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Sensitive and specific blood-based assays for the detection of pulmonary and extrapulmonary tuberculosis would reduce mortality associated with missed diagnoses, particularly in children. Here we report a nanoparticle-enhanced immunoassay read by dark-field microscopy that detects twoMycobacterium tuberculosisvirulence factors (the glycolipid lipoarabinomannan and its carrier protein) on the surface of circulating extracellular vesicles. In a cohort study of 147 hospitalized and severely immunosuppressed children living with HIV, the assay detected 58 of the 78 (74%) cases of paediatric tuberculosis, 48 of the 66 (73%) cases that were missed by microbiological assays, and 8 out of 10 (80%) cases undiagnosed during the study. It also distinguished tuberculosis from latent-tuberculosis infections in non-human primates. We adapted the assay to make it portable and operable by a smartphone. With further development, the assay may facilitate the detection of tuberculosis at the point of care, particularly in resource-limited settings.

Oral Cancer is one of the most common causes of morbidity and mortality. Screening and mobile Health (mHealth) based approach facilitates remote early detection of Oral cancer in a resource-constrained settings. The emerging eHealth technology has aided specialist reach to rural areas enabling remote monitoring and triaging to downstage Oral cancer. Though the diagnostic accuracy of the remote specialist has been evaluated, there are no studies evaluating the consistency among the remote specialists, to the best of our knowledge. The purpose of the study was to evaluate the interobserver agreement between the specialists through telemedicine systems in real-world settings using store and forward technology. Two remote specialists independently diagnosed the clinical images from image repositories, and the diagnostic accuracy was compared with onsite specialist and histopathological diagnosis when available. Moderate agreement (k = 0.682) between two remote specialists and (k = 0.629) between the onsite specialist and two remote specialists in diagnosing oral lesions. The sensitivity and specificity of remote specialist 1 were 92.7% and 83.3%, whereas remote specialist 2 was 95.8% and 60%, respectively, compared to histopathology. The store and forward technology and telecare can be effective tools in triaging and surveillance of patients.

The cell maintains its intracellular pH in a narrow physiological range and disrupting the pH-homeostasis could cause dysfunctional metabolic states. Anion exchanger 2 (AE2) works at high cellular pH to catalyze the exchange between the intracellular HCO3− and extracellular Cl−, thereby maintaining the pH-homeostasis. Here, we determine the cryo-EM structures of human AE2 in five major operating states and one transitional hybrid state. Among those states, the AE2 shows the inward-facing, outward-facing, and intermediate conformations, as well as the substrate-binding pockets at two sides of the cell membrane. Furthermore, critical structural features were identified showing an interlock mechanism for interactions among the cytoplasmic N-terminal domain and the transmembrane domain and the self-inhibitory effect of the C-terminal loop. The structural and cell-based functional assay collectively demonstrate the dynamic process of the anion exchange across membranes and provide the structural basis for the pH-sensitive pH-rebalancing activity of AE2.

Glycoprotein (GP) Ib-IX-V is the second most abundant platelet receptor for thrombin and other ligands crucial for hemostasis and thrombosis. Its activity is involved in platelet adhesion to vascular injury sites and thrombin-induced platelet aggregation. GPIb-IX-V is a heteromeric complex composed of four subunits, GPIbα, GPIbβ, GPV and GPIX, in a stoichiometric ratio that has been wildly debated. Despite its important physiological roles, the overall structure and molecular arrangement of GPIb-IX-V are not yet fully understood. Here, we purify stable and functional human GPIb-IX-V complex from reconstituted EXPi293F cells in high homogeneity, and perform biochemical and structural characterization of this complex. Single-particle cryo-electron microscopy structure of GPIb-IX-V is determined at ∼11 Å resolution, which unveils the architecture of GPIb-IX-V and its subunit organization. Size-exclusion chromatography-multi-angle static light scattering analysis reveals that GPIb-IX-V contains GPIb-IX and GPV at a 1:1 stoichiometric ratio and surface plasmon resonance assays show that association of GPV leads to slow kinetics of thrombin binding to GPIb-IX-V. Taken together, our results provide the first three-dimensional architecture of the intact GPIb-IX-V complex, which extends our understanding of the structure and functional mechanism of this complex in hemostasis and thrombosis.

Telomerase, a multi-subunit ribonucleoprotein complex, is a unique reverse transcriptase that catalyzes the processive addition of a repeat sequence to extend the telomere end using a short fragment of its own RNA component as the template. Despite recent structural characterizations of human and Tetrahymena telomerase, it is still a mystery how telomerase repeatedly uses its RNA template to synthesize telomeric DNA. Here, we report the cryo-EM structure of human telomerase holoenzyme bound with telomeric DNA at resolutions of 3.5 Å and 3.9 Å for the catalytic core and biogenesis module, respectively. The structure reveals that a leucine residue Leu980 in telomerase reverse transcriptase (TERT) catalytic subunit functions as a zipper head to limit the length of the short primer–template duplex in the active center. Moreover, our structural and computational analyses suggest that TERT and telomerase RNA (hTR) are organized to harbor a preformed active site that can accommodate short primer–template duplex substrates for catalysis. Furthermore, our findings unveil a double-fingers architecture in TERT that ensures nucleotide addition processivity of human telomerase. We propose that the zipper head Leu980 is a structural determinant for the sequence-based pausing signal of DNA synthesis that coincides with the RNA element-based physical template boundary. Functional analyses unveil that the non-glycine zipper head plays an essential role in both telomerase repeat addition processivity and telomere length homeostasis. In addition, we also demonstrate that this zipper head mechanism is conserved in all eukaryotic telomerases. Together, our study provides an integrated model for telomerase-mediated telomere synthesis.

SignificanceOral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.AimWe aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.ApproachThis work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.ResultsThe proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.ConclusionsOur study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model's prediction can be improved.Oral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.We aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.This work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.The proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.Our study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model’s prediction can be improved.

RNA polymerase III (Pol III) synthesizes structured, essential small RNAs, such as transfer RNA, 5S ribosomal RNA and U6 small nuclear RNA. Pol III, the largest nuclear RNA polymerase, is composed of a conserved core region and eight constitutive regulatory subunits, but how these factors jointly regulate Pol III transcription remains unclear. Here, we present cryo-EM structures of human Pol III in both apo and elongating states, which unveil both an orchestrated movement during the apo-to-elongating transition and an unexpected apo state in which the RPC7 subunit tail occupies the DNA-RNA-binding cleft of Pol III, suggesting that RPC7 plays important roles in both autoinhibition and transcription initiation. The structures also reveal a proofreading mechanism for the TFIIS-like subunit RPC10, which stably retains its catalytic position in the secondary channel, explaining the high fidelity of Pol III transcription. Our work provides an integrated picture of the mechanism of Pol III transcription regulation.

In this paper, the behaviors of single bubble in shear-thinning viscoelastic fluids were investigated using OpenFOAM. The volume-of-fluid method was used to capture the interface, and the Giesekus model was adopted for describing the rheological behaviors of shear-thinning viscoelastic fluids. The bubble cusp, negative wake, and velocity jump phenomenons in viscoelastic fluids were obtained, and the effects of wall effect, mobility factor α, and Weissenberg number ( Wi) on bubble behaviors were investigated. The results showed that the viscoelastic stress is the main reason for the formation of bubble cusp, the relaxation of polymer macromolecules leads to the formation of negative wake, and the negative wake may be the main reason for the velocity jump. The open angle θ of the negative wake decreases and the distribution region in the vertical direction increases with the increasing Reynolds numbers ( Re). In addition, the magnitude of the maximum negative velocity increases with Re and appears further away from the bubble cusp. Moreover, decreasing the wall effect can accelerate the formation of viscoelastic stress and make the bubble cusp appear earlier. As the mobility factor α increases, the viscosity and viscoelastic stress of the fluid near the bubble decrease; this causes the terminal velocity of the bubble to increase. As the Wi increases, both the maximum velocity and terminal velocity of the bubble increase, and the time lag occur. The viscoelastic stress τyy has a longer linear distribution at the tail of the bubble with the increase in Wi.

The bubble motion behavior in viscoelastic fluid is very important the in related gas-liquid processes. Thus, the emphasis of this review is to discuss the three peculiar phenomena of bubbles rising in viscoelastic fluids, i.e. the formation of the cusp, negative wake and velocity jump discontinuity and to highlight the possible future directions of the subject. The mechanism as well as influence factors on the three peculiar phenomena is discussed in detail. The comparison of results has shown that the change of bubble shape is mainly related to the viscoelasticity of the fluid. However, the mechanism of two-dimensional cusp, the tip-streaming, “blade-edge” tip, “fish-bone” tip and the tail breaking into two different threads phenomena in some special viscoelastic fluids is not very clear. For bubble velocity jump discontinuity phenomenon, viscoelasticity is the most critical factor, and the cusp of bubble, surface modification only played ancillary role.

Significance: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network’s attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications. Aim: Develop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image. Approach: We utilized Selvaraju et al.’s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.’s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation. Results: The network’s attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions. Conclusions: We demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification.

Adapted to the task at hand RNA-based catalysts perform fundamental tasks in cellular RNA metabolism, especially in eukaryotes, where RNAs are cut by specialized ribonucleoproteins (RNPs) as part of ribosome assembly or messenger RNA regulation or splicing. Both RNA and protein components play a role in shaping how these large catalytic complexes interact with their RNA substrates. Lan et al. determined the cryo–electron microscopy structures of a yeast RNP called ribonuclease MRP both alone and bound to a small RNA substrate. Comparison with the related ribonuclease P revealed differences in both protein and RNA components that enable ribonuclease MRP to recognize substrates with a specific sequence motif, rather than purely recognizing RNA structure as ribonuclease P does. These structures aid in considering how RNPs evolved and why they remain central to eukaryotic RNA processing. Science , this issue p. 656

The mass-transfer characteristics of bubbles rising in yield stress fluids was investigated numerically using volume-of-fluid and user-defined function methods in this study. The CO2 concentration profiles inside the liquid phase near the bubble equator at different bubble diameters, yield stresses, consistency coefficients, and flow indices were observed. The results revealed that the rate of mass transfer decreased with the increase of yield stress, consistency coefficient, and flow index of the liquid phase and the decrease of bubble diameter. Moreover, two empirical correlations for the drag coefficients and Sherwood numbers were developed by introducing one correction factor X for CD correlation and another correction factor fc for Sherwood numbers for correcting the influence of yield stress behavior on bubble motion and mass transfer, respectively. The predictions of the two correlations were compared with the simulated data, and a satisfactory agreement was found.

As the major component of cell membranes, phosphatidylcholine (PC) is synthesized de novo in the Kennedy pathway and then undergoes extensive deacylation-reacylation remodeling via Lands’ cycle. The re-acylation is catalyzed by lysophosphatidylcholine acyltransferase (LPCAT) and among the four LPCAT members in human, the LPCAT3 preferentially introduces polyunsaturated acyl onto the sn-2 position of lysophosphatidylcholine, thereby modulating the membrane fluidity and membrane protein functions therein. Combining the x-ray crystallography and the cryo-electron microscopy, we determined the structures of LPCAT3 in apo-, acyl donor-bound, and acyl receptor-bound states. A reaction chamber was revealed in the LPCAT3 structure where the lysophosphatidylcholine and arachidonoyl-CoA were positioned in two tunnels connected near to the catalytic center. A side pocket was found expanding the tunnel for the arachidonoyl CoA and holding the main body of arachidonoyl. The structural and functional analysis provides the basis for the re-acylation of lysophosphatidylcholine and the substrate preference during the reactions., During phosphatidylcholine (PC) remodeling re-acylation is catalyzed by lysophosphatidylcholine acyltransferases (LPCAT). Here, the authors present crystal and cryo-EM structures of chicken LPCAT3 in the apo-, acyl donor-bound and acyl receptor-bound states, and based on the structures and further functional analysis they discuss the mechanism of the enzyme.

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

Pyruvate carboxylase (PC) catalyzes the two-step carboxylation of pyruvate to produce oxaloacetate, playing a key role in the maintenance of metabolic homeostasis in cells. Given its involvement in multiple diseases, PC has been regarded as a potential therapeutic target for obesity, diabetes, and cancer. Albeit acetyl-CoA has been recognized as the allosteric regulator of PC for over 60 years, the underlying mechanism of how acetyl-CoA induces PC activation remains enigmatic. Herein, by using time-resolved cryo-electron microscopy, we have captured the snapshots of PC transitional states during its catalytic cycle. These structures and the biochemical studies reveal that acetyl-CoA stabilizes PC in a catalytically competent conformation, which triggers a cascade of events, including ATP hydrolysis and the long-distance communication between the two reactive centers. These findings provide an integrated picture for PC catalysis and unveil the unique allosteric mechanism of acetyl-CoA in an essential biochemical reaction in all kingdoms of life.

Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings.

In this Letter, a microLED-based chromatic confocal microscope with a virtual confocal slit is proposed and demonstrated for three-dimensional (3D) profiling without any mechanical scanning or external light source. In the proposed method, a micro-scale light-emitting diode (microLED) panel works as a point source array to achieve lateral scanning. Axial scanning is realized through the chromatic aberration of an aspherical objective. A virtual pinhole technique is utilized to improve the contrast and precision of depth reconstruction. The system performance has been demonstrated with a diamond-turned copper sample and onion epidermis. The experimental results show that the microLED panel could be a potential solution for portable 3D confocal microscopy. Several considerations and prospects are proposed for future microLED requirements in confocal imaging.

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

In medical imaging, deep learning-based solutions have achieved state-of-the-art performance. However, reliability restricts the integration of deep learning into practical medical workflows since conventional deep learning frameworks cannot quantitatively assess model uncertainty. In this work, we propose to address this shortcoming by utilizing a Bayesian deep network capable of estimating uncertainty to assess oral cancer image classification reliability. We evaluate the model using a large intraoral cheek mucosa image dataset captured using our customized device from high-risk population to show that meaningful uncertainty information can be produced. In addition, our experiments show improved accuracy by uncertainty-informed referral. The accuracy of retained data reaches roughly 90% when referring either 10% of all cases or referring cases whose uncertainty value is greater than 0.3. The performance can be further improved by referring more patients. The experiments show the model is capable of identifying difficult cases needing further inspection.

A new laser differential confocal measurement method with a high axial resolution and insensitivity to surface inclination is proposed for high-precision measurement of the outer surface profile of a laser inertial confinement fusion (ICF) capsule. This method uses an eccentricity automatic adjustment system to rapidly adjust the capsule’s centre to be aligned with the centreline of the rotary shaft. The capsule is then rotated by a high-precision rotation system, and the laser differential confocal measurement system (LDCS) radially measures the geometrical coordinates of each sampling point position in order to obtain the outer surface profile data of the ICF capsule. The proposed method achieves high-precision, non-contact, automatic, and rapid measurement of the outer surface profile of an ICF capsule. Theoretical analyses and preliminary experiments indicate that for the proposed method the measurement repeatability for the outer surface profile of an ICF capsule can reach 20 nm.

The structure of human Derlin-1 reveals the mechanism of misfolded protein transport across the ER membrane., Endoplasmic reticulum–associated degradation (ERAD) is a process directing misfolded proteins from the ER lumen and membrane to the degradation machinery in the cytosol. A key step in ERAD is the translocation of ER proteins to the cytosol. Derlins are essential for protein translocation in ERAD, but the mechanism remains unclear. Here, we solved the structure of human Derlin-1 by cryo–electron microscopy. The structure shows that Derlin-1 forms a homotetramer that encircles a large tunnel traversing the ER membrane. The tunnel has a diameter of about 12 to 15 angstroms, large enough to allow an α helix to pass through. The structure also shows a lateral gate within the membrane, providing access of transmembrane proteins to the tunnel, and thus, human Derlin-1 forms a protein channel for translocation of misfolded proteins. Our structure is different from the monomeric yeast Derlin structure previously reported, which forms a semichannel with another protein.

Background: Early detection of oral cancer in low-resource settings necessitates a robust, Point-of-Care screening tool that can empower Frontline-Health-Workers (FHW) for triaging high-risk populations. This study was conducted to validate the accuracy of Artificial-Neural-Network (ANN) enabled m(mobile)-Health device deployed with FHWs. Methods: The dual-imaging device enables white-light as well as auto-fluorescence imaging with both a wide and focused view. The effectiveness of the device was tested in tertiary-care/dental hospitals and low-resource settings of South/North-East India. Demographics and clinical details were documented in the device, and the subjects were screened independently, either by FHWs or along with specialists, in addition to a remote evaluation by oral cancer specialists. Simple and complex ANN were built using the images for classification of oral-potentially-malignant/malignant lesions and integrated into the mobile phone/cloud. The specialist diagnoses were compared with the histopathology, FHW, and ANN-based diagnosis to evaluate the efficacy of the oral cancer detection program. Findings: The program screened 5025 subjects with 95% (n=4728) having tele-diagnosis. Among the 16% (n=752) assessed by onsite specialists, biopsy was advised for 515, out of which 20% (n=102) underwent biopsy. The onsite specialist diagnosis showed high sensitivity (94%) and moderate specificity (72%) when compared to histology diagnosis, while tele-diagnosis showed high accuracy when compared with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, when compared with tele-diagnosis, identified suspicious lesions with less sensitivity (60%). Further, the efficacy of the FHWs in delineating suspicious lesions correlated (r= 0·80) with their prior experience in the project. The ANN (MobileNet) integrated with the phone for a real-time diagnosis could accurately delineate lesions (n=1416; sensitivity: 82%), while the cloud-based ANN (VGG19) had higher accuracy (sensitivity: 87%) when compared to tele-diagnosis. Interpretation: This study suggests that an automated mHealth-enabled dual-image system could prove a useful triaging tool, empowering FHWs towards accurate screening in low-resource settings. Clinical Trial: The study protocol was registered in the Clinical Trial Registry of the Indian Council of Medical Research (CTRI/2019/11/022167). Funding: This study is primarily funded by the National Institutes of Health (NIH), USA grants (UH2EB022623, UH3CA239682). This work is also supported by LAMMP NIH/NIBIB P41EB05890, Arnold and Mabel Beckman Foundation, California Tobacco-Related Diseases Program: T31IR1825. Declaration of Interest: We declare no competing interests. Ethical Approval: Institutional Ethics Committee approvals were obtained from the three nodal centers- The KLE Society’s Institute of Dental Sciences (KLE; ECR/887/Inst/KA/2016), Bengaluru, India, Christian Institute of Health Sciences and Research (CIHSR; EC/NEW/INST/2020/782), Dimapur, Nagaland, India, and Mazumdar Shaw Medical Center (MSMC; NNH/MEC-CL-2016-394), Bengaluru, India prior to the initiation of the clinical trial.

The bubble formation under the influence of orifice diameter submerged in yield-stress fluids was studied numerically using a coupled level-set (LS) and the volume-of-fluid (VOF) method and was in contrast with that seen in Newtonian fluids. The bubble formation process had a good consistency by virtue of comparing numerical simulation and experiment. The process of bubble formation could be divided into two parts, bubble expansion stage and stretch stage. The influence of orifice diameter and liquid rheological characteristics (consistency coefficient, flow index, and yield stress) on the bubble formation parameters (the bubble formation time, detachment volume, and aspect ratio) was investigated. The results revealed that the bubble detachment volume increases with the increase of orifice diameter, consistency coefficient, flow index, and yield stress. In different kinds of fluids, the formation time and detachment volume of bubbles in the shear-thinning fluid were the lowest, followed by the Newtonian fluid, and finally, in yield-stress fluids.

In this paper, a digital mirror device (DMD)-based chromatic confocal microscopy is proposed and demonstrated for three-dimensional (3D) surface profiling without any mechanical scanning. In this method, the DMD works as the multipoint source and multi-pinhole at the same time to achieve the lateral scanning. Moreover, axial scanning is realized through the chromatic aberration of the confocal optics. Since the micromirror array of the DMD is not perpendicular to the confocal imaging axis, a corresponding calibration is needed to eliminate the tilt effects and perform accurate 3D imaging. The measurement range with the current optical system is 45 µm over 505–650 nm working spectrum and can be increased by using a custom objective with large chromatic aberration. The system performance has been demonstrated with a multistep sample.

We use both data- and algorithm-level methods to improve the neural network performance of oral cancer image classification with imbalanced multi-class dataset captured from high-risk populations during oral cancer screening.

We will report a mobile-based dual-mode image classification method for oral cancer detection in low-resource settings.

Standard deep learning algorithms for clinical image classification are unable to understand their confidence in a decision. We developed a Bayesian deep network could estimate uncertainty to assess the reliability of oral cancer image classification.

This study experimentally investigates velocity evolution for the coalescence of two in-line bubbles rising in non-Newtonian fluids by using a high speed camera. Due to the wake of the leading bubble and the shear-thinning effect of non-Newtonian fluids, the following bubble is accelerated to approach the leading bubble, leading to the coalescence of the two bubbles. Based on the Newton’s second law and Schlichting’s wake theory, a theoretical model was developed to estimate the instantaneous rising velocity of the following bubble in the coalescence process of the two bubbles. The predicted values by the present model showed a good agreement with the experimental data.

In this study, the effects of surfactants on the hydrodynamic characteristics of bubbles in shear-thinning fluids at low Reynolds number (Re

In this study, the effect of pH on the mass transfer of oxygen bubble swarms in non-Newtonian fluids was experimentally studied. The volumetric liquid side mass transfer coefficient (kLa), liquid side mass transfer coefficient (kL), and specific interfacial area (a) were investigated. The pH was regulated by the addition of hydrochloric acid and sodium hydroxide (NaOH). It was found that the kLa increased with the gas flow rate increasing and decreased with the apparent viscosity of the liquid increasing. In the case of pH < 7, a marginal effect of pH on the gas?liquid mass transfer was observed, but when pH value was higher than 7, mass transfer was promoted with the increase of pH value. Via investigating the impact of pH on kL and a, the variation of mass transfer at pH > 7 was attributed to the decomposition of the Xanthan molecular structure by the hydroxyl of NaOH.