EpCAM Is an Endoderm-Specific Wnt Derepressor that Licenses Hepatic Development

SummaryMechanisms underlying cell-type-specific response to morphogens or signaling molecules during embryonic development are poorly understood. To learn how response to the liver-inductive Wnt2bb signal is achieved, we identify an endoderm-enriched, single transmembrane protein, epithelial-cell-adhesion-molecule (EpCAM), as an endoderm-specific Wnt derepressor in zebrafish. hi2151/epcam mutants exhibit defective liver development similar to prt/wnt2bb mutants. EpCAM directly binds to Kremen1 and disrupts the Kremen1-Dickkopf2 (Dkk2) interaction, which prevents Kremen1-Dkk2-mediated removal of Lipoprotein-receptor-related protein 6 (Lrp6) from the cell surface. These data lead to a model in which EpCAM derepresses Lrp6 and cooperates with Wnt ligand to activate Wnt signaling through stabilizing membrane Lrp6 and allowing Lrp6 clustering into active signalosomes. Thus, EpCAM cell autonomously licenses and cooperatively activates Wnt2bb signaling in endodermal cells. Our results identify EpCAM as the key molecule and its functional mechanism to confer endodermal cells the competence to respond to the liver-inductive Wnt2bb signal

CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition

Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous work alleviates the data scarcity problem by translating source-language labeled data or performing knowledge distillation on target-language unlabeled data. However, these methods may suffer from label noise due to the automatic labeling process. In this paper, we propose CoLaDa, a Collaborative Label Denoising Framework, to address this problem. Specifically, we first explore a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other. We then present an instance-collaboration-based strategy that considers the label consistency of each token's neighborhood in the representation space for denoising. Experiments on different benchmark datasets show that the proposed CoLaDa achieves superior results compared to previous methods, especially when generalizing to distant languages.Comment: ACL 2023. Our code is available at https://github.com/microsoft/vert-papers/tree/master/papers/CoLaD

PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor). In this paper, we propose PIT, a deep-learning compiler for dynamic sparsity. PIT proposes a novel tiling mechanism that leverages Permutation Invariant Transformation (PIT), a mathematically proven property, to transform multiple sparsely located micro-tiles into a GPU-efficient dense tile without changing the computation results, thus achieving both high GPU utilization and low coverage waste. Given a model, PIT first finds feasible PIT rules for all its operators and generates efficient GPU kernels accordingly. At runtime, with the novel SRead and SWrite primitives, PIT rules can be executed extremely fast to support dynamic sparsity in an online manner. Extensive evaluation on diverse models shows that PIT can accelerate dynamic sparsity computation by up to 5.9x (average 2.43x) over state-of-the-art compilers

Acoustic Emission Simulation on Coal Specimen Subjected to Cyclic Loading

The damage and failure state of the loaded coal and rock masses is indirectly reflected by its acoustic emission (AE) characteristics. Therefore, it is of great significance to study the AE evolution of loaded coal and rock masses for the evaluation of damage degree and prediction of collapse. The paper mainly represents a numerical simulation investigation of the AE characteristics of coal specimen subjected to cyclic loading under three confining pressures, loading-unloading rates, and valley stresses. From the numerical simulation tests, the following conclusions can be drawn: (1) The final cycle number of coal specimen subjected to cyclic loading is significantly influenced by the confining pressure, followed the valley stress. With the increase in confining pressure or valley stress, the cycle number tends to increase. However, the loading-unloading rate has a little influence on it. (2) The AE counts of coal specimen subjected to cyclic loading are greatly influenced by the confining pressure and the valley stress. With the increase in the confining pressure, the cumulative AE counts at the 1st cycle tend to increase but decrease at a cycle before failure; with the decrease in the valley stress, the cumulative AE counts per cycle increase in the relatively quiet phase. However, the loading-unloading rate has a little influence on it. (3) The failure mode of coal specimen subjected to cyclic loading is significantly influenced by the confining pressure. Under the uniaxial stress state, there is an inclined main fractured plane in the coal specimen, under the confining pressures of 5 and 10 MPa, the coal specimen represents dispersion failure. The loading-unloading rate and valley stress have little influence on it. (4) The AE ratio is proposed, and its evolution can better reflect the different stages of coal specimen failure under cyclic loading. (5) The influence of confining pressure on the broken degree of coal specimen subjected to cyclic loading is analyzed, and the higher the confining pressure, the more broken the failed coal specimen

The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system.

In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it's effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries

Amperometric Biosensor of Matrix Metalloproteinase-7 Enhanced by Pd-Functionalized Carbon Nanocomposites

Abstract Matrix metalloproteinase-7 plays a pivotal role in tumour progression and metastasis as an enzyme that can degrade the cell-matrix composition and cleave peptides between alanine and leucine in various biomolecular activation processes. In this work, a Pd-functionalised carbon nanocomposite was designed as a new impedance enhancer for an amperometric sensor of MMP-7. Pd nanoparticles in the enhancer can catalyse the oxidation of 4-chloro-1-naphthol with H2O2 to generate insoluble precipitation in situ, forming high-resistance precipitation on electrodes. In addition, poorly conductive carbon nanospheres of the nanocomposite increased the precipitation resistance, further causing a dramatic increase in resistivity of the enhancer and, subsequently, a significant decrease in current. This can significantly promote the current signal difference between the biosensor treated with and without the target analyte, which is directly related to the sensitivity of the amperometric biosensor. Overall, electrochemical biosensor can sensitively detect MMP-7 in the range of 100 fg mL−1 to 100 ng mL−1 with a limit of detection for MMP-7 of 17.38 fg mL−1

Magnetic target recognition and localization method unaffected by attitude

This letter presents a magnetic target inversion method that does not vary with changes in the coordinate system and is based on the cross-product of the intermediate eigenvectors of any two points in the dipole field which is in the same/opposite direction as the magnetic moment vector. We used tensor geometric invariants to interpret this new physical property and obtain the unit magnetic moment vector. Using this, the unit vector of the measurement point-source displacement vector was derived. The distance between the measurement point and the source was obtained via the Frobenius norm of the gradient tensor matrix. Simulations verified that the proposed method is unaffected by attitudes and yields unique inversion results, and the results revealed that the inversion accuracy of the proposed method is high. The simulation results also show that conditional cosine and measurement noise have considerable influence on the inversion accuracy in the proposed method

Comparison of index under different denoising methods.

Comparison of index under different denoising methods.</p

Operation procedure of CNN-BiLSTM model.

In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it’s effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries.</div