Taxonomic and functional homogenization of an endemic desert fish fauna

Aim The highly endemic fishes of the arid Southwest USA have been heavily impacted by human activities resulting in one of the most threatened fish faunas in the world. The aim of this study was to examine the patterns and drivers of taxonomic and functional beta diversity of freshwater fish in the Lower Colorado River Basin across the 20th century. Location Lower Colorado River Basin (LCRB). Methods The taxonomic and functional similarities of watersheds were quantified to identify patterns of biotic homogenization or differentiation over the period 1900–1999. Path analysis was used to identify the relative influence of dam density, urban land use, precipitation regimes and non‐native species richness on observed changes in fish faunal composition. Results The fish fauna of the LCRB has become increasingly homogenized, both taxonomically (1.1% based on β_sim index) and functionally (6.2% based on Bray–Curtis index), over the 20th century. The rate of homogenization varied substantially; range declines of native species initially caused taxonomic differentiation (?7.9% in the 1960s), followed by marginal homogenization (observed in the 1990s) in response to an influx of non‐native species introductions. By contrast, functional homogenization of the basin was evident considerably earlier (in the 1950s) because of the widespread introduction of non‐native species sharing similar suites of biological traits. Path analysis revealed that both taxonomic and functional homogenization were positively related to the direct and indirect (facilitation by dams and urbanization) effects of non‐native species richness. Main conclusions Our study simultaneously examines rates of change in multiple dimensions of the homogenization process. For the endemic fish fauna of the LCRB, we found that the processes of taxonomic and functional homogenization are highly dynamic over time, varying both in terms of the magnitude and rate of change over the 20th century.     

Dark diversity in Amazonian stream fish communities: What factors determine species absence along environmental gradients?

1. Species distribution models often fail to predict observed patterns of species diversity, and this is because some species within a regional pool that are tolerant of conditions at a given location may nevertheless be absent from the local community. These missing species have been termed “dark diversity”. In the present study, we investigated which factors explain dark diversity among fish assemblages in Amazonian streams.
2. We sampled 71 streams in areas with different types of land use within two river basins and estimated dark diversity from patterns of species co-occurrence, using Beals’ index, along environmental gradients. From this procedure, taxa are designated as dark diversity components when they are absent from a given stream, but often co-occur with the local species at other streams, indicating similar ecological requirements. We used generalised linear models both to determine whether environmental or landscape variables, connectivity, instream environmental heterogeneity or some combination of these factors explained dark diversity of fishes, and to evaluate whether ecomorphology is associated with the extent to which a species contributes to dark diversity and which specific traits contribute the most to explaining variation in dark diversity.
3. Mean local diversity exceeded observed dark diversity. The magnitude of dark diversity was directly associated with the proportion of secondary forest in the immediate catchment and with the index of proximity to anthropogenic impact. Species that have high affinity for environments with higher current velocity, low swimming ability and that capture food mainly on the surface contributed more to dark diversity, which suggests that swimming ability, habitat preference and aspects related to diet are key predictors of the probability that a given species will be present at locations with suitable habitat.
4. Our findings reinforce the idea that dark diversity results from interactions between species traits and environmental factors, including anthropogenic impacts. Understanding the interplay among environmental factors and species traits that contribute to dark diversity provides targets for improved ecosystem restoration and sustainability of native species assemblages.

     

上海农作物种质资源数据库的设计和构建

随着农作物种质资源数量的不断增加,种质资源交流的日益频繁以及种质资源利用的逐步深入,农作物种质资源数据库的建设对农作物的应用研究和基础性研究至关重要。上海市农业生物基因中心借助于本中心保存的近21万份种质资源的信息,开发了一套上海农作物种质资源数据库,本文主要阐述了该数据库设计和构建的目的、原则和数据库的架构、运行环境以及主要功能模块。上海农作物种质资源数据库采用了B/S结构,平台系统利用多层逻辑架构实现了对农作物种质资源数据信息的收集、存储、查询、统计分析等功能。外部用户通过浏览器即可访问平台系统,浏览查询种质资源信息。内部管理员可使用平台的受限功能,需要认证成功后才可使用后台管理功能,实现对整个平台数据的管理。注册用户还可通过基因资源数据库提交引种申请,管理员结合管理信息系统实现对外供种全流程的网络化,借此可提高工作效率,增加工作流程的透明度。     

Polydatin promotes the neuronal differentiation of bone marrow mesenchymal stem cells in vitro and in vivo: Involvement of Nrf2 signalling pathway

Bone marrow mesenchymal stem cell (BMSC) transplantation represents a promising repair strategy following spinal cord injury (SCI), although the therapeutic effects are minimal due to their limited neural differentiation potential. Polydatin (PD), a key component of the Chinese herb Polygonum cuspidatum, exerts significant neuroprotective effects in various central nervous system disorders and protects BMSCs against oxidative injury. However, the effect of PD on the neuronal differentiation of BMSCs, and the underlying mechanisms remain inadequately understood. In this study, we induced neuronal differentiation of BMSCs in the presence of PD, and analysed the Nrf2 signalling and neuronal differentiation markers using routine molecular assays. We also established an in vivo model of SCI and assessed the locomotor function of the mice through hindlimb movements and electrophysiological measurements. Finally, tissue regeneration was evaluated by H&E staining, Nissl staining and transmission electron microscopy. PD (30 μmol/L) markedly facilitated BMSC differentiation into neuron‐like cells by activating the Nrf2 pathway and increased the expression of neuronal markers in the transplanted BMSCs at the injured spinal cord sites. Furthermore, compared with either monotherapy, the combination of PD and BMSC transplantation promoted axonal rehabilitation, attenuated glial scar formation and promoted axonal generation across the glial scar, thereby enhancing recovery of hindlimb locomotor function. Taken together, PD augments the neuronal differentiation of BMSCs via Nrf2 activation and improves functional recovery, indicating a promising new therapeutic approach against SCI.     

Activation of Ni Particles into Single Ni–N Atoms for Efficient Electrochemical Reduction of CO2

Electrochemical reduction of carbon dioxide (CO₂) to fuels and value‐added industrial chemicals is a promising strategy for keeping a healthy balance between energy supply and net carbon emissions. Here, the facile transformation of residual Ni particle catalysts in carbon nanotubes into thermally stable single Ni atoms with a possible NiN₃ moiety is reported, surrounded with a porous N‐doped carbon sheath through a one‐step nanoconfined pyrolysis strategy. These structural changes are confirmed by X‐ray absorption fine structure analysis and density functional theory (DFT) calculations. The dispersed Ni single atoms facilitate highly efficient electrocatalytic CO₂ reduction at low overpotentials to yield CO, providing a CO faradaic efficiency exceeding 90%, turnover frequency approaching 12 000 h^?1, and metal mass activity reaching about 10 600 mA mg^?1, outperforming current state‐of‐the‐art single atom catalysts for CO₂ reduction to CO. DFT calculations suggest that the Ni@N₃ (pyrrolic) site favors *COOH formation with lower free energy than Ni@N₄, in addition to exothermic CO desorption, hence enhancing electrocatalytic CO₂ conversion. This finding provides a simple, scalable, and promising route for the preparation of low‐cost, abundant, and highly active single atom catalysts, benefiting future practical CO₂ electrolysis.     

Nonwoven rGO Fiber‐Aramid Separator for High‐Speed Charging and Discharging of Li Metal Anode

Li metal, which has a high theoretical specific capacity and low redox potential, is considered to the most promising anode material for next‐generation Li ion‐based batteries. However, it also exhibits a disadvantageous solid electrolyte interphase (SEI) layer problem that needs to be resolved. Herein, an advanced separator composed of reduced graphene oxide fiber attached to aramid paper (rGOF‐A) is introduced. When rGOF‐A is applied, F^? anions, generated from the decomposition of the LiPF₆ electrolyte during the SEI layer formation process form semi‐ionic C? F bonds along the surface of rGOF. As Li⁺ ions are plated, the “F‐doped” rGO surface induces the formation of LiF, which is known as a component of a chemically stable SEI, therefore it helps the Li metal anode to operate stably at a high current of 20 mA cm^?2 with a high capacity of 20 mAh cm^?2. The proposed rGOF‐A separator successfully achieves a stable SEI layer that could resolve the interfacial issues of the Li metal anode.     

Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Atomic catalysts (AC) are emerging as a highly attractive research topic, especially in sustainable energy fields. Lack of a full picture of the hydrogen evolution reaction (HER) impedes the future development of potential electrocatalysts. In this work, the systematic investigation of the HER process in graphdyine (GDY) based AC is presented in terms of the adsorption energies, adsorption trend, electronic structures, reaction pathway, and active sites. This comprehensive work innovatively reveals GDY based AC for HER covering all the transition metals (TM) and lanthanide (Ln) metals, enabling the screening of potential catalysts. The density functional theory (DFT) calculations carefully explore the HER performance beyond the comparison of sole H adsorption. Therefore, the screened catalysts candidates not only match with experimental results but also provide significant references for novel catalysts. Moreover, the machine learning (ML) technique bag‐tree approach is innovatively utilized based on the fuzzy model for data separation and converse prediction of the HER performance, which indicates a similar result to the theoretical calculations. From two independent theoretical perspectives (DFT and ML), this work proposes pivotal guidelines for experimental catalyst design and synthesis. The proposed advanced research strategy shows great potential as a general approach in other energy‐related areas.