Miracle-dz/UniFAR-FTUs huggingface dataset:
An Innovative Corn to Silage-Grass-Legume Intercropping System With Oversown Black Oat and Soybean to Silage in Succession for the Improvement of Nutrient Cycling

In the context of sustainable tropical agriculture, an innovative corn (Zea mays L.) to silage-grass-legume intercropping system can promotes plant diversity, improves agronomic performance and land-use efficiency, and increases the yield of oversown black oat (Avena strigosa Schreb) and soybean [Glycine max (L.) Merr. ] to silage in succession. Thus, during three growing seasons on a Typic Haplorthox in Botucatu, São Paulo State, Brazil, four treatments of a corn to silage production system were implemented in summer/autumn with black oat oversown in winter/spring: (1) corn intercropped with palisade grass (Urochloa brizantha “Marandu”) and black oat overseeded in lines; (2) corn intercropped with palisade grass and black oat overseeded in a broadcast system with superficial incorporation; (3) corn intercropped with palisade grass + pigeon pea [Cajanus cajan (L.) Millsp. ] and black oat overseeded in lines; and (4) corn intercropped with palisade grass + pigeon pea and black oat overseeded in a broadcast system with superficial incorporation. During winter/spring, the black oat pastures were grazed by lambs, but results on forage allowance and nutritive value for animal grazing and on animal performance are not reported in the present manuscript. In the fourth growing season, the effect of soybean to silage intercropped with guinea grass (Panicum maximum “Aruana”), with only a residual effect of the four production systems from the previous three growing seasons, was evaluated. Despite greater interspecific competition of palisade grass and pigeon pea intercropped with corn, this more complex system produced better results. Thus, when analyzing this system as a whole, the triple intercrop (corn + pigeon pea + palisade grass) combined with oversown black oat in lines was the most effective option for silage production and for the improvement of other elements of system productivity, such higher surface mulch quantity, leaf nutrient concentrations, and yield of soybean to silage intercropped with guinea grass. This intercrop also generated better nutrient cycling because an increased quantity of nutrients was retained in standing plant residue and surface mulch, which resulted in better land- and nutrient-use efficiency, with an emphasis on nitrogen and potassium.
Memory‐Efficient Bijective Parameterizations of Very‐Large‐Scale Models

As high‐precision 3D scanners become more and more widespread, it is easy to obtain very‐large‐scale meshes containing at least millions of vertices. However, processing these very‐large‐scale meshes is still a very challenging task due to memory limitations. This paper focuses on a fundamental geometric processing task, i.e., bijective parameterization construction. To this end, we present a spline‐enhanced method to compute bijective and low distortion parameterizations for very‐large‐scale disk topology meshes. Instead of computing descent directions using the mesh vertices as variables, we estimate descent directions for each vertex by optimizing a proxy energy defined in spline spaces. Since the spline functions contain a small set of control points, it significantly decreases memory requirement. Besides, a divide‐and‐conquer method is proposed to obtain bijective initializations, and a submesh‐based optimization strategy is developed to reduce distortion further. The capability and feasibility of our method are demonstrated over various complex models. Compared to the existing methods for bijective parameterizations of very‐large‐scale meshes, our method exhibits better scalability and requires much less memory.
Hybrid Recanalization for the Treatment of Carotid/Vertebral In-stent Restenosis or Occlusion: Pilot Surgery Experiences From One Single Center

Background : The hybrid recanalization of internal carotid artery (ICA) and vertebral artery (VA) in-stent restenosis or occlusion using a combination of endarterectomy and endovascular intervention has achieved technical success. We present our surgical experiences to further evaluate the safety and efficacy of the hybrid technique for the treatment of in-stent restenosis and occlusion. Methods : A cohort of 12 refractory patients with in-stent restenosis or occlusion who underwent hybrid recanalization, a combination of endarterectomy and endovascular intervention, were retrospectively analyzed. Medical records, including presenting symptoms, comorbidities, contralateral ICA/VA findings, use of antiplatelet drugs, postoperative complications, and angiographic outcomes, were collected. Results : Among 415 consecutive patients with ICA, common carotid artery, and V1 segment lesions, 12 refractory patients (2.89%) with 13 cases were enrolled in our study (1 female and 11 male). All patients underwent successful hybrid recanalization. There were no cases of postoperative stroke or death. Only two patients sustained hoarseness, but it resolved within 2 weeks after surgery. Three patients were treated with dual antiplatelet (aspirin and clopidogrel), seven with single antiplatelet (aspirin), one with single antiplatelet (clopidogrel), and one with single antiplatelet (ticagrelor). All patients were followed up in the outpatient department according to the protocol, with a mean follow-up period of 13 months (range, 6–24 months). No death or recurrent symptoms occurred during the regular follow-up period. Conclusion : The hybrid technique maybe a safe and feasible treatment option to recanalize in-stent restenosis or occlusion with acceptable complications.
Field-Effect Sensors for Virus Detection: From Ebola to SARS-CoV-2 and Plant Viral Enhancers

Coronavirus disease 2019 (COVID-19) is a novel human infectious disease provoked by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, no specific vaccines or drugs against COVID-19 are available. Therefore, early diagnosis and treatment are essential in order to slow the virus spread and to contain the disease outbreak. Hence, new diagnostic tests and devices for virus detection in clinical samples that are faster, more accurate and reliable, easier and cost-efficient than existing ones are needed. Due to the small sizes, fast response time, label-free operation without the need for expensive and time-consuming labeling steps, the possibility of real-time and multiplexed measurements, robustness and portability (point-of-care and on-site testing), biosensors based on semiconductor field-effect devices (FEDs) are one of the most attractive platforms for an electrical detection of charged biomolecules and bioparticles by their intrinsic charge. In this review, recent advances and key developments in the field of label-free detection of viruses (including plant viruses) with various types of FEDs are presented. In recent years, however, certain plant viruses have also attracted additional interest for biosensor layouts: Their repetitive protein subunits arranged at nanometric spacing can be employed for coupling functional molecules. If used as adapters on sensor chip surfaces, they allow an efficient immobilization of analyte-specific recognition and detector elements such as antibodies and enzymes at highest surface densities. The display on plant viral bionanoparticles may also lead to long-time stabilization of sensor molecules upon repeated uses and has the potential to increase sensor performance substantially, compared to conventional layouts. This has been demonstrated in different proof-of-concept biosensor devices. Therefore, richly available plant viral particles, non-pathogenic for animals or humans, might gain novel importance if applied in receptor layers of FEDs. These perspectives are explained and discussed with regard to future detection strategies for COVID-19 and related viral diseases.
Potential of Soluble Decellularized Extracellular Matrix for Musculoskeletal Tissue Engineering – Comparison of Various Mesenchymal Tissues

Background It is well studied that preparations of decellularized extracellular matrix (ECM) obtained from mesenchymal tissues can function as biological scaffolds to regenerate injured musculoskeletal tissues. Previously, we reported that soluble decellularized ECMs derived from meniscal tissue demonstrated excellent biocompatibility and produced meniscal regenerate with native meniscal anatomy and biochemical characteristics. We therefore hypothesized that decellularized mesenchymal tissue ECMs from various mesenchymal tissues should exhibit tissue-specific bioactivity. The purpose of this study was to test this hypothesis using porcine tissues, for potential applications in musculoskeletal tissue engineering. Methods Nine types of porcine tissue, including cartilage, meniscus, ligament, tendon, muscle, synovium, fat pad, fat, and bone, were decellularized using established methods and solubilized. Although the current trend is to develop tissue specific decellularization protocols, we selected a simple standard protocol across all tissues using Triton X-100 and DNase/RNase after mincing to compare the outcome. The content of sulfated glycosaminoglycan (sGAG) and hydroxyproline were quantified to determine the biochemical composition of each tissue. Along with the concentration of several growth factors, known to be involved in tissue repair and/or maturation, including bFGF, IGF-1, VEGF, and TGF-β1. The effect of soluble ECMs on cell differentiation was explored by combining them with 3D collagen scaffold culturing human synovium derived mesenchymal stem cells (hSMSCs). Results The decellularization of each tissue was performed and confirmed both histologically [hematoxylin and eosin (H&E) and 4’,6-diamidino-2-phenylindole (DAPI) staining] and on the basis of dsDNA quantification. The content of hydroxyproline of each tissue was relatively unchanged during the decellularization process when comparing the native and decellularized tissue. Cartilage and meniscus exhibited a significant decrease in sGAG content. The content of hydroxyproline in meniscus-derived ECM was the highest when compared with other tissues, while sGAG content in cartilage was the highest. Interestingly, a tissue-specific composition of most of the growth factors was measured in each soluble decellularized ECM and specific differentiation potential was particularly evident in cartilage, ligament and bone derived ECMs. Conclusion In this study, soluble decellularized ECMs exhibited differences based on their tissue of origin and the present results are important going forward in the field of musculoskeletal regeneration therapy.
Comparison of Psychosocial Distress in Areas With Different COVID-19 Prevalence in Korea

Objectives: This study aimed to compare psychosocial distress in areas in Korea with different prevalence of coronavirus disease 2019 (COVID-19) after validating a questionnaire on psychological experiences and stress associated with the disease outbreak. Methods: Using an online-based survey, psychosocial distress associated with COVID-19 was investigated in three regions, which were selected according to their prevalence of COVID-19. A total of 1,500 people from an online public panel in the three regions participated in the study. The questionnaire included sociodemographic information, psychosocial experience and stress related to COVID-19, and the perceived stress scale (PSS), patient health questionnaire-9 (PHQ-9), and generalized anxiety disorder-7 (GAD-7). Those questionnaires and scales were compared by level of prevalence of COVID-19 after validating the questionnaire on psychosocial distress associated with COVID-19. Results: The 19 items on psychosocial experience associated with COVID-19 comprised 5 subscales, with favorable Cronbach's α ranging from 0.69 to 0.88. Six stress items related to COVID-19 had a Cronbach's α of 0.79. Disturbance in eating and sleeping, difficulty with outside activities, stress from COVID-19, and PSS scores were greater in the areas where COVID-19 was highly prevalent. Economic problems, daily activity changes, and anger toward society were higher in the higher-prevalence regions. Discussion: Psychosocial distress associated with COVID-19 was closely related to the prevalence of the disease in the areas where participants lived. Psychosocial interventions for distress associated with COVID-19 should be developed and prepared for people during this lengthy pandemic.
Two‐stage Photograph Cartoonization via Line Tracing

Cartoon is highly abstracted with clear edges, which makes it unique from the other art forms. In this paper, we focus on the essential cartoon factors of abstraction and edges, aiming to cartoonize real‐world photographs like an artist. To this end, we propose a two‐stage network, each stage explicitly targets at producing abstracted shading and crisp edges respectively. In the first abstraction stage, we propose a novel unsupervised bilateral flattening loss, which allows generating high‐quality smoothing results in a label‐free manner. Together with two other semantic‐aware losses, the abstraction stage imposes different forms of regularization for creating cartoon‐like flattened images. In the second stage we draw lines on the structural edges of the flattened cartoon with the fully supervised line drawing objective and unsupervised edge augmenting loss. We collect a cartoon‐line dataset with line tracing, and it serves as the starting point for preparing abstraction and line drawing data. We have evaluated the proposed method on a large number of photographs, by converting them to three different cartoon styles. Our method substantially outperforms state‐of‐the‐art methods in terms of visual quality quantitatively and qualitatively.
Coarse to Fine: Weak Feature Boosting Network for Salient Object Detection

Salient object detection is to identify objects or regions with maximum visual recognition in an image, which brings significant help and improvement to many computer visual processing tasks. Although lots of methods have occurred for salient object detection, the problem is still not perfectly solved especially when the background scene is complex or the salient object is small. In this paper, we propose a novel Weak Feature Boosting Network (WFBNet) for the salient object detection task. In the WFBNet, we extract the unpredictable regions (low confidence regions) of the image via a polynomial function and enhance the features of these regions through a well‐designed weak feature boosting module (WFBM). Starting from a coarse saliency map, we gradually refine it according to the boosted features to obtain the final saliency map, and our network does not need any post‐processing step. We conduct extensive experiments on five benchmark datasets using comprehensive evaluation metrics. The results show that our algorithm has considerable advantages over the existing state‐of‐the‐art methods.
Design of intelligent trash can be based on machine vision

This article designs smart trash that can be based on machine vision. Its primary function is to complete the automatic identification, classification, and bucketing operations of the typical garbage types in the community. The system uses a Raspberry Pi equipped with a convolutional neural network. It embeds a deep learning model to realize the identification of garbage and online communication to control the mechanical system to complete the class i f icat ion of was te into bucket s . In this paper, the accuracy of the AlexNet model, ZFNet model, and Inception V1 model top-1 are 62.5%, 64%, and 69.8%, respectively. The top-1 accuracy rate of the vgg model has reached 74%, and the accuracy of the model training can be improved to 94% by enhancing the vgg model.
A sparse aperture imaging system with optimal rotating pupil

Space-based high-resolution imaging system has a trend to broadband spectrum imaging in geosynchronous orbits. The resolution of an imaging system is related to the aperture size, the height of the orbit of the system, and the detection wavelength. For traditional imaging system, it is difficult to increase the aperture size due to the limitation of manufacturing level, processing cost and load weight. The optical sparse aperture (OSA) system uses a number of independent subaperture systems with a smaller size and the same standard to collect light from objects and synthetizes a blur image in focal plane, which can achieve a large equivalent aperture size. However, with the increasing number of the sub-aperture systems, the system structure becomes more complicated, which is difficult to be used in practice. This paper presents an approach to search for an optimal OSA system based on annular pupil structure and reduce the number of sub-aperture systems by rotating the pupil to compensate PSF information and improve the image quality in OSA system. In this paper, a 12-aperture OSA system applied for earth observation in geosynchronous orbit are designed optimally to realize the resolution of 1m. The optimal scheme for rotating the pupil with twelve apertures under the threesymmetric structure was determined, and the MTF of the hexagonal structure was obtained. The simulation results show that the method can be applied to design a OSA system with a rotating pupil, synthesize all images within the rotation period, collect all frequency information, and restore high-resolution images using image restoration techniques.
Image enhancement using convolutional neural network

One common interest in radiography is producing radiographs with as low as possible radiation exposures to patients. In clinical practices, radiation exposure factors are preset for optimal image qualities to avoid underexposures which will lead to repeating examinations hence increasing radiation exposures to patients. Underexposed radiographs mainly suffer from Poisson noises due to inadequate photons reaching the detector. Radiographs are often degraded by scatter radiations and the severity of image quality degradations depends on the amount of scatters reaching the detectors. In this work, a convolutional neural network (CNN) algorithm was used to predict scatters and reduce Poisson noises. Monte Carlo simulation images and an adult abdomen radiograph were used to evaluate this CNN algorithm. The radiograph was underexposed by 60% radiation exposures. The simulation images were produced with one-thousandth of a typical clinical exposure. The results show that Poisson noises are successfully reduced, and image contrast and details are improved. After the underexposed radiograph which is not useful for making a confident diagnosis was processed using the CNN algorithm, the contrast and details in the radiograph were greatly improved and are adequate for making a diagnosis, therefore a 60% radiation dose reduction was achieved. This work shows that radiograph qualities can be improved by reducing scatters and Poisson noises. A potential application of this CNN algorithm is for patient radiation dose reductions by reducing current preset optimal radiation exposures and then using this algorithm to enhance the image contrast and details by reducing both scatters and Poisson noises.
Urban waterlogging identification system based on public surveillance video

Aiming at the problems of limited coverage, limited construction and high maintenance cost of the traditional urban waterlogging early warning system, a large-scale waterlogging automatic recognition system based on urban public security monitoring video is designed. By manually organizing the existing urban policing video image data, a set of flood image data set is constructed to train and evaluate a variety of deep convolutional neural networks, and the networks with moderate recognition performance and computational cost are deployed in the actual identification system. The experimental results show that the system can identify the image with 97.72% accuracy, and has a recall rate of 95.16%, and a single image recognition server can support 135 video points, and support elastic scaling, which can meet the needs of large-scale deployment.
Research on perceptual fusion of audio and video based on deep learning

In view of the technical problems existing in the perception and fusion of unstructured data such as audio and video, the method of deep learning is used to realize the pixel level sound source location of video by combining the synchronization of audio and video content in the physical scene and analyzing the sound and image jointly. At the same time, aiming at the problem of low resolution and poor quality of face image in video, a recognition method based on face super-resolution is proposed to realize the identity attribute calculation of the target person.
Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward‐looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.
InstanceFusion: Real‐time Instance‐level 3D Reconstruction Using a Single RGBD Camera

We present InstanceFusion, a robust real‐time system to detect, segment, and reconstruct instance‐level 3D objects of indoor scenes with a hand‐held RGBD camera. It combines the strengths of deep learning and traditional SLAM techniques to produce visually compelling 3D semantic models. The key success comes from our novel segmentation scheme and the efficient instance‐level data fusion, which are both implemented on GPU. Specifically, for each incoming RGBD frame, we take the advantages of the RGBD features, the 3D point cloud, and the reconstructed model to perform instance‐level segmentation. The corresponding RGBD data along with the instance ID are then fused to the surfel‐based models. In order to sufficiently store and update these data, we design and implement a new data structure using the OpenGL Shading Language. Experimental results show that our method advances the state‐of‐the‐art (SOTA) methods in instance segmentation and data fusion by a big margin. In addition, our instance segmentation improves the precision of 3D reconstruction, especially in the loop closure. InstanceFusion system runs 20.5Hz on a consumer‐level GPU, which supports a number of augmented reality (AR) applications (e.g., 3D model registration, virtual interaction, AR map) and robot applications (e.g., navigation, manipulation, grasping). To facilitate future research and reproduce our system more easily, the source code, data, and the trained model are released on Github: https://github.com/Fancomi2017/InstanceFusion.