How good are collimated Gaussian beams produced with engineered diffusers?

Collimating a Gaussian beam from an uncollimated laser source has been achieved via the deployment of engineered diffusers (EDs)-also referred to as light shaping diffusers. When compared to conventional pinhole-based optical collimation systems, this method of beam collimation ensures high optical transmission efficiency at the expense of the introduction of additional speckle and a resulting reduction in spatial coherence. Despite a lower collimation quality, these ED-produced collimated beams are attractive and promising in terms of their deployment in various benchtop or tabletop systems that involve shorter beam propagation distances of up to a few meters while requiring a high optical power throughput. This paper aims to further the understanding of collimation quality and propagation properties of ED-produced Gaussian collimated beams via carefully designed experiments and accompanying analysis. We measure and document the beam divergence, Rayleigh distance, and M 2 factor, as well as evolution of the wavefront radius of curvature (RoC), of these ED-generated beams over a few meters of propagation-a propagation distance which encapsulates a vast majority of optical systems. We further investigate the changes in the beam profile with the addition of a laser speckle reducer (SR) and compare the ED-produced beams with a near-ideal collimated beam produced with spatial filtering systems.

Measuring the radius of curvature of spherical surfaces with actively tunable Fizeau and Twyman-Green interferometers.

Accurate and repeatable measurement of the radius of curvature (RoC) of spherical sample surfaces is of great importance in optics. This importance lies in the ubiquitous use of spherical optical elements such as curved mirrors and lenses. Due to a high measurement sensitivity, interferometric techniques are often deployed for accurate characterization of the sample surface RoC. One method by which a typical commercial Fizeau or Twyman-Green (TG) interferometer measures surface RoC is via scanning between two principal retroreflective optical configurations-namely, the confocal and catseye configurations. Switching between these two configurations is typically achieved by moving an optical head along the axis of the propagating laser beam and the RoC is estimated by measuring the magnitude of mechanical motion to switch between the two principal configurations. In this paper, we propose a motion-free catseye/confocal-imaging-based sample RoC measurement system. The necessity of bulk motion to switch between the two configurations is circumvented via the use of an actively controlled varifocal lens. We demonstrate the usefulness of the proposed innovation in RoC measurements with either the TG or the Fizeau interferometer. Furthermore, we convert a commercial motion-based Zygo RoC measurement system into a motion-free one by introducing a tunable lens inside the apparatus and using it to accurately characterize the RoC of different test samples. We also compute the wavefront aberrations for all spherical sample surfaces from the recorded measurement data.

Weak grip strength among persons with multiple sclerosis having minimal disability is not related to agility or integrity of the corticospinal tract.

INTRODUCTION: Mobility impairment is common in multiple sclerosis (MS); however, agility has received less attention. Agility requires strength and neuromuscular coordination to elicit controlled propulsive rapid whole-body movement. Grip strength is a common method to assess whole body force production, but also reflects neuromuscular integrity and global brain health. Impaired agility may be linked to loss of neuromuscular integrity (reflected by grip strength or corticospinal excitability). OBJECTIVES: We aimed to determine whether grip strength would be associated with agility and transcranial magnetic stimulation (TMS)-based indices of corticospinal excitability and inhibition in persons with MS having low disability. We hypothesized that low grip strength would predict impaired agility and reflect low corticospinal excitability. METHODS: We recruited 34 persons with relapsing MS (27 females; median [range] age 45.5 [21.0-65.0] years) and mild disability (median [range] Expanded Disability Status Scale 2.0 [0-3.0]), as well as a convenience sample of age- and sex-matched apparently healthy controls. Agility was tested by measuring hop length during bipedal hopping on an instrumented walkway. Grip strength was measured using a calibrated dynamometer. Corticospinal excitability and inhibition were examined using TMS-based motor evoked potential (MEP) and corticospinal silent period (CSP) recruitment curves, respectively. RESULTS: MS participants had significantly lower grip strength than controls independent of sex. Females with and without MS had weaker grip strength than males. There were no statistically significant sex or group differences in agility. After controlling for sex, weaker grip strength was associated with shorter hop length in controls only (r = 0.645, p < .05). Grip strength did not significantly predict agility in persons with MS, nor was grip strength predicted by corticospinal excitability or inhibition. CONCLUSIONS: In persons with MS having low disability, grip strength (normalized to body mass) was reduced despite having intact agility and walking performance. Grip strength was not associated with corticospinal excitability or inhibition, suggesting peripheral neuromuscular function, low physical activity or fitness, or other psychosocial factors may be related to weakness. Low grip strength is a putative indicator of early neuromuscular aging in persons with MS having mild disability and normal mobility.

Texture Estimation for Abnormal Tissue Segmentation in Brain MRI.

This chapter discusses multifractal texture estimation and characterization of brain lesions (necrosis, edema, enhanced tumor, nonenhanced tumor, etc.) in magnetic resonance (MR) images. This work formulates the complex texture of tumor in MR images using a stochastic model known as multifractional Brownian motion (mBm). Mathematical derivations of the mBm model and corresponding algorithm to extract the spatially varying multifractal texture feature are discussed. Extracted multifractal texture feature is fused with other effective features to enhance the tissue characteristics. Segmentation of the tissues is performed using a feature-based classification method. The efficacy of the mBm texture feature in segmenting different abnormal tissues is demonstrated using a large-scale publicly available clinical dataset. Experimental results and performance of the methods confirm the efficacy of the proposed technique in an automatic segmentation of abnormal tissues in multimodal (T1, T2, Flair, and T1contrast) brain MRIs.

Towards a more accurate light transport model for non-line-of-sight imaging.

Non-line-of-sight (NLOS) imaging systems involve the measurement of an optical signal at a diffuse surface. A forward model encodes the physics of these measurements mathematically and can be inverted to generate a reconstruction of the hidden scene. Some existing NLOS imaging techniques rely on illuminating the diffuse surface and measuring the photon time of flight (ToF) of multi-bounce light paths. Alternatively, some methods depend on measuring high-frequency variations caused by shadows cast by occluders in the hidden scene. While forward models for ToF-NLOS and Shadow-NLOS have been developed separately, there has been limited work on unifying these two imaging modalities. Dove et al introduced a unified mathematical framework capable of modeling both imaging techniques [Opt. Express27, 18016 (2019)10.1364/OE.27.018016]. The authors utilize this general forward model, known as the two frequency spatial Wigner distribution (TFSWD), to discuss the implications of reconstruction resolution for combining the two modalities but only when the occluder geometry is known a priori. In this work, we develop a graphical representation of the TFSWD forward model and apply it to novel experimental setups with potential applications in NLOS imaging. Furthermore, we use this unified framework to explore the potential of combining these two imaging modalities in situations where the occluder geometry is not known in advance.

Repeatability of computed tomography liver radiomic features in a nonhuman primate model of diet-induced steatosis.

Purpose: We describe a method to identify repeatable liver computed tomography (CT) radiomic features, suitable for detection of steatosis, in nonhuman primates. Criteria used for feature selection exclude nonrepeatable features and may be useful to improve the performance and robustness of radiomics-based predictive models. Approach: Six crab-eating macaques were equally assigned to two experimental groups, fed regular chow or an atherogenic diet. High-resolution CT images were acquired over several days for each macaque. First-order and second-order radiomic features were extracted from six regions in the liver parenchyma, either with or without liver-to-spleen intensity normalization from images reconstructed using either a standard (B-filter) or a bone-enhanced (D-filter) kernel. Intrasubject repeatability of each feature was assessed using a paired t-test for all scans and the minimum p-value was identified for each macaque. Repeatable features were defined as having a minimum p-value among all macaques above the significance level after Bonferroni's correction. Features showing a significant difference with respect to diet group were identified using a two-sample t-test. Results: A list of repeatable features was generated for each type of image. The largest number of repeatable features was achieved from spleen-normalized D-filtered images, which also produced the largest number of second-order radiomic features that were repeatable and different between diet groups. Conclusions: Repeatability depends on reconstruction kernel and normalization. Features were quantified and ranked based on their repeatability. Features to be excluded for more robust models were identified. Features that were repeatable but different between diet groups were also identified.

Novel machine-learning analysis of SARS-CoV-2 infection in a subclinical nonhuman primate model using radiomics and blood biomarkers.

Detection of the physiological response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is challenging in the absence of overt clinical signs but remains necessary to understand a full subclinical disease spectrum. In this study, our objective was to use radiomics (from computed tomography images) and blood biomarkers to predict SARS-CoV-2 infection in a nonhuman primate model (NHP) with inapparent clinical disease. To accomplish this aim, we built machine-learning models to predict SARS-CoV-2 infection in a NHP model of subclinical disease using baseline-normalized radiomic and blood sample analyses data from SARS-CoV-2-exposed and control (mock-exposed) crab-eating macaques. We applied a novel adaptation of the minimum redundancy maximum relevance (mRMR) feature-selection technique, called mRMR-permute, for statistically-thresholded and unbiased feature selection. Through performance comparison of eight machine-learning models trained on 14 feature sets, we demonstrated that a logistic regression model trained on the mRMR-permute feature set can predict SARS-CoV-2 infection with very high accuracy. Eighty-nine percent of mRMR-permute selected features had strong and significant class effects. Through this work, we identified a key set of radiomic and blood biomarkers that can be used to predict infection status even in the absence of clinical signs. Furthermore, we proposed and demonstrated the utility of a novel feature-selection technique called mRMR-permute. This work lays the foundation for the prediction and classification of SARS-CoV-2 disease severity.

Artificial Intelligence and Infectious Disease Imaging.

The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.

Simultaneous thickness and phase index measurements with a motion-free actively tunable Twyman-Green interferometer.

In this paper, we present a scheme to simultaneously measure the thickness and refractive index of parallel plate samples, involving no bulk mechanical motion, by deploying an electronically tunable Twyman-Green interferometer configuration. The active electronic control with no bulk mechanical motion is realized via the introduction of a tunable focus lens within the classical motion-based Twyman-Green interferometer configuration. The resulting interferometer is repeatable and delivers accurate estimates of the thickness and refractive index of a sample under test. Elimination of bulk motion also promises a potential for miniaturization. We develop a theoretical model for estimating sample thickness and index values using this reconfigurable interferometer setup and present detailed experimental results that demonstrate the working principle of the proposed interferometer.

Low-loss tunable beam collimator and expander assembly with no moving parts using an engineered diffuser and varifocal lenses.

In this paper, we present a novel design for a tunable beam collimator. A variable collimator assists in achieving an adaptive size of an output collimated beam. Alternatively, it can also provide an adjustable output beam divergence angle for a noncollimated beam output. Tunable collimators are highly desirable for various applications in testing, engineering, and measurements. Such devices are also useful in providing tunable illumination of samples or targets in microscopes and emulating different target distances for characterizing the performance of camera systems in laboratory settings. The proposed collimator has two distinct advantages: it is light-efficient compared with pinhole-based collimator designs, and it delivers a large range of output beam sizes without involving the mechanical motion of bulk components. These attributes are achieved via the use of an engineered diffuser (in the place of a pinhole) and a pair of large aperture tunable focus lenses, which deliver a tunable magnification to the output collimated beam. In laboratory experiments, we achieve an optical transmission efficiency of 90% for the proposed tunable collimator.

Computed Tomography Imaging for Monitoring of Marburg Virus Disease: a Nonhuman Primate Proof-Of-Concept Study.

Marburg virus (MARV) is a highly virulent zoonotic filovirid that causes Marburg virus disease (MVD) in humans. The pathogenesis of MVD remains poorly understood, partially due to the low number of cases that can be studied, the absence of state-of-the-art medical equipment in areas where cases are reported, and limitations on the number of animals that can be safely used in experimental studies under maximum containment animal biosafety level 4 conditions. Medical imaging modalities, such as whole-body computed tomography (CT), may help to describe disease progression in vivo, potentially replacing ethically contentious and logistically challenging serial euthanasia studies. Towards this vision, we performed a pilot study, during which we acquired whole-body CT images of 6 rhesus monkeys before and 7 to 9 days after intramuscular MARV exposure. We identified imaging abnormalities in the liver, spleen, and axillary lymph nodes that corresponded to clinical, virological, and gross pathological hallmarks of MVD in this animal model. Quantitative image analysis indicated hepatomegaly with a significant reduction in organ density (indicating fatty infiltration of the liver), splenomegaly, and edema that corresponded with gross pathological and histopathological findings. Our results indicated that CT imaging could be used to verify and quantify typical MVD pathogenesis versus altered, diminished, or absent disease severity or progression in the presence of candidate medical countermeasures, thus possibly reducing the number of animals needed and eliminating serial euthanasia. IMPORTANCE Marburg virus (MARV) is a highly virulent zoonotic filovirid that causes Marburg virus disease (MVD) in humans. Much is unknown about disease progression and, thus, prevention and treatment options are limited. Medical imaging modalities, such as whole-body computed tomography (CT), have the potential to improve understanding of MVD pathogenesis. Our study used CT to identify abnormalities in the liver, spleen, and axillary lymph nodes that corresponded to known clinical signs of MVD in this animal model. Our results indicated that CT imaging and analyses could be used to elucidate pathogenesis and possibly assess the efficacy of candidate treatments.

Robust, motion-free optical characterization of samples using actively-tunable Twyman-Green interferometry.

Optical interferometry-based techniques are ubiquitous in various measurement, imaging, calibration, metrological, and astronomical applications. Repeatability, simplicity, and reliability of measurements have ensured that interferometry in its various forms remains popular-and in fact continues to grow-in almost every branch of measurement science. In this paper, we propose a novel actively-controlled optical interferometer in the Twyman-Green configuration. The active beam control within the interferometer is a result of using an actively-controlled tunable focus lens in the sample arm of the interferometer. This innovation allows us to characterize transparent samples cut in the cubical geometry without the need for bulk mechanical motion within the interferometer. Unlike thickness/refractive index measurements with conventional Twyman-Green interferometers, the actively-tunable interferometer enables bulk-motion free thickness or refractive index sample measurements. With experimental demonstrations, we show excellent results for various samples that we characterized. The elimination of bulk motion from the measurement process promises to enable miniaturization of actively-tunable Twyman-Green interferometers for various applications.

Deep-Learning-Based Whole-Lung and Lung-Lesion Quantification Despite Inconsistent Ground Truth: Application to Computerized Tomography in SARS-CoV-2 Nonhuman Primate Models.

RATIONALE AND OBJECTIVES: Animal modeling of infectious diseases such as coronavirus disease 2019 (COVID-19) is important for exploration of natural history, understanding of pathogenesis, and evaluation of countermeasures. Preclinical studies enable rigorous control of experimental conditions as well as pre-exposure baseline and longitudinal measurements, including medical imaging, that are often unavailable in the clinical research setting. Computerized tomography (CT) imaging provides important diagnostic, prognostic, and disease characterization to clinicians and clinical researchers. In that context, automated deep-learning systems for the analysis of CT imaging have been broadly proposed, but their practical utility has been limited. Manual outlining of the ground truth (i.e., lung-lesions) requires accurate distinctions between abnormal and normal tissues that often have vague boundaries and is subject to reader heterogeneity in interpretation. Indeed, this subjectivity is demonstrated as wide inconsistency in manual outlines among experts and from the same expert. The application of deep-learning data-science tools has been less well-evaluated in the preclinical setting, including in nonhuman primate (NHP) models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection/COVID-19, in which the translation of human-derived deep-learning tools is challenging. The automated segmentation of the whole lung and lung lesions provides a potentially standardized and automated method to detect and quantify disease. MATERIALS AND METHODS: We used deep-learning-based quantification of the whole lung and lung lesions on CT scans of NHPs exposed to SARS-CoV-2. We proposed a novel multi-model ensemble technique to address the inconsistency in the ground truths for deep-learning-based automated segmentation of the whole lung and lung lesions. Multiple models were obtained by training the convolutional neural network (CNN) on different subsets of the training data instead of having a single model using the entire training dataset. Moreover, we employed a feature pyramid network (FPN), a CNN that provides predictions at different resolution levels, enabling the network to predict objects with wide size variations. RESULTS: We achieved an average of 99.4 and 60.2% Dice coefficients for whole-lung and lung-lesion segmentation, respectively. The proposed multi-model FPN outperformed well-accepted methods U-Net (50.5%), V-Net (54.5%), and Inception (53.4%) for the challenging lesion-segmentation task. We show the application of segmentation outputs for longitudinal quantification of lung disease in SARS-CoV-2-exposed and mock-exposed NHPs. CONCLUSION: Deep-learning methods should be optimally characterized for and targeted specifically to preclinical research needs in terms of impact, automation, and dynamic quantification independently from purely clinical applications.

Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019.

Purpose: We propose a method to identify sensitive and reliable whole-lung radiomic features from computed tomography (CT) images in a nonhuman primate model of coronavirus disease 2019 (COVID-19). Criteria used for feature selection in this method may improve the performance and robustness of predictive models. Approach: Fourteen crab-eating macaques were assigned to two experimental groups and exposed to either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a mock inoculum. High-resolution CT scans were acquired before exposure and on several post-exposure days. Lung volumes were segmented using a deep-learning methodology, and radiomic features were extracted from the original image. The reliability of each feature was assessed by the intraclass correlation coefficient (ICC) using the mock-exposed group data. The sensitivity of each feature was assessed using the virus-exposed group data by defining a factor R that estimates the excess of variation above the maximum normal variation computed in the mock-exposed group. R and ICC were used to rank features and identify non-sensitive and unstable features. Results: Out of 111 radiomic features, 43% had excellent reliability ( ICC > 0.90 ), and 55% had either good ( ICC > 0.75 ) or moderate ( ICC > 0.50 ) reliability. Nineteen features were not sensitive to the radiological manifestations of SARS-CoV-2 exposure. The sensitivity of features showed patterns that suggested a correlation with the radiological manifestations. Conclusions: Features were quantified and ranked based on their sensitivity and reliability. Features to be excluded to create more robust models were identified. Applicability to similar viral pneumonia studies is also possible.

Performance of NI-RADS on CECT Alone to Predict Recurrent Head and Neck Squamous Cell Carcinoma after Chemoradiotherapy: Added Value of RECIST 1.1.

Background The Head and Neck Imaging Reporting and Data System (NI-RADS) is a standardized reporting format for the categorization of the degree of suspicion for recurrent head and neck malignancies on positron emission tomography/computed tomography. Purpose The purpose of our study was to analyze the efficacy of the NI-RADS rating scale and criteria for contrast-enhanced computed tomography (CECT) alone in predicting the local and regional recurrence of malignancies after chemoradiotherapy. Material and Methods CECT of the patients with head and neck cancers receiving radiotherapy and concurrent chemotherapy as a primary treatment was obtained 3 months after the completion of radiotherapy and NI-RADS scoring was done using components of Response Evaluation Criteria in Solid Tumors (RECIST 1.1) criteria. Their management was guided according to the recommendations based on their NI-RADS score. Results Thirty patients with squamous cell carcinoma of the neck were included in this study. The positive or negative status of the recurrent disease was based on biopsy results or follow-up protocol as recommended in NI-RADS rating scale. Fifteen patients had path proven recurrence at the primary tumor site. For primary tumor site, disease persistence rates of 4% for NI-RADS 1, 24% for NI-RADS 2, and 80% for NI-RADS 3 scores were seen. Five patients had recurrent lymph nodal disease. For lymph nodal assessment, NI-RADS categories 1, 2, and 3 revealed nodal disease recurrence rates of 5.3, 25, and 66.7%, respectively. Conclusion CECT alone may be used to assign the NI-RADS rating scale using RECIST 1.1 criteria to predict the presence or absence of recurrent tumor in patients with neck malignancies.

Extubation in neurocritical care patients: the ENIO international prospective study.

PURPOSE: Neurocritical care patients receive prolonged invasive mechanical ventilation (IMV), but there is poor specific information in this high-risk population about the liberation strategies of invasive mechanical ventilation. METHODS: ENIO (NCT03400904) is an international, prospective observational study, in 73 intensive care units (ICUs) in 18 countries from 2018 to 2020. Neurocritical care patients with a Glasgow Coma Score (GCS) ≤ 12, receiving IMV ≥ 24 h, undergoing extubation attempt or tracheostomy were included. The primary endpoint was extubation failure by day 5. An extubation success prediction score was created, with 2/3 of patients randomly allocated to the training cohort and 1/3 to the validation cohort. Secondary endpoints were the duration of IMV and in-ICU mortality. RESULTS: 1512 patients were included. Among the 1193 (78.9%) patients who underwent an extubation attempt, 231 (19.4%) failures were recorded. The score for successful extubation prediction retained 20 variables as independent predictors. The area under the curve (AUC) in the training cohort was 0.79 95% confidence interval (CI95) [0.71-0.87] and 0.71 CI95 [0.61-0.81] in the validation cohort. Patients with extubation failure displayed a longer IMV duration (14 [7-21] vs 6 [3-11] days) and a higher in-ICU mortality rate (8.7% vs 2.4%). Three hundred and nineteen (21.1%) patients underwent tracheostomy without extubation attempt. Patients with direct tracheostomy displayed a longer duration of IMV and higher in-ICU mortality than patients with an extubation attempt (success and failure). CONCLUSIONS: In neurocritical care patients, extubation failure is high and is associated with unfavourable outcomes. A score could predict extubation success in multiple settings. However, it will be mandatory to validate our findings in another prospective independent cohort.

Thank you, COVID-19: Positive social psychology towards the new normal.

During the time being, while everybody else is busy conversing about the adverse effects of COVID-19, researchers solemnly look forward to intensifying the positive effects of COVID-19 on political, economic, social, technological, environmental, and ethical (PESTEL) aspects, as it was unrevealed. The FGD (Focus Group Discussion) and Delphi methods were conducted from April 2020 to January 2021 through the online platform to collect the data. In this research, 40 graduates from 40 families were taken as our sample size who carried out the opinions of their family members. The average duration of the interview was 30-40 min. This article highlighted that COVID-19 has some positive effects on social-psychological aspects (PESTEL) and, people are trying to adapt to new practices (New Normal) to improve their lifestyles against the deadly virus. COVID-19 would pass, but life will never be the same; thus, researchers conduct the research based on a country, which may not be similar to the other cultures and countries' perceptions. This is the first and foremost study on the positive effects of covid-19 and a guideline to cope with any pandemic in the near future. The study's findings are intended to assist the community in developing positive social psychology to call the covid-19 into question and look forward to a new standard.

Plasmonic metal-organic framework nanocomposites enabled by degenerately doped molybdenum oxides.

Metal-organic frameworks (MOFs) nanocomposites are under the limelight due to their unique electronic, optical, and surface properties for various applications. Plasmonic MOFs enabled by noble metal nanostructures are an emerging class of MOF nanocomposites with efficient solar light-harvesting capability. However, major concerns such as poor photostability, sophisticated synthesis processes, and high fabrication cost are raised. Here, we develop a novel plasmonic MOF nanocomposite consisting of the ultra-thin degenerately doped molybdenum oxide core and the flexible iron MOF (FeMOF) shell through a hydrothermal growth, featuring low cost, facile synthesis, and non-toxicity. More importantly, the incorporation of plasmonic oxides in the highly porous MOF structure enhances the visible light absorbability, demonstrating improved photobleaching performances of various azo and non-azo dyes compared to that of pure FeMOF without the incorporation of oxidative agents. Furthermore, the nanocomposite exhibits enhanced sensitivity and selectivity towards NO2 gas at room temperature, attributed to the electron-rich surface of plasmonic oxides. This work possibly broadens the exploration of plasmonic MOF nanocomposites for practical and efficient solar energy harvesting, environmental remediation, and environmental monitoring applications.

Printable Single-Unit-Cell-Thick Transparent Zinc-Doped Indium Oxides with Efficient Electron Transport Properties.

Ultrathin transparent conductive oxides (TCOs) are emerging candidates for next-generation transparent electronics. Indium oxide (In2O3) incorporated with post-transition-metal ions (e.g., Sn) has been widely studied due to their excellent optical transparency and electrical conductivity. However, their electron transport properties are deteriorated at the ultrathin two-dimensional (2D) morphology compared to that of intrinsic In2O3. Here, we explore the domain of transition-metal dopants in ultrathin In2O3 with the thicknesses down to the single-unit-cell limit, which is realized in a large area using a low-temperature liquid metal printing technique. Zn dopant is selected as a representative to incorporate into the In2O3 rhombohedral crystal framework, which results in the gradual transition of the host to quasimetallic. While the optical transmittance is maintained above 98%, an electron field-effect mobility of up to 87 cm2 V-1 s-1 and a considerable sub-kΩ-1 cm-1 ranged electrical conductivity are achieved when the Zn doping level is optimized, which are in a combination significantly improved compared to those of reported ultrathin TCOs. This work presents various opportunities for developing high-performance flexible transparent electronics based on emerging ultrathin TCO candidates.

Deep Learning for Automated Liver Segmentation to Aid in the Study of Infectious Diseases in Nonhuman Primates.

With the advent of deep learning, convolutional neural networks (CNNs) have evolved as an effective method for the automated segmentation of different tissues in medical image analysis. In certain infectious diseases, the liver is one of the more highly affected organs, where an accurate liver segmentation method may play a significant role to improve the diagnosis, quantification, and follow-up. Although several segmentation algorithms have been proposed for liver or liver-tumor segmentation in computed tomography (CT) of human subjects, none of them have been investigated for nonhuman primates (NHPs), where the livers have a wide range in size and morphology. In addition, the unique characteristics of different infections or the heterogeneous immune responses of different NHPs to the infections appear with a diverse radiodensity distribution in the CT imaging. In this study, we investigated three state-of-the-art algorithms; VNet, UNet, and feature pyramid network (FPN) for automated liver segmentation in whole-body CT images of NHPs. The efficacy of the CNNs were evaluated on 82 scans of 37 animals, including pre and post-exposure to different viruses such as Ebola, Marburg, and Lassa. Using a 10-fold cross-validation, the best performance for the segmented liver was provided by the FPN; an average 94.77% Dice score, and 3.6% relative absolute volume difference. Our study demonstrated the efficacy of multiple CNNs, wherein the FPN outperforms VNet and UNet for liver segmentation in infectious disease imaging research.