BACH1 deficiency prevents neointima formation and maintains the differentiated phenotype of vascular smooth muscle cells by regulating chromatin accessibility.

The transcription factor BTB and CNC homology 1(BACH1) has been linked to coronary artery disease risk by human genome-wide association studies, but little is known about the role of BACH1 in vascular smooth muscle cell (VSMC) phenotype switching and neointima formation following vascular injury. Therefore, this study aims to explore the role of BACH1 in vascular remodeling and its underlying mechanisms. BACH1 was highly expressed in human atherosclerotic plaques and has high transcriptional factor activity in VSMCs of human atherosclerotic arteries. VSMC-specific loss of Bach1 in mice inhibited the transformation of VSMC from contractile to synthetic phenotype and VSMC proliferation and attenuated the neointimal hyperplasia induced by wire injury. Mechanistically, BACH1 suppressed chromatin accessibility at the promoters of VSMC marker genes via recruiting histone methyltransferase G9a and cofactor YAP and maintaining the H3K9me2 state, thereby repressing VSMC marker genes expression in human aortic smooth muscle cells (HASMCs). BACH1-induced repression of VSMC marker genes was abolished by the silencing of G9a or YAP. Thus, these findings demonstrate a crucial regulatory role of BACH1 in VSMC phenotypic transition and vascular homeostasis and shed light on potential future protective vascular disease intervention via manipulation of BACH1.

Deletion of BACH1 Attenuates Atherosclerosis by Reducing Endothelial Inflammation.

BACKGROUND: The transcription factor BACH1 (BTB and CNC homology 1) suppressed endothelial cells (ECs) proliferation and migration and impaired angiogenesis in the ischemic hindlimbs of adult mice. However, the role and underlying mechanisms of BACH1 in atherosclerosis remain unclear. METHODS: Mouse models of atherosclerosis in endothelial cell (EC)-specific-Bach1 knockout mice were used to study the role of BACH1 in the regulation of atherogenesis and the underlying mechanisms. RESULTS: Genetic analyses revealed that coronary artery disease-associated risk variant rs2832227 was associated with BACH1 gene expression in carotid plaques from patients. BACH1 was upregulated in ECs of human and mouse atherosclerotic plaques. Endothelial Bach1 deficiency decreased turbulent blood flow- or western diet-induced atherosclerotic lesions, macrophage content in plaques, expression of endothelial adhesion molecules (ICAM1 [intercellular cell adhesion molecule-1] and VCAM1 [vascular cell adhesion molecule-1]), and reduced plasma TNF-α (tumor necrosis factor-α) and IL-1ß levels in atherosclerotic mice. BACH1 deletion or knockdown inhibited monocyte-endothelial adhesion and reduced oscillatory shear stress or TNF-α-mediated induction of endothelial adhesion molecules and/or proinflammatory cytokines in mouse ECs, human umbilical vein ECs, and human aortic ECs. Mechanistic studies showed that upon oscillatory shear stress or TNF-α stimulation, BACH1 and YAP (yes-associated protein) were induced and translocated into the nucleus in ECs. BACH1 upregulated YAP expression by binding to the YAP promoter. BACH1 formed a complex with YAP inducing the transcription of adhesion molecules. YAP overexpression in ECs counteracted the antiatherosclerotic effect mediated by Bach1-deletion in mice. Rosuvastatin inhibited BACH1 expression by upregulating microRNA let-7a in ECs, and decreased Bach1 expression in the vascular endothelium of hyperlipidemic mice. BACH1 was colocalized with YAP, and the expression of BACH1 was positively correlated with YAP and proinflammatory genes, as well as adhesion molecules in human atherosclerotic plaques. CONCLUSIONS: These data identify BACH1 as a mechanosensor of hemodynamic stress and reveal that the BACH1-YAP transcriptional network is essential to vascular inflammation and atherogenesis. BACH1 shows potential as a novel therapeutic target in atherosclerosis.

Structural and functional insights into the epigenetic regulator MRG15.

MORF4-related gene on chromosome 15 (MRG15), a chromatin remodeller, is evolutionally conserved and ubiquitously expressed in mammalian tissues and cells. MRG15 plays vital regulatory roles in DNA damage repair, cell proliferation and division, cellular senescence and apoptosis by regulating both gene activation and gene repression via associations with specific histone acetyltransferase and histone deacetylase complexes. Recently, MRG15 has also been shown to rhythmically regulate hepatic lipid metabolism and suppress carcinoma progression. The unique N-terminal chromodomain and C-terminal MRG domain in MRG15 synergistically regulate its interaction with different cofactors, affecting its functions in various cell types. Thus, how MRG15 elaborately regulates target gene expression and performs diverse functions in different cellular contexts is worth investigating. In this review, we provide an in-depth discussion of how MRG15 controls multiple physiological and pathological processes.

Incidence and risk factors of subsequent vertebral fracture following percutaneous vertebral augmentation in postmenopausal women.

PURPOSE: Subsequent vertebral fracture (SVF) is a severe advent event of percutaneous vertebral augmentation (PVA). However, the incidence and risk factors of SVF following PVA for OVCF in postmenopausal women remain unclear. This research aims to investigative the incidence and risk factors of SVF after PVA for OVCF in postmenopausal women. METHODS: Women who underwent initial PVA for OVCF between August 2019 and December 2021 were reviewed. Univariate logistic regression analysis was performed to identify possible risk factors of SVF, and independent risk factors were determined by multivariate logistic regression. RESULTS: A total of 682 women after menopause were enrolled in the study. Of these women, 100 cases had an SVF after PVA, with the incidence of 14.66%. Univariate logistic regression analysis demonstrated that age (p = 0.001), body mass index (BMI) (p < 0.001), steroid use (p = 0.008), history of previous vertebral fracture (p < 0.001), multiple vertebral fracture (p = 0.033), postoperative wedge angle (p = 0.003), and HU value (p < 0.001) were significantly correlated with SVF following PVA. Furthermore, BMI (OR [95%CI] = 0.892 [0.825 - 0.965]; p = 0.004), steroid use (OR [95%CI] = 3.029 [1.211 - 7.574]; p = 0.018), history of previous vertebral fracture (OR [95%CI] = 1.898 [1.148 - 3.139]; p = 0.013), postoperative wedge angle (OR [95%CI] = 1.036 [1.004 - 1.070]; p = 0.028), and HU value (OR [95%CI] = 0.980 [0.971 - 0.990]; p < 0.001) were identified as independent risk factors of SVF after PVA by multivariate logistic regression analysis. CONCLUSIONS: The incidence of SVF following PVA for OVCF in postmenopausal women was 14.66%. BMI, steroid use, history of previous vertebral fracture, postoperative wedge angle, and HU value were independent risk factors of SVF after PVA for OVCF in postmenopausal women.

A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising.

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance.

Consistency of limb preference across unimanual feeding, bipedal locomotion, and social grooming in golden snub-nosed monkeys (Rhinopithecus roxellana).

The golden snub-nosed monkey (Rhinopithecus roxellana) is a typical arboreal group-living Old World primate. While limb preference has been extensively studied in this species, limb preference consistency has not yet been explored. Here, based on 26 R. roxellana adults, we investigated whether individuals exhibit consistent motor biases in manual- (e.g., unimanual feeding and social grooming) and foot-related (e.g., bipedal locomotion) tasks and whether limb preference consistency is influenced by increased social interactions during social grooming. Results showed no consistency in the direction or strength of limb preference among tasks, except for lateral strength in handedness for unimanual feeding and footedness in the initiation of locomotion. Population-level foot preference was only found among right-handers. Marked lateral bias was found in unimanual feeding, indicating that it may be a sensitive behavioural measure for assessing manual preference, especially for provisioned populations. This study not only improves our understanding of the relationship between hand and foot preference in R. roxellana but also reveals potential differential hemispheric regulation of limb preference and the influence of increased social interaction on handedness consistency.

A bibliometric analysis of global research on spinal cord injury: 1999-2019.

STUDY DESIGN: Bibliometric review. OBJECTIVE: The spatial structure of the global spinal cord injury (SCI) research field has not been summarized or analyzed. The objective of this study was to understand the current status and global trends of SCI research, and provide scholars knowledge to integrate into their plans for future research. SETTING: Not applicable. METHODS: The Web of Science database was searched for articles related to SCI published between 1999 and 2019. Metrics based on publication data, including publication counts, H indices, countries, institutions, authors, and journals were extracted. Co-citation analysis, collaboration analysis, and co-occurrence analysis of keywords were conducted using CiteSpace. RESULTS: The search identified a total of 41,012 articles related to SCI. Overall, the number of publications increased annually. The United States was the top ranked country by publication count, H index, and citation count. Harvard University and the University of Toronto made the most contributions. M.G. Fehlings was the top ranked author. Spinal Cord published the largest number of articles, and was the most frequently cited journal. The top 5 ranked keywords that appeared most frequently were spinal cord injury, functional recovery, adult rat rehabilitation, and paraplegia. Twelve major clusters of keywords and 15 clusters of co-cited references were generated. CONCLUSIONS: This study comprehensively analyzed and summarized the trends in SCI research during the past 20 years. Findings should provide scholars information on the countries, institutions, authors, and journals that are active in the field of SCI research, and a knowledge base for future projects.

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems.

Fault diagnosis and health condition monitoring have always been critical issues in the engineering research community [...].

Advanced Sensing, Fault Diagnostics, and Structural Health Management.

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries [...].

Partial Transfer Ensemble Learning Framework: A Method for Intelligent Diagnosis of Rotating Machinery Based on an Incomplete Source Domain.

Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the training stage in actual engineering; this causes the training dataset to be incomplete. These existing methods may not be favorably implemented with an incomplete training dataset. To address this problem, a novel deep-learning-based model called partial transfer ensemble learning framework (PT-ELF) is proposed in this paper. The major procedures of this study consist of three steps. First, the missing health states in the training dataset are supplemented by another dataset. Second, since the training dataset is drawn from two different distributions, a partial transfer mechanism is explored to train a weak global classifier and two partial domain adaptation classifiers. Third, a particular ensemble strategy combines these classifiers with different classification ranges and capabilities to obtain the final diagnosis result. Two case studies are used to validate our method. Results indicate that our method can provide robust diagnosis results based on an incomplete source domain under variable working conditions.

Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings.

When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability.

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions.

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.

Information Theory and Its Application in Machine Condition Monitoring.

Rotating machinery is part and parcel of modern industrial applications [...].

Modular prosthesis fracture in a patient with developmental dysplasia of the hip: a case report and literature review.

BACKGROUND: Modular prosthesis fracture, especially distal femoral fracture, is a rare complication of total hip arthroplasty (THA). However, it is catastrophic, and may have a serious impact on the patients. A distal femoral prosthesis fracture in a patient with developmental dysplasia of the hip (DDH) with nonunion at the subtrochanteric osteotomy site has not yet been reported in any literature. This report presents the first such case, with a purpose of analyzing the causes of modular prosthesis fractures and nonunion of the osteotomy area, so as to provide orthopedic surgeons with experience and lessons. CASE PRESENTATION: We report the case of a 52-year-old woman with the distal femoral prosthesis fracture after THA and subtrochanteric osteotomy for Crowe type IV DDH. The patient had severe pain in the left thigh and her activities were limited. Plain radiographs revealed fracture of the left distal femoral prosthesis and nonunion in the subtrochanteric osteotomy region of the left femur. After a revision of the THA, the patient's symptoms were resolved. CONCLUSIONS: A prosthesis fracture combined with nonunion at the subtrochanteric osteotomy site is a rare complication. Modular THA combined with a subtrochanteric osteotomy in the treatment of Crowe type IV DDH should reduce the damage to blood supply and avoid further nonunion of the osteotomy area, which may otherwise lead to modular prosthesis fractures. A detailed preoperative plan and suitable rehabilitation program may help minimize the occurrence of subtrochanteric osteotomy nonunion and reduce complications, including femoral prosthesis fractures, in patients with DDH.

Short-term outcomes of laparoscopic intersphincteric resection with intraoperative radiotherapy using low-energy X-rays for primary locally advanced low rectal cancer: a single center experience.

BACKGROUND: Even with the augmentative application of anal-preservation surgery in low rectal cancer, the role and indications of laparoscopic intersphincteric resection (Lap ISR) are still under debate, especially for T3 or node-positive (T3N0M0, T1-3N+M0) cancer, mainly due to the oncological safety and functional outcomes. INTRABEAM (Carl Zeiss, Germany) intraoperative radiotherapy (IORT) using low-energy X-rays features in accurate irradiation, less exposure, and reduced complications. Taking advantages of Lap ISR and INTRABEAM IORT, this innovative approach aims to increase the probability of the anal preservation with acceptable postoperative outcomes. MATERIALS AND METHODS: From December 2015 to August 2019, we retrospectively analyzed the short-term outcomes of 12 patients evaluated preoperatively with T3 or node-positive (T3N0M0, T1-3N+M0) primary locally advanced low rectal cancer. They all had received Lap ISR and INTRABEAM IORT with a dose of 16-18 Gy applied by an applicator through the anus (natural orifice). Then, with no pre- or postoperative radiotherapy given, the patients were suggested to receive 6-8 cycles of the XELOX chemotherapy regimen (oxaliplatin, 130 mg/m2 and capecitabine, 1000 mg/m2). RESULTS: All patients achieved R0 resection. The median radiation time was 27 min and 15 s, and the mean radiative dose was 17.3 Gy (range 16-18 Gy). The median follow-up time was 18.5 months (range 3-45 months). Two patients experienced local recurrence. Two male patients experienced anastomotic stenosis. Furthermore, one of them experienced perianal abscess and the other one experienced pulmonary metastasis after refusing to receive chemotherapy. One female patient with internal anal sphincter invasion experienced distant metastases to the liver and gluteus maximus muscle 35 months after IORT. No acute radiation injuries or symptoms were observed. Although they experienced a reduction in anal function, every patient was satisfied with the postoperative outcomes. CONCLUSIONS: For patients evaluated preoperatively with T3 or node-positive (T3N0M0, T1-3N+M0) primary locally advanced low rectal cancer, Lap ISR with INTRABEAM IORT may be a safe and feasible approach for anal preservation without compromising oncological outcomes.

Robust 3D Hand Detection from a Single RGB-D Image in Unconstrained Environments.

Three-dimensional hand detection from a single RGB-D image is an important technology which supports many useful applications. Practically, it is challenging to robustly detect human hands in unconstrained environments because the RGB-D channels can be affected by many uncontrollable factors, such as light changes. To tackle this problem, we propose a 3D hand detection approach which improves the robustness and accuracy by adaptively fusing the complementary features extracted from the RGB-D channels. Using the fused RGB-D feature, the 2D bounding boxes of hands are detected first, and then the 3D locations along the z-axis are estimated through a cascaded network. Furthermore, we represent a challenging RGB-D hand detection dataset collected in unconstrained environments. Different from previous works which primarily rely on either the RGB or D channel, we adaptively fuse the RGB-D channels for hand detection. Specifically, evaluation results show that the D-channel is crucial for hand detection in unconstrained environments. Our RGB-D fusion-based approach significantly improves the hand detection accuracy from 69.1 to 74.1 comparing to one of the most state-of-the-art RGB-based hand detectors. The existing RGB- or D-based methods are unstable in unseen lighting conditions: in dark conditions, the accuracy of the RGB-based method significantly drops to 48.9, and in back-light conditions, the accuracy of the D-based method dramatically drops to 28.3. Compared with these methods, our RGB-D fusion based approach is much more robust without accuracy degrading, and our detection results are 62.5 and 65.9, respectively, in these two extreme lighting conditions for accuracy.

Insulin and 20-hydroxyecdysone oppose each other in the regulation of phosphoinositide-dependent kinase-1 expression during insect pupation.

Insulin promotes larval growth of insects by stimulating the synthesis of the steroid hormone 20-hydroxyecdysone (20E), which induces pupation and apoptosis. However, the mechanism underlying the coordinate regulation of insect pupation and apoptosis by these two functionally opposing hormones is still unclear. Here, using the lepidopteran insect and serious agricultural pest Helicoverpa armigera (cotton bollworm) as a model, we report that phosphoinositide-dependent kinase-1 (PDK1) and forkhead box O (FoxO) play key roles in these processes. We found that the transcript levels of the PDK1 gene are increased during the larval feeding stages. Moreover, PDK1 expression was increased by insulin, but repressed by 20E. dsRNA-mediated PDK1 knockdown in the H. armigera larvae delayed pupation and resulted in small pupae and also decreased Akt/protein kinase B expression and increased FoxO expression. Furthermore, the PDK1 knockdown blocked midgut remodeling and decreased 20E levels in the larvae. Of note, injecting larvae with 20E overcame the effect of the PDK1 knockdown and restored midgut remodeling. FoxO overexpression in an H. armigera epidermal cell line (HaEpi) did not induce apoptosis, but promoted autophagy and repressed cell proliferation. These results reveal cross-talk between insulin and 20E and that both hormones oppose each other's activities in the regulation of insect pupation and apoptosis by controlling PDK1 expression and, in turn, FoxO expression. We conclude that sufficiently high 20E levels are a key factor for inducing apoptosis during insect pupation.

Accurate Hand Detection from Single-Color Images by Reconstructing Hand Appearances.

Hand detection is a crucial pre-processing procedure for many human hand related computer vision tasks, such as hand pose estimation, hand gesture recognition, human activity analysis, and so on. However, reliably detecting multiple hands from cluttering scenes remains to be a challenging task because of complex appearance diversities of dexterous human hands (e.g., different hand shapes, skin colors, illuminations, orientations, and scales, etc.) in color images. To tackle this problem, an accurate hand detection method is proposed to reliably detect multiple hands from a single color image using a hybrid detection/reconstruction convolutional neural networks (CNN) framework, in which regions of hands are detected and appearances of hands are reconstructed in parallel by sharing features extracted from a region proposal layer, and the proposed model is trained in an end-to-end manner. Furthermore, it is observed that the generative adversarial network (GAN) could further boost the detection performance by generating more realistic hand appearances. The experimental results show that the proposed approach outperforms the state-of-the-art on public challenging hand detection benchmarks.

Synthesizing Depth Hand Images with GANs and Style Transfer for Hand Pose Estimation.

Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks.

As an important machine component, the gearbox is widely used in industry for power transmission. Condition monitoring (CM) of a gearbox is critical to provide timely information for undertaking necessary maintenance actions. Massive research efforts have been made in the last two decades to develop vibration-based techniques. However, vibration-based methods usually include several inherent shortages including contact measurement, localized information, noise contamination, and high computation costs, making it difficult to be a cost-effective CM technique. In this paper, infrared thermal (IRT) images, which can contain information covering a large area and acquired remotely, are based on developing a cost-effective CM method. Moreover, a convolutional neural network (CNN) is employed to automatically process the raw IRT images for attaining more comprehensive feature parameters, which avoids the deficiency of incomplete information caused by various feature-extraction methods in vibration analysis. Thus, an IRT-CNN method is developed to achieve online remote monitoring of a gearbox. The performance evaluation based on a bevel gearbox shows that the proposed method can achieve nearly 100% correctness in identifying several common gear faults such as tooth pitting, cracks, and breakages and their compounds. It is also especially robust to ambient temperature changes. In addition, IRT also significantly outperforms its vibration-based counterparts.