EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model

Large-scale, big-variant, and high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called EMIT-Diff. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints and preserve the underlying structure of imaging data. Due to the random sampling process by the diffusion model, we can generate an arbitrary number of synthetic images with diverse appearances. To validate the effectiveness of our proposed method, we conduct an extensive set of medical image segmentation experiments on multiple datasets, including Ultrasound breast (+13.87%), CT spleen (+0.38%), and MRI prostate (+7.78%), achieving significant improvements over the baseline segmentation methods. For the first time, to our best knowledge, the promising results demonstrate the effectiveness of our EMIT-Diff for medical image segmentation tasks and show the feasibility of introducing a first-ever text-guided diffusion model for general medical image segmentation tasks. With carefully designed ablation experiments, we investigate the influence of various data augmentation ratios, hyper-parameter settings, patch size for generating random merging mask settings, and combined influence with different network architectures.Comment: 15 page

Nonlinear adaptive speed control of a permanent magnet synchronous motor: A perturbation estimation approach

This paper presents a nonlinear adaptive control (NAC) scheme for the speed regulation of a permanent magnet synchronous motor (PMSM) based on perturbation estimation and feedback linearizing control. All PMSM system’s unknown nonlinearities, parameter uncertainties, and external disturbances including unknown time-varying load torque disturbance, are defined as lumped perturbation terms, which are estimated by designing perturbation observers. The estimates are used to adaptively compensate the real perturbations and achieve adaptive feedback linearizing control of the original nonlinear system. The proposed control scheme does not require accurate system model and full state feedback. Stability of the close-loop system with proposed NAC is investigated via Lyapunov theory, and the effectiveness of proposed NAC scheme is verified through both simulation and experimental studies. Both simulation and experimental results show that the proposed NAC scheme can provide less regulation error in speed tracking, better dynamic performance and robustness against parameter uncertainties and load torque disturbance, compared with conventional vector control and load torque estimated based control

Comparison of the Interactions of Different Growth Factors and Glycosaminoglycans

Most growth factors are naturally occurring proteins, which are signaling molecules implicated in cellular multiple functions such as proliferation, migration and differentiation under patho/physiological conditions by interacting with cell surface receptors and other ligands in the extracellular microenvironment. Many of the growth factors are heparin-binding proteins (HBPs) that have a high affinity for cell surface heparan sulfate proteoglycans (HSPG). In the present study, we report the binding kinetics and affinity of heparin interacting with different growth factors, including fibroblast growth factor (FGF) 2,7,10, hepatocyte growth factor (HGF) and transforming growth factor (TGF β-1), using a heparin chip. Surface plasmon resonance studies revealed that all the tested growth factors bind to heparin with high affinity (with KD ranging from ~0.1 to 59 nM) and all the interactions are oligosaccharide size dependent except those involving TGF β-1. These heparin-binding growth factors also interact with other glycosaminoglycans (GAGs), as well as various chemically modified heparins. Other GAGs, including heparan sulfate, chondroitin sulfates A, B, C, D, E and keratan sulfate, showed different inhibition activities for the growth factor-heparin interactions. FGF2, FGF7, FGF10 and HGF bind heparin but the 2-O-sulfo and 6-O-sulfo groups on heparin have less impact on these interactions than do the N-sulfo groups. All the three sulfo groups (N-, 2-O and 6-O) on heparin are important for TGFβ-1-heparin interaction

Overall Adaptive Controller Design of PMSG Under Whole Wind Speed Range: A Perturbation Compensation Based Approach

This paper proposes an adaptive overall control strategy of the permanent magnet synchronous generator-based wind energy conversion system (WECS) in the whole wind speed range. For the machine side, the maximum power point tracking (MPPT) operation is realized by stator current and mechanical rotation speed control under below-rated wind speeds. Under above-rated wind speeds, the extracted wind power is limited via pitch control. For the grid side, the reactive and active power injected into grid is regulated by DC-Link voltage and grid current control loop. In addition, under grid voltage dips, the pitch control is employed for limiting grid current and maintaining the DC-Link voltage around its rated value. The fault ride-through capability (FRTC) can be enhanced. The overall control strategy is based on perturbation estimation technique. A designed observer is used for estimating the perturbation term including all system nonlinearities, uncertainties and disturbances, so as to compensate the real perturbation. Then, an adaptive control for the original nonlinear system can be realized. The effectiveness of the proposed overall control strategy is verified by applying the strategy to a 2-MW WECS in MATLAB/Simulink. The results show that, compared with the feedback linearizing control (FLC) strategy and conventional vector control (VC) strategy, the proposed perturbation observer based adaptive control (PO-AC) strategy realizes the control objectives without knowing full state information and accurate system model, and improves the robustness of the WECS parameter uncertainties and FRTC

Machine learning assisted design of high entropy alloys with desired property

We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved

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CS5931, a Novel Polypeptide in Ciona savignyi, Represses Angiogenesis via Inhibiting Vascular Endothelial Growth Factor (VEGF) and Matrix Metalloproteinases (MMPs)

CS5931 is a novel polypeptide from Ciona savignyi with anticancer activities. Previous study in our laboratory has shown that CS5931 can induce cell death via mitochondrial apoptotic pathway. In the present study, we found that the polypeptide could inhibit angiogenesis both in vitro and in vivo. CS5931 inhibited the proliferation, migration and formation of capillary-like structures of HUVECs (Human Umbilical Vein Endothelial Cell) in a dose-dependent manner. Additionally, CS5931 repressed spontaneous angiogenesis of the zebrafish vessels. Further studies showed that CS5931 also blocked vascular endothelial growth factor (VEGF) production but without any effect on its mRNA expression. Moreover, CS5931 reduced the expression of matrix metalloproteinases (MMP-2 and MMP-9) both on protein and mRNA levels in HUVEC cells. We demonstrated that CS5931 possessed strong anti-angiogenic activity both in vitro and in vivo, possible via VEGF and MMPs. This study indicates that CS5931 has the potential to be developed as a novel therapeutic agent as an inhibitor of angiogenesis for the treatment of cancer

Deformation and failure of the CrCoNi medium-entropy alloy subjected to extreme shock loading

The extraordinary work hardening ability and fracture toughness of the face-centered cubic (fcc) high-entropy alloys render them ideal candidates for many structural applications. Here, the deformation and failure mechanisms of an equiatomic CrCoNi medium-entropyalloy (MEA) were investigated by powerful laser-driven shock experiments. Multiscale characterization demonstrates that profuse planar defects including stacking faults, nanotwins, and hexagonal nanolamella were generated during shock compression, forming a three-dimensional network. During shock release, the MEA fractured by strong tensile deformation and numerous voids was observed in the vicinity of the fracture plane. High defect populations, nanorecrystallization, and amorphization were found adjacent to these areas of localized deformation. Molecular dynamics simulations corroborate the experimental results and suggest that deformation-induced defects formed before void nucleation govern the geometry of void growth and delay their coalescence. Our results indicate that the CrCoNi-based alloys are impact resistant, damage tolerant, and potentially suitable in applications under extreme conditions