Assessing biogeochemical effects and best management practice for a wheat–maize cropping system using the DNDC model

Contemporary agriculture is shifting from a single-goal to a multi-goal strategy, which in turn requires choosing best management practice (BMP) based on an assessment of the biogeochemical effects of management alternatives. The bottleneck is the capacity of predicting the simultaneous effects of different management practice scenarios on multiple goals and choosing BMP among scenarios. The denitrification–decomposition (DNDC) model may provide an opportunity to solve this problem. We validated the DNDC model (version 95) using the observations of soil moisture and temperature, crop yields, aboveground biomass and fluxes of net ecosystem exchange of carbon dioxide, methane, nitrous oxide (N2O), nitric oxide (NO) and ammonia (NH3) from a wheat–maize cropping site in northern China. The model performed well for these variables. Then we used this model to simulate the effects of management practices on the goal variables of crop yields, NO emission, nitrate leaching, NH3 volatilization and net emission of greenhouse gases in the ecosystem (NEGE). Results showed that no-till and straw-incorporated practices had beneficial effects on crop yields and NEGE. Use of nitrification inhibitors decreased nitrate leaching and N2O and NO emissions, but they significantly increased NH3 volatilization. Irrigation based on crop demand significantly increased crop yield and decreased nitrate leaching and NH3 volatilization. Crop yields were hardly decreased if nitrogen dose was reduced by 15% or irrigation water amount was reduced by 25%. Two methods were used to identify BMP and resulted in the same BMP, which adopted the current crop cultivar, field operation schedules and full straw incorporation and applied nitrogen and irrigation water at 15 and 25% lower rates, respectively, than the current use. Our study indicates that the DNDC model can be used as a tool to assess biogeochemical effects of management alternatives and identify BMP

Screening of Multimeric β-Xylosidases from the Gut Microbiome of a Higher Termite, \u3cem\u3eGlobitermes brachycerastes\u3c/em\u3e

Termite gut microbiome is a rich reservoir for glycoside hydrolases, a suite of enzymes critical for the degradation of lignocellulosic biomass. To search for hemicellulases, we screened 12,000 clones from a fosmid gut library of a higher termite, Globitermes brachycerastes. As a common Southeastern Asian genus, Globitermes distributes predominantly in tropical rain forests and relies on the lignocellulases from themselves and bacterial symbionts to digest wood. In total, 22 positive clones with β-xylosidase activity were isolated, in which 11 representing different restriction fragment length polymorphism (RFLP) patterns were pooled and subjected to 454 pyrosequencing. As a result, eight putative β-xylosidases were cloned and heterologously expressed in Escherichia coli BL21 competent cells. After purification using Ni-NTA affinity chromatography, recombinant G. brachycerastes symbiotic β-xylosidases were characterized enzymatically, including their pH and temperature optimum. In addition to β-xylosidase activity, four of them also exhibited either β-glucosidase or α-arabinosidases activities, suggesting the existence of bifunctional hemicellulases in the gut microbiome of G. brachycerastes. In comparison to multimeric protein engineering, the involvement of naturally occurring multifunctional biocatalysts streamlines the genetic modification procedures and simplifies the overall production processes. Alternatively, these multimeric enzymes could serve as the substitutes for β-glucosidase, β-xylosidase and α-arabinosidase to facilitate a wide range of industrial applications, including food processing, animal feed, environment and waste management, and biomass conversion

Layer-refined Graph Convolutional Networks for Recommendation

Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological structure of the user-item interaction graph. However, these GCN-based recommendation models not only suffer from over-smoothing when stacking too many layers but also bear performance degeneration resulting from the existence of noise in user-item interactions. In this paper, we first identify a recommendation dilemma of over-smoothing and solution collapsing in current GCN-based models. Specifically, these models usually aggregate all layer embeddings for node updating and achieve their best recommendation performance within a few layers because of over-smoothing. Conversely, if we place learnable weights on layer embeddings for node updating, the weight space will always collapse to a fixed point, at which the weighting of the ego layer almost holds all. We propose a layer-refined GCN model, dubbed LayerGCN, that refines layer representations during information propagation and node updating of GCN. Moreover, previous GCN-based recommendation models aggregate all incoming information from neighbors without distinguishing the noise nodes, which deteriorates the recommendation performance. Our model further prunes the edges of the user-item interaction graph following a degree-sensitive probability instead of the uniform distribution. Experimental results show that the proposed model outperforms the state-of-the-art models significantly on four public datasets with fast training convergence. The implementation code of the proposed method is available at https://github.com/enoche/ImRec.Comment: 12 pages, 5 figure

Hélice Tríplice: inovação e empreendedorismo universidade-indústria-governo

A Hélice Tríplice tornou-se um modelo reconhecido internacionalmente, que está no âmago da disciplina emergente de estudos de inovação, e um guia de políticas e práticas nos âmbitos local, regional, nacional e multinacional. As interações universidade-indústria-governo, que formam uma “hélice tríplice” de inovação e empreendedorismo, são a chave para o crescimento econômico e o desenvolvimento social baseados no conhecimento. O artigo apresenta a origem do modelo, seu conceito, dinâmica, fontes e rotas alternativas.The Triple Helix has developed into an internationally recognized model that is at the heart of the emerging discipline of innovation studies, and a guide to policy and practice at the local, regional, national and multinational levels. University-industry-government interactions, forming a “triple helix” for innovation and entrepreneurship, are the key to knowledge-based economic growth and social development. The article discusses the model’s origin, concept, dynamics, sources, and alternate routes

Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub-optimal performance on target languages, the pseudo labels are often noisy and limit the overall performance. In this work, we aim to improve self-training for cross-lingual NER by combining representation learning and pseudo label refinement in one coherent framework. Our proposed method, namely ContProto mainly comprises two components: (1) contrastive self-training and (2) prototype-based pseudo-labeling. Our contrastive self-training facilitates span classification by separating clusters of different classes, and enhances cross-lingual transferability by producing closely-aligned representations between the source and target language. Meanwhile, prototype-based pseudo-labeling effectively improves the accuracy of pseudo labels during training. We evaluate ContProto on multiple transfer pairs, and experimental results show our method brings in substantial improvements over current state-of-the-art methods.Comment: Accepted by ACL202

Molecular analysis and expression of phenylalanine ammonia-lyase from poinsettia (Euphorbia pulcherrima willd.)

Phenylalanine ammonia-lyase (PAL, EC 4.3.1.5) is a key regulatory enzyme that link primary and secondary metabolism in plants by catalyzing the conversion of l-phenylalanine to cinnamic acid. In this study, the cDNA and genomic DNA of PAL (named EpPAL) in poinsettia (Euphorbia pulcherrima willd.) were isolated and submitted in GenBank with accession number FJ594466 and FJ943503, respectively. The full-length of cDNA was 2429 bp with a poly (A) tail and contains a 2166-bp open reading frame (ORF) encoding 721 amino acids. The sequence of genomic DNA was 3315 bp, and the transcript was divided into two exons by an 886-bp long intron which located at 416 bp downstream initiation codon. Expression analysis of EpPAL in poinsettia revealed that expression levels were higher in roots and bracts, but lower in stems and green leaves. Meanwhile, expression levels increased in the order: green leaves - turning color leaves - bracts, which were consistent with their anthocyanin content during growth and development of bracts. The curve of diurnal variation of EpPAL expression level in bracts exhibited two highest peaks at 9:00 and 18:00, respectively, and reached the lowest level at 12:00 in a clear day. With the maturation and senescence of bracts, expression levels reduced gradually in both green leaves and bracts, but decreased more rapidly in bracts than green leaves.Keywords: Cloning, expression, phenylalanine ammonia-lyase, poinsetti

SelfCF: A Simple Framework for Self-supervised Collaborative Filtering

Collaborative filtering (CF) is widely used to learn an informative latent representation of a user or item from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. That is, observed user-item pairs are treated as positive instances; unobserved pairs are considered as negative instances and are sampled under a defined distribution for training. Training with negative sampling on large datasets is computationally expensive. Further, negative items should be carefully sampled under the defined distribution, in order to avoid selecting an observed positive item in the training dataset. Unavoidably, some negative items sampled from the training dataset could be positive in the test set. Recently, self-supervised learning (SSL) has emerged as a powerful tool to learn a model without negative samples. In this paper, we propose a self-supervised collaborative filtering framework (SelfCF), that is specially designed for recommender scenario with implicit feedback. The main idea of SelfCF is to augment the output embeddings generated by backbone networks, because it is infeasible to augment raw input of user/item ids. We propose and study three output perturbation techniques that can be applied to different types of backbone networks including both traditional CF models and graph-based models. By encapsulating two popular recommendation models into the framework, our experiments on three datasets show that the best performance of our framework is comparable or better than the supervised counterpart. We also show that SelfCF can boost up the performance by up to 8.93\% on average, compared with another self-supervised framework as the baseline. Source codes are available at: https://github.com/enoche/SelfCF

Characteristics of multiple‐year nitrous oxide emissions from conventional vegetable fields in southeastern China

The annual and interannual characteristics of nitrous oxide (N2O) emissions from conventional vegetable fields are poorly understood. We carried out 4 year measurements of N2O fluxes from a conventional vegetable cultivation area in the Yangtze River delta. Under fertilized conditions subject to farming practices, approximately 86% of the annual total N2O release occurred following fertilization events. The direct emission factors (EFd) of the 12 individual vegetable seasons investigated ranged from 0.06 to 14.20%, with a mean of 3.09% and a coefficient of variation (CV) of 142%. The annual EFd varied from 0.59 to 4.98%, with a mean of 2.88% and an interannual CV of 74%. The mean value is much larger than the latest default value (1.00%) of the Intergovernmental Panel on Climate Change. Occasional application of lagoon‐stored manure slurry coupled with other nitrogen fertilizers, or basal nitrogen addition immediately followed by heavy rainfall, accounted for a substantial portion of the large EFds observed in warm seasons. The large CVs suggest that the emission factors obtained from short‐term observations that poorly represent seasonality and/or interannual variability will inevitably yield large uncertainties in inventory estimation. The results of this study indicate that conventional vegetable fields associated with intensive nitrogen addition, as well as occasional applications of manure slurry, may substantially account for regional N2O emissions. However, this conclusion needs to be further confirmed through studies at multiple field sites. Moreover, further experimental studies are needed to test the mitigation options suggested by this study for N2O emissions from open vegetable fields

Adrenomedullin delivery in microsphere-scaffold composite for remodeling of the alveolar bone following tooth extraction: an experimental study in the rat

BACKGROUND: Alveolar ridge resorption, as a significant problem in implant and restorative dentistry, has long been considered as an inevitable outcome following tooth extraction. Recently, adrenomedullin (ADM) is reported to be able to stimulate the proliferation and migration of various cells including osteoblasts. The purpose of this study was to investigate the influence of local ADM application in the tooth extraction socket in vivo. METHODS: Chitosan micropheres were developed by an emulsion-ionic cross-linking method for ADM delivery. Poly (L -lactic-co-glycolic) acid (PLGA) and nano-hydroxyapatite (nHA) were used to prepare scaffolds to contain the micrspheres with ADM. In vivo experiment was evaluated by transplanting the composite into the rat socket right after the incisor extraction. After 4, 8, 12 weeks implantation, radiographic and histological tests were carried out to evaluate the effect of released ADM on the alveolar bone. RESULTS: The microspheres had a spherical structure and a relative rough and uniform surface, and the particle size was under a normal distribution, with the average diameter of 38.59 μm. The scaffolds had open and interconnected pores. In addition, the high porosity of the composite was 88.93%. Radiographic and histological examination revealed that the PLGA/nHA/CMs/ADM composite could accelerate the alveolar bone remodeling and reduce the residual ridge resorption compared with the PLGA/nHA/CMs scaffold. CONCLUSIONS: The results of this study suggest that local application of ADM has the potential to preserve the residual alveolar ridge and accelerate the alveolar bone remodeling