The design and development of a dashboard for improving sustainable healthy food choices.

Over the last decade, several digital tools have been designed to provide consumers with nutritional and environmental impact information about their food choices post-consumption. Many of these tools lack behavioral change modules, have low user engagement, and ignore inherent environmental nutrition trade-offs to stimulate dietary change. This study presents the design and development of a decision support system to enhance consumer health while meeting sustainability goals from a pre-consumption perspective. The proposed decision support system, Dashboard for Improving Sustainable Healthy (DISH) food choices, employs behavioral features, traffic light labels, and nudges to inform end-users about the nutritional health performance and environmental impact of meals. DISH uses a simple metric that allows end-users to explore the potential minutes of healthy and productive life gained or lost from consuming 100 kcal of a meal. The metric combines the positive or negative nutritional health effects (µ-DALYs) of consuming a meal and environmental damage (endpoint impact expressed in DALYs) on human health. In the DISH application, end-users are rewarded or deducted EnCoins, which represent the number of silver or gold coins lost or gained based on the cost ($) of environmental damage (midpoint impacts) of a meal compared to reference sustainable healthy and unsustainable and unhealthy meal. DISH's gamification module enables end-users to track the potential minutes of healthy and productive life gained/lost and gold or silver rewards or deductions from consuming 100 kcal of a selected meal through cumulative minutes gained or lost and EnCoins. In promoting a sustainable diet culture, the gamification module enables users to create groups and communities where friends and families can track their sustainability performance through meal decisions. The DISH application is currently available online and can be accessed by an end-user through any device. Further pilot studies will focus on testing the technology in partner campus cafeterias.

Active Dual Line-Laser Scanning for Depth Imaging of Piled Agricultural Commodities for Itemized Processing Lines.

The accurate depth imaging of piled products provides essential perception for the automated selection of individual objects that require itemized food processing, such as fish, crabs, or fruit. Traditional depth imaging techniques, such as Time-of-Flight and stereoscopy, lack the necessary depth resolution for imaging small items, such as food commodities. Although structured light methods such as laser triangulation have high depth resolution, they depend on conveyor motion for depth scanning. This manuscript introduces an active dual line-laser scanning system for depth imaging static piled items, such as a pile of crabs on a table, eliminating the need for conveyor motion to generate high-resolution 3D images. This advancement benefits robotic perception for loading individual items from a pile for itemized food processing. Leveraging a unique geometrical configuration and laser redundancy, the dual-laser strategy overcomes occlusions while reconstructing a large field of view (FOV) from a long working distance. We achieved a depth reconstruction MSE of 0.3 mm and an STD of 0.5 mm on a symmetrical pyramid stage. The proposed system demonstrates that laser scanners can produce depth maps of complex items, such as piled Chesapeake Blue Crab and White Button mushrooms. This technology enables 3D perception for automated processing lines and offers broad applicability for quality inspection, sorting, and handling of piled products.

Simple and field-adapted species identification of biological specimens combining multiplex multienzyme isothermal rapid amplification, lateral flow dipsticks, and universal primers for initial rapid screening without standard PCR laboratory.

Species identification of biological specimens can provide the valuable clues and accelerate the speed of prosecution material processing for forensic investigation, especially when the case scene is inaccessible and the physical evidence is cumbersome. Thus, establishing a rapid, simple, and field-adapted species identification method is crucial for forensic scientists, particularly as first-line technology at the crime scene for initial rapid screening. In this study, we established a new field-adapted species identification method by combining multiplex multienzyme isothermal rapid amplification (MIRA), lateral flow dipstick (LFD) system, and universal primers. Universal primers targeting COX I and COX II genes were used in multiplex MIRA-LFD system for seven species identification, and a dedicated MIRA-LFD system primer targeting CYT B gene was used to detect the human material. DNA extraction was performed by collecting DNA directly from the centrifuged supernatant. Our study found that the entire amplification process took only 15 min at 37 °C and the results of LFDs could be visually observed after 10 min. The detection sensitivity of human material could reach 10 pg, which is equivalent to the detection of single cell. Different common animal samples mixed at the ratio of 1 ng:1 ng, 10 ng:1 ng, and 1 ng:10 ng could be detected successfully. Furthermore, the damaged and degraded samples could also be detected. Therefore, the convenient, feasible, and rapid approach for species identification is suitable for popularization as first-line technology at the crime scene for initial rapid screening and provides a great convenient for forensic application.

Realization of a Z-Classified Chiral-Symmetric Higher-Order Topological Insulator in a Coupling-Inverted Acoustic Crystal.

Higher-order topological band theory has transformed the landscape of topological phases in quantum and classical systems. Here, we experimentally demonstrate a two-dimensional higher-order topological phase, referred to as the multiple chiral topological phase, which is protected by a multipole chiral number (MCN). Our realization differs from previous higher-order topological phases in that it possesses a larger-than-unity MCN, which arises when the nearest-neighbor couplings are weaker than long-range couplings. Our phase has an MCN of 4, protecting the existence of 4 midgap topological corner modes at each corner. The multiple topological corner modes demonstrated here could lead to enhanced quantum-inspired devices for sensing and computing. Our study also highlights the rich and untapped potential of long-range coupling manipulation for future research in topological phases.

Visualization of erythrocyte stasis in the living human eye in health and disease.

Blood cells trapped in stasis have been reported within the microcirculation, but their relevance to health and disease has not been established. In this study, we introduce an in vivo imaging approach that reveals the presence of a previously-unknown pool of erythrocytes in stasis, located within capillary segments of the CNS, and present in 100% of subjects imaged. These results provide a key insight that blood cells pause as they travel through the choroidal microvasculature, a vascular structure that boasts the highest blood flow of any tissue in the body. Demonstration of clinical utility using deep learning reveals that erythrocyte stasis is altered in glaucoma, indicating the possibility of more widespread changes in choroidal microvascular than previously realized. The ability to monitor the choroidal microvasculature at the single cell level may lead to novel strategies for tracking microvascular health in glaucoma, age-related macular degeneration, and other neurodegenerative diseases.

Methamphetamine Shows Different Joint Toxicity for Different Types of Microplastics on Zebrafish Larvae by Mediating Oxidative Stress.

The coexistence of polystyrene (PS) and polypropylene (PVC) microplastics (MPs) and methamphetamine (METH) in aquatic systems is evident. However, the joint toxicity is unclear. Here, zebrafish larvae were exposed to single PS and PVC MPs (20 mg L-1) and combined with METH (250 and 500 µg L-1) for 10 days. The results indicated that acute exposure to PS and PVC MPs induced lethal effects on zebrafish larvae (10-20%). Treatment with MPs markedly suppressed the locomotion of zebrafish, showing as the lengthy immobility (51-74%) and lower velocity (0.09-0.55 cm s-1) compared with the control (1.07 cm s-1). Meanwhile, histopathological analysis revealed pronounced depositions of MPs particles in fish's intestinal tract, triggering inflammatory responses (histological scores: 1.6-2.0). In the coexposure groups, obviously inflammatory responses were found. Furthermore, the up-regulations of the genes involved in the oxidative kinase gene and inflammation related genes implied that oxidative stress triggered by MPs on zebrafish larvae might be responsible for the mortality and locomotion retardant. The antagonistic and stimulatory effects of METH on the expression changes of genes found in PVC and PS groups implied the contrary combined toxicity of PS/PVC MPs and METH. This study for the first time estimated the different toxicity of PS and PVC MPs on fish and the joint effects with METH at high environmental levels. The results suggested PS showed stronger toxicity than PVC for fish larvae. The addition of METH stimulated the effects of PS but antagonized the effects of PVC, promoting control strategy development on MPs and METH in aquatic environments.

Ginsenoside Rg1 Nanoparticles Induce Demethylation of H3K27me3 in VEGF-A and Jagged 1 Promoter Regions to Activate Angiogenesis After Ischemic Stroke.

Background: Compared with traditional drugs, nanomaterial drugs have the benefits of improving the solubility, bioavailability, and absorption rate of insoluble drugs. Nanoporous complexes can increase the efficiency with which drugs can penetrate the blood-brain barrier and reach target organs. Ginsenoside Rg1 is an effective drug that promotes angiogenesis. Ginsenoside Rg1 composite nanoparticles were employed to induce the expression of several key epigenetic enzymes and then activate the VEGF and Notch pathways after the onset of ischemic brain lesions. Methods: We constructed nanoparticles to fully encapsulate the therapeutic drug (ginsenoside Rg1), which can be transferred into brain tissue via the receptor-mediated transfer of drug-encapsulated nanoparticles. Evaluation of the therapeutic effect of ginsenoside Rg1 complex nanovesicles (CNV) was performed by in vitro and in vivo experiments. Real-time polymerase chain reaction (RT- PCR), Western blot, immunohistochemistry staining (IHC), and Co-immunoprecipitation (co-IP) were employed to screen for epigenetic enzymes with an up-regulated expression post ginsenoside Rg1-CNV intervention. RNA sequencing, shRNA knockdown, and chromatin Immunoprecipitation (ChIP) sequencing were performed to detect the target genes of ginsenoside Rg1-CNV that regulate angiogenesis. Then, bioinformatic analysis was performed to investigate the mechanism of action of epigenetic modifying enzymes in regulating target genes. Results: The average of the synthesized ginsenoside Rg1-CNV was 203.78±6.83 nm, the polydispersion index was 0.135±0.007, and the Zeta potential was 23.13±1.65 mV. Through in vivo and in vitro experiments, we found that it promotes the proliferation, migration, and tubular formation of brain microvascular endothelial cells (BMECs). Meanwhile, the intervention of ginsenoside Rg1-CNV promoted the demethylation of H3K27me3 within the promoter region of VEGF-A and Jagged1 genes and reduced the H3K27me3 modification within this region. Conclusion: The ginsenoside Rg1 nanoparticles may be an available blood-brain barrier penetrating agent for ischemic stroke.

Copper-Promoted N-Alkylation and Bromination of Arylamines/Indazoles Using Alkyl Bromides as Reagents for Difunctionalization.

Practical copper-promoted N-alkylation and bromination of arylamines/indazoles with alkyl bromides are described; the N-alkylation-C-4-bromination and N-dialkylation-C-4-bromination of arylamines, and N-alkylation-C-3-bromination of indazoles, with alkyl bromides have been analyzed. The full use of alkyl bromides as alkylating and brominating building blocks without atom wastage, indicating excellent atom and step economy, has been highlighted. Eco-friendly oxygen and water are the reaction oxidant and byproduct, respectively.

Prediction of Aqueous Glucose Concentration Using Hyperspectral Imaging.

Near infrared hyperspectral imaging (HSI) is an emerging optical imaging modality which boasts several advantages. Compared to conventional spectroscopy, HSI pro-vides thousands of spectral samples with embedded spatial information in a single image. This allows the collection of high quality and high volume spectral signals in a short time. In this paper, transmissive HSI combined with Partial Least Squares Regression (PLSR) was used to non-invasively predict aqueous glucose concentration. Aqueous glucose samples are prepared with concentration ranging from 0 - 1000 mg/dL at intervals of 100 mg/dL and 100 - 300 mg/dL at intervals of 20 mg/dL. Our results are validated using leave-one-concentration-out cross validation, and demonstrate the feasibility of the proposed aqueous glucose concentration detection method using the combination of HSI and PLSR.

Paeoniflorin-loaded pH-sensitive liposomes alleviate synovial inflammation by altering macrophage polarity via STAT signaling.

Macrophage polarization plays a prominent role in the pathogenesis of rheumatoid arthritis (RA) and could be regulated by natural extracts paeoniflorin (Pae) but with low bioavailability. In the present study, Pae-loaded liposomes (Pae-LS) with co-conjugation of folate and PEG were prepared for the improvement of therapeutic benefits. We evaluated biophysical characterizations of Pae-LS and macrophage uptake of liposomes, as well as gain insight into whether Pae-LS can improve synovial inflammation in CIA rats and how Pae-LS promoted RAW 264.7 macrophages phenotype switch. We found that Pae-LS showed physical stability, sustained release, long circulation, pH-responsive properties, and higher uptake by active macrophages than free Pae. Furthermore, Pae-LS could repress STAT1 phosphorylation to reduce the levels of pro-inflammatory cytokines (IL-1ß, IL-6, and TNF-α) and iNOS expression, as well as lead to a marked increase in anti-inflammatory cytokine (IL-10) and CD206 levels via elevated p-STAT6. In contrast to free Pae, Pae-LS treatment was more effective in alleviating synovial inflammation and hyperplasia in the ankle joint of CIA rats. Our study revealed Pae-LS could effectively suppress synovial inflammation of CIA rats by regulating macrophage polarization via STAT signaling and had the potential for RA treatment as liposome delivery carriers systems.

Flexible Self-Powered Integrated Sensing System with 3D Periodic Ordered Black Phosphorus@MXene Thin-Films.

Accurate and continuous detection of physiological signals without the need for an external power supply is a key technology for realizing wearable electronics as next-generation biomedical devices. Herein, it is shown that a MXene/black phosphorus (BP)-based self-powered smart sensor system can be designed by integrating a flexible pressure sensor with direct-laser-writing micro-supercapacitors and solar cells. Using a layer-by-layer (LbL) self-assembly process to form a periodic interleaving MXene/BP lamellar structure results in a high energy-storage capacity in a direct-laser-writing micro-supercapacitor to drive the operation of sensors and compensate the intermittency of light illumination. Meanwhile, with MXene/BP as the sensitive layer in a flexible pressure sensor, the pressure sensitivity of the device can be improved to 77.61 kPa-1 at an optimized elastic modulus of 0.45 MPa. Furthermore, the smart sensor system with fast response time (10.9 ms) shows a real-time detection capability for the state of the human heart under physiological conditions. It is believed that the proposed study based on the design and integration of MXene materials will provide a general platform for next-generation self-powered electronics.

Efficient generation of complex vectorial optical fields with metasurfaces.

Vectorial optical fields (VOFs) exhibiting arbitrarily designed wavefronts and polarization distributions are highly desired in photonics. However, current methods to generate them either require complicated setups or exhibit limited functionalities, which is unfavorable for integration-optics applications. Here, we propose a generic approach to efficiently generate arbitrary VOFs based on metasurfaces exhibiting full-matrix yet inhomogeneous Jones-matrix distributions. We illustrate our strategy with analytical calculations on a model system and an experimental demonstration of a meta-device that can simultaneously deflect light and manipulate its polarization. Based on these benchmark results, we next experimentally demonstrate the generation of a far-field VOF exhibiting both a vortex wavefront and an inhomogeneous polarization distribution. Finally, we design/fabricate a meta-device and experimentally demonstrate that it can generate a complex near-field VOF-a cylindrically polarized surface plasmon wave possessing orbital angular momentum-with an efficiency of ~34%. Our results establish an efficient and ultracompact platform for generating arbitrary predesigned VOFs in both the near- and far-fields, which may find many applications in optical manipulation and communications.

Wireless Optogenetic Modulation of Cortical Neurons Enabled by Radioluminescent Nanoparticles.

While offering high-precision control of neural circuits, optogenetics is hampered by the necessity to implant fiber-optic waveguides in order to deliver photons to genetically engineered light-gated neurons in the brain. Unlike laser light, X-rays freely pass biological barriers. Here we show that radioluminescent Gd2(WO4)3:Eu nanoparticles, which absorb external X-rays energy and then downconvert it into optical photons with wavelengths of ∼610 nm, can be used for the transcranial stimulation of cortical neurons expressing red-shifted, ∼590-630 nm, channelrhodopsin ReaChR, thereby promoting optogenetic neural control to the practical implementation of minimally invasive wireless deep brain stimulation.

Hard Attention Net for Automatic Retinal Vessel Segmentation.

Automated retinal vessel segmentation is among the most significant application and research topics in ophthalmologic image analysis. Deep learning based retinal vessel segmentation models have attracted much attention in the recent years. However, current deep network designs tend to predominantly focus on vessels which are easy to segment, while overlooking vessels which are more difficult to segment, such as thin vessels or those with uncertain boundaries. To address this critical gap, we propose a new end-to-end deep learning architecture for retinal vessel segmentation: hard attention net (HAnet). Our design is composed of three decoder networks: the first of which dynamically locates which image regions are "hard" or "easy" to analyze, while the other two aim to segment retinal vessels in these "hard" and "easy" regions independently. We introduce attention mechanisms in the network to reinforce focus on image features in the "hard" regions. Finally, a final vessel segmentation map is generated by fusing all decoder outputs. To quantify the network's performance, we evaluate our model on four public fundus photography datasets (DRIVE, STARE, CHASE_DB1, HRF), two recent published color scanning laser ophthalmoscopy image datasets (IOSTAR, RC-SLO), and a self-collected indocyanine green angiography dataset. Compared to existing state-of-the-art models, the proposed architecture achieves better/comparable performances in segmentation accuracy, area under the receiver operating characteristic curve (AUC), and f1-score. To further gauge the ability to generalize our model, cross-dataset and cross-modality evaluations are conducted, and demonstrate promising extendibility of our proposed network architecture.

Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN.

Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.

Biocompatible and Biodegradable Functional Polysaccharides for Flexible Humidity Sensors.

Using wearable devices to monitor respiration rate is essential for reducing the risk of death or permanent injury in patients. Improving the performance and safety of these devices and reducing their environmental footprint could advance the currently used health monitoring technologies. Here, we report high-performance, flexible bioprotonic devices made entirely of biodegradable biomaterials. This smart sensor satisfies all the requirements for monitoring human breathing states, including noncontact characteristic and the ability to discriminate humidity stimuli with ultrahigh sensitivity, rapid response time, and excellent cycling stability. In addition, the device can completely decompose after its service life, which reduces the risk to the human body. The cytotoxicity test demonstrates that the device shows good biocompatibility based on the viability of human skin fibroblast-HSAS1 cells and human umbilical vein endothelial (HUVECs), illustrating the safety of the sensor upon integration with the human skin.

Bioinspired Interlocked Structure-Induced High Deformability for Two-Dimensional Titanium Carbide (MXene)/Natural Microcapsule-Based Flexible Pressure Sensors.

Achieving high deformability in response to minimal external stimulation while maximizing human-machine interactions is a considerable challenge for wearable and flexible electronics applications. Various natural materials or living organisms consisting of hierarchical or interlocked structures exhibit combinations of properties (e.g., natural elasticity and flexibility) that do not occur in conventional materials. The interlocked epidermal-dermal microbridges in human skin have excellent elastic moduli, which enhance and amplify received tactile signal transport. Herein, we use the sensing mechanisms inspired by human skin to develop Ti3C2/natural microcapsule biocomposite films that are robust and deformable by mimicking the micro/nanoscale structure of human skin-such as the hierarchy, interlocking, and patterning. The interlocked hierarchical structures can be used to create biocomposite films with excellent elastic moduli (0.73 MPa), capable of high deformability in response to various external stimuli, as verified by employing theoretical studies. The flexible sensor with a hierarchical and interlocked structure (24.63 kPa-1) achieves a 9.4-fold increase in pressure sensitivity compared to that of the planar structured Ti3C2-based flexible sensor (2.61 kPa-1). This device also exhibits a rapid response rate (14 ms) and good cycling reproducibility and stability (5000 times). In addition, the flexible pressure device can be used to detect and discriminate signals ranging from finger motion and human pulses to voice recognition.

Automated retinal microvascular velocimetry based on erythrocyte mediated angiography.

Retinal blood flow is an emerging biomarker in ocular and systemic disease. Erythrocyte mediated angiography (EMA) is a novel technique that provides an easily interpretable blood flow velocity quantification by directly tracing individual moving erythrocyte ghosts over time in vivo, imaged using a scanning laser ophthalmoscope (Heidelberg Retina Angiograph platform). This tracking procedure, however, requires time-consuming manual analysis to determine blood flow. To overcome this current bottleneck, we developed an objective and automated velocimetry approach, EMA - Automated Velocimetry (EMA-AV), which is based on a modified sequential Monte Carlo method. The intra-class correlation coefficient (ICC) between trained human graders and EMA-AV is 0.98 for mean vessel velocity estimation and 0.92 for frame by frame erythrocyte velocity estimation. This study proves EMA-AV is a reliable tool for quantification of retinal microvascular velocity and flow and establishes EMA-AV as a reliable and interpretable tool for quantifying retinal microvascular velocity.

Early Detection of Tomato Spotted Wilt Virus by Hyperspectral Imaging and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN).

Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts' attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.

Variable weighted convolutional neural network for the nitrogen content quantization of Masson pine seedling leaves with near-infrared spectroscopy.

Spectroscopy is a powerful non-destructive quantization tool. In this paper, the technology is used to predict the nitrogen content of Masson pine seedling leaves. Masson pine is widely planted in China, and its nitrogen content is an important index for evaluating the vigour of seedings. To establish a better prediction model, an improved 1D convolutional neural network architecture, named the variable weighted convolutional neural network (VWCNN), is proposed in this research. The new model can automatically force the network attention onto the important spectrum wavelengths and is able to improve the generalization ability of the basic 1D-CNN model. For 219 fresh Masson pine seedling leaves, it shows better results in the prediction accuracy and robustness compared to those derived from the traditional shallow prediction model and other CNN-based models. VWCNN can achieve a 0.984 R2 and 0.038 RMSE value in training dataset and 0.925 R2 value and 0.075 RMSE value in the test dataset. The proposed model was also tested on a public corn kernel dataset. For the dataset output, moisture, oil, protein, and starch, the new model also achieves state-of-the-art prediction results.