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Compression optical coherence elastography forms images based on the mechanical properties of tissue by mapping the local strain in response to a compressive load. Strain is described by a second-order tensor, comprising six independent components. The majority of compression elastography methods, however, measure and form their analyses from a single axial component, which relies on the assumption that the stress in tissue is uniform and uniaxial. However, in general, tissues are complex and heterogeneous, and rarely comply with this assumption. This can lead to inaccuracies and misinterpretation of image contrast in compression optical coherence elastography. Here, we image the full strain tensor and demonstrate its utility in unambiguously characterizing deformation in structured phantoms and ex vivo tissues. We derive additional parameters from the strain tensor, and map the local compressibility, anisotropy of deformation, and total equivalent strain. Such analysis is enabled by an efficient non-iterative approach to measuring the three-dimensional displacement field via a closed-form solution to a collection of the amplitude of complex correlation coefficients across multiple digitally shifted images. Strain tensor imaging is likely to lead to more accurate estimation of tissue mechanical properties, improving the utility of compression optical coherence elastography in clinical and biological applications.

The importance of cellular-scale mechanical properties is well-established, yet it is challenging to map subcellular elasticity in three dimensions. We present subcellular mechano-microscopy, an optical coherence microscopy (OCM)-based variant of three-dimensional (3-D) compression optical coherence elastography (OCE) that provides an elasticity system resolution of 5 × 5 × 5 µm: a 7-fold improvement in system resolution over previous OCE studies of cells. The improved resolution is achieved through a ∼5-fold improvement in optical resolution, refinement of the strain estimation algorithm, and demonstration that mechanical deformation of subcellular features provides feature resolution far greater than that demonstrated previously on larger features with diameter >250 µm. We use mechano-microscopy to image adipose-derived stem cells encapsulated in gelatin methacryloyl. We compare our results with compression OCE and demonstrate that mechano-microscopy can provide contrast from subcellular features not visible using OCE.

We demonstrate a convolutional neural network (CNN) for multi-class breast tissue classification as adipose tissue, benign dense tissue, or malignant tissue, using multi-channel optical coherence tomography (OCT) and attenuation images, and a novel Matthews correlation coefficient (MCC)-based loss function that correlates more strongly with performance metrics than the commonly used cross-entropy loss. We hypothesized that using multi-channel images would increase tumor detection performance compared to using OCT alone. 5,804 images from 29 patients were used to fine-tune a pre-trained ResNet-18 network. Adding attenuation images to OCT images yields statistically significant improvements in several performance metrics, including benign dense tissue sensitivity (68.0% versus 59.6%), malignant tissue positive predictive value (PPV) (79.4% versus 75.5%), and total accuracy (85.4% versus 83.3%), indicating that the additional contrast from attenuation imaging is most beneficial for distinguishing between benign dense tissue and malignant tissue.

Quantitative micro-elastography (QME), a variant of compression optical coherence elastography (OCE), is a technique to image tissue elasticity on the microscale. QME has been proposed for a range of applications, most notably tumor margin assessment in breast-conserving surgery. However, QME sensitivity, a key imaging metric, has yet to be systematically analyzed. Consequently, it is difficult to optimize imaging performance and to assess the potential of QME in new application areas. To address this, we present a framework for analyzing sensitivity that incorporates the three main steps in QME image formation: mechanical deformation, its detection using optical coherence tomography (OCT), and signal processing used to estimate elasticity. Firstly, we present an analytical model of QME sensitivity, validated by experimental data, and demonstrate that sub-kPa elasticity sensitivity can be achieved in QME. Using silicone phantoms, we demonstrate that sensitivity is dependent on friction, OCT focus depth, and averaging methods in signal processing. For the first time, we show that whilst lubrication of layer improves accuracy by reducing surface friction, it reduces sensitivity due to the time-dependent effect of lubricant exudation from the layer boundaries resulting in increased friction. Furthermore, we demonstrate how signal processing in QME provides a trade-off between sensitivity and resolution that can be used to optimize imaging performance. We believe that our framework to analyze sensitivity can help to sustain the development of QME and, also, that it can be readily adapted to other OCE techniques.

Due to the explosion of new sources of human language data and the rapid progression of computational methods for extracting meaning from natural language, language analysis is a promising, though complicated, category of psychological research. In this chapter, we give a modern perspective on language analysis as it applies to psychology, uniting historical context, the diverse range of domains studied in psychology via language, and the methodological rigor of natural language processing (NLP) and machine learning. Top–down methods (e.g., dictionary approaches, text annotation) are presented alongside bottom–up methods (e.g., topic modeling, word embedding, language modeling) in order to give the reader a comprehensive grounding in the tools available and the recommended practices involved in applying them. We conclude with a view of the future of language analysis, specifically the ways in which psychology and NLP will continue to co-develop.

Online radicalization is among the most vexing challenges the world faces today. Here, we demonstrate that homogeneity in moral concerns results in increased levels of radical intentions. In Study 1, we find that in Gab—a right-wing extremist network—the degree of moral convergence within a cluster predicts the number of hate-speech messages members post. In Study 2, we replicate this observation in another extremist network, Incels. In Studies 3 to 5 (N = 1,431), we demonstrate that experimentally leading people to believe that others in their hypothetical or real group share their moral views increases their radical intentions as well as willingness to fight and die for the group. Our findings highlight the role of moral convergence in radicalization, emphasizing the need for diversity of moral worldviews within social networks.

Bias mitigation approaches reduce models' dependence on sensitive features of data, such as social group tokens (SGTs), resulting in equal predictions across the sensitive features. In hate speech detection, however, equalizing model predictions may ignore important differences among targeted social groups, as hate speech can contain stereotypical language specific to each SGT. Here, to take the specific language about each SGT into account, we rely on counterfactual fairness and equalize predictions among counterfactuals, generated by changing the SGTs. Our method evaluates the similarity in sentence likelihoods (via pre-trained language models) among counterfactuals, to treat SGTs equally only within interchangeable contexts. By applying logit pairing to equalize outcomes on the restricted set of counterfactuals for each instance, we improve fairness metrics while preserving model performance on hate speech detection., 4-page paper Workshop on Online Abuse and Harms (ACL 2021)

Intraoperative margin assessment is needed to reduce the re-excision rate of breast-conserving surgery. One possibility is optical palpation, a tactile imaging technique that maps stress (force applied across the tissue surface) as an indicator of tissue stiffness. Images (optical palpograms) are generated by compressing a transparent silicone layer on the tissue and measuring the layer deformation using optical coherence tomography (OCT). This paper reports, for the first time, the diagnostic accuracy of optical palpation in identifying tumor within 1 mm of the excised specimen boundary using an automated classifier. Optical palpograms from 154 regions of interest (ROIs) from 71 excised tumor specimens were obtained. An automated classifier was constructed to predict the ROI margin status by first choosing a circle diameter, then searching for a location within the ROI where the circle was ≥ 75% filled with high stress (indicating a positive margin). A range of circle diameters and stress thresholds, as well as the impact of filtering out non-dense tissue regions, were tested. Sensitivity and specificity were calculated by comparing the automated classifier results with the true margin status, determined from co-registered histology. 83.3% sensitivity and 86.2% specificity were achieved, compared to 69.0% sensitivity and 79.0% specificity obtained with OCT alone on the same dataset using human readers. Representative optical palpograms show that positive margins containing a range of cancer types tend to exhibit higher stress compared to negative margins. These results demonstrate the potential of optical palpation for margin assessment.

Two-photon lithography (2 PL) has been proposed as an additive manufacturing method to fabricate structures at the microscale, including curing protein-based bioinks. In this study, we explored the use of 2 PL for biomedical applications to photo-cross-link silk fibroin solution into a solid hydrogel with directly tuneable physico-chemical properties. 2 PL allowed control of the cross-linking degree and the porosity of the resulting hydrogel at the micrometric scale by regulating the intensity of the IR laser exposure, tested between 800 and 2400 mW, during the 3D printing process. The regulation of the cross-linking degree in this way allowed for the production of hydrogels with significantly different degradation rates, ranging from 0.5 to 2 h during accelerated enzymatic degradation assay, while their β-sheet content, assessed by Fourier-transform infra-red spectroscopy (FTIR), did not show significant changes. The hydrogels Young’s modulus, assessed by quantitative microelastography, could also be controlled within a 9.6 to 47.2 kPa range and was found to increase with the rate of cross-linking. When human dermal fibroblasts were included in the silk fibroin solution for bioprinting testing at different laser exposures, cell viability was assessed over 95% during 21 days of in vitro culture. Moreover, cells showed a higher proliferation rate in less cross-linked hydrogels and were able to align and migrate to areas of increased stiffness printed in gradient hydrogels. Overall, this versatile additive manufacturing method represents an advancement of silk fibroin biomaterials for soft tissue engineering, enabling control of the microstructure and degradation rate of the products and the proliferation of the engrafted cells.

Quantitative mapping of deformation and elasticity in optical coherence tomography has attracted much attention of researchers during the last two decades. However, despite intense effort it took ~15 years to demonstrate optical coherence elastography (OCE) as a practically useful technique. Similarly to medical ultrasound, where elastography was first realized using the quasi-static compression principle and later shear-wave-based systems were developed, in OCE these two approaches also developed in parallel. However, although the compression OCE (C-OCE) was proposed historically earlier in the seminal paper by J. Schmitt in 1998, breakthroughs in quantitative mapping of genuine local strains and the Young's modulus in C-OCE have been reported only recently and have not yet obtained sufficient attention in reviews. In this overview, we focus on underlying principles of C-OCE; discuss various practical challenges in its realization and present examples of biomedical applications of C-OCE. The figure demonstrates OCE-visualization of complex transient strains in a corneal sample heated by an infrared laser beam.

Effective intraoperative tumor margin assessment is needed to reduce re-excision rates in breast-conserving surgery (BCS). Mapping the attenuation coefficient in optical coherence tomography (OCT) throughout a sample to create an image (attenuation imaging) is one promising approach. For the first time, three-dimensional OCT attenuation imaging of human breast tissue microarchitecture using a wide-field (up to ~45 × 45 × 3.5 mm) imaging system is demonstrated. Representative results from three mastectomy and one BCS specimen (from 31 specimens) are presented with co-registered postoperative histology. Attenuation imaging is shown to provide substantially improved contrast over OCT, delineating nuanced features within tumors (including necrosis and variations in tumor cell density and growth patterns) and benign features (such as sclerosing adenosis). Additionally, quantitative micro-elastography (QME) images presented alongside OCT and attenuation images show that these techniques provide complementary contrast, suggesting that multimodal imaging could increase tissue identification accuracy and potentially improve tumor margin assessment.

This presentation reports a comparison between two handheld quantitative micro elastography (QME) methods: PZT actuated compression QME and manual compression QME. PZT actuated compression QME utilizes a PZT actuator to provide a periodic compression against the scanned sample, whilst manual compression QME utilizes the continuous motion of the user’s hand holding the probe to create compression against the sample. From our results, each method has its own advantages, and both methods are capable of measuring elasticity of the sample and distinguishing stiff tumor from regions of soft benign tissue on excised human breast specimens.

13 pags., 7 figs., Compression optical coherence elastography (OCE) typically requires a mechanical actuator to impart a controlled uniform strain to the sample. However, for handheld scanning, this adds complexity to the design of the probe and the actuator stroke limits the amount of strain that can be applied. In this work, we present a new volumetric imaging approach that utilizes bidirectional manual compression via the natural motion of the user's hand to induce strain to the sample, realizing compact, actuator-free, handheld compression OCE. In this way, we are able to demonstrate rapid acquisition of three-dimensional quantitative microelastography (QME) datasets of a tissue volume (6 × 6 × 1 mm) in 3.4 seconds. We characterize the elasticity sensitivity of this freehand manual compression approach using a homogeneous silicone phantom and demonstrate comparable performance to a benchtop mounted, actuator-based approach. In addition, we demonstrate handheld volumetric manual compression-based QME on a tissue-mimicking phantom with an embedded stiff inclusion and on freshly excised human breast specimens from both mastectomy and wide local excision (WLE) surgeries. Tissue results are coregistered with postoperative histology, verifying the capability of our approach to measure the elasticity of tissue and to distinguish stiff tumor from surrounding soft benign tissue., This research was supported by grants and fellow shipsfrom the Australian Research Council, the National Health and Medical Research Council (Australia), the National Breast Cancer Foundation (Australia), the Department of Health, Western Australia, the Cancer Council, Western Australia and through a research con-tract with OncoRes Medical, Australia

Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous works focus on detecting these biases, reducing bias in data representations, and using auxiliary training objectives to mitigate bias during fine-tuning. Although these techniques achieve bias reduction for the task and domain at hand, the effects of bias mitigation may not directly transfer to new tasks, requiring additional data collection and customized annotation of sensitive attributes, and re-evaluation of appropriate fairness metrics. We explore the feasibility and benefits of upstream bias mitigation (UBM) for reducing bias on downstream tasks, by first applying bias mitigation to an upstream model through fine-tuning and subsequently using it for downstream fine-tuning. We find, in extensive experiments across hate speech detection, toxicity detection, occupation prediction, and coreference resolution tasks over various bias factors, that the effects of UBM are indeed transferable to new downstream tasks or domains via fine-tuning, creating less biased downstream models than directly fine-tuning on the downstream task or transferring from a vanilla upstream model. Though challenges remain, we show that UBM promises more efficient and accessible bias mitigation in LM fine-tuning., Comment: 14 pages; Accepted at NAACL 2021

This chapter describes the main technology and methods of optical elastography—a suite of techniques that use optical methods to form images on the microscale of the mechanical properties of tissues. Optical elastography has recently shown compelling potential for the microscale characterization of soft tissues and cellular mechanics. Current developments are described in the context of their application, placed in perspective by touching briefly upon the history and likely future directions of the field. This chapter is mainly focused on two optical elastography techniques that have become prominent over the past 5 years: optical coherence elastography and Brillouin microscopy; however, other techniques, such as laser speckle imaging, photoacoustic elastography, and ultrasound-modulated optical tomography are briefly discussed in the context of tissue elasticity imaging.

Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways. Such biases manifest in false positives when these identifiers are present, due to models' inability to learn the contexts which constitute a hateful usage of identifiers. We extract SOC post-hoc explanations from fine-tuned BERT classifiers to efficiently detect bias towards identity terms. Then, we propose a novel regularization technique based on these explanations that encourages models to learn from the context of group identifiers in addition to the identifiers themselves. Our approach improved over baselines in limiting false positives on out-of-domain data while maintaining or improving in-domain performance. Project page: https://inklab.usc.edu/contextualize-hate-speech/., Comment: To appear in Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics; Updated references and discussions

Recent studies have found discordant mechanosensitive outcomes when comparing 2D and 3D, highlighting the need for tools to study mechanotransduction in 3D across a wide spectrum of stiffness. A gelatin methacryloyl (GelMA) hydrogel with a continuous stiffness gradient ranging from 5 to 38 kPa was developed to recapitulate physiological stiffness conditions. Adipose-derived stem cells (ASCs) were encapsulated in this hydrogel, and their morphological characteristics and expression of both mechanosensitive proteins (Lamin A, YAP, and MRTFa) and differentiation markers (PPARγ and RUNX2) were analyzed. Low-stiffness regions (∼8 kPa) permitted increased cellular and nuclear volume and enhanced mechanosensitive protein localization in the nucleus. This trend was reversed in high stiffness regions (∼30 kPa), where decreased cellular and nuclear volumes and reduced mechanosensitive protein nuclear localization were observed. Interestingly, cells in soft regions exhibited enhanced osteogenic RUNX2 expression, while those in stiff regions upregulated the adipogenic regulator PPARγ, suggesting that volume, not substrate stiffness, is sufficient to drive 3D stem cell differentiation. Inhibition of myosin II (Blebbistatin) and ROCK (Y-27632), both key drivers of actomyosin contractility, resulted in reduced cell volume, especially in low-stiffness regions, causing a decorrelation between volume expansion and mechanosensitive protein localization. Constitutively active and inactive forms of the canonical downstream mechanotransduction effector TAZ were stably transfected into ASCs. Activated TAZ resulted in higher cellular volume despite increasing stiffness and a consistent, stiffness-independent translocation of YAP and MRTFa into the nucleus. Thus, volume adaptation as a function of 3D matrix stiffness can control stem cell mechanotransduction and differentiation.

High extracellular matrix (ECM) content in solid cancers impairs tumour perfusion and thus access of imaging and therapeutic agents. We have devised a new approach to degrade tumour ECM, which improves uptake of circulating compounds. We target the immune‐modulating cytokine, tumour necrosis factor alpha (TNFα), to tumours using a newly discovered peptide ligand referred to as CSG. This peptide binds to laminin–nidogen complexes in the ECM of mouse and human carcinomas with little or no peptide detected in normal tissues, and it selectively delivers a recombinant TNFα‐CSG fusion protein to tumour ECM in tumour‐bearing mice. Intravenously injected TNFα‐CSG triggered robust immune cell infiltration in mouse tumours, particularly in the ECM‐rich zones. The immune cell influx was accompanied by extensive ECM degradation, reduction in tumour stiffness, dilation of tumour blood vessels, improved perfusion and greater intratumoral uptake of the contrast agents gadoteridol and iron oxide nanoparticles. Suppressed tumour growth and prolonged survival of tumour‐bearing mice were observed. These effects were attainable without the usually severe toxic side effects of TNFα., This study establishes a new approach to treat solid tumours by immune modulation and ECM depletion. The developed agent, TNFα‐CSG, has dual capacity as an immunotherapeutic and ECM reducing agent which improves tumour perfusion and drug delivery.

Objective To evaluate the recent developments in optical coherence tomography (OCT) for tympanic membrane (TM) and middle ear (ME) imaging and to identify what further development is required for the technology to be integrated into common clinical use. Data Sources PubMed, Embase, Google Scholar, Scopus, and Web of Science. Review Methods A comprehensive literature search was performed for English language articles published from January 1966 to January 2018 with the keywords "tympanic membrane or middle ear,""optical coherence tomography," and "imaging." Conclusion Conventional imaging techniques cannot adequately resolve the microscale features of TM and ME, sometimes necessitating diagnostic exploratory surgery in challenging otologic pathology. As a high-resolution noninvasive imaging technique, OCT offers promise as a diagnostic aid for otologic conditions, such as otitis media, cholesteatoma, and conductive hearing loss. Using OCT vibrometry to image the nanoscale vibrations of the TM and ME as they conduct acoustic waves may detect the location of ossicular chain dysfunction and differentiate between stapes fixation and incus-stapes discontinuity. The capacity of OCT to image depth and thickness at high resolution allows 3-dimensional volumetric reconstruction of the ME and has potential use for reconstructive tympanoplasty planning and the follow-up of ossicular prostheses. Implications for Practice To achieve common clinical use beyond these initial discoveries, future in vivo imaging devices must feature low-cost probe or endoscopic designs and faster imaging speeds and demonstrate superior diagnostic utility to computed tomography and magnetic resonance imaging. While such technology has been available for OCT, its translation requires focused development through a close collaboration between engineers and clinicians.

Cellular-scale imaging of the mechanical properties of tissue has helped to reveal the origins of disease; however, cellular-scale resolution is not readily achievable in intact tissue volumes. Here, we demonstrate volumetric imaging of Young’s modulus using ultrahigh-resolution optical coherence elastography, and apply it to characterizing the stiffness of mouse aortas. We achieve isotropic resolution of better than 15 μm over a 1-mm lateral field of view through the entire depth of an intact aortic wall. We employ a method of quasi-static compression elastography that measures volumetric axial strain and uses a compliant, transparent layer to measure surface axial stress. This combination is used to estimate Young’s modulus throughout the volume. We demonstrate differentiation by stiffness of individual elastic lamellae and vascular smooth muscle. We observe stiffening of the aorta in regulator of G protein signaling 5-deficient mice, a model that is linked to vascular remodeling and fibrosis. We observe increased stiffness with proximity to the heart, as well as regions with micro-structural and micro-mechanical signatures characteristic of fibrous and lipid-rich tissue. High-resolution imaging of Young’s modulus with optical coherence elastography may become an important tool in vascular biology and in other fields concerned with understanding the role of mechanics within the complex three-dimensional architecture of tissue.

Language is a psychologically rich medium for human expression and communication. While it is often used in moral psychology as an intermediary between researcher and participant, much of the human experience that occurs through language — our relationships, conversations, and, in general, the everyday transmission of our thoughts — has yet to be studied in association with moral concerns. In order to understand how moral concerns relate to observed language usage, we paired Facebook status updates (N = 107,798) from English-speaking participants (n = 2,691) with their responses on the Moral Foundations Questionnaire, which measures Care, Fairness, Loyalty, Authority, and Purity concerns. Overall, we found consistent evidence that participants’ self-reported moral concerns can be predicted from their language, though the magnitude of this effect varied considerably among concerns. Across a diverse selection of Natural Language Processing methods, cross-validated R2 values ranged from 0.04 for predicting Fairness concerns to 0.21for predicting Purity concerns. In follow-up analyses, each moral concern was found to be related to distinct patterns of relational, emotional, and social language. Our results are the first finding relating internally valid measures of moral concerns to observations of language, motivating several new avenues for exploring and investigating how the moral domain intersects with language usage.

Smartphones are now integral to many telehealth services that provide remote patients with an improved diagnostic standard of care. The ongoing management of burn wounds and scars is one area in which telehealth has been adopted, using video and photography to assess the repair process over time. However, a current limitation is the inability to evaluate scar stiffness objectively and repeatedly: an essential measurement for classifying the degree of inflammation and fibrosis. Optical elastography detects mechanical contrast on a micrometer- to millimeter-scale, however, typically requires expensive optics and bulky imaging systems, making it prohibitive for wide-spread adoption in telehealth. More recently, a new variant of optical elastography, camera-based optical palpation, has demonstrated the capability to perform elastography at low cost using a standard digital camera. In this paper, we propose smartphone-based optical palpation, adapting camera-based optical palpation by utilizing a commercially available smartphone camera to provide sub-millimeter resolution imaging of mechanical contrast in scar tissue in a form factor that is amenable to telehealth. We first validate this technique on a silicone phantom containing a 5 × 5 × 1 mm3 embedded inclusion, demonstrating comparative image quality between mounted and handheld implementations. We then demonstrate preliminary in vivo smartphone-based optical palpation by imaging a region of healthy skin and two scars on a burns patient, showing clear mechanical contrast between regions of scar tissue and healthy tissue. This study represents the first implementation of elastography on a smartphone device, extending the potential application of elastography to telehealth.

Optical elastography, the use of optics to characterize and map the mechanical properties of biological tissue, involves measuring the deformation of tissue in response to a load. Such measurements may be used to form an image of a mechanical property, often elastic modulus, with the resulting mechanical contrast complementary to the more familiar optical contrast. Optical elastography is experiencing new impetus in response to developments in the closely related fields of cell mechanics and medical imaging, aided by advances in photonics technology, and through probing the microscale between that of cells and whole tissues. Two techniques — optical coherence elastography and Brillouin microscopy — have recently shown particular promise for medical applications, such as in ophthalmology and oncology, and as new techniques in cell mechanics.

Shear-wave elastography (SWE) is an ultrasound based technology that can provide reliable measurements (velocity) of scar stiffness. The aim of this research was to evaluate the concurrent validity of using both the measured velocity and the calculated difference in velocity between scars and matched controls, in addition to evaluating potential patient factors that may influence the interpretation of the measurements. Methods A cross-sectional study of 32 participants, with 48 burn scars and 48 matched contralateral control sites were evaluated with SWE, the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS) tactile sub-scores. Results Spearman’s rho demonstrated high correlations (r > 0.7) between the measured scar velocity and both the POSAS and VSS pliability sub-scores, whereas moderate correlations (r > 0.6) were found with the calculated difference in velocity. Regression analysis indicated that the association of increased velocity in scars, varied by length of time after burn injury and gender. Body location and Fitzpatrick skin type also demonstrated significant associations with velocity, whereas age did not. Conclusion SWE shows potential as a novel tool to quantify burn scar stiffness, however patient factors need to be considered when interpreting results. Further research is recommended on a larger variety of scars to support the findings.

Acts of hate have been used to silence, terrorize, and erase marginalized social groups throughout history. The rising rates of these behaviors in recent years underscores the importance of developing a better understanding of when, why, and where they occur. In this work, we present a program of research that suggests that acts of hate may often be best understood not just as responses to threat, but also as morally motivated behaviors grounded in people’s moral values and perceptions of moral violations. As evidence for this claim, we present findings from five studies that rely on a combination of natural language processing, spatial modeling, and experimental methods to investigate the relationship between moral values and acts of hate toward marginalized groups. Across these studies, we find consistent evidence that moral values oriented around ingroup preservation are disproportionately evoked in hate speech, predictive of the county-level prevalence of hate groups, and associated with the belief that acts of hate against marginalized groups are justified. Additional analyses suggest that the association between group-oriented moral values and hate acts against marginalized groups can be partly explained by the belief that these groups have done something morally wrong. By accounting for the role of moralization in acts of hate, this work provides a unified framework for understanding hateful behaviors and the events or dynamics that trigger them.

Inadequate margins in breast-conserving surgery (BCS) are associated with an increased likelihood of local recurrence of breast cancer. Currently, approximately 20% of BCS patients require repeat surgery due to inadequate margins at the initial operation. Implementation of an accurate, intraoperative margin assessment tool may reduce this re-excision rate. This study determined, for the first time, the diagnostic accuracy of quantitative micro-elastography (QME), an optical coherence tomography (OCT)–based elastography technique that produces images of tissue microscale elasticity, for detecting tumor within 1 mm of the margins of BCS specimens. Simultaneous OCT and QME were performed on the margins of intact, freshly excised specimens from 83 patients undergoing BCS and on dissected specimens from 7 patients undergoing mastectomy. The resulting three-dimensional images (45 × 45 × 1 mm) were coregistered with postoperative histology to determine tissue types present in each scan. Data from 12 BCS patients and the 7 mastectomy patients served to build a set of images for reader training. One hundred and fifty-four subimages (10 × 10 × 1 mm) from the remaining 71 BCS patients were included in a blinded reader study, which resulted in 69.0% sensitivity and 79.0% specificity using OCT images, versus 92.9% sensitivity and 96.4% specificity using elasticity images. The quantitative nature of QME also facilitated development of an automated reader, which resulted in 100.0% sensitivity and 97.7% specificity. These results demonstrate high accuracy of QME for detecting tumor within 1 mm of the margin and the potential for this technique to improve outcomes in BCS. Significance: An optical imaging technology probes breast tissue elasticity to provide accurate assessment of tumor margin involvement in breast-conserving surgery.

14 pags., 7 figs., We present a finger-mounted quantitative micro-elastography (QME) probe, capable of measuring the elasticity of biological tissue in a format that avails of the dexterity of the human finger. Finger-mounted QME represents the first demonstration of a wearable elastography probe. The approach realizes optical coherence tomography-based elastography by focusing the optical beam into the sample via a single-mode fiber that is fused to a length of graded-index fiber. The fiber is rigidly affixed to a 3D-printed thimble that is mounted on the finger. Analogous to manual palpation, the probe compresses the tissue through the force exerted by the finger. The resulting deformation is measured using optical coherence tomography. Elasticity is estimated as the ratio of local stress at the sample surface, measured using a compliant layer, to the local strain in the sample. We describe the probe fabrication method and the signal processing developed to achieve accurate elasticity measurements in the presence of motion artifact. We demonstrate the probe’s performance in motion-mode scans performed on homogeneous, bi-layer and inclusion phantoms and its ability to measure a thermally-induced increase in elasticity in ex vivo muscle tissue. In addition, we demonstrate the ability to acquire 2D images with the finger-mounted probe where lateral scanning is achieved by swiping the probe across the sample surface., Australian Research Council (ARC); the Cancer Council, Western Australia; OncoRes Medical; War Widows' Guild of Western Australia Scholarship.

Approximately a quarter of patients undergoing breast conserving surgery will need further surgery as close or involved surgical margins suggest they may have residual tumour in the breast. Handheld imaging probes capable of scanning the surgical cavity during the surgery have the potential to improve intraoperative assessment of surgical margins in breast conserving surgery thus allow real time assessment of completeness of tumour excision. In this paper, we present a handheld optical coherence elastography (OCE) probe, allowing us to acquire a 3D quantitative elastogram of a 6×6×1.5 mm volume in 3.4 seconds. Our technique is based on a compression OCE technique, referred to as quantitative micro-elastography (QME), where a compliant silicone layer is incorporated to measure stress at the tissue surface. To perform handheld scanning, we implemented a rapid scan pattern to enable B-scan rates of 215 Hz using a microelectromechanical system (MEMS) scanner: minimizing the time difference between B-scan pairs used to generate displacement maps thus minimizing the motion artefact caused by hand motion. We present handheld scans acquired from silicone phantoms where the motion artefact is barely noticeable. In addition, freshly dissected human breast tissue from a mastectomy was scanned with the handheld probe. The breast tissue elastograms are validated using standard histology and demonstrate our ability to distinguish stiff regions of tumour from benign tissue using this probe.

Variations in the mechanical properties of the extracellular environment can alter important aspects of cell function such as proliferation, migration, differentiation and survival. However, many of the techniques available to study these effects lack the ability to characterise cell-to-cell and cell-to-environment interactions on the microscopic scale in three dimensions (3D). Quantitative micro-elastography (QME) is an extension of compression optical coherence elastography that utilizes a compliant layer with known mechanical properties to estimate the axial stress at the tissue surface, which combined with axial strain, is used to map the 3D microscale elasticity of tissue into an image. Despite being based on OCT, limitations in post-processing techniques used to determine axial strain prevented QME to quantify the elasticity of individual cells. In this study we extend the capability of QME to present, to the best of our knowledge, the first images of the elasticity of cells and their environment in 3D over millimeter field-of-views. We improve the accuracy and resolution of QME by incorporating an efficient, iterative solution to the inverse elasticity problem using adjoint elasticity equations to enable QME to visualize individual cells for the first time. We present images of human stem cells embedded in soft gelatin methacryloyl (GelMa) hydrogels and demonstrate these cells elevate the stiffness of the GelMa from 3-kPa to approximately 25-kPa. Our QME system is developed using commercially available components that can be readily made available to biologists, highlighting the potential for QME to emerge as an important tool in the field of mechanobiology.

16 pags., 7 figs., 1 tab., Optical coherence elastography (OCE) has been proposed for a range of clinical applications. However, the majority of these studies have been performed using bulky, lab-based imaging systems. A compact, handheld imaging probe would accelerate clinical translation, however, to date, this had been inhibited by the slow scan rates of compact devices and the motion artifact induced by the user’s hand. In this paper, we present a proof-of-concept, handheld quantitative micro-elastography (QME) probe capable of scanning a 6 × 6 × 1 mm volume of tissue in 3.4 seconds. This handheld probe is enabled by a novel QME acquisition protocol that incorporates a custom bidirectional scan pattern driving a microelectromechanical system (MEMS) scanner, synchronized with the sample deformation induced by an annular PZT actuator. The custom scan pattern reduces the total acquisition time and the time difference between B-scans used to generate displacement maps, minimizing the impact of motion artifact. We test the feasibility of the handheld QME probe on a tissue-mimicking silicone phantom, demonstrating comparable image quality to a bench-mounted setup. In addition, we present the first handheld QME scans performed on human breast tissue specimens. For each specimen, quantitative micro-elastograms are co-registered with, and validated by, histology, demonstrating the ability to distinguish stiff cancerous tissue from surrounding soft benign tissue., Australian Research Council (ARC); Department of Health, Western Australia; Cancer Council, Western Australia; OncoRes Medical.

Cognitive tests have traditionally resorted to standardizing testing materials in the name of equality and because of the onerous nature of creating test items. This approach ignores participants’ diverse language experiences that potentially significantly affect testing outcomes. Here, we seek to explain our prior finding of significant performance differences on two cognitive tests (reading span and SPiN) between clusters of participants based on their media consumption. Here, we model the language contained in these media sources using an LSTM trained on corpora of each cluster’s media sources to predict target words. We also model semantic similarity of test items with each cluster’s corpus using skip-thought vectors. We find robust, significant correlations between performance on the SPiN test and the LSTMs and skip-thought models we present here, but not the reading span test.

Meaning depends on context. This applies in obvious cases like deictics or sarcasm as well as more subtle situations like framing or persuasion. One key aspect of this is the identity of the participants in an interaction. Our interpretation of an utterance shifts based on a variety of factors, including personal history, background knowledge, and our relationship to the source. While obviously an incomplete model of individual differences, demographic factors provide a useful starting point and allow us to capture some of this variance. However, the relevance of specific demographic factors varies between situations-where age might be the key factor in one context, ideology might dominate in another. To address this challenge, we introduce a method for combining demographics and context into situated demographic embeddings-mapping representations into a continuous geometric space appropriate for the given domain, showing the resulting representations to be functional and interpretable. We further demonstrate how to make use of related external data so as to apply this approach in low-resource situations. Finally, we show how these representations can be incorporated into improve modeling of real-world natural language understanding tasks, improving model performance and helping with issues of data sparsity.

Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations, there are no official reports from a large number of US cities regarding incidents of hate. Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of local news articles, can be used to predict instances of hate crime. We then use the trained model to detect incidents of hate in cities for which the FBI lacks statistics. Lastly, we train models on predicting homicide and kidnapping, compare the predictions to FBI reports, and establish that incidents of hate are indeed under-reported, compared to other types of crimes, in local press.

Funding: Australian Research Council; the Cancer Council Western Australia; OncoRes Medical; William and Marlene Schrader Trust of the University of Western Australia scholarship. Optical coherence elastography (OCE) is emerging as a method to image the mechanical properties of tissue on the microscale. However, the spatial resolution, a main advantage of OCE, has not been investigated and is not trivial to evaluate. To address this, we present a framework to analyze resolution in phase-sensitive compression OCE that incorporates the three main determinants of resolution: mechanical deformation of the sample, detection of this deformation using optical coherence tomography (OCT), and signal processing to estimate local axial strain. We demonstrate for the first time, through close correspondence between experiment and simulation of structured phantoms, that resolution in compression OCE is both spatially varying and sample dependent, which we link to the discrepancies between the model of elasticity and the mechanical deformation of the sample. We demonstrate that resolution is dependent on factors such as feature size and mechanical contrast. We believe that the analysis of image formation provided by our framework can expedite the development of compression OCE. Publisher PDF

We demonstrate the use of the near-infrared attenuation coefficient, measured using optical coherence tomography (OCT), in longitudinal assessment of hypertrophic burn scars undergoing fractional laser treatment. The measurement method incorporates blood vessel detection by speckle decorrelation and masking, and a robust regression estimator to produce 2D en face parametric images of the attenuation coefficient of the dermis. Through reliable co-location of the field of view across pre- and post-treatment imaging sessions, the study was able to quantify changes in the attenuation coefficient of the dermis over a period of ~20 weeks in seven patients. Minimal variation was observed in the mean attenuation coefficient of normal skin and control (untreated) mature scars, as expected. However, a significant decrease (13 ± 5%, mean ± standard deviation) was observed in the treated mature scars, resulting in a greater distinction from normal skin in response to localized damage from the laser treatment. By contrast, we observed an increase in the mean attenuation coefficient of treated (31 ± 27%) and control (27 ± 20%) immature scars, with numerical values incrementally approaching normal skin as the healing progressed. This pilot study supports conducting a more extensive investigation of OCT attenuation imaging for quantitative longitudinal monitoring of scars. (Figure presented.) En face 2D OCT attenuation coefficient map of a treated immature scar derived from the pre-treatment (top) and the post-treatment (bottom) scans. (Vasculature (black) is masked out.) The scale bars are 0.5 mm.

Quantitative elasticity imaging seeks to retrieve spatial maps of elastic moduli of tissue. Unlike strain, which is commonly imaged in compression elastography, elastic moduli are intrinsic properties of tissue, and therefore, this approach reconstructs images that are largely operator and system independent, enabling objective, longitudinal, and multisite diagnoses. Recently, novel quantitative elasticity imaging approaches to compression elastography have been developed. These methods use a calibration layer with known mechanical properties to sense the stress at the tissue surface, which combined with strain, is used to estimate the tissue's elastic moduli by assuming homogeneity in the stress field. However, this assumption is violated in mechanically heterogeneous samples. We present a more general approach to quantitative elasticity imaging that overcomes this limitation through an efficient iterative solution of the inverse elasticity problem using adjoint elasticity equations. We present solutions for linear elastic, isotropic, and incompressible solids; however, this method can be employed for more complex mechanical models. We retrieve the spatial distribution of shear modulus for a tissue-simulating phantom and a tissue sample. This is the first time, to our knowledge, that the iterative solution of the inverse elasticity problem has been implemented on experimentally acquired compression optical coherence elastography data.

Funding: Australian Research Council; Cancer Council Western Australia; Industrial Transformation Training Centre; The William and Marlene Schrader Trust of the University of Western Australia. Recent studies in mechanobiology have revealed the importance of cellular and extracellular mechanical properties in regulating cellular function in normal and disease states. Although it is established that cells should be investigated in a three-dimensional (3-D) environment, most techniques available to study mechanical properties on the microscopic scale are unable to do so. In this study, for the first time, we present volumetric images of cellular and extracellular elasticity in 3-D biomaterials using quantitative micro-elastography (QME). We achieve this by developing a novel strain estimation algorithm based on 3-D linear regression to improve QME system resolution. We show that QME can reveal elevated elasticity surrounding human adipose-derived stem cells (ASCs) embedded in soft hydrogels. We observe, for the first time in 3-D, further elevation of extracellular elasticity around ASCs with overexpressed TAZ; a mechanosensitive transcription factor which regulates cell volume. Our results demonstrate that QME has the potential to study the effects of extracellular mechanical properties on cellular functions in a 3-D micro-environment. Publisher PDF

16 pags., 8 figs., Handheld imaging probes are needed to extend the clinical translation of optical elastography to in vivo applications, yet such probes have received little attention. In this paper, we present the first demonstration of optical palpation using a handheld probe. Optical palpation is a variant of optical elastography that uses three-dimensional optical coherence tomography (3D-OCT) to provide maps of stress at the tissue surface under static compression. Using this technique, stiff features present beneath the surface of turbid tissues are identified, providing mechanical contrast complementary to the optical contrast provided by OCT. However, during handheld operation, relative motion between the probe and the tissue can induce motion artifact, causing spatial distortion of 3D-OCT and in turn, optical palpation images. We overcome this issue using a novel, dual-function bi-layer that provides both a fiducial marker for co-registration and a compliant section for estimation of the stress at the tissue surface. Co-registration of digital photographs of the bi-layer laid out over the tissue surface is used to measure and correct for motion in the lateral (xy) plane. We also demonstrate, for the first time, that optical palpation can be used as a method for monitoring pressure applied to the tissue during handheld operation, thus providing a more repeatable and robust imaging technique between different users. Handheld optical palpation is demonstrated on a structured phantom, in vivo human skin and excised human breast tissue. In each case, image quality comparable to bench-top 3D-OCT and optical palpation is achieved., Australian Research Council; Cancer Council, Western Australia; OncoRes Medical.

We present the Gab Hate Corpus (GHC), consisting of 27,665 posts from the social network service gab.com, each annotated for the presence of “hate-based rhetoric” by a minimum of three annotators. Posts were labeled according to a coding typology derived from a synthesis of hate speech definitions across legal precedent, previous hate speech coding typologies, and definitions from psychology and sociology, comprising hierarchical labels indicating dehumanizing and violent speech as well as indicators of targeted groups and rhetorical framing. We provide inter-annotator agreement statistics and perform a classification analysis in order to validate the corpus and establish performance baselines. The GHC complements existing hate speech datasets in its theoretical grounding and by providing a large, representative sample of richly annotated social media posts.

It is widely accepted that accurate mechanical properties of three-dimensional soft tissues and cellular samples are not available on the microscale. Current methods based on optical coherence elastography can measure displacements at the necessary resolution, and over the volumes required for this task. However, in converting this data to maps of elastic properties, they often impose assumptions regarding homogeneity in stress or elastic properties that are violated in most realistic scenarios. Here, we introduce novel, rigorous, and computationally efficient inverse problem techniques that do not make these assumptions, to realize quantitative volumetric elasticity imaging on the microscale. Specifically, we iteratively solve the three-dimensional elasticity inverse problem using displacement maps obtained from compression optical coherence elastography. This is made computationally feasible with adaptive mesh refinement and domain decomposition methods. By employing a transparent, compliant surface layer with known shear modulus as a reference for the measurement, absolute shear modulus values are produced within a millimeter-scale sample volume. We demonstrate the method on phantoms, on a breast cancer sample ex vivo, and on human skin in vivo. Quantitative elastography on this length scale will find wide application in cell biology, tissue engineering and medicine. Publisher PDF

Australian Research Council; Cancer Council Western Australia; Department of Health, Government of Western Australia; OncoResMedical; William and Marlene Schrader Trust of The University of Western Australia Accurate and effective removal of tumor in one operation is an important goal of breast-conserving surgery. However, it is not always achieved. Surgeons often utilize manual palpation to assess the surgical margin and/or the breast cavity. Manual palpation, however, is subjective and has relatively low resolution. Here, we investigate a tactile imaging technique, optical palpation, for the visualization of tumor. Optical palpation generates maps of the stress at the surface of tissue under static preload compression. Stress is evaluated by measuring the deformation of a contacting thin compliant layer with known mechanical properties using optical coherence tomography. In this study, optical palpation is performed on 34 freshly excised human breast specimens. Wide field-of-view (up to ~46 × 46 mm) stress images, optical palpograms, are presented from four representative specimens, demonstrating the capability of optical palpation to visualize tumor. Median stress reported for adipose tissue, 4 kPa, and benign dense tissue, 8 kPa, is significantly lower than for invasive tumor, 60 kPa. In addition, we demonstrate that optical palpation provides contrast consistent with a related optical technique, quantitative micro-elastography. This study demonstrates that optical palpation holds promise for visualization of tumor in breast-conserving surgery. Postprint

We present a graphics processing unit (GPU)-accelerated optical coherence elastography (OCE) system capable of generating strain images (elastograms) of soft tissue at near video-rates. The system implements phase-sensitive compression OCE using a pipeline of GPU kernel functions to enable a highly parallel implementation of OCE processing using the OpenCL framework. Developed on a commercial-grade GPU and desktop computer, the system achieves a processing rate of 21 elastograms per second at an image size of 960 × 400 pixels, enabling high-rate visualization during acquisition. The system is demonstrated on both tissue-simulating phantoms and fresh ex vivo mouse muscle. To the best of our knowledge, this is the first implementation of near video-rate OCE and the fastest reported OCE processing rate, enabling, for the first time, a system capable of computing and displaying OCE elastograms interactively during acquisition. This advance provides new opportunities for medical imaging of soft tissue stiffness using optical methods.

An accurate intraoperative identification of malignant tissue is a challenge in the surgical management of breast cancer. Imaging techniques that help address this challenge could contribute to more complete and accurate tumor excision, and thereby help reduce the current high reexcision rates without resorting to the removal of excess healthy tissue. Optical coherence microelastography (OCME) is a three-dimensional, high-resolution imaging technique that is sensitive to microscale variations of the mechanical properties of tissue. As the tumor modifies the mechanical properties of breast tissue, OCME has the potential to identify, on the microscale, involved regions of fresh, unstained tissue. OCME is based on the use of optical coherence tomography (OCT) to measure tissue deformation in response to applied mechanical compression. In this feasibility study on 58 ex vivo samples from patients undergoing mastectomy or wide local excision, we demonstrate the performance of OCME as a means to visualize tissue microarchitecture in benign and malignant human breast tissues. Through a comparison with corresponding histology and OCT images, OCME is shown to enable ready visualization of features such as ducts, lobules, microcysts, blood vessels, and arterioles and to identify invasive tumor through distinctive patterns in OCME images, often with enhanced contrast compared with OCT. These results lay the foundation for future intraoperative studies. Cancer Res; 75(16); 3236–45. ©2015 AACR.