Detonation performance and shock sensitivity of energetic material NTO with embedded small molecules: a deep neural network potential accelerated molecular dynamics study.

Accurate description of detonation performance for explosives remains a challenge for current experimental and theoretical methodologies. Herein, we address this issue through combining a multi-scale shock technique and a first-principles based deep neural network potential. This approach enables us to conduct molecular dynamics simulations encompassing over a thousand atoms and extending for several nanoseconds, allowing us to evaluate the detonation performance of the insensitive explosive NTO crystal. Utilizing the ZND model, we successfully determine the detonation velocity (7.9 km s-1), and detonation pressure (33 GPa) of the NTO crystals at the C-J state, which align well with experimental results. Additionally, we predict the detonation performance of three host-guest materials: NTO/H2O2, NTO/CO2, and NTO/N2O, all of which exhibit higher detonation temperatures compared to the NTO crystals in the present model. Moreover, we proposed the time to reach the C-J state as a shock sensitivity descriptor for explosives. Our findings reveal that the order of shock sensitivity for these materials is NTO/H2O2 > NTO/N2O > NTO/CO2 > NTO, and the trend can be explained in terms of bulk modulus, electronic band gap and oxygen balance. The enhanced shock sensitivity by embedded small molecules arises not only from the reduction in initial reaction barriers, but also from the faster evolution rate of final products and the release of more heat. Our research might present a cutting-edge framework for precisely, quickly, and safely evaluating and modulating the detonation performance and shock sensitivity of explosives.

Force Field Limitations of All-Atom Continuous Constant pH Molecular Dynamics.

All-atom constant pH molecular dynamics simulations offer a powerful tool for understanding pH-mediated and proton-coupled biological processes. As the protonation equilibria of protein sidechains are shifted by electrostatic interactions and desolvation energies, pK a values calculated from the constant pH simulations may be sensitive to the underlying protein force field and water model. Here we investigated the force field dependence of the all-atom particle mesh Ewald (PME) continuous constant pH (PME-CpHMD) simulations of a mini-protein BBL. The replica-exchange titration simulations based on the Amber ff19SB and ff14SB force fields with the respective water models showed significantly overestimated pK a downshifts for a buried histidine (His166) and for two glutamic acids (Glu141 and Glu161) that are involved in salt-bridge interactions. These errors (due to undersolvation of neutral histidines and overstabilization of salt bridges) are consistent with the previously reported pK a's based on the CHARMM c22/CMAP force field, albeit in larger magnitudes. The pK a calculations also demonstrated that ff19SB with OPC water is significantly more accurate than ff14SB with TIP3P water, and the salt-bridge related pK a downshifts can be partially alleviated by the atom-pair specific Lennard-Jones corrections (NBFIX). Together, these data suggest that the accuracies of the protonation equilibria of proteins from constant pH simulations can significantly benefit from improvements of force fields.

Free-standing cubic gauche nitrogen stable at 760 K under ambient pressure.

Cubic gauche nitrogen (cg-N) has received wide attention for its exceptionally high energy density and environmental friendliness. However, traditional synthesis methods for cg-N predominantly rely on high-pressure techniques or the utilization of nanoconfined effects using highly toxic and sensitive sodium azide as precursor, which substantially restrict its practical application. On the basis of the first-principles simulations, we found that adsorption of potassium on the cg-N surface exhibits superior stabilization compared to sodium. Then, we chose safer potassium azide as precursor for synthesizing cg-N. Through plasma-enhanced chemical vapor deposition treatment, the free-standing cg-N was successfully synthesized without the need for high-pressure and nanoconfined effects. It demonstrated excellent thermal stability up to 760 K, and then rapid and intense thermal decomposition occurred, exhibiting typical thermal decomposition behaviors of high-energy-density materials. The explosion parameters were also measured using laser-induced plasma spectroscopy. Our work has substantially promoted the practical application of cg-N as HEDMs.

The application of metaverse in healthcare.

While metaverse is widely discussed, comprehension of its intricacies remains limited to a select few. Conceptually akin to a three-dimensional embodiment of the Internet, the metaverse facilitates simultaneous existence in both physical and virtual domains. Fundamentally, it embodies a visually immersive virtual environment, striving for authenticity, where individuals engage in real-world activities such as commerce, gaming, social interaction, and leisure pursuits. The global pandemic has accelerated digital innovations across diverse sectors. Beyond strides in telehealth, payment systems, remote monitoring, and secure data exchange, substantial advancements have been achieved in artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and blockchain technologies. Nevertheless, the metaverse, in its nascent stage, continues to evolve, harboring significant potential for revolutionizing healthcare. Through integration with the Internet of Medical Devices, quantum computing, and robotics, the metaverse stands poised to redefine healthcare systems, offering enhancements in surgical precision and therapeutic modalities, thus promising profound transformations within the industry.

The immunotherapy in gastrointestinal stromal tumors.

Using Tyrosine Kinase Inhibitors (TKIs) for gastrointestinal stromal tumors (GIST) has significantly reduced the risk of recurrence and prolonged survival. Immunotherapy has demonstrated efficacy in multiple solid tumors, but its effectiveness in GIST remains uncertain. Although early clinical studies indicate good tolerability of immunotherapy in patients, the efficacy is not as desired. Therefore, identifying the subset of GIST patients who benefit from immunotherapy and coordinating the relationship between immunotherapy and TKI treatment are crucial issues to be explored. In this review, we aims to provide a retrospective analysis of relevant literature and find that GIST patients exhibit a rich presence of tumor-infiltrating immune cells, which play critical roles in the immune surveillance and evasion processes of tumors. This review incorporates a selection of 48 articles published between 2002 and 2023, sourced from PubMed, EBSCO, and Google Scholar databases.

Machine Learned Classification of Ligand Intrinsic Activities at Human µ-Opioid Receptor.

Opioids are small-molecule agonists of µ-opioid receptor (µOR), while reversal agents such as naloxone are antagonists of µOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human µOR based on the SMILES strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured Emax values at the human µOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5 ± 3.9% and 91.8 ± 4.4%, respectively. To overcome the challenge of a small data set, a student-teacher learning method called tritraining with disagreement was tested using an unlabeled data set comprised of 15,816 ligands of human, mouse, and rat µOR, κOR, and δOR. We found that the tritraining scheme was able to increase the hold-out AUC of MPNN models to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of µOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Advancements in brain-machine interfaces for application in the metaverse.

In recent years, with the shift of focus in metaverse research toward content exchange and social interaction, breaking through the current bottleneck of audio-visual media interaction has become an urgent issue. The use of brain-machine interfaces for sensory simulation is one of the proposed solutions. Currently, brain-machine interfaces have demonstrated irreplaceable potential as physiological signal acquisition tools in various fields within the metaverse. This study explores three application scenarios: generative art in the metaverse, serious gaming for healthcare in metaverse medicine, and brain-machine interface applications for facial expression synthesis in the virtual society of the metaverse. It investigates existing commercial products and patents (such as MindWave Mobile, GVS, and Galea), draws analogies with the development processes of network security and neurosecurity, bioethics and neuroethics, and discusses the challenges and potential issues that may arise when brain-machine interfaces mature and are widely applied. Furthermore, it looks ahead to the diverse possibilities of deep and varied applications of brain-machine interfaces in the metaverse in the future.

Laser-induced shock wave two-dimensional extraction technology based on a beam deflection method.

This paper introduces a method for analyzing the spatiotemporal progression of laser-induced shock waves using the beam deflection technique. This method allows for the accurate measurement of the shock wave evolution and can replace high-speed cameras. The results demonstrate the detection signals at various distances and energies, as well as the extraction and reconstruction of the shock wave velocities and propagation trajectories. The characteristic velocities of the shock waves propagating in air from various metals and energetic materials were measured and compared with the results obtained from high-speed cameras. The study also predicts the macroscopic detonation velocity of energetic materials based on the characteristic velocity. Overall, this approach offers a reliable and cost-effective method for studying the shock waves and has potential applications in various fields.

Construction and Validation of Novel Ferroptosis-related Risk Score Signature and Prognostic Prediction Nomogram for Patients with Colorectal Cancer.

Background: Colorectal cancer (CRC) has a high morbidity and mortality. Ferroptosis is a phenomenon in which metabolism and cell death are closely related. The role of ferroptosis-related genes in the progression of CRC is still not clear. Therefore, we screened and validated the ferroptosis-related genes which could determine the prevalence, risk and prognosis of patients with CRC. Methods: We firstly screened differentially expressed ferroptosis-related genes by The Cancer Genome Atlas (TCGA) database. Then, these genes were used to construct a risk-score model using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The function and prognosis of the ferroptosis-related genes were confirmed using multi-omics analysis. The gene expression results were validated using publicly available databases and qPCR. We also used publicly available data and ferroptosis-related genes to construct a prognostic prediction nomogram. Results: A total of 24 differential expressed genes associated with ferroptosis were screened in this study. A three-gene risk score model was then established based on these 24 genes and GPX3, CDKN2A and SLC7A11 were selected. The significant prognostic value of this novel three-gene signature was also assessed. Furthermore, we conducted RT-qPCR analysis on cell lines and tissues, and validated the high expression of CDKN2A, GPX3 and low expression of SLC7A11 in CRC cells. The observed mRNA expression of GPX3, CDKN2A and SLC7A11 was consistent with the predicted outcomes. Besides, eight variables including selected ferroptosis related genes were included to establish the prognostic prediction nomogram for patients with CRC. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations. Also, the time-dependent AUC (>0.7) indicated satisfactory discriminative ability of the nomogram. Conclusions: The present study constructed and validated a novel ferroptosis-related three-gene risk score signature and a prognostic prediction nomogram for patients with CRC. Also, we screened and validated the ferroptosis-related genes GPX3, CDKN2A, and SLC7A11 which could serve as novel biomarkers for patients with CRC.

Emission behaviors from ZnO microwire pumped CsPbBr3 perovskite microwire.

Perovskite semiconductor materials have attracted significant attention in the fields of photovoltaics and luminescence due to their excellent photoelectric properties, such as high carrier mobility, high absorption coefficient, and high fluorescence quantum yield. In particular, low-dimensional metal-halide perovskite microcrystalline materials have been reported to exhibit low-dimensional lasing phenomena and laser devices due to their high gain and widely tunable bandgap. In this Letter, one-dimensional (1-D) ZnO microwires with their ultraviolet lasing emissions are utilized as an excitation source to pump CsPbBr3 microwire on hybrid ZnO-CsPbBr3 microscale structures. At higher excitation, the amplified spontaneous emission (ASE) behaviors from CsPbBr3 microwire are realized with ultralow threshold by indirect pumping from the ZnO lasing emission for the first time, to the best of our knowledge. In comparison, the ASE behaviors from the CsPbBr3 microwire directly pumped by Nd:YAG Q-switched laser and continuous wave laser are also performed at room temperature. There are also no multimode lasing behaviors observed. The paper provides a new method to achieve a low threshold on-chip microlaser by a high-quality perovskite micro-nano structure.

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines.

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here, we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94 and 93% area under the receiver operating characteristic curves for the tree models and CNNs, respectively. Based on the AlphaFold2 predicted structures, the ML models recapitulated the newly liganded cysteines in the PDB with over 90% recall values. To assist the community of covalent drug discoveries, we report the predicted ligandable cysteines in 392 human kinases and their locations in the sequence-aligned kinase structure, including the PH and SH2 domains. Furthermore, we disseminate a searchable online database LigCys3D (https://ligcys.computchem.org/) and a web prediction server DeepCys (https://deepcys.computchem.org/), both of which will be continuously updated and improved by including newly published experimental data. The present work represents an important step toward the ML-led integration of big genome data and structure models to annotate the human proteome space for the next-generation covalent drug discoveries.

Machine Learned Classification of Ligand Intrinsic Activities at Human µ-Opioid Receptor.

Opioids are small-molecule agonists of µ-opioid receptor (µOR), while reversal agents such as naloxone are antagonists of µOR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human µOR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured E max values at the human µOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5±3.9% and 91.8± 4.4%, respectively. To overcome the challenge of small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or rat µOR, κOR, or δOR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of µOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Why is the Omicron main protease of SARS-CoV-2 less stable than its wild-type counterpart? A crystallographic, biophysical, and theoretical study of the free enzyme and its complex with inhibitor 13b-K.

During the continuing evolution of SARS-CoV-2, the Omicron variant of concern emerged in the second half of 2021 and has been dominant since November that year. Along with its sublineages, it has maintained a prominent role ever since. The Nsp5 main protease (Mpro) of the Omicron virus is characterized by a single dominant mutation, P132H. Here we determined the X-ray crystal structures of the P132H mutant (or O-Mpro) as free enzyme and in complex with the Mpro inhibitor, the alpha-ketoamide 13b-K, and we conducted enzymology, biophysical as well as theoretical studies to characterize the O-Mpro. We found that O-Mpro has a similar overall structure and binding with 13b-K; however, it displays lower enzymatic activity and lower thermal stability compared to the WT-Mpro (with "WT" referring to the original Wuhan-1 strain). Intriguingly, the imidazole ring of His132 and the carboxylate plane of Glu240 are in a stacked configuration in the X-ray structures determined here. The empirical folding free energy calculations suggest that the O-Mpro dimer is destabilized relative to the WT-Mpro due to the less favorable van der Waals interactions and backbone conformation in the individual protomers. The all-atom continuous constant pH molecular dynamics (MD) simulations reveal that His132 and Glu240 display coupled titration. At pH 7, His132 is predominantly neutral and in a stacked configuration with respect to Glu240 which is charged. In order to examine whether the Omicron mutation eases the emergence of further Mpro mutations, we also determined crystal structures of the relatively frequent P132H+T169S double mutant but found little evidence for a correlation between the two sites.

Gamma-band-based dynamic functional connectivity in pigeon entopallium during sample presentation in a delayed color matching task.

Birds have developed visual cognitions, especially in discriminating colors due to their four types of cones in the retina. The entopallium of birds is thought to be involved in the processing of color information during visual cognition. However, there is a lack of understanding about how functional connectivity in the entopallium region of birds changes during color cognition, which is related to various input colors. We therefore trained pigeons to perform a delayed color matching task, in which two colors were randomly presented in sample stimuli phrases, and the neural activity at individual recording site and the gamma band functional connectivity among local population in entopallium during sample presentation were analyzed. Both gamma band energy and gamma band functional connectivity presented dynamics as the stimulus was presented and persisted. The response features in the early-stimulus phase were significantly different from those of baseline and the late-stimulus phase. Furthermore, gamma band energy showed significant differences between different colors during the early-stimulus phase, but the global feature of the gamma band functional network did not. Further decoding results showed that decoding accuracy was significantly enhanced by adding functional connectivity features, suggesting the global feature of the gamma band functional network did not directly contain color information, but was related to it. These results provided insight into information processing rules among local neuronal populations in the entopallium of birds during color cognition, which is important for their daily life.

Correction to: Gamma-band-based dynamic functional connectivity in pigeon entopallium during sample presentation in a delayed color matching task.

[This corrects the article DOI: 10.1007/s11571-022-09916-w.].

Machine Learning Models to Interrogate Proteomewide Covalent Ligandabilities Directed at Cysteines.

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1,000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94% and 93% AUCs (area under the receiver operating characteristic curve) for the tree models and CNNs, respectively. Based on the AlphaFold2 predicted structures, the ML models recapitulated the newly liganded cysteines in the PDB with over 90% recall values. To assist the community of covalent drug discoveries, we report the predicted ligandable cysteines in 392 human kinases and their locations in the sequence-aligned kinase structure including the PH and SH2 domains. Furthermore, we disseminate a searchable online database LigCys3D (https://ligcys.computchem.org/) and a web prediction server DeepCys (https://deepcys.computchem.org/), both of which will be continuously updated and improved by including newly published experimental data. The present work represents a first step towards the ML-led integration of big genome data and structure models to annotate the human proteome space for the next-generation covalent drug discoveries.

Two-Dimensional Cr5Te8@Graphite Heterostructure for Efficient Electromagnetic Microwave Absorption.

Two-dimensional (2D) transition metal chalcogenides (TMCs) hold great promise as novel microwave absorption materials owing to their interlayer interactions and unique magnetoelectric properties. However, overcoming the impedance mismatch at the low loading is still a challenge for TMCs due to the restricted loss pathways caused by their high-density characteristic. Here, an interface engineering based on the heterostructure of 2D Cr5Te8 and graphite is in situ constructed via a one-step chemical vapor deposit to modulate impedance matching and introduce multiple attenuation mechanisms. Intriguingly, the Cr5Te8@EG (ECT) heterostructure exhibits a minimum reflection loss of up to - 57.6 dB at 15.4 GHz with a thin thickness of only 1.4 mm under a low filling rate of 10%. The density functional theory calculations confirm that the splendid performance of ECT heterostructure primarily derives from charge redistribution at the abundant intimate interfaces, thereby reinforcing interfacial polarization loss. Furthermore, the ECT coating displays a remarkable radar cross section reduction of 31.9 dB m2, demonstrating a great radar microwave scattering ability. This work sheds light on the interfacial coupled stimulus response mechanism of TMC-based heterogeneous structures and provides a feasible strategy to manipulate high-quality TMCs for excellent microwave absorbers.

SnS2/MoS2 van der Waals Heterostructure Photodetector with Ultrahigh Responsivity Realized by a Photogating Effect.

Photoresponsivity is a fundamental parameter used to quantify the ability of photoelectric conversion of a photodetector device. High-responsivity photodetectors are essential for numerous optoelectronic applications. Due to the strong light-matter interactions and the high carrier mobility, two-dimensional (2D) materials are promising candidates for the next-generation photodetectors. However, poor light absorption, lack of photoconductive gain, and the interfacial recombination lead to the relatively low responsivity of 2D photodetectors. The photogating effect, which extends the lifetime of photoexcited carriers, provides a simple approach to enhance responsivity in photodetector devices. Here, the O2 plasma treatment introduced surface traps on the SnS2 surface, leading to a gate-tunable photogating effect in SnS2/MoS2 heterojunctions. The heterojunction device exhibits an ultrahigh responsibility of up to 28 A/W. Moreover, the photodetector possesses a wide spectral photoresponse spanning from 300 to 1100 nm and a high specific detectivity (D*) of 4 × 1011 Jones under a 532 nm laser at VDS = 1 V. These results demonstrate that O2 plasma treatment is an efficient and simple avenue to achieve photogating effects, which can be employed to enhance the performance of van der Waals heterostructure photodetector devices and make them suitable for future integration into advanced electronic and optoelectronic systems.

Temporary impact on medical system and effectiveness of mitigation strategies after COVID-19 policy adjustment in China: a modeling study.

Background: As China amends its "zero COVID" strategy, a sudden increase in the number of infections may overwhelm medical resources and its impact has not been quantified. Specific mitigation strategies are needed to minimize disruption to the healthcare system and to prepare for the next possible epidemic in advance. Method: We develop a stochastic compartmental model to project the burden on the medical system (that is, the number of fever clinic visits and admission beds) of China after adjustment to COVID-19 policy, which considers the epidemiological characteristics of the Omicron variant, age composition of the population, and vaccine effectiveness against infection and severe COVD-19. We also estimate the effect of four-dose vaccinations (heterologous and homologous), antipyretic drug supply, non-pharmacological interventions (NPIs), and triage treatment on mitigating the domestic infection peak. Result: As to the impact on the medical system, this epidemic is projected to result in 398.02 million fever clinic visits and 16.58 million hospitalizations, and the disruption period on the healthcare system is 18 and 30 days, respectively. Antipyretic drug supply and booster vaccination could reduce the burden on emergency visits and hospitalization, respectively, while neither of them could not reduce to the current capacity. The synergy of several different strategies suggests that increasing the heterologous booster vaccination rate for older adult to over 90% is a key measure to alleviate the bed burden for respiratory diseases on the basis of expanded healthcare resource allocation. Conclusion: The Omicron epidemic followed the adjustment to COVID-19 policy overloading many local health systems across the country at the end of 2022. The combined effect of vaccination, antipyretic drug supply, triage treatment, and PHSMs could prevent overwhelming medical resources.

Real time and high-precision online determination of main components in iron ore using spectral refinement algorithm based LIBS.

The real-time online quantitative analysis instrument is highly desirable for many industrial fields. Herein, a new laser-induced breakdown spectroscopy (LIBS) setup with optimized optical route and high accuracy algorithm is designed and applied in a real industrial site. The components of total iron (TFe), silica (SiO2), aluminum oxide (Al2O3), and phosphorus (P) are quantitatively determined by the online LIBS system. The key optical part is a Maksutov-Cassegrain telescope, in which, two aspherical mirrors are specially designed and fabricated to reflect the broadband emission from ultraviolet 240 nm to infrared 890 nm with reflectivity over 90%, and pass the excited laser line of 1064 nm. The system could automatically adjust the focal length in the range of 780 mm to 940 mm. Based on the online LIBS system, the spectral pretreatment algorithm is also optimized including baseline removal and spectral normalization. The overlapped window slide (OWS) algorithm avoids the deformation of emission peaks in spectral baseline removal, in addition, two normalization steps by total back area and total spectral intensity within the sub-channel are applied to improve the spectral data stabilization. The calibration and validation are performed by utilizing the emissions that are insensitive to the detection distance. Compared with the traditional method, the prediction result shows that the root of mean square error of prediction (RMSEP) decreased from 5.091% to 1.2328%, and the mean absolute error (MAE) reduced from 4.801% to 0.9126% for TFe. Eventually, the online measurement shows good agreement with the official standard results. The high-precision online determination system based on LIBS will upgrade low frequency sampling of traditional detection to high-frequency real online determination in many industrial fields.