The use of a multivariate statistical procedure in analysing the germination process of two bean cultivars, compared with a univariate approach

Abstract Several studies on plant physiology are aimed at describing or assessing seed germination processes under laboratory conditions. With respect to seed germination of crop species, some statistical complexities have been discussed, but they have not been developed much in practice. That is, such discussions are not as common as in other areas of plant biology. Additionally, the current literature that is concerned directly with the application of statistics in seed germination indicates that simple and well-known statistical procedures still merit further consideration. Regarding the use of multivariate statistical methods in agriculture, several field studies have used such procedures as a means of clarifying some underlying ecological principles that govern crop production. Nonetheless, multivariate tests have not been widely employed in germination experiments. Therefore, in the present study a simple multivariate statistical procedure (Hotelling's T 2 statistic) was utilised in order to compare two common bean cultivars, using germination indices as variables. The outcome derived from the multivariate approach was compared with that obtained from the utilisation of the univariate t test. The simultaneous application of both methods (that is, the classical univariate t test and the multivariate T 2 test) showed that the outcomes may well depend on the approach utilised

Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R(2)) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Methodological difficulties of conducting agroecological studies from a statistical perspective

André Bianconi, Tommy Dalgaard, Bryan F. J. Manly, José S. Govone, Michael J. Watts, Peter Nkala, Gustavo Habermann, Yanbo Huang, and Adriane B. S. Serapiã

The structural dynamics of the flavivirus fusion peptide-membrane interaction.

Membrane fusion is a crucial step in flavivirus infections and a potential target for antiviral strategies. Lipids and proteins play cooperative roles in the fusion process, which is triggered by the acidic pH inside the endosome. This acidic environment induces many changes in glycoprotein conformation and allows the action of a highly conserved hydrophobic sequence, the fusion peptide (FP). Despite the large volume of information available on the virus-triggered fusion process, little is known regarding the mechanisms behind flavivirus-cell membrane fusion. Here, we evaluated the contribution of a natural single amino acid difference on two flavivirus FPs, FLA(G) ((98)DRGWGNGCGLFGK(110)) and FLA(H) ((98)DRGWGNHCGLFGK(110)), and investigated the role of the charge of the target membrane on the fusion process. We used an in silico approach to simulate the interaction of the FPs with a lipid bilayer in a complementary way and used spectroscopic approaches to collect conformation information. We found that both peptides interact with neutral and anionic micelles, and molecular dynamics (MD) simulations showed the interaction of the FPs with the lipid bilayer. The participation of the indole ring of Trp appeared to be important for the anchoring of both peptides in the membrane model, as indicated by MD simulations and spectroscopic analyses. Mild differences between FLA(G) and FLA(H) were observed according to the pH and the charge of the target membrane model. The MD simulations of the membrane showed that both peptides adopted a bend structure, and an interaction between the aromatic residues was strongly suggested, which was also observed by circular dichroism in the presence of micelles. As the FPs of viral fusion proteins play a key role in the mechanism of viral fusion, understanding the interactions between peptides and membranes is crucial for medical science and biology and may contribute to the design of new antiviral drugs

Charge neutralization as the major factor for the assembly of nucleocapsid-like particles from C-terminal truncated hepatitis C virus core protein

Submitted by Repositório Arca ([email protected]) on 2019-03-07T16:36:32Z No. of bitstreams: 2 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) F151-peerj-04-2670.pdf: 10071325 bytes, checksum: 9a45a96fbd259bc84f1c0510c81483a2 (MD5)Approved for entry into archive by monique santos ([email protected]) on 2019-04-08T19:29:00Z (GMT) No. of bitstreams: 2 F151-peerj-04-2670.pdf: 10071325 bytes, checksum: 9a45a96fbd259bc84f1c0510c81483a2 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Made available in DSpace on 2019-04-08T19:29:00Z (GMT). No. of bitstreams: 2 F151-peerj-04-2670.pdf: 10071325 bytes, checksum: 9a45a96fbd259bc84f1c0510c81483a2 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2016Universidade Federal do Rio de Janeiro. Faculdade de Farmácia. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto Nacional de Ciência e Tecnologia de Biologia Estrutural e Bioimagem. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Programa de Biologia Estrutural, Instituto de Bioquímica Médica Leopoldo de Meis. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos. Rio de Janeiro, RJ, Brasil.University of New Mexico. Department of Molecular Genetics and Microbiology and Cancer Research and Treatment Center. Albuquerque, USA.Universidade Federal do Rio de Janeiro. Instituto Nacional de Ciência e Tecnologia de Biologia Estrutural e Bioimagem. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Programa de Biologia Estrutural, Instituto de Bioquímica Médica Leopoldo de Meis. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto Nacional de Ciência e Tecnologia de Biologia Estrutural e Bioimagem. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Programa de Biologia Estrutural, Instituto de Bioquímica Médica Leopoldo de Meis. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto Nacional de Ciência e Tecnologia de Biologia Estrutural e Bioimagem. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Programa de Biologia Estrutural, Instituto de Bioquímica Médica Leopoldo de Meis. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto Nacional de Ciência e Tecnologia de Biologia Estrutural e Bioimagem. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Programa de Biologia Estrutural, Instituto de Bioquímica Médica Leopoldo de Meis. Rio de Janeiro, RJ, Brasil.BACKGROUND: Hepatitis C virus (HCV) core protein, in addition to its structural role to form the nucleocapsid assembly, plays a critical role in HCV pathogenesis by interfering in several cellular processes, including microRNA and mRNA homeostasis. The C-terminal truncated HCV core protein (C124) is intrinsically unstructured in solution and is able to interact with unspecific nucleic acids, in the micromolar range, and to assemble into nucleocapsid-like particles (NLPs) in vitro. The specificity and propensity of C124 to the assembly and its implications on HCV pathogenesis are not well understood. METHODS: Spectroscopic techniques, transmission electron microscopy and calorimetry were used to better understand the propensity of C124 to fold or to multimerize into NLPs when subjected to different conditions or in the presence of unspecific nucleic acids of equivalent size to cellular microRNAs. RESULTS: The structural analysis indicated that C124 has low propensity to self-folding. On the other hand, for the first time, we show that C124, in the absence of nucleic acids, multimerizes into empty NLPs when subjected to a pH close to its isoelectric point (pH ≈ 12), indicating that assembly is mainly driven by charge neutralization. Isothermal calorimetry data showed that the assembly of NLPs promoted by nucleic acids is enthalpy driven. Additionally, data obtained from fluorescence correlation spectroscopy show that C124, in nanomolar range, was able to interact and to sequester a large number of short unspecific nucleic acids into NLPs. DISCUSSION: Together, our data showed that the charge neutralization is the major factor for the nucleocapsid-like particles assembly from C-terminal truncated HCV core protein. This finding suggests that HCV core protein may physically interact with unspecific cellular polyanions, which may correspond to microRNAs and mRNAs in a host cell infected by HCV, triggering their confinement into infectious particles

Effect of High-Fat Diet upon Inflammatory Markers and Aortic Stiffening in Mice

Changes in lifestyle such as increase in high-fat food consumption are an important cause for vascular diseases. The present study aimed to investigate the involvement of ACE and TGF-β in the aorta stiffness induced by high-fat diet. C57BL/6 male mice were divided in two groups according to their diet for 8 weeks: standard diet (ST) and high-fat diet (HF). At the end of the protocol, body weight gain, adipose tissue content, serum lipids and glucose levels, and aorta morphometric and biochemical measurements were performed. Analysis of collagen fibers by picrosirius staining of aorta slices showed that HF diet promoted increase of thin (55%) and thick (100%) collagen fibers deposition and concomitant disorganization of these fibers orientations in the aorta vascular wall (50%). To unravel the mechanism involved, myeloperoxidase (MPO) and angiotensin I converting enzyme (ACE) were evaluated by protein expression and enzyme activity. HF diet increased MPO (90%) and ACE (28%) activities, as well as protein expression of ACE. TGF-β was also increased in aorta tissue of HF diet mice after 8 weeks. Altogether, we have observed that the HF diet-induced aortic stiffening may be associated with increased oxidative stress damage and activation of the RAS in vascular tissue

Fluorescence spectroscopy data of FLA_G and FLA_H in the presence of micelles.

<p>All measurements were performed in triplicate in the same experiment, and the results were obtained from at least six independent experiments. ΔCM and S/S₀ are expressed as the mean ± SD.</p>[1]<p>Blue shift was determined by subtracting emission wavelength from control.</p