Synthesis, Crystallization, Structure Memory Effects, and Molecular Dynamics of Biobased and Renewable Poly(n-alkylene succinate)s with n from 2 to 10

In this article, we synthesize five poly(n-alkylene succinate)s, PnASs, with n = 2, 4, 6, 8, and 10 via multi-step polycondensation methods. Next, we comparatively investigate these renewable and biobased polyesters from the points of view of structure, crystallinity, and molecular mobility, employing 1H nuclear magnetic resonance spectroscopy, size-exclusion chromatography, viscometry, X-ray diffraction, differential scanning calorimetry (DSC, conventional and temperature modulation modes), polarized optical microscopy (POM), and broadband dielectric relaxation spectroscopy (BDS). Next to the successful synthesis of the materials, we evaluate the characteristics of crystallization (temperature and fraction); moreover, we explore for the first time, on the same type of succinic polyesters, the impact of n on the structure memory related to crystal nucleation as well as the changes in the semicrystalline morphology. We demonstrate that the structure/crystal memory is stronger for the lower n (shorter alkylene succinate monomers) because of more chain-chain associations, the result being independent from the overall length of the polymer chain (molar mass, Mn 13-80 kg/mass). The crystalline fraction (CF ∼12-34%) increases with n, also independently from Mn; however, the chain length affects directly the nucleation rate as Tc increases with Mn. The direct effects of n, in the inter-/intrachain interactions, as well as the indirect ones, on the CF and distribution of crystallites were found to be responsible for the alternations in the static glass transition temperature in DSC (lowering of Tg with n) and the dynamic glass transition (α, αc relaxations in BDS). For the sum of these PnASs, the molecular dynamics mapping is shown here, also for the first time. With increasing n, segmental dynamics accelerates, whereas, interestingly, the cooperativity drops (elimination for n = 10). Comparing these results with the recorded alternations in the semicrystalline morphology (POM), we conclude spatial confinement to be imposed on the mobile amorphous polymer by the tightly distributed crystallites when n increases. Overall, these data provide proofs for the potential for tuning of the final polymer properties connected with crystallization (mechanical performance and permeability), envisaging future biomedical, packaging, and other application for these PnASs. © 2021 American Chemical Society

The effect of leg kick on sprint front crawl swimming

The aim of this study was to examine the influence of leg kick on the pattern, the orientation and the propulsive forces produced by the hand, the efficiency of the arm stroke, the trunk inclination, the inter-arm coordination and the intra-cyclic horizontal velocity variation of the hip in sprint front crawl swimming. Nine female swimmers swam two maximal trials of 25 m front crawl, with and without leg kick. Four camcorders were used to record the underwater movements. Using the legs, the mean swimming velocity increased significantly. On the contrary, the velocity and the orientation of the hand, the magnitude and the direction of the propulsive forces, as well as the Froude efficiency of the arm stroke were not modified. The hip intra-cyclic horizontal velocity variation was also not changed, while the index of coordination decreased significantly. A significant decrease (13%) was also observed in the inclination of the trunk. Thus, the positive effect of leg kick on the swimming speed, besides the obvious direct generation of propulsive forces from the legs, could probably be attributed to the reduction of the body's inclination, while the generation of the propulsive forces and the efficiency of the arm stroke seem not to be significantly affected. © 2013 Taylor & Francis

Implementation of a decision support system for prediction of the total soluble solids of industrial tomato using machine learning models

Tomato is the second most important vegetable in the world, both in terms of production and consumption. Especially for the cultivation of industrial tomato, harvest is conducted when the total soluble solids, a major quality characteristic, are as high as possible. Advancements in technology have made Decision Support Systems simpler and more applicable in an everyday basis. Data Analysis, combined with Machine Learning algorithms are considered the future of sustainable agriculture, allowing farmers to be advised about the best possible decisions for their cultivation. Farmers need to adopt this kind of technology in order to be able to know when the quality of tomatoes is at its peak, in order to gather their product from the field. The implementation of a Decision Support System to predict the total soluble solids was conducted, based on data from previous years, including quality data (pH, Bostwick, L, a/b, Mean Weight, °Brix), the type of hybrid used, weather data and soil data from the fields. Data derived from fields in 6 different regions in the northwestern Peloponnese, Greece over 6 cultivation periods, created a dataset of 33 different inputs. Thirteen different algorithms were put into evaluation in order to find the best one in terms of speed and efficiency. In this research, we developed a Decision Support System using the K-nearest algorithm, which proved to be the best for our dataset. The predicted °Brix were following the same pattern as the actual °Brix. This means that the DSS could advise the farmer about the ideal harvesting period where the °Brix will be maximized. This DSS which is using real time weather data as an input is expected to be a valuable tool for the farmers. © 202

Assessment of plant growth promoting bacteria strains on growth, yield and quality of sweet corn

The use of plant growth promoting bacteria (PGPB) is increasingly gaining acceptance from all the stakeholders of the agricultural production. Different strains of PGPB species had been found to have a vast variety of mechanisms of action, while at the same time, affect differently a variety of crops. This study investigated the effectiveness of ten PGPB strains, on sweet corn cultivation under Mediterranean soil and climatic conditions. A field experiment that followed a completely randomized design was conducted at the region of Attica at Oropos. The results indicated that B. mojavensis increased yield by 16%, B. subtilis by 13.8%, B. pumilus by 11.8% and B. pseudomycoides by 9.8% compared to control. In addition, the harvested grains of the plants treated with B. mojavensis, B. subtilis and B. pumilus presented the highest values of protein and fiber content. Moreover, in most of the cases, high values of photosynthetic rate, transpiration rate and stomatal conductance during the cultivation period, resulted in high productivity. Regarding the texture, the size, the sphericity and the ash content of corn grains, it was found that they were not influenced by the application of different treatments of PGPB. The use of certain strains of PGPB, under specific soil and climatic conditions could contribute to better understand which strains are better suited to certain crops. © 2022, The Author(s)

The search for sexually antagonistic genes : Practical insights from studies of local adaptation and statistical genomics

Sexually antagonistic (SA) genetic variation—in which alleles favored in one sex are disfavored in the other—is predicted to be common and has been documented in several animal and plant populations, yet we currently know little about its pervasiveness among species or its population genetic basis. Recent applications of genomics in studies of SA genetic variation have highlighted considerable methodological challenges to the identification and characterization of SA genes, raising questions about the feasibility of genomic approaches for inferring SA selection. The related fields of local adaptation and statistical genomics have previously dealt with similar challenges, and lessons from these disciplines can therefore help overcome current difficulties in applying genomics to study SA genetic variation. Here, we integrate theoretical and analytical concepts from local adaptation and statistical genomics research—including FST and FIS statistics, genome‐wide association studies, pedigree analyses, reciprocal transplant studies, and evolve‐and‐resequence experiments—to evaluate methods for identifying SA genes and genome‐wide signals of SA genetic variation. We begin by developing theoretical models for between‐sex FST and FIS, including explicit null distributions for each statistic, and using them to critically evaluate putative multilocus signals of sex‐specific selection in previously published datasets. We then highlight new statistics that address some of the limitations of FST and FIS, along with applications of more direct approaches for characterizing SA genetic variation, which incorporate explicit fitness measurements. We finish by presenting practical guidelines for the validation and evolutionary analysis of candidate SA genes and discussing promising empirical systems for future work