Genome-wide identification of the endonuclease family genes implicates potential roles of TaENDO23 in drought-stressed response and grain development in wheat.

BACKGROUND: Endonucleases play a crucial role in plant growth and stress response by breaking down nuclear DNA. However, the specific members and biological functions of the endonuclease encoding genes in wheat remain to be determined. RESULTS: In this study, we identified a total of 26 TaENDO family genes at the wheat genome-wide level. These genes were located on chromosomes 2 A, 2B, 2D, 3 A, 3B, and 3D and classified into four groups, each sharing similar gene structures and conserved motifs. Furthermore, we identified diverse stress-response and growth-related cis-elements in the promoter of TaENDO genes, which were broadly expressed in different organs, and several TaENDO genes were significantly induced under drought and salt stresses. We further examined the biological function of TaENDO23 gene since it was rapidly induced under drought stress and exhibited high expression in spikes and grains. Subcellular localization analysis revealed that TaENDO23 was localized in the cytoplasm of wheat protoplasts. qRT-PCR results indicated that the expression of TaENDO23 increased under PEG6000 and abscisic acid treatments, but decreased under NaCl treatment. TaENDO23 mainly expressed in leaves and spikes. A kompetitive allele-specific PCR (KASP) marker was developed to identify single nucleotide polymorphisms in TaENDO23 gene in 256 wheat accessions. The alleles with TaENDO23-HapI haplotypes had higher grain weight and size compared to TaENDO23-HapII. The geographical and annual frequency distributions of the two TaENDO23 haplotypes revealed that the elite haplotype TaENDO23-HapI was positively selected in the wheat breeding process. CONCLUSION: We systematically analyzed the evolutionary relationships, gene structure characteristics, and expression patterns of TaENDO genes in wheat. The expression of TaENDO23, in particular, was induced under drought stress, mainly expressed in the leaves and grains. The KASP marker of TaENDO23 gene successfully distinguished between the wheat accessions, revealing TaENDO23-HapI as the elite haplotype associated with improved grain weight and size. These findings provide insights into the evolution and characteristics of TaENDO genes at the genome-wide level in wheat, laying the foundation for further biological analysis of TaENDO23 gene, especially in response to drought stress and grain development.

Genome-wide identification and analysis of the GGCT gene family in wheat.

BACKGROUND: γ-glutamylcyclotransferase (GGCT), an enzyme to maintain glutathione homeostasis, plays a vital role in the response to plant growth and development as well as the adaptation to various stresses. Although the GGCT gene family analysis has been conducted in Arabidopsis and rice, the family genes have not yet been well identified and analyzed at the genome-wide level in wheat (Triticum aestivum L.). RESULTS: In the present study, 20 TaGGCT genes were identified in the wheat genome and widely distributed on chromosomes 2A, 2B, 2D, 3A, 4A, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, and 7D. Phylogenetic and structural analyses showed that these TaGGCT genes could be classified into three subfamilies: ChaC, GGGACT, and GGCT-PS. They exhibited similar motif compositions and distribution patterns in the same subgroup. Gene duplication analysis suggested that the expansion of TaGGCT family genes was facilitated by segmental duplications and tandem repeats in the wheat evolutionary events. Identification of diverse cis-acting response elements in TaGGCT promoters indicated their potential fundamental roles in response to plant development and abiotic stresses. The analysis of transcriptome data combined with RT-qPCR results revealed that the TaGGCTs genes exhibited ubiquitous expression across plant organs, with highly expressed in roots, stems, and developing grains. Most TaGGCT genes were up-regulated after 6 h under 20% PEG6000 and ABA treatments. Association analysis revealed that two haplotypes of TaGGCT20 gene displayed significantly different Thousand-kernel weight (TKW), Kernel length (KL), and Kernel width (KW) in wheat. The geographical and annual distribution of the two haplotypes of TaGGCT20 gene further revealed that the frequency of the favorable haplotype TaGGCT20-Hap-I was positively selected in the historical breeding process of wheat. CONCLUSION: This study investigated the genome-wide identification, structure, evolution, and expression analysis of TaGGCT genes in wheat. The motifs of TaGGCTs were highly conserved throughout the evolutionary history of wheat. Most TaGGCT genes were highly expressed in roots, stems, and developing grains, and involved in the response to drought stresses. Two haplotypes were developed in the TaGGCT20 gene, where TaGGCT20-Hap-I, as a favorable haplotype, was significantly associated with higher TKW, KL, and KW in wheat, suggesting that the haplotype is used as a function marker for the selection in grain yield in wheat breeding.

Genome­wide identification and expression analysis of the UBC gene family in wheat (Triticum aestivum L.).

BACKGROUND: Ubiquitination is an important regulatory step of selective protein degradation in the plant UPS (ubiquitin-proteasome system), which is involved in various biological processes in eukaryotes. Ubiquitin-conjugating enzymes play an intermediate role in the process of protein ubiquitination reactions and thus play an essential role in regulating plant growth and response to adverse environmental conditions. However, a genome-wide analysis of the UBC gene family in wheat (Triticum aestivum L.) has not yet been performed. RESULTS: In this study, the number, physiochemical properties, gene structure, collinearity, and phylogenetic relationships of TaUBC family members in wheat were analyzed using bioinformatics methods. The expression pattern of TaUBC genes in different tissues/organs and developmental periods, as well as the transcript levels under abiotic stress treatment, were analyzed using RNA-Seq data and qRT-PCR. Meanwhile, favorable haplotypes of TaUBC25 were investigated based on wheat resequencing data of 681 wheat cultivars from the Wheat Union Database. The analyses identified a total of 93 TaUBC family members containing a UBC domain in wheat genome. These genes were unevenly distributed across 21 chromosomes, and numerous duplication events were observed between gene members. Based on phylogenetic analysis, the TaUBC family was divided into 13 E2 groups and a separate UEV group. We investigated the expression of TaUBC family genes under different tissue/organ and stress conditions by quantitative real-time PCR (qRT-PCR) analysis. The results showed that some TaUBC genes were specifically expressed in certain tissues/organs and that most TaUBC genes responded to NaCl, PEG6000, and ABA treatment with different levels of expression. In addition, we performed association analysis for the two haplotypes based on key agronomic traits such as thousand-kernel weight (TKW), kernel length (KL), kernel weight (KW), and kernel thickness (KT), examining 122 wheat accessions at three environmental sites. The results showed that TaUBC25-Hap II had significantly higher TKW, KL, KW, and KT than TaUBC25-Hap I. The distribution analysis of haplotypes showed that TaUBC25-Hap II was preferred in the natural population of wheat. CONCLUSION: Our results identified 93 members of the TaUBC family in wheat, and several genes involved in grain development and abiotic stress response. Based on the SNPs detected in the TaUBC sequence, two haplotypes, TaUBC25-Hap I and TaUBC25-Hap II, were identified among wheat cultivars, and their potential value for wheat breeding was validated by association analysis. The above results provide a theoretical basis for elucidating the evolutionary relationships of the TaUBC gene family and lay the foundation for studying the functions of family members in the future.

Effectiveness of Sodium-Glucose Cotransporter 2 Inhibitors in Patients With Acute Myocardial Infarction With or Without Type 2 Diabetes: A Systematic Review and Meta-analysis.

ABSTRACT: Recent studies have revealed the benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in heart failure patients. However, their effects on acute myocardial infarction (AMI) remain uncertain. Therefore, we conducted this meta-analysis to assess the effectiveness of SGLT2i in patients with AMI with or without diabetes. We conducted a comprehensive search of PubMed, Embase, and Cochrane Library encompassing data from inception until November 30, 2023. Relevant studies comparing SGLT2i with placebo or non-SGLT2i in patients with AMI were included. The mean difference and/or odds ratio (OR) with 95% confidence intervals were pooled using a fixed-effects model when the heterogeneity statistic (I2) was less than 50%; otherwise, a random-effects model was employed. Four randomized controlled trials and 4 observational studies involving 9397 patients with AMI were included in this meta-analysis. Patients treated with SGLT2i exhibited a significantly lower rate of hospitalization for heart failure (OR = 0.50, 95% CI: 0.32-0.80) and all-cause death (OR = 0.65, 95% CI: 0.44-0.95) compared with those treated with placebo or non-SGLT2i. Furthermore, the use of SGLT2i was associated with a significant increase in left ventricular ejection fraction (mean difference = 1.90, 95% CI: 1.62-2.17) and a greater reduction of N-terminal prohormone of brain natriuretic peptide (OR = 0.88, 95% CI 0.82-0.94). Subgroup analysis revealed that in patients with diabetes, SGLT2i exhibited similar effects. The present meta-analysis provided evidence indicating the effectiveness of SGLT2i in patients with AMI; SGLT2i may serve as an additional therapeutic option for patients with AMI, regardless of the presence or absence of diabetes.

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks.

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.

[Stapled anoplin peptide combined with photothermal therapy enhances oncolytic immunotherapy of triple-negative breast cancer].

This study constructed a nano-drug delivery system, A3@GMH, by co-delivering the stapled anoplin peptide(Ano-3, A3) with the light-harvesting material graphene oxide(GO), and evaluated its oncolytic immunotherapy effect on triple-negative breast cancer(TNBC). A3@GMH was prepared using an emulsion template method and its physicochemical properties were characterized. The in vivo and in vitro photothermal conversion abilities of A3@GMH were investigated using an infrared thermal imager. The oncoly-tic activity of A3@GMH against TNBC 4T1 cells was evaluated through cell counting kit-8(CCK-8), lactate dehydrogenase(LDH) release, live/dead cell staining, and super-resolution microscopy. The targeting properties of A3@GMH on 4T1 cells were assessed using a high-content imaging system and flow cytometry. In vitro and in vivo studies were conducted to investigate the antitumor mechanism of A3@GMH in combination with photothermal therapy(PTT) through inducing immunogenic cell death(ICD) in 4T1 cells. The results showed that the prepared A3@GMH exhibited distinct mesoporous and coated structures with an average particle size of(308.9±7.5) nm and a surface potential of(-6.79±0.58) mV. The encapsulation efficiency and drug loading of A3 were 23.9%±0.6% and 20.5%±0.5%, respectively. A3@GMH demonstrated excellent photothermal conversion ability and biological safety. A3@GMH actively mediated oncolytic features such as 4T1 cell lysis and LDH release, as well as ICD effects, and showed enhanced in vitro antitumor activity when combined with PTT. In vivo, A3@GMH efficiently induced ICD effects with two rounds of PTT, activated the host's antitumor immune response, and effectively suppressed tumor growth in 4T1 tumor-bearing mice, achieving an 88.9% tumor inhibition rate with no apparent toxic side effects. This study suggests that the combination of stapled anoplin peptide and PTT significantly enhances the oncolytic immunotherapy for TNBC and provides a basis for the innovative application of anti-tumor peptides derived from TCM in TNBC treatment.

Red blood cell distribution width and maximum left ventricular wall thickness predict poor outcomes in patients with hypertrophic cardiomyopathy.

AIM: To evaluate the prognostic utility of red blood cell distribution width (RDW) and maximum left ventricular wall thickness (MLVWT) in patients with hypertrophic cardiomyopathy (HCM). PATIENTS AND METHODS: This study is a retrospective cohort analysis. Patients diagnosed with HCM at the First Affiliated Hospital of Sun Yat-sen University from March 2014 to March 2019 were included. HCM patients were stratified into two groups based on the occurrence of major adverse cardiac events (MACE). Receiver operating characteristic (ROC) curves were then constructed and Cox regression models were employed to gauge the prognostic relevance of RDW and MLVWT for HCM patients. Kaplan-Meier analysis evaluated the survival and MACE-free rate in patients with different level of RDW and MLVWT. RESULTS: A total of 300 patients with HCM were enrolled in this study and followed up for 40.56±18.33 months. Among them, 117 MACE (39.00%), 40 all-cause deaths (13.33%), and 29 cardiovascular deaths (9.67%). The level of RDW, MLVWT, creatinine (Cr), and B-type pro-brain natriuretic peptide (NT-ProBNP) were statistically different between the MACE group and non-MACE group (P < .05). Multivariate analysis showed that after adjusting for confounding factors, RDW and MLVWT were independent predictors of all-cause mortality and MACE in HCM patients. ROC showed that RDW > .13 and MLVWT > 23 mm are the cut-off value to predict all-cause mortality and MACE. The area under the ROC curve AUC of the combination predicting the occurrence of all-cause mortality and MACE are .823 and .820, respectively. Kaplan-Meier analysis showed that the survival rate and MACE-free survival rate of group 1 (RDW≦.13 and MLVWT≦23 mm) were significantly higher than group 2 (RDW > .13 or MLVWT > 23 mm), and group 3 (RDW > .13 and MLVWT > 23 mm) (P = .000). CONCLUSION: We determined that increased RDW and MLVWT was independently associated with MACE incidence and risk of mortality in HCM patients. Combined evaluation of RDW and MLVWT yielded a more accurate predictive model of HCM patient outcomes relative to the use of either of these metrics in isolation. Our research can provide a theoretical basis in the occurrence of MACE for the high-risk HCM and intervene them properly and timely.

Online Detection of Fabric Defects Based on Improved CenterNet with Deformable Convolution.

The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of lights sources, eight cameras, and a mirror was developed. The image acquisition speed of the developed device is up to 65 m per minute of fabric. This study treats the problem of fabric defect detection as an object detection task in machine vision. Considering the real-time and precision requirements of detection, we improved some components of CenterNet to achieve efficient fabric defect detection, including the introduction of deformable convolution to adapt to different defect shapes and the introduction of i-FPN to adapt to defects of different sizes. Ablation studies demonstrate the effectiveness of our proposed improvements. The comparative experimental results show that our method achieves a satisfactory balance of accuracy and speed, which demonstrate the superiority of the proposed method. The maximum detection speed of the developed system can reach 37.3 m per minute, which can meet the real-time requirements.

Multi-Agent Reinforcement Learning Based Fully Decentralized Dynamic Time Division Configuration for 5G and B5G Network.

Future network services must adapt to the highly dynamic uplink and downlink traffic. To fulfill this requirement, the 3rd Generation Partnership Project (3GPP) proposed dynamic time division duplex (D-TDD) technology in Long Term Evolution (LTE) Release 11. Afterward, the 3GPP RAN#86 meeting clarified that 5G NR needs to support dynamic adjustment of the duplex pattern (transmission direction) in the time domain. Although 5G NR provides a more flexible duplex pattern, how to configure an effective duplex pattern according to services traffic is still an open research area. In this research, we propose a distributed multi-agent deep reinforcement learning (MARL) based decentralized D-TDD configuration method. First, we model a D-TDD configuration problem as a dynamic programming problem. Given the buffer length of all UE, we model the D-TDD configuration policy as a conditional probability distribution. Our goal is to find a D-TDD configuration policy that maximizes the expected discount return of all UE's sum rates. Second, in order to reduce signaling overhead, we design a fully decentralized solution with distributed MARL technology. Each agent in MARL makes decisions only based on local observations. We regard each base station (BS) as an agent, and each agent configures uplink and downlink time slot ratio according to length of intra-BS user (UE) queue buffer. Third, in order to solve the problem of overall system revenue caused by the lack of global information in MARL, we apply leniency control and binary LSTM (BLSTM) based auto-encoder. Leniency controller effectively controls Q-value estimation process in MARL according to Q-value and current network conditions, and auto-encoder makes up for the defect that leniency control cannot handle complex environments and high-dimensional data. Through the parallel distributed training, the global D-TDD policy is obtained. This method deploys the MARL algorithm on the Mobile Edge Computing (MEC) server of each BS and uses the storage and computing capabilities of the server for distributed training. The simulation results show that the proposed distributed MARL converges stably in various environments, and performs better than distributed deep reinforcement algorithm.

Green Synthesis of Nano-Zero Valence Iron with Green Tea and It's Implication in Lead Removal.

The nano-zero valence iron (nZVI) via green synthesis for heavy metal remediation has attracted many attentions due to its low-cost, environmental-safety, relative reproductivity, and high stability. However, influence of synthesis conditions on the physiochemical properties of nZVI via green tea extracts and the responding suspensibility, which is required for high reactivity, has not been fully elucidated. In this study, we investigated the zeta potentials, sedimentation and lead (Pb2+) removal capacity of various nZVIs synthesized using green tea extracts. The results showed that the tea extracts extracted at 80oC presented an excellent activity, which contributed to the outstanding suspensibility and reaction activity of nZVI synthesized in a volume ratio of 1:1 (tea extraction versus Fe2+ solution). Thus, the optimized nZVI was successfully prepared with a Pb2+ removal capacity (377.3 mg/g), which was seven times stronger than 50.31 mg/g of traditional chemical synthesized nZVI.

Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks.

Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity.

An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity.

In the context of "double carbon", as a traditional high energy consumption industry, the textile industry is facing the severe challenges of energy saving and emission reduction. To improve production efficiency in the textile industry, we propose the use of content-based image retrieval technology to shorten the fabric production cycle. However, fabric retrieval has high requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. This paper presents a novel method for fabric image retrieval. Firstly, we define a fine-grained similarity to measure the similarity between two fabric images. Then, a convolutional neural network with a compact structure and cross-domain connections is designed to narrow the gap between fabric images and similarities. To overcome the problems of probabilistic missing and difficult training in classical hashing, we introduce a variational network module and structural module into the hashing model, which is called DVSH. We employ list-wise learning to perform similarity embedding. The experimental results demonstrate the superiority and efficiency of the proposed hashing model, DVSH.

Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System.

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

Spatio-temporal heterogeneity of air pollution and its key influencing factors in the Yellow River Economic Belt of China from 2014 to 2019.

The Yellow River Economic Belt (YREB) plays an important role in China's socio-economic development and ecological security. However, this region has suffered from serious atmospheric pollution in recent years due to intense human activity. Identifying and qualifying the spatio-temporal characteristics of the region's air pollution and its driving forces would help in the formulation of effective mitigation policies. Here, the YREB's spatio-temporal characteristics of air quality were meticulously investigated using air pollution observation, synchronous meteorological, and socio-economic data from 103 cities, for the period of 2014-2019. Furthermore, the factors influencing air pollution were analyzed and qualified. Although air quality improved in the cities of the YREB following the implementation of the Air Pollution Action Plan, the region's quality index (AQI) remained higher than the national average. Annual variations of AQI in the YREB followed a U-shaped pattern, being high in autumn and winter and low in spring and summer; this U shape became shallower following improvements in air quality during autumn and winter. From 2014 to 2019, the annual average AQI values of cities in the eastern, middle, and western YREB dropped from 109.66, 111.70, and 94.65 to 92.00, 103.85, and 73.95, respectively. The air pollution trends of cities revealed obvious spatial agglomeration, and those cities with poor air quality were primarily the western cities of Shandong province, most cities in Henan province, and the eastern cities of Shanxi province. Due to the improvement of air quality in eastern cities, the pollution center of gravity moved gradually from Changzhi (113°3411"E, 36°040"N) to Linfen (110°5222″E, 36°2344″N). The results of the spatial Durbin model (SDM) indicated that air pollution had an apparent spillover effect in the YREB at the watershed scale, and that government technical expenditure, gross domestic product (GDP) per capita, population density, annual wind speed, and relative humidity all had significant negative overall effects on the AQI values of cities. The green cover rate, ratio of secondary industry, and temperature, meanwhile, all had significant positive total effects. Due to differences the natural conditions and stages of socio-economic development between the eastern, middle, and western cities of the YREB, the impact directions and functional strengths of their key factors differed greatly.

Power Control and Clustering-Based Interference Management for UAV-Assisted Networks.

Unmanned Aerial Vehicle (UAV) has been widely used in various applications of wireless network. A system of UAVs has the function of collecting data, offloading traffic for ground Base Stations (BSs) and illuminating coverage holes. However, inter-UAV interference is easily introduced because of the huge number of LoS paths in the air-to-ground channel. In this paper, we propose an interference management framework for UAV-assisted networks, consisting of two main modules: power control and UAV clustering. The power control is executed first to adjust the power levels of UAVs. We model the problem of power control for UAV networks as a non-cooperative game which is proved to be an exact potential game and the Nash equilibrium is reached. Next, to further improve system user rate, coordinated multi-point (CoMP) technique is implemented. The cooperative UAV sets are established to serve users and thus transforming the interfering links into useful links. Affinity propagation is applied to build clusters of UAVs based on the interference strength. Simulation results show that the proposed algorithm integrating power control with CoMP can effectively reduce the interference and improve system sum-rate, compared to Non-CoMP scenario. The law of cluster formation is also obtained where the average cluster size and the number of clusters are affected by inter-UAV distance.

Depth recovery of hairy fibers for precise yarn hairiness measurement.

Hairiness is a concept describing the amount of hairy fibers (hairs) protruding from a yarn core in different spatial orientations and shapes. Most image-based hairiness assessment methods measure hairs by projecting a yarn on a 2D image plane, which suffers from two major problems: 1) not detecting defocused hairs (fuzzy hairs) when hairs are out of the field of view of the imaging system and 2) miscalculating real lengths of spatially curved hairs. The objective of this research was to develop a new image-based hairiness measurement method to mitigate these problems. The proposed method included two tasks: yarn image segmentation and hairiness assessment. The first task was to improve the detection rate of fuzzy hairs with a hybrid algorithm combining double homomorphic filtering and region-growing algorithms. The second task was to establish a width-depth mapping model for defocused hairs to compensate measurable lengths of defocused hairs based on their width information. The experiment results demonstrated that the proposed segmentation algorithm can detect fuzzy hairs usually missed by the previously used algorithm, and can produce more accurate hair length measurements than the previous algorithm when compared to the corresponding manual measurements, which were considered as the gold standard in this study.

Determining the colorimetric attributes of multicolored materials based on a global correction and unsupervised image segmentation method.

Fast and accurate measurement of colors in multicolored prints using commercial instruments or existing computer vision systems remains a challenge due to limitations in image segmentation methods and the size and complexity of the colored patterns. To determine the colorimetric attributes (L*a*b*) of multicolored materials, an approach based on global color correction and an effective unsupervised image segmentation is presented. The colorimetric attributes of all patches in a ColorChecker chart were measured spectrophotometrically, and an image of the chart was also captured. Images were segmented using a modified Chan-Vese method, and the sRGB values of each patch were extracted and then transformed into L*a*b* values. In order to optimize the transformation process, the performance of 10 models was examined by minimizing the average color differences between measured and calculated colorimetric values. To assess the performance of the model, a set of printed samples was employed and the color differences between the predicted and measured L*a*b* values of samples were compared. The results show that the modified Chan-Vese method, with suitable settings, generates satisfactory segmentation of the printed images with mean and maximum ΔE00 values of 2.43 and 4.28 between measured and calculated values.

Fluid upstream shear stress of rabbit aortic stenosis inhibits neointimal hyperplasia by promoting endothelization after balloon injury.

BACKGROUND: Atherosclerosis is associated with disturbed blood flow characterized by low and oscillatory shear stress (SS), however, few study directly links SS to neointimal hyperplasia in animal model. This study was focused on the effects of changed SS upon the neointimal hyperplasia which responded to balloon injury in a novel rabbit model with partially-constricted abdominal aorta. METHODS: We established a rabbit model subjected to partial abdominal aortic constriction with a cylinder-shaped cannula as a model of disturbed flow, which was similar to the hemodynamic features of stenosis caused by atherosclerosis plaque. Further, balloon injury was performed to investigate the relationship between SS and neointimal hyperplasia. Four weeks later, the abdominal aorta was assessed with digital subtraction angiography (DSA) and intravascular ultrasound (IVUS). The vascular sections were embedded in paraffin blocks for morphometric analysis to evaluate neointimal hyperplasia, and anti-CD31 immunohistochemical staining was for endothelialization ratio. RESULTS: In upstream the stenosis, the changed SS leads to neointimal hyperplasia compared with normal SS (11,729 ± 1205 vs 8418 ± 737, P = 0.023). However, the upstream SS of the stenosis can promote vascular re-endothelialization after balloon injury compared with normal SS, verified by endothelialization ratio (0.36 ± 0.03 vs 0.32 ± 0.03, P = 0.017), thereby attenuate neointimal hyperplasia (64,851 ± 3995 vs 68,335 ± 3867, P = 0.018). CONCLUSION: The upstream SS of stenosis, not downstream SS, inhibits the neointimal hyperplasia after balloon injury by promoting vascular re-endothelializtion.