**3. Background information on peanut seed**

It has been noted that runner-type peanut has an indeterminate growth habit which hampers the identification of an optimum harvest date [20, 21]. Due to this indeterminate growth, just prior to harvest there will be a range of pods with different maturities present on the same plant. When harvested all pods that have reached a given size and weight, regardless of maturity, are collected. This can result in high levels of immature pods in the harvested product. After shelling, this mix of seed maturities has critical implications for overall seed quality. Seed germination is often the trait that is commonly utilized to establish seed quality; but this presents significant limitations because there is often a difference between germination and overall seed vigor [20]. The maturity pattern of runner-type peanuts will vary by cultivar and one from one year to another. For virginia-type peanut, it has been demonstrated that seed maturity impacted not only germination capacity, but overall seed vigor [21]. However information about the effect on maturity for runner-type peanut germination and vigor has not reported.

### **3.1. Runner-type cultivars**

One distinct aspect of runner-type peanut is that almost all cultivars are releases from public institutions from the Southeast region including the University of Georgia, University of Florida, Auburn University, and United States Department of Agriculture Research Service (USDA-ARS) [14]. These institutions have maintained constant releases over the past 25 years, and as previously noted, some cultivars have not been successful due to poor stand establishment [17], leading to their rapid demise from production even though they had desirable traits for disease resistance and improved quality [22]. In contrast, some cultivars have been rapidly adapted and garnered greater than 80% of field planting in some years. For example, 'Georgia Green' [23] was released in 1996 and from then to the mid-2000s was widely adapted and in some growing seasons, planted to greater than 90% of production hectares in the Southeast [24]. 'Georgia-06G' [25] replaced Georgia Green with plantings of greater than 75% (estimated) of the Southeast hectares. One advantage to using the aforementioned cultivars is their demonstrated resistance to TSWV. This has prompted the recommendation that growers begin planting peanut again in late April and early May [26]. This change has occurred as over the past few years as noted by a shift to late April-early May peanut planting (**Figure 2B**). For example, comparing the May week 1 in 2007–2012 and 2015 there is a clear indication that producers made that shift to planting earlier after Georgia-06G and other more TSWV cultivars were released. In contrast, for the May week 4 planting in the early 2000's, over 20% of the crop was planted the last week of May [27]. As prior noted, planting in late May and early June often reduces yield and quality of runnertype peanut due to maturity issues [8, 15, 16]. By 2015 and 2016, 15% or less was planted in late May and less than 10% in June. These changes and rapid acceptance of new cultivars has led to a need to ensure high quality seed and prevent any future stand establishment issues.

#### **3.2. Seed quality**

seed germination testing, this did not always guarantee adequate stand establishment in the field. Seeds can be a substantial cost of growing peanuts due to its large size, often requiring greater than 170 kg ha−1. When there is poor stand establishment, replanting can be expensive due to additional seed expenditures and trips through the field, and can reduce yield due to planting outside optimum time for peanut production. This can result in difficulty in determining optimum maturity, can promote weed escapes due to soil disturbance, incur greater disease opportunities, and can potentially incur additional pesticide costs. Therefore, planting cultivars with high germination and vigor to maximize net returns on input costs

It has been noted that runner-type peanut has an indeterminate growth habit which hampers the identification of an optimum harvest date [20, 21]. Due to this indeterminate growth, just prior to harvest there will be a range of pods with different maturities present on the same plant. When harvested all pods that have reached a given size and weight, regardless of maturity, are collected. This can result in high levels of immature pods in the harvested product. After shelling, this mix of seed maturities has critical implications for overall seed quality. Seed germination is often the trait that is commonly utilized to establish seed quality; but this presents significant limitations because there is often a difference between germination and overall seed vigor [20]. The maturity pattern of runner-type peanuts will vary by cultivar and one from one year to another. For virginia-type peanut, it has been demonstrated that seed maturity impacted not only germination capacity, but overall seed vigor [21]. However information about the effect on maturity for runner-type peanut germination and vigor has

One distinct aspect of runner-type peanut is that almost all cultivars are releases from public institutions from the Southeast region including the University of Georgia, University of Florida, Auburn University, and United States Department of Agriculture Research Service (USDA-ARS) [14]. These institutions have maintained constant releases over the past 25 years, and as previously noted, some cultivars have not been successful due to poor stand establishment [17], leading to their rapid demise from production even though they had desirable traits for disease resistance and improved quality [22]. In contrast, some cultivars have been rapidly adapted and garnered greater than 80% of field planting in some years. For example, 'Georgia Green' [23] was released in 1996 and from then to the mid-2000s was widely adapted and in some growing seasons, planted to greater than 90% of production hectares in the Southeast [24]. 'Georgia-06G' [25] replaced Georgia Green with plantings of greater than 75% (estimated) of the Southeast hectares. One advantage to using the aforementioned cultivars is their demonstrated resistance to TSWV. This has prompted the recommendation that growers begin planting peanut again in late April and early May [26].

is essential.

106 Advances in Seed Biology

not reported.

**3.1. Runner-type cultivars**

**3. Background information on peanut seed**

In reviews of factors that contribute to seed deterioration over time during storage, it has been noted that seed moisture content, mechanical and insect damage, pathogen attack, seed maturity, relative humidity, and temperature can have negative impacts [17]. These impacts have been quantified by previous research [14, 28]. Vigor testing can be utilized to evaluate seed for successful field establishment under different environmental conditions [29]. Strong primary seedling development in standard germination testing is regarded as an indicator of strong vigor [30], but this does not always translate into adequate field performance.

#### **3.3. Seed testing**

One method of testing seed quality, germination, and vigor is the use of a thermal gradient device [31–34]. This method has been used for weed and other crop seed evaluations [35, 36] to determine germination speed and vigor. A thermogradient allows investigators to examine a single seed lot, or multiple seed lots, at different temperatures simultaneously without the use of growth media, such as soil or growth chambers. Previous research using this process has demonstrated grower stock-dependent differences in seed vigor [22]. Quantifying genotypic by phenotypic differences in vigor have also been evaluated where site-specific differences in environmental factors during seed development kept all management variables equal (irrigation, pesticides, fertility) in order to minimize environmental variation [37]. Data indicated that eight runner-type cultivars exhibited phenotypic vigor variation by year, over the course of six years during the experiment, with genotype consistency across years.


**5. Cytochrome P450 monooxygenases (P450S) and evolved herbicide resistance**

Sanger sequencing [13, 124]. In many studies, these approaches were used to uncover TSR in

Recognizing alleles of non-target-site-based resistance requires the identification of alleles specific for resistant genotypes while contrasted with sensitive genotypes, and to eliminate "false positives." These alleles are diverse in both genotypes (resistant and susceptible), however, do not involve in non-target-site-based resistance. In plant genomes, there are numerous alleles reported to be associated with non-target-site-based herbicide resistance. Alleles associated with quantitative characteristics are mostly identifies using genetic marker approaches (quantitative trait loci (QTL) mapping) [132]. QTL mapping is intricate, time consuming, and not easily applied to natural or field populations of nonmodel organisms such as weeds [133]. Another approach utilized for identification of alleles dedicating quantitative properties is to interrupt or imitate the phenotype of interest through genetic transformation [134]. Owing to the recent technical and scientific "omics" revolution, the genetic basis of quantitative characteristics, such as NTSR, can be explained even in nondemonstrate species, for example weeds.

To accomplish this objective, three stages ought to be completed (**Figure 2**).

**Figure 2.** Three-step procedure to identify NTSR alleles [8].

weeds [125–131].

**4.2. Non-target-site resistance**

112 Herbicide Resistance in Weeds and Crops

P450s are one of the largest super families of enzymes. Plants have the most noteworthy number of P450 genes. It is well demonstrated that tolerance to several modes of action to some herbicides is associated with Cyt P450 mediated enhanced metabolism. Cytochrome P450s in plants synthesize sterols, fatty acid derivatives, and hormones and in some cases, these are involved in plant secondary metabolism. Usually, plant cytochrome P450s are limited in the endoplasmic reticulum (in a few cases to plastid membranes). When a vast variety in plant metabolism catalyzes by cytochrome P450s, the role of cytochrome P450s is often hydroxylation or dealkylation in the herbicide alteration. Several plant cytochrome P450s will metabolize some herbicides to further inactivated productions so that their phytotoxicity will decrease or alter. Mostly, this process occurs by conjugation to glucose and subsequent transport into the vacuole. [36, 46, 135]. As crops can P450 metabolize many different herbicides, their use on large weed populations is a strong selection pressure for weed individuals possessing the same ability. Indeed, in weeds, P450-based herbicide resistance is a very threatening resistance mechanism because P450 enzymes can simultaneously metabolize herbicides of different modes of action, potentially including never-used herbicides [136, 137]. Subsequently, *in vivo* studies on herbicide metabolism and P450 inhibitors in resistant biotypes showed that P450s catalyzed enhanced rates of metabolism of several herbicides [138–140]. In addition to *L. rigidum* and *A. myosuroides*, the evolution of resistance due to P450-catalyzed enhanced rates of herbicide metabolism has been demonstrated in resistant biotypes of a further nine weed species [57, 141]. Cytochrome P450s can metabolize low dose of diclofop-methyl in some susceptible biotypes of *L. rigidum* and therefore the weeds were treated at a dose bringing around 50% mortality. Some survivors were grown for producing seeds to create the next generation, and in this direction, the selection was repeated. Nontarget-site resistance was progress of high level in lonely three generations [142]. Importantly, there was concomitant evolution of cross-resistance to other P450-metabolizable herbicides of different modes of action [85, 88]. In *L. rigidum*, evolved P450-based herbicide resistance can be correlated with a fitness cost [143, 144]. Up to now, slight data are obtained through biochemical investigation to determine P450-based herbicide resistance in some evolved resistant weed species. Cytochrome P450 microsomes which degrade herbicides have not been successfully isolated from resistant *A. myosuroides* and *L. rigidum*, while they have been successfully isolated from resistant *E. phyllopogon* [145]. Isolated 16 P450 genes from a resistant *L. rigidum* biotype were not imputed to herbicide metabolism [76, 146]. One of three fulllength P450s, CYP71R4, were gained from resistant *L. rigidum* biotypes metabolized a PSII herbicide, while expressed in yeast [147]. P450 proteins can share as little as 16% amino acid identity, and there are more than 2000 plant P450 sequences in the P450 database. Reportages about P450-based evolved herbicide resistance in grass weed species has often been more than dicot species, whereas dicot weed species have less P450 genes toward grass weed species. This fact reflects the attitude of some investigate to test only for target-site-based resistance [17].


3. Study 2

reversed relations it did not. In this case, Condition 3a achieved the highest score. Regarding to double reversed relations, Condition 3a achieved the best performance for these two types of

Non-parametric tests (Mann-Whitney U) revealed significant differences between Condition 1 and Condition 2 in NOW-THEN reversed relations (<0.04), between Condition 2 and Condition 3a in HERE-THERE reversed relations (<0.01), and between Condition 2 and Condition 3b

In connection with the number of trials being repeated per condition, Condition 3 was the only one in which no trial was repeated. Furthermore, in Condition 1 and 2 some participants asked whether the trial had been done before (e.g. "but you have already asked me this question before", "I don't understand why you are asking me again", "Did I make a mistake? Because

The results from Condition 1-3a/b can be discussed as follows. Condition 1 relinquished first position when the two questions per trial were presented as separate trials (see Condition 2 in Tables 3, 4). These findings are consistent with the results of other studies [14, 22], and they confirmed our initial hypothesis that a correct response to the first question of a trial could serve as a discriminative stimulus and facilitate a correct response for the second question of that trial. Overall, Condition 3 finished in first or second position for the majority of relation types (see Table 4). However, it gave the weakest performances on I-YOU reversed relations. It should be recalled that the structure of the I-You relations in Condition 3 can be described as Other-Other relations because the trials enquiring about the perspective of the characters included in the scenario. According to Lovett and Rehfeldt [22], it could be that Other-Other relations are a

these relations.

80 Behavior Analysis

in HERE-THERE reversed relations (<0.04).

Note: The best result for each relational type is shaded.

Table 4. Means and standard deviations for relational type in each condition.

you are asking me that question again").

With regard to the performances observed in Study 1, the current study was designed to determine whether a specific deictic protocol, maintaining the structure of the I-YOU and NOW-THEN relations in Condition 1, but following the variability in scenarios developed by Vilardaga et al. [20], would facilitate the participant's relational performances.


**4. Conclusions**

This study reports on the influence of different patterned piezoelectric P(VDF-TrFE) scaffolds in the preosteoblasts' proliferation and differentiation. It was observed that MC3T3-E1 cells differentiation can be induced solely by a physical stimulus, specifically by an isotropic hexagonal surface topography, with no need of using a chemical inducer. Finding that bone cells differentiation can be positively influenced by this type of geometry, it is concluded that novel scaffolds based on electromechanically active materials with specific geometries can provide the necessary geometrical and electroactive components of the bone microenvironment to support bone regeneration.

**Supplementary Materials:** Supplementary Materials can be found at http://www.mdpi.com/1422-0067/21/21/ 8352/s1. Figure S1. FTIR-ATR spectra of P(VDF-TrFE) scaffolds. Figure S2. Schematic representation of the produced patterned P(VDF-TrFE) scaffolds with linear microstructures. Figure S3. Schematic representation of the patterned P(VDF-TrFE) scaffolds with hexagonal microstructures. Figure S4. Cell adhesion of MC3T3-E1 on dense non-patterned P(VDF-TrFE) scaffolds.

**Author Contributions:** Conceptualization, V.F.C., S.L.-M. and C.R.; Data curation, T.M.-A.; Formal analysis, T.M.-A. and C.R.; Funding acquisition, S.L.-M. and C.R.; Investigation, T.M.-A., V.F.C., M.G., S.L.-M. and C.R.; Methodology, C.R.; Resources, M.G.; Supervision, S.L.-M. and C.R.; Writing—original draft, T.M.-A.; Writing—review & editing, V.F.C., M.G., S.L.-M. and C.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by national funds through the Fundação para a Ciência e Tecnologia (FCT) and by ERDF through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI) in the framework of the Strategic Programs UID/FIS/04650/2020 and UIDB/04436/2020 and projects PTDC/EMD-EMD/28159/2017 and PTDC/BTM-MAT/28237/2017. TA thank FCT for the grant SFRH/BD/141136/2018 and CR thanks the FCT for the contract under the Stimulus of Scientific Employment (DL57/2016 junior researcher contract). Finally, the authors acknowledge funding by Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project PID2019-106099RB-C43/AEI/10.13039/501100011033 and from the Basque Government Industry and Education Departments under the ELKARTEK and PIBA (PIBA-2018-06) programs, respectively.

**Conflicts of Interest:** The authors declare no conflict of interest.


*2.11. Analysis of Enzymatic Activities*

Promastigotes (5 × 106 protozoa/mL) were treated with 1/5 IC50/4 h NaNO2 in HBSS at 25 ◦C for 4 h, washed with PBS, and kept dry at −20 ◦C until use to ensure that all biological replicas were extracted and analyzed at the same time and under the same experimental conditions. Protein homogenates were prepared by sonication, as previously described [32]. Briefly, pellet was resuspended in cold PBS containing protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA) and disrupted on ice by sonication for 10 cycles of 7 s with intervals of 7 s, using a Markson GE50 Ultrasonic Processor. Amplitude was set to 70%, and parasite disruption was monitored by light microscopy. Soluble fraction was obtained, and protein concentration was measured using Pierce™ BCA protein assay kit (Thermo Fisher Scientific, Palo Alto, CA, USA). For glutathione peroxidase (GPx) assay, soluble protein extract (0.5 mg/mL) was incubated in 100 mM KH2PO4 (pH 7.8) buffer, supplemented with 1 mM reduced glutathione (Sigma-Aldrich, St. Louis, MO, USA), 5 U/mL glutathione reductase (Sigma-Aldrich, St. Louis, MO, USA), 200 μM NADPH (Sigma-Aldrich, St. Louis, MO, USA), and 300 μM H2O2. The rate of the NADP (ε = 6.22 M<sup>−</sup>1cm−1) reduction was determined at 340 nm. Lactate dehydrogenase activity was evaluated using a commercial kit (Doles, Goiânia, Brazil) following the manufacturer's protocol, with some modifications. Briefly, 0.5 mg/mL of protein was added to 100 μL manufacturer's substrate solution and 10 μL ferric alum for 2 min at 37 ◦C. After that, 10 μL NAD+ + phenazine methosulfate (PMS) was added, and absorbance measured for 45 min (every 1 min) at 510 nm. A standard curve was generated with lactate dehydrogenase (Sigma-Aldrich, St. Louis, MO, USA). All enzyme activities were measured at 37 ◦C in a total reaction volume of 200 μL using a SpectraMax Plus384 spectrophotometer (Molecular Devices, Sunnyvale, CA, USA).