Automated turnkey microcomb for low-noise microwave synthesis

Microresonator-based optical frequency comb (microcomb) has the potential to revolutionize the accuracy of frequency synthesizer in radar and communication applications. However, fundamental limit exists for low noise microcomb generation, especially in low size, weight, power and cost (SWaP-C) package. Here we resolve this limit, by the demonstration of an automated turnkey microcomb, operating close to its low quantum-limited phase noise, within a compact setup size of 85 mm * 90 mm * 25 mm. High quality factor fiber Fabry-Perot resonator (FFPR), with Q up to 4.0 * 10^9, is the key for both low quantum noise and pump noise limit, in the diode-pump case in a self-injection locking scheme. Low phase noise of -80 and -105 dBc/Hz at 100 Hz, -106 and -125 dBc/Hz at 1 kHz, -133 and -148 dBc/Hz at 10 kHz is achieved at 10.1 GHz and 1.7 GHz repetition frequencies, respectively. With the simultaneous automated turnkey, low-noise and direct-diode-pump capability, our microcomb is ready to be used as a low-noise frequency synthesizer with low SWaP-C and thus field deployability

Tomato short internodes and pedicels encode an LRR receptor-like serine/threonine-protein kinase ERECTA regulating stem elongation through modulating gibberellin metabolism

Plant height is an important agronomic trait. Dwarf varieties present several advantages, such as lodging resistance, increased yield, and suitability for mechanized harvesting, which are crucial for crop improvement. However, limited research is available on dwarf tomato varieties suitable for production. In this study, we report a novel short internode mutant named “short internode and pedicel (sip)” in tomato, which exhibits marked internode and pedicel shortening due to suppressed cell elongation. This mutant plant has a compact plant structure and compact inflorescence, and has been demonstrated to produce more fruits, resulting in a higher harvest index. Genetic analysis revealed that this phenotype is controlled by a single recessive gene, SlSIP. BSA analysis and KASP genotyping indicated that ERECTA (ER) is the possible candidate gene for SlSIP, which encodes a leucine-rich receptor-like kinase. Additionally, we obtained an ER functional loss mutant using the CRISPR/Cas9 gene-editing technology. The 401st base A of ER is substituted with T in sip, resulting in a change in the 134th amino acid from asparagine (N) to isoleucine (I). Molecular dynamics(MD) simulations showed that this mutation site is located in the extracellular LRR domain and alters nearby ionic bonds, leading to a change in the spatial structure of this site. Transcriptome analysis indicated that the genes that were differentially expressed between sip and wild-type (WT) plants were enriched in the gibberellin metabolic pathway. We found that GA3 and GA4 decreased in the sip mutant, and exogenous GA3 restored the sip to the height of the WT plant. These findings reveal that SlSIP in tomatoes regulates stem elongation by regulating gibberellin metabolism. These results provide new insights into the mechanisms of tomato dwarfing and germplasm resources for breeding dwarfing tomatoes

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Estimates of Wildfire Emissions in Boreal Forests of China

Wildfire emissions in the boreal forests yield an important contribution to the chemical budget of the troposphere. To assess the contribution of wildfire to the emissions of atmospheric trace species in the Great Xing’an Mountains (GXM), which is also the most severe fire-prone boreal forest region in China, we estimated various wildfire activities by combining explicit spatio-temporal remote sensing data with fire-induced emission models. We observed 9998 fire scars with 46,096 km2 in the GXM between the years 1986 and 2010. The years 1987 and 2003 contributed 33.2% and 22.9%, respectively, in burned area during the 25 years. Fire activity is the strongest in May. Most large fires occurred in the north region of the GXM between 50° N and 54° N latitude due to much drier weather and higher fire danger in the northern region than in the southern region of the study domain. Evergreen and deciduous needleleaf forest and deciduous broadleaf forest are the main sources of emissions, accounting for 84%, 81%, 84%, 87%, 89%, 86%, 85% and 74% of the total annual CO2, CH4, CO, PM10, PM2.5, SO2, BC and NOx emissions, respectively. Wildfire emissions from shrub, grassland and cropland only account for a small fraction of the total emissions level (approximately 4%–11%). Comparisons of our results with other published estimates of wildfire emissions show reasonable agreement

Long-Term Satellite Detection of Post-Fire Vegetation Trends in Boreal Forests of China

This paper describes the long-term effects on vegetation following the catastrophic fire in 1987 on the northern Great Xing'an Mountain by analyzing the AVHRR GIMMS 15-day composite normalized difference vegetation index (NDVI) dataset. Both temporal and spatial characteristics were analyzed for natural regeneration and tree planting scenarios from 1984 to 2006. Regressing post-fire NDVI values on the pre-fire values helped identify the NDVI for burnt pixels in vegetation stands. Stand differences in fire damage were classified into five levels: Very High (VH), High (H), Moderate (M), Low (L) and Slight (S). Furthermore, intra-annual and inter-annual post-fire vegetation recovery trajectories were analyzed by deriving a time series of NDVI and relative regrowth index (RRI) values for the entire burned area. Finally, spatial pattern and trend analyses were conducted using the pixel-based post-fire annual stands regrowth index (SRI) with a nonparametric Mann-Kendall (MK) statistics method. The results show that October was a better period compared to other months for distinguishing the post-and pre-fire vegetation conditions using the NDVI signals in boreal forests of China because colored leaves on grasses and shrubs fall down, while the leaves on healthy trees remain green in October. The MK statistics method is robustly capable of detecting vegetation trends in a relatively long time series. Because tree planting primarily occurred in the severely burned area (approximately equal to the Medium, High and Very High fire damage areas) following the Daxing'anling fire in 1987, the severely burned area exhibited a better recovery trend than the lightly burned regions. Reasonable tree planting can substantially quicken the recovery and shorten the restoration time of the target species. More detailed satellite analyses and field data will be required in the future for a more convincing validation of the results

Static viscoelasticity of biomass polyethylene composites

The biomass polyethylene composites filled with poplar wood flour, rice husk, cotton stalk or corn stalk were prepared by extrusion molding. The static viscoelasticity of composites was investigated by the dynamic thermal mechanical analyzer (DMA). Through the stress-strain scanning, it is found that the linear viscoelasticity interval of composites gradually decreases as the temperature rises, and the critical stress and strain values are 0.8 MPa and 0.03% respectively. The experiment shows that as the temperature rises, the creep compliance of biomass polyethylene composites is increased; under the constant temperature, the creep compliance decreases with the increase of content of biomass and calcium carbonate. The biomass and calcium carbonate used to prepare composites as filler can improve damping vibration attenuation and reduce stress deformation of composites. The stress relaxation modulus of composites is reduced and the relaxation rate increases at the higher temperature. The biomass and calcium carbonate used to prepare composites as filler not only can reduce costs, but also can increase stress relaxation modulus and improve the size thermostability of composites. The corn stalk is a good kind of biomass raw material for composites since it can improve the creep resistance property and the stress relaxation resistance property of composites more effectively than other three kinds of biomass (poplar wood flour, rice husk and cotton stalk). Keywords: Biomass, Composites, Calcium carbonate, Static viscoelasticity, Creep, Stress relaxatio

Trends of greening and browning in terrestrial vegetation in China from 2000 to 2020

Terrestrial vegetation condition is altering generally as a result of climate change and anthropogenic activity during the past few decades. To reveal the impact of long-term climate factors and artificial protection on multiple vegetation types, it is crucial to understand the spatial distribution of vegetation greening and browning and the effect of national ecological restoration programs. In this study, we established a persistent vegetation change index (P-value) to characterize greening (restoration) and browning (degradation) in China in 2000–2020. Firstly, we generated annual time-series normalized difference vegetation index (NDVI) data from MODIS product by averaging the monthly maximum NDVI values for each year. Secondly, we calculated the P-value to investigate the continuous change in vegetation state by incremental time interval. Finally, patterns and trends of greening and browning in forests, shrublands, and grasslands were quantified and mapped at pixel and sample point levels. The findings of our study revealed that Chinese wild vegetated lands greened up by ∼3.4 × 104 km2 (25%) and turned brown in ∼1.6 × 104 km2 (11%) between 2000 and 2020. Net greening was detected in all biomes, most conspicuously in several ecological program regions in northern China. The NDVI time-series data in 31% of field plots showed a consistent result, 11% of field plots showed a browning trend, and 58% of field plots showed a stable state. These results indicated a synergistic effect on forests, shrublands, and grasslands, but with regional variations attributed to differences in precipitation abundance, the implementation of positive ecological programs by the government and negative human activities. Additionally, these findings provide valuable insight into large-scale terrestrial vegetation transitions and have practical applications for decision-making and policy development in the assessment and restoration of ecosystems aimed at reducing carbon emissions, mitigating climate change, preserving biodiversity, and conserving water resources in China