Natural channel protein inserts and functions in a completely artificial, solid-supported bilayer membrane

Reconstitution of membrane proteins in artificial membrane systems creates a platform for exploring their potential for pharmacological or biotechnological applications. Previously, we demonstrated amphiphilic block copolymers as promising building blocks for artificial membranes with long-term stability and tailorable structural parameters. However, the insertion of membrane proteins has not previously been realized in a large-area, stable, and solid-supported artificial membrane. Here, we show the first, preliminary model of a channel membrane protein that is functionally incorporated in a completely artificial polymer, tethered, solid-supported bilayer membrane (TSSBM). Unprecedented ionic transport characteristics that differ from previous results on protein insertion into planar, free-standing membranes, are identified. Our findings mark a change in understanding protein insertion and ion flow within natural channel proteins when inserted in an artificial TSSBM, thus holding great potential for numerous applications such as drug screening, trace analyzing, and biosensing

Which Matters More in Incidental Category Learning: Edge-Based Versus Surface-Based Features

Although many researches have shown that edge-based information is more important than surface-based information in object recognition, it remains unclear whether edge-based features play a more crucial role than surface-based features in category learning. To address this issue, a modified prototype distortion task was adopted in the present study, in which each category was defined by a rule or a similarity about either the edge-based features (i.e., contours or shapes) or the corresponding surface-based features (i.e., color and textures). The results of Experiments 1 and 2 showed that when the category was defined by a rule, the performance was significantly better in the edge-based condition than in the surface-based condition in the testing phase, and increasing the defined dimensions enhanced rather than reduced performance in the edge-based condition but not in the surface-based condition. The results of Experiment 3 showed that when each category was defined by a similarity, there was also a larger learning effect when the category was defined by edge-based dimensions than by surface-based dimensions in the testing phase. The current study is the first to provide convergent evidence that the edge-based information matters more than surface-based information in incidental category learning

Acceleration for Timing-Aware Gate-Level Logic Simulation with One-Pass GPU Parallelism

Witnessing the advancing scale and complexity of chip design and benefiting from high-performance computation technologies, the simulation of Very Large Scale Integration (VLSI) Circuits imposes an increasing requirement for acceleration through parallel computing with GPU devices. However, the conventional parallel strategies do not fully align with modern GPU abilities, leading to new challenges in the parallelism of VLSI simulation when using GPU, despite some previous successful demonstrations of significant acceleration. In this paper, we propose a novel approach to accelerate 4-value logic timing-aware gate-level logic simulation using waveform-based GPU parallelism. Our approach utilizes a new strategy that can effectively handle the dependency between tasks during the parallelism, reducing the synchronization requirement between CPU and GPU when parallelizing the simulation on combinational circuits. This approach requires only one round of data transfer and hence achieves one-pass parallelism. Moreover, to overcome the difficulty within the adoption of our strategy in GPU devices, we design a series of data structures and tune them to dynamically allocate and store new-generated output with uncertain scale. Finally, experiments are carried out on industrial-scale open-source benchmarks to demonstrate the performance gain of our approach compared to several state-of-the-art baselines

Dual processing of sulfated steroids in the olfactory system of an anuran amphibian

Chemical communication is widespread in amphibians, but if compared to later diverging tetrapods the available functional data is limited. The existing information on the vomeronasal system of anurans is particularly sparse. Amphibians represent a transitional stage in the evolution of the olfactory system. Most species have anatomically separated main and vomeronasal systems, but recent studies have shown that in anurans their molecular separation is still underway. Sulfated steroids function as migratory pheromones in lamprey and have recently been identified as natural vomeronasal stimuli in rodents. Here we identified sulfated steroids as the first known class of vomeronasal stimuli in the amphibian Xenopus laevis. We show that sulfated steroids are detected and concurrently processed by the two distinct olfactory subsystems of larval Xenopus laevis, the main olfactory system and the vomeronasal system. Our data revealed a similar but partially different processing of steroid-induced responses in the two systems. Differences of detection thresholds suggest that the two information channels are not just redundant, but rather signal different information. Furthermore, we found that larval and adult animals excrete multiple sulfated compounds with physical properties consistent with sulfated steroids. Breeding tadpole and frog water including these compounds activated a large subset of sensory neurons that also responded to synthetic steroids, showing that sulfated steroids are likely to convey intraspecific information. Our findings indicate that sulfated steroids are conserved vomeronasal stimuli functioning in phylogenetically distant classes of tetrapods living in aquatic and terrestrial habitats

Establishment of stable cell line for inducing KAP1 protein expression

Generation of the stable cell lines is a highly efficient tool in functional studies of certain genes or proteins, where the particular genes or proteins are inducibly expressed. The KRAB-associated protein-1 (KAP1) is an important transcription regulatory protein, which is investigated in several molecular biological studies. In this study, we have aimed to generate a stable cell line for inducing KAP1 expression. The recombinant plasmid pcDNA5/FRT/TO-KAP1 was constructed at first, which was then transfected into Flp-In™T-REx™-HEK293 cells to establish an inducible pcDNA5/FRT/TO-KAP1-HEK293 cell line. The Western blot analysis showed that the protein level of KAP1 is over-expressed in the established stable cell line by doxycycline induction, both dose and time dependently. Thus we have successfully established stable pcDNA5/FRT/TO-KAP1-HEK293 cell line, which can express KAP1 inducibly. This inducible cell line might be very useful for KAP1 functional studies

Temporal and spatial patterns in a diffusive ratio-dependent predator-prey system with linear stocking rate of prey species

The ratio-dependent predator–prey model exhibits rich interesting dynamics due to the singularity of the origin. It is one of prototypical pattern formation models. Stocking in ratio-dependent predator–prey models is relatively an important research subject from both ecological and mathematical points of view. In this paper, we study the temporal, spatial patterns of a ratio-dependent predator–prey diffusive model with linear stocking rate of prey species. For the spatially homogeneous model, we derive conditions for determining the direction of Hopf bifurcation and the stability of the bifurcating periodic solution by the center manifold and the normal form theory. For the reaction-diffusion model, firstly it is shown that Turing (diffusion-driven) instability occurs, which induces spatial inhomogeneous patterns. Then it is demonstrated that the model exhibits Hopf bifurcation which produces temporal inhomogeneous patterns. Finally, the non-existence and existence of positive non-constant steady-state solutions are established. We can see spatial inhomogeneous patterns via Turing instability, temporal periodic patterns via Hopf bifurcation and spatial patterns via the existence of positive non-constant steady state. Moreover, numerical simulations are performed to visualize the complex dynamic behavior

Temporal and spatial patterns in a diffusive ratio-dependent predator–prey system with linear stocking rate of prey species

The ratio-dependent predator–prey model exhibits rich interesting dynamics due to the singularity of the origin. It is one of prototypical pattern formation models. Stocking in a ratio-dependent predator–prey models is relatively an important research subject from both ecological and mathematical points of view. In this paper, we study the temporal, spatial patterns of a ratio-dependent predator–prey diffusive model with linear stocking rate of prey species. For the spatially homogeneous model, we derive conditions for determining the direction of Hopf bifurcation and the stability of the bifurcating periodic solution by the center manifold and the normal form theory. For the reaction-diffusion model, firstly it is shown that Turing (diffusion-driven) instability occurs, which induces spatial inhomogeneous patterns. Then it is demonstrated that the model exhibits Hopf bifurcation which produces temporal inhomogeneous patterns. Finally, the non-existence and existence of positive non-constant steady-state solutions are established. We can see spatial inhomogeneous patterns via Turing instability, temporal periodic patterns via Hopf bifurcation and spatial patterns via the existence of positive non-constant steady state. Moreover, numerical simulations are performed to visualize the complex dynamic behavior

RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray Projection

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiration limits its accuracy. Recently, 3D imaging from a single X-ray projection has received extensive attention as a promising approach to address this issue. However, current methods can only reconstruct 3D images without directly locating the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirements of motion control in radiotherapy. In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle. Furthermore, the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules have been proposed to aid feature extraction and improve segmentation accuracy. The proposed method was evaluated on fifteen patient cases and compared with three state-of-the-art methods. It not only delivers superior 3D reconstruction but also demonstrates commendable tumor segmentation results. Simultaneous reconstruction and segmentation can be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacies of both AEC and URE have also been validated in ablation studies. The code of work is available at https://github.com/ZywooSimple/RT-SRTS