Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation

Medical image segmentation has seen significant improvements with transformer models, which excel in grasping far-reaching contexts and global contextual information. However, the increasing computational demands of these models, proportional to the squared token count, limit their depth and resolution capabilities. Most current methods process D volumetric image data slice-by-slice (called pseudo 3D), missing crucial inter-slice information and thus reducing the model's overall performance. To address these challenges, we introduce the concept of \textbf{Deformable Large Kernel Attention (D-LKA Attention)}, a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context. This mechanism operates within a receptive field akin to self-attention while sidestepping the computational overhead. Additionally, our proposed attention mechanism benefits from deformable convolutions to flexibly warp the sampling grid, enabling the model to adapt appropriately to diverse data patterns. We designed both 2D and 3D adaptations of the D-LKA Attention, with the latter excelling in cross-depth data understanding. Together, these components shape our novel hierarchical Vision Transformer architecture, the \textit{D-LKA Net}. Evaluations of our model against leading methods on popular medical segmentation datasets (Synapse, NIH Pancreas, and Skin lesion) demonstrate its superior performance. Our code implementation is publicly available at the: https://github.com/mindflow-institue/deformableLK

Comparison of the structural dynamic and mitochondrial electron-transfer properties of the proapoptotic human cytochrome c variants, G41S, Y48H and A51V

Mitochondrial cytochrome c is associated with electron transfer in the respiratory chain and in apoptosis. Four cytochrome c variants have been identified in families that suffer from mild autosomal dominant thrombocytopenia, a platelet disorder associated with increased apoptosis. Three out of the four substitutions, G41S, Y48H and A51V are located on the 40–57 Ω-loop. The G41S and Y48H variants perturb key physicochemical and dynamic properties that result in enhanced functional features associated with apoptotic activity. Herein we characterise the ferric A51V variant. We show by chemical denaturation that this variant causes the native state to be destabilized. Through azide binding kinetics, the population of a pentacoordinate heme form, whereby the Met80 axial ligand is dissociated, is estimated to be of equal magnitude to that found in the Y48H variant. This pentacoordinate form gives rise to peroxidase activity, which despite the similar pentacoordinate population of the A51V variant to that of the Y48H variant, the peroxidase activity of the A51V variant is suppressed. Far-UV circular dichroism spectroscopy and pH jump studies, suggest that a combination of structural and dynamic features in addition to the population of the pentacoordinate form regulate peroxidase activity in these disease variants. Additionally, the steady-state ratio of ferric/ferrous cytochrome c when in turnover with cytochrome c oxidase has been investigated for all 40–57 Ω-loop variants. These studies show that the lower pKa of the alkaline transition for the disease causing variants increases the ferric to ferrous heme ratio, indicating a possible influence on respiration in vivo

Impact of the Ohmic Electrode on the Endurance of Oxide-Based Resistive Switching Memory

As one of the key aspects in the reliability of redox-based resistive switching memories (ReRAMs), maximizing their endurance is of high relevance for industrial applications. The major limitation regarding endurance is considered the excessive generation of oxygen vacancies during cycling, which eventually leads to irreversible RESET failures. Thus, the endurance could be increased by using combinations of switching oxide and ohmic electrode (OE) metal that provides a high barrier for the generation of oxygen vacancies [defect formation energy (DFE)]. In this work, we present a sophisticated programming algorithm that aims to maximize the endurance within reasonable measurement time. Using this algorithm, we compare ReRAM devices with four different OE metals and confirm the theoretically predicted trend. Thus, our work provides valuable information for device engineering toward higher endurance

Impact of the Ohmic Electrode on the Endurance of Oxide-Based Resistive Switching Memory

As one of the key aspects in the reliability of redox-based resistive switching memories (ReRAMs), maximizing their endurance is of high relevance for industrial applications. The major limitation regarding endurance is considered the excessive generation of oxygen vacancies during cycling, which eventually leads to irreversible RESET failures. Thus, the endurance could be increased by using combinations of switching oxide and ohmic electrode (OE) metal that provides a high barrier for the generation of oxygen vacancies [defect formation energy (DFE)]. In this work, we present a sophisticated programming algorithm that aims to maximize the endurance within reasonable measurement time. Using this algorithm, we compare ReRAM devices with four different OE metals and confirm the theoretically predicted trend. Thus, our work provides valuable information for device engineering toward higher endurance