Specific Growth Stages Research Articles

Maintaining higher leaf photosynthesis after heading stage could promote biomass accumulation in rice

Leaf photosynthetic rate changes across the growing season as crop plants age. Most studies of leaf photosynthesis focus on a specific growth stage, leaving the question of which pattern of photosynthetic dynamics maximizes crop productivity unanswered. Here we obtained high-frequency data of canopy leaf CO2 assimilation rate (A) of two elite rice (Oryza sativa) cultivars and 76 inbred lines across the whole growing season. The integrated A value after heading was positively associated with crop growth rate (CGR) from heading to harvest, but that before heading was not. A curve-smoothing analysis of A after heading showed that accumulated A at > 80% of its maximum (A80) was positively correlated with CGR in analyses of all lines mixed and of lines grouped by genetic background, while the maximum A and accumulated A at ≤ 80% were less strongly correlated with CGR. We also found a genomic region (~ 12.2 Mb) that may enhance both A80 and aboveground biomass at harvest. We propose that maintaining a high A after heading, rather than having high maximum A, is a potential target for enhancing rice biomass accumulation.

Spatiotemporal Heterogeneity of Chlorophyll Content and Fluorescence Response Within Rice (Oryza sativa L.) Canopies Under Different Nitrogen Treatments.

Accurate acquisition of plant phenotypic information has raised long-standing concerns in support of crop breeding programs. Different methods have been developed for high throughput plant phenotyping, while they mainly focused on the canopy level without considering the spatiotemporal heterogeneity at different canopy layers and growth stages. This study aims to phenotype spatiotemporal heterogeneity of chlorophyll (Chl) content and fluorescence response within rice leaves and canopies. Multipoint Chl content and high time-resolved Chl a fluorescence (ChlF) transient (OJIP transient) of rice plants were measured at different nitrogen levels and growth stages. Results showed that the Chl content within the upper leaves exhibited an increasing trend from the basal to the top portions but a decreasing pattern within the lower leaves at the most growth stages. Leaf Chl content within the rice canopy was higher in the lower leaves in the vegetative phase, while from the initial heading stage the pattern gradually reversed with the highest Chl content appearing in the upper leaves. Nitrogen supply mainly affects the occurrence time of the reverse vertical pattern. This could be the result of different nutritional demands of leaves transforming from sinks to sources, and it was further confirmed by the fall of the JI phase of OJIP transient in the vegetative phase and the rise in the reproductive phase. We further deduced that the vertical distribution of Chl content could have a defined pattern at a specific growth stage. Furthermore, the reduction of end acceptors at photosystem I (PSI) electron acceptor side per cross section (RE0/CS) was found to be a potential sensitive predictor for identifying the vertical heterogeneity of leaf Chl content. These findings provide prior knowledge on the vertical profiles of crop physiological traits, which explore the opportunity to develop more efficient plant phenotyping tools for crop breeding.

Effect of Foliar Micronutrients (B, Mn, Fe, Zn) on Maize Grain Yield, Micronutrient Recovery, Uptake, and Partitioning.

Timing of micronutrient demand and acquisition by maize (Zea mays L.) is nutrient specific and associated with key vegetative and reproductive growth stages. The objective of this study was to determine the fate of foliar-applied B, Fe, Mn, Zn, and Fe/Zn together, evaluate the effect of foliar micronutrients applied at multiple rates and growth stages on maize grain yield, and determine their apparent nutrient recovery efficiency (ANR). Five Randomized Complete Block Design (RCBD) experiments were conducted in 2014 and 2015 at five locations across Nebraska. Total dry matter was collected at 5–6 stages, and separated into leaves, stalk, and reproductive tissue as appropriate to determine micronutrient uptake, partitioning, and translocation. Foliar B, Mn, Zn, and Fe/Zn had no effect on grain yield for most application time by rate levels, though, at the foliar Mn site, there was a 19% yield increase due to a V18 application of 0.73 kg Mn ha−1 which corresponded with reduced Mn uptake in maize grown in control plots. At the foliar Zn site, there was 4.5% decrease in yield due to a split foliar application of 0.84 kg Zn ha−1 total, applied at V11 and V15 stage, which increased leaf Zn concentrations greater than the established toxic level. Only the Fe site had consistent grain yield response and was the only experiment that had visual signs of micronutrient deficiency. Regardless of application time from V6 to R2, there was a 13.5–14.6% increase in grain yield due to 0.22 kg Fe ha−1 foliar application. Most micronutrients had limited or no translocation, however, early season applications of B, prior to V10, had significant mobilization to reproductive tissues at or after VT. Foliar Mn, Zn, and B application had ANR LSmeans of 9.5, 16.9, and 2.5%, respectively, whereas the Fe/Zn mix had negative ANR LSmeans of −9.1% Fe and −1.3% Zn which indicate suppression. These data highlight the importance of confirming a micronutrient deficiency prior to foliar application, guide specific growth stages to target with specific micronutrients, track the fate of foliar-applied micronutrients, and describe the variable effect of foliar-applied micronutrients on grain yield.

Evaluation of evapotranspiration deficit index for agricultural drought monitoring in North China

Drought is a major natural hazard that causes significant economic loss across the world. Drought indexes derived from remote sensing data can provide dynamic information in real time, facilitating drought assessments. The evapotranspiration (ET) deficit index (ETDI) is derived from observations of ET and potential ET. ETDI is an indicator of agricultural drought. In this study, actual ET was modeled using the Boreal Ecosystem Productivity Simulator with remote sensing data and ground-station observation data as input. We investigated the efficacy of ETDI to indicate regional agricultural drought in a major region of winter wheat production in Northern China. Spatiotemporal changes in ETDI agreed well with the standardized precipitation index (SPI) on a 5 month scale and with the Palmer drought severity index (PDSI). ETDI also agreed well with the composite index of yield reduction (CI), which represents the provincial percentage area of cropland subject to drought stress. CI performed better in agricultural regions where water was supplied by precipitation rather than irrigation. Changes in winter wheat yield due to meteorological events were highly correlated with ETDI, rather than SPI or PDSI, averaged across specific growth stages. Weather conditions had significant impacts in April (jointing and boot stages of winter wheat development) in Hebei province, in May (heading and grain-filling stages) in Shanxi province, and from January through April in other provinces. These results demonstrate that ETDI is a suitable index for indicating regional agricultural drought in order to inform drought mitigation activities.

Temperature and precipitation at specific growth stages influence soybean tocopherol and lutein concentrations

AbstractSoybean [Glycine max (L.) Merr.] is an important source of health beneficial compounds, including tocopherols and lutein. Seeds with elevated concentrations of these health beneficial compounds may be required to meet the increasing needs of the functional food market. A study was conducted for a period of 11 years in Ottawa, ON, Canada to examine the relationship between temperature and precipitation during specific growth stage intervals (GSI) and the concentration of tocopherol (toc) and lutein in the seeds at harvest. Tocopherol concentrations in soybean were most influenced by air temperature changes, while lutein concentration was more sensitive to precipitation. Heat stress via the accumulation of temperatures greater than 31°C during the mid‐vegetative to physiological maturity GSI was positively correlated (r2 = 0.94) with α‐toc concentration. Lutein concentration was negatively correlated (r2 = 0.61) with mean cumulative precipitation (cppt) during the seed development to maturation GSI. Overall, reproductive stages were more responsive to climatic variables than vegetative stages. In addition, the cultivars with higher toc or lutein concentrations were less affected by climatic variables than cultivars with lower concentrations. Climatic factors and abiotic stress during specific growth stages may have significant impact on concentrations of health beneficial compounds in mature seeds and this should be considered in the production of soybean for the functional food market.

Zeytin (Olea europaea) meyvesindeki bitki besin maddelerinin değişimi

Türkiye’de zeytin veriminin dünya ortalamalarının altında olmasının sebeplerinden biri de gübre uygulamalarının doğru bir şekilde yapılmamasıdır. Zeytin verimini arttırmak, kaliteyi yükseltmek, periyodisiteyi azaltmak, ülke çiftçisi ve ekonomisine katkıda bulunabilmek için toprak ve yaprak analizlerine dayalı olarak doğru bir gübreleme yapılması gerekmektedir. Son yıllarda yaygın bir şekilde kullanılan fertigasyon sisteminden uygulanacak gübrelerin verilme zamanlarının belirlenmesinde meyvedeki besin elementi hareketleri önem kazanmaktadır. Bu amaç doğrultusunda, verim çağındaki ayvalık zeytin çeşidi meyvelerinin farklı gelişme dönemlerindeki N, P, K, Ca, Mg değişimi ve 100 kg ürün ile kalkan N, P2O5, K2O, CaO ve MgO miktarları belirlenmiştir. Çalışma Zeytincilik Araştırma Enstitüsü bahçesinde bulunan 32 yaşındaki Ayvalık zeytin çeşidinde yapılmıştır. Tam çiçeklenmeden sonra (Haziran-Kasım) hasada kadar 6 dönemde alınan meyve örneklerinin bitki besin maddesi içeriklerinin değişimi belirlenmiştir. Bu çalışma ile zeytin meyvenin büyümesi ile birlikte dönemlere göre N ve K’un çok değişken bir durum gösterdiği P, Ca ve Mg içeriklerinin ise daha stabil olduğu gözlenmiştir. Bu da zeytin ağacının beslenmesinde ürün ile kaldırılan besin maddeleri içerisinde en çok K’un olduğunu göstermektedir. Bu çalışma sonucunda toprak ve yaprak analiz sonuçlarına göre fertigasyon programları hazırlanırken, meyvenin gelişme dönemlerine göre ihtiyacı olan bitki besin elementlerinin uygun dönem ve miktarlarda kullanılmasına katkı sağlayacaktır.

Impact of high night temperature stress on different growth stages of wheat

Increasing temperature especially the High night temperature (HNT) is a major constriction for sustaining global food production under changing climate scenario. Recent climate change has resulted into events of abrupt night temperature rise either throughout the crop period or at specific growth stage. Historical temperature observations and model projections have predicted a more pronounced increase in night temperature [minimum temperature (Tmin.)] compared to that of day temperatures [maximum temperature (Tmax.)]. Hence, the present study was carried out to identify the most critical growth stage affected by HNT and their effects on physiological, growth and yield traits of wheat. A unique field-based movable temperature controlling system was designed to enhance the night temperature by 5 °C than ambient temperature. The three critical phenological stages of wheat i.e. at GS 15 (before tillering initiation), GS 45 (booting) and GS 75 (grain filling) of Zadok’s scale were separately subjected to HNT treatment. The results reveals that GS 75 (grain filling) was the most sensitive stage affected by HNT, which has recorded remarkable reduction in grain yield (GY), Harvest Index (HI), Thousand grain weight (TGW) and grain weight per spike (GWPS) compared to control treatment. Pearson correlation analysis showed that breeders can choose lines with higher TGW, GWPS and grain number per spike (GNPS) for obtaining better GY under HNT conditions and vegetative/booting stage HNT in field condition may not attribute for any significant yield loss.

Seasonal Progression in Nutrition Density of Mandarin Cultivars and Their Relationship with Dry Matter Accumulation during Fruit Development Period

ABSTRACT Seasonal progression in the nutritional concentration of fruit peel, pulp, and leaves of mandarin cultivar Kinnow (KN) and W. Murcott (WM) were evaluated at monthly intervals during fruit development. Marked variations in nutritional content during specific fruit growth stages were observed in both the cultivars. Overall, leaves were the main sight of nutrient accumulation than fruit parts, and concentration was increased with the advancement in fruit maturity. The general order of abundance of macro and micronutrients in leaves was calcium (Ca)>nitrogen (N)>potassium (K)>magnesium (Mg)>phosphorus (P) and iron (Fe)>manganese (Mn)>zinc (Zn)>copper (Cu) respectively. P and K were higher in the pulp, while most of the micronutrients were more in the peel. However, in the case of N equal distribution was observed in peel and pulp of both the cultivars. Leaf nutrient showed a strong association with peel, pulp, and dry matter accumulation. These results emphasized that mandarin can be a good source of minerals, and they have the capacity to reinstate in nutrient-rich foods.

Heat-responsive and time-resolved transcriptome and metabolome analyses of Escherichia coli uncover thermo-tolerant mechanisms

Current understanding of heat shock response has been complicated by the fact that heat stress is inevitably accompanied by changes in specific growth rates and growth stages. In this study, a chemostat culture was successfully performed to avoid the physico-chemical and biological changes that accompany heatshock, which provided a unique opportunity to investigate the full range of cellular responses to thermal stress, ranging from temporary adjustment to phenotypic adaptation at multi-omics levels. Heat-responsive and time-resolved changes in the transcriptome and metabolome of a widely used E. coli strain BL21(DE3) were explored in which the temperature was upshifted from 37 to 42 °C. Omics profiles were categorized into early (2 and 10 min), middle (0.5, 1, and 2 h), and late (4, 8, and 40 h) stages of heat stress, each of which reflected the initiation, adaptation, and phenotypic plasticity steps of the stress response. The continued heat stress modulated global gene expression by controlling the expression levels of sigma factors in different time frames, including unexpected downregulation of the second heatshock sigma factor gene (rpoE) upon the heat stress. Trehalose, cadaverine, and enterobactin showed increased production to deal with the heat-induced oxidative stress. Genes highly expressed at the late stage were experimentally validated to provide thermotolerance. Intriguingly, a cryptic capsular gene cluster showed considerably high expression level only at the late stage, and its expression was essential for cell growth at high temperature. Granule-forming and elongated cells were observed at the late stage, which was morphological plasticity occurred as a result of acclimation to the continued heat stress. Whole process of thermal adaptation along with the genetic and metabolic changes at fine temporal resolution will contribute to far-reaching comprehension of the heat shock response. Further, the identified thermotolerant genes will be useful to rationally engineer thermotolerant microorganisms.

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation.

An Analytical View on Water Use Efficiency of Some Main Crops Grown in the Non-costal Parts of Thrace Region

The article contains data obtained from evaluations related to irrigation water use efficiency (IWUE) and water use efficiency (WUE), for the main crops, irrigated at different stages of growth, on the basis of some findings obtained in the Research Institute in Kırklareli. Each of the experimental crops was sown and farmed following procedures applied by the farmers in the region, except of the irrigation applications which were based on the sensitivity of a certain crop to water shortage in the soil, during the specific growth stages. Similar procedures were applied and all the experimental treatments were irrigated at growth stages, as predicted in the research methodology, and water amounts required to fill the 0-90 cm soil depth to field capacity were implied. Evaluation data obtained from the field experiments with three major crops, grown on the non-coastal lands of Thrace Region showed, that the productivity of irrigation water, as well as water use efficiencies of all analysed crops, are growth stage controlled. The highest IWUE and WUE efficiencies of 0.87 and 0.92 kg da-1 m-3; and 1.08 kg da-1 m-3 and 0.81 kg da-1 m-3; were determined for wheat and sunflower crops, irrigated at booting and flowering stages, respectively. Each m3 of irrigation water, applied during the most sensitive fruit formation stage (Ff) of pumpkin crop, provided additionally 8.47 kg da-1 fruit yield, 8.09 fruit numbers and 0.28 kg da-1 seed yields, more than those of rainfed farming (R).

Nutrient Status Diagnosis of Infield Oilseed Rape via Deep Learning-Enabled Dynamic Model

The symptoms of the nutrient stress in plant canopies differ among different growth stages. It is a challenge to develop individual diagnosis models to evaluate the nutrient status for a specific growth stage in time. Therefore, this article encoded spatiotemporal information of plants in a single time-series model to evaluate the nutrient status of oilseed rape more efficiently. Specifically, in this article, we combined the convolutional neural network (CNN) and long short-term memory (LSTM) to classify the oilseed rape crops according to their nutrient status. The model was validated on a large number of sequential images acquired from oilseed rape canopies at different growth stages during a two-year experiment. Different pretrained CNNs were used to extract distinctive features from every time step of sequential images and then, these features were considered as the input of LSTM to classify the oilseed rape into nine classes of nutrient statuses. We demonstrated that the LSTM outperformed the traditional machine-learning method and the deep features showed better performance compared with hand-crafted features. The Inceptionv3-LSTM obtained the highest overall classification accuracy of 95% when tested on the dataset of 2017/2018 and it also provided a good generalization when using a cross-dataset validation, with the highest overall accuracy of 92%. Our proposed approach presents a pathway toward automatic nutrient status diagnosis during the whole life cycle of the plants, and the LSTM technique would play an essential role in the near future for time-series analysis for precision agriculture.

Impact of climatic factors at specific growth stages on soybean soyasaponin I concentration

Soybean [Glycine max (L.) Merr.] is an important source of health-beneficial compounds, including soyasaponin I. A field study was conducted for 11 yr in Ottawa, ON, Canada, to assess the impacts of temperature and precipitation during specific growth stage intervals on soyasaponin I concentration in three soybean cultivars. Soyasaponin I concentration response to air temperature and precipitation variables were observed at specific growth stage intervals for some cultivars. The response was complex and cultivar specific. Overall, reproductive stages were more responsive and 20 °C appeared to be a critical threshold in determining soybean soyasaponin I concentration response to air temperature.

Impacts of drought stress on the morphology, physiology, and sugar content of Lanzhou lily (Lilium davidii var. unicolor)

Water shortage is a key environmental factor that negatively effects plant growth. Lanzhou lily (Lilium davidii var. unicolor) is a perennial herbaceous drought-tolerant crop widely used as a food and medicine in China. The effects of drought stress on plant growth, osmotic pressure, and secondary metabolite content differ. Here, we investigate alterations in basic physiological processes and in the accumulation of osmolytes by Lanzhou lily under drought stress. Plants were grown at three drought intensities, being irrigated at 5, 15, and 25 day intervals (either throughout the study or during specific growth stages). Water stress markedly decreased plant height and leaf length. With increasing drought stress, the chlorophyll content, bulb weight, and contents of soluble sugars, polysaccharides, and fructose decreased. In contrast, the proline, glucose, and trehalose contents increased under severe drought stress. In addition, glucose and trehalose contents differed significantly under drought stress at different growth stages, whereas other indicators differed significantly only under drought stress throughout the growth period. Our results demonstrate that Lanzhou lily may adapt to drought stress in different growth stages were different. During the shoot stage, the adaptation strategy was to reduce the growth of aboveground part to sustain the underground parts, but during the bulbs expansion stage Lanzhou lily appears to adapt to drought stress by consuming nutrients from underground bulbs to sustain the growth of the aboveground parts and complete the plant's life cycle and by changing osmotic regulation and the levels of secondary metabolites to improve resistance to drought stress.

Assessment on water requirement and irrigation scheduling in Field Pea under elevated thermal regimes: A Simulation Study

The present experiment was laid out on Field Pea (cv. AP 3) at the research farm of Bidhan Chandra Krishi Viswavidyalaya, Kalyani, Nadia, West Bengal during rabi seasons of 2017-18 and 2018-19 to estimate the ETo and water requirement from Pea field using CROPWAT 8.0 model which will eventually help farmers in irrigating the crop in future also. Seasonal mean reference crop evapotranspiration was relatively higher (2.12 mm day-1) during first season than the second one (2.04 mm day-1). Radiation use efficiency has been found to be directly proportional to the ETo rate. Model derived total irrigation requirement and actual crop evapotranspiration remained maximum as 159.3 mm and 161 mm respectively during rabi 2017-18. Increment of temperature by 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 oC from the current scenario, resulted in increasing the seasonal ETo by 1.44, 2.40, 3.84, 5.76, 6.73 and 8.17%, respectively and water requirement by 2.41, 5.40, 7.56, 10.16, 13.02 and 14.54% in alike order. Two specific crucial crop growth stages viz. initial (7-10 DAS) and mid development (35-40 DAS) were simulated based on critical depletion for scheduling irrigation under already over exploited water resource zones. Therefore, the study concluded that need based judicious irrigation supplementation at critical stages could be a realizable option for effective planning and management of moisture regimes at optimum level in view of persisting climate change conditions.

Real-time and multi-stage recommendations for nitrogen fertilizer topdressing rates in winter oilseed rape based on canopy hyperspectral data

Due to the intensive rotation and environmental concerns related with the over-fertilization, optimizing nitrogen (N) fertilization is crucial to winter oilseed rape cultivation in the Yangtze River Basin in China. Although remote sensing techniques have been applied to aid the site-specific crop N management, most studies are conducted for a specific growth stage, ignoring that multi-stage topdressing are commonly applied for crops. This study aimed to determine the real-time and multi-stage N fertilizer topdressing recommendations for winter oilseed rape by developing site-specific topdressing recommendation (SSTR) models using hyperspectral canopy reflectance data. The in-situ hyperspectral data were collected from five N fertilization experiments conducted at two sites in Hubei province, China during 2013–2016. A ratio vegetation index (RVI) based on reflectance at 764 and 657 nm wavelengths (RVI(764,657)) was proposed as the effective indicator of the N status in winter oilseed rape. For each critical growth stage, the empirical relationships between the RVI(764,657), the leaf N concentration (LNC), and the yield were used to develop SSTR models and determine the threshold below which topdressing was needed. In the growth season of 2016–2017, the performance of the SSTR models were evaluated by conducting the experiment of the real-time and multi-stage topdressing recommendation. This experiment included 54 multi-stage topdressing strategies. At each growth stage, whether the topdressing was needed and the corresponding topdressing rates were determined with the canopy spectra measurements and the SSTR model at this stage. The site-specific N topdressing was then applied on the next day after spectra measurements were taken. The LNC, the dry mass (DM), and the yield of this experiment were compared with the reference experiment of the local farmer’s topdressing strategy. Results showed that the maximal yield was achieved by topdressing either in the eight-leaf stage (yield = 2983 kg/hm2) or the over-wintering stage (yield = 2902 kg/hm2) for the N120 treatment. The yield of 2983 kg/hm2 was 9.8 % lower than the theoretical maximal yield of 3306 kg/hm2, but 8.3 % higher than the maximal yield in the reference experiment. This maximal yield was achieved by reducing 25 % of the theoretically optimal total N rates. Moreover, we found that neither the LNC nor the DM and the yield were linearly related with the total N fertilizer application rates. The timing of the N fertilization had more important impacts on the DM and the yield than the total N fertilizer rates. Postponing the timing of topdressing could decrease the DM and the yield. Results from this study suggested that the newly developed SSTR models could help effectively distribute the N fertilizer based on the real-time demand of winter oilseed rape in critical growth stages.

Growing period classification of Gynura bicolor DC using GL-CNN

Determining the optimal growth stage of leafy vegetables suitable for harvesting is of great importance. Despite the availability of many intelligent programs designed for plant classification, identifying the growth stage of a vegetable from its leaf features remains a major challenge. Gynura bicolor DC (G. bicolor) is an important vegetable, and its leaves are harvested for culinary use at a specific growth stage. The produce quality of leaves is compromised when they are harvested at improper stage. A classification model named GL-CNN was proposed based on convolutional neural networks to address this issue. The proposed model merges the features using a network fusion strategy to expand the feature representation on the basis of the intact leaf and leaf patch image sets. The networks were validated using a new dataset of G. bicolor planted and collected by ourselves. “Early fusion” and “late fusion” networks were designed and compared with GL-CNN to verify the rationality of network fusion location. The test accuracy of GL-CNN reaches 95.63%, which is the best in the classification task.

Hfq CLASH uncovers sRNA-target interaction networks linked to nutrient availability adaptation.

By shaping gene expression profiles, small RNAs (sRNAs) enable bacteria to efficiently adapt to changes in their environment. To better understand how Escherichia coli acclimatizes to nutrient availability, we performed UV cross-linking, ligation and sequencing of hybrids (CLASH) to uncover Hfq-associated RNA-RNA interactions at specific growth stages. We demonstrate that Hfq CLASH robustly captures bona fide RNA-RNA interactions. We identified hundreds of novel sRNA base-pairing interactions, including many sRNA-sRNA interactions and involving 3'UTR-derived sRNAs. We rediscovered known and identified novel sRNA seed sequences. The sRNA-mRNA interactions identified by CLASH have strong base-pairing potential and are highly enriched for complementary sequence motifs, even those supported by only a few reads. Yet, steady state levels of most mRNA targets were not significantly affected upon over-expression of the sRNA regulator. Our results reinforce the idea that the reproducibility of the interaction, not base-pairing potential, is a stronger predictor for a regulatory outcome.

Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model

Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt.

Selection of potential aptamers for specific growth stage detection of Yersinia enterocolitica.

Yersinia enterocolitica remains a threat to public health, and a sensitive detection method is a prerequisite due to its complicated diagnosis associated with slow growth. Recently, aptamer-based detection systems have played a vital role in the development of simple, rapid, sensitive, and specific detection methods. Herein, highly specific ssDNA aptamers were screened against Y. enterocolitica at the different growth stages by whole cell-SELEX. Cells at different growth stages were harvested and incubated with an ssDNA library to get an enriched pool of specific aptamer candidates. After the 10th round of SELEX, the enriched pool was sequenced and grouped into seven families based on homology and similarity of the secondary structure. Flow cytometry analysis revealed that the aptamers M1, M5, and M7 with Kd values of 37.93 ± 7.88 nM, 74.96 ± 21.34 nM, and 73.02 ± 18.76 nM had the highest affinity and specificity to the target, respectively. The selected aptamers showed binding to the different growth stages of Y. enterocolitica with a significant increase in the gated fluorescence. Our aptamer selection strategy is convenient, and the developed aptamer can be useful for an accurate and reliable detection system.