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Instance-level tooth segmentation extracts abundant localization and shape information from panoramic radiographs (PRs). The aim of this study was to evaluate the performance of a mask refinement network that extracts precise tooth edges.

A public dataset which consists of 543 PRs and 16211 labelled teeth was utilized. The structure of a typical Mask Region-based Convolutional Neural Network (Mask RCNN) was used as the baseline. A novel loss function was designed focus on producing accurate mask edges. In addition to our proposed method, 3 existing tooth segmentation methods were also implemented on the dataset for comparative analysis. The average precisions (APs), mean intersection over union (mIoU), and mean Hausdorff distance (mHAU) were exploited to evaluate the performance of the network.

A novel mask refinement region-based convolutional neural network was designed based on Mask RCNN architecture to extract refined masks for individual tooth on PRs. A total of 3311 teeth were correctly detected from 3382 tested teeth in 111 PRs. The AP, precision, and recall were 0.686, 0.979, and 0.952, respectively. Moreover, the mIoU and mHAU achieved 0.941 and 9.7, respectively, which are significantly better than the other existing segmentation methods.

This study proposed an efficient deep learning algorithm for accurately extracting the mask of any individual tooth from PRs. Precise tooth masks can provide valuable reference for clinical diagnosis and treatment. This algorithm is a fundamental basis for further automated processing applications.

 

Metallic nanostructures supporting surface plasmon modes can concentrate optical fields, and enhance luminescence processes from the metal surface at plasmonic hotspots. Such nanoplasmonic metal luminescence contributes to the spectral background in surface-enhanced Raman spectroscopy (SERS) measurements and is helpful in bioimaging, nano-thermometry, and chemical reaction monitoring applications. Despite increasing interest in nanoplasmonic metal luminescence, little attention has been paid to investigating its dependence on voltage modulation. Also, the hyphenated electrochemical surface-enhanced Raman spectroscopy (EC-SERS) technique typically ignores voltage-dependent spectral background information associated with nanoplasmonic metal luminescence due to limited mechanistic understanding and poor measurement reproducibility. Here, we report a combined experiment and theory study on dynamic voltage-modulated nanoplasmonic metal luminescence from hotspots at the electrode-electrolyte interface using multiresonant nanolaminate nano-optoelectrode arrays. Our EC-SERS measurements under 785 nm laser excitation demonstrate that short-wavenumber nanoplasmonic metal luminescence associated with plasmon-enhanced electronic Raman scattering (PE-ERS) exhibits a negative voltage modulation slope (up to ≈30 % V-1) in physiological ionic solutions. Furthermore, we have developed a phenomenological model to intuitively capture plasmonic, electronic, and ionic characteristics at the metal-electrolyte interface to understand the observed dependence of the PE-ERS voltage modulation slope on voltage polarization and ionic strength. The current work represents a critical step toward the general application of nanoplasmonic metal luminescence signals in optical voltage biosensing, hybrid optical-electrical signal transduction, and interfacial electrochemical monitoring. 

Many outbreaks of emerging disease ( e.g. , avian influenza, SARS, MERS, Ebola, COVID-19) are caused by viruses. In addition to direct person-to-person transfer, the movement of these viruses through environmental matrices (water, air, and food) can further disease transmission. There is a pressing need for rapid and sensitive virus detection in environmental matrices. Nanomaterial-based sensors (nanosensors), which take advantage of the unique optical, electrical, or magnetic properties of nanomaterials, exhibit significant potential for environmental virus detection. Interactions between viruses and nanomaterials (or recognition agents on the nanomaterials) can induce detectable signals and provide rapid response times, high sensitivity, and high specificity. Facile and field-deployable operations can be envisioned due to the small size of the sensing elements. In this frontier review, we summarize virus transmission via environmental pathways and then comprehensively discuss recent applications of nanosensors to detect various viruses. This review provides guidelines for virus detection in the environment through the use of nanosensors as a tool to decrease environmental transmission of current and emerging diseases. 

Surface-enhanced Raman spectroscopy (SERS) has great potential as an analytical technique for environmental analyses. In this study, we fabricated highly porous gold (Au) supraparticles ( i.e. , ∼100 μm diameter agglomerates of primary nano-sized particles) and evaluated their applicability as SERS substrates for the sensitive detection of environmental contaminants. Facile supraparticle fabrication was achieved by evaporating a droplet containing an Au and polystyrene (PS) nanoparticle mixture on a superamphiphobic nanofilament substrate. Porous Au supraparticles were obtained through the removal of the PS phase by calcination at 500 °C. The porosity of the Au supraparticles was readily adjusted by varying the volumetric ratios of Au and PS nanoparticles. Six environmental contaminants (malachite green isothiocyanate, rhodamine B, benzenethiol, atrazine, adenine, and gene segment) were successfully adsorbed to the porous Au supraparticles, and their distinct SERS spectra were obtained. The observed linear dependence of the characteristic Raman peak intensity for each environmental contaminant on its aqueous concentration reveals the quantitative SERS detection capability by porous Au supraparticles. The limit of detection (LOD) for the six environmental contaminants ranged from ∼10 nM to ∼10 μM, which depends on analyte affinity to the porous Au supraparticles and analyte intrinsic Raman cross-sections. The porous Au supraparticles enabled multiplex SERS detection and maintained comparable SERS detection sensitivity in wastewater influent. Overall, we envision that the Au supraparticles can potentially serve as practical and sensitive SERS devices for environmental analysis applications. 

Plant growth generally responds positively to an increase in ambient temperature. Hence, most Earth system models project a continuous increase in vegetation cover in the future due to elevated temperatures. Over the last 40 years, a considerable warming trend has affected the alpine ecosystem across the Tibetan Plateau. However, we found vegetation growth in the moderately vegetated areas of the plateau were negatively related to the warming temperatures, thus resulting in a significant degradation of the vegetative cover (LAI: slope  = −0.0026 per year, p  < 0.05). The underlying mechanisms that caused the decoupling of the relationship between vegetation growth and warming in the region were elaborated with the analysis of water and energy variables in the ecosystem. Results indicate that high temperatures stimulated evapotranspiration and increased the water consumption of the ecosystem (with an influence coefficient of 0.34) in these degrading areas, significantly reducing water availability (with an influence coefficient of −0.68) and limiting vegetation growth. Moreover, the negative warming effect on vegetation was only observed in the moderately vegetated areas, as evapotranspiration there predominantly occupied a larger proportion of available water (compared to the wet and highly vegetated areas) and resulted in a greater increase in total water consumption in a warmer condition (compared to dry areas with lower levels of vegetation cover). These findings highlight the risk of vegetation degradation in semi-arid areas, with the degree of vulnerability depending on the level of vegetation cover. Furthermore, results demonstrate the central role of evapotranspiration in regulating water stress intensity on vegetation under elevated temperatures.