Differentiation in detoxification gene complements, including neofunctionalization of duplicated cytochrome P450 genes, between lineages of cotton bollworm, Helicoverpa armigera.

Here we investigate the evolutionary dynamics of five enzyme superfamilies (CYPs, GSTs, UGTs, CCEs and ABCs) involved in detoxification in Helicoverpa armigera. The reference assembly for an African isolate of the major lineages, H. a. armigera, has 373 genes in the five superfamilies. Most of its CYPs, GSTs, UGTs and CCEs and a few of its ABCs occur in blocks and most of the clustered genes are in subfamilies specifically implicated in detoxification. Most of the genes have orthologues in the reference genome for the Oceania lineage, H. a. conferta. However, clustered orthologues and subfamilies specifically implicated in detoxification show greater sequence divergence and less constraint on non-synonymous differences between the two assemblies than do other members of the five superfamilies. Two duplicated CYPs, which were found in the H. a. armigera but not H. a. conferta reference genome, were also missing in 16 Chinese populations spanning two different lineages of H. a. armigera. The enzyme produced by one of these duplicates has higher activity against esfenvalerate than a previously described chimeric CYP mutant conferring pyrethroid resistance. Various transposable elements were found in the introns of most detoxification genes, generating diverse gene structures. Extensive resequencing data for the Chinese H. a. armigera and H. a. conferta lineages also revealed complex copy number polymorphisms in 17 CCE001s in a cluster also implicated in pyrethroid metabolism, with substantial haplotype differences between all three lineages. Our results suggest that cotton bollworm has a versatile complement of detoxification genes which are evolving in diverse ways across its range.

Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security.

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

Efficient Solvent-Free Synthesis of Indolizines Using CuBr Catalyst from Pyridine, Acetophenone, and Electron-Deficient Alkenes.

Herein, we have developed a new approach for the synthesis of indolizine via Cu-catalyzed reaction of pyridine, acetophenone, and nitroolefin under mild conditions in high yields. This reaction involved the formation of C-N and C-C bonds and new indolizine compounds with high stereoselectivity and excellent functional group tolerance.

Constructing lactylation-related genes prognostic model to effectively predict the disease-free survival and treatment responsiveness in prostate cancer based on machine learning.

Background: Prostate cancer (PCa) is one of the most common malignancies in men with a poor prognosis. It is therefore of great clinical importance to find reliable prognostic indicators for PCa. Many studies have revealed the pivotal role of protein lactylation in tumor development and progression. This research aims to analyze the effect of lactylation-related genes on PCa prognosis. Methods: By downloading mRNA-Seq data of TCGA PCa, we obtained the differential genes related to lactylation in PCa. Five machine learning algorithms were used to screen for lactylation-related key genes for PCa, then the five overlapping key genes were used to construct a survival prognostic model by lasso cox regression analysis. Furthermore, the relationships between the model and related pathways, tumor mutation and immune cell subpopulations, and drug sensitivity were explored. Moreover, two risk groups were established according to the risk score calculated by the five lactylation-related genes (LRGs). Subsequently, a nomogram scoring system was established to predict disease-free survival (DFS) of patients by combining clinicopathological features and lactylation-related risk scores. In addition, the mRNA expression levels of five genes were verified in PCa cell lines by qPCR. Results: We identified 5 key LRGs (ALDOA, DDX39A, H2AX, KIF2C, RACGAP1) and constructed the LRGs prognostic model. The AUC values for 1 -, 3 -, and 5-year DFS in the TCGA dataset were 0.762, 0.745, and 0.709, respectively. The risk score was found a better predictor of DFS than traditional clinicopathological features in PCa. A nomogram that combined the risk score with clinical variables accurately predicted the outcome of the patients. The PCa patients in the high-risk group have a higher proportion of regulatory T cells and M2 macrophage, a higher tumor mutation burden, and a worse prognosis than those in the low-risk group. The high-risk group had a lower IC50 for certain chemotherapeutic drugs, such as Docetaxel, and Paclitaxel than the low-risk group. Furthermore, five key LRGs were found to be highly expressed in castration-resistant PCa cells. Conclusion: The lactylation-related genes prognostic model can effectively predict the DFS and therapeutic responses in patients with PCa.

Micro and Macro Flooding Mechanism and Law of a Gel Particle System in Strong Heterogeneous Reservoirs.

To address the issue of ineffective injection resulting from the consistent channeling of injected water through highly permeable channels in ultra-deep, high-temperature, high-salinity, and strongly heterogeneous reservoirs during the production process, a gel particle profile control agent suitable for high-temperature and high-salinity conditions was chosen. With the help of the glass etching visual microscopic model and the heterogeneous long core model, the formation mechanism of a water flooding channeling path and the distribution law of the remaining oil were explored, the microscopic profile control mechanism of the different parameters was clarified, and the profile control effect of macroscopic core displacement was analyzed. The research shows that the formation mechanism of a water flooding channeling path is dominated by the distribution law of the permeability section and the connection mode between different penetration zones. The remaining oil types after water flooding are mainly contiguous block, parallel throats, and multi-branch clusters. The profile control effect of gel particles on reservoir vertical heterogeneity is better than that of reservoir lateral heterogeneity. It was found that 10 wt% submicron particles with a median diameter of 600 nm play a good role in profiling and plugging pores of 5-20 µm. In addition, 10 wt% micron-sized particles with a median diameter of 2.63 µm mainly play a strong plugging role in the pores of 20-30 µm, and 5 wt% micron-sized particles with a median diameter of 2.63 µm mainly form a weak plugging effect on the pores of 10-20 µm. The overall profile control effect of 10 wt% submicro particles is the best, and changes in concentration parameters have a more significant effect on the profile control effect. In the macroscopic core profile control, enhanced oil recovery (EOR) can reach 16%, and the gel particles show plugging, deformation migration, and re-plugging. The research results provide theoretical guidance for tapping the potential of the remaining oil in strong heterogeneous reservoirs. To date, the gel particles have been applied in the Tahe oilfield and have produced an obvious profile control effect; the oil production has risen to the highest value of 26.4 t/d, and the comprehensive water content has fallen to the lowest percentage of 32.1%.

Porous nanocomposites with enhanced intrinsic piezoresistive sensitivity for bioinspired multimodal tactile sensors.

In this work, we propose porous fluororubber/thermoplastic urethane nanocomposites (PFTNs) and explore their intrinsic piezoresistive sensitivity to pressure. Our experiments reveal that the intrinsic sensitivity of the PFTN-based sensor to pressure up to 10 kPa increases up to 900% compared to the porous thermoplastic urethane nanocomposite (PTN) counterpart and up to 275% compared to the porous fluororubber nanocomposite (PFN) counterpart. For pressures exceeding 10 kPa, the resistance-pressure relationship of PFTN follows a logarithmic function, and the sensitivity is 221% and 125% higher than that of PTN and PFN, respectively. With the excellent intrinsic sensitivity of the thick PFTN film, a single sensing unit with integrated electrode design can imitate human skin for touch detection, pressure perception and traction sensation. The sensing range of our multimodal tactile sensor reaches ~150 Pa, and it exhibits a linear fit over 97% for both normal pressure and shear force. We also demonstrate that an electronic skin, made of an array of sensing units, is capable of accurately recognizing complex tactile interactions including pinch, spread, and tweak motions.

FBLN5 was Regulated by PRDM9, and Promoted Senescence and Osteogenic Differentiation of Human Periodontal Ligament Stem Cells.

OBJECTIVES: Periodontal ligament stem cells (PDLSCs) are ideal seed cells for periodontal tissue regeneration. Our previous studies have indicated that the histone methyltransferase PRDM9 plays an important role in human periodontal ligament stem cells (hPDLSCs). Whether FBLN5, which is a downstream gene of PRDM9, also has a potential impact on hPDLSCs is still unclear. METHODS: Senescence was assessed using ß-galactosidase and Enzyme-linked immunosorbent assay (ELISA). Osteogenic differentiation potential of hPDLSCs was measured through Alkaline phosphatase (ALP) activity assay and Alizarin red detection, while gene expression levels were evaluated using western blot and RT-qPCR analysis. RESULTS: FBLN5 overexpression promoted the osteogenic differentiation and senescence of hPDLSCs. FBLN5 knockdown inhibited the osteogenic differentiation and senescence of hPDLSCs. Knockdown of PRDM9 decreased the expression of FBLN5 in hPDLSCs and inhibited senescence of hPDLSCs. Additionally, both FBLN5 and PRDM9 promoted the expression of phosphorylated p38 MAPK, Erk1/2 and JNK. The p38 MAPK pathway inhibitor SB203580 and the Erk1/2 pathway inhibitor PD98059 have the same effects on inhibiting the osteogenic differentiation and senescence of hPDLSCs. The JNK pathway inhibitor SP600125 reduced the senescence of hPDLSCs. CONCLUSION: FBLN5 promoted senescence and osteogenic differentiation of hPDLSCs via activation of the MAPK signaling pathway. FBLN5 was positively targeted by PRDM9, which also activated the MAPK signaling pathway.

A new secondary metabolite from the marine-derived fungus Phomopsis lithocarpus FS508.

Based on the One Strain-Many Compounds (OSMAC) strategy, the secondary metabolites of Phomopsis lithocarpus FS508 were investigated. As a result, a new secondary metabolite, 4-methoxy-3-[4-(acetyloxy)-3-methyl-2-butenyl]benzoic acid (1) as well as eleven known compounds were isolated from the fermentation product of the strain FS508. Their structures were determined by NMR, IR, UV, and MS spectroscopic data analyses. All the isolated compounds were evaluated for cytotoxic and anti-inflammatory activities. Among them, compounds 3 and 9 displayed potent cytotoxicity against HepG-2 cell line, and compounds 2, 3 and 12 showed significant anti-inflammatory activities.

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

INTRODUCTION: Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT. METHODS: In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC. RESULTS: A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively. CONCLUSION: A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

ReFs: A hybrid pre-training paradigm for 3D medical image segmentation.

Self-supervised learning (SSL) has achieved remarkable progress in medical image segmentation. The application of an SSL algorithm often follows a two-stage training process: using unlabeled data to perform label-free representation learning and fine-tuning the pre-trained model on the downstream tasks. One issue of this paradigm is that the SSL step is unaware of the downstream task, which may lead to sub-optimal feature representation for a target task. In this paper, we propose a hybrid pre-training paradigm that is driven by both self-supervised and supervised objectives. To achieve this, a supervised reference task is involved in self-supervised learning, aiming to improve the representation quality. Specifically, we employ the off-the-shelf medical image segmentation task as reference, and encourage learning a representation that (1) incurs low prediction loss on both SSL and reference tasks and (2) leads to a similar gradient when updating the feature extractor from either task. In this way, the reference task pilots SSL in the direction beneficial for the downstream segmentation. To this end, we propose a simple but effective gradient matching method to optimize the model towards a consistent direction, thus improving the compatibility of both SSL and supervised reference tasks. We call this hybrid pre-training paradigm reference-guided self-supervised learning (ReFs), and perform it on a large-scale unlabeled dataset and an additional reference dataset. The experimental results demonstrate its effectiveness on seven downstream medical image segmentation benchmarks.

Comparative cytogenetic analysis of diploid and triploid pacific abalone, Haliotis discus hannai.

Cytogenetic analysis of triploid Haliotis discus hannai larvae (produced by chemical treatment) and its diploids were performed. The results showed that triploid H. discus hannai had a chromosome number of 3n = 54, consisting of 30 metacentric (m) and 24 submetacentric (sm) chromosomes, while the diploids had a chromosome number of 2n = 36, consisting of 20 metacentric (m) and 16 submetacentric (sm) chromosomes. Notably, both triploids and diploids displayed variation in the number of NORs and/or their diameter. The average number of NORs was significantly higher in triploids than in diploids (P < 0.05), while there was no significant difference in the average diameter of NORs between the two groups (P > 0.05). Additionally, 5S rDNA localization to 3 submetacentric chromosomes was observed in triploids, compared to 2 submetacentric chromosomes in diploids. The number of 18S rDNA sites displayed positional conservancy and quantitative variability in both diploids and triploids. Specifically, 18S rDNA was found at the end of the chromosome in both groups, with triploids exhibiting a significantly higher number of loci than diploids (P < 0.01). This study provides valuable insights into the cytogenetic characteristics of triploid H. discus hannai, which could facilitate further research on the stability of the chromosome set in this species.

Pimarane-Type Diterpenes with Anti-Inflammatory Activity from Arctic-Derived Fungus Eutypella sp. D-1.

The Arctic-derived fungus Eutypella sp. D-1 can produce numerous secondary metabolites, and some compounds exhibit excellent biological activity. Seven pimarane-type diterpenes, including three new compounds eutypellenone F (1), libertellenone Y (2), and libertellenone Z (3), and four known compounds (4-7), were isolated from fermentation broth of Eutypella sp. D-1 by the OSMAC strategy of adding ethanol as a promoter in the culture medium. Compound 2 has a rare tetrahydrofuran-fused pimarane diterpene skeleton. The anti-inflammatory activity of all compounds was evaluated. Compounds 3-6 showed a significant inhibitory effect on cell NO release at 10 µmol/L by in vitro experiments, of which 3-5 had inhibitory rates over 60% on nitric oxide (NO) release. Subsequently, the anti-inflammatory activity of 3-5 was evaluated based on a zebrafish model, and the results showed that 3 had a significant inhibitory effect on inflammatory cells migration at 40 µmol/L, while 4 and 5 had a significant inhibitory effect at 20 µmol/L. Moreover, compounds 3-5 have the same conjugated double bond structure, which may be an important group for these compounds to exert anti-inflammatory activity.

High Bioaffinity Controllable Assembly Nanocarrier UiO-66-NH2@Quantum Dot-Based Immunochromatographic Assay for Simultaneous Detection of Five Mycotoxins in Cereals and Feed.

Herein, the UiO-66-NH2@quantum dot (NU66@QD) was synthesized with excellent fluorescence intensity and biocompatibility, which was used to develop a multiple immunochromatographic assay (ICA) for the detection of aflatoxin B1 (AFB1), fumonisin B1 (FB1), deoxynivalenol (DON), T-2 toxins (T-2), and zearalenone (ZEN) in cereals and feed. Five monoclonal antibodies and NU66@QD were efficiently labeled by a one-step mixed method to form a multiple detection probe. The limits of detection of the proposed NU66@QD-ICA for AFB1/FB1/DON/T-2/ZEN were 0.04/0.28/0.25/0.09/0.08 µg/kg. The recoveries ranged from 82.83-117.44%, with the coefficient of variation from 2.88-11.80%. A parallel analysis in 35 naturally contaminated cereal and feed samples was confirmed by LC-MS/MS, and the results showed a good correlation (R2 > 0.9), indicating the practical reliability of the multiple NU66@QD-ICA. Overall, the introduction of the novel nanomaterial NU66@QD provides a highly sensitive and efficient multiplex detection strategy for the development of ICA.

Fine mapping of two recessive powdery mildew resistance genes from Aegilops tauschii accession CIae8.

KEY MESSAGE: Two recessive powdery mildew resistance loci pmAeCIae8_2DS and pmAeCIae8_7DS from Aegilops tauschii were mapped and two synthesized hexaploid wheat lines were developed by distant hybridization. Wheat powdery mildew (Pm), one of the worldwide destructive fungal diseases, causes significant yield loss up to 30%. The identification of new Pm resistance genes will enrich the genetic diversity of wheat breeding for Pm resistance. Aegilops tauschii is the ancestor donor of sub-genome D of hexaploid wheat. It provides beneficial genes that can be easily transferred into wheat by producing synthetic hexaploid wheat followed by genetic recombination. We assessed the Pm resistance level of 35 Ae. tauschii accessions from different origins. Accession CIae8 exhibited high Pm resistance. Inheritance analysis and gene mapping were performed using F2 and F2:3 populations derived from the cross between CIae8 and a Pm susceptible accession PI574467. The Pm resistance of CIae8 was controlled by two independent recessive genes. Bulked segregate analysis using a 55 K SNP array revealed the SNPs were mainly enriched into genome regions, i.e. 2DS (13.5-20 Mb) and 7DS (4.0-15.5 Mb). The Pm resistance loci were named as pmAeCIae8_2DS and pmAeCIae8_7DS, respectively. By recombinant screening, we narrowed the pmAeCIae8_2DS into a 370-kb interval flanked by markers CINAU-AE7800 (14.89 Mb) and CINAU-AE20 (15.26 Mb), and narrowed the pmAeCIae8_7DS into a 260-kb interval flanked by markers CINAU-AE58 (4.72 Mb) and CINAU-AE25 (4.98 Mb). The molecular markers closely linked with the resistance loci were developed, and two synthesized hexaploid wheat (SHW) lines were produced. These laid the foundation for cloning of the two resistance loci and for transferring the resistance into common wheat.

Learning From Partially Labeled Data for Multi-Organ and Tumor Segmentation.

Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream approaches follow the practice of one network solving one task. With this pipeline, not only the performance is limited by the typically small dataset of a single task, but also the computation cost linearly increases with the number of tasks. To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets. Specifically, TransDoDNet has a hybrid backbone that is composed of the convolutional neural network and Transformer. A dynamic head enables the network to accomplish multiple segmentation tasks flexibly. Unlike existing approaches that fix kernels after training, the kernels in the dynamic head are generated adaptively by the Transformer, which employs the self-attention mechanism to model long-range organ-wise dependencies and decodes the organ embedding that can represent each organ. We create a large-scale partially labeled Multi-Organ and Tumor Segmentation benchmark, termed MOTS, and demonstrate the superior performance of our TransDoDNet over other competitors on seven organ and tumor segmentation tasks. This study also provides a general 3D medical image segmentation model, which has been pre-trained on the large-scale MOTS benchmark and has demonstrated advanced performance over current predominant self-supervised learning methods.

MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images.

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were 0.614±0.231 and 0.644±0.153 for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

A New Signature That Predicts Progression-Free Survival of Clear Cell Renal Cell Carcinoma with Anti-PD-1 Therapy.

Immunotherapy has greatly improved the survival time and quality of life of patients with renal cell carcinoma, but the benefits are limited to a small portion of patients. There are too few new biomarkers that can be used to identify molecular subtypes of renal clear cell carcinoma and predict survival time with anti-PD-1 treatment. Single-cell RNA data of clear cell renal cell carcinoma (ccRCC) treated with anti-PD-1 were obtained from public databases, then 27,707 high-quality CD4 + T and CD8 + T cells were obtained for subsequent analysis. Firstly, genes set variation analysis and CellChat algorithm were used to explore potential molecular pathway differences and intercellular communication between the responder and non-responder groups. Additionally, differentially expressed genes (DEGs) between the responder and non-responder groups were obtained using the "edgeR" package, and ccRCC samples from TCGA-KIRC (n = 533) and ICGA-KIRC (n = 91) were analyzed by the unsupervised clustering algorithm to recognize molecular subtypes with different immune characteristics. Finally, using univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression, and multivariate Cox regression, the prognosis model of immunotherapy was established and verified to predict the progression-free survival of ccRCC patients treated with anti-PD-1. At the single cell level, there are different signal pathways and cell communication between the immunotherapy responder and non-responder groups. In addition, our research also confirms that the expression level of PDCD1/PD-1 is not an effective marker for predicting the response to immune checkpoint inhibitors (ICIs). The new prognostic immune signature (PIS) enabled the classification of ccRCC patients with anti-PD-1 therapy into high- and low-risk groups, and the progression-free survival times (PFS) and immunotherapy responses were significantly different between these two groups. In the training group, the area under the ROC curve (AUC) for predicting 1-, 2- and 3-year progression-free survival was 0.940 (95% CI: 0.894-0.985), 0.981 (95% CI: 0.960-1.000), and 0.969 (95% CI: 0.937-1.000), respectively. Validation sets confirm the robustness of the signature. This study revealed the heterogeneity between the anti-PD-1 responder and non-responder groups from different angles and established a robust PIS to predict the progression-free survival of ccRCC patients receiving immune checkpoint inhibitors.

Seismic performance of the intermediate unit of a scrap tire-granular material composite column.

This study focused on the seismic performance of an intermediate unit of scrap tire-granular material composite columns under cyclic loading. A Laboratory test study regarding the dynamic deformation characteristics of the intermediate unit of a scrap tire-granular material composite column was conducted. The hysteresis loops, skeleton curves, stiffness degradation curves, and energy-dissipation capacities of the three specimens with different initial vertical loads were experimentally evaluated. Based on the test results, a restoring force model of the tire-granular material unit is established. The results demonstrated that the external force had a significant influence on the strength of the intermediate unit of the scrap tire-granular material composite column. The area of the hysteresis loop of the tire-granular material unit was large, indicating a good seismic performance. As the initial vertical load increased, the horizontal bearing capacity and lateral stiffness of the intermediate unit of the scrap tire-granular material composite column increased. The calculation results of the skeleton curve and hysteresis curve were in good agreement with the test results. As a base damping material, the scrap tire-granular material composite column is feasible, and provides a new concept for the resource utilization of the original scrap tire.

Domain and Content Adaptive Convolution Based Multi-Source Domain Generalization for Medical Image Segmentation.

The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model to unseen clinical data. To address this issue, domain generalization methods have been proposed, which however usually use static convolutions and are less flexible. In this paper, we propose a multi-source domain generalization model based on the domain and content adaptive convolution (DCAC) for the segmentation of medical images across different modalities. Specifically, we design the domain adaptive convolution (DAC) module and content adaptive convolution (CAC) module and incorporate both into an encoder-decoder backbone. In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain. In the CAC module, a dynamic convolutional head is conditioned on the global image features to make our model adapt to the test image. We evaluated the DCAC model against the baseline and four state-of-the-art domain generalization methods on the prostate segmentation, COVID-19 lesion segmentation, and optic cup/optic disc segmentation tasks. Our results not only indicate that the proposed DCAC model outperforms all competing methods on each segmentation task but also demonstrate the effectiveness of the DAC and CAC modules. Code is available at https://git.io/DCAC.

Global population genomic signature of Spodoptera frugiperda (fall armyworm) supports complex introduction events across the Old World.

Native to the Americas, the invasive Spodoptera frugiperda (fall armyworm; FAW) was reported in West Africa in 2016, followed by its chronological detection across the Old World and the hypothesis of an eastward Asia expansion. We explored population genomic signatures of American and Old World FAW and identified 12 maternal mitochondrial DNA genome lineages across the invasive range. 870 high-quality nuclear single nucleotide polymorphic DNA markers identified five distinct New World population clusters, broadly reflecting FAW native geographical ranges and the absence of host-plant preferences. We identified unique admixed Old World populations, and admixed and non-admixed Asian FAW individuals, all of which suggested multiple introductions underpinning the pest's global spread. Directional gene flow from the East into eastern Africa was also detected, in contrast to the west-to-east spread hypothesis. Our study demonstrated the potential of population genomic approaches via international partnership to address global emerging pest threats and biosecurity challenges.