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Abstract The coherent continental-wide speleothem water isotope records are accompanied by regionally inconsistent moisture patterns within the Asian summer monsoon (ASM) region, leaving a disputed explanation of monsoonal hydroclimate variability at the orbital timescale. Here, the transient simulation forced by orbital parameters during the past 300,000 years in an isotope-enabled fully coupled model shows that the complex ASM response can be understood as a grand dipole pattern of oxygen isotope and rainfall between South Asia and Japan, with South China lying in a hybrid transition region. While the dipole monsoon rainfall change between South Asia and Japan is related to the large-scale circulation adjustment in response to Northern Hemisphere summer insolation, the dipole oxygen isotope pattern is associated with the changing contribution of moisture sources. Our results provide new clues to reconcile ASM hydroclimate patterns at orbital timescale as revealed by the water isotope records and other hydrological proxies.

Abstract Upwelling-induced productivity in the Arabian Sea infers an out-of-phase relation between the Indian summer monsoon and Northern Hemisphere summer insolation at the precession band but an in-phase relation at the obliquity band. These records contrast speleothem oxygen records showing an in-phase relation between the monsoon and summer insolation in two bands, leaving the driving mechanism for the Indian summer monsoon still unknown. Here we use an isotope-enabled Community Earth System Model, showing that the Indian summer monsoon rainfall and water isotopes respond directly to Northern Hemisphere summer insolation in two bands. This contrasts with the Arabian Sea upwelling which is weakened at precessional minimum but strengthened at obliquity maximum by a stronger northward shift of the monsoon wind due to the stronger summer insolation at precessional minimum compared with obliquity maximum. This paper resolves the longstanding question, supporting the Northern Hemisphere-driving mechanism for the Indian summer monsoon at orbital timescale.

Introduction Non-combustible nicotine products (NCNPs), such as heated tobacco products (HTPs) and nicotine vaping products (NVPs) have gained a significant nicotine market share in South Korea. This descriptive study examined patterns of regular cigarette and NCNP use among South Korean adults. Methods Data were from the 2020 International Tobacco Control Korea Survey and included 4016 adults (aged ≥19 years) in the Republic of Korea who were regularly (at least weekly) using at least one NCNP (NVP/HTP, n=2117) and/ or smoked cigarettes (n=3763) at the time of the survey. Weighted descriptive estimates were computed to assess respondents’ nicotine product use among all respondents (exclusive, dual, or triple use). Thereafter, we identified sociodemographic characteristics associated with NCNP use (n=2117). Results Among Korean adults who were smoking cigarettes, 83.1% (95% CI: 81.6– 84.6) did so exclusively, and 16.9% (95% CI: 15.4–18.4) smoked cigarettes and used NCNPs. Among those who used HTPs (n=1877), 14.9% (95% CI: 11.5–18.4) did so exclusively, 59.6% used HTPs and smoked cigarettes (95% CI: 55.4–63.1), 4.2% used HTPs and vaped (95% CI: 11.5–18.4), and 21.6 % (95% CI: 18.9–24.2) used all three products. Of adults who used HTPs and smoked cigarettes, 86.6% smoked daily. Among those who vaped (n=865), 13.3% did so exclusively (95% CI: 9.4–17.1), 55.6% (95% CI: 49.6–61.5) vaped and smoked cigarettes, 5.1% (95% CI: 1.7–8.6) used HTPs and vaped, and 26.1% (95% CI: 22.1–30.1) used all three products. Of adults who vaped and smoked cigarettes, 82.4% (95% CI: 77.1–87.7) smoked daily. Conclusions Cigarettes remain the most commonly used nicotine product in South Korea, and among adults using heated tobacco and/or vaping products, the majority were also smoking. Research is urgently needed to assess whether adults who are using an NCNP are doing so to quit, or rather to complement their cigarette smoking.

Soft magnetic composites (SMCs) play a crucial role in modern electronics and electrical devices, for which challenges remain in maintaining magnetic performance and reducing loss at megahertz (MHz) frequencies. Here, CeFeCo/FeSiAl@ polyurethane (PU) SMCs have been rationally designed with the simultaneous achievement of large relative permeability, high domain-wall resonance frequency, and low power loss. As a rare-earth soft magnetic alloy, the CeFeCo is promising for high-frequency applications due to its easy-plane anisotropy, whereas FeSiAl is advantageous with increased electrical resistivity as well as approaching-zero magnetocrystalline anisotropy and magnetostriction. Hot pressing has been adopted to achieve a dense and homogeneous distribution of CeFeCo and FeSiAl powders in the PU for reduced magnetic dilution and eddy current loss. The resultant SMCs exhibit optional soft magnetic performance with large relative permeability of 21.0, low power loss of 3677.5 kW·m−3 at 5 MHz, and high domain-wall resonance frequency of 386.1 MHz. As such this work provides insights into the design of high-frequency SMCs incorporating novel rare-earth soft magnetic alloys.

Abstract The practical implementation of aqueous Zn‐ion batteries (ZIBs) for large‐scale energy storage is impeded by the challenges of water‐induced parasitic reactions and uncontrolled dendrite growth. Herein, we propose a strategy to regulate both anions and cations of electrolyte solvation structures to address above challenges, by introducing an electrolyte additive of 3‐hydroxy‐4‐(trimethylammonio)butyrate (HTMAB) into ZnSO4 electrolyte. Consequently, the deposition of Zn is significantly improved leading to a highly reversible Zn anode with paralleled texture. The Zn/Zn cells with ZnSO4/HTMAB exhibit outstanding cycling performance, showcasing a lifespan exceeding 7500 h and an exceptionally high accumulative capacity of 16.47 Ah cm−2. Zn/NaV3O8·1.5H2O full cell displays a specific capacity of ~130 mAh g−1 at 5 A g−1 maintaining a capacity retention of 93% after 2000 cycles. This work highlights the regulation on both cations and anions of electrolyte solvation structures in optimizing interfacial stability during Zn plating/stripping for high performance ZIBs.

The industrial landscape constitutes a crucial aspect of a region’s historical and cultural identity, serving as a valuable asset in the development of industrial tourism. Exploring the industrial landscape supports initiatives in industrial tourism, acts as a catalyst for community revitalization, and contributes to sustainable urban progress. The primary objective of this research was to investigate the spatial distribution characteristics and underlying determinants of China’s industrial landscape (CIL) to inform urban planning, cultural heritage preservation, and sustainable development initiatives. This study utilized analytical tools, such as the nearest neighbor index, geographic concentration index, and hot spot analysis, to comprehensively examine the spatial distribution of CIL. Additionally, Geodetector was employed to explore the correlating factors behind this distribution. The findings reveal the following: (1) CIL exhibited a pronounced agglomerative spatial pattern characterized by a high degree of concentration, significant disparities, and substantial spatial autocorrelation. (2) Over time, the agglomeration of CIL varied, intensifying initially and then diminishing, with the center of gravity of its distribution shifting eastward before subsequently moving westward in a directional trend resembling “northeast–southwest”. (3) There was a diverse array of industrial landscape types within China, with notable disparities in the prevalence of different categories. The manufacturing and transportation sectors boasted the highest number of heritage sites. (4) The distribution pattern of CIL was shaped by factors such as the level of economic development, socio-demographic conditions, transportation infrastructure, and cultural milieu. The interplay between these factors had a substantial impact on this distribution pattern.

The combination of magnetic fields and magnetic nanoparticles (MNPs) to kill cancer cells by magneto-mechanical force represents a novel therapy, offering advantages such as non-invasiveness, among others. Pulsed magnetic fields (PMFs) hold promise for application in this therapy due to advantages such as easily adjustable parameters; however, they suffer from the drawback of narrow pulse width. In order to fully exploit the potential of PMFs and MNPs in this therapy, while maximizing therapeutic efficacy within the constraints of the narrow pulse width, a feature-matching theory is proposed, encompassing the matching of three aspects: (1) MNP volume and critical volume of Brownian relaxation, (2) relaxation time and pulse width, and (3) MNP shape and the intermittence of PMF. In the theory, a microsecond-PMF generator was developed, and four kinds of MNPs were selected for in vitro cell experiments. The results demonstrate that the killing rate of the experimental group meeting the requirements of the theory is at least 18% higher than the control group. This validates the accuracy of our theory and provides valuable guidance for the further application of PMFs in this therapy. [ABSTRACT FROM AUTHOR]

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning‐based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning‐based Strategy) by using dataset of disease‐direct‐related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery. [ABSTRACT FROM AUTHOR]

Objective: To explore role of nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome in idiopathic membranous nephropathy (IMN) and its possible mechanism of activation. Methods: A total of 135 patients with no adrenal corticosteroid and immunosuppressive therapy who were diagnosed with IMN by renal biopsy were collected, and 10 patients with renal tuberculosis and renal tumors who underwent nephrectomy were selected as control group. Immunohistochemical was used to detect NLRP3, tumor necrosis factor receptor-related factor 6 (TRAF6), Caspase-1, NF-κB p65, MAPK signaling pathway-related proteins, including phosphorylated ERK1/2 (p-ERK1/2), phosphorylated p38 (p-p38), phosphorylated JNK (p-JNK) expressions. Blood and 24-hour urine of IMN patients were collected, and 24-hour urine protein quantification, serum creatinine, creatinine clearance rate, blood urea nitrogen and other general clinical data were were analyzed, correlation between urine protein and NLRP3, Caspase-1, IL-1β, TNF- α, NLRP3 and TRAF6 were analyzed, respectively. Results: Compared with control group, 24-hour urine protein and serum creatinine of IMN patients were significantly increased (both P<0.05), and protein expressions of NLRP3 and Caspase-1 in kidney tissues of IMN patients were significantly increased (both P<0.05), among which renal tissue of stage II IMN patients had the highest protein expressions of NLRP3 and Caspase-1, IL-1β and TNF- α expressions were enhanced (both P<0.05), and IL-1β and TNF-α protein expressions in renal tissue of stage II IMN patients were the strongest, protein expressions of NF-κB p65, p-ERK1/2, p-p38 and p-JNK were significantly increased (all P<0.05) . 24-hour urinary protein of IMN patients was positively correlated with expression of NLRP3 protein in kidney tissues (r=0.689, P<0.01), positively correlated with Caspase-1 protein (r=0.614, P<0.000 1), positively correlated with IL-1β (r=0.708, P<0.000 1), positively correlated with TNF-α (r=0.594, P<0.01) . Conclusion: TRAF6 induces ERK1/2, p38 and JNK signaling pathways to stimulate activation of NLRP3 inflammasome through activation of NF-κB, which promotes occurrence of IMN and production of large amounts of urine protein. [ABSTRACT FROM AUTHOR]

Obesity-induced chronic inflammation exacerbates multiple types of tissue/organ deterioration and stem cell dysfunction; however, the effects on skeletal tissue and the underlying mechanisms are still unclear. Here, we show that obesity triggers changes in the microRNA profile of macrophage-secreted extracellular vesicles, leading to a switch in skeletal stem/progenitor cell (SSPC) differentiation between osteoblasts and adipocytes and bone deterioration. Bone marrow macrophage (BMM)-secreted extracellular vesicles (BMM-EVs) from obese mice induced bone deterioration (decreased bone volume, bone microstructural deterioration, and increased adipocyte numbers) when administered to lean mice. Conversely, BMM-EVs from lean mice rejuvenated bone deterioration in obese recipients. We further screened the differentially expressed microRNAs in obese BMM-EVs and found that among the candidates, miR-140 (with the function of promoting adipogenesis) and miR-378a (with the function of enhancing osteogenesis) coordinately determine SSPC fate of osteogenic and adipogenic differentiation by targeting the Pparα-Abca1 axis. BMM miR-140 conditional knockout mice showed resistance to obesity-induced bone deterioration, while miR-140 overexpression in SSPCs led to low bone mass and marrow adiposity in lean mice. BMM miR-378a conditional depletion in mice led to obesity-like bone deterioration. More importantly, we used an SSPC-specific targeting aptamer to precisely deliver miR-378a-3p-overloaded BMM-EVs to SSPCs via an aptamer-engineered extracellular vesicle delivery system, and this approach rescued bone deterioration in obese mice. Thus, our study reveals the critical role of BMMs in mediating obesity-induced bone deterioration by transporting selective extracellular-vesicle microRNAs into SSPCs and controlling SSPC fate.

A droop-free distributed energy storage control strategy based on the diffusion algorithm is proposed to address the inability of droop control to simultaneously achieve bus voltage stability and accurate power distribution among energy storage systems when the line impedance is mismatched and bus voltage is inconsistent. First, the diffusion algorithm is applied to the distributed estimation of direct current (DC) microgrid to obtain the global average, and the difference between the voltage rating and the average voltage is used as a compensation term to restore the bus voltage deviation. Then, in order to achieve the accurate distribution of power between energy storage systems with different rated capacities and different states of charge (SOC), a standard power of energy storage is designed and the difference between the standard power and the average standard power is used as a compensation term for the equilibrium of SOC between energy storage systems. Finally, a model based on RT-LAB is built to verify the effectiveness of the designed control strategy in four different operating modes. The experimental results show that the proposed control strategy can achieve the bus voltage recovery and the accurate distribution of energy storage power in the isolated DC microgrid.

Abstract Lower-grade gliomas (LGGs) exhibit highly variable clinical behaviors, while classic histology characteristics cannot accurately reflect the authentic biological behaviors, clinical outcomes, and prognosis of LGGs. In this study, we carried out analyses of whole exome sequencing, RNA sequencing and DNA methylation in primary vs. recurrent LGG samples, and also combined the multi-omics data to construct a prognostic prediction model. TCGA-LGG dataset was searched for LGG samples. 523 samples were used for whole exome sequencing analysis, 532 for transcriptional analysis, and 529 for DNA methylation analysis. LASSO regression was used to screen genes with significant association with LGG survival from the frequently mutated genes, differentially expressed genes, and differentially methylated genes, whereby a prediction model for prognosis of LGG was further constructed and validated. The most frequently mutated diver genes in LGGs were IDH1 (77%), TP53 (48%), ATRX (37%), etc. Top significantly up-regulated genes were C6orf15, DAO, MEOX2, etc., and top significantly down-regulated genes were DMBX1, GPR50, HMX2, etc. 2077 genes were more and 299 were less methylated in recurrent vs. primary LGG samples. Thirty-nine genes from the above analysis were included to establish a prediction model of survival, which showed that the high-score group had a very significantly shorter survival than the low-score group in both training and testing sets. ROC analysis showed that AUC was 0.817 for the training set and 0.819 for the testing set. This study will be beneficial to accurately predict the survival of LGGs to identify patients with poor prognosis to take specific treatment as early, which will help improve the treatment outcomes and prognosis of LGG.

TP53, a guardian of the genome, suppresses or enhances tumors through various regulatory pathways. However, the role of p53-related long non-coding RNAs (lncRNAs) in immune regulation of tumor microenvironment and prognosis of gastric cancer (GC) is so far unelucidated. We analyzed the role of TP53-associated lncRNAs (obtained from the TP53LNC-DB database) in immune regulation, immune cell infiltration and RNA modification in gastric cancer. Firstly, using multivariate COX regression analysis, we identified eight lncRNAs related to the prognosis of GC. Furthermore, based on the expression of the lncRNA signature and risk score, the GC patients were divided into high-risk and low-risk groups. We found that M2-macrophages have significantly higher infiltration in the high-risk group. Similarly, significant differences in immune function (APC_co_stimulation, CCR, and checkpoint) and m6A modification (FTO, ZC3H13, YTHDC1, and RBM15), and m5C modification (NOP2 and TET1) between both groups were also observed. These signature lncRNAs were also positively associated with oxidative stress-related genes (MPO, MAPK14, HMOX1, and APP). Additionally, we found that high expression of GAS5 and low expression of MALAT1 in Helicobacter pylori (H-pylori) positive GC patients. Finally, GC patients in the low-risk group showed higher resistance to immunotherapy while patients in the high-risk group were more sensitive to various chemotherapy drugs. Based on these findings, we conclude that p53-associated lncRNAs signature could potentially predict the immune status and overall survival, and may also be used for risk management and planning immunotherapy for gastric cancer patients.

Sun-induced chlorophyll fluorescence (SIF) has been a promising indicator of plants’ physiological status, but its response to physiological changes under the verticillium wilt (VW) stress of cotton plants is complicated by concurrent nonphysiological changes. The relative contributions of the above two components to SIF variations are unclear at different VW stress severities, hindering the accurate diagnosis of VW levels. Therefore, the objective of this study is to investigate the dynamic responses of SIF, physiological and nonphysiological factors, and to evaluate the contributions of the two factors to SIF variations under different VW stress degrees of cotton. We continuously observed the diurnal variation of the top-of-canopy reflectance and SIF on healthy and VW-infected cotton during the peak incidence period of VW disease. To accurately quantify the relative contribution of each component to SIF, we proposed a practical strategy to estimate unmeasurable parameter when using Lindeman, Merenda, and Gold method. The results demonstrated the dominant role of physiological factors in SIF with an arch diurnal change pattern at the early stages of VW development. As the VW severity increased, the contribution of physiological components declined, with the maximum contribution decreasing by 47.7%, and the diurnal pattern was disrupted, with the diurnal variation amplitude of the parameter declining by 63.4%, followed by a shift in the regulatory role toward nonphysiological factors. This study contributes to a further understanding of the roles of physiological and nonphysiological components in SIF variations of cotton under VW stress, thus advancing the accurate monitoring of VW stress severity.

Abstract Skeletal stem/progenitor cell (SSPC) senescence is a major cause of decreased bone regenerative potential with aging, but the causes of SSPC senescence remain unclear. In this study, we revealed that macrophages in calluses secrete prosenescent factors, including grancalcin (GCA), during aging, which triggers SSPC senescence and impairs fracture healing. Local injection of human rGCA in young mice induced SSPC senescence and delayed fracture repair. Genetic deletion of Gca in monocytes/macrophages was sufficient to rejuvenate fracture repair in aged mice and alleviate SSPC senescence. Mechanistically, GCA binds to the plexin-B2 receptor and activates Arg2-mediated mitochondrial dysfunction, resulting in cellular senescence. Depletion of Plxnb2 in SSPCs impaired fracture healing. Administration of GCA-neutralizing antibody enhanced fracture healing in aged mice. Thus, our study revealed that senescent macrophages within calluses secrete GCA to trigger SSPC secondary senescence, and GCA neutralization represents a promising therapy for nonunion or delayed union in elderly individuals.

In response to the defects of the traditional centralized economic dispatching strategy which needs to obtain global information and cannot adapt to the flexible topology of the system, and considering the coexistence of energy in the form of electricity and heat, a completely distributed cogeneration economic dispatching strategy based on the consistency algorithm is proposed. The incremental cost of electricity price and heat price of each unit in the cogeneration system is taken as the consistent variable to conduct the iterative calculation until the incremental cost converges to achieve the economic optimum of the system. In addition, the case of individual unit transmitting to neighboring units with deviating incremental cost values in order to enhance their own interests is also analyzed, and an incremental cost compensation term is designed to eliminate the impact of this selfish behavior. The simulation results indicate that the proposed strategy is effective and viable in the solution of the cogeneration economic dispatching problems.

Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices and textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nitrogen nutrition status using unmanned aerial vehicle (UAV) image features and the nitrogen nutrient index (NNI). Random frog algorithm - and random forest-screened image feature sets significantly correlated with the NNI, which were substituted into four machine learning algorithms for NNI estimation modeling. The composite scores (F) of optimal image feature sets were calculated using the coefficient of variation method for comprehensive cotton nitrogen nutrient diagnosis. Validation of the model for determining the critical nitrogen concentration in cotton yielded a coefficient of determination R2 = 0.89, root mean square error RMSE = 0.50 g (100 g)-1, and mean absolute error MAE = 0.44, demonstrating improved performance. Additionally, our novel NNI estimation model constructed based on the optimal image feature sets exhibited R2c = 0.97, RMSEc = 0.02, MAEc = 0.02, R2v = 0.85, RMSEv = 0.05, and MAEv = 0.04. Polynomial fitting of the composite index with NNI indicated that the model was reliable and yielded the following diagnostic criterion: 0.48 < F2 < 0.67 indicated nitrogen overapplication, whereas F2 < 0.48 or F2 > 0.67 indicated nitrogen deficiency. This study demonstrates the superior effectiveness of using UAV RGB image feature sets for NNI estimation and the quick, accurate diagnosis of cotton nitrogen levels, which will help guide nitrogen fertilizer application.

This case report documents the first instance of Penicillin-Susceptible Methicillin-Resistant Staphylococcus aureus (PS-MRSA) in a Chinese psychiatric hospital. The strain was isolated from a patient with Alzheimer’s disease who had a lower respiratory tract infection. Clinical and laboratory analyses, including mass spectrometry, antibiotic susceptibility testing, and whole-genome sequencing, confirmed the PS-MRSA strain. In this case, we systematically introduce the clinical symptoms, laboratory findings, and treatment responses associated with this PS-MRSA strain. This discovery offers a new perspective on our understanding of resistance mechanisms and expands our considerations for existing antibiotic treatments. It may fill a gap in the classification of MRSA strains, enhance the spectrum of MRSA resistance, and complete the therapeutic strategies for MRSA.

Introduction: Although previous studies investigated the potential adverse effects of endocrine-disrupting chemicals (EDCs) on biological age acceleration and aging-related diseases, the mixed effect of multiple types of EDCs on biological age acceleration, including its potential underlying mechanism, remains unclear. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) were used to analyze biological age measures, including Klemera-Doubal method biological age (KDM-BA), phenotypic age, and homeostatic dysregulation (HD). Weight quantile sum (WQS) regression was performed to screen biological age-related EDCs (BA-EDCs) and assess the mixed effect of BA-EDCs on biological age acceleration and aging-related disease. Targets of BA-EDCs were obtained from three databases, while heart aging-related genes were obtained from the Aging Anno database. Protein–protein interaction (PPI) network and MCODE algorithm were applied to identify potential interactions between BA-EDC targets and heart aging-related genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to identify related pathways. Results: This cross-sectional study included 1,439 participants. A decile increase in BA-EDCs co-exposure was associated with 0.31 years and 0.17 years of KDM-BA and phenotypic age acceleration, respectively. The mixed effect of BA-EDCs was associated with an increased prevalence of atherosclerotic cardiovascular disease (ASCVD). Vitamins C and E demonstrated a significant interaction effect on the association between BA-EDCs and KDM-BA acceleration. PPI network and functional enrichment analysis indicated that the AGE-RAGE signaling pathway in diabetic complications was significantly enriched. Conclusion: Our results showed that the co-exposure effect of BA-EDCs was associated with biological age acceleration and ASCVD, with the AGE-RAGE signaling pathway being the underlying mechanism. Vitamins C and E may also be an actionable target for preventing EDC-induced biological aging.

Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R2 of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R2 increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R2 = 0.9612, RMSE = 66.27 kg·ha−1, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R2 = 0.8898) and Ada Boost algorithm (R2 = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield.

AbstractAn anti-neuroinflammatory activities-guided phytochemical research of Wikstroemia lungtzeensis was performed for the first time. Three undescribed carotane-type sesquiterpenes, excoecafolinols C-E (1–3), and nine known sesquiterpene derivatives were isolated from the effective ethyl acetate extract of W. lungtzeensis. Their structures were determined based on multiple spectroscopic techniques and electronic circular dichroism (ECD) spectra. Furthermore, the anti-neuroinflammatory activities of the identified compounds were evaluated in lipopolysaccharide-stimulated BV-2 cells. Among them, six components (1, 2, 4, 7, 11, 12) exhibited significant inhibitory effects on nitric oxide (NO) production, with IC50 values ranging from 10.48 to 49.41 μM (positive control minocycline, IC50 53.20 μM). Carotane-type sesquiterpenes (1, 2, 4) with high content and significant inhibitory effects, are considered to be major active ingredients of W. lungtzeensis, which might serve as potential therapeutic agents for neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Given the multitude of extracellular enzymes at their disposal, many of which are designed to degrade nature's polymers (lignin, cutin, cellulose, etc.), fungi are adept at targeting synthetic polyesters with similar chemical composition. Microbial-influenced deterioration of xenobiotic polymeric surfaces is an area of interest for material scientists as these are important for the conservation of the underlying structural materials. Here, we describe the isolation and characterization of the Papiliotrema laurentii 5307AH (P. laurentii) cutinase, Plcut1. P. laurentii is basidiomycete yeast with the ability to disperse Impranil-DLN (Impranil), a colloidal polyester polyurethane, in agar plates. To test whether the fungal factor involved in this clearing was a secreted enzyme, we screened the ability of P. laurentii culture supernatants to disperse Impranil. Using size exclusion chromatography (SEC), we isolated fractions that contained Impranil-clearing activity. These fractions harbored a single ~22 kD band, which was excised and subjected to peptide sequencing. Homology searches using the peptide sequences identified, revealed that the protein Papla1 543643 (Plcut1) displays similarities to serine esterase and cutinase family of proteins. Biochemical assays using recombinant Plcut1 confirmed that this enzyme has the capability to hydrolyze Impranil, soluble esterase substrates, and apple cutin. Finally, we confirmed the presence of the Plcut1 in culture supernatants using a custom antibody that specifically recognizes this protein. The work shown here supports a major role for the Plcut1 in the fungal degradation of natural polyesters and xenobiotic polymer surfaces. [ABSTRACT FROM AUTHOR]

Metal anodes are emerging as culminating solutions for the development of energy-dense batteries in either aprotic, aqueous, or solid battery configurations. However, unlike traditional intercalation electrodes, the low utilization of "hostless" metal anodes due to the intrinsically disordered plating/stripping impedes their practical applications. Herein, we report ordered planar plating/stripping in a bulk zinc (Zn) anode to achieve an extremely high depth of discharge exceeding 90% with negligible thickness fluctuation and long-term stable cycling. The Zn can be plated/stripped with (0001)Zn preferential orientation throughout the consecutive charge/discharge process, assisted by a self-assembled supramolecular bilayer at the Zn anode-electrolyte interface. Through real-time tracking of the Zn atoms migration, we reveal that the ordered planar plating/stripping is driven by the construction of in-plane Zn-N bindings and the gradient energy landscape at the reaction fronts. The breakthrough results provide alternative insights into the ordered plating/stripping of metal anodes toward rechargeable energy-dense batteries. [ABSTRACT FROM AUTHOR]

Three photoactive Cd(II) coordination polymers (CPs), [Cd (Fsbpe)(DBBA) 2 ]·2DMF (CP 1 ), [Cd(Fepbpe)(DBBA) 2 ]·2DMF (CP 2 ) and [Cd(Fsbpeb)(DBBA) 2 ] (CP 3 ) (DBBA = 3,5-dibromobenzoic acid, DMF = dimethyl formamide) with similar 1D chain motifs exhibited completely different photosalient behaviors (PS) in response to UV light. Mechanical motion was triggered by [2+2] photocycloaddition and regulated by positioning of the photoactive alkene centers relative to the crystal axis. This solid-state reaction was reversed by heating and photomechanical behaviour was repeated over several cycles. A simple photoactuating device was prepared using a CP3-PVA composite.