•A simulation model for assessment of Integrated Weed Management was developed.•The model simulates multiannual weed dynamics and weed-crop competitive interaction.•The model was evaluated on Avena ...fatua – cereal crops rotation systems.•Chemical control as well as cultural management alternatives were simulated.•The model provides bioecological, economic and environmental outputs.
A mathematical simulation model for the multi-annual assessment of Integrated Weed Management strategies is presented. The model allows the simulation of the competitive interaction between annual weeds and crops. For weed development, the following processes are represented: (i) demographic dynamics on a daily basis considering the numeric composition of the different phenological states, (ii) intra and inter specific competition, (iii) seed production and (v) the effect of different control methods. For the crop development, the computed variables are: (i) Leaf Area Index, (ii) competitive effect over the weed, (iii) the expected yield as a function of weed competition. The model was developed in close collaboration with agricultural technicians and extensionists aiming to target a decision-making related audience. Results are provided for a wild oat (Avena fatua) – winter wheat (Triticum aestivum) / malting barley (Hordeum distichum) rotation system, typical of the Semiarid Pampean Region of Argentina. Multi-annual scenarios were generated to evaluate the effect of different management strategies against common herbicide-based practices in susceptible and resistant weed populations. Parts of the model were validated with independent experimental data. Finally, future improvements of the model and some guidelines towards the development of a long-term Decision Support System for weed management are provided.
The nucleus is highly organized, such that factors involved in the transcription and processing of distinct classes of RNA are confined within specific nuclear bodies
. One example is the nuclear ...speckle, which is defined by high concentrations of protein and noncoding RNA regulators of pre-mRNA splicing
. What functional role, if any, speckles might play in the process of mRNA splicing is unclear
. Here we show that genes localized near nuclear speckles display higher spliceosome concentrations, increased spliceosome binding to their pre-mRNAs and higher co-transcriptional splicing levels than genes that are located farther from nuclear speckles. Gene organization around nuclear speckles is dynamic between cell types, and changes in speckle proximity lead to differences in splicing efficiency. Finally, directed recruitment of a pre-mRNA to nuclear speckles is sufficient to increase mRNA splicing levels. Together, our results integrate the long-standing observations of nuclear speckles with the biochemistry of mRNA splicing and demonstrate a crucial role for dynamic three-dimensional spatial organization of genomic DNA in driving spliceosome concentrations and controlling the efficiency of mRNA splicing.
We report Single Molecule Cluster Analysis (SiMCAn), which utilizes hierarchical clustering of hidden Markov modeling-fitted single-molecule fluorescence resonance energy transfer (smFRET) ...trajectories to dissect the complex conformational dynamics of biomolecular machines. We used this method to study the conformational dynamics of a precursor mRNA during the splicing cycle as carried out by the spliceosome. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify the signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified an open conformation adopted late in splicing by a 3' splice-site mutant, invoking a mechanism for substrate proofreading. SiMCAn enables rapid interpretation of complex single-molecule behaviors and should prove useful for the comprehensive analysis of a plethora of dynamic cellular machines.
Avena fatua is an invasive weed of the semiarid region of Argentina. Seedling emergence patterns are very irregular along the season showing a great year-to-year variability mainly due to a highly ...unpredictable precipitation regime. Non-linear regression techniques are usually unable to accurately predict field emergence under such environmental conditions. Artificial Neural Networks (ANNs) are known for their capacity to describe highly non-linear relationships among variables thus showing a high potential applicability in ecological systems. The objectives of the present work were to develop different ANN models for A. fatua seedling emergence prediction and to compare their predictive capability against non-linear regression techniques. Classical hydrothermal-time indices were used as input variable for the development of univariate models, while thermal-time and hydro-time were used as independent input variables for developing bivariate models. The accumulated proportion of seedling emergence was the output variable in all cases. A total of 528 input/output data pairs corresponding to 11years of data collection were used in this study. Obtained results indicate a higher accuracy and generalization performance of the optimal ANN model in comparison to non-linear regression approaches. It is also demonstrated that the use of thermal-time and hydro-time as independent explanatory variables in ANN models yields better prediction than using combined hydrothermal-time indices in classical NLR models. The best obtained ANN model outperformed in 43.3% the best NLR model in terms of RMSE of the test set. Moreover, the best obtained ANN predicted accumulated emergence within the first 50% of total emergence 48.3% better in average than the best developed NLR model. These outcomes suggest the potential applicability of the proposed modeling approach in weed management decision support systems design.
•A conceptual operational planning model for weed control is proposed.•The model selects herbicides to be applied and determines their application times.•The objective is to maximize the economic ...benefit.•The environmental impact is quantified as an external cost.•Sensitivity analysis shows that the model solutions are robust.
Weeds cause crop yield loss due to competition, interfere with agricultural activities and reduce grain quality due to seed contamination. Among the numerous methods for weed control, the use of herbicides is the most common practice. Nowadays, the optimization of herbicide application is pursued to reduce the environmental impact, delay the appearance of herbicide-resistant weed populations, and improve the cost/benefit ratio of the agronomic business. This work proposes an operational planning model, aimed at calculating the optimal application times of herbicides in no-tillage systems within a growing season in order to maximize the economic benefit of the activity while rationalizing the intensity of the applications with respect to expert-knowledge-based recommendations. The model can decide on herbicide applications on a daily basis, consistent with timing of agricultural activities, and provides an explicit quantification of the environmental impact as an external cost. The proposed approach was tested on a winter wheat (Triticum aestivum)–wild oat (Avena fatua) system, typical of the semiarid region of Argentina. In all the studied scenarios at least two pre-sowing applications of non-selective herbicides were required to effectively control early emerging weed seedlings. Additional pre-sowing and post-emergence applications were also advised in cases when competitive pressure was significant.
Many lnc
RNA
s are thought to interact with the polycomb repressive complex 2 (
PRC
2) in order to regulate gene expression. A central example of this lnc
RNA
–
PRC
2 paradigm in gene regulation is
...HOTAIR
. In this issue of
The
EMBO
Journal,
a study (Portoso
et al,
) reports that while
HOTAIR
binds
PRC
2 with high affinity, the complex itself is dispensable for
HOTAIR
‐mediated transcriptional silencing. This study raises important questions about the role of
PRC
2 interactions for lnc
RNA
‐mediated functions and argues for a re‐evaluation of this lnc
RNA
–
PRC
2 functional paradigm.
The Selective Reminding Test (SRT) and the Free and Cued Selective Reminding Test (FCSRT) are multitrial memory tests that use a common "selective reminding" paradigm that aims to facilitate learning ...by presenting only the missing words from the previous recall trial. While in the FCSRT semantic cues are provided to elicit recall, in the SRT, participants are merely reminded of the missing items by repeating them. These tests have been used to assess age-related memory changes and to predict dementia. The performance of healthy elders on these tests has been compared before, and results have shown that twice as many words were retrieved from long-term memory in the FCSRT compared with the SRT. In this study, we compared the tests' properties and their accuracy in discriminating amnestic mild cognitive impairment (aMCI; n = 20) from Alzheimer disease (AD; n = 18). Patients with AD performed significantly worse than patients with aMCI on both tests. The percentage of items recalled during the learning trials was significantly higher for the FCSRT in both groups, and a higher number of items were later retrieved, showing the benefit of category cueing. Our key finding was that the FCSRT showed higher accuracy in discriminating patients with aMCI from those with AD.
Most popular emergence prediction models require species-specific population-based parameters to modulate thermal/hydrothermal accumulation. Such parameters are frequently unknown and difficult to ...estimate. Moreover, such models also rely on hardly available and difficult to estimate soil site-specific microclimate conditions, which in turn depend on soil heterogeneity at a field spatial level. On the other hand, modern agriculture benefits from easily available real-time information, in particular on-line meteorological data generated by forecasts and automatic local weather stations. In this context, Artificial Neural Networks (ANN) provide a flexible option for the development of prediction models, especially to study species which show a highly distributed emergence pattern along the year. In this work, an ANN approach based on easily obtainable meteorological data (daily minimum and maximum temperatures; daily precipitation) is proposed for weed emergence prediction. Relative Daily Emergence (RDE), expressed as a proportion of the total emergence, was the adopted output variable. Field emergence data recorded on a weekly basis were used to generate RDE patterns through linear interpolation. Results for three study cases from the Semiarid Pampean Region of Argentina (Lolium multiflorum, Avena fatua and Vicia villosa), which show irregular and time-distributed field emergence patterns, are reported. In all cases, ANN model selection was based on the Root Mean Square Error of the test set which showed better consistency than other typical Information Theory performance metrics. The combination of large ANN with a Bayesian Regularization Algorithm generated satisfactory estimations based on the RMSE values for independent Cumulative Emergence data.
•ANN are proposed for real-time weed emergence prediction.•Emergence rates and cumulative patterns are predicted.•Raw daily field meteorological data are directly used.•Large ANN provide accurate emergence prediction for three weed species.•Results suggest the potential practical use of ANN within IWMSS.
The spliceosome is the dynamic RNA-protein machine responsible for faithfully splicing introns from precursor messenger RNAs (pre-mRNAs). Many of the dynamic processes required for the proper ...assembly, catalytic activation, and disassembly of the spliceosome as it acts on its pre-mRNA substrate remain poorly understood, a challenge that persists for many biomolecular machines. Here, we developed a fluorescence-based Single Molecule Cluster Analysis (SiMCAn) tool to dissect the manifold conformational dynamics of a pre-mRNA through the splicing cycle. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified a conformation adopted late in splicing by a 3′ splice site mutant, invoking a mechanism for substrate proofreading. SiMCAn presents a novel framework for interpreting complex single molecule behaviors that should prove widely useful for the comprehensive analysis of a plethora of dynamic cellular machines.
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