Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network.

The authentic traditional Chinese medicines (TCMs) including Angelicae Sinensis Radix (ASR) are the representative of high-quality herbals in China. However, ASR from authentic region being adulterated or counterfeited is frequently occurring, and there is still a lack of rapid quality evaluation methods for identifying the authentic ASR. In this study, the color features of ASR were firstly characterized. The results showed that the authentic ASR cannot be fully identified by color characteristics. Then near-infrared (NIR) spectroscopy combined with Bayesian optimized long short-term memory (BO-LSTM) was used to evaluate the quality of ASR, and the performance of BO-LSTM with common classification and regression algorithms was compared. The results revealed that following the pretreatment of NIR spectra, the optimal NIR spectra combined with BO-LSTM not only successfully distinguished authentic, non-authentic, and adulterated ASR with 100 % accuracy, but also accurately predicted the adulteration concentration of authentic ASR (R2 > 0.99). Moreover, BO-LSTM demonstrated excellent performance in classification and regression compared with common algorithms (ANN, SVM, PLSR, etc.). Overall, the proposed strategy could quickly and accurately evaluate the quality of ASR, which provided a reference for other TCMs.

Self-Assembling Sulfated Lactobacillus Exopolysaccharide Nanoparticles as Adjuvants for SARS-CoV-2 Subunit Vaccine Elicit Potent Humoral and Cellular Immune Responses.

Coronavirus disease 2019 (COVID-19) has caused a global pandemic since its onset in 2019, and the development of effective vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to induce potent and long-lasting immunity remains a priority. Herein, we prepared two Lactobacillus exopolysaccharide (EPS) nanoparticle adjuvants (NPs 7-4 and NPs 8-2) that were constructed by using sulfation-modified EPS and quaternization-modified chitosan. These two NPs displayed a spherical morphology with sizes of 39 and 47 nm. Furthermore, the zeta potentials of NPs 7-4 and NPs 8-2 were 50.40 and 44.40 mV, respectively. In vitro assays demonstrated that NPs could effectively adsorb antigenic proteins and exhibited a sustained release effect. Mouse immunization tests showed that the NPs induced the expression of cytokines and chemokines at the injection site and promoted the uptake of antigenic proteins by macrophages. Mechanically, the NPs upregulated the expression of pattern recognition receptors (toll-like receptors and nod-like receptors) and activated the immune response of T cells and the production of neutralizing antibodies. In addition, the NP adjuvants had favorable immune-enhancing effects in cats, which are of great significance for controlling the trans-host transmission and re-endemicity of SARS-CoV-2. Overall, we demonstrated that NP-adjuvanted SARS-CoV-2 receptor binding domain proteins could induce robust specific humoral and cellular immunity.

Active Dendrite Suppression by Ferroelectric Membrane Separators in Rechargeable Batteries.

Anodic dendrite formation is a critical issue in rechargeable batteries and often leads to poor cycling stability and quick capacity loss. Prevailing strategies for dendrite suppression aim at slowing down the growth rate kinetically but still leaving possibilities for dendrite evolution over time. Herein, we report a complete dendrite elimination strategy using a mesoporous ferroelectric polymer membrane as the battery separator. The dendrite suppression is realized by spontaneously reversing the surface energetics for metal ion reduction at the protrusion front, where a positive piezoelectric polarization is generated and superimposed as the protrusion compresses the separator. This effect is demonstrated first in a Zn electroplating process, and further in Zn-Zn symmetric cells and Zn-NaV3O8·1.5H2O full cells, where the dendritic Zn anode surfaces are completely turned into featureless flat surfaces. Consequently, a substantially longer charging/discharging cycle is achieved. This study provides a promising pathway toward high-performance dendrite-free rechargeable batteries.

EndoSRR: a comprehensive multi-stage approach for endoscopic specular reflection removal.

PURPOSE: Specular reflections in endoscopic images not only disturb visual perception but also hamper computer vision algorithm performance. However, the intricate nature and variability of these reflections, coupled with a lack of relevant datasets, pose ongoing challenges for removal. METHODS: We present EndoSRR, a robust method for eliminating specular reflections in endoscopic images. EndoSRR comprises two stages: reflection detection and reflection region inpainting. In the reflection detection stage, we adapt and fine-tune the segment anything model (SAM) using a weakly labeled dataset, achieving an accurate reflection mask. For reflective region inpainting, we employ LaMa, a fast Fourier convolution-based model trained on a 4.5M-image dataset, enabling effective inpainting of arbitrarily shaped reflection regions. Lastly, we introduce an iterative optimization strategy for dual pre-trained models to refine the results of specular reflection removal, named DPMIO. RESULTS: Utilizing the SCARED-2019 dataset, our approach surpasses state-of-the-art methods in both qualitative and quantitative evaluations. Qualitatively, our method excels in accurately detecting reflective regions, yielding more natural and realistic inpainting results. Quantitatively, our method demonstrates superior performance in both segmentation evaluation metrics (IoU, E-measure, etc.) and image inpainting evaluation metrics (PSNR, SSIM, etc.). CONCLUSION: The experimental results underscore the significance of proficient endoscopic specular reflection removal for enhancing visual perception and downstream tasks. The methodology and results presented in this study are poised to catalyze advancements in specular reflection removal, thereby augmenting the accuracy and safety of minimally invasive surgery.

The Development of a Novel Fiber-2 Subunit Vaccine against Fowl Adenovirus Serotype 4 Formulated with Oil Adjuvants.

Hepatitis-hydropericardium syndrome (HHS), caused by fowl adenovirus serotype 4 (FAdV-4), has been widely spread across China, resulting in great financial losses in the poultry industry. Therefore, efficient vaccines against this disease urgently need to be developed. In our study, the fiber-2 and penton base proteins derived from the FAdV-4 JS strain were expressed in a prokaryotic system (E. coli) in a soluble form. Then, the efficacy of the two recombinant proteins formulated with cheap and widely used adjuvants (Marcol™ 52 white oil) were respectively tested, and the minimum immune doses and safety of the above proteins were also determined. It was indicated that the fiber-2 (20 µg/bird, 200 µg/bird) and penton base (200 µg/bird) could provide complete protection against the highly pathogenic FAdV-4 and suppress its replication and shedding. Unfortunately, only the fiber-2 protein could induce complete protection (10/10) at a low dose (10 µg/bird). In addition, we confirmed that the fiber-2 subunit vaccine formulated with oil adjuvants was safe for vaccinated chickens. Conclusively, all of our results suggest that we successfully prepared an efficient and cheap fiber-2 subunit vaccine with few side effects.

Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping.

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1ρ estimations. In in vivo studies, the estimated T1, T2, and T1ρ values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 ± 53.80 vs. 1228.34 ± 52.91 ms, p > 0.1; T2: 40.70 ± 2.89 vs. 41.19 ± 2.91 ms, p > 0.1; T1ρ: 45.09 ± 4.47 vs. 45.23 ± 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1ρ maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.

Some Recent Advances in Density-Based Reactivity Theory.

Establishing a chemical reactivity theory in density functional theory (DFT) language has been our intense research interest in the past two decades, exemplified by the determination of steric effect and stereoselectivity, evaluation of electrophilicity and nucleophilicity, identification of strong and weak interactions, and formulation of cooperativity, frustration, and principle of chirality hierarchy. In this Featured Article, we first overview the four density-based frameworks in DFT to appreciate chemical understanding, including conceptual DFT, use of density associated quantities, information-theoretic approach, and orbital-free DFT, and then present a few recent advances of these frameworks as well as new applications from our studies. To that end, we will introduce the relationship among these frameworks, determining the entire spectrum of interactions with Pauli energy derivatives, performing topological analyses with information-theoretic quantities, and extending the density-based frameworks to excited states. Applications to examine physiochemical properties in external electric fields and to evaluate polarizability for proteins and crystals are discussed. A few possible directions for future development are followed, with the special emphasis on its merger with machine learning.

Toward the Rational Design of Organic Catalysts for Organocatalysed Atom Transfer Radical Polymerisation.

Thanks to their diversity, organic photocatalysts (PCs) have been widely used in manufacturing polymeric products with well-defined molecular weights, block sequences, and architectures. Still, however, more universal property-performance relationships are needed to enable the rational design of such PCs. That is, a set of unique descriptors ought to be identified to represent key properties of the PCs relevant for polymerisation. Previously, the redox potentials of excited PCs (PC*) were used as a good descriptor for characterising very structurally similar PCs. However, it fails to elucidate PCs with diverse chromophore cores and ligands, among which those used for polymerisation are a good representative. As showcased by model systems of organocatalysed atom transfer radical polymerisation (O-ATRP), new universal descriptors accounting for additional factors, such as the binding and density overlap between the PC* and initiator, are proposed and proved to be successful in elucidating the experimental performances of PCs in polymerisation. While O-ATRP is exemplified here, the approach adopted is general for studying other photocatalytic systems.

Unified construction of relativistic Hamiltonians.

It is shown that the four-component (4C), quasi-four-component (Q4C), and exact two-component (X2C) relativistic Hartree-Fock equations can be implemented in a unified manner by making use of the atomic nature of the small components of molecular 4-spinors. A model density matrix approximation can first be invoked for the small-component charge/current density functions, which gives rise to a static, pre-molecular mean field to be combined with the one-electron term. As a result, only the nonrelativistic-like two-electron term of the 4C/Q4C/X2C Fock matrix needs to be updated during the iterations. A "one-center small-component" approximation can then be invoked in the evaluation of relativistic integrals, that is, all atom-centered small-component basis functions are regarded as extremely localized near the position of the atom to which they belong such that they have vanishing overlaps with all small- or large-component functions centered at other nuclei. Under these approximations, the 4C, Q4C, and X2C mean-field and many-electron Hamiltonians share precisely the same structure and accuracy. Beyond these is the effective quantum electrodynamics Hamiltonian that can be constructed in the same way. Such approximations lead to errors that are orders of magnitude smaller than other sources of errors (e.g., truncation errors in the one- and many-particle bases as well as uncertainties of experimental measurements) and are, hence, safe to use for whatever purposes. The quaternion forms of the 4C, Q4C, and X2C equations are also presented in the most general way, based on which the corresponding Kramers-restricted open-shell variants are formulated for "high-spin" open-shell systems.

Effects of Continuous Nutritional Intervention on Nutritional Status and Serum Albumin During Maintenance Therapy in Children with Acute Lymphoblastic Leukemia.

Background: Nutritional imbalances can significantly impact clinical efficacy and chemotherapy tolerance in cases of acute lymphoblastic leukemia. Despite the potential significance, there is limited research in this domain, and clinicians have paid limited attention to it. Objective: This study aims to investigate the impact of continuous nutritional intervention on pediatric patients with acute lymphoblastic leukemia. Methods: A comparative analysis was conducted by dividing the children into observation and control groups, examining the effects of intermittent diet intervention and continuous nutrition intervention post-nutritional risk assessment. Results: After the intervention, the observation group exhibited a higher proportion of good nutrition and elevated serum albumin levels compared to the control group (χ2=4.79, 5.49, P = .029, 0.019, t =-2.819, -5.559, P = .01, P < .001). Additionally, the complication rate in the observation group was significantly lower than that in the control group (χ2=5.247, P = .022). Conclusions: Continuous nutrition intervention emerges as a valuable strategy for improving the nutritional status and serum albumin levels in children undergoing maintenance treatment for acute lymphoblastic leukemia. Moreover, it contributes to a noteworthy reduction in the incidence of complications.

HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data.

Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.

Tumor-derived Exosomal ENO2 Modulates Polarization of Tumor-associated Macrophages through Reprogramming Glycolysis to Promote Progression of Diffuse Large B-cell Lymphoma.

Macrophages can be polarized into functional classically activated (M1) or alternatively activated (M2) phenotype. Tumor-associated macrophages (TAMs) mainly exhibit M2 phenotype. Previous works determined that up-regulation of enolase 2 (ENO2) in diffuse large B-cell lymphoma (DLBCL) cells can promote macrophages to an M2-like phenotype, thereby consequently promoting the progression of DLBCL. Exosomes are a subset of extracellular vesicles, carrying various bioactive molecules, mediate signals transduction and regulate immune cells. In our study, we investigated the role and related mechanisms of DLBCL-derived exosomal ENO2 in regulating macrophage polarization during DLBCL progression via bioinformatics analysis and a series of experiments. The results of bioinformatics analysis indicated that high expression of ENO2 was positively correlated with DLBCL progression and macrophages M2/M1 ratio. ENO2 protein levels were increased in the exosomes of the sera of DLBCL patients and DLBCL cells. Moreover, the DLBCL-derived exosomes were assimilated by macrophages and then regulated macrophage polarization. The results of in vitro and in vivo experiments showed that DLBCL-derived exosomal ENO2 modulated macrophages polarization (increased M2 phenotype and decreased M1 phenotype), thereby promoting DLBCL proliferation, migration, and invasion. We then revealed that the modulation of macrophages polarization by DLBCL-derived exosomal ENO2 depended on glycolysis and was promoted through GSK3ß/ß-catenin/c-Myc signaling pathway. These findings suggested that DLBCL-derived exosomal ENO2 accelerated glycolysis via GSK3ß/ß-catenin/c-Myc signaling pathway to ultimately promote macrophages to an M2-like phenotype, which can promote the proliferation, migration and invasion of DLBCL, suggesting that exosomal ENO2 may be a promising therapeutic target and prognostic biomarker for DLBCL.

On committor functions in milestoning.

As an optimal one-dimensional reaction coordinate, the committor function not only describes the probability of a trajectory initiated at a phase space point first reaching the product state before reaching the reactant state but also preserves the kinetics when utilized to run a reduced dynamics model. However, calculating the committor function in high-dimensional systems poses significant challenges. In this paper, within the framework of milestoning, exact expressions for committor functions at two levels of coarse graining are given, including committor functions of phase space point to point (CFPP) and milestone to milestone (CFMM). When combined with transition kernels obtained from trajectory analysis, these expressions can be utilized to accurately and efficiently compute the committor functions. Furthermore, based on the calculated committor functions, an adaptive algorithm is developed to gradually refine the transition state region. Finally, two model examples are employed to assess the accuracy of these different formulations of committor functions.

When do tripdoublet states fluoresce? A theoretical study of copper(II) porphyrin.

Open-shell molecules rarely fluoresce, due to their typically faster non-radiative relaxation rates compared to closed-shell ones. Even rarer is the fluorescence from states that have two more unpaired electrons than the open-shell ground state, since they involve excitations from closed-shell orbitals to vacant-shell orbitals, which are typically higher in energy compared to excitations from or out of open-shell orbitals. States that are dominated by the former type of excitations are known as tripdoublet states when they can be described as a triplet excitation antiferromagnetically coupled to a doublet state, and their description by unrestricted single-reference methods (e.g., U-TDDFT) is notoriously inaccurate due to large spin contamination. In this work, we applied our spin-adapted TDDFT method, X-TDDFT, and the efficient and accurate static-dynamic-static second order perturbation theory (SDSPT2), to the study of the excited states as well as their relaxation pathways of copper(II) porphyrin; previous experimental works suggested that the photoluminescence of some substituted copper(II) porphyrins originate from a tripdoublet state, formed by a triplet ligand π → π* excitation antiferromagnetically coupled with the unpaired d electron. Our results demonstrated favorable agreement between the X-TDDFT, SDSPT2 and experimental excitation energies, and revealed noticeable improvements of X-TDDFT compared to U-TDDFT, not only for vertical excitation energies but also for adiabatic energy differences. These suggest that X-TDDFT is a reliable tool for the study of tripdoublet state fluorescence. Intriguingly, we showed that the aforementioned tripdoublet state is only slightly above the lowest doublet excited state and lies only slightly higher than the lowest quartet state, which suggests that the tripdoublet of copper(II) porphyrin is long-lived enough to fluoresce due to a lack of efficient non-radiative relaxation pathways; an explanation for this unusual state ordering is given. Indeed, thermal vibration correlation function (TVCF)-based calculations of internal conversion, intersystem crossing, and radiative transition rates confirm that copper(II) porphyrin emits thermally activated delayed fluorescence (TADF) and a small amount of phosphorescence at low temperature (83 K), in accordance with experiment. The present contribution is concluded by a few possible approaches of designing new molecules that fluoresce from tripdoublet states.

[Correlation between frailty and erectile dysfunction in elderly males].

OBJECTIVE: To investigate the correlation of frailty with ED in Chinese elderly men. METHODS: This community-based study was conducted with a sample of 258 Chinese men aged 60 to 83 years old in Fuyang City, Anhui Province. All the participants completed a standard questionnaire on demographics, lifestyle, underlying diseases and medical and sexual histories. They also scored on the Chinese version of Tilburg Frailty Indicator (TFI) and International Index of Erectile Function-5 (IIEF-5). RESULTS: The incidence rates of ED and frailty in the elderly men were 85.27% and 75.58%, respectively. The ED patients, compared with the non-ED males, had a significantly older age (ï¼»71.25 ± 5.83ï¼½ vs ï¼»66.92 ± 5.44ï¼½ yr, P < 0.01) and higher body mass index (ï¼»24.37 ± 3.31ï¼½ vs ï¼»23.35 ± 2.97ï¼½ kg/m 2, P < 0.05), incidence of diabetes mellitus (38.0% vs 19.2%, P < 0.05) and TFI scores (8.61 ± 4.29 vs 5.95 ± 4.36, P < 0.05), but lower education and frequency of irregular intercourse (less than once a week) (all P<0.05). Multivariate analysis indicated that diabetes (OR：3.292，95% CI：1.236－8.768), irregular intercourse (OR：2.425，95% CI：1.114－5.279), and scores of frailty (OR：4.502，95% CI：1.905－10.640) were regarded as independent risk factors for ED (all P < 0.05). CONCLUSION: There is a strong correlation between ED and frailty in elderly men. Sexual health care for elderly ED patients should be more focused on the multidimensional assessment and treatment of senile frailty.

SOiCISCF: Combining SOiCI and iCISCF for Variational Treatment of Spin-Orbit Coupling.

It has recently been shown that the SOiCI approach [Zhang, N.; J. Phys.: Condens. Matter 2022, 34, 224007], in conjunction with the spin-separated exact two-component relativistic Hamiltonian, can provide very accurate fine structures of systems containing heavy elements by treating electron correlation and spin-orbit coupling (SOC) on an equal footing. Nonetheless, orbital relaxations/polarizations induced by SOC are not yet fully accounted for due to the use of scalar relativistic orbitals. This issue can be resolved by further optimizing the still real-valued orbitals self-consistently in the presence of SOC, as done in the spin-orbit coupled CASSCF approach [Ganyushin, D.; et al. J. Chem. Phys. 2013, 138, 104113] but with the iCISCF algorithm [Guo, Y.; J. Chem. Theory Comput. 2021, 17, 7545-7561] for large active spaces. The resulting SOiCISCF employs both double group and time reversal symmetries for computational efficiency and the assignment of target states. The fine structures of p-block elements are taken as showcases to reveal the efficacy of SOiCISCF.

Approximating First Hitting Point Distribution in Milestoning for Rare Event Kinetics.

Milestoning is an efficient method for rare event kinetics calculation using short trajectory parallelization. Mean first passage time (MFPT) is the key kinetic output of Milestoning, whose accuracy crucially depends on the initial distribution of the short trajectory ensemble. The true initial distribution, i.e., the first hitting point distribution (FHPD), has no analytic expression in the general case. Here, we introduce two algorithms, local passage time weighted Milestoning (LPT-M) and Bayesian inference Milestoning (BI-M), to accurately and efficiently approximate FHPD for systems at equilibrium condition. Starting from sampling the Boltzmann distribution on milestones, we calculate the proper weighting factor for the short trajectory ensemble. The methods are tested on two model examples for illustration purpose. Both methods improve significantly over the widely used classical Milestoning (CM) method in terms of the accuracy of MFPT. In particular, BI-M covers the directional Milestoning method as a special case in deterministic Hamiltonian dynamics. LPT-M is especially advantageous in terms of computational costs and robustness with respect to the increasing number of intermediate milestones. Furthermore, a locally iterative correction algorithm for nonequilibrium stationary FHPD is developed for exact MFPT calculation, which can be combined with LPT-M/BI-M and is much cheaper than the exact Milestoning (ExM) method.

Dynamic Causal Modeling and Online Collaborative Forecasting of Air Quality in Hong Kong and Macao.

The Hong Kong and Macao Special Administrative Regions, situated within China's Guangdong-Hong Kong-Macao Greater Bay Area, significantly influence and are impacted by their air quality conditions. Rapid urbanization, high population density, and air pollution from diverse factors present challenges, making the health of the atmospheric environment in these regions a research focal point. This study offers three key contributions: (1) It applied an interpretable dynamic Bayesian network (DBN) to construct a dynamic causal model of air quality in Hong Kong and Macao, amidst complex, unstable, multi-dimensional, and uncertain factors over time. (2) It investigated the dynamic interaction between meteorology and air quality sub-networks, and both qualitatively and quantitatively identified, evaluated, and understood the causal relationships between air pollutants and their determinants. (3) It facilitated an online collaborative forecast of air pollutant concentrations, enabling pollution warnings. The findings proposed that a DBN-based dynamic causal model can effectively explain and manage complex atmospheric environmental systems in Hong Kong and Macao. This method offers crucial insights for decision-making and the management of atmospheric environments not only in these regions but also for neighboring cities and regions with similar geographical contexts.

Identification of Intestinal Microbial Community in Gallstone Patients with Metagenomic Next-Generation Sequencing.

Gallstone disease (GD) is one of the most common gastrointestinal diseases worldwide. Nowadays, intestinal microbiota are thought to play important roles in the formation of gallstones. In our study, human fecal samples were extracted for metagenomic next-generation sequencing (mNGS) on the Illumina HiSeq platform, followed by bioinformatics analyses. Our results showed that there was a particular intestinal micro-ecosystem in GD patients. In contrast to healthy people, the sequences of Bacteroidetes, Bacteroides and Thetaiotaomicron were obviously more abundant in GD patients at phylum, genus and species levels, respectively. On the other hand, the glycan metabolism and drug resistance, especially for the ß-lactams, were the most profound functions of gut microbes in GD patients compared to those in normal subjects. Furthermore, a correlation analysis drew out that there existed a significant relationship between the serum levels of biochemical indicators and abundances of intestinal microbes in GD patients. Our results illuminate both the composition and functions of intestinal microbiota in GD patients. All in all, our study can broaden the insight into the potential mechanism of how gut microbes affect the progression of gallstones to some extent, which may provide potential targets for the prevention, diagnosis or treatment of GD.