Two Undescribed Germacrane-Type Sesquiterpenoids from Salvia cavaleriei var. simplicifolia Stib. and Their Anti-Alzheimer's Disease Activity.

Two undescribed germacrane-type sesquiterpenoids, salcasins A (1) and B (2), together with three known compounds (3-5) were isolated and identified from the whole plant of Salvia cavaleriei var. simplicifolia Stib. The structures of the undescribed compounds were elucidated on the basis of spectroscopic methods, such as HR-ESI-MS, 1D and 2D NMR data. The relative configurations of 1 and 2 were established by analyzing their NOESY spectra as well as by 13C NMR calculations with DP4+ probability analyses. The absolute configurations of 1 and 2 were determined by comparing experimental and calculated ECD spectra. Furthermore, the in vivo anti-Alzheimer's disease activities of 1-5 were evaluated using Caenorhabditis elegans AD pathological model. Among all isolated compounds, salcasin A (1) significantly delayed AD-like symptoms of worm paralysis, which may be a potential anti-AD candidate agent.

The effects of four paralogous piscidin antimicrobial peptides on the chemotaxis, macrophage respiratory burst, phagocytosis and expression of immune-related genes in orange-spotted grouper (Epinephelus coicodes).

Antimicrobial peptides (AMPs) are an essential part of the vertebrate innate immune system. Piscidins are a family of AMPs specific in fish. In our previous investigation, we identified four paralogous genes of piscidins in the orange-spotted grouper (Epinephelus coicodes), which exhibited distinct activities against bacteria, fungi, and parasitic ciliated protozoa. Piscidins demonstrated their capability to modulate the expression of diverse immune-related genes; however, their precise immunoregulatory functions remain largely unexplored. In this study, we examined the immunomodulatory properties of putative mature peptides derived from four E. coicodes piscidins (ecPis1S, ecPis2S, ecPis3S, and ecPis4S) in head kidney leukocytes (HKLs) or monocytes/macrophages (MO/MΦ)-like cells isolated from E. coicodes. Our data demonstrate that E. coicodes piscidins exhibit immunomodulatory activities supported by multiple lines of evidence. Firstly, all four piscidins displayed chemotactic activities towards HKLs, with the most potent chemotactic activity observed in ecPis2S. Secondly, stimulation with E. coicodes piscidins enhanced respiratory burst and phagocytic activity in MO/MФ-like cells, with ecPis3S showing the highest efficacy in increasing phagocytosis of MO/MΦ-like cells. Thirdly, mRNA expression levels of chemokine receptors, Toll-like receptors, T cell receptors, and proinflammatory cytokines were modulated to varying extents by the four piscidins in E. coicodes HKLs. Overall, our findings indicate that the immunological activities of these four paralogous piscidins from E. coicodes are exhibited in a paralog-specific and concentration-dependent manner, highlighting their distinct and versatile immunomodulatory properties. This study makes a significant contribution to the field of fish AMPs immunology by elucidating the novel mechanisms through which members of the piscidin family exert their immunomodulatory effects. Moreover, it provides valuable insights for further exploration of fish immunomodulating agents.

Experimental Study on the Fracture Toughness of Bamboo Scrimber.

In the past decade, bamboo scrimber has developed rapidly in the field of building materials due to its excellent mechanical properties, such as high toughness and high tensile strength. However, when the applied stress exceeds the ultimate strength limit of bamboo scrimber, cracks occur, which affects the performance of bamboo scrimber in structural applications. Due to the propensity of cracks to propagate, it reduces the load-bearing capacity of the bamboo scrimber material. Therefore, research on the fracture toughness of bamboo scrimber contributes to determining the material's load-bearing capacity and failure mechanisms, enabling its widespread application in engineering failure analysis. The fracture toughness of bamboo scrimber was studied via the single-edge notched beam (SENB) experiment and compact compression (CC) method. Nine groups of longitudinal and transverse samples were selected for experimental investigation. The fracture toughness of longitudinal bamboo scrimber under tensile and compressive loadings was 3.59 MPa·m1/2 and 2.39 MPa·m1/2, respectively. In addition, the fracture toughness of transverse bamboo scrimber under tensile and compressive conditions was 0.38 MPa·m1/2 and 1.79 MPa·m1/2, respectively. The results show that, for this material, there was a significant distinction between longitudinal and transverse. Subsequently, three-point bending tests and simulations were studied. The results show that the failure mode and the force-displacement curve of the numerical simulation were highly consistent compared with the experimental results. It could verify the correctness of the test parameters. Finally, the flexural strength of bamboo scrimber was calculated to be as high as 143.16 MPa. This paper provides data accumulation for the numerical simulation of bamboo scrimber, which can further promote the development of bamboo scrimber parameters in all aspects of the application.

Experimental investigation on mechanical properties of aged bamboo fiber-reinforced composites under quasi-static loading.

Bamboo fiber-reinforced composites (BFRC) have gradually attracted the interest of researchers due to their excellent mechanical properties. However, the age factor can affect these composites' mechanical properties and weaken them. This study aims to explore the mechanical properties of BFRC subjected different aging tests. First, the BFRC is prepared from raw bamboo through a series of technological processes. Second, the BFRC sheets are processed into tensile and compressive samples with different directions. Third, the samples are treated with a cold-hot test and a UV dry-wet test, respectively. Finally, quasi-static experiments are carried out to study the mechanical properties of aged BFRC. The results reveal that the BFRC has significant anisotropy. The tensile strength of BFRC parallel with the axis of the reinforcing fiber is as high as 148.53 MPa. It is 26.47 times greater than the tensile strength of BFRC perpendicular to the axis of the reinforcing fiber. The mechanical responses and SEM images show that the UV dry-wet test significantly affects on the BFRC compared to the cold-hot test. It dramatically reduces the mechanical properties of the BFRC. The SEM images further indicate that cracks occur on the surface of BFRC samples under the UV dry-wet test. In addition, two aging tests have an interactive effect on BFRC, which can reduce the sensitivity of BFRC to a UV dry-wet environment. These results are expected to deepen the understanding of the mechanical properties of BFRC and provide guidance for its applicable conditions.

Nomogram for predicting the risk of preterm birth in women undergoing in vitro fertilization cycles.

BACKGROUND: The aim of this study was to develop a nomogram for predicting the risk of preterm birth in women undergoing in vitro fertilization (IVF) cycles. METHODS: A retrospective study of 4266 live birth cycles collected from January 2016 to October 2021 at the Center for Reproductive Medicine, First Hospital of Jilin University was performed. The sample size was sufficient based on the minimal ten events per variable (EPV) rule. The primary outcome of this study was preterm birth. The cycles were divided into the preterm birth group (n = 827) and the full-term delivery group (n = 3439). A nomogram was established based on the multivariate logistic regression analysis results. The area under the curve (AUC) was calculated to assess the prediction accuracy of the nomogram model. The calibration curve was used to measure the calibration of the nomogram. RESULTS: Multivariate logistic regression analyses showed that female obesity or overweight (OR = 1.366, 95% CI: 1.111-1.679; OR = 1.537, 95% CI: 1.030-2.292), antral follicle count (AFC) of more than 24 (OR = 1.378, 95% CI: 1.035-1.836), multiple pregnancies (OR = 6.748, 95% CI: 5.559-8.190), gestational hypertension (OR = 9.662, 95% CI: 6.632-14.078) and gestational diabetes (OR = 4.650, 95% CI: 2.289-9.445) were the independent risk factors for preterm birth in IVF patients. The area under curve (AUC) under the receiver operating characteristic (ROC) curve in the prediction model was 0.781(95%CI: 0.763-0.799). The calibration curve of the nomogram showed that the prediction model had a good calibration. CONCLUSIONS: We used five risk factors to conduct a nomogram to predict preterm birth rates for patients undergoing IVF cycles. This nomogram can provide a visual assessment of the risk of preterm birth for clinical consultation.

Antimicrobial diterpenoids from the leaves and twigs of Croton kongensis Gagnepain.

Croton kongensis Gagnepain. belongs to the genus Croton, the Euphorbiaceae family, mainly distributed in Hainan and southern Yunnan, China. The aim of present study was to acquire secondary metabolites of the ethanol extract obtained from the leaves and twigs of C. kongensis. Three new abietane-type diterpenoids, crokongenolides A-C (1-3), together with seven known diterpenoids (4-10), were isolated from the leaves and twigs of C. kongensis. The structures of the new compounds were determined by extensive spectroscopic methods (1D and 2D NMR, IR, and HRESIMS), and their absolute configurations were confirmed by single-crystal X-ray diffraction analysis or electronic circular dichroism (ECD) calculations. The absolute configuration of 4 was determined for the first time by single-crystal X-ray diffraction analysis with Cu-Kα irradiation. Some compounds were evaluated for their antimicrobial properties by assessing their inhibitory effects on Staphylococcus aureus, Candida albicans, and Escherichia coli. Compound 10 showed significant antimicrobial activity against S. aureus with MIC value of 1.56 µg/ml.

An end-to-end deep learning approach for extracting stochastic dynamical systems with α-stable Lévy noise.

Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained much attention in various fields. Moreover, a growing amount of research work tends to transfer deterministic dynamical systems to stochastic dynamical systems, especially those driven by non-Gaussian multiplicative noise. However, many log-likelihood based algorithms that work well for Gaussian cases cannot be directly extended to non-Gaussian scenarios, which could have high errors and low convergence issues. In this work, we overcome some of these challenges and identify stochastic dynamical systems driven by α-stable Lévy noise from only random pairwise data. Our innovations include (1) designing a deep learning approach to learn both drift and diffusion coefficients for Lévy induced noise with α across all values, (2) learning complex multiplicative noise without restrictions on small noise intensity, and (3) proposing an end-to-end complete framework for stochastic system identification under a general input data assumption, that is, an α-stable random variable. Finally, numerical experiments and comparisons with the non-local Kramers-Moyal formulas with the moment generating function confirm the effectiveness of our method.

Extracting stochastic governing laws by non-local Kramers-Moyal formulae.

With the rapid development of computational techniques and scientific tools, great progress of data-driven analysis has been made to extract governing laws of dynamical systems from data. Despite the wide occurrences of non-Gaussian fluctuations, the effective data-driven methods to identify stochastic differential equations with non-Gaussian Lévy noise are relatively few so far. In this work, we propose a data-driven approach to extract stochastic governing laws with both (Gaussian) Brownian motion and (non-Gaussian) Lévy motion, from short bursts of simulation data. Specifically, we use the normalizing flows technology to estimate the transition probability density function (solution of non-local Fokker-Planck equations) from data, and then substitute it into the recently proposed non-local Kramers-Moyal formulae to approximate Lévy jump measure, drift coefficient and diffusion coefficient. We demonstrate that this approach can learn the stochastic differential equation with Lévy motion. We present examples with one- and two-dimensional decoupled and coupled systems to illustrate our method. This approach will become an effective tool for discovering stochastic governing laws and understanding complex dynamical behaviours. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.

Learning the temporal evolution of multivariate densities via normalizing flows.

In this work, we propose a method to learn multivariate probability distributions using sample path data from stochastic differential equations. Specifically, we consider temporally evolving probability distributions (e.g., those produced by integrating local or nonlocal Fokker-Planck equations). We analyze this evolution through machine learning assisted construction of a time-dependent mapping that takes a reference distribution (say, a Gaussian) to each and every instance of our evolving distribution. If the reference distribution is the initial condition of a Fokker-Planck equation, what we learn is the time-T map of the corresponding solution. Specifically, the learned map is a multivariate normalizing flow that deforms the support of the reference density to the support of each and every density snapshot in time. We demonstrate that this approach can approximate probability density function evolutions in time from observed sampled data for systems driven by both Brownian and Lévy noise. We present examples with two- and three-dimensional, uni- and multimodal distributions to validate the method.

Detecting the maximum likelihood transition path from data of stochastic dynamical systems.

In recent years, data-driven methods for discovering complex dynamical systems in various fields have attracted widespread attention. These methods make full use of data and have become powerful tools to study complex phenomena. In this work, we propose a framework for detecting dynamical behaviors, such as the maximum likelihood transition path, of stochastic dynamical systems from data. For a stochastic dynamical system, we use the Kramers-Moyal formula to link the sample path data with coefficients in the system, then use the extended sparse identification of nonlinear dynamics method to obtain these coefficients, and finally calculate the maximum likelihood transition path. With two examples of stochastic dynamical systems with additive or multiplicative Gaussian noise, we demonstrate the validity of our framework by reproducing the known dynamical system behavior.

Discovering transition phenomena from data of stochastic dynamical systems with Lévy noise.

It is a challenging issue to analyze complex dynamics from observed and simulated data. An advantage of extracting dynamic behaviors from data is that this approach enables the investigation of nonlinear phenomena whose mathematical models are unavailable. The purpose of this present work is to extract information about transition phenomena (e.g., mean exit time and escape probability) from data of stochastic differential equations with non-Gaussian Lévy noise. As a tool in describing dynamical systems, the Koopman semigroup transforms a nonlinear system into a linear system, but at the cost of elevating a finite dimensional problem into an infinite dimensional one. In spite of this, using the relation between the stochastic Koopman semigroup and the infinitesimal generator of a stochastic differential equation, we learn the mean exit time and escape probability from data. Specifically, we first obtain a finite dimensional approximation of the infinitesimal generator by an extended dynamic mode decomposition algorithm. Then, we identify the drift coefficient, diffusion coefficient, and anomalous diffusion coefficient for the stochastic differential equation. Finally, we compute the mean exit time and escape probability by finite difference discretization of the associated nonlocal partial differential equations. This approach is applicable in extracting transition information from data of stochastic differential equations with either (Gaussian) Brownian motion or (non-Gaussian) Lévy motion. We present one- and two-dimensional examples to demonstrate the effectiveness of our approach.

Genome-Wide Identification, Expression Profiling, and Functional Validation of Oleosin Gene Family in Carthamus tinctorius L.

Carthamus tinctorius L., commonly known as safflower, is an important oilseed crop containing oil bodies. Oil bodies are intracellular organelles in plant cells for storing triacylglycerols (TAGs) and sterol esters. Oleosins are the most important surface proteins of the oil bodies. We predicted and retrieved the sequences of eight putative C. tinctorius oleosin (Ctoleosin) genes from the genome database of safflower. The bioinformatics analyses revealed the size of their open reading frames ranging from 414 to 675 bp, encoding 137 to 224 aa polypeptides with predicted molecular weights of 14.812 to 22.155 kDa, all containing the typical "proline knot" motif. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) determined the spatiotemporal expression pattern of Ctoleosin genes, which gradually increased and peaked during flowering and seed ripening, and decreased thereafter. To validate their role in plant development, we transformed and overexpressed these eight putative Ctoleosin genes in Arabidopsis. Overexpressing Ctoleosins did not affect leaf size, although silique length was altered. Arabidopsis transformed with Ctoleosin3, 4, and 5 grew longer siliques than did the wild-type plants, without altering seed quantity. The 100-grain weight of the transgenic Arabidopsis seeds was slightly more than that of the wild-type seeds. The seed germination rates of the plants overexpressing Ctoleosin4 and 6 were slightly lower as compared with that of the wild-type Arabidopsis, whereas that in the other transgenic lines were higher than that in the wild-type plants. The overexpression of Ctoleosin genes elevated the oil content in the seeds of transgenic Arabidopsis. Our findings not only provide an approach for increasing the oil content, but also for elucidating the intricate mechanisms of oil body synthesis.

A new nanoscale transdermal drug delivery system: oil body-linked oleosin-hEGF improves skin regeneration to accelerate wound healing.

BACKGROUND: Epidermal growth factor (EGF) can promote cell proliferation as well as migration, which is feasible in tissue wound healing. Oil bodies have been exploited as an important platform to produce exogenous proteins. The exogenous proteins were expressed in oil bodies from plant seeds. The process can reduce purification steps, thereby significantly reducing the purification cost. Mostly, the diameter of oil body particle ranges between 1.0 and 1.5 µm in the safflower seeds, however, it reduces to 700-1000 nm in the transgenic safflower seeds. The significant reduction of particle size in transgenic seeds is extremely beneficial to skin absorption. RESULTS: The diameter of oil body in the transgenic safflower seeds was recorded in the range of 700-1000 nm. The smaller particle size improved their skin absorption. The expression level of oleosin-hEGF-hEGF in T3 transgenic seeds was highest at 69.32 mg/g of seeds. The oil body expressing oleosin-hEGF-hEGF had significant proliferative activity on NIH/3T3 cells and improved skin regeneration thereby accelerating wound healing in rats. The wound coverage rate exceeded 98% after treatment for 14 days with oil body expressing oleosin-hEGF-hEGF, while the saline without EGF group and wild type oil body group both showed less than 80%. The neonatal fibroblast and collagen were found to be increased in the safflower oil body expressing oleosin-hEGF-hEGF treatment group. TGF-ß1, bFGF and VEGF were noted as important growth factors in the repair of cutaneous wounds. Their expression level increased after 4 and 7 day treatment, but decreased after 14 days. Therefore, it can promote skin regeneration to accelerate wounds healing. CONCLUSIONS: The expression of oleosin-hEGF-hEGF in T3 transgenic seeds was 80.43 ng/µL oil body. It had significant proliferative activity on NIH/3T3 cells and improved skin regeneration to accelerate wound healing in rats. The expression process of TGF-ß1, bFGF and VEGF increased at first and then gradually declined.

Separation of phosphatidylcholine from soybean phospholipids by simulated moving bed.

A simulated moving bed (SMB), equipped with eight silica-gel columns, was used to separate phosphatidylcholine (PC) from soybean phospholipids. The effects of flow rate in Sections 2 (Q(2)) and 3 (Q(3)), switching time, feed flow rate and feed concentration on the operating performance parameters: purity, recovery, productivity and desorbent consumption were studied. Operating conditions leading to more than 90% purity in both outlet streams have been identified, together with those achieving optimal performance. Regions leading to complete separation are observed and explained theoretically. As the mass-transfer effect was not considered, the triangle theory only gives initial guesses for the optimal operating conditions.