Search

Idiopathic pulmonary fibrosis (IPF) is a prevalent chronic pulmonary fibrosis disease characterized by alveolar epithelial cell damage, fibroblast proliferation and activation, excessive extracellular matrix deposition, and abnormal epithelial-mesenchymal transition (EMT), resulting in tissue remodeling and irreversible structural distortion. The mortality rate of IPF is very high, with a median survival time of 2–3 years after diagnosis. The exact cause of IPF remains unknown, but increasing evidence supports the central role of epigenetic changes, particularly microRNA (miRNA), in IPF. Approximately 10% of miRNAs in IPF lung tissue exhibit differential expression compared to normal lung tissue. Diverse miRNA phenotypes exert either a pro-fibrotic or anti-fibrotic influence on the progression of IPF. In the context of IPF, epigenetic factors such as DNA methylation and long non-coding RNAs (lncRNAs) regulate differentially expressed miRNAs, which in turn modulate various signaling pathways implicated in this process, including transforming growth factor-β1 (TGF-β1)/Smad, mitogen-activated protein kinase (MAPK), and phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) pathways. Therefore, this review presents the epidemiology of IPF, discusses the multifaceted regulatory roles of miRNAs in IPF, and explores the impact of miRNAs on IPF through various pathways, particularly the TGF-β1/Smad pathway and its constituent structures. Consequently, we investigate the potential for targeting miRNAs as a treatment for IPF, thereby contributing to advancements in IPF research.

Cytotoxic CD8+ T lymphocytes (CTLs) have been implicated in the severity of COVID-19. The TCR-pMHC ternary complex, formed by the T cell receptor (TCR) and peptide-MHC (major histocompatibility complex), constitutes the molecular basis of CTL responses against SARS-CoV-2. While numerous studies have been conducted on T cell immunity, the molecular mechanisms underlying CTL-mediated immunity against SARS-CoV-2 infection have not been well elaborated. In this review, we described the association between HLA variants and different immune responses to SARS-CoV-2 infection, which may lead to varying COVID-19 outcomes. We also summarized the specific TCR repertoires triggered by certain SARS-CoV-2 CTL epitopes, which might explain the variations in disease outcomes among different patients. Importantly, we have highlighted the primary strategies used by SARS-CoV-2 variants to evade T-cell killing: disrupting peptide-MHC binding, TCR recognition, and antigen processing. This review provides valuable insights into the molecule mechanism of CTL responses during SARS-CoV-2 infection, aiding efforts to control the pandemic and prepare for future challenges.

Pulmonary fibrosis is a progressive interstitial lung disease associated with aging. The pathogenesis of pulmonary fibrosis remains unclear, however, alveolar epithelial cell injury, myofibroblast activation, and extracellular matrix (ECM) accumulation are recognized as key contributors. Moreover, recent studies have implicated cellular senescence, endothelial-mesenchymal transition (EndMT), and epigenetic modifications in the pathogenesis of fibrotic diseases. Various signaling pathways regulate pulmonary fibrosis, including the TGF-β, Notch, Wnt, Hedgehog, and mTOR pathways. Among these, the TGF-β pathway is extensively studied, while the Notch pathway has emerged as a recent research focus. The Notch pathway influences the fibrotic process by modulating immune cell differentiation (e.g., macrophages, lymphocytes), inhibiting autophagy, and promoting interstitial transformation. Consequently, inhibiting Notch signaling represents a promising approach to mitigating pulmonary fibrosis. In this review, we discuss the role of Notch signaling pathway in pulmonary fibrosis, aiming to offer insights for future therapeutic investigations.

Tourism in urban forests is rapidly becoming an increasing trend; however, rather few studies have used quantitative measurement to describe the relationship between tourism intensity and ecological functions. This study provides a practical framework that integrates ecosystem service value (ESV) assessment, Internet big data mining and spatial regression analysis to identify the spatial response of ESV and land use/land cover change to tourism activities from 2009 to 2019 in the Siming Mountain Region (SMR), a famous tourist resort located in the eastern coastal China. Results showed that between 2009 and 2019 total ESV increased by 7.1%. Nevertheless, there have been drastic transitions in land use types with function adjustments from traditional agricultural production to diversified tourism-oriented services. Significant spatial autocorrelation was identified for the patterns of ESV changes. GWR further highlighted that the relationship between ESV change and rural tourism indicators varied in space. ESV change in the core zone was negatively correlated with changes in catering service spots and recreational venues, whereas it was positively correlated with local lodgings. Ultimately, targeted recommendations and countermeasures for spatial planning and sustainable tourism development of urban forests under new circumstances were discussed.

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather.

The observation and analysis of sediment transport in oceans is an important means for the protection of the marine environment, resource development, construction engineering, and element cycling. However, traditional methods of observing sediment transport are either limited by the range of the instruments used or their own observational attributes, such that they cannot be used to accurately detect and analyze the process of transport of marine sediment. A 3D sediment trap has been proposed to compensate for the shortcomings of the various monitoring tools in our team, but no mature method for the analytical inversion of the data obtained from this device has been developed to date. In this paper, we developed analytical methods to invert sediment transport processes using corrected capture efficiency, sample inversion, and transport flux analysis. Through an annular flume test, we measured the turbidity, pressure, and particle size of the water stream and substituted them into the proposed analytical equations, thus verifying the applicability of the analytical methods. We used the slice experiment of the time series of the sediment samples, to determine the validity of the sample inversion, and establish the relationship between the particle size and concentration of the captured samples. We performed restoration tests on the process of sediment transport to establish a set of methods of flux analysis based on the velocity and turbidity of flow. And finally corrected for capture efficiency by particle size. The combination of analytical methods and 3D sediment trap could provide technological support for investigating the evolution of the sea, ecological cycle, and marine engineering.

Climate change and intensive anthropogenic activities have severely challenged the water quality of China’s Pearl River Estuary (PRE). Further investigations into long-term water quality variation and associated driving mechanisms are therefore necessary to support the sustainable development of the PRE’s Greater Bay Area (GBA). This study used remote sensing retrieval to address long-term spatiotemporal chlorophyll-a (Chl-a) variation characteristics in the PRE and the relationship between Chl-a concentrations and socioeconomic/environmental indicators. Three decades of Landsat satellite images and measured data were collected, and a two-band global algorithm was used to retrieve Chl-a concentration data. Results reveal significant spatiotemporal variability in Chl-a concentrations. The space-averaged Chl-a concentration exhibited a slight downward trend during the past three decades, and the multi-year mean value was 5.20 mg/L. Changes to environmental protection policies in recent years have improved overall PRE water quality. The western section of the PRE had the highest Chl-a concentration (i.e., 5.92 mg/L average) while the eastern section had the lowest (i.e., 3.98 mg/L average). This discrepancy was likely caused by the western section’s more intensive industrial activities, resulting in a higher overall wastewater discharge volume. Affected by climatic conditions, winter Chl-a concentrations were evenly distributed while summer concentrations were significantly higher. Additionally, Chl-a concentrations significantly and positively correlated with total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), and the biotic oxygen demand (BOD5). Chl-a concentrations also correlated with external factors (i.e., climate and anthropogenic activities). Among these factors, industrial wastewater discharge and the proportion of primary industries in coastal cities significantly and positively correlated with water quality. This study is intended to help direct water quality improvement management and urban sustainable development in the GBA.

ObjectiveTo address a novel lower-dose rituximab (RTX) therapy strategy based on our clinical experience and assess its efficacy and safety in neuromyelitis optica spectrum disorder (NMOSD).MethodsA multicenter, open-label, self-controlled, prospective follow-up study. Totally, 108 NMOSD patients were enrolled and a lower-dose RTX strategy was applied including 100 mg weekly for 3 weeks and then reinfusions every 6 months. Annualized relapse rate (ARR), the expanded disability status scale (EDSS) score and length of spinal cord lesions were included to evaluate the efficacy. Side effects were recorded to assess the safety profile.ResultsOf 108 patients, 80 (74.1%) initiated low-dose RTX therapy immediately after acute attack treatment and 33 (30.6%) initiated it after the first attack. During a median treatment period of 35.5 (22.0–48.8) months, significant decreases were observed in median ARR (1.1 [0.8–2.0] versus 0 [0–0.2], p < 0.001), EDSS score (3.5 [2.5–4.0] versus 2.0 [1.0–3.0], p < 0.001) and spinal cord lesion segments (5.0 [4.0–8.0] versus 3.0 [1.0–6.0], p < 0.001). The cumulative risk of relapses significantly decreased during the post- versus pre-RTX period (HR 0.238, 95%CI 0.160–0.356, p < 0.001) and on early therapy initiated within 24 months after disease onset versus delayed therapy (HR 0.506, 95%CI 0.258–0.994, p = 0.041). No serious side effects were recorded and all the subjects did not discontinue treatment due to RTX-related side effects.ConclusionOur research provided evidence supporting the lower-dose RTX strategy in treating NMOSD and reopened the issues of optimal dosage and therapy initiation timing.

With the increasing demand for reliable power supply and the widespread integration of distributed energy sources, the topology of distribution networks is subject to frequent changes. Consequently, the dynamic alterations in the connection relationships between distribution transformers and feeders occur frequently, and these changes are not accurately monitored by grid companies in real-time. In this paper, we present a data-driven machine learning approach for identifying the feeder-transformer relationship in distribution networks. Initially, we preprocess the collected three-phase voltage magnitude data of distribution transformers, addressing data quality and enhancing usability through three-phase voltage normalization. Subsequently, we derive the correlation coefficient calculations between distribution transformers, as well as between distribution transformers and feeders. To tackle the challenging task of determining the correlation coefficient threshold, we propose a multi-feature fusion approach. We extracted additional features from the feeders and combined them with the correlation coefficients to create a feature matrix. Machine learning algorithms were then applied to calculate the results. Through experimentation on a real distribution network in Jiangxi province, we demonstrated the effectiveness of the proposed method. When compared to other approaches, our method achieved outstanding results with an F1 score of 0.977, indicating high precision and recall. The precision value was 0.973 and the recall value was 0.981. Importantly, our method eliminates the need for additional measurement installations, as the required data can be obtained using existing collection devices. This significantly reduces the application cost associated with implementing our approach.

In an increasingly complex and changing competitive environment, organizations inevitably face various conflicting demands, such as the contradiction between the psychological needs of employees and the organization’s performance requirements. Paradoxical leadership could focus on these competing needs of the organization and employees in multiple ways simultaneously. According to the trickle-down effect of social learning theory, we investigated whether and how paradoxical leadership may increase team adaptation and team performance. The study had a time-lagged survey design and included 254 team members and 60 leaders in 60 work teams in mainland China. The results of the structural equation modeling analysis indicated that paradoxical leadership is an essential predictor of team adaptation and performance, and that inclusive climate is mediating in this relationship. Our findings reveal a mechanism underlying the benefits of paradoxical leadership on team adaptation and team performance from a team-level perspective.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease (ILD) without an identifiable cause. If not treated after diagnosis, the average life expectancy is 3–5 years. Currently approved drugs for the treatment of IPF are Pirfenidone and Nintedanib, as antifibrotic drugs, which can reduce the decline rate of forced vital capacity (FVC) and reduce the risk of acute exacerbation of IPF. However these drugs can not relieve the symptoms associated with IPF, nor improve the overall survival rate of IPF patients. We need to develop new, safe and effective drugs to treat pulmonary fibrosis. Previous studies have shown that cyclic nucleotides participate in the pathway and play an essential role in the process of pulmonary fibrosis. Phosphodiesterase (PDEs) is involved in cyclic nucleotide metabolism, so PDE inhibitors are candidates for pulmonary fibrosis. This paper reviews the research progress of PDE inhibitors related to pulmonary fibrosis, so as to provide ideas for the development of anti-pulmonary fibrosis drugs.

Coastlines and estuaries as one of the most dynamic systems in the marine environment are influenced by not only natural factors but also various anthropogenic activities, but there have been limited comprehensive studies on the relationship between coastline variation and anthropogenic activities at the watershed and local scales. The Pearl River Estuary (PRE) in China has experienced remarkable coastline variation along with rapid economic development and urbanization. In this study, the coastline of the PRE was extracted using Landsat images during 1987-2017, and the impacts of multiple factors on coastline variation were explored with correlation analysis and regression analysis. Results suggested coastlines of the PRE demonstrated a clear expanding trend over recent decades, and the coastline length had increased by 74.81 km and the land area had increased by 679.25 km2 by 2017, and results also showed rapid coastline variation during 1987-2004 and relatively slow variation during 2004-2017. Artificial coastlines accounted for 80.90% of the total coastline length in 2017, while natural coastlines accounted for only 19.10%. In addition, reduction in the sand content caused by construction of reservoirs and river dredging in upstream areas played a dominant role in affecting natural coastline variation in the PRE, especially on the west coast. Moreover, the artificial coastline in the study area expanded quickly due to the influence of various human activities, among which the urbanization and land reclamation for aquaculture played a dominant role in affecting the artificial coastline variation on the east and west coasts, respectively. Results of this study have important value for understanding the individual and joint effects of various human activities on coastline variation and can provide important reference information for the sustainable management of the PRE as well as other estuaries.

The NLRP1 inflammasome functions as canonical cytosolic sensor in response to intracellular infections and is implicated in auto-inflammatory diseases. But the regulation and signal transduction mechanisms of NLRP1 are incompletely understood. Here, we show that the T60 variant of CARD8, but not the canonical T48 isoform, negatively regulates the NLRP1 inflammasome activation by directly interacting with the receptor molecule NLRP1 and inhibiting inflammasome assembly. Furthermore, our results suggest that different ASC preference in three types of inflammasomes, namely the ASC-indispensable NLRP1 inflammasome, ASC-dispensable mNLRP1b inflammasome and ASC-independent CARD8 inflammasome, is mainly caused by the CARD domain, not the UPA subdomain. Based on the systematic site-directed mutagenesis and structural analysis, we find that signal transduction of the NLRP1 inflammasome relies on multiple interaction surfaces at its CARD domain. Finally, our results partly explain how mutations in NLRP1 lead to its constitutive activation in auto-inflammatory diseases. In conclusion, our study not only reveals how CARD8 downregulates the NLRP1 inflammasome activation, but also provides insights into the assembly mechanisms of CARD-containing inflammasomes.

NUCLEAR FACTOR Y subunit alpha (NF-YA), together with NF-YB and NF-YC, regulates plant growth and development, as well as plant responses to biotic and abiotic stresses. Although extensive studies have examined the functions of NF-YAs in Arabidopsis thaliana, the roles of NF- YAs in Glycinme max are poorly understood. In this study, we identified a phosphorus (P) starvation-responsive NF-YA8 in soybean. The expression of GmNF-YA8 is induced by low P or low nitrogen in leaves, but not by potassium or iron starvation, respectively. GmNF-YA8 is localized in the nucleus and plasma membrane. Ectopic expression of GmNF-YA8 inhibits plant growth and delayed flowering in Arabidopsis. Exogenous application of gibberellic acid (GA) rescues the delayed flowering phenotype in Arabidopsis overexpressing GmNF-YA8 lines GmNF-YA8OE-05 and GmNF-YA8OE-20. Moreover, quantitative real time PCR (qRT-PCR) verified that overexpression of GmNF-YA8 downregulates GA20ox2 and GA3ox2 expression, but upregulates GA2ox2 and GA2ox3 that encode enzymes, which inactive bioactive GAs. Consistent with the late flowering phenotype of Arabidopsis trangenic lines that overexpress GmNF-YA8, the transcript levels of flowering-promoting genes AP1, CO, LFY, and SOC1 are reduced. In addition, overexpression of GmNF-YA8 promotes the emergence of lateral root (LR) primordium from epidermis rather than the initiation of LR in low P, and increases the LR density in low nitrogen. Our results provide insights into the roles of GmNF-YA8.

Hydrologic (water temperature) and hydrodynamic (water depth, flow velocity, and Froude number) factors affect fish spawning activities, and spawning grounds provide suitable hydrologic and hydrodynamic conditions for fish spawning to occur. However, locating fish spawning grounds is encumbered by uncertainty, particularly for pelagic spawners. This may be because such fish species have unique hydrologic and hydrodynamic requirements during their spawning periods, resulting in the wide-ranging approaches used to locate their spawning grounds. Accordingly, this study was designed to accurately locate fish spawning grounds through means of spatial suitability evaluation. For this experiment, the four major “Asian carp” target species were selected in the Dongta spawning reach, a tributary of the Pearl River. First, we investigated the historical information on the location of the spawning reaches. An acoustic doppler current profiler (ADCP) was used to measure topographic and hydrodynamic data of the spawning reaches during the spawning period. Then, based on the spatial clustering method, cluster analysis on spatial attributes (water depth, flow velocity, water temperature, and Froude number) was conducted on potential spawning grounds. The cluster analysis method uses k-means clustering; a method often employed to classify large amounts of data. Finally, we analyzed and evaluated the spatial suitability of spawning reaches by combining fish spawning suitability curves to obtain spatial preferences associated with fish spawning activities. Proportionally, results revealed a high suitability (>0.4) area (60.86%). Moreover, spawning suitability in curved river sections and deep pools in straight river sections were significantly higher than bifurcated sections. Furthermore, areas near the riverbanks were more suitable than mid-course sections of the river. Finally, the locations of six potential Asian carp spawning grounds were determined according to their spatial suitability. This study provides technical support to accurately locate spawning grounds for the fish that produce drifting eggs.

In a modern power system, to realize the safe operation of units and maintain the frequency stability of the power network, various means of frequency modulation can be adopted. However, there are some deficiencies in the real-time monitoring and assessment management of primary frequency modulation (PFM). To solve these problems, a PFM online monitoring and management method based on the “master–sub” control station model in a wide area measurement system (WAMS) is proposed as a control strategy to support frequency modulation of units and assist power network assessment. In the hybrid monitoring system strategy of the master control station and sub control station, the sub control station is built locally in the power plant to monitor the operational status of the unit in real-time and calculate the PFM assessment index. The master control station is built in the dispatching center, receiving the assessment data uploaded by the sub control station and sending the PFM simulation test instruction. This strategy can effectively improve the real-time performance of dynamic monitoring and parameter calculation, and reduce the probability of frame loss and communication instability in long-distance data transmission. The experimental results show that the PFM online monitoring management control strategy based on the “master–sub” control station model can effectively monitor the unit and provide data support for PFM assessment.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease, and its occurrence and development are mediated by cellular senescence. Drugs targeting senescent cells seem like a promising and efficacious strategy for IPF treatment. Previous studies have illustrated that pentoxifylline (PTX) may play a certain role in inhibiting pulmonary fibrosis and combating cellular senescence. In this study, we demonstrated that PTX administration inhibits pulmonary fibrosis development and cellular senescence in the bleomycin (BLM)-induced IPF mice model. Moreover, the expression levels of fibrosis-related genes and senescence-related genes in mice lung tissue and primary pulmonary fibroblasts illustrated lung fibroblasts’ vital role in these two processes. And the curative effect of PTX was completed mainly by acting on lung fibroblasts. Besides, during the whole treatment, delayed initiation or advanced halt of PTX administration would influence its effectiveness in reducing fibrotic and senescent traits in various degrees, and the latter influenced more. We further determined that a long period of PTX administration could bring noticeable benefits to mice in recovering BLM-induced lung fibrosis and suppressing age-associated cellular senescence. Moreover, it was still effective when PTX administration was used to treat senescent human fibroblasts. Thus, our findings manifested that PTX therapy is an efficient remedy for pulmonary fibrosis by suppressing cellular senescence.

The high-tech industry plays a crucial role in reducing carbon emission and achieving green economic development. This research uses Meta-Frontier data envelopment analysis to measure the innovation efficiency level of the high-tech industry in China’s provinces from 1999 to 2018, compares the difference in this industry’s innovation efficiency under the regional Frontier and common Frontier, and inspects the convergence condition of its innovation efficiency in the three major areas of eastern, central, and western China. The results show under the regional Frontier that the difference in innovation efficiency of the western region’s high-tech industry is the biggest, while the difference in the central region is the smallest, and under the national common Frontier the innovation efficiency level of the eastern region’s high-tech industry is the highest, while that of the western region is the lowest. The regional pattern of innovation efficiency in the high-tech industry is consistent with the development trend of the regional economy. Moreover, by using the ratio of the technology gap ratio, we find that the eastern region has the potential optimal technology in China, whereas the central and western regions have large room for improvement. Lastly, the stochastic convergence test shows that the innovation efficiency of the central region’s high-tech industry presents a convergence trend, but the same trend does not occur in the western and eastern regions as well as for the whole country.

When terahertz imaging technology is used for the nondestructive testing of composite materials, the signal is often affected by the experimental environment and internal noise of the system, as well as the absorption and scattering effect of the tested materials. The obtained image has degradation phenomena such as low contrast, poor resolution of small targets and blurred details. In order to improve the image quality, this paper proposes a novel method for the enhancement of composite materials’ terahertz image by using unsharp masking and guided filtering technology. The method includes the processing steps of hard threshold shrinkage denoising based on discrete wavelet transform, amplitude imaging, unsharp masking, guided filtering, contrast stretching, and pseudo-color mapping. In this paper, these steps are reasonably combined and optimized to obtain the final resulting image. To verify the effectiveness of the proposed method, a 150–220 GHz high frequency terahertz frequency modulated radar imaging system was used to image three commonly used sandwich structure composites, and the enhancement processing were carried out. The resulting images with significantly enhanced contrast, detail resolution and edge information were obtained, and the prefabricated defects were all detected; Five objective evaluation indexes including standard deviation, mean gradient, information entropy, energy gradient and local contrast were used to compare and analyze the processing results of different image enhancement methods. The subjective and objective evaluation results showed that the proposed method can effectively suppress the noise in terahertz detection signals, enhance the ability of defect detection and positioning, and improve the accuracy of detection. The proposed method in this paper is expected to play a positive role in improving the practicability of terahertz imaging detection technology and expanding its application fields.

Aging is an inevitable and complex natural phenomenon due to the increase in age. Cellular senescence means a non-proliferative but viable cellular physiological state. It is the basis of aging, and it exists in the body at any time point. Idiopathic pulmonary fibrosis (IPF) is an interstitial fibrous lung disease with unknown etiology, characterized by irreversible destruction of lung structure and function. Aging is one of the most critical risk factors for IPF, and extensive epidemiological data confirms IPF as an aging-related disease. Senescent fibroblasts in IPF show abnormal activation, telomere shortening, metabolic reprogramming, mitochondrial dysfunction, apoptosis resistance, autophagy deficiency, and senescence-associated secretory phenotypes (SASP). These characteristics of senescent fibroblasts establish a close link between cellular senescence and IPF. The treatment of senescence-related molecules and pathways is continually emerging, and using senolytics eliminating senescent fibroblasts is also actively tried as a new therapy for IPF. In this review, we discuss the roles of aging and cellular senescence in IPF. In particular, we summarize the signaling pathways through which senescent fibroblasts influence the occurrence and development of IPF. On this basis, we further talk about the current treatment ideas, hoping this paper can be used as a helpful reference for future researches.

Force control of manipulators could enhance compliance and execution capabilities, and has become a key issue in the field of robotic control. However, it is challenging for redundant manipulators, especially when there exist risks of collisions. In this paper, we propose a collision-free compliance control strategy based on recurrent neural networks. Inspired by impedance control, the position-force control task is rebuilt as a reference command of task-space velocities, by combing kinematic properties, the compliance controller is then described as an equality constraint in joint velocity level. As to collision avoidance strategy, both robot and obstacles are approximately described as two sets of key points, and the distances between those points are used to scale the feasible workspace. In order to save unnecessary energy consumption while reducing impact of possible collisions, the secondary task is chosen to minimize joint velocities. Then a RNN with provable convergence is established to solve the constraint-optimization problem in realtime. Numerical results validate the effectiveness of the proposed controller.

Obstacle avoidance is an important subject in the control of robot manipulators, but is remains challenging for robots with redundant degrees of freedom, especially when there exist complex physical constraints. In this paper, we propose a novel controller based on deep recurrent neural networks. By abstracting robots and obstacles into critical point sets respectively, the distance between the robot and obstacles can be described in a simpler way, then the obstacle avoidance strategy is established in form of inequality constraints by general class-K functions. Using minimal-velocity-norm (MVN) scheme, the control problem is formulated as a quadratic-programming case under multiple constraints. Then a deep recurrent neural network considering system models is established to solve the QP problem online. Theoretical conduction and numerical simulations show that the controller is capable of avoiding static or dynamic obstacles, while tracking the predefined trajectories under physical constraints.

Cytotoxic CD8+ T lymphocytes (CTLs) have been implicated in the severity of COVID-19. The TCR-pMHC ternary complex, formed by the T cell receptor (TCR) and peptide-MHC (major histocompatibility complex), constitutes the molecular basis of CTL responses against SARS-CoV-2. While numerous studies have been conducted on T cell immunity, the molecular mechanisms underlying CTLmediated immunity against SARS-CoV-2 infection have not been well elaborated. In this review, we described the association between HLA variants and different immune responses to SARS-CoV-2 infection, which may lead to varying COVID-19 outcomes. We also summarized the specific TCR repertoires triggered by certain SARS-CoV-2 CTL epitopes, which might explain the variations in disease outcomes among different patients. Importantly, we have highlighted the primary strategies used by SARS-CoV-2 variants to evade T-cell killing: disrupting peptide-MHC binding, TCR recognition, and antigen processing. This review provides valuable insights into the molecule mechanism of CTL responses during SARS-CoV-2 infection, aiding efforts to control the pandemic and prepare for future challenges. [ABSTRACT FROM AUTHOR]

IntroductionHigh-resolution whole-heart coronary magnetic resonance angiography (CMRA) often suffers from unreasonably long scan times, rendering imaging acceleration highly desirable. Traditional reconstruction methods used in CMRA rely on either hand-crafted priors or supervised learning models. Although the latter often yield superior reconstruction quality, they require a large amount of training data and memory resources, and may encounter generalization issues when dealing with out-of-distribution datasets.MethodsTo address these challenges, we introduce an unsupervised reconstruction method that combines deep image prior (DIP) with compressed sensing (CS) to accelerate 3D CMRA. This method incorporates a slice-by-slice DIP reconstruction and 3D total variation (TV) regularization, enabling high-quality reconstruction under a significant acceleration while enforcing continuity in the slice direction. We evaluated our method by comparing it to iterative SENSE, CS-TV, CS-wavelet, and other DIP-based variants, using both retrospectively and prospectively undersampled datasets.ResultsThe results demonstrate the superiority of our 3D DIP-CS approach, which improved the reconstruction accuracy relative to the other approaches across both datasets. Ablation studies further reveal the benefits of combining DIP with 3D TV regularization, which leads to significant improvements of image quality over pure DIP-based methods. Evaluation of vessel sharpness and image quality scores shows that DIP-CS improves the quality of reformatted coronary arteries.DiscussionThe proposed method enables scan-specific reconstruction of high-quality 3D CMRA from a five-minute acquisition, without relying on fully-sampled training data or placing a heavy burden on memory resources.

NUCLEAR FACTOR Y subunit alpha (NF-YA), together with NF-YB and NF-YC, regulates plant growth and development, as well as plant responses to biotic and abiotic stresses. Although extensive studies have examined the functions of NFYAs in Arabidopsis thaliana, the roles of NF-YAs in Glycinme max are poorly understood. In this study, we identified a phosphorus (P) starvation-responsive NFYA8 in soybean. The expression of GmNF-YA8 is induced by low P or low nitrogen in leaves, but not by potassium or iron starvation, respectively. GmNF-YA8 is localized in the nucleus and plasma membrane. Ectopic expression of GmNF-YA8 inhibits plant growth and delayed flowering in Arabidopsis. Exogenous application of gibberellic acid (GA) rescues the delayed flowering phenotype in Arabidopsis overexpressing GmNF-YA8 lines GmNF-YA8OE-05 and GmNF-YA8OE-20. Moreover, quantitative real time PCR (qRT-PCR) verified that overexpression of GmNF-YA8 downregulates GA20ox2 and GA3ox2 expression, but upregulates GA2ox2 and GA2ox3 that encode enzymes, which inactive bioactive GAs. Consistent with the late flowering phenotype of Arabidopsis trangenic lines that overexpress GmNF-YA8, the transcript levels of flowering-promoting genes AP1, CO, LFY, and SOC1 are reduced. In addition, overexpression of GmNF-YA8 promotes the emergence of lateral root (LR) primordium from epidermis rather than the initiation of LR in low P, and increases the LR density in low nitrogen. Our results provide insights into the roles of GmNF-YA8. [ABSTRACT FROM AUTHOR]

Evaluating the computational complexity is critical for assessing the time-domain anti-jamming performance of GNSS receivers. The multiplier is the core component that contributes to the computational complexity in time-domain anti-jamming. However, current algorithms aimed at reducing the complexity of time-domain anti-jamming typically concentrate on shortening the filter length, which fails to address the high computational complexity introduced by the use of multipliers. This paper introduces a cascaded multiplier-free approach for implementing time-domain anti-jamming in navigation receivers. We propose a numerical power decomposition technique based on optimal Canonical Signed Digit coding and coefficient decomposition. By substituting the multiplier with minimal adder and shift operations, the computational complexity of the anti-jamming filter with a high quantization bit-width can be considerably decreased. An optimization strategy is presented, and the low-complexity multiplier-free technique is applied to the time-domain anti-jamming filter. Compared to the traditional Canonical Signed Digit multiplier-free technique, our method can reduce the components required for a 12-bit quantization anti-interference filter by one adder, 20 shift operations, and five coded word lengths, while maintaining a pseudo-range measurement deviation below 0.27 ns.

The global navigation satellite system is a high-precision radio navigation system based on artificial satellites. Among the factors affecting the positioning accuracy of the navigation satellite system, the multipath signal is the most significant and challenging to eliminate. Furthermore, the multipath environment of the receiver is also becoming more complex, and it seriously threatens the measurement accuracy and stability of the receiver. The multipath mitigation technology is also continuously improved and developed in practical application. This paper firstly introduces the concept and characteristics of multipath signals, and summarizes the influence of multipath signals on navigation satellite systems from two aspects of code tracking loop and carrier tracking loop. Furthermore, the existing multipath mitigation technology is summarized in four stages: signal system design, antenna design, baseband signal processing, and navigation data processing. Finally, according to the summary of the existing technology and the new demand, the future development direction of multipath suppression technology is prospected.