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Over the past few years alone, the lensing community has discovered thousands of strong lens candidates, and spectroscopically confirmed hundreds of them. In this time of abundance, it becomes pragmatic to focus our time and resources on the few extraordinary systems, in order to most efficiently study the Universe. In this paper, we present such a system: DESI-090.9854-35.9683, a cluster-scale lens at z l = 0.49, with seven observed lensed sources around the core, and additional lensed sources further out in the cluster. From the number and the textbook configuration of the lensed images, a tight constraint on the mass potential of the lens is possible. This would allow for detailed analysis on the dark and luminous matter content within galaxy clusters, as well as a probe into dark energy and high-redshift galaxies. We present our spatially resolved kinematic measurements of this system from the Very Large Telescope Multi Unit Spectroscopic Explorer, which confirm five of these source galaxies (in ascending order, at z s = 0.962, 0.962, 1.166, 1.432, and 1.432). With previous Hubble Space Telescope imaging in the F140W and F200LP bands, we also present a simple flux-based lens model consisting of two power-law profiles that, for a cluster lens, well models the five lensed arc families with redshifts. We determine the mass to be M(< θ E) = 4.78 × 1013 M ⊙ for the primary mass potential. From the model, we extrapolate the redshift of one of the two source galaxies not yet spectroscopically confirmed to be at z s = 4.52 − 0.71 + 1.03 .

In this paper, we study two pieces of malware that attempted to create blackouts in Ukraine. In particular, we design and develop a new sandbox that emulates different networks, devices, and other characteristics so that we can execute malware targeting substation equipment and understand in detail the specific sequence of actions the attackers could perform on substation equipment. We also study the effects that future similar malware can have. Our findings include new malware behavior not previously documented (such as the detailed algorithm for the MMS protocol payload) and an illustration of how attacking different targets will produce different effects.

Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding.

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.

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.

SignificanceConvolutional 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.AimDevelop 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.ApproachWe 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.ResultsThe 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.ConclusionsWe demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification.

Remdesivir (RDV) prodrug can be metabolized into a triphosphate form nucleotide analogue (RDV-TP) to bind and insert into the active site of viral RNA dependent RNA polymerase (RdRp) to further interfere with viral genome replication. In this work, we computationally studied how RDV-TP binds and inserts to the SARS-CoV-2 RdRp active site, in comparison with natural nucleotide substrate adenosine triphosphate (ATP). To do that, we first constructed atomic structural models of an initial binding complex (active site open) and a substrate insertion complex (active site closed), based on high-resolution cryo-EM structures determined recently for SARS-CoV-2 RdRp or non-structural protein (nsp) 12, in complex with accessory protein factors nsp7 and nsp8. By conducting all-atom molecular dynamics simulation with umbrella sampling strategies on the nucleotide insertion between the open and closed state RdRp complexes, our studies show that RDV-TP can initially bind in a comparatively stabilized state to the viral RdRp active site, as it primarily forms base stacking with the template uracil nucleotide (nt +1), which under freely fluctuations supports a low free energy barrier of the RDV-TP insertion (∼1.5 kcal mol-1). In comparison, the corresponding natural substrate ATP binds initially to the RdRp active site in Watson-Crick base pairing with the template nt, and inserts into the active site with a medium low free energy barrier (∼2.6 kcal mol-1), when the fluctuations of the template nt are well quenched. The simulations also show that the initial base stacking of RDV-TP with the template can be specifically stabilized by motif C-S759, S682 (near motif B) with the base, and motif G-K500 with the template backbone. Although the RDV-TP insertion can be hindered by motif F-R555/R553 interaction with the triphosphate, the ATP insertion seems to be facilitated by such interactions. The inserted RDV-TP and ATP can be further distinguished by specific sugar interaction with motif B-T687 and motif A-D623, respectively.

Remdesivir (RDV) prodrug can be metabolized into a triphosphate form nucleotide analogue (RDV-TP) to bind and insert into the active site of viral RNA dependent RNA polymerase (RdRp) to further interfere with viral genome replication. In this work, we computationally studied how RDV-TP binds and inserts to the SARS-CoV-2 RdRp active site, in comparison with natural nucleotide substrate adenosine triphosphate (ATP). To do that, we first constructed atomic structural models of an initial binding complex (active site open) and a substrate insertion complex (active site closed), based on high-resolution cryo-EM structures determined recently for SARS-CoV-2 RdRp or non-structural protein (nsp) 12, in complex with accessory protein factors nsp7 and nsp8. By conducting all-atom molecular dynamics simulation with umbrella sampling strategies on the nucleotide insertion between the open and closed state RdRp complexes, our studies show that RDV-TP can initially bind in a comparatively stabilized state to the viral RdRp active site, as it primarily forms base stacking with the template uracil nucleotide (nt +1), which under freely fluctuations supports a low free energy barrier of the RDV-TP insertion (∼1.5 kcal mol-1). In comparison, the corresponding natural substrate ATP binds initially to the RdRp active site in Watson-Crick base pairing with the template nt, and inserts into the active site with a medium low free energy barrier (∼2.6 kcal mol-1), when the fluctuations of the template nt are well quenched. The simulations also show that the initial base stacking of RDV-TP with the template can be specifically stabilized by motif C-S759, S682 (near motif B) with the base, and motif G-K500 with the template backbone. Although the RDV-TP insertion can be hindered by motif F-R555/R553 interaction with the triphosphate, the ATP insertion seems to be facilitated by such interactions. The inserted RDV-TP and ATP can be further distinguished by specific sugar interaction with motif B-T687 and motif A-D623, respectively.

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.

SignificanceEarly 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.AimTo reduce the class bias caused by data imbalance.ApproachWe 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.ResultsBy 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.ConclusionsExperimental 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.

SignificanceEarly 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.AimTo reduce the class bias caused by data imbalance.ApproachWe 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.ResultsBy 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.ConclusionsExperimental 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.

ObjectiveThe hypotension prediction index (HPI) is a novel parameter developed by Edwards Lifesciences (Irvine, CA) that is obtained through an algorithm based on arterial pressure waveform characteristics. Past studies have demonstrated its accuracy in predicting hypotensive events in noncardiac surgeries. The authors aimed to evaluate the use of the HPI in cardiac surgeries requiring cardiopulmonary bypass (CPB).DesignProspective cohort feasibility study.SettingSingle university medical center.ParticipantsSequential adult patients undergoing elective cardiac surgeries requiring CPB between October 1, 2018, and December 31, 2018.InterventionsHPI monitor was connected to the patient's arterial pressure transducer. Anesthesiologists and surgeons were blinded to the monitor output.Measurements and main resultsHPI values and hypotensive events were recorded before and after CPB. The primary outcomes were the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity of HPI predicting hypotension. The AUC, sensitivity, and specificity for HPI lead time to hypotension five minutes before the event were 0.90 (95% confidence interval [CI]: 0.853-0.949), 84% (95% CI: 77.7-90.5), and 84% (95% CI: 70.9-96.8), respectively. Ten minutes before the event AUC, sensitivity, and specificity for HPI lead time to hypotension were 0.83 (95% CI: 0.750-0.905), 79% (95% CI: 69.8-88.1), and 74% (95% CI: 58.8-89.6), respectively. Fifteen minutes before the hypotensive event AUC, sensitivity, and specificity for HPI lead time to hypotension were 0.83 (95% CI: 0.746-0.911), 79% (95% CI: 68.4-89.0), and 74% (95% CI: 58.8-89.6), respectively.ConclusionHPI predicted hypotensive episodes during cardiac surgeries with a high degree of sensitivity and specificity.

SignificanceOral 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.AimTo develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection.ApproachThe 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.ResultsWe 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.ConclusionsOur study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

SignificanceOral 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.AimTo develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection.ApproachThe 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.ResultsWe 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.ConclusionsOur study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

Idiopathic intracranial hypertension (IIH)• Elevated intracranial pressure (ICP) with no clear cause• Results in stenosed venous transverse sinus (TS)•Stenosis causes pulsatile tinnitusTreatment – Venous Sinus Stenting (VSS)• Performed when the intravascular pressure (IVP) gradient across the stenosis is above 8 mmHg• 10.3% revision surgery rate• Most common reason for revision is the occurrence of a new stenosis upstream of the stent• There is currently no standardized criteria for the selection of stents to be used in this procedure; they are used off-label

HypothesisWe hypothesize patient-specific flow models to be an adequate in vitro surrogate to allow for characterization of pulsatile tinnitus (PT) that affects three to five million Americans.BackgroundPT, rhythmic sounds without an extracorporeal source that patients appreciate, can be caused by aberrant blood flow in large cerebral veins near the cochlea. To investigate the sound production mechanism, we created 3D printed flow models based on patient-specific cerebral venous anatomies.MethodsMagnetic resonance angiography datasets from two patients with PT were used to generate patient-specific 3D printed flow models. A flow circuit connecting the patient-specific models to a pulsatile, continuous flow pump simulating cardiac cycle was created. Sound recordings were made along the surface of the models using an electronic stethoscope. Peak-to-rms amplitude, and area under the power spectral density (PSD) curve values were computed to evaluate the sound measurements. Wilcoxon rank sum test was used to statistically determine the differences in measurements between the patient-specific models.ResultsIn patient-1, the recordings (peak-to-rms) from the internal jugular vein stenosis of baseline model (4.29 ± 1.26 for 146 samples) were significantly louder (p

Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.

Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.

The Internet of Things (IoT) enables us to sense and share information of real-world events, including potentially privacy-sensitive information about the users' choices and behaviors. In this paper we focus on the security and privacy problems of Internet-connected cameras. We study two cameras: a consumer camera marketed as a baby monitor, and a surveillance camera marketed for enterprise (physical) security. We show how a generic algorithm can be used to infer actions recorded by the camera, even if the traffic is encrypted, and we also show how both cameras have security vulnerabilities that allow a remote attacker to gain access to the video frames captured by the camera. We also discuss new findings such as the fact that one camera has multiple vendors and domains that connect to a single cloud system supported by a single company, which is a trend we have previously seen in other IoT devices with one company designing the core-functionality of the device and then multiple vendors selling the device under their own brand name and developing different mobile applications for them.

With the goal to screen high-risk populations for oral cancer in low- and middle-income countries (LMICs), we have developed a low-cost, portable, easy to use smartphone-based intraoral dual-modality imaging platform. In this paper we present an image classification approach based on autofluorescence and white light images using deep learning methods. The information from the autofluorescence and white light image pair is extracted, calculated, and fused to feed the deep learning neural networks. We have investigated and compared the performance of different convolutional neural networks, transfer learning, and several regularization techniques for oral cancer classification. Our experimental results demonstrate the effectiveness of deep learning methods in classifying dual-modal images for oral cancer detection.

With the goal to screen high-risk populations for oral cancer in low- and middle-income countries (LMICs), we have developed a low-cost, portable, easy to use smartphone-based intraoral dual-modality imaging platform. In this paper we present an image classification approach based on autofluorescence and white light images using deep learning methods. The information from the autofluorescence and white light image pair is extracted, calculated, and fused to feed the deep learning neural networks. We have investigated and compared the performance of different convolutional neural networks, transfer learning, and several regularization techniques for oral cancer classification. Our experimental results demonstrate the effectiveness of deep learning methods in classifying dual-modal images for oral cancer detection.

AimThe incidence of oral cancer is high in India, which can be reduced by early detection. We aimed to empower frontline health care providers (FHP) for early detection and connect specialist to rural population through mHealth.Materials and methodsWe provided training to FHPs in examination of oral cavity, use of mobile phone for image capture, and risk factor analysis. The FHPs were selected from different cohorts in resource-constrained settings. The workflow involved screening of high-risk individuals in door-to-door and workplace settings, and capture of images of suspected lesions. Uploaded data were interpreted and recommendation was sent by specialist from a remote location. Their recommendation was intimated to FHPs who arranged for further action. Two more initiatives, one for multiple dental schools and another for private practitioners, were undertaken.ResultsDuring the period from 2010 to 2018, 42,754 subjects have been screened, and 5,406 subjects with potentially malignant disorders have been identified. The prevalence of potentially malignant disorders varied from 0.8 to 62% at different cohorts; 516 biopsies have been performed at remote locations.ConclusionConnecting specialists to rural population was made possible through the use of mobile health. Trained FHP were able to reach out to the population. Electronic data capture facilitated efficient follow-up. The program was very cost-effective with screening completed under $1 per person.Clinical significanceIn view of the high incidence of oral cancer in India, and the resource-constrained settings, mobile health paves the way for better access to specialist care for the rural population.

AimThe incidence of oral cancer is high in India, which can be reduced by early detection. We aimed to empower frontline health care providers (FHP) for early detection and connect specialist to rural population through mHealth.Materials and methodsWe provided training to FHPs in examination of oral cavity, use of mobile phone for image capture, and risk factor analysis. The FHPs were selected from different cohorts in resource-constrained settings. The workflow involved screening of high-risk individuals in door-to-door and workplace settings, and capture of images of suspected lesions. Uploaded data were interpreted and recommendation was sent by specialist from a remote location. Their recommendation was intimated to FHPs who arranged for further action. Two more initiatives, one for multiple dental schools and another for private practitioners, were undertaken.ResultsDuring the period from 2010 to 2018, 42,754 subjects have been screened, and 5,406 subjects with potentially malignant disorders have been identified. The prevalence of potentially malignant disorders varied from 0.8 to 62% at different cohorts; 516 biopsies have been performed at remote locations.ConclusionConnecting specialists to rural population was made possible through the use of mobile health. Trained FHP were able to reach out to the population. Electronic data capture facilitated efficient follow-up. The program was very cost-effective with screening completed under $1 per person.Clinical significanceIn view of the high incidence of oral cancer in India, and the resource-constrained settings, mobile health paves the way for better access to specialist care for the rural population.

Oral cancer is a growing health issue in a number of low- and middle-income countries (LMIC), particularly in South and Southeast Asia. The described dual-modality, dual-view, point-of-care oral cancer screening device, developed for high-risk populations in remote regions with limited infrastructure, implements autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform, enabling early detection of pre-cancerous and cancerous lesions in the oral cavity with the potential to reduce morbidity, mortality, and overall healthcare costs. Using a custom Android application, this device synchronizes external light-emitting diode (LED) illumination and image capture for AFI and WLI. Data is uploaded to a cloud server for diagnosis by a remote specialist through a web app, with the ability to transmit triage instructions back to the device and patient. Finally, with the on-site specialist's diagnosis as the gold-standard, the remote specialist and a convolutional neural network (CNN) were able to classify 170 image pairs into 'suspicious' and 'not suspicious' with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81.25% to 94.94%.

Oral cancer is a growing health issue in a number of low- and middle-income countries (LMIC), particularly in South and Southeast Asia. The described dual-modality, dual-view, point-of-care oral cancer screening device, developed for high-risk populations in remote regions with limited infrastructure, implements autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform, enabling early detection of pre-cancerous and cancerous lesions in the oral cavity with the potential to reduce morbidity, mortality, and overall healthcare costs. Using a custom Android application, this device synchronizes external light-emitting diode (LED) illumination and image capture for AFI and WLI. Data is uploaded to a cloud server for diagnosis by a remote specialist through a web app, with the ability to transmit triage instructions back to the device and patient. Finally, with the on-site specialist's diagnosis as the gold-standard, the remote specialist and a convolutional neural network (CNN) were able to classify 170 image pairs into 'suspicious' and 'not suspicious' with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81.25% to 94.94%.

There are increasing evidences suggesting bone marrow adiposity tissue (MAT) plays a critical role in affecting both bone quantity and quality. However, very limited studies that have investigated the association between the composition of MAT and bone mineral density (BMD). The goal of this study was to quantify MAT unsaturation profile of marrow samples from post-menopausal women using ex vivo high-resolution magic angle spinning (HRMAS) proton nuclear magnetic resonance (1H NMR) spectroscopy, and to investigate the relationship between MAT composition and BMD. Bone marrow samples were obtained by iliac crest aspiration during surgical procedures from 24 postmenopausal women (65-89years) who had hip surgery due to bone fracture or arthroplasty. Marrow fat composition parameters, in particular, unsaturation level (UL), mono-unsaturation level (MUL) and saturation level (SL), were quantified using HRMAS 1H NMR spectroscopy. The patients were classified into three groups based on the DXA BMD T-scores: controls, osteopenia and osteoporosis. Marrow fat composition was compared between these three groups as well as between subjects with and without factures using ANOCOVA, adjusted for age. Subjects with lower BMD (n=17) had significantly lower MUL (P=0.003) and UL (P=0.039), and significantly higher SL (P=0.039) compared to controls (n=7). When separating lower BMD into osteopenia (n=9) and osteoporosis (n=8) groups, subjects with osteopenia had significantly lower MUL (P=0.002) and UL (P=0.010), and significantly higher SL (P=0.010) compared to healthy controls. No significant difference was observed between subjects with osteopenia and osteoporosis. Using HRMAS 1H NMR, significantly lower unsaturation and significantly higher saturation levels were observed in the marrow fat of subjects with lower BMD. HRMAS 1H NMR was shown to be a powerful tool for identifying novel MR markers of marrow fat composition that are associated with bone quality and potentially fracture, and other bone pathologies and changes after treatment. A better understanding of the relationship between bone marrow composition and bone quality in humans may identify novel treatment targets, and provide guidance on novel interventions and therapeutic strategies for bone preservation.

PurposeTo quantitatively evaluate longitudinal changes in water and lipid in knee bone marrow with and without bone marrow edema-like lesions (BMELs) in subjects with acutely ruptured anterior cruciate ligaments (ACLs) or osteoarthritis (OA) using three-dimensional magnetic resonance spectroscopic imaging (3D MRSI).Material and methodsTen ACL and 10 OA subjects who presented with BMEL and seven BMEL-free controls were scanned at 3T. All ACL and OA subjects had one-year follow-up scans. 3D MRSI was acquired in BMEL and adjacent bone marrow, and water content (WC) and unsaturated lipid index (UI) were calculated in each region of interest.ResultsAt baseline, ACL BMEL WC was significantly higher than ACL non-BMEL, OA BMEL, and control WC; ACL non-BMEL WC, ACL BMEL UI, and OA BMEL WC were significantly higher than control. ACL BMEL WC decreased significantly one year post-reconstruction; UI decreased non-significantly (p=0.09). No significant changes in OA BMEL or ACL and OA non-BMEL WC and UI were observed.Conclusion3D MRSI is a powerful method of quantitatively assessing the biochemical composition of bone marrow in OA and ACL-injured knees, which may serve as imaging markers to improve comprehension of primary and secondary OA pathology.

Osteoarthritis (OA) is a common multifactorial and heterogeneous degenerative joint disease, and biochemical changes in cartilage matrix occur during the early stages of OA before morphological changes occur. Thus, it is desired to measure regional biochemical changes in the joint. High-resolution magic angle spinning (HRMAS) NMR spectroscopy is a powerful method of observing cartilaginous biochemical changes ex vivo, including the concentrations of alanine and N-acetyl, which are markers of collagen and total proteoglycan content, respectively. Previous studies have observed significant changes in chondrocyte metabolism of OA cartilage via the altered gene expression profiles of ACAN, COL2A1 and MMP13, which encode aggrecan, type II collagen and matrix metalloproteinase 13 (a protein crucial in the degradation of type II collagen), respectively. Employing HRMAS, this study aimed to elucidate potential relationships between N-acetyl and/or alanine and ACAN, COL2A1 and/or MMP13 expression profiles in OA cartilage. Thirty samples from the condyles of five subjects undergoing total knee arthroplasty to treat OA were collected. HRMAS spectra were obtained at 11.7 T for each sample. RNA was subsequently extracted to determine gene expression profiles. A significant negative correlation between N-acetyl metabolite and ACAN gene expression levels was observed; this provides further evidence of N-acetyl as a biomarker of cartilage degeneration. The alanine doublet was distinguished in the spectra of 15 of the 30 specimens of this study. Alanine can only be detected with HRMAS NMR spectroscopy when the collagen framework has been degraded such that alanine is sufficiently mobile to form a distinguished peak in the spectrum. Thus, HRMAS NMR spectroscopy may provide unique localized measurements of collagenous degeneration in OA cartilage. The identification of imaging markers that could provide a link between OA pathology and chondrocyte metabolism will facilitate the development of more sensitive diagnostic techniques and will improve methods of monitoring treatment for patients suffering from OA.

PurposeTo develop a robust sequence that combines T1ρ and T2 quantifications and to examine the in vivo repeatability and diurnal variation of T1ρ and T2 quantifications in knee cartilage.Materials and methodsSix healthy volunteers were scanned in the morning and afternoon on 2 days using a combined T1ρ and T2 quantification sequence developed in this study. Repeatability of T1ρ and T2 quantification was estimated using root-mean-square coefficients-of-variation (RMS-CV). T1ρ and T2 values from morning scans were compared to those from afternoon scans using paired t-tests.ResultsThe overall RMS-CV of in vivo T1ρ and T2 quantification was 5.3% and 5.2%, respectively. The RMS-CV of am scans was 4.2% and 5.0% while the RMS-CV of pm scans was 6.0% and 6.3% for T1ρ and T2 , respectively. No significant difference was found between T1ρ or T2 values in the morning and in the afternoon.ConclusionA sequence that combines T1ρ and T2 quantification with scan time less than 10 minutes and is robust to B0 and B1 inhomogeneity was developed with excellent repeatability. For a cohort with low-level daily activity, although no significant diurnal variation of cartilage MR relaxation times was observed, the afternoon scans had inferior repeatability compared to morning scans.

BACKGROUND: Small non-coding RNAs (sRNA) are emerging as major components of the cells regulatory network, several possessing their own regulons. A few sRNAs have been reported as being involved in general or toxic-metabolite stress, mostly in Gram- prokaryotes, but hardly any in Gram+ prokaryotes. Significantly, the role of sRNAs in the stress response remains poorly understood at the genome-scale level. It was previously shown that toxic-metabolite stress is one of the most comprehensive and encompassing stress responses in the cell, engaging both the general stress (or heat-shock protein, HSP) response as well as specialized metabolic programs. RESULTS: Using RNA deep sequencing (RNA-seq) we examined the sRNome of C. acetobutylicum in response to the native but toxic metabolites, butanol and butyrate. 7.5% of the RNA-seq reads mapped to genome outside annotated ORFs, thus demonstrating the richness and importance of the small RNome. We used comparative expression analysis of 113 sRNAs we had previously computationally predicted, and of annotated mRNAs to set metrics for reliably identifying sRNAs from RNA-seq data, thus discovering 46 additional sRNAs. Under metabolite stress, these 159 sRNAs displayed distinct expression patterns, a select number of which was verified by Northern analysis. We identified stress-related expression of sRNAs affecting transcriptional (6S, S-box &solB) and translational (tmRNA & SRP-RNA) processes, and 65 likely targets of the RNA chaperone Hfq. CONCLUSIONS: Our results support an important role for sRNAs for understanding the complexity of the regulatory network that underlies the stress response in Clostridium organisms, whether related to normophysiology, pathogenesis or biotechnological applications.

ContextBone marrow fat (BMF) and bone mineral density (BMD) by dual x-ray energy absorptiometry (DXA) are negatively correlated. However, little is known about the association of BMF with fracture or with separate trabecular and cortical bone compartments.ObjectiveOur objective was to assess the relationships between vertebral BMF, BMD by quantitative computed tomography, and fracture in older adults.Design, setting, and participantsWe conducted a cross-sectional study in the Age Gene/Environment Susceptibility-Reykjavik cohort.Main outcome measuresOutcomes measures included vertebral BMF (L1-L4) measured with magnetic resonance spectroscopy, quantitative computed tomography and DXA scans of the hip and spine, and DXA vertebral fracture assessments. Previous clinical fracture was determined from medical records.ResultsIn 257 participants without recent bone-active medication use, mean age was 79 (SD 3.1) years. Mean BMF was 53.5% ± 8.1% in men and 55.0% ± 8.4% in women. Those with prevalent vertebral fracture (21 men, 32 women) had higher mean BMF in models adjusted for BMD. In separate models by sex, the difference was statistically significant only in men (57.3% vs 52.8%, P = 0.02). BMF was associated with lower trabecular volumetric BMD (vBMD) at the spine (-10.5% difference for each 1 SD increase in BMF, P < 0.01), total hip, and femoral neck, but not with cortical vBMD, in women. In men, BMF was marginally associated with trabecular spine vBMD (-6.1%, P = 0.05). Total hip and spine areal BMD (aBMD) were negatively correlated with BMF in women only.ConclusionHigher marrow fat correlated with lower trabecular, but not cortical, BMD in older women but not men. Higher marrow fat was associated with prevalent vertebral fracture in men, even after adjustment for BMD.

ContextBone marrow fat (BMF) and bone mineral density (BMD) by dual x-ray energy absorptiometry (DXA) are negatively correlated. However, little is known about the association of BMF with fracture or with separate trabecular and cortical bone compartments.ObjectiveOur objective was to assess the relationships between vertebral BMF, BMD by quantitative computed tomography, and fracture in older adults.Design, setting, and participantsWe conducted a cross-sectional study in the Age Gene/Environment Susceptibility-Reykjavik cohort.Main outcome measuresOutcomes measures included vertebral BMF (L1-L4) measured with magnetic resonance spectroscopy, quantitative computed tomography and DXA scans of the hip and spine, and DXA vertebral fracture assessments. Previous clinical fracture was determined from medical records.ResultsIn 257 participants without recent bone-active medication use, mean age was 79 (SD 3.1) years. Mean BMF was 53.5% ± 8.1% in men and 55.0% ± 8.4% in women. Those with prevalent vertebral fracture (21 men, 32 women) had higher mean BMF in models adjusted for BMD. In separate models by sex, the difference was statistically significant only in men (57.3% vs 52.8%, P = 0.02). BMF was associated with lower trabecular volumetric BMD (vBMD) at the spine (-10.5% difference for each 1 SD increase in BMF, P < 0.01), total hip, and femoral neck, but not with cortical vBMD, in women. In men, BMF was marginally associated with trabecular spine vBMD (-6.1%, P = 0.05). Total hip and spine areal BMD (aBMD) were negatively correlated with BMF in women only.ConclusionHigher marrow fat correlated with lower trabecular, but not cortical, BMD in older women but not men. Higher marrow fat was associated with prevalent vertebral fracture in men, even after adjustment for BMD.