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.

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.

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.

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 Activity at Human µ-Opioid Receptor.

Opioids are small-molecule agonists of µ-opioid receptor (µOR), while reversal agents such as naloxone are antagonists of mOR. Here we developed machine learning models to classify the intrinsic activities of ligands at the human µOR. 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 of the extra-tree (ET) and MPNN models are 91.5±3.9% and 91.8±4.4%, respectively, while the respective balanced accuracies (BAs) are 83.3±5.0% and 85.1±5.0%. 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 MPNN AUC to as high as 9.7%. Taken together, our work provides a proof of concept for developing machine learning models to predict µOR ligand intrinsic activities despite small data size. We envisage many future applications of these models, including evaluation of pharmacologically uncharacterized substances that may pose a risk to public safety and discovery of new rescue agents to combat 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.

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.].

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.

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.

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.

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.

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.

Visualized and Nondestructive Quality Identification of Two-Dimensional MoS2 Based on Principal Component Analysis.

To date, the common quality characterizations for MoS2 are inefficient or cause irreversible damage to the samples, which have limited scalability and low throughput. Here, we propose a visualized and nondestructive approach to evaluate the quality of MoS2 based on the PCA machine learning method. Through PCA processing of PL mapping, the CVD grown MoS2 with different edge defect densities can be well distinguished. Furthermore, six twin GBs along the sulfur zigzag direction of the six pointed MoS2 stars are also successfully identified. To verify the correctness of the identification results, we measured the lifetime mapping and thermal expansion coefficient of the synthesized MoS2 samples. It is found that the high quality MoS2 samples have a shorter carrier lifetime (∼0.291 ns) and lower thermal expansion coefficient (∼2.03 × 10-5K-1). Therefore, our work offers a new approach to evaluate the quality of MoS2 to drive their practical application.

Quantum Descriptors for Predicting and Understanding the Structure-Activity Relationships of Michael Acceptor Warheads.

Predictive modeling and understanding of chemical warhead reactivities have the potential to accelerate targeted covalent drug discovery. Recently, the carbanion formation free energies as well as other ground-state electronic properties from density functional theory (DFT) calculations have been proposed as predictors of glutathione reactivities of Michael acceptors; however, no clear consensus exists. By profiling the thiol-Michael reactions of a diverse set of singly- and doubly-activated olefins, including several model warheads related to afatinib, here we reexamined the question of whether low-cost electronic properties can be used as predictors of reaction barriers. The electronic properties related to the carbanion intermediate were found to be strong predictors, e.g., the change in the Cß charge accompanying carbanion formation. The least expensive reactant-only properties, the electrophilicity index, and the Cß charge also show strong rank correlations, suggesting their utility as quantum descriptors. A second objective of the work is to clarify the effect of the ß-dimethylaminomethyl (DMAM) substitution, which is incorporated in the warheads of several FDA-approved covalent drugs. Our data suggest that the ß-DMAM substitution is cationic at neutral pH in solution and promotes acrylamide's intrinsic reactivity by enhancing the charge accumulation at Cα upon carbanion formation. In contrast, the inductive effect of the ß-trimethylaminomethyl substitution is diminished due to steric hindrance. Together, these results reconcile the current views of the intrinsic reactivities of acrylamides and contribute to large-scale predictive modeling and an understanding of the structure-activity relationships of Michael acceptors for rational TCI design.

Light Amplification in Fe-Doped CsPbBr3 Crystal Microwire Excited by Continuous-Wave Laser.

Electrically pumped halide perovskite laser diodes remain unexplored, and it is widely acknowledged that continuous-wave (CW) lasing will be a crucial step. Here, we demonstrate room-temperature amplified spontaneous emission of Fe-doped CsPbBr3 crystal microwire excited by a CW laser. Temperature-dependent photoluminescence spectra indicate that the Fe dopant forms a shallow level trap states near the band edge of the lightly doped CsPbBr3 microcrystal. Pump intensity-dependent time-resolved PL spectra show that the introduced Fe dopant level makes the electron more stable in excited states, suitable for the population inversion. The emission peak intensity of the lightly Fe-doped microwire increases nonlinearly above a threshold of 12.3 kW/cm2 under CW laser excitation, indicating a significant light amplification. Under high excitation, the uniform crystal structure and surface outcoupling in Fe-doped perovskite crystal microwires enhanced the spontaneous emission. These results reveal the considerable promise of Fe-doped perovskite crystal microwires toward low-cost, high-performance, room-temperature electrical pumping perovskite lasers.

A triphenylamine-based fluorescence probe for detection of hypochlorite in mitochondria.

The level of HClO/ClO- in mitochondria is essential to keep the normal function of mitochondria. Therefore, it is meaningful to accurately and quickly monitor ClO- in mitochondria. In this work, a new triphenylamine-based fluorescence probe PDTPA was designed and synthesized, in which pyridinium salt and dicyano-vinyl group were introduced as mitochondria targeting site and reaction site for ClO-. The probe showed high sensitivity and fast fluorescence response (<10 s) in the detection of ClO-. Moreover, the probe PDTPA had good linearity in a wide concentration range of ClO- and its detection limit was calculated as 10.5 µM. Confocal fluorescence images demonstrated that the probe could target mitochondria and track the fluctuations of endogenous/exogenous ClO- levels in the mitochondria of living cells.

Real-time assessment of COVID-19 epidemic in Guangdong Province, China using mathematical models.

China government has relaxed the response measures of COVID-19 in early December 2022. In this report, we assessed the number of infections, the number of severe cases based on the current epidemic trend (October 22, 2022 to November 30, 2022) using a transmission dynamics model, called modified susceptible-exposed-infectious-removed (SEIR) to provide valuable information to ensure the medical operation of the healthcare system under the new situation. Our model showed that the present outbreak in Guangdong Province peaked during December 21, 2022 to December 25, 2022 with about 14.98 million new infections (95% CI: 14.23-15.73 million). The cumulative number of infections will reach about 70% of the province's population from December 24, 2022 to December 26, 2022. The number of existing severe cases is expected to peak during January 1, 2023 to January 5, 2023 with a peak number of approximately 101.45 thousand (95% CI: 96.38-106.52 thousand). In addition, the epidemic in Guangzhou which is the capital city of Guangdong Province is expected to have peaked around December 22, 2022 to December 23, 2022 with the number of new infections at the peak being about 2.45 million (95% CI: 2.33-2.57 million). The cumulative number of infected people will reach about 70% of the city's population from December 24, 2022 to December 25, 2022 and the number of existing severe cases is expected to peak around January 4, 2023 to January 6, 2023 with the number of existing severe cases at the peak being about 6.32 thousand (95% CI: 6.00-6.64 thousand). Predicted results enable the government to prepare medically and plan for potential risks in advance.

Analysis of the ERK Pathway Cysteinome for Targeted Covalent Inhibition of RAF and MEK Kinases.

The ERK pathway is one of the most important signaling cascades involved in tumorigenesis. So far, eight noncovalent inhibitors of RAF and MEK kinases in the ERK pathway have been approved by the FDA for the treatment of cancers; however, their efficacies are limited due to various resistance mechanisms. There is an urgent need to develop novel targeted covalent inhibitors. Here we report a systematic study of the covalent ligandabilities of the ERK pathway kinases (ARAF, BRAF, CRAF, KSR1, KSR2, MEK1, MEK2, ERK1, and ERK2) using constant pH molecular dynamics titration and pocket analysis. Our data revealed that the hinge GK (gate keeper)+3 cysteine in RAF family kinases (ARAF, BRAF, CRAF, KSR1, and KSR2) and the back loop cysteine in MEK1 and MEK2 are reactive and ligandable. Structure analysis suggests that the type II inhibitors belvarafenib and GW5074 may be used as scaffolds for designing pan-RAF or CRAF-selective covalent inhibitors directed at the GK+3 cysteine, while the type III inhibitor cobimetinib may be modified to label the back loop cysteine in MEK1/2. The reactivities and ligandabilities of the remote cysteine in MEK1/2 and the DFG-1 cysteine in MEK1/2 and ERK1/2 are also discussed. Our work provides a starting point for medicinal chemists to design novel covalent inhibitors of the ERK pathway kinases. The computational protocol is general and can be applied to the systematic evaluation of covalent ligandabilities of the human cysteinome.

On-Chip Ultralow-Threshold Tunable CdSSe Nanobelt Lasers Excited by the Emission of Linked ZnO Nanowire.

The integration of optical waveguide and on-chip nanolasers source has been one of the trends in photonic devices. For on-chip nanolasers, the integration of nanowires and high antidamage ability are imperative. Herein, we realized the on-chip ultralow-threshold and wavelength-tunable lasing from alloyed CdSSe nanobelt chip that is excited by the emission from linked ZnO nanowires. ZnO nanowire arrays are integrated into CdSSe nanobelt chips by the dry transfer method. A one-dimensional (1D) ZnO nanowire forms high-quality optical resonators and serves as an indirect pumping light to stimulate CdSSe nanobelt chips, and then wavelength-tunable lasing is generated with the ultralow threshold of 3.88 µW. The lasing mechanism is quite different than direct excitation by nanosecond laser pulse and indirect pumping by ZnO emission. The ZnO-CdSSe blocks provide a new solution to realize nanowire lasing from linked nanowires rather than direct laser pumping and thus avoid the light direct damage under general nanosecond laser excitation.