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Modified Jiawei Juanbi decoction (MJD) is used for the treatment of early-stage knee osteoarthritis (KOA). Here, modified Jiawei Juanbi decoction (MJD) was employed for the treatment of early-stage knee osteoarthritis (KOA) and its mechanisms were assessed via metabonomics and network pharmacology. A total of 24 male Sprague-Dawley rats were randomly allocated into a normal control group, a model group, and an MJD group (n = 8 rats per group). Each rat group was further equally divided into two subgroups for investigation for either 14 or 28 days. A rat model of early-stage KOA was constructed and rats were treated with MJD. Effects were evaluated based on changes in knee circumference, mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL). We also analyzed histopathological changes in articular cartilage. High-resolution mass spectrometry was used to analyze the chemical profile of MJD, identifying 228 components. Using an LC-Q-TOF-MS metabonomics approach, 33 differential metabolites were identified. The relevant pathways significantly associated with MJD include arginine and proline metabolism, vitamin B6 metabolism, as well as the biosynthesis of phenylalanine, tyrosine and tryptophan. The system pharmacology paradigm revealed that MJD contains 1027 components and associates with 1637 genes, of which 862 disease genes are related to osteoarthritis. The construction of the MJD composition-target-KOA network revealed a total of 140 intersection genes. A total of 39 hub genes were identified via integration of betweenness centrality values greater than 100 using CytoHubba. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed several significantly affected signaling pathways including the HIF-1, AGE-RAGE (in diabetic complications), IL-17, rheumatoid arthritis and TNF pathways. Integrated-omics and network pharmacology approaches revealed a necessity for further detailed investigation focusing on two major targets, namely NOS2 and NOS3, along with their essential metabolite (arginine) and associated pathways (HIF-1 signaling and arginine and proline metabolism). Real-time PCR validated significantly greater downregulation of NOS2 and HIF-1ɑ in the MJD as compared to the model group. Molecular docking analysis further confirmed the binding of active MJD with key active components. Our findings elucidate the impact of MJD on relevant pathophysiological and metabolic networks relevant to KOA and assess the drug efficacy of MJD and its underlying mechanisms of action.

Magnetic position sensors have extensive applications in various industrial sectors and consumer products. However, measuring angles in the full range of 0–360° in a wide field range using a single magnetic sensor remains a challenge. Here, we propose a magnetic position sensor based on a single Wheatstone bridge structure made from a single ferromagnetic layer. By measuring the anisotropic magnetoresistance (AMR) signals from the bridge and two sets of anomalous Nernst effect (ANE) signals from the transverse ports on two perpendicular Wheatstone bridge arms concurrently, we show that it is possible to achieve 0–360° angle detection using a single bridge sensor. The combined use of AMR and ANE signals allows a mean angle error in the range of 0.51–1.05° within a field range of 100 Oe–10,000 Oe to be achieved.

Lower limb sensory disturbance can be a false localizing sign of cervical spondylotic myelopathy. It may lead to delayed or missed diagnosis, resulting in an inappropriate management plan, especially in the presence of concurrent lumbar lesions. Three Asian patients with lower limb sensory disturbance were ineffectively treated in the lumbar region. Magnetic resonance imaging showed cervical disc herniation and cervical spinal cord compression. The patients underwent anterior cervical discectomy and interbody fusion using a stand-alone cervical interbody fusion cage. Postoperative imaging showed that the spinal cord compression was relieved. On the first postoperative day, the lower limb sensory disturbance was also relieved. The patients showed good recovery at the 3-month postoperative follow-up. These three cases of cervical cord compression with lower limb sensory disturbance were easily misdiagnosed as lumbar spondylosis. Anterior cervical discectomy and fusion had a good therapeutic effect. Therefore, the presence of lower limb sensory disturbance in a non-radicular classic pattern should always raise suspicion of a possible cord compression at a higher level.

Under an applied current, chiral antiferromagnets, such as Mn3Sn, can produce a spin-polarized current. Here, by varying the thickness of a buffering layer, the authors show that this spin-polarized current can drive self-induced switching in polycrystalline Mn3Sn.

Postoperative discal pseudocyst (PDP) is a rare complication that can occur after percutaneous endoscopic lumbar discectomy (PELD), microendoscopic discectomy, and laminectomy. The PDP pathogenesis and pathological process remain unclear. We described two PDP cases following PELD, with long-term follow-up results. The first patient was an Asian male, 30 years old, who experienced unbearable low back pain with right lower limb radiating pain for 2 years. The second patient was also an Asian male, 21 years old, who experienced low back pain with bilateral lower limb numbness. Both patients were diagnosed with lumbar disc herniation, underwent PELD, and relapsed after discharge. The diagnosis was PDP in each case, and conservative treatment was initiated with oral anti-inflammatory drugs and rest. Eventually, the symptoms in both patients resolved. Magnetic resonance imaging showed that the discal cysts had disappeared. The follow-up of these two PDP cases after PELD showed good results, demonstrating that PDP may be a self-healing disease. Conservative treatment is effective, and surgery should be performed only in an emergency. These case reports and literature review can help improve the understanding of PDP.

Abstract In recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

A new species belonging to the genus Parathalestris Brady & Robertson, 1873 was identified and described here based on samples recently collected from a survey on fouling organisms at the Zhonggang wharf in Qingdao, China. The new species differs from its congeners in the following characteristics: caudal rami 2.0 times longer than wide, with bulbous setae II and setae III of caudal ramus issuing terminally and laterodistally, respectively; exopod of antenna two-segmented, with two setae on first segment and four setae on second one;maxilla with two bipinnate setae on proximal endite, and three spinulose setae on middle and distal endites; basis of maxilliped with concave inner edge; P1 exopod and endopod approximately same length, enp-1 about 6.7 times as long as greatest width, with inner seta reaching proximal 2/3 of segment; baseoendopod and exopod of female P5 with transversal rows of spinules on its surface, exopod bearing six marginal setae; male P1 carrying an inner curved spine on basis; exopod of male P5 bearing six elements. The DNA barcode (COI) sequence of the new species was obtained and submitted to GenBank. This is the first report of Parathalestris from the China Seas.

Phthalate esters (PAEs) are frequently tracked in water environments worldwide. As a typical class of endocrine disruptor chemicals (EDCs), PAEs posed adverse effects on aquatic organisms at low concentration. Thus, they have attracted wide attention in recent years. In the present study, the concentrations of seven typical PAEs from 30 sampling sites in Baiyangdian Lake were measured, and the environmental exposure data of PAEs were gathered in typical rivers in China. Then, based on the aquatic life criteria (ALCs) derived from the reproductive toxicity data of aquatic organisms, two risk assessment methods, including hazard quotient (HQ) and probabilistic ecological risk assessment (PERA), were adopted to evaluate the ecological risks of PAEs in water. The sediment quality criteria (SQCs) of DEHP, DBP, BBP, DIBP and DEP were deduced based on the equilibrium partitioning method. Combined with the gathered environmental exposure data of seven PAEs in sediments from typical rivers in China, the ecological risk assessments of five PAEs in sediment were conducted only by the HQ method. The results of ecological risk assessment showed that in terms of water, DBP and DIBP posed low risk, while the risk of DEHP in Baiyangdian Lake cannot be ignored and should receive attention. In typical rivers in China, BBP and DEP posed no risk, while DIBP and DBP posed potential risk. Meanwhile, DEHP posed a high ecological risk. As far as sediment is concerned, DBP posed a high risk in some typical rivers in China, and the other rivers had medium risk. DEHP posed a high risk only in a few rivers and low to medium risk in others. This study provides an important reference for the protection of aquatic organisms and the risk management of PAEs in China.

The discharge of natural rivers is one of the important hydrological factors that are considered when responding to ice-flood disasters during ice periods. Traditionally, holes need to be dug along the cross-section on the ice cover to gauge velocity distributions along the flow depth at each hole, and to calculate the cross-sectional flow discharge by integrating velocity profiles over the entire area. This method is time consuming, costly, and inefficient. The discharge measurement can be improved using the sectional flow-depth distribution and stream-tube methods. However, the selection of both the depth-averaged–velocity-estimation method and the typical survey-point position in the cross-section affects the estimation accuracy. This study first compared the estimation methods of the depth-averaged velocity, such as the one-, two-, three-, and six-point methods, and their estimation accuracy. Furthermore, the variations in relative-unit discharge distributions in common channels with cross-sectional topographies were analyzed, and the effects of the cross-section characteristic coefficient and typical survey-point position on the flow-discharge estimation accuracy were compared. The results show that the average errors of the depth-averaged velocity estimated by the one-point method at 0.5H, new three-point method, and six-point method were 1.96%, 1.22%, and 0.45%, respectively. The new three-point method is recommended if measurement workload and accuracy are key considerations. The cross-section characteristic coefficient is considered to be 0.5 and 0.25 for the natural river and artificial channel, respectively, and the maximum-flow-depth position in the mainstream area of the cross-section is selected as the typical survey-point position. Thus, the flow-discharge estimation accuracy can be improved. In conclusion, this study provides an improved stream-tube method for the measurement of flow discharge and velocity distribution in ice periods, which can be used as a reference during practical applications.

Recently, we have developed a spin Hall magnetoresistance (SMR) sensor featuring high-sensitivity, low-noise, zero-offset, and negligible hysteresis. Here, we demonstrate that the SMR sensor can be used to map the self-magnetic leakage field (SMLF) of stainless steel sheets. The SMLF serves as an effective knob for probing magnetism in steel sheets induced by either stress or composition inhomogeneity, which, in turn, affects its mechanical properties and corrosion resistance. However, the SMLF is typically quite weak, and thus the high-sensitivity and low-noise SMR sensor is uniquely suited for this application. The self-magnetic flux leakage mapping experiments have been conducted on SUS304 steel sheets with emphasis on the lift-off effect. Both analytical model and finite element modeling have been used to account for the experimental results. Although the SMLF mapping alone is unable to pinpoint the root cause of magnetism, i.e., whether it is due to stress or composition inhomogeneity, it does provide an efficient way for pre-screening of steel sheets to prevent defect formation at early stages.

Background: Generally, it is difficult to obtain accurate pose and depth for a non-rigid moving object from a single RGB camera to create augmented reality (AR). In this study, we build an augmented reality system from a single RGB camera for a non-rigid moving human by accurately computing pose and depth, for which two key tasks are segmentation and monocular Simultaneous Localization and Mapping (SLAM). Most existing monocular SLAM systems are designed for static scenes, while in this AR system, the human body is always moving and non-rigid. Methods: In order to make the SLAM system suitable for a moving human, we first segment the rigid part of the human in each frame. A segmented moving body part can be regarded as a static object, and the relative motions between each moving body part and the camera can be considered the motion of the camera. Typical SLAM systems designed for static scenes can then be applied. In the segmentation step of this AR system, we first employ the proposed BowtieNet, which adds the atrous spatial pyramid pooling (ASPP) of DeepLab between the encoder and decoder of SegNet to segment the human in the original frame, and then we use color information to extract the face from the segmented human area. Results: Based on the human segmentation results and a monocular SLAM, this system can change the video background and add a virtual object to humans. Conclusions: The experiments on the human image segmentation datasets show that BowtieNet obtains state-of-the-art human image segmentation performance and enough speed for real-time segmentation. The experiments on videos show that the proposed AR system can robustly add a virtual object to humans and can accurately change the video background. Keywords: Augmented reality, Moving object, Reconstruction and tracking, Camera pose, Human segmentation

Objective Synovitis is a joint disease that seriously affects patient quality of life, but there are currently no diagnostic markers. The albumin to fibrinogen ratio (AFR) and monocyte to lymphocyte ratio (MLR) are non-invasive and cost-effective markers for various systemic inflammatory diseases. However, these markers have not yet been investigated for synovitis. This cross-sectional study evaluated the predictive ability of AFR and MLR in patients with non-specific knee synovitis. Methods One hundred fifty-five patients with knee synovitis and 108 healthy control patients were enrolled. Patient characteristics, blood parameters, AFRs, and MLRs were assessed, and the diagnostic value of these factors was determined. Results Among 125 patients included, patients with synovitis had a lower AFR and higher MLR than control subjects. The diagnostic values of AFR and MLR were 0.86 and 0.84, respectively, and higher compared with other parameters by receiver operating characteristic curve assessments. Additionally, MLR was negatively correlated with AFR. Late-stage patients showed significantly lower AFRs and significantly higher MLRs than early-stage patients. Binary logistic regression analyses indicated that AFR was an independent predictor for synovitis severity. Conclusions The AFR and MLR had high diagnostic value for knee synovitis. The AFR was an independent predictor for synovitis severity.

Myocardial infarction (MI) has become one of the serious diseases threatening human life and health. However, traditional treatment methods for MI have some limitations, such as irreversible myocardial necrosis and cardiac dysfunction. Fortunately, recent endeavors have shown that hydrogel materials can effectively prevent negative remodeling of the heart and improve the heart function and long-term prognosis of patients with MI due to their good biocompatibility, mechanical properties, and electrical conductivity. Therefore, this review aims to summarize the research progress of injectable hydrogel in the treatment of MI in recent years and to introduce the rational design of injectable hydrogels in myocardial repair. Finally, the potential challenges and perspectives of injectable hydrogel in this field will be discussed, in order to provide theoretical guidance for the development of new and effective treatment strategies for MI.

In water-deficient area, river-changed ponds are common and vital for aquatic species. This paper studies the relationship between the environmental factors and the benthic invertebrate communities in varying ponds under river habitat modifications (RHM). For analysis, numerous samples are collected from river-changed ponds in Beijing, China. These ponds are classified into five types, including totally reinforced ponds, channel reinforced ponds, bank reinforced ponds, natural ponds, and restored ponds. Results indicate: 1) the biodiversity of benthic invertebrates is dependent strongly on the pond type; 2) habitat quality explains a considerable amount of variations in benthic invertebrate communities, and RHM show strong power in explaining variations in habitat quality; 3) RHM on the pond bank has stronger impact on the habitat quality than that on the pond bottom. These findings suggest that RHM can affect the benthic invertebrate community through changing the habitat quality. And we suggest that ecological restoration of pond bank should be a useful way to improve invertebrate biodiversity for rive-changed ponds in Beijing.

The 3D reconstruction is an important topic in computer vision with many applications, such as robotics and augmented reality. Since the raw depth images captured by consumer RGB-D cameras are often low resolution (LR), noisy, and incomplete. How to obtain high-quality 3D models with a consumer RGB-D camera is still a challenge for the existing systems. In this paper, we propose a new depth super-resolution and completion method implemented in a deep learning framework and build a high-quality 3D reconstruction system. We first improve the resolution of LR depth image with a depth super-resolution network and remove the outliers in high-resolution (HR) depth image based on gradient saliency. To further enhance the quality of HR depth image with the guide of HR color image, we learn surface normal and occlusion boundary images from the corresponding HR color image through two deep fully convolutional networks. In particular, the blurriness of HR color image is also detected and pixel-wise quantized. Finally, we obtain a completed HR depth image by optimizing the HR depth image with the surface normal, occlusion boundary, and color image blurriness. We have carried out qualitative and quantitative evaluations with baseline methods on public datasets. The experimental results demonstrate that our method has better performance both on single depth image enhancement and 3D reconstruction.

Abstract Virtual reality, augmented reality, robotics, and autonomous driving, have recently attracted much attention from both academic and industrial communities, in which image-based camera localization is a key task. However, there has not been a complete review on image-based camera localization. It is urgent to map this topic to enable individuals enter the field quickly. In this paper, an overview of image-based camera localization is presented. A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced. Trends for future development are also discussed. This will be useful not only to researchers, but also to engineers and other individuals interested in this field.

Viral pathogens are a major threat to stable crop production. Using a backcross strategy, we find that integrating a dominant brown planthopper (BPH) resistance gene Bph3 into a high-yield and BPH-susceptible indica rice variety significantly enhances BPH resistance. However, when Bph3-carrying backcross lines are infested with BPH, these BPH-resistant lines exhibit sterile characteristics, displaying panicle enclosure and failure of seed production at their mature stage. As we suspected, BPH-mediated viral infections could cause the observed sterile symptoms, and we characterized rice-infecting viruses using deep metatranscriptomic sequencing. Our analyses revealed eight novel virus species and five known viruses, including a highly divergent virus clustered within a currently unclassified family. Additionally, we characterized rice plant antiviral responses using small RNA sequencing. The results revealed abundant virus-derived small interfering RNAs in sterile rice plants, providing evidence for Dicer-like and Argonaute-mediated immune responses in rice plants. Together, our results provide insights into the diversity of viruses in rice plants, and our findings suggest that multiple virus infections occur in rice plants.

Magnetoresistance in ferromagnetic materials and heterostructures have been enabling advanced understanding of electron transport in solids, as well as new concepts for applications. Here the authors demonstrate a different type of magnetoresistance arising from anomalous Hall effect associated spin–charge mutual conversion.

Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.

The application of spin-orbit torque in topological insulator heterostructures is limited only under low temperature. Here, the authors report room temperature magnetization switching in topological insulator-ferromagnet heterostructures by spin-orbit torques.

A novel label fusion method for multi-atlas based image segmentation method is developed by integrating semi-supervised and supervised machine learning techniques. Particularly, our method is developed in a pattern recognition based multi-atlas label fusion framework. We build random forests classification models for each image voxel to be segmented based on its corresponding image patches of atlas images that have been registered to the image to be segmented. The voxelwise random forests classification models are then applied to the image to be segmented to obtain a probabilistic segmentation map. Finally, a semi-supervised label propagation method is adapted to refine the probabilistic segmentation map by propagating its reliable voxelwise segmentation labels, taking into consideration consistency of local and global image appearance of the image to be segmented. The proposed method has been evaluated for segmenting the hippocampus in MR images and compared with alternative machine learning based multi-atlas based image segmentation methods. The experiment results have demonstrated that our method could obtain competitive segmentation performance (average Dice index > 0.88), compared with alternative multi-atlas based image segmentation methods under comparison. Source codes of the methods under comparison are publicly available at www.nitrc.org/frs/?group_id=1242.

Citrus huanglongbing (HLB) is a devastating disease for the citrus industry. The previous studies demonstrated that oxytetracycline and penicillin are effective antibiotics against Candidatus Liberibacter asiaticus (CLas). However, since CLas is uncultured, the mechanisms of action of antibiotics against CLas are still unclear. It was recently reported that the endophytic microbial communities are associated with the progression of citrus HLB after oxytetracycline and penicillin treatment. Therefore, we hypothesize that penicillin has greater antibacterial activity against CLas than oxytetracycline, which may be associated with the alteration of the structure and function of endophytic microbial communities in HLB-affected citrus in response to these antibiotics. To test this hypothesis, the microbiome of HLB-affected citrus leaves treated with these two antibiotics was analyzed using a metagenomic method. Our results indicate that the microbial structure and function in HLB-affected citrus were altered by these two antibiotics. The relative abundance of beneficial bacterial species, including Streptomyces avermitilis and Bradyrhizobium, was higher in penicillin-treated plants compared to those treated with oxytetracycline, and the relative abundance of the bacterial species (such as Propionibacterium acnes and Synechocystis sp PCC 6803) associated with CLas survival was lower for penicillin-treated plants compared to oxytetracycline-treated plants. These results indicate that penicillin has greater antibacterial activity against CLas. Based on the metagenomic analysis, this study elucidated the mechanism for the observed increase in antibacterial activity of penicillin against CLas. The data presented here are not only invaluable for developing eco-friendly and effective biocontrol strategies to combat citrus HLB, but also provide a method for revealing mechanism of antimicrobial against uncultured bacteria in host.

For the additive manufacturing (AM) of metal objects, the powder-based fusion (PBF) method is routinely utilized to fabricate macroscale parts. On the other hand, electrochemical additive manufacturing (ECAM), in which metallic structures are deposited through the electrochemical reduction of metal ions, is a promising technique for producing micro- and nanoscale objects. However, a gap exists in terms of fabricating mesoscale objects within the current AM techniques. The PBF method is limited by fabrication precision due to pronounced residual stresses, and most current ECAM systems are difficult to scale up to print mesoscale objects. In the present paper, the novel design of a low-cost ECAM 3D printer based on a microfluidic system is proposed for fabricating mesoscale metal parts. The meniscus-guided electrodeposition approach is utilized, in which a meniscus is formed between the print head and substrate, and electrodeposition is confined within the meniscus. A 3D object is fabricated by the meniscus moving with the print head according to the programmed pattern and the material subsequently being deposited at the designated locations. The key to the proposed design is to maintain a mesoscale meniscus, which normally cannot be sustained by the electrolyte surface tension with a print nozzle having a mesoscale diameter. Therefore, a microfluidic system, called the fountain pen feed system, constituting a semi-open main channel and comb structure, was designed to maintain a mesoscale meniscus throughout the printing process. Two materials, copper and nickel, with various geometric shapes were attempted to print by the proposed ECAM system, and, during the printing process, both fluid leaking and meniscus breaking were completely prevented. Free standing tilted copper pillars with controlled angles were printed to show the ability of the proposed design in fabricating 3D structures. A copper circuit was also printed on a non-conductive substrate to demonstrate a possible application of the proposed ECAM system in the fabrication of functional electronics.

Understanding the swimming characteristics of micro-organisms is significant for modelling the migration of motile cells and corresponding ecological risk assessments associated with harmful algae in oceans and estuaries. Presented in this paper is an experimental and numerical investigation of swimming characteristics of a typical gyrotactic micro-organism, Heterosigma akashiwo (H. akashiwo) in water, based on the technology of planar laser-induced fluorescence and the finite volume method. Two-dimensional swimming velocity of algal cells are obtained by analyzing cells’ trajectories in the vertical plane, and three-dimensional swimming velocity is reconstructed based on the assumption that cells’ swimming is isotropic in the horizontal plane. Four important parameters are given to reflect the swimming characteristics of gyrotactic cells in still water, including the mean swimming speed (Vs = 146 μm/s), the relative strength of reorientation by gravitational torque to rotational diffusion (λ = 1.96), the time scale of reorientation (B = 5.6 s), and rotational diffusivity (Dr = 0.046 rad2/s). A database of the ambient vorticity, mean swimming velocity and diffusivity tensor is established, by solving Fokker-Planck equation for the probability density function of cells’ swimming under the combined action of gravity, rotational diffusion, and the ambient vorticity. The mean swimming velocity and translational diffusion tensor of H. akashiwo are found to change with the horizontal and vertical vorticity. It is also shown that gyrotactic cells swim in a given direction for a weak horizontal vorticity, in contrast to cells’ tumbling and being trapped for a strong horizontal vorticity.

3D scene reconstruction is an important topic in computer vision. A complete scene is reconstructed from views acquired along the camera trajectory, each view containing a small part of the scene. Tracking in textureless scenes is well known to be a Gordian knot of camera tracking, and how to obtain accurate 3D models quickly is a major challenge for existing systems. For the application of robotics, we propose a robust CPU-based approach to reconstruct indoor scenes efficiently with a consumer RGB-D camera. The proposed approach bridges feature-based camera tracking and volumetric-based data integration together and has a good reconstruction performance in terms of both robustness and efficiency. The key points in our approach include: (i) a robust and fast camera tracking method combining points and edges, which improves tracking stability in textureless scenes; (ii) an efficient data fusion strategy to select camera views and integrate RGB-D images on multiple scales, which enhances the efficiency of volumetric integration; (iii) a novel RGB-D scene reconstruction system, which can be quickly implemented on a standard CPU. Experimental results demonstrate that our approach reconstructs scenes with higher robustness and efficiency compared to state-of-the-art reconstruction systems.

We investigated spin Hall magnetoresistance in FeMn/Pt bilayers, which was found to be one order of magnitude larger than that of heavy metal and insulating ferromagnet or antiferromagnet bilayer systems, and comparable to that of NiFe/Pt bilayers. The spin Hall magnetoresistance shows a non-monotonic dependence on the thicknesses of both FeMn and Pt. The former can be accounted for by the thickness dependence of net magnetization in FeMn thin films, whereas the latter is mainly due to spin accumulation and diffusion in Pt. Through analysis of the Pt thickness dependence, the spin Hall angle, spin diffusion length of Pt and the real part of spin mixing conductance were determined to be 0.2, 1.1 nm, and 5.5 × 1014 Ω−1m−2, respectively. The results corroborate the spin orbit torque effect observed in this system recently.

Metal/two-dimensional carbon junctions are characterized by using a nanoprobe in an ultrahigh vacuum environment. Significant differences were found in bias voltage (V) dependence of differential conductance (dI/dV) between edge- and side-contact; the former exhibits a clear linear relationship (i.e., dI/dV ∝ V), whereas the latter is characterized by a nonlinear dependence, dI/dV ∝ V3/2. Theoretical calculations confirm the experimental results, which are due to the robust two-dimensional nature of the carbon materials under study. Our work demonstrates the importance of contact geometry in graphene-based electronic devices.