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Whether intentionally generating acoustic waves or attempting to mitigate unwanted noise, sound control is an area of challenge and opportunity. This study investigates traditional fabrics as emitters and suppressors of sound. When attached to a single strand of a piezoelectric fiber actuator, a silk fabric emits up to 70 dB of sound. Despite the complex fabric structure, vibrometer measurements reveal behavior reminiscent of a classical thin plate. Fabric pore size relative to the viscous boundary layer thickness is found-through comparative fabric analysis-to influence acoustic-emission efficiency. Sound suppression is demonstrated using two distinct mechanisms. In the first, direct acoustic interference is shown to reduce sound by up to 37 dB. The second relies on pacifying the fabric vibrations by the piezoelectric fiber, reducing the amplitude of vibration waves by 95% and attenuating the transmitted sound by up to 75%. Interestingly, this vibration-mediated suppression in principle reduces sound in an unlimited volume. It also allows the acoustic reflectivity of the fabric to be dynamically controlled, increasing by up to 68%. The sound emission and suppression efficiency of a 130 µm silk fabric presents opportunities for sound control in a variety of applications ranging from apparel to transportation to architecture.

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Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve 'nontrivial' empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.

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Significance: Imaging through scattering media is critical in many biomedical imaging applications, such as breast tumor detection and functional neuroimaging. Time-of-flight diffuse optical tomography (ToF-DOT) is one of the most promising methods for high-resolution imaging through scattering media. ToF-DOT and many traditional DOT methods require an image reconstruction algorithm. Unfortunately, this algorithm often requires long computational runtimes and may produce lower quality reconstructions in the presence of model mismatch or improper hyperparameter tuning. Aim: We used a data-driven unrolled network as our ToF-DOT inverse solver. The unrolled network is faster than traditional inverse solvers and achieves higher reconstruction quality by accounting for model mismatch. Approach: Our model "Unrolled-DOT" uses the learned iterative shrinkage thresholding algorithm. In addition, we incorporate a refinement U-Net and Visual Geometry Group (VGG) perceptual loss to further increase the reconstruction quality. We trained and tested our model on simulated and real-world data and benchmarked against physics-based and learning-based inverse solvers. Results: In experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10× reduction in runtime and mean-squared error, compared to traditional physics-based solvers. Conclusion: We demonstrated a learning-based ToF-DOT inverse solver that achieves state-of-the-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging.

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BACKGROUND: We attempted to identify the most reliable immune-related index for predicting nasopharyngeal carcinoma (NPC) prognosis and to reveal its precise and integrated relationship with NPC progression. METHOD: One thousand seven hundred and six patients with newly diagnosed NPC (1320 from the primary cohort and 386 from the validated cohort) from January 2010 to March 2014 were enrolled. Clinical features and 12 immune-related variables were analyzed. RESULTS: A high absolute lymphocyte count (ALC; >3.2 × 109 /L) correlated with a poor prognosis of patients with NPC. Significant OS differences were discovered between patients with high ALC and no ALC elevation (p < 0.05, in primary cohort), showing similar prognostic risk to patients with advanced NPC (p > 0.05, in validated cohort). ALC improved the predictive performance of the basic tumor-node-metastasis prognostic model (p = 0.025), which was reliably validated in the external independent cohort. CONCLUSION: High ALC is a surrogate marker for improved prognostic risk stratification in NPC.

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We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l 0-norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm3 spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.

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Light scattering by tissue severely limits how deep beneath the surface one can image, and the spatial resolution one can obtain from these images. Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue - well beyond the conventional  âˆ¼ 10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy. Unfortunately, existing DOT systems are limited, achieving only centimeter-scale resolution. Furthermore, they suffer from slow acquisition times and slow reconstruction speeds making real-time imaging infeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT) and its confocal variant (CToF-DOT), by exploiting the photon travel time information, allow us to achieve millimeter spatial resolution in the highly scattered diffusion regime ( mean free paths). In addition, we demonstrate two additional innovations: focusing on confocal measurements, and multiplexing the illumination sources allow us to significantly reduce the measurement acquisition time. Finally, we rely on a novel convolutional approximation that allows us to develop a fast reconstruction algorithm, achieving a 100× speedup in reconstruction time compared to traditional DOT reconstruction techniques. Together, we believe that these technical advances serve as the first step towards real-time, millimeter resolution, deep tissue imaging using DOT.

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OBJECTIVES: Weight loss has been validated as a prognostic predictor of nasopharyngeal carcinoma (NPC); however, no global unitary indicator and criteria exist for the definition of weight loss as a prognostic factor. The aim of this study was to determine the most effective indicator for weight loss, evaluate its effect on the prognosis of NPC, and further propose a cutoff value to identify patients in need of nutritional care. METHODS: This retrospective cohort analysis with a median follow-up of 62.3 mo included 681 newly diagnosed patients with NPC. Principal component analysis was performed to select the best continuous variable including weight loss (kg; value of weight loss [VWL]), percent weight loss (PWL), and body mass index loss (BMIL). Multivariable Cox regression analysis and multiple correspondence analysis were performed to select the best cutoff values by different cutoff methods including the median, receiver operating characteristic curve, and threshold searching. RESULTS: PWL was the highest contributor to the prognosis of NPC compared with VWL and BMIL. Cutoff values of PWL (6.3 and 12.3%) were confirmed to be more important and were proposed to differentiate patients into low-, medium-, and high-risk NPC groups, with their 5-y progression-free survival (84.5 versus 77.9%, P = 0.046; 77.9 versus 67.3%, P = 0.046). PWL was an independent adverse prognostic factor (P = 0.002) for NPC. CONCLUSIONS: PWL is a promising predictor for NPC, and cutoff values could be validated for nutritional risk grading in patients with NPC. These stratified criteria may help accelerate the extensive application of grading nutritional management in NPC therapy.

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Knowledge regarding the anti-inflammatory capability of quercetin remains inconclusive and controversial due to the heterogeneous methods and inconsistent results of RCTs. We performed a series of meta-analyses of RCTs to evaluate the impact of quercetin supplementation on inflammatory biomarkers. Three cytokines (CRP, IL-6, TNF-α) with enough eligible studies (n = 6, 5 and 4, respectively) were selected for further meta-analyses. Data from these RCTs were pooled, and both overall effect and stratified subgroup analyses were performed. No relevant overall effects on peripheral CRP, IL-6 and TNF-α were observed. Subgroup analyses revealed a significant reduction in circulating CRP in participants with diagnosed diseases (SMD: -0.24, 95% CI: -0.49, 0.00) and IL-6 in females (SMD: -1.37, 95% CI: -1.93, -0.81), subjects with diagnosed diseases (SMD: -1.37, 95% CI: -1.93, -0.81) and with high-dose interventions (SMD: -0.69, 95% CI: -1.10, -0.38). In conclusion, consumption of quercetin is a promising therapeutic strategy for chronic disease management.

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Fluorescent chemosensors based on a new macrocyclic compound, multifarene[2,2], with modification by triazole-linked pyrene or anthracene were synthesized. These macrocyclic sensors exhibited high affinity and selectivity toward Ag+ over other metal ions, with ratiometric or enhanced response of their fluorescence emissions depending upon the substituent species for coordination to Ag+, and an unexpected response to a concentration threshold of the metal cations was discovered. The experimental evidences of fluorescence spectra, 1H NMR titration, IR spectra, and high-resolution mass spectra suggested the coordination behaviors of the sensors with Ag+, that is, the 1:1 complexes were formed with moderate association constants of about 105 L·mol-1, and the sulfur atoms on macrocyclic ligand should affinite to the metal cations. Energy-minimized structures and frontier orbitals were estimated by quantum chemical calculations with a view to rationalizing the fluorescence response of the multifarene[2,2] sensors upon binding to Ag+.

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A selective and sensitive fluorescent chemosensor based on an anthracene-functionalized triazole-linked multifarene[2,2] was successfully synthesized and investigated with regard to the recognition of metal ions using fluorescence spectroscopy, 1H NMR titration, and IR spectroscopy. The proposed sensor exhibited desirable properties for potential fluorescence enhanced chemosensor applications, including selective affinity and low Zn2+ and Cd2+ detection limits compared with other metal ions. Quantum chemical calculations described the synthesized chemosensor's static structure and its coordination to Zn2+ and Cd2+. Frontier molecular orbital distribution and energy changes suggested a possible mechanism for increased receptor fluorescence intensity with Zn2+ and Cd2+ addition.

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We sought to assess light characteristics and user acceptability of a prototype Bright Classroom (BC), designed to prevent children's myopia by exposing them to light conditions resembling the outdoors. Conditions were measured throughout the school year in the glass-constructed BC, a traditional classroom (TC) and outdoors. Teachers and children completed user questionnaires, and children rated reading comfort at different light intensities. A total of 230 children (mean age 10.2 years, 57.4% boys) and 13 teachers (36.8 years, 15.4% men) completed questionnaires. The median (Inter Quartile Range) light intensity in the BC (2,540 [1,330-4,060] lux) was greater than the TC (477 [245-738] lux, P < 0.001), though less than outdoors (19,500 [8,960-36,000] lux, P < 0.001). A prominent spectral peak at 490-560 nm was present in the BC and outdoors, but less so in the TC. Teachers and children gave higher overall ratings to the BC than TC, and light intensity in the BC in summer and on sunny days (>5,000 lux) was at the upper limit of children's comfort for reading. In summary, light intensity in the BC exceeds TC, and is at the practical upper limit for routine use. Children and teachers prefer the BC.

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In recent times, atomically thin alloys of boron, nitrogen, and carbon have generated significant excitement as a composition-tunable two-dimensional (2D) material that demonstrates rich physics as well as application potentials. The possibility of tunably incorporating oxygen, a group VI element, into the honeycomb sp(2)-type 2D-BNC lattice is an intriguing idea from both fundamental and applied perspectives. We present the first report on an atomically thin quaternary alloy of boron, nitrogen, carbon, and oxygen (2D-BNCO). Our experiments suggest, and density functional theory (DFT) calculations corroborate, stable configurations of a honeycomb 2D-BNCO lattice. We observe micrometer-scale 2D-BNCO domains within a graphene-rich 2D-BNC matrix, and are able to control the area coverage and relative composition of these domains by varying the oxygen content in the growth setup. Macroscopic samples comprising 2D-BNCO domains in a graphene-rich 2D-BNC matrix show graphene-like gate-modulated electronic transport with mobility exceeding 500 cm(2) V(-1) s(-1), and Arrhenius-like activated temperature dependence. Spin-polarized DFT calculations for nanoscale 2D-BNCO patches predict magnetic ground states originating from the B atoms closest to the O atoms and sizable (0.6 eV < E g < 0.8 eV) band gaps in their density of states. These results suggest that 2D-BNCO with novel electronic and magnetic properties have great potential for nanoelectronics and spintronic applications in an atomically thin platform.