Amplified Arctic warming by phytoplankton under greenhouse warming

Phytoplankton have attracted increasing attention in climate science due to their impacts on climate systems. A new generation of climate models can now provide estimates of future climate change, considering the biological feedbacks through the development of the coupled physical–ecosystem model. Here we present the geophysical impact of phytoplankton, which is often overlooked in future climate projections. A suite of future warming experiments using a fully coupled ocean−atmosphere model that interacts with a marine ecosystem model reveals that the future phytoplankton change influenced by greenhouse warming can amplify Arctic surface warming considerably. The warming-induced sea ice melting and the corresponding increase in shortwave radiation penetrating into the ocean both result in a longer phytoplankton growing season in the Arctic. In turn, the increase in Arctic phytoplankton warms the ocean surface layer through direct biological heating, triggering additional positive feedbacks in the Arctic, and consequently intensifying the Arctic warming further. Our results establish the presence of marine phytoplankton as an important potential driver of the future Arctic climate changes.Phytoplankton, aquatic photosynthetic microalgae, play a key role in marine ecology, forming the foundation of the marine food chain. Besides this ecological importance, the climatic importance of phytoplankton is also evident, given their role in carbon fixation, which potentially reduces human-induced carbon dioxide (CO₂) in the atmosphere (1–3). The great strides made in the field of biogeochemical modeling have improved climate models, enabling the investigation of carbon−climate feedback, such as diagnosing the strength of biogeochemical feedback and quantifying its importance in total carbon cycle responses. In fact, several modeling groups provide future climate projections including the biogeochemical process, as seen in a recent version of the Coupled Model Intercomparison Project (CMIP), i.e., CMIP5.In addition to their biogeochemical feedback, phytoplankton also modify physical properties of the ocean. Chlorophyll and related pigments in phytoplankton affect the radiant heating in the ocean by decreasing both the ocean surface albedo and shortwave penetration (4–6). Thus, higher phytoplankton biomass generally results in warmer ocean surface layer. This biogeophysical feedback is known to significantly impact the global climate (7–10) and large-scale climate variability, such as the El Niño–Southern Oscillation (11–13) and Indian Ocean dipole (14). Unlike biogeochemical feedback, however, biogeophysical feedback has been overlooked in many future climate projections simulated by state-of-the-art climate models, even in projections by so-called Earth System Models that include interactive marine ecosystem components.Greenhouse warming generally involves changes in physical fields that inevitably affect growth factors of phytoplankton such as temperature, light, and nutrients. Hence, they lead to changes in phytoplankton responding to future climate warming. Recent analyses based on the historical observation of phytoplankton and the future phytoplankton population estimated by modeling works have suggested substantial future changes in global phytoplankton, with the opposite sign of their trends in different regions (15–17). Such climate change-induced phytoplankton response would impact climate systems, given the aforementioned biological feedbacks.Previous studies discovered an increase in the annual area-integrated primary production in the Arctic, which is caused by the thinning and melting of sea ice and the corresponding increase in the phytoplankton growing area (18, 19). Considering that the biogeophysical feedback of phytoplankton leads to a warmer ocean surface layer, the increased area of Arctic phytoplankton bloom can induce first-order warming in the Arctic Ocean. The Arctic is a particularly vulnerable region where various positive feedback processes are involved, such as ice albedo feedback, temperature lapse rate feedback, and water vapor/cloud feedback (20–22). Thus, Arctic warming is generally greater than the globally averaged warming, a phenomenon known as Arctic amplification (23). Due to such strong positive feedback processes, an initial warming induced by the Arctic phytoplankton increase can be amplified and can potentially contribute to the Arctic amplification. In this sense, recent trends in annual mean surface temperature, sea ice, and chlorophyll may suggest the potential role of biogeophysical feedback on the current Arctic warming trend (Fig. 1). The strong trends of surface warming and the related sea ice reduction appear over Barents, Kara, Laptev, and Chukchi Seas. The pattern of increased annual mean chlorophyll, which can be caused by stronger Arctic phytoplankton bloom or by extended growing season, is closely linked with that of surface warming and ice melting trends. Although, in this observational result, the causality of their relationship and the quantitative impact of phytoplankton cannot be established, the similar patterns between the different variables suggest the possibility of interactive phytoplankton−climate feedback in the Arctic.Open in a separate windowFig. 1.Observational Arctic climate trends in recent warming. Annual mean trends of observational (A) surface temperature, (B) sea ice concentration, (C) ice-free days, and (D) chlorophyll over the period 1998–2013 when the satellite-retrieved chlorophyll data are available. The ice-free days are defined as the number of days when sea ice concentration is lower than 5%.The same feedback loop may hold for the future. If global warming continues with increased ice-free regions as projected by most climate models, the phytoplankton growing area would increase in the Arctic Ocean, and this, in turn, may intensify the Arctic amplification. However, most future climate projections have been produced without considering the impact of biogeophysical feedback, as mentioned earlier. Therefore, the effects of phytoplankton feedback need to be investigated in the context of the phytoplankton−Arctic warming hypothesis.     

Effect of reducing frequency of augmented feedback on manual dexterity training and its retention

OBJECTIVE: The study addressed the impact of the frequency of tutorial-enriched augmented visual feedback, provided by a virtual simulation system (DentSim), on the skill acquisition for a cavity preparation task in novice dental students. METHODS: Thirty-six subjects were assigned to two training groups and a control group. The task consisted of a geometrical cross preparation on the lower left first molar. All subjects performed a pre-test to assess their basic skill level. The training groups received simulation feedback, enriched with tutorial information, across acquisition. One group trained under continuous augmented feedback, while a second group trained under an intermittent (66% of the time) feedback. At both 1-day and 4-month interval, subjects performed a retention test to explore learning specific effects. Two transfer tests were added to assess the extrapolation of the learned skills to an adjacent molar. All tests were performed in the absence of feedback. A control group performed all the tests, without preceding training. All preparations were graded by the simulation system. RESULTS: The training groups performed similarly across acquisition and improved with practice (ANOVA, P<0.001). After 1 day and 4 months of no practice, the training groups outperformed the control group on a retention test (ANOVA, P<0.001) and transfer test (ANOVA, P<0.001). CONCLUSIONS: Performance and learning of a cavity preparation task on a simulation unit was independent of the frequency of tutorial-enriched augmented visual feedback within the range tested. Training sessions on a simulation unit could be alternated with training sessions in the traditional phantom head laboratory.     

A pilot study comparing the effectiveness of conventional training and virtual reality simulation in the skills acquisition of junior dental students

The use of virtual reality (VR) in the training of operative dentistry is a recent innovation and little research has been published on its efficacy compared to conventional training methods. To evaluate possible benefits, junior undergraduate dental students were randomly assigned to one of three groups: group 1 as taught by conventional means only; group 2 as trained by conventional means combined with VR repetition and reinforcement (with access to a human instructor for operative advice); and group 3 as trained by conventional means combined with VR repetition and reinforcement, but without instructor evaluation/advice, which was only supplied via the VR-associated software. At the end of the research period, all groups executed two class 1 preparations that were evaluated blindly by 'expert' trainers, under traditional criteria (outline, retention, smoothness, depth, wall angulation and cavity margin index). Analyses of resulting scores indicated a lack of significant differences between the three groups except for scores for the category of 'outline form', for group 2, which produced significantly lower (i.e. better) scores than the conventionally trained group. A statistical comparison between scores from two 'expert' examiners indicated lack of agreement, despite identical written and visual criteria being used for evaluation by both. Both examiners, however, generally showed similar trends in evaluation. An anonymous questionnaire suggested that students recognized the benefits of VR training (e.g. ready access to assessment, error identification and how they can be corrected), but the majority felt that it would not replace conventional training methods (95%), although participants recognized the potential for development of VR systems in dentistry. The most common reasons cited for the preference of conventional training were excessive critical feedback (55%), lack of personal contact (50%) and technical hardware difficulties (20%) associated with VR-based training.     

消化内镜专科护士培训中应用目标反馈教学的效果研究

目的　探讨目标反馈教学在消化内镜专科护士培训中的应用效果。方法　以参加重庆市消化内镜专科护士培训的68名学员为研究对象,将2019年的30名学员作为观察组,采用目标反馈教学法进行培训;2018年的38名学员作为对照组,采用传统培训方法。培训结束后,从理论成绩、技能操作成绩及综合成绩3个方面评价并比较两组学员的培训效果,同时针对学员的满意度进行问卷调查。采用SPSS 25.0进行t检验、卡方检验和Mann-Whitney U检验。结果　观察组学员的技能操作成绩（84.90±4.92）、综合成绩（86.30±4.62）高于对照组（82.39±4.10）（83.86±5.10）,差异有统计学意义（P<0.05）;理论成绩差异不大,无统计学意义[（85.80±5.63）vs.（83.68±4.51）,P>0.05]。学员对新的培训方法满意度较高。结论　目标反馈教学能很好地提高消化内镜专科护士的操作技能和综合能力,提高培训效果,确保专科护士对培训的满意度。     

评价理论下高校英语教学有效反馈策略探讨

目的：通过积极话语分析,得出教师提高其反馈素养之话语策略,为高校语言评估政策和实践提供参考。方法：以评价理论为分析框架,通过基于课堂观摩与访谈之积极话语分析,观察学生在口语活动中参加课堂陈述并获得同伴反馈的过程,探讨大学英语教师提供反馈的特点及结果。结果：教师反馈过程中认知支架学习论和社会情感支持策略能培养学生以合理的知识和技能对同伴陈述做出反馈。教师需逐步适当抽离教学及语言支持,使其话语策略顺应教学情况,实现口语课堂教学范式的创新。结论：本研究为口语课堂有效反馈策略的初步构建,提供了经验及今后研究的方向。     

Linking internal and external signals for performance monitoring: An event‐related potential study

The present study explored the relevance of internal signals for the dynamics of personal and nonpersonal feedback processing. To this end, pairs of participants performed concurrently a choice‐response task and received external signals in four feedback contexts. In two contexts, feedback was informative about the personal performance (personal/private and personal/public); in the other two contexts, instructions suggested that feedback was informative about the other participant's performance (nonpersonal/other) or that it was random (nonpersonal/random). Since personal feedback was contingent on performance, the two contexts with personal feedback allowed a reference between internal and external signals. This reference significantly affected personal feedback processing. On the one hand, in the processing of personal feedback, the feedback‐related negativities (FRNs) evoked by feedback associated with distinctively fast or slow responses were less negative than the FRN elicited by feedback related to responses made with average speed. On the other hand, feedback signals evoked FRNs with similar amplitudes in the two contexts with nonpersonal feedback. Furthermore, personal and nonpersonal feedback elicited ERPs with different strength. Starting with the P2 potential, personal feedback evoked a more positive electrophysiological response than nonpersonal feedback. Based on these results, we conclude that a link between internal and external signals, as for personal feedback, is a key factor influencing the dynamics of feedback processing.     

运用多渠道信息反馈制提高急诊观察室护理质量

目的提高急诊观察室的护理工作质量。方法通过建立信息反馈小组、质量反馈本、护理质量座谈会、病人建议征集的方法及时整改工作中的不足,达到持续改进。结果护理流程更加合理,病人零投诉,护理质量得到提高。结论运用信息反馈制有助于提高急诊观察室护理质量。