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