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The value of honey as a natural food product is influenced by its pollen content, with the dominant type of pollen conferring specific medicinal properties. The present study examines the pollen spectra of 31 honeys from urban (linden, acacia, polyfloral, honeydew) and rural (rape, acacia, polyfloral, honeydew) apiaries in Poland. The pollen in content in honey ranged from 0.2 to 88 %. In total, 76 plant taxa were identified, 21 of which were assigned to forms A, B and C. Higher pollen grain content and a greater diversity of honey plant taxa were found in the urban honey, particularly polyfloral honey; this could be attributed to the rich variety of plants found in urban green areas compared to rural areas ones.

Pollen allergies have become one of the most wide-spread afflictions that impact quality of life. This has made automatic pollen detection, classification and monitoring a very important topic of research. This paper introduces a new public annotated image data-set of pollen with almost 45 thousand samples obtained from an automatic instrument. In this work we apply some of the best performing convolutional neural networks architectures on the task of pollen classification as well as some fully convolutional networks optimized for image segmentation on complex microscope images. We obtain an F1 scores of 0.95 on the new data-set when the best trained model is used as a fully convolutional classifier and a class mean Intersection over Union (IoU) of 0.88 when used as an object detector.

Pollen allergies are seasonal epidemic diseases that are accompanied by high incidence rates, especially in Beijing, China. With the development of deep learning, key progress has been made in the task of automatic pollen grain classification, which could replace the time-consuming and laborious manual identification process using a microscope. In China, few pioneering works have made significant progress in automatic pollen grain classification. Therefore, we first constructed a multi-class and large-scale pollen grain dataset for the Beijing area in preparation for the task of pollen classification. Then, a deblurring pipeline was designed to enhance the quality of the pollen grain images selectively. Moreover, as pollen grains vary greatly in size and shape, we proposed an easy-to-implement and efficient multi-scale deep learning architecture. Our experimental results showed that our architecture achieved a 97.7% accuracy, based on the Resnet-50 backbone network, which proved that the proposed method could be applied successfully to the automatic identification of pollen grains in Beijing. [ABSTRACT FROM AUTHOR]

In a large number of scientific areas, such as immunology, forensics, paleoecology, and archeology, the study of pollen, i.e., palynology, plays an important role: from tracking climate changes, studying allergies, to forensic investigations or honey origin analysis. Since the mid-nineties of the last century, the idea for an automated solution to the problem of pollen identification and classification was formulated and since then, several attempts and proposals have been made and presented, based on different technologies, in particular in the field of Computer Vision. However, as of 2021 microscopic analyses are performed mainly manually by highly trained specialists, although the capabilities of artificial intelligence, especially Deep Neural Networks, are steadily increasing. In this work, we analyzed various state-of-the-art research work concerning pollen detection and classification and compared their methods and results. The problems, such as data accessibility, different methods of Machine Learning, and the intended applicability of the proposed solutions are explored. We also identified crucial issues that require further work and research. Our work will provide a thorough view on the current state of the art, its issues, and possibilities for the future. [ABSTRACT FROM AUTHOR]

Pollen allergies are seasonal epidemic diseases that are accompanied by high incidence rates, especially in Beijing, China. With the development of deep learning, key progress has been made in the task of automatic pollen grain classification, which could replace the time-consuming and laborious manual identification process using a microscope. In China, few pioneering works have made significant progress in automatic pollen grain classification. Therefore, we first constructed a multi-class and large-scale pollen grain dataset for the Beijing area in preparation for the task of pollen classification. Then, a deblurring pipeline was designed to enhance the quality of the pollen grain images selectively. Moreover, as pollen grains vary greatly in size and shape, we proposed an easy-to-implement and efficient multi-scale deep learning architecture. Our experimental results showed that our architecture achieved a 97.7% accuracy, based on the Resnet-50 backbone network, which proved that the proposed method could be applied successfully to the automatic identification of pollen grains in Beijing.

Pollen identification and quantification are used in many fields of application and research has been conducted to attain accurate automatic pollen recognition aiming to reduce the laborious work and subjectivity in human identification. The aim of our study was to evaluate the capacity of Raman parameters of pollen spectra, calculated for only 7 common band intervals in a limited spectral range, to be used as future technique in pollen automatic identification. There were analyzed 15 different pollen species considered to induce allergic reactions. Raman spectra were acquired at an excitation wavelength of 785 nm in a spectral region from 1000 to 1800 cm−1, preprocessed and deconvoluted to determine the Raman parameters: wavenumber, full width at half maximum of the band and integrated intensity. Seven common band intervals of all Raman spectra, in the fingerprint areas 1000–1010, 1300–1460 and 1500–1700 cm−1, were chosen for the classification of the pollen species using SVM (support vector machine). Our results showed that the classification accuracy of all pollen species was 100% in the training step, while in the testing step 14 out of the 15 pollen species were correctly assigned (93.3%), including the discrimination between 5 Poaceae species and between Betula pendula and Corylus avellana. It was also observed that all Raman parameters are important in the classification as well as all wavenumber areas considered. So, our study indicates that the Raman parameters of pollen spectra can be a promising methodology for automatic pollen recognition. [ABSTRACT FROM AUTHOR]

The automation of honey pollen visual sorting overcomes the limitations of the conventional procedure helping the specialist in this time-consuming task. In this work, a novel and comprehensive Ground Truth of almost 19,000 images (from optical microscopy) of the 16 most abundant types of grains/pollen particles present in citrus and rosemary honey from Spain was constructed. This task was assisted by a HoneyApp (also developed herein) for the labelling and annotation process. Subsequently, the effectiveness of different pre-existing automatic pollen recognizers based on convolutional neural networks (CNN) (VGG16, VGG19, InceptionV3, Xception, ResNet50, DenseNet201, MobileNetV2 and EfficientNetV2M) was tested together with a new network proposed in this paper (PolleNetV1). The extreme complexity of those pre-existing CNN and extensive use of millions of parameters makes this new proposal especially promising. Although with a slightly lower accuracy (average 96%) in determining the relative frequencies of different types of pollen grains/particles, it has considerable advantages such as simplicity and ability to be included in the future functionality to automate pollen recognition in honey. This is the first step to finally achieving an objective tool that allows the correct labelling of any types of pollen in honey, thus contributing to its transparency in the market. • A pollen images Ground Truth dataset from citrus and rosemary honeys was built. • A specific application (HoneyApp) to label/annotate pollen images was developed. • CNN networks were effective in counting the frequencies of pollen types in hone. • A new simpler, low memory and robust network (PolleNetV1) was created. • PolleNetV1 is expected to produce promising outcomes for other monoflorals. [ABSTRACT FROM AUTHOR]

eDNA metabarcoding is an emergent tool to inform aerobiome complexity, but few studies have applied this technology with real-world environmental pollen monitoring samples. Here we apply eDNA metabarcoding to assess seasonal and regional differences in the composition of airborne pollen from routine samples collected across successive years. Airborne pollen concentrations over two sampling periods were determined using a continuous flow volumetric impaction air sampler in sub-tropical (Mutdapilly and Rocklea) and temperate (Macquarie Park and Richmond), sites of Australia. eDNA metabarcoding was applied to daily pollen samples collected once per week using the rbcL amplicon. Composition and redundancy analysis of the sequence read counts were examined. The dominant pollen families were mostly consistent between consecutive years but there was some heterogeneity between sites and years for month of peak pollen release. Many more families were detected by eDNA than counted by light microscopy with 211 to 399 operational taxonomic units assigned to family per site from October to May. There were 216 unique and 119 taxa shared between subtropics (27°S) and temperate (33°S) latitudes, with, for example, Poaceae, Myrtaceae and Causurinaceae being shared, and Manihot, Vigna and Aristida being in subtropical, and Ceratodon and Cerastium being in temperate sites. Certain genera were observed within the same location and season over the two years; Chloris at Rocklea in autumn of 2017-18 (0.625, p ≤ 0.004) and 2018-19 (0.55, p ≤ 0.001), and Pinus and Plantago at Macquarie Park in summer of 2017-18 (0.58, p ≤ 0.001 and 0.53, p ≤ 0.003, respectively), and 2018-19 (0.8, p ≤ 0.003 and 0.8, p ≤ 0.003, respectively). eDNA metabarcoding is a powerful tool to survey the complexity of pollen aerobiology and distinguish spatial and temporal profiles of local pollen to a far deeper level than traditional counting methods. However, further research is required to optimise the metabarcode target to enable reliable detection of pollen to genus and species level., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: JMD and PJB report the grant from the Australian National Health and Medical Research CouncilGNT1116107, with financial co-contribution from Asthma Australia and Stallergenes Greer Australia Pty Ltd., as well as in kind non-financial support from Australasian Society for Clinical Immunology and Allergy, Asthma Australia, Bureau of Meteorology, Commonwealth Scientific and Industrial Research Organisation, Stallergenes Greer Australia Pty Ltd., and Federal Office of Meteorology and Climatology (MeteoSwiss), for the conduct of part of this project. JMD and PB report grants from Australian Research Council (DP210100347; DP170101630) for this research. JMD reports grants from the Australian Research Council (DP190100376; LP190100216), National Foundation of Medical Research Innovation, Abionic SA, The Emergency Medicine Foundation, and Queensland Chief Scientist Citizen Science Grant, outside the scope of submitted work. PJB and JMD report grants from Bureau of Meteorology outside the submitted work. JMD reports QUT has patents broadly relevant US PTO 14/311944 and AU2008/316301 issued., (Copyright © 2022. Published by Elsevier B.V.)

Poaceae pollen grains are notoriously difficult to use as a tool for eco-palynological studies due to their relatively uniform morphology. This article uses light and scanning electron microscopy to describe the pollen morphology of 31 species of Malagasy grasses, including 11 endemic species and seven endemic genera that are described here for the first time. Our ultimate goal is to use these data to differentiate between primary and secondary grasslands in Madagascar, which is of important conservation significance. Our data show some species differences that are promising for future identification of fossil grains. Pollen morphological details are imaged and discussed. [ABSTRACT FROM PUBLISHER]

The digitization of pollen grain images would permit the creation of a semi-automated system that could aid the expert palynologists in pollen classification. It would reduce cost and time-to-answer as well as improve analyst productivity. These issues are particularly critical in forensic applications. There are numerous factors that should be considered when establishing a digital database intended for semi-automated pollen classification. This paper explores a number of these issues through computer vision and machine learning assessments. The main topics evaluated are morphologically similar species-level classification, optimal training data size, how best to utilize three-dimensional data, accuracy changes due to the availability of metadata, i.e., fluctuations in analysts' confidence in taxa labeling, and using fossil data to classify modern data. This is the first known application of training on fossil data to classify modern taxa. Performances of 95.4% and 93.8% correct classification were achieved on two distinct sets of morphologically similar species-level data, surpassing previous records. We determined that a minimum of 5–10 training images per class was required to yield reasonable performance. Additionally, we established that all depth dimension slices associated with each grain were required to yield the best performance possible. Lastly, the error rate doubles due to decreasing analyst confidence and almost triples when using data from grains of varying ages, further solidifying the importance of comprehensive metadata. [ABSTRACT FROM AUTHOR]

Jacquemontiais one of the larger genera in Convolvulaceae, with around 120 species, and is considered taxonomically difficult. The family is eurypalynous and pollen morphology has been considered as an important taxonomic character. Pollen morphology of 43 species, representing all morphological groups ofJacquemontia, was analysed with light microscopy and/or scanning electron microscopy. Three pollen types were characterised. These pollen types do not corroborate the current circumscription of sections inJacquemontia, which is based on inflorescence structure. Conversely, however, some macro-morphological features are discussed that support groups defined on the basis of pollen analysis. [ABSTRACT FROM AUTHOR]

• The paper addresses the lack of labeled training samples in biology and medicine. • It presents a data augmentation method which generates additional training samples. • New samples are obtained by combining two samples that belong to the same class. • The data augmentation procedure is based on morphing and image registration. • Results show that the accuracy improves compared to other commonly-used techniques. Background and objective: Training a deep convolutional neural network (CNN) for automatic image classification requires a large database with images of labeled samples. However, in some applications such as biology and medicine only a few experts can correctly categorize each sample. Experts are able to identify small changes in shape and texture which go unnoticed by untrained people, as well as distinguish between objects in the same class that present drastically different shapes and textures. This means that currently available databases are too small and not suitable to train deep learning models from scratch. To deal with this problem, data augmentation techniques are commonly used to increase the dataset size. However, typical data augmentation methods introduce artifacts or apply distortions to the original image, which instead of creating new realistic samples, obtain basic spatial variations of the original ones. Methods: We propose a novel data augmentation procedure which generates new realistic samples, by combining two samples that belong to the same class. Although the idea behind the method described in this paper is to mimic the variations that diatoms experience in different stages of their life cycle, it has also been demonstrated in glomeruli and pollen identification problems. This new data augmentation procedure is based on morphing and image registration methods that perform diffeomorphic transformations. Results: The proposed technique achieves an increase in accuracy over existing techniques of 0.47%, 1.47%, and 0.23% for diatom, glomeruli and pollen problems respectively. Conclusions: For the Diatom dataset, the method is able to simulate the shape changes in different diatom life cycle stages, and thus, images generated resemble newly acquired samples with intermediate shapes. In fact, the other methods compared obtained worse results than those which were not using data augmentation. For the Glomeruli dataset, the method is able to add new samples with different shapes and degrees of sclerosis (through different textures). This is the case where our proposed DA method is more beneficial, when objects highly differ in both shape and texture. Finally, for the Pollen dataset, since there are only small variations between samples in a few classes and this dataset has other features such as noise which are likely to benefit other existing DA techniques, the method still shows an improvement of the results. [ABSTRACT FROM AUTHOR]

Premise: Pollinator sharing of co-flowering plants may result in interspecific pollen receipt with a fitness cost. However, the underlying factors that determine the effects of heterospecific pollen (HP) are not fully understood. Moreover, the cost of stigma closure induced by HP may be more severe for plants with special touch-sensitive stigmas than for plants with non-touch-sensitive stigmas. Very few studies have assessed HP effects on stigma behavior., Methods: We conducted hand-pollination experiments with 10 HP donors to estimate HP effects on stigma behavior and stigmatic pollen germination in Campsis radicans (Bignoniaceae) at low and high pollen loads. We assessed the role of phylogenetic distance between donor and recipient, pollen size, and pollen aperture number in mediating HP effects. Additionally, we observed pollen tube growth to determine the conspecific pollen-tube-growth advantage., Results: Stigma behavior differed significantly with HP of different species. Pollen load increased, while pollen size decreased, the percentage of permanent closure and stigmatic germination of HP. Stigmatic HP germination increased with increasing aperture number. However, HP effects did not depend on phylogenetic distance. In addition, conspecific pollen had a pollen-tube-growth advantage over HP., Conclusions: Our results provide a good basis for understanding the stigma-pollen recognition process of plant taxa with touch-sensitive stigmas. We concluded that certain flowering traits drive the HP effects on the post-pollination period. To better understand the impact of pollinator sharing and interspecific pollen transfer on plant evolution, we highlight the importance of evaluating more factors that determine HP effects at the community level., (© 2022 Botanical Society of America.)

Abstract: Palynological analysis, as applied in vegetation reconstruction, climate change studies, allergy research, melissopalynology and forensic science, is a slow, laborious process. Here, we present an ongoing project aimed at the realisation of a low-cost, automatic, trainable system for the location, recognition and counting of pollen on standard glass microscope slides. This system is designed to dramatically reduce the time that the palynologist must spend at the microscope, thus considerably increasing productivity in the pollen lab. The system employs robotics, image processing and neural network technology to locate, photograph and classify pollen on a conventionally prepared pollen slide. After locating pollen grains on a microscope slide, it captures images of them. The individual images of the pollen are then analysed using a set of mathematically defined features. These feature sets are then classified by the system by comparison with feature sets previously obtained from the analysis of images of known pollen types. The classified images are then presented to the palynologist for checking. This ability for post-classification checking is a key part of the automated palynology process, as it is likely that under the current technology, it will be very difficult to produce an automated pollen counting and classifier system that is 100% correct 100% of the time. However, it is important to remember that pollen counts performed by human palynologists are seldom 100% correct 100% of the time as well. The system has been tested on slides containing fresh pollen of six different species. The slides were counted repeatedly by both the system and by human palynologists. The results of these tests show that the machine can produce counts with very similar proportions to human palynologists (typically within 1–4%). Although the means of the machine counts were usually slightly lower than those of the human counts, the variance was also lower, demonstrating that the machine counts pollen more consistently than human palynologists. The system described herein should be viewed as a potentially very valuable tool in the palynological laboratory. Its ability to discriminate between the bulk of pollen and debris on a slide and capture and store images of each pollen grain is in itself a very useful feature. This capability combined with the relatively positive results from this first all-of-system capture-and-classify test clearly demonstrate the potential of the system to considerably improve the efficiency of palynological analysis. However, more tests are required before the extent of the system''s potential can be fully realised. The next step, testing the system on fossil pollen samples, is now underway. [Copyright &y& Elsevier]

Abstract: A new method to classify pollen species was developed by monitoring autofluorescence images of pollen grains. The pollens of nine species were selected, and their autofluorescence images were captured by a microscope equipped with a digital camera. The pollen size and the ratio of the blue to red pollen autofluorescence spectra (the B/R ratio) were calculated by image processing. The B/R ratios and pollen size varied among the species. Furthermore, the scatter-plot of pollen size versus the B/R ratio showed that pollen could be classified to the species level using both parameters. The pollen size and B/R ratio were confirmed by means of particle flow image analysis and the fluorescence spectra, respectively. These results suggest that a flow system capable of measuring both scattered light and the autofluorescence of particles could classify and count pollen grains in real time. [Copyright &y& Elsevier]

The article presents a system that realizes the benefits of the development of a computer system to pollinic analysis. It classifies and count the grains of pollen in a slice which represents the visual information reported by an optical microscope. The method involves the detection of pollen grains in the slice and their classification. Based on results, it has been recommended that a wider test of the system will be needed before applying this method in systematic pollinic counting.

Recent advances in microscopy, imaging, and data analyses have permitted both the greater application of quantitative methods and the collection of large data sets that can be used to investigate plant morphology. This special issue, the first for Applications in Plant Sciences, presents a collection of papers highlighting recent methods in the quantitative study of plant form. These emerging biometric and bioinformatic approaches to plant sciences are critical for better understanding how morphology relates to ecology, physiology, genotype, and evolutionary and phylogenetic history. From microscopic pollen grains and charcoal particles, to macroscopic leaves and whole root systems, the methods presented include automated classification and identification, geometric morphometrics, and skeleton networks, as well as tests of the limits of human assessment. All demonstrate a clear need for these computational and morphometric approaches in order to increase the consistency, objectivity, and throughput of plant morphological studies.

Background: Although the main method for authentication of monofloral honey is pollen analysis, other classification approaches have been also applied. However, the majority of the existing classification models so far have utilized a few honey types or a few honey samples of each honey type, which can lead to inaccurate results. Aiming at addressing this, the goal of the present study was to create a classification model by analysing in total 250 honey samples from 15 different monofloral honey types in ten physicochemical parameters and then, multivariate analysis [multivariate analysis of variance (MANOVA), principal component analysis (PCA) and multi-discriminant analysis (MDA)] was applied in an effort to distinguish and classify them., Results: Electrical conductivity and colour were found to have the highest discriminative power, allowing the classification of monofloral honey types, such as oak, knotgrass and chestnut honey, as well as the differentiation between honeydew and nectar honeys. The classification model had a high predictive power, as the 84.4% of the group cases was correctly classified, while for the cases of chestnut, strawberry tree and sunflower honeys the respective prediction was correct by 91.3%, 95% and 100%, allowing further determination of unknown honey samples., Conclusion: It seems that the characterization of monofloral honeys based on their physicochemical parameters through the proposed model can be achieved and further applied on other honey types. The results could contribute to the development of methodologies for the determination of honey's botanical origin, based on simple techniques, so that these can be applied for routine analysis. © 2021 Society of Chemical Industry., (© 2021 Society of Chemical Industry.)

Sanguisorba , commonly known as burnet, is a genus in the family Rosaceae native to the temperate regions of the Northern hemisphere. Five of its thirty species are distributed in Korea: Sanguisorba officinalis , S. stipulata , S. hakusanensis , S. longifolia , and S. tenuifolia . S. officinalis has been designated as a medicinal remedy in the Chinese and Korean Herbal Pharmacopeias. Despite being a valuable medicinal resource, the morphological and genomic information, as well as the genetic characteristics of Sanguisorba , are still elusive. Therefore, we carried out the first comprehensive study on the floral micromorphology, palynology, and complete chloroplast (cp) genome of the Sanguisorba species. The outer sepal waxes and hypanthium characters showed diagnostic value, despite a similar floral micromorphology across different species. All the studied Sanguisorba pollen were small to medium, oblate to prolate-spheroidal, and their exine ornamentation was microechinate. The orbicules, which are possibly synapomorphic, were consistently absent in this genus. Additionally, the cp genomes of S. officinalis , S. stipulata , and S. hakusanensis have been completely sequenced. The comparative analysis of the reported Sanguisorba cp genomes revealed local divergence regions. The nucleotide diversity of trnH-psbA and rps2-rpoC2 , referred to as hotspot regions, revealed the highest pi values in six Sanguisorba . The ndhG indicated positive selection pressures as a species-specific variation in S. filiformis . The S. stipulata and S. tenuifolia species had psbK genes at the selected pressures. We developed new DNA barcodes that distinguish the typical S. officinalis and S. officinalis var. longifolia , important herbal medicinal plants, from other similar Sanguisorba species with species-specific distinctive markers. The phylogenetic trees showed the positions of the reported Sanguisorba species; S. officinalis , S. tenuifolia , and S. stipulata showed the nearest genetic distance. The results of our comprehensive study on micromorphology, pollen chemistry, cp genome analysis, and the development of species identification markers can provide valuable information for future studies on S. officinalis , including those highlighting it as an important medicinal resource.

Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image analysis transfers visual information to mathematical descriptions. In this work, the suitability of three microscopic techniques for automatic analysis of pollen grains was studied. 2D and 3D morphological characteristics, textural and colour features, and extended depth of focus characteristics were used for the pollen discrimination. In this study, 7 botanical taxa and a total of 2482 pollen grains were evaluated. The highest correct classification rate of 93.05% was achieved using the phase contrast microscopy, followed by the dark field microscopy reaching 91.02%, and finally by the light field microscopy reaching 88.88%. The most significant discriminant characteristics were morphological (2D and 3D) and colour characteristics. Our results confirm the potential of using automatic pollen analysis to discriminate pollen taxa in honey. This work provides the basis for further research where the taxa dataset will be increased, and new descriptors will be studied., Competing Interests: The authors have declared that no competing interests exist.

The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology, and melissopalynology. This paper presents a new public annotated image dataset for the Brazilian Savanna called POLLEN73S composed of 2523 images from 73 pollen types. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide a baseline for pollen grain classification. Our experiments showed evidence that DenseNet-201 and ResNet-50 have superior performance against the other CNNs tested, achieving precision results of 95.7% and 94.0%, respectively. Due to its category coverage and satisfactory diversity of examples, POLLEN73S offers a diversity of pollen grain to guide progress in computer vision to solve Palynology problems. • Publication of the POLLEN73S dataset, a repository of 2523 pollen grain images distributed in 73 types. • The definition of a baseline for pollen grain classification using the state-of-the-art Convolutional Neural Networks (CNNs). • Automation of pollen grain analysis. [ABSTRACT FROM AUTHOR]

Context: The species-specific differences and mechanisms of action of bee pollen on reproduction have not been well studied., Objective: We compared the effects of bee pollen extracts from different plants on ovarian cell functions., Materials and Methods: We compared the effects of pollens from black alder, dandelion, maize, rapeseed, and willow at 0, 0.01, 0.1, 1, 10, or 100 µg/mL on cultured porcine ovarian granulosa cells. Cell viability was assessed with a Trypan blue test, the cell proliferation marker (PCNA), and an apoptosis marker (BAX) were assessed by immunocytochemistry. Insulin-like growth factor (IGF-I) release was measured by an enzyme-linked immunosorbent assay., Results: Addition of any bee pollen reduced cell viability, promoted accumulation of both proliferation and apoptosis markers, and promoted IGF-I release. The ability of various pollens to suppress cell viability ranked as follows: rapeseed > dandelion > alder > maize > willow. The biological activity of bee pollens regarding their stimulatory action on ovarian cell proliferation ranked as follows: dandelion > willow > maize > alder > rapeseed. Cell apoptosis was promoted by pollens as follows: range > dandelion > alder > rapeseed > willow > maize. The ability of the pollens to stimulate IGF-I output are as follows: willow > dandelion > rapeseed > maize > alder., Discussion: Bee pollen can promote ovarian cell proliferation by promoting IGF-I release, but it induces the dominance of apoptosis over proliferation and the reduction in ovarian cell viability in a species-specific manner., Conclusions: This is the first demonstration of adverse effects of bee pollen on ovarian cell viability and of its direct stimulatory influence on proliferation, apoptosis, and IGF-I release. The biological potency of bee pollen is dependent on the plant species.

Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution microscopy and machine learning to create a quantitative and higher throughput workflow for producing palynological identifications and hypotheses of biological affinity. We developed three convolutional neural network (CNN) classification models: maximum projection (MPM), multislice (MSM), and fused (FM). We trained the models on the pollen of 16 genera of the legume tribe Amherstieae, and then used these models to constrain the biological classifications of 48 fossil Striatopollis specimens from the Paleocene, Eocene, and Miocene of western Africa and northern South America. All models achieved average accuracies of 83 to 90% in the classification of the extant genera, and the majority of fossil identifications (86%) showed consensus among at least two of the three models. Our fossil identifications support the paleobiogeographic hypothesis that Amherstieae originated in Paleocene Africa and dispersed to South America during the Paleocene-Eocene Thermal Maximum (56 Ma). They also raise the possibility that at least three Amherstieae genera ( Crudia , Berlinia , and Anthonotha ) may have diverged earlier in the Cenozoic than predicted by molecular phylogenies., Competing Interests: The authors declare no competing interest.

Green lacewings (Neuroptera: Chrysopidae) are key biological control agents found in a broad range of crops. Given the importance of enhancing their presence and conservation, in this study, we aim to identify and to determine the relative importance of the pollen consumed by Chrysoperla lucasina (Lacroix, 1936) from 29 pollen types offered by 51 native plant species sown in an experimental farm in Villarrubia in the south of Spain. For the purposes of this study, C. lucasina specimens were captured in the late spring of 2016 and 2017. The pollen types and other components in the alimentary canal of C. lucasina were microscopically identified using the transparency method, which is a novel technique applied to green lacewings captured in the field. The results show that (i) C. lucasina feeds on over half of the pollen types offered by the sown plant species, with no differences in behaviour by sex or year; (ii) Capsella bursa-pastoris was the most frequently identified pollen type in the alimentary canal; (iii) the majority of pollen types identified correspond to sown native plant species and not to surrounding plant species; and that (iv) most of the adults studied also consumed honeydew. Our feeding study has important implications for the selection of plant mixtures for ground cover restoration and flower vegetation strips in Mediterranean agroecosystems, which complements our previous findings on how C. lucasina use native plant species as host and reproduction sites. The plant species Capsella bursa-pastoris and Biscutella auriculata, which are best suited to provide pollen, host and reproduction sites for C. lucasina in late spring, should consequently be included in the proposed plant mixtures for Mediterranean agroecosystems., Competing Interests: The authors have declared that no competing interests exist.

In the Brazilian Tropical Dry Forest, the Caatinga, stingless bees (Apidae, Meliponini) need to adjust their foraging behavior to a very short and unpredictable blooming period. Melipona subnitida Ducke 1910 is one of the few meliponine species adapted to the environmental peculiarities of this biome. To get an insight into how these highly eusocial bees are able to maintain their perennial colonies despite extended periods of food scarcity, we asked the following questions: (1) At which plant species do colonies of M. subnitida collect their food during the rainy season? And (2) are there any plant species during the dry season, from which the colonies may profit for replenishing their food stores? During 1 year, we collected monthly honey and pollen samples from recently built storage pots of five colonies of M. subnitida and identified the botanical origin of the collected resources. In the course of our study, the colonies foraged at native trees, shrubs, and herbaceous species, demonstrating the importance of all plant strata for the bees' diet. Profitable plants, which bloom mainly during the rainy season and usually produce a great number of flowers, were frequently sampled in new pots throughout the entire study, even during the dry season. From our results, we compiled a list of the most important plant species providing floral resources for bees throughout the year, including periods of drought. We recommend these plants for restoration areas to improve the conservation of native bee species and local beekeeping in the Brazilian Tropical Dry Forest.

Vibrating bees are the main pollinators of the tomato plant (Solanum lycopersicum L.). Knowledge of other alternative food resources for these bees is fundamental for pollinator management actions in agricultural areas. The objective of this study was to evaluate the plants used as food resources for the main pollinators Bombus morio (Swederus) and Exomalopsis analis Spinola in plantation areas. The study was conducted in 12 plantation areas in São José de Ubá, southeastern Brazil, during the flowering period of S. lycopersicum. The pollen material contained on the hind legs of 40 B. morio females and 72 E. analis females was analyzed and compared with the reference slides made from 155 flowering plant species (35 botanical families) sampled close to the plantations. The pollen material was submitted to acetolysis and mounted in glycerin gelatin and analyzed under optical microscope. From B.morio corbiculae were identified 188 pollen types (52 identified from reference slides) and 189 types from E. analis scopae (54 in reference slides). Besides tomato pollen, other most abundant types belong to Fabaceae (8%) in B. morio samples, and Hyptis and Solanum sp in E. analis samples. The trophic niche overlap was close to zero when the tomato pollen was disregarded, indicating that both pollinators use distinct sources. The results confirm the generalist character of tomato pollinators; in addition, the use of floral resources from several other plants, even at tomato flowering peak, emphasizes the importance of maintaining flowering plant composition around agricultural areas.

Mutualistic interactions between bees and flowering plants have been widely recognized as one of the most important for the maintenance of these communities throughout ecosystems. Consequently, understanding how these interactions occur is highly important, especially in seasonal dry tropical forest (SDTF), one of the most endangered ecosystems in northern South America. In this study, we analyzed the changes between interaction networks across two well-defined seasons, dry and wet, in a SDTF of the Colombian Caribbean in Taganga, Magdalena. We also determined changes in species composition and their role in interaction networks. To study this system, we compared two approaches: (1) networks constructed with data from direct collections in flowering plants, and (2) networks constructed with pollen data obtained from bees' bodies. A total of 44 species were collected in 18 species of flowering plants; also, we registered 16 additional plants presented in the records only as pollen types. We found that network metrics, connectance, nestedness, specialization (H2'), and interaction strength asymmetry remain stable through seasons. However, when the two types of approximations were compared, there were significant differences. Networks constructed with pollen data are more connected, less specialized, and with lower values of interaction strength asymmetry. The major difference between seasons relied on the interacting species composition, due to a high species turnover. Bee community was more diverse in dry season. Apidae family, mainly eusocial species, persisted in the community, being more abundant and relevant in wet season. For dry season, Megachile and other solitary species from Apidae and Halictidae families were better represented and relevant for the community. We found that Fabaceae is an important resource for bees in both seasons. In addition, herbaceous species from Asteraceae and Convolvulaceae were preferred in wet season, while shrub and tree species from Fabaceae and Polygonaceae were the main resource in dry season.

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models. Our proposal manages to properly classify up to 98% of the examples from a dataset with 46 different classes of pollen grains, produced by the Classifynder classification system. This is an unprecedented result which surpasses all previous attempts both in accuracy and number and difficulty of taxa under consideration, which include types previously considered as indistinguishable., Competing Interests: The authors have declared that no competing interests exist.

The genus Rubus L. (Rosaceae) not been investigated satisfactorily in terms of palynology. This genus is taxonomically very difficult due to the large number of species and problems with their delimitation, as well as very different distribution areas of particular species. The aim of this study was to investigate pollen morphology and for the first time the ranges of intrageneric and interspecific variability of Rubus species, as well as verify the taxonomic usefulness of these traits in distinguishing studied taxa from this genus. The selected species of the genus Rubus were analysed for 11 quantitative pollen characteristics and the following qualitative ones: exine ornamentation, pollen outline and shape, as well as bridge structure. Analyses were conducted on a total of 1740 pollen grains, which represent 58 blackberry species belonging to a majority of subgenera and all the sections and series found in Poland. The most important characters included exine ornamentation (exine ornamentation type, width and direction of grooves and striae, number and diameter of perforations) and length of the polar axis (P). The arrangement of the examined species on the dendrogram does not corroborate division of the genus Rubus into subgenera, sections and series currently adopted in taxonomy. This fact is not surprising because the taxonomy of the genus was not based on pollen characters. Pollen features should be treated in taxonomy as auxiliary, because they fail to differentiate several (10) individual species, while the other ones create groups with similar pollen traits., Competing Interests: No authors have competing interests

Plant community response to climate change ranges from synchronous tracking to strong mismatch. Explaining this variation in climate change response is critical for accurate global change modeling. Here we quantify how closely assemblages track changes in climate (match/mismatch) and how broadly climate niches are spread within assemblages (narrow/broad ecological tolerance, or "filtering") using data for the past 21,000 years for 531 eastern North American fossil pollen assemblages. Although climate matching has been strong over the last 21 millennia, mismatch increased in 30% of assemblages during the rapid climate shifts between 14.5 and 10 ka. Assemblage matching rebounded toward the present day in 10%-20% of assemblages. Climate-assemblage mismatch was greater in tree-dominated and high-latitude assemblages, consistent with persisting populations, slower dispersal rates, and glacial retreat. In contrast, climate matching was greater for assemblages comprising taxa with higher median seed mass. More than half of the assemblages were climatically filtered at any given time, with peak filtering occurring at 8.5 ka for nearly 80% of assemblages. Thus, vegetation assemblages have highly variable rates of climate mismatch and filtering over millennial scales. These climate responses can be partially predicted by species' traits and life histories. These findings help constrain predictions for plant community response to contemporary climate change.

The number of pollen grains is a critical part of the reproductive strategies in plants and varies greatly between and within species. In agriculture, pollen viability is important for crop breeding. It is a laborious work to count pollen tubes using a counting chamber under a microscope. Here, we present a method of counting the number of pollen grains using a cell counter. In this method, the counting step is shortened to 3 min per flower, which, in our setting, is more than five times faster than the counting chamber method. This technique is applicable to species with a lower and higher number of pollen grains, as it can count particles in a wide range, from 0 to 20,000 particles, in one measurement. The cell counter also estimates the size of the particles together with the number. Because aborted pollen shows abnormal membrane characteristics and/or a distorted or smaller shape, a cell counter can quantify the number of normal and aborted pollen separately. We explain how to count the number of pollen grains and measure pollen size in Arabidopsis thaliana, Arabidopsis kamchatica, and wheat (Triticum aestivum).

Bee pollen loads generally have a homogeneous and monospecific pollen content and assume a typical form and color, due to the typical bee foraging habits, thus having a typical composition related to the botanical origin. The present study aims to characterize bee pollen loads belonging to different botanical species using morphological, spectroscopic and color properties and to find relationships between these variables. IR spectra analysis allowed to have a reliable picture of the components present in the different samples; color and granulometry permits a visual identification of pollen load belonging to different species. Multivariate analysis enabled differentiation among the botanical origin of most of the bee pollen samples, grouping them according to the family and the genus and confirming the possibility to use IR and color measurements for the evaluative analysis and classification of bee pollen samples, to promote the consumption of this bee product as functional food.

The medicinal plants are utilized globally considering the cheap and chemical free source, but their correct identification and authentication is prerequisite for safety and efficacy of plant-based medicines. The present study encompassed traded medicinal plants (16) with high therapeutic value from diverse families like Brassicaceae, Berberidaceae, Malvaceae, Salicaceae, Myrtaceae, Papilionaceae, Ascelpiadaceae, Colchicaceae, Violaceae, and Vitaceae for detailed microscopic study of characters that is, morphology, pollen shape and sizes, P/E ratio, pore length and width, spine length, colpi dimensions, and exine sculpture pattern. The plants showed noteworthy differences in microscopy of Wattakaka volubilis having pollinia, translator and corpusculum like structures while pores were visible in Colchicum luteum, Alcea rosea, and Hibiscus syriacus. The spines were observed in Centipeda minima, A. rosea, and H. syriacus being dimorphic spines in A. rosea and monomorphic in H. syriacus. The exine sculpturing pattern was reticulate in mostly studied plants however distinctive exine pattern was noted in Berberis aristata and Berberis lyceum. The highest polar diameter, equatorial diameter and exine thickness among studied plants were observed in H. syriacus (161 μm), C. luteum (50 μm) and Vitis jacquemontii (1.10), respectively. Thus, microscopy of medicinal plants in addition to other taxonomic evidence offers a supportive skill in authentication, consequently utilization by local consumers and pharmaceutical industries., (© 2019 Wiley Periodicals, Inc.)

Surghar mountain belt has comparatively less natural resources of floral diversity because it is composed of minerals of different kinds making it less favorable for the growth of different vegetation. The pollen morphology of some selected plants from Surghar belt Mainwali has been evaluated. The pollen grains were measured and demonstrated using scanning electron microscopy (SEM). The examined plant specimens have a difference in size, shape, polarity, and their exine ornamentation. The pollen taxa show a huge variation in size and sculpture. Pollen fertility has also been estimated, shows that the selected plants are well-known in the Surghar belt. The need of the hour is to conserve these plants having a higher fertility rate to cope with the deforestation in an area. The conclusion does not favor theories in which deforestation results in fast growth in population. It shows that the irrational management and unlawful cutting down of woods neglected by the forest department are the main causes of deforestation in the mountain belt of Mianwali. The findings show the importance of morphological characteristics in the identification of natural resource species in the area., (© 2019 Wiley Periodicals, Inc.)

Methods of reconstructing changes in plant traits over long time scales are needed to understand the impact of changing environmental conditions on ecosystem processes and services. Although Holocene pollen have been extensively used to provide records of vegetation history, few studies have adopted a functional trait approach that is pertinent to changes in ecosystem processes. Here, for woody and herbaceous fen peatland communities, we use modern pollen and vegetation data combined with pollen records from Holocene deposits to reconstruct vegetation functional dynamics. The six traits chosen (measures of leaf area-to-mass ratio and leaf nutrient content) are known to modulate species' fitness and to vary with changes in ecosystem processes. We fitted linear mixed effects models between community weighted mean (CWM) trait values of the modern pollen and vegetation to determine whether traits assigned to pollen types could be used to reconstruct traits found in the vegetation from pollen assemblages. We used relative pollen productivity (RPP) correction factors in an attempt to improve this relationship. For traits showing the best fit between modern pollen and vegetation, we applied the model to dated Holocene pollen sequences from Fenland and Romney Marsh in eastern and southern England and reconstructed temporal changes in trait composition. RPP adjustment did not improve the linear relationship between modern pollen and vegetation. Leaf nutrient traits (leaf C and N) were generally more predictable from pollen data than mass-area traits. We show that inferences about biomass accumulation and decomposition rates can be made using Holocene trait reconstructions. While it is possible to reconstruct community-level trends for some leaf traits from pollen assemblages preserved in sedimentary archives in wetlands, we show the importance of testing methods in modern systems first and encourage further development of this approach to address issues concerning the pollen-plant abundance relationship and pollen source area., Competing Interests: The authors have declared that no competing interests exist.

The unique medicinal and nutritional properties of honey are determined by its chemical composition. To evaluate the quality of honey, it is essential to study the surrounding vegetation where honeybees forage. In this study we used conventional melissopalynological and DNA barcoding techniques to determine the floral source of honey samples collected from different districts of the state of Mizoram, India. Pollen grains were isolated and genomic DNA was extracted from the honey samples. PCR amplification was carried out using universal barcode candidates ITS2 and rbcL to identify the plant species. Furthermore, TA cloning was carried out to screen the PCR amplicon libraries to identify the presence of multiple plant species. Results from both the melissopalynological and DNA barcoding analyses identified almost exactly the same 22 species, suggesting that both methods are suitable for analysis. However, DNA barcoding is easier and widely practiced. Hence, it can be concluded that DNA barcoding is a useful tool in determining the medicinal and commercial value of honey.