Quantum simulation of conical intersections.

We explore the simulation of conical intersections (CIs) on quantum devices, setting the groundwork for potential applications in nonadiabatic quantum dynamics within molecular systems. The intersecting potential energy surfaces of H3+ are computed from a variance-based contracted quantum eigensolver. We show how the CIs can be correctly described on quantum devices using wavefunctions generated by the anti-Hermitian contracted Schrödinger equation ansatz, which is a unitary transformation of wavefunctions that preserves the topography of CIs. A hybrid quantum-classical procedure is used to locate the seam of CIs. Additionally, we discuss the quantum implementation of the adiabatic to diabatic transformation and its relation to the geometric phase effect. Results on noisy intermediate-scale quantum devices showcase the potential of quantum computers in dealing with problems in nonadiabatic chemistry.

Detection of lung cancer through SERS analysis of serum.

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Periampullary cancer and neurological interactions: current understanding and future research directions.

Periampullary cancer is a malignant tumor occurring around the ampullary region of the liver and pancreas, encompassing a variety of tissue types and sharing numerous biological characteristics, including interactions with the nervous system. The nervous system plays a crucial role in regulating organ development, maintaining physiological equilibrium, and ensuring life process plasticity, a role that is equally pivotal in oncology. Investigations into nerve-tumor interactions have unveiled their key part in controlling cancer progression, inhibiting anti-tumor immune responses, facilitating invasion and metastasis, and triggering neuropathic pain. Despite many mechanisms by which nerve fibers contribute to cancer advancement still being incompletely understood, the growing emphasis on the significance of nerves within the tumor microenvironment in recent years has set the stage for the development of groundbreaking therapies. This includes combining current neuroactive medications with established therapeutic protocols. This review centers on the mechanisms of Periampullary cancer's interactions with nerves, the influence of various types of nerve innervation on cancer evolution, and outlines the horizons for ongoing and forthcoming research.

Temperature-Controlled Selective Formation of Silver Nanoclusters and Their Transformation to the Same Product.

Herein, two atomically precise silver nanoclusters, Ag54 and Ag33, directed by inner anion templates (CrO42- and/or Cl-), are initially isolated as a mixed phase from identical reactants across a wide temperature range (20-80 °C). Interestingly, fine-tuning the reaction temperature can realize pure phase synthesis of the two nanoclusters; that is, a metastable Ag54 is kinetically formed at a low temperature (20 °C), whereas such a system is steered towards a thermodynamically stable Ag33 at a relatively high temperature (80 °C). Electrospray ionization mass spectrometry illustrates that the stability of Ag33 is superior to that of Ag54, which is further supported by density functional theory calculations. Importantly, the difference in structural stability can influence the pathway of 1,4-bis(pyrid-4-yl)benzene induced transformation reaction starting from Ag54 and Ag33. The former undergoes a dramatic breakage-reorganization process to form an Ag31 dimer (Ag31), while the same product can be also achieved from the latter following a noninvasive ligand exchange process. Both the Ag54 and Ag33 have the potential for further remote laser ignition applications. This work not only demonstrates how temperature controls the isolation of a specific phase, but also sheds light on the structural transformation pathway of nanoclusters with different stability.

The spatial spillover effects of clean energy consumption and production on sustainable economic development in China.

The massive consumption of fossil energy has resulted in high CO2 emissions, posing a formidable challenge to global sustainable economic development (SED). As countries endeavor to shift from fossil to clean energy sources to achieve SED, research on the impact of clean energy is scarce, and quantitative analysis is lacking. This study measured China's SED and used a spatial econometric model to examine the impact of clean energy consumption and production on SED across 30 provinces in China from 2008 to 2020. Results show that (1) China's SED exhibits significant positive spatial autocorrelation characteristics, forming a "point-to-area" development pattern. (2) Clean energy consumption, production, and consumption structure all contribute to the promotion of SED in the region and have positive spatial spillover effects. (3) A considerable regional disparity exists in the spatial impact of clean energy on SED. The eastern and central regions have significant positive spatial spillover effects, whereas the western region is opposite. Notably, the estimated coefficient of the spatial Durbin model is relatively small, reflecting China's ongoing transition to clean energy and its limited role in promoting economic sustainability. Joint efforts and differentiated policies are essential to develop clean energy and sustainable economic.

DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning.

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.

Construction of Functional Phenolic Resin and Carbon Hollow Spheres by Manipulating Structural Inhomogeneity.

Hollow carbonaceous spheres are extraordinarily attractive for their unique structural features and wide applications in various fields. Herein, a facile and effective synthesis methodology based on the extended Stöber process for construction of phenolic resin hollow spheres has been presented. Combined with a series of characterization techniques, the synthesis process was systematically investigated, and a possible synthesis mechanism was proposed. It is revealed that the structural inhomogeneity of the polymer product achieved by using dodecylamine and alkane is responsible for the formation of hollow architecture, which depends on spontaneous selective dissolution during the synthesis process. Different metal-doped carbonaceous hollow spheres can be obtained by introducing corresponding precursors into the synthetic system and meeting requirements of different application fields. This work presented a novel synthesis strategy of hollow carbonaceous spheres, which is significant for building a new platform of advanced functional carbon-based composites.

Emergency Department Trauma Activation Fees by Payer Type.

This cross-sectional study examines the wide variations in prices of emergency medical services at US hospitals.

A Novel Induction-Type Pressure Sensor based on Magneto-Stress Impedance and Magnetoelastic Coupling Effect for Monitoring Hand Rehabilitation.

Visualization of training effectiveness is critical to patients' confidence and eventual rehabilitation. Here, an innovative magnetoinductive pressure sensor is proposed for monitoring hand rehabilitation in stroke hemiplegic patients. It couples the giant magneto and stress-impedance effects of a square spiral amorphous wire with the giant magnetoelastic effect of a polymer magnet (NdFeB@PDMS). The addition of the magnetoelastic layer results in a sensitivity improvement of 178%, a wide sensing range (up to 1 MPa), fast response/recovery times (40 ms), and excellent mechanical robustness (over 15 000 cycles). Further integration with an LC oscillation circuit enables frequency adjustment into the MHz range resulting in a sensitivity of 6.6% kPa-1 and outstanding linearity (R2 =  0.99717) over a stress range of up to 100 kPa. When attached to a commercial split-fingerboard, the sensor is capable of dynamically monitoring the force in each finger, providing a reading of the rehabilitation process. Unlike conventional inductive sensors, the sensor is based on an inductive force-responsive material (amorphous wire), which significantly boosts the sensitivity. The approach also demonstrates the potential of magnetoelasticity in static pressure sensing, which is highly sensitive to dynamic pressure only through electromagnetic induction. This makes it more suitable for long-term and continuous human health monitoring.

Chelerythrine induces apoptosis and ferroptosis through Nrf2 in ovarian cancer cells.

Ovarian cancer is a prevalent malignancy in the female reproductive system, representing a significantly fatal and incurable tumor. Chelerythrine (CHE), a natural benzopyridine alkaloid, has demonstrated a broad spectrum of anticancer activities. Nevertheless, the ovarian cancer inhibitory impact of CHE remains unclear. In this study, we investigated the cytotoxic mechanism and potential targets of CHE on in vitro cultures of A2780 and SKOV3 cells derived from ovarian cancer. Additionally, in vivo experiments were conducted to confirm the suppressive impact of CHE on tumor growth in nude mice. The findings revealed that CHE impeded the growth of A2780 and SKOV3 cells in a concentration-time-dependent manner and significantly suppressed the development of tumors in nude mice. CHE elevated the level of oxidative stress in tumor cells, prompted cell cycle halt in the S phase, and increased their mitochondrial membrane potential. Western blotting results demonstrated that CHE could modulate the expression of proteins associated with apoptotic and ferroptosis processes in A2780 and SKOV3 cells. Nrf2 was verified to be an upstream key target mediating the inhibitory impact of CHE on ovarian cancer cells. In summary, CHE exerts its anti-cancer effects on ovarian cancer by modulating Nrf2, inhibiting cellular proliferation, and promoting apoptosis and ferroptosis.

Omics-based approaches for discovering active ingredients and regulating gut microbiota of Actinidia arguta exosome-like nanoparticles.

Exosome-like nanoparticles (ELNs) are novel naturally occurring plant ultrastructures and contain unique bioactive components. However, the potential applications and biological functions of plant ELNs, especially in the context of health promotion and disease prevention, remain largely unexplored. This study aimed to explore the biological activities and functional mechanisms of Actinidia arguta-derived exosome-like nanoparticles (AAELNs). We reported the development of AAELNs, which possess particle sizes of 157.8 nm and a negative surface charge of -23.07 mV, uptaking by RAW264.7 cells, and reduction of oxidative stress by decreasing the activity of GSH-Px and T-SOD and increasing the content of MDA. Through the use of high-throughput sequencing technology, 12 known miRNA families and 23 additional miRNAs were identified in AAELNs, GO and KEGG term enrichment analysis revealed the potential of AAELNs-miRNAs in modulating neural-relevant behaviors. Additionally, LC-MS/MS analysis detected a total of 32 major lipid classes, 430 lipid subclasses, and 1345 proteins in AAELNs. Furthermore, in vivo fluorescence disappearance and in vitro fermentation experiments demonstrated that AAELNs were able to enter the colon and improve the microbial structure. These findings suggest that AAELNs could serve as nanoshuttles in food, potentially offering health-enhancing properties.

WTAP-mediated N6-methyladenosine modification promotes the inflammation, mitochondrial damage and ferroptosis of kidney tubular epithelial cells in acute kidney injury by regulating LMNB1 expression and activating NF-κB and JAK2/STAT3 pathways.

Acute kidney injury (AKI) is a serious complication of sepsis patients, but the pathogenic mechanisms underlying AKI are still largely unclear. In this view, the roles of the key component of N6-methyladenosine (m6A)-wilms tumor 1 associated protein (WTAP) in AKI progression were investigated. AKI mice model was established by using cecal ligation and puncture (CLP). AKI cell model was established by treating HK-2 cells with LPS. Cell apoptosis was analyzed by TdT-mediated dUTP Nick-End Labeling (TUNEL) staining and flow cytometry analysis. Cell viability was analyzed by 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT) assay. The concentrations of inflammatory factors were examined with ELISA kits. Reactive oxygen species (ROS), malondialdehyde (MDA), glutathione (GSH) and Fe2+ levels were detected with related kits. Gene expression was detected by western blot assay or quantitative real-time polymerase chain reaction (qRT-PCR) assay. The relation between WTAP and lamin B1 (LMNB1) was verified by Methylated RNA Immunoprecipitation (meRIP) assay, RIP assay, dual-luciferase reporter assay and Actinomycin D assay. CLP induced significant pathological changes in kidney tissues in mice and promoted inflammation, mitochondrial damage and ferroptosis. LMNB1 level was induced in HK-2 cells by LPS. LMNB1 knockdown promoted LPS-mediated HK-2 cell viability and inhibited LPS-mediated HK-2 cell apoptosis, inflammation, mitochondrial damage and ferroptosis. Then, WTAP was demonstrated to promote LMNB1 expression by m6A Methylation modification. Moreover, WTAP knockdown repressed LPS-treated HK-2 cell apoptosis, inflammation, mitochondrial damage and ferroptosis, while LMNB1 overexpression reversed the effects. Additionally, WTAP affected the pathways of NF-κB and JAK2/STAT3 by LMNB1. WTAP-mediated m6A promoted the inflammation, mitochondrial damage and ferroptosis in LPS-induced HK-2 cells by regulating LMNB1 expression and activating NF-κB and JAK2/STAT3 pathways.

Phonon-mediated ultrafast energy- and momentum-resolved hole dynamics in monolayer black phosphorus.

The electron-phonon scattering plays a crucial role in determining the electronic, transport, optical, and thermal properties of materials. Here, we employ a non-Markovian stochastic Schrödinger equation (NMSSE) in momentum space, together with ab initio calculations for energy bands and electron-phonon interactions, to reveal the phonon-mediated ultrafast hole relaxation dynamics in the valence bands of monolayer black phosphorus. Our numerical simulations show that the hole can initially remain in the high-energy valence bands for more than 100 fs due to the weak interband scatterings, and its energy relaxation follows single-exponential decay toward the valence band maximum after scattering into low-energy valence bands. The total relaxation time of holes is much longer than that of electrons in the conduction band. This suggests that harnessing the excess energy of holes may be more effective than that of electrons. Compared to the semiclassical Boltzmann equation based on a hopping model, the NMSSE highlights the persistence of quantum coherence for a long time, which significantly impacts the relaxation dynamics. These findings complement the understanding of hot carrier relaxation dynamics in two-dimensional materials and may offer novel insights into harnessing hole energy in photocatalysis.

Proton tunneling in the dissociation of H2+ and its asymmetric isotopologues driven by circularly polarized THz laser pulses.

Light-induced deprotonation of molecules is an important process in photochemical reactions. Here, we theoretically investigate the tunneling deprotonation of H2+ and its asymmetric isotopologues driven by circularly polarized THz laser pulses. The quasi-static picture shows that the field-dressed potential barrier is significantly lowered for the deprotonation channel when the mass asymmetry of the diatomic molecule increases. Our numerical simulations demonstrate that when the mass symmetry breaks, the tunneling deprotonation is significantly enhanced and the proton tunneling becomes the dominant dissociation channel in the THz driving fields. In addition, the simulated nuclear momentum distributions show that the emission of the proton is directed by the effective vector potential for the deprotonation channel and, meanwhile, the angular distribution of the emitting proton is affected by the alignment and rotation of the molecule induced by the rotating field.

Revelation of adhesive proteins affecting cellular contractility through reference-free traction force microscopy.

Over the past few decades, the critical role played by cellular contractility associated mechanotransduction in the regulation of cell functions has been revealed. In this case, numerous biomaterials have been chemically or structurally designed to manipulate cell behaviors through the regulation of cellular contractility. In particular, adhesive proteins including fibronectin, poly-L-lysine and collagen type I have been widely applied in various biomaterials to improve cell adhesion. Therefore, clarifying the effects of adhesive proteins on cellular contractility has been valuable for the development of biomaterial design. In this study, reference-free traction force microscopy with a well-organized microdot array was designed and prepared to investigate the relationship between adhesive proteins, cellular contractility, and mechanotransduction. The results showed that fibronectin and collagen type I were able to promote the assembly of focal adhesions and further enhance cellular contraction and YAP activity. In contrast, although poly-L-lysine supported cell spreading and elongation, it was inefficient at inducing cell contractility and activating YAP. Additionally, compared with cellular morphogenesis, cellular contraction was essential for YAP activation.

ResDeepSurv: A Survival Model for Deep Neural Networks Based on Residual Blocks and Self-attention Mechanism.

Survival analysis, as a widely used method for analyzing and predicting the timing of event occurrence, plays a crucial role in the medicine field. Medical professionals utilize survival models to gain insight into the effects of patient covariates on the disease, and the correlation with the effectiveness of different treatment strategies. This knowledge is essential for the development of treatment plans and the enhancement of treatment approaches. Conventional survival models, such as the Cox proportional hazards model, require a significant amount of feature engineering or prior knowledge to facilitate personalized modeling. To address these limitations, we propose a novel residual-based self-attention deep neural network for survival modeling, called ResDeepSurv, which combines the benefits of neural networks and the Cox proportional hazards regression model. The model proposed in our study simulates the distribution of survival time and the correlation between covariates and outcomes, but does not impose strict assumptions on the basic distribution of survival data. This approach effectively accounts for both linear and nonlinear risk functions in survival data analysis. The performance of our model in analyzing survival data with various risk functions is on par with or even superior to that of other existing survival analysis methods. Furthermore, we validate the superior performance of our model in comparison to currently existing methods by evaluating multiple publicly available clinical datasets. Through this study, we prove the effectiveness of our proposed model in survival analysis, providing a promising alternative to traditional approaches. The application of deep learning techniques and the ability to capture complex relationships between covariates and survival outcomes without relying on extensive feature engineering make our model a valuable tool for personalized medicine and decision-making in clinical practice.

Enzyme-Assisted Ultrasonic Extraction and Antioxidant Activities of Polysaccharides from Schizochytrium limacinum Meal.

Enzyme-assisted ultrasonic extraction (EAUE) was utilized and optimized for extracting polysaccharides from Schizochytrium limacinum meal (SLMPs) via the response surface methodology. The optimal EAUE conditions were determined as follows: enzyme concentration at 5.18%, ultrasonic temperature at 53 °C, ultrasonic duration of 40 min, ultrasonic power at 60 W, and a liquid-to-material ratio of 34 mL/g, achieving a polysaccharide extraction yield of 11.86 ± 0.61%. The purified polysaccharide component, SLMP1-1, isolated using DEAE Sepharose Fast Flow and Sephadex G-100 columns, exhibited potent antioxidant activity. SLMP1-1, with a molecular weight of 25.5 kDa, comprises glucose, mannose, arabinose, and galactose in a molar ratio of 16.39:14.75:1:693.03. 1H NMR analysis revealed the α configuration of SLMP1-1. Antioxidant assessments, including DPPH, ABTS, and ferric ion reduction assays, were detected with inhibitory values at 21.82-82.98%, 38.21-98.46%, and 3.30-20.30% at 0.2-1.0 mg/mL. This confirmed the effective antioxidant capacity of SLMP1-1, which is notably enhanced post oral and gastric digestion. The findings suggest that polysaccharides extracted from Schizochytrium limacinum meal hold significant promise as natural antioxidants.

Molecular cloning, expression, purification, and characterization of Bacillus subtilis hydrolyzed ginsenoside Rc of α-L-arabinofuranosidase in Escherichia coli.

The α-L-arabinofuranosidase enzyme plays a crucial role in the degradation of ginsenosides. In this study, we successfully cloned and expressed a novel α-L-arabinofuranosidase bsafs gene (1503 bp, 501 amino acids, 55 kDa, and pI = 5.4) belonging to glycosyl hydrolase (GH) family 51 from Bacillus subtilis genome in Escherichia coli BL21 cells. The recombinant protein Bsafs was purified using Ni2+ sepharose fastflow affinity chromatography and exhibited a specific activity of 2.91 U/mg. Bsafs effectively hydrolyzed the α-L-arabinofuranoside at C20 site of ginsenoside Rc to produce Rd as the product. The Km values for hydrolysis of pNP-α-L-arabinofuranoside (pNPαAraf) and ginsenoside Rc were determined as 0.74 and 4.59 mmol/L, respectively; while the Vmax values for these substrates were found to be 24 and 164 µmol/min/mg, respectively; furthermore, the Kcat values for these enzymes were calculated as 22.3 and 1.58 S-1 correspondingly.

A universal ANN-to-SNN framework for achieving high accuracy and low latency deep Spiking Neural Networks.

Spiking Neural Networks (SNNs) have become one of the most prominent next-generation computational models owing to their biological plausibility, low power consumption, and the potential for neuromorphic hardware implementation. Among the various methods for obtaining available SNNs, converting Artificial Neural Networks (ANNs) into SNNs is the most cost-effective approach. The early challenges in ANN-to-SNN conversion work revolved around the susceptibility of converted SNNs to conversion errors. Some recent endeavors have attempted to mitigate these conversion errors by altering the original ANNs. Despite their ability to enhance the accuracy of SNNs, these methods lack generality and cannot be directly applied to convert the majority of existing ANNs. In this paper, we present a framework named DNISNM for converting ANN to SNN, with the aim of addressing conversion errors arising from differences in the discreteness and asynchrony of network transmission between ANN and SNN. The DNISNM consists of two mechanisms, Data-based Neuronal Initialization (DNI) and Signed Neuron with Memory (SNM), designed to respectively address errors stemming from discreteness and asynchrony disparities. This framework requires no additional modifications to the original ANN and can result in SNNs with improved accuracy performance, simultaneously ensuring universality, high precision, and low inference latency. We verify it experimentally on challenging object recognition datasets, including CIFAR10, CIFAR100, and ImageNet-1k. Experimental results show that the SNN converted by our framework has very high accuracy even at extremely low latency.

Investigation on screening, identification, and fermentation characteristics of Yunnan olive in the fermented liquid utilizing five strains of Saccharomyces cerevisiae.

Refined indigenous Saccharomyces cerevisiae can enhance refinement, sophistication, and subtlety of fruit wines by showcasing exceptional regional characteristics. In order to identify exceptional indigenous S. cerevisiae strains from Yunnan olive, this study isolated 60 yeast strains from wild Yunnan olive fermentation mash. The five S. cerevisiae strains were subjected to morphological and molecular biological identification, followed by evaluation of their fermentation performance, ethanol production capacity, ester production capacity, H2S production capacity, killing capacity, and tolerance. Strains LJM-4, LJM-10, and LJM-26 exhibited robust tolerance to 6% ethanol volume fraction, pH 2.8, sucrose concentration of 400 g/L, SO2 concentration of 0.3 g/L, glucose concentration of 400 g/L at both 40 °C and 15 °C. Additionally, strain LJM-10 demonstrated a faster fermentation rate compared to the other strains. Among the tested S. cerevisiae strains evaluated in this study for olive wine fermentation process in Yunnan region; strain LJM-10 displayed superior abilities in terms of ester and ethanol production while exhibiting the lowest H2S production levels. These findings suggest that strain LJM-10 holds great potential as an excellent candidate for optimizing fruit wine S. cerevisiae fermentation processes in Yunnan olive fruit wine.