High-Throughput Screening Strategy for Electrocatalysts for Selective Catalytic Oxidation of Formaldehyde to Formic Acid.

Electrocatalytic oxidation of formaldehyde (FOR) is an effective way to prevent the damage caused by formaldehyde and produce high-value products. A screening strategy of a single-layer MnO2-supported transition metal catalyst for the selective oxidation of formaldehyde to formic acid was designed by high-throughput density functional calculation. N-MnO2@Cu and MnO2@Cu are predicted to be potential FOR electrocatalysts with potential-limiting steps (PDS) of 0.008 and -0.009 eV, respectively. Electronic structure analysis of single-atom catalysts (SACs) shows that single-layer MnO2 can regulate the spin density of loaded transition metal and thus regulate the adsorption of HCHO (Ead), and Ead is volcanically distributed with the magnetic moment descriptor -|mM - mH|. In addition, the formula quantifies Ead and |mM - mH| to construct a volcano-type descriptor α describing the PDS [ΔG(*CHO)]. Other electronic and structural properties of SACs and α are used as input features for the GBR method to construct machine learning models predicting the PDS (R2 = 0.97). This study hopes to provide some insights into FOR electrocatalysts.

Ultrasound image segmentation based on Transformer and U-Net with joint loss.

Background: Ultrasound image segmentation is challenging due to the low signal-to-noise ratio and poor quality of ultrasound images. With deep learning advancements, convolutional neural networks (CNNs) have been widely used for ultrasound image segmentation. However, due to the intrinsic locality of convolutional operations and the varying shapes of segmentation objects, segmentation methods based on CNNs still face challenges with accuracy and generalization. In addition, Transformer is a network architecture with self-attention mechanisms that performs well in the field of computer vision. Based on the characteristics of Transformer and CNNs, we propose a hybrid architecture based on Transformer and U-Net with joint loss for ultrasound image segmentation, referred to as TU-Net. Methods: TU-Net is based on the encoder-decoder architecture and includes encoder, parallel attention mechanism and decoder modules. The encoder module is responsible for reducing dimensions and capturing different levels of feature information from ultrasound images; the parallel attention mechanism is responsible for capturing global and multiscale local feature information; and the decoder module is responsible for gradually recovering dimensions and delineating the boundaries of the segmentation target. Additionally, we adopt joint loss to optimize learning and improve segmentation accuracy. We use experiments on datasets of two types of ultrasound images to verify the proposed architecture. We use the Dice scores, precision, recall, Hausdorff distance (HD) and average symmetric surface distance (ASD) as evaluation metrics for segmentation performance. Results: For the brachia plexus and fetal head ultrasound image datasets, TU-Net achieves mean Dice scores of 79.59% and 97.94%; precisions of 81.25% and 98.18%; recalls of 80.19% and 97.72%; HDs (mm) of 12.44 and 6.93; and ASDs (mm) of 4.29 and 2.97, respectively. Compared with those of the other six segmentation algorithms, the mean values of TU-Net increased by approximately 3.41%, 2.62%, 3.74%, 36.40% and 31.96% for the Dice score, precision, recall, HD and ASD, respectively.

Bicarbonate ringer's solution could improve the intraoperative acid-base equilibrium and reduce hepatocellular enzyme levels after deceased donor liver transplantation: a randomized controlled study.

BACKGROUND: Bicarbonate Ringer's (BR) solution is a direct liver and kidney metabolism-independent HCO3- buffering system. We hypothesized that BR solution would be more effective in improving acid-base equilibrium and more conducive to better liver function than Acetate Ringer's (AR) solution in conventional orthotopic liver transplantation (OLT) patients. METHODS: Sixty-nine adult patients underwent OLT. Patients in the bicarbonate and acetate groups received BR solution or AR solution as infused crystalloids and graft washing solution, respectively. The primary outcome was the effect on pH and base excess (BE) levels. The secondary outcome measures were the incidence and volume of intraoperative 5% sodium bicarbonate infusion and laboratory indicates of liver and kidney function. RESULTS: The pH and absolute BE values changed significantly during the anhepatic phase and immediately after transplanted liver reperfusion in the bicarbonate group compared with the acetate group (all P < 0.05). The incidence and volume of 5% sodium bicarbonate infusion were lower in the bicarbonate group than in the acetate group (all P < 0.05). The aspartate transaminase (AST) level at 7 postoperative days and the creatine level at 30 postoperative days were significantly higher in the acetate group than in the bicarbonate group (all P < 0.05). CONCLUSION: Compared with AR solution, BR solution was associated with improved intraoperative acid-base balance and potentially protected early postoperative liver graft function and reduced late-postoperative renal injury.

CBMC: A Biomimetic Approach for Control of a 7-Degree of Freedom Robotic Arm.

Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot's operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules. The first module consists of a cerebellum-like spiking neural network that employs spiking timing-dependent plasticity to learn the dynamics mechanisms and adjust the synapses connecting the spiking neurons. The second module constructed using an artificial neural network, mimicking the regulation ability of the cerebral cortex to the cerebellum in the CNS, learns by reinforcement learning to supervise the cerebellum module with instructive input. The third and last modules are the cerebral sensory module and the spinal cord module, which deal with sensory input and provide modulation to torque commands, respectively. To validate our method, CBMC was applied to the trajectory tracking control of a 7-DoF robotic arm in simulation. Finally, experiments are conducted on the robotic arm using various payloads, and the results of these experiments clearly demonstrate the effectiveness of the proposed methodology.

Balanced Standing on One Foot of Biped Robot Based on Three-Particle Model Predictive Control.

Balancing is a fundamental task in the motion control of bipedal robots. Compared to two-foot balancing, one-foot balancing introduces new challenges, such as a smaller supporting polygon and control difficulty coming from the kinematic coupling between the center of mass (CoM) and the swinging leg. Although nonlinear model predictive control (NMPC) may solve this problem, it is not feasible to implement it on the actual robot because of its large amount of calculation. This paper proposes the three-particle model predictive control (TP-MPC) approach. It combines with the hierarchical whole-body control (WBC) to solve the one-leg balancing problem in real time. The bipedal robot's torso and two legs are modeled as three separate particles without inertia. The TP-MPC generates feasible swing leg trajectories, followed by the WBC to adjust the robot's center of mass. Since the three-particle model is linear, the TP-MPC requires less computational cost, which implies real-time execution on an actual robot. The proposed method is verified in simulation. Simulation results show that our method can resist much larger external disturbance than the WBC-only control scheme.

Lysine 92 amino acid residue of USP46, a gene associated with 'behavioral despair' in mice, influences the deubiquitinating enzyme activity.

Deubiquitinating enzymes (DUBs) regulate diverse cellular functions by their activity of cleaving ubiquitin from specific protein substrates. Ubiquitin-Specific Protease 46 (USP46) has recently been identified as a quantitative trait gene responsible for immobility in the tail suspension test and forced swimming test in mice. Mice with a lysine codon (Lys 92) deletion in USP46 exhibited loss of 'behavioral despair' under inescapable stresses in addition to abnormalities in circadian behavioral rhythms and the GABAergic system. However, whether this deletion affects enzyme activity is unknown. Here we show that USP46 has deubiquitinating enzyme activity detected by USP cleavage assay using GST-Ub52 as a model substrate. Interestingly, compared to wild type, the Lys 92 deletion mutant resulted in a decreased deubiquitinating enzyme activity of 27.04%. We also determined the relative expression levels of Usp46 in rat tissues using real-time RT-PCR. Usp46 mRNA was expressed in various tissues examined including brain, with the highest expression in spleen. In addition, like rat USP46, both human and mouse USP46 are active toward to the model substrate, indicating the USP cleavage assay is a simple method for testing the deubiquitinating enzyme activity of USP46. These results suggest that the Lys 92 deletion of USP46 could influence enzyme activity and thereby provide a molecular clue how the enzyme regulating the pathogenesis of mental illnesses.