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Recently, there has been a growing interest in predicting human motion, which involves forecasting future body poses based on observed pose sequences. This task is complex due to modeling spatial and temporal relationships. The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks. However, RNNs have several drawbacks, such as vanishing or exploding gradients. Other researchers have attempted to solve the communication problem in the spatial dimension by integrating Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) models. These works deal with temporal and spatial information separately, which limits the effectiveness. To fix this problem, we propose a novel approach called the multi-graph convolution network (MGCN) for 3D human pose forecasting. This model simultaneously captures spatial and temporal information by introducing an augmented graph for pose sequences. Multiple frames give multiple parts, joined together in a single graph instance. Furthermore, we also explore the influence of natural structure and sequence-aware attention to our model. In our experimental evaluation of the large-scale benchmark datasets, Human3.6M, AMSS and 3DPW, MGCN outperforms the state-of-the-art in pose prediction., arXiv admin note: text overlap with arXiv:2110.04573 by other authors

Along with the development of sonar technology, the detection accuracy and stability of sonar have been improved. A large amount of seabed sediment information can be obtained through sonar detection. However, this information is often accompanied by noise interference, resulting in poor quality of the generated images. Moreover, sonar images are different from conventional images. There are single-channel images. The model needs to classify the images according to the texture features in the images. Coupled with the scarcity of sonar data, this makes it difficult to accurately classify the seabed sediment. According to the characteristics of sonar images, we propose the ShuffleNet-DSE which is a classification model based on deep learning. The ShuffleNet-DSE network is based on ShuffleNet-V2, while ensuring the lightweight of the network, it incorporates feature dense connection and Squeeze-and-Excitation (SE) structure channel self-attention. And combined with the sonar image's characteristics, the partial activation function of the model is changed to the Swish. The experimental results show that compared with the traditional machine learning classification method, ShuffleNet-DSE has greatly improved the classification accuracy and the computational cost. Compared with excellent neural network models such as AlexNet, MobileNet-V3, GoogLeNet and ResNet, it is more suitable for sonar image processing.

Keloids are benign dermal tumors that arise from abnormal wound healing processes following skin lesions. Surgical excision followed by radiotherapy plays an important role in the treatment of keloids. Nevertheless, radioresistance remains a serious impediment to treatment efficacy. Investigation of the molecular response of keloids to radiation may contribute to radiosensitizing strategies. Primary keloid fibroblasts from human keloids were isolated and irradiated with X-ray. The expression profiles of messenger RNA (mRNA) in nonradiated and irradiated primary keloid fibroblasts were measured by mRNA sequencing analysis. Then, we identified common motifs and corresponding transcription factors of dysregulated mRNAs by using bioinformatic analysis of the proximal promoters. Whereafter, GO and KEGG were used to analyze the functional enrichment of the differentially expressed genes. We found that radiation not only suppressed proliferation but also increased cell senescence of primary keloid fibroblasts. There were 184 mRNAs and 204 mRNAs that showed significant changes in 4 and 8 Gy irradiated primary keloid fibroblasts, respectively. Among them, 8 upregulated and 30 downregulated mRNAs showed consistent alterations in 4 and 8 Gy irradiated primary keloid fibroblasts. More importantly, the xForkhead box O1 (FOXO1) signaling pathway was involved in the irradiation response. Pretreatment with the FOXO1 signaling inhibitor AS1842856 significantly promoted LDH release, apoptosis and senescence of primary keloid fibroblasts following irradiation. Our findings illustrated the molecular changes in human keloid fibroblasts in response to radiation, and FOXO1 pathway inhibition is expected to provide a novel strategy for the radiosensitization of keloids.

Supplemental Material for Transcriptome Profiling Unveils a Critical Role of IL-17 Signaling-Mediated Inflammation in Radiation-Induced Esophageal Injury in Rats by Jia Yao, Jinkang Zhang, Jinlong Wang, Qian Lai, Weijun Yuan, Zhongyu Liu, Shuanghua Cheng, Yahui Feng, Zhiqiang Jiang, Yuhong Shi, Sheng Jiang, and Wenling Tu in Dose-Response

ZnMn2O4, with theoretical specific capacity of 1008 mAh g−1, is a promising anode material for lithium-ion batteries. It attracts intensive research interests not only because of its high specific capacity, but also due to its low cost, abundant resources and environmental friendliness. However, its poor reaction kinetics, low intrinsic electrical conductivity and large volume changes upon cycling impede its commercial application. In this work, we demonstrate a method to effectively synthesize a three-dimensional, layered network architecture utilizing reduced graphene oxide (rGO) and cross-linked carbon nanotubes (CNTs) as substrates for ZnMn2O4 nanoparticles. The ZnMn2O4 nanoparticles are grown in-situ on the surface of both the rGO and the CNTs. The results show that the composite samples display a significantly enhanced electrochemical performance when compared with their pristine counterparts. In particular, the ZnMn2O4-rGO/CNT composite anode delivered an enhanced capacity of 606 mAh g−1 at 1.0 A g−1 after 1000 cycles with an 83% capacity retention. The synergistic effect between the rGO and the CNTs is believed to be the reason for the improved performance. In-situ X-ray diffraction (XRD) was used to successfully reveal the electrochemical conversion reaction mechanism of the new ZnMn2O4 anode.

Vibrio alginolyticus is the main Vibrio pathogen in aquaculture in the south of China,the number of which is the largest in marine Vibrio class.In the present study,the outer membrane protein K gene(OmpK)of V.alginolyticus strain ATCC17749 was amplified by PCR and cloned into high efficient expression vector pET28a.The results of sequencing and restriction enzyme analysis combined with agarose gel electrophoresis showed that the open reading frame sequence was correct and fully consistent with what had reported.The recombinant plasmid of pET-28a-OmpK was successfully constructed and transformed into E.coli BL21 pLys E.The fusion protein was expressed under the IPTG inducing condition,and the expression product was purified by an affinity chromatographic method.Mouse anti-OmpK poly-clonal antibodies(PAbs)were obtained via applying protein OmpK as antigen to immune mice.Western-blotting analysis proved the recombinant protein has a good reactive ability against OmpK positive serum.Then,an indirect Enzyme-Linked Immunosorbent Assay(ELISA)for the rapid diagnosis of V.alginolyticus has been developed using the PAbs.The lowest V.alginolyticus suspension was 104 CFU/mL.Cross reactions of antisera with other bacteria were detected,and all results were negative.The results indicated the suitability and simplicity of the test as a rapid,field diagnostic tool for V.alginolyticus and it can be used for the rapid detection of V.alginolyticus.Further investigation will be focused on the development of an indirect ELISA Kit for the detection of V.alginolyticus.

In this study,the growth characteristics of four Pseudosciaena crocea groups including the selfing of DD(Daiquyang♀×Daiquyang♂),the selfing of GG(Guanjingyang♀×Guanjingyang ♂),their direct cross DG(Daiquyang ♀×Guanjingyang ♂),and back-cross GD(Guanjingyang ♀ × Daiquyang ♂) were determined.Results after 526 days of grow-out showed that the final mean weights were higher for the GG(330.514 g) and the GD(336.694 g) than those of the DD(278.975 g) and the DG(243.297 g).The 1-year-old DD grew in a lower rate,but after the winter,the growth rate of DD accelerated significantly:the growth rate in length was higher than the other three groups while the weight gain was not significantly different from the GG and the GD.That is,the growth potential of the DD at later stage was high.The order of the fullness on 526th day was DDDGGDGG.A superior cross GD was constructed,which displayed significant heterosis for growth and body shape.It was concluded that the GD had excellent economic and breeding potential.The growth curves(DD:W=0.020 6L2.942 7,R2=0.994 9;GG:W=0.013 9L3.099 4,R2=0.978 5;DG:W=0.022 9L2.913 6,R2=0.990 5;GD:W=0.017 7L3.007 9,R2=0.994 9) fitted according to weight and body length had the high degree.The weight could be estimated by their body length,and this growth curves model could be applied in the production.