*2.2. Relationship between the Total Process Gas Flow and the Resulting Film Stress*

Knisely [14] introduced a model describing the power law relation between the RF substrate bias and the resulting AlN film stress. For a fixed Sc alloy this power law equation can be adapted to instead describe the total Al1-xScxN films stress Tave as a function of N2 process gas flow

$$\mathbf{T\_{ave}} = \mathfrak{a}\left(\mathfrak{F}(\mathbf{t}\_{\mathbf{f}})^{\mathcal{Y}} + \mathbf{F\_{N\_2}}\right) \tag{2}$$

where α is determined from the slope of the stress versus N2 gas flow, FN2 is the N2 gas flow in the chamber, and β and γ are empirical fits based on the deposition parameters and environment. Similar to Knisely [14] we implement a film that utilizes multiple layers deposited under different sputtering gas conditions where the average film stress of each layer is used to compensate for the through-thickness stress gradient. Where Knisely [14] utilizes a different RF substrate bias to control the stress of each AlN layer, we utilize a different N2 flow to control the stress of each Al0.68Sc0.32N layer. The layer film stress, tf , based on the thickness and N2 flow of the layer is derived from integrating the average film stress [14] and is given by

$$\mathbf{T\_{f}(t, F\_{N\_{2}})} = \alpha \left(\boldsymbol{\beta} (1 + \boldsymbol{\gamma}) (\mathbf{t\_{f}})^{\boldsymbol{\gamma}} - F\_{N\_{2}, \boldsymbol{\mu}}\right) \tag{3}$$

where FN2 is the constant flow applied to the layer and t is the thickness of the layer. Equation (3) can be used to calculate the layer stresses needed to compensate for the through-thickness stress gradient within the film. The stress gradient is calculated from the average stress measurements taken from different wafers deposited using identical deposition process parameters and at varying AlScN thicknesses. Equation (3) is then used to interpolate between the experimental measurements of films with different thicknesses to find the through-thickness stress gradient.


**3. Results**

The Results section is structured in two parts. In this first part, in situ annealing experiments on the fiber textured Al0.73Sc0.27N/Mo(110)/AlN(0001)/Si(001) (sample set #1) thin films are discussed with respect to intrinsic and extrinsic effects of observed anomalous thermal expansion. In the second part, the effect size of the thermal expansion behavior and the microstructural changes to epitaxial thin film samples of Al1−xSc*x*N/Al2O3(0001) are analyzed depending on Sc concentration (0 < *x* < 0.40) (sample set #2). Structural data recorded before and after annealing are discussed and data recorded during annealing will be compared. Eventually, we conclude on our hypotheses considering data from sample #3.


**Author details**

10 Current Insights in Pollen Allergens

al, and Zienkiewicz et al

**5. Conclusions and future perspectives**

numerous cDNA and peptide/glucan sequences from Ole e 1 (Napoli et al, 2008; Hamman Khalifa et al, 2010; Castro et al, 2012; Jiménez-López et al, 2011; Soleimani et al, 2012a,b), Ole e 5 (Zafra, 2007), Ole e 2 (Jiménez López, 2008; Morales et al, 2008; Jiménez-López et al, 2012b), and Ole e 11 (Jiménez-López et al, 2012a). Moreover, the reactivity of a broad panel of olive pollen cultivar extracts to diverse patient' sera has been also analyzed in Jordan (Jaradat et al, 2011). Recently, a novel multiplex method for the simultaneous detection and relative quantification of pollen allergens has been set up (Morales 2012; Morales et al, 2012). This method will help to investigate pollen allergen polymorphism within cultivars in combination with patient's reactivity, by notably improving the specificity and capacity of the biochemical and immunological assays. The present book also includes remarkable analyses of olive varietal polymorphism in those chapters by Jimenez-Lopez et al, Morales et

The past and recent developments in the analysis of the differential allergenicity of pollens from heterogeneous infraspecific taxonomic ranks described above, confirm the need of rethinking current strategies for basic research on pollen allergen characterization, and the design of diagnosis and specific immunotherapy approaches. These issues, raised and discussed initially by us (Alché et al, 2007) for olive pollen allergens, seem to be valid for a broader number of species, as stated here. Extensive pollen allergen polymorphism is known to represent a general feature over the plant kingdom. The limitation of the study of this polymorphism just to the level of species represents a restriction which may limit both basic knowledge and more importantly the efficacy and the future development of strategies to detect and contest human pollen allergy. Although the use of marker allergens for order, genera or even plant families may represent an invaluable tool (Mothes et al, 2004), relevant differences in patient's reactivity occur even among closerelated taxonomical ranks (e.g. van Ree 2002; Asero et al, 2005; Fenaille et al, 2009; Wallner et al, 2009a,b; Jaradat et al, 2011) therefore determining that even close allergenic compositions are not always "fully equivalent". The analysis of allergenic variability in infraspecific taxonomical ranks should be considered a "must" that can be easily incorporated into most developing and evolving trends in allergy analysis and clinics, namely the design of highly specific and personalized natural extracts, hypoallergens, the design and production of recombinant allergens, hybrid molecules, high-throughput diagnosis, new forms of allergen administration and release, the analysis of allergen cross-

reactivity etc. (Schenk et al, 2006, 2011; Gao et al, 2008; Wallner et al, 2009a,b).

Agricultural and environmental strategies to reduce the impact of pollen allergy involving the use of differential infraspecific taxonomic ranks are not to be discarded either. They may include the primary screening of relatively less allergenic varieties as proposed for wheat, buckwheat and other food sources (Nair and Adachi, 2002; Nakamura et al, 2005; Spangenberg et al, 2006), and the future design of varieties/hybrids with reduced pollen production, limited period of flowering, or even androsteril characteristics in a similar way Juan de Dios Alché\* , Adoración Zafra, Jose Carlos Jiménez-López, Antonio Jesús Castro and María Isabel Rodríguez-García *Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), Granada, Spain*

#### Sonia Morales

*Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), Granada, Spain Proteomic Research Service, Hospital Universitario San Cecilio, Granada, Spain*

Fernando Florido *Allergy Service, Hospital Universitario San Cecilio, Granada, Spain*


**1. Introduction**

Recently surround-sound systems have become popular. The effect of "surrounding" the listener in sound is achieved by employing acoustic phenomena which influence localizing the source of the sound. Similarly to stereophony system the time, volume and phase interrelations in signals coming from each sound source are taken into account. Additionally the influence of the acoustic system created by the pinna, head and torso on the frequency characteristic of the sound is taken into consideration. This influence is described by the Head-Related Transfer Function (HRTF). The knowledge of the human physical body characteristics' influence on the perception of the sound source location in space is being more and more frequently applied to building sound systems.

So far the best method of including the influence of the human body on the frequency characteristic of the sound is the HRTF measurement for different locations of the sound in relation to the listener. Then the achieved measurement results are used for creating a database meant for the sound reproduction. Creating a proper HRTF database is a difficult problem – every human exhibits individual body characteristics therefore it is not possible to create one universal database for all the listeners. For this reason applying the knowledge of the human body influence on the frequency characteristic of the sound is impeded. In order to include these parameters it is necessary to conduct all these laborious measurements for each individual.


*2.3. Species Composition Measurement*

In each plot, we placed three 50 cm × 50 cm quadrates as replicates to survey the species by recording their names, coverage (%), and richness (i.e., the number of species) and then classified all the species into the four plant functional groups: graminoids, forbs, legumes, and shrubs (if any). The coverage was estimated visually, but significantly positive correlation of coverage and species richness confirmed the data reliability (R2 = 0.418, *p* < 0.001).

### *2.4. Data Analysis*

Firstly, the effects of slope aspect on leaf [N], [P], and N:P ratio were detected using a linear mixed model by treating "site" as the random factor (LMM) for the mixed samples, with species-level value as a replicate unit and one common species, *Anaphalis lacteal*, that occurred in each of the six plots. Correlations among leaf [N], [P], and N:P ratio were evaluated in each site using linear models. Comparison of the leaf N:P stoichiometry between different PFGs was also conducted using the LMM. Secondly, the effects of slope aspect on species richness and coverage and their correlations with leaf N:P stoichiometry (using plot-level means) were analyzed using LMM.

All the variables were log10-transformed. All analyses were performed with R version 4.0.3 (R Core Team, 2020) in RStudio version 1.3.1093 (RStudio Team, 2020).

**Table 1.** Mean value of leaf nitrogen (N) and phosphorus (P) concentrations of the 40 measured species. PFG represents plant functional group.

### **3. Results**

*3.1. Variations in Leaf [N], [P], and N:P Ratio across the Slope Aspects*

The leaf [N], [P], and N:P ratios ranged from 10.09 to 42.96, 0.89 to 3.10, and 8.10 to 23.63 mg g−1 in Hezuo and from 4.36 to 33.53, 0.536 to 2.33, and 4.14 to 21.21 mg g−1 in Maqu, respectively. Overall, the leaf [N], [P], and N:P ratios were all significantly lower in Maqu, with higher elevations than in Hezuo (Figure 2A,C,E).

**Figure 2.** Comparisons of leaf [N] (**A**,**B**), [P] (**C**,**D**), and N:P ratio (**E**,**F**) between sites and among slope aspects. SFS, WFS, and NFS represent south-, west-, and north-facing slope aspects, respectively. Different letters represent significant differences at the level of *p* < 0.05. Variables are in log10 scale.

Although leaf [N] and [P] were not significantly different between slope aspects, leaf N:P ratio was significantly lower on the NFS than on the SFS for the mixed samples (Figure 2). The one common species, *Anaphalis lacteal*, showed the same patterns as those of the mixed samples (Figure S1). Moreover, significant correlations between leaf [N] and [P] and between the leaf N:P ratio and leaf [N] rather than [P] were found in each plot (Figure 3). In addition, legumes showed a significantly higher leaf [N] and [P] than forbs and graminoids and a higher leaf N:P ratio than forbs (Figure S2).

### *3.2. Correlations of Leaf Traits with Species Richness and Coverage*

Significantly higher richness of all species and forbs and lower richness of legumes were found on the NFS than on the SFS and WFS (Figure 4A–D); however, the richness of graminoids and plant coverage for each PFG and for all species did not differ significantly among the slope aspects (Figure 4E–H).

**Figure 3.** Linear regressions between leaf [N] and [P], between leaf [N] and leaf N:P ratio, and between leaf [P] and leaf N:P ratio in Hezuo (**A**–**C**) and Maqu (**D**–**F**). Solid and dashed lines indicate significant and non-significant regressions, respectively. SFS, WFS, and NFS represent south-, west-, and north-facing slope aspects, respectively. Variables are in log10 scale.

**Figure 4.** Comparisons of species richness (**A**–**D**) and coverage (**E**–**H**) for each plant functional group and the whole community among slope aspects. SFS, WFS, and NFS represent south-, west-, and north-facing slope aspects, respectively. Variables are log10-transformed.

**Figure 5.** Correlations of species richness of each plant functional group and the whole community with leaf N concentration (**A**–**D**), with leaf P concentration (**E**–**H**), and with leaf N:P ratio (**I**–**L**). Variables are log10-transformed.

The correlations were found to be significantly negative between leaf [N] and richness of all species (R2 = 0.615, *p* = 0.047) and forb species (R2 = 0.570, *p* = 0.061), between leaf [P] and graminoid richness (R2 = 0.362, *p* = 0.093), and between leaf N:P ratios and forb richness (R2 = 0.675, *p* = 0.032) (Figure 5). On the other hand, legume coverage was positively correlated with leaf [N] (R2 = 0.809, *p* = 0.010), and the coverage of graminoids and all species was negatively and positively correlated with leaf [P] and with marginal significance and significance, respectively (R2 = 0.051 and 0.704, *p* = 0.103 and 0.029, respectively) (Figure 6).

**Figure 6.** Correlations of species coverage of each plant functional group and the whole community with leaf N concentration (**A**–**D**), with leaf P concentration (**E**–**H**), and with leaf N:P ratio (**I**–**L**). Variables are log10-transformed.

### **4. Discussion**

### *4.1. Variations in Leaf [N], [P], and N:P Ratio across Different Slope Aspects*

Although leaf [N] and [P] did not differ significantly between slope aspects, we found a significantly lower N:P ratio on the NFS, with a mean of 11, and a higher N:P ratio on the SFS, with a mean of 16, which was consistent with a previous study reporting that the lowest N:P ratio occurred on the NFS out of the four slope aspects [39]. Tessier and Raynal [8] pointed out that plants were subject to N-, P-, and co-limitation when the plant N:P ratio was less than 14, higher than 16, and between 14 and 16, respectively. Therefore, our results indicate that plants on the NFS suffered N limitation. The tight correlation of leaf [N] with N:P ratio suggests that leaf N:P is determined by leaf [N] along the slope aspect gradient. A lower soil N availability reported in our previous studies [36]

was probably the reason for the lower leaf N:P ratio on the NFS, which supports the biogeochemical hypothesis suggesting that plant N and P are influenced by the availability of soil nutrients [9]. The lower soil N on the NFS was perhaps due to lower temperatures reducing the decomposition and mineralization of organic material.

In addition, the leaf [N], [P], and N:P ratio were lower in Maqu, which has a higher elevation, which is consistent with previous studies of elevational gradients [14,40], indicating an increase in N-limitation along altitudinal gradients.

### *4.2. Correlations of Leaf N:P Stoichiometry with Species Richness and Coverage across the Slope Aspects*

Significantly higher species richness in each quadrate was found on the NFS than on the SFS, which was mainly caused by the variation in forb richness. These results are consistent with previous studies in which species richness was significantly lower on the SFS [31,41], with the possible mechanism of lower soil water content as the limiting factor.

Community species richness was negatively correlated with leaf [N], which is consistent with the negative effects of N-fertilization on species diversity [42–45]. Many Nfertilization experiments showed considerable species loss, with the underlying improved growth of dominating clonal graminoids excluding forbs through competition [45,46]. The possible mechanisms may be that N-fertilization led to a tendency of P limitation, but graminids are often considered stress-tolerant species due to their low requirement for P and K [47]. Therefore, we found negative correlations of leaf [P] with graminoid richness and coverage because higher leaf [P] indicated the rapid growth of P-sensitive species. The significant negative correlations between leaf N:P ratio and forb richness provide direct evidence that on the natural environmental gradient, a higher N:P ratio is associated with fewer forbs and more graminoids, as hypothesized by Güsewell [6].

In addition, the higher legume richness on the SFS and the positive correlation of legume coverage with leaf [N] were consistent with the higher N:P ratio on the SFS due to their N-fixing functions.