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Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer.

The significant regulatory role of palmitoylation modification in cancer-related targets has been demonstrated previously. However, the biological functions of Nrf2 in stomach cancer and whether the presence of Nrf2 palmitoylation affects gastric cancer (GC) progression and its treatment have not been reported. Several public datasets were used to look into the possible link between the amount of palmitoylated Nrf2 and the progression and its outcome of GC in patients. The palmitoylated Nrf2 levels in tumoral and peritumoral tissues from GC patients were also evaluated. Both loss-of-function and gain-of-function via transgenic experiments were performed to study the effects of palmitoylated Nrf2 on carcinogenesis and the pharmacological function of 2-bromopalmitate (2-BP) on the suppression of GC progression in vitro and in vitro. We discovered that Nrf2 was palmitoylated in the cytoplasmic domain, and this lipid posttranslational modification causes Nrf2 stabilization by inhibiting ubiquitination, delaying Nrf2 destruction via the proteasome and boosting nuclear translocation. Importantly, we also identify palmitoyltransferase zinc finger DHHC-type palmitoyltransferase 2 (DHHC2) as the primary acetyltransferase required for the palmitoylated Nrf2 and indicate that the suppression of Nrf2 palmitoylation via 2-bromopalmitate (2-BP), or the knockdown of DHHC2, promotes anti-cancer immunity in vitro and in mice model-bearing xenografts. Of note, based on the antineoplastic mechanism of 2-BP, a novel anti-tumor drug delivery system ground 2-BP and oxaliplatin (OXA) dual-loading gold nanorods (GNRs) with tumor cell membrane coating biomimetic nanoparticles (CM@GNRs-BO) was established. In situ photothermal therapy is done using near-infrared (NIR) laser irradiation to help release high-temperature-triggered drugs from the CM@GNRs-BO reservoir when needed. This is done to achieve photothermal/chemical synergistic therapy. Our findings show the influence and linkage of palmitoylated Nrf2 with tumoral and peritumoral tissues in GC patients, the underlying mechanism of palmitoylated Nrf2 in GC progression, and novel possible techniques for addressing Nrf2-associated immune evasion in cancer growth. Furthermore, the bionic nanomedicine developed by us has the characteristics of dual drugs delivery, homologous tumor targeting, and photothermal and chemical synergistic therapy, and is expected to become a potential platform for cancer treatment.

Abstract This study explored whether amygdala reactivity predicted the greed personality trait (GPT) using both task-based and resting-state functional connectivity analyses (ntotal = 452). In Cohort 1 (n = 83), task-based functional magnetic resonance imaging (t-fMRI) results from a region-of-interest (ROI) analysis revealed no direct correlation between amygdala reactivity to fearful and angry faces and GPT. Instead, whole-brain analyses revealed GPT to robustly negatively vary with activations in the right ventromedial prefrontal cortex (vmPFC), supramarginal gyrus, and angular gyrus in the contrast of fearful + angry faces > shapes. Moreover, task-based psychophysiological interaction (PPI) analyses showed that the high GPT group showed weaker functional connectivity of the vmPFC seed with a top-down control network and visual pathways when processing fearful or angry faces compared to their lower GPT counterparts. In Cohort 2, resting-state functional connectivity (rs-FC) analyses indicated stronger connectivity between the vmPFC seed and the top-down control network and visual pathways in individuals with higher GPT. Comparing the two cohorts, bilateral amygdala seeds showed weaker associations with the top-down control network in the high group via PPI analyses in Cohort 1. Yet, they exhibited distinct rs-FC patterns in Cohort 2 (e.g., positive associations of GPT with the left amygdala-top-down network FC but negative associations with the right amygdala-visual pathway FC). The study underscores the role of the vmPFC and its functional connectivity in understanding GPT, rather than amygdala reactivity.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function’s generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot.

Summary: DeepContact is a deep learning software for high-throughput quantification of membrane contact site (MCS) in 2D electron microscopy images. This protocol will guide users through incorporating available DeepContact models in Amira’s artificial intelligence module, thereby allowing invoking of DeepContact functions in organelle segmentation and quantifying of MCS with a user-friendly graphical user interface of Amira software.For complete details on the use and execution of this protocol, please refer to Liu et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-β-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-β-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization.

Abstract The metabolic networks underlying skotomorphogenesis in seedlings remain relatively unknown. On the basis of our previous study on the folate metabolism in seedlings grown in darkness, the plastidial folylpolyglutamate synthetase gene (AtDFB) T‐DNA insertion Arabidopsis thaliana mutant (atdfb‐3) was examined. Under the nitrate‐sufficient condition, the mutant exhibited deficient folate metabolism and hypocotyl elongation, which affected skotomorphogenesis. Further analyses revealed changes to multiple intermediate metabolites related to carbon and nitrogen metabolism in the etiolated atdfb‐3 seedlings. Specifically, the sugar, polyol, and fatty acid contents decreased in the atdfb‐3 mutant under the nitrate‐sufficient condition, whereas the abundance of various organic acids and amino acids increased. In response to nitrate‐limited stress, multiple metabolites, including sugars, polyols, fatty acids, organic acids, and amino acids, accumulated more in the mutant than in the wild‐type control. The differences in the contents of multiple metabolites between the atdfb‐3 and wild‐type seedlings decreased following the addition of exogenous 5‐F‐THF under both nitrogen conditions. Additionally, the mutant accumulated high levels of one‐carbon metabolites, such as Cys, S‐adenosylmethionine, and S‐adenosylhomocysteine, under both nitrogen conditions. Thus, our data demonstrated that the perturbed folate metabolism in the atdfb‐3 seedlings, which was caused by the loss‐of‐function mutation to AtDFB, probably altered carbon and nitrogen metabolism, thereby modulating skotomorphogenesis. Furthermore, the study findings provide new evidence of the links among folate metabolism, metabolic networks, and skotomorphogenesis.

Abstract In order to effectively detect the internal insulation defects of large transformers, a miniature patrol robot fish is designed and used to observe the pressboard inside the transformer, which can visually inspect the insulation condition of the pressboard. In the process of visual inspection by a patrol robot fish, insulation defects observed in the transformer (such as discharge carbon marks, pressboard cracks, etc.) are small size, indistinctive colour contrast and different shapes. The key purpose of the patrol robot fish is to identify the defect targets and defect types intelligently and quickly from the images taken by the camera on the fish. Considering that there are abundant insulation defects in transformers and a lack of samples for learning, a vision detection method based on deep learning network is proposed in this study. The proposed method integrates the variable autoencoder into the traditional Faster‐RCNN target detection network and constructs an improved Faster‐RCNN that enhances feature extraction. This method expands the small‐scale training sample set and improves the generalisation performance of the model. In order to verify the effectiveness of the proposed method, the improved network is trained and tested, and the test results show that the improved network training model can accurately identify and mark the carbon marks on the pressboard surface.

Foot-and-mouth disease virus (FMDV) is an acute, highly contagious, and economically destructive pathogen of vesicular disease that affects domestic and wild cloven-hoofed animals. The FMDV VP1 protein is an important part of the nucleocapsid and plays a significant role during FMDV infection. However, the signal pathways mediated by VP1 in the life cycle of FMDV and the related mechanisms are not yet fully understood. Here, we performed RNA-seq to compare gene expression profiles between pCAGGS-HA-VP1 transfected PK-15 cells and pCAGGS-HA (empty vector) transfected PK-15 cells. The results showed 5,571 genes with significantly different expression levels, of which 2,981 were up-regulated and 2,590 were down-regulated. GO enrichment analysis showed that 51 GO terms were significantly enriched in cell components including protein complex, membrane and organelle part. KEGG enrichment analysis showed 11 KEGG pathways were significantly enriched which were mainly related to the immune system, infectious viral disease, and signal transduction. Among the up-regulated genes, the chemokines such as CCL5, CXCL8, and CXCL10 in turn promoted FMDV replication. In contrast, GBP1, an interferon-stimulated gene that was suppressed by VP1 and FMDV, could effectively inhibit FMDV replication. Our research provides a comprehensive overview of the response of host cells to VP1 protein and a basis for further research to understand the roles of VP1 in FMDV infection including the genes involved in FMDV replication.

The control of interfacial thermal conductivity is the key to two−dimensional heterojunction in semiconductor devices. In this paper, by using non−equilibrium molecular dynamics (NEMD) simulations, we analyze the regulation of interfacial thermal energy transport in graphene (Gr)/hexagonal boron nitride (h-BN) heterojunctions and reveal the variation mechanism of interfacial thermal energy transport. The calculated results show that 2.16% atomic doping can effectively improve interfacial heat transport by more than 15.6%, which is attributed to the enhanced phonon coupling in the mid−frequency region (15–25 THz). The single vacancy in both N and B atoms can significantly reduce the interfacial thermal conductivity (ITC), and the ITC decreases linearly with the increase in vacancy defect concentration, mainly due to the single vacancy defects leading to an increased phonon participation rate (PPR) below 0.4 in the low-frequency region (0–13 THz), which shows the phonon the localization feature, which hinders the interfacial heat transport. Finally, a BP neural network algorithm is constructed using machine learning to achieve fast prediction of the ITC of Gr/h-BN two-dimensional heterogeneous structures, and the results show that the prediction error of the model is less than 2%, and the method will provide guidance and reference for the design and optimization of the ITC of more complex defect-state heterogeneous structures.

Tandem CCCH zinc finger (TZF) proteins are the essential components of processing bodies (PBs) and stress granules (SGs), which play critical roles in growth development and stress response in both animals and plants through posttranscriptional regulation of target mRNA. In this study, we characterized the biological and molecular functions of a novel tandem zinc finger protein, OsTZF7. The expression of OsTZF7 was upregulated by abiotic stresses, including polyethylene glycol (PEG) 4000, NaCl, and abscisic acid (ABA) in rice. Accordingly, the overexpression of OsTZF7 increased drought tolerance and enhanced sensitivity to exogenous ABA in rice, whereas the knockdown of OsTZF7 resulted in the opposite phenotype. RNA-seq analysis revealed that genes related to “response to stress,” “abscisic acid signaling,” “methylated histone binding,” and “cytoplasmic mRNA processing body” are regulated by OsTZF7. We demonstrated that OsTZF7 can traffic between the nucleus and PBs/SGs, and the leucine-rich nuclear export signal (NES) mediates the nuclear export of OsTZF7. Additionally, we revealed that OsTZF7 can bind adenine- and uridine-rich (AU-rich) element (ARE) or ARE-like motifs within the 3′ untranslated region of downregulated mRNAs, and interact with PWWP family proteins in vitro. Together, these results indicate that OsTZF7 positively regulates drought response in rice via ABA signaling and may be involved in mRNA turnover.

In complex power systems, when power equipment fails, multiple concurrent failures usually occur instead of a single failure. Concurrent failures are so common and hidden in complex systems that diagnosis requires not only analysis of failure characteristics, but also correlation between failures. Therefore, in this paper, a concurrent fault diagnosis method is proposed for power equipment based on graph neural networks and knowledge graphs. First, an electrical equipment failure knowledge map is created based on operational and maintenance records to emphasize the relevance of the failed equipment or component. Next, a lightweight graph neural network model is built to detect concurrent faults in the graph data. Finally, a city’s transformer concurrent fault is taken as an example for simulation and validation. Simulation results show that the accuracy and acquisition rate of graph neural network mining in Knowledge Graph is superior to traditional algorithms such as convolutional neural networks, which can achieve the effectiveness and robustness of concurrent fault mining.

Due to the development of intelligent electric devices and advanced metering infrastructures, demand response will be widely utilized in the trading of the electricity market and maintain energy balance of the power system. In this paper, a two-stage nested bilevel model for the optimal bidding strategy of a load agent (LA) with incentive-based demand response in day-ahead and balancing markets is proposed. In the upper-level model, the optimal trading strategy of the LA is formulated to maximize the operating profit of the LA in the day-ahead energy and balancing markets. On the other hand, the lower-level proposes the clearing market model of the independent system operator, which aim to maximize social welfare. The LA acts as a price-maker in the first-stage of the bilevel model, which is bilevel nonlinear programming (BNLP) problem. Karush-Kuhn-Tucker conditions and dual theory are used to transform the BNLP into single-level programming. The LA acts as a price-taker accepts the day-ahead energy clearing-price in the second-stage of the bilevel model, which is a bilevel mixed-integer linear stochastic programming problem. Finally, implementing the two-stage nested bilevel model on modifying the 8-bus power system demonstrates the applicability of the proposed model and analysis the sensitivity of the LA' profit to unit price and the committed reserve capacity demand of the day-ahead reserve market.

In this study, the structural, morphological, and magnetic properties and the local structure of Bi1−xLaxFe0.95Co0.05O3 (x = 0.05, 0.10, 0.15, 0.20) nanoparticles were systematically investigated. In the (La, Co) co-doped BiFeO3 samples, a structural transition from a rhombohedral to orthorhombic structure was observed via X-ray diffraction. This structural phase transition resulted in significantly improved magnetic properties (Ms = 1.706 emu/g at 60 kOe). SEM analysis revealed that grain size decreased with increasing La concentration. The magnetic hysteresis loops confirmed that the significant enhancement of magnetization for all samples. Finally, combining the experimental synchrotron radiation techniques, we studied the samples’ local structure in order to analyze the reasons why the samples’ magnetic properties were enhanced.

Performing continuous sets to failure is fatiguing during the plyometric training. Cluster sets have been used to redistribute total rest time to create short frequent sets so that muscle fatigue can be avoided. The purpose of the study was to investigate the effects of inter-set recovery time on lower extremity explosive power, neuromuscular activity, and tissue oxygenation during plyometric exercise and recovery. An integrated assessment of explosive power, muscle electrical activity, and tissue oxygenation was adopted in the present study to help understand local muscle metabolism and fatigue during plyometric exercise and recovery. Ten university male basketball players participated in this study. Subjects performed 4 groups of exercise, each group comprised of 3 sets of jumps: 1, 2, 3, or 5 min. Surface electromyography (sEMG) signals were collected from 9 lower extremity muscles; near-infrared spectroscopy (NIRS) was recorded on vastus lateralis; mechanical data during plyometric exercise were collected from a force plate. No significant differences among sets and among groups were found regarding explosive power, jump height, EMG intensity, mean power frequency, the rate of tissue saturation index, and HbO2 changes between baseline and recovery. The current study has shown no muscular fatigue induced during the 4 groups of exercise. The results of this study may help inform recommendations concerning the recovery time during plyometric exercises at low loads (30% 1 RM).

The ZnO@TiO2 core/shell nanowire arrays with different thicknesses of the TiO2 shell were synthesized, through depositing TiO2 on the ZnO nanowire arrays using the pulsed laser deposition process. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images show that these core/shell nanowires were homogeneously coated with TiO2 nanoparticles with high crystallinity, appearing to be a rather rough surface compared to pure ZnO nanowires. The efficiency of ZnO@TiO2 core/shell structure-based dye-sensitized solar cells (DSSCs) was improved compared with pure ZnO nanowires. This is mainly attributed to the enlarged internal surface area of the core/shell structures, which increases dye adsorption on the anode to improve the light harvest. In addition, the energy barrier which formed at the interface between ZnO and TiO2 promoted the charge separation and suppressed the carrier recombination. Furthermore, the efficiency of DSSCs was further improved by increasing the thickness of the TiO2 shell. This work shows an efficient method to achieve high power conversion efficiency in core/shell nanowire-based DSSCs.

Self-referential emotion refers to the process of evaluating emotional stimuli with respect to the self. Processes indicative of a self-positivity bias are reflected in electroencephalogram (EEG) signals at ~400 ms when the task does not require a discrimination of self from other. However, when distinguishing between self-referential and other-referential emotions is required, previous studies have shown inconsistent temporal dynamics of EEG signals in slightly different tasks. Based on the observation of early self–other discrimination, we hypothesized that self would be rapidly activated in the early stage to modulate emotional processing in the late stage during an implicit self-referential emotion. To test this hypothesis, we employed an implicit task in which participants were asked to judge the order of Chinese characters of trait adjectives preceded by a self (“I”) or other pronoun (“He” or “She”). This study aimed to explore the difference of social-related emotional evaluation from self-reference; the other pronoun was not defined to a specific person, rather it referred to the general concept. Sixteen healthy Chinese subjects participated in the experiment. Event-related potentials (ERPs) showed that there were self-other discrimination effects in the N1 (80–110 ms) and P1 (170–200 ms) components in the anterior brain. The emotional valence was discriminated in the later component of N2 (220–250 ms). The interaction between self-reference and emotional valence occurred during the late positive potential (LPP; 400–500 ms). Moreover, there was a positive correlation between response time (RT) and N1 in the self-reference condition based on the positive-negative contrast, suggesting a modulatory effect of the self-positivity bias. The results indicate that self-reference emerges earlier than emotion and then combines with emotional processing in an implicit task. The findings extend the view that the self plays a highly integrated and modulated role in self-referential emotion processing.

With increasing age cognitive performance slows down. This includes cognitive processes essential for motor performance. Additionally, performance of motor tasks becomes less accurate. The objective of the present study was to identify general neural correlates underlying age-related behavioral slowing and the reduction in motor task accuracy. To this end, we continuously recorded EEG activity from 18 younger and 24 older right-handed healthy participants while they were performing a simple finger tapping task. We analyzed the EEG records with respect to local changes in amplitude (power spectrum) as well as phase locking between the two age groups. We found differences between younger and older subjects in the amplitude of post-movement synchronization in the β band of the sensory-motor and medial prefrontal cortex (mPFC). This post-movement β amplitude was significantly reduced in older subjects. Moreover, it positively correlated with the accuracy with which subjects performed the motor task at the electrode FCz, which detects activity of the mPFC and the supplementary motor area. In contrast, we found no correlation between the accurate timing of local neural activity, i.e. phase locking in the δ-θ frequency band, with the reaction and movement time or the accuracy with which the motor task was performed. Our results show that only post-movement β amplitude and not δ-θ phase locking is involved in the control of movement accuracy. The decreased post-movement β amplitude in the mPFC of older subjects hints at an impaired deactivation of this area, which may affect the cognitive control of stimulus-induced motor tasks and thereby motor output.

The complete mitochondrial genome of Thryssa hamiltonii has been determined. The whole sequence was 16,894 bp in length and included 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNA genes and one control region (D-loop). The overall base composition is A 30.77%, C 27.97%, G 16.25%, T 25.01%, with a slightly A + T bias of 55.78%. With the exception of ND6 and eight tRNA genes, all other mitochondrial genes are encoded on the heavy strand. Three tandem repeat sequences were observed in the control region. Phylogenetic tree was constructed based on 13 protein-coding genes sequences of 21 clupeoidei species, and the result showed that T. hamiltonii is most closely related to T. dussumieri. These mitogenome sequence data will be useful for population genetics and phylogenetic analysis of the Clupeoidei.

The complete mitochondrial genome of Thryssa setirostris has been determined. The whole sequence was 16,926 bp in length and included 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes, and 1 control region (D-loop). The overall base composition is A 29.88%, C 29.23%, G 16.77%, T 24.02%, with a slightly A + T bias of 54%. With the exception of ND6 and eight tRNA genes, all other mitochondrial genes are encoded on the heavy strand. Phylogenetic tree was constructed based on 13 protein-coding genes sequences of 21 clupeoidei species, and the result showed that T. setirostris is most closely related to T. dussumieri. These mitogenome sequence data will be useful for population genetics and phylogenetic analysis of the Clupeoidei.

Trachidermus fasciatus is a small catadromous fish and has been listed as a second class state protected aquatic animal since 1988 in China due to the declines in its abundance. We describe the complete mitogenome of T. fasciatus in this study. The mitogenome is 16,536 nucleotides long and contains 13 protein-coding genes (PCGs), two ribosomal RNA genes, 22 transfer RNA genes, and two main non-coding regions. The overall base composition includes C (30%), A (26.3%), T (25.5%), and G (18.2%). Moreover, the 13 PCGs encode 3800 amino acids in total, all the PCGs use the initiation codon ATG except COI uses GTG. Most of them have TAA or TAG as the stop codon, except COII, ND4 and Cytb use an incomplete stop codon T. The phylogenetic tree based on the neighbour joining method was constructed to provide relationship within Cottidae, which could be a useful basis for management of this species.