Proton-Coupled Electron Transfer on CuO/TiCT MXene for Propane (CH) Synthesis from Electrochemical CO Reduction.

Electrochemical CO reduction reaction (CORR) to produce value-added multi-carbon chemicals has been an appealing approach to achieving environmentally friendly carbon neutrality in recent years. Despite extensive research focusing on the use of CO to produce high-value chemicals like high-energy-density hydrocarbons, there have been few reports on the production of propane (CH), which requires carbon chain elongation and protonation. A rationally designed 0D/2D hybrid CuO anchored-TiCT MXene catalyst (CuO/MXene) is demonstrated with efficient CORR activity in an aqueous electrolyte to produce CH.

Decoding force production of skeletal muscle from the female brain using functional near-infrared spectroscopy.

Objective: Noninvasive neural decoding enables predicting motor output from neural activities without physically damaging the human body. A recent study demonstrated the applicability of functional near-infrared spectroscopy (fNIRS) to decode muscle force production from hemodynamic signals measured in the male brain. However, given the sex differences in cerebral blood flow and muscle physiology, whether the fNIRS approach can also be applied to the female brain remains elusive.

A dynamic calcium-force relationship model for sag behavior in fast skeletal muscle.

In vitro studies using isolated or skinned muscle fibers suggest that the sigmoidal relationship between the intracellular calcium concentration and force production may depend upon muscle type and activity. The goal of this study was to investigate whether and how the calcium-force relationship changes during force production under physiological conditions of muscle excitation and length in fast skeletal muscles. A computational framework was developed to identify the dynamic variation in the calcium-force relationship during force generation over a full physiological range of stimulation frequencies and muscle lengths in cat gastrocnemius muscles.

Wafer map failure pattern classification using geometric transformation-invariant convolutional neural network.

Wafer map defect pattern classification is essential in semiconductor manufacturing processes for increasing production yield and quality by providing key root-cause information. However, manual diagnosis by field experts is difficult in large-scale production situations, and existing deep-learning frameworks require a large quantity of data for learning. To address this, we propose a novel rotation- and flip-invariant method based on the labeling rule that the wafer map defect pattern has no effect on the rotation and flip of labels, achieving class discriminant performance in scarce data situations.

An evolution-based machine learning to identify cancer type-specific driver mutations.

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance.

Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder.

Discrimination of ovarian tumors is necessary for proper treatment. In this study, we developed a convolutional neural network model with a convolutional autoencoder (CNN-CAE) to classify ovarian tumors. A total of 1613 ultrasound images of ovaries with known pathological diagnoses were pre-processed and augmented for deep learning analysis.

Enzyme activity engineering based on sequence co-evolution analysis.

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection.

Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes.

Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor.

Gallbladder Polyp Classification in Ultrasound Images Using an Ensemble Convolutional Neural Network Model.

Differential diagnosis of true gallbladder polyps remains a challenging task. This study aimed to differentiate true polyps in ultrasound images using deep learning, especially gallbladder polyps less than 20 mm in size, where clinical distinction is necessary. A total of 501 patients with gallbladder polyp pathology confirmed through cholecystectomy were enrolled from two tertiary hospitals.

Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images.

Background And Aim: We aimed to develop a convolutional neural network (CNN)-based object detection model for the discrimination of gastric subepithelial tumors, such as gastrointestinal stromal tumors (GISTs), and leiomyomas, in endoscopic ultrasound (EUS) images.

Methods: We used 376 images from 114 patients with histologically confirmed gastric GIST or leiomyoma to train the EUS-CNN. We constructed the EUS-CNN using an EfficientNet CNN model for feature extraction and a weighted bi-directional feature pyramid network for object detection.

Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning.

Advanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network.