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Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles1. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours2–6. Here we use connectomics7 to analyse the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. By contrast, wing premotor networks lack proportional synaptic connectivity, which may enable more flexible recruitment of wing steering muscles. Through comparison of the architecture of distinct motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.We use connectomics to compare the wiring logic of premotor circuits controlling the Drosophila leg and wing, finding that both premotor networks cluster into modules that link motor neurons innervating muscles with related functions. [ABSTRACT FROM AUTHOR]

A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.Automated reconstruction of dense neural networks in the ventral nerve cord of the fruit fly provides a resource for investigating the neural control of movement. [ABSTRACT FROM AUTHOR]

The thermal expansion effects of GNSS stations are influenced by not only temperature variations, but also bedrock depths and types. Unfortunately, the current studies treat the subsurface GNSS monument and their nearby bedrock as a whole, without taking into account the inconsistencies among bedrock depths and types, while the existing full-spectrum finite element method (FEM) cannot be easily extended to consider the bedrock information. To solve this problem, we propose a refined full-spectrum temperature-induced subsurface thermal expansion model (FSHBDT) that considers both seasonal and non-seasonal temperature variations as well as bedrock information based on the half-space harmonic model. Results show that the full-spectrum half-space harmonic model (FSH), which considers only seasonal and non-seasonal temperature variations, can obtain comparable results to the FEM and even outperform the FEM for inland stations. In addition, the depth and type of bedrock have significant effects on the annual amplitude and phase of thermal expansion-induced vertical displacement. In particular, we find that the station displacement increases by more than 1 mm and the annual phase delays by up to 10° for high-latitude and deeper bedrock stations when bedrock depths are taken into account. The FSHBDT improves the correlation coefficient between GNSS height and mass load displacements by up to 42.3% compared to the FEM and explains up to 8.2% of the nonlinear variation in the GNSS height time series. Our work confirms the advantage of rigorous subsurface thermal expansion modeling to correct the nonlinear variations of global GNSS stations, which might provide a potential opportunity to improve the terrestrial reference frame toward the goal of 1 mm accuracy. [ABSTRACT FROM AUTHOR]

The thermal expansion effect exhibited by the GNSS monument and their nearby bedrock due to surface temperature variations is an important factor affecting the nonlinear variation of GNSS height. However, current thermal expansion models either consider only the above-surface GNSS monuments or only non-seasonal temperature variations of the subsurface bedrock, and lack a comprehensive thermal expansion model. Furthermore, previous studies on the contribution of thermal expansion effects to nonlinear variations in GNSS height have been analyzed only against one set of GNSS time series products per single research, but in fact the GNSS time series provided by each agency varied considerably. In this study, we use a refined comprehensive thermal expansion model (TEVDFSD) to evaluate its contribution to the nonlinear variations from several GNSS height products (SOPAC, JPL, and COMBINED) obtained using different data processing strategies. The results show that the most GNSS stations (about 95%) using the TEVDFSD model exhibit an annual amplitude increase and phase lag, with an amplitude increase of up to 0.5 mm and phase lag of up to 13° compared with the finite element model, especially for inland and those with deeper GNSS monument stations. This phase lag improves its correlation with the GNSS height, which reduces the GNSS height value to improve the geophysical interpretation. The TEVDFSD model estimates an annual amplitude of up to 7.5 mm, explaining at most 13.6% of the nonlinear variation in the COMBINED height. The COMBINED product exhibits a further WRMS reduction of up to 20% and 18.7% compared with the SOPAC and JPL products, respectively, which are likely due to the higher accuracy of the combined GNSS solution than of the independent data processing strategy. Our work indicates that differences in data processing strategies for GNSS height time series products significantly affect the interpretability of thermal expansion effects to nonlinear variations. [ABSTRACT FROM AUTHOR]

The new generation of BDS-3 broadcasted open service signals B1C and B2a, which are compatible and interoperable with GPS and Galileo overlapping frequency signals, are suitable for multi-constellation global navigation satellite system (multi-GNSS) precise point positioning ambiguity resolution (PPP-AR). However, multipath errors caused by an actual complex environment can affect the ability of ambiguity resolution, thereby restricting the positioning performance of multi-GNSS. Due to different orbital repeat periods of GNSS systems, the implementation complexity of a multipath correction method based on time-domain repeatability is relatively high, while that based on spatial-domain repeatability are research hotspot at present, thanks to the advantages of simple algorithms, easy implementation, and real-time correction. Based on the original multipath hemispherical map (MHM) and trend-surface analysis MHM (TMHM) methods, four multipath processing schemes, namely, the independent modeling and correction (I-MHM, I-TMHM), together with the joint modeling and correction (C-MHM, C-TMHM) of different GNSS systems are proposed in this paper. We find that the residuals of GPS, BDS-3, and Galileo overlapping frequency show a strong correlation at the same spatial position after considering the GNSS inter-system biases in static PPP-AR modes, while the multipath joint modeling and correction method can improve the positioning performance more than the independent modeling and correction. This can be attributed to the ability of multi-GNSS to improve the space coverage within grids, making the modeling results more explanatory. Compared to C-MHM, the C-TMHM derived positioning accuracy and convergence time of combined GCE in 3D component can be improved by up to 29.3% and 40.7%, respectively. In addition, through using multi-GNSS data for multipath modeling, the modeling time can be shortened by more than half to obtain a correction effect similar to that of full orbit period modeling, specifically, 3-day data for GC modeling, while 4-day data for GE, CE, and GCE modeling. Finally, the performances of our improved multipath modeling method were verified and evaluated by using the observation data in environment with fewer blind areas. Compared with the uncorrected cases, the positioning accuracies of GC, GE, CE, and GCE in 3D component improve by 51.7, 63.8, 59.7, and 65.7%, after correcting the multipath error by the proposed C-TMHM method, while the convergence time can also be shortened by 55.3, 51.0, 52.2, and 64.2%, respectively. This research has significant applicability for mitigating multipath errors in various scenarios to improve positioning accuracy and reliability. [ABSTRACT FROM AUTHOR]

Recently, social networks have been regarded as auxiliary information to mitigate the data sparsity issue in recommender systems. However, most existing social recommendation methods fail to effectively capture the relations between multiple behaviors, resulting in the correlated behaviors being unable to make semantic complements to the target behavior and sparse behavior data features. To alleviate the above problems, we propose a novel method based on graph neural network, namely Multiple Behavior Interactive Enhanced Social-aware Recommendation (MISR), which can dynamically acquire more fine-grained relations and differences between different behaviors and combine features of temporal sequences to capture potential interactions. In addition, we develop a global enhanced module to fully learn the enhanced user representation, empowering MISR to capture jointly the heterogeneous strengths of global social context and social relations. Extensive experiments on three real-world recommendation datasets validate the rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Objective: Patients with systemic lupus erythematosus (SLE) often develop multi-organ damages including heart and kidney complications. We sought to better define the underlying mechanisms with a focus on the chemokine receptor CX3CR1. Methods: We generated Cx3cr1-deficient MRL/lpr lupus-prone mice through backcrossing. We then employed heterozygous intercross to generate MRL/lpr littermates that were either sufficient or deficient of CX3CR1. The mice were also treated with either Lactobacillus spp. or a high-fat diet (HFD) followed by assessments of the kidney and heart, respectively. Results: Cx3cr1–/– MRL/lpr mice exhibited a distinct phenotype of exacerbated glomerulonephritis compared to Cx3cr1+/+ littermates, which was associated with a decrease of spleen tolerogenic marginal zone macrophages and an increase of double-negative T cells. Interestingly, upon correction of the gut microbiota with Lactobacillus administration, the phenotype of exacerbated glomerulonephritis was reversed, suggesting that CX3CR1 controls glomerulonephritis in MRL/lpr mice through a gut microbiota-dependent mechanism. Upon treatment with HFD, Cx3cr1–/– MRL/lpr mice developed significantly more atherosclerotic plaques that were promoted by Ly6C+ monocytes. Activated monocytes expressed ICOS-L that interacted with ICOS-expressing follicular T-helper cells, which in turn facilitated a germinal center reaction to produce more autoantibodies. Through a positive feedback mechanism, the increased circulatory autoantibodies further promoted the activation of Ly6C+ monocytes and their display of ICOS-L. Conclusions: We uncovered novel, Cx3cr1 deficiency-mediated pathogenic mechanisms contributing to SLE-associated glomerulonephritis and cardiovascular disease. [ABSTRACT FROM AUTHOR]

To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions. [ABSTRACT FROM AUTHOR]

Fibroblasts and myofibroblasts are major mesenchymal cells in the lamina propria of colon mucosa and in colon cancer tissues. Detailed insight into the highly specific populations of fibroblasts and myofibroblasts is required to understand the integrity and homeostasis of human colon mucosa and colon cancer. Based on gene expression profiles of single cells, we identified fibroblast populations that produce extracellular matrix components, Wnt ligand- and BMP-secreting fibroblasts, chemokine- and chemokine ligand-generating fibroblasts, highly activated fibroblasts, immune-modulating fibroblasts, epithelial cell-modulating myofibroblasts, stimuli-responsive myofibroblasts, proliferating myofibroblasts, fibroblast-like myofibroblasts, matrix producing myofibroblasts, and contractile myofibroblasts in human colon mucosa. In colon cancer tissue, the compositions of fibroblasts and myofibroblasts were highly altered, as were the expressing patterns of genes including BMPs, Wnt ligands, chemokines, chemokine ligands, growth factors and extracellular matrix components in fibroblasts and myofibroblasts. Our work expands the working atlas of fibroblasts and myofibroblasts and provides a framework for interrogating the complexity of stromal cells in human healthy colon mucosa and colon cancer tissues. The heterogeneity of fibroblasts in normal colon mucosa and colorectal cancer (CRC) is examined using single cell data, showing their potential function in normal gut homeostasis and in CRC. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment classification has been widely used by researchers as a fine-grained sentiment classification task to predict the sentiment polarity of specific aspect words in a given sentence. Previous studies have shown relatively good experimental results using graph convolutional networks, so more and more approaches are beginning to exploit sentence structure information for this task. However, these methods do not link aspect word and context well. To address this problem, we propose a method that utilizes a hierarchical multi-head attention mechanism and a graph convolutional network (MHAGCN). It fully considers syntactic dependencies and combines semantic information to achieve interaction between aspect words and context. To fully validate the effectiveness of the method proposed in this paper, we conduct extensive experiments on three benchmark datasets, which, according to the experimental results, show that the method outperforms current methods. [ABSTRACT FROM AUTHOR]

Multipath effect is one of the main challenges of precise point positioning (PPP) in complex environments. Nowadays, the BeiDou global navigation satellite system (BDS-3) constellation was fully operational. We evaluated the multipath characteristics of BDS-3 open-service signals. The results indicate that the B2a signal had the best anti-multipath performance, and B1C signal had the worst capability. Since BDS-3 satellites with different orbital types have different orbital repeat time, the traditional method based on multipath time-domain repeatability is complicated to alleviate the multipath error on BDS-3 satellites. In contrast, the multipath spatial-domain repeatability method does not need to calculate the orbital repeat times and is only related to the position of the satellite in the sky. It has the advantages of simple algorithm and easy implementation. We selected a multipath hemispherical map (MHM) and a MHM based on trend-surface analysis (T-MHM) to evaluate the effects of BDS-3 PPP multipath correction. The positioning results for the inclined geosynchronous orbit (IGSO) and medium earth orbit (MEO) satellites, which were separately modeled and corrected, are slightly better than those obtained when they were modeled and corrected together. Compared with the uncorrected multipath, the positioning accuracy of B1I/B3I and B1C/B2a ionospheric-free (IF) combinations using the MHM can be improved by 52.7% and 51.6% and the convergence time can be shortened by 48.6% and 57.5%, respectively. The positioning accuracy of B1I/B3I and B1C/B2a IF combinations using the T-MHM can be improved by 67% and 66.9% and the convergence time can be shortened by 69.3% and 76.5%, respectively. The T-MHM introduces trend-surface analysis to model the spatial variation of the multipath inside the grid, which effectively alleviates high-frequency and low-frequency multipath. This study is of great significance for further improvements to the application of BDS-3 in complex environments. [ABSTRACT FROM AUTHOR]

Purpose: Development of best practices for dealing with incidental findings on neuroimaging requires insight in their frequency and clinical relevance. Methods: Here, we delineate prevalence estimates with 95% confidence intervals and clinical management of incidental findings, based on the first 3589 participants of the population-based Rhineland Study (age range 30–95 years) who underwent 3 Tesla structural neuroimaging (3D, 0.8 mm3 isotropic resolution). Two trained raters independently assessed all scans for abnormalities, with confirmation and adjudication where needed by neuroradiologists. Participants were referred for diagnostic work-up depending on the potential benefit. Results: Of 3589 participants (mean age 55 ± 14 years, 2072 women), 867 had at least one possible incidental finding (24.2%). Most common were pituitary abnormalities (12.3%), arachnoid cysts (4.1%), developmental venous anomalies (2.5%), non-acute infarcts (1.8%), cavernomas (1.0%), and meningiomas (0.7%). Forty-six participants were informed about their findings, which was hitherto unknown in 40 of them (1.1%). Of these, in 19 participants (48%), a wait-and-see policy was applied and nine (23%) received treatment, while lesions in the remainder were benign, could not be confirmed, or the participant refused to inform us about their clinical diagnosis. Conclusion: Nearly one-quarter of participants had an incidental finding, but only 5% of those required referral, that mostly remained without direct clinical consequences. [ABSTRACT FROM AUTHOR]

Lower extremity deep venous thrombosis (LEDVT) is a venous reflux disorder caused by abnormal coagulation of blood. LEDVT can obstruct the lumen and LEDVT is the third vascular disease after cerebrovascular diseases and coronary artery diseases. miRNAs are associated with thrombosis, and miR-185 was reported to affect the proliferation and apoptosis of vascular endothelial cells by regulating receptor of advanced glycation end products (RAGE). However, no study has reported the effect of miR-185 on LEDVT. Here, we studied the effects of miR-185 on the PI3K/AKT and MAPK signaling pathways in the LEDVT cells. The results showed that miR-185 promotes cell proliferation through activating the PI3K/AKT and MAPK signaling pathways and then inhibits tissue factor and fibrin expression to reduce thrombosis. In short, our study provides new ideas and a theoretical basis for research on the prevention, diagnosis, and treatment of LEDVT. [ABSTRACT FROM AUTHOR]

Generalizing wavelets by adding desired redundancy and flexibility, framelets (i.e., wavelet frames) are of interest and importance in many applications such as image processing and numerical algorithms. Several key properties of framelets are high vanishing moments for sparse multiscale representation, fast framelet transforms for numerical efficiency, and redundancy for robustness. However, it is a challenging problem to study and construct multivariate nonseparable framelets, mainly due to their intrinsic connections to factorization and syzygy modules of multivariate polynomial matrices. Moreover, all the known multivariate tight framelets derived from spline refinable scalar functions have only one vanishing moment, and framelets derived from refinable vector functions are barely studied yet in the literature. In this paper, we circumvent the above difficulties through the approach of quasi-tight framelets, which behave almost identically to tight framelets. Employing the popular oblique extension principle (OEP), from an arbitrary compactly supported M-refinable vector function ϕ with multiplicity greater than one, we prove that we can always derive from ϕ a compactly supported multivariate quasi-tight framelet such that: (i) all the framelet generators have the highest possible order of vanishing moments; (ii) its associated fast framelet transform has the highest balancing order and is compact. For a refinable scalar function ϕ (i.e., its multiplicity is one), the above item (ii) often cannot be achieved intrinsically but we show that we can always construct a compactly supported OEP-based multivariate quasi-tight framelet derived from ϕ satisfying item (i). We point out that constructing OEP-based quasi-tight framelets is closely related to the generalized spectral factorization of Hermitian trigonometric polynomial matrices. Our proof is critically built on a newly developed result on the normal form of a matrix-valued filter, which is of interest and importance in itself for greatly facilitating the study of refinable vector functions and multiwavelets/multiframelets. This paper provides a comprehensive investigation on OEP-based multivariate quasi-tight multiframelets and their associated framelet transforms with high balancing orders. This deepens our theoretical understanding of multivariate quasi-tight multiframelets and their associated fast multiframelet transforms. [ABSTRACT FROM AUTHOR]

Poly-ether-ether-ketone (PEEK) has attracted increasing attention as a promising orthopaedic implant material owing to its excellent mechanical properties and biocompatibility. However, its antibacterial properties must be improved as an implant material. In this study, PEEK was sulfonated to obtain a porous surface, and graphene oxide (GO) was deposited to form a coating with antibacterial activity and biocompatibility. After PEEK was sulfonated for different durations, GO was deposited on the surface to prepare the coating (SPEEK-GO), which was then characterised using scanning electron microscopy (SEM), Raman spectroscopy, and contact angle measurements. The in vitro study included antimicrobial and cellular tests. The results showed that the PEEK sulfonated using a 10-min treatment exhibited a uniform porous structure and provided a better basal surface for the deposition of GO. The SPEEK-GO coating displayed strong antibacterial activity against two common dental pathogens. It exhibited good adhesion and proliferation of MC3T3-E1. Moreover, it showed osteogenic differentiation as bone implant material. [ABSTRACT FROM AUTHOR]

The multipath effect reduces the accuracy of pseudorange and carrier phase observations and significantly affects the convergence of precise point positioning (PPP) in an urban high-obstruction environment. The multipath hemispherical map (MHM) is based on the spatial repeatability of the multipath and is used to mitigate the multipath effect. This method is widely used because it is suitable for real-time dynamic and static situations with multipath invariance. Although the MHM algorithm is simple and easy to implement, it ignores the spatial distribution of multipath inside the sky grid. It is not suitable for use in high-frequency multipath corrections. By fitting the multipath trend inside the sky grid, the MHM based on a trend-surface analysis (T-MHM) alleviates the high-frequency and low-frequency multipath simultaneously, improving the accuracy of the baseline solution in the differential mode. We mainly demonstrate the application of T-MHM in PPP, analyze the unmodeled errors, evaluate the multipath correction effect of T-MHM, discuss the optimal modeling days, and test its sensitivity in the scale of the sky grid. Compared to MHM, the experimental results show that using T-MHM in multipath correction improves the positioning accuracy in the horizontal and vertical directions by 21.6 and 13.6%, respectively, and shortens the convergence time by 24.2 and 7.2%, respectively. The T-MHM method is not sensitive to the grid scale of the sky, thus resulting in high efficiency. [ABSTRACT FROM AUTHOR]

Levan is a type of fructan with β-(2, 6) fructosyl linkage. In recent years, levan has been used in the medicinal, food and chemical industries for its excellent physicochemical properties of good thermal stability, low viscosity, pseudoplasticity, and non-swelling in water. The levansucrase is key enzyme responsible for biosynthesis of levan from substrate sucrose. In the present study cell lysate of Escherichia coli was used as support for convenient immobilization of Ba-SacB (i.e., Ba-SacB-Cell-Lysate) under the action of dopamine. Compared with the free Ba-SacB, the Ba-SacB-Cell-Lysate showed surprisingly enhanced pH stability in the pH range 6–9, and improved relative activities in the pH ranges 4–5 and 7–10. With the presence of 10% (v/v) butyl alcohol, 5mmol/L Fe3+, and after vortex for 90 min, the Ba-SacB-Cell-Lysate showed up to 14.32 times, 4.63 times, and 9.2 times higher relative activities than the free Ba-SacB. The Ba-SacB-Cell-Lysate kept nearly 81% of the residual activity after 9 cycles of reuse. The fructo-oligosaccharide syrup (FOS) was efficiently produced through the catalysis of levanase with the substrate of levan produced by Ba-SacB-Cell-Lysate, and the preparation conditions were optimized by Box-Behnken response surface methodology. [ABSTRACT FROM AUTHOR]

Sequential Recommendation systems play a crucial role in predicting users’ preferences based on their behavioral history. However, the existing methods ignore the extrapolation nature of sequences and do not make deep use of item provider information. This oversight limits the model’s ability to fully utilize relevant external properties. To alleviate these limitations, we design a recommendation model that incorporates Position encoding and external reinforcement (Item -Provider), named DPSRec. Specifically, we design an Embed Encoding layer, in order to distinguish the Position Embedding of previous sequence models, we combine the time variability with the position encoding with extrapolation property, and encode the item and item provider sequences accordingly. Meanwhile, to avoid the noise that the initial item embeddings might cause with Position Encoding, we calculate the position Encoding separately from the item embedding. In addition, we design a Cross Propagation layer to capture implicit higher-order dependencies between item sequences. Extensive experiments on three real-world datasets demonstrate that the proposed model generally outperforms the baselines by about 1–12.5%. Our source code will be published after the paper is published. [ABSTRACT FROM AUTHOR]