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Abstract Despite progress significant advances in immunotherapy for some solid tumors, pancreatic ductal adenocarcinoma (PDAC) remains unresponsive poorly responsive to such interventions, largely due to its highly immunosuppressive tumor microenvironment (TME) with limited CD8+ T cell infiltration. This study explores the role of the epigenetic factor Sin3B in the PDAC TME. Using murine PDAC models, we found that tumor cell‐intrinsic Sin3B loss reshapes the TME, increasing CD8+ T cell infiltration and cytotoxicity, thus impeding tumor progression and enhancing sensitivity to anti‐PD1 treatment. Sin3B‐deficient tumor cells exhibited amplified CXCL9/10 secretion in response to Interferon‐gamma (IFNγ), creating a positive feedback loop via the CXCL9/10‐CXCR3 axis, thereby intensifying the anti‐tumor immune response against PDAC. Mechanistically, extensive epigenetic regulation is uncovered by Sin3B loss, particularly enhanced H3K27Ac distribution on genes related to immune responses in PDAC cells. Consistent with the murine model findings, analysis of human PDAC samples revealed a significant inverse correlation between SIN3B levels and both CD8+ T cell infiltration and CXCL9/10 expression. Notebly, PDAC patients with lower SIN3B expression showed a more favorable response to anti‐PD1 therapy. The findings suggest that targeting SIN3B can enhance cytotoxic T cell infiltration into the tumor site and improve immunotherapy efficacy in PDAC, offering potential avenues for therapeutic biomarker or target in this challenging disease.

Hippophae tibetana, belonging to the Elaeagnaceae family, is an endemic plant species of the Qinghai–Tibet Plateau, valued for its remarkable ecological restoration capabilities, as well as medicinal and edible properties. Despite being acknowledged as a useful species, its mitochondrial genome data and those of other species of the Elaeagnaceae family are lacking to date. In this study, we, for the first time, successfully assembled the mitochondrial genome of H. tibetana, which is 464,208 bp long and comprises 31 tRNA genes, 3 rRNA genes, 37 protein-coding genes, and 3 pseudogenes. Analysis of the genome revealed a high copy number of the trnM-CAT gene and a high prevalence of repetitive sequences, both of which likely contribute to genome rearrangement and adaptive evolution. Through nucleotide diversity and codon usage bias analyses, we identified specific genes that are crucial for adaptation to high-altitude conditions. Notably, genes such as atp6, ccmB, nad4L, and nad7 exhibited signs of positive selection, indicating the presence of unique adaptive traits for survival in extreme environments. Phylogenetic analysis confirmed the close relationship between the Elaeagnaceae family and other related families, whereas intergenomic sequence transfer analysis revealed a substantial presence of homologous fragments among the mitochondrial, chloroplast, and whole genomes, which may be linked to the high-altitude adaptation mechanisms of H. tibetana. The findings of this study not only enrich our knowledge of H. tibetana molecular biology but also advance our understanding of the adaptive evolution of plants on the Qinghai–Tibet Plateau. This study provides a solid scientific foundation for the molecular breeding, conservation, and utilization of H. tibetana genetic resources.

The existing body of research underscores the critical impact of intratumoral microbiomes on the progression of pancreatic ductal adenocarcinoma (PDAC), particularly in reshaping the tumor microenvironment and influencing gemcitabine resistance. However, peritumoral tissues’ microbiome, distinct from PDAC tumors, remain understudied, and Western-centric analyses overlooking potential variations in dietary-influenced microbiomes. Our study addresses this gap by 16 S rRNA sequencing of PDAC tumors and matched peritumoral tissues from Chinese Mainland patients. Our research has uncovered that the microbiome composition within tumors and paired peritumoral tissues exhibits a high degree of similarity, albeit with certain discrepancies. Notably, Exiguobacterium is found to be more abundant within the tumor tissues. Further investigations have revealed that a lower Exiguobacterium/Bacillus ratio in both the tumor and peritumoral tissues of PDAC patients is indicative of a more favorable prognosis. Further exploration utilizing an orthotopic tumor model demonstrates that the probiotic Bacillus Coagulans impedes PDAC progression, accompanied by an increased infiltration of inflammatory neutrophils in tumors. Additionally, in the subgroup with a low Exiguobacterium/Bacillus ratio, whole-exome sequencing reveals elevated missense mutations in ABL2 and MSH2. The elevated expression of ABL2 and MSH2 has been correlated with poorer prognostic outcomes in PDAC patients. Together, these insights shed light on risk factors influencing PDAC progression and unveil potential therapeutic targets, alongside probiotic intervention strategies.

Vision-Language Pre-training (VLP) models have shown promising capabilities in grounding natural language in image data, facilitating a broad range of cross-modal tasks. However, we note that there exists a significant gap between the objective forms of model pre-training and fine-tuning, resulting in a need for large amounts of labeled data to stimulate the visual grounding capability of VLP models for downstream tasks. To address the challenge, we present Color-based Prompt Tuning (CPT), a novel paradigm for tuning VLP models, which reformulates visual grounding into a fill-in-the-blank problem with color-based co-referential markers in image and text, maximally mitigating the gap. In this way, CPT enables strong few-shot and even zero-shot visual grounding capabilities of VLP models. Comprehensive experimental results show that CPT achieves state-of-the-art performance on zero/few-shot visual grounding (e.g., 75.1 zero-shot accuracy in RefCOCO evaluation), outperforming fine-tuned and other prompt-tuned models by a large margin. Moreover, CPT can also be easily extended to achieve promising zero/few-shot performance on other vision-language tasks, such as visual relation detection, visual commonsense reasoning and visual question answering. We make the data and codes publicly available at https://github.com/thunlp/CPT.

It is difficult for the existing Burgers model to accurately depict the off-axis cyclic drawing process of woven coatings. In this paper, the mechanical deformation of woven PVC (polyvinyl chloride)-coated film at different temperatures is investigated. One-dimensional (1D) and two-dimensional (2D) constitutive models were established to characterize cyclic deformation processes. The 1D model is an improved Burgers model. The effects of the time dependence of the viscosity coefficient and the ratio of elastic to viscous deformation are considered simultaneously. The accuracy of the 1D model for predicting the cyclic nonlinear deformation at different temperatures and loading rates is improved. The 2D model is a nonlinear orthotropic model using polynomials. On the basis of the single-objective genetic algorithm, the inverse algorithm is used to obtain the shear polynomial coefficients in the tension phase and the shear modulus in the unloading phase, which circumvents performing the difficult shear test. UMAT subroutines of off-axis stretching and off-axis cyclic stretching are written separately. The intelligent inverse algorithm program consists of a single-objective genetic algorithm program, a finite element parametric modelling program, and a UMAT subroutine. The simulation results are compared with the off-axis cyclic tensile test data to validate the effectiveness and accuracy of the proposed 2D model for the analysis of the woven PVC-coated films in the tension–shear coupling state.

Numb is an evolutionarily conserved protein that regulates the differentiation of neuronal progenitor cells through unknown mechanisms. Numb has four alternative splice variants with different lengths of phosphotyrosine-binding (PTB) and proline-rich regions (PRR) domains. In this study, we demonstrated that Numb expression was increased in the primary cultures of rat cortical and hippocampal neurons over time in vitro, and Numb antisense inhibited neurite outgrowth. We verified that cells overexpressing short PTB (SPTB) or long PTB (LPTB) domains exhibited differentiation or proliferation, respectively. SPTB-mediated differentiation was related to the PRR domains, as cells expressing SPTB/LPRR had longer dendrites and more branched dendrites than cells expressing SPTB/SPRR. The differentiation of both cell types was completely blocked by the Ca2+ chelator. Western blot analysis revealed the increased total protein expression of voltage-gated calcium channel (VGCC) subunit α1C and α1D in cells expressing SPTB and LPTB Numb. The increased expression of the VGCC β3 subunit was only observed in cells expressing SPTB Numb. Immunocytochemistry further showed that SPTB-mediated cell differentiation was associated with increased membrane expression of VGCC subunits α1C, α1D and β3, which corresponded to the higher Ca2+ current (ICa) densities. Furthermore, we found that VGCC of cells transfected with SPTB/SPRR or SPTB/LPRR Numb isoforms exhibit steady-state inactivation (SSI) in both differentiated and undifferentiated phenotypes. A similar SSI of VGCC was observed in the differentiated cells transfected with SPTB/SPRR or SPTB/LPRR Numb isoforms, whereas a left shift SSI of VGCC in cells expressing SPTB/LPRR was detected in the undifferentiated cells. Collectively, these data indicate that SPTB domain is essential for neurite outgrowth involving in membrane expression of VGCC subunits, and LPRR plays a role in neuronal branching and the regulation of VGCC inactivation kinetics.

The energy saving and emissions reduction of the construction industry are crucial for China to achieve the “carbon peaking and carbon neutrality” goals. In order to promote the green development of the life cycle of the construction industry and improve the efficiency of emissions reduction. This paper examines the spatial-temporal distribution of life cycle carbon emissions in China’s construction industry (LCCECI) from 2004 to 2018. It uses the SBM-Malmquist total factor productivity (TFP) index to measure technological progress and establishes the spatial econometric model based on the STIRPAT model. The study investigates the driving factors of the LCCECI at the provincial and regional levels, aiming to provide suggestions for low-carbon development in the construction industry. The research results are as follows. ① The growth in the SBM-Malmquist TFP index of the construction industry distinctly curbs the LCCECI. ② Total population and urbanization level are not the primary driving factors for the LCCECI. The growth of per capita GDP significantly induces the LCCECI, while concurrently exhibiting a notable inhibitory effect on the LCCECI of neighboring regions. ③ The improvement of the SBM-Malmquist TFP index is conducive to the reduction of the LCCECI in the three major regions. The per capita GDP has the largest positive driving effect of the LCCECI in the eastern region, and the urbanization level the urbanization rate only significantly inhibits the growth of the LCCECI in the central region.

AbstractTokenization is fundamental to pretrained language models (PLMs). Existing tokenization methods for Chinese PLMs typically treat each character as an indivisible token. However, they ignore the unique feature of the Chinese writing system where additional linguistic information exists below the character level, i.e., at the sub-character level. To utilize such information, we propose sub-character (SubChar for short) tokenization. Specifically, we first encode the input text by converting each Chinese character into a short sequence based on its glyph or pronunciation, and then construct the vocabulary based on the encoded text with sub-word segmentation. Experimental results show that SubChar tokenizers have two main advantages over existing tokenizers: 1) They can tokenize inputs into much shorter sequences, thus improving the computational efficiency. 2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to homophone typos. At the same time, models trained with SubChar tokenizers perform competitively on downstream tasks. We release our code and models at https://github.com/thunlp/SubCharTokenization to facilitate future work.

Non-alcoholic fatty liver disease (NAFLD) is a liver disease syndrome. The prevalence of NAFLD has continued to increase globally, and NAFLD has become a worldwide public health problem. Glucosamine (GLC) is an amino monosaccharide derivative of glucose. GLC has been proven to not only be effective in anti-inflammation applications, but also to modulate the gut microbiota effectively. Therefore, in this study, the therapeutic effect of GLC in the NAFLD context and the mechanisms underlying these effects were explored. Specifically, an NAFLD model was established by feeding mice a high-fat and high-sugar diet (HFHSD), and the HFHSD-fed NAFLD mice were treated with GLC. First, we investigated the effect of treating NAFLD mice with GLC by analyzing serum- and liver-related indicator levels. We found that GLC attenuated insulin resistance and inflammation, increased antioxidant function, and attenuated serum and liver lipid metabolism in the mice. Then, we investigated the mechanism underlying liver lipid metabolism, inflammation, and intestinal barrier function in these mice. We found that GLC can improve liver lipid metabolism and relieve insulin resistance and oxidative stress levels. In addition, GLC treatment increased intestinal barrier function, reduced LPS translocation, and reduced liver inflammation by inhibiting the activation of the LPS/TLR4/NF-κB pathway, thereby effectively ameliorating liver lesions in NAFLD mice.

Due to the fact that point clouds are always corrupted by significant noise and large transformations, aligning two point clouds by deep neural networks is still challenging. This paper presents a semi-supervised point cloud registration (PCR) method for accurately estimating point correspondences and handling large transformations using limited prior datasets. Firstly, a modified autoencoder is introduced as the feature extraction module to extract the distinctive and robust features for the downstream registration task. Unlike optimization-based pairwise PCR strategies, the proposed method treats two point clouds as two implementations of a Gaussian mixture model (GMM), which we call latent GMM. Based on the above assumption, two point clouds can be regarded as two probability distributions. Hence, the PCR of two point clouds can be approached by minimizing the KL divergence between these two probability distributions. Then, the correspondence between the point clouds and the latent GMM components is estimated using the augmented regression network. Finally, the parameters of GMM can be updated by the correspondence and the transformation matrix can be computed by employing the weighted singular value decomposition (SVD) method. Extensive experiments conducted on both synthetic and real-world data validate the superior performance of the proposed method compared to state-of-the-art registration methods. These experiments also highlight the method’s superiority in terms of accuracy, robustness, and generalization.

An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals.

Distributed array radar achieves high angular resolution and measurement accuracy, which could provide a solution to suppress digital radio frequency memory (DRFM) repeater jamming. However, owing to the large aperture of a distributed radar, the far-field plane wave assumption is no longer satisfied. Consequently, traditional adaptive beamforming methods cannot work effectively due to mismatched steering vectors. To address this issue, a DRFM repeater jamming suppression method based on joint range-angle sparse recovery and beamforming for distributed array radar is proposed in this paper. First, the steering vectors of the distributed array are reconstructed according to the spherical wave model under near-field conditions. Then, a joint range-angle sparse dictionary is generated using reconstructed steering vectors, and the range-angle position of jamming is estimated using the weighted L1-norm singular value decomposition (W-L1-SVD) algorithm. Finally, beamforming with joint range-angle nulling is implemented based on the linear constrained minimum variance (LCMV) algorithm for jamming suppression. The performance and effectiveness of proposed method is validated by simulations and experiments on an actual ground-based distributed array radar system.

Introduction: Winter wheat plays a crucial role in ensuring food security and sustainable agriculture. Accurate identification and recognition of winter wheat in remote sensing images are essential for monitoring crop growth and yield estimation. In recent years, attention-based convolutional neural networks have shown promising results in various image recognition tasks. Therefore, this study aims to explore the application of attention-based convolutional neural networks for winter wheat identification on GF-2 high-resolution images and propose improvements to enhance recognition accuracy. Method: This study built a multi-band winter wheat sample dataset based on GF-2 images. In order to highlight the characteristics of winter wheat, this study added two bands, NDVI and NDVIincrease, to the dataset and proposed a SUNet network model. In this study, the batch normalization layer was added to the basic structure of the UNet convolutional network to speed up network convergence and improve accuracy. In the jump phase, shuffle attention was added to the shallow features extracted from the coding structure for feature optimization and spliced with the deep features extracted by upsampling. The SUNet made the network pay more attention to the important features to improve winter wheat recognition accuracy. In order to overcome the sample imbalance problem, this study used the focus loss function instead of the traditional cross-entropy loss function. Result: The experimental data show that its mean intersection over union, overall classification accuracy, recall, F1 score and kappa coefficient are 0.9514, 0.9781, 0.9707, 0.9663 and 0.9501, respectively. The results of these evaluation indicators are better than those of other comparison methods. Compared with the UNet, the evaluation indicators have increased by 0.0253, 0.0118, 0.021, 0.0185, and 0.0272, respectively. Conclusion: The SUNet network can effectively improve winter wheat recognition accuracy in multi-band GF-2 images. Furthermore, with the support of a cloud platform, it can provide data guarantee and computing support for winter wheat information extraction.

Digital radio frequency memory (DRFM) based repeater jamming can create false targets, which can lead to a loss of situational awareness, misidentification of targets, and decreased overall performance of the radar system. Traditional jamming suppression methods do not give due importance to the accurate estimation of the jamming edge, resulting in jamming residual and poor anti-jamming performance. To tackle this issue, this paper explores the reason and impact of inaccurate jamming edge estimation and proposes a DRFM-based repeater jamming reconstruction and cancellation method with accurate edge detection. In the proposed method, firstly, multiple jamming parameters are obtained by computing the short-time fractional Fourier transformation (STFRFT) spectrogram of the received signal. To avoid jamming residue, the proposed method estimates the accurate jamming edges by the joint use of the difference of box (DOB) filters and time domain deconvolution (TDDC) curves. Numerical simulations and experiments are conducted to evaluate the algorithm’s effectiveness in countering smeared spectrum (SMSP) and interrupted sampling repeater jamming (ISRJ). The results demonstrate its superior jamming reconstruction and suppression performance than other methods.

AbstractPre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abundant textual information. In this paper, we propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation (KEPLER), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs. In KEPLER, we encode textual entity descriptions with a PLM as their embeddings, and then jointly optimize the KE and language modeling objectives. Experimental results show that KEPLER achieves state-of-the-art performances on various NLP tasks, and also works remarkably well as an inductive KE model on KG link prediction. Furthermore, for pre-training and evaluating KEPLER, we construct Wikidata5M1 , a large-scale KG dataset with aligned entity descriptions, and benchmark state-of-the-art KE methods on it. It shall serve as a new KE benchmark and facilitate the research on large KG, inductive KE, and KG with text. The source code can be obtained from https://github.com/THU-KEG/KEPLER.

Abstract Electrohydrodynamic (EHD) printing has great potential to be used in the field of electronic industry, precision optics and biological engineering. However, it is still a challenge to achieve a high scaling ratio for line patterns production by EHD jet printing technique, which can effectively prevent the needles to be blocked. In this paper, the influence of the key process parameters on the line width of EHD jet printing was investigated. The printing needle with the inner diameter of 890 μm was used, and the line with width of 3.02 μm was printed by optimizing the parameters. The scaling ratio between the needle inner diameter and the printed line width was up to 295 times. At the same time, the function model between the flow rate and printed line width of PVP/PEO composite was deduced. The error was found to be less than 1% by the verification of experiments. By utilizing this model, the printed line width can be predicted by controlling the flow rate, which will greatly improve the work efficiency. By controlling the flow rate, the goal of printing on demand can be realized. The proposed method and model can provide support for the fabrication of micro‐nano scale structure.

In recent years, the size of pre-trained language models (PLMs) has grown by leaps and bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real-world scenarios. We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference. (1) We introduce knowledge inheritance to accelerate the pre-training process by exploiting existing PLMs instead of training models from scratch. (2) We explore the best practice of prompt tuning with large-scale PLMs. Compared with conventional fine-tuning, prompt tuning significantly reduces the number of task-specific parameters. (3) We implement a new inference toolkit, namely infmoe, for using large-scale PLMs with limited computational resources. Based on our cost-effective pipeline, we pre-train two models: an encoder-decoder bilingual model with 11 billion parameters (CPM-2) and its corresponding MoE version with 198 billion parameters. In our experiments, we compare CPM-2 with mT5 on downstream tasks. Experimental results show that CPM-2 has excellent general language intelligence. Moreover, we validate the efficiency of infmoe when conducting inference of large-scale models having tens of billions of parameters on a single GPU. All source code and model parameters are available at https://github.com/TsinghuaAI/CPM.

Abstract ADMSCs were isolated from subcutaneous adipose tissue, characterized and cultured in vitro. GFP-labeled ADMSCs can grow and proliferate well on the Atelocollagen scaffolds, and induced by 5-aza the cells can differentiate into cardio-like cells. 3D cultured ADMSCs on Atelocollagen scaffolds were transplanted into mice ischemia myocardium, and have good biocompatibility with host cardio tissue.

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.

Mesoporous silica nanocarrier (MSN) preparations have a wide range of medical applications. Studying the biocompatibility of MSN is an important part of clinical transformation. Scientists have developed different types of mesoporous silica nanocarriers (MSNs) for different applications to realize the great potential of MSNs in the field of biomedicine, especially in tumor treatment. MSNs have achieved good results in diagnostic bioimaging, tissue engineering, cancer treatment, vaccine development, biomaterial application and diagnostics. MSNs can improve the therapeutic efficiency of drugs, introduce new drug delivery strategies, and provide advantages that traditional drugs lack. It is necessary not only to innovate MSNs but also to comprehensively understand their biological distribution. In this review, we summarize the various medical uses of MSN preparations and explore the factors that affect their distribution and biocompatibility in the body based on metabolism. Designing more reasonable therapeutic nanomedicine is an important task for the further development of the potential clinical applications of MSNs.

Microstructure evolution and precipitation behaviors before and after tension at elevated temperature of Nb and Nb-Mo microalloyed fire-resistant steels were investigated by scanning electron microscopy and transmission electron microscopy, physical–chemical phase analysis and small-angle X-ray scattering. Results showed that the martensite and austenite (M/A) islands in a rolled state disappeared after tempering, and cementite and a large number of nanometer-sized carbide precipitated. Those nanosized particles were identified as NbC in Nb steel and (Nb, Mo)C in Nb-Mo steel. The precipitation amount of Nb is nearly equal for the two steels in a rolled state. However, after tempering treatment at 600 °C, the precipitation of Nb increased in Nb-Mo steel more obviously than that in Nb steel, indicating that Mo promotes the precipitation of Nb. Correspondingly, the amount of dissolved Nb was reduced, which results in decreased coarsening kinetics of (Nb, Mo)C in Nb-Mo steel as compared with that of NbC in Nb steel.

Laser Powder Bed Fusion (LPBF) presents a more extensive allowable design complexity and manufacturability compared with the traditional manufacturing processes by depositing materials in a layer-wised manner. However, the process variability in the LPBF process induces quality uncertainty and inconsistency. Specifically, the mechanical properties, e.g., tensile strength, are hard to be predicted and controlled in the LPBF process. Much research has recently been reported exploring the qualitative influence of single/two process parameters on tensile strength. In fact, mechanical properties are comprehensively affected by multiple correlated process parameters with unclear and complex interactions. Thus, the study on the quantitative process-quality model of the metal LPBF process is urgently needed to provide an enough-strength component via the metal LPBF process. Recent progress in artificial intelligence (AI) and machine learning (ML) provides new insight into quality prediction in terms of computational accuracy and speed. However, the predictive model quality through the traditional AL/ML is heavily determined by the training data size, and the experimental analysis can be expansive on LPBF. This paper explores the comprehensive effect of the tensile strength of 316L stainless-steel parts on LPBF and proposes a valid quantitative predictive model through a novel self-growing machine-learning framework. The self-growing framework can autonomously expand and classify the growing dataset to provide a high-accuracy prediction with fewer input data. To verify this predictive model of tensile strength, specimens manufactured by the LPBF process with different group process parameters (laser power, scanning speed, and hatch spacing) are collected. The experimental results validate the predicted tensile strengths within a less than 3% deviation.

Resonant walking with preferred gait features is a self-optimized consequence of long-term human locomotion. Minimal energy expenditure can be achieved in this resonant condition. This unpowered multi-joint soft exoskeleton is designed to test whether: (1) there is an obvious improvement in preferred speed and other gait features; (2) resonant walking still exists with exoskeleton assistance. Healthy participants (N = 7) were asked to perform the following trials: (1) walking at 1.25 m/s without assistance (normal condition); (2) walking at 1.25 m/s with assistance (general condition); (3) walking at preferred speed with assistance (preferred condition); (4) walking at the speed in trial (3) without assistance (comparison condition). Participants walked at the preferred frequency and ±10% of it. An average 21% increase in preferred speed was observed. The U-shaped oxygen consumption and lower limb muscle activity curve with the minimum at preferred frequency indicated that the resonant condition existed under the preferred condition. Average metabolic reductions of 4.53% and 7.65% were found in the preferred condition compared to the general and comparison condition, respectively. These results demonstrate that the resonant condition in assisted walking could benefit energy expenditure and provide a new perspective for exoskeleton design and evaluation.

Farnesoid X receptor (FXR) is a nuclear receptor for bile acids (BAs) that is widely expressed in the intestine, liver and kidney. FXR has important regulatory impacts on a wide variety of metabolic pathways (such as glucose, lipid, and sterol metabolism) and has been recognized to ameliorate obesity, liver damage, cholestasis and chronic inflammatory diseases. The types of BAs are complex and diverse. BAs link the intestine with the liver through the enterohepatic circulation. BAs derivatives have entered clinical trials for liver disease. In addition to the liver, the intestine is also targeted by BAs. This article reviews the effects of different BAs on the intestinal tract through the enterohepatic circulation from the perspective of FXR, aiming to elucidate the effects of different BAs on the intestinal tract and lay a foundation for new treatment methods.

Neutrophil extracellular traps (NETs) are characterized as extracellular DNA fibers comprised of histone and cytoplasmic granule proteins. NETs were first described as a form of innate response against pathogen invasion, which can capture pathogens, degrade bacterial toxic factors, and kill bacteria. Additionally, NETs also provide a scaffold for protein and cell binding. Protein binding to NETs further activate the coagulation system which participates in thrombosis. In addition, NETs also can damage the tissues due to the proteins they carry. Many studies have suggested that the excessive formation of NETs may contribute to a range of diseases, including thrombosis, atherosclerosis, autoimmune diseases, and sepsis. In this review, we describe the structure and components of NETs, models of NET formation, and detection methods. We also discuss the molecular mechanism of NET formation and their disease relevance. Modulation of NET formation may provide a new route for the prevention and treatment of releated human diseases.

Conventional lower-limb rehabilitation robots cannot provide in-time rehabilitation training for stroke patients in the acute stage due to their large size and mass as well as their complex wearing process. Aiming to solve the problems, first, a novel hybrid end-traction lower-limb rehabilitation robot (HE-LRR) was designed as the lower-limb rehabilitation requirement of patients in the acute stage, in this paper. The usage of (2-UPS + U)&(R + RPS)&(2-RR) hybrid mechanism and a mirror motion actuator had the advantages of compact structure, large working space and short wearing time to the HE-LRR. Then, the mobility of the HE-LRR was calculated and the motion property was analyzed based on screw theory. Meanwhile, the trajectory planning of the HE-LRR was carried out based on MOTOmed® motion training. Finally, the motion capture and surface electromyography (sEMG) signal acquisition experiments in the MOTOmed motion training were performed. The foot trajectory experimental effect and the lower-limb muscle groups activation rules were studied ulteriorly. The experimental results showed that the HE-LRR achieved good kinematic accuracy and lower limb muscle groups training effect, illustrating that the HE-LRR possessed good application prospects for the lower-limb rehabilitation of patients in the acute stage. This research could also provide a theoretical basis for improving the standardization and compliance of lower-limb robot rehabilitation training.

Image repair often has errors in filling. An exemplar-based technique has the advantage of filling texture and structure simultaneously, but there exist some problems with this, such as not considering the overall situation of inpainting, the unreasonable calculation of priority, and random selection when there are multiple candidate patches. In view of these situations, this paper proposes a method of screen window propagating where the upper layer guides the lower layer inpainting from the top layer by means of multiple resolution decomposition. Screen window propagating aims to constrain the inpainting of the lower layer and maintain the overall profile. Improving the priority calculation using the upper repair result can lead to getting a better repair order. When there is more than one candidate patch, the structural similarity index measure (SSIM) is used to obtain the best candidate patch. We consider four cases of rotation and inversion when looking for the best exemplar. The experiments show that our method can obtain more satisfactory results than other methods based on subjective and objective indicators, such as peak signal-tonoise ratio (PSNR), SSIM, and feature similarity index measurement. Screen window propagating for image inpainting aims toward image restoration. Whole contours are preserved and the lower layer can utilize more upper layer information. Our method considers information from the whole and its parts.

Cu-bearing age-hardening steel has significant potential in shipbuilding applications due to its excellent weldability as compared to conventional NiCrMoV steel. Not much research has been carried out to analyze the differences in the mechanisms of strength and toughness between Cu-bearing age-hardening and NiCrMoV steel. Both steels were heat treated under the same conditions: they were austenized at 900 °C and then quenched to room temperature, followed by tempering at 630 °C for 2 h. The uniaxial tensile test reveals that the Cu-bearing age-hardening steel exhibits relatively lower strength but larger plasticity than NiCrMoV steel. The lower contents of Carbon and other alloying elements is one of possible reasons for these differences in mechanical properties. Transmission Electron Microscope observations show that two types of precipitates, Cr carbides and Cu-rich particles, exist in tempered Cu-bearing age-hardening steel. Cu-rich particles with sizes of 20–40 nm can inhibit the dislocation motion during deformation, which then results in dislocation pile ups and multiplication; this makes up the strength loss of Cu-bearing age-hardening steel and simultaneously improves its plasticity.

Intraflagellar transport moves proteins in and out of flagella/cilia and it is essential for the assembly of these organelles. Using whole-genome sequencing, we identified splice site mutations in two IFT genes, IFT81 (fla9) and IFT121 (ift121-2), which lead to flagellar assembly defects in the unicellular green alga Chlamydomonas reinhardtii. The splicing defects in these ift mutants are partially corrected by mutations in two conserved spliceosome proteins, DGR14 and FRA10. We identified a dgr14 deletion mutant, which suppresses the 3′ splice site mutation in IFT81, and a frameshift mutant of FRA10, which suppresses the 5′ splice site mutation in IFT121. Surprisingly, we found dgr14-1 and fra10 mutations suppress both splice site mutations. We suggest these two proteins are involved in facilitating splice site recognition/interaction; in their absence some splice site mutations are tolerated. Nonsense mutations in SMG1, which is involved in nonsense-mediated decay, lead to accumulation of aberrant transcripts and partial restoration of flagellar assembly in the ift mutants. The high density of introns and the conservation of noncore splicing factors, together with the ease of scoring the ift mutant phenotype, make Chlamydomonas an attractive organism to identify new proteins involved in splicing through suppressor screening.

Bioceramics have frequent use in functional restoration of hard tissues to improve human well-being. Additive manufacturing (AM) also known as 3D printing is an innovative material processing technique extensively applied to produce bioceramic parts or scaffolds in a layered perspicacious manner. Moreover, the applications of additive manufacturing in bioceramics have the capability to reliably fabricate the commercialized scaffolds tailored for practical clinical applications, and the potential to survive in the new era of effective hard tissue fabrication. The similarity of the materials with human bone histomorphometry makes them conducive to use in hard tissue engineering scheme. The key objective of this manuscript is to explore the applications of bioceramics-based AM in bone tissue engineering. Furthermore, the article comprehensively and categorically summarizes some novel bioceramics based AM techniques for the restoration of bones. At prior stages of this article, different ceramics processing AM techniques have been categorized, subsequently, processing of frequently used materials for bone implants and complexities associated with these materials have been elaborated. At the end, some novel applications of bioceramics in orthopedic implants and some future directions are also highlighted to explore it further. This review article will help the new researchers to understand the basic mechanism and current challenges in neophyte techniques and the applications of bioceramics in the orthopedic prosthesis.

In this paper, we consider the problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple targets for bistatic multiple-input multiple-output (MIMO) radar. A high-precision tracking algorithm for target angle is proposed. First, the linear relationship between the covariance matrix difference and the angle difference of the adjacent moment was obtained through three approximate relations. Then, the proposed algorithm obtained the relationship between the elements in the covariance matrix difference. On this basis, the performance of the algorithm was improved by averaging the covariance matrix element. Finally, the least square method was used to estimate the DOD and DOA. The algorithm realized the automatic correlation of the angle and provided better performance when compared with the adaptive asymmetric joint diagonalization (AAJD) algorithm. The simulation results demonstrated the effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

CCDC39 and CCDC40 were first identified as causative mutations in primary ciliary dyskinesia patients; cilia from patients show disorganized microtubules, and they are missing both N-DRC and inner dynein arms proteins. In Chlamydomonas, we used immunoblots and microtubule sliding assays to show that mutants in CCDC40 (PF7) and CCDC39 (PF8) fail to assemble N-DRC, several inner dynein arms, tektin, and CCDC39. Enrichment screens for suppression of pf7; pf8 cells led to the isolation of five independent extragenic suppressors defined by four different mutations in a NIMA-related kinase, CNK11. These alleles partially rescue the flagellar length defect, but not the motility defect. The suppressor does not restore the missing N-DRC and inner dynein arm proteins. In addition, the cnk11 mutations partially suppress the short flagella phenotype of N-DRC and axonemal dynein mutants, but do not suppress the motility defects. The tpg1 mutation in TTLL9, a tubulin polyglutamylase, partially suppresses the length phenotype in the same axonemal dynein mutants. In contrast to cnk11, tpg1 does not suppress the short flagella phenotype of pf7. The polyglutamylated tubulin in the proximal region that remains in the tpg1 mutant is reduced further in the pf7; tpg1 double mutant by immunofluorescence. CCDC40, which is needed for docking multiple other axonemal complexes, is needed for tubulin polyglutamylation in the proximal end of the flagella. The CCDC39 and CCDC40 proteins are likely to be involved in recruiting another tubulin glutamylase(s) to the flagella. Another difference between cnk11-1 and tpg1 mutants is that cnk11-1 cells show a faster turnover rate of tubulin at the flagellar tip than in wild-type flagella and tpg1 flagella show a slower rate. The double mutant shows a turnover rate similar to tpg1, which suggests the faster turnover rate in cnk11-1 flagella requires polyglutamylation. Thus, we hypothesize that many short flagella mutants in Chlamydomonas have increased instability of axonemal microtubules. Both CNK11 and tubulin polyglutamylation play roles in regulating the stability of axonemal microtubules.