Determining the Most Important Physiological and Agronomic Traits Contributing to Maize Grain Yield through Machine Learning Algorithms: A New Avenue in Intelligent Agriculture

Prediction is an attempt to accurately forecast the outcome of a specific situation while using input information obtained from a set of variables that potentially describe the situation. They can be used to project physiological and agronomic processes; regarding this fact, agronomic traits such as yield can be affected by a large number of variables. In this study, we analyzed a large number of physiological and agronomic traits by screening, clustering, and decision tree models to select the most relevant factors for the prospect of accurately increasing maize grain yield. Decision tree models (with nearly the same performance evaluation) were the most useful tools in understanding the underlying relationships in physiological and agronomic features for selecting the most important and relevant traits (sowing date-location, kernel number per ear, maximum water content, kernel weight, and season duration) corresponding to the maize grain yield. In particular, decision tree generated by C&RT algorithm was the best model for yield prediction based on physiological and agronomical traits which can be extensively employed in future breeding programs. No significant differences in the decision tree models were found when feature selection filtering on data were used, but positive feature selection effect observed in clustering models. Finally, the results showed that the proposed model techniques are useful tools for crop physiologists to search through large datasets seeking patterns for the physiological and agronomic factors, and may assist the selection of the most important traits for the individual site and field. In particular, decision tree models are method of choice with the capability of illustrating different pathways of yield increase in breeding programs, governed by their hierarchy structure of feature ranking as well as pattern discovery via various combinations of features.     

Production of a Recombinant Peptide (Lasioglossin LL ΙΙΙ) and Assessment of Antibacterial and Antioxidant Activity

International Journal of Peptide Research and Therapeutics - The use of antimicrobial peptides (AMPs) as biopreservative to replace chemical preservatives has become of interest among consumers....     

Modeling and systematic analysis of biomarker validation using selected reaction monitoring

Background

Discovery and validation of protein biomarkers with high specificity is the main challenge of current proteomics studies. Different mass spectrometry models are used as shotgun tools for the discovery of biomarkers. Validation of a set of selected biomarkers from a list of candidates is an important stage in the biomarker identification pipeline. Validation is typically done by triple quadrupole (QQQ) mass spectrometry (MS) running in selected reaction monitoring (SRM) mode. Although the individual modules of this pipeline have been studied, there is little work on integrating the components from a systematic point of view.

Results

This paper analyzes the SRM experiment pipeline in a systematic fashion, by modeling the main stages of the biomarker validation process. The proposed models for SRM and protein mixture are then used to study the effect of different parameters on the final performance of biomarker validation. Sample complexity, purification, peptide ionization, and peptide specificity are among the parameters of the SRM experiment that are studied. We focus on the sensitivity of the SRM pipeline to the working parameters, in order to identify the bottlenecks where time and energy should be spent in designing the experiment.

Conclusions

The model presented in this paper can be utilized to observe the effect of different instrument and experimental settings on biomarker validation by SRM. On the other hand, the model would be beneficial for optimization of the work flow as well as identification of the bottlenecks of the pipeline. Also, it creates the required infrastructure for predicting the performance of the SRM pipeline for a specific setting of the parameters.

     

miRNAs; a novel strategy for the treatment of COVID-19

Coronavirus disease 2019 (COVID-19) is the seventh member of the bat severe acute respiratory syndrome family. COVID-19 can fuse their envelopes with the host cell membranes and deliver their genetic material. COVID-19 attacks the respiratory system and stimulates the host inflammatory responses, enhances the recruitment of immune cells, and promotes angiotensin-converting enzyme 2 activities. Patients with confirmed COVID-19 may have experienced fever, dry cough, headache, dyspnea, acute kidney injury, acute respiratory distress syndrome, and acute heart injury. Several strategies such as oxygen therapy, ventilation, antibiotic or antiviral therapy, and renal replacement therapy are commonly used to decrease COVID-19-associated mortality. However, these approaches may not be good treatment options. Therefore, the search for an alternative-novel therapy is urgently important to prevent the disease progression. Recently, microRNAs (miRNAs) have emerged as a promising strategy for COVID-19. The design of oligonucleotide against the genetic material of COVID-19 might suppress virus RNA translation. Several previous studies have shown that host miRNAs play an antiviral role and improve the treatment of patients with COVID-19. miRNAs by binding to the 3′-untranslated region (UTR) or 5′-UTR of viral RNA play an important role in COVID-19-host interplay and viral replication. miRNAs interact with multiple pathways and reduce inflammatory biomarkers, thrombi formation, and tissue damage to accelerate the patient outcome. The information in this review provides a summary of the current clinical application of miRNAs for the treatments of patients with COVID-19.     

A New Angle on Second Harmonic Generation from a Small Hole in a Metal Film

Plasmonics - Bethe’s theory treats a subwavelength aperture in a metal film as the combination of a parallel magnetic dipole and transverse electric dipole. For linear optics, this gives the...     

Emerging roles of epigenetic mechanisms in Parkinson’s disease

Epigenetic mechanisms have emerged as important components of a variety of human diseases, including cancer and central nervous system disorders. Despite recent studies highlighting the role of epigenetic mechanisms in several neurodegenerative and neuropsychiatric disorders, to date, there has been a paucity of studies exploring the role of epigenetic factors in Parkinson’s disease (PD). PD is a progressive neurological disorder with characteristic motor and non-motor symptoms, including a range of neuropsychiatric features, for which neither preventative nor effective long-term treatment strategies are available. It is one of the most common neurodegenerative disorders and the second most prevalent after Alzheimer’s disease. In this review, we present several lines of evidence suggesting that epigenetic factors may play an important role in the pathogenesis of PD and propose on this basis a framework to guide future investigations into epigenetic mechanisms and systems biology of PD. These notions, together with technical advances in the ability to perform genome-wide analysis of epigenomic states, and newly available small-molecule probes targeting chromatin-modifying enzymes, may help design new treatment strategies for PD and other human diseases involving epigenetic dysregulation.     

From interfacial ring-opening polymerization to melt processing of cellulose nanowhisker-filled polylactide-based nanocomposites

In the present work, cellulose nanowhiskers (CNWs), extracted from ramie fibers, were incorporated in polylactide (PLA)-based composites. Prior to the blending, PLA chains were chemically grafted on the surface of CNW to enhance the compatibilization between CNW and the hydrophobic polyester matrix. Ring-opening polymerization of l-lactide was initiated from the hydroxyl groups available at the CNW surface to yield CNW-g-PLA nanohybrids. PLA-based nanocomposites were prepared by melt blending to ensure a green concept of the study thereby limiting the use of organic solvents. The influence of PLA-grafted cellulose nanoparticles on the mechanical and thermal properties of the ensuing nanocomposites was deeply investigated. The thermal behavior and mechanical properties of the nanocomposites were determined using differential scanning calorimetry (DSC) and dynamical mechanical and thermal analysis (DMTA), respectively. It was clearly evidenced that the chemical grafting of CNW enhances their compatibility with the polymeric matrix and thus improves the final properties of the nanocomposites. Large modification of the crystalline properties such as the crystallization half-time was evidenced according to the nature of the PLA matrix and the content of nanofillers.     

Prediction of thermostability from amino acid attributes by combination of clustering with attribute weighting: a new vista in engineering enzymes

The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a basis for engineering thermostable enzymes in the laboratory.     

A fluorescence method to detect and quantitate sterol esterification by lecithin:cholesterol acyltransferase

We describe a simple but sensitive fluorescence method to accurately detect the esterification activity of lecithin:cholesterol acyltransferase (LCAT). The new assay protocol employs a convenient mix, incubate, and measure scheme. This is possible by using the fluorescent sterol dehydroergosterol (DHE) in place of cholesterol as the LCAT substrate. The assay method is further enhanced by incorporation of an amphiphilic peptide in place of apolipoprotein A-I as the lipid emulsifier and LCAT activator. Specific fluorescence detection of DHE ester synthesis is achieved by employing cholesterol oxidase to selectively render unesterified DHE nonfluorescent. The assay accurately detects LCAT activity in buffer and in plasma that is depleted of apolipoprotein B lipoproteins by selective precipitation. Analysis of LCAT activity in plasmas from control subjects and sickle cell disease (SCD) patients confirms previous reports of reduced LCAT activity in SCD and demonstrates a strong correlation between plasma LCAT activity and LCAT content. The fluorescent assay combines the sensitivity of radiochemical assays with the simplicity of nonradiochemical assays to obtain accurate and robust measurement of LCAT esterification activity.     

The role of epiphytic Pseudomonas and Pantoea population diversity on prevalence of fire blight disease

Several microbial populations on plants interact with each other and their host through the actions of secreted metabolites. However, the role of diverse microorganisms and their metabolites on plant health has yet to be fully appreciated. Here, we investigated the population diversity of two dominant epiphytic bacterial genuses in different area and their role in biological control of fire blight disease. To do so, we isolated and calculated population diversity of different Pantoea spp. and Pseudomonas spp. using serial dilution methods. The growth inhibition of Erwinia amylovora in vitro by some of these bacteria indicated the ecological significance of secondary metabolites produced by these bacteria and discuss how they might contribute to biological control of fire blight disease. Although, we did not work on the ability of these bacteria on induction of disease resistance but it could be considered for future, because they represent very different but important types of secondary metabolites. We also described how the weather conditions in different geographical regions can effect on the population of these epiphytic bacterial phenotypes leading to plant health promotion. In conclusion, we demonstrated the role of Pantoea and Pseudomonas population diversity on prevalence of fire blight in different area of north-east of Iran.