A site-specific and dynamic modeling system for zoning and optimizing variable rate irrigation in cotton

Cotton irrigation has been rapidly expanding in west Tennessee during the past decade. Variable rate irrigation is expected to enhance water use efficiency and crop yield in this region due to the significant field-scale soil spatial heterogeneity. A detailed understanding of the soil available water content within the effective root zone is needed to optimally schedule irrigation. In addition, site-specific crop-yield mathematical relationships should be established to identify optimum irrigation management. This study aimed to design and evaluate a site-specific modeling system for zoning and optimizing variable rate irrigation in cotton. The specific objectives of this study were to investigate (i) the spatial variability of soil attributes at the field-scale, (ii) site-specific cotton lint yieldwater relationships across all soil types, and (iii) multiple zoning strategies for variable rate irrigation scenarios. The field (73 ha) was sampled and apparent soil electrical conductivity (ECa) was measured. Landsat 8 satellite data was acquired, processed, and transformed to compare indicators of vegetation and soil response to cotton lint yields, variable irrigation rates, and the spatial variability of soil attributes. Multiple modeling scenarios were developed and examined. Although experiments were performed during two wet years, supplemental irrigation enhanced cotton yield across all soil types in comparison with rain-fed conditions. However, length of cropping season and rainfall distribution remarkably affected cotton response to supplemental irrigation. Geostatistical analysis showed spatial variability in soil textural components and water content was significant and correlated to yield patterns. There was as high as four-fold difference between available water content between coarse-textured and fine-textured soils on the study site. A good agreement was observed (RMSE = 0.052 cm3 cm-3 [cubic centimeter per cubic centimeter] and r = 0.88) between predicted and observed water contents. ECa and space images were useful proximal data to investigate soil spatial variability. The site-specific water production functions performed well at predicting cotton lint yield with RMSE equal to 0.131 Mg ha-1 [megagram per hectare] and 0.194 Mg ha-1 in 2013 and 2014, respectively. The findings revealed that variable rate irrigation with pie shape zones could enhance cotton lint yield under supplemental irrigation in west Tennessee

Assessing Heat Management Practices in High Tunnels to Improve the Production of Romaine Lettuce

A three-year experiment evaluated the beneficial effects of independent and combined practices on thermal conditions inside high tunnels (HTs), and further investigated the temperature impacts on lettuce production. Specific practices included mulching (polyethylene and biodegradable plastic films, and vegetative), row covers, cover crops, and irrigation with collected rainwater or city water. The study conducted in eastern Tennessee was a randomized complete block split-split plot design (RCBD) with three HTs used as replicates to determine fall lettuce weight (g/plant) and lettuce survival (#/plot), and the changes in soil and air temperature. The black and clear plastic mulches worked best for increasing plant weight, but when compared to the bare ground, the higher soil temperature from the plastics may have caused a significant reduction in lettuce plants per plot. Moreover, the biodegradable mulch did not generate as much soil warming as black polyethylene, yet total lettuce marketable yield was statistically similar to that for the latter mulch treatment; while the white spunbond reduced plant weight when compared with black plastic. Also, row covers provided an increased nighttime air temperature that increased soil temperature, hence significantly increased lettuce production. Cover crops reduced lettuce yield, but increased soil temperatures. Additionally, irrigation using city water warmed the soil and provided more nutrients for increased lettuce production over rainwater irrigation

Anticancer Properties of Saccharomyces boulardii Metabolite Against Colon Cancer Cells

Saccharomyces cerevisiae var. boulardii has been used as a probiotic yeast in the medical and food industries. Colon cancers have been known as the third most common cancer type worldwide. Nowadays, cell-free extract and metabolites of probiotics have been employed for the treatment or prevention of different cancer diseases. This study investigates the anticancer properties of S. boulardii metabolites against human colon carcinoma. We evaluated cytotoxicity, apoptosis induction, and suppression of survivin, IL-8, and NFƙB gene expression effects of SBM against caco-2 cells after 24 and 48 h. IC50 concentrations of SBM were measured at 815 and 1411 µg/mL for 24 and 48 h treatments, respectively. The total proportion of apoptotic caco-2 cells treated with SBM after 24 and 48 h were calculated at 62.23 and 88.7%, respectively. Also, relative expression of survivin, IL-8, and NFƙB genes were significantly suppressed in caco-2 cells treated with SBM after 24 and 48 h. In conclusion, we found that SBM induced apoptosis, inhibited the growth rate, and suppressed the expression of the survivin, IL-8, and NFƙB genes in human colorectal cancer cells and it can be considered as a perspective supplement or drug for the treatment or prevention of colon cancer in humans

Decoding the regulatory network of early blood development from single-cell gene expression measurements.

Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.We thank J. Downing (St. Jude Children's Research Hospital, Memphis, TN, USA) for the Runx1-ires-GFP mouse. Research in the authors' laboratory is supported by the Medical Research Council, Biotechnology and Biological Sciences Research Council, Leukaemia and Lymphoma Research, the Leukemia and Lymphoma Society, Microsoft Research and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute. V.M. is supported by a Medical Research Council Studentship and Centenary Award and S.W. by a Microsoft Research PhD Scholarship.This is the accepted manuscript for a paper published in Nature Biotechnology 33, 269–276 (2015) doi:10.1038/nbt.315

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

A single-cell survey of the small intestinal epithelium

Intestinal epithelial cells (IECs) absorb nutrients, respond to microbes, provide barrier function and help coordinate immune responses. We profiled 53,193 individual epithelial cells from mouse small intestine and organoids, and characterized novel subtypes and their gene signatures. We showed unexpected diversity of hormone-secreting enteroendocrine cells and constructed their novel taxonomy. We distinguished between two tuft cell subtypes, one of which expresses the epithelial cytokine TSLP and CD45 (Ptprc), the pan-immune marker not previously associated with non-hematopoietic cells. We also characterized how cell-intrinsic states and cell proportions respond to bacterial and helminth infections. Salmonella infection caused an increase in Paneth cells and enterocytes abundance, and broad activation of an antimicrobial program. In contrast, Heligmosomoides polygyrus caused an expansion of goblet and tuft cell populations. Our survey highlights new markers and programs, associates sensory molecules to cell types, and uncovers principles of gut homeostasis and response to pathogens

Development and evaluation of temperature-based deep learning models to estimate reference evapotranspiration

Efficient irrigation management of urban landscapes is critical in arid/semi-arid environments and depends on the reliable estimation of reference evapotranspiration (ETo). However, the available measured climatic data in urban areas are typically insufficient to use the standard Penman-Monteith for ETo estimation. Therefore, smart landscape irrigation controllers often use temperature-based ETo models for autonomous irrigation scheduling. This study focuses on developing deep learning temperature-based ETo models and comparing their performance with widely used empirical temperature-based models, including FAO Blaney & Criddle (BC), and Hargreaves & Samani (HS). We also developed a simple free and easy-to-access tool called DeepET for ETo estimation using the best-performing deep learning models developed in this study. Four artificial neural network (ANN) models were developed using raw weather data as inputs and the reconstructed signal obtained from the wavelet transform as inputs. In addition, long short-term memory (LSTM) recurrent neural network (NN) and one-dimensional convolution neural network (CNN) models were developed. A total of 101 active California Irrigation Management Information System (CIMIS) weather stations were selected for this study, with >725,000 data points expanding from 1985 to 2019. The performance of the models was evaluated against the standard CIMIS ETo. When evaluated at the independent sites, the temperature-based DL (Deep Learning) models showed 15–20% lower mean absolute error values than the calibrated HS model. No improvement in the performance of the ANN models was observed using reconstructed signals obtained from the wavelet transform. Our study suggests that DL models offer a promising alternative for more accurate estimations of ETo in urban areas using only temperature as input. The DeepET can be accessed from the Haghverdi Water Management Group website: http://www. ucrwater.com/software-and-tools.html