A high-performance organic fluorescent probe with aggregation-induced emission properties for long-term tumor monitoring.

Near-infrared organic fluorescent probes have great need in biological sciences and medicine but most of them are still largely unable to meet demand. In this work, a delicate multipurpose organic fluorescent probe (DPPM-TPA) with aggregation-induced emission performances is designed and prepared by facile method to reflect fluorescence labeling, two-photon imaging, and long-term fluorescent tracking. Specifically, DPPM-TPA NPs was constructed from 4-(diphenylamino)phenylboronic acid and DPPM-Br by classical Suzuki coupling reaction and then coated with F127. Such nanoprobe possessed high stability in diverse medium under ambient temperatures, low cytotoxicity, and brilliant fluorescence performance. More importantly, DPPM-TPA NPs showed excellent two-photon imaging and extraordinary long-term fluorescence tracing capacity to malignant tumor, and it can last up to 9 days. These results indicated that DPPM-TPA NPs is expected to serve as a fluorescent probe for photodiagnostic and providing a new idea for the development of long-term fluorescent tracker.

The characteristics and environmental significance of BVOCs released by aquatic macrophytes.

Biogenic volatile organic compounds (BVOCs) emitted by plants serve crucial biological functions and potentially impact atmospheric environment and global carbon cycling. Despite their significance, BVOC emissions from aquatic macrophytes have been relatively understudied. In this study, for the first time we identified there were 68 major BVOCs released from 34 common aquatic macrophytes, and these compounds referred to alcohols, aldehydes, alkanes, alkenes, arenes, ethers, furans, ketones, phenol. For type of BVOC emissions from different life form and phylogenetic group of aquatic macrophytes, 34 of the 68 BVOCs from emergent and submerged macrophytes are classified into alkene and alcohol compounds, over 50% BVOCs from dicotyledon and monocotyledon belong to alcohol and arene compounds. Charophyte and pteridophyte emitted significantly fewer BVOCs than dicotyledon and monocotyledon, and each of them only released 12 BVOCs. These BVOCs may be of great importance for the growth and development of macrophytes, because many BVOCs, such as azulene, (E)-ß-farnesene, and dimethyl sulfide are proved to play vital roles in plant growth, defense, and information transmission. Our results confirmed that both life form and phylogenetic group of aquatic macrophytes had significantly affected the BVOC emissions form macrophytes, and suggested that the intricate interplay of internal and external factors that shape BVOC emissions from aquatic macrophytes. Thus, further studies are urgently needed to investigate the influence factors and ecological function of BVOCs released by macrophytes within aquatic ecosystem.

Multi-model analysis of gallbladder cancer reveals the role of OxLDL-absorbing neutrophils in promoting liver invasion.

BACKGROUND: Gallbladder cancer (GBC) is the most common and lethal malignancy of the biliary tract that lacks effective therapy. In many GBC cases, infiltration into adjacent organs or distant metastasis happened long before the diagnosis, especially the direct liver invasion, which is the most common and unfavorable way of spreading. METHODS: Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), proteomics, and multiplexed immunohistochemistry (mIHC) were performed on GBC across multiple tumor stages to characterize the tumor microenvironment (TME), focusing specifically on the preferential enrichment of neutrophils in GBC liver invasion (GBC-LI). RESULTS: Multi-model Analysis reveals the immunosuppressive TME of GBC-LI that was characterized by the enrichment of neutrophils at the invasive front. We identified the context-dependent transcriptional states of neutrophils, with the Tumor-Modifying state being associated with oxidized low-density lipoprotein (oxLDL) metabolism. In vitro assays showed that the direct cell-cell contact between GBC cells and neutrophils led to the drastic increase in oxLDL uptake of neutrophils, which was primarily mediated by the elevated OLR1 on neutrophils. The oxLDL-absorbing neutrophils displayed a higher potential to promote tumor invasion while demonstrating lower cancer cytotoxicity. Finally, we identified a neutrophil-promoting niche at the invasive front of GBC-LI that constituted of KRT17+ GBC cells, neutrophils, and surrounding fibroblasts, which may help cultivate the oxLDL-absorbing neutrophils. CONCLUSIONS: Our study reveals the existence of a subset of pro-tumoral neutrophils with a unique ability to absorb oxLDL via OLR1, a phenomenon induced through cell-cell contact with KRT17+ GBC cells in GBC-LI.

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based kcat data.

BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila. To enhance the GEM's performance, an ecGEM was developed for M. thermophila in this study. RESULTS: Initially, the model iDL1450 underwent refinement and updates, resulting in a new version named iYW1475. These updates included adjustments to biomass components, correction of gene-protein-reaction (GPR) rules, and a consensus on metabolites. Subsequently, the first ecGEM for M. thermophila was constructed using machine learning-based kcat data predicted by TurNuP within the ECMpy framework. During the construction, three versions of ecGEMs were developed based on three distinct kcat collection methods, namely AutoPACMEN, DLKcat and TurNuP. After comparison, the ecGEM constructed using TurNuP-predicted kcat values performed better in several aspects and was selected as the definitive version of ecGEM for M. thermophila (ecMTM). Comparing ecMTM to iYW1475, the solution space was reduced and the growth simulation results more closely resembled realistic cellular phenotypes. Metabolic adjustment simulated by ecMTM revealed a trade-off between biomass yield and enzyme usage efficiency at varying glucose uptake rates. Notably, hierarchical utilization of five carbon sources derived from plant biomass hydrolysis was accurately captured and explained by ecMTM. Furthermore, based on enzyme cost considerations, ecMTM successfully predicted reported targets for metabolic engineering modification and introduced some new potential targets for chemicals produced in M. thermophila. CONCLUSIONS: In this study, the incorporation of enzyme constraint to iYW1475 not only improved prediction accuracy but also broadened the model's applicability. This research demonstrates the effectiveness of integrating of machine learning-based kcat data in the construction of ecGEMs especially in situations where there is limited measured enzyme kinetic parameters for a specific organism.

The origins of odor (ß-cyclocitral) under different water nutrient conditions: Algae or submerged plants?

Among the problems caused by water eutrophication, the issue of odor compounds has attracted notable attention. ß-Cyclocitral, a widely distributed and versatile odor compound, is commonly derived from both algae and aquatic plants. Planting aquatic plants is a common method of water purification. However, there is limited study on their impact on ß-cyclocitral levels in water. Here, we conducted a study on the ß-cyclocitral levels in water and the submerged plant leaves under three nutrient levels and six plant density treatments. Our findings revealed the following: (1) Chlorophyll-a (Chla), ß-cyclocitral in the water (Wcyc), ß-cyclocitral in Potamogeton lucens leaves (Pcyc) and the biomass of the submerged plants increase with rising nutrient concentration, which increased about 83 %, 95 %, 450 %, 320 % from eutrophic treatment to oligotrophic treatment, respectively. (2) In water, ß-cyclocitral is influenced not only by algae but also by submerged plants, with primary influencing factors varying across different nutrient levels and plant densities. The main source of ß-cyclocitral in water becomes from plants to algae as the water eutrophication and plant density decrease. (3) As submerged plants have the capability to emit ß-cyclocitral, the release of ß-cyclocitral increases with the density of submerged plants. Hence, when considering planting submerged plants for water purification purposes, it is crucial to carefully manage submerged plant density to mitigate the risk of odor pollution emanating from aquatic plants. This study offers fresh insights into selecting optimal water density for submerged plants and their role in mitigating the release of ß-cyclocitral.

The plant rhabdovirus viroporin P9 facilitates insect-mediated virus transmission in barley.

Potassium (K+) plays crucial roles in both plant development and immunity. However, the function of K+ in plant-virus interactions remains largely unknown. Here, we utilized Barley yellow striate mosaic virus (BYSMV), an insect-transmitted plant cytorhabdovirus, to investigate the interplay between viral infection and plant K+ homeostasis. The BYSMV accessory P9 protein exhibits viroporin activity by enhancing membrane permeability in Escherichia coli. Additionally, P9 increases K+ uptake in yeast (Saccharomyces cerevisiae) cells, which is disrupted by a point mutation of Glycine 14 to Threonine (P9G14T). Furthermore, BYSMV P9 forms oligomers and targets to both the viral envelope and the plant membrane. Based on the recombinant BYSMV-green fluorescent protein (BYGFP) virus, a P9-deleted mutant (BYGFPΔP9) was rescued and demonstrated infectivity within individual plant cells of Nicotiana benthamiana and insect vectors. However, BYGFPΔP9 failed to infect barley plants after transmission by insect vectors. Furthermore, infection of barley plants was severely impaired for BYGFP-P9G14T lacking P9 K+ channel activity. In vitro assays demonstrate that K+ facilitates virion disassembly and the release of genome RNA for viral mRNA transcription. Altogether, our results show that the K+ channel activity of viroporins is conserved in plant cytorhabdoviruses and plays crucial roles in insect-mediated virus transmission.

Five undescribed meroterpenoids from Ganoderma lucidum and their inhibitory activities against renal fibrosis.

Five undescribed meroterpenoids, baosglucidnes A - E (1-5), were isolated from the fruiting bodies of Ganoderma lucidum. Among them, baosglucidne B (2) as a racemic mixture was obtained. Chiral HPLC was employed to separate a pair of enantiomers (+)-2 and (-)-2. The structures and stereochemical features of these substances were characterized by utilizing spectroscopic data and ECD calculations. Finally, the results of anti-renal fibrosis activity evaluation showed that baosglucidne E (5) could inhibit the expression of collagen I in TGF-ß1-induced rat kidney proximal tubular cells at 20 µM.

ARPES detection of superconducting gap sign in unconventional superconductors.

The superconducting gap symmetry is crucial in understanding the underlying superconductivity mechanism. Angle-resolved photoemission spectroscopy (ARPES) has played a key role in determining the gap symmetry in unconventional superconductors. However, it has been considered so far that ARPES can only measure the magnitude of the superconducting gap but not its phase; the phase has to be detected by other phase-sensitive techniques. Here we propose a method to directly detect the superconducting gap sign by ARPES. This method is successfully validated in a cuprate superconductor Bi2Sr2CaCu2O8+δ with a well-known d-wave gap symmetry. When two bands have a strong interband interaction, the resulted electronic structures in the superconducting state are sensitive to the relative gap sign between the two bands. Our present work provides an approach to detect the gap sign and can be applied to various superconductors, particularly those with multiple orbitals like the iron-based superconductors.

Conservative strip tillage system in maize maintains high yield and mitigates GHG emissions but promotes N2O emissions.

Optimizing N application under straw-covered strip tillage is of great significance to the rational utilization of stover resources as well as ensure food and ecosystem security, and especially N2O emissions from agricultural systems. Quantifying N2O emissions and even the carbon footprint (CF) from agricultural systems is crucial for future protecting agricultural production systems. A two-year field experiment was conducted on black soil in Northeast China, which set up two tillage systems: strip tillage with straw returning (ST) and conventional tillage (control: CT) without straw and three nitrogen rates: 0, farmers' practice (Nfp 240 kg hm-2), and optimized nitrogen fertilizer (Nopt 180 kg hm-2). We examined the characteristics of N2O emissions and CF under the ST and CT systems. Among them, we indirectly calculated GHG emissions using the LCA method. Compared with CT, the ST system significantly reduces indirect GHG emissions, but did significantly increase direct cumulative N2O emissions by 20.7 %, most likely because the higher soil residual nitrate nitrogen content, WFPS, and soil temperature under ST was 13.0 %, 2 % and 5.7 % higher than that under CT. Nopt treatment markedly reduced cumulative N2O emissions by 36.0 %, CFarea, CFyield, and CFNPV by 22.4 %, 23.1 %, and 23.5 % in ST, respectively, compared to Nfp. The reduction in energy use of machinery in ST results in lower fuel consumption and thus generating less CF. What's more, the decrease of CFyield and CFNPV between nitrogen application treatments under ST was 5.2 % and 7.7 % higher than CT, respectively. ST system can effectively achieve higher grain yield and mitigate GHG emissions on black soil in Northeast China compared with CT, but attention should be paid to N2O emissions in the soil during the maize growth period. The sustainability of balancing GHG emissions, and economic and environmental benefits can be achieved by optimizing nitrogen fertilizer manage.

Sensitive Detection of OCS Using Thermal Conversion Combined with Spectral Reconstruction Filtering Differential Optical Absorption Spectroscopy.

Carbonyl sulfide (OCS) is a toxic gas produced during industrial processes that poses risks to both human health and industrial equipment. Therefore, detecting OCS concentrations plays a crucial role in early hazard warning. This paper presents an online system for detecting OCS at the ppb level using thermal conversion and spectral reconstruction filtering differential optical absorption spectroscopy (SRF-DOAS). First, OCS, which is not suitable for DOAS due to its weak absorption characteristics, is completely transformed into SO2 with strong absorption characteristics under high-temperature conditions. Then, the spectral reconstruction filtering method (SRF) is proposed to eliminate the noise and interference. The core idea of the method is to arrange the spectrum according to the spectral intensity from small to large rather than wavelength, reconstructing the spectrum into a new spectrum with linear characteristics. The reconstructed spectrum can remove noise and interference by linear fitting and retain the characteristic of SO2 oscillation absorption. Next, we demonstrate the ability of the reconstructed spectral method to remove noise and interference by comparing the spectra of the inverse-reconstructed gas mixture and SO2. The relative deviation of 0.88% at 100 ppb and detection limit of 7.26 ppb*m for OCS were obtained using the SRF-DOAS method. Finally, the reliability of the system was confirmed by measurements of OCS concentrations in mixture gas of OCS and air, as well as in human exhaled breath.

A blueprint for tumor-infiltrating B cells across human cancers.

B lymphocytes are essential mediators of humoral immunity and play multiple roles in human cancer. To decode the functions of tumor-infiltrating B cells, we generated a B cell blueprint encompassing single-cell transcriptome, B cell-receptor repertoire, and chromatin accessibility data across 20 different cancer types (477 samples, 269 patients). B cells harbored extraordinary heterogeneity and comprised 15 subsets, which could be grouped into two independent developmental paths (extrafollicular versus germinal center). Tumor types grouped into the extrafollicular pathway were linked with worse clinical outcomes and resistance to immunotherapy. The dysfunctional extrafollicular program was associated with glutamine-derived metabolites through epigenetic-metabolic cross-talk, which promoted a T cell-driven immunosuppressive program. These data suggest an intratumor B cell balance between extrafollicular and germinal-center responses and suggest that humoral immunity could possibly be harnessed for B cell-targeting immunotherapy.

MYC and NCAPG2 as molecular targets of colorectal cancer and gastric cancer in nursing.

Colorectal cancer is a common malignant tumor in intestinal tract, the early symptoms are not obvious. Gastric cancer is a malignant tumor originating from the gastric mucosal epithelium. However, the role of MYC and non-SMC condensin II complex subunit G2 (NCAPG2) in colorectal cancer and gastric cancer remains unclear. The colorectal cancer datasets GSE49355 and gastric cancer datasets GSE19826 were downloaded from gene expression omnibus database. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. Functional enrichment analysis, gene set enrichment analysis (GSEA) and immune infiltration analysis was performed. Construction and analysis of protein-protein interactions (PPI) network. Survival analysis and comparative toxicogenomics database (CTD) were performed. A heat map of gene expression was drawn. A total of 751 DEGs were obtained. According to the gene ontology (GO) analysis, in Biological process (BP) analysis, they are mainly enriched in cell differentiation, cartilage development, and skeletal development. In cellular component (CC) analysis, they are mainly enriched in the cytoskeleton of muscle cells and actin filaments. In molecular function (MF) analysis, they are mainly concentrated in Rho GTPase binding, DNA binding, and fibronectin binding. In Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, they are mainly enriched in the MAPK signaling pathway, apoptosis, and cancer pathways. The soft threshold power for WGCNA analysis was set to 9, resulting in the generation of 40 modules. Ultimately, 2 core genes (MYC and NCAPG2) were identified. The heatmap of core gene expression showed high expression of MYC and NCAPG2 in colorectal cancer tissue samples and low expression in normal tissue samples, while they were core molecules in gastric cancer. Survival analysis indicated that MYC and NCAPG2 were risk factors, showing an upregulation trend with increasing risk scores. CTD analysis revealed associations of MYC and NCAPG2 with colorectal cancer, gastric cancer, inflammation, and immune system diseases. MYC and NCAPG2 are highly expressed in colorectal cancer. The higher the expression of MYC and NCAPG2, the worse the prognosis. MYC and NCAPG2 are core molecules in gastric cancer.

Preservation of Topological Surface States in Millimeter-Scale Transferred Membranes.

Ultrathin topological insulator membranes are building blocks of exotic quantum matter. However, traditional epitaxy of these materials does not facilitate stacking in arbitrary orders, while mechanical exfoliation from bulk crystals is also challenging due to the non-negligible interlayer coupling therein. Here we liberate millimeter-scale films of the topological insulator Bi2Se3, grown by molecular beam epitaxy, down to 3 quintuple layers. We characterize the preservation of the topological surface states and quantum well states in transferred Bi2Se3 films using angle-resolved photoemission spectroscopy. Leveraging the photon-energy-dependent surface sensitivity, the photoemission spectra taken with 6 and 21.2 eV photons reveal a transfer-induced migration of the topological surface states from the top to the inner layers. By establishing clear electronic structures of the transferred films and unveiling the wave function relocation of the topological surface states, our work lays the physics foundation crucial for the future fabrication of artificially stacked topological materials with single-layer precision.

A prediction model of rubber content in the dried root of Taraxacum kok-saghyz Rodin based on near-infrared spectroscopy.

BACKGROUND: Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified. RESULT: The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS-FD CASR-RF and MWS-FD chemical characteristic band RF, respectively. Their respective R P 2 , RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively. CONCLUSIONS: This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.

A model-based sliding mode control with intelligent distribution for a proportional valve driven by digital valve arrays.

Parallel-connected digital valve arrays are commonly utilized in the pilot stage of the proportional directional valve to enhance dynamic performance and reliability. However, when the digital valve array is driven by a digital signal, it is difficult to optimally assign the signal pulses to each valve. If the assignment is not well executed, it can significantly reduce the switching uniformity of the digital valves or lead to performance degradation of the system. In this paper, a model-based sliding mode control strategy based on the intelligent distribution of control law is proposed and successfully applied to a proportional valve driven by digital valve arrays. The intelligent distribution strategy encompasses a logic distribution algorithm and a circular sliding distribution algorithm that automatically assigns control laws to different valves based on the rolling of the PWM signal cycle. Experimental results confirm that the proposed strategy not only simultaneously reduces the total number of valve switches and enhances the switching uniformity among the valves, but also adapts to the variation in the number of valves. The proposed strategy is not limited to the application of digital valve arrays, it is also applicable in other fields of multi-actuators driven by digital signals, and can simultaneously improve the control accuracy, lifetime, and maintenance friendliness.

Can environmental policies improve marine ecological efficiency? Examining China's Ecological Civilization Pilot Zones.

Terrestrial ecosystems can benefit from environmental protection policies; however, their impact on marine ecological efficiency deserves further exploration. This study uses China's Ecological Civilization Pilot Zone (ECZ) policy as an example of a quasi-natural experimental study, with data from 11 coastal provinces in China from 2006 to 2019 as the initial sample. First, a Super-SBM model considers undesired outputs to measure marine eco-efficiency, while a synthetic control method (SCM) investigates the effect of environmental regulations on marine eco-efficiency. The results show that ECZ policies can promote marine eco-efficiency and the effect mechanisms of these policies are discussed from national and regional perspectives. This study contributes to the current literature by theoretically evaluating the impact of ECZ policies on the marine environment in coastal areas, enriching the mechanism of integrated environmental policies on marine ecological protection, and providing references for formulating and implementing environmental policies.

Electrifying Energy and Chemical Transformations with Single-Atom Alloy Nanoparticle Catalysts.

Single-atom alloys (SAAs) have attracted considerable attention as promising electrocatalysts in reactions central to energy conversion and chemical transformation. In contrast to monometallic nanocrystals and metal alloys, SAAs possess unique and intriguing physicochemical properties, positioning them as ideal model systems for studying structure-property relationships. However, the field is still in its early stages. In this Perspective, we first review and summarize rational synthesis methods and advanced characterization techniques for SAA nanoparticle catalysts. We then emphasize the extensive applications of SAAs in a range of electrocatalytic reactions, including fuel cell reactions, water splitting, and carbon dioxide and nitrate reductions. Finally, we provide insights into existing challenges and prospects associated with the controlled synthesis, characterization, and design of SAA catalysts.

Clinical characteristics and assessment of risk factors in patients with influenza A-induced severe pneumonia after the prevalence of SARS-CoV-2.

Purpose: The aim of this study is to describe the novel epidemiological and clinical characteristics of influenza A-induced severe pneumonia occurring after the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and to further assess its potential risk factors for mortality. Methods: We retrospectively studied the consecutive case series of 30 patients with confirmed influenza A-induced severe pneumonia treated in the intensive care unit at Dazhou Central Hospital in Sichuan, China, from March 1 to April 30, 2023. Logistic regression was used to analyze the independent risk factors, and receiver operating characteristic (ROC) curves were applied to evaluate the predictive efficacy of associated risk factors for mortality. Results: The mortality rate was 33.3% in this study. Independent risk factors for mortality of patients were acute respiratory distress syndrome (ARDS) (p = 0.044) and septic shock (p = 0.012). ROC statistics for ARDS and septic shock to predict mortality in patients with influenza A-induced severe pneumonia demonstrated an area under the curve of 0.800 (sensitivity 80.0%, specificity 80.0%) and 0.825 (sensitivity 70.0%, specificity 95.0%), respectively. Conclusion: ARDS and septic shock were the independent risk factors for mortality in patients with influenza A-induced severe pneumonia following the end of the SARS-CoV-2 pandemic. But high level of next generation sequencing reads Aspergillus coinfection, and comorbidities did not increase death risk of the study population.