The effect of thyrotropin and thyroid hormones in in vitro follicle growth of mouse primary preantral follicles, in in vitro fertilization and early embryo development to the blastocyst stage, and the expression of their receptors during follicle maturation

ΑΙΜThe aim of the study was to assess the effect of Thyrotropin (TSH) and thyroid hormones Triiodothyronine (T3) and Thyroxine (T4) in in vitro follicle growth of mouse primary preantral follicles, in in vitro fertilization and early embryo development to the blastocyst stage, and the expression of their receptors during follicle maturation. MATERIALS & METHODSPre-pubertal female mice, aged 13-14 days, and adult male mice, aged 3-4 months, all C57BL/6 x CBA F1 hybrids, were used in this animal experimental study. Primary preantral follicles were dissected from the ovaries, cultured in vitro for 10 days and mature oocytes were fertilized using sperm retrieved from adult male F1 hybrids. The effect of TSH in a concentration of 2 mIU/ml, 1 mIU/ml and 0.2 mIU/ml and the effect of T3 and in a concentration of 10-9 M, 10-8 M, 10-7 M each, were evaluated. Furthermore, on culture day 0, 2, 5 and 8, follicles were collected, and on culture day 10, cumulus oocyte complexes (COCs) and denuded oocytes (DO) were collected for total RNA extraction, which was used for complementary DNA synthesis. This in turn was subjected to Real-Time PCR for the detection and quantification of TSH receptor (TSHR) and thyroid hormone (TRα and TRβ) gene expression.RESULTSΤ3 was found to negatively affect follicular growth and maturation in the lower 10-9 M and intermediate 10-8 Μ concentrations. Similarly, Τ4 was found to negatively affect follicular growth and maturation of mouse preantral follicles in the intermediate concentration 10-8 Μ. Contrary to thyroid hormones, no impact of TSH was found in the process of folliculogenesis.Τ3 had a negative effect in fertilization and early embryo development to the 2-cell stage at a concentration of 10-8 M and T4 negatively affected fertilization at a concentration of 10-9 Μ and 10-7 Μ. Interestingly, higher 2-cell, 4-cell and morula/blastocyst stage rates were shown at a concentration of 10-8 Μ. TSH had no effect on fertilization and early embryo development.With regards to hormone receptor gene expression, on day 0, none of TSHR, TRα and TRβ were expressed. TRβ was not expressed at all throughout folliculogenesis (days 2, 5 and 8) and also was not expressed in COCs and DO. TRα and TRβ were expressed in follicles collected on days 2, 5 and 8 as well as in COCs, whereas their expression was absent in DO. CONCLUSIONSControversial results have been published regarding TSH, T3 and T4 effect on folliculogenesis in different species. But even within the same species, discrepancies have been observed among studies, which could be attributed to cell culture type, culture medium selection and supplementation, stage of development/differentation at follicle retrieval and duration of culture. The negative effect of T3 and the positive effect of T4 at the intermediate concentration 10-8 Μ in early embryo development, may encourage researchers to work on the effect of these hormones in various combinations during folliculogenesis, fertilization and early embryo development, that is, in conditions that mimic follicular physiology, given that in vivo these hormones co-exist in the follicular milieu.ΣΚΟΠΟΣ. Ο σκοπός της μελέτης ήταν η αξιολόγηση της επίδρασης της Θυρεοειδοτρόπου ορμόνης (TSH) και των θυρεοειδικών ορμονών Τριιωδοθυρονίνη (T3) και Θυροξίνη (Τ4) στην in vitro ανάπτυξη πρώιμων πρωτογενών προκοιλοτικών ωοθυλακίων επίμυος, στη γονιμοποίηση και στην πρώιμη εμβρυϊκή ανάπτυξη στο στάδιο της βλαστοκύστης. Επιπλέον, σκοπός της μελέτης ήταν η ανίχνευση της έκφρασης των υποδοχέων των ανωτέρω ορμονών στα ωοθυλάκια κατά τα διάφορα στάδια της ωοθυλακικής ανάπτυξης. ΥΛΙΚΟ & ΜΕΘΟΔΟΣ. Στη μελέτη χρησιμοποιήθηκαν άνηβα θήλεα υβρίδια F1 ηλικίας 13-14 ημερών και ενήλικα άρρενα υβρίδια F1 ηλικίας 3-4 μηνών, προϊόντα διασταύρωσης επίμυων C57BL/6 (θήλυ) X CBA (άρρεν). Πρώιμα προκοιλοτικά ωοθυλάκια απομονώθηκαν από τις ωοθήκες, καλλιεργήθηκαν in vitro για 10 ημέρες και τα ώριμα ωάρια γονιμοποιήθηκαν με σπέρμα που ελήφθη από τα ενήλικα άρρενα υβρίδια. Αξιολογήθηκε η επίδραση της TSH σε συγκέντρωση 2 mIU/ml, 1 mIU/ml και 0,2 mIU/ml και η επίδραση των T3 και T4 σε συγκέντρωση 10-9 M, 10-8 M, 10-7 M η καθεμιά. Επιπλέον, τις ημέρες 0, 2, 5 και 8 της καλλιέργειας, συλλέχθηκαν ωοθυλάκια, ενώ την ημέρα 10 συμπλέγματα κοκκωδών κυττάρων-ωοκυττάρου (COCs) και απογυμνωμένα ωάρια (DO) προς απομόνωση ολικού RNΑ, από το οποίο συντέθηκε συμπληρωματικό DNA, το οποίο με τη σειρά του υπεβλήθη στην αλυσιδωτή αντίδραση πολυμεράσης πραγματικού χρόνου προς ανίχνευση και ποσοτικοποίηση της έκφρασης του υποδοχέα της TSH, TSHR, και των υποδοχέων των θυρεοειδικών ορμονών TRα και TRβ. ΑΠΟΤΕΛΕΣΜΑΤΑ. Η Τ3 είχε μία αρνητική επίδραση στην ωοθυλακική ανάπτυξη στην μικρότερη, 10-9 M, και ενδιάμεση συγκέντρωση, 10-8 Μ. Παρόμοια, η Τ4 είχε μία αρνητική επίδραση στην ωοθυλακική ανάπτυξη στην ενδιάμεση συγκέντρωση 10-8Μ. Σε αντίθεση με τις θυρεοειδικές ορμόνες, η TSH δεν είχε καμία επίδραση στην ωοθυλακιογένεση. Η Τ3 είχε μία αρνητική επίδραση στη γονιμοποίηση και την πρώιμη εμβρυική ανάπτυξη στο στάδιο των 2-κυττάρων στην ενδιάμεση συγκέντρωση 10-8 M και η T4 μία αρνητική επίδραση στη γονιμοποίηση στη μικρότερη και στη μεγαλύτερη συγκέντρωση, 10-9 Μ και 10-7 Μ αντίστοιχα. Ωστόσο, στην ενδιάμεση συγκέντρωση 10-8 Μ είχε μία θετική επίδραση, αφού οδήγησε σε μεγαλύτερα ποσοστά εμβρύων 2-κυττάρων, 4-κυττάρων και μοριδίου/βλαστοκύστης. Η TSH δεν είχε καμία επίδραση στη γονιμοποίηση και στην πρώιμη εμβρυική ανάπτυξη.Τέλος σε ότι αφορά την έκφραση των ορμονικών υποδοχέων, την ημέρα 0 δεν εκφραζόταν κανένας από τους TSHR, TRα και TRβ. Ο υποδοχέας TRβ, εξακολουθούσε να μην εκφράζεται σε όλα τα μετέπειτα στάδια της ωοθυλακιογένεσης, όπως και στα COCs και DO. Οι υποδοχείς ΤRα και TSHR εκφράζονταν στα ωοθυλάκια τις ημέρες 2, 5, 8 και στα COCs, ενώ δεν εκφράζονταν στα DO. ΣΥΜΠΕΡΑΣΜΑΤΑ. Στα διάφορα είδη ζώων υπάρχουν αντικρουόμενες αναφορές για την επίδραση της TSH και των T3 και T4 στην ωοθυλακιογένεση. Ωστόσο όμως, και στο ίδιο είδος, οι επιδράσεις φαίνεται ότι διαφοροποιούνται ανάλογα με τον τύπο των κυττάρων που καλλιεργούνται, το είδος και τον εμπλουτισμό του καλλιεργητικού μέσου, το στάδιο ανάπτυξης των ωοθυλακίων, και τη χρονική διάρκεια της καλλιέργειας.Η διαπίστωση της αρνητικής επίδρασης της Τ3 και της θετικής επίδρασης της ενδιάμεσης συγκέντρωσης 10-8 Μ της Τ4 στην πρώιμη εμβρυική ανάπτυξη, ανοίγει νέους δρόμους έρευνας της επίδρασης ορμονών αυτών σε διάφορους συνδυασμούς μεταξύ τους, σε συνθήκες που προσομοιάζουν την φυσιολογία της ωοθυλακικής ανάπτυξης, με δεδομένο ότι οι ορμόνες αυτές in vivo συνυπάρχουν στο μικροπεριβάλλον των ωοθυλακίων

A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output

Plant viruses induce plant volatiles that are detected by aphid parasitoids

Abstract Aphis gossypii (Sternorrhyncha: Aphididae) aphids are vectors of important plant viruses among which cucumber mosaic virus (CMV) and potato virus Y (PVY). Virus-infected plants attract aphid vectors and affect their behavior and growth performance either positively or negatively depending on mode of transmission. Viruses cause changes in the composition and the amount of volatile organic compounds (VOCs) released by the plant that attract aphids. The aphid parasitoid Aphidius colemani (Hymenoptera: Aphelinidae) has been shown to have higher parasitism and survival rates on aphids fed on virus-infected than aphids fed on non-infected plants. We hypothesized that parasitoids distinguish virus-infected plants and are attracted to them regardless of the presence of their aphid hosts. Herein, we examined the attraction of the A. colemani parasitoid to infected pepper plants with each of CMV or PVY without the presence of aphids. The dynamic headspace technique was used to collect VOCs from non-infected and CMV or PVY-infected pepper plants. Identification was performed with gas chromatography-mass spectrometry (GC–MS). The response of the parasitoids on virus-infected vs non-infected pepper plants was tested by Y-tube olfactometer assays. The results revealed that parasitoids displayed a preference to CMV and PVY infected plants compared to those that were not infected

Brown marmorated stink bugs are invading Europe: potential pathways of origin of the alien pest populations of Halyomorpha halys (Heteroptera, Pentatomidae)

The brown marmorated stink bug, Halyomorpha halys is an agricultural and household pest originating from Asia. In the last years it has become an invasive pest in North America causing severe economic losses to agricultural crops in the United States. Recently, H. halys has been retrieved in Europe (Switzerland, Germany, France, Hungary, and Greece) and, since September 2012, it has also been found in Italy. Tracing back the patterns of introduction and monitoring the spread of H. halys in the Italian territory in its initial phase of colonization will be useful in the view to implement better pest control strategies. The present study aims to identify the potential pathways of entry of H. halys by detecting the genetic diversity of specimens collected in Northern Italy, Southern Switzerland and Greece. The analyses of 1,175 base pairs of mitochondrial DNA cytochrome c oxidase I and II genes (cox1 and cox2) on more than 130 specimens led to the identification of ten haplotypes: one, scored in Italy and Greece, is the same found both in China and North America, while two haplotypes found in Switzerland and Lombardy are shared only with Chinese specimens. The other seven haplotypes are new and present high similarity with Asian haplotypes. Present data show that the introduction of the brown marmorated stink bug in Europe has occurred by means of multiple events, probably both from Asia and North America, and that H. halys is currently expanding its range in the European continent

Genetic diversity of the brown marmorated stink bug Halyomorpha halys in the invaded territories of Europe and its patterns of diffusion in Italy

Halyomorpha halys is an invasive stink bug pest originating from East Asia. In Europe, it was first detected in Switzerland in 2004. It is now present in thirteen countries, and seems to be spreading throughout the continent. In Italy, where it has been recorded since 2012, other than being an urban nuisance, it is causing severe damage in commercial fruit orchards. An integrated approach, using current and previous observational data in space and time and molecular information, was used to identify the genetic diversity of this pest in Europe, its invasion history, and the potential pathways of entry and diffusion. The analysis of 1175 bp of mitochondrial DNA cytochrome c oxidase I and II genes (cox1, cox2) led to the identification of twenty previously unknown haplotypes. The European distribution of H. halys is the result of multiple invasions that are still in progress, and, in some cases, it was possible to identify the specific Asian areas of origin. Moreover, secondary invasions could have occurred among European countries by a bridgehead effect. In Italy, the data were more clearly related to their temporal occurrence, allowing for a clearer reading of the patterns of invasion and dispersion. After having successfully established in localized areas, H. halys further expanded its range by an active dispersion process and/or by jump dispersal events due to passive transport. The multiple introductions from different areas of the native range together with the different patterns of diffusion of H. halys, may hamper the pest management strategies for its containment

A deep learning and GIS approach for the optimal positioning of wave energy converters

Summarization: Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.Presented on: Energie

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

Accurate photovoltaic (PV) diagnosis is of paramount importance for reducing investment risk and increasing the bankability of the PV technology. The application of fault diagnostic solutions and troubleshooting on operating PV power plants is vital for ensuring optimal energy harvesting, increased power generation production and optimised field operation and maintenance (O&M) activities. This study aims to give an overview of the existing approaches for PV plant diagnosis, focusing on unmanned aerial vehicle (UAV)-based approaches, that can support PV plant diagnostics using imaging techniques and data-driven analytics. This review paper initially outlines the different degradation mechanisms, failure modes and patterns that PV systems are subjected and then reports the main diagnostic techniques. Furthermore, the essential equipment and sensor's requirements for diagnosing failures in monitored PV systems using UAV-based approaches are provided. Moreover, the study summarizes the operating conditions and the various failure types that can be detected by such diagnostic approaches. Finally, it provides recommendations and insights on how to develop a fully functional UAV-based diagnostic tool, capable of detecting and classifying accurately failure modes in PV systems, while also locating the exact position of faulty modules

Automated geolocation in urban environments using a simple camera-equipped Unmanned Aerial Vehicle: a rapid mapping surveying alternative?

Summarization: GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.Presented on: ISPRS International Journal of Geo-Informatio