Simulation of CSSTs astrometric capability

The China Space Station Telescope (CSST) will enter a low Earth orbit around 2024 and operate for 10 years, with seven of those years devoted to surveying the area of the median-to-high Galactic latitude and median-to-high Ecliptic latitude of the sky. To maximize the scientific output of CSST, it is important to optimize the survey schedule. We aim to evaluate the astrometric capability of CSST for a given survey schedule and to provide independent suggestions for the optimization of the survey strategy. For this purpose, we first construct the astrometric model and then conduct simulated observations based on the given survey schedule. The astrometric solution is obtained by analyzing the simulated observation data. And then we evaluate the astrometric capability of CSST by analyzing the properties of the astrometric solution. We find that the accuracy of parallax and proper motion of CSST is better than 1 mas( yr1) for the sources of 18-22 mag in g band, and about 1-10 mas( yr1) for the sources of 22-26 mag in g band, respectively. The results from real survey could be worse since the assumptions are optimistic and simple. We find that optimizing the survey schedule can improve the astrometric accuracy of CSST. In the future, we will improve the astrometric capability of CSST by continuously iterating and optimizing the survey schedule.Comment: 17 pages, 10 figure

Anatexis of former arc magmatic rocks during oceanic subduction: A case study from the North Wulan gneiss complex

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.gr.2018.04.016 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Migmatites are widespread in the North Wulan gneiss complex from the South Qilian–North Qaidam orogenic belt, but their petrogenesis and ages are poorly constrained. Here, an integrated study of petrography, whole-rock geochemistry, geochronology, zircon trace element and Hf isotope analysis deciphers the nature and timing of partial melting in migmatitic amphibole-biotite gneiss. Zircon U–Pb geochronology reveals that the protoliths crystallized at 506–494 Ma followed by metamorphism and anatexis at ca. 465 to 450 Ma. Hafnium isotope compositions of inherited cores and anatectic rims are very similar, suggesting that partial melting occurred in a relatively closed isotopic system and new zircon rims grew via dissolution–reprecipitation of pre-existing zircon cores. Anatexis occurred by water-fluxed melting of mafic-intermediate rocks through the breakdown of biotite and growth of peritectic amphibole. The protolith of the migmatites records Cambrian arc magmatism in an active continental margin, which was induced by northward subduction of the South Qilian ocean slab. Contemporary arc-like magmatism and high-temperature/low-pressure metamorphism in the region suggest that anatexis in the North Wulan gneiss complex likely took place in a continental arc setting, which reflects the reworking of former arc magmatic rocks in a late stage of oceanic subduction.National Natural Science Foundation of China [41372207, 41772228, 41272221]China Scholarship Council [201606690007]Key Program of the Ministry of Land and Resources of China [DD20160022, 12120115069401, 1212011120159

Physical activity improves the visual–spatial working memory of individuals with mild cognitive impairment or Alzheimer’s disease: a systematic review and network meta-analysis

ObjectiveOur network meta-analysis aimed to ascertain the effect of physical activity on the visual–spatial working memory of individuals with mild cognitive impairment and Alzheimer’s disease as well as to propose tailored exercise interventions for each group.MethodsEmploying a frequentist approach, we performed a network meta-analysis to compare the effectiveness of different exercise interventions in improving the visual–spatial working memory of individuals with mild cognitive impairment and Alzheimer’s disease. Subsequently, we explored the moderating variables influencing the effectiveness of the exercise interventions through a subgroup analysis.ResultsWe included 34 articles involving 3,074 participants in the meta-analysis, comprised of 1,537 participants from studies on mild cognitive impairment and 1,537 participants from studies on Alzheimer’s disease. The articles included exhibited an average quality score of 6.6 (score studies) and 6.75 (reaction time [RT] studies), all passing the inconsistency test (p > 0.05). In the mild cognitive impairment literature, mind–body exercise emerged as the most effective exercise intervention (SMD = 0.61, 95% CI: 0.07–1.14). In Alzheimer’s disease research, aerobic exercise was identified as the optimal exercise intervention (SMD = 0.39, 95% CI: 0.06–0.71).ConclusionThe results of the subgroup analysis suggest that the most effective approach to enhancing the visual–spatial working memory of individuals with mild cognitive impairment entails exercising at a frequency of three or more times per week for over 60 min each time and at a moderate intensity for more than 3 months. Suitable exercise options include mind–body exercise, multicomponent exercise, resistance exercise, and aerobic exercise. For individuals with Alzheimer’s disease, we recommend moderately intense exercise twice per week for over 90 min per session and for a duration of 3 months or longer, with exercise options encompassing aerobic exercise and resistance exercise

A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar–acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch “Spring Belle”) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model

Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine

Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control

Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine

Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control

Deficiency of Auxin Efflux Carrier <i>OsPIN1b</i> Impairs Chilling and Drought Tolerance in Rice

Significant progress has been made in the functions of auxin efflux transporter PIN-FORMED (PIN) genes for the regulation of growth and development in rice. However, knowledge on the roles of OsPIN genes in abiotic stresses is limited. We previously reported that the mutation of OsPIN1b alters rice architecture and root gravitropism, while the role of OsPIN1b in the regulation of rice abiotic stress adaptations is still largely elusive. In the present study, two homozygous ospin1b mutants (C1b-1 and C1b-2) were employed to investigate the roles of OsPIN1b in regulating abiotic stress adaptations. Low temperature gradually suppressed OsPIN1b expression, while osmotic stress treatment firstly induced and then inhibited OsPIN1b expression. Most OsPIN genes and auxin biosynthesis key genes OsYUC were up-regulated in ospin1b leaves, implying that auxin homeostasis is probably disturbed in ospin1b mutants. The loss of function of OsPIN1b significantly decreased rice chilling tolerance, which was evidenced by decreased survival rate, increased death cells and ion leakage under chilling conditions. Compared with the wild-type (WT), ospin1b mutants accumulated more hydrogen peroxide (H2O2) and less superoxide anion radicals (O2−) after chilling treatment, indicating that reactive oxygen species (ROS) homeostasis is disrupted in ospin1b mutants. Consistently, C-repeat binding factor (CBF)/dehydration-responsive element binding factor (DREB) genes were downregulated in ospin1b mutants, implying that OsDREB genes are implicated in OsPIN1b-mediated chilling impairment. Additionally, the mutation of OsPIN1b led to decreased sensitivity to abscisic acid (ABA) treatment in seed germination, impaired drought tolerance in the seedlings and changed expression of ABA-associated genes in rice roots. Taken together, our investigations revealed that OsPIN1b is implicated in chilling and drought tolerance in rice and provide new insight for improving abiotic stress tolerance in rice