Within the examined drylands, SIC, originating from aeolian deposits and soil processes, assumes a more prominent role in total carbon storage than SOC, particularly within subsurface soils. Notably, over 90% of total carbon storage exists in the form of inorganic carbon in soils.Studying total soil carbon (STC), which encompasses organic (SOC) and inorganic carbon (SIC), as well as investigating the influence of soil carbon on other soil properties, is crucial for effective global soil carbon management. This knowledge is invaluable for evaluating carbon sequestration, although its scope is currently limited. Boosting soil carbon sequestration, particularly in arid regions, has direct and indirect implications for achieving over four Sustainable Development Goals: mitigating hunger, extreme poverty, enhancing environmental preservation, and addressing global climate concerns. Research into changes within SOC and SIC across surface and subsurface soils was conducted on aeolian deposits. In this specific case study, two sites sharing similar climates and conditions were chosen as sources of wind-blown sediment parent material. The aim was to discern variations in SOC, SIC, and STC storage in surface and subsurface soils between Sistan and Baluchistan Province (with rapeseed and date orchard cultivation) and Kerman Province (with maize cultivation) in southeastern Iran. The findings highlighted an opposing pattern in SOC and storage concerning soil depth, unlike SIC. The average SOC content was higher in maize cultivation (0.2%) compared to date orchard and rapeseed cultivation (0.11%), attributed to the greater evolution of these arid soils (aridisols) in comparison to the other region (entisols). Conversely, SIC content in the three soil uses demonstrated minimal variation. The mean STC storage was greater in maize cultivation (60.35 Mg ha The southeastern region of Iran is located in an arid region2. The 120-day winds of Sistan are considered one of the most important and well-known climatic phenomena that have a great impact on the morphology and environment of eastern and southeastern Iran during the hot season4. They have various effects on the region. For example, these winds cause dust storms, more evaporation and transpiration, and sand dunes in the region (wind erosion)3. Wind erosion destroys soil nutrients that are less than 2 m high, such as humus, clay, and solutes, and transports them to distant areas in the form of dust storms. Dust storms occur in arid and semiarid regions when strong to violent winds stir up large amounts of soil dust and reduce visibility, affecting human health7. Particles from heavy dust/sand storms typically cover cropland and grassland, causing crop damage and filling rivers and water channels with aeolian material8. In recent decades, the importance of the SOC and SIC sources to the global carbon (C) cycle has been largely elucidated17. Soil carbon is known to be the largest source of C in the terrestrial biosphere, and C changes are one of the most important indicators of the effects of climate on soil formation. Management of soil organic carbon (SOC) requires knowledge of its quantity and effective parameters19. In recent decades, soil and environmental scientists have conducted extensive studies of soil C. However, the study of various aspects of soil-C is needed in global research programs, especially in arid regions that have recently been affected by global warming. More studies should be conducted to address the problems caused by changes in soil carbon balance in different ecosystems.Aeolian sediments are wind-deposited materials consisting mainly of sandy or silty particles. Windblown dust, which consists of carbonates, clays, and salts, has been shown to play an important role in the development of desert soils and associated desert soils in arid regions20. Climate and parent material can introduce a range of C contents into ecosystems21. Depending on human activities, soils can play an important role as a source or sink of C22. In addition, changes in land use and vegetation affect various soil properties, including soil carbon (SOC).This is because the balance of soil carbon in agricultural soils can also affect soil quality improvement, climate change, and crop productivity19. Soil organic carbon is one of the most important components involved in global climate change, which is due to its sensitivity to environmental changes23. Understanding the mechanisms involved in carbon sequestration in soil profiles is critical for assessing regional, continental, and global soil C pools and predicting the consequences of global change24.As mentioned earlier, soil carbon (C) is the largest C pool in the terrestrial biosphere and consists of inorganic and organic components. Therefore, it is important and necessary to determine the changes in SOC and soil inorganic carbon (SIC) in different climatic zones to assess the amount of sequestered carbon25. Improving organic C content (OC) and promoting C sequestration in arable soils could be important not only for food security and soil health, but also for achieving the Paris Climate Agreement global target of less than 1.5 °C26.Knowledge of the spatial variation of SOC is important in drylands as it provides information on soil fertility, water conservation, carbon sequestration, climate change, and the impact of land use practices28. Lithogenic and pedogenic SIC30, also play an important role in carbon storage9. Several studies have not considered IC as a carbon store31, probably due to the longer time required for changes in carbonates compared to the shorter time for SOC33. Most of SIC occurs in arid and semiarid regions and accumulates as carbonate minerals, especially calcite27, but the factors affecting the dynamics of SIC are poorly understood22.In addition to SOC, many soils also contain inorganic C (IC). Soil inorganic carbon stocks (SIC) and their dynamics in arid and semiarid regions covering about one-third of the Earth's surface are very important because the total accumulation rate of SIC is higher than in other biomes16. However, little is known about OC /IC storage in arid climates, particularly in soils formed on aeolian deposits. On the other hand, with the continuous growth of the world's population, more and more cultivated land is being converted into production land, which currently leads to a degradation of about 3334 to 40%35 of the world's soils. For this reason, it is very important to study agricultural soils with different uses from an ecological perspective and with an eye on the future. For this study, we selected stable sites without the effects of climate change, topography, parent material, and time in different land uses (agriculture and horticulture) to reduce the effects of other variables on the relationships between SOC and SIC. We investigated the effects of different land uses on SOC, SIC and STC content, storage, and spatial distribution in soils formed on aeolian deposits in the arid regions of southeastern Iran.Understanding the distribution of OC/IC storage in surface and subsurface soils is critical for assessing regional, continental, and global soil C storage and predicting the consequences of global changeThe study was conducted in two regions of southeastern Iran  and Kerman province (land use: maize cultivation) Fig. .Figure 136 software. The soil moisture and temperature regimes in the studied regions were aridic-hyper thermic ic Table . The stu37, four representative pedons from each soil use  with the most distinctive characteristics of the soil taxonomic units were selected for presentation ) were determined by the Walkley–Black method and the calcimetry method (with increasing reaction time), respectively41. STC content was calculated as the sum of SOC and SIC.18 pedons containing 63 samples from the genetic horizons/layers and 99 surface samples were selected for laboratory analyzes Fig. . After con Table . All ana29:−1), ρi is the bulk density (Mg m−3), Pi is the OC content (g C g−1) in horizon i, Di is the thickness of the horizon (m), and Si is the volume fraction of fragments > 2 mm. Similarly, SIC was calculated using the Eq. (−1), ρi is the bulk density (Mg m−3), Pi is the IC content (g C g−1) in horizon i, Di is the thickness of the horizon (m), and Si is the volume fraction of fragments > 2 mm. The coefficient of 0.12 is the molar fraction of C in CaCO3 to convert the measured carbonates to SIC42. The volume fraction of fragments (Si) was calculated for each horizon using the method of the Soil Survey Staff43.Equation  was usedTo understand the importance of different forms of carbon in deep soils, we also determined SIC, SOC, and STC storage at 0–30, 30–60, and 60–120 cm soil depth based on weighted averages. Descriptive statistical analyzes, including minimum, maximum, mean, and S.E., were performed using SPSS 17.0 software .A factorial design was performed to compare the storage of SOC, SIC, and STC at different depths  in soils with different land uses , with four replicates in homogeneous delineations. Data were analyzed using a mixed linear model and all comparisons were tested using SAS software version 9.4 at a 0.05% significance level.−3, respectively , compared to date orchard (STCs = 54.67 Mg h−1) and maize (STCs = 53.42 Mg h−1). Table Table 44, precipitation was found to positively correlate with SOC levels, whereas temperature showed a negative correlation in Inner Mongolia. This pattern could be explained by the accelerated production of SOC relative to decomposition with higher precipitation, and conversely, faster decomposition compared to production with higher temperatures. As depth increases, the quantity of OC in profiles diminishes, while IC exhibits irregular fluctuations. In the context of sustainable agriculture, emphasis is placed on soil quality and organic matter (OM), with plant residues representing a vital source of soil OM. These residues play a crucial role in mitigating climate change by capturing carbon, thereby offsetting carbon dioxide emissions and other greenhouse gases45. As shown in Table The low levels of SOC and SIC observed in the examined region can be attributed to reduced chemical weathering, biomass growth, and residue production. The arid climate significantly limits biomass generation, leading to the lowest SOC levels within this area. According to Evans et al.46.Plant residues play the most important role in OM production and soil productivity47 showed that maize residues led to an improvement in soil OM such that conservation of residues at the surface (0–10 cm depth) caused a 2% increase at OM and a 1% increase at depth. Heidari48 also reported that corn residues increased SOC content by 25% compared to wheat residues. The effect of cultivation on the next crop in the rotation depends on several factors, such as crop type, length of growing season, soil moisture, plowing method, irrigation method, use of nitrogen fertilizer, and the amount and quality of crop residue returned to the soil49.The results of MirzavandMaize, rapeseed, and dates stand out as some of the most commonly consumed crops for sustenance and deriving nourishment from the soil. The cultivation of these crops in the southern and southeastern regions of Iran takes advantage of the soil's light texture and helps mitigate wind erosion, ultimately resulting in increased food production from the land. However, it's important to note that the deficiency of organic carbon (OC) within the soil may stem from several factors, including excessive utilization of chemical fertilizers, the failure to reintegrate plant residues into the local soil, and even their combustion, all of which contribute to a decrease in soil organic matter (OM).Chemical results show that the amount of calcium carbonate increases with depth in all three regions, while the content of SIC does not show significant changes in the three land uses studied. Signs of secondary calcium carbonate accumulation were not detected in the studied profiles, and wind deposits transferred from other sites cause the presence of IC in the samples.−1) were determined in the rapeseed crop due to poor vegetation cover, few root residues, and lower OC input to the soil. Fast-growing crops such as rapeseed decompose quickly when returned to the soil and therefore have little effect on increasing SOC content50. The results of Lupwayi et al.51 showed that rotation of rapeseed with field crops increased SOC content and soil microbial community activity. On the other hand, the highest amount of SOC (average 3.88 Mg h−1) was associated with date orchard, as it was influenced by proper human management and fertilized with animal manure in recent years to increase fertility and crop yield.For each pedon, the highest OM content was observed in the surface soil horizons (A horizons). Table −1, 50.78 Mg h−1, and 50.76 Mg h−1 for maize, date, and rapeseed, respectively , along with a lower local pH53.The average SIC values displayed variations of 57.19 Mg hly Table . The reg24. Some studies have indicated that soil carbon stocks are greatly underestimated if the amounts stored in the subsurface are not taken into account54. Table Although it is known that subsurface horizons are important for soil carbon storage, most studies have focused on carbon storage in the upper soil horizons13, Wang et al.16, and Jobbágy and Jackson55.In contrast, the results of this work showed that SIC storage in the deeper soils is also very important and the highest percentage of SIC (more than 43%) was observed in the depths of 60–120 cm for all land uses. Also, more than 43% of the total carbon storage (SOC + SIC) in the studied soils occurred in the depths of 60–120 cm. Thus, our results are in agreement with the studies of Raheb et al.−1) compared to date orchards (54.67 Mg h−1) and rapeseed areas (53.42 Mg h−1). Additionally, over 43% of STC storage in the studied regions was located in deep soils (> 60 cm). Among the three land use types, soil inorganic carbon constituted the majority (> 90%) of STC, indicating that in dry and underdeveloped soils, IC predominates, while conditions are less favorable for OC formation.The region has faced formidable challenges in planning agricultural development projects and maintaining crop yields due to recent droughts, scarce water resources, inadequate irrigation efficiency, declining soil fertility, acute scarcity of SOC and nutrients for sustainable production, and the inability to meet water demands for cultivation. Upholding or increasing soil organic matter (OM), particularly in arid areas (due to high temperatures) and sandy soils (due to abundant aeration), remains challenging due to elevated decomposition rates. The influx of wind-driven sediments in recent times has hindered soil development in the study area, rendering soils relatively young and underdeveloped. Low levels of SOC and SIC in the region stem from diminished chemical weathering, biomass growth, and residue production. Consequently, soil formation processes have been limited, resulting in reduced soil carbon content. Unlike SIC contents, SOC stocks exhibited an inverse relationship with increasing soil depth. In the maize-growing zones, the mean STC storage (STCs) was higher . When losses escalate while inputs remain constant, soil OM diminishes. Conversely, when losses stabilize and inputs increase, soil OM rises (see Table


Models trained on inconsistent BB sizes showed a significant decrease of performance when deviating 20% or more from the original when tested on noisy data , or 30% or more when tested on accurate data . In conclusion, accurate predictions need accurate labeled data in the training process. Testing on noisy data may disguise the effects of noisy training data. Researchers should be aware of the relevance of accurately annotated data, especially when testing model performances.Supervised deep learning requires labelled data. On medical images, data is often labelled inconsistently  with varying accuracies. We aimed to assess the impact of such label noise on dental calculus detection on bitewing radiographs. On 2584 bitewings calculus was accurately labeled using bounding boxes (BBs) and artificially increased and decreased stepwise, resulting in 30 consistently and 9 inconsistently noisy datasets. An object detection network (YOLOv5) was trained on each dataset and evaluated on noisy and accurate test data. Training on accurately labeled data yielded an mAP50: 0.77 (SD: 0.01). When trained on consistently too small BBs model performance significantly decreased on accurate and noisy test data. Model performance trained on consistently too large BBs decreased immediately on accurate test data (e.g., 200% BBs: mAP50: 0.24; SD: 0.05;  Object detection is a computer vision technique which seeks to identify and label object instances within images by means of outlining rectangles, also called bounding boxes (BBs). Often, object detection models are trained in a supervised manner, with annotators labeling objects of interest by drawing BBs over them. In natural scenes of images this seems to be a relatively easy task for annotators as humans process such visual data without actively thinking about it. However, object detection has long moved beyond natural image scenes and has become an important component of medical image analysis ,2.Providing labels for medical data remains challenging. Besides datasets being smaller in comparison to open datasets of common objects (like cats or dogs)  experts Noisy labels in supervised learning are challenging researchers and data scientists. Supervised object detection models are trained by iterating through data. During the training process the model is being optimized to minimize the difference between the model predictions and the provided labels. Given noisy labels, the model may learn incorrect features to recognize an object. Different types of label noise have been described, e.g., inter-observer variability (inconsistent labels) and class-independent errors  . The preIn recent years first proposals have been developed to reduce the negative impact of noisy labels in machine learning, e.g., choice of loss function, data weighting or filtering of noisy labels ,5. Even Demonstrating that noise is detrimental would underpin the relevance of accurate, consistent labeling. Automated detection of dental calculus on radiographs has so far not been studied but is relevant in the context of this study as calculus is represented by small irregular objects with oftentimes blurred boundaries, i.e., objects which are hard to label accurately. Bitewings are a type of dental radiograph that is used to visualize the coronal part of the posterior teeth. The main indication for bitewing radiographs is caries diagnosis. Clinically, the automated detection of calculus on radiographs could warrant further clinical examination and trigger certain therapies like professional tooth cleaning or scaling and root planning.Our objective was to assess the impact of label noise on the performance of a state-of-the-art deep learning based object detection model for one particular problem: detection of dental calculus on bitewing radiographs. Our hypothesis was that both consistent and inconsistent noise significantly affects model performance. We further investigate the object detection models with explainable artificial intelligence (XAI) methods to visualize the impact of noisy labels on an exemplary model prediction.This study employed a commonly used deep learning based single-shot object detector: YOLOv5, which demands accurate labels for optimal performance. We employed a dataset of bitewing radiographs labelled for dental calculus by two calibrated experts using optimal (accurate) BBs. To simulate noise we first consistently increased or decreased the BBs sizes to generate consistently too small or too large labels. In a second step we increased and decreased only parts of the dataset, i.e., generated an inconsistently labeled dataset. We then explored the performance of YOLOv5 to detect calculus on these datasets and further employed methods of XAI to assess which image features were particularly relevant for the model’s decision when trained on differently noisy datasets. The waiver for informed consent is approved by ethics committee Charité—Universitätsmedizin Berlin. Reporting of this study follows the checklist for authors for artificial intelligence in dental research .Our dataset contained 4837 bitewings collected during routine care at a public university clinic in Berlin, Germany with radiographic machines from Dürr Dental SE  and Sirona Densply Inc. . The prevalence of calculus on image level was 36.1%. Bitewings without calculus were excluded, resulting in 1746 included images from a German subpopulation with a mean  age of 38.5 years , 51.0% males and 49.0% females. Two calibrated dentists, experienced in image analysis performed the labeling process. In the first cycle one expert labeled dental calculus using BBs with the aim of achieving the most accurate label . A second expert checked all images in a second pass and controlled them once more resulting in the “accurate” base-case dataset. A comprehensive sample of available bitewings was used.α. For this purpose, the center of the BBs was kept and the height and width were each multiplied by the square root of α. These labels will be referred to as manipulated labels below:α values: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 . For the first third we kept the original BBs to represent an accurate labeler. For the second and third parts we increased and decreased the BBs respectively, to simulate too small and too large labeling. The manipulation of the labels was performed as described above with different deviations of too large and too small BBs. The deviation δ from the original annotation of the BB area was systematically increased from 0.1 to 0.9 in 0.1 steps. A δ of 0.1 means an α of 1.1 for manipulating the third of the too large labeler and an α of 0.9 for manipulating the third of the too small labeler, etc.Noise was introduced to the overall dataset , while for the evaluation (see below) performance testing was performed on both noisy test data  and accurate (non-noisy) test data to evaluate the “true” effect of noise, which may be disguised by testing on noisy data but also to gauge if only paying specific attention to labeling the test dataset would be an option.Data preprocessing and manipulation was done with Pythons pandas library version 1.4.1 .In this study the state-of-the-art object detection model architecture “You Only Look Once” version 5 (YOLOv5) was employed . YOLOv5 Each model was trained for up to 300 epochs, referring to a complete pass through the entire training dataset. The training process was stopped after 100 epochs without improvement on validation data (early stopping). Mosaic and right-left-flip data augmentation, techniques to increase the dataset with image modifications to build a more robust model, were applied. The model was optimized using stochastic gradient descent, an optimization algorithm to minimize the difference between the model predictions and the true labels (loss function). The number of images used in each iteration (batch size) was set to 16, while the step size to updates the model parameters (learning rate) was set to 0.01. Training was performed with an image resolution of 640 × 640. Five-fold cross validation was performed. Data was randomly split into separate training, validation and test sets of 60%, 20% and 20% respectively. For each fold the model for evaluation on the test set was selected based on the epoch with best performance on the validation set. All computations were performed on Nvidia A100 40 GB GPU.IoU) threshold set to 50% (mAP50). IoU describes the overlap of the predicted BB (pBB) with the ground truth BB (gBB) in relation to the total area of unified BBs:Performance was evaluated using mean average precision with the intersection over union (IoU being higher than the given threshold the object is counted as correctly detected (true positive—tp) or not . Average precision (AP) is the weighted mean of Precision in the Precision-Recall-Curve—calculating Precision and Recall (sensitivity) with different model confidence thresholds.With the P) describes what proportion of the detected calculus is truly dental calculus:Precision (R), in medical domain better known as sensitivity, describes how many of all existing concretions are detected:fn are false negative/not detected BB. Mean average precision (mAP) is the mean of AP over all classes:Recall  based on Shapley Values was applied . ShapleyThe model trained and tested on the base-case (accurate) dataset resulted in a mean  mAP50 of 0.77 . The base-case model is used as reference model and all models trained on noisy labels were compared against it (blue dashed line in p < 0.05/Mann-Whitney). In contrast, increasing BB size up to α = 60 did not lead to significant changes in mAP50 (p = 0.15); moderate increase  even led to a significant improvement (p = 0.02). However, if tested on accurate test data (green graph in p = 0.008 for both). Performance of models trained on consistent noisy labels were listed in Models trained on consistently smaller BBs showed significantly and escalatingly decreased performance when tested on noisy test data (orange graph in p = 0.008). When the model was tested on accurate data (green graph in p = 0.03), mean  mAP50 of 0.76 . Performance of models trained on inconsistent noisy labels were listed in When inconsistently noisy data (to simulate different annotators) was used, increasing noise had escalating detrimental effects when tested on inconsistently noisy data .Our findings need to be discussed in detail. If the model was both trained and tested on consistently noisy data its performance remained moderately high for most datasets, indicating that inaccurate labeling and its impact on performance may be disguised if testing is performed on the resulting noisy data too. This was particularly true for larger BBs: When tested on noisy data the effect of enlarged BBs during training was absent for considerable time, even for massively increased sizes (α = 100) played a subordinate role for the prediction, likely as the standardization of the image  allowed the model to learn the background and consequently ignore it successfully even if BBs were too large. The illustration of the predictions further show that the object  remained center of the predicted BBs. This reinforces the assumption that the model has learned the annotation error (consistently too large BBs). This phenomenon might be further explained by the learning process of neural networks: the model is optimized to predict BBs as close to the ground truth as possible. To increase the performance the objective thus becomes: (1) identification of the object of interest ; (2) fit BB with dimensions to maximize area overlap with ground truth .Evaluating the models with XAI methods as shown in IoU threshold not being reached. When tested on accurate data, the detrimental effect of noise on training success was demonstrated; researchers should pay special attention to consistent and accurate labeling of their test set to allow providing reliable information about the true accuracy of their model. This aspect of the results should be emphasized, since other studies dealing with noisy data tested their models solely on accurate data [In contrast, if the BB were too small, we observed poor performance regardless of the chosen test (noisy or accurate). It can be assumed that given that calculus is already a very small object, models trained on too small BBs simply did not receive enough information to allow learning the features of calculus. Furthermore, with small objects, a small error already leads to the required ate data ,9,12. TeIn contrast, testing on noisy data may even lead to false conclusions, for example if a model was trained on accurate data and the (correctly) predicted BBs would not fit to the provided test data, leading to low performance metrics. A similar effect was demonstrated when dealing with inconsistent noise: Testing on accurate data showed no significant performance deterioration while δ ≤ 0.3, indicating that the model was capable of learning from inconsistent noisy labels. However, testing on noisy labels disguises this capability and already suggests a decrease in model performance when δ ≥ 0.2.As discussed, several methods have been proposed to handle label noise through technical methods: a different loss function, measuring the difference between the model predictions and the desired output showed promising results when the underlying data set for a neural network contained noisy labels ,20,21. HThis study has a number of limitations. First, our goal was not to develop the best model for our specific problem; we did not aim to optimize performance by employing, for example, hyperparameter tuning but to understand the impact of noise in principle. Secondly, our trained models were not tested for generalizability while being developed using data from one German subpopulation only. Again, we accept this caveat in the context of our studys aims. Third, we assessed the impact of noise for one particular task, detecting calculus on bitewings. The effects on other modeling tasks like segmentation  or other clinical problems  or images  may differ; we hence cannot claim transferability of our findings. Similarly only one model—YOLOv5—was employed; the effect of noise on other models may differ to some degree, as shown on histopathological images for cell segmentation .Accurate predictions need accurate labeled data in the training process. Testing on noisy data may disguise the effects of noisy training data. Modelers should be aware of the relevance of accurately annotated data, especially when testing model performance, and users should scrutinize models accordingly for labeling quality.


Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae), is a destructive pest that affects protected cultivation worldwide. The Indian Himalayan region is particularly vulnerable to GWF introduction, invasion, and spread due to the expansion of protected cultivation and climate change. In this study, we collected 32 naturally occurring GWF populations, mainly from the Uttarakhand state in the Indian Himalayan region, to investigate the distribution pattern and genetic diversity of T. vaporariorum. Our sampling was representative of the region's vegetation diversity and geographical location, and we collected samples from multiple sites within each locality to account for local variations. The mtCOI gene was used to accurately detect and identify GWF and to sequence haplotypes prevalent in the Uttarakhand state. The maximum likelihood method used for phylogenetic studies revealed that all 32 whitefly samples in this study belonged to T. vaporariorum and were prevalent in all the collected localities. Our population genetic study using mtCOI showed variation within T. vaporariorum populations, with 20 distinct haplotypes present. Notably, haplotype 2 (H2) was the most dominant haplotype among the sampled populations. These results provide fundamental knowledge for understanding the geographical distribution and ecology of T. vaporariorum in the Uttarakhand state of the Indian Himalayan region. The discovery of geospatial and genetic diversity of GWF in the Himalayan region underscores the importance of pest alertness, research prioritization, and the development of sustainable management strategies to protect crops.The Greenhouse whitefly (GWF),  Unfortunately, the Indian Himalaya is vulnerable to climate change, which poses a risk of species emergence, outbreak, and extinction, and threatens food security in the region2. Protected agriculture, which creates favorable microclimates to protect crops from biotic and abiotic stresses, has emerged as a new paradigm to improve food security, income, and crop protection in the region3. Polyhouse structures have been used extensively for commercial cultivation of high-value and off-season vegetables in the Himalayas, but the microclimate under these structures can favor the growth and development of pests, diseases, and soil-borne nematodes, which can threaten crop productivity6.The Indian Himalaya is a biodiverse region, hosting 30,377 species of protozoa and animalia, representing about 30.16% of the total Indian fauna, with arthropods contributing 26,393 speciesTrialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae) is a destructive pest problem for protected cultivation globally, infesting over 800 species of plants8. The wide host range comprises tomato, egg-plant, pepper, cucumber, chrysanthemum and other agriculturally important crops.Greenhouse whitefly (GWF), 7. These molds affect photosynthesis in leaves, leading to significant economic loss to the crops9.Due to its polyphagous nature and potential to act as a virus vector, GWF has become a major scourge for protected cultivation practicing farmers. Adult and nymph of the GWF reduce plant vigor by sucking the sap from foliage, flower, seeds, secreting honey dew, which attracts the development of soot molds fungi10 and successively has been reported in other parts of the country14. The increase in area under protected cultivation and climate change in the Uttarakhand states of Indian Himalayan region are critical factors in vulnerability to introduction, invasion, and emergence of GWF in new areas. Therefore, updating the status of GWF, mapping the geospatial distribution pattern, and intraspecific genetic diversity of the burgeoning population is obligatory to ensure timely mitigation of insect pests through suitable pest management operations. Additionally, keeping an eye on GWF springing into new regions is key in sending alertness to the farming community, safeguarding crops, and ensuring maximum returns to farmers. However, the geospatial distribution and genetic diversity for GWF is unknown from the Uttarakhand states of Indian Himalayan region. Moreover, identification of genetic variation in populations of GWF is a key objective to formulate any management plan and pest management decision making.From India, GWF was first recorded from Nilgiri hills of Tamilnadu, southern state of India17. DNA barcoding-based species identification utilizing mitochondrial cytochrome C oxidase subunit I (mtCOI) offers rapid, reliable detection and study of genetic variation among GWF18. Therefore, in this study, we aimed to use mtCOI gene markers to unravel the distribution pattern and genetic diversity among GWF (T. vaporariorum) for monitoring, tackling the pestilence to protect the protected cultivations.Traditional identification of GWF is based on morphological traits, which has several drawbacks, including reliance on expert taxonomists, time-consuming, low resolution in presence of conspecific character, presence of cryptic population, and ergonomic issues20, no populations and genetic diversity study has been conducted for Uttarakhand states, where protected cultivation is an important agricultural enterprise on a large scale. Therefore, we conducted extensive geographical sampling of GWF from protected cultivations, collecting 32 naturally occurring GWF populations representing Uttarakhand states of India. We aimed to identify the mtCOI gene sequence variation-based haplotypes most commonly representing in the sampled geographical region to formulate any management plan and pest management decision making.Despite a few genetic diversity and distribution studies on GWF from IndiaT. vaporariorum whitefly samples were collected from commercial host crops including tomato, capsicum, french bean, brinjal, cauliflower, snake guard, okra, bottle guard, garden pea and ornamentals like salvia, marigold. These crops provided a significant food source and habitat for the greenhouse whitefly (GWF) population in the study area. These crops were widely grown in open and protected structures and were heavily infested by GWF. While the chosen host crops may not be exhaustive in terms of their contribution to the genetic variation of GWF, they were important sources for the pest species. The extensive geographical sampling of GWF populations from protected cultivations conducted in this study aimed to capture a wide range of genetic diversity and distribution patterns among GWF in the Indian Himalayan region. The combination of the chosen host crops and the extensive geographical sampling approach was expected to provide a comprehensive understanding of the genetic diversity and distribution patterns of GWF in the study area.A total of 32 mtCOI gene were successfully amplified and sequenced for molecular identification and phylogeny estimation. The identity of T. vaporariorum in collected samples was supported by morphological and molecular diagnostic technique. The result suggests the frequent occurrence of T. vaporariorum in the surveyed locality including open and protected structures on range of host crops. The surveys were conducted for the first time in few districts like Chamoli, Dehradun, Nainital and Pithoragarh of Uttarakhand state and greenhouse whitefly infestation was recorded for the first time in commercial crops like Tomato, Capsicum, French bean, Brinjal and Cauliflower.Most of the samples were collected from Uttarakhand and three samples from Himachal Pradesh, one each from Delhi and Tripura. The partial mtCOI gene sequences of collected individuals have been submitted in GenBank database citing the information of locality, host range, geospatial data etc. and the accession numbers have been received and summarized in Table T. vaporariorum accessions viz., AY521265, NC006280, LN614547 to exploit as ingroups whereas the Genbank accessions viz., KU761949 Aleurocanthus camelliae, NC029155 Aleurocanthus spiniferus, AY572538 Aleurochiton aceris, NC005939 Aleurodicus dugesii, MT880225 Aleyrodes shizuokensis, KR819174 Bemisia afer, MH205754 Bemisia tabaci, AY572539 Neomaskellia andropogonis, NC006292 Tetraleurodes acacia were used as outgroups. Phylogenetic studies based on maximum likelihood method reveals that all the 32 whitefly samples from the study belonged to T. vaporariorum and it is found prevalent in all the collected localities . The number of haplotypes, total number of variable sites, and haplotype diversity, and nucleotide diversity, average number of nucleotide differences, G+C content and total number of mutations were represented in Table The variations within the haplotypes of T. vaporariorum belongs to India were represented in Table Nucleotide diversity of the Uttarakhand observed to be 0.00346 whereas in Himachal Pradesh it was 0.00233 and the group-others (one sequence each of New Delhi and Tripura) was 0.00349. In the same way, haplotype diversity observed higher in others 1.000) followed by Uttarakhand (0.843) and Himachal Pradesh (0.667) , is a highly destructive pest of cold climate greenhouses21. It has been reported to infest over 102 herbaceous plants belonging to 36 families across the world22. The infestation of greenhouse whitefly in the Indian Himalayas was first reported in the 1990s; however, its impact on commercial cultivation of vegetable crops and ornamental plants under protected and open field conditions was only reported after 2000s23. Currently, T. vaporariorum is classified as one of the key pests of tomato, cucumber, capsicum, french bean, gerbera, salvia and gladiolus under protected environment in the Indian Himalayan region6. During our surveys, we observed mixed populations of T. vaporariorum and B. tabaci in open field conditions; however, T. vaporariorum was the predominant species under protected cultivation systems in the Alpine and Humid subtropical climatic zones of the northwestern Indian Himalayas in the states of Uttarakhand and Himachal Pradesh. We also found that T. vaporariorum infestation was consistently recorded on all ornamental plants in high elevation locations, such as the Valley of Flowers (Chamoli), with an average pest infestation rate of 15–30%. Given the potential economic impact of T. vaporariorum infestations on various crops, it is crucial to develop effective management strategies for this pest. Integrated pest management approaches that combine cultural, physical, biological, and chemical control methods have been found to be effective in controlling T. vaporariorum infestations24. In addition, the development of resistant plant varieties could also serve as an effective long-term management strategy25. It is also important to note that T. vaporariorum infestations have been reported in other regions with similar climatic conditions, such as parts of Europe and the United States27. Overall, our findings highlight the need for continued monitoring and research on T. vaporariorum infestations in the Uttarakhand state of Indian Himalayas and other regions with similar climatic conditions.The polyphagous whitefly species, T. vaporariorum populations. Whitefly survey using aspirator tools are widely used tools to collect whitefly for phylogeography study28. The molecular diagnostic technique deployed was convenient to identify whitefly populations and expanding our knowledge of T. vaporariorum occurrence and diversity in Uttarakhand state.In the present study, the Uttarakhand state located in Indian Himalayan region of India, was surveyed to detect diversity of 29, biotypes30, cryptic species31 and haplotypes32. The presence of T. vaporariorum was recorded most frequently from all the surveyed plant host and respective locations. We have detected T. vaporariorum from all the regions ranging from the 300 AMSL (Pantnagar) to the highest altitude 2400 AMSL (Chamoli) of survey. In our study, populations of T. vaporariorum were recorded for the first time from higher altitudes having temperate climate including Almora, Nainital, Bageshwar and Chamoli districts. The finding of T. vaporariorum in all the thirty-two locations, shows the frequent occurrence (100%) and prevalent of GWF in Uttarakhand state compare to the survey in Karnataka, Tamil Nadu and Andhra Pradesh states of India19, Costa Rica33. This study suggests that T. vaporariorum have successfully colonized and spread in the surveyed region of Uttarakhand irrespective of sampled crop in polyhouses as well open fields. In the Indian Himalayan region, incidence of T. vaporariorum have been recorded long back from protected crops of Himachal Pradesh23. The presence of T. vaporariorum may have harmful impact on yield of protected crops grown in the regions due to agility in adaptiveness, colonization in newer habitats and development of insecticidal resistance35.Molecular diversity techniques have been proven useful in unravelling diverse insect pest of agricultural importance including new speciesmtCOI gene sequence amplification found useful for the detection of whitefly species. mtCOI gene have been used as potential marker in the past for discrimination of white fly species complex, evolutionary and genetic diversity study with accuracy and reliability38. The phylogenetic tree reconstructed by retrieving and aligning mtCOI sequences with the expansion of mtCOI supports the relatedness and validity of the molecular identification of collected T. vaporariorum.The mtCOI suggest the high variation with presence of 20 haplotype in the T. vaporariorum populations. Most of the haplotype differs with single nucleotide mutations with their corresponding haplotypes except the haplotype number 1, 13, 15, 16. Haplotype 2 represents the primitive population and most frequent among all haplotypes. This suggests that the most of the populations are descendants of Haplotype 2 populations. Most of the populations are recently introduced in the area and their dispersal agents haven’t had time to disperse. Since these populations are recently introduced, the decision on regular monitoring of these areas may be recommended before the population reaches economic injury level. Presence of twenty haplotype suggests the high diversification among all the haplotypes. However, mtCOI based phylogeny showed the close relation. The obtained results are in consensus with the results of Wainaina et al. and Barboza et al. who reported sampled sequences grouped in monophyletic clade39. Similarly, Despoina et al., 2015 have shown limited sequence divergence among T. vaporariorum populations37. Hence, low level of genetic variation may exist in mtCOI sequences around the world for T. vaporariorum.Population genetic study using T. vaporariorum in Uttarakhand. The host factor and ecological adaptability may be the reason for high abundance in different area33. However, the effect of several other factors such as altitudinal variation, temperature effect, crop cycle, rainfall pattern, wind directions, wind speed, dispersal through planting material and existence of natural population could be explored for future studies to get real scenario and cause of distribution. We have revealed here, the wide distribution pattern of T. vaporariorum on different host at different altitudes exhibiting uniformity in genetic level. The detection of the whitefly population in Uttarakhand state areas may help us to extend surveillance, monitoring and decision making about T. vaporariorum management initiative in the identified areas. Additionally, the present study attracts special attention to study the associated viruses being vectored by T. vaporariorum on different host plants.The result of this study gives fundamental knowledge on understanding of geographical distribution and ecology of Trialeurodes vaporariorum (Westwood) is a serious concern for farming community. The spread of GWF into new area may cause mayhem to the crops. Therefore, regular survey, identification, documentation on geospatial distribution of GWF is urgent need. Present study, suggest the frequent occurrence of GWF from Indian Himalayan states. Population genetic study using mtCOI suggest the high variation with presence of 20 haplotype in the T. vaporariorum populations, where haplotype 2 (H2) was found most dominant. The obtained result draws attention of researchers, policy makers and farmers for pest alertness, decision making research prioritization, development and adoption of sustainable management strategies of GWF to safeguard the crops.Global distribution, polyphagous nature and virus vector potential of Greenhouse whitefly (GWF) A total of 32 greenhouse whitefly samples were collected from Uttarakhand, Himachal Pradesh, New Delhi and Tripura states during 2019–2021 from both protected cultivation systems as well open field conditions Table . Both Th40. Each individual whitefly was properly rinsed in sterile distilled water, crushed and homogenized using a hand-held homogenizer (Sigma Aldrich). 120 μl of CTAB solution  to extract the genomic DNA and ice-cold isopropyl alcohol was used to precipitate the DNA at − 20 °C for 30 min. The DNA pellet obtained after centrifugation at 10,000 rpm for 7 min was washed with 70% chilled ethyl alcohol to remove the excess salts and was suspended in 20 μl of TE buffer. DNase-free RNase A treatment was followed for 20 min at 37 °C to remove the RNA residues. Electrophoresis with 0.8% agarose gel was carried out to visualize intact genomic DNA. Furthermore, the DNA concentration were quantified in Nanodrop (NanoDrop™ 2000/2000c) subsequently used as template for PCR amplification. The segments of mtCOI gene were amplified using pair of universal primers (COX1: LC01490- “5′-GGTCAACAAATCATAAAGATA-3′” and HC02198 “5′-TAAACTTCAGGGTGACCAAAAAA-3′”) with 710 bp product41. The PCR reaction programme was set using 25 µl reaction mixtures including 12.5 µl of ready to use PCR master mix (Promega M750A), 5.5 µl of nuclease free water, 1 µl each of forward and reverse primer and 5 µl of DNA template. The thermo-cycler programme was run with the following cycles; 94 °C for 5 min as initial denaturation followed by 35 cycles of denaturation at 94 °C for 45 s, annealing at 52 °C for 45 s, extension at 72 °C for 45 s and the final extension step for 10 min at 72 °C. The presence of amplified PCR product was visualized and confirmed in the gel documentation system  by 1.2% agarose-EtBr 10 mg/ml gel electrophoresis with 2.5 μl PCR product. Gel elution columns (Sigma) were used for purification of the amplified products of the target gene. The purified products were sequenced directly by an automated DNA sequencer (ABI 377) following manufacturers guidelines for the Big Dye terminator kit (Applied Biosystems).The standard methodology of CTAB as described by Subbanna et al. was followed for extraction of genomic DNA from an individual whitefly specimenmtCOI gene sequence were assembled using Clustal Omega (1.2.2) multiple sequence alignment programme42. The sequences were exported to NCBI and BLASTn similarity search performed with default parameters. All the available whitefly sequences from databases were retrieved and aligned with ClustalW and bases were edited in BioEdit v7.2.5. Phylogeny was estimated with Mega X44 following Kimura 2-parameter model and the maximum likelihood approach. To test the phylogenies, a bootstrap replication of 1000 was performed45. Genbank accessions Bemicia tabaci AY057182.1 was included as outgroups. All the generated sequences from this study have been submitted to the NCBI GenBank with respective accession numbers  are shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. This analysis involved 50 nucleotide sequences. Codon positions included were 1st + 2nd + 3rd + Noncoding. There was a total of 883 positions in the final dataset. Evolutionary analyses were conducted in MEGA X45.The evolutionary history was inferred by using the Maximum Likelihood method and Tamura-Nei modelT. vaporariorum is not an endangered species that threatens biodiversity. So, no permits were required for collecting it from the field.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Results: No correlations were found between age, gender, previous extraction, tooth site (maxillar on mandible), pain score, and pain area. Patterns of third molar pericoronitis pain among 86 patients were reported. A significant correlation was found between pain score and pain area . Conclusions: Pain intensity has indeed some kind of responsibility in determining the orofacial distribution of pain. The pain area referral patterns of the present article could be considered as a pain model resulting from the pericoronitis of maxillar and mandibular third molars.Background: Mandibular third molar (M3M) removal and the management of postoperative complications represent a common matter of interest in oral and maxillofacial surgery. Pain represents a great symptom for patients affected by pericoronitis and it is the most common indication for third molar removal. The aim of the present article is to search for patterns of pre-operative pain in patients before undergoing third molar surgery and to test for a relation between some patterns of symptoms, such as pain intensity, site of symptomatic tooth, and referred area of pain. Methods: This retrospective observational study enrolled a total of 86 patients, aged (mean ± SD) 34.54 ± 13.62 years (range 17–78 years), scheduled for outpatient third molar extraction at the Oral Surgery School, Department of Medical Biotechnologies, Policlinico “Le Scotte”, University of Siena. Pericoronitis and pain were the symptoms of the patients and the indication of extraction. Inclusion criteria were the presence of partially impacted third molars, confirmed with a preoperative panoramic radiograph, and preoperative pain. Exclusion criteria were known neurological disease , impaired communicative or cognitive disease, diagnosed diabetes mellitus, and oral surgical intervention within 30 days before data collection. Patients were visited and asked to answer a morphometric analytic questionnaire about their perception of pain referred to the third molar. Analyses were performed on statistical evaluation on age, age ranges, patient gender, prior third molar extraction, site of pericoronitis, pain score (1–10), and pain area. Two-tailed  Third molar removal is one of the most common interventions in oral and maxillofacial surgery. The most frequent pathology of the third molar is dysodontiasis and pericoronitis; dysodontiasis regards alterations related to the tooth inclusion, the lack of dragging competence of the periodontal ligament, and probably an eruptive deficit during dental root development . This paPericoronitis is a typical inflammatory pathology of the impacted or partially impacted third molar that influences the quality of life of the patients before the extraction of the tooth, more commonly in the 20s and 30s [The common symptoms of third molar pathology are pain, swelling, and trismus; a characteristic of this pathology is the difficulty for the patient to refer to the upper or lower tooth, as trigeminal innervation often confuses the patient due to the maxillary and mandibular branches . The impAs important as the pre-operative symptoms of the third molar, the post-operative conditions after third molar surgery raise relevant issues; several studies have underlined the correlation of many factors  with oral disability and severe pain after third molar surgery ,6,7,8. FApart from correlations with postoperative complications, the entity and facial distribution of preoperative pain due to third molar pericoronitis, in our opinion, has often been underestimated in the international literature. We hypothesize that this condition may be due to the fact that third-molar-referred preoperative pain has not been studied in detail, and a better definition could lead to a more confident diagnosis and a better understanding of the pathology.With regard to pericoronitis pathology, Caymaz and Buhara in their paper reported evidence of a positive association between the amount of dental plaque and third molar pericoronitis; therefore, they encouraged the improvement of oral hygiene and control of dental plaque in order to prevent third molar pericoronitis . The intPericoronitis appears to be a bacterial infection that commonly evokes the most common pain in third molar impaction; however, a recent article reported the presence of viral infection in pericoronitis . Even ifThe implementation of clinically relevant preoperative pain studies in patients with symptomatic third molars has been advocated over the past years: Rudin et al. in their paper on 38 consecutive patients reported with a multiple regression model that the combination of psychological vulnerability and heat pain perception rendered a predictive model that could account for 15 to 30% of the variance in postoperative pain during resting and dynamic conditions . Yuasa aIn order to lead to a better comprehension of pain derived from third molar pericoronitis, the authors of the present article conducted a retrospective observational study with a morphometrical, analytical evaluation of preoperative pain on 86 patients with symptomatic third molars. The purpose of this research was to evaluate pain intensity as a function of symptomatic tooth site (maxillar or mandibular) and to test the possible relationship between pain intensity and the referred area of pain.The study had an observational retrospective design. This study enrolled subjects previously visited and scheduled for outpatient third molar surgery at the Oral Surgery School, Dentistry and Dental Prosthodontics, Department of Medical Biotechnologies, University of Siena. All participants signed an informed consent agreement. For all cases, acute pericoronitis was the indication for surgery.Inclusion criteria were the presence of partially impacted third molars, confirmed with a preoperative panoramic radiograph, and preoperative pain.Exclusion criteria were known neurological disease , impaired communicative or cognitive disease, diagnosed diabetes mellitus, and oral surgical intervention within 30 days before data collection. The presence of a previous extraction was evaluated if in the past.Patients were referred to the oral surgery service, Policlinico “Le Scotte”, Siena for third molar pain and requested clinical evaluation. All patients provided a preoperative panoramic radiograph for diagnosis of an impacted third molar and pericoronitis. At the preoperative evaluation, patients were asked to answer a questionnaire about their perception of pain referred to the third molar. The morphometric analytic questionnaire (MAQ) we realized consisted of 5 parts. The first part of the MAQ concerned the possibility that the patient already underwent third molar surgery, as we thought that previous pain sensitization could influence the modulation of pre-operative pain. The second part concerned the third-molar-referred pain as perceived by the patient on a subjective scale from 1 to 10. The third part requested the patient to display over two graphs depicted with a standard face   the percAll patients received an explanation of their third molar pathology and symptoms after the clinical evaluation, with referral of the maxillary or mandibular third molar. This explanation and clinical evaluation were performed before the filling and compilation of the questionnaire, in order to reduce possible influences on the patient.Analyses were performed on statistical evaluations regarding patient gender; patient age, both linear and by age range; prior third molar extraction; and site(s) of pericoronitis (maxillar and/or mandible).As it concerns pain parameters, the evaluated items were pain score (1–10) and pain area  .The observational retrospective design did not require the approval of an ethics committee, as per Italian legislation on clinical investigations at the time of the study. Nevertheless, the investigation was carried out following the rules of the Declaration of Helsinki of 1975, revised in 2013, and performed according to the principles of the ICH Good Clinical Practice.t test , Mann–Whitney rank sum test , chi-square statistics  of Fisher’s exact test , one-way analysis of variance (ANOVA), Student–Newman–Keuls post hoc test, or Kruskal–Wallis test. Associations between variables were tested by univariate regression analysis, and two-tailed p values of less than 0.05 were considered significant if not otherwise specified. The MedCalc version 11.3.0.0 statistical software package  was used.All variables were tested for normal distributions (D’Agostino–Pearson test) and data were presented as means with 95% confidence intervals (95% C.I.) for normally distributed variables or median means with 95% C.I. for non-normally distributed data. Differences were evaluated using the independent-sample All the 86 patients completely filled the questionnaire. A summary of anagraphical data of the patient population is reported in Between January and October 2019, a total of 86 patients, aged (mean ± SD) 34.54 ± 13.62 years (range 17–78 years), were included in this study: 36 patients were male, aged (mean ± SD) 37.38 ± 14.73 years (range 19–78 years); 50 patients were female, aged (mean ± SD) 32.50 ± 12.51 years (range 17–68 years).The mean pain score was (mean ± SD) 5.9 ± 2.5, with a mean pain area of 9.9 ± 14.4 (range 2–112). Male patients referred a pain score of 5.9 ± 2.5, with a mean pain area of 12 ± 20.6 (range 2–112). Female patients referred a pain score of 5.9 ± 2.5, with a mean pain area of 8.3 ± 6.6 (range 2–28). No correlations were found between age and age range with pain area. However, greater values were reported in the 16–20 age range, with reduced values for the greater ranges . Age andp = 0.0111, rs = 0.3131, A significant correlation was found between pain score and pain area  seeking third molar extraction due to orofacial pain . As compIn this study, no correlations were found between age, gender, previous extraction, and tooth site, while the results suggested a strong influence of pain score on orofacial pain distribution (pain area). This influence of high pain that leads to a diffuse orofacial distribution of pain could let the authors speculate on possible influences of postoperative recovery after third molar surgery ,4,5,6,7.The main limits of this paper are the exclusive study of preoperative pain, without examination of postoperative recovery. Further, even if 86 patients represent an interesting number for statistics, the age and patient gender were not completely equally matched; this limit especially was felt in the age range evaluations of pain area pain score, where in some ranges, the distribution of patient gender was not homogeneous. Further, the greater presence of the mandibular third molar over the maxillary third molar impacted the study in some ways. Diabetes mellitus was reported among the exclusion criteria from this retrospective study. Diabetes mellitus is an endocrine disease with documented evidence of interference in nerve sensitivity and transmission that takes the name of diabetic neuropathy . As far On the basis of this research, the authors suggest different characteristics of preoperative pain to investigate. Future prospectives of third molar pericoronitis pain research should begin with the examination of different possible pathologies of the follicular sac surrounding unerupted third molars. We advocate the use of the histological study of the follicular sac and pericoronitis symptoms with evaluation of the state of pericoronitis. The study of these preoperative conditions could be correlated with postoperative conditions, such as postoperative non-pain complications, swelling, and infection. For this purpose, a recent study underlined a different bacterial retention on sutures after third molar surgery, and we advocate studies of pericoronitis and postoperative bacterial retention .Further, the study of preoperative pain versus no pain in third molar impaction should be investigated in order to relate with postoperative pain; this information could be useful for the clinician in order to counsel properly patients with regard to oral disability after tooth extraction .The present research study investigated and reported the intensity, distribution, and correlations between variables of preoperative-third-molar-related pain. The results of the present article allow the authors to link the third molar preoperative pain and the third molar orofacial distribution of preoperative pain by a direct correlation.


Within the plots, we sampled all understory woody plants and measured three metrics of physiological leaf drought tolerance—turgor loss point (TLP), cuticular conductance (Gmin), and solute leakage (SL)—and assessed how the community-level means of these three traits related to the mean annual precipitation (MAP) and elevation . We did not find any correlations between the three metrics of leaf drought tolerance, suggesting that they represent independent strategies for coping with a low water availability. Despite being widely used metrics of leaf drought tolerance, neither the TLP nor Gmin showed any significant relationships with elevation or the MAP. In contrast, SL, which has only recently been developed for use in ecological field studies, increased significantly at higher precipitations and at lower elevations . Our results illustrate that differences in water availability may affect the physiology of tropical montane plants and thus play a strong role in structuring plant communities even in the super humid tropics. Our results also highlight the potential for SL assays to be efficient and effective tools for measuring drought tolerances in the field.Little is known about how differences in water availability within the “super humid” tropics can influence the physiology of understory plant species and the composition of understory plant communities. We investigated the variation in the physiological drought tolerances of hundreds of understory plants in dozens of plant communities across an extreme elevation and precipitation gradient. Specifically, we established 58 understory plots along a gradient of 400–3600 m asl elevation and 1000–6000 mm yr Elevational and environmental gradients can be powerful tools for investigating the importance of habitat filtering and community assembly processes. Indeed, the potential value of environmental gradients in demonstrating how different factors drive patterns of plant diversity and vegetation structure has long been recognized ,2, and pStudies of elevational gradients can also provide important insights into how tropical species, communities, and ecosystems are responding to ongoing anthropogenic climate change ,10,11,12The importance of changes in water availability through time for driving shifts in species’ distributions is widely acknowledged in many temperate ecosystems ,24,25,26Rainfall is commonly used as a proxy for water availability. Soil water deficit can occur when evapotranspiration exceeds the seasonal or annual precipitation; as a result, the soil moisture is depleted, and many water-dependent physiological processes in plants are negatively impacted . The airSince plant water availability is a complex phenomenon involving the interplay between multiple climate variables, soil and environmental factors, and intrinsic species traits related to water uptake and use, the characterization of water availability is nearly impossible for diverse species assemblages spread across large environmental gradients. Water availability is generally believed to decrease at high elevations in the tropics due to reduced rainfall, increased rainfall seasonality, and thinner soil organic material ,38; howeAs the water availability decreases, plant hydraulic conductance may decrease through different biophysical and physiological mechanisms to prevent embolism or desiccation . In thismin) is the conductance to vapor diffusion across the leaf epidermis  once the stomata are closed [min are generally considered to be more drought tolerant than plants with a high Gmin .Responses at the cellular level may also prevent damage to plants during droughts ,52,53. Tmin, and SL in hundreds of individual understory saplings and shrubs and assessed the community-level metrics of leaf drought tolerance in approximately 60 plots located along a 3000+ m elevation and a 5000 mm yr−1 rainfall gradient from lowland rainforests to highland gallery forests and high-elevation shrubs. We tested the relationships of these community drought tolerance metrics with elevation  and the mean annual precipitation. Specifically, we addressed the two following questions and corresponding a priori hypotheses: (1) Are the three metrics of drought tolerance coordinated or independent of each other? We hypothesized that the TLP, Gmin, and SL would all be correlated at the individual plant and plot levels due to a coupling of leaf-level mechanisms to resist and tolerate drought [In this study, we measured the leaf TLP, G drought . (2) How−1 at low elevations to 5000 mm yr−1 at middle elevations and <1000 mm yr−1 at the very highest elevations. The MAP at each plot location was estimated based on the observed relationship of precipitation vs. elevation in nearby weather stations (adjusted R2 = 0.99) and interpolating to the plot locations based on their elevations as measured with a handheld GPS  each. Approximately four plots were installed every 250 m in elevation between 900 and 3600 m asl. Four additional plots were installed at approximately 400 m asl in the lowland rainforest near the Cocha Cashu Biological Station . For every individual, we measured basal diameter, total height, and diameter at breast height (dbh) when height was >1.3 m. Most individuals (90%) were identified to family level in the field, and some to genus level. Unidentified individuals were assigned a temporary name in the field. Subsequently, the unidentified individuals were compared with specimens in regional botanical collections and determined to species level or vouchered morphospecies. Vouchers were compared across all plots and taxonomy was standardized. Since all analyses were conducted at the plot level using the averages of individual-level measurements, the high frequency of morphospecies does not affect the results.One terminal branch was collected from each individual understory plant in each plot and placed in black plastic bags along with wet paper towels inside, and stem ends were submerged in water in plastic bags to avoid desiccation during transport to temporary field laboratories established near the top and bottom of the main elevation gradient at 3500 m asl and at 1300 m asl, respectively, and at Cocha Cashu Biological Station at 400 m asl. Once in the “laboratory”, branches were placed in buckets, stems were recut under water, and leaves were allowed to rehydrate covered with plastic. For each individual, all traits were measured on mature leaves from the same branch. Leaves were only included if they looked healthy and were not discolored; any leaves that were noticeably affected by pathogens were excluded.Ψ leaf, MPa) were measured periodically. Leaf mass was measured with an analytical balance  and Ψ leaf was measured using a Scholander-type pressure chamber . Plotting the inverse of leaf water potential against relative water content allows for the determination of the turgor loss point (TLP) as the point of transition between linear and nonlinear portions of the pressure–volume (PV) curve. A lower TLP  indicates a higher resistance to leaf wilting [Turgor loss point (TLP). Before measurements, branches were allowed to rehydrate for 10 h. After rehydration, three mature leaves were collected from each branch and allowed to slowly desiccate on a table over a period of 6–8 h between 02:00 and 10:00 a.m. local time. During this desiccation period, leaf mass (g) and leaf water potential ( wilting . We weremin). To measure the leaf Gmin, we harvested three fully expanded leaves from each individual’s sample branch after ≥4 h rehydration. We cut the leaf petioles and then sealed the petiole ends with paraffin. Leaves were dried at night using LED lanterns (PPFD 1.73–4.6 µmol m−2s−1) on a table for 1 h in order to induce stomatal closure. The leaves were then massed six times at intervals of 30 min. Cuticular conductance was calculated as the rate of water loss (g) over time [sat, kPa). Following standard protocols, the value of Gmin (mmol m−2 s−1) was calculated as the water loss rate divided by the VPD (mol mol−1) and twice the total leaf area as measured using scanned leaf images (cm2) [min indicates a high drought tolerance since a lower cuticular conductance enables the maintenance of hydration based on stored water.Cuticular conductance (Gver time . At eaches (cm2) . A lower0) using an Oakton CON 6+ Meter conductivity meter. After 12 h in the distilled water, we took a third conductivity measurement (C1). The leaf pieces and water were then boiled in the sealed plastic tubes for 20 min to rupture any remaining intact cells. Twelve hours after boiling, we took a second conductivity measurement (C2). We calculated the relative solute leakage (%) as (C1 − C0)/(C2 − C0) × 100 [Solute leakage (SL). To measure solute leakage (SL), we harvested five mature leaves from the sample branches of each individual after ≥4 h rehydration. We lightly cleaned each leaf to remove surface debris and cut three pieces from the apical, middle, and bottom areas of the leaf with a 1.27 cm diameter hole punch. We submerged the leaf pieces in 20 mL of 50% Polyethylene glycol 3350 (PEG), which is a hypertonic solution with an osmotic potential of approximately −8.4 MPa, for 12 h . After t0) × 100 ,60. High0) × 100 , and tha0) × 100 . In addi0) × 100 , water u0) × 100 , and tis0) × 100 , we findmin and five leaves per individual to obtain SL. The values for all individuals in each plot were then averaged to obtain community-weighted mean (CWM) trait values. We measured SL on each individual plant in each plot. In the case of TLP and Gmin, we were not able to sample every individual, and we therefore calculated the CWM trait values as the mean of the species-level average trait values weighted by the number of stems per species. Species-level averages were always based on measurements of individuals within each focal plot; in other words, drought tolerance measurements were never generalized between plots. Using plot-specific measurements accounts for any intraspecific variation due to local adaptation or acclimation and eliminates the need for taxonomic standardization across plots. We conducted Pearson correlation analyses between the traits to determine possible relationships between the different metrics of physiological drought tolerance. Linear regression analyses were used to assess the relationships between the plots’ CWMs of the SL, TLP, and Gmin with the elevation and MAP. All analyses were conducted in R [We used three leaves per individual to calculate average TLP and Gted in R .Melastomataceae , Rubiaceae , and Chloranthaceae . The most abundant genera were Miconia , Hedyosmum , and Palicourea . Of the 834 individuals, 501 were saplings and 333 were shrubs species, representing 89 genera and 50 families . The mose shrubs . On avere shrubs .min, and TLP for each individual plant are reported in min, and TLP for each plot are reported in min varied between plots from 4.18 to 99.11 mmol m−2 s−1  (min was only measured in middle and high elevations (1189–3604 m asl). The mean SL varied markedly among plots from 13% to 81%  . As with% to 81% . The SL min, p = 0.06) or at the plot level   .min with either elevation or the mean annual precipitation  and especially the MAP (SL ~ 27.23 + 0.0058 × MAP (in mm); p < 0.00005; adjusted R2 = 0.50), such that the SL was the lowest (indicating a greater drought tolerance) at high elevations and in areas with lower precipitation .There were no significant relationships between the TLP or Gpitation . In contpitation . When boLittle is known about whether climatic variation within the super humid tropics can cause variation in the drought tolerance and community assembly of plant species. We assessed the changes in the relative drought tolerance of nearly 60 understory woody plant communities along a 3000+ m elevational gradient in the “super humid” tropics by characterizing the relationships of three different metrics of physiological leaf drought tolerance with precipitation and elevation. In total, we performed at least one measurement of drought tolerance on almost 800 individual understory plants and measured all three measures of drought tolerance on almost 100 individual plants . Using tmin, and SL would all be correlated at the individual and plot levels due to a coupling of leaf-level mechanisms to resist and tolerate drought.Our a priori hypothesis was that the TLP, GContrary to this hypothesis, we did not find covariation between our three metrics of drought tolerance at either the plot level or the individual level . This suOur a priori hypothesis was that drought-tolerant species would be relatively infrequent in sites with high rainfall and that plant communities at lower elevations with hotter climates would have greater average drought tolerances than plants at higher elevations.The turgor loss point (TLP) values that we measured in the understory plants growing along our elevational gradient were generally higher  than those reported from other functional groups and habitats. Specifically, the TLP of cloud forest vascular epiphytes (−2.71 to −1.67 MPa) , understOther studies have also found the TLP to be related to precipitation and the VPD such that plants generally have lower TLPs (indicating a greater drought tolerance) in drier places ,76. The min) with either precipitation or elevation along our study gradient  and can be extremely humid. The high SL values of the understory plants in the sub-montane forests suggest that these communities are adapted to super wet conditions and may therefore suffer due to any future climate-change-driven decreases in rainfall or humidity.High-elevation shrubs had the lowest SL values  in the gradient, possibly due to the combination of the dry and cold (sometimes freezing) conditions that can occur in these habitats . LowlandIt is particularly noteworthy that the solute leakage had the only significant relationships with the environmental variables . The SL min or TLP). Temperatures in the Andes are increasing rapidly, and there is an overall tendency of increasing seasonality and decreasing annual rainfall through time [Studies of tropical montane forests can be particularly challenging given the large number of species involved, the frequent absence of local taxonomic treatments, the diverse array of plant habitats, and the difficulty of accessing the field sites. As a result of these and other challenges, the relationships between the water availability, elevation, and functional traits in Andean forests remain largely unknown. This study helps to advance our understanding of how the variation in water availability drives trait adaptation and community assembly in hyperdiverse tropical understory plant communities. We show that because of habitat filtering and/or local adaptation and acclimation, the plant assemblages in areas with different water availabilities have significant differences in their drought tolerance as measured by the solute leakage (but not by the Gugh time ,88. Incrugh time . Changesugh time . Given tugh time , and the


Vitis vinifera L.) being a well-known noble red grape variety of northern Greece, little is known about its ‘‘bouquet’’ typicity. Volatile compounds of Xinomavro wines produced using a common vinification protocol were analyzed by gas chromatography–mass spectrometry and sensory descriptive analysis was carried out with a trained panel. Wines were characterized by the presence of fatty acids, ethyl and acetate esters, and alcohols, with contributions from terpenes and a volatile phenol. The most active aroma compounds were determined to be 3-methylbutyl acetate, β-damascenone, ethyl esters of octanoic and hexanoic acids, and eugenol. Those compounds positively correlated with fruity and spicy odor descriptors, with the wines being mostly characterized by five typical aroma terms: strawberry, berry fruit, spices, tomato, and green bell pepper. Partial least squares regression (PLSR) analysis was used to visualize relationship between the orthonasal sensory attributes and the volatile aroma compounds with calculated OAVs > 1. Key aroma-active volatiles in the wines were identified using GC-MS/olfactometry, providing a list of 40 compounds, among which 13 presented a modified detection frequency > 70%. This study is the first of its kind and provided strong indications regarding the aroma compounds defining the sensory characteristics of Xinomavro wines.Despite Xinomavro ( Vitis vinifera varieties [Vitis vinifera L.) is the flagship red variety of northern Greece and is cultivated in the winegrowing regions of Amyndeon and Naoussa, producing protected designation of origin (PDO) wines [From antiquity to today, knowledge of viticulture and enology in Greece has yielded many red grape arieties  that havarieties ,3. Nowadarieties . In partO) wines , maturinO) wines . The subO) wines  but richO) wines , with seO) wines . These wO) wines  or the aO) wines .The quality of wines is a multidimensional concept for which sensory characteristics have a significant role . This deVarious studies involving the identification of volatile compounds contributing to the unique sensory properties of wines have been published over the past ten years ,10,11,12Xinomavro wines have recently represented Greece on the international stage , but thev/v), highlighting that the grapes were harvested at technological maturity [maturity . Titratamaturity , and pH maturity ,6. All pmaturity .A total of 25 volatile compounds were quantified by GC-MS in the Xinomavro research wines . These cEsters were the most numerous class and comprised 40% of the number of identified compounds. Generally, they contribute to the fruity aroma , mainly Higher alcohols are considered to strongly affect overall wine aroma . They ar13-norisoprenoid β-damascenone  provides guidance about the most potent odorants for a given product. OAVs are frequently used to indicate the relative importance of volatile compounds and provide insight into the compounds having the greatest influence on aroma . GeneralFour ethyl esters also had OAV > 1 : ethyl hIn the case of less desirable aromas such as those from volatile fatty acids, 3-methylbutyric, butanoic, and hexanoic acids had OAVs in the range 4–67. 3-Methylbutyric acid was an order of magnitude higher than the other volatile fatty acids, with an OAV ≈ 31–67 among the wines, followed by butanoic acid with an OAV ≈ 5–8, while hexanoic acid presented a lower OAV ≈ 4–6 . VolatilThe Xinomavro research wines presented a high content of eugenol (OAV ≈ 14–23), which from a sensory point of view could have an impact by providing spicy aromas . In contIt is worth mentioning that the aroma compounds with the greatest influence (OAVs > 1) presented differences among the wine samples produced from different vineyards within the PDO zone. Wine sample Xin3 tended to have the highest OAVs for most aroma compound families, such as the terpenes, higher alcohols along with Xin1, ethyl esters with Xin4, and volatile fatty acids with Xin2, whereas Xin5 presented the highest OAVs in the acetate ester group. This phenomenon indicates that despite the common vinification protocol followed, differences could still exist among the wines due to location (terroir) and meso-climate of subregions within the PDO zone , as alluPLSR multivariate analysis was applied to visualize the association between the orthonasal sensory attributes and the OAVs of the volatile aroma compounds (OAV > 1). This approach has been previously used in the modeling of the aroma of wines ,34,35,36More specifically, the upper left side of GC-O was conducted to characterize the odor active zones of Xinomavro research wines. The selection of samples for solvent-assisted flavor evaporation (SAFE) and GC-O analysis was based on their different aroma characteristics, as revealed in the PCA biplot . The finp = 0.036) with the score for strawberry attribute arising from sensory evaluation of the wines. Likewise, the summed scores of compounds with spicy character from GC-O was correlated well  with the wine sensory scores for the spicy character. The sum of fruity odorants from GC-O correlated well  with the berry fruit intensity of the wines, with the summation of the three esters that presented OAV > 10 according to the quantitative data being correlated even more strongly  with the berry fruit sensory score. There may also be a correlation between the summed odorants with vegetal character and the green bell pepper sensory attribute of the wines, although the relationship was barely significant .From another point of view, it is important to recognize that GC-O analysis differs from classical sensory analysis because of the chromatographic separation of components, whereas mixtures of odorants present in the original matrix can have a synergistic effect in sensory analysis . NotablyThe strawberry/sweet group included three aroma odorants, i.e., dimethylmethoxyfuranone, furaneol, and homofuraneol. It has been reported that relatively small quantities of furaneol, by acting in a synergistic manner with homofuraneol, can exert a great impact on the strawberry and caramel notes of wines ,40. DimeThe fruity group included ethyl and acetate ester odorants. Esters contribute to the fruity character of young wines , even at13-norisoprenoid varietal aroma compound, found in grapes and the respective wines, is derived from the breakdown of different aglycones and neoxanthin from grape [The last group of odorants that correlated well with the sensory attribute was the spicy group consisting of β-damascenone, eugenol, and sotolon. Sotolon is a lactone that can be found in some young red wines, but is more clearly found in aged wines, which could indicate that it forms during wine maturation . β-Damasom grape . The lasom grape , as mentSaccharomyces cerevisiae strain HDS 135 . Then, 12 h after yeast addition and again after fermentations was 1/3 completed, 125 mg/L of diammonium phosphate (DAP) and 125 mg/L of Springferm  were added. Maceration was conducted by pumping the juice over twice a day and alcoholic fermentation was performed between 21–23 °C, until sugar depletion, as determined using an enzymatic analyzer . Once dry, ferments were pressed with the use of a hydraulic press  to separate the wine from skin and seed. The wines were transferred to 30 L stainless steel tanks and inoculated with Viniflora CH11  for malolactic fermentation (MLF). Once the malic acid concentration was below 0.2 g/L, as determined enzymatically, MLF was considered complete. After racking and addition of 50 mg/L of sulfur dioxide, wines were stabilized in a cold room (~5 °C) for a period of 2 months. Amber glass bottles of 0.75 L were filled, the headspace was flushed with N2, and the bottles were closed with a technical stopper . Bottled wines were stored horizontally in cellar conditions . Wines produced in this manner were labeled as Xin1, Xin2, Xin3, Xin4, Xin5, and Xin6 according to the respective vineyard, with the three fermentation replicates for each wine bottled separately.Xinomavro grapes were hand harvested at technological maturity  during tAnalysis of enological parameters and determination of chromatic characteristics was undertaken after vinification. Reducing sugars, alcoholic degree, TA, pH, and VA were determined according to OIV methods . Color iThe method described by Ivanova et al.  for the m/z 40–400 using selected ion full ion (SIFI) mode. A total of 25 wine volatile compounds were identified and quantified using commercial standards with external calibration curves.Analysis of wine extracts was performed with liquid injection using a Perkin Elmer Clarus 590 gas chromatograph with an HT3000A automatic liquid sampler  coupled to a Perkin Elmer Clarus SQ8S mass spectrometer . A DB-WAX column  was used with helium as carrier gas at a constant flow rate of 1.9 mL/min. Injection volume was 1 µL in splitless mode, the injector temperature was set at 250 °C, and the split vent was opened after 120 s. The oven temperature was held at 40 °C for 2 min, heated at 5 °C/min to 240 °C, and held at 240 °C for 20 min. The transfer line and quadrupole temperatures were both 250 °C. Electron ionization (EI) mass spectra were recorded at 70 eV in the range The sensory assessment was carried out by 12 trained panelists  who gave informed consent to take part in the study. Panelists attended three training and two evaluation sessions over a period of 2 weeks. The training period included an initial session in which the panelists were served a set of representative Xinomavro samples to familiarize themselves with this kind of wine variety. The panelists were asked to describe the perceived aroma attributes, as described by Nanou et al. . The secTraining was repeated and the panel’s performance was evaluated, with two final sessions being used to assess the samples (six wines in duplicate), as described by Nanou et al. . Each wi−3 Pa) was maintained and the apparatus was thermostated at 40 °C. The dichloromethane extract of a sample was introduced dropwise at a rate of 10 mL/min. Vacuum to the apparatus was released 5 min after the last drops of extract were added and the collected distillate was allowed to reach room temperature [A 500 mL sample of wine was extracted sequentially using 80, 80, and 50 mL of dichloromethane, with magnetic stirring for 10 min. The collected organic phases were combined and dried over anhydrous sodium sulfate . After fperature . This SATM Olfactory Port, N6590100) was fitted to the GC-MS system, connected by a flow splitter to the column exit , to achieve a 1:5 split ratio. GC effluent was combined with humidified air  at the bottom of the detector port. Injection of 1 μL sample of each concentrated dichloromethane extract was undertaken in splitless mode (injector temperature 250 °C) and the split vent was opened after 5 min. A DB-Wax column  was used with helium as carrier gas in constant flow rate of 1.9 mL/min. The temperature program was as follows: 40 °C for 2 min, then increasing by 4 °C/min to 240 °C, with a 20-min isothermal phase at 240 °C [m/z 40–400.Analysis of SAFE extracts was conducted on a Perkin Elmer Clarus 590 gas chromatography coupled to a Perkin Elmer Clarus SQ8S mass spectrometer . A Perkin Elmer olfactory detector port  trained in descriptive sensory analysis and able to identify the odors of different wine samples of Xinomavro grape variety were used as sniffers. The panelists noted each odor-active compound’s perceived characteristics, intensity, and retention time as they sniffed via the olfactory mask. The odor intensities were assessed using a three-point discrete intensity scale (1–3) and the modified frequency, MF (%), was calculated :MF (%) =The MS data were analyzed with TurboMass Ver6.1.2 GC/MS Software  and compared with the NIST 2008 Mass Spectra library . The Kovats retention index (RI) of the target compound was calculated  and compared with a NIST database. Each analyte’s aroma description was compared to the literature and to the Flavornet website .p < 0.05) was conducted for volatile compounds and sensory attributes in the six Xinomavro wines followed by Tukey’s HSD post-hoc test (α = 0.05). Sensory data means were subjected to principal component analysis (PCA). Pearson’s correlation coefficient was calculated to reveal the relationship between the summed scores of the odorants according to GC-O analysis and the sensory attributes. Partial least squares regression (PLSR) analysis (PLS2) was conducted using XLSTAT  with the volatile compounds having OAV > 1 (x-variables) and sensory DA data (y-variables). The optimal number of components for the PLSR model was determined from the variable importance in projection (VIPs) plots, including only the variables with VIP > 0.8.Values are presented as the mean ± standard deviation with statistical analyses being undertaken with JMP Pro Statistical Software (version 13). One-way ANOVA (This study investigated the chemical composition, aroma-active compounds, and sensory characters of Xinomavro red wines arising from six vineyards within the Amyndeon PDO zone. Correlations between the chemical and sensory results were undertaken and the experiments highlighted a clear relationship between the summed groups of aroma-active odorants that were identified via GC-O and the scores from sensory descriptive analysis. The strongest correlations were presented between the strawberry/caramel group and the strawberry attribute, along with the spicy group with the spices attribute, followed by the fruity group with the berry fruit attribute. According to the quantitative results, the quantified esters of the fruity group presented OAVs > 1, whereas the esters of the berries group, and β-damascenone and eugenol of the spicy group, presented OAVs >10. PLSR analysis effectively distinguished the three different groups of Xinomavro wines based on the aroma characters—fruity, spice, and vegetative/green—which were associated with certain aroma compounds. As the first study of its kind, this work has provided strong indications regarding the aroma compounds defining the sensory characteristics of strawberry, berries and spices that may typify Xinomavro PDO wines. Most of these aroma compounds contribute to the primary aroma of the wines, whether arising from the terpenic family and released in the wine after glycoside hydrolysis or some others, such as the acetate and ethyl esters, produced during the vinification process derived from the precursors of the grape must.Armed with this knowledge, winemakers could use suitable vinification protocols, altering winemaking variables such as yeast strain and/or fermentation temperature, to produce a wine with a prototypical wine style. Manipulation of the wine style could also potentially start in the vineyard through understanding the viticultural practices affecting the formation of precursors in the grape berry. As such, viticultural practices could be adjusted to produce grapes that contain either higher or lower concentrations of precursors to fit the desired wine style that is characteristic of the PDO wine.While the primary focus of this initial research was the correlations between quantitative GC-MS, GC-O, and sensory data, the work provides opportunities for additional methodologies that can improve and supplement the results of this study. For instance, research could also focus on the identification of the aroma compounds related to the tomato character of Xinomavro PDO wines, considering that the perceived intensity of the tomato attribute was quite high, but no correlation was presented with the vegetal group of odorants.