Research on nondestructive detection of sweet-waxy corn seed varieties and mildew based on stacked ensemble learning and hyperspectral feature fusion technology.

The variety and quality of corn seeds are crucial factors affecting crop yield and farmers' economic benefits. This study adopts an innovative method based on a hyperspectral imaging system combined with stacked ensemble learning, aiming to achieve varieties classification and mildew detection of sweet-waxy corn seeds. First, data interference is eliminated by extracting the spectral and texture information of each corn sample and preprocessing the data. Secondly, a stacked ensemble learning model (Stack) was constructed by stacking base models and meta-models. Its results were compared with those of the base models, including Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF).Finally, the overall performance of the model is improved through the information fusion strategy of hyperspectral data and texture information. The research results indicate that the GBDT-Stack model, which integrates spectral and texture data, demonstrated optimal performance in the comprehensive classification of both corn seed varieties and mold detection. On the test set, the model achieved an average prediction accuracy of 97.01%. Specifically, the model achieved a test set accuracy ranging from 94.49% to 97.58% for different corn seed varieties and a test set accuracy of 98.89% for mildew detection. This model not only classifies corn seed varieties but also accurately detects mildew, demonstrating its wide applicability. The method has huge potential and is of great significance for improving crop yield and quality.

Diel transcriptional responses of coral-Symbiodiniaceae holobiont to elevated temperature.

Coral exhibits diel rhythms in behavior and gene transcription. However, the influence of elevated temperature, a key factor causing coral bleaching, on these rhythms remains poorly understood. To address this, we examined physiological, metabolic, and gene transcription oscillations in the Acropora tenuis-Cladocopium sp. holobiont under constant darkness (DD), light-dark cycle (LD), and LD with elevated temperature (HLD). Under LD, the values of photosystem II efficiency, reactive oxygen species leakage, and lipid peroxidation exhibited significant diel oscillations. These oscillations were further amplified during coral bleaching under HLD. Gene transcription analysis identified 24-hour rhythms for specific genes in both coral and Symbiodiniaceae under LD. Notably, these rhythms were disrupted in coral and shifted in Symbiodiniaceae under HLD. Importantly, we identified over 20 clock or clock-controlled genes in this holobiont. Specifically, we suggested CIPC (CLOCK-interacting pacemaker-like) gene as a core clock gene in coral. We observed that the transcription of two abundant rhythmic genes encoding glycoside hydrolases (CBM21) and heme-binding protein (SOUL) were dysregulated by elevated temperature. These findings indicate that elevated temperatures disrupt diel gene transcription rhythms in the coral-Symbiodiniaceae holobiont, affecting essential symbiosis processes, such as carbohydrate utilization and redox homeostasis. These disruptions may contribute to the thermal bleaching of coral.

YOLOC-tiny: a generalized lightweight real-time detection model for multiripeness fruits of large non-green-ripe citrus in unstructured environments.

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-green-ripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head's efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layer-based adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province-navel orange, Ehime Jelly orange, and Harumi tangerine-YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection.

Comprehensive Analysis Reveals the Difference in Volatile Oil between Bupleurum marginatum var. stenophyllum (Wolff) Shan et Y. Li and the Other Four Medicinal Bupleurum Species.

Volatile oil serves as a traditional antipyretic component of Bupleuri Radix. Bupleurum marginatum var. stenophyllum (Wolff) Shan et Y. Li belongs to the genus Bupleurum and is distinguished for its high level of saikosaponins and volatile oils; nonetheless, prevailing evidence remains inconclusive regarding its viability as an alternative resource of other official species. This study aims to systematically compare the volatile oil components of both dried and fresh roots of B. marginatum var. stenophyllum and the four legally available Bupleurum species across their chemical, molecular, bionics, and anatomical structures. A total of 962 compounds were determined via GC-MS from the dried roots; B. marginatum var. stenophyllum showed the greatest differences from other species in terms of hydrocarbons, esters, and ketones, which was consistent with the results of fresh roots and the e-nose analysis. A large number of DEGs were identified from the key enzyme family of the monoterpene synthesis pathway in B. marginatum var. stenophyllum via transcriptome analysis. The microscopic observation results, using different staining methods, further showed the distinctive high proportion of phloem in B. marginatum var. stenophyllum, the structure which produces volatile oils. Together, these pieces of evidence hold substantial significance in guiding the judicious development and utilization of Bupleurum genus resources.

Widely targeted metabolomic analysis reveals effects of yellowing process time on the flavor of vine tea (Ampelopsis grossedentata).

The bitter and astringent taste and miscellaneous smell of vine tea prevent its further development. In this study, we used a processing technology that mimics yellow tea to improve the flavor of vine tea and revealed its internal reasons through metabolomics. Sensory evaluation showed the yellowing process for 6-12 h reduced the bitterness and astringency significantly, and enriched the aroma. The improvement of taste was mainly related to the down-regulation of anthocyanins (54.83-97.38%), the hydrolysis of gallated catechins (34.80-47.81%) and flavonol glycosides (18.56-44.96%), and the subsequent accumulation of d-glucose (33.68-78.04%) and gallic acid (220.96-252.09%). For aroma, increase of total volatile metabolite content (23.88-25.44%) and key compounds like geraniol (239.32-275.21%) induced the changes. These results identified the positive effects of yellowing process on improvements in vine tea flavor and the key compounds that contribute to these changes.

Comprehensive analysis of the mitochondrial genome of Rehmannia glutinosa: insights into repeat-mediated recombinations and RNA editing-induced stop codon acquisition.

Rehmannia glutinosa is an economically significant medicinal plant. Yet, the structure and sequence of its mitochondrial genome has not been published, which plays a crucial role in evolutionary analysis and regulating respiratory-related macromolecule synthesis. In this study, the R. glutinosa mitogenome was sequenced employing a combination of Illumina short reads and Nanopore long reads, with subsequent assembly using a hybrid strategy. We found that the predominant configuration of the R. glutinosa mitogenome comprises two circular chromosomes. The primary structure of the mitogenome encompasses two mitochondrial chromosomes corresponding to the two major configurations, Mac1-1 and Mac1-2. The R. glutinosa mitogenome encoded an angiosperm-typical set of 24 core genes, nine variable genes, three rRNA genes, and 15 tRNA genes. A phylogenetic analysis using the 16 shared protein-coding genes (PCG) yielded a tree consistent with the phylogeny of Lamiales species and two outgroup taxa. Mapping RNA-seq data to the coding sequences (CDS) of the PCGs revealed 507 C-to-U RNA editing sites across 31 PCGs of the R. glutinosa mitogenome. Furthermore, one start codon (nad4L) and two stop codons (rpl10 and atp6) were identified as products of RNA editing events in the R. glutinosa mitogenome.

Comparative chloroplast genomes: insights into the identification and phylogeny of rapid radiation genus Rhodiola.

Rhodiola L. is a genus exhibiting rapid radiation and represents a typical case for studying plastid gene adaptation in species that spread from high altitudes to low altitudes. In this study, 23 samples of 18 Rhodiola species were collected from the Qinghai-Tibetan Plateau and five scattered alpine areas, and the plastid genomes (plastomes) of these species were sequenced, annotated, and compared between high-altitude and widely distributed groups. The plastomes of Rhodiola were found to be highly conserved in terms of gene size, content, and order but highly variable in several lineage-specific features, such as codon usage bias, IR boundary shifting, and distinct repeat sequence structures binding to SSRs. Codon usage in the genes of photosystem II exhibited an obvious preference, reflecting significant environmental adaptation pressures. In this study, three repeat regions compounded with trinucleotide and mononucleotide repeats were found for the first time in R. forrestii, R. himalensis, and R. yunnanensis. High-variability regions such as ndhF, ycf1, trnH-psbA, and rpoC1-rpoB were screened, laying the foundation for the precise identification of these species. The phylogenetic analysis revealed the occurrence of cyto-nuclear discordance, likely originating from the frequent interspecific hybridization events observed within Rhodiola species during rapid radiation. Dioecious and hermaphrodite species can be broadly categorized into two subclades, probably they have different environmental adaptation strategies in response to climate change. In addition, the phylogenetic tree supported the monophyly of R. forrestii and R. yunnanensis, which compose R. Sect. Pseudorhodiola. In conclusion, plastome data enrich the genetic information available for the Rhodiola genus and may provide insight into species migration events during climate change.

Linking coral fluorescence phenotypes to thermal bleaching in the reef-building Galaxea fascicularis from the northern South China Sea.

Coral fluorescence phenotypes have been suggested as an adaptation to a broad range of environmental conditions, yet the mechanisms linking thermal bleaching tolerance in reef-building coral populations, associated with fluorescence phenotypes due to GFP-like proteins, remains unclear. In this study, the relationship between the thermal sensitivity and phenotypic plasticity of corals was investigated using two phenotypes of Galaxea fascicularis, green and brown. The results reveal that brown G. fascicularis was more susceptible to bleaching than green G. fascicularis when exposed to a higher growth temperature of 32 °C. Both phenotypes of G. fascicularis were associated with the thermotolerant Symbiodiniaceae symbiont, Durusdinium trenchii. However, the brown G. fascicularis showed a significant decrease in Symbiodiniaceae cell density and a significant increase in pathogenic bacteria abundance when the growth temperature was raised from 29 to 32 °C. The physiological traits and transcriptomic profiles of Symbiodiniaceae were not notably affected, but there were differences in the transcriptional levels of certain genes between the two phenotype hosts of G. fascicularis. Under heat stress of 32 °C, the gene encoding green fluorescent protein (GFP)-like and chromosome-associated proteins, as well as genes related to oxidative phosphorylation, cell growth and death showed lower transcriptional levels in the brown G. fascicularis compared to the green G. fascicularis. Overall, the results demonstrate that the green form of G. fascicularis is better able to tolerate ocean warming and defend against pathogenic bacteria, likely due to higher gene transcription levels and defense ability. Supplementary Information: The online version contains supplementary material available at 10.1007/s42995-023-00190-1.

Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method.

The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.

The microbiome dynamics and interaction of endosymbiotic Symbiodiniaceae and fungi are associated with thermal bleaching susceptibility of coral holobionts.

The thermal bleaching percentage of coral holobionts shows interspecific differences under heat-stress conditions, which are closely related to the coral-associated microbiome. However, the ecological effects of community dynamics and interactions between Symbiodiniaceae and fungi on coral thermal bleaching susceptibility remain unclear. In this study, we analyzed the diversity, community structure, functions, and potential interaction of Symbiodiniaceae and fungi among 18 coral species from a high thermal bleaching risk atoll using next-generation sequencing. The results showed that heat-tolerant C3u sub-clade and Durusdinium dominated the Symbiodiniaceae community of corals and that there were no core amplicon sequence variants in the coral-associated fungal community. Fungal richness and the abundance of confirmed functional animal-plant pathogens were significantly positively correlated with the coral thermal bleaching percentage. Fungal indicators, including Didymellaceae, Chaetomiaceae, Schizophyllum, and Colletotrichum, were identified in corals. Each coral species had a complex Symbiodiniaceae-fungi interaction network (SFIN), which was driven by the dominant Symbiodiniaceae sub-clades. The SFINs of coral holobionts with low thermal bleaching susceptibility exhibited low complexity and high betweenness centrality. These results indicate that the extra heat tolerance of coral in Huangyan Island may be linked to the high abundance of heat-tolerant Symbiodiniaceae. Fungal communities have high interspecific flexibility, and the increase of fungal diversity and pathogen abundance was correlated with higher thermal bleaching susceptibility of corals. Moreover, fungal indicators were associated with the degrees of coral thermal bleaching susceptibility, including both high and intermediate levels. The topological properties of SFINs suggest that heat-tolerant coral have limited fungal parasitism and strong microbial network resilience.IMPORTANCEGlobal warming and enhanced marine heatwaves have led to a rapid decline in coral reef ecosystems worldwide. Several studies have focused on the impact of coral-associated microbiomes on thermal bleaching susceptibility in corals; however, the ecological functions and interactions between Symbiodiniaceae and fungi remain unclear. We investigated the microbiome dynamics and potential interactions of Symbiodiniaceae and fungi among 18 coral species in Huangyan Island. Our study found that the Symbiodiniaceae community of corals was mainly composed of heat-tolerant C3u sub-clade and Durusdinium. The increase in fungal diversity and pathogen abundance has close associations with higher coral thermal bleaching susceptibility. We first constructed an interaction network between Symbiodiniaceae and fungi in corals, which indicated that restricting fungal parasitism and strong interaction network resilience would promote heat acclimatization of corals. Accordingly, this study provides insights into the role of microorganisms and their interaction as drivers of interspecific differences in coral thermal bleaching.

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning.

A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.

Legacy and emerging per- and polyfluoroalkyl substances in the Shuidong bay of South China: Occurrence, partitioning behavior, and ecological risks.

With the phase-out of legacy per- and polyfluoroalkyl substances (PFASs), PFAS alternatives have been increasingly used in industrial production and daily life. However, available information on the occurrence of PFASs and PFAS alternatives in semi-enclosed bays remains limited. As a representative semi-enclosed bay in Guangdong Province, China, Shuidong Bay has experienced severe anthropogenic pollution (industrial, shipping, cultural, and domestic) in recent decades. Water pollution in Shuidong Bay has worsened, and PFASs have been identified as ubiquitous environmental pollutants in this bay. In this study, 23 PFASs, including 5 emerging PFASs, were analyzed in water, suspended particulate matter (SPM), and sediment samples collected from Shuidong Bay. We determined that perfluorobutanoic acid (PFBA) was the predominant PFAS compound in seawater, whereas 6:2 fluorotelomer sulfonic acid (FTS) and perfluorooctane sulfonamide acetate (FOSAA) were dominant in SPM and sediment, respectively. The sediment-water partitioning coefficients were greatly dependent on the perfluorinated carbon chain length. Chlorophyll a concentration had a significant effect on the dissolved concentrations of PFASs in seawater. The ecological risk assessment indicated that the PFASs detected in the seawater and sediment samples posed no considerable risks to aquatic organisms. This study provides a valuable reference for evaluating PFAS contamination in Shuidong Bay and conducting ecological risk assessments for aquatic organisms.

Evolutionary radiation and microbial community dynamics shape the thermal tolerance of Fungiidae in the southern South China Sea.

Fungiidae have shown increased thermal adaptability in coral reef ecosystems under global warming. This study analyzes the evolutionary divergence and microbial communities of Fungiidae in the Sanjiao Reef of the southern South China Sea and explores the impact of coral evolution radiation and microbial dynamics on the heat tolerance of Fungiidae. The results found that Cycloseris was an ancient branch of Fungiidae, dating back approximately 147.8953 Mya, and Fungiidae differentiated into two ancestral clades (clades I and II) before 107.0312 Ma. Fungiidae exhibited specific symbioses with the Cladocopium C27 sub-clade. Notably, the Cladocopium C1 sub-clade has a high relative abundance in clade I, whereas the heat-tolerant Cladocopium C40 and C3u sub-clades subdominante in clade II. Regarding bacterial communities, Cycloseris costulata, the earliest divergent species, had higher bacterial ß-diversity, while the latest divergent species, Lithophyllon scabra, displayed lower bacterial α-diversity and higher community stability. Beneficial bacteria dominante Fungiidae's bacterial community (54%). The co-occurrence network revealed that microbial networks in clade II exhibited lower complexity and greater resilience than those in clade I. Our study highlights that host evolutionary radiation and microbial communities shaped Fungiidae's thermal tolerance. The variability in subdominant Symbiodiniaceae populations may contribute to interspecific differences in thermal tolerance along the evolutionary branches of Fungiidae. The presence of abundant beneficial bacteria may further enhance the thermal ability of the Fungiidae. Furthermore, the later divergent species of Fungiidae have stronger heat tolerance, possibly driven by the increased regulation ability of the host on the bacterial community, greater microbial community stability, and interaction network resistance.IMPORTANCECoral reefs are facing significant threats due to global warming. The heat tolerance of coral holobionts depends on both the coral host and its microbiome. However, the association between coral evolutionary radiation and interspecific differences in microbial communities remains unclear. In this study, we investigated the role of evolutionary radiation and microbial community dynamics in shaping the thermal acclimation potential of Fungiidae in the Sanjiao Reef of the southern South China Sea. The study's results suggest that evolutionary radiation enhances the thermal tolerance of Fungiidae. Fungiidae species that have diverged more recently have exhibited a higher presence of heat-tolerant Symbiodiniaceae taxa, more stable bacterial communities, and a robust and resilient microbial interaction network, improving the thermal adaptability of Fungiidae. In summary, this study provides new insights into the thermal adaptation patterns of corals under global warming conditions.

The effect of vine tea (Ampelopsis grossedentata) extract on fatigue alleviation via improving muscle mass.

ETHNOPHARMACOLOGICAL RELEVANCE: Vine Tea (VT, Ampelopsis grossedentata), boasts a venerable tradition in China, with a recorded consumption history exceeding 1200 years. Predominantly utilized by ethnic groups in southwest China, this herbal tea is celebrated for its multifaceted therapeutic attributes. Traditionally, VT has been employed to alleviate heat and remove toxins, exhibit anti-inflammatory properties, soothe sore throats, lower blood pressure, and fortify bones and muscles. In the realm of functional foods derived from plant resources, VT has garnered attention for its potential in crafting anti-fatigue beverages or foods, attributed to its promising efficacy and minimal side effects. Currently, in accordance with the Food Safety Standards set forth by the Monitoring and Evaluation Department of the National Health and Family Planning Commission in China, VT serves as a raw material in various beverages. AIM OF THE STUDY: VT has an anti-fatigue or similar effect in folk. However, the underlying molecular mechanisms contributing to VT's anti-fatigue effects remain elusive. This study endeavors to investigate the influence of Vine Tea Aqueous Extract (VTE) on fatigue mitigation and to elucidate its operative mechanisms, with the objective of developing VTE as a functional beverage. MATERIALS AND METHODS: The preparation of VTE involved heat extraction and freeze-drying processes, followed by the identification of its metabolites using UPLC-QTOF-MS to ascertain the chemical composition of VTE. A fatigue model was established using a forced swimming test in mice. Potential molecular targets were identified through network pharmacology, transcriptome analysis, and molecular docking. Furthermore, RT-PCR and Western blot techniques were employed to assess mRNA and protein expressions related to the AMPK and FoxO pathways. RESULTS: VTE significantly prolonged the duration of swimming time in an exhaustive swimming test in a dose-dependent manner, while simultaneously reducing the concentrations of blood lactic acid (LA), lactate dehydrogenase (LDH), serum urea nitrogen (SUN), and creatine kinase (CK). Notably, the performance of the high-dose VTE group surpassed that of the well-recognized ginsenoside. VTE demonstrated a regulatory effect akin to ginsenoside on the AMPK energy metabolism pathway and induced downregulation in the expression of Gadd45α, Cdkn1a, FOXO1, and Fbxo32 genes, suggesting an enhancement in skeletal muscle mass. These findings indicate that VTE can improve energy metabolism and muscle mass concurrently. CONCLUSIONS: VTE exhibits significant anti-fatigue effects, and its mechanism is intricately linked to the modulation of the AMPK and FoxO pathways. Crucially, no caffeine or other addictive substances with known side effects were detected in VTE. Consequently, vine tea shows substantial promise as a natural resource for the development of anti-fatigue beverages within the food industry.

Measuring the intensive care experience of intensive care unit patients: A cross-sectional study in western China.

BACKGROUND: The negative experiences of intensive care unit (ICU) patients seriously affect their quality of life and survival outcomes. Thus, it is of great significance to evaluate the monitoring experience of ICU patients for the clinical improvement of their experiences and promote interventions. OBJECTIVES: The objective of this study was to investigate patients' experiences of ICU and to understand the sources of patient experience and influencing factors. METHODS: From November 2021 to September 2022, a cross-sectional survey was conducted with 600 inpatients from four grade A-III hospitals in western China. Data were collected using the Chinese version of the Intensive Care Experience Questionnaire. RESULTS: 585 valid questionnaires were collected, the response rate was 97.5%. ICU patients in western China scored below-the-average for their intensive care experience. Family monthly income, occupation types, medical payment method, type of ICU, ICU admission plan, ICU admission times, mechanical ventilation use, fertility status, analgesia, sedation, and Acute Physiology and Chronic Health Evaluation II scores are important factors influencing ICU patients' intensive care experience. CONCLUSIONS: Medical staff need to pay attention to patient experience, improve the awareness of patient stressors and influencing factors, design nursing programs conducive to patient-positive experience, and promote interventions to further improve the long-term prognosis of patients. The results of this study can also be used as a set of nursing-sensitive indicators for evaluating nursing structure, process, and outcomes.

Porous Carbonaceous Aerogels Composed of Multiscale Carbon-Based Units for High-Performance Microwave Absorption.

Structural engineering is definitely a promising and effective approach to develop excellent microwave absorbing materials with quantities of advantages. Especially, when carbon materials act as the constituents, the fabricated absorbers are available to gain more prominent absorption performance. However, extra high conductivities and the widespread aggregations and stacking of low-dimensional carbon materials always detrimentally affect the impedance matching and weaken the attenuation capacity, inevitably confining their further absorption applications. Herein, by introducing the amorphous chiral carbon nanocoils to overcome the challenges and achieve the strategies of structure optimization and multicomponent recombination, the reduced graphene oxide/carbon nanocoil/carbon nanotube aerogels were successfully synthesized by a successive hydrothermal method and freeze-drying strategy. The as-obtained aerogels possess a porous architecture that contribute to the extraordinary impedance matching and multiple reflections, which integrate the multifarious dielectric loss mechanisms of diverse carbon materials simultaneously. Benefiting from the tricomponent synergistic effect, the ultralight aerogels reach an outstanding microwave absorption property with an extremely low filler content of only 6 wt %. This work provides a helpful approach to design hierarchical absorbers consisted by multidimensional carbon materials for fantastic microwave absorption.

Site-selected in situ polymerization for living cell surface engineering.

The construction of polymer-based mimicry on cell surface to manipulate cell behaviors and functions offers promising prospects in the field of biotechnology and cell therapy. However, precise control of polymer grafting sites is essential to successful implementation of biomimicry and functional modulation, which has been overlooked by most current research. Herein, we report a biological site-selected, in situ controlled radical polymerization platform for living cell surface engineering. The method utilizes metabolic labeling techniques to confine the growth sites of polymers and designs a Fenton-RAFT polymerization technique with cytocompatibility. Polymers grown at different sites (glycans, proteins, lipids) have different membrane retention time and exhibit differential effects on the recognition behaviors of cellular glycans. Of particular importance is the achievement of in situ copolymerization of glycomonomers on the outermost natural glycan sites of cell membrane, building a biomimetic glycocalyx with distinct recognition properties.

Unfolding the secrets of microbiome (Symbiodiniaceae and bacteria) in cold-water coral.

Recent deep-ocean exploration has uncovered a variety of cold-water coral (CWC) ecosystems around the world ocean, but it remains unclear how microbiome is associated with these corals at a molecular levels. This study utilized metabarcoding, tissue section observation, and metatranscriptomes to investigate the microbiome (Symbiodiniaceae and bacteria) of CWC species (Narella versluysi, Heterogorgia uatumani, and Muriceides sp.) from depths ranging from 260 m to 370 m. Warm-water coral (WWC) species (Acropora pruinosa, Pocillopora damicornis, and Galaxea fascicularis) were used as control groups. Results revealed that CWC host diverse bacteria and Symbiodiniaceae cells were observed in endoderm of CWC tissues. Several new candidate bacterial phyla were found in both CWC and WWC, including Coralsanbacteria, Coralqiangbacteria, Coralgsqaceae, Coralgongineae, etc. Both the 16S rRNA gene sequencing and metatranscriptomes revealed that Actinobacteria and Proteobacteria were abundant bacterial phyla in CWC. At the gene transcription level, the CWC-associated Symbiodiniaceae community showed a low-level transcription of genes involved in photosynthesis, CO2 fixation, glycolysis, citric acid cycle, while bacteria associated with CWC exhibited a high-level transcription of genes for carbon fixation via the Wood-Lijungdahl pathway, short chain fatty acids production, nitrogen, and sulfur cycles. IMPORTANCE This study shed new light on the functions of both Symbiodiniaceae and bacteria in cold-water coral (CWC). The results demonstrated that Symbiodiniaceae can survive and actively transcribe genes in CWC, suggesting a possible symbiotic or parasitic relationship with the host. This study also revealed complete non-photosynthetic CO2 fixation pathway of bacteria in CWC, as well as their roles in short chain fatty acids production and assimilation of host-derived organic nitrogen and sulfur. These findings highlight the important role of bacteria in the carbon, nitrogen sulfur cycles in CWC, which were possibly crucial for CWC survival in in deep-water environments.

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family.

Introduction: Insect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy. Methods: To address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters. Results: Experimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%. Discussion: Our model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment.

Surface Enhancement Effects of Tiny SnO2 Nanoparticle Modification on α-Fe2O3 for Room-Temperature NH3 Sensing.

The development of a gas sensor capable of detecting ammonia with high selectivity and rapid response at room temperature has consistently posed a formidable challenge. To address this issue, the present study utilized a one-step solvothermal method to co-assemble α-Fe2O3 and SnO2 by evenly covering SnO2 nanoparticles on the surface of α-Fe2O3. By controlling the morphology and Fe/Sn mole ratio of the composite, the as-prepared sample exhibits high-performance detection of NH3. At room temperature conditions, a gas sensor composed of α-Fe2O3@3%SnO2 demonstrates a rapid response time of 14 s and a notable sensitivity of 83.9% when detecting 100 ppm ammonia. Experiments and density functional theory (DFT) calculations suggest that the adsorption capacity of α-Fe2O3 to ammonia is enhanced by the surface effect provided by SnO2. Meanwhile, the existence of SnO2 tailors the pore structure and effective surface area of α-Fe2O3, creating multiple channels for the diffusion and adsorption of ammonia molecules. Additionally, an N-N heterostructure is formed between α-Fe2O3 and SnO2, which enhances the potential energy barrier and improves the ammonia sensing performance. Demonstration experiments have proved that the sensor shows significant advantages over commercial sensors in the process of ammonia detection in agricultural facilities. This work provides new insights into the perspectives on ammonia detection at room temperature.