Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning

Parameter regularization or allocation methods are effective in overcoming catastrophic forgetting in lifelong learning. However, they solve all tasks in a sequence uniformly and ignore the differences in the learning difficulty of different tasks. So parameter regularization methods face significant forgetting when learning a new task very different from learned tasks, and parameter allocation methods face unnecessary parameter overhead when learning simple tasks. In this paper, we propose the Parameter Allocation & Regularization (PAR), which adaptively select an appropriate strategy for each task from parameter allocation and regularization based on its learning difficulty. A task is easy for a model that has learned tasks related to it and vice versa. We propose a divergence estimation method based on the Nearest-Prototype distance to measure the task relatedness using only features of the new task. Moreover, we propose a time-efficient relatedness-aware sampling-based architecture search strategy to reduce the parameter overhead for allocation. Experimental results on multiple benchmarks demonstrate that, compared with SOTAs, our method is scalable and significantly reduces the model's redundancy while improving the model's performance. Further qualitative analysis indicates that PAR obtains reasonable task-relatedness.Comment: Accepted by CVPR2023. Code is available at https://github.com/WenjinW/PA

The Elastic Constants Measurement of Metal Alloy by Using Ultrasonic Nondestructive Method at Different Temperature

The ultrasonic nondestructive method is introduced into the elastic constants measurement of metal material. The extraction principle of Poisson’s ratio, elastic modulus, and shear modulus is deduced from the ultrasonic propagating equations with two kinds of vibration model of the elastic medium named ultrasonic longitudinal wave and transverse wave, respectively. The ultrasonic propagating velocity is measured by using the digital correlation technique between the ultrasonic original signal and the echo signal from the bottom surface, and then the elastic constants of the metal material are calculated. The feasibility of the correlation algorithm is verified by a simulation procedure. Finally, in order to obtain the stability of the elastic properties of different metal materials in a variable engineering application environment, the elastic constants of two kinds of metal materials in different temperature environment are measured by the proposed ultrasonic method

Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect

The common modeling of digital twins uses an information model to describe the physical machines. The integration of digital twins into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop and investigate a new method for building cloud-based digital twins (CBDT), which can be adapted to the CPCM platform. Our method helps reduce computing resources in the information processing center for efficient interactions between human users and physical machines. We introduce a knowledge resource center (KRC) built on a cloud server for information intensive applications. An information model for one type of 3D printers is designed and integrated into the core of the KRC as a shared resource. Several experiments are conducted and the results show that the CBDT has an excellent performance compared to existing methods

Screening of linear B-cell epitopes and its proinflammatory activities of Haemophilus parasuis outer membrane protein P2

Haemophilus parasuis is a commensal organism of the upper respiratory tract of pigs, but virulent strains can cause Glässer’s disease, resulting in significant economic losses to the swine industry. OmpP2 is an outer membrane protein of this organism that shows considerable heterogeneity between virulent and non-virulent strains, with classification into genotypes I and II. It also acts as a dominant antigen and is involved in the inflammatory response. In this study, 32 monoclonal antibodies (mAbs) against recombinant OmpP2 (rOmpP2) of different genotypes were tested for reactivity to a panel of OmpP2 peptides. Nine linear B cell epitopes were screened, including five common genotype epitopes (Pt1a, Pt7/Pt7a, Pt9a, Pt17, and Pt19/Pt19a) and two groups of genotype-specific epitopes (Pt5 and Pt5-II, Pt11/Pt11a, and Pt11a-II). In addition, we used positive sera from mice and pigs to screen for five linear B-cell epitopes (Pt4, Pt14, Pt15, Pt21, and Pt22). After porcine alveolar macrophages (PAMs) were stimulated with overlapping OmpP2 peptides, we found that the epitope peptides Pt1 and Pt9, and the loop peptide Pt20 which was adjacent epitopes could all significantly upregulated the mRNA expression levels of IL-1α, IL-1β, IL-6, IL-8, and TNF-α. Additionally, we identified epitope peptides Pt7, Pt11/Pt11a, Pt17, Pt19, and Pt21 and loop peptides Pt13 and Pt18 which adjacent epitopes could also upregulate the mRNA expression levels of most proinflammatory cytokines. This suggested that these peptides may be the virulence-related sites of the OmpP2 protein, with proinflammatory activity. Further study revealed differences in the mRNA expression levels of proinflammatory cytokines, including IL-1β and IL-6, between genotype-specific epitopes, which may be responsible for pathogenic differences between different genotype strains. Here, we profiled a linear B-cell epitope map of the OmpP2 protein and preliminarily analyzed the proinflammatory activities and effects of these epitopes on bacterial virulence, providing a reliable theoretical basis for establishing a method to distinguish strain pathogenicity and to screen candidate peptides for subunit vaccines

Resolving power of diffraction imaging with an objective: a numerical study

Diffraction imaging in the far-field can detect 3D morphological features of an object for its coherent nature. We describe methods for accurate calculation and analysis of diffraction images of scatterers of single and double spheres by an imaging unit based on microscope objective at non-conjugate positions. A quantitative study of the calculated diffraction imaging in spectral domain has been performed to assess the resolving power of diffraction imaging. It has been shown numerically that with coherent illumination of 532 nm in wavelength the imaging unit can resolve single spheres of 2 μm or larger in diameters and double spheres separated by less than 300 nm between their centers.ECU Open Access Publishing Fun

PKM2 promotes glucose metabolism and cell growth in gliomas through a mechanism involving a let-7a/c-Myc/hnRNPA1 feedback loop

AbstrAct Tumor cells metabolize more glucose to lactate in aerobic or hypoxic conditions than non-tumor cells. Pyruvate kinase isoenzyme type M2 (PKM2) is crucial for tumor cell aerobic glycolysis. We established a role for let-7a/c-Myc/hnRNPA1/PKM2 signaling in glioma cell glucose metabolism. PKM2 depletion via siRNA inhibits cell proliferation and aerobic glycolysis in glioma cells. C-Myc promotes up-regulation of hnRNPA1 expression, hnRNPA1 binding to PKM pre-mRNA, and the subsequent formation of PKM2. This pathway is downregulated by the microRNA let-7a, which functionally targets c-Myc, whereas hnRNPA1 blocks the biogenesis of let-7a to counteract its ability to downregulate the c-Myc/hnRNPA1/PKM2 signaling pathway. The down-regulation of c-Myc/ hnRNPA1/PKM2 by let-7a is verified using a glioma xenograft model. These results suggest that let-7a, c-Myc and hnRNPA1 from a feedback loop, thereby regulating PKM2 expression to modulate glucose metabolism of glioma cells. These findings elucidate a new pathway mediating aerobic glycolysis in gliomas and provide an attractive potential target for therapeutic intervention

GWAS Identifies Novel Susceptibility Loci on 6p21.32 and 21q21.3 for Hepatocellular Carcinoma in Chronic Hepatitis B Virus Carriers

Genome-wide association studies (GWAS) have recently identified KIF1B as susceptibility locus for hepatitis B virus (HBV)–related hepatocellular carcinoma (HCC). To further identify novel susceptibility loci associated with HBV–related HCC and replicate the previously reported association, we performed a large three-stage GWAS in the Han Chinese population. 523,663 autosomal SNPs in 1,538 HBV–positive HCC patients and 1,465 chronic HBV carriers were genotyped for the discovery stage. Top candidate SNPs were genotyped in the initial validation samples of 2,112 HBV–positive HCC cases and 2,208 HBV carriers and then in the second validation samples of 1,021 cases and 1,491 HBV carriers. We discovered two novel associations at rs9272105 (HLA-DQA1/DRB1) on 6p21.32 (OR = 1.30, P = 1.13×$10^{−19}$) and rs455804 (GRIK1) on 21q21.3 (OR = 0.84, P = 1.86×$10^{−8}$), which were further replicated in the fourth independent sample of 1,298 cases and 1,026 controls (rs9272105: OR = 1.25, P = 1.71×$10^{−4}$; rs455804: OR = 0.84, P = 6.92×$10^{−3}$). We also revealed the associations of HLA-DRB1*0405 and 0901*0602, which could partially account for the association at rs9272105. The association at rs455804 implicates GRIK1 as a novel susceptibility gene for HBV–related HCC, suggesting the involvement of glutamate signaling in the development of HBV–related HCC

100 essential questions for the future of agriculture

Publication history: Accepted - 8 March 2023; Published online - 11 April 2023.The world is at a crossroad when it comes to agriculture. The global population is growing, and the demand for food is increasing, putting a strain on our agricultural resources and practices. To address this challenge, innovative, sustainable, and inclusive approaches to agriculture are urgently required. In this paper, we launched a call for Essential Questions for the Future of Agriculture and identified a priority list of 100 questions. We focus on 10 primary themes: transforming agri-food systems, enhancing resilience of agriculture to climate change, mitigating climate change through agriculture, exploring resources and technologies for breeding, advancing cultivation methods, sustaining healthy agroecosystems, enabling smart and controlled-environment agriculture for food security, promoting health and nutrition-driven agriculture, exploring economic opportunities and addressing social challenges, and integrating one health and modern agriculture. We emphasise the critical importance of interdisciplinary and multidisciplinary research that integrates both basic and applied sciences and bridges the gaps among various stakeholders for achieving sustainable agriculture. Key points Growing demand and resource limitations pose a critical challenge for agriculture, necessitating innovative and sustainable approaches. The paper identifies 100 priority questions for the future of agriculture, indicating current and future research directions. Sustainable agriculture depends on interdisciplinary and multidisciplinary research that harmonises basic and applied sciences and fosters collaboration among different stakeholders