A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket's advancement and practicality for protein-ligand binding site prediction.

BioEGRE: a linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network.

BACKGROUND: Automatic and accurate extraction of diverse biomedical relations from literature is a crucial component of bio-medical text mining. Currently, stacking various classification networks on pre-trained language models to perform fine-tuning is a common framework to end-to-end solve the biomedical relation extraction (BioRE) problem. However, the sequence-based pre-trained language models underutilize the graphical topology of language to some extent. In addition, sequence-oriented deep neural networks have limitations in processing graphical features. RESULTS: In this paper, we propose a novel method for sentence-level BioRE task, BioEGRE (BioELECTRA and Graph pointer neural net-work for Relation Extraction), aimed at leveraging the linguistic topological features. First, the biomedical literature is preprocessed to retain sentences involving pre-defined entity pairs. Secondly, SciSpaCy is employed to conduct dependency parsing; sentences are modeled as graphs based on the parsing results; BioELECTRA is utilized to generate token-level representations, which are modeled as attributes of nodes in the sentence graphs; a graph pointer neural network layer is employed to select the most relevant multi-hop neighbors to optimize representations; a fully-connected neural network layer is employed to generate the sentence-level representation. Finally, the Softmax function is employed to calculate the probabilities. Our proposed method is evaluated on three BioRE tasks: a multi-class (CHEMPROT) and two binary tasks (GAD and EU-ADR). The results show that our method achieves F1-scores of 79.97% (CHEMPROT), 83.31% (GAD), and 83.51% (EU-ADR), surpassing the performance of existing state-of-the-art models. CONCLUSION: The experimental results on 3 biomedical benchmark datasets demonstrate the effectiveness and generalization of BioEGRE, which indicates that linguistic topology and a graph pointer neural network layer explicitly improve performance for BioRE tasks.

Ultrasonic Thickness Measurement Method and System Implementation Based on Sampling Reconstruction and Phase Feature Extraction.

The existing ultrasonic thickness measurement systems require high sampling frequencies for echo signal acquisition, leading to complex circuit designs and high costs. Moreover, extracting the characteristics of ultrasonic echo signals for accurate thickness measurement poses significant challenges. To address these issues, this paper proposes a method that utilizes conventional sampling frequencies to acquire high-frequency ultrasonic echo signals, overcoming the limitations of high-frequency data acquisition imposed by the Nyquist-Shannon sampling theorem. By employing an improved sampling reconstruction technique, the multi-cycle sampling signals are reconstructed and rearranged within a single cycle, effectively increasing the equivalent sampling frequency. Additionally, a combination of coarse estimation using fast Fourier transform (FFT) and precise phase extraction using the moving sine fitting algorithm is proposed for accurate thickness measurement, resolving the limitations of common thickness measurement methods such as peak detection, envelope detection, and Hilbert autocorrelation in terms of low measurement accuracy. Experimental results obtained from thickness measurements on 45 steel ultrasonic test blocks within the range of 3 mm to 20 mm indicate a measurement error of ±0.01 mm, while for thicknesses ranging from 1 mm to 50 mm, the measurement error is ±0.05 mm.

Auto-balancing bridge based wide impedance spectrum measurement with consideration of Op-Amp input impedance.

In an auto-balancing bridge for high impedance measurements, an operational amplifier (Op-Amp) is used to follow the intermediate potential. However, the input impedance of the Op-Amp introduces significant effects in high impedance measurements. This paper proposes a two-step excitation method (TSEM) and an incremental iterative method (IIM). The TSEM determines the magnitude of the Op-Amp input impedance and the initial value of the device under test. The IIM utilizes the TSEM results as initial conditions to quickly bring the bridge to equilibrium. To overcome the distortion issues associated with small amplitude excitation signals generated by the DAC under low resolution conditions, a programmable gain amplifier is designed. Additionally, a half-cycle difference algorithm is developed prior to the three-parameter sine fit to mitigate low-frequency direct-current drift caused by power frequency, thus improving measurement accuracy. Experimental results demonstrate that when the reference impedance is set to 1 MΩ, impedance measurements ranging from 1 kΩ to 100 MΩ can be achieved within the frequency range of 1 to 100 kHz. The precision evaluation reveals a relative standard deviation (RSD) of the modulus better than 0.384% and a standard deviation (SD) of the phase angle better than 3.49 mrad; especially for the impedance under test of 1 MΩ, the RSD is better than 0.006% and the SD is better than 0.1 mrad.

Continuous medium approach to approximate the high concentrated aqueous electrolyte with different types of electrochemical structure.

Superconcentrated aqueous electrolytes have recently emerged as a new class of electrolytes, called water-in-salt electrolytes. They are distinguished, in both weight and volume, by a quantity of salt greater than water. Currently, these electrolytes are attracting major interest, particularly for application in aqueous rechargeable batteries. These electrolytes have only a small amount of free water due to an ultrahigh salt concentration. Consequently, the electrochemical stability window of water is wider than the predicted thermodynamic value of 1.23 V. Hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) have been shown to be shifted to more negative and positive potentials, respectively. The decrease in free water population is recognized as being involved in the increase in the electrochemical stability window of water. Here, we study the quantitative contribution of the decrease in the free water molecule concentration to the permittivity of the solution and of the activity of water to the OER and HER overpotentials when the salt concentration increases. We compare our model with that of Kornyshev and get three types of electrolyte structures: diluted, gradient of water contents, and aggregation. The theoretical calculation of the redox potentials of the OER and HER is compared with the experimentally determined electrochemical properties of aqueous LiTFSI electrolytes.

Visualizing Water Reduction with Diazonium Grafting on a Glassy Carbon Electrode Surface in a Water-in-Salt Electrolyte.

Aqueous batteries are regaining interest, thanks to the extended working stability voltage window in a highly concentrated electrolyte, namely the water-in-salt electrolyte. A solid-electrolyte interphase (SEI) forms on the negative electrode to prevent water access to the electrode surface. However, we further reported that the formed SEI layer was not uniform on the surface of the glassy carbon electrode. The SEI after passivation will also show degradation during the remaining time of open-circuit voltage (OCV); hence, it calls for a more stable passivation layer to cover the electrode surface. Here, a surface modification was successfully achieved via artificial diazonium grafting using monomers, such as poly(ethylene glycol), α-methoxy, ω-allyloxy (PEG), and allyl glycidyl cyclocarbonate (AGC), on glassy carbon. Physical and electrochemical measurements indicated that the hydrophobic layer composed of PEG or AGC species was well grafted on the electrode surface. The grafted hydrophobic coatings could protect the electrode surface from the water molecules in the bulk electrolyte and then suppress the free water decomposition (from LSV) but still migrating lithium ions. Furthermore, multiple cycles of CV with one-hour resting OCV identified the good stability of the hydrophobic grafting layer, which is a highlight compared with our precious work. These findings relying on the diazonium grafting design may offer a new strategy to construct a stable artificial SEI layer that can well protect the electrode surface from the free water molecule.

Diffraction characteristics of orthogonal gratings analysis based on a spatial light modulator.

The diffraction characteristics of orthogonal gratings with variable duty cycles and phase modulation depths are analyzed by using a spatial light modulator. The calculation methods of the transmission function, far-field diffraction light field, and diffraction efficiency of orthogonal gratings are deduced in theory. Meanwhile, the influences of the duty cycle and phase modulation depth on the diffraction characteristics of the orthogonal grating are discussed. The simulation and experimental results verify the correctness of the theoretical derivation. This method can be widely used in the fields of an optical vortex array, laser parallel processing, optical computing, optical communication, and optoelectronic hybrid processing.

Beam shaping with high energy utilization and uniformity using gradient orthogonal gratings.

A flattop beam is useful in ultrafast laser processing. A laser beam shaping method for high energy utilization and uniformity is presented using a complex hologram displayed on a spatial light modulator. The hologram consists of a geometric mask, an external blazed grating, and internal gradient orthogonal gratings. The gradient orthogonal gratings can change the incident light energy distribution and obtain flattop beams with high energy utilization. Experimental results show that the presented method can obtain an arbitrary geometric shape with a steep edge and high uniformity. Meanwhile, the bigger the geometric mask size, the higher the energy utilization will be, and it is up to 78.70%.