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Abstract Background The objective of this study was to examine and analyze differential methylation profiles in order to investigate the influence of hyper-methioninemia (HM) on the development of diabetic nephropathy (DN). Male Wistar rats, aged eight weeks and weighing 250–300 g, were randomly assigned into four groups: a control group (Healthy, n = 8), streptozocin-induced rats (STZ group, n = 8), HM + STZ group (n = 8), and the Tangshen Formula (TSF) treatment group (TSF group, n = 8). Blood glucose levels and other metabolic indicators were monitored before treatment and at four-week intervals until 12 weeks. Total DNA was extracted from the aforementioned groups, and DNA methylation landscapes were analyzed via reduced representative bisulfite sequencing. Results Both the STZ group and HM + STZ group exhibited increased blood glucose levels and urinary albumin/creatinine ratios in comparison with the control group. Notably, the HM + STZ group exhibited a markedly elevated urinary albumin/creatinine ratio (411.90 ± 88.86 mg/g) compared to the STZ group (238.41 ± 62.52 mg/g). TSF-treated rats demonstrated substantial reductions in both blood glucose levels and urinary albumin/creatinine ratios in comparison with the HM + STZ group. In-depth analysis of DNA methylation profiles revealed 797 genes with potential therapeutic effects related to TSF, among which approximately 2.3% had been previously reported as homologous genes. Conclusion While HM exacerbates DN through altered methylation patterns at specific CpG sites, TSF holds promise as a viable treatment for DN by restoring abnormal methylation levels. The identification of specific genes provides valuable insights into the underlying mechanisms of DN pathogenesis and offers potential therapeutic targets for further investigation.

Abstract Epigenetic modifications have long been recognized as an essential level in transcriptional regulation linking behavior and environmental conditions or stimuli with biological processes and disease development. Among them, methylation is the most abundant of these reversible epigenetic marks, predominantly occurring on DNA, RNA, and histones. Methylation modification is intimately involved in regulating gene transcription and cell differentiation, while aberrant methylation status has been linked with cancer development in several malignancies. Early detection and precise restoration of dysregulated methylation form the basis for several epigenetics-based therapeutic strategies. In this review, we summarize the current basic understanding of the regulation and mechanisms responsible for methylation modification and cover several cutting-edge research techniques for detecting methylation across the genome and transcriptome. We then explore recent advances in clinical diagnostic applications of methylation markers of various cancers and address the current state and future prospects of methylation modifications in therapies for different diseases, especially comparing pharmacological methylase/demethylase inhibitors with the CRISPRoff/on methylation editing systems. This review thus provides a resource for understanding the emerging role of epigenetic methylation in cancer, the use of methylation-based biomarkers in cancer detection, and novel methylation-targeted drugs.

The gas tungsten arc welding (GTAW) molten pool are numerical and experimentally determined to be wetting and stirring with an extra transverse alternating magnetic field (ETAMF). The trace particle behaviors in the molten pool were identified to demonstrate molten pool flow behavior with the use of a camera system in the GTAW process. Taking advantage of mathematical models, we present the calculated results of temperature fields, flow fields, pressure fields with and without ETAMF for comparison. The result revealed that reduction of the solid/liquid surface tension attributed to the periodic heating of the side walls facilitates the wetting and flow of the molten pool's edge, which takes on a critical significance to the formation of a concave liquid level in a narrow gap. Arc pressure, Lorentz force and arc shear force primarily accounted for the molten pool's flow and morphological change. As the arc deflected to one side wall, the liquid melt accumulated at the bottom of the side under the driving forces, accompanied by wetting and flow behavior. At equilibrium, the molten pool flowed outward the center of the anode point and eventually converged to the bottom of the other side wall. The variation of the arc deflection direction broke the previous equilibrium state, and the molten pool's flow direction is forced to change, resulting in a stirring effect.

Abstract Daptomycin is a cyclic lipodepsipeptide antibiotic reserved for the treatment of serious infections by multidrug-resistant Gram-positive pathogens. Its mode of action is considered to be multifaceted, encompassing the targeting and depolarization of bacterial cell membranes, alongside the inhibition of cell wall biosynthesis. To characterize the daptomycin mode of action, 15N cross-polarization at magic-angle spinning NMR measurements were performed on intact whole cells of Staphylococcus aureus grown in the presence of a sub-inhibitory concentration of daptomycin in a chemically defined media containing l-[ϵ-15N]Lys. Daptomycin-treated cells showed a reduction in the lysyl-ε-amide intensity that was consistent with cell wall thinning. However, the reduced lysyl-ε-amine intensity at 10 ppm indicated that the daptomycin-treated cells did not accumulate in Park’s nucleotide, the cytoplasmic peptidoglycan (PG) precursor. Consequently, daptomycin did not inhibit the transglycosylation step of PG biosynthesis. To further elucidate the daptomycin mode of action, the PG composition of daptomycin-susceptible Enterococcus faecalis grown in the presence of daptomycin was analyzed using liquid chromatography-mass spectrometry. Sixty-nine muropeptide ions correspond to PG with varying degrees of modifications including crosslinking, acetylation, alanylation, and 1,6-anhydrous ring formation at MurNAc were quantified. Analysis showed that the cell walls of daptomycin-treated E. faecalis had a significant reduction in PG crosslinking which was accompanied by an increase in lytic transglycosylase activities and a decrease in PG-stem modifications by the carboxypeptidases. The changes in PG composition suggest that daptomycin inhibits cell wall biosynthesis by impeding the incorporation of nascent PG into the cell walls by transpeptidases and maturation by carboxypeptidases. As a result, the newly formed cell walls become highly susceptible to degradation by the autolysins, resulting in thinning of the cell wall.

In a simultaneous wireless power and data transfer (SWPDT) system using common coils, achieving high-speed communication in high-power wireless charging systems is challenging due to power transfer interference on communication. Based on high-frequency data carrier-based SWPDT (HFDC-SWPDT) technology, this paper proposes an orthogonal-frequency simultaneous wireless power and data transfer (OF-SWPDT) method to minimize interference where the data carrier frequency is orthogonal to the power carrier frequency and its harmonics. In addition, a guard band is inserted between the spectra of power harmonics and data in order to further separate the power and data spectra. Thus, a high-power, high-speed SWPDT system is achieved. Finally, an 11 kW prototype with 64.125 kbps full-duplex communication is developed to validate the proposed method.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle–bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

Femoral head necrosis (FHN) is a common leg disease in broilers, resulting in economic losses in the poultry industry. The occurrence of FHN is closely related to the decrease in the number of bone marrow mesenchymal stem cells (BMSCs) and the change in differentiation direction. This study aimed to investigate the function of differentiation of BMSCs in the development of FHN. We isolated and cultured BMSCs from spontaneous FHN-affected broilers and normal broilers, assessed the ability of BMSCs into three lineages by multiple staining methods, and found that BMSCs isolated from FHN-affected broilers demonstrated enhanced lipogenic differentiation, activated Notch-RBPJ signaling pathway, and diminished osteogenic and chondrogenic differentiation. The treatment of BMSCs with methylprednisolone (MP) revealed a significant decrease in the expressions of Runx2, BMP2, Col2a1 and Aggrecan, while the expressions of p-Notch1/Notch1, Notch2 and RBPJ were increased significantly. Jagged-1 (JAG-1, Notch activator)/DAPT (γ-secretase inhibitor) could promote/inhibit the osteogenic or chondrogenic ability of MP-treated BMSCs, respectively, whereas the differentiation ability of BMSCs was restored after transfection with si-RBPJ. The above results suggest that the Notch-RBPJ pathway plays important role in FHN progression by modulating the osteogenic and chondrogenic differentiation of BMSCs.

Background estimation is an efficient infrared (IR) small target detection method. However, to deal with unknown targets, the estimation window in existing algorithms should be adjusted to perform multiscale detection and requires a lot of calculations. Besides, the stages during and after estimation have received wide attention in existing algorithms, but the research on the stages before estimation is insufficient. Moreover, existing algorithms typically regard the maximum value of different orientations as the estimation value. However, when a dim target is adjacent to high-brightness background, it is easily submerged. This article proposes a three-layer estimation window to detect targets of different sizes with only a single-scale calculation. The enhanced closest-mean background estimation method is then proposed and carefully designed before, during, and after the estimation. Before estimation, the matched filter is adopted to improve the image signal-to-noise ratio. During estimation, the principle of closest-mean is proposed to suppress high-brightness background. After estimation, a ratio-difference operation is performed to enhance the true target and suppress the background simultaneously. A simple checking mechanism is proposed to further improve the detection performance. Experiments on some IR images demonstrate the effectiveness and robustness of the proposed method. Compared with existing algorithms, the proposed method has better target enhancement, background suppression, and computational efficiency.

When traditional 3D reconstruction techniques were used to reconstruct a scene with hidden and disguised heat source targets, the reconstructed scene could not contain these targets; thus, we could not recognize them. The disadvantages of such problems are particularly acute in the military and remote-sensing areas. For this application problem, the authors proposed a visible/infrared combined 3D reconstruction scheme. The 3D scene containing thermal radiation information could be reconstructed by fusing the data from RGB optical and infrared images combined with computer vision passive optical 3D scene reconstruction technique. Meanwhile, the authors proposed the nonrigid registration of multi-modality images with mixed features to solve the problem that the registration algorithm, which is widely used in traditional passive reconstruction technology, cannot accurately match visible and infrared images. Registration accuracy was improved by approximately 40%, experimental results showed that the visible/infrared combined 3D reconstruction scenes retain the visual reality of the traditional 3D reconstruction, and the hidden targets are highlighted in the scenes, which is conducive to the detection and recognition of interesting targets.

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Abstract Mycobacteria possess a multi-layered cell wall that requires extensive remodelling during cell division. We investigated the role of an amidase_3 domain-containing N-acetylmuramyl-L-alanine amidase, a peptidoglycan remodelling enzyme implicated in cell division. We demonstrated that deletion of MSMEG_6281 (Ami1) in Mycobacterium smegmatis resulted in the formation of cellular chains, illustrative of cells that were unable to complete division. Suprisingly, viability in the Δami1 mutant was maintained through atypical lateral branching, the products of which proceeded to form viable daughter cells. We showed that these lateral buds resulted from mislocalization of DivIVA, a major determinant in facilitating polar elongation in mycobacterial cells. Failure of Δami1 mutant cells to separate also led to dysregulation of FtsZ ring bundling. Loss of Ami1 resulted in defects in septal peptidoglycan turnover with release of excess cell wall material from the septum or newly born cell poles. We noted signficant accumulation of 3-3 crosslinked muropeptides in the Δami1 mutant. We further demonstrated that deletion of ami1 leads to increased cell wall permeability and enhanced susceptiblity to cell wall targeting antibiotics. Collectively, these data provide novel insight on cell division in actinobacteria and highlights a new class of potential drug targets for mycobacterial diseases.

Current measurement is a key part of the monitoring system for power transmission lines. Compared with the conventional current sensor, the distributed, self-powered and contactless current sensor has great advantages of safety and reliability. By integrating the current sensing function and the energy harvesting function of current transformer (CT), a time-multiplexed self-powered wireless sensor that can measure the power transmission line current is presented in this paper. Two operating modes of CT, including current sensing mode and energy harvesting mode, are analyzed in detail. Through the design of mode-switching circuit, harvesting circuit and measurement circuit are isolated using only one CT secondary coil, which eliminates the interference between energy harvesting and current measurement. Thus, the accurate measurement in the current sensing mode and the maximum energy collection in the energy harvesting mode are both realized, all of which simplify the online power transmission line monitoring. The designed time-multiplexed working mode allows the sensor to work at a lower transmission line current, at the expense of a lower working frequency. Finally, the proposed sensor is verified by experiments.

Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection will dramatically improve the health outcomes of the older population and reduce the associated healthcare cost after a fall. To achieve the goal, a multi-features semi-supervised support vector machines (MFSS-SVM) algorithm utilizing measurements from structural floor vibration obtained through accelerometers is proposed in this study to detect falling events with limited labeled samples. In this MFSS-SVM algorithm, the peak value, energy, and correlation coefficient of the accelerometer signal are used as classification features. The performance of the proposed algorithm was validated with laboratory experiments among activities including falling, walking, free jumping, rhythmic jumping, bag dropping, and ball dropping. To further illustrate the performance of the algorithm, a benchmark database was adopted and expanded to test its ability to accurately identify falling, compared with the algorithm used in the benchmark study. Results show that by using the proposed algorithm, the falling events can be identified with high accuracy and confidence, even with small training datasets and test nodes.

Distributed computing strategy over a network of wireless sensors has emerged as a promising application in order to enable real-time structural health monitoring. Most previous studies of distributed computing strategy consider relatively simple operations like modal parameters identification (e.g. frequencies and modal shapes) that cannot directly assess structural health condition demanded by many practical engineering applications. This article focuses on developing distributed computing strategy techniques in wireless sensor networks used for direct stiffness estimation of structural damage locations and extent. An improved frequency-domain regression method is proposed to detect and quantify damage in frame structures. In the proposed method, the static condensation technique is adopted to reconstruct the rotational responses on joints, which is considered as inputs to estimate stiffness. Therefore, this improved method provides a more practical and accurate tool for structural damage detection. A numerical simulation of a five-story two-bay frame structure demonstrates that the proposed damage detection method using a limited number of wireless sensors can identify, locate, and quantify the structural stiffness changes accurately.

Structural strain under external environmental loads is one of the main monitoring parameters in structural health monitoring or dynamic tests. This paper presents a wireless strain sensor network (WSSN) design for monitoring structural dynamic strain field. A precision strain sensor board is developed and integrated with the IRIS mote hardware/software platform for multichannel strain gauge signal conditioning and wireless monitoring. Measurement results confirm the sensor’s functionality regarding its static and dynamic characterization. Furthermore, in order to verify the functionality of the designed wireless strain sensor for dynamic strain monitoring, a cluster-star network evaluation system is developed for strain modal testing on an experimental steel truss structure. Test results show very good agreement with the finite element (FE) simulations. This paper demonstrates the feasibility of the proposed WSSN for large structural dynamic strain monitoring.

Recently, the sparse representation based classification methods have received particular attention in the classification of hyperspectral imagery. However, current sparse representation based classification models have not considered all the test pixels simultaneously. In this paper, we propose a hyperspectral classification method with spatial filtering and \(l_{(2,1)}\) norm (SFL) that can deal with all the test pixels simultaneously. The \(l_{(2,1)}\) norm regularization is used to extract relevant training samples among the whole training data set with joint sparsity. In addition, the \(l_{(2,1)}\) norm loss function is adopted to make it robust for samples that deviate significantly from the rest of the samples. Moreover, to take the spatial information into consideration, a spatial filtering step is implemented where all the training and testing samples are spatially averaged with its nearest neighbors. Furthermore, the non-negative constraint is added to the sparse representation matrix motivated by hyperspectral unmixing. Finally, the alternating direction method of multipliers is used to solve SFL. Experiments on real hyperspectral images demonstrate that the proposed SFL method can obtain better classification performance than some other popular classifiers.

This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.

Heterogeneous wireless sensor networks (HWSNs) are widely adopted in structural health monitoring systems due to their potential for implementing sophisticated algorithms by integrating a diverse set of devices and improving a network’s sensing performance. However, deploying such a HWSN is still in a challenge due to the heterogeneous nature of the data and the energy constraints of the network. To respond to these challenges, an optimal deployment framework in terms of both modal information quality and energy consumption is proposed in this study. This framework generates a multi-objective function aimed at maximizing the quality of the modal information identified from heterogeneous data while minimizing the consumption of energy within the network at the same time. Particle swarm optimization algorithm is then implemented to seek solutions to the function effectively. After laying out the proposed sensor-optimization framework, a methodology is present to determine the clustering of the sensors to further conserve energy. Finally, a numerical verification is performed on a four-span pre-stressed reinforced concrete box-girder bridge. Results show that a set of strategically positioned heterogeneous sensors can maintain a balanced trade-off between the modal information accuracy and energy consumption. It is also observed that an appropriate cluster-tree network topology can further achieve energy saving in HWSNs.

Recently, sparse unmixing (SU) of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (i.e., nonlinearity). In this paper, we propose a new method named robust collaborative sparse regression (RCSR) based on the robust LMM (rLMM) for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM) is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.