize diagnostic visual patterns in medical images has been broadly recognized. However, systematic approaches to doing so remain poorly established. Using mammography as an exemplar case, we use a series of experiments to demonstrate that deep learning (DL) techniques can, in principle, be used to train naïve subjects to reliably detect certain diagnostic visual patterns of cancer in medical images. In the main experiment, subjects were required to learn to detect statistical visual patterns diagnostic of cancer in mammograms using only the mammograms and feedback provided following the subjects’ response. We found not only that the subjects learned to perform the task at statistically significant levels, but also that their eye movements related to image scrutiny changed in a learning-dependent fashion. Two additional, smaller exploratory experiments suggested that allowing subjects to re-examine the mammogram in light of various items of diagnostic information may help further improve DL of the diagnostic patterns. Finally, a fourth small, exploratory experiment suggested that the image information learned was similar across subjects. Together, these results prove the principle that DL methodologies can be used to train nonprofessional subjects to reliably perform those aspects of medical image perception tasks that depend on visual pattern recognition expertise.

Ovarian cancer is the fifth leading cause of female cancer mortality in the U.S. and accounts for five percent of all cancer deaths among women. No environmental risk factors for ovarian cancer have been confirmed. We previously reported that ovarian cancer incidence rates at the state level were significantly correlated with the extent of pulp and paper manufacturing. We evaluated that association using county-level data and advanced geospatial methods. Specifically, we investigated the relationship of spatial patterns of ovarian cancer incidence rates with toxic emissions from pulp and paper facilities using data from the Environmental Protection Agency&rsquo, s Toxic Release Inventory (TRI). Geospatial analysis identified clusters of counties with high ovarian cancer incidence rates in south-central Iowa, Wisconsin, New York, Pennsylvania, Alabama, and Georgia. A bivariate local indicator of spatial autocorrelation (LISA) analysis confirmed that counties with high ovarian cancer rates were associated with counties with large numbers of pulp and paper mills. Regression analysis of state level data indicated a positive correlation between ovarian cancer and water pollutant emissions. A similar relationship was identified from the analysis of county-level data. These data support a possible role of water-borne pollutants from pulp and paper mills in the etiology of ovarian cancer.

The age of a relatively old document is one of the pending issues to be resolved in the field of forensic documentary examination. Although nowadays there are a variety of analytical methodologies focused in the analysis of inks for dating documents, the paper analysis has attained little attention. This work aims to develop two complementary approaches for estimating the age of documents based on paper analysis employing the pyrolysis technique coupled to gas chromatography with detection by mass spectrometry (Py-GC/MS): (i) a direct approach using the pyrolytic fingerprints and multivariate regression with artificially aged samples, and (ii) an indirect approach based on the identification of compounds characteristic of the document period. The direct approach has successfully allowed the age estimation of relatively old documents under police custody (up to 30 years of age) and the determination of a relation between the natural and the accelerated aging of paper under the used conditions. This approach is applicable to papers that have the same (or similar) composition and have been stored under comparable storage conditions. Additionally, the indirect approach is presented as an interesting perspective to ratify valuable information of the document age.

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

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.

Background The long-term availability of online Web services is of utmost importance to ensure reproducibility of analytical results. However, because of lack of maintenance following acceptance, many servers become unavailable after a short period of time. Our aim was to monitor the accessibility and the decay rate of published Web services as well as to determine the factors underlying trends changes. Methods We searched PubMed to identify publications containing Web server-related terms published between 1994 and 2017. Automatic and manual screening was used to check the status of each Web service. Kruskall–Wallis, Mann–Whitney and Chi-square tests were used to evaluate various parameters, including availability, accessibility, platform, origin of authors, citation, journal impact factor and publication year. Results We identified 3649 publications in 375 journals of which 2522 (69%) were currently active. Over 95% of sites were running in the first 2 years, but this rate dropped to 84% in the third year and gradually sank afterwards (P

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.