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Abstract Inspired by the success of large language models (LLMs), we develop a long-context generative model for genomes. Our multiscale transformer model, megaDNA, is pre-trained on unannotated bacteriophage genomes with nucleotide-level tokenization. We demonstrate the foundational capabilities of our model including the prediction of essential genes, genetic variant effects, regulatory element activity and taxonomy of unannotated sequences. Furthermore, it generates de novo sequences up to 96 K base pairs, which contain potential regulatory elements and annotated proteins with phage-related functions.

Abstract Constructing powerful anode/cathode interphases by modulate ion solvation structure is the principle of electrolyte design. However, the methodological and theoretical design principles of electrolyte/solvation structure and their effect on electrochemical performance are still vague. Here, we propose a cationic weakly coordinating-intervention strategy for modulating the Na+ solvation sheathes and constructing robust anode/cathode interphases in sodium-metal batteries. Unlike the local highly concentrated electrolytes, 1,2-difluorobenzene can weakly coordinate with Na+ thus transforming the solvation structure into Na+-anion-incorporated structures and strengthening anode/cathode interphases formation by combining with salt decomposition. Furthermore, the correlations between the electrode interface properties and solvation structure are revealed, which can be tuned by the weakly coordination. Ultimately, the modulated electrolyte achieves 97.5% Coulombic efficiency for 600 cycles in Na‖Cu cells at 1 mA cm−2 and a beneficial lifetime (2500 h) in Na‖Na cells. Meanwhile, Na‖PB cells have achieved long-term operation at 4.8 V, along with operation at wide temperatures.

Abstract Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurements of translation at the genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts in these data and identify sequence determinants of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. When trained on an unbiased dataset, Riboformer corrects experimental artifacts in previously unseen datasets, which reveals subtle differences in synonymous codon translation and uncovers a bottleneck in translation elongation. Further, we show that Riboformer can be combined with in silico mutagenesis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.

Abstract As the core component of solid-state batteries, neither current inorganic solid-state electrolytes nor solid polymer electrolytes can simultaneously possess satisfactory ionic conductivity, electrode compatibility and processability. By incorporating efficient Li+ diffusion channels found in inorganic solid-state electrolytes and polar functional groups present in solid polymer electrolytes, it is conceivable to design inorganic-organic hybrid solid-state electrolytes to achieve true fusion and synergy in performance. Herein, we demonstrate that traditional metal coordination compounds can serve as exceptional Li+ ion conductors at room temperature through rational structural design. Specifically, we synthesize copper maleate hydrate nanoflakes via bottom-up self-assembly featuring highly-ordered 1D channels that are interconnected by Cu2+/Cu+ nodes and maleic acid ligands, alongside rich COO− groups and structural water within the channels. Benefiting from the combination of ion-hopping and coupling-dissociation mechanisms, Li+ ions can preferably transport through these channels rapidly. Thus, the Li+-implanted copper maleate hydrate solid-state electrolytes shows remarkable ionic conductivity (1.17 × 10−4 S cm−1 at room temperature), high Li+ transference number (0.77), and a 4.7 V-wide operating window. More impressively, Li+-implanted copper maleate hydrate solid-state electrolytes are demonstrated to have exceptional compatibility with both cathode and Li anode, enabling long-term stability of more than 800 cycles. This work brings new insight on exploring superior room-temperature ionic conductors based on metal coordination compounds.

Leaf Trait Networks (LTNs) visually display the intricate trait connections in offering insights into how plants adapt to diverse environments. The pervasive environmental heterogeneity can influence plant diversity and functional traits. Various habitats sustain distinct species diversity and leaf trait differentiation, precisely as the degraded karst-heterogenous ecosystem caused by inhomogeneous microhabitats. However, how the LTNs respond to understory habitat heterogeneity and species diversity in degraded karst forests remains unclear. This study selected fifty-three forest plots for plant community survey and habitat investigation in a typical karst topography to visualize LTNs using twenty-one woody leaf traits. LTN architecture variations were analyzed in relation to habitat heterogeneity and species diversity through network-architectural parameters, including edge density, diameter, average path length, and average clustering coefficient, utilizing regression, principal component analysis, and structural equation model. The results showed a 35% variation in modularity across all plots, indicating distinct network architectural modularization differentiation. Network diameter and average path length displayed significant initial decreases followed by increases with ascending PC1 heterogeneity scores, indicating moderate heterogeneity sustained greater network connectivity. In addition, the network diameter, average path length, and average clustering coefficient significantly decreased with increasing species diversity, indicating that high species diversity enables greater connectivity and complexity. Moreover, the habitat heterogeneity significantly affected species diversity directly and affected the average clustering coefficient, diameter, average path length, and edge density indirectly through species diversity effects via SEM pathway analysis. Overall, habitat heterogeneity and woody plant diversity of karst forests significantly influence LTN architectures in degraded karst forests. In conclusion, moderate heterogeneity sustains greater network connectivity while species diversity jointly enhances connectivity and complexity by combining modular regulation of plant functional traits.

Abstract Electron transfer kinetics at the single‐layer graphene/ionic liquids interfaces have been examined for different common imidazolium‐based ionic liquids and ferrocene derivatives. The heterogeneous kinetics are characterized by slow standard charge transfer rates and an unusual low variation of the rate with the applied potential corresponding to charge transfer coefficients lower than 0.1. This behavior is specific to the electrochemistry on a single‐layer graphene as we found that the oxidation of the same substituted ferrocenes in the same ionic liquids follows the classical behavior predicted by the Butler‐Volmer law.

Abstract Macrophages play an important role as effector cells in innate immune system. Meanwhile, macrophages activated in a pro-inflammatory direction alter intracellular metabolism and damage intact tissues by increasing reactive oxygen species (ROS). Electrical stimulation (ES), a predominant physical agent to control metabolism in cells and tissues, has been reported to exert anti-inflammatory effect on immune cells. However, the mechanism underlying the anti-inflammatory effects by ES is unknown. This study aimed to investigate the effect of ES on metabolism in glycolytic-tricarboxylic acid cycle (TCA) cycle and inflammatory responses in macrophages. ES was performed on bone marrow-derived macrophages and followed by a stimulation with LPS. The inflammatory cytokine expression levels were analyzed by real-time polymerase chain reaction and ELISA. ROS production was analyzed by CellRox Green Reagent and metabolites by capillary electrophoresis-mass spectrometry. As a result, ES significantly reduced proinflammatory cytokine expression levels and ROS generation compared to the LPS group and increased glucose-1-phosphate, a metabolite of glycogen. ES also increased intermediate metabolites of the pentose phosphate pathway (PPP); ribulose-5-phosphate, rebose-5 phosphate, and nicotinamide adenine dinucleotide phosphate, a key factor of cellular antioxidation systems, as well as α-Ketoglutarate, an anti-oxidative metabolite in the TCA cycle. Our findings imply that ES enhanced NADPH production with enhancement of PPP, and also decreased oxidative stress and inflammatory responses in macrophages.

Transmission measurements of the soft X-ray beamline to the Small Quantum Systems (SQS) scientific instrument at the SASE3 undulator of European XFEL are presented. Measurements are reported for a wide range of photon energies (650 eV to 2400 eV), using X-ray gas monitors as well as a bolometric radiometer. The results are in good agreement with simulations for the beam transport and show a transmission of up to 80% over the whole photon energy range. The contribution of second- and third-harmonic radiation of the soft X-ray undulator is determined at selected photon energies by performing transmission measurements using a gas absorber to provide variable attenuation of the incoming photon flux. A comparison of the results with semi-analytic calculations for the generation of free-electron laser pulses in the SASE3 undulator reveals an influence of apertures along the beam transport on the exact harmonic content to be accounted for at the experiment. The second-harmonic content is measured to be in the range of 0.1% to 0.3%, while the third-harmonic contributed a few percent to the SASE3 emission. For experiments at the SQS instrument, these numbers can be reduced through specific selections of the mirror reflection angles.

Leaf trait networks (LTNs) visualize the intricate linkages reflecting plant trait-functional coordination. Typical karst vegetation, developed from lithological dolomite and limestone, generally exhibits differential communities, possibly due to habitat rock exposure, soil depth, and soil physicochemical properties variations, leading to a shift from plant trait variation to functional linkages. However, how soil and habitat quality affect the differentiation of leaf trait networks remains unclear. LTNs were constructed for typical dolomite and limestone habitats by analyzing twenty-one woody plant leaf traits across fifty-six forest subplots in karst mountains. The differences between dolomite and limestone LTNs were compared using network parameters. The network association of soil and habitat quality was analyzed using redundancy analysis (RDA), Mantle's test, and a random forest model. The limestone LTN exhibited significantly higher edge density with lower diameter and average path length when compared to the dolomite LTN. It indicates LTN differentiation, with the limestone network displaying a more compact architecture and higher connectivity than the dolomite network. The specific leaf phosphorus and leaf nitrogen contents of dolomite LTN, as well as the leaf mass and leaf carbon contents of limestone LTN, significantly contributed to network degree and closeness, serving as crucial node traits regulating LTN connectedness. Additionally, both habitat LTNs significantly correlated with soil nitrogen and phosphorus, stoichiometric ratios, pH, and organic carbon, as well as soil depth and rock exposure rates, with soil depth and rock exposure showing greater relative importance. Soil depth and rock exposure dominate trait network differentiation, with the limestone habitat exhibiting a more compact network architecture than the dolomite habitat.

To achieve a stable size distribution over multiple generations, proliferating cells require a means of counteracting stochastic noise in the rate of growth, the time spent in various phases of the cell cycle, and the imprecision in the placement of the plane of cell division. In the most widely accepted model, cell size is thought to be regulated at the G1/S transition, such that cells smaller than a critical size pause at the end of G1 phase until they have accumulated mass to a predetermined size threshold, at which point the cells proceed through the rest of the cell cycle. However, a model, based solely on a specific size checkpoint at G1/S, cannot readily explain why cells with deficient G1/S control mechanisms are still able to maintain a very stable cell size distribution. Furthermore, such a model would not easily account for stochastic variation in cell size during the subsequent phases of the cell cycle, which cannot be anticipated at G1/S. To address such questions, we applied computationally enhanced quantitative phase microscopy (ceQPM) to populations of cultured human cell lines, which enables highly accurate measurement of cell dry mass of individual cells throughout the cell cycle. From these measurements, we have evaluated the factors that contribute to maintaining cell mass homeostasis at any point in the cell cycle. Our findings reveal that cell mass homeostasis is accurately maintained, despite disruptions to the normal G1/S machinery or perturbations in the rate of cell growth. Control of cell mass is generally not confined to regulation of the G1 length. Instead mass homeostasis is imposed throughout the cell cycle. In the cell lines examined, we find that the coefficient of variation (CV) in dry mass of cells in the population begins to decline well before the G1/S transition and continues to decline throughout S and G2 phases. Among the different cell types tested, the detailed response of cell growth rate to cell mass differs. However, in general, when it falls below that for exponential growth, the natural increase in the CV of cell mass is effectively constrained. We find that both mass-dependent cell cycle regulation and mass-dependent growth rate modulation contribute to reducing cell mass variation within the population. Through the interplay and coordination of these 2 processes, accurate cell mass homeostasis emerges. Such findings reveal previously unappreciated and very general principles of cell size control in proliferating cells. These same regulatory processes might also be operative in terminally differentiated cells. Further quantitative dynamical studies should lead to a better understanding of the underlying molecular mechanisms of cell size control.

The regulation of inflammatory responses is an important intervention in biological function and macrophages play an essential role during inflammation. Skeletal muscle is the largest organ in the human body and releases various factors which mediate anti-inflammatory/immune modulatory effects. Recently, the roles of extracellular vesicles (EVs) from a large variety of cells are reported. In particular, EVs released from skeletal muscle are attracting attention due to their therapeutic effects on dysfunctional organs and tissues. Also, ultrasound (US) promotes release of EVs from skeletal muscle. In this study, we investigated the output parameters and mechanisms of US-induced EV release enhancement and the potential of US-treated skeletal muscle-derived EVs in the regulation of inflammatory responses in macrophages. High-intensity US (3.0 W/cm2) irradiation increased EV secretion from C2C12 murine muscle cells via elevating intracellular Ca2+ level without negative effects. Moreover, US-induced EVs suppressed expression levels of pro-inflammatory factors in macrophages. miRNA sequencing analysis revealed that miR-206-3p and miR-378a-3p were especially abundant in skeletal myotube-derived EVs. In this study we demonstrated that high-intensity US promotes the release of anti-inflammatory EVs from skeletal myotubes and exert anti-inflammatory effects on macrophages.

Abstract Background Detecting and counting wheat spikes is essential for predicting and measuring wheat yield. However, current wheat spike detection researches often directly apply the new network structure. There are few studies that can combine the prior knowledge of wheat spike size characteristics to design a suitable wheat spike detection model. It remains unclear whether the complex detection layers of the network play their intended role. Results This study proposes an interpretive analysis method for quantitatively evaluating the role of three-scale detection layers in a deep learning-based wheat spike detection model. The attention scores in each detection layer of the YOLOv5 network are calculated using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, which compares the prior labeled wheat spike bounding boxes with the attention areas of the network. By refining the multi-scale detection layers using the attention scores, a better wheat spike detection network is obtained. The experiments on the Global Wheat Head Detection (GWHD) dataset show that the large-scale detection layer performs poorly, while the medium-scale detection layer performs best among the three-scale detection layers. Consequently, the large-scale detection layer is removed, a micro-scale detection layer is added, and the feature extraction ability in the medium-scale detection layer is enhanced. The refined model increases the detection accuracy and reduces the network complexity by decreasing the network parameters. Conclusion The proposed interpretive analysis method to evaluate the contribution of different detection layers in the wheat spike detection network and provide a correct network improvement scheme. The findings of this study will offer a useful reference for future applications of deep network refinement in this field.

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient training, slow convergence, and infrequent rewards. To tackle these challenges, this study introduces an improved two-delay depth deterministic policy gradient algorithm (LP-TD3) for local planning navigation. Initially, the integration of the long–short-term memory (LSTM) module with the Prioritized Experience Re-play (PER) mechanism into the existing TD3 framework was performed to optimize training and improve the efficiency of experience data utilization. Furthermore, the incorporation of an Intrinsic Curiosity Module (ICM) merges intrinsic with extrinsic rewards to tackle sparse reward problems and enhance exploratory behavior. Experimental evaluations using ROS and Gazebo simulators demonstrate that the proposed method outperforms the original on various performance metrics.

Water-in-salt electrolytes can be useful for future electrochemical energy storage systems. Here, the authors investigate the potential-dependent double-layer structures at the interface between a gold electrode and a highly concentrated aqueous electrolyte solution via in situ Raman measurements.

Abstract Ionic Liquids (ILs) and deep eutectic solvents (DESs) are promising candidate electrolytes in electrochemical fields due to their excellent properties. They can absorb water from the environment quickly, the existence of water in ILs/DESs benefits or harms their performance depending on the purpose of the applications. Therefore, studies on the effect of water on the properties of ILs/DESs have received much attention in recent years. This mini‐review provides an overview of the structure of the electrochemical interface in ILs/DESs incorporated with water by summarizing the information acquired from a variety of characterization technologies and simulations. Both our understanding of the interfacial structure and our perspective on further research in the field are presented.

Previous studies recognized three major lineages of the family Costaceae: a South American clade, an Asian clade and a Costus clade. However, the genus Hellenia within the Asian clade has been shown to be non-monophyletic and its morphology has not been studied carefully. Therefore, the complete plastid genomes of Hellenia species were obtained and the monophyly of Hellenia was tested through four different datasets in this study. Plastid phylogenomic analyses of Costaceae revealed that Hellenia is strongly supported as paraphyletic. Two major clades are recovered, namely the Hellenia s.s. subclade and the Parahellenia subclade. Phylogenetic analyses based on an enlarged taxon sampling of the Asian clade using a two chloroplast markers dataset (trnK intron and trnL-F spacer) confirmed the paraphyly of Hellenia. Meanwhile, morphological analyses suggested that members of the Parahellenia subclade differ from the remaining Hellenia species in many characters including inflorescences, bracts, stigma, axillary buds, floral tubes and labellum. According to the present molecular and morphological evidence, the latter subclade is recognized as a new genus, Parahellenia. Two new species are described, four new combinations are made, and identification keys are also provided.

IntroductionMacrophages play an important role in the innate immunity. While macrophage inflammation is necessary for biological defense, it must be appropriately controlled. Extracellular vesicles (EVs) are small vesicles released from all types of cells and play a central role in intercellular communication. Skeletal muscle has been suggested to release anti-inflammatory factors, but the effect of myotube-derived EVs on macrophages is unknown. As an anti-inflammatory mechanism of macrophages, the immune responsive gene 1 (IRG1)-itaconate pathway is essential. In this study, we show that skeletal muscle-derived EVs suppress macrophage inflammatory responses, upregulating the IRG1-itaconate pathway.MethodsC2C12 myoblasts were differentiated into myotubes and EVs were extracted by ultracentrifugation. Skeletal myotube-derived EVs were administered to mouse bone marrow-derived macrophages, then lipopolysaccharide (LPS) stimulation was performed and inflammatory cytokine expression was measured by RT-qPCR. Metabolite abundance in macrophages after addition of EVs was measured by CE/MS, and IRG1 expression was measured by RT-PCR. Furthermore, RNA-seq analysis was performed on macrophages after EV treatment.ResultsEVs attenuated the expression of LPS-induced pro-inflammatory factors in macrophages. Itaconate abundance and IRG1 expression were significantly increased in the EV-treated group. RNA-seq analysis revealed activation of the PI3K-Akt and JAK-STAT pathways in macrophages after EV treatment. The most abundant miRNA in myotube EVs was miR-206-3p, followed by miR-378a-3p, miR-30d-5p, and miR-21a-5p.DiscussionSkeletal myotube EVs are supposed to increase the production of itaconate via upregulation of IRG1 expression and exhibited an anti-inflammatory effect in macrophages. This anti-inflammatory effect was suggested to involve the PI3K-Akt and JAK-STAT pathways. The miRNA profiles within EVs implied that miR-206-3p, miR-378a-3p, miR-30d-5p, and miR-21a-5p may be responsible for the anti-inflammatory effects of the EVs. In summary, in this study we showed that myotube-derived EVs prevent macrophage inflammatory responses by activating the IRG1-itaconate pathway.

Variational auto-encoders (VAE) have been widely used in process modeling due to the ability of deep feature extraction and noise robustness. However, the construction of a supervised VAE model still faces huge challenges. The data generated by the existing supervised VAE models are unstable and uncontrollable due to random resampling in the latent subspace, meaning the performance of prediction is greatly weakened. In this paper, a new multi-layer conditional variational auto-encoder (M-CVAE) is constructed by injecting label information into the latent subspace to control the output data generated towards the direction of the actual value. Furthermore, the label information is also used as the input with process variables in order to strengthen the correlation between input and output. Finally, a neural network layer is embedded in the encoder of the model to achieve online quality prediction. The superiority and effectiveness of the proposed method are demonstrated by two real industrial process cases that are compared with other methods.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

Karst ecosystems represent a typical heterogeneous habitat, and it is ubiquitous with varying interactive patches of rock and soil associated with differential weathering patterns of carbonate rocks. Arbuscular mycorrhizae fungi (AMF) play an important role in regulating plant growth and nutrition in heterogeneous karst habitats. However, it remains unclear how AMF affects the growth and nutrition of plants in heterogeneous karst soil with varying patches and weathering gravel. A heterogeneous experiment with Bidens pilosa L. was conducted in a grid microcosm through patching karst soil with different gravel contents. The experimental treatments included the AMF treatments inoculated with (M+) or without (M-) fungus Glomus etunicatum; the substrate patchiness treatments involved different sizes of the homogeneous patch (Homo), the heterogeneous large patch (Hetl), and the heterogeneous small patch (Hets); the substrate gravel treatments in the inner patch involved the free gravel (FG), the low gravel (LG) 20% in 80% soil, and the high gravel (HG) 40% in 60% soil. Plant traits related to growth and nutrients were analyzed by comparing substrate gravel content and patch size. The results showed that AMF was more beneficial in increasing the aboveground biomass of B. pilosa under the LG and HG substrates with a higher root mycorrhizal colonization rate than under the FG substrate with a lower root mycorrhizal colonization rate. AMF enhanced higher growth and nutrients for B. pilosa under the LG and HG substrates than under the FG substrate and under the Hets than under the Hetl. Moreover, AMF alleviated the limited supply of N for B. pilosa under all heterogeneous treatments. Furthermore, the response ratio LnRR of B. pilosa presented that the substrate gravel promoted the highest growth, N and P absorption than the substrate patchiness with M+ treatment, and the gravel content had a more effect on plant growth and nutrition as compared to the patch size. Overall, this study suggests that plant growth and nutrition regulated by AMF mainly depend on the substrate gravel content rather than the spatial patchiness in the heterogeneous karst habitat.

To address the problems of low productivity and sizeable dispensing positioning errors in manual and semi-automatic dispensing processes in small- and medium-sized electronic enterprises, this study proposes a fully automatic dispensing method based on visual positioning with RJDNEL-type PCBs as the research object. The fully automatic dispensing system is constructed through the construction of a mechanical structure, the selection of optical equipment, and the debugging of the control system. The method is based on optical imaging technology. Firstly, the dispensing area is extracted through image preprocessing; then, the edge is detected by the Sobel operator, and the Hilditch operator optimizes the dispensing sharpness; then, the minimum outer rectangle algorithm is used to calculate the positioning information of the dispensing area by using the relationship between geometric transformations, which provides the database support for the realization of automatic dispensing; finally, the simulated annealing algorithm is adopted to optimize the dispensing path. Through the image acquisition matching experiments and positioning accuracy experimental analysis, it is concluded that the matching success rate is more than 99%, and the image positioning information repeated extraction accuracy error is less than 0.02 mm. Analysis of the total path of dispensing, as well as computing time, was performed as follows: for the complete dispensing process, 20 PCB boards were used; the path optimization of the dispensing path was reduced by 723.4 mm, and the dispensing efficiency was improved by 20% to 30%; after dispensing, the dispensing area measurement was performed on the PCB boards. PCB boards meet the quality requirements of dispensing, and the proposed method for meeting the quality of dispensing at the same time effectively improves the dispensing accuracy and efficiency.

The characteristics of flexible production lines, i.e., “multiple steps and few processes”, increase the complexity of the process and the difficulty of process quality control, but are not conducive for improving the quality and efficiency of a production line. In this study, we use a flexible production line processing spindle box as the research object. Using an extensive data analysis method, we study the key influencing factors of process quality and the prediction and optimization of process quality characteristics, aiming to accurately predict the machining accuracy of flexible production line processes and to achieve efficient quality control. A fuzzy hierarchical analysis-based impact factor model is developed to obtain a process quality impact factor model consistent with the spindle box of a production line. By verifying the prediction accuracy of 24 sets of quality spindle bore data, a prediction model with a relative error of less than 0.01 is obtained, which provides a prediction sample for analyzing potential problems of process quality in a production line. The SPC control principle is used to monitor process quality by using the standard control method, and the change trends between the actual and predicted values of quality characteristics are compared to achieve predictive control of the process quality. The product qualification rate of this control scheme under this monitoring method is 96%.

Plant invasion has severely damaged ecosystem stability and species diversity worldwide. The cooperation between arbuscular mycorrhizal fungi (AMF) and plant roots is often affected by changes in the external environment. Exogenous phosphorus (P) addition can alter the root absorption of soil resources, thus regulating the root growth and development of exotic and native plants. However, it remains unclear how exogenous P addition regulates the root growth and development of exotic and native plants mediated by AMF, affecting the exotic plant invasion. In this experiment, the invasive plant Eupatorium adenophorum and native plant Eupatorium lindleyanum were selected and cultured under intraspecific (Intra-) competition and interspecific (Inter-) competition conditions, involving inoculation with (M+) and without AMF (M−) and three different levels of P addition including no addition (P0), addition with 15 mg P kg−1 soil (P15), and addition with 25 mg P kg−1 soil (P25) for the two species. Root traits of the two species were analyzed to study the response of the two species’ roots to AMF inoculation and P addition. The results showed that AMF significantly promoted the root biomass, length, surface area, volume, tips, branching points, and carbon (C), nitrogen (N), and P accumulation of the two species. Under M+ treatment, the Inter- competition decreased the root growth and nutrient accumulation of invasive E. adenophorum but increased the root growth and nutrient accumulation of native E. lindleyanum relative to the Intra- competition. Meanwhile, the exotic and native plants responded differently to P addition, exhibiting root growth and nutrient accumulation of invasive E. adenophorum increased with P addition, whereas native E. lindleyanum reduced with P addition. Further, the root growth and nutrition accumulation of native E. lindleyanum were higher than invasive E. adenophorum under Inter- competition. In conclusion, exogenous P addition promoted the invasive plant but reduced the native plant in root growth and nutrient accumulation regulated by AMF, although the native plant outcompeted the invasive plant when the two species competed. The findings provide a critical perspective that the anthropogenic P fertilizer addition might potentially contribute to the successful invasion of exotic plants.

An excellent high-temperature-resistant scale inhibitor, polyaspartic acid-prolineamide (PASP-Pro), was synthesized by polysuccinimide (PSI) and L-prolineamide (L-Pro), and then characterized by 1H-NMR and FTIR analysis. The inhibition performance of PASP-Pro on CaCO3 precipitation was studied at different temperatures through static tests; at the same time, the influence of PASP-Pro on the crystallization process of CaCO3 was investigated by combining the electrical conductivity test of CaCO3 solution with different CaCO3 scale characterizations. The suitable synthesis and evaluation conditions for PASP-Pro were obtained, and a possible multi-stage scale inhibition mechanism of PASP-Pro for CaCO3 scale was then suggested. PASP-Pro has better thermal stability and high-temperature scale inhibition performance (exceeds 87% after pretreatment at 150 °C) than PASP. In addition, PASP-Pro exhibited a promising anti-scaling property by inhibiting the crystallization of CaCO3; the induction period and the nucleation period of the CaCO3 crystallization process were prolonged nearly four times. It was found from XRD patterns that vaterite, an unstable crystalline phase, gradually emerged with the addition of the scale inhibitors, and the aragonite crystals are clearly observed in SEM images. Finally, the possible multi-stage scale inhibition mechanism of PASP-based inhibitors was proposed, including coating impurities, electrostatic repulsion, and inhibiting dehydration and rearrangement of CaCO3 crystallization.

Abstract In this study, amino hyperbranched polymers (HBP)-grafted polyacrylonitrile (PAN) fiber was prepared through an amidation reaction in an autoclave. The prepared PAN-G-HBP fiber can complex Ag+ through amino groups of amino HBP, and in a hot steaming condition, Ag+ can be converted to Ag0 through the reducibility of HBP. PAN-G-HBP and Ag nanoparticles (NPs)-coated fibers were then characterized through FTIR, UV–VIS DRS, FE-SEM, EDS, XPS and antibacterial measurement. FTIR results confirmed HBP was grafted on the surface of PAN fiber. FE-SEM showed that after grafting with HBP, the average diameter of PAN fibers was amplified. EDS, XPS, and UV–VIS DRS method indicated that under hot steaming condition and with the reducibility of HBP, Ag NPs uniform coating on the PAN-G-HBP. Ag NPs-coated fibers exhibits excellent antibacterial property against Escherichia coli and Staphylococcus aureus. Even under 20 times home washing conditions, the antibacterial reduction of Ag NPs-coated PAN fiber can achieved more than 98.94%.

Achieving efficient photoelectrochemical cells requires insights into the ion kinetics at the surface of photoelectrode. Here, the authors show a new platform for investigating strong coupling between hydroxide ions and surface charge carriers by using monolayer InSe as the anode with high photocurrent density.

BackgroundThe long non-coding RNA (lncRNA)-mRNA regulation network plays an important role in the development of diffuse large B-cell lymphoma (DLBCL). This study uses bioinformatics to find an innovative regulation axis in DLBCL that will provide a positive reference for defining the mechanism of disease progression.MethodsBatch Cox regression was used to screen prognosis-related lncRNAs, and a random forest model was used to identify hub lncRNA. The clinical value of the lncRNA was evaluated and Spearman correlation analysis was used to predict the candidate target genes. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were used to define the biological function of the lncRNA. A batch Cox regression model, expression validation, and Spearman correlation analysis were used to select the best downstream target genes. The expression and prognostic value validation of this gene was conducted using public data. Gene Set Enrichment Analysis (GSEA) was performed to explore potential mechanisms for this gene in DLBCL.ResultsLINC00654 was identified as the hub lncRNA and 1443 mRNAs were selected as downstream target genes of the lncRNA. The target genes were enriched in the regulation of GTPase and Notch signaling pathways. After validation, the ninein-like (NINL) gene was selected as the potential target of LINC00654 and the LINC00654-NINL axis was constructed. Patients with better responses to therapy were shown to have high NINL gene expression (p-value = 0.036). NINL also had high expression in the DB cell line and low expression in the OCILY3 cell line. Survival analysis showed that high NINL expression was a risk factor for overall survival (OS) and disease-specific survival (DSS) within older patients and those with advanced-stage cancer. GSEA results showed that NINL may be involved in neutrophil-mediated immunity and NF-κB signaling.ConclusionThis study identified a novel LncRNA00654-NINL regulatory axis in DLBCL, which could provide a favorable reference for exploring the possible mechanisms of disease progression.

Background: The role of long-chain noncoding RNA (lncRNA) in genomic instability has been demonstrated to be increasingly importance. Therefore, in this study, lncRNAs associated with genomic instability were identified and kidney renal papillary cell carcinoma (KIRP)-associated predictive features were analysed to classify high-risk patients and improve individualised treatment.Methods: The training (n = 142) and test (n = 144) sets were created using raw RNA-seq and patient’s clinical data of KIRP obtained from The Cancer Genome Atlas (TCGA).There are 27 long-chain noncoding RNAs (lncRNAs) that are connected with genomic instability, these lncRNAs were identified using the ‘limma’ R package based on the numbers of somatic mutations and lncRNA expression profiles acquired from KIRP TCGA cohort. Furthermore, Cox regression analysis was carried out to develop a genome instability-derived lncRNA-based gene signature (GILncSig), whose prognostic value was confirmed in the test cohort as well as across the entire KIRP TCGA dataset.Results: A GILncSig derived from three lncRNAs (BOLA3-AS1, AC004870, and LINC00839), which were related with poor KIRP survival, was identified, which was split up into high- and low-risk groups. Additionally, the GILncSig was found to be an independent prognostic predictive index in KIRP using univariate and multivariate Cox analysis. Furthermore, the prognostic significance and characteristics of GilncSig were confirmed in the training test and TCGA sets. GilncSig also showed better predictive performance than other prognostic lncRNA features.Conclusion: The function of lncRNAs in genomic instability and the genetic diversity of KIRP were elucidated in this work. Moreover, three lncRNAs were screened for prediction of the outcome of KIRP survival and novel insights into identifying cancer biomarkers related to genomic instability were discussed.

Solid-state electrolyte (SSE), as the core component of solid-state batteries, plays a critical role in the performance of the batteries. Currently, the development of SSE is still hindered by its high price, low ionic conductivity, and poor interface stability. In this work, we report the tailored synthesis of a high ionic conductive and low cost sulfide SSE for all-solid-state lithium batteries. The Li10.35Si1.35P1.65S12 with favorable tetragonal structure was synthesis by increasing the concentration of Si4+, which shows an ionic conductivity of 4.28 × 10−3 S cm−1 and a wide electrochemical stability window of up to 5 V. By further modifying the composition of the electrolyte via ionic doping, the ionic conductivity of Li10.35Si1.35P1.65S12 can be further enhanced. Among them, the 1% Co4+-doped Li10.35Si1.35P1.65S12 shows the highest ionic conductivity of 6.91 × 10−3 S cm−1, 40% higher than the undoped one. This can be attributed to the broadened MS4− tetrahedrons and increased Li+ concentration. As a demonstration, an all-solid-state Li metal battery was assembled using TiS2 as the cathode and 1% Co4+-doped Li10.35Si1.35P1.65S12 as the electrolyte, showing capacity retention of 72% at the 110th cycle. This strategy is simple and can be easily extended for the construction of other high-performance sulfide SSEs.

The zero-temperature physics of interacting quantum dots attached to superconducting leads is now well understood. The overall qualitative picture is obtained from the static mean-field approximation. The situation drastically changes at non-zero temperatures. No reliable solutions apart from numerical simulations exist there. We show that any static mean-field approximation fails at non-zero temperatures since magnetic fluctuations induce dynamical corrections that lead to broadening of the in-gap state energies to energy bands. Spin-symmetric equilibrium state at non-zero temperatures is unstable with respect to magnetic fluctuations and the zero magnetic field can be reached only as a weak limit of the spin-polarized solution like in a magnetically ordered phase.

Water adsorbed on the electrode surface of electrochemical energy storage devices narrows the electrochemical windows. Combining atomistic simulations and cyclic voltammetry experiments the authors demonstrate an approach to expand the voltage window of humid ionic liquids based on the addition of lithium salt.

ABSTRACTEffective strategy to mitigate the ongoing pandemic of 2019 novel coronavirus (COVID-19) require a comprehensive understanding of humoral responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the emerging virus causing COVID-19. The dynamic profile of viral replication and shedding along with viral antigen specific antibody responses among COVID-19 patients started to be reported but there is no consensus on their patterns. Here, we conducted a serial investigation on 21 individuals infected with SARS-CoV-2 in two medical centres from Jiangsu Province, including 11 non-severe COVID-19 patients, and 5 severe COVID-19 patients and 5 asymptomatic carriers based on nucleic acid test and clinical symptoms. The longitudinal swab samples and sera were collected from these people for viral RNA testing and antibody responses, respectively. Our data revealed different pattern of seroconversion among these groups. All 11 non-severe COVID-19 patients and 5 severe COVID-19 patients were seroconverted during hospitalization or follow-up period, suggesting that serological testing is a complementary assay to nucleic acid test for those symptomatic COVID-19 patients. Of note, immediate antibody responses were identified among severe cases, compared to non-severe cases. On the other hand, only one were seroconverted for asymptomatic carriers. The SARS-CoV-2 specific antibody responses were well-maintained during the observation period. Such information is of immediate relevance and would assist COVID-19 clinical diagnosis, prognosis and vaccine design.

CNC machine tools are a measure of national manufacturing and comprehensive national strength and how to reduce CNC machine errors in modern industry has received much attention from all walks of life. The thermal deformation of the feed axis of CNC machine tools is affected by the machine’s structure, installation, material properties, motion position, working conditions, and environmental temperature. Therefore, adopting a single thermal error model to the compensation implementation needs is difficult under changing motion parameters. In this paper, VMC655H is used as the research object and an embedded system is used as the control core. The model classification method is based on the approximate matching of the motion parameters of the thermal error model. Dynamic loading technology is used to fit multiple thermal error models as a technical means to reduce the impact of the machine feed axis’s thermal deformation on the positioning accuracy during the manufacturing process. The thermal error classification model for machine tool feed axes with variable motion parameters in an embedded environment can classify, identify, and load compensation device models within a limited range of motion parameters. This strategy can determine the current operating environment and compensate for the poor adaptability of a single thermal error model. The study of the actual operation effect of the compensation device under this strategy shows that the compensation strategy proposed in this paper can compensate for the thermal deformation of the ball screw feed axis under variable motion, and the X-axis positioning accuracy of its feed system can be improved by 53.11%. This study provides a new idea for the compensation method for thermogenic errors in machine tools.

The occurrence of adventitious roots and somatic embryos is a crucial step in micropropagation that frequently limits the application of this technique in woody plants. Recent studies demonstrated that they can be negatively or positively regulated with γ-aminobutyric acid (GABA), which is a four-carbon non-proteinous amino acid that not only acts as a main inhibitory neurotransmitter in mammals. It has been reported that GABA affects plant growth and their response to stress although its mode of action is still unclear. This review dealt with the effects of GABA on adventitious root formation and growth as well as on somatic embryogenesis. Furthermore, we focused on discussing the interaction of GABA with phytohormones, such as auxin, ethylene, abscisic acid, and gibberellin, as well as with the carbon and nitrogen metabolism during adventitious root development. We suggested that research on GABA will contribute to the application of micropropagation in the recalcitrant fruit and forest species.

Zinc oxide (ZnO) nanorods were grown on the surface of a polypropylene (PP) nonwoven fabric (NWF) using the low-temperature hydrothermal method. The finishing process was investigated, and the prepared PP NWF was characterized. Results of the scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, anti-ultraviolet (UV) properties, and energy dispersive spectrometry indicate that the ZnO nanorods can be synthesized and uniformly coated on the surface of the PP NWF. The UV-degradation behavior and antibacterial activity of 0.025 M zinc nitrate hexahydrate-treated PP NWFs was good, with an ultraviolet protection factor value exceeding 100. Furthermore, the ZnO nanorods can inhibit the photodegradation of PP NWF. No obvious cracks were found on the fiber surface under 72 h of UV-light irradiation. The longitudinal and cross-breaking strength reduction properties were enhanced substantially. The ZnO nanorod-coated PP NWF achieved 99.5% bacterial reduction of Staphylococcus aureus and 99.9% bacterial reduction of Escherichia coli.

Ionic liquid electrolytes can impart increased operational voltage and energy density in supercapacitors, but water may diminish performance. Here the authors show that the hydrophilicity/hydrophobicity of ionic liquids can influence electrosorption of water and ultimately the supercapacitor performance.

We study the low-temperature critical behavior of the one-dimensional Hubbard model near half filling caused by enhanced antiferromagnetic fluctuations. We use a mean-field-type approximation with a two-particle self-consistency renormalizing the bare interaction. It allows us to control a transition from high to low temperatures as well as from weak to strong-coupling. We show that there is a crossover temperature T0 = t exp{−1/Uρ(0)} for arbitrary interaction U > 0 and the bare density of states at the Fermi energy ρ(0) > 0. The solution at lower temperatures goes over to strong coupling and approaches a quantum critical point with the diverging staggered susceptibility and a gap in the excitation spectrum at zero temperature.

There is an urgent need for effective treatment and preventive vaccine to contain this devastating global pandemic, which requires a comprehensive understanding of humoral responses specific to SARS-CoV-2 during the disease progression and convalescent phase of COVID-19 patients. We continuously monitored the serum IgM and IgG responses specific to four SARS-CoV-2 related antigens, including the nucleoprotein (NP), receptor binding domain (RBD), S1 protein, and ectodomain (ECD) of the spike protein among non-severe and severe COVID-19 patients for seven weeks since disease onset. Most patients generated humoral responses against NP and spike protein-related antigens but with their distinct kinetics profiles. Combined detection of NP and ECD antigens as detecting antigen synergistically improved the sensitivity of the serological assay, compared to that of using NP or RBD as detection antigen. 80.7% of convalescent sera from COVID-19 patients revealed that the varying extents of neutralization activities against SARS-CoV-2. S1-specific and ECD-specific IgA responses were strongly correlated with the neutralization activities in non-severe patients, but not in severe patients. Moreover, the neutralizing activities of the convalescent sera were shown to significantly decline during the period between 21 days to 28 days after hospital discharge, accompanied by a substantial drop in RBD-specific IgA response. Our data provide evidence that are crucial for serological testing, antibody-based intervention, and vaccine design of COVID-19.

As a material with high hydrogen storage density, ammonia borane has attracted the attention of researchers. Ammonia borane can release hydrogen gas through hydrolytic dehydrogenation and pyrolysis dehydrogenation, where hydrolytic dehydrogenation has a low actual hydrogen storage density and is expensive, which is not conducive to practical application. Direct thermal dehydrogenation research shows that improving the dehydrogenation performance of ammonia borane by metal substitution can not only increase the dehydrogenation rate, but also suppress the generation of impurity gases. In this paper, the density functional theory (DFT) is used to calculate the structural parameters, density of states, and HOMO-LUMO energy difference to obtain the dehydrogenation stability of ammonia borane and alkali metal substituted ammonia borane. Subsequently, the reaction energy barriers of the N-H ... H-B path and the M-H ... H-N path are calculated to obtain which dehydrogenation path is easy to occur. The results show that alkali metal substituted ammonia borane MAB(M = Li, Na, K) is easier to dehydrogenate than AB, and MAB is more likely to dehydrogenate by forming M-H ... H-N double hydrogen bonds. Density functional theory calculations can obtain the structural parameters and related thermodynamic information of each stagnation point on the dehydrogenation reaction path, which provides theoretical guidance for the research and improvement of the dehydrogenation performance of ammonia borane.

Carbon ion therapy is a promising modality in radiotherapy to treat tumors, however, a potential risk of induction of late normal tissue damage should still be investigated and protected. The aim of the present study was to explore the long-term cognitive deficits provoked by a high-linear energy transfer (high-LET) carbon ions in mice by targeting to hippocampus which plays a crucial role in memory and learning. Our data showed that, one month after 4 Gy carbon ion exposure, carbon ion irradiation conspicuously resulted in the impaired cognitive performance, neurodegeneration and neuronal cell death, as well as the reduced mitochondrial integrity, the disrupted activities of tricarboxylic acid cycle flux and electron transport chain, and the depressed antioxidant defense system, consequently leading to a decline of ATP production and persistent oxidative damage in the hippocampus region. Mechanistically, we demonstrated the disruptions of mitochondrial homeostasis and redox balance typically characterized by the disordered mitochondrial dynamics, mitophagy and glutathione redox couple, which is closely associated with the inhibitions of PINK1 and NRF2 signaling pathway as the key regulators of molecular responses in the context of neurotoxicity and neurodegenerative disorders. Most importantly, we found that administration with melatonin as a mitochondria-targeted antioxidant promoted the PINK1 accumulation on the mitochondrial membrane, and augmented the NRF2 accumulation and translocation. Moreover, melatonin pronouncedly enhanced the molecular interplay between NRF2 and PINK1. Furthermore, in the mouse hippocampal neuronal cells, overexpression of NRF2/PINK1 strikingly protected the hippocampal neurons from carbon ion-elicited toxic insults. Thus, these data suggest that alleviation of the sustained mitochondrial dysfunction and oxidative stress through co-modulation of NRF2 and PINK1 may be in charge of restoration of the cognitive impairments in a mouse model of high-LET carbon ion irradiation. Keywords: Carbon ions, Cognitive deficits, NRF2, PINK1, Mitochondrial dysfunction, Mitochondrial homeostasis, Redox balance

Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring.

The increasing demand of mobile devices (MDs) for data services brings tremendous pressure to cellular networks. It has become a great challenge for traditional offloading techniques to balance the energy efficiency and quality of service. The concept of device-to-device (D2D) communication shows a huge potential in cellular offloading. In this paper, we investigate the scenario where MDs have the same demand for a common content and they cooperate to download the content by multihop relaying. We aim to minimize the total power consumption by grouping MDs in multihop D2D networks, while satisfying the minimum rate required by each MD. As the problem is NP-complete and the optimal solution cannot be found in polynomial time, we propose three greedy algorithms with different grouping strategies to trade off the performance and complexity. Simulation results demonstrate that the total power consumption can be saved significantly in the content delivery situation using cooperative D2D communication, and the proposed algorithms are suitable for static and dynamic networks with different advantages.

In view of the lack of applicability of the existing equivalent evaporation duct feature calculation scheme in China's sea area and the requirement of the evaporation duct model test, this study used the actual sounding data in China's coastal areas and studied the applicability of the three computational schemes proposed by J.F. Howell, S.P. Oncley, and G.J. Steeneveld in different sea areas and different seasons by comparing the error and standard deviation. The applicability of the sea area shows that the best calculation scheme for the equivalent evaporating duct feature in the South China Sea is the scheme proposed by G.J. Steeneveld et al., while the optimal calculation method for the East China Sea is the scheme proposed by S.P. Oncley et al.; the seasonal applicability shows that the scheme proposed by G.J. Steeneveld et al. has the best-fitting results in the spring, while in the summer and winter, the scheme proposed by S.P. Oncley et al. fits best. These results provide a theoretical basis for the follow-up study.

Bearings are key components of cone bit, thus its rapid failure is a major cause of leading to lower life of the bit. To improve the bearing performance and prolong working life, contact mechanics of hollow cylindrical roller bearing of cone bit was simulated. Effects of hollow size, drilling pressure, friction coefficient and fitting clearance on mechanics performance of the bearing were studied. The results show that the maximum equivalent stress of the hollow cylindrical roller bearing appears on the claw journal, and the maximum contact stress appears on the contact pair of the hollow roller. Besides, hollow sizes have a greater impact on the equivalent stress and contact stress of the cylindrical roller, while the influence on the stress of the cone and claw journal is relatively small. With the increasing of the drilling pressure and fitting clearance, equivalent stress and contact stress of bearing parts increase. The friction coefficient has little impact on mechanical performance of the bearing. As the 121/4SWPI517 type hollow cylindrical roller bearing of cone bit an example, the optimal hollow size is 55%, the drilling pressure is 140 kN and the fitting clearance is 0–0.02 mm.

Here we provide raw and processed data and methods behind mTOR siRNA loaded DNA nanotubes (siRNA-DNA-NTs) in the growth of pulmonary arterial smooth muscle cells (PASMCs) under both normoxic and hypoxic condition, and also related to (You et al., Biomaterials, 2015, 67:137–150, [1]). The MTT analysis, Semi-quantitative RT-PCR data presented here were used to probe cytotoxicity of mTOR siRNA-DNA-NT complex in its TAE-Mg2+ buffer. siRNA-DNA-NTs have a lower cytotoxicity and higher transfection efficiency and can, based on inhibition of mTOR expression, decrease PASMCs growth both hypoxic and normal condition.

Inorganic polyphosphate (polyP) is a linear polymer of orthophosphate residues. It is reported to be present in all life forms. Experimental studies showed that polyP plays important roles in bacterial durability and virulence. Here we investigated the relationships of polyP with bacterial durability and virulence theoretically. Bacterial lifestyle, environmental persistence, virulence factors (VFs), and species evolution are all included in the analysis. The presence of seven genes involved in polyP metabolism (ppk1, ppk2, pap, surE, gppA, ppnK, and ppgK) and 2595 core VFs were verified in 944 bacterial reference proteomes for distribution patterns via HMMER. Proteome size and VFs were compared in terms of gain and loss of polyP pathway. Literature mining and phylogenetic analysis were recruited to support the study. Our analyzes revealed that the presence of polyP metabolism is positively correlated with bacterial proteome size and the number of virulence genes. A potential relationship of polyP in bacterial lifestyle and environmental durability is suggested. Evolutionary analysis shows that polyP genes are randomly lost along the phylogenetic tree. In sum, based on our theoretical analysis, we confirmed that bacteria with polyP metabolism are associated with high environmental durability and more VFs.

The intriguing sit-and-wait hypothesis predicts that bacterial durability in the external environment is positively correlated with their virulence. Since its first proposal in 1987, the hypothesis has been spurring debates in terms of its validity in the field of bacterial virulence. As a special case of the vector-borne transmission versus virulence tradeoff, where vector is now replaced by environmental longevity, there are only sporadic studies over the last three decades showing that environmental durability is possibly linked with virulence. However, no systematic study of these works is currently available and epidemiological analysis has not been updated for the sit-and-wait hypothesis since the publication of Walther and Ewald’s (2004) review. In this article, we put experimental evidence, epidemiological data and theoretical analysis together to support the sit-and-wait hypothesis. According to the epidemiological data in terms of gain and loss of virulence (+/-) and durability (+/-) phenotypes, we classify bacteria into four groups, which are: sit-and-wait pathogens (++), vector-borne pathogens (+-), obligate-intracellular bacteria (--), and free-living bacteria (-+). After that, we dive into the abundant bacterial proteomic data with the assistance of bioinformatics techniques in order to investigate the two factors at molecular level thanks to the fast development of high-throughput sequencing technology. Sequences of durability-related genes sourced from Gene Ontology and UniProt databases and virulence factors collected from Virulence Factor Database are used to search 20 corresponding bacterial proteomes in batch mode for homologous sequences via the HMMER software package. Statistical analysis only identified a modest, and not statistically significant correlation between mortality and survival time for eight non-vector-borne bacteria with sit-and-wait potentials. Meanwhile, through between-group comparisons, bacteria with higher host-mortality are significantly more durable in the external environment. The results of bioinformatics analysis correspond well with epidemiological data, that is, non-vector-borne pathogens with sit-and-wait potentials have higher number of virulence and durability genes compared with other bacterial groups. However, the conclusions are constrained by the relatively small bacterial sample size and non-standardized experimental data.

Herein, ZnO nanoparticles (NPs) were synthesized using zinc acetate and an amino hyperbranched polymer. The methods of transmission electron microscope (TEM) and X-ray Diffraction (XRD) were applied to the characterization of ZnO NPs. Polypropylene (PP)/ZnO fiber was prepared using 1−5 wt% ZnO NPs via melt spinning. The ultraviolet light (UV)-degradation behavior, antibacterial activity and mechanical properties of PP fibers were characterized. The PP fiber filled with ZnO NPs presents better mechanical properties and the resistance to UV light degradation. For the best effect, the contents of ZnO NPs were set 4 wt% in PP fiber. FTIR analysis shows significant photo-degradation of PP induced by UV irradiation and a remarkable reduction in the UV degradation of the fiber filled with ZnO NPs. It is also observed that the ZnO NPs-filled fiber has good antibacterial actives against Escherichia coli and staphylococcus aureus.

The parallel operation of multiple undulator lines with a wide spectral range is an important way to increase the usability of x-ray free electron laser (XFEL) facilities, especially for machines with high-repetition rate. In this paper, a delay system based on four double bend achromats is proposed to delay electron beams, thereby changing the arrival time of those delayed electron beams in the accelerating structure behind the system. Combined with kickers, the delay system can be used to generate bunch-to-bunch energy changed electron beams in a continuous wave XFEL facility. Start-to-end simulations based on the parameters of the Shanghai high-repetition-rate XFEL and extreme light facility are performed to demonstrate that the delay system can flexibly control electron beam energy from 1.48 to 8.74 GeV at the end of the linac.

Herein, the amino-capped TiO2 nanoparticles were synthesized using tetrabutyl titanate and amino polymers by a two-step sol-gel and hydrothermal method technique for the fabrication of functional cotton fabric. The prepared TiO2 nanoparticles and the treated cotton fabric were characterized by transmission electron microscope (TEM), X-ray diffraction (XRD), field emission scanning electron microcopy (FE-SEM) photocatalytic and antibacterial measurement. The results indicate the typical characteristic anatase form of the amino-capped TiO2 NPs with an average crystallite size of 14.9 nm. The treated cotton fabrics exhibit excellent antibacterial property and good photocatalytic degradation of methylene blue.