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Background: The community of gut microbes is a key factor controlling the intestinal barrier that communicates with the nervous system through the gut–brain axis. Based on our clinical data showing that populations of Roseburia intestinalis are dramatically decreased in the gut of patients with ulcerative colitis, we studied the efficacy of a strain belonging to this species in the context of colitis and stress using animal models. Methods: Dextran sulfate sodium was used to induce colitis in rats, which then underwent an enema with R. intestinalis as a treatment. The disease activity index, fecal changes and body weight of rats were recorded to evaluate colitis, while histological and immunohistochemical analyses were carried out to examine colon function, and 16S rRNA sequencing was performed to evaluate the gut microbiota change. Behavioral assays and immunohistochemical staining of brain were performed to assess the effect of R. intestinalis on the gut–brain axis. Results: Colitis-related symptoms in rats were significantly relieved after R. intestinalis enema, and the stool traits and colon length of rats were significantly recovered after treatment. The gut epithelial integrity and intestinal barrier were restored in treated rats, as evidenced by the higher expression of Zo-1 in colon tissues, accompanied by the restoration of gut microbiota. Meanwhile, depressive-like behaviors of rats were reduced after treatment, and laboratory experiments on neuronal cells also showed that IL-6, IL-7 and 5-HT were downregulated by R. intestinalis treatment in both serum and brain tissue, while Iba-1 expression was reduced in treated rats. Conclusions: The administration of R. intestinalis contributes to restoration of the gut microbiota, promoting colon repair and the recovery of gastrointestinal function. These alterations are accompanied by the relief of depressive-like behaviors through a process modulated by the neuronal network and the regulation of inflammation by the gut–brain axis.

A new reliability estimation method based on partial multiplicative dimensional reduction is proposed for probabilistic and non-probabilistic hybrid structural systems. The proposed method is characterized by decorrelating interval input variables from random input variables using the partial multiplicative dimensional reduction method in conjunction with the weakest-link theory. In this method, the failure statistics of the original performance function are equivalent to a statical chain of two elements, in which one of the two elements represents the failures due to random input variables and the other represents the failures due to interval variables. Rather than yielding an estimated interval of failure probability, the proposed method produces a single value for failure probability, which is more meaningful for engineering. In addition, the accuracy, validity, and superiority of the proposed method are demonstrated, and the error-related properties of the proposed method are investigated.

LVDC buildings use a low-voltage direct current (LVDC) distribution system for energy transmission and integrate photovoltaic (PV), battery energy storage (BES) and the utility grid as building energy resources. This technology can reduce energy loss in conversion to a certain extent and increase renewable energy compared with traditional AC distribution systems. Under the national goal of carbon peaking and carbon neutrality, LVDC buildings have been proven to be a new approach to energy conservation and emission reductions, and have been applied in engineering in China. However, the construction methods and integrated technologies of those projects are not clear, and technical barriers and policy constraints for the engineering application of LVDC buildings are not systematically discussed yet. This paper presents the latest study of LVDC building engineering applications the advantages and drawbacks of LVDC development in China. First, relevant policies and standards which support the development of LVDC building industries are summarized. More than 60 practical LVDC projects are investigated, and the application characteristics and the technology status of building types, and their capacity and design methods, etc., are analyzed. The attitudes of stakeholders toward LVDC buildings are surveyed to determine the policy direction according to the demands of this technology, with reference to building practitioners who intend to engage in LVDC projects.

Rare earth element recovery in molten salt electrolysis is approximately between 91 and 93%, whereof 8% is lost in waste molten salt slag. Presently, minimal research has been conducted on the technology for recycling waste rare earth molten salt slag, which is either discarded as industrial garbage or mixed with waste slag into qualified molten salt. The development of a new approach toward the effective treatment of rare earth fluoride molten salt electrolytic slag, which can recycle the remaining rare earth and improve the utilization rate, is essential. Herein, weak magnetic iron separation, sulfuric acid leaching transformation, water leaching, hydrogen fluoride water absorption, and cycle precipitation of rare earth are used to recover rare earth from their fluoride molten salt electrolytic slag, wherein the thermodynamic and kinetic processes of sulfuric acid leaching transformation are emphatically studied. Thermodynamic results show that temperature has a great influence on sulfuric acid leaching. With rising temperature, the equilibrium constant of the reaction gradually increases, and the stable interval of NdF3 decreases, while that of Nd3+ increases, indicating that high temperature is conducive to the sulfuric acid leaching process, whereof the kinetic results reveal that the activation energy E of Nd transformation is 41.57 kJ/mol, which indicates that the sulfuric acid leaching process is controlled by interfacial chemical reaction. According to the Nd transformation rate equation in the sulfuric acid leaching process of rare earth fluoride molten salt electrolytic slag under different particle size conditions, it is determinable that with the decrease of particle size, the reaction rate increases accordingly, while strengthening the leaching kinetic process. According to the equation of Nd transformation rate in the sulfuric acid leaching process under different sulfuric acid concentration conditions, the reaction series of sulfuric acid concentration K = 6.4, which is greater than 1, indicating that increasing sulfuric acid concentration can change the kinetic-control region and strengthen the kinetic process.

Idiopathic pulmonary fibrosis (IPF) is serious chronic lung disease with limited therapeutic approaches. Inflammation and immune disorders are considered as the main factors in the initiation and development of pulmonary fibrosis. Inspired by the key roles of macrophages during the processes of inflammation and immune disorders, here, we report a new method for direct drug delivery into the in-situ fibrotic tissue sites in vitro and in vivo. First, liposomes containing dexamethasone (Dex-L) are prepared and designed to entry into the macrophages in the early hours, forming the macrophages loaded Dex-L delivery system (Dex-L-MV). Chemokine and cytokine factors such as IL-6, IL-10, Arg-1 are measured to show the effect of Dex-L to the various subtypes of macrophages. Next, we mimic the inflammatory and anti-inflammatory microenvironment by co-culture of polarized/inactive macrophage and fibroblast cells to show the acute inflammation response of Dex-L-MV. Further, we confirm the targeted delivery of Dex-L-MV into the inflammatory sites in vivo, and surprisingly found that injected macrophage containing Dex can reduce the level of macrophage infiltration and expression of the markers of collagen deposition during the fibrotic stage, while causing little systematic toxicity. These data demonstrated the suitability and immune regulation effect of Dex-L-MV for the anti-pulmonary process. It is envisaged that these findings are a step forward toward endogenous immune targeting systems as a tool for clinical drug delivery.

Insects are behaviorally and physiologically affected by different light conditions, including photoperiod, light intensity, and spectrum. Light at night has important influences on nocturnal insects, including most moth species. Moth copulation and mating usually occur at night. Although a few studies examine changes in insect mating under artificial light at night, detailed influences of light, such as that of monochromatic light, on moth mating remain largely unknown. In this study, on the basis of long-term insects rearing experience, dim red light (spectrum range: 610–710nm, with a peak at 660nm; 2.0 Lux) during scotophase was hypothesized to enhance mating in the yellow peach moth, Conogethes punctiferalis. To test the hypothesis, the mating of moths under dim red, blue, and white lights during scotophase was observed. Under the dim red light, the enhancement of mating in C. punctiferalis was observed. In addition, the electroantennografic response of males against the female sex pheromone increased with red light treatment during scotophase. In an analysis of the differentially expressed genes in the antennae of males under red light and dark conditions, the expression levels of two odorant-binding protein (OBP) genes, CpunOBP2 and CpunPBP5, were up-regulated. Two genes were then expressed in Escherichia coli, and the recombinant proteins showed strong binding to female pheromone components in fluorescence-binding assays. Thus, the results of this study indicated that dim red light at night enhanced the mating of C. punctiferalis. One of the mechanisms for the enhancement was probably an increase in the antennal sensitivity of males to the female sex pheromone under red light that was caused by increases in the expression levels of pheromone-binding protein genes in male antennae.

To overcome the problem of arsenic separation and enrichment from an alkaline leaching solution in arsenic-containing dust, a CO32--type tri-n-octylmethyl-ammonium chloride (TOMAC) method for extracting thioarsenite is proposed in this paper. Considering an alkaline leaching solution as the research object, after vulcanization pretreatment, TOMAC transformation and organic phase saturated extraction capacity were measured, and the extraction mechanism was preliminarily studied. First, Cl−-type quaternary ammonium salt was effectively transformed to HCO3--type by treating organic phase with saturated NaHCO3five times. TOMAC was effectively transformed from HCO3- to CO32- type by alkaline washing with 1.0 mol/l NaOH solution; this washing was repeated thrice. Thereafter, the effects of organic phase composition, phase ratio, extraction time, and temperature on the extraction and separation of arsenic were investigated. The results show that under the conditions of 30% CO32--type TOMAC + 15% sec-octanol + 55% sulfonated kerosene, VO/VA = 1/1, and 5 min extraction at room temperature, the single-stage extraction rate of AsIII is 85.2%. The AsIII concentration in raffinate can be reduced to less than 1.33 × 10−3 mol/l by four-stage countercurrent extraction, and the extraction rate of AsIII can exceed 98.4%.

Antiplatelet therapy has become a cornerstone in the treatment of coronary heart disease (CHD). However, due to high-residual-platelet-reactivity, clopidogrel resistance (CR) is a common phenomenon, and it is rarely known about the relationship between CR and epigenetic changes. This study compared the whole genomic methylation patterns of blood samples from patients with CR (n = 6) and non-CR (n = 6) with the Human Methylation 850K BeadChip assay. We explored differentially methylated CpG sites, genes, and pathways using bioinformatics profiling. The CR and control groups showed significantly different DNA methylation at 7,098 sites, with 979 sites showing hypermethylation and 6,119 sites showing hypomethylation. The pyrosequencing method was used to validate four differentially methylated CpG loci (cg23371584, cg15971518, cg04481923, cg22507406), confirming that DNA methylation was associated with the risk of CR (30 CR vs. 30 non-CR). The relative mRNA expression of the four genes (BTG2, PRG2, VTRNA2-1, PER3) corresponding to the loci above was also associated with CR, suggesting that alterations in DNA methylation may affect the expression of these four genes, eventually resulting in CR. Additionally, differentially methylated sites are partially related to genes and pathways that play key roles in process of circadian entrainment, insulin secretion, and so on. Hence, the mechanism and biological regulation of CR might be reflected through these epigenetic alterations, but future research will need to address the causal relationships.

MicroRNAs (miRNAs) are emerging as critical mediators in tumors, including triple-negative breast cancer (TNBC). The role of miR-518a-3p in TNBC was investigated to identify potential therapeutic target. Data from KM Plotter database ( www.kmplot.com ) showed that high miR-518a-3p expression was significantly associated with overall survival of patients with TNBC ( p = 0.04). The expression of miR-518a-3p was dysregulated in TNBC cells. Functional assays revealed that over-expression of miR-518a-3p inhibited cell invasion and migration of TNBC. Additionally, miR-518a-3p could target TMEM2 (transmembrane protein 2), and decreased protein and mRNA expression of TMEM2 in TNBC cells. Knockdown of TMEM2 suppressed cell invasion and migration through inhibiting phospho (p)-JAK1 (Janus kinase 1) and p-STAT (signal transducer and activator of transcription protein) 1/2. Moreover, over-expression of TMEM2 counteracted the suppressive effect of miR-518a-3p on TNBC invasion and migration through promoting the levels of p-JAK1 and p-STAT1/2. In conclusion, miR-518a-3p negatively regulates the JAK/STAT pathway via targeting TMEM2 and suppresses invasion and migration in TNBC, suggesting that miR-518a-3p may be a potential therapeutic target in TNBC.

Spinal cord injury (SCI) remains among the most challenging pathologies worldwide and has limited therapeutic possibilities and a very bleak prognosis. Biomaterials and stem cell transplantation are promising treatments for functional recovery in SCI. Seven patients with acute complete SCI diagnosed by a combination of methods were included in the study, and different lengths (2.0–6.0 cm) of necrotic spinal cord tissue were surgically cleaned under intraoperative neurophysiological monitoring. Subsequently, NeuroRegen scaffolds loaded with autologous bone marrow mononuclear cells (BMMCs) were implanted into the cleaned site. All patients participated in 6 months of rehabilitation and at least 3 years of clinical follow-up. No adverse symptoms associated with stem cell or functional scaffold implantation were observed during the 3-year follow-up period. Additionally, partial shallow sensory and autonomic nervous functional improvements were observed in some patients, but no motor function recovery was observed. Magnetic resonance imaging suggested that NeuroRegen scaffold implantation supported injured spinal cord continuity after treatment. These findings indicate that implantation of NeuroRegen scaffolds combined with stem cells may serve as a safe and promising clinical treatment for patients with acute complete SCI. However, determining the therapeutic effects and exact application methods still requires further study.

Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand‐crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)‐based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy.

An aero flywheel motor (AFM) is an integration of a starter, generator, and inertial flywheel on the traditional aero piston-engine (APE). When performing as a starter, the AFM is responsible for dragging the APE to a high speed (600-800 rpm) in a short time (0.2-0.4 s). In order to obtain better performance, two modifications are applied to the direct torque control (DTC) method. First, the switching table is modified by using only the positive voltage vectors. Second, a new low-pass filter (LPF) flux estimator is proposed to eliminate the initial flux error using the pulse width modulated (PWM) flux orientation method and the resistance estimation error by using real-time varying (RTV) equivalent resistance. Three DTC-based starting strategies are developed for the AFM, namely, the constant acceleration strategy (CAS), the constant torque strategy (CTS), and the modified constant current strategy (MCCS). The modified and traditional DTC methods are compared to the three starting strategies. The results show that the proposed switching table significantly reduces the switching frequency, and the proposed LPF flux estimator improves the convergence of the DTC method; in addition, the robustness against the resistance error is improved. The performances of the three starting strategies based on the modified DTC method are also compared. The results show that the MCCS significantly reduces the peak value of the battery current, thus reducing the capacity, weight, and size of the battery.

Medical images of brain tumors are critical for characterizing the pathology of tumors and early diagnosis. There are multiple modalities for medical images of brain tumors. Fusing the unique features of each modality of the magnetic resonance imaging (MRI) scans can accurately determine the nature of brain tumors. The current genetic analysis approach is time-consuming and requires surgical extraction of brain tissue samples. Accurate classification of multi-modal brain tumor images can speed up the detection process and alleviate patient suffering. Medical image fusion refers to effectively merging the significant information of multiple source images of the same tissue into one image, which will carry abundant information for diagnosis. This paper proposes a novel attentive deep-learning-based classification model that integrates multi-modal feature aggregation, lite attention mechanism, separable embedding, and modal-wise shortcuts for performance improvement. We evaluate our model on the RSNA-MICCAI dataset, a scenario-specific medical image dataset, and demonstrate that the proposed method outperforms the state-of-the-art (SOTA) by around 3%.

This paper proposes an innovative rail pressure sensor signal processing algorithm to determine the start of injection and end of injection for single or multiple injections of diesel engines with a common rail injection system (CRIS). The importance of fuel injection timing (FIT) and fuel injection volume (FIV) in the CRIS is discussed, as well as the current progress of research in this field. The working principle of a solenoid valve injector is also described, pressure wave propagation is analyzed, and the characteristics of pressure waves are studied by simulation and experiments. The proposed algorithm has high reliability and flexibility and can be adjusted in real time according to engine operating status. The algorithm can effectively avoid the defects and shortcomings of traditional engine matching calibration, enhance the control precision of the FIT and FIV, improve the consistency and stability of fuel injection, and is expected to promote the performance of engine on fuel economy and pollutant emissions. The hardware implementation of this algorithm in practical ECU is also presented.

This paper presents an energy-efficient wireless transmission scheme for battery-less vehicle tire pressure monitoring system (TPMS). Our proposed transmission scheme includes a wake-up communication link with 125-kHz carrier frequency and a data communication link with 433-MHz carrier frequency. Considering the TPMS application requirements and the special nature of the vehicle environment, we derive the relevant circuit parameters. In order to verify the reliability of the wireless communication system under the derived circuit parameters, we design an in-tire data transmitter and wake-up receiver. The 125-kHz wake-up receiver adopts dual-channel to improve the communication reliability and logarithmic amplifiers to achieve ASK demodulation and dynamic range compression. The receiver is implemented in 0.35-μm high voltage (HV) BCD process. Experiment results show that typical power consumption of the receiver is no more than 5 μA under 3.3 V supply voltage; the maximum data rate is 35 kb/s with 0.5 mVpp sensitivity. On the other hand, the data transmitter is implemented in 0.18-μm MMRF process. Experiment results show that the typical power consumption is 7.3 mA under 1.8 V supply voltage, and the emission power is -10 dBm@433.92 MHz with a phase noise of -103 dBc/Hz@300 kHz. System level experiments demonstrate that the proposed wireless transmission scheme fulfills the vehicle TPMS requirements. The data transmitter and wake-up receiver can communicate with the commercial data receiver and wake-up transmitter in the range of 20 m, which meets the requirements of most vehicles.

The aim of image-based localization (IBL) is to localize the real location of query image by matching reference image in database with GNSS-tags. Popular methods related to IBL commonly use street-level images, which have high value in practical application. Using street-level image to tackle IBL task has the primary challenges: existing works have not made targeted optimization for urban IBL tasks. Besides, the matching result is over-reliant on the quality of image features. Methods should address their practicality and robustness in engineering application, under metropolitan-scale. In response to these, this paper made following contributions: firstly, given the critical of buildings in distinguishing urban scenes, we contribute a feature called Building-Aware Feature (BAF). Secondly, in view of negative influence of complex urban scenes in retrieval process, we propose a retrieval method called Patch-Region Retrieval (PRR). To prove the effectiveness of BAF and PRR, we established an image-based localization experimental framework. Experiments prove that BAF can retain the feature points that fall on the building, and selectively lessen the feature points that fall on other things. While this effectively compresses the storage amount of feature index, we can also improve recall of localization results; implemented in the stage of geometric verification, PRR compares matching results of regional features and selects the best ranking as final result. PRR can enhance effectiveness of patch-regional feature. In addition, we fully confirmed the superiority of our proposed methods through a metropolitan-scale street-level image dataset.

High-resolution remote sensing image scene classification is a challenging visual task due to the large intravariance and small intervariance between the categories. To accurately recognize the scene categories, it is essential to learn discriminative features from both global and local critical regions. Recent efforts focus on how to encourage the network to learn multigranularity features with the destruction of the spatial information on the input image at different scales, which leads to meaningless edges that are harmful to training. In this study, we propose a novel method named Semantic Multigranularity Feature Learning Network (SMGFL-Net) for remote sensing image scene classification. The core idea is to learn both global and multigranularity local features from rearranged intermediate feature maps, thus, eliminating the meaningless edges. These features are then fused for the final prediction. Our proposed framework is compared with a collection of state-of-the-art (SOTA) methods on two fine-grained remote sensing image scene datasets, including the NWPU-RESISC45 and Aerial Image Datasets (AID). We justify several design choices, including the branch granularities, fusion strategies, pooling operations, and necessity of feature map rearrangement through a comparative study. Moreover, the overall performance results show that SMGFL-Net consistently outperforms other peer methods in classification accuracy, and the superiority is more apparent with less training data, demonstrating the efficacy of feature learning of our approach.

In this research, a CFD solver is developed for solving the 2D/3D compressible flow problem: the finite volume method based on multi-block structural grids is used to solve the compressible Reynolds averaged Navier–Stokes equations (RANS). Included in the methodology are multiple high-order reconstruction schemes, such as the 3rd-order MUSCL (Monotone Upstreamcentered Schemes for Conservation Laws), 5th-order WENO (Weight Essentially Non-Oscillatory), and 5th-order MP (Monotonicity-Preserving) schemes. Of the variety of turbulence models that are embedded, this solver is mainly based on the shear stress transport model (SST), which is compatible with OpenMP/MPI parallel algorithms. This research uses the CFD solver to conduct steady-state flow simulation for a two-dimensional supersonic inlet/isolator, incorporating these high-precision reconstruction schemes to accurately capture the shock wave/expansion wave interaction and shock wave/turbulent boundary layer interaction (SWTBLI), among other effects. By comparing the 2D/3D computation results of the same inlet configuration, it is found that the 3D effects of the side wall cannot be ignored due to the existing strong lateral flow near the corner. To obtain a more refined turbulence simulation, the commercial software ANSYS Fluent 18.0 is used to carry out the detached eddy simulation (DES) and the large eddy simulation (LES) of the same supersonic inlet, so as to reveal the flow details near the separation area and boundary layers.

Abstract Calcific aortic valve disease (CAVD) is the most common heart valve disorder, yet its mechanism remains poorly understood. Valve interstitial cells (VICs) are the prevalent cells in aortic valve and their osteogenic differentiation may be responsible for calcific nodule formation in CAVD pathogenesis. Emerging evidence shows microRNA (miRNA, or miR) can function as important regulators of many pathological processes, including osteogenic differentiation. Here, we aimed to explore the function of miR-449c-5p in CAVD pathogenesis. In this study, we demonstrated the role of miR-449c-5p in VICs osteogenesis. MiRNA microarray assay and qRT-PCR results revealed miR-449c-5p was significantly down-regulated in calcified aortic valves compared with non-calcified valves. MiR-449c-5p overexpression inhibited VICs osteogenic differentiation in vitro, whereas down-regulation of miR-449c-5p enhanced the process. Target prediction analysis and dual-luciferase reporter assay confirmed Smad4 was a direct target of miR-449c-5p. Furthermore, knockdown of Smad4 inhibited VICs osteogenic differentiation, similar to the effect observed in up-regulation miR-449c-5p. In addition, animal experiments proved indirectly miR-449c-5p could alleviate aortic valve calcification. Our data suggested miR-449c-5p could function as a new inhibitory regulator of VICs osteogenic differentiation, which may act by targeting Smad4. MiR-449c-5p may be a potential therapeutic target for CAVD.

Abstract Background Previous nomograms for intrahepatic cholangiocarcinoma (ICC) were conducted to predict overall survival, which could be influenced by various factors. Herein, we conducted our nomogram to predict recurrence of the tumor only after hepatic resection. Methods The nomogram was established with prognostic factors for the relapse-free survival (RFS) analyzed from our single center cohort and was evaluated by comparing with the American Joint Committee on Cancer (AJCC) staging system for the predictive accuracy. Results Seropositivity of hepatitis B surface antigen (hazard ratio [HR], 0.505; 95% confidence interval [CI], 0.279 to 0.914; P = 0.024), tumor size of larger than 5 cm (HR, 1.947; 95% CI, 1.177 to 3.219; P = 0.009), Child-Pugh score of B (HR, 3.067; 95% CI, 1.293 to 7.275; P = 0.011), and lymph node metastasis (HR, 2.790; 95% CI, 1.628 to 4.781; P

Spinal cord ischemia is a severe clinical complication induced by thoracoabdominal aortic surgery, severe trauma, or compression to the spinal column. As one of the most important functional cells in the spinal cord, spinal motor neurons (SMNs) suffer most during the process since they are vulnerable to ischemic injury due to high demands of energy. Previous researches have tried various animal models or organotypic tissue experiments to mimic the process and get to know the pathogenesis and mechanism. However, little work has been performed on the cellular model of spinal cord ischemia, which has been hampered by the inability to obtain a sufficient number of pure primary SMNs for in vitro study. By optimizing the isolation and culture of SMNs, our laboratory has developed an improved culture system of primary SMNs, which allows cellular models and thus mechanism studies. In the present study, by establishing an in vitro model of spinal cord ischemia, we intended to observe the dynamic time-course changes of SMNs and investigate the role of autophagy in SMNs during the process. It was found that oxygen-glucose deprivation (OGD) resulted in destruction of neural networks and decreased cell viability of primary SMNs, and the severity increased with the prolonging of the OGD time. The OGD treatment enhanced autophagy, which reached a peak at 5 h. Further investigation demonstrated that inhibition of autophagy exacerbated the injury, evidencing that autophagy plays a protective role during the process.

Inflammasomes, multiprotein complex induced by harmful factors in the body, play a crucial role in innate immunity. Activation of inflammasomes lead to the activation of casepase-1 and then the secretion of inflammatory cytokines, including IL-1β and IL-18, subsequently leading to a type of cell death called pyroptosis. There are two types of signaling pathways involved in the process of inflammasome activation: the canonical and the non-canonical signaling pathway. The canonical signaling pathway is mainly dependent on casepase-1; the non-canonical signal pathway, which was recently discovered, is mainly dependent on caspase-11, but is also meditated by caspase-4, caspase-5, and caspase-8. Kidney inflammation is basically associated with inflammatory factor exudation and inflammatory cell infiltration. Several studies have showed that inflammasomes are closely related to kidney diseases, especially the NOD-, LRR- and pyrin domain-containing 3 (NLRP3) inflammasome, which play a role in regulating kidney inflammation and fibrosis. In this review, we focus on the relationship between inflammasomes and kidney diseases, especially the role of the NLRP3 inflammasome in different kinds of kidney disease via both canonical and non-canonical signal pathways.

Osteoporosis is a bone disease characterized by increasing osseous fragility and fracture due to the reduced bone mass and microstructural degradation. Primary pharmacological strategies for the treatment of osteoporosis, hormone replacement treatment (HRT), and alendronate therapies may produce adverse side-effects and may not be recommended for long-term usage. Some classic and bone-specific natural Chinese medicine are very popularly used to treat osteoporosis and bone fracture effectively in clinical with their potential value in bone growth and development, but with few adverse side-effects. Current evidence suggests that the treatments appear to improve bone metabolism and attenuate the osteoporotic imbalance between bone formation and bone resorption at a cellular level by promoting osteoblast activity and inhibiting the effects of osteoclasts. The valuable therapies might, therefore, provide an effective and safer alternative to primary pharmacological strategies. Therefore, the purpose of this article is to comprehensively review these classic and bone-specific drugs in natural Chinese medicines for the treatment of osteoporosis that had been deeply and definitely studied and reported with both bone formation and antiresorption effects, including Gynochthodes officinalis (F.C.How) Razafim. & B.Bremer (syn. Morinda officinalis F.C.How), Curculigo orchioides Gaertn., Psoralea corylifolia (L.) Medik Eucommia ulmoides Oliv., Dipsacus inermis Wall. (syn. Dipsacus asperoides C.Y.Cheng & T.M.Ai), Cibotium barometz (L.) J. Sm., Velvet Antler, Cistanche deserticola Ma, Cuscuta chinensis Lam., Cnidium monnieri (L.) Cusson, Epimedium brevicornum Maxim, Pueraria montana (Lour.) Merr. and Salvia miltiorrhiza Bunge., thus providing evidence for the potential use of alternative Chinese medicine therapies to effectively treat osteoporosis.

Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato surface defect detection. In particular, we applied transfer learning by fine-tuning a base model through three DCNN models—SSD Inception V2, RFCN ResNet101, and Faster RCNN ResNet101—on a self-developed dataset, and achieved an accuracy of 92.5%, 95.6%, and 98.7%, respectively. RFCN ResNet101 presented the best overall performance in detection speed and accuracy. It was selected as the final model for out-of-sample testing, further demonstrating the model’s ability to generalize.

Digital twinning technology originated in the field of aerospace. The real-time and bidirectional feature of data interaction guarantees its advantages of high accuracy, real-time performance and scalability. In this paper the digital twin technology was introduced to electric vehicle energy consumption research. First, an energy consumption model of an electric vehicle of BAIC BJEV was established, then the model was optimized and verified through the energy consumption data of the drum test. Based on the data of the vehicle real-time monitoring platform, a digital twin model was built, and it was trained and updated by daily new data. Eventually it can be used to predict and verify the data of vehicle. In this way the prediction of energy consumption of vehicles can be achieved.

Relation extraction (RE) is an essential task in natural language processing. Given a context, RE aims to classify an entity-mention pair into a set of pre-defined relations. In the biomedical field, building an efficient and accurate RE system is critical for the construction of a domain knowledge base to support upper-level applications. Recent advances have witnessed a focus shift from sentence to document-level RE problems, which are more challenging due to the need for inter- and intra-sentence semantic reasoning. This type of distant dependency is difficult to understand and capture for a learning algorithm. To address the challenge, prior efforts either attempted to improve the cross sentence text representation or infuse domain or local knowledge into the model. Both strategies demonstrated efficacy on various datasets. In this paper, a keyword-attentive knowledge infusion strategy is proposed and integrated into BioBERT. A domain keyword collection mechanism is developed to discover the most relation-suggestive word tokens for bio-entities in a given context. By manipulating the attention masks, the model can be guided to focus on the semantic interaction between bio-entities linked by the keywords. We validated the proposed method on the Biocreative V Chemical Disease Relation dataset with an F1 of 75.6%, outperforming the state-of-the-art by 5.6%.

Vegetable and fruit recognition can be considered as a fine-grained visual categorization (FGVC) task, which is challenging due to the large intraclass variances and small interclass variances. A mainstream direction to address the challenge is to exploit fine-grained local/global features to enhance the feature extraction and representation in the learning pipeline. However, unlike the human visual system, most of the existing FGVC methods only extract features from individual images during training. In contrast, human beings can learn discriminative features by comparing two different images. Inspired by this intuition, a recent FGVC method, named Attentive Pairwise Interaction Network (API-Net), takes as input an image pair for pairwise feature interaction and demonstrates superior performance in several open FGVC data sets. However, the accuracy of API-Net on VegFru, a domain-specific FGVC data set, is lower than expected, potentially due to the lack of spatialwise attention. Following this direction, we propose an FGVC framework named Attention-aware Interactive Features Network (AIF-Net) that refines the API-Net by integrating an attentive feature extractor into the backbone network. Specifically, we employ a region proposal network (RPN) to generate a collection of informative regions and apply a biattention module to learn global and local attentive feature maps, which are fused and fed into an interactive feature learning subnetwork. The novel neural structure is verified through extensive experiments and shows consistent performance improvement in comparison with the SOTA on the VegFru data set, demonstrating its superiority in fine-grained vegetable and fruit recognition. We also discover that a concatenation fusion operation applied in the feature extractor, along with three top-scoring regions suggested by an RPN, can effectively boost the performance.

The cooling system is an important subsystem of an internal combustion engine, which plays a vital role in the engine’s dynamical characteristic, the fuel economy, and emission output performance at each speed and load. This paper proposes an economical and precise model for an electric cooling system, including the modeling of engine heat rejection, water jacket temperature, and other parts of the cooling system. This model ensures that the engine operates precisely at the designated temperature and the total power consumption of the cooling system takes the minimum value at some power proportion of fan and pump. Speed maps for the cooling fan and pump at different speeds and loads of engine are predicted, which can be stored in the electronic control unit (ECU). This model was validated on a single-cylinder diesel engine, called the DK32. Furthermore, it was used to tune the temperature of the water jacket precisely. The results show that in the common use case, the electric cooling system can save the power of 255 W in contrast with the mechanical cooling system, which is about 1.9% of the engine’s power output. In addition, the validation results of the DK32 engine meet the non-road mobile machinery China-IV emission standards.

The performance of fruit surface defect detection is easily affected by factors such as noisy background and foliage occlusion. In this study, we choose lychee as a fruit type to investigate its surface quality. Lychees are hard to preserve and have to be stored at low temperatures to keep fresh. Additionally, the surface of lychees is subject to scratches and cracks during harvesting/processing. To explore the feasibility of the automation of defective surface detection for lychees, we build a dataset with 3743 samples divided into three categories, namely, mature, defects, and rot. The original dataset suffers an imbalanced distribution issue. To address it, we adopt a transformer-based generative adversarial network (GAN) as a means of data augmentation that can effectively enhance the original training set with more and diverse samples to rebalance the three categories. In addition, we investigate three deep convolutional neural network (DCNN) models, including SSD-MobileNet V2, Faster RCNN-ResNet50, and Faster RCNN-Inception-ResNet V2, trained under different settings for an extensive comparison study. The results show that all three models demonstrate consistent performance gains in mean average precision (mAP), with the application of GAN-based augmentation. The rebalanced dataset also reduces the inter-category discrepancy, allowing a DCNN model to be trained equally across categories. In addition, the qualitative results show that models trained under the augmented setting can better identify the critical regions and the object boundary, leading to gains in mAP. Lastly, we conclude that the most cost-effective model, SSD-MobileNet V2, presents a comparable mAP (91.81%) and a superior inference speed (102 FPS), suitable for real-time detection in industrial-level applications.

Synthetic rutile was prepared from titanium slag melt with low energy consumption and a small amount of additive (B2O3) in our previous work. The modification mechanism of titanium slag was not clear enough. The migration and enrichment behaviors of Ca and Mg elements during cooling and crystallization of boron-bearing titanium slag melt were characterized by XRF, FESEM, EMPA, and XPS. Results show that when additive (B2O3) is added, Ti elements are migrated and enriched in the area to generate rutile, while Ca, Mg, and B elements are migrated and enriched in another area to generate borate. With the additive (B2O3) amount increased, Ca and Mg element migration is complete and more thorough. Additive (B2O3) promotes rutile formation and inhibits the formation of anosovite during cooling and crystallization of titanium slag melt. With the additive (B2O3) amount increasing from 0% to 6%, the proportion of Ti3+ in the modified titanium slag reduces from 9.15% to 0%, and the proportion of Ti4+ increases from 90.85% to 100% under the same cooling and crystallization condition. The result will lay the foundation for the efficient preparation of synthetic rutile by adding B2O3 to the titanium slag melt.

3D Object detection is a critical mission of the perception system of a self-driving vehicle. Existing bounding box-based methods are hard to train due to the need to remove duplicated detections in the post-processing stage. In this paper, we propose a center point-based deep neural network (DNN) architecture named RCBi-CenterNet that predicts the absolute pose for each detected object in the 3D world space. RCBi-CenterNet is composed of a recursive composite network with a dual-backbone feature extractor and a bi-directional feature pyramid network (BiFPN) for cross-scale feature fusion. In the detection head, we predict a confidence heatmap that is used to determine the position of detected objects. The other pose information, including depth and orientation, is regressed. We conducted extensive experiments on the Peking University/Baidu-Autonomous Driving dataset, which contains more than 60,000 labeled 3D vehicle instances from 5277 real-world images, and each vehicle object is annotated with the absolute pose described by the six degrees of freedom (6DOF). We validated the design choices of various data augmentation methods and the backbone options. Through an ablation study and an overall comparison with the state-of-the-art (SOTA), namely CenterNet, we showed that the proposed RCBi-CenterNet presents performance gains of 2.16%, 2.76%, and 5.24% in Top 1, Top 3, and Top 10 mean average precision (mAP). The model and the result could serve as a credible benchmark for future research in center point-based object detection.

Vibration is an important issue faced by reciprocating piston engines, and is caused by reciprocating inertia forces of the piston set. To reduce the vibration without changing the main structure and size of the original engine, we proposed a novel coaxial balance mechanism design based on a compact unit body. By introducing a second-order balance mass, this mechanism can significantly increase the efficiency of vibration reduction. The proposed mechanism can effectively balance the first-order and second-order inertia forces with the potential of balancing high-order inertia forces. To accurately determine the second-order balance mass, a closed-form method was developed. Simulation results with a single-cylinder piston DK32 engine demonstrate the effectiveness and advantage of the proposed mechanism. At a crankshaft speed of 2350 r/min, compared with the first-order balance device, the average root mean square velocity of the test points on the engine’s cylinder was reduced by 97.31%, and the support reaction force was reduced by 96.54%.

Prostate cancer (PCa) is one of the most prevalent cancers worldwide. As the demand for prostate biopsies increases, a worldwide shortage and an uneven geographical distribution of proficient pathologists place a strain on the efficacy of pathological diagnosis. Deep learning (DL) is able to automatically extract features from whole-slide images of prostate biopsies annotated by skilled pathologists and to classify the severity of PCa. A whole-slide image of biopsies has many irrelevant features that weaken the performance of DL models. To enable DL models to focus more on cancerous tissues, we propose a Multi-Channel and Multi-Spatial (MCMS) Attention module that can be easily plugged into any backbone CNN to enhance feature extraction. Specifically, MCMS learns a channel attention vector to assign weights to channels in the feature map by pooling from multiple attention branches with different reduction ratios; similarly, it also learns a spatial attention matrix to focus on more relevant areas of the image, by pooling from multiple convolutional layers with different kernel sizes. The model is verified on the most extensive multi-center PCa dataset that consists of 11,000 H&E-stained histopathology whole-slide images. Experimental results demonstrate that an MCMS-assisted CNN can effectively boost prediction performance in accuracy (ACC) and quadratic weighted kappa (QWK), compared with prior studies. The proposed model and results can serve as a credible benchmark for future research in automated PCa grading.

Multilingual characteristics, lack of annotated data, and imbalanced sample distribution are the three main challenges for toxic comment analysis in a multilingual setting. This paper proposes a multilingual toxic text classifier which adopts a novel fusion strategy that combines different loss functions and multiple pre-training models. Specifically, the proposed learning pipeline starts with a series of pre-processing steps, including translation, word segmentation, purification, text digitization, and vectorization, to convert word tokens to a vectorized form suitable for the downstream tasks. Two models, multilingual bidirectional encoder representation from transformers (MBERT) and XLM-RoBERTa (XLM-R), are employed for pre-training through Masking Language Modeling (MLM) and Translation Language Modeling (TLM), which incorporate semantic and contextual information into the models. We train six base models and fuse them to obtain three fusion models using the F1 scores as the weights. The models are evaluated on the Jigsaw Multilingual Toxic Comment dataset. Experimental results show that the best fusion model outperforms the two state-of-the-art models, MBERT and XLM-R, in F1 score by 5.05% and 0.76%, respectively, verifying the effectiveness and robustness of the proposed fusion strategy.

Abstract Bone marrow mesenchymal stem cells (BMSCs) are a good candidate for tissue engineering and clinical application. One of the challenges in its cell therapy is how to quickly obtain an adequate number of seed cells and meanwhile maintain suitable differentiation potential. In this study we combined three-dimensional (3D) collagen porous scaffolds with rotary cell culture system (RCCS) (RCCS-3D) to create a stereoscopic dynamic environment for the amplification of rat BMSCs in vitro. The results revealed that this RCCS-3D system could enhance BMSCs’ proliferation and colony formation, as well as maintain the differentiation potential compared with conventional static two-dimensional (2D) and 3D cell culture conditions. In addition, high-throughput microarray analysis showed that gene expressions of RCCS-3D system displayed significant differences in cell proliferation and differentiation compared with static-2D conditions. Thus, RCCS-3D system could provide an effective means for BMSCs cell proliferation in vitro and meanwhile maintain differentiation potential in tissue engineering.

The simulation of electronic control function failure has been utilized broadly as an evaluation method when determining the Automotive Safety Integrity Level (ASIL). The driver model is quite critical in the ASIL evaluation simulation. A new driver model that can consider drivers of different driving skills is proposed in this paper. It can simulate the overall performance of different drivers driving vehicles by adjusting parameters, with which the impact of a certain electronic control function failure and the ASIL are evaluated. This paper has taken the function failure of regenerative braking as the simulation object in the double-lane-change driving scenario to simulate typical driving conditions with the designed driver model, and then has obtained the ASIL of regenerative braking function, which is applied to a BAIC new energy vehicle development project.

Object detection is a challenging task in aerial images, where many objects have large aspect ratios and are densely arranged. Most anchor-based rotating detectors assign anchors for ground-truth objects by a fixed restriction of the rotation Intersection-over-Unit (IoU) between anchors and objects, which directly follow horizontal detectors. Due to many directional objects with a large aspect ratio, the object-anchor IoU is heavily influenced by the angle, which may cause few anchors assigned for some ground-truth objects. In this study, we propose an anchor selection method based on sample balance assigning anchors adaptively, which we name the Self-Adaptive Anchor Selection (A2S-Det) method. For each ground-truth object, A2S-Det selects a set of candidate anchors by horizontal IoU. Then, an adaptive threshold module is adopted on the set of candidate anchors, which calculates a boundary of these candidate anchors aiming to keep a balance between positive and negative anchors. In addition, we propose a coordinate regression of relative reference (CR3) module to precisely regress the rotating bounding box. We test our method on a public aerial image dataset, and prove better performance than many other one-stage detectors and two-stage detectors, achieving the mAP of 70.64. An efficiency anchor matching method helps the detector achieve better performance for objects with large aspect ratios.

Deep sea oil resources worldwide possess great potential for exploration; however, multiphase medium technology requires urgent development. The multiphase pump has achieved great success as one of the most advanced machinery in underwater oil and gas exploration. Tip clearance is inevitable between the rotating and stationary components of the multiphase pump. In this study, tip clearance sizes of 0.0, 0.2, 0.5, and 0.8 mm are selected to investigate the effect of tip clearance on energy performance and flow characteristics of a multiphase pump. Results show that pressure rises decrease by 10.72%, 24.96%, and 41.39% with gas volume fraction = 0% under different tip clearance sizes, while the pressure rises decrease by 17.10%, 25.35%, and 38.11% with gas volume fraction = 10%. The dominant frequencies and maximum amplitudes of pressure fluctuation rise with the increase in tip clearance. The entrainment effect between the tip leakage flow and main flow in the impeller strengthens with the increase in tip clearance size; the induced vortex area and leakage flow rate also increase.

Stem cells and biomaterials transplantation hold a promising treatment for functional recovery in spinal cord injury (SCI) animal models. However, the functional recovery of complete SCI patients was still a huge challenge in clinic. Additionally, there is no clinical standard procedure available to diagnose precisely an acute patient as complete SCI. Here, two acute SCI patients, with injury at thoracic 11 (T11) and cervical 4 (C4) level respectively, were judged as complete injury by a stricter method combined with American Spinal Injury Association (ASIA) Impairment Scale, magnetic resonance imaging (MRI) and nerve electrophysiology. Collagen scaffolds, named NeuroRegen scaffolds, with human umbilical cord mesenchymal stem cells (MSCs) were transplanted into the injury site. During 1 year follow up, no obvious adverse symptoms related to the functional scaffolds implantation were found after treatment. The recovery of the sensory and motor functions was observed in the two patients. The sensory level expanded below the injury level, and the patients regained the sense function in bowel and bladder. The thoracic SCI patient could walk voluntary with the hip under the help of brace. The cervical SCI patient could raise his lower legs against the gravity in the wheelchair and shake his toes under control. The injury status of the two patients was improved from ASIA A complete injury to ASIA C incomplete injury. Furthermore, the improvement of sensory and motor functions was accompanied with the recovery of the interrupted neural conduction. These results showed that the supraspinal control of movements below the injury was regained by functional scaffolds implantation in the two patients who were judged as the complete injury with combined criteria, it suggested that functional scaffolds transplantation could serve as an effective treatment for acute complete SCI patients.

The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs) can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB® and Carsim® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption.

Recently, with the development of the space program there are growing concerns about the influence of spaceflight on tissue engineering. The purpose of this study was thus to determine the variations of neural stem cells (NSCs) during spaceflight. RNA-Sequencing (RNA-Seq) based transcriptomic profiling of NSCs identified many differentially expressed mRNAs and miRNAs between space and earth groups. Subsequently, those genes with differential expression were subjected to bioinformatic evaluation using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) and miRNA-mRNA network analyses. The results showed that NSCs maintain greater stemness ability during spaceflight although the growth rate of NSCs was slowed down. Furthermore, the results indicated that NSCs tended to differentiate into neuron in outer space conditions. Detailed genomic analyses of NSCs during spaceflight will help us to elucidate the molecular mechanisms behind their differentiation and proliferation when they are in outer space.

Regeneration of damaged neurons and recovery of sensation and motor function after complete spinal cord injury (SCI) are challenging. We previously developed a collagen scaffold, NeuroRegen, to promote axonal growth along collagen fibers and inhibit glial scar formation after SCI. When functionalized with multiple biomolecules, this scaffold promoted neurological regeneration and functional recovery in animals with SCI. In this study, eight patients with chronic complete SCI were enrolled to examine the safety and efficacy of implanting NeuroRegen scaffold with human umbilical cord mesenchymal stem cells (hUCB-MSCs). Using intraoperative neurophysiological monitoring, we identified and surgically resected scar tissues to eliminate the inhibitory effect of glial scarring on nerve regeneration. We then implanted NeuroRegen scaffold loaded with hUCB-MSCs into the resection sites. No adverse events (infection, fever, headache, allergic reaction, shock, perioperative complications, aggravation of neurological status, or cancer) were observed during 1 year of follow-up. Primary efficacy outcomes, including expansion of sensation level and motor-evoked potential (MEP)-responsive area, increased finger activity, enhanced trunk stability, defecation sensation, and autonomic neural function recovery, were observed in some patients. Our findings suggest that combined application of NeuroRegen scaffold and hUCB-MSCs is safe and feasible for clinical therapy in patients with chronic SCI. Our study suggests that construction of a regenerative microenvironment using a scaffold-based strategy may be a possible future approach to SCI repair.

Orientated object detection in aerial images is still a challenging task due to the bird’s eye view and the various scales and arbitrary angles of objects in aerial images. Most current methods for orientated object detection are anchor-based, which require considerable pre-defined anchors and are time consuming. In this article, we propose a new one-stage anchor-free method to detect orientated objects in per-pixel prediction fashion with less computational complexity. Arbitrary orientated objects are detected by predicting the axis of the object, which is the line connecting the head and tail of the object, and the width of the object is vertical to the axis. By predicting objects at the pixel level of feature maps directly, the method avoids setting a number of hyperparameters related to anchor and is computationally efficient. Besides, a new aspect-ratio-aware orientation centerness method is proposed to better weigh positive pixel points, in order to guide the network to learn discriminative features from a complex background, which brings improvements for large aspect ratio object detection. The method is tested on two common aerial image datasets, achieving better performance compared with most one-stage orientated methods and many two-stage anchor-based methods with a simpler procedure and lower computational complexity.

A UHPLC-QQQ-MS/MS method was developed to quantify the significant constituents in Wen-Dan Decoction (WDD), a traditional Chinese medicine. Analysis of 19 compounds was conducted on an ACQUITY UPLC® BEH C18 Column (2.1 × 50 mm, 1.7 μm) using elution with a gradient elution of acetonitrile and 0.05% (v/v) formic acid in water. A triple quadrupole mass spectrometer was operated in negative ionization mode and positive ionization mode by multiple reaction monitoring (MRM), respectively. All calibration curves showed acceptable linearity (r ≥ 0.9950). The RSDs of intra- and inter-day precisions of low, mid and high concentrations were ≤ 8.88%. The repeatabilities (RSDs ≤ 7.17%) and stabilities (RSD ≤ 4.79%) of the samples were qualified. The recoveries were found in the range of 93.07 ± 3.86 to 103.98 ± 2.98% with the RSD varying between 1.30 and 7.86%. The final rapid, sensitive, precise, accurate and reliable UHPLC-QQQ-MS/MS method was used for the simultaneous quantification of 19 constituents in WDD and its commercial preparations. The strategy of combining the contents of the 19 chemicals in a daily dose of the WDD preparations with the hierarchical cluster analysis and the 3D principal component analysis was employed to effectively distinguish the WDD preparations provided by the different suppliers, which represents a contribution to the evaluation and control of the quality of WDD (or other decoctions consisting of the same herbs) and the preparations of WDD in other dosage forms such as tablets and granules.

Intestinal phosphate (Pi) absorption across the apical membrane of small intestinal epithelial cells is mainly mediated by the type IIb Na-coupled phosphate co-transporter (NaPi-IIb), but its expression and regulation in the chicken remain unclear. In the present study, we investigated the mRNA and protein levels of NaPi-IIb in three regions of chicken small intestine, and related their expression levels to the rate of net phosphate absorption. Our results showed that maximal phosphate absorption occurs in the jejunum, however the highest expression levels of NaPi-IIb mRNA and protein occurs in the duodenum. In response to a low-Pi diet (TP 0.2%), there is an adaptive response restricted to the duodenum, with increased brush border membrane (BBM) Na-Pi transport activity and NaPi-IIb protein and mRNA abundance. However, when switched from a low- (TP 0.2%) to a normal diet (TP 0.6%) for 4 h, there is an increase in BBM NaPi-IIb protein abundance in the jejunum, but no changes in BBM NaPi-IIb mRNA. Therefore, our study indicates that Na-Pi transport activity and NaPi-IIb protein expression are differentially regulated in the duodenum vs the jejunum in the chicken.

Patch-based texture synthesis techniques from a small sample texture generate a new texture image efficiently, but one significant problem is that these existing texture synthesis algorithms suffer from structure mismatching across patch boundaries due to inaccurate similarity measure. In this paper, we propose a desired distance metric to measure structural similarity when especially handling textures with salient features, which has the ability to find best matches during the patch-based sampling. To do this, we introduce the concept of “layers” to describe the correlation of pixels through computing each pixel weight, and construct the orientation fields to capture the inherent direction and variation of texture features through computing the dominant local orientation angle and the degree of anisotropy of each pixel. Our experimental results demonstrate that the proposed method can significantly alleviate structure discontinuities in synthesized textures.

As an important type of centrifugal pump, the double-suction pump has been widely used due to its high efficiency region and large flow rate. In the present study, the complex flow in volute of a double-suction centrifugal pump is investigated by numerical simulation using a re-normalization group (RNG) k-ε model with experimental validation. Axial flows are observed in volute near the impeller outlet and compared with four staggered angles. The net area-weighted average axial velocities decrease as the staggered angle increases. The axial flows are mainly caused by the different circumferential pressure distribution at the twin impeller outlet. The dominant frequencies of the axial velocities for different staggered angles are fBP and its harmonic. The pressure fluctuations in most regions of the volute are obtained by superimposing the pressure generated by the two impellers.

The variable-speed rotor has proven to be a promising means to improve helicopter performance. Previous investigations on the aeromechanics of the variable-speed rotor are scarce. This work studies the mechanism of the variable-speed rotor affecting the rotor aerodynamic performance by means of dimensionless parameter analysis and reveals the various effect mechanisms under the various flight speeds and take-off weights. The current work shows that, during hover, the variable-speed rotor improves the rotor aerodynamic performance by reducing the blade dynamic pressure, while the non-uniform thrust distribution is attributed to the reduced rotor speed suppresses the performance improvement. In slow forward flight, the blade dynamic pressure reduction improves the rotor aerodynamic performance. In cruise and fast forward flight, both the blade dynamic pressure and advancing blade tip compression loss reductions improve the rotor aerodynamic performance. The study also shows that in forward flight, the rotor loading is smaller, and the effect of reducing the advancing blade tip compression loss through the variable-speed rotor is greater.

The carmine spider mite (Tetranychus cinnabarinus) is one of the most serious pests on crops and its control mainly depends on chemical acaricides. The excessive and improper acaricides use has resulted in mite resistance to many acaricides, including fenpropathrin. Previous studies have indicated fenpropathrin resistance is a complex development process involving many genes, but information on resistance mechanism of post-transcription regulation is rare. Using Illumina sequencing, several categories of sRNAs were identified from susceptible (TS) and fenpropathrin-resistant strains (TR) of T. cinnabarinus, including 75 known microRNAs (miRNAs) and 64 novel miRNAs, whose target genes containing 78592 miRNA-target pairs were predicted by 6 algorithms. Also, 12 significantly differently expressed miRNAs were identified between the TS and TR libraries and RT-qPCR validation also performed a well consistency with sequencing. The targets of significantly differentially expressed miRNAs included 7 glutathione S-transferase, 7 cytochrome P450 and 16 carboxyl/choline esterase genes, their function in fenpropathrin resistance were further analyzed. The present study provides the firstly large-scale characterization of miRNAs in T. cinnabarinus and the comparison between TS and TR strains gives a clue on how miRNA involves in fenpropathrin resistance.