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When large amounts of data continuously arrive in streams, online updating is an effective way to reduce storage and computational burden. The key idea of online updating is that the previous estimators are sequentially updated only using the current data and summary statistics of historical data. In this article, we develop a renewable learning method for the multiplicative regression model with streaming data, where the parameter estimator based on a least product relative error (LPRE) criterion is renewed without revisiting historical data. Under some regularity conditions, we establish the consistency and asymptotic normality of the renewable estimator. Moreover, our proposed renewable estimator has an identical asymptotic distribution with that of the full data LPRE estimator. Numerical studies and two real-world datasets are provided to evaluate the performance of our proposed method. [ABSTRACT FROM AUTHOR]

The style of the underwater images varies according to the region of the sea. However, Tidal Stream Turbine (TST) rotor images captured in the laboratory environment cannot reflect the real underwater environment in image style, resulting in poor generalization of image signal-based fault detection algorithms. Due to the fixed capture position of the camera, the TST rotor image dataset has a high semantic similarity between images, resulting in content loss in conventional image-to-image translation networks. Meanwhile, the one-to-one translation feature in other works cannot meet our requirements. In this work, a Domain Variable Prior-based Multi-style Transfer Network (DVP-MSTN) is proposed to achieve TST rotor image style augmentation. First, the backbone network is trained using a public paired dataset to acquire prior knowledge of domain variable (Knowledge Acquiring, KA). Next, a Multi-domain Transfer Unit (MDT unit) is introduced to enable the conversion of style representations in low-dimensional space. Finally, the prior knowledge is shared to train the MDT unit by fixing the parameters of the backbone network optimized from the KA process (Knowledge Sharing, KS). In addition, an algorithm based on the dark channel of the image is proposed to improve the transfer of low-contrast features. Specifically, a discriminator is used to discriminate the image dark channel to guide the MDT unit to generate low-contrast style representation conditionally. Meanwhile, color loss is employed to preserve the color feature of the image. By controlling the weights of the style code, this method enables control over the image style transfer process, thereby expanding the variety of image styles in the dataset for the purpose of data augmentation. [ABSTRACT FROM AUTHOR]

Simple Summary: The effect of optimized mating methods for long-term selection has not been studied in cattle breeding. In this study, the linear programming and optimal contribution selection methods on the genetic gain and inbreeding level of beef cattle were explored and evaluated using a simulation strategy. Our results showed that the linear programming method can effectively improve the genetic gain in the population during long-term selection in the breeding process, and the optimal contribution selection method can maintain a balance between improving genetic gain and controlling inbreeding level. Our findings can provide theoretical guidance for the long-term and sustainable genetic gain in breeding populations in farm animals. The optimized selection method can maximize the genetic gain in offspring under the premise of controlling the inbreeding level of the population. At present, genetic gain has been largely improved by using genomic selection in multiple farm animals. However, the design of the optimal selection method and assessment of its effects during long-term selection in beef cattle breeding are yet to be fully explored. In this study, a simulated beef cattle population was constructed, and 15 generations of simulated breeding were carried out using the linear programming breeding strategy (LP) and optimal contribution selection strategy (OCS), respectively. The truncation selection strategy (TS−I and TS−II) was used as the control. During the breeding process, genetic parameters including genetic gain, average kinship coefficient, QTL effect variance, and average observed heterozygosity were calculated and compared across generations. Our results showed that the LP method can significantly improve the genetic gain in the population, especially the genetic performance of the traits with high heritability and the traits with high weight in the breeding process, but the inbreeding level of the population is higher under LP strategy. Although the genetic gain in the population under the OCS strategy is lower than the TS−II strategy, this method can effectively control the inbreeding level of the population. Our findings also suggest that the LP and OCS method can be used as an effective means to improve genetic gain, while the OCS method is a more ideal method to obtain sustainable genetic gain during long-term selection. [ABSTRACT FROM AUTHOR]

Tidal stream turbines (TST) are susceptible to fouling by organisms and suffer blade faults. Accurate blade fault diagnosis is of great significance. The single sensor-based diagnosis methods generally cannot meet the requirement for high-quality blade fault diagnosis, especially in complex underwater environments affected by the swell effects and turbid water. To address this issue, a confidence-guided Dempster-Shafer (CDS) method is proposed for multi-blade fault diagnosis. Specifically, the proposed CDS method adaptively fuses the stator current signals and images in the decision-level fusion module, which designs the confidence coefficients of the stator current signals and images to adaptively fuse the two sensor signals with complementary fault features and diagnosis performance. The proposed CDS method alleviates the limitations of the stator current signal-based methods in diagnosing multi-blade faults under the swell effects and the limitations of the image-based methods in turbid water states. The comparative experiments and ablation experiments on a 230 W TST prototype verified that the proposed CDS method achieves high diagnosis accuracy in the swell effects and turbid water environments. • A CDS method is proposed to fuse the stator current signals and images at the decision-level. • The proposed CDS method adaptively allocates contributions of two sensor signals by designing confidence coefficients. • The proposed CDS method improves TST multi-blade fault diagnosis accuracy under the swell effects and turbid water states. • The comparative and ablation experimental results on 230W TST prototype verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world's food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs' proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle. [ABSTRACT FROM AUTHOR]

Faced with massive data, subsampling is a popular way to downsize the data volume for reducing computational burden. The key idea of subsampling is to perform statistical analysis on a representative subsample drawn from the full data. It provides a practical solution to extracting useful information from big data. In this article, we develop an efficient subsampling method for large‐scale multiplicative regression model, which can largely reduce the computational burden due to massive data. Under some regularity conditions, we establish consistency and asymptotic normality of the subsample‐based estimator, and derive the optimal subsampling probabilities according to the L‐optimality criterion. A two‐step algorithm is developed to approximate the optimal subsampling procedure. Meanwhile, the convergence rate and asymptotic normality of the two‐step subsample estimator are established. Numerical studies and two real data applications are carried out to evaluate the performance of our subsampling method. [ABSTRACT FROM AUTHOR]

The impact fault caused by ocean creatures poses a risk to the safe operation of tidal stream turbine blades. However, it is difficult to detect the weak impact fault directly because the collected signal is disturbed by the waves, turbulence, and continuously variable flow velocity. To solve this problem, a fusion method of feature sample screening and local outlier factor is proposed in this paper. This method consists of three main parts. First, the Teager–Kaiser energy operator and the sliding window technique are introduced to extract the envelope statistical features from the current signal. Second, a parameter optimized density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to perform feature sample screening before detecting. Notably, the conventional DBSCAN algorithm is sensitive to the parameter selection and lacks the capability of adaptive screening, so this paper proposes an adaptive sand cat swarm optimization algorithm to optimize the parameters. Finally, the local outlier factor is utilized to detect faults based on the screened feature samples. The experimental results show that the proposed method stands out in reducing the false alarm rate compared with traditional methods. Specifically, within the flow velocity ranges of 1.0–1.3 m/s and 1.3–1.6 m/s, the false alarm rates can reach 0% and 0.17%, respectively. [ABSTRACT FROM AUTHOR]

Background: Beef cuts in different regions of the carcass have different meat quality due to their distinct physiological function. The objective of this study was to characterize the region-specific expression differences using comparative transcriptomics analysis among five representative beef cuts (tenderloin, longissimus lumborum, rump, neck, chuck). Results: We obtained 15,701 expressed genes in 30 muscle samples across five regions from carcass meat. We identified a total of 80 region-specific genes (RSGs), ranging from three (identified in the rump cut) to thirty (identified in the longissimus lumborum cut), and detected 25 transcription factors (TFs) for RSGs. Using a co-expression network analysis, we detected seven region-specific modules, including three positively correlated modules and four negatively correlated modules. We finally obtained 91 candidate genes related to meat quality, and the functional enrichment analyses showed that these genes were mainly involved in muscle fiber structure (e.g., TNNI1, TNNT1), fatty acids (e.g., SCD, LPL), amino acids (ALDH2, IVD, ACADS), ion channel binding (PHPT1, SNTA1, SUMO1, CNBP), protein processing (e.g., CDC37, GAPDH, NRBP1), as well as energy production and conversion (e.g., ATP8, COX8B, NDUFB6). Moreover, four candidate genes (ALDH2, CANX, IVD, PHPT1) were validated using RT-qPCR analyses which further supported our RNA-seq results. Conclusions: Our results provide valuable insights into understanding the transcriptome regulation of meat quality in different beef cuts, and these findings may further help to improve the selection for health-beneficial meat in beef cattle. [ABSTRACT FROM AUTHOR]

Background: A comprehensive analysis of gene expression profiling across tissues can provide necessary information for an in-depth understanding of their biological functions. We performed a large-scale gene expression analysis and generated a high-resolution atlas of the transcriptome in beef cattle. Results: Our transcriptome atlas was generated from 135 bovine tissues in adult beef cattle, covering 51 tissue types of major organ systems (e.g., muscular system, digestive system, immune system, reproductive system). Approximately 94.76% of sequencing reads were successfully mapped to the reference genome assembly ARS-UCD1.2. We detected a total of 60,488 transcripts, and 32% of them were not reported before. We identified 2654 housekeeping genes (HKGs) and 477 tissue-specific genes (TSGs) across tissues. Using weighted gene co-expression network analysis, we obtained 24 modules with 237 hub genes (HUBGs). Functional enrichment analysis showed that HKGs mainly maintain the basic biological activities of cells, while TSGs were involved in tissue differentiation and specific physiological processes. HKGs in bovine tissues were more conserved in terms of expression pattern as compared to TSGs and HUBGs among multiple species. Finally, we obtained a subset of tissue-specific differentially expressed genes (DEGs) between beef and dairy cattle and several functional pathways, which may be involved in production and health traits. Conclusions: We generated a large-scale gene expression atlas across the major tissues in beef cattle, providing valuable information for enhancing genome assembly and annotation. HKGs, TSGs, and HUBGs further contribute to better understanding the biology and evolution of multiple tissues in cattle. DEGs between beef and dairy cattle also fill in the knowledge gaps about differential transcriptome regulation of bovine tissues underlying economically important traits. [ABSTRACT FROM AUTHOR]

The rational layout of tidal stream turbines (TSTs) is beneficial for making full use of tidal stream energy. It is essential to consider the wake radius and velocity distribution for determining the spacing between the TSTs. The wake is primarily affected by the turbulence intensity. The attenuation of turbulence results in the non-linear expansion of the wake. Additionally, the high turbulence in the near wake region inhibits the velocity deficit, which is more evident under high ambient turbulence intensity. Therefore, a radius and minimum velocity Jensen (RMV-Jensen) model is proposed to predict the wake radius and the wake velocity distribution downstream of a TST. The RMV-Jensen model consists of a radius block (R-Block) and a minimum velocity block (MV-Block). The R-Block is a piecewise exponential function based on the turbulence attenuation in the wake region, accurately describing the change of the wake expansion coefficient. The MV-Block is a wake minimum velocity model, and the inhibitory effect of turbulence on velocity deficit is considered for the first time. The RMV-Jensen model is applied to predict the wake distribution in the Zhoushan sea area. The prediction accuracy of the RMV-Jensen model is improved by 10%–20% compared to that of the classical Jensen model, according to the experimental results. [ABSTRACT FROM AUTHOR]

Marine current power is a new renewable energy source, it is significant to keep the marine current turbine healthy. But harsh marine conditions, such as regular thermal cycling, high humidity and heavy salt mist, bring severe challenges to the marine current turbine, especially to the winding insulation system. Therefore, it is of great importance to perform fault detection on the insulation system. In this paper, a Modified Extended Kalman Filter (M-EKF) fault detection strategy based on the electrical parametric model derived from the RLC network modeling of Roebel bars is proposed. The proposed fault detection strategy mainly includes two parts. The first step is to establish reasonable state space equations for the model structure extracted to monitor the state of the insulation system. In the second step, the conventional continuous EKF is modified to follow more accurately the state of the marine current turbine. Simulation and experiment results show that the proposed method can realize timely monitoring of the winding insulation system of marine current turbine with good performance. [ABSTRACT FROM AUTHOR]

• A four-state carrier reconfiguration strategy for activating fault behavior to distinguish similar faults is proposed. • A four-state fusion diagnosis method is proposed to locate fault under four-state carrier reconfiguration. • In the cascaded H-bridge multilevel inverter with level-shifted pulse width modulation strategy, the switch at the diagonal position of the inner bridge are similar faults due to the occurrence of the zero current state. • The discrepancy of similar fault is composed of two parts: the voltage spike caused by the non-ideal characteristics of the Insulated-gate bipolar transistors and the voltage spike caused by the discharge on the load side. The cascaded multilevel inverters have many semiconductor switches with high failure rate, which require timely diagnosis techniques. It is a difficult point that the diagonally positioned switches have similar fault characteristics. By analyzing the output characteristics under fault, it is found that the position and the cause of similar faults are related to the permutation of carrier in pulse width modulation. A four-state active fault diagnosis method is proposed in this article. First, the permutation of carriers is reconstructed to activate the fault behavior after the fault is detected. Under the specific four modulation states, the fault characteristics can be fully behaved. Next, based on the machine learning algorithm, the effective fault features are extracted from the voltage signals in four modulation states and the fault location is achieved. The experiments show that the proposed method could effectively distinguish similar faults, improve diagnostic accuracy and have better robustness. [ABSTRACT FROM AUTHOR]

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions. [ABSTRACT FROM AUTHOR]

Multilevel inverters, for their distinctive performance, have been widely used in high voltage and high-power applications in recent years. As power electronics equipment reliability is very important and to ensure multilevel inverter systems stable operation, it is important to detect and locate faults as quickly as possible. In this context and to improve fault diagnosis accuracy and efficiency of a cascaded H-bridge multilevel inverter system (CHMLIS), a fault diagnosis strategy based on the principle component analysis and the multiclass relevance vector machine (PCA-mRVM), is elaborated and proposed in this paper. First, CHMLIS output voltage signals are selected as input fault classification characteristic signals. Then, a fast Fourier transform is used to preprocess these signals. PCA is used to extract fault signals features and to reduce samples dimensions. Finally, an mRVM model is used to classify faulty samples. Compared to traditional approaches, the proposed PCA-mRVM strategy not only achieves higher model sparsity and shorter diagnosis time, but also provides probabilistic outputs for every class membership. Experimental tests are carried out to highlight the proposed PCA-mRVM diagnosis performances. [ABSTRACT FROM AUTHOR]

Summary: Objectives: The aims of this study were to investigate the effect of hepatitis B virus (HBV) replication on the metastatic ability of hepatocellular carcinoma (HCC) cells and to explore a potential mechanism from the perspective of epithelial–mesenchymal transition (EMT). Methods: Two short-interfering RNAs (siRNAs) against the HBV S gene were used to inhibit HBV replication in HepG2.2.15 cells. To evaluate the level of HBV replication and interference efficiency, HBV antigen and HBV DNA were detected by ELISA and quantitative PCR (Q-PCR). Invasion and metastatic abilities were compared between different groups by wound healing and trans-well assays. Immunofluorescent staining and Western blotting were utilized to detect EMT markers. Results: Both siRNAs effectively inhibited HBV replication in HepG2.2.15 cells. Compared to control HepG2.2.15 cells, cells transfected with the siRNAs showed characteristics of the mesenchymal phenotype and augmented their ability to invade and metastasize. Inhibition of HBV replication suppressed E-cadherin and induced a switch to vimentin expression. Western blots confirmed the decrease in E-cadherin expression. The level of E-cadherin expression was also lower in HepG2 cells than in HepG2.2.15 cells. Conclusions: siRNAs were able to effectively inhibit HBV replication in vitro. A high load of HBV replication may inhibit the invasion and metastatic ability of HCC cells by reversing the EMT process. [Copyright &y& Elsevier]

The multi-level inverter has been widely applied in high-power applications, especially in grid-connected systems. In addition to some of the benefits provided by the multi-level inverter, it has the disadvantage to be more prone to failure because of the large number of components used. Therefore, fault-tolerant control of multi-level inverters is critical to ensure the stability and reliability of the system. This paper introduces a fault-tolerant reconfiguration system (FTRS) based on pilot switch for grid-connected inverters. FTRS includes the grid-connected inverter using pilot switches and a fault-tolerant modulation algorithm. FTRS aims to reduce the damage of insulated gate bipolar transistor (IGBT) failure of cascaded H-bridge (CHB) inverter and meet such application requirements as grid-connected systems. The grid-connected inverter using pilot switches and its modulation algorithm can complete fault isolation without the redundant backup H-bridge and can make H-bridge reconfiguration after the system receives the fault signal so that the healthy H-bridge will keep working with voltage peak. Reliability evaluation and comparison of the FTRS and other inverters are presented. The validation of the proposed method is confirmed by simulation and experiment results on the five-level grid-connecter inverter. • It reduces the complexity and cost of the circuit. • It keeps the voltage peak unchanged. • Compared with other inverters, it has higher reliability. [ABSTRACT FROM AUTHOR]

Background: IgA nephropathy (IgAN) is prevalent among both children and adults. Illumination of the differences between them is important for clinical doctors. Methods: We retrospectively compared clinicopathological features in 110 children and 908 adults with IgAN. Results: The male to female ratio was 1.62:1 in children and 0.85:1 in adults. Most patients lacked triggers, but IgAN was preceded by upper respiratory infection (URI) in 45.5% of children and 20.2% of adults. Gross hematuria was the most common initial symptom in children (53.6%), especially in those associated with URI (82.0%), while other symptoms and abnormal laboratory parameters were more common in adults. Estimated glomerular filtration rate (eGFR) was higher in children than in adults. Co-deposition of IgA and C3 were found in 50.9% of children, while IgA deposit was often accompanied by two or more immune complexes in adults. The frequency of subclass I was significantly higher in children than in adults. Mild histological lesions were more common in pediatric IgAN patients associated with URI than other patients. Conclusions: Pediatric patients showed relatively mild clinical manifestations and histological lesions compared with adult patients. URI was the most important trigger for IgAN, particularly in children. IgAN associated with URI was relatively mild. [ABSTRACT FROM AUTHOR]

[Display omitted] • Epidemiological studies indicate that physical activity/exercise has a protective effect against colon cancer progression. • Changes in shear pressure, systemic milieu, extracellular vesicles, and immune system by exercise may impact colon cancer. • Research into mechanisms will enhance development of targeted exercise prescription for patients with colon cancer. Numerous epidemiological studies indicate that physical activity has a protective effect against colon cancer development and progression. Further, the relevant biological mechanisms where physical activity or exercise may improve survival have also been initially examined. In this review, we provide an overview of the epidemiological evidence to date which comprises 16 cohort studies of the effects of physical activity on colon cancer outcomes including cancer recurrence, cancer-specific and overall survival. Moreover, we present four potential mechanisms involving shear pressure, systemic milieu alteration, extracellular vesicles, and immune function by which physical activity and exercise may favorably impact colon cancer. Research currently in progress will provide definitive evidence of survival benefits resulting from exercise and future work will help clarify the role of targeted exercise and the relevant mechanisms involved. [ABSTRACT FROM AUTHOR]

Marine current turbines (MCTs) has progressively attracted wider interest from the industry and many research initiatives due to its potential as a novel world energy resource. However, several technological issues amongst others fault detection of MCT blades still require further progress for their successful implementation. Indeed, power generation is likely to progressively degrade due to blade faults and then causes disruptive disturbance when the marine current generator is connected to the grid. Fault detection of MCT blades still presents several challenges due to the complexity of the underwater environment. It appears that attachment like plankton or biofouling may have a considerable influence on the turbine blade as these may be triggering different imbalance faults. This survey reviews different blade fault types under the condition of wave and turbulence. We also review current blade fault detection methods, including multi-domain approaches. It appears from our study that built-in sensor-based fault detection methods, which use phase currents and voltages across the generator windings, provide several advantages for MCT blade fault detection. It also proposes several trends. • An overview of marine current turbine (MCT) blade fault detection is given. • We analyze the pros & cons of MCT blade fault detection methods. • Four research directions for future research work are also suggested. [ABSTRACT FROM AUTHOR]

The conversion of marine current energy into electricity with marine current turbines (MCTs) promises renewable energy. However, the reliability and power quality of marine current turbines are degraded due to marine biological attachments on the blades. To benefit from all the information embedded in the three phases, we created a fault feature that was the derivative of the current vector modulus in a Concordia reference frame. Moreover, because of the varying marine current speed, fault features were non-stationary. A transformation based on new adaptive proportional sampling frequency (APSF) transformed them into stationary ones. The fault indicator was derived from the amplitude of the shaft rotating frequency, which was itself derived from its power spectrum. The method was validated with data collected from a test bed composed of a marine current turbine coupled to a 230 W permanent magnet synchronous generator. The results showed the efficiency of the method to detect an introduced imbalance fault with an additional mass of 80–220 g attached to blades. In comparison to methods that use a single piece of electrical information (phase current or voltage), the fault indicator based on the three currents was found to be, on average, 2.2 times greater. The results also showed that the fault indicator increased monotonically with the fault severity, with a 1.8 times-higher variation rate, as well as that the method is robust for the flow current speed that varies from 0.95 to 1.3 m/s. [ABSTRACT FROM AUTHOR]

• A total of 18 SNPs located in BTA14 were detected which were associated with at least one milk traits using mixed linear model after Bonferroni correction. • One 2.5 Mb region (range from 1.5 to 4.0 Mb) with high linkage disequilibrium (LD) on BTA14 were identified for fat percentage trait. • Five novel SNPs as candidate variants for fat percentage, protein percentage and cheese merit traits, also our haplotype-based analysis confirmed the region of potential association for these traits. Genome-wide association study (GWAS) has been widely utilized to identify candidate genes and causal loci for important traits in farm animals. In this study, we performed a GWAS for five milk- related traits using the GeneSeek Genomic Profiler Bovine 50K SNP chip in 300 Chinese Holstein cows. Using a mixed linear model, we detected 28 candidate SNPs surpassing the threshold level (P -value < 1.2−6). A total of 18 SNPs located in Bos Taurus autosome (BTA)14 were significantly associated with at least one milk traits after Bonferroni correction. Interestingly, we observed a 2.5 Mb region (range from 1.5 to 4.0 Mb) with high linkage disequilibrium (LD) on BTA14 for fat percentage trait. Among them, four SNPs were identified for five traits including fat percentage, fat yield, protein percentage, milk yield and cheese merit, one SNP was associated with four traits fat percentage, fat yield, protein percentage and cheese merit, and nine SNPs were associated with fat percentage and fat yield traits. Moreover, our haplotype-based analysis confirmed the region of potential association for these traits. Notably, despite most identified SNPs have been reported as previous studies, we also found five novel SNPs as candidate variants for fat percentage, protein percentage and cheese merit traits. [ABSTRACT FROM AUTHOR]

Federated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federation can result in negative transfer within the FL framework. Traditional regularization-based FL methods can partially mitigate the performance disparity between local models. Nevertheless, they do not adequately address the inconsistency in model optimization directions caused by variations in fault information distribution under different working conditions, thereby diminishing the applicability of the global model. This paper proposes a federated adversarial fault diagnosis method driven by fault information discrepancy (FedAdv_ID) to address the challenge of constructing an optimal global model under multiple working conditions. A consistency evaluation metric is introduced to quantify the discrepancy between local and global average fault information, guiding the federated adversarial training mechanism between clients and the federated center to minimize feature discrepancy across clients. In addition, an optimal aggregation strategy is developed based on the information discrepancies among different clients, which adaptively learns the aggregation weights and model parameters needed to reduce global feature discrepancy, ultimately yielding an optimal global model. Experiments conducted on benchmark and real-world motor-bearing datasets demonstrate that FedAdv_ID achieves a fault diagnosis accuracy of 93.09% under various motor operating conditions, outperforming model regularization-based FL methods by 17.89%. [ABSTRACT FROM AUTHOR]

Affected by high density, non-uniform, and unstructured seawater environment, fault detection of Marine Current Turbine (MCT) faces various fault features and strong interferences. To solve these problems, a harmonic analysis strategy based on zero-crossing estimation and Empirical Mode Decomposition (EMD) filter banks is proposed. First, the detection problems of rotor imbalance fault under strong interference conditions are described through an analysis of the fault mechanism and operation environment of MCT. Therefore, against various fault features, a zero-crossing estimation is proposed to calculate instantaneous frequency. Last, and in order to solve the problem that the frequency and amplitude of the operating parameters are partially or completely covered by interference, a band-pass filter based on EMD is used, together with a characteristic frequency selected by a Pearson correlation coefficient. This strategy can accurately detect the multiplicative faults under strong interference conditions, and can be applied to the MCT fault detection system. Theoretical and experimental results verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if they are correctly classified. For the fault detection, the kernel principal component analysis (KPCA), kernel independent component analysis (KICA) and support vector domain description (SVDD) were used and combined with a fusion operator. For the classification, extreme learning machine (ELM) was used with different activation functions combined with an average fusion function. The performance of the methodology was evaluated with a set of experimental vibration data collected from a test-to-failure bearing test rig. The results show the effectiveness of the proposed approach compared to conventional methods. The fault detection was achieved with a false alarm rate of 2.29% and a null missing alarm rate. The data is also successfully classified with a rate of 99.17%. [ABSTRACT FROM AUTHOR]

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Surfactant-assisted biodegradation of wheat straw by Inonotus obliquus under SmF. • Ligninolytic enzymes change the chemical and surface structures of wheat straw. • Surfactant-assisted fermentation is an efficient method for the lignocellulose bioconversion. • Synergistic effects of the surfactant supplement and lignocellulose biodegradation. • Enhanced production and antioxidant activity of the medicinal mushroom exo -polysaccharides. Abstract Current work proposes an innovative wheat straw biomass utilization strategy that connects efficient lignocellulose biodegradation with exo -polysaccharide (EPS) production in I. obliquus under submerged fermentation. The addition of Tween 80 increased the activities of ligninolytic enzymes MnP, LiP and Lac by 1200%, 125% and 39.9%, respectively. When wheat straw lignin recalcitrance was substantially reduced with the aid of Tween 80, I. obliquus was capable of utilizing the substrates and in turn accumulated EPS. The degradation of cellulose, hemicellulose and lignin reached 46.1%, 46.4% and 44.1% on Day 9 of growth, respectively. Meanwhile, the maximum mycelial biomass and EPS production increased by 23.3% and 142.9%, respectively. The EPS had higher contents of sugar, protein, uronic acid, and mannose ratio, and higher antioxidant activity against 2, 2-diphenyl-1-picrylhydrazyl (DPPH), 2,2-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+) and hydroxyl radicals. [ABSTRACT FROM AUTHOR]

In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, the existing semi-supervised federated learning methods may lead to a negative transfer problem since they fail to filter out unreliable model information from the unlabeled client. Therefore, in this study, a dynamic semi-supervised federated learning fault diagnosis method with an attention mechanism (SSFL-ATT) is proposed to prevent the federation model from experiencing negative transfer. A federation strategy driven by an attention mechanism was designed to filter out the unreliable information hidden in the local model. SSFL-ATT can ensure the federation model's performance as well as render the unlabeled client capable of fault classification. In cases where there is an unlabeled client, compared to the existing semi-supervised federated learning methods, SSFL-ATT can achieve increments of 9.06% and 12.53% in fault diagnosis accuracy when datasets provided by Case Western Reserve University and Shanghai Maritime University, respectively, are used for verification. [ABSTRACT FROM AUTHOR]

The development of marine current energy productions has recently gained attention. However, the variation in marine current will lead to weak fault feature and fault misclassification of marine current turbines (MCTs). This paper proposes a new method called extended Concordia transform-latitudinal summation linear discriminant analysis (ECT-LSLDA) to alleviate this problem. First, the extended Concordia transform (ECT) method projects the acquired three-phase currents into the stationary coordinate system to achieve the derivative modulus signals as an effective fault feature. Then, the latitudinal summation linear discriminant analysis (LSLDA) method normalizes and classifies the modulus to realize MCT faults recognition. Experimental results show that the proposed fault diagnosis method achieves high accuracy compare with the state of the art. • A stator current measurement-based way solves the problem that the current signal is non -Gauss distribution without additional measurements. • An ECT based on the derivation is studied in the proposed diagnosis method to extract MCT multi-fault features. • An LSLDA classifier is proposed to solve the problem of fault features diversity and classify the MCT faults. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is an effective method when a single client cannot provide enough samples for multiple condition fault diagnosis of bearings since it can combine the information provided by multiple clients. However, some of the client's working conditions are different; for example, different clients are in different stages of the whole life cycle, and different clients have different loads. At this point, the status of each client is not equal, and the traditional FL approach will lead to some clients' useful information being ignored. The purpose of this paper is to investigate a multiscale recursive FL framework that makes the server more focused on the useful information provided by the clients to ensure the effectiveness of FL. The proposed FL method can build reliable multiple working condition fault diagnosis models due to the increased focus on useful information in the FL process and the full utilization of server information through local multiscale feature fusion. The validity of the proposed method was verified with the Case Western Reserve University benchmark dataset. With less local client training data and complex fault types, the proposed method improves the accuracy of fault diagnosis by 23.21% over the existing FL fault diagnosis. [ABSTRACT FROM AUTHOR]

The objective of this paper is to introduce a method that quantifies the degree of visibility of an offshore wind farm from an observer located along the coast. Due to the relative vicinity with living areas, the installation of an offshore wind farm leads in many cases to public opposition. Therefore, there is still a need to provide planners and decision makers appropriate methods and objective criteria to actually evaluate the visual impact of offshore wind farms. The aim is to provide an intelligible method for visual assessment that can be applied and used within a full decision-aid system oriented towards offshore wind turbines. A visual operator that takes into account the visual distance of an observer located on the ground to the farms layout is introduced. Such operator is based on the aggregation of three different indexes. The horizon occupation is the main index, it evaluates the horizon surface impact. This parameter is modulated by a second index that computes the number of distinguishable turbines and a third index considered as an aesthetic one based on turbine alignments. The whole method is implemented on top of a Geographical Information System (GIS). Overall, the approach provides an estimation of the visual impact without further need for local surveys, it is conceived as a decision-aided mechanism oriented to decision-makers and that can be used at the preliminary steps of the study of an offshore wind farm. The whole approach is experimented in the context of a wind farm in North West France. [ABSTRACT FROM AUTHOR]

This article presents an intelligent cavitation diagnosis method for hydro turbines, employing the sparrow search algorithm (SSA), variational mode decomposition (VMD), and deep learning. Initially, hydroacoustic signals from the hydro turbines are decomposed using SSA-VMD, with the key parameters of VMD optimized by SSA. Subsequently, the decomposed signal components are all fed into a multiscale convolutional neural network (CNN) to adaptively extract features and classify states effectively. This method combines the optimization capabilities of SSA with the adaptive signal decomposition characteristic of VMD and the potent classification capacity of CNN, eliminating the need for complex manual feature extraction typically found in existing approaches. The proposed multiscale CNN based on SSA-optimized VMD (SSA-VMD-MSCNN) offers three primary advantages. Firstly, the added SSA-VMD layer legitimately processes nonstationary hydroacoustic data to adaptively obtain components at multiple characteristic scales. It fully capitalizes on VMD, thereby enabling the CNN to extract multiscale features. Secondly, the SSA-VMD layer directly feeds multiscale components into the hierarchical CNN, effectively extracting rich cavitation information while avoiding the loss of valuable information owing to hand-crafted feature extraction. Finally, the number of channels remains constant through zero-padding, addressing the issue of uncertain component quantity caused by adaptive decomposition of the raw signal. Hydroacoustic data captured by hydrophones, provided by a research institute, demonstrate that the SSA-VMD-MSCNN method presented in this study outperforms traditional CNN, wavelet packet decomposition-based multiscale CNN (WPD-MSCNN), and the shallow machine learning method support vector machine (SVM) for cavitation diagnosis. • The paper presents a novel cavitation diagnosis method for hydro turbines by integrating SSA, VMD, and CNN to develop the SSA-VMD-MSCNN model. • This method extracts multiscale features from hydroacoustic signals, boosting diagnostic accuracy over traditional CNN, WPD-MSCNN, and SVM approaches. • The SSA-VMD-MSCNN model demonstrates superior classification performance, achieving higher accuracy and F1 scores during both training and testing phases. • The method is robust across various conditions and proves effective in handling nonstationary hydroacoustic signals, which are typical in hydro turbine operations. [ABSTRACT FROM AUTHOR]

The freshwater shortage has become a global issue due to the population explosion. As an effective response to the water crisis, forward osmosis (FO) desalination technology is applied because of its comparatively low membrane fouling and low energy consumption. Hydrogels, as draw solutions in FO, have received increasing attention due to their properties of no reverse solute flux, and strong water recovery capability. However, the concentration in the feed solution is below 8000 ppm restricted by the lack of a clear mechanism for osmotic pressure regulation. In this work, a sodium alginate-graphene oxide (SA-GO) hydrogel was proposed with excellent performance in drawing water. The hydrogel has a homogeneous porous structure that enables easy water recovery through compression. When the SA-GO-1 hydrogel was used for the treatment of the simulated seawater (35,000 ppm NaCl solutions), the initial water flux achieved 0.4429 L m−2 h−1 (LMH), and the average water flux remained at 0.1783LMH. In contrast to conventional tactics, the osmotic pressure of hydrogel was regulated based on Flory-Rehner theory (FR). Moreover, the interaction energy was computed to reveal the mechanism of osmotic pressure regulation. This strategy provides a potential solution for the design of high-performance draw solutions for FO systems. • The FR theory was employed to mentor the osmotic pressure regulation of hydrogel. • An osmotic pressure regulation hydrogel was proposed as a draw solution in FO. • The hydrogel can obtain freshwater under 35,000 ppm NaCl solution. • The hydrogel possessed a stable performance during continuous FO operation. [ABSTRACT FROM AUTHOR]

• An improved GANormaly model for fast and accurate diagnosis of blade attachment in marine current turbines is proposed. • The method is based on unsupervised learning, eliminating the need for human tagging and improving the efficiency of human capital use. • Deblurring recovery of underwater images to improve the efficiency of turbine blade attachment cleaning. • Enables fast and accurate diagnosis of blade adhesion on marine current turbines, promoting clean energy production and the development of marine current power generation technology. Marine current energy is a clean and renewable resource that is an available energy source with future power generation potential. However, complex subsea conditions can cause blade failures in marine current turbines (MCTs); therefore, the fault diagnosis of MCTs is essential to ensure their good operation. Image-based diagnosis is a nondestructive approach. To improve the efficiency of using human capital, this study uses the unsupervised learning network GANomaly trained only with normal samples to diagnose the leaf attachment of MCTs. Marine biofouling is a lengthy process, and according to the literature, the blades were contaminated by a 1.1-mm-thick layer of lithium lubricating grease, therefore, this study uses ropes to simulate this attachment. First, we address the problem of blurred underwater images collected using a multiscale retinex enhancement algorithm with color recovery to form a new dataset. GANomaly is modified to diagnose MCT blades with different attachment degrees. The experimental results show that the accuracy of the enhanced dataset is improved by 1.75 %, and the accuracy of the improved GANomaly model reaches 91.23 %, thereby enhancing the efficiency of the fault diagnosis of MCTs. [ABSTRACT FROM AUTHOR]

ARID3A is upregulated in colorectal cancer and can promote the proliferation and metastasis of cancer cells. However, patients with higher level of ARID3A have a better prognosis. This study aimed to uncover the mechanism by which ARID3A benefits the prognosis of colon cancer. Our results indicated that ARID3A upregulation enhanced the chemosensitivity of colon cancer cells to 5‐fluorouracil (5‐FU), whereas ARID3A downregulation inhibited the chemosensitivity of colon cancer cells to 5‐FU. Through database analysis, we found that AKR1C3, a drug resistance‐related gene, was the target of ARID3A. Moreover, AKR1C3 was downregulated in colon cancer tissues compared to normal tissues. Next, we assessed the interaction between AKR1C3 and ARID3A, and found that ARID3A inhibited the transcription of AKR1C3, leading to the downregulation of AKR1C3 in colon cancer cells. We also verified that AKR1C3 inhibited the chemosensitivity of colon cancer cells to 5‐FU. Moreover, patients with higher ratio of ARID3A to AKR1C3 had a better prognosis. This study suggested that ARID3A promoted chemosensitivity of colon cancer cells by inhibiting AKR1C3 in colon cancer. The ratio of ARID3A to AKR1C3 is a good marker to predict the prognosis of colon cancer patients. [ABSTRACT FROM AUTHOR]

Abstract The tomato leaf miner, Tuta absoluta is a destructive pest for Solanaceae crops. While pheromone traps are widely used to monitor this pest, the efficiency of this technique remains to be improved. To improve the efficacy of the pheromone traps, a field study was performed in Southwest China where there is a current outbreak of this pest. Different trap heights (0.5, 1.5 and 2.5 m), hanging methods (ladder and vertical), and concentrations of pheromone compounds (0, 1, 2 and 3 mg) were investigated, and their trapping efficiencies were assessed. The results showed that the traps at a height of 0.5 m achieved the best trapping efficiencies among all the tested heights, with trapping effect values ranging between 40.19% and 48.59%. Further, the performance of the ladder hanging method was superior to that of the vertical hanging method. The maximum trapping amount of T. absoluta was achieved with the use of 2.8 mg of pheromone compound mixture. The dose was extrapolated from calculation. This study provides a theoretical basis for the monitoring and trapping assessment of T. absoluta. [ABSTRACT FROM AUTHOR]

In recent years, with the development of battery technology, hybrid electric ship (HES), as a promising solution to reduce the fuel consumption and emissions, has become a research hotspot. However, frequent use of the battery will accelerate the aging of the battery, and the replacement of scrapped battery will increase the cost of the ship. Therefore, it is necessary to consider delaying battery aging into the energy control strategy of HES. The equivalent consumption minimization strategy (ECMS) is a feasible energy control strategy because it can be implemented in real time. However, under the condition of uncertain initial state of charge (SOC) of the battery, ECMS cannot effectively reduce the fuel consumption unless the equivalent factor (EF) is optimized in real time. In this paper, an adaptive equivalent consumption minimization strategy (A‐ECMS) is proposed, which extracts the global optimal EF trajectory from the dynamic programming (DP) solution and uses the back propagation (BP) neural network to adjust the EF in real time. A trade‐off between the fuel consumption and battery aging is made in the cost function by introducing a weight coefficient. Finally, the effectiveness and the adaptability of the proposed strategy are verified in MATLAB. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies have confirmed that abnormal circadian rhythms are associated with tumorigenesis in breast cancer. However, few studies have investigated the pathological roles of rhythm genes in breast cancer progression. In this study, we aimed to evaluate the aberrant expression of 32 rhythm genes in breast cancer and detect the pathological roles and molecular mechanisms of the altered rhythm gene in regulating the progression of triple negative breast cancer (TNBC). Methods: The aberrant expression of rhythm genes in breast cancer was screened by searching the GEPIA database and validated by using qRT-PCR and immunohistochemistry staining. Bioinformatics analysis combined with luciferase reporter experiment and chromatinimmunopercitation (ChIP) were used to investigate the molecular mechanism about aberrant expression of identified rhythm gene in breast cancer. The pathological roles of identified rhythm gene in TNBC progression was evaluated by colony formation assay, wound healing experiment, transwell assay, subcutaneous tumor formation and the mouse tail vein injection model through gain-of-function and loss-of-function strategies respectively. mRNA array, bioinformatics analysis, luciferase reporter experiment, ChIP and immunoflurescence assay were employed to investigate the key molecules and signaling pathways by which the identified rhythm gene regulating TNBC progression. Results: We identified that nuclear factor interleukin 3 regulated (NFIL3) expression is significantly altered in TNBC compared with both normal breast tissues and other subtypes of breast cancer. We found that NFIL3 inhibits its own transcription, and thus, downregulated NFIL3 mRNA indicates high expression of NFIL3 protein in breast cancer. We demonstrated that NFIL3 promotes the proliferation and metastasis of TNBC cells in vitro and in vivo, and higher expression of NFIL3 is associated with poor prognosis of patients with TNBC. We further demonstrated that NFIL3 enhances the activity of NF-κB signaling. Mechanistically, we revealed that NFIL3 directly suppresses the transcription of NFKBIA, which blocks the activation of NF-κB and inhibits the progression of TNBC cells in vitro and in vivo. Moreover, we showed that enhancing NF-κB activity by repressing NFKBIA largely mimics the oncogenic effect of NFIL3 in TNBC, and anti-inflammatory strategies targeting NF-κB activity block the oncogenic roles of NFIL3 in TNBC. Conclusion: NFIL3 promotes the progression of TNBC by suppressing NFKBIA transcription and then enhancing NF-κB signaling-mediated cancer-associated inflammation. This study may provide a new target for TNBC prevention and therapy. [ABSTRACT FROM AUTHOR]

• A rotation augmentation strategy is utilized to generate sufficient labeled data without laborious manual labeling. • A coarse-fine semantic segmentation network (CSSN) is proposed to recognize the attachment from blurry underwater images. • An adaptive method is proposed to train the CSSN. • Precise attachment area percentage and recognition uncertainty are inferred. Microorganisms attached to marine current turbine may induce imbalance faults that badly affect the power generation efficiency. Therefore, it is necessary to conduct attachment recognition for prompt device maintenance. This paper proposes a coarse-fine semantic segmentation network (CSSN) to adaptively recognize the attachment location and size from blurry underwater images. The CSSN contains a deep coarse branch to perform global segmentation and a shallow fine branch to obtain local contours. The two branches are adaptively fused with dynamic weights in the training process. The final segmentation maps are produced by a softmax layer, after which the precise attachment area percentage can be computed. Besides, dropout is applied to estimate the recognition uncertainty that provides intuitive guidance for the maintenance decision. Experimental results show that the proposed method is efficient to recognize the attachment under turbid submerged conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

Carcass merits are widely considered as economically important traits affecting beef production in the beef cattle industry. However, the genetic basis of carcass traits remains to be well understood. Here, we applied multiple methods, including the Composite of Likelihood Ratio (CLR) and Genome-wide Association Study (GWAS), to explore the selection signatures and candidate variants affecting carcass traits. We identified 11,600 selected regions overlapping with 2214 candidate genes, and most of those were enriched in binding and gene regulation. Notably, we identified 66 and 110 potential variants significantly associated with carcass traits using single-trait and multi-traits analyses, respectively. By integrating selection signatures with single and multi-traits associations, we identified 12 and 27 putative genes, respectively. Several highly conserved missense variants were identified in OR5M13D , NCAPG, and TEX2. Our study supported polygenic genetic architecture of carcass traits and provided novel insights into the genetic basis of complex traits in beef cattle. • We explored the selection signatures and candidate variants affecting carcass traits. • By integrating selection signatures with single and multi-traits associations, we identified 12 and 27 genes for carcass traits, respectively. • Our study supported polygenic genetic basis of carcass traits and provided novel insights into the genetic basis of complex traits in beef cattle. [ABSTRACT FROM AUTHOR]

As tidal stream energy is clean, sustainable, and can be extracted by tidal stream turbine (TST), many countries have paid more attention to it. However, the imbalance faults, caused by the rotor attachment of TST, have an adverse effect on power generation efficiency. Therefore, rotor attachment detection is of great importance for the operation and maintenance of TST. This paper proposes a new method based on asymmetric depthwise convolution and convolution block attention module (ADC-CBAM) to solve this problem. First, rotor images of TST are preprocessed by normalization and centering. Then, asymmetric depthwise convolution (ADC) separates the convolution structure and combines depthwise convolution to extract features and reduce the convolution computation. Furthermore, the convolution block attention module (CBAM) focuses on the feature region to reduce redundant information. Finally, the proposed method is used to detect the rotor attachment. The experimental results demonstrate that the method has higher accuracy and is more lightweight than other state-of-the-art lightweight methods. Moreover, the generalization is verified on the public dataset CIFAR-10. • A rotor attachment detection method based on ADC-CBAM is proposed for TST by combining the ADC and CBAM. • The ADC is proposed to solve the problem of heavy convolution computation and feature extraction of rotor attachment. • The CBAM is studied in the rotor attachment method to focus on the feature region and reduce the redundant information of rotor images. [ABSTRACT FROM AUTHOR]

The marine current turbine (MCT) is becoming more and more popular to produce eco-friendly electricity. However, its performance is negatively affected by MCT imbalance fault. In this paper, an integration fault detection method using stator voltage for MCT is proposed. This method uses an integration way to detect the imbalance fault. The proposed method comprises three steps: First, the data conversion is based on Hilbert transform and the extreme value searching, and then the imbalance fault signature extraction based on the frequency sequences subtraction (FSS). Last, to reduce the data dimension and to set the fault detection limit, a data vector selection method based on principal components analysis (PCA) (called preliminary-selection-based PCA (PS-PCA)) is proposed, the adaptive fault detection is realized by calculating Hotelling T2 and SPE (squared prediction error). Finally, a marine current prototype experimental platform was built to verify the proposed methods. The experimental results show that the method in this paper has high detection accuracy in the fault detection of MCT imbalance under the variable flow rate. • Fault signatures are extracted by Hilbert transform and the extreme value searching. • With the preliminary-selection-based PCA, the fault detection is insensitive to the rotation frequency. • With the proposed method, the fault signature is almost free of harmonic disturbances. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most common malignant tumours, and its morbidity and mortality rates are relatively high. However, the aetiology and pathogenesis of CRC have not been clearly elucidated to date. AT-rich interaction domain 3A (ARID3A) is a member of the ARID3 family and a transcription factor that can bind to specific DNA sites to regulate gene expression. It was reported that ARID3A is involved in various biological processes and may be related to carcinogenesis. In this study, by assessing the mRNA level of ARID3A in TCGA database, we found that ARID3A expression increased in CRC tissues, and proposed that ARID3A could act as a tumour-promoting factor in the development of CRC. To verify this hypothesis, we used cell proliferation, migration and invasion assays to assess the effect of ARID3A on CRC cells. We revealed that ARID3A overexpression enhanced tumour cell proliferation, migration and invasion. ARID3A could target Aurora kinase A (AURKA) to facilitate the malignant phenotype of CRC cells, and patients with a higher ratio of AURKA and ARID3A had a better overall survival. Conclusively, this study showed that ARID3A targeted AURKA to facilitate the development of CRC. The ratio of ARID3A and AURKA could be used as a potential biomarker to predict prognosis, providing a new strategy for the diagnosis and prognosis of CRC. [ABSTRACT FROM AUTHOR]

Marine current energy is attracting more and more attention in the world as a reliable and highly predictable energy resource. However, conventional proportional integral (PI) control will be sensitive to the numerous challenges that exist in a marine current turbine system (MCTs) such as marine current disturbance, torque disturbance and other uncertain parameters. This paper proposes a fuzzy adaptive backstepping control (F-A-BC) approach for a marine current turbine system. The proposed F-A-BC strategy consisted of two parts. First, an adaptive backstepping control approach with the compensation of disturbance and uncertainty was designed to improve anti-interference of the MCT so that the maximum power point tracking (MPPT) was realized. Then, a fuzzy logic control approach was combined to adjust parameters of an adaptive backstepping control approach in real time. The effectiveness of the proposed controller was verified by the simulation of a direct-drive marine current turbine system. The simulation results showed that the F-A-BC has better anti-interference ability and faster convergence compared to the adaptive backstepping control, sliding mode control and fuzzy PI control strategies under disturbances. The error percentage of rotor speed could be reduced by 3.5% under swell effect compared to the conventional controller. Moreover, the robustness of the F-A-BC method under uncertainties was tested and analyzed. The simulation results also indicated that the proposed approach could slightly improve the power extraction capability of the MCTs under variable marine current speed. [ABSTRACT FROM AUTHOR]

With the wide application of lithium batteries, battery fault prediction and health management have become more and more important. This article proposes a method for predicting the remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems caused by continuing to use the battery after reaching its service life threshold. Since the battery capacity is not easy to obtain online, we propose that some measurable parameters should be used in the battery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace the measured value of the particle filter (PF) based on the Kendall rank correlation coefficient (KCCPF) to predict the RUL of the lithium batteries. Simulation results show that the proposed method has high prediction accuracy, stability, and practical value. [ABSTRACT FROM AUTHOR]

AURKA, a cell cycle-regulated kinase, is associated with malignant transformation and progression in many cancer types. We analyzed the expression change of AURKA in pan-cancer and its effect on the prognosis of cancer patients using the TCGA dataset. We revealed that AURKA was extensively elevated and predicted a poor prognosis in most of the detected cancer types, with an exception in colon cancer. AURKA was elevated in colon cancer, but the upregulation of AURKA indicated better outcomes of colon cancer patients. Then we revealed that undermethylation of the AURKA gene and several transcription factors contributed to the upregulation of AURKA in colon cancer. Moreover, we demonstrated that AURKA overexpression promoted the death of colon cancer cells induced by Oxaliplatin, whereas knockdown of AURKA significantly weakened the chemosensitivity of colon cancer cells to Oxaliplatin. Mechanistically, AURKA inhibited DNA damage response by suppressing the expression of various DNA damage repair genes in a TP53-dependent manner, which can partly explain that ARUKA is associated with a beneficial outcome of colon cancer. This study provided a possibility to use AURKA as a biomarker to predict the chemosensitivity of colon cancer to platinum in the clinic. [ABSTRACT FROM AUTHOR]

With the development of oil and gas exploration, the conventional seismic migration imaging technology based on the isotropic assumption no longer meets our current requirements for high-resolution images. Migration in anisotropic media has become an essential requirement for oil and gas exploration. Marine seismic exploration has gradually entered the wide-azimuth and high-density seismic data acquisition stage. However, even for current large high-performance computer clusters, it is still very difficult to implement pre-stack depth migration based on shot gathers. Thus, we present a double-square-root (DSR) equation based on three-dimensional (3D) pre-stack depth migration in midpoint-offset domain for a wide-azimuth dataset in transversely isotropic media with a vertical symmetry axis (VTI media). Considering VTI media, the DSR migration requires extensive memory and computation; we adopted the phase-shift plus interpolation approach to improve the computational efficiency. Then, we extract the angle-domain common-image gathers (ADCIGs) during DSR migration. For real large-scale seismic data, we designed an effective parallel implementation of 3D DSR migration with ADCIGs outputs. Finally, we applied the proposed angle-domain VTI DSR migration on wide-azimuth SEG/EAGE salt dome-based data and real data from the China South Sea. Numerical and practical data illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Although emerging studies showed that certain rhythm genes regulate cancer progression, the expression and roles of the vast majority of rhythm genes in human cancer are largely unknown, and the hallmarks of cancer regulated by rhythm genes have not been detected. In this study, we detected the expression changes of rhythm genes in pan‐cancer and found that almost all rhythm genes mutated in all cancer types, and their expression level was significantly altered partially due to abnormal methylation, and several rhythm genes regulate the expression of other rhythm genes in various cancer types. Furthermore, we revealed that rhythm genes are significantly enriched in genome instability and the expression of certain rhythm genes is correlated with the tumor mutation burden, microsatellite instability, and the expression of DNA damage repair genes in most of the detected cancer types. Moreover, rhythm genes are associated with the infiltration of immune cells and the efficiency of immune blockade therapy. This study provides a comprehensive understanding of the roles of rhythm genes in cancer immunity, which may provide a novel method for the diagnosis and treatment of cancer. [ABSTRACT FROM AUTHOR]

Tidal stream turbine (TST) is a reliable machine for converting tidal stream energy to electric energy. However, the efficiency of power generation will be severely affected by the increasing marine attachment on TST blades. Accurate identification of attachments is very important for their removal. This paper proposes a semi-supervised video segmentation network (SVSN) to recognize attachments in underwater video sources. To alleviate the labeling burden, only a small number of video frames need manual annotation, and sufficient labeled data are collected by a data augmentation algorithm. For utilizing the remaining video frames, these unlabeled data and the labeled data are used to train the SVSN based on semi-supervised adversarial learning. In this way, these two kinds of data benefit from each other, making the network to learn general features for better generalization ability. The SVSN includes a generator and a discriminator. The generator is a modified SegNet to obtain initial segmentation maps. The discriminator, a simple fully convolutional network, provides pixel-wise confidence maps for the semi-supervised adversarial learning. Besides, it enhances the generator segmentation quality via considering object boundary areas. Experimental results demonstrate that precise attachment recognition and fast uncertainty estimation can be accomplished under harsh submerged conditions. • Sufficient labeled images are acquired by a continuous rotation generation algorithm. • During the training stage, the generator is a modified SegNet. The discriminator provides pixel-wise confidence maps. • The method - a semi-supervised video segmentation network, is used to recognize attachment in underwater video sources. [ABSTRACT FROM AUTHOR]

Thyroid cancer is the most common endocrine cancer with an increasing incidence and mortality. Epithelial-mesenchymal transition (EMT) is a biological process contributing to tumor progression, metastasis, and the acquisition of chemotherapy resistance. The impact of the REGγ proteasome activator on EMT in human thyroid cancer cells and the molecular mechanism is still unclear. Here, we found silencing REGγ in thyroid cancer cells inhibited cell migration and invasion, with concurrent upregulation of E-cadherin and Smurf2 expression. Mechanistically, REGγ dependent regulation of Smurf2, an E3 ligase for Smad3, contributed to alteration of Zeb1/2, Snail, Slug, and Twist. Consistently, TGF-β mediated suppression of E-cadherin was attenuated in REGγ deficient cells, coupled with changes in cell morphology, migration and invasion. Furthermore, xenograft metastasis mouse model showed a reduced E-cadherin expression at both mRNA and protein levels, and decreased cell migration. Taken together, our findings provided an important evidence for the role of REGγ in tumor suppression, thereby implicating REGγ as a potential anti-cancer strategy in thyroid cancer therapy. • In this study, we found silencing REGγ in thyroid cancer cells inhibited cell migration and invasion, with concurrent upregulation of E-cadherin and Smurf2 expression. • Mechanistically, REGγ dependent regulation of Smurf2, an E3 ligase for Smad3, contributed to alteration of Zeb1/2, Snail, Slug, and Twist. • Consistently, TGF-β mediated suppression of E-cadherin was attenuated in REGγ deficient cells, coupled with changes in cell morphology, migration and invasion. • Furthermore, xenograft metastasis mouse model showed a reduced E-cadherin expression at both mRNA and protein levels, and decreased cell migration. • Taken together, our findings provided an important evidence for the role of REGγ in tumor suppression, thereby implicating REGγ as a potential anti-cancer strategy in thyroid cancer therapy. [ABSTRACT FROM AUTHOR]

Purpose: Aspirin, one of the most commonly used nonsteroidal anti-inflammatory drugs (NAIDS), not only shows cancer chemoprevention effects but also improves cancer therapeutic effects when combined with other therapies. Studies that focus on aspirin regulation of the hallmarks of cancer and the associated molecular mechanisms facilitate a more thorough understanding of aspirin in mediating chemoprevention and may supply additional information for the development of novel cancer therapeutic agents. Methods: The relevant literatures from PubMed have been reviewed in this article. Results: Current studies have revealed that aspirin regulates almost all the hallmarks of cancer. Within tumor tissue, aspirin suppresses the bioactivities of cancer cells themselves and deteriorates the tumor microenvironment that supports cancer progression. In addition to tumor tissues, blocking of platelet activation also contributes to the ability of aspirin to inhibit cancer progression. In terms of the molecular mechanism, aspirin targets oncogenes and cancer-related signaling pathways and activates certain tumor suppressors. Conclusion: Beyond a chemopreventive agent, aspirin is a master regulator of the hallmarks of cancer. [ABSTRACT FROM AUTHOR]