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Riverine nitrogen pollution is ubiquitous and attracts considerable global attention. Nitrate is commonly the dominant total nitrogen (TN) constituent in surface and ground waters; thus, stable isotopes of nitrate (δ15N/δ18O-NO3-) are widely used to differentiate nitrate sources. However, δ15N/δ18O-NO3- approach fails to present a holistic perspective of nitrogen pollution for many coastal-plain river networks because diverse nitrogen species contribute to high TN loads. In this study, multiple isotopes, namely, δ15N/δ18O-NO3-, δ18O-H2O, δ15N-NH4+, δ15N-PN, and δ15Nbulk/δ18O/SP-N2O in the Wen-Rui Tang River, a typical coastal-plain river network of Eastern China, were investigated to identify transformation processes and sources of nitrogen. Then, a stable isotope analysis in R (SIAR) model-TN source apportionment method was developed to quantify the contributions of different nitrogen sources to riverine TN loads. Results showed that nitrogen pollution in the river network was serious with TN concentrations ranging from 1.71 to 8.09 mg/L (mean ± SD: 3.77 ± 1.39 mg/L). Ammonium, nitrate, and suspended particulate nitrogen were the most prominent nitrogen components during the study period, constituting 45.4 %, 28.9 %, and 19.9 % of TN, respectively. Multiple hydrochemical and isotopic analysis identified nitrification as the dominant N cycling process. Biological assimilation and denitrification were minor N cycling processes, whereas ammonia volatilization was deemed negligible. Isotopic evidence and SIAR modeling revealed municipal sewage was the dominant contributor to nitrogen pollution. Based on quantitative estimates from the SIAR model, nitrogen source contributions to the Wen-Rui Tang River watershed followed: municipal sewage (40.6 %) ≈ soil nitrogen (39.5 %) > nitrogen fertilizer (9.7 %) > atmospheric deposition (2.8 %) during wet season; and municipal sewage (59.1 %) > soil nitrogen (30.4 %) > nitrogen fertilizer (4.1 %) > atmospheric deposition (1.0 %) during dry season. This study provides a deeper understanding of nitrogen dynamics in eutrophic coastal-plain river networks, which informs strategies for efficient control and remediation of riverine nitrogen pollution.

Peer feedback is known to have positive effects on knowledge improvement in a collaborative learning environment. Attributed to technology affordances, class-wide peer feedback could be garnered at a wider range in the networked learning environment. However, more empirical studies are needed to explore further the effects of type and depth of feedback on knowledge improvement. In this mixed method research, 38 students underwent a computer-supported collaborative learning (CSCL) lesson in an authentic classroom environment. Both quantitative and qualitative analyses were conducted on the collected data. Pre- and post-test comparison results showed that students' conceptual knowledge on adaptations improved significantly after the CSCL lesson. Qualitative analysis was conducted to examine how the knowledge improved before and after the peer feedback process. The results showed that the class-wide intergroup peer feedback supported learners, with improvement to the quality of their conceptual knowledge when cognitive capacity had reached its maximum at the group level. The peer comments that seek further clarity and suggestions prompted deeper conceptual understanding, leading to knowledge improvement. However, such types of feedback were cognitively more demanding to process. The implications of the effects of type of peer feedback on knowledge improvement and the practical implications of the findings for authentic classroom environments are discussed.

AIM:To assess the evolving burden of cataracts in China from 1990 to 2019.METHODS: Data on disease burden related to cataracts in China were retrieved from the Global Burden of Disease(GBD)2019 study based on large public databases. Utilizing data from the GBD 2019 study, we extracted information on cataract-related disease burden in China from extensive public databases. Analysis of prevalence and disability-adjusted life years(DALYs)associated with cataracts in China was conducted based on GBD 2019 findings. The variable characteristics of age-standardized prevalence rates(ASPR)and age-standardized DALYs rates(ASDR)in China and its neighboring countries were also explored.RESULTS: Between 1990 and 2019, the number of prevalent cases of blindness and vision loss caused by cataracts in China increased by 223.54%, and the corresponding DALYs raised by 142.14%. Over the past 30 years, females exhibited higher age-standardized prevalence and DALYs rates compared to males. Meanwhile, individuals aged 65 to 84 years were found to be more susceptible to cataracts than other age groups. Compared with neighboring countries, China ranked from the 9th position in 1990(867.09, 95%UI: 761.36 to 975.42, per 100 000 population)to the 11th in 2019(991.56, 95%UI: 861.52 to 1131.04, per 100 000 population)in ASPR, while from the 9th in 1990(65.85, 95%UI: 46.39 to 89.41, per 100 000 population)to the 10th position in 2019(59.16, 95%UI: 41.70 to 80.15, per 100 000 population)in ASDR. However, on a global scale, China maintained relatively low ASDR and ASPR for cataracts in 2019.CONCLUSION: The study highlights a substantial rise in the prevalence and DALYs associated with blindness and vision loss due to cataracts from 1990 to 2019 in China, and underscores the urgent need for increased early screening of cataracts, particularly among the elderly and females.

Nitrate pollution is of considerable global concern as a threat to human health and aquatic ecosystems. Nowadays, δ15N/δ18O-NO3- combined with a Bayesian-based SIAR model are widely used to identify riverine nitrate sources. However, little is known regarding the effect of variations in pollution source isotopic composition on nitrate source contributions. Herein, we used δ15N/δ18O-NO3-, SIAR modeling, probability statistical analysis and a perturbing method to quantify the contributions and uncertainties of riverine nitrate sources in the Wen-Rui Tang River of China and to further investigate the model sensitivity of each nitrate source. The SIAR model confirmed municipal sewage (MS) as the major nitrate source (58.5-75.7%). Nitrogen fertilizer (NF, 8.6-20.9%) and soil nitrogen (SN, 7.8-20.1%) were also identified as secondary nitrate sources, while atmospheric deposition (AD,

Self-directed learning and self-assessment require student responsibility over learning needs, goals, processes, and outcomes. However, this student-led learning can be challenging to achieve in a classroom limited by a one-to-many teacher-led instruction. We, thus, have designed and prototyped a generative artificial intelligence chatbot application (GAIA), named TeacherGAIA, that can be used to asynchronously support students in their self-directed learning and self-assessment outside the classroom. We first identified diverse constructivist learning approaches that align with, and promote, student-led learning. These included knowledge construction, inquiry-based learning, self-assessment, and peer teaching. The in-context learning abilities of large language model (LLM) from OpenAI were then leveraged via prompt engineering to steer interactions supporting these different learning approaches. These interactions contrasted with ChatGPT, OpenAI's chatbot which by default engaged in the traditional transmissionist mode of learning reminiscent of teacher-led instruction. Preliminary design, prompt engineering and prototyping suggested fidelity to the learning approaches, cognitive guidance, and social-emotional support, all of which were implemented in a generative AI manner without pre-specified rules or "hard-coding". Other affordances of TeacherGAIA are discussed and future development outlined. We anticipate TeacherGAIA to be a useful application for teachers in facilitating self-directed learning and self-assessment among K-12 students.

Nurturing twenty-first-century competency is one important agenda in this era, especially in developing collaborative learning and critical thinking skills. Yet, facilitating such a computer-supported collaborative learning (CSCL) environment is challenging. Although several technological platforms from past research studies were developed to support collaborative learning, having a system that comprises a collaboration script, and graph-based workspaces to facilitate explicit externalization of cognitive processes, at the same time, armed with learning analytics to monitor the collaboration and cognitive processes is rare. In this paper, the AppleTree system is presented to illustrate three unique features. Firstly, the AppleTree system is designed with a graph-based workspace to facilitate explicit externalization of knowledge co-constructions and argumentation. Secondly, the system is embedded with a four-phased switch to structure the CSCL process. Thirdly, the system possesses real-time learning analytics, including contribution counts, social network analysis, and the learning artifacts' argumentation structure to promote the development of arguments. An exploratory case study was conducted to examine the design of the AppleTree system in an authentic classroom. Empirical findings showed that the AppleTree system helped regulate effective collaboration and supported learners in developing strong arguments. These results could address the social participation and interaction issues related to collaborative learning and enable the development of students' argumentation skills. The implications of the study to educational technology and the CSCL research field are discussed.

Identifying the sources and transformations of riverine nitrate plays a critical role in mitigating nitrogen enrichment of river networks. Several previous studies have used δ18O-NO3− to quantitatively assess riverine nitrate contributed by atmospheric nitrate and terrestrial sources, but their results have great uncertainty due to the wide range of δ18O-NO3− values and isotopic fractionation during nitrogen-cycling processes in terrestrial environment. The nitrate 17O anomaly (△17O-NO3−), as an unambiguous tracer of atmospheric nitrate, is a promising tool to effectively separate atmospheric nitrate from microbially produced nitrate. However, to our knowledge, △17O-NO3− approach has not been previously applied to identify nitrate pollution sources in plain river networks of eastern China. In this study, we used a multiple isotope approach (δD/δ18O-H2O and δ15N/δ18O/△17O-NO3−) for the first time to quantitatively identify sources and transformations of riverine nitrate in a hypereutrophic coastal plain river network namely Wen-Rui Tang River located in eastern China, which is a region receiving high inputs of atmospheric nitrogen deposition. The △17O-NO3− values in precipitation and river water during the study period (April–June of 2021) varied from 14.83‰ to 31.39‰ and from − 2.82‰ to 9.66‰, respectively. The δD/δ18O-H2O values revealed that river water mainly originated from recent precipitation with little evaporation. Moreover, the δ15N/δ18O-NO3− values indicated that microbial nitrification, not denitrification, was the predominant nitrogen-cycling process in the watershed. Based on a Bayesian mixing model (Stable Isotope Analysis in R, SIAR) using δ15N/△17O-NO3−, municipal sewage was identified as the dominant nitrate source (50.5 ± 11.7%), followed by soil nitrogen (23.8 ± 13.7%), atmospheric nitrate deposition (14.3 ± 2.9%), and nitrogen fertilizer (11.4 ± 8.7%). Finally, an uncertainty analysis for nitrate source apportionment demonstrated that the greatest uncertainty was associated with soil nitrogen, followed by municipal sewage, nitrogen fertilizer, and atmospheric nitrate deposition. This study provides important scientific information on riverine nitrate source apportionment to guide pollution control/remediation strategies and highlights the benefits of utilizing △17O-NO3− to enhance nitrate source apportionment.

Objective To evaluate the effect and unravel the mechanism of rapamycin on the ovarian tissues in rat models with polycystic ovary syndrome （PCOS） induced by dehydroepiandrosterone （DHEA）. Methods After 1-week accustomed feeding， 55 rats were randomly divided into the blank （n=13）， control （n=13） and model groups （n=29）. PCOS rat models were established by subcutaneous injection of DHEA solution. In the control group， rats were subcutaneously injected with soybean oil， while those in the blank group were not treated throughout the whole experiment. After the model was successfully established， all rats were divided into the blank group， control group （intraperitoneal injection of an equivalent volume of normal saline）， the model group， low-dose group （intraperitoneal injection of 1 mg/kg rapamycin） and high-dose group （intraperitoneal injection of 10 mg/kg rapamycin） for 28 d，8 rats in each group. Blood glucose and hormone levels were detected， and HE， Masson and immunohistochemical staining was performed after corresponding interventions. The changes of the expression levels of mTOR， p-mTOR and p-P70S6 were assessed among different groups. TUNEL assay was used to detect the apoptosis of ovarian tissues among different groups. Results PCOS rats showed irregular estrous cycle， and the follicles were manifested with polycystic changes. The body weight， ovarian weight， volume， relative weight， sex hormone levels and positive area of ovarian fibrosis of PCOS rats were all higher than those in the control group （all P < 0.05）， whereas these parameters in the low- and high-dose groups were lower than those in the model control group （all P < 0.05）， and the polycystic changes of ovarian tissues were significantly mitigated. The positive area of ovarian fibrosis in the low- and high-dose groups was increased than those in the blank and control groups （all P < 0.05）. The positive area of ovarian fibrosis in the high-dose group was increased than that in the control group （P < 0.05）. No significant difference was noted in the OD value of mTOR protein among five groups （all P > 0.05）. The OD value of mTOR protein in the model control group was higher compared with those in the remaining four groups （all P < 0.05）. The OD value of mp-P70S6 protein in the model control group was similar to that in the control group （P > 0.05）， whereas the OD value of mp-P70S6 protein in the high-dose group was lower compared with those in the blank， control and low-dose groups （all P < 0.05）. TUNEL staining revealed that the apoptosis rate of ovarian tissues in the model control group was higher than those in the blank， control and high-dose groups （all P < 0.005）， whereas the apoptosis rate in the blank group was lower than that in the low-dose group （P < 0.005）. Conclusions Rapamycin can reduce body weight， decrease ovarian mass and volume， lower hormone levels， mitigate polycystic changes in follicular tissues and alleviate apoptosis of ovarian tissues in DHEA-induced PCOS rat models， which may be related to the inhibition of abnormal activation of mTOR signaling pathway by rapamycin. However， rapamycin may aggravate ovarian interstitial fibrosis in PCOS rats. The body weight， ovarian mass and volume of PCOS rats are lower than the normal levels.