**Abstract**

The effects of prefermentative cold skin-contact technique using Malvasia *aromatica* were studied as a first step to adapt to the climate change related effects in order to intensify the aroma potential of white wines of the D.O. "Vinos de Madrid" keeping the organoleptic characteristics of the region. Major volatile compounds were extracted by liquid–liquid extraction and quantified by GC-FID. Minor volatile compounds were determined by HS-SPME/GC–MS. Sensory analysis were also carried out to describe and quantify attributes of the wines. A total of 37 components were identified and quantified. Volatile components showed mixed behavior depending on the skin-contact time. Skin-contact for longer helps to enhance the floral character provided by some compounds contained in the skin, especially linalool and 2-phenyl etanol and were impact odorants of Malvasia *aromatica* wine based on odor activity values (OAVs).

 Introduction**

Skin-contact treatment has been proposed as a technique to try to increase the extraction of varietal aromas from the skins in different white cultivars [1–3]. It is a technique extensively used in the production of young white wines with the aim of improving their intensity and aroma profile by transferring free and glycosidically bound aroma compounds from the grape skins to the must before fermentation begins. The compounds responsible for the varietal aromas of wines depend on grape variety, climate, and soil and will determine the quality and local character of wines. Early winemaking procedures such as skin contact and the amount of pressure applied during pressing together with temperature conditions applied, will affect the extraction of aroma compounds and their precursors into the grape juice and consequently their concentrations in the resulting wine [4–7]. In the course of maceration, the concentration of aromas may increase in the must but there are not always changes at the sensory level in the wines. The varietal characteristics of the wine may be enhanced with the skin contact, however, there is some risk of the

apparition of herbaceous aromas, bitter flavors and excessive color in the musts. For these reasons, the conditions of temperature and contact time between the skins and the juice must be carefully chosen.

The vineyard is a crop with a wide range of adaptation to different environmental and agronomic conditions whose correct development is strongly influenced by the climate. In particular, the suitability of wine-growing areas to reach optimum levels of sugar, pH, color and aromatic components, which are necessary for the production of quality wines, depends on weather conditions throughout the growing period [8, 9]. As a result, climatic fluctuations will make very difficult to produce the same kind of wine in a particular area over seasons. The wines would lose the typicity and distinction of the region being affected the local economy by the decrease of the value of the final product.

The adaptation responses to deal with climate change related effects on winemaking can be implemented at the winery level or at the vineyard level [10]. In oenology, innovations could serve to correct fluctuations in grape quality. Also, can be considered as the first strategy to protect against climate variations related effects by focusing on specific hazards in order to improve the production. These techniques include changes in winemaking practices.

Skin-contact treatment has been proposed as a first measure of adaptation to climate change related effects. This study was focused on variations skin-contact time in order to intensify the aroma potential of winemaking white wines in D.O. "Vinos de Madrid". The purpose of the present paper was to evaluate differences in white musts and wines, which would arise due to different skin-contact time using the same temperature. In particular, the aromatic and sensory characteristics of the wines. To achieve this aim we choose cv. Malvasia *aromatica*, a white grape variety of Italian origin that has been grown in Spain since the 14th century. The main characteristics of this cultivar are: from an aromatic point of view, the presence of terpenes responsible of citrus and floral aromas similar to Muscat varieties [11] and fermentation aroma compounds, mainly fatty acids and their esters, provide it with fruity aromas [3, 12, 13]. On the other hand, physical–chemical characteristics that give rise to musts with high acidity and low pH, which make it a suitable varietal for trying to improve the organoleptic quality of its white wines of D.O. "Vinos de Madrid".

### **2. Material and methods**

#### **2.1 Vintage**

Grapes from *Vitis vinifera* L. cv. Malvasia *aromatica* were hand-collected from an experimental vineyard of the Instituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario (IMIDRA), located in "Finca El Socorro" in D.O. "Vinos de Madrid", Arganda del Rey, Spain (40°8'N, 3°22'W, 715 m altitude). Final harvest time was determined when berries reached 23°Brix and transported to the Experimental Winery from IMIDRA at the "Finca El Encín", in Alcalá de Henares, Spain (40°31'N, 3°17'W, 605 m altitude).

#### **2.2 Skin-contact treatment**

After harvest, grapes were divided into two batches for each assay (1 and 2). One batch was treated in the conventional way (C) without skin-contact and was used as control. In this way grapes were crushed and pressed in a hand-press and 5 g/hl of sulfur dioxide was added. The juice was then settled at 10°C for 12–18 h,

*Influence of Skin-Contact Treatment on Aroma Profile of Malvasia* Aromatica *Wines… DOI: http://dx.doi.org/10.5772/intechopen.99216*

and then racked. The total acidity in the must was corrected with tartaric acid to 6 g/L. The must was racked, dividing the volume equally in three stainless steel tanks. Commercial yeast was added for its fermentation which took place at 16°C and was followed daily by measuring density. The conventional way samples (C) of each assay (1 and 2) were different from each other, they came from different grapes.

For the skin contact treatment, the grapes were destemmed and crushed. The pomace (musts and skin) was mixed 5 g/hl of sulfur dioxide, kept at 10°C for 18 h (A1) and 6 h (A2). At the end were pressed in a hand-press (M18 and M6 assays). The juice was settled, racked and divided as mentioned in the conventional way. The rest of the process was equal to the conventional way.

#### **2.3 Physical-chemical analysis and fermentation kinetics**

Oenological parameters (°Brix, free and total sulfur dioxide, pH, total acidity, volatile acidity, ethanol (% v/v) and residual sugars) were analyzed following OIV official methods [14]. Yeast assimilable nitrogen (YAN) was determined following the Sörensen method.

A daily control of temperature and density was carried out to determine the influence of pre-fermentative skin contact on the kinetics of the fermentations. Fermentation velocity (VF) was measured checking daily the sugar percentage lost during the fermentation. On the other hand, V50 amount of sugar daily transformed by the yeasts when 50% of the sugar content had been used up was also evaluated [15].

#### **2.4 Aromatic analysis of the wines**

Analysis of free aroma compounds was performed by quantification of minor and mayor volatile compounds. Quantification of major volatile compounds was undertaken by GC-FID (Agilent Technologies, Santa Clara, CA, USA) with a DB-Wax column (60 mx 0.32 mm x 0.5 m) from J&W Scientific (Folsom, CA, USA) following the procedures proposed by Ortega [16]. The liquid phase extraction (LPE) of aroma compounds was performed in dichloromethane. The method conditions were: oven temperature 40°C for 5 min, then increased to 3°C/ min up to 200°C, and helium as carrier gas at 2 ml/min. Two mL of aroma extract were injected at 250°C in splitless mode. The total run time was 75 minutes per sample. Analyses were carried out in duplicate.

Minor volatile compounds (terpenoids and C13-norisoprenoids) were determined by HS-SPME/GC–MS following the method proposed by Yuan & Qian [17]. A 50/30 μm DVB/CAR/PDMS fiber (Supelco Inc., Bellefonte, PA) was used for volatile extraction. 20 mL vials were used for chromatography (Agilent Technologies). Two mL of the wine sample were diluted with 8 mL of a citric acid solution (0.5 g/L citric acid, pH 3 saturated with sodium chloride) and 20 μL of 4-octanol (100 μg/L) was used as internal standard were added with a small magnetic stir bar. The vials were capped and equilibrated at 50°C in a thermostatic bath for 10 min. The aromatic compounds were extracted through SPME fiber for 50 min at 50°C with stirring (1000 rpm). The fiber was inserted into the injection port of the GC (230°C) to desorb the compounds. The injection into the chromatograph was manual. An Agilent 6890 gas chromatograph equipped with an Agilent 5973 mass selective detector (Agilent, Santa Clara, CA) was used. Compound separation was achieved with a DB-WAX de J&W Scientific (Folsom, CA, USA) (60 m x 0.32 mm x 0.5 μm film thickness, Phenomenex, Torrance, CA). A constant helium column

flow rate of 1.0 mL/min was used. The chromatographic program was set at 40°C for 3 min, raised to 230°C at 5°C/min for 15 min. Splitless injection mode was used.

### **2.5 Sensory analysis**

Descriptive sensory analyses were performed by a trained panel of 8 people (4 expert tasters and 4 habitual consumers) from the IMIDRA Institute. This panel had been previously trained in the recognition of wine flavor. Sensory descriptive analysis was performed to describe and quantify attributes of the wines based on a scale from 1 (low intensity) to 10 (high intensity). A hedonic classification was also carried out establishing the order of preference of the samples presented. The final score was obtained as the mean of the wine evaluations with their respective standard deviation and interpreted by graphical representation.

### **2.6 Statistical analyses**

The statistical processing of the data was carried out with software SPSS ver. 20.0 (SPSS, Inc., Chicago, USA). Analysis of variance (ANOVA) was applied on oenological parameters, volatile compounds and sensory attributes of the wines. Tukey HSD post-hoc tests were used to establish the significance of differences between means to assess significance (p < 0.05).


**1. Introduction**

Prostate cancer (PCa) diagnosis and prognosis are based on histopathological interpretation of biopsy cores to assess the grade and volume of cancer present as well as the relative proportions of the various Gleason patterns (GP) present. A number of studies have investigated the importance of amount of GP4 on the patient's prognosis and survival. Stark et al. [1] indicated that Gleason Grade group (GG) 3 cancers which have a majority of GP4 cancer were associated with a three-fold higher rate of lethal outcome compared to GG2 cancers [1] which have a majority of GP3 cancer. Additional studies have highlighted that a binary cut-off for relative amount of GP4 cancer as used for defining GG2 and GG3, whilst useful, is not sufficient for complete prognostic discrimination. Specifically Choy et al. [2] reported that an increase in the percentage of tumor in radical prostatectomy (RPx)

**Citation:** Ball, R.Y.; Cardenas, R.; Winterbone, M.S.; Hanna, M.Y.; Parker, C.; Hurst, R.; Brewer, D.S.; D'Sa, L.; Mills, R.; Cooper, C.S.; et al. The Urine Biomarker PUR-4 Is Positively Associated with the Amount of Gleason 4 in Human Prostate Cancers. *Life* **2021**, *11*, 1172. https://doi.org/10.3390/life11111172

Academic Editors: Ana Faustino and Paula A. Oliveira

Received: 30 September 2021 Accepted: 20 October 2021 Published: 3 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

samples that is GP4 or greater is associated with an increase in biochemical recurrence (BCR)—ranging from 16% BCR in men where GP4 content was 1–20% to 68% BCR where GP4 was >70% [2]. The percentage of biopsy cores positive for tumor has also been found to link to prognosis [3] and is factored into the CAPRA (Cancer of the Prostate Risk Assessment) score [4]. There is further complexity in that tumor foci in up to 87% of RPx can contain multiple Gleason patterns [5–7].

Most tumors arise in the peripheral and transitional zones of the prostate, which are secretory in nature. Prostatic secretions carry tumor biomarkers into the urethra from where they can be harvested in urine. PCa-biomarkers in urine have shown utility in assessing prognosis. PCa-specific RNA transcripts such as *TMPRSS2:ERG* are detectable in urine from men with prostate cancer [8] and there has been much interest in development of a urine diagnostic test. The majority of such biomarker tests, including the PCA3 test (*PCA3* and *KLK3*), MiPS (serum PSA plus urine *TMPRSS2:ERG* and *PCA3*) and ExosomeDX Prostate Intelliscore (*ERG*, *PCA3*, *SPDEF*), rely on detecting transcripts from a very small number of genes which may not be expressed in every cancer [9–11]. In contrast we have developed the Prostate Urine Risk (PUR) signatures using a NanoString panel of 36 gene probes [12]. PUR detected PCa in urine samples that were negative for *PCA3* and/or *TMPRSS2:ERG*. The PUR signatures were designed to correspond to patients in four clinical groups: those with no evidence of cancer (NEC, PUR-1), and three D'Amico categories of PCa: low-(PUR-2), intermediate-(PUR-3) and high-risk PCa (PUR-4). The PUR-4 signature could detect intermediate and high-risk disease (AUC = 0.77, 95% confidence interval [CI] 0.70–0.84) and was able to predict disease progression in men on an active surveillance monitoring regime up to 5 years after a single urine sample was collected (HR 8.23, 95% CI 3.26–20.81; *p* < 0.001). To understand how PUR-4 related to the structure of the cancerous prostate we now examine its relationship to the amounts and grade of tumor in biopsy-sampled prostates and in a series of radical prostatectomy specimens.

#### **2. Materials and Methods**

#### *2.1. Urine RNA Extraction and PUR Signatures*

All urine samples (≤30 mL) were collected from 295 men who attended urology clinics at the Norfolk and Norwich University Hospitals NHS Foundation Trust (NNUH) UK (*n* = 214) and at the Royal Marsden Hospital (RMH) UK (*n* = 81) between September 2012 to September 2014 [12]. Ethical approval for this study was gained from the Health Research Authority and the NRES Committee East of England in 2012, REC reference: 12/EE/0058, IRAS project ID: 196199. Informed consent was obtained from all subjects involved in the study. All urine samples (≤30 mL) were collected pre-biopsy. Urine cell-free RNA (cfRNA) was extracted and the PUR signatures were generated from NanoString (NanoString Technologies Inc, Seattle, WA, USA) expression analysis as per Connell et al. (2019) [12].

### *2.2. Biopsy Tissue Analyses*

Full biopsy data required for tumor volume analysis (prostate volume, total number of cores taken and number of cores positive for PCa) were available for 215/295 patients (Table 1). The majority of the biopsies were trans-rectal ultrasound guided (TRUS) with a range of 2–23 cores taken per prostate (median 9, mean 9). Biopsy data were recorded as part of a standard biopsy assessment for PCa diagnosis at the NNUH and the RMH. Ultrasound prostate volumes ranged from 5.5–353.9 g (mean 53.9, SD 41.9). For clinical characteristics of Gleason grade groups divided into small and large tumors see Supplementary Table S1.

**Table 1.** Clinical characteristics and PUR scores for biopsy cases. All data presented as: 'Median (IQR)'.

Abbreviations: IQR, interquartile range; GG, Gleason Grade; NEC, No Evidence of Cancer; US, Ultrasound; Age and PSA at time of urine collection; PSA, prostate specific antigen; PSAD, PSA density (PSA/US prostate volume). PUR-1–4, Prostate urine risk signatures 1–4, NA, Not applicable.

#### *2.3. Radical Prostatectomy Analyses*

Nine men had a radical prostatectomy (RPx) within 4 months of their biopsy, and these were analyzed in detail. The RPx were fixed, embedded, sectioned at ~5mm intervals and megablock slides were stained with hematoxylin and eosin (H&E) as part of the standard histopathological reporting process for the NNUH. The H&E stained megablock sections were re-examined for this study by two NNUH histopathologists (RYB, LDS) and marked to indicate all areas of tumor. Each tumor focus was given a Gleason grade (GG) and an estimate for the percentage of Gleason Pattern (GP) 3 and 4 present (see main text and Figure 1). GG2 and GG3 tumor foci were then divided into 6 groups, depending on the percent of Gleason 4 present (see Figures 1 and 2). The tumor-marked H & E-stained slides were scanned on a flatbed scanner at 300 dpi, and Fiji software (Image J version 1.42 q, https://imagej.net/software/fiji/) was used to analyze the images and measure the areas of prostate and of each focus of cancer. Prior to analysis of each tissue section Fiji was calibrated using the known width of the H&E slide in the scanned image. Total areas of GP3 and GP4 tumor in each prostate were calculated by multiplying area of each tumor foci by the percentage of GP3 or GP4 present.

#### *2.4. Statistical Analyses*

R (v4.0.3) was utilized to perform all statistical analyses. Prior to test selection data were checked for appropriateness for normality and variance using QQ-plots and flinger/shapiro tests, respectively, using the base R package. The Rstatix package was used to perform Kruskal-Wallis rank sum test and Games Howell post-hoc test. All correlation analyses were performed using base R and used Cohen's effect sizes [13]. Plots were developed using 'ggplot' in combination with multiple package extensions including 'ggsci', 'ggrepel' and 'ggpubr'. The collection of 'tidyverse' packages were utilized for data manipulation. Default options were opted for unless otherwise stated.

**Figure 1.** Assessment of prostatectomy RPx9. (**A**) RPx9 was cut into 4 sections, for which H&E slides were prepared and areas of tumor marked up. (**B**) the sections are arranged for viewing from base to apex. The PCa areas have been colored to correspond to the Gleason Grade Group (GG) and % GP4 found: red indicates GG3 with 75–90% GP4, orange indicates GG3 with 50–75% GP4 (see Figure 2). (**C**) the information in 'B' was used to create a 3D projection of what the prostate and tumor may have looked like. Dotted tumor indicates a tumor focus behind a more anterior tumor.

**Figure 2.** Image projections of tumor content for 9 radical prostatectomies (RPx1–9). Images were constructed for each prostate as shown in Figure 1. The different colors correspond to the % GP4 detected (RPx-Key). PA: prostate area summed for all slices. TA: Tumor Area. '+5' indicates a tertiary region of GP5 tumor. Dotted tumor indicates a tumor focus behind a more anterior tumor. Horizontal dotted lines indicate approximate positions of H&E sections examined. The grey boxes underneath each 3D construct contain the biopsy data taken prior to prostatectomy, including the highest GG found on biopsy. Each circle represents a core taken, data is divided up into left and right cores and also into anterior (upper boxes) and posterior (lower boxes) for one prostate (RPx6).

## **3. Results**

#### *3.1. Analyses of Prostate Needle Biopsy Tissue*

PUR signatures (PUR-1 to PUR-4) were generated as published in Connell et al. [12] from urine cell-free-RNA NanoString expression data (36 NanoString gene probes) collected from patients with a suspicion of prostate cancer (Movember cohort, *n* = 535). PUR signatures represent the probability of membership of each of the following clinical groups: PUR-1, NEC (No clinical Evidence of Cancer, PSA normal for age); and PUR-2 to 4, respectively the three D'Amico risk categories low-(PUR-2), intermediate-(PUR-3) and high-risk (PUR-4) [12]. Each sample is represented by a readout in all four PUR signatures with the sum adding up to '1' (see Figure 3A for examples).

**Figure 3.** PUR status in biopsy samples. (**A**) Distribution of the four PUR signatures from nine patients that were subsequently treated by radical prostatectomy. See Figures 1 and 2 for the cancer maps corresponding to these cases. PUR-1 (green), PUR-2 (blue), PUR-3 (yellow), PUR-4 (red). The sum of PUR-1-4 in each sample is '1'. (**B**–**D**) Data were analyzed using a non-parametric Kruskal-Wallis analysis of variance followed by Games-Howell post-hoc analysis. (**B**) % Biopsy cores positive also referred to as tumor volume (TV) was calculated as PCa-positive cores/total cores taken. The clinical categories are 'NEC': No Evidence of Cancer (*n* = 62) and Biopsy Gleason Grade (GG) groups GG1 (*n* = 93), GG2 (*n* = 61), GG3 (*n* = 38), GG4 (*n* = 12), GG5 (*n* = 11). 'NS' indicates 'Not Significant' difference in pair-wise analysis of the groups, all other pairwise comparisons were significant (*p* < 0.05). (**C**) PUR-4 signature for samples in Figure 3B. \* indicates a significant difference between groups. Green brackets indicate that a significant difference was found between NEC patient samples and each of the GG patient groups. (**D**) Serum PSA in GG 1, 2, 3 samples dichotomized based on tumor volume/ultrasound prostate volume (TV/US) into small (S) and large (L) groups, only pairwise comparisons with NEC were significant. No significant differences between GG (S) and (L) groups were found. (**E**) PUR-4 signal for samples dichotomized as 'D' above.

The 'Biopsy cohort' consisted of men with PCa detected on biopsy in the Movember cohort for whom full biopsy data were available: number of cores positive; Gleason score; pre-biopsy PSA; and prostate ultrasound volume, *n* = 215. Additionally, 62 men designated as NEC from the Movember cohort were used in comparisons of PUR values. PUR-4 correlated with increasing D'Amico risk group (Supplementary Figure S1), and PUR-4 was significantly different in pairwise comparisons between NEC, D'Amico low, and intermediate-risk groups. PSA also correlated with increasing D'Amico risk group and was significantly different in pairwise comparisons between all clinical categories (Figure S1).

Comparison of PUR-4 to DRE data was not performed due to unreliability of data (see discussion). As expected, the percentage of cores positive for prostate cancer correlated with Gleason Grade group (GG) (Spearman's coefficient of rank correlation *ρ* = 0.63, *p* = 1.77 × 10−25, Figure 3B). PUR-4 signature values showed a significant positive correlation with increasing GG (Spearman's correlation *ρ* = 0.58, *p* = 3.5 × 10−24, Figure 3C). When intercomparing the different clinical groups, PUR-4 was significantly different between NEC and each individual GG (all *p* < 0.00035); GG1 cancers were significantly different to both GG2 (Games-Howell post-hoc test, *p* = 0.003) and GG3 tumors (*p* = 0.036). There was no significant difference in tumor volume or PUR-4 between GG2 and GG3 tumors (both *p* > 0.05; Games-Howell post-hoc test).

We next examined the relationships between PUR-4 value and tumor volume. To achieve this the results for each GG were divided into large (L) and small (S) volume cancers relative to the median tumor volume. Tumor volume was calculated as the number of PCa-positive cores/total number of cores taken, with individual values corrected for their patient's prostate volume determined by ultrasound (US). GG4 (*n* = 12) and GG5 groups (*n* = 11) were not subdivided due to the small numbers of samples. There were no significant differences in PSA between (S) and (L) groups for GG1, GG2 or GG3 (Figure 3D, Games-Howell post-hoc test, all *p* > 0.05). There was no difference in PUR-4 values comparing GG1(S) and GG1(L) (Games-Howell post-hoc test, *p* = 0.98, Figure 3E). Since GG1 cancers contain only Gleason Pattern 3 cancer this observation showed that the volume of Gleason Pattern 3 cancer present had no impact on PUR-4 values. PUR-4 for GG2 (L) was significantly greater than for GG2 (S) (*p* = 0.005; Games-Howell post-hoc test), while PUR-4 values for GG3 (L) were greater than for GG3 (S) but did not reach significance (Figure 3E). Since GG2 and GG3 contain both Gleason Pattern 3 and 4 cancer these observations suggest that Gleason Pattern 4 cancer may be contributing to PUR4 status.

#### *3.2. Prostatectomy Analyses*

Nine radical prostatectomy specimens were chosen for histopathological re-examination; three GG1, four GG2, and two GG3 based on pre-surgical histopathological assessment of biopsies. H&E analysis was carried out on all individual sections from each RPx as shown in Figure 1 allowing construction of 3D cancer maps (Figure 2). Each section was examined independently by 2 histopathologists (RYB and LD) and areas of cancers were marked depending on the estimated percentage of GP4 present: (i) <10% GP4; (ii) 10–25% GP4; (iii) 26–50% GP4; (iv) 51–75% GP4; (v) 76–90% GP4; and (vi) >90% GP4 (Figures 1 and 2). For each focus of tumor the range midpoint was multiplied by the focus area to provide an estimated amount of GP3 and GP4. These values were summed for each prostate (Table 2, biopsy data for RPx1-9 are presented in Supplementary Table S2.

No significant correlation was observed between PUR-4 score and PSA (Figure S1), or the size of the prostate as measured by ultrasound or H&E tissue area (Figure 4), (Pearson's correlation, *r* = −0.33 *p* = 0.4; *r* = −0.47 *p* = 0.2; *r* = –0.3 *p* > 0.4 respectively). No significant associations were identified between PUR-4 scores and the total area of tumor or the area of GP3 tumor with or without adjustment for prostate size (Pearson's correlation all *p* ≥ 0.48) (Figure 4). The amount of GP4 tumor was found to have a strong significant positive correlation with PUR-4 score (*r* = 0.71, *p* = 0.035; Pearson's correlation; Figure 4D), and similar strong associations were found after adjustment for total prostate area on H&E slides (*r* = 0.72, *p* = 0.028; Figure 4E) or for ultrasound prostate volume (*r* = 0.73, *p* = 0.024).

**Table 2.** Clinical characteristics for the 9 radical prostatectomy (RPx) cases examined.

GG = Gleason Grade group; Majority, data for most common GG found in each prostate; GP3, GP4, GP5, Gleason Pattern 3, 4, 5 respectively; Age, age at recruitment; US, Ultrasound; PCa, prostate cancer; All areas measured from scans of H&E slides (see methods); for GP3 and GP4 amount calculation see results; GG2+5 indicates GG2 cancer with a tertiary GP5 (<5% of all PCa), similarly GG1+4 indicates a GG1 cancer with tertiary GP4; PUR-4, Prostate urine risk signatures 4.

**Figure 4.** *Cont*.

**Figure 4.** Correlation of PUR-4 with prostate parameters. Measurements were taken from the 9 whole mount prostate H&E sections shown in Figure 2. Each sample is colored by the Gleason score of the predominant tumor area (see key). No correlation was found between PUR-4 and (**A**) prostate area of the H&E sections, (**B**) tumor area, (**C**) amount of Gleason pattern 3. (**D**) PUR-4 correlated with amount of Gleason pattern 4 cancer both before, and (**E**) after adjustment for prostate area (see body text for details). '*r*' is the Pearson's correlation coefficient, an '*r*' > 0.7 indicates a strong correlation, '*p*' = probability that the correlation has not arisen by chance.

#### **4. Discussion**

When estimating Gleason Grade Evans et al. [14] reported that only 55% of cases were concordant between biopsy and radical prostatectomy, while Yang et al. [15] and Epstein et al. [16] reported upgrading in 30–36% of RPxs. Comparably we found disease upgrading in 55% (5 of 9) prostatectomy specimens (Figure 2). Two out of three RPx reported as GG1 on biopsy had GP4 in their RPxs, 2/4 biopsy GG2 were upgraded to GG3, and GP5 was found in one prostate (RPx4). As the interval between biopsy and prostatectomy was ≤4 months for all samples, these Gleason differences were inferred to be due to biopsy sampling issues. Despite potential limitations of the tumor volume assessment, our results support the hypothesis that there is a relationship between increasing amounts of GP4 PCa and increasing PUR-4 signal in GG2 and GG3 tumors both in biopsy specimens and for prostatectomies. The importance of detecting and estimating the amount of Gleason pattern 4 tumor is highly relevant to risk stratification and selecting the appropriate treatment pathway for a patient [17–19].

No difference in PUR-4 signals were observed between GG2 (S) and the GG1 (S) and GG1 (L) subgroups (*p* > 0.05). We have previously shown that a PUR-4 signature of <0.174 identified a subgroup of men whose disease did not progress to require treatment intervention up to 5 years later (HR 8.23, 95% CI 3.26–20.81; *p* < 0.001) [12]. Significantly, 80% of the GG2 (S) tumors were below the 0.174 PUR-4 cutoff, and the vast majority of these (83%) had <30% PCa-positive cores (average 21%, median 19%), a figure comparable to that found in the GG1 cancers (94% <30% cores positive, median 13%, average 15%). These results suggest that PUR-4 may be identifying a group of low tumor-volume GG2 men who could benefit from an active surveillance monitoring regime.

One limitation of this study is that we could not interrogate an increase in PUR-4 in patients with GG4 and GG5 disease due to the small sample numbers involved. Anecdotally the PUR-4 signature for RPx4 which was found to have an area of GP4+5 on RPx was much higher than expected for the volume of GP4 calculated in this largely GG2 < 10% GP4 cancer, which may imply that PUR-4 can respond to disease that is becoming more aggressive.

PUR-4 increases with D'Amico risk group, and D'Amico categorization includes data from Gleason, PSA and DRE. However, while PSA increased with D'Amico risk group, there were no differences in PSA between the GG (S) and GG (L) groups. In addition, there was no correlation between PSA and PUR-4 for the 9 RPx. When taken together these

results indicate that PUR-4 does not appear to mirror serum PSA. DRE information was not considered reliable data for this cohort, DRE data is considered to be subjective, for example Gosselaar et al. [20] found that suspicion of cancer on DRE by 6 clinicians varied from 4–28%, while Ankerst [21] found that 70% of abnormal DREs were DRE-normal the following year. A metanalysis by Naji et al. [22] of 8217 studies led them to the conclusion that there was no evidence for the efficacy of using a DRE to detect prostate cancer [22]. The percentage of cores positive was considered to be a more solid assessment of extent of tumor, and as approximately equal numbers of cores were taken on each side of the prostate, a figure of >50% cancer positive would indicate disease on both sides of the prostate.

We propose that the PUR urine test could help in the diagnostic pathway for patients who are worried about their risk of prostate cancer and those with suspected prostate cancer. mpMRI has aided the diagnosis of significant disease, but negative aspects of mpMRI are that it can miss significant cancers (GP ≥ 4) [23], identify insignificant cancers (GG1) [24], and has been reported to have a high false-positive rate of around 50% [23], with problems of interoperator inconsistencies [25]. We believe that the PUR test could add to the information from multiparametric magnetic resonance imaging (mpMRI) and aid decision making on whether a biopsy is necessary.

#### **5. Conclusions**

A strong association was observed between an increasing amount of Gleason pattern 4 cancer and increasing PUR-4 signal. These data suggest that the PUR-4 signature could provide useful additional information in determining the amount of clinically significant tumor within a prostate and thereby help guide the patient treatment pathway with essential information for triage, improved management and prognostic utility.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/life11111172/s1, Figure S1: PSA and PUR-4 data in biopsy patients categorized on D'Amico and PSA in the nine RPx men, Table S1: Clinical characteristics and PUR scores for biopsy cases categorized by Gleason Grade and subcategorized by % biopsy cores positive, Table S2: Combined RPx and Biopsy clinical characteristics for the 9 radical prostatectomy (RPx) cases examined.

**Author Contributions:** Conceptualization, J.C. and R.Y.B.; methodology, J.C. and R.Y.B.; software, J.C. and R.C.; formal analysis, M.S.W. and R.C.; resources, R.Y.B., L.D., R.M., R.H., C.P. and M.Y.H.; data curation, D.S.B.; writing—original draft preparation, J.C.; writing—review & editing, J.C., C.S.C., D.S.B., R.H., R.Y.B., R.C. and M.S.W.; visualization, J.C. and R.C.; supervision, J.C., C.S.C. and D.S.B.; project administration, J.C.; funding acquisition, C.S.C. and D.S.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was possible thanks to the Movember Foundation GAP1 Urine Biomarker project, Prostate Cancer UK, The Masonic Charitable Foundation, The Bob Champion Cancer Trust, the King family, Big C Cancer Charity, The Andy Ripley Memorial Fund and the Stephen Hargrave Trust. The sponsors had no role in the design, execution, interpretation, or writing of the study.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Health Research Authority and the NRES Committee East of England Ethics Committee, 2012. REC reference: 12/EE/0058, IRAS project ID: 196199.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data is contained within the article or supplementary material.

**Conflicts of Interest:** A patent application has been filed by C.S.C., J.C. and D.S.B. for the use of the PUR biomarkers in prostate cancer diagnosis and prognosis. There are no other conflicts of interest to disclose.

## **References**


**3. HF radar observations of the 2011 Japan tsunami leading to an empirical detection algorithm**

Radar echoes are produced by reflection of the radar wave from ocean waves with wavelengths half that of the radar; such waves have periods between 1.5 and 4.5 s. In contrast, the tsunami wave period lies between 20 and 50 min, corresponding to wavelengths between 400 and 800 km in the open ocean. Tsunami orbital velocities add to the shortwave radial velocities producing the radar echo; in deep water, tsunami velocities are too small to be seen by the radar, but they increase as the tsunami moves onto the continental shelf and the water depth decreases below 200 m and can then be observed by radars located on the coast.

Radial current velocities are obtained from the first‐order radar echo spectra measured at individual radar sites [13, 14]. In usual practice, several radar echo spectra are averaged over time before analysis. As time resolution is critical for local tsunami detection, unaveraged spectra are analyzed. The Doppler shift from the ideal Bragg frequency defines the radial current speed; spectral values at that frequency are interpreted to give the azimuth angles at which this speed occurs. Together with range defined by the time delay, estimates follow for the radial current velocity at locations spaced 1° apart around a circular range cell centered on the radar site.

error estimate of about 25% when using Green's Law for estimating tsunami height change

Solving Eqs (2–3) provides both the tsunami height and orbital velocity profiles as the wave approaches the coast, as impacted by the decreasing depth on the journey toward shore. This yields the most accurate estimate for the arrival time from any offshore point along its path and is useful in near field of the coastal radar because the computational effort is manageable. However, for an approach from much farther out, or in order to get a quick, rough estimate of the tsunami arrival time, the phase velocity *vph*(*d*) at depth *d*, time *t*, and distance *s* along a

> ( ) *ph s v d*

> > 0

*t*

Eqs (5) and (9) lead to the following approximate solution for the elapsed time Δ*T* for the

( ) *s s T*

*gd s*

Because of inherent smoothing of the integration process, the depth profile need not be defined to great resolution, allowing quick use of Eq. (10) over large ocean distances to give rough

**3. HF radar observations of the 2011 Japan tsunami leading to an empirical**

Radar echoes are produced by reflection of the radar wave from ocean waves with wavelengths half that of the radar; such waves have periods between 1.5 and 4.5 s. In contrast, the tsunami wave period lies between 20 and 50 min, corresponding to wavelengths between 400 and 800 km in the open ocean. Tsunami orbital velocities add to the shortwave radial velocities producing the radar echo; in deep water, tsunami velocities are too small to be seen by the radar, but they increase as the tsunami moves onto the continental shelf and the water depth

Radial current velocities are obtained from the first‐order radar echo spectra measured at individual radar sites [13, 14]. In usual practice, several radar echo spectra are averaged over time before analysis. As time resolution is critical for local tsunami detection, unaveraged spectra are analyzed. The Doppler shift from the ideal Bragg frequency defines the radial

decreases below 200 m and can then be observed by radars located on the coast.

¶ = ¶ (9)

¶ D = ò (10)

due to sharp depth changes.

tsunami great‐circle ray path can be expressed as follows:

tsunami arrival at distance *S* from the start:

estimates of the tsunami arrival time.

**detection algorithm**

**2.5. Tsunami arrival time**

80 Tsunami

Total current velocities are obtained by combining radial velocities from the radar sites [13]. A grid is formed over the radar coverage area and averaging circles form surrounding each grid point. Total velocity vector components are calculated by fitting to radial velocities from the different radar sites that fall within the averaging circle.

On March 11, 14:46 2011 Japan Standard Time (JST), a magnitude‐9 earthquake off Sendai, Japan, unleashed a large tsunami that was observed by HF radars around the Pacific Rim, see **Figure 1**. The earthquake and resulting tsunami are known as the Tohoku event. As our first example, we show current velocities measured by two SeaSonde radars located near Usujiri and Kinaoshi, Hokkaido, Japan during the 2011 Tohoku tsunami.

**Figure 1.** (a) The North Pacific Ocean showing the location of the radars that detected the tsunami. Abbreviated radar field names are as follows; corresponding geographic locations are given in Section 1. Japan: A087 and A088. USA: STV2, SEA1, YHS2, TRIN, GCVE, BML1, PREY, COMM, ESTR, and LUIS. (b) The location of the Japan earthquake and the radars in Hokkaido. (c) The bathymetry offshore from the radars and radial velocities measured by the Kinaoshi radar, March 11, 2011, 21:00 JST.

The radars had a transmit frequency of 42 MHz and a range increment of 0.5 km. The water depth over the entire radar coverage area is less than 200 m. The 42 MHz frequency band is used for high‐resolution, short‐range current observations and results in a radar range less than 15 km, due to significant attenuation of the surface wave passing across the sea at these higher HF frequencies.

### **3.1. Total velocity current maps**

The direction and strength of the flow were measured at approximate 4‐min intervals with a cell resolution of 0.5 km × 0.5 km. **Figure 2** shows total current‐velocity maps, the first

**Figure 2.** Tsunami current velocity vectors with the tsunami height shown in colors. Radar measurements from A087 (blue circle) and A088 (red circle) made on March 11, 2011 (a) the tsunami arrival at 15:53 JST and (b) outward flow occurring later at 21:00 JST.

demonstrating the arrival of the tsunami, indicated by strong inward flow, the second an example of outward flow. Eqs (6) and (7) relate the tsunami height to the current velocity; heights are displayed in colors superimposed on the velocity vectors shown by the arrows. As noted in Section 2.3.2, the accuracy of Green's Law estimates of height decreases close to shore.

A video showing the current velocity/height flow from March 11, 14:06 JST to March 12, 13:54 JST is shown in Video 1, available at http://bit.ly/29lOdXw

Time in JST is given in each frame of the movie. The video shows the tsunami arriving at 15:53 JST and then sweeping in and out of Uchiura Bay.


Preface

There is nothing better than the exact solution of mathematical problems, for example, equa‐ tions or integrals. There are a huge number of such relatively simple cases but the number of complex problems, which have only approximate solutions, is bigger, even tends to infinity. The approximate solutions are looked for either by analytical methods or by numerical anal‐ ysis. A powerful and widely applicable analytical approach to the obtaining of reliable ap‐ proximate solutions is the perturbation method, namely, the development of a perturbation theory of the selected problem. This general approach is well combined with numerical cal‐ culations and computer simulations.

The perturbation theories look alike and can easily be recognized through their main fea‐ tures. Their results are represented by infinite series expansions around an exact solution of a simpler problem. The latter is usually a result of a suitable reduction of the initial problem when the mathematical part, which is responsible for the lack of exact solution, is ignored. The ignored part is called "perturbation part." Under the supposition that the perturbation term has a small effect on the final result, the solution of the entire problem is represented as an infinite series in powers of some expansion parameter, which is a small factor in the ig‐ nored term. The first term in this expansion is usually labeled by the subscript "0" (zero-or‐ der term) and represents the solution of the exactly solved reduced task, whereas the other terms are in powers of the expansion parameter.

The perturbation series are infinite, but in the self-consistent theories, the magnitude of the terms in nonzero power decreases with the increase of the expansion parameter powers, and the final sum of the perturbation terms is smaller than the zero-order term. In this much desired case, the perturbation leads to a relatively small correction to the result for the exact‐ ly solvable part of the problem. This usually happens under some conditions that depend on the features of the specific task and are to be deduced within the development of the theory. In case of a number of relevant problems, both in mathematics and natural sciences, the per‐ turbation contributions are larger than the zero-order solution. This circumstance requires a more specific interpretation of the final results.

In other important cases, the perturbation series are divergent for some parameters of the theory. This situation is frequent in research problems in natural sciences, in particular, in physics. Significant efforts to extract useful information from asymptotic perturbation series are the daily concern of many theoretical physicists working on the most important physics problems, particularly in the field of quantum field theory and in the theory of phase transi‐ tions, where the interparticle interactions are relatively strong. Namely, the interaction terms in the Hamiltonian of a physical system are usually chosen as the perturbation part of perturbation expansions in quantum field theory and statistical physics, where the perturba‐ tion methods are widely used on the basis of the so-called Green's function approach. In modern theory of strongly interacting systems, the perturbation expansions are combined with ideas of scaling and renormalization, and thus these expansions are in the basis of the so-called renormalization group. The latter is a powerful tool of investigation of the effect of strong interactions in field theories.

Once introduced and highly developed in physics, perturbation methods of study are also spread in chemistry—mainly in quantum chemistry, in physical chemistry, in chemical physics, and in biophysics. In the last three–four decades, new interdisciplinary research fields appeared, for example, sociophysics and econophysics, where perturbation theories together with numerical analysis and computer simulations will undoubtedly be very im‐ portant.

The book contains seven chapters, written by noted experts and young researchers who present their recent studies of both pure mathematical problems of perturbation theories and application of perturbation methods to the study of important topics in physics, for ex‐ ample, renormalization group theory and applications to basic models in theoretical physics (Y. Takashi), the quantum gravity and its detection and measurement (F. Bulnes), atom-pho‐ ton interactions (E. G. Thrapsaniotis), treatment of spectra and radiation characteristics by relativistic perturbation theory (A. V. Glushkov et al.), and Green's function approach and some applications (Jing Huang). The pure mathematical issues are related to the problem of generalization of the boundary layer function method for bisingularly perturbed differential equations (K. Alymkulov and D. A. Torsunov) and to the development of new homotopy asymptotic methods and their applications (Baojian Hong).

> **Dimo I. Uzunov** Professor of Physics Bulgarian Academy of Sciences Sofia, Bulgaria


*3.6. Fluorescence Image Analysis*

The cell-killing efficacy of the released Cur was determined by staining HepG2 cells with propidium iodide and acridine orange to distinguish between living and dead cells. As seen in Figure 6C, the cells treated with PNIPAm-co-PAAm-Mela HG fluoresced green, indicating that the majority of the cells were alive. The Cur-loaded PNIPAm-co-PAAm-Mela/Cur HG sample treated cells, on the other hand, exhibit red fluorescence, indicating that around >95% of cells were killed [38,39]. This might be due to the released Cur drugs from the PNIPAm-co-PAAm-Mela/Cur HG system, which induced cell death. As seen in Figure 6D, the cell killing efficiency is a function of sample concentration. The cell killing efficiency rose when the PNIPAm-co-PAAm-Mela/Cur HG sample concentration increased, which may be attributed to an increase in released Cur from the PNIPAm-co-PAAm-Mela/Cur HG system, and the released Cur induced more cell death.

#### **4. Conclusions**

In this study, we developed a dual-stimulus PNIPAm-co-PAAm-Mela/Cur HG copolymer system for temperature-responsive and pH-induced drug delivery applications. To evaluate the drug delivery behavior of the PNIPAm-co-PAAm-Mela HG copolymer, we employed Cur as a model drug. The PNIPAm-co-PAAm-Mela HG system had much higher Cur loading efficiency (73%) due to the existence of more drug-binding sites of amide, amine, and imine sites in the PNIPAm and Mel cross-linked PAAm segments. The drug release investigation revealed that a nearly complete release of Cur was accomplished in the presence of the combined pH and temperature stimulation conditions (pH 5.0/45 ◦C Furthermore, the results of the cytocompatibility investigation show that the prepared PNIPAm-co-PAAm-Mela HG system is cytocompatible and that the loaded drug may be released in the intracellular microenvironment. The overall study findings demonstrated that the PNIPAm-co-PAAm-Mela HG system might be used for controlled drug release to specific sites in chemotherapeutic applications.

**Author Contributions:** Conceptualization, methodology, formal analysis, K.T. and T.T.V.P.; validation, M.S. and V.R.; data curation, K.T.; writing—original draft preparation, T.T.V.P., M.S. and V.R.; writing—review and editing; supervision, project administration, funding acquisition, S.-C.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2020R1I1A3052258). In addition, the work was also supported by the Technology Development Program (S3060516), funded by the Ministry of SMEs and Startups (MSS, Republic of Korea), in 2021.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

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

#### **References**

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.


**4. Satellite estimates of temperature versus ground measurements**

In this Section, a methodology is presented in which the temperature estimates from the MODIS sensor onboard the Terra Satellite is contrasted with ground measurements. The methodology consists of a neural network approach in which measurements on the ground are used as input to the neural network, whereas, the temperature estimate from the satellite is considered as the network's output.

The neural network methodology adopted has successfully been implemented before in tackling several climatological problems in Cyprus: the prediction of maximum daily total solar irradiance (Kalogirou et al., 2002), the prediction of the daily average solar radiation (Tymvios et al., 2002, 2005a), the modeling of photosynthetic radiation (Tymvios et al., 2005b) and others.

#### **4.1. Data**

**Figure 7.** The height pattern at 500hPa (Cluster 5) from 24 November 1962

8 Remote Sensing of Environment: Integrated Approaches

of the atmosphere.

850hPa, 700hPa).

Although it appears that some Clusters are associated with heat events over Cyprus, the connection between heat events and atmospheric circulation at 500hPa did not give definite results that any of these patterns dominate heat event occurrences (as it was possible to demonstrate in previous studies on rainfall and extreme rainfall events). There might be two reasons for this inadequacy. The first is that the window that was chosen for the classifica‐ tion does not include the synoptic patterns that influence the area sufficiently; the second reason is that, although upper air patterns at 500hPa contribute significantly to the evolution of certain surface features (such as dynamical or extreme rainfall), such an association is not so clear for the temperature field. In the search for associations of the temperature fields with synoptic patterns in the Mediterranean, it is important to consider also the lower parts

Future research concerning the connection of the weather classification patterns will be fo‐ cused into a new, much larger window that will include Northern Africa and the Middle East and a combination of classification of patterns at lower levels of the atmosphere (e.g., For the needs of this research, data from MODIS onboard the Terra satellite have been used. More specifically, the level-3 product MOD11A1 (version 5) for the period 2000-2009 was exploited, at a resolution of about 1km by 1km (0.927km). Using the available Land Surface Temperature (LST) fields derived from MODIS, a time series was established corresponding to the position of ground stations. Wan & Dozier (1996) have developed the Generalized Split Window (GSW) algorithm for the retrieval of LST, using the thermal (infrared) channels of MODIS and under different atmospheric condi‐ tions (see also, Wan, 1999, 2008). This algorithm retrieves LST on the basis of emissivi‐ ties in bands 31 and 32 of MODIS. The accuracy in estimating LST was found to be better than 1K, whereas in most cases it was better than 0.5K (Hulley & Hook, 2009; Coll et al., 2009).

The data base for the surface measurements used in this research consist of the hourly re‐ corded temperature at each of the automatic meteorological stations of the network operat‐ ed by the Cyprus Meteorological Service (see Fig. 4), in the period 2000-2007. Based on these data, the maximum temperature recorded in the time period 1100 – 1300 UTC (local stand‐ ard time=UTC+2 hours) was considered as the day's maximum and was subsequently used in the study.

The training of the neural network implemented requires that there are no missing data in the time series used, because the data are used in groups and are therefore inter-dependent. Therefore, the estimated LST (by the neural network implemented) is based on the data of a whole day and missing values result in the rejection of that day. Following quality control based on the above constraint, the number of automatic stations that were subsequently used was reduced to twelve, as shown in Table 1.

#### **4.2. Methodology**

Artificial Neural networks (ANN) are small autonomous computational units (algorithms) with inter-connections which, to a large extent, resemble the functioning of natural compu‐ tational units, namely, the neurons of the human brain. ANN can be trained and learn through repeated examples so that they can reach conclusions and results without human intervention. Since their invention, ANN covered a wide spectrum of research and disci‐ plines and their application has been phenomenal. A few of the numerous examples of ANN applications are mentioned here: medical systems' automation for the recognition of malignant tumors, control of military equipment and aircraft, estimation of environmental variables, quality verification in production factories, forecasting of financial indices, weath‐ er diagnosis and forecasting etc.

**Ground station**

**Latitude (N)**

**Longitude (E)**

**Altitude Above sea level (m)**

**Astromeritis** 35°03´ 32°26´ 175 6.81 -7.31 0.22 2.35

**Athalassa** 35°04´ 33°58´ 162 7.14 -7.58 0.63 2.77

**Athienou** 35°03´ 33°32´ 185 6.45 -5.74 0.23 2.27

**Dasaki** 35°03´ 33°47´ 50 5.74 -4.52 0.60 2.17

**Kannaviou** 34°55´ 32°35´ 419 7.03 -6.23 0.48 2.41

**Kathikas** 34°55´ 32°26´ 650 8.25 -5.30 0.63 2.53

**Kato Pyrgos** 35°11´ 32°41´ 5 7.19 -10.12 0.62 2.99

**Malia** 34°49´ 32°47´ 645 7.59 -7.75 -0.11 2.56

**Mennogeia** 34°51´ 33°26´ 140 7.36 -7.78 0.28 3.07

**Paphos** 34°47´ 32°26´ 82 8.34 -7.45 0.37 2.83

**Paralimni** 35°04´ 33°58´ 65 7.51 -8.42 0.68 2.61

**Polis** 35°03´ 32°26´ 20 6.87 -4.83 0.40 2.40

In this research, an attempt has been made to relate ground measurements of temperature with the temperature as it is estimated from MODIS and develop a neural network method‐ ology that can be used in the estimation of ground temperatures by using the satellite im‐ agery. Although the methodology performs sufficiently, overall, it seems that further refinement is needed in order to improve the approach. The adoption of a single network for all the time series of data seems to limit the application of the methodology. For example, the present single neural network developed for each station does not take into account the large seasonal variations in the parameter concerned. It could be more effective if several

**Table 1.** Errors of LST estimation for the independent set of data for each ground station

neural networks are developed based on seasonally grouped data.

**Maximum (°C)**

Satellite and Ground Measurements for Studying the Urban Heat Island Effect in Cyprus

**Minimum (°C)**

**Average (°C)**

http://dx.doi.org/10.5772/39313

**Standard deviation (°C)**

11

For the implementation of the ANN methodology in the present research, the Multi-Layer Perceptron (MLP) was adopted (see Haykin, 1998). The input to this network is the surface temperature recorded at the ground stations and the output is the temperature estimated by the satellite (LST). Fig. 8 displays the MLP implemented.

**Figure 8.** The Multi-Layer Perceptron (MLP) network implemented for the prediction of LST

The data from the twelve ground stations and the respective MODIS estimations of LST were used as follows: 60% were used for Training of the network, 20% for Validation and the remaining 20% as an independent set for Testing.

#### **4.3. Results**

Table 1 displays the errors in the estimation of LST with the neural network, by using the independent set of data. In this table, the maximum, minimum and average errors along with the standard deviation are shown for each of the ground stations. Overall, the perform‐ ance of the neural network is considered as very satisfactory. However, there are cases where the error is unacceptable and this requires further investigation.

The relation between input data (ground temperature) and satellite estimated temperature LST (target) is shown in Fig. 9. The results are shown for all the data but also separately for the Training, Validation and Testing data sets. For the Training set, the correlation coeffi‐ cient is R=0.96991, for the Validation set R=0.89692 and for the Testing set R=0.9145, whereas, for all the data R=0.94747. Based on these findings, the performance of the network in pre‐ dicting LST is considered as satisfactory.

Satellite and Ground Measurements for Studying the Urban Heat Island Effect in Cyprus http://dx.doi.org/10.5772/39313 11

**Table 1.** Errors of LST estimation for the independent set of data for each ground station

tational units, namely, the neurons of the human brain. ANN can be trained and learn through repeated examples so that they can reach conclusions and results without human intervention. Since their invention, ANN covered a wide spectrum of research and disci‐ plines and their application has been phenomenal. A few of the numerous examples of ANN applications are mentioned here: medical systems' automation for the recognition of malignant tumors, control of military equipment and aircraft, estimation of environmental variables, quality verification in production factories, forecasting of financial indices, weath‐

For the implementation of the ANN methodology in the present research, the Multi-Layer Perceptron (MLP) was adopted (see Haykin, 1998). The input to this network is the surface temperature recorded at the ground stations and the output is the temperature estimated by

The data from the twelve ground stations and the respective MODIS estimations of LST were used as follows: 60% were used for Training of the network, 20% for Validation and

Table 1 displays the errors in the estimation of LST with the neural network, by using the independent set of data. In this table, the maximum, minimum and average errors along with the standard deviation are shown for each of the ground stations. Overall, the perform‐ ance of the neural network is considered as very satisfactory. However, there are cases

The relation between input data (ground temperature) and satellite estimated temperature LST (target) is shown in Fig. 9. The results are shown for all the data but also separately for the Training, Validation and Testing data sets. For the Training set, the correlation coeffi‐ cient is R=0.96991, for the Validation set R=0.89692 and for the Testing set R=0.9145, whereas, for all the data R=0.94747. Based on these findings, the performance of the network in pre‐

er diagnosis and forecasting etc.

10 Remote Sensing of Environment: Integrated Approaches

the satellite (LST). Fig. 8 displays the MLP implemented.

**Figure 8.** The Multi-Layer Perceptron (MLP) network implemented for the prediction of LST

where the error is unacceptable and this requires further investigation.

the remaining 20% as an independent set for Testing.

dicting LST is considered as satisfactory.

**4.3. Results**

In this research, an attempt has been made to relate ground measurements of temperature with the temperature as it is estimated from MODIS and develop a neural network method‐ ology that can be used in the estimation of ground temperatures by using the satellite im‐ agery. Although the methodology performs sufficiently, overall, it seems that further refinement is needed in order to improve the approach. The adoption of a single network for all the time series of data seems to limit the application of the methodology. For example, the present single neural network developed for each station does not take into account the large seasonal variations in the parameter concerned. It could be more effective if several neural networks are developed based on seasonally grouped data.

**5. Land surface temperature analysis**

Cyprus during the extreme heat wave of August 2010.

**5.1. MODIS LST temporal evolution and temperature anomaly maps**

thermal bands.

2005; Wan, 2008).

and day Aqua passes.

The MODIS sensor, onboard Terra and Aqua polar satellites, provides one day and one night image under clear sky conditions. MODIS is particularly suitable for the land sur‐ face temperature (LST) product due to its global coverage, radiometric resolution and dy‐ namic ranges for a variety of land cover types and high calibration accuracy in multiple

Satellite and Ground Measurements for Studying the Urban Heat Island Effect in Cyprus

http://dx.doi.org/10.5772/39313

13

MODIS LST product is based on the generalized split-window (GSW) algorithm (Wan & Dozier, 1996) using as input the MODIS thermal bands 31 and 32. The parameters in the MODIS GSW depend on the satellite zenith view angles, column water vapor and also on the low atmosphere boundary temperature. The band emissivities rely on the classificationbased method (Snyder et al., 1998) according to land cover types in the pixel (Monteiro et al., 2007). Temperatures are extracted in Kelvin; accuracy of 1 Kelvin is yielded for materials with known emissivities (Wan, 1999), while a number of studies have also tested the accura‐ cy of the MODIS LST product with favorable results (Wan, 2002; Wan et al., 2004; Coll et al.,

The MODIS Aqua product MYD11A1 (V5) and MODIS Terra product MOD11A1 (V5) – Land Surface Temperature and Emissivity Daily L3 Global 1 km Grid SIN were used. Terra and Aqua overpass times for the study area are considered at approximately 1030 and 1330 UTC for day passes, and at approximately 2230 and 0130 UTC for night passes, respectively. The use of MODIS LST data for examining the temporal evolution and the retrieved temper‐ ature anomaly maps for a heat wave event occurred on 24 June 2007 is presented. Moreover, MODIS LST data are used for calculating the urban heat island (UHI) at four urban areas of

MODIS LST data were initially used for generating mean monthly climatology LST maps for June in the period of 2003-2008. The mean and maximum Aqua day and night LST values for June are presented in Fig. 10 for the period 2003-2008 for two urban areas (Nicosia, Lar‐ naca) and one rural area (Ag. Marina). The curves show that the mean night LST values for the two urban areas are similar, while for the area of Ag. Marina, the temperature levels are 3-4 °C lower. For all sites, a minimum was observed for year 2005. The situation is different though regarding day LST values. The coastal site of Larnaca exhibited the lowest values among the three areas, while Nicosia and Ag. Marina exhibited similar patterns and temper‐

ature levels. The overall trend over time for the three areas showed a positive trend.

The intense heat wave event of 24 June 2007 was next examined in order to study the LST behavior during such events since satellite derived LST is controlled by land cover and topographic effect factors. In Fig.11, temperature anomaly maps, in terms of temper‐ ature deviation from the long-term monthly mean values (calculated for the period 2003-2008), are presented for the heat wave event under consideration and for both night

**Figure 9.** Neural network performance for the Training, Validation, Test and All data sets