Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.

Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV (2DUV) spectra as pattern recognition descriptors, aiming at automated protein secondary structure determination from spectroscopic features. Accurate secondary structure recognition is obtained for homologous (97%) and nonhomologous (91%) protein segments, randomly selected from simulated model datasets. The advantage of 2DUV descriptors over one-dimensional linear absorption and circular dichroism spectra lies in the cross-peak information that reflects interactions between local regions of the protein. Thanks to their ultrafast (∼200 fs) nature, 2DUV measurements can be used in the future to probe conformational variations in the course of protein dynamics.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics.

The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

Amino-Acid-Encoded Bioinspired Supramolecular Self-Assembly of Multimorphological Nanocarriers.

Supramolecular self-assembly has emerged as an efficient tool to construct well-organized nanostructures for biomedical applications by small organic molecules. However, the physicochemical properties of self-assembled nanoarchitectures are greatly influenced by their morphologies, mechanical properties, and working mechanisms, making it challenging to design and screen ideal building blocks. Herein, using a biocompatible firefly-sourced click reaction between the cyano group of 2-cyano-benzothiazole (CBT) and the 1,2-aminothiol group of cysteine (Cys), an amino-acid-encoded supramolecular self-assembly platform Cys(SEt)-X-CBT (X represents any amino acid) is developed to incorporate both covalent and noncovalent interactions for building diverse morphologies of nanostructures with bioinspired response mechanism, providing a convenient and rapid strategy to construct site-specific nanocarriers for drug delivery, cell imaging, and enzyme encapsulation. Additionally, it is worth noting that the biodegradation of Cys(SEt)-X-CBT generated nanocarriers can be easily tracked via bioluminescence imaging. By caging either the thiol or amino groups in Cys with other stimulus-responsive sites and modifying X with probes or drugs, a variety of multi-morphological and multifunctional nanomedicines can be readily prepared for a wide range of biomedical applications.

Mechanochemical Synthesis of Cuprous Complexes for X-ray Scintillation and Imaging.

Cuprous complex scintillators show promise for X-ray detection with abundant raw materials, diverse luminescent mechanisms, and adjustable structures. However, their synthesis typically requires a significant amount of organic solvents, which conflict with green chemistry principles. Herein, we present the synthesis of two high-performance cuprous complex scintillators using a simple mechanochemical method for the first time, namely [CuI(PPh3)2R] (R = 4-phenylpyridine hydroiodide (PH, Cu-1) and 4-(4-bromophenyl)pyridine hydroiodide (PH-Br, Cu-2). Both materials demonstrated remarkable scintillation performances, exhibiting radioluminescence (RL) intensities 1.52 times (Cu-1) and 2.52 times (Cu-2) greater than those of Bi4Ge3O12 (BGO), respectively. Compared to Cu-1, the enhanced RL performance of Cu-2 can be ascribed to its elevated quantum yield of 51.54%, significantly surpassing that of Cu-1 at 37.75%. This excellent luminescent performance is derived from the introduction of PH-Br, providing a more diverse array of intermolecular interactions that effectively constrain molecular vibration and rotation, further suppressing the nonradiative transition process. Furthermore, Cu-2 powder can be prepared into scintillator film with excellent X-ray imaging capabilities. This work establishes a pathway for the rapid, eco-friendly, and cost-effective synthesis of high-performance cuprous complex scintillators.

High-efficiency reflective terahertz cross-polarization converter with broadband and wide-angle response.

In this paper, a broadband terahertz metasurface dedicated to cross-polarization conversion was designed, fabricated, and assessed. The metasurface, comprising two nested double-split rings, features an inherent insensitivity to the angle of incidence. Simulations reveal that the converter achieves a >99% polarization conversion efficiency across the 90-140 GHz range. Moreover, it maintains a >90% polarization conversion ratio (PCR), even at a 50° incidence angle. The sample, featuring 50×70 arrays, was fabricated, and the relevant experimental results align closely with the simulated outcomes. The metasurface characteristics can markedly enhance the performance of cross-polarization converters operating in the terahertz range.

AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2.

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE2 and the spike glycoprotein (S protein) of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines, yet the dynamic nature of these interactions in fluctuating surroundings is very challenging to probe by those structure determination techniques requiring the structures of samples to be fixed. Here we demonstrate, by a proof-of-concept simulation of infrared (IR) spectra of S protein and hACE2, that time-resolved spectroscopy may monitor the real-time structural information of the protein-protein complexes of interest, with the help of machine learning. Our machine learning protocol is able to identify fine changes in IR spectra associated with variation of the secondary structures of S protein of the coronavirus. Further, it is three to four orders of magnitude faster than conventional quantum chemistry calculations. We expect our machine learning protocol would accelerate the development of real-time spectroscopy study of protein dynamics.

Electrosynthesis of an Improbable Directly Bonded Phosphorene-Fullerene Heterodimensional Hybrid toward Boosted Photocatalytic Hydrogen Evolution.

Phosphorene and fullerene are representative two-dimensional (2D) and zero-dimensional (0D) nanomaterials respectively, constructing their heterodimensional hybrid not only complements their physiochemical properties but also extends their applications via synergistic interactions. This is however challenging because of their diversities in dimension and chemical reactivity, and theoretical studies predicted that it is improbable to directly bond C60 onto the surface of phosphorene due to their strong repulsion. Here, we develop a facile electrosynthesis method to synthesize the first phosphorene-fullerene hybrid featuring fullerene surface bonding via P-C bonds. Few-layer black phosphorus nanosheets (BPNSs) obtained from electrochemical exfoliation react with C602- dianion prepared by electroreduction of C60, fulfilling formation of the "improbable" phosphorene-fullerene hybrid (BPNS-s-C60). Theoretical results reveal that the energy barrier for formation of [BPNS-s-C60]2- intermediate is significantly decreased by 1.88 eV, followed by an oxidization reaction to generate neutral BPNS-s-C60 hybrid. Surface bonding of C60 molecules not only improves significantly the ambient stability of BPNSs, but also boosts dramatically the visible light and near-infrared (NIR) photocatalytic hydrogen evolution rates, reaching 1466 and 1039 µmol h-1 g-1 respectively, which are both the highest values among all reported BP-based metal-free photocatalysts.

Catalytic Structure Design by AI Generating with Spectroscopic Descriptors.

Generative artificial intelligence has depicted a beautiful blueprint for on-demand design in chemical research. However, the few successful chemical generations have only been able to implement a few special property values because most chemical descriptors are mathematically discrete or discontinuously adjustable. Herein, we use spectroscopic descriptors with machine learning to establish a quantitative spectral structure-property relationship for adsorbed molecules on metal monatomic catalysts. Besides catalytic properties such as adsorption energy and charge transfer, the complete spatial relative coordinates of the adsorbed molecule were successfully inverted. The spectroscopic descriptors and prediction models are generalized, allowing them to be transferred to several different systems. Due to the continuous tunability of the spectroscopic descriptors, the design of catalytic structures with continuous adsorption states generated by AI in the catalytic process has been achieved. This work paves the way for using spectroscopy to enable real-time monitoring of the catalytic process and continuous customization of catalytic performance, which will lead to profound changes in catalytic research.

Manipulation of sub-terahertz waves using digital coding metasurfaces based on liquid crystals.

This paper presents a novel sub-terahertz liquid crystal (LC) phase shifter based on digital coding metasurfaces. The proposed structure consists of metal gratings and resonant structures. They are both immersed in LC. The metal gratings function as reflective surfaces for electromagnetic waves and electrodes for controlling the LC layer. The proposed structure changes the state of the phase shifter by switching the voltage on every grating. It allows the deflection of LC molecules within a subregion of the metasurface structure. Four switchable coding states of the phase shifter are obtained experimentally. The phase of the reflected wave varies by 0°, 102°, 166°, and 233° at 120 GHz. Due to the presence of the transverse control electric field, modulation speed is approximately doubled compared to the free relaxation state. This work provides a novel idea for wavefront modulation of phase.

Two Species of Stemphylium, S. astragali and S. henanense sp. nov., Causing Leaf and Stem Spot Disease on Astragalus sinicus in China.

Astragalus sinicus is a versatile legume crop, primarily utilized as a green manure in China. During 2020 and 2021, A. sinicus plants exhibiting dark brown or reddish-brown lesions or spots on leaves and stems were collected from fields in the Henan, Sichuan, and Guangxi provinces of China. Sixteen single-spore isolates were isolated from the infected leaf and stem tissue samples. Phylogenetic analyses based on the concatenated internal transcribed spacer, gapdh, and cmdA sequences indicated that 14 of them belong to Stemphylium astragali, whereas two isolates can be well separated from other known species in this genus. Based on the morphological characteristics and nucleotide polymorphisms with sister taxa, the two isolates were identified as a new species named S. henanense. Furthermore, pathogenicity assays showed that the S. astragali and S. henanense isolates caused leaf and stem spot symptoms on A. sinicus. Altogether, we describe a new species of Stemphylium (i.e., S. henanense sp. nov.) causing leaf spot disease of A. sinicus. In addition, this is the first report of S. astragali causing stem spot disease of A. sinicus.

Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes.

Solid electrolytes (SEs) with superionic conductivity and interfacial stability are highly desirable for stable all-solid-state Li-metal batteries (ASSLMBs). Here, we employ neural network potential to simulate materials composed of Li, Zr/Hf, and Cl using stochastic surface walking method and identify two potential unique layered halide SEs, named Li2ZrCl6 and Li2HfCl6, for stable ASSLMBs. The predicted halide SEs possess high Li+ conductivity and outstanding compatibility with Li metal anodes. We synthesize these SEs and demonstrate their superior stability against Li metal anodes with a record performance of 4000 h of steady lithium plating/stripping. We further fabricate the prototype stable ASSLMBs using these halide SEs without any interfacial modifications, showing small internal cathode/SE resistance (19.48 Ω cm2), high average Coulombic efficiency (∼99.48%), good rate capability (63 mAh g-1 at 1.5 C), and unprecedented cycling stability (87% capacity retention for 70 cycles at 0.5 C).

Bone marrow mesenchymal stem cells-derived exosomes mediated delivery of tetramethylpyrazine attenuate cerebral ischemic injury.

OBJECTIVES: Tetramethylpyrazine (TEP) can protect the brain from ischemic damage, but it has defects such as short half-life, fast absorption, wide distribution, and rapid elimination, which limits its application. Exosomes (Exos) have the property of loading drugs and transporting signal substances. Here, we elucidated the effect of TEP-loaded bone marrow mesenchymal stem cell (BMSC)-derived Exos (Exo-TEP) on cerebral ischemic injury. MATERIALS AND METHODS: The Exos were extracted by ultracentrifugation and TEP was loaded into the Exos by electroporation. Oxygen-glucose deprivation (OGD) induced-primary cortical neurons and middle cerebral artery occlusion (MCAO)-induced mouse models were used to determine the effect of Exo-TEP on cerebral ischemic injury in vitro and in vivo. RESULTS: Exo-TEP exhibited a stable and sustained release pattern compared to free TEP. Exo-TEP treatment was more significant in improving OGD-mediated decrease in cell activity, as well as a elevation in apoptosis and ROS production in cortical neurons. In comparison with Exo and free TEP treatment, Exo-TEP treatment significantly improved pathological changes, shrunk cerebral infarction volume, as well reduced neurological deficit scores and neuronal apoptosis, and oxidative stress. CONCLUSIONS: Exo-TEP was superior to free TEP in improving cerebral ischemic injury by reducing neuronal apoptosis and oxidative stress.

Epsilon-MnO2 simply prepared by redox precipitation as an efficient catalyst for ciprofloxacin degradation by activating peroxymonosulfate.

Four kinds of manganese oxides were successfully prepared by hydrothermal and redox precipitation methods, and the obtained oxides were used for CIP removal from water by activating PMS. The microstructure and surface properties of four oxides were systematically characterized. The results showed that ε-MnO2 prepared by the redox precipitation method had large surface area, low crystallinity, high surface Mn(III)/Mn(Ⅳ) ratio and the highest activation efficiency for PMS, that is, when the concentration of PMS was 0.6 g/L, 0.2 g/L ε-MnO2 could degrade 93% of CIP within 30 min. Multiple active oxygen species, such as sulfate radical, hydroxyl radical and singlet oxygen, were found in CIP degradation, among which sulfate radical was the most important one. The degradation reaction mainly occurred on the surface of the catalyst, and the surface hydroxyl group played an important role in the degradation. The catalyst could be regenerated in situ through the redox reaction between Mn4+ and Mn3+. The ε-MnO2 had the advantages of simple preparation, good stability and excellent performance, which provided the potential for developing new green antibiotic removal technology.

Water quality characteristics and evaluation of Qilian Mountain National Park section in Heihe River Basin based on water quality indices and 3D fluorescence technology.

The water quality of the Heihe River Basin affects the life quality and health of tens of thousands of residents along it. However, there are relatively few studies that evaluate its water quality. In this study, we used principal component analysis (PCA), an improved comprehensive water quality index (WQI), and three-dimensional (3D) fluorescence technology to identify pollutants and evaluate water quality at nine monitoring sites in the Qilian Mountain National Park in Heihe River Basin. PCA was applied to concentrate the water quality indices into nine items. The analysis shows that the water quality in the study area is mainly polluted by organic matter, nitrogen, and phosphorus. According to the revised WQI model, the water quality of the study area is from moderate to good, while the water quality of Qinghai section is worse than that of Gansu section. According to the 3D fluorescence spectrum analysis of the monitoring sites, the organic pollution of water comes from vegetation decay, animal feces, and some human activities. This study can not only provide support and basis for water environment protection and management in the Heihe River Basin, but also promote the healthy development of the water environment in the Qilian Mountains.

Electronic Perturbation of Copper Single-Atom CO2 Reduction Catalysts in a Molecular Way.

Fine-tuning electronic structures of single-atom catalysts (SACs) plays a crucial role in harnessing their catalytic activities, yet challenges remain at a molecular scale in a controlled fashion. By tailoring the structure of graphdiyne (GDY) with electron-withdrawing/-donating groups, we show herein the electronic perturbation of Cu single-atom CO2 reduction catalysts in a molecular way. The elaborately introduced functional groups (-F, -H and -OMe) can regulate the valance state of Cuδ+ , which is found to be directly scaled with the selectivity of the electrochemical CO2 -to-CH4 conversion. An optimum CH4 Faradaic efficiency of 72.3 % was achieved over the Cu SAC on the F-substituted GDY. In situ spectroscopic studies and theoretical calculations revealed that the positive Cuδ+ centers adjusted by the electron-withdrawing group decrease the pKa of adsorbed H2 O, promoting the hydrogenation of intermediates toward the CH4 production. Our strategy paves the way for precise electronic perturbation of SACs toward efficient electrocatalysis.

Quantitatively Determining Surface-Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning.

Learning microscopic properties of a material from its macroscopic measurables is a grand and challenging goal in physical science. Conventional wisdom is to first identify material structures exploiting characterization tools, such as spectroscopy, and then to infer properties of interest, often with assistance of theory and simulations. This indirect approach has limitations due to the accumulation of errors from retrieving structures from spectral signals and the lack of quantitative structure-property relationship. A new pathway directly from spectral signals to microscopic properties is highly desirable, as it would offer valuable guidance toward materials evaluation and design via spectroscopic measurements. Herein, we exploit machine-learned vibrational spectroscopy to establish quantitative spectrum-property relationships. Key interaction properties of substrate-adsorbate systems, including adsorption energy and charge transfer, are quantitatively determined directly from Infrared and Raman spectroscopic signals of the adsorbates. The machine-learned spectrum-property relationships are presented as mathematical formulas, which are physically interpretable and therefore transferrable to a series of metal/alloy surfaces. The demonstrated ability of quantitative determination of hard-to-measure microscopic properties using machine-learned spectroscopy will significantly broaden the applicability of conventional spectroscopic techniques for materials design and high throughput screening under operando conditions.

Exogenous Nicotinamide Adenine Dinucleotide Attenuates Postresuscitation Myocardial and Neurologic Dysfunction in a Rat Model of Cardiac Arrest.

OBJECTIVES: To investigate the therapeutic potential and underlying mechanisms of exogenous nicotinamide adenine dinucleotide+ on postresuscitation myocardial and neurologic dysfunction in a rat model of cardiac arrest. DESIGN: Thirty-eight rats were randomized into three groups: 1) Sham, 2) Control, and 3) NAD. Except for the sham group, untreated ventricular fibrillation for 6 minutes followed by cardiopulmonary resuscitation was performed in the control and NAD groups. Nicotinamide adenine dinucleotide+ (20 mg/kg) was IV administered at the onset of return of spontaneous circulation. SETTING: University-affiliated research laboratory. SUBJECTS: Sprague-Dawley rats. INTERVENTIONS: Nicotinamide adenine dinucleotide+. MEASUREMENTS AND MAIN RESULTS: Hemodynamic and myocardial function were measured at baseline and within 4 hours following return of spontaneous circulation. Survival analysis and Neurologic Deficit Score were performed up to 72 hours after return of spontaneous circulation. Adenosine triphosphate (adenosine triphosphate) level was measured in both brain and heart tissue. Mitochondrial respiratory chain function, acetylation level, and expression of Sirtuin3 and NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 9 (NDUFA9) in isolated mitochondrial protein from both brain and heart tissue were evaluated at 4 hours following return of spontaneous circulation. The results demonstrated that nicotinamide adenine dinucleotide+ treatment improved mean arterial pressure (at 1 hr following return of spontaneous circulation, 94.69 ± 4.25 mm Hg vs 89.57 ± 7.71 mm Hg; p < 0.05), ejection fraction (at 1 hr following return of spontaneous circulation, 62.67% ± 6.71% vs 52.96% ± 9.37%; p < 0.05), Neurologic Deficit Score (at 24 hr following return of spontaneous circulation, 449.50 ± 82.58 vs 339.50 ± 90.66; p < 0.05), and survival rate compared with that of the control group. The adenosine triphosphate level and complex I respiratory were significantly restored in the NAD group compared with those of the control group. In addition, nicotinamide adenine dinucleotide+ treatment activated the Sirtuin3 pathway, down-regulating acetylated-NDUFA9 in the isolated mitochondria protein. CONCLUSIONS: Exogenous nicotinamide adenine dinucleotide+ treatment attenuated postresuscitation myocardial and neurologic dysfunction. The responsible mechanisms may involve the preservation of mitochondrial complex I respiratory capacity and adenosine triphosphate production, which involves the Sirtuin3-NDUFA9 deacetylation.

Active continuous control of terahertz wave based on a reflectarray element-liquid crystal-grating electrode hybrid structure.

In this work, a new and efficient terahertz reflective phase shifter is proposed. The phase shifter is composed of a metal-dielectric-metal structure with a double dipole patch array, as well as copper grating electrodes immersed within the nematic liquid crystal. More specifically, the employed copper grating electrodes consist of two sets of cross-distributed comb grids, whereas at each set of comb grids can be applied an external bias voltage separately. On top of that, the electric field in the liquid crystal (LC) layer can be continuously changed by enforcing an innovative technique. Consequently, the orientation of the LC molecules was fully controlled by the applied electric field, since the dielectric constant of the LC is controlled by the biased voltage. The phase of the reflective electromagnetic wave can be continuously manipulated. Under this direction, the experimental results show that the phase shift exceeds the value of 180° in the range of 102.5 GHz-104.3 GHz, where the maximum phase shift is 249° at 103 GHz. The proposed work provides a new regulation concept for the implementation of LC-based terahertz devices and the respective applications in the terahertz reconfigurable antennas field.

Rapid terahertz wave manipulation in a liquid-crystal-integrated metasurface structure.

A terahertz phase shifter based on liquid-crystal-integrated metasurface is proposed, which contains a three-slotted array structure and comb grating. The orientation of the liquid crystal molecules can be completely controlled by the direction of the electric field. From the acquired experimental results, it was demonstrated that the phase shift exceeds 300° in the range of 378.6 - 390.8 GHz, whereas the maximum phase shift reaches 374.1° at 383.1 GHz. The molecular reorientation transient process induced by the external electric field in the liquid crystal was measured and analyzed. Based on the molecular reorientation mechanism, which can be divided into three processes, a rapid modulation mechanism was demonstrated. From the performance of the proposed device, an actively controllable phase delay and reflectance with a cycle switching time of approximately 0.3 s was achieved, which is remarkably faster than the usual cycle time that exceeds 8 s. Our work provides useful ideas for improving the response speed of LC-based terahertz devices, which is considered of great significance for several applications, in terms of terahertz reconfigurable devices.