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Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists. We developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution. In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.

Efficient interconversion of both classical and quantum information between microwave and optical frequency is an important engineering challenge. The optomechanical approach with gigahertz-frequency mechanical devices has the potential to be extremely efficient due to the large optomechanical response of common materials, and the ability to localize mechanical energy into a micron-scale volume. However, existing demonstrations suffer from some combination of low optical quality factor, low electrical-to-mechanical transduction efficiency, and low optomechanical interaction rate. Here we demonstrate an on-chip piezo-optomechanical transducer that systematically addresses all these challenges to achieve nearly three orders of magnitude improvement in conversion efficiency over previous work. Our modulator demonstrates acousto-optic modulation with V π = 0.02 V. We show bidirectional conversion efficiency of 1 0 − 5 with 3.3 μW  red-detuned optical pump, and 5.5 % with 323 μW blue-detuned pump. Further study of quantum transduction at millikelvin temperatures is required to understand how the efficiency and added noise are affected by reduced mechanical dissipation, thermal conductivity, and thermal capacity. Current optomechanical implementations of microwave and optical frequency interconversion are lacking in efficiency and interaction strength. The authors design and demonstrate an on-chip piezo-optomechanical solution which overcomes several technical barriers to reach several orders of magnitude improvement in efficiency.

Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation. Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

Tension-induced tunable mode coupling in graphene drums enables coherent energy transfer between mechanical modes to realize strong coupling and amplification. Mechanical resonators are ubiquitous in modern information technology. With the possibility of coupling them to electromagnetic and plasmonic modes, they hold promise as the key building blocks in future quantum information technology. Graphene-based resonators are of interest for technological applications due to their high resonant frequencies, multiple mechanical modes and low mass 1 , 2 , 3 , 4 , 5 , 6 , 7 . The tension-mediated nonlinear coupling between various modes of the resonator can be excited in a controllable manner 8 , 9 , 10 , 11 . Here we engineer a graphene resonator with large frequency tunability at low temperatures, resulting in a large intermodal coupling strength. We observe the emergence of new eigenmodes and amplification of the coupled modes using red and blue parametric excitation, respectively. We demonstrate that the dynamical intermodal coupling is tunable. A cooperativity of 60 between two resonant modes of ∼100 MHz is achieved in the strong coupling regime. The ability to dynamically control the coupling between the high-frequency eigenmodes of a mechanical system opens up the possibility of quantum mechanical experiments at low temperatures 12 , 13 .

Optically addressable spins associated with defects in wide-bandgap semiconductors are versatile platforms for quantum information processing and nanoscale sensing, where spin-dependent inter-system crossing transitions facilitate optical spin initialization and readout. Recently, the van der Waals material hexagonal boron nitride (h-BN) has emerged as a robust host for quantum emitters, promising efficient photon extraction and atom-scale engineering, but observations of spin-related effects have remained thus far elusive. Here, we report room-temperature observations of strongly anisotropic photoluminescence patterns as a function of applied magnetic field for select quantum emitters in h-BN. Field-dependent variations in the steady-state photoluminescence and photon emission statistics are consistent with an electronic model featuring a spin-dependent inter-system crossing between triplet and singlet manifolds, indicating that optically-addressable spin defects are present in h-BN. The observation of magnetic field dependence of defects hosted in hBN has been elusive so far. Here, the authors perform an investigation of spin-related effects in the optical emission from hBN defects, and observe a magnetic field dependence in the intensity of the photoluminescence spectrum.

Birth of a black-hole relativistic jet Two groups report observations of the X-ray source Swift J164449.3+573451, which was discovered when it triggered the Swift Burst Alert Telescope on 28 March 2011. Burrows et al . report that the source has increased in brightness in the X-ray band more than 10,000-fold since 1990, and by more than 100-fold since early 2010. They conclude that we are observing the onset of relativistic jet activity from a supermassive black hole. Zauderer et al . arrive at a similar conclusion based on their observation of a radio transient associated with the source, and extensive monitoring at centimetre to millimetre wavelengths during the first month of its evolution. They estimate the mass of the black hole at around 10 6 solar masses. Active galactic nuclei, which are powered by long-term accretion onto central supermassive black holes, produce 1 relativistic jets with lifetimes of at least one million years, and the observation of the birth of such a jet is therefore unlikely. Transient accretion onto a supermassive black hole, for example through the tidal disruption 2 , 3 of a stray star, thus offers a rare opportunity to study the birth of a relativistic jet. On 25 March 2011, an unusual transient source (Swift J164449.3+573451) was found 4 , potentially representing 5 , 6 such an accretion event. Here we report observations spanning centimetre to millimetre wavelengths and covering the first month of evolution of a luminous radio transient associated with Swift J164449.3+573451. The radio transient coincides 7 with the nucleus of an inactive galaxy. We conclude that we are seeing a newly formed relativistic outflow, launched by transient accretion onto a million-solar-mass black hole. A relativistic outflow is not predicted in this situation, but we show that the tidal disruption of a star naturally explains the observed high-energy properties and radio luminosity and the inferred rate of such events. The weaker beaming in the radio-frequency spectrum relative to γ-rays or X-rays suggests that radio searches may uncover similar events out to redshifts of z  ≈ 6.

In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management.

Accurate and quantitative regional estimates of the carbon budget require an integration of eddy covariance (EC) flux-tower observations and remote sensing in ecosystem models. In this study, a simple remote sensing driven light use efficiency (LUE) model was used to estimate the primary productivity for major cropping systems using multi-temporal satellite data over the Saharanpur district in India. The model is based on radiation absorption and its conversion into biomass. The LUE model was implemented for major crop rotations derived from the time-series of Sentinel-2 and Landsat 8 with monthly satellite-based spatially explicit fields of photosynthetically active radiation (PAR), fraction of absorbed PAR ( fAPAR ) and down-regulated light use efficiency. Incident PAR and fAPAR were estimated on monthly basis from the ground-calibrated empirical equation using INSAT-3D insolation product and remote sensing–based vegetation indices, respectively. Spatial LUE maps created by down-regulating maximum LUE (EC tower-based) with water and temperature stressors derived from land surface water index (LSWI) and EC-based cardinal temperature, respectively. LUE-based modeled GPP over the sugarcane-wheat system was found higher than the rice-wheat system in Saharanpur district. This is because C4 crop (sugarcane) has very high photosynthetic efficiency compared to C3 crops (rice and wheat). Modeled GPP over the sugarcane-wheat system was found in good agreement with observed EC tower-based GPP (Index of Agreement = 0.93). Further regionally calibrated remote sensing–based LUE model well captures gross photosynthesis rates (GPP) over cropland ecosystem compared to globally modeled MODIS GPP product.

Background: Human papillomavirus (HPV)-positive oropharyngeal cancer (OPSCC) is associated with improved survival compared with HPV-negative disease. However, a minority of HPV-positive patients have poor prognosis. Currently, there is no generally accepted strategy for identifying these patients. Methods: We retrospectively analysed 270 consecutively treated OPSCC patients from three centres for effects of clinical, pathological, immunological, and molecular features on disease mortality. We used Cox regression to examine associations between factors and OPSCC death, and developed a prognostic model for 3-year mortality using logistic regression analysis. Results: Patients with HPV-positive tumours showed improved survival (hazard ratio (HR), 0.33 (0.21–0.53)). High levels of tumour-infiltrating lymphocytes (TILs) stratified HPV-positive patients into high-risk and low-risk groups (3-year survival; HPV-positive/TIL high =96%, HPV-positive/TIL low =59%). Survival of HPV-positive/TIL low patients did not differ from HPV-negative patients (HR, 1.01; P =0.98). We developed a prognostic model for HPV-positive tumours using a ‘training’ cohort from one centre; the combination of TIL levels, heavy smoking, and T-stage were significant (AUROC=0·87). This model was validated on patients from the other centres (detection rate 67%; false-positive rate 5.6%; AUROC=0·82). Interpretation: Our data suggest that an immune response, reflected by TIL levels in the primary tumour, has an important role in the improved survival seen in most HPV-positive patients, and is relevant for the clinical evaluation of HPV-positive OPSCC.

Extreme heat poses significant threats to human life and ecosystems. Quantifying the effects of anthropogenic climate change on extreme heat has remained challenging, in part due to the short observational record. Here, we isolate the most slowly varying component of the frequency at which the historical 90th and 99th percentiles were exceeded in observational records from 1955 to 2021 by using a statistical method called low‐frequency component analysis. The emerging spatiotemporal signal in the changing frequency of temperature extremes can be attributed to a shift of the temperature distribution by local warming of the annual‐mean daily maximum temperature. The shift explains over 80% of the interannual variability in the frequency at which the historical 90th percentile is exceeded in the tropics and up to 50% in higher latitudes. This work connects variability in the frequency of extreme surface temperatures to variability in mean local warming. Plain Language Summary Over the past few decades, regions across the globe have experienced substantial increases in surface temperature extremes, posing significant threats to human life, as well as critical agriculture and energy sectors. Due to the relatively short observational record, it has been difficult to disentangle the relative roles of natural variability and anthropogenic forcing in driving changes to temperature extremes. Here, we introduce a simple framework for understanding the increasing frequency of surface temperature extremes by employing a statistical method to isolate the most slowly changing, and hence most likely anthropogenic, component of surface temperature extremes. We find that the emerging signal in the changing frequency of temperature extremes is largely driven by a shift in the temperature distribution by mean local warming. The shift explains over 80% of the observed variability in exceeding the 90th percentile in the tropics and up to 50% in higher latitudes. It also explains why changes in the frequency of extremes appear to be more rapid than changes closer to the center of the temperature distribution. This work offers guidance for climate risk assessment and adaptation strategies by connecting variability in the frequency of extreme temperatures to variability in mean warming at a given location. Key Points Shifting the surface temperature distribution by mean local warming explains much of the frequency increase in observed temperature extremes Mean local warming explains 80% of the observed variability in 90th percentile exceedance in the tropics and up to 50% in higher latitudes Narrower temperature distributions in the tropics are associated with a larger increase in extreme heat frequency compared to midlatitudes