Comparative transcriptome analysis of Fusarium graminearum challenged with distinct fungicides and functional analysis of FgICL gene.

Fusarium graminearum is an economically important phytopathogenic fungus. Chemical control remains the dominant approach to managing this plant pathogen. In the present study, we performed a comparative transcriptome analysis to understand the effects of four commercially used fungicides on F. graminearum. The results revealed a significant number of differentially expressed genes related to carbohydrate, amino acid, and lipid metabolism, particularly in the carbendazim and phenamacril groups. Central carbon pathways, including the TCA and glyoxylate cycles, were found to play crucial roles across all treatments except tebuconazole. Weighted gene co-expression network analysis reinforced the pivotal role of central carbon pathways based on identified hub genes. Additionally, critical candidates associated with ATP-binding cassette transporters, heat shock proteins, and chitin synthases were identified. The crucial functions of the isocitrate lyase in F. graminearum were also validated. Overall, the study provided comprehensive insights into the mechanisms of how F. graminearum responds to fungicide stress.

Highly Flexible and Superelastic Graphene Nanofibrous Aerogels for Intelligent Sign Language.

Highly flexible and superelastic aerogels at large deformation have become urgent mechanical demands in practical uses, but both properties are usually exclusive. Here a trans-scale porosity design is proposed in graphene nanofibrous aerogels (GNFAs) to break the trade-off between high flexibility and superelasticity. The resulting GNFAs can completely recover after 1000 fatigue cycles at 60% folding strain, and notably maintain excellent structural integrity after 10000 cycles at 90% compressive strain, outperforming most of the reported aerogels. The mechanical robustness is demonstrated to be derived from the trans-scale porous structure, which is composed of hyperbolic micropores and porous nanofibers to enable the large elastic deformation capability. It is further revealed that flexible and superelastic GNFAs exhibit high sensitivity and ultrastability as an electrical sensors to detect tension and flexion deformation. As proof, The GNFA sensor is implemented onto a human finger and achieves the intelligent recognition of sign language with high accuracy by multi-layer artificial neural network. This study proposes a highly flexible and elastic graphene aerogel for wearable human-machine interfaces in sensor technology.

Capillarity Constructed Open Siphon for Sustainable Drainage.

Siphon is an effective method to transfer liquid from a higher to a lower level, which has many applications in hygienic design, clinical apparatus, and hydraulic engineering. Traditional operation requires energy to overcome gravity and establish flow in a closed system. Achieving sustainable high flux siphon drainage without energy input remains a challenge due to viscous dissipation. Here, an unexpected open siphon behavior on the South American pitcher plant Heliamphora minor consisting of trichomes covered pitcher and a wedge-shaped sheath is examined. Exploiting the concept of Digital Twin, a new biomimetic research method by transforming the biological sample to a virtual 3D model is proposed and unveiled that maintained connection of wicking on sub-millimeter long trichomes due to asymmetric pressure distribution and ascending in wedge sheath under unbalanced pressure forms continuous surface flow. Exploring this mechanism, a biomimetic siphon device achieving continuous high flux exposed to ambient air is constructed. Besides, particles floating on the meniscus in the outside wedge move under a curvature gradient as water ascends, which implies a biological nutrient capture method and new dust collection manner in the drainage system. Applying the underlying principle enhances the siphon efficiency of floor drains and has the potential for other liquid transfer device design improvements.

Vegetation communities and soil properties along the restoration process of the Jinqianghe mine site in the Qilian Mountains, China.

The study explores the impact of mine grassland restoration on plant communities and soil properties in alpine grasslands, a subject of significant interest due to the observed relationship between grassland changes, plant communities, and soil properties. While prior research has mainly focused on the consequences of grassland degradation on plant diversity and soil characteristics, the specific effects of varying restoration degrees in alpine mining grasslands at the regional scale remain poorly understood. To address this knowledge gap, we established 15 sampling plots (0.5m×0.5m) across five different restoration degrees within alpine mining grasslands in the Qilian Mountains, China. Our objective was to assess the variations in plant diversity and soil properties along these restoration gradients. We conducted comprehensive analyses, encompassing soil properties [soil water content (SWC), available nitrogen (AN), total phosphorus (TP), nitrate nitrogen (NO3-N), ammonium nitrogen (NH4-N), total nitrogen (TN), available phosphorus (AP), soil organic carbon (SOC), nitrate nitrogen, soil pH, and electrical conductivity (EC)], plant characteristics (height, density, frequency, coverage, and aboveground biomass), and plant diversity indices (Simpson, Shannon-Wiener, Margalef, Dominance, and Evenness indexes). Our findings included the identification and collection of 18 plant species from 11 families and 16 genera across the five restoration degrees: Very Low Restoration Degree (VLRD), Low Restoration Degree (LRD), Moderate Restoration Degree (MRD), High Restoration Degree (HRD), and Natural Grassland (NGL). Notably, species like Carex duriuscula, Cyperus rotundus, and Polygonum viviparum showed signs of recovery. Principal component analysis and Pearson correlation analysis revealed that soil pH, SWC, SOC, NO3-N, and AN were the primary environmental factors influencing plant communities. Specifically, soil pH and EC decreased as restoration levels increased, while SWC, AN, TP, NH4-N, TN, AP, SOC, and NO3-N exhibited a gradual increase with greater restoration efforts. Furthermore, the HRD plant community demonstrated similarities to the NGL, indicating the most effective natural recovery. In conclusion, our study provides valuable insights into the responses of plant community characteristics, plant diversity, and soil properties across varying restoration degrees to environmental factors. It also elucidates the characteristics of plant communities along recovery gradients in alpine grasslands.

Effect of GnRH Active Immunisation on Reproductive Performance of Male Sprague Dawley Rats.

To investigate the effect of active immunisation with gonadotropin-releasing hormone (GnRH) on the reproductive function in male Sprague Dawley (SD) rats, 24 42-day-old rats were randomly assigned to treatment with GnRH6-MAP, GnRH-OVA, a surgical castration group, and a blank control group. Each rat in the treatment groups was intramuscularly injected at 6, 8, and 10 weeks of age. The serum concentrations of testosterone (T), follicle-stimulating hormone (FSH), luteinising hormone (LH), and anti-GnRH antibodies were determined using enzyme-linked immunosorbent assays. The results showed that active immunisation with recombinant GnRH6-MBP and GnRH-OVA significantly increased the serum levels of anti-GnRH antibodies and reduced the serum concentrations of testosterone compared to the black control. Eight weeks after immunisation, the rats' testes were surgically removed for morphological evaluation, showing atrophy of the convoluted vasculature, relative emptying of the lumen, and insignificant differentiation of spermatogonial cells, which were increased in weight and volume compared with the blank control group. These findings indicated that active immunisation with GnRH can lead to testicular atrophy and reduce gonadal hormone concentrations, suggesting that GnRH is a highly effective immunogen.

Pontederia crassipes inspired bottom overflow for fast and stable drainage.

Fast and stable water drainage is essential for living organisms, drainage plane construction, and protection of infrastructure from damage during rainfall. Unlike traditional anti-overflow drainage methods that rely on hydrophobic or sharped edges, this study demonstrates a bottom overflow-induced drainage model inspired by the water path employed by Pontederia crassipes leaves, leading to fast and stable drainage. A superhydrophilic bottom surface guides water to overflow and pin at the bottom of a thin sheet, resulting in dripping at a higher frequency and reduced water retention. This bottom drainage idea assists large-scale thin sheets to function as efficient and stable drainage surfaces in simulated rain environments. The flexible thin sheet can also be feasibly attached to dusty substrates to effectively remove dusty rainwater with slight dust residue. The bioinspired approach presented herein suggests a promising potential for efficient water drainage on outdoor functional photovoltaic surfaces, such as solar panels and radomes, thus ensuring effective energy conversion and stable signal transmission.

Efficient agricultural drip irrigation inspired by fig leaf morphology.

Irrigation is limited by water scarcity. Here, we show how a drip irrigation system inspired by the leaf of the fig tree Ficus religiosa (also known as the bodhi tree) can improve irrigation efficiency. The reverse curvature of the leaf regulates the convergence process of multiple water streams, while its long-tail apex allows for fast water drainage with the droplet separation centroid beyond the leaf apex. We explain why drip frequency increases after the break-up of contact line pinning at the apex tip by using scaling laws for drip volume and analyzing drainage dynamics. We build a drip irrigation emitter inspired by the bodhi leaf apex and compare the germination efficiency of wheat, cotton, and maize under different irrigation modes. These results show that the proposed bodhi-leaf-apex-mimetic (BLAM) drip irrigation can improve water saving while ensuring germination and seedling growth.

Viscous-capillary entrainment on bioinspired millimetric structure for sustained liquid transfer.

Liquid entrainment with a solid architecture passing through the fluid-fluid interface is ubiquitous and widely used in industrial processes as a liquid transfer method. Besides liquid properties, solid structures play a core role in entrainment. Although the influence of its macroscopic curvatures and microscale roughness has attracted years of research, the effect and potential of the commonly seen millimetric structures have not been sufficiently explored and exploited. Here, we demonstrate enhanced liquid entrainment on the millimetric structured surface by the co-effect of viscosity and capillarity for sustained liquid transfer of small deviation, including high-quantity uptake and practically operational drainage with small and relatively uniform droplet dripping time of varied liquid viscosities. With the overall process of viscous-capillary entrainment, we achieve stable cyclical arrayed liquid transport, showing its potential for sustained liquid transfer in intractable situations in laboratory, industry, and even daily life.

Research on Abrasive Water Jet Polishing of Silicon Carbide Based on Fluid Self-Excited Oscillation Pulse Characteristics.

A self-excited oscillating pulsed abrasive water jet polishing method is proposed to solve the problems of low removal efficiency in traditional abrasive water jet polishing and the influence of an external flow field on the material surface removal rate. The self-excited oscillating chamber of the nozzle was used to generate pulsed water jets to reduce the impact of the jet stagnation zone on material surface removal and increase the jet speed to improve processing efficiency. ANSYS Fluent was employed to simulate the processing flow field characteristics for different lengths of oscillation cavities. The simulation results indicate that the velocity of the jet shaft reached a maximum of 178.26 m/s when the length of the oscillation cavity was 4 mm. The erosion rate of the material is linear with the processing angle. A nozzle with a length of 4 mm of the self-excited oscillating cavity was fabricated for SiC surface polishing experiments. The results were compared with those of ordinary abrasive water jet polishing. The experimental results showed that the self-excited oscillation pulse fluid enhanced the erosion ability of the abrasive water jet on the SiC surface and significantly improved the material-removal depth of the abrasive water jet polishing SiC. The maximum surface erosion depth can be increased by 26 µm.

Dual-bionic superwetting gears with liquid directional steering for oil-water separation.

Developing an effective and sustainable method for separating and purifying oily wastewater is a significant challenge. Conventional separation membrane and sponge systems are limited in their long-term usage due to weak antifouling abilities and poor processing capacity for systems with multiple oils. In this study, we present a dual-bionic superwetting gears overflow system with liquid steering abilities, which enables the separation of oil-in-water emulsions into pure phases. This is achieved through the synergistic effect of surface superwettability and complementary topological structures. By applying the surface energy matching principle, water and oil in the mixture rapidly and continuously spread on preferential gear surfaces, forming distinct liquid films that repel each other. The topological structures of the gears facilitate the overflow and rapid transfer of the liquid films, resulting in a high separation flux with the assistance of rotational motion. Importantly, this separation model mitigates the decrease in separation flux caused by fouling and maintains a consistently high separation efficiency for multiple oils with varying densities and surface tensions.

Colloidal gold and fluorescent immunochromatographic test strips for canine parvovirus detection.

Canine parvovirus (CPV) is an acute and highly infectious virus causing disease in puppies and, thus, affecting the global dog industry. The current CPV detection methods are limited by their sensitivity and specificity. Hence, the current study sought to develop a rapid, sensitive, simple, and accurate immunochromatographic (ICS) test to detect and control the spread and prevalence of CPV infection. More specifically, 6A8, a monoclonal antibody (mAb) with high specificity and sensitivity, was obtained by preliminary screening. The 6A8 antibody was labelled with colloidal gold particles. Subsequently, 6A8 and goat anti-mouse antibodies were coated onto a nitrocellulose membrane (NC) as the test and control lines, respectively. Furthermore, 6A8 and rabbit IgG antibodies were labelled with fluorescent microspheres and evenly sprayed onto a glass fibre membrane. Both strips could be prepared in 15 min with no noticeable cross-reactivity with other common canine intestinal pathogens. The strips were simultaneously used to detect CPV in 60 clinical samples using real-time quantitative PCR, hemagglutination, and hemagglutination inhibition assays. The colloidal gold (fluorescent) ICS test strip was stable for 6 (7) and 4 (5) months at 4 °C and room temperature (18-25 °C). Both test strips were easy to prepare and rapidly detected CPV with high sensitivity and specificity. Moreover, the results were easily interpretable. This study establishes a simple method for two CPV diseases, colloidal gold and fluorescent immunochromatographic (ICS) test strips. KEY POINTS: • CPV test strips do not exhibit cross-reactivity with other canine intestinal pathogens. • The strips are stable for months at 4 °C and at room temperature (18-25 °C). • These strips are a promising approach for the timely diagnosis and treatment of CPV.

Programmable Microfluidics Enabled by 3D Printed Bionic Janus Porous Matrics for Microfluidic Logic Chips.

Numerous structures have been functionally optimized for directional liquid transport in nature. Inspired by lush trees' xylem that enable liquid directional transportation from rhizomes to the tip of trees, a new kind of programmable microfluidic porous matrices using projection micro-stereolithography (PµSL) based 3D printing technique is fabricated. Structural matrices with internal superhydrophilicity and external hydrophobicity are assembled for ultra-fast liquid rising enabled by capillary force. Moreover, the unidirectional microfluidic performance of the bionic porous matrices can be theoretically optimized by adjusting its geometric parameters. Most significantly, the successive programmable flow of liquid in a preferred direction inside the bionic porous matrices with tailored wettability is achieved, validating by a precisely printed liquid displayer and a microfluidic logic chip. The programmable and functional microfluidic matrices promise applications of patterned liquid flow, displayer, logic chip, cell screening, gas-liquid separation, and so on.

Survival in desert: Extreme water adaptations and bioinspired structural designs.

Deserts are the driest places in the world, desert creatures have evolved special adaptations to survive in this extreme water shortage environment. The collection and transport of condensed water have been of particular interest regarding the potential transfer of the underlying mechanisms to technical applications. In this review, the mechanisms of water capture and transport were first summarized. Secondly, an introduction of four typical desert creatures including cactus, desert beetles, lizards, and snakes which have special adaptations to manage water was elaborated. Thirdly, the recent progress of biomimetic water-collecting structures including cactus, desert beetles, and lizards inspired designs and the influence of overflow on water collection was demonstrated. Finally, the conclusions were drawn, and future issues were pointed out. The present study will further promote research on bioinspired water management strategies.

Redistribution effect of irrigation on shallow groundwater recharge source contributions in an arid agricultural region.

Recharge sources such as precipitation, mountain front recharge, mountain block recharge and confined water are the sources usually considered in quantitative studies of groundwater recharge. Changes in recharge processes caused by irrigation practices need to be fully considered for the accurate budgeting and management of water resources. Here, we put forward a conceptual framework for evaluating the shallow groundwater recharge process in arid irrigated agricultural areas using hydrochemical and stable isotope techniques, combined with an assessment of hydrogeological conditions and quantitative models. In general, the recharge effect of atmospheric precipitation on shallow groundwater in arid areas is relatively small. The contributions made by recharge sources in the studied river irrigated area, from greater to smaller, were confined groundwater (46.98 %), river water (45.48 %) and precipitation (7.55 %). The original range in groundwater recharge levels caused by river leakage also appeared to have expanded in response to the establishment of canal irrigation networks. Lateral groundwater flow and confined groundwater were the main recharge sources of shallow groundwater in areas fed by well irrigation and well-/spring-water irrigation (not taking into account any groundwater irrigation leakage). However, had the recharge of shallow groundwater by groundwater irrigation leakage, which reached 19.8-41.1 %, not been counted as contributing to actual groundwater recharge, the recharge contributions made by lateral groundwater flow and confined groundwater to shallow groundwater would have been significantly overestimated. This is because the groundwater recharge process has been modified by the various irrigation measures employed in arid agricultural areas, leading to a redistribution effect in groundwater recharge source contributions. This study provides a new perspective and intuitive data support for the development and utilization of water resources in arid regions.

Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting.

Urban traffic forecasting is the cornerstone of the intelligent transportation system (ITS). Existing methods focus on spatial-temporal dependency modeling, while two intrinsic properties of the traffic forecasting problem are overlooked. First, the complexity of diverse forecasting tasks is nonuniformly distributed across various spaces (e.g., suburb versus downtown) and times (e.g., rush hour versus off-peak). Second, the recollection of past traffic conditions is beneficial to the prediction of future traffic conditions. Based on these properties, we propose a bidirectional spatial-temporal adaptive transformer (Bi-STAT) for accurate traffic forecasting. Bi-STAT adopts an encoder-decoder architecture, where both the encoder and the decoder maintain a spatial-adaptive transformer and a temporal-adaptive transformer structure. Inspired by the first property, each transformer is designed to dynamically process the traffic streams according to their task complexities. Specifically, we realize this by the recurrent mechanism with a novel dynamic halting module (DHM). Each transformer performs iterative computation with shared parameters until DHM emits a stopping signal. Motivated by the second property, Bi-STAT utilizes one decoder to perform the present → past recollection task and the other decoder to perform the present → future prediction task. The recollection task supplies complementary information to assist and regularize the prediction task for a better generalization. Through extensive experiments, we show the effectiveness of each module in Bi-STAT and demonstrate the superiority of Bi-STAT over the state-of-the-art baselines on four benchmark datasets. The code is available at https://github.com/chenchl19941118/Bi-STAT.git.

An Efficient Method for Modeling Nonoccurring Behaviors by Negative Sequential Patterns With Loose Constraints.

The sequence analysis handles sequential discrete events and behaviors, which can be represented by temporal point processes (TPPs). However, TPP models only occurring events and behaviors. This article explores an efficient method for the negative sequential pattern (NSP) mining to leverage TPP in modeling both frequently occurring and nonoccurring events and behaviors. NSP mining is good at the challenging modeling of nonoccurrences of events and behaviors and their combinations with occurring events, with existing methods built on incorporating various constraints into NSP representations, e.g., simplifying NSP formulations and reducing computational costs. Such constraints restrict the flexibility of NSPs, and nonoccurring behaviors (NOBs) cannot be comprehensively exposed. This article addresses this issue by loosening some inflexible constraints in NSP mining and solves a series of consequent challenges. First, we provide a new definition of negative containment with the set theory according to the loose constraints. Second, an efficient method quickly calculates the supports of negative sequences. Our method only uses the information about the corresponding positive sequential patterns (PSPs) and avoids additional database scans. Finally, a novel and efficient algorithm, NegI-NSP, is proposed to efficiently identify highly valuable NSPs. Theoretical analyses, comparisons, and experiments on four synthetic and two real-life data sets clearly show that NegI-NSP can efficiently discover more useful NOBs.

Continuous resin refilling and hydrogen bond synergistically assisted 3D structural color printing.

3D photonic crystals (PCs) have attracted extensive attention due to their unique optical properties. However, fabricating 3D PCs structure by 3D printing colloidal particles is limited by control of assembly under a fast-printing speed. Here, we employ continuous digital light processing (DLP) 3D printing strategy with hydrogen bonds assisted colloidal inks for fabricating well-assembled 3D PCs structures. Stable dispersion of colloidal particles inside UV-curable system induced by hydrogen bonding and suction force induced by continuous curing manner cooperatively realize the simultaneous macroscopic printing and microscopic particle assembly, which endows volumetric color property. Structural color can be well regulated by controlling the particle diameter and printing speed, through which various complex 3D structures with desired structural color distribution and optical light-guide properties are acquired. This 3D color construction approach shows great potential in customized jewelry accessories, decoration and optical device preparation, and will innovate the development of structural color.

Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention.

Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.

Whole blood viscosity is an independent early predictor for metabolic syndrome and its components in men: A prospective cohort study in Northern Chinese population.

The association between whole blood viscosity (WBV) and metabolic syndrome (MetS) is still scarcely investigated in the population-based prospective cohort. We aim to explore the longitudinal effect of WBV on MetS, and to verify whether WBV measures can be used as early predictors for MetS. The longitudinal cohort consisted of 3,508 adults (2,350 males and 1,158 females) who visited the health check-up system twice. WBV were measured at four shear rate (200, 50, 10 and 1 s-1), and their values were classified into quartiles. Multivariate Cox models were used to estimate the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) in men and women, respectively. A total of 444 (12.66%) incident MetS were observed at follow-up period. The incidences of MetS significantly increased with increasing quartiles of WBVs at all of the shear rate in men. After adjusting for baseline age, smoking, obesity, hypertension, hyperglycemia, and hyperlipidemia status, all of the WBV measures were significantly associated with incident MetS in men, and the HRs showed clear increasing trend across the quartiles of baseline WBVs. There were no significant association between WBVs and incident MetS in women. These findings suggest that MetS has a hemodynamic basis, and WBVs could be used as independent early predictor for MetS in men.

Reinforcement Learning With Multiple Relational Attention for Solving Vehicle Routing Problems.

In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs). Recent works have shown that attention-based RL models outperform recurrent neural network-based methods on these problems in terms of both effectiveness and efficiency. However, existing RL models simply aggregate node embeddings to generate the context embedding without taking into account the dynamic network structures, making them incapable of modeling the state transition and action selection dynamics. In this work, we develop a new attention-based RL model that provides enhanced node embeddings via batch normalization reordering and gate aggregation, as well as dynamic-aware context embedding through an attentive aggregation module on multiple relational structures. We conduct experiments on five types of VRPs: 1) travelling salesman problem (TSP); 2) capacitated VRP (CVRP); 3) split delivery VRP (SDVRP); 4) orienteering problem (OP); and 5) prize collecting TSP (PCTSP). The results show that our model not only outperforms the learning-based baselines but also solves the problems much faster than the traditional baselines. In addition, our model shows improved generalizability when being evaluated in large-scale problems, as well as problems with different data distributions.