542 research outputs found
Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation
Breast cancer diagnosis challenges both patients and clinicians, with early
detection being crucial for effective treatment. Ultrasound imaging plays a key
role in this, but its utility is hampered by the need for precise lesion
segmentation-a task that is both time-consuming and labor-intensive. To address
these challenges, we propose a new framework: a morphology-enhanced, Class
Activation Map (CAM)-guided model, which is optimized using a computer vision
foundation model known as SAM. This innovative framework is specifically
designed for weakly supervised lesion segmentation in early-stage breast
ultrasound images. Our approach uniquely leverages image-level annotations,
which removes the requirement for detailed pixel-level annotation. Initially,
we perform a preliminary segmentation using breast lesion morphology knowledge.
Following this, we accurately localize lesions by extracting semantic
information through a CAM-based heatmap. These two elements are then fused
together, serving as a prompt to guide the SAM in performing refined
segmentation. Subsequently, post-processing techniques are employed to rectify
topological errors made by the SAM. Our method not only simplifies the
segmentation process but also attains accuracy comparable to supervised
learning methods that rely on pixel-level annotation. Our framework achieves a
Dice score of 74.39% on the test set, demonstrating compareable performance
with supervised learning methods. Additionally, it outperforms a supervised
learning model, in terms of the Hausdorff distance, scoring 24.27 compared to
Deeplabv3+'s 32.22. These experimental results showcase its feasibility and
superior performance in integrating weakly supervised learning with SAM. The
code is made available at: https://github.com/YueXin18/MorSeg-CAM-SAM
Seismic Earth Pressures of Retaining Wall from Large Shaking Table Tests
To ascertain seismic response of retaining wall in the Wenchuan earthquake, large shaking table tests are performed and an acceleration record is acted in 3 directions. In the tests, acceleration time history recorded at Wolong station in the Wenchuan earthquake is used to excite the model wall. Results from the tests show that the location of dynamic resultant earth pressure is 0.35–0.49 H from toe of the wall for road shoulder retaining wall on rock foundation, 0.33–0.42 H for embankment retaining wall on rock foundation, and 0.46–0.77 H for road shoulder retaining wall on soil foundation. Besides, dynamic earth pressure increases with the increase of ground shaking from 0.1 g to 0.9 g and the relationship is nonlinear. The distribution is closed to for PGA less than 0.4 g but larger for PGA larger than and equal to 0.4 g, especially on the soil foundation. After the comparison of measured earth pressures and theoretical results by pseudodynamic method and pseudostatic method, results of the former are consistent with those of the shaking table test, but results of the latter method are smaller than measured
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits
The identification of addiction-related circuits is critical for explaining
addiction processes and developing addiction treatments. And models of
functional addiction circuits developed from functional imaging are an
effective tool for discovering and verifying addiction circuits. However,
analyzing functional imaging data of addiction and detecting functional
addiction circuits still have challenges. We have developed a data-driven and
end-to-end generative artificial intelligence(AI) framework to address these
difficulties. The framework integrates dynamic brain network modeling and novel
network architecture networks architecture, including temporal graph
Transformer and contrastive learning modules. A complete workflow is formed by
our generative AI framework: the functional imaging data, from neurobiological
experiments, and computational modeling, to end-to-end neural networks, is
transformed into dynamic nicotine addiction-related circuits. It enables the
detection of addiction-related brain circuits with dynamic properties and
reveals the underlying mechanisms of addiction
Relating Admissibility Standards for Digital Evidence to Attack Scenario Reconstruction
Attackers tend to use complex techniques such as combining multi-step, multi-stage attack with anti-forensic tools to make it difficult to find incriminating evidence and reconstruct attack scenarios that can stand up to the expected level of evidence admissibility in a court of law. As a solution, we propose to integrate the legal aspects of evidence correlation into a Prolog based reasoner to address the admissibility requirements by creating most probable attack scenarios that satisfy admissibility standards for substantiating evidence. Using a prototype implementation, we show how evidence extracted by using forensic tools can be integrated with legal reasoning to reconstruct network attack scenarios. Our experiment shows this implemented reasoner can provide pre-estimate of admissibility on a digital crime towards an attacked network
Sphere-shaped Mn3O4 catalyst with remarkable low-temperature activity for Methyl-Ethyl-Ketone combustion
Mn3O4, FeMnOx, and FeOx catalysts synthesized via a solvothermal method were employed for catalytic oxidation of methyl−ethyl−ketone (MEK) at low temperature. Mn3O4 with sphere-like morphology exhibited the highest activity for MEK oxidation, over which MEK was completely oxidized to CO2 at 200 °C, and this result can be comparable to typical noble metal loaded catalysts. The activation energy of MEK over Mn3O4 (30.8 kJ/mol) was much lower than that of FeMnOx (41.5 kJ/mol) and FeOx (47.8 kJ/mol). The dominant planes, surface manganese species ratio, surface-absorbed oxygen, and redox capability played important roles in the catalytic activities of catalysts, while no significant correlation was found between specific surface area and MEK removal efficiency. Mn3O4 showed the highest activity,
accounting for abundant oxygen vacancies, low content of surface Mn4+ and strong reducibility. The oxidation of MEK to CO2 via an intermediate of diacetyl is a reaction pathway on Mn3O4 catalyst. Due to high efficiency and low cost, sphere-shaped Mn3O4 is a promising catalyst for VOCs abatement
Recent Advances in the Catalytic Depolymerization of Lignin towards Phenolic Chemicals : A Review
The efficient valorization of lignin could dictate the success of the 2nd generation biorefinery. Lignin, accounting for on average a third of the lignocellulosic biomass, is the most promising candidate for sustainable production of value-added phenolics. However, the structural alteration induced during lignin isolation is often depleting its potential for value-added chemicals. Recently, catalytic reductive depolymerization of lignin has appeared to be a promising and effective method for its valorization to obtain phenolic monomers. The present study systematically summarizes the far-reaching and state-of-the-art lignin valorization strategies during different stages, including conventional catalytic depolymerization of technical lignin, emerging reductive catalytic fractionation of protolignin, stabilization strategies to inhibit the undesired condensation reactions, and further catalytic upgrading of lignin-derived monomers. Finally, the potential challenges for the future researches on the efficient valorization of lignin and possible solutions are proposed
Screening of Spirulina strains for high copper adsorption capacity through Fourier transform infrared spectroscopy
Microalgae have emerged as promising biosorbents for the removal of toxic metals from industrial effluents due to the presence of various free functional groups. While the constitutes are distinct among different algal strains, it needs to screen the algae with high adsorption capacities for heavy metal ions by analyzing the algal components. In this study, a rapid and nondestructive Fourier transform infrared (FTIR) method combined PCA algorithm was used to discriminate algal strains according to their cellular components. With FTIR spectroscopy, we have found that the algal strains for high copper adsorption capacity (RH44, XS58, AH53, and RZ22) can be well differentiated from other strains via assessing the components involved in the biosorption of copper ions at the spectral window range of 1,200–900 cm−1 mainly attributed to polysaccharides. Correspondingly, the copper removal efficiency by different Spirulina strains was also measured by biochemical assay and scanning electron microscopy (SEM) in order to confirm the screening result. Compared with the chemical measurement, the assessment based on spectral features appears fairly good in the evaluation and differentiation of copper adsorption capacity in various Spirulina strains. This study illustrates that FTIR spectroscopy may serve as a fast and effective tool to investigate the functional groups for copper ions binding in the Spirulina cell and it even offers a useful and accurate new approach to rapidly assess potential adsorbents for the high capacity of copper adsorption
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