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North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable in the world's oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25‐year leadtimes. Layer‐wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream‐North Atlantic Current region for accurate predictions. Additionally, CESM1‐trained NNs successfully predict the phasing of multidecadal variability in an observational data set, suggesting consistency in physical processes driving NASST variability between CESM1 and observations. Plain Language Summary: North Atlantic sea surface temperatures, particularly in the subpolar region, are among the most predictable locations in the world's oceans. However, it remains uncertain if processes in the atmosphere or ocean are more important for driving temperature fluctuations in this region occurring over multiple decades. We use a machine learning approach to predict the sea surface temperature state from climate model outputs, given snapshots of atmospheric or oceanic variables. Ocean variables lead to more accurate predictions relative to atmospheric variables and standard prediction baselines out to 25 years ahead if processes that drive the trends in climate, such as human‐induced warming, are present in the data. These successful predictions arise consistently from the same region near the Gulf Stream‐North Atlantic Current region. Despite being trained on climate models, the neural networks can predict the timing of observed positive and negative states of real‐world sea surface temperatures, suggesting that there is potential for using model output to train neural networks at predicting the actual North Atlantic sea surface variability. Key Points: Neural networks outperform persistence forecasts in predicting extreme states of North Atlantic sea surface temperature out to 25 yearsAn explainable neural network technique reveals successful predictions rely consistently on the Transition Zone regionNeural networks trained on climate model output predict the phasing of multidecadal variability on an observation‐based data set [ABSTRACT FROM AUTHOR]

Hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs) are potent greenhouse gases regulated under the Montreal Protocol and its amendments. Emission estimates generally use constant atmospheric lifetimes accounting for loss via hydroxyl radical (OH) reactions. However, chemistry‐climate models suggest OH increases after 1980, implying underestimated emissions. Further, HCFCs and HFCs are soluble in seawater and could be destroyed through in situ oceanic microbial activity. These ocean sinks are largely overlooked. Using a coupled atmosphere‐ocean model, we show that increases in modeled OH imply underestimated HCFC and HFC emissions by ∼10% near their respective peak emissions. Our model results also suggest that oceanic processes could lead to up to an additional 10% underestimation in these halocarbon emissions in the 2020s. Ensuring global compliance to the Protocol and accurate knowledge of contributions to global warming from these gases therefore requires understanding of these processes. Plain Language Summary: Man‐made hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs) contribute to global warming, prompting worldwide agreement to control the production of these chemicals. It is important to estimate their emissions to ensure global compliance with the agreed phaseout. But correct emission estimation requires knowledge of different loss pathways. One major loss pathway of halocarbons is through chemical reactions with the atmospheric "scrubber" called OH. OH is difficult to measure and usually assumed to be constant with time. But some models suggest OH has increased, which implies increased emissions to match observed abundances. These halocarbons also dissolve into the oceans, where microbes may also metabolize them, but these processes are not included in current emission estimates. We show that if these halocarbons are being consumed in the oceans, this would also lead to an additional underestimation of human emissions. Confidence in the success in the Montreal Protocol and its Kigali amendment to reduce HFCs will therefore require a better understanding of both OH trends and ocean sinks, along with use of HFC and HCFC measurements. Key Points: Increasing OH suggested by Coupled Model Intercomparison Project Phase 6 models can lead to a 5%–7% underestimation in hydrochlorofluorocarbon (HCFC) and hydrofluorocarbon (HFC) emission estimations in 2005If there is significant ocean degradation through microbial activity, HCFC, and HFC emissions could be underestimated by up to 10%Our study suggests an uncertainty in the combined contribution to global warming from HCFCs and HFCs up to 15%–20% in the 2020s [ABSTRACT FROM AUTHOR]

Subgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that machine‐learning (ML) parameterizations based on high‐resolution model output data could be superior to traditional parameterizations. Currently, both traditional and ML parameterizations of subgrid processes in the atmosphere are based on a single‐column approach, which only use information from single atmospheric columns. However, single‐column parameterizations might not be ideal since certain atmospheric phenomena, such as organized convective systems, can cross multiple grid boxes and involve slantwise circulations that are not purely vertical. Here we train neural networks (NNs) using non‐local inputs spanning over 3 × 3 columns of inputs. We find that including the non‐local inputs improves the offline prediction of a range of subgrid processes. The improvement is especially notable for subgrid momentum transport and for atmospheric conditions associated with mid‐latitude fronts and convective instability. Using an interpretability method, we find that the NN improvements partly rely on using the horizontal wind divergence, and we further show that including the divergence or vertical velocity as a separate input substantially improves offline performance. However, non‐local winds continue to be useful inputs for parameterizating subgrid momentum transport even when the vertical velocity is included as an input. Overall, our results imply that the use of non‐local variables and the vertical velocity as inputs could improve the performance of ML parameterizations, and the use of these inputs should be tested in online simulations in future work. Plain Language Summary: Current global climate models cannot resolve small‐scale processes, such as clouds and convection, which are crucial for accurate simulations of climate, and the effect of these processes is approximated using parameterizations. Traditionally, these parameterizations rely on simple conceptual models, but in recent years machine learning (ML) has also been used to develop new parameterizations. Both traditional and ML parameterizations rely on a simple approach in which the vertical structure of a single atmospheric column is used to predict the effect of unresolved small‐scale processes on the column itself. Here we use ML to show that this single‐column approach might hamper the accuracy of parameterizations. We demonstrate that a machine‐learning parameterization that uses information from multiple atmospheric columns simultaneously (rather than information from a single atmospheric column) better predicts the effects of small‐scale processes compared to the same approach but only using information from a single column. We show that non‐local inputs are especially important for parameterizing subgrid momentum transport and for mid‐latitude situations with atmospheric fronts. Including only neighboring columns is sufficient to improve the parameterization in climate model simulations, and therefore the increase in computational expense should not be a barrier in the implementation of non‐local parameterizations. Key Points: Using non‐local inputs from neighboring atmospheric columns improves offline performance of a neural network parameterizationImprovements in mid‐latitudes are associated with cases with mid‐latitude fronts, where subgrid squall‐line features are presentAn explainable artificial intelligence technique shows non‐local winds are especially useful for parameterizing subgrid momentum transport [ABSTRACT FROM AUTHOR]

This study evaluates formaldehyde (HCHO) over the U.S. from 2006 to 2015 by comparing ground monitor data from the Air Quality System (AQS) and a satellite retrieval from the Ozone Monitoring Instrument (OMI). Our comparison focuses on the utility of satellite data to inform patterns, trends, and processes of ground-based HCHO across the U.S. We find that cities with higher levels of biogenic volatile organic compound (BVOC) emissions, including primary HCHO, exhibit larger HCHO diurnal amplitudes in surface observations. These differences in hour-to-hour variability in surface HCHO suggests that satellite agreement with ground-based data may depend on the distribution of emission sources. On a seasonal basis, OMI exhibits the highest correlation with AQS in summer and the lowest correlation in winter. The ratios of HCHO in summer versus other seasons show pronounced seasonal variability in OMI, likely due to seasonal changes in the vertical HCHO distribution. The seasonal variability in HCHO from satellite is more pronounced than at the surface, with seasonal variability 20–100% larger in satellite than surface observations. The seasonal variability also has a latitude dependency, with more variability in higher latitude regions. OMI agrees with AQS on the interannual variability in certain periods, whereas AQS and OMI do not show a consistent decadal trend. This is possibly due to a rather large interannual variability in HCHO, which makes the small decadal drift less significant. Temperature also explains part of the interannual variabilities. Small temperature variations in the western U.S. are reflected with more quiescent HCHO interannual variability in that region. The decrease in summertime HCHO in the southeast U.S. could also be partially explained by a small and negative trend in local temperatures. [ABSTRACT FROM AUTHOR]

The outbreak of aggregative diseases in the process of sea cucumber cultivation has brought huge economic losses to aquaculture farmers. It is of positive significance to realize intelligent detection of abnormal behavior to avoid the outbreak of aggregative diseases. Therefore, this paper researches the approaches of intelligent recognition and behavior tracking of sea cucumbers. Fusing the Coordinated Attention and Bi-directional Feature Pyramid Network, the DT-YOLOv5 intelligent recognition model is proposed to enhance the representation ability and feature extraction ability. A multi-object behavior tracking approach is presented based on the automatic frame-matching coordinates, which can track multiple objects and calculate the volumes of exercise. The experimental results show that the precision, recall and AP 50:95 are 99.43%, 98.91% and 84.89%, respectively. This research provides a theoretical support for the detection of abnormal behavior of aquatic animals during intensive aquaculture and has potential practical application value for protecting the welfare of sea cucumbers and improving the intelligence level of aquaculture. • A nondestructive detection and tracking approach for sea cucumbers infected with Vibrio alginolyticus is proposed. • This paper proposes improved DT-YOLOv5 model combined with Coordinated Attention and Bi-directional Feature Pyramid Network. • An animal behavior tracking approach is presented, which can track multiple objects and calculate the volumes of exercise. • This research provides an approach for the qualitative and quantitative analysis of the diseased behavior of sea cucumbers. [ABSTRACT FROM AUTHOR]

This paper briefly reviews the common-mode voltage (CMV) and neutral point (NP) voltage for neutral point clamped three-level inverter (NPC TLI). Then, the space vector pulsewidth modulation (SVPWM) and traditional virtual SVPWM (VSVPWM) are discussed in terms of these two issues, revealing the drawbacks in reducing CMV or eliminating NP voltage oscillation. A novel virtual space vector modulation, named as RCMV_VSVPWM, is proposed in this paper to reduce CMV and eliminate NP voltage oscillation for NPC TLI. By selecting vectors with lower CMVs, a set of novel virtual voltage vectors are generated. The highlight of the method is zero average NP current in one control cycle and lower CMV. Furthermore, the active NP voltage control suitable for RCMV_VSVPWM is presented and evaluated. The corresponding experimental results are given, which are well-consistent with theoretical analysis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel dynamic filtering method using Gaussian wavelet filters is proposed to remove noise from ultrasound echo signal. In the proposed method, a mother wavelet is first selected with its central frequency (CF) and frequency bandwidth (FB) equal to those of the transmitted signal. The actual frequency of the received signal at a given depth is estimated through the autocorrelation technique. Then the mother wavelet is dilated using the ratio between the transmitted central frequency and the actual frequency as the scale factor. The generated daughter wavelet is finally used as the dynamic filter at this depth. Frequency-demodulated Gaussian wavelet is chosen in this paper because its power spectrum is well-matched with that of the transmitted ultrasound signal. The proposed method is evaluated by simulations using Field II program. Experiments are also conducted out on a standard ultrasound phantom using a 192-element transducer with the center frequency of 5MHz. The phantom contains five point targets, five circular high scattering regions with diameters of 2, 3, 4, 5, 6mm respectively, and five cysts with diameters of 6, 5, 4, 3, 2mm respectively. Both simulation and experimental results show that optimal signal-to-noise ratio (SNR) can be obtained and useful information can be extracted along the depth direction irrespective of the diagnostic objects. [Copyright &y& Elsevier]

Wireless communication technology has been widely used in marine engineering, marine ranching and Marine environmental monitoring. However, structural redundancy and functional confusion exist in applying neural networks in signal separation technology in underwater communication environments, which can result in a slower rate of signal separation and lead to confusion of parameters during transfer learning. Based on this, an end-to-end, internal functionally structured decoupled neural network (D-CNN) blind source separation (BSS) model is proposed in this paper, which can realize a neural network BSS algorithm with a well-defined structure and function. The one-dimensional convolutional neural network layer is used in algorithm to automatically extract observed signal's features, based on the features, and there are two generation modules of separation matrix and scaling coefficients. Then the two modules can be used to separate the observed signal and adjust the signal coefficients to obtain the separated signal. Finally the transfer learning technique is used to generalize the model, which reduces the transfer cost of the model in different application scenarios. Experimental results show that when the communication distance is set to 0.02 km–2 km, the MSE of independent signal and related signal can be reduced by 14.24% and 14.95% respectively compared with the nearest Neural FCA algorithm. The results prove that the proposed algorithm can accurately estimate the source signal and improve the signal reception quality. • Decouple neural network to meet the functional and structural requirements of signal separation. • No independence assumption, so the algorithm can separate the correlated signals. • Carry out simulation communication experiment using Bellhop to simulate underwater environment. • The accuracy of MSE improved by about 44% and 58% when separating independent signals and correlated signals in-5.5 dB. [ABSTRACT FROM AUTHOR]

This study investigates and compares large-scale moisture and heat budgets over the eastern rainy sea area around Dongsha, the western rainless sea area around Xisha, and the northern coastland of the South China Sea. Ten-year (2011–20) surface, balloon-sounding, satellite measurements, and ERA5 reanalysis are merged into the physically consistent data to study annual and vertical variations of the budgets. It shows that the surface and column-integrated heat and moisture budgets have the smallest annual evolution over the coastland. The latent heat as a key heat contributor in summer is mainly offset by total cold advection and partially offset by net radiative cooling. The horizontal moisture advection below 700 hPa presents moistening over the sea whereas drying over the coastland during rainy months, in which the vertical moisture advection presents moistening up to 250 hPa for all three subregions. The horizontal temperature advection is weak throughout the year over the sea but displays strong top warming and bottom cooling in summer and nearly the opposite in winter over the coastland. The diabatic cooling with a peak at ∼700 hPa in winter is largely due to the enhanced radiative cooling and latent cooling. While the diabatic heating with a peak at ∼500 hPa in summer is largely due to the enhanced latent heating. The earliest atmospheric heating and moistening occur in spring over the coastland, inducing the earliest precipitation increase. The enhanced heating and moistening over Xisha have a 1-month lag relative to Dongsha, resulting in lagging precipitation. [ABSTRACT FROM AUTHOR]

Describes tunable vertical cavity surface emitting lasers with a half-symmetric cavity structure. Induction of a curvature in the top movable dielectric mirror; Forcing of lasing oscillations in a single fundamental spatial mode; Achievement of continuous wavelength tuning of 44 nanometer.

Cannabidiol (CBD), a natural phenolic compound, has many promising biological activities, but its use in food or other fields is restricted owing to inferior water solubility. So hydroxypropyl-β-cyclodextrin (HP-β-CD) was selected to form the inclusion complex (IC) of CBD to improve its solubility. The results showed CBD/HP-β-CD IC was formed at a 1 : 1 molar ratio. The complexation was spontaneous, endothermic, and entropy-driven. The loading efficiency (LE) and complexation efficiency (CE) were 16.2% and 95.1%, respectively. NMR revealed CBD/HP-β-CD IC was formed through incorporating the benzene ring of CBD into the HP-β-CD cavity. The water solubility was greatly enhanced to 9.4 μg mL−1. About 80% of CBD molecules in the IC were released in simulated intestinal juice. The permeability, radical scavenging ability, and in vitro cytotoxicity of CBD were enhanced significantly after complexation. And the radical scavenging ability of digested IC was less potent compared with undigested IC but still stronger than that of CBD. [ABSTRACT FROM AUTHOR]

Based on the daily maximum and minimum temperature data from the Guangzhou National Basic Weather Station from 1951 to 2020, the study calculates 13 kinds of extreme temperature indices recommended by the World Meteorological Organization and two new integrated extreme temperature indices with local conditions considered. Then the study analyzes how they change monthly and annually by using linear trend method, anomaly and cumulative anomaly method and Mann-Kendall test. Moreover, the study examines the influence of the choice of base period which was rarely explored in previous studies. The results show that: (1) On an annual scale, SU25, SU35, TR20, TR26, TXx, TNx, TNx, TN90p, TX90p and WSDI in Guangzhou increased, while TN10p, TX10p and CSDI decreased, and DTR and TXn had no significant change. (2) The rising rate of the new extreme temperature indices SU35 and TR26 is significantly higher than that of SU25 and TR20, which can better reflect the significant increase in the number of days that are extremely hot both in the daytime and at night in recent 70 years. The annual largest rising rate of extreme temperature indices is that of TX90p which has reached 8.18 d/(10 a). (3) In the past 70 years, the monthly warm series extreme temperature indices TXx and TNx increased significantly in summer in Guangzhou and June and July witnessed the most substantial increase. TX90p reached an extremely significant level (P<0.001) in local climatological summer (from April to October) except for May. The rise of warm (hot) days in summer (especially in June) is the main reason for the rise of warm (hot) days in the whole year in Guangzhou. (4) Based on the results of three different base periods (1961--1990/1971--2000/ 1981--2010), it is found that different base periods affect the statistical results of extreme temperature indices but do not affect the trends of extreme temperature indices. (5) Mann-Kendall test shows that the mutations of SU25, SU35, TMAXmean, TX90p and TXx occurred around 1997; while those of TR20, TR26, TNn, TN10p, TMINmean, TNx, TX10p and TN90p occurred around 1985. The results of this study are the same with the general trend of global climate change and thus can provide scientific reference for people in Guangzhou to respond to climate change and prevent extreme weather disasters. [ABSTRACT FROM AUTHOR]

Highlights: [•] Two novel 3-D 4d–4f (Ln–Ag) coordination polymers have been hydrothermally assembled by two mixed ligands (HIN and ). [•] Both 1 and 2 based on zigzag [Ln2] n chains exhibit the first double wave-like [Ln2Ag2IN4(OH)2] n layers. [•] Compound 1 displays intensely red luminescence in solid state. [ABSTRACT FROM AUTHOR]

For trace gases destroyed in the stratosphere, mass flux across the tropopause can substantially influence observed surface hemispheric differences (NH‐SH). Here, we quantify associations between observed stratospheric and tropospheric NH‐SH growth rate anomalies of CFC‐11, CFC‐12, and N2O. We employ a chemistry climate model along with satellite and global surface station observations. Our model explains 60% of observed N2O NH‐SH growth rate variability from 2005 to 2019, compared to 30% for CFC‐11% and 40% for CFC‐12, supporting evidence that unexpected anthropogenic emissions caused sustained positive NH‐SH anomalies in these CFCs from 2012 to 2017. Between 2012 and 2015, the observed CFC‐11 NH‐SH difference grew by 1.7 ppt; our model explains 0.5 ± 0.1 ppt of this growth, but not the duration. Our model suggests that in the absence of further emission anomalies, new NH‐SH positive tracer anomalies should have occurred in 2020, and predicts small negative anomalies in 2021. Plain Language Summary: Changes in the North‐South difference of surface trace gas abundances, denoted NH‐SH, are often used as evidence of new emissions. However, atmospheric dynamics can also influence measurements of NH‐SH. Importantly, anomalies in trace gas abundances in the stratosphere are transported down to the troposphere, influencing tropospheric NH‐SH values. We quantify the stratospheric influence on NH‐SH in models and observations for CFC‐11, CFC‐12, and N2O. We provide a simple model to account for future stratospheric anomalies at the surface. Our model suggests that 60% of the variability in NH‐SH can be explained by stratospheric anomalies. Following 2013, there was a sustained positive NH‐SH anomaly in observations. Our results indicate that stratospheric influences can only partially explain this anomaly, supporting earlier work that an expected emission led to the observed positive NH‐SH anomaly. This work shows that stratospheric observations improves interpretation of future emissions. Key Points: Stratospheric anomalies in trace gas mole fractions influence tropospheric North‐South tracer differences and provide predictabilityOur model suggests 0.5 ± 0.1 ppt of the NH‐SH surface growth in CFC‐11 after 2012 can be explained by stratospheric anomaliesIn the absence of anomalous emissions, 60%–70% of the variability in NH‐SH tracers can be explained by stratospheric anomalies [ABSTRACT FROM AUTHOR]

Background: China’s standardized training for residents of General Practitioners (GPs) is aimed at providing the postgraduate training for family doctors who will serve the primary health care institutions. The aim of this paper is to investigate the standardized training situation, satisfaction with standardized training, work situation, intention, satisfaction and attitude of GPs who have finished standardized training. Methods: This study was undertaken in 6 training hospitals in Gansu province using a questionnaire with 45 questions. Results: Approximately 275 residents of GPs were enrolled. Finally, 263 residents completed the questionnaire (95.64% response rate), including 133 females (50.57%) and 130 males (49.43%) with an average age of 28 years (standard deviation, 1.93 years; range, 25–36 years). Additionally, 56.65% were single and 43.35% were married. Of all subjects, 92.40% residents had obtained certification of standardized training for residents of GPs and only 39.54% residents were satisfied with monthly income during training. There were 171 oriented rural medical graduates, of whom, only 42.69% expressed the willing to continue working in the primary health care institutions after the serve time (6 years) expired. Around 86.31% of residents of GPs who had finished standardized training got jobs with more than half serving in the primary health care institutions. For medical institutions and sanitary bureau were clear about general medicine policies, only 29.96% subjects registered as GPs. Among the residents in general practice department, 68.42% were engaged in the diagnosis and treatment of common disease and frequently-occurring diseases as well as referral of patients. The percentage of residents who were satisfied with the job and income were 30.40 and 14.98%, respectively. Conclusion: Standardized training for residents of GPs in China is gradually improving. In order to cultivate more GPs and increase their willing to serve in the primary health care institutions, it is necessary to formulate and execute better policy of GPs, publize general medicine and improve the training quality. [ABSTRACT FROM AUTHOR]

Underwater object recognition is a prerequisite for the realization of automated seafood harvesting. To solve the problems of low recognition accuracy and poor generalization ability of existing underwater small object, this paper takes sea cucumber as research objects to research the small object recognition approach in complex underwater environment. In this paper, a sea cucumber dataset is established to solve the problem of lacking sea cucumber dataset in the real seabed environment. A deep learning model SO-YOLOv5 is proposed based on YOLOv5 for underwater small object recognition, which improves the recognition ability of the algorithm for different sizes of objects in complex environment. The proposed model embeds a large-scale feature extraction layer in structure to increase the detection ability of small objects by referring to the idea of the Bi-directional Feature Pyramid Network. SO-YOLOv5 fuses the features between deep and shallow layers and balances the feature information of different scales, and integrates the Coordinate Attention mechanism to enhance the sensitivity of the algorithm to the direction and position information. The experimental results illustrate that the mAP of the proposed approach achieves 95.47% for sea cucumber recognition in the complex seabed environment, which is 3.42%, 6.79%, and 5.46% higher than the traditional Faster R-CNN, SSD, and YOLOv5 algorithms, respectively. This research not only provides an effective approach for sea cucumber recognition in complex environment but also has certain reference significance for the recognition of small objects in other complex environments. In addition, the proposed approach has practical application value to improve intelligence level in aquaculture. • A sea cucumbers dataset was established. • Feature extraction capability was enhanced through fusing large-size features to improve small object detection ability. • The deep and shallow layers of features are bi-directionally fused to balance the different scales of features. • The Coordinate Attention mechanism is embedded to enhance the algorithm's sensitivity to the direction and location. [ABSTRACT FROM AUTHOR]

Femoral medical image registration is the main research content of the femur 3D reconstruction technology, registration parameters obtained through the auxiliary machine determines the accuracy of the 3D reconstruction. In view of the problem of traditional ICP algorithm has the low accuracy and robustness, this paper put forward a kind of method based on affine CPD femoral medical image registration, to make registration automation and precision. The method obtained floating and reference point set through edge detection; then got result parameters after registration; through the result parameters, femoral universal model was put into individual femoral fracture posture. The result shows that the average error of translation and rotation is less than 2.1 mm, 1.6° respectively; registration failure rate is only around 10% ; the validity of 3D image after 3D reconstruction is higher. The method described by this paper for image registration and 3D reconstruction is feasible and efficient. [ABSTRACT FROM AUTHOR]

During the last decade, saliva has emerged as a potentially ideal diagnostic biofluid for noninvasive testing. In this paper, we present an automated, integrated platform useable by minimally trained personnel in the field for the diagnosis of respiratory diseases using human saliva as a sample specimen. In this platform, a saliva sample is loaded onto a disposable microfluidic chip containing all the necessary reagents and components required for saliva analysis. The chip is then inserted into the automated analyzer, the SDReader, where multiple potential protein biomarkers for respiratory diseases are measured simultaneously using a microsphere-based array via fluorescence sandwich immunoassays. The results are read optically, and the images are analyzed by a custom-designed algorithm. The fully automated assay requires as little as 10 μL of saliva sample, and the results are reported in 70 min. The performance of the platform was characterized by testing protein standard solutions, and the results were comparable to those from the 3.5 h lab bench assay that we have previously reported. The device was also deployed in two clinical environments where 273 human saliva samples collected from different subjects were successfully tested, demonstrating the device's potential to assist clinicians with the diagnosis of respiratory diseases by providing timely protein biomarker profiling information. This platform, which combines noninvasive sample collection and fully automated analysis, can also be utilized in point-of-care diagnostics. [ABSTRACT FROM AUTHOR]

Quinoa is known for its rich nutrients and bioactive compounds. In order to elucidate the preliminary structural characteristics and biological activity of polysaccharides from quinoa (QPs), five crude polysaccharides (QPE50, QPE60, QPE70, QPE80 and QPE90) were successively fractionated by gradient ethanol, and their physicochemical properties, antioxidant and antidiabetic activities were analyzed. The results implied that their total sugar contents were 52.82%, 63.69%, 67.15%, 44.56%, and 41.01%, and their weight-average molecular weights were 13,785 Da, 6489 Da, 4732 Da, 3318 Da, and 1960 Da, respectively. Glucose was a predominantly monosaccharide in these QPs, which together in QPE50, QPE60, QPE70, QPE80, and QPE90, respectively, made up 94.37%, 87.92%, 92.21%, 100%, and 100% of the total polysaccharide. Congo red test showed that all five QPs contained triple-helix structure. The Fourier transform-infrared spectroscopy (FT-IR) and X-ray diffractometry (XRD) results suggest that the QPs form a semi-crystalline polymer constituted typical functional groups of polysaccharide including CO, CH and OH. The thermogravimetric analysis (TGA) of QPs showed that weight loss was at about 200 °C and 320 °C. The observation from scanning electron microscope (SEM) and atomic force microscope (AFM) image indicated that the morphology of QPs exhibited spherical shape. Antioxidant and antidiabetic assay exhibited that all five QPs samples had certain antioxidant and antidiabetic activities, and QPE90 showed the best antioxidant and antidiabetic activity. Overall, QPs present a promising natural source of food antioxidants and antidiabetic agents. [ABSTRACT FROM AUTHOR]

• A robust and water-induced shape memory CNP was prepared with CNFs and CS. • Fast response to water of the CNP is due to synergistic hydration of CS and CNFs. • The final CNP shows excellent wet strength and folding endurance. • The final CNP exhibits amazing barrier and antibacterial properties. • The final CNP has potential use in humidity sensor, packaging, medicine, etc. Water-induced shape memory polymers (SMPs) are the desired candidates for the uses in the fields of surgical operation, smart textile, and aerospace. However, most of water-induced SMPs are non-biodegradable petroleum-based polymers, and a quick response to water of SMPs is usually accompanied with the loss of wet strength due to the over hydration. To address this issue, in this work, a water resistant cellulose nanopaper (CNP) was firstly prepared with the cellulose nanofibrils (CNFs) produced by formic acid hydrolysis, and then the obtained CNP was modified via immersing in chitosan (CS) for the preparation of a robust CNP with quick water and humidity responses. Results showed that the original shape of the modified CNP after folding could immediately respond to water (<3 s) and humidity (<10 min). This quick response of the modified CNP was due to the synergistic hydration of CS and CNFs network, and the presence of CNFs with ester groups could prevent the over hydration of the modified CNP. Furthermore, the strong interactions between CS and CNFs and the reduced defects of CNP after modified with CS endow the final CNP with excellent mechanical performance (particular for the robust folding resistance (over 1190 times) and high wet tensile strength (65 MPa)), high transparency, as well as good barrier properties and antibacterial properties. Hence, the resultant CNP has potential applications in the fields of surgical operation, smart textile, humidity sensor, and high-quality packaging. [ABSTRACT FROM AUTHOR]