Taizhou University

Biological invasions are a global consequence of an increasingly connected world and the rise in human population size. The numbers of invasive alien species - the subset of alien species that spread widely in areas where they are not native, affecting the environment or human livelihoods - are increasing. Synergies with other global changes are exacerbating current invasions and facilitating new ones, thereby escalating the extent and impacts of invaders. Invasions have complex and often immense long-term direct and indirect impacts. In many cases, such impacts become apparent or problematic only when invaders are well established and have large ranges. Invasive alien species break down biogeographic realms, affect native species richness and abundance, increase the risk of native species extinction, affect the genetic composition of native populations, change native animal behaviour, alter phylogenetic diversity across communities, and modify trophic networks. Many invasive alien species also change ecosystem functioning and the delivery of ecosystem services by altering nutrient and contaminant cycling, hydrology, habitat structure, and disturbance regimes. These biodiversity and ecosystem impacts are accelerating and will increase further in the future. Scientific evidence has identified policy strategies to reduce future invasions, but these strategies are often insufficiently implemented. For some nations, notably Australia and New Zealand, biosecurity has become a national priority. There have been long-term successes, such as eradication of rats and cats on increasingly large islands and biological control of weeds across continental areas. However, in many countries, invasions receive little attention. Improved international cooperation is crucial to reduce the impacts of invasive alien species on biodiversity, ecosystem services, and human livelihoods. Countries can strengthen their biosecurity regulations to implement and enforce more effective management strategies that should also address other global changes that interact with invasions.

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

The WRKY gene family is among the largest families of transcription factors (TFs) in higher plants. By regulating the plant hormone signal transduction pathway, these TFs play critical roles in some plant processes in response to biotic and abiotic stress. Various bodies of research have demonstrated the important biological functions of WRKY TFs in plant response to different kinds of biotic and abiotic stresses and working mechanisms. However, very little summarization has been done to review their research progress. Not just important TFs function in plant response to biotic and abiotic stresses, WRKY also participates in carbohydrate synthesis, senescence, development, and secondary metabolites synthesis. WRKY proteins can bind to W-box (TGACC (A/T)) in the promoter of its target genes and activate or repress the expression of downstream genes to regulate their stress response. Moreover, WRKY proteins can interact with other TFs to regulate plant defensive responses. In the present review, we focus on the structural characteristics of WRKY TFs and the research progress on their functions in plant responses to a variety of stresses.

Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application.

Biological invasions have steadily increased over recent centuries. However, we still lack a clear expectation about future trends in alien species numbers. In particular, we do not know whether alien species will continue to accumulate in regional floras and faunas, or whether the pace of accumulation will decrease due to the depletion of native source pools. Here, we apply a new model to simulate future numbers of alien species based on estimated sizes of source pools and dynamics of historical invasions, assuming a continuation of processes in the future as observed in the past (a business-as-usual scenario). We first validated performance of different model versions by conducting a back-casting approach, therefore fitting the model to alien species numbers until 1950 and validating predictions on trends from 1950 to 2005. In a second step, we selected the best performing model that provided the most robust predictions to project trajectories of alien species numbers until 2050. Altogether, this resulted in 3,790 stochastic simulation runs for 38 taxon-continent combinations. We provide the first quantitative projections of future trajectories of alien species numbers for seven major taxonomic groups in eight continents, accounting for variation in sampling intensity and uncertainty in projections. Overall, established alien species numbers per continent were predicted to increase from 2005 to 2050 by 36%. Particularly, strong increases were projected for Europe in absolute (+2,543 ± 237 alien species) and relative terms, followed by Temperate Asia (+1,597 ± 197), Northern America (1,484 ± 74) and Southern America (1,391 ± 258). Among individual taxonomic groups, especially strong increases were projected for invertebrates globally. Declining (but still positive) rates were projected only for Australasia. Our projections provide a first baseline for the assessment of future developments of biological invasions, which will help to inform policies to contain the spread of alien species.

Our ability to predict the identity of future invasive alien species is largely based upon knowledge of prior invasion history. Emerging alien species-those never encountered as aliens before-therefore pose a significant challenge to biosecurity interventions worldwide. Understanding their temporal trends, origins, and the drivers of their spread is pivotal to improving prevention and risk assessment tools. Here, we use a database of 45,984 first records of 16,019 established alien species to investigate the temporal dynamics of occurrences of emerging alien species worldwide. Even after many centuries of invasions the rate of emergence of new alien species is still high: One-quarter of first records during 2000-2005 were of species that had not been previously recorded anywhere as alien, though with large variation across taxa. Model results show that the high proportion of emerging alien species cannot be solely explained by increases in well-known drivers such as the amount of imported commodities from historically important source regions. Instead, these dynamics reflect the incorporation of new regions into the pool of potential alien species, likely as a consequence of expanding trade networks and environmental change. This process compensates for the depletion of the historically important source species pool through successive invasions. We estimate that 1-16% of all species on Earth, depending on the taxonomic group, qualify as potential alien species. These results suggest that there remains a high proportion of emerging alien species we have yet to encounter, with future impacts that are difficult to predict.

Hydrogen has been deemed as an ideal substitute fuel to fossil energy because of its renewability and the highest energy density among all chemical fuels. One of the most economical, ecofriendly, and high-performance ways of hydrogen production is electrochemical water splitting. Recently, 2D transition metal dichalcogenides (also known as 2D TMDs) showed their utilization potentiality as cost-effective hydrogen evolution reaction (HER) catalysts in water electrolysis. Herein, recent representative research efforts and systematic progress made in 2D TMDs are reviewed, and future opportunities and challenges are discussed. Furthermore, general methods of synthesizing 2D TMDs materials are introduced in detail and the advantages and disadvantages for some specific methods are provided. This explanation includes several important regulation strategies of creating more active sites, heteroatoms doping, phase engineering, construction of heterostructures, and synergistic modulation which are capable of optimizing the electrical conductivity, exposure to the catalytic active sites, and reaction energy barrier of the electrode material to boost the HER kinetics. In the last section, the current obstacles and future chances for the development of 2D TMDs electrocatalysts are proposed to provide insight into and valuable guidelines for fabricating effective HER electrocatalysts.

Early detection has the potential to reduce cancer mortality, but an effective screening test must demonstrate asymptomatic cancer detection years before conventional diagnosis in a longitudinal study. In the Taizhou Longitudinal Study (TZL), 123,115 healthy subjects provided plasma samples for long-term storage and were then monitored for cancer occurrence. Here we report the preliminary results of PanSeer, a noninvasive blood test based on circulating tumor DNA methylation, on TZL plasma samples from 605 asymptomatic individuals, 191 of whom were later diagnosed with stomach, esophageal, colorectal, lung or liver cancer within four years of blood draw. We also assay plasma samples from an additional 223 cancer patients, plus 200 primary tumor and normal tissues. We show that PanSeer detects five common types of cancer in 88% (95% CI: 80-93%) of post-diagnosis patients with a specificity of 96% (95% CI: 93-98%), We also demonstrate that PanSeer detects cancer in 95% (95% CI: 89-98%) of asymptomatic individuals who were later diagnosed, though future longitudinal studies are required to confirm this result. These results demonstrate that cancer can be non-invasively detected up to four years before current standard of care.

Non-fullerene acceptors based organic solar cells represent the frontier of the field, owing to both the materials and morphology manipulation innovations. Non-radiative recombination loss suppression and performance boosting are in the center of organic solar cell research. Here, we developed a non-monotonic intermediate state manipulation strategy for state-of-the-art organic solar cells by employing 1,3,5-trichlorobenzene as crystallization regulator, which optimizes the film crystallization process, regulates the self-organization of bulk-heterojunction in a non-monotonic manner, i.e., first enhancing and then relaxing the molecular aggregation. As a result, the excessive aggregation of non-fullerene acceptors is avoided and we have achieved efficient organic solar cells with reduced non-radiative recombination loss. In PM6:BTP-eC9 organic solar cell, our strategy successfully offers a record binary organic solar cell efficiency of 19.31% (18.93% certified) with very low non-radiative recombination loss of 0.190 eV. And lower non-radiative recombination loss of 0.168 eV is further achieved in PM1:BTP-eC9 organic solar cell (19.10% efficiency), giving great promise to future organic solar cell research.

Abstract Amorphous and heterojunction materials have been widely used in the field of electrocatalytic hydrogen evolution due to their unique physicochemical properties. However, the current used individual strategy still has limited effects. Hence efficient tailoring tactics with synergistic effect are highly desired. Herein, the authors have realized the deep optimization of catalytic activity by a constructing crystalline–amorphous CoSe 2 /CoP heterojunction. Benefiting from the strong electronic coupling at the interfaces, the d‐band center of the material moves further down compared to its crystalline–crystalline counterpart, optimizing the valence state and the H adsorption of Co and lowering the kinetic barrier of hydrogen evolution reaction (HER). The heterojunction shows an overpotential of 65 mV to drive a current density of 10 mA cm −2 in the acidic medium. Besides, it also shows competitive properties in both neutral and basic media. This work provides inspiration for optimizing the catalytic activity through combining a crystalline and amorphous heterojunction, which can be implemented for other transition metal compound electrocatalysts.

Adsorption is considered to be one of the most effective technologies widely used in global environmental protection areas. Modeling of experimental adsorption isotherm data is an essential way for predicting the mechanisms of adsorption, which will lead to an improvement in the area of adsorption science. In this paper, we employed three isotherm models, namely: Langmuir, Freundlich, and Dubinin-Radushkevich to correlate four sets of experimental adsorption isotherm data, which were obtained by batch tests in lab. The linearized and non-linearized isotherm models were compared and discussed. In order to determine the best fit isotherm model, the correlation coefficient (r2) and standard errors (S.E.) for each parameter were used to evaluate the data. The modeling results showed that non-linear Langmuir model could fit the data better than others, with relatively higher r2 values and smaller S.E. The linear Langmuir model had the highest value of r2, however, the maximum adsorption capacities estimated from linear Langmuir model were deviated from the experimental data.

Speech emotion recognition is challenging because of the affective gap between the subjective emotions and low-level features. Integrating multilevel feature learning and model training, deep convolutional neural networks (DCNN) has exhibited remarkable success in bridging the semantic gap in visual tasks like image classification, object detection. This paper explores how to utilize a DCNN to bridge the affective gap in speech signals. To this end, we first extract three channels of log Mel-spectrograms (static, delta, and delta delta) similar to the red, green, blue (RGB) image representation as the DCNN input. Then, the AlexNet DCNN model pretrained on the large ImageNet dataset is employed to learn high-level feature representations on each segment divided from an utterance. The learned segment-level features are aggregated by a discriminant temporal pyramid matching (DTPM) strategy. DTPM combines temporal pyramid matching and optimal Lp-norm pooling to form a global utterance-level feature representation, followed by the linear support vector machines for emotion classification. Experimental results on four public datasets, that is, EMO-DB, RML, eNTERFACE05, and BAUM-1s, show the promising performance of our DCNN model and the DTPM strategy. Another interesting finding is that the DCNN model pretrained for image applications performs reasonably good in affective speech feature extraction. Further fine tuning on the target emotional speech datasets substantially promotes recognition performance.

The number of alien plants escaping from cultivation into native ecosystems is increasing steadily. We provide an overview of the historical, contemporary and potential future roles of ornamental horticulture in plant invasions. We show that currently at least 75% and 93% of the global naturalised alien flora is grown in domestic and botanical gardens, respectively. Species grown in gardens also have a larger naturalised range than those that are not. After the Middle Ages, particularly in the 18th and 19th centuries, a global trade network in plants emerged. Since then, cultivated alien species also started to appear in the wild more frequently than non-cultivated aliens globally, particularly during the 19th century. Horticulture still plays a prominent role in current plant introduction, and the monetary value of live-plant imports in different parts of the world is steadily increasing. Historically, botanical gardens - an important component of horticulture - played a major role in displaying, cultivating and distributing new plant discoveries. While the role of botanical gardens in the horticultural supply chain has declined, they are still a significant link, with one-third of institutions involved in retail-plant sales and horticultural research. However, botanical gardens have also become more dependent on commercial nurseries as plant sources, particularly in North America. Plants selected for ornamental purposes are not a random selection of the global flora, and some of the plant characteristics promoted through horticulture, such as fast growth, also promote invasion. Efforts to breed non-invasive plant cultivars are still rare. Socio-economical, technological, and environmental changes will lead to novel patterns of plant introductions and invasion opportunities for the species that are already cultivated. We describe the role that horticulture could play in mediating these changes. We identify current research challenges, and call for more research efforts on the past and current role of horticulture in plant invasions. This is required to develop science-based regulatory frameworks to prevent further plant invasions.

Non-alcoholic fatty liver disease (NAFLD) is considered the most common liver disease. Dietary supplementation has become a promising strategy for managing NAFLD. Hesperetin, a citrus flavonoid, is mainly found in citrus fruits (oranges, grapefruit, and lemons) and possesses multiple pharmacological properties, including anti-cancer, anti-Alzheimer and anti-diabetic effects. However, the anti-NAFLD effect and mechanisms of hesperetin remain unclear. In this study, we investigated the therapeutic effect of hesperetin against NAFLD and the underlying mechanism in vitro and in vivo. In oleic acid (OA)-induced HepG2 cells, hesperetin upregulated antioxidant levels (SOD/GPx/GR/GCLC/HO-1) by triggering the PI3 K/AKT-Nrf2 pathway, alleviating OA-induced reactive oxygen species (ROS) overproduction and hepatotoxicity. Furthermore, hesperetin suppressed NF-κB activation and reduced inflammatory cytokine secretion (TNF-α and IL-6). More importantly, we revealed that this anti-inflammatory effect is attributed to reduced ROS overproduction by the Nrf2 pathway, as pre-treatment with Nrf2 siRNA or an inhibitor of superoxide dismutase (SOD) or/and glutathione peroxidase (GPx) abolished hesperetin-induced NF-κB inactivation and reductions in inflammatory cytokine secretion. In a rat model of high-fat diet (HFD)-induced NAFLD, we confirmed that hesperetin relieved hepatic steatosis, oxidative stress, inflammatory cell infiltration and fibrosis. Moreover, hesperetin activated the PI3 K/AKT-Nrf2 pathway in the liver, increasing antioxidant expression and inhibiting NF-κB activation and inflammatory cytokine secretion. In summary, our results demonstrate that hesperetin ameliorates hepatic oxidative stress through the PI3 K/AKT-Nrf2 pathway and that this antioxidative effect further suppresses NF-κB-mediated inflammation during NAFLD progression. Thus, our study suggests that hesperetin may be an effective dietary supplement for improving NAFLD by suppressing hepatic oxidative stress and inflammation.

There is increasing evidence showing that the dynamic changes in the gut microbiota can alter brain physiology and behavior. Cognition was originally thought to be regulated only by the central nervous system. However, it is now becoming clear that many non-nervous system factors, including the gut-resident bacteria of the gastrointestinal tract, regulate and influence cognitive dysfunction as well as the process of neurodegeneration and cerebrovascular diseases. Extrinsic and intrinsic factors including dietary habits can regulate the composition of the microbiota. Microbes release metabolites and microbiota-derived molecules to further trigger host-derived cytokines and inflammation in the central nervous system, which contribute greatly to the pathogenesis of host brain disorders such as pain, depression, anxiety, autism, Alzheimer's diseases, Parkinson's disease, and stroke. Change of blood-brain barrier permeability, brain vascular physiology, and brain structure are among the most critical causes of the development of downstream neurological dysfunction. In this review, we will discuss the following parts: Overview of technical approaches used in gut microbiome studiesMicrobiota and immunityGut microbiota and metabolitesMicrobiota-induced blood-brain barrier dysfunctionNeuropsychiatric diseases ■ Stress and depression■ Pain and migraine■ Autism spectrum disordersNeurodegenerative diseases ■ Parkinson's disease■ Alzheimer's disease■ Amyotrophic lateral sclerosis■ Multiple sclerosisCerebrovascular disease ■ Atherosclerosis■ Stroke■ Arteriovenous malformationConclusions and perspectives.

Lightweight polymeric foam is highly attractive as thermal insulation materials for energy-saving buildings but is plagued by its inherent flammability. Fire-retardant coatings are suggested as an effective means to solve this problem. However, most of the existing fire-retardant coatings suffer from poor interfacial adhesion to polymeric foam during use. In nature, snails and tree frogs exhibit strong adhesion to a variety of surfaces by interfacial hydrogen-bonding and mechanical interlocking, respectively. Inspired by their adhesion mechanisms, we herein rationally design fire-retardant polymeric coatings with phase-separated micro/nanostructures via a facile radical copolymerization of hydroxyethyl acrylate (HEA) and sodium vinylsulfonate (VS). The resultant waterborne poly(VS-co-HEA) copolymers exhibit strong interfacial adhesion to rigid polyurethane (PU) foam and other substrates, better than most of the current adhesives because of the combination of interfacial hydrogen-bonding and mechanical interlocking. Besides a superhydrophobic feature, the poly(VS-co-HEA)-coated PU foam can self-extinguish a flame, exhibiting a desired V-0 rating during vertical burning and low heat and smoke release due to its high charring capability, which is superior to its previous counterparts. Moreover, the foam thermal insulation is well-preserved and agrees well with theoretical calculations. This work offers a facile biomimetic strategy for creating advanced adhesive fire-retardant polymeric coatings for many flammable substrates.

Pyridinium salts are valuable building blocks, which have been widely applied in various organic transformations during the past few decades. In particular, N-functionalized pyridinium salts have been explored as convenient radical precursors, which would go through reductive single-electron transfer. As a result, the chemistry of such pyridinium compounds for generating carbon-, nitrogen-, and oxygen-centered radicals has been witnessed, and a remarkable progress has been achieved, making it a hot topic over the last five years. This Review describes recent advances in the area of pyridinium salts as radical precursors, concerning the development of radical reactions involving pyridinium salts in organic synthesis.

Emotion recognition is challenging due to the emotional gap between emotions and audio-visual features. Motivated by the powerful feature learning ability of deep neural networks, this paper proposes to bridge the emotional gap by using a hybrid deep model, which first produces audio-visual segment features with Convolutional Neural Networks (CNNs) and 3D-CNN, then fuses audio-visual segment features in a Deep Belief Networks (DBNs). The proposed method is trained in two stages. First, CNN and 3D-CNN models pre-trained on corresponding large-scale image and video classification tasks are fine-tuned on emotion recognition tasks to learn audio and visual segment features, respectively. Second, the outputs of CNN and 3D-CNN models are combined into a fusion network built with a DBN model. The fusion network is trained to jointly learn a discriminative audio-visual segment feature representation. After average-pooling segment features learned by DBN to form a fixed-length global video feature, a linear Support Vector Machine is used for video emotion classification. Experimental results on three public audio-visual emotional databases, including the acted RML database, the acted eNTERFACE05 database, and the spontaneous BAUM-1s database, demonstrate the promising performance of the proposed method. To the best of our knowledge, this is an early work fusing audio and visual cues with CNN, 3D-CNN, and DBN for audio-visual emotion recognition.

BACKGROUND AND OBJECTIVES: Ischemic stroke (IS), 1 of the 2 main subtypes of stroke, occurs because of brain ischemia caused by thrombosis of a cerebral blood vessel. IS is one of the most important neurovascular causes of death and disability. It is affected by many risk factors, such as smoking and a high body mass index (BMI), which are also critical in the preventive control of other cardiovascular and cerebrovascular diseases. However, there are still few systematic analyses of the current and predicted disease burden and the attributable risk factors of IS. METHODS: Based on the Global Burden of Disease 2019 database, we used age-standardized mortality rate and disability-adjusted life year to systematically present the geographical distribution and trends of IS disease burden worldwide from 1990 to 2019 by calculating the estimated annual percentage change and to analyze and predict the death number of IS accounted by 7 major risk factors for 2020-2030. RESULTS: Between 1990 and 2019, the global number of IS deaths increased from 2.04 million to 3.29 million and is expected to increase further to 4.90 million by 2030. The downward trend was more pronounced in women, young people, and high sociodemographic index (SDI) regions. At the same time, a study of attributable risk factors of IS found that 2 behavioral factors, smoking and diet in high sodium, and 5 metabolic factors, including high systolic blood pressure, high low-density lipoprotein cholesterol, kidney dysfunction, high fasting plasma glucose, and a high BMI, are major contributors to the increased disease burden of IS now and in the future. DISCUSSION: Our study provides the first comprehensive summary for the past 30 years and the prediction of the global burden of IS and its attributable risk factors until 2030, providing detailed statistics for decision-making on the prevention and control of IS globally. An inadequate control of the 7 risk factors would lead to an increased disease burden of IS in young people, especially in low SDI regions. Our study identifies high-risk populations and helps public health professionals develop targeted preventive strategies to reduce the global disease burden of IS.

Abstract: Despite growing extensive applications, until now the poor thermal stability and flame retardancy properties of wood-plastic composites (WPC) remain effectively unsolved. Meanwhile, industrial lignin has emerged as a potential component for polymer composites due to many advantages including abundance, rich reactive functional groups, high carbon content and tailored capability for chemical transformations. Herein, we have fabricated one biobased flame retardant based on industrial lignin chemically grafted with phosphorus, nitrogen and copper elements, as the functional additive for the WPC. Compared with unmodified lignin (O-lignin), functionalized lignin (F-lignin) is more effective to improve the thermal stability and flame retardancy of WPC because of presence of the flame retardancy elements (P and N) and the catalytic effect of Cu2+ on the char-formation. The presence of F-lignin not only reduces the heat release rate, total heat release and slows down the combustion process but also decreases total smoke production rate during combustion. The char residue shows that it is the increased char residues and its continuous compact char formed during burning that are responsible for enhanced flame retardancy properties. This work suggests a novel green strategy for improving flame retardancy performance of WPC and promoting the utilization of industrial lignin.