Heavy metal ions exchange driven protein phosphorylation cascade functions in genomic instability in spermatocytes and male infertility.

DNA double-strand breaks (DSBs) are functionally linked to genomic instability in spermatocytes and to male infertility. The heavy metal cadmium (Cd) is known to induce DNA damage in spermatocytes by unknown mechanisms. Here, we showed that Cd ions impaired the canonical non-homologous end-joining (NHEJ) repair pathway, but not the homologous recombination (HR) repair pathway, through stimulation of Ser2056 and Thr2609 phosphorylation of DNA-PKcs at DSB sites. Hyper-phosphorylation of DNA-PKcs led to its premature dissociation from DNA ends and the Ku complex, preventing recruitment of processing enzymes and further ligation of DNA ends. Specifically, this cascade was initiated by the loss of PP5 phosphatase activity, which results from the dissociation of PP5 from its activating ions (Mn), that is antagonized by Cd ions through a competitive mechanism. In accordance, in a mouse model Cd-induced genomic instability and consequential male reproductive dysfunction were effectively reversed by a high dosage of Mn ions. Together, our findings corroborate a protein phosphorylation-mediated genomic instability pathway in spermatocytes that is triggered by exchange of heavy metal ions.

Targeted PLK1 suppression through RNA interference mediated by high-fidelity Cas13d mitigates osteosarcoma progression via TGF-ß/Smad3 signalling.

Osteosarcoma is the most common primary bone malignancy in children and adolescents. Overexpression of polo-like kinase 1 (PLK1) is frequent in osteosarcoma and drives disease progression and metastasis, making it a promising therapeutic target. In this study, we explored PLK1 knockdown in osteosarcoma cells using RNA interference mediated by high-fidelity Cas13d (hfCas13d). PLK1 was found to be significantly upregulated in osteosarcoma tumour tissues compared to normal bone. sgRNA-mediated PLK1 suppression via hfCas13d transfection inhibited osteosarcoma cell proliferation, induced G2/M cell cycle arrest, promoted apoptosis, reduced cell invasion and increased expression of the epithelial marker E-cadherin. Proximity labelling by TurboID coupled with co-immunoprecipitation identified novel PLK1 interactions with Smad3, a key intracellular transducer of TGF-ß signalling. PLK1 knockdown impaired Smad2/3 phosphorylation and modulated TGF-ß/Smad3 pathway inactivation. Finally, in vivo delivery of hfCas13d vectors targeting PLK1 substantially attenuated osteosarcoma xenograft growth in nude mice. Taken together, this study highlights PLK1 as a potential therapeutic target and driver of disease progression in osteosarcoma. It also demonstrates the utility of hfCas13d-mediated gene knockdown as a strategy for targeted therapy. Further optimization of PLK1 suppression approaches may ultimately improve clinical outcomes for osteosarcoma patients.

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer.

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

Development and validation of a nomogram to predict the risk of constipation after lumbar interbody fusion surgery.

INTRODUCTION: To understand the incidence of postoperative constipation and the risk factors of constipation in patients with lumbar interbody fusion, we constructed and verified the constipation risk prediction model, so as to provide reference for the prevention and treatment of postoperative constipation. METHODS: The data of patients undergoing lumbar interbody fusion in our hospital were retrospectively analyzed from December 2021 to December 2022. According to postoperative constipation, the patients were divided into constipation group and non-constipation group. Univariate logistic regression analysis and multivariate logistic regression analysis were used to determine independent risk factors for postoperative constipation. Based on independent risk factors, a nomogram was developed to predict the risk of constipation after lumbar interbody fusion. The prediction performance was assessed using receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA). Finally, bootstrapping method for internal validation was further evaluated the nomogram. RESULTS: A total of 282 patients participated in the study. 176 patients (62.41%) after lumbar interbody occurred constipation, and 106 patients were asymptomatic. Multivariate regression analysis showed independent risk factors, including the use of calcium channel blockers, polypharmacy, postoperative bed time, and constipation history. Multivariate regression analysis was used to establish the model. The C-index of the nomogram was 0.827 (95% CI 0.779-0.875), and the C-index of interval bootstrapping validation was 0.813 (95% CI 0.765-0.861), and the area under the AUC was 0.800. The nomogram showed good discrimination ability. CONCLUSIONS: The use of calcium channel blockers, polypharmacy, postoperative bed time, and history of constipation are independent risk factors for postoperative constipation in patients undergoing lumbar interbody fusion. The constructed risk prediction model has good discriminative ability.

A robust NIR fluorescence-activated probe for peroxynitrite imaging in cells and mice osteoarthritis models.

Osteoarthritis (OA) is the most common type of joint disease, which is difficult to treat, but early standardized diagnosis and treatment can effectively alleviate the pain and symptoms of patients. Therefore, it is important to construct an effective tool to assist in the early diagnosis and evaluation of the therapeutic effect of OA. In this work, a near-infrared (NIR) fluorescence-activated fluorescent probe, YB-1, was constructed for the evaluation of the diagnostic and therapeutic efficacy of OA via detection and imaging of the biomarker of ONOO- in inflammatory cells and mice osteoarthritis models. YB-1 exhibited high selectivity, high sensitivity, and a high ratio yield (I668/I0) fluorescence increasing (∼30 folds). Besides, YB-1 can be used effectively to image endogenous and exogenous ONOO- in living human chondrocytes cells (TC28a2), as well as to evaluate the effect of drug (Chrysosplenol D, CD) treatment in IL-1ß-induced inflammatory cells model. Interestingly, YB-1 was available for OONO- imaging analysis in the collagenase-induced mice OA models and assessment of the effect of CD treatment in mice OA models, with good results. Thus, the newly constructed YB-1 is a powerful molecular tool for the diagnosis and treatment of OA-related diseases.

A phase III randomized, double-blind, placebo-controlled trial of the denosumab biosimilar QL1206 in postmenopausal Chinese women with osteoporosis and high fracture risk.

The current study evaluated the efficacy and safety of a denosumab biosimilar, QL1206 (60 mg), compared to placebo in postmenopausal Chinese women with osteoporosis and high fracture risk. At 31 study centers in China, a total of 455 postmenopausal women with osteoporosis and high fracture risk were randomly assigned to receive QL1206 (60 mg subcutaneously every 6 months) or placebo. From baseline to the 12-month follow-up, the participants who received QL1206 showed significantly increased bone mineral density (BMD) values (mean difference and 95% CI) in the lumbar spine: 4.780% (3.880%, 5.681%), total hip :3.930% (3.136%, 4.725%), femoral neck 2.733% (1.877%, 3.589%) and trochanter: 4.058% (2.791%, 5.325%) compared with the participants who received the placebo. In addition, QL1206 injection significantly decreased the serum levels of C-terminal crosslinked telopeptides of type 1 collagen (CTX): -77.352% (-87.080%, -66.844%), and N-terminal procollagen of type l collagen (P1NP): -50.867% (-57.184%, -45.217%) compared with the placebo over the period from baseline to 12 months. No new or unexpected adverse events were observed. We concluded that compared with placebo, QL1206 effectively increased the BMD of the lumbar spine, total hip, femoral neck and trochanter in postmenopausal Chinese women with osteoporosis and rapidly decreased bone turnover markers. This study demonstrated that QL1206 has beneficial effects on postmenopausal Chinese women with osteoporosis and high fracture risk.

Cardiac CIP protein regulates dystrophic cardiomyopathy.

Heart failure is a leading cause of fatality in Duchenne muscular dystrophy (DMD) patients. Previously, we discovered that cardiac and skeletal-muscle-enriched CIP proteins play important roles in cardiac function. Here, we report that CIP, a striated muscle-specific protein, participates in the regulation of dystrophic cardiomyopathy. Using a mouse model of human DMD, we found that deletion of CIP leads to dilated cardiomyopathy and heart failure in young, non-syndromic mdx mice. Conversely, transgenic overexpression of CIP reduces pathological dystrophic cardiomyopathy in old, syndromic mdx mice. Genome-wide transcriptome analyses reveal that molecular pathways involving fibrogenesis and oxidative stress are affected in CIP-mediated dystrophic cardiomyopathy. Mechanistically, we found that CIP interacts with dystrophin and calcineurin (CnA) to suppress the CnA-Nuclear Factor of Activated T cells (NFAT) pathway, which results in decreased expression of Nox4, a key component of the oxidative stress pathway. Overexpression of Nox4 accelerates the development of dystrophic cardiomyopathy in mdx mice. Our study indicates CIP is a modifier of dystrophic cardiomyopathy and a potential therapeutic target for this devastating disease.

Regulation of myonuclear positioning and muscle function by the skeletal muscle-specific CIP protein.

The appropriate arrangement of myonuclei within skeletal muscle myofibers is of critical importance for normal muscle function, and improper myonuclear localization has been linked to a variety of skeletal muscle diseases, such as centronuclear myopathy and muscular dystrophies. However, the molecules that govern myonuclear positioning remain elusive. Here, we report that skeletal muscle-specific CIP (sk-CIP) is a regulator of nuclear positioning. Genetic deletion of sk-CIP in mice results in misalignment of myonuclei along the myofibers and at specialized structures such as neuromuscular junctions (NMJs) and myotendinous junctions (MTJs) in vivo, impairing myonuclear positioning after muscle regeneration, leading to severe muscle dystrophy in mdx mice, a mouse model of Duchenne muscular dystrophy. sk-CIP is localized to the centrosome in myoblasts and relocates to the outer nuclear envelope in myotubes upon differentiation. Mechanistically, we found that sk-CIP interacts with the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex and the centriole Microtubule Organizing Center (MTOC) proteins to coordinately modulate myonuclear positioning and alignment. These findings indicate that sk-CIP may function as a muscle-specific anchoring protein to regulate nuclear position in multinucleated muscle cells.

An attenuated multiple genetic mutant of Mycoplasma pneumoniae imparts good immuno-protection against M. pneumoniae pneumonia in BALB/c mice.

Mycoplasma pneumoniae (M. pneumoniae) is the causative agent of both upper and lower respiratory infections that can lead to pneumonia, extrapulmonary complications and devastating sequela. With the increasing rate of macrolide-resistant strains, the severe clinical consequence of refractory mycoplasma pneumonia in children health calls for the need of vaccine research for this pathogen. In this report, the immunomodulatory effectiveness of a live attenuated M. pneumoniae vaccine was evaluated. The vaccine strain was a mutant strain of M. pneumoniae, MUT129, obtained after multiple passages of M129 strain in PPLO broth. The SNP/InDel detection results showed that mutations were present in genes encoding the adhesion organelle-associated proteins and lipoproteins of M. pneumoniae MUT129. Upon intranasal challenge of BALB/c mice with 1 × 107 CFU of MUT129, there were very small amount of Mycoplasma antigens and almost no M. pneumoniae present in the lung tissues of BALB/c mice. Besides, there was almost no inflammatory cell infiltration in the lung tissue. Results of the M. pneumoniae challenge study showed that mice immunized with MUT129 presented with less inflammation, lower detectable number of M. pneumoniae in the lungs when compared with the unimmunized mice. These results indicated that the live attenuated vaccine can efficiently prevent the proliferation of M. pneumonia in the lungs, reduce but not completely prevent the pulmonary inflammatory response.

Adjacent segment disease after minimally invasive transforaminal lumbar interbody fusion for degenerative lumbar diseases: incidence and risk factors.

STUDY DESIGN: Retrospective study. OBJECTIVES: To explore the incidence and risk factors for symptomatic adjacent segment disease (ASD) in patients enveloped in degenerative lumbar diseases after minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF). METHODS: Data were retrospectively analyzed on 744 patients who underwent MIS-TLIF for degenerative lumbar diseases in our hospital from October 2012 to December 2018. The patients were divided into the ASD group and non-ASD (N-ASD) group on the basis of developing ASD at follow-up, and then the incidence of ASD was calculated. Clinical and radiological risk factors were assessed over time to determine their association with ASD by excluding less important factors. RESULTS: Data were missing for 26 patients, while a total of 718 patients were successfully monitored after MIS-TLIF. Of the 718 individuals participated in the study, 34 (4.7%) patients plagued by ASD required surgical intervention. The average onset time of ASD was 62.7 ± 15.1 months. Univariate analysis results shows that age, bone mineral density (BMD), body mass index (BMI), preoperative adjacent intervertebral disc height and preoperative adjacent segment disc degeneration were significantly different between the ASD and N-ASD groups (p < 0.05). The logistic regression analysis results demonstrated that BMD (p = 0.039, OR = 0.986, 95% CI 0.899-1.115), BMI (p = 0.041, OR = 1.119, 95% CI 1.103-2.397), and preoperative adjacent intervertebral disc degeneration (p = 0.023, OR = 1.215, 95% CI 1.015-1.986) may be seen as risk factors for ASD after MIS-TLIF. CONCLUSIONS: The incidence of ASD was about 4.7% in patients suffer from degenerative lumbar diseases after MIS-TLIF. BMD, BMI and preoperative adjacent intervertebral disc degeneration might be the risk factors for the occurrence of ASD after MIS-TLIF. Our research also suggested that patients with lower BMD, higher BMI and disc preoperative adjacent segment disc degeneration were more likely to develop ASD after MIS-TLIF.

Relapsed boyhood tibia polymicrobial osteomyelitis linked to dermatophytosis: a case report.

BACKGROUND: Relapsed childhood polymicrobial osteomyelitis associated with dermatophytosis has not been reported in the literature. CASE PRESENTATION: Here we report on a case of a 45-year-old man who had left tibial osteomyelitis for 29 years, accompanied by skin fungal infection of the ipsilateral heel for 20 years, and underwent a second operation due to recurrence of polymicrobial infection 6 years ago. The patient had a history of injury from a rusty object, which penetrated the anterior skin of the left tibia middle segment causing subsequent bone infection, but was asymptomatic after receiving treatments in 1983. The patient was physically normal until dermatophytosis occurred on the ipsilateral heel skin in 1998. The patient complained that the dermatophytosis was gradually getting worse, and the tibial wound site became itchy, red, and swollen. The left tibial infection resurged in May 2012, leading to the patient receiving debridement and antibiotic treatment. H&E and Gram-stained histology was performed on biopsy specimens of sequestrum and surrounding inflammatory tissue. Tissue culture and microbiology examination confirmed polymicrobial infection with Staphylococcus aureus (S. aureus) and Corynebacterium and a fungus. Additionally, the patient also received potassium permanganate for dermatophytosis when he was admitted into the hospital. CONCLUSIONS: Together with longitudinal follow-up of medical history, surgical findings, histopathological and microbiology culture evidence, we conclude that boyhood tibia polymicrobial osteomyelitis with S. aureus and Corynebacterium occurred in this patient, and the fungal activation of dermatophytosis may have led to osteomyelitis relapse.

Resource Mapping Allocation Scheme in 6G Satellite Twin Network.

The sixth generation (6G) satellite twin network is an important solution to achieve seamless global coverage of 6G. The deterministic geometric topology and the randomness of the communication behaviors of 6G networks limit the realism and transparency of cross-platform and cross-object communication, twin, and computing co-simulation networks. Meanwhile, the parallel-based serverless architecture has a high redundancy of computational resource allocation. Therefore, for the first time, we present a new hypergraph hierarchical nested kriging model, which provides theoretical analysis and modeling of integrated relationships for communication, twin, and computing. We explore the hierarchical unified characterization method which joins heterogeneous topologies. A basis function matrix for local flexible connectivity of the global network is designed for the connection of huge heterogeneous systems to decouple the resource mapping among heterogeneous networks. To improve the efficiency of resource allocation in communication, twin, and computing integrated network, a multi-constraint multi-objective genetic algorithm (MMGA) based on the common requirements of operations, storage, interaction, and multi-layer optimal solution conflict is proposed for the first time. The effectiveness of the algorithm and architecture is verified through simulation and testing.

A Novel Carrier Loop Based on Coarse-to-Fine Weighted Adaptive Kalman Filter for Weak Communication-Positioning Integrated Signal.

We propose a communication-navigation integrated signal (CPIS), which is superimposed on the communication signal with power that does not affect the communication service, and realizes high-precision indoor positioning in a mobile communication network. Due to the occlusion of indoor obstacles and the power limitation of the positioning signal, existing carrier loop algorithms have large tracking errors in weak signal environments, which limits the positioning performance of the receiver in a complex environment. The carrier loop based on Kalman filtering (KF) has a good performance in respect of weak signals. However, the carrier frequency error of acquisition under weak signals is large, and the KF loop cannot converge quickly. Moreover, the KF algorithm based on fixed noise covariance increases or diverges in filtering error in complex environments. In this paper, a coarse-to-fine weighted adaptive Kalman filter (WAKF)-based carrier loop algorithm is proposed to solve the above problems of the receiver. In the coarse tracking stage, acquisition error reduction and bit synchronization are realized, and then a carrier loop based on Sage-Husa adaptive filtering is entered. Considering the shortcomings of the filter divergence caused by the negative covariance matrix of Sage-Husa in the filter update process, the weighted factor is given and UD decomposition is introduced to suppress the filtering divergence and improve the filtering accuracy. The simulation and actual environment test results show that the tracking sensitivity of the proposed algorithm is better than that based on the Sage-Husa adaptive filtering algorithm. In addition, compared with the weighted Sage-Husa AKF algorithm, the coarse-to-fine WAKF-based carrier loop algorithm converges faster.

Robust Visual Odometry Leveraging Mixture of Manhattan Frames in Indoor Environments.

We propose a robust RGB-Depth (RGB-D) Visual Odometry (VO) system to improve the localization performance of indoor scenes by using geometric features, including point and line features. Previous VO/Simultaneous Localization and Mapping (SLAM) algorithms estimate the low-drift camera poses with the Manhattan World (MW)/Atlanta World (AW) assumption, which limits the applications of such systems. In this paper, we divide the indoor environments into two different scenes: MW and non-MW scenes. The Manhattan scenes are modeled as a Mixture of Manhattan Frames, in which each Manhattan Frame in itself defines a Manhattan World of a specific orientation. Moreover, we provide a method to detect Manhattan Frames (MFs) using the dominant directions extracted from the parallel lines. Our approach is designed with lower computational complexity than existing techniques using planes to detect Manhattan Frame (MF). For MW scenes, we separately estimate rotational and translational motion. A novel method is proposed to estimate the drift-free rotation using MF observations, unit direction vectors of lines, and surface normal vectors. Then, the translation part is recovered from point-line tracking. In non-MW scenes, the tracked and matched dominant directions are combined with the point and line features to estimate the full 6 degree of freedom (DoF) camera poses. Additionally, we exploit the rotation constraints generated from the multi-view dominant directions observations. The constraints are combined with the reprojection errors of points and lines to refine the camera pose through local map bundle adjustment. Evaluations on both synthesized and real-world datasets demonstrate that our approach outperforms state-of-the-art methods. On synthesized datasets, average localization accuracy is 1.5 cm, which is equivalent to state-of-the-art methods. On real-world datasets, the average localization accuracy is 1.7 cm, which outperforms the state-of-the-art methods by 43%. Our time consumption is reduced by 36%.

LRF-WiVi: A WiFi and Visual Indoor Localization Method Based on Low-Rank Fusion.

In this paper, a WiFi and visual fingerprint localization model based on low-rank fusion (LRF-WiVi) is proposed, which makes full use of the complementarity of heterogeneous signals by modeling both the signal-specific actions and interaction of location information in the two signals end-to-end. Firstly, two feature extraction subnetworks are designed to extract the feature vectors containing location information of WiFi channel state information (CSI) and multi-directional visual images respectively. Then, the low-rank fusion module efficiently aggregates the specific actions and interactions of the two feature vectors while maintaining low computational complexity. The fusion features obtained are used for position estimation; In addition, for the CSI feature extraction subnetwork, we designed a novel construction method of CSI time-frequency characteristic map and a double-branch CNN structure to extract features. LRF-WiVi jointly learns the parameters of each module under the guidance of the same loss function, making the whole model more consistent with the goal of fusion localization. Extensive experiments are conducted in a complex laboratory and an open hall to verify the superior performance of LRF-WiVi in utilizing WiFi and visual signal complementarity. The results show that our method achieves more advanced positioning performance than other methods in both scenarios.

MHSA-EC: An Indoor Localization Algorithm Fusing the Multi-Head Self-Attention Mechanism and Effective CSI.

Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. The CSI signals collected by different fingerprint points have a high degree of discrimination due to the influence of multi-path effects. This multi-path effect is reflected in the correlation between subcarriers and antennas. However, in mining such correlations, previous methods are difficult to aggregate non-adjacent features, resulting in insufficient multi-path information extraction. In addition, the existence of the multi-path effect makes the relationship between the original CSI signal and the distance not obvious, and it is easy to cause mismatching of long-distance points. Therefore, this paper proposes an indoor localization algorithm that combines the multi-head self-attention mechanism and effective CSI (MHSA-EC). This algorithm is used to solve the problem where it is difficult for traditional algorithms to effectively aggregate long-distance CSI features and mismatches of long-distance points. This paper verifies the stability and accuracy of MHSA-EC positioning through a large number of experiments. The average positioning error of MHSA-EC is 0.71 m in the comprehensive office and 0.64 m in the laboratory.

A Three-in-One Strategy: Injectable Biomimetic Porous Hydrogels for Accelerating Bone Regeneration via Shape-Adaptable Scaffolds, Controllable Magnesium Ion Release, and Enhanced Osteogenic Differentiation.

The repair of bone defects with irregular shapes, particularly in a minimally invasive manner, remains a major challenge. For synthetic bone grafts, injectable hydrogels are superior to conventional scaffolds because they can adapt satisfactorily to the defect margins and can be injected into deeper areas of injury via a minimally invasive procedure. Based on the poly(lactide-co-glycolide)(PLGA)/1-methyl-2-pyrrolidinone solution reported in our previous study, we successfully synthesized injectable MgO/MgCO3@PLGA (PMM) hydrogels, namely, injectable biomimetic porous hydrogels (IBPHs), to accelerate bone regeneration. In addition to exhibiting excellent injectability, PMM hydrogels could transform into porous scaffolds in situ through a liquid-to-solid phase transition and completely fill irregular bone defects via their superb shape adaptability. Moreover, sustainable and steady release of Mg2+ was achieved by regulating the weight ratio of the incorporated MgO and MgCO3 particles. Via controlled release of Mg2+, PMM hydrogels significantly promoted proliferation, osteogenic differentiation, migration, and biomineral deposition of immortalized mouse embryonic fibroblasts. More importantly, micro-CT imaging and histological analysis indicated that concomitant with their gradual degradation, PMM hydrogels effectively stimulated in situ bone regeneration in rat calvarial defects with an increase in the bone volume fraction of almost 2-fold compared with that in the control group. These findings suggest that injectable PMM hydrogels can satisfactorily match bone defects and form porous scaffolds in situ and can significantly promote bone regeneration via controllable Mg2+ release. The remarkable features of IPBHs may open a new avenue for the exploration of in situ repair systems for irregular bone defects to accelerate bone regeneration and have great potential for clinical translation.

LRTM1 promotes the differentiation of myoblast cells by negatively regulating the FGFR1 signaling pathway.

The proliferation and differentiation of myoblast cells are regulated by the fibroblast growth factor receptor (FGFR) signaling pathway. Although the regulation of FGFR signaling cascades has been widely investigated, the inhibitory mechanism that particularly function in skeletal muscle myogenesis remains obscure. In this study, we determined that LRTM1, an inhibitory regulator of the FGFR signaling pathway, negatively modulates the activation of ERK and promotes the differentiation of myoblast cells. LRTM1 is dynamically expressed during myoblast differentiation and skeletal muscle regeneration after injury. In mouse myoblast C2C12 cells, knockout (KO) of Lrtm1 significantly prevents the differentiation of myoblast cells; this effect is associated with the reduction of MyoD transcriptional activity and the overactivation of ERK kinase. Notably, further studies demonstrated that LRTM1 associates with p52Shc and inhibits the recruitment of p52Shc to FGFR1. Taken together, our findings identify a novel negative regulator of FGFR1, which plays an important role in regulating the differentiation of myoblast cells.

Design and Implementation of Opportunity Signal Perception Unit Based on Time-Frequency Representation and Convolutional Neural Network.

The traditional signal of opportunity (SOP) positioning system is equipped with dedicated receivers for each type of signal to ensure continuous signal perception. However, it causes a low equipment resources utilization and energy waste. With increasing SOP types, problems become more serious. This paper proposes a new signal perception unit for SOP positioning systems. By extracting the perception function from the positioning system and operating independently, the system can flexibly schedule resources and reduce waste based on the perception results. Through time-frequency joint representation, time-frequency image can be obtained which provides more information for signal recognition, and is difficult for traditional single time/frequency-domain analysis. We also designed a convolutional neural network (CNN) for signal recognition and a negative learning method to correct the overfitting to noisy data. Finally, a prototype system was built using USRP and LabVIEW for a 2.4 GHz frequency band test. The results show that the system can effectively identify Wi-Fi, Bluetooth, and ZigBee signals at the same time, and verified the effectiveness of the proposed signal perception architecture. It can be further promoted to realize SOP perception in almost full frequency domain, and improve the integration and resource utilization efficiency of the SOP positioning system.

JLGBMLoc-A Novel High-Precision Indoor Localization Method Based on LightGBM.

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, with the localization system based on received signal strength (RSS), is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm-named joint denoising auto-encoder (JDAE)-which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on the UJIIndoorLoc dataset and the Tampere dataset, the experimental results show that the proposed model increases the positioning accuracy dramatically compared with other existing methods.