Four timely and broadly available remotely sensed datasets were assessed for inclusion into county-level corn and soybean yield forecasting efforts focused on the Corn Belt region of the central ...United States (US). Those datasets were the (1) Normalized Difference Vegetation Index (NDVI) as derived from the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS), (2) daytime and (3) nighttime land surface temperature (LST) as derived from Aqua satellite's MODIS, and (4) precipitation from the National Weather Service (NWS) Nexrad-based gridded data product. The originating MODIS data utilized were the globally produced 8-day, clear sky composited science products (MOD09Q1 and MYD11A2), while the US-wide NWS data were manipulated to mesh with the MODIS imagery both spatially and temporally by regridding and summing the otherwise daily measurements. The crop growing seasons of 2006–2011 were analyzed with each year bounded by 32 8-day periods from mid-February through late October. Land cover classifications known as the Cropland Data Layer as produced annually by the National Agricultural Statistics Service (NASS) were used to isolate the input dataset pixels as to corn and soybeans for each of the corresponding years. The relevant pixels were then averaged by crop and time period to produce a county-level estimate of NDVI, the LSTs, and precipitation. They in turn were related to official annual NASS county level yield statistics. For the Corn Belt region as a whole, both corn and soybean yields were found to be positively correlated with NDVI in the middle of the summer and negatively correlated to daytime LST at that same time. Nighttime LST and precipitation showed no correlations to yield, regardless of the time prior or during the growing season. There was also slight suggestion of low NDVI and high daytime LST in the spring being positively related to final yields, again for both crops. Taking only NDVI and daytime LST as inputs from the 2006–2011 dataset, regression tree-based models were built and county-level, within-sample coefficients of determination (R2) of 0.93 were found for both crops. Limiting the models by systematically removing late season data showed the model performance to remain strong even at mid-season and still viable even earlier. Finally, the derived models were used to predict out-of-sample for the 2012 season, which ended up having an anomalous drought. Yet, the county-level results compared reasonably well against official statistics with R2=0.77 for corn and 0.71 for soybeans. The root-mean-square errors were 1.26 and 0.42metrictonsper hectare, respectively.
•MODIS NDVI was found to be positively correlated to crop yields mid-summer.•MODIS daytime land surface temperature was negatively correlated mid-summer.•MODIS nighttime land surface temperature showed no relationship to yields anytime.•NWS Nexrad rainfall data also showed no relationship to yields anytime.•A rule-based decision tree model predicted well for the anomalous 2012 crop season.
Landsat Thematic Mapper has been collecting multispectral imagery at 30 m resolution globally since 1984. One utility of the data has been for detailed mapping of agricultural regions and seasonal ...identification of crops grown within them. However, the ability to do so has only been applied sporadically and eluded widespread adoption due to cost of the imagery, burdensome preprocessing requirements, and computing not being up to the task. These hurdles have become much reduced of recent with the free and open distribution of the Landsat imagery, emphasis on ready-to-use surface reflectance data products, and distributed high performance computing infrastructures available online in “the cloud.” As such, this work leverages these aspects and investigates the ability to retrospectively map summer crops over the United States (US) annually from 1984 to 2007. Google's Earth Engine Internet-based analytical platform containing the historical Landsat archive in surface reflectance format was used as a foundation for the classification work. Robust 30 m Cropland Data Layer (CDL) information from US Department of Agriculture (USDA) for years 2008 through 2011 were leveraged to train rule-based classifiers which were applied back through time to each year 1984 through 2007. Focus crops were corn, soybeans, and winter wheat – the three largest by area in the US. A large sampling of highly intensive counties throughout the country were prototyped for generation of the 24 years of historical crop cover. For validation, crop area statistics were calculated for each county-year and compared to survey-based information existing from the USDA. Results were muted overall with the average crop area coefficient of determination (R2) correlations for the years 1984–2007 found to be 0.192, 0.159, and 0.142 for corn, soybeans, and winter wheat, respectively. Furthermore, the standard deviations were variable at 0.132, 0.177, and 0.133, also respectively. While unimpressive, it was found as a benchmark that the R2 between the 2008 through 2017 CDL classifications were only 0.478, 0.686, and 0.726 and thus a suggestion that the USDA area statistics are an imperfect measure of map accuracy. Deletion of approximately one third to one half of the grossest 1984–2007 outlier years from the historical outputs pulled the correlations to the benchmark standard. Qualitatively, most of the remaining years classified looked of high quality and were believed useful as field-level thematic crop area maps. These historical cropland maps could provide the ability to better detail the role farming has played on the broad US landscape over recent decades.
•Google Earth Engine was used to prototype annual historical crop maps for the USA.•Landsat 5 and 7 satellite imagery from 1984 to 2007 was useful as a foundation.•Generalized classification rules were derivable and applied retroactively.•Assessment of the maps against official area statistics showed weak correlations.•Removal of outlier years showed remaining classifications to look relatively robust.
Crop type maps were created without the traditional need for in-season training data across the Corn Belt and Great Plains regions of the United States. This was accomplished through machine learning ...of historical land cover information, paired with a time-series of multi-spectral satellite imagery composites spanning the growing season, to develop rulesets, which were used for real-time prediction in the current year. Specifically, a decade's worth of annual 30 m resolution crop specific maps, known as the Cropland Data Layer (CDL), provided the foundation, and prior and current year's satellite imagery from Landsat 7 and 8 and Sentinel-2a and -2b built upon it. Four modeling scenarios, all using random forests, were performed to understand the crop mapping abilities of the datasets independently and combined. They were 1) use of CDLs only (i.e. prediction based solely on crop rotation history) 2) use of Landsat 7 and 8 bottom-of-atmosphere surface reflectance imagery only, 3) use of Sentinel-2a and -2b top-of-atmosphere imagery only, and 4) integration of the CDL, Landsat, and Sentinel-2 information together in a unified effort. Furthermore, the model runs were generated monthly, beginning in April, through the growing season to provide understanding of classification performance as a function of time. The 2020 crop year, relatively normal in terms of planting and weather, was used for the test. Accuracy statistics were generated by randomly sampling 50 counties and comparing those classification outputs to the actual 2020 CDL. Pixel-level results showed that prediction by midsummer using only the CDL information provided a crop type map with corn and soybean consumer and producer agreement above 70% and winter wheat just below 50%. The early season imagery-based classifications were markedly worse. However, as Landsat or Sentinel-2 imagery accumulated through July, those classifications became significantly better than those reliant on the use of the CDL information only. Ultimately, the very best crop maps resulted from integrating the CDLs with a full season's worth of Landsat and Sentinel-2 imagery. At that point in late September, the corn and soybean agreements were around 85% and winter wheat near 70%. All analysis was performed within Google Earth Engine cloud-based public imagery repository and high-performance computing system. The classification outputs provide practitioners with US crop type maps in near real-time.
•Pre-growing season crop type maps were generated by modeling crop rotation history.•Those maps were improved within-season by including Landsat and Sentinel-2 imagery.•Mid-July was the time at which the imagery brought improvement to the initial maps.•Best case pixel-level agreement with the Cropland Data Layer was 79% overall.•The classification methodology did not use within-season training data.
The retinoblastoma (RB) tumor suppressor is known as a master regulator of the cell cycle. RB is mutated or functionally inactivated in the majority of human cancers. This transcriptional regulator ...exerts its function in cell cycle control through its interaction with the E2F family of transcription factors and with chromatin remodelers and modifiers that contribute to the repression of genes important for cell cycle progression. Over the years, studies have shown that RB participates in multiple processes in addition to cell cycle control. Indeed, RB is known to interact with over 200 different proteins and likely exists in multiple complexes. RB, in some cases, acts through its interaction with E2F1, other members of the pocket protein family (p107 and p130), and/or chromatin remodelers and modifiers. RB is a tumor suppressor with important chromatin regulatory functions that affect genomic stability. These functions include the role of RB in DNA repair, telomere maintenance, chromosome condensation and cohesion, and silencing of repetitive regions. In this review we will discuss recent advances in RB biology related to RB, partner proteins, and their non-transcriptional functions fighting back against genomic instability.
The Current State of Peritoneal Dialysis Mehrotra, Rajnish; Devuyst, Olivier; Davies, Simon J ...
Journal of the American Society of Nephrology,
11/2016, Letnik:
27, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Technical innovations in peritoneal dialysis (PD), now used widely for the long-term treatment of ESRD, have significantly reduced therapy-related complications, allowing patients to be maintained on ...PD for longer periods. Indeed, the survival rate for patients treated with PD is now equivalent to that with in-center hemodialysis. In parallel, changes in public policy have spurred an unprecedented expansion in the use of PD in many parts of the world. Meanwhile, our improved understanding of the molecular mechanisms involved in solute and water transport across the peritoneum and of the pathobiology of structural and functional changes in the peritoneum with long-term PD has provided new targets for improving efficiency and for intervention. As with hemodialysis, almost half of all deaths on PD occur because of cardiovascular events, and there is great interest in identifying modality-specific factors contributing to these events. Notably, tremendous progress has been made in developing interventions that substantially reduce the risk of PD-related peritonitis. Yet the gains have been unequal among individual centers, primarily because of unequal clinical application of knowledge gained from research. The work to date has further highlighted the areas in need of innovation as we continue to strive to improve the health and outcomes of patients treated with PD.
Focusing on well-known and obscure literary texts from the 1880s to the 1970s, as well as the many manifestos and programmes setting out visions of the future, this book charts the dreams of freedom ...of five major traditions of anti-colonial and anti-apartheid resistance.
Although several methods have been developed to allow for the analysis of data in the presence of missing values, no clear guide exists to help family researchers in choosing among the many options ...and procedures available. We delineate these options and examine the sensitivity of the findings in a regression model estimated in three random samples from the National Survey of Families and Households (n = 250-2,000). These results, combined with findings from simulation studies, are used to guide answers to a set of 10 common questions asked by researchers when selecting a missing data approach. Modern missing data techniques were found to perform better than traditional ones, but differences between the types of modern approaches had minor effects on the estimates and substantive conclusions. Our findings suggest that the researcher has considerable flexibility in selecting among modern options for handling missing data.
Electronic cigarettes (EC) have evolved rapidly toward higher powered devices that produce more vaping aerosol and a more satisfying vaping experience. This research characterized the particle size ...distribution and estimated the mass concentration of vaping aerosols produced at power outputs spanning the operating range typical of second generation variable voltage EC devices.
EC aerosol was characterized from a single coil atomizer powered by a variable voltage EC battery at the minimum and maximum dial settings (3.3, 11.2 Watts, W), and a lab controlled power supply (3-11.9 W). Aerosol particle size distribution was measured by a Scanning Mobility Particle Sizer and Aerodynamic Particle Sizer, spanning 16 nm to 19.8 μm. A mouth puff was simulated using a 100 mL glass syringe.
Consistent with prior studies, sub-micron EC aerosol size distributions were bimodal, with peaks at 40 and 200 nm, however a previously unreported third mode was observed at approximately 1000 nm. The ~1000 nm mode accounted for 7-20x the aerosol mass of the smaller modes. Increasing atomizer power decreased count concentration of particles <600 nm but increased particle count >600 nm. Particle mass distribution shifted toward micron sized particles with increasing power and increased the respirable fraction of aerosol, likely due to increased coagulation and condensation around nano-sized particles.
Vaping power greatly affects EC aerosol count and mass distribution. Mouth puffed EC aerosol spans a much wider particle size range than previously reported, although the major portion of the mass is still well within the alveolar size range the larger particles will deposit within the oro-pharyngeal cavity at 2-3x greater efficiency than in alveoli. These observations have major clinical implications, as aerosol particle size distribution determines deposition sites along the respiratory tract. The results of this experiment stress the need for further research to inform the design, regulation and use of e-cigarette products.
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