Acute appendicitis secondary to Enterobius vermicularis infection in a middle-aged man: a case report

<p>Abstract</p> <p>Introduction</p> <p>Acute appendicitis due to <it>Enterobius vermicularis </it>is very rare, affecting mostly children. Whether pinworms cause inflammation of the appendix or just appendiceal colic has been a matter of controversy.</p> <p>Case presentation</p> <p>A Caucasian 52-year-old man was referred to our Emergency Department with acute abdominal pain in his right lower quadrant. The physical and laboratory examination revealed right iliac fossa tenderness and leukocytosis with neutrophilia. An open appendectomy was performed. The pathological examination showed the lumen containing pinworms. Two oral doses of mebendazole were administered postoperatively. The follow-up to date was without incident and he was free of symptoms one year after the operation.</p> <p>Conclusion</p> <p>The finding of <it>E. vermicularis </it>in appendectomy pathological specimens is infrequent. Parasitic infections rarely cause acute appendicitis, especially in adults.</p> <p>One should keep in mind that the clinical signs of intestinal parasite infection may mimic acute appendicitis, although rare. A careful evaluation of symptoms such as pruritus ani, or eosinophilia on laboratory examination, could prevent unnecessary appendectomies.</p

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

Revision breast and chest wall reconstruction in Poland and pectus excavatum following implant complication using free deep inferior epigastric perforator flap

This study aims to present the case of a female patient with Poland′s syndrome and pectus excavatum deformity who underwent breast and chest wall reconstruction with a pre-shaped free deep inferior epigastric perforator flap. A 57-year-old female patient with Poland′s syndrome and pectus excavatum presented with a Baker III capsular contracture following a previously performed implant-based right breast reconstruction. After a chest and abdominal CT angiography, she was staged as 2A1 chest wall deformity according to Park′s classification and underwent implant removal and capsulectomy, followed by a pre-shaped free abdominal flap transfer, providing both breast reconstruction and chest wall deformity correction in a single stage operation. Post-operative course was uneventful, and the aesthetic result remains highly satisfactory 24 months after surgery. Deep inferior epigastric free flap represents an interesting reconstructive solution when treating Poland′s syndrome female patients with chest wall and breast deformities

Optimal siting of wave energy converters using machine learning and GIS

Περίληψη: Στις μέρες μας, οι Ανανεώσιμες Πηγές Ενέργειας αποτελούν μία διέξοδο στο πρόβλημα της ολοένα και εντονότερης κλιματικής αλλαγής. Γι' αυτόν τον λόγο, αρκετές χώρες προχωρούν στην παραγωγή ηλεκτρικής ενέργειας από εναλλακτικές πηγές όπως, φωτοβολταϊκά και αιολικά συστήματα, με σκοπό τον σταδιακό περιορισμό λειτουργίας των ρυπογόνων συμβατικών μέσων παραγωγής ηλεκτρικής ενέργειας (λιγνίτης, άνθρακας). Μια εκ των εναλλακτικών πηγών παραγωγής ηλεκτρικής ενέργειας αποτελεί η κυματική. Οι Μετατροπείς Κυματικής Ενέργειας είναι συστήματα τα οποία μετατρέπουν την κυματική ενέργεια σε ηλεκτρική. Εκτιμάται ότι το ποσοστό της παραγόμενης ενέργειας από ανανεώσιμες πηγές με αξιοποίηση της κυματικής ενέργειας είναι δυνατόν να αυξηθεί σε μεγάλη κλίμακα μελλοντικά σε περιοχές όπως η Σκανδιναβία, η Μεσόγειος, το Ηνωμένο Βασίλειο, η Ωκεανία και η θαλάσσια περιοχή της αμερικανικής ηπείρου. Στη συγκεκριμένη εργασία εξετάζεται η βέλτιστη χωροθέτηση εγκατάστασης Μετατροπέων Κυματικής Ενέργειας. Προκειμένου να βρεθεί η βέλτιστη χωροθέτηση είναι απαραίτητο να ληφθούν υπόψη γεωχωρικοί και τεχνικοί περιορισμοί. Οι γεωχωρικοί περιορισμοί εξαρτώνται τόσο από την θαλάσσια χλωρίδα όσο και από την χρήση της εγγύτερης παραθαλάσσιας γης. Οι τεχνικοί περιορισμοί περιλαμβάνουν τις μετεωρολογικές συνθήκες και η μορφολογία του θαλάσσιου πυθμένα. Οι ιδανικές περιοχές εγκατάστασης επιλέγονται μετά τον αποκλεισμό των σημείων που δεν πληρούν τους προαναφερθέντες περιορισμούς. Η αναζήτηση ιδανικών τοποθεσιών επιτυγχάνεται μέσω της αξιοποίησης αλγορίθμων Μηχανικής Μάθησης. Αρχικά, υλοποιείται ένα Νευρωνικό Δίκτυο που λειτουργεί βάσει της συγχώνευσης ετερογενών δεδομένων, εν προκειμένω δορυφορικών εικόνων και χρονοσειρών μετεωρολογικών δεδομένων. Το γεγονός αυτό συνεπάγεται τον καθορισμό αρχιτεκτονικής δύο διακλαδώσεων. Η διακλάδωση που εκπαιδεύεται με δεδομένα εικόνων προβλέπει τον εντοπισμό δυναμικών γεωχωρικών κλάσεων στην υποψήφια περιοχή εγκατάστασης, ενώ η δεύτερη διακλάδωση είναι υπεύθυνη για την ταξινόμησή της περιοχής, αξιοποιώντας χρονοσειρές ύψους και περιόδου κυμάτων. Στη λήψη της τελικής απόφασης για τη καταλληλότητα της υποψήφιας περιοχής διαδραματίζουν σημαντικό ρόλο και πλήθος στατικών δεδομένων χρήσης γης, των οποίων η αξιοποίηση δεν απαιτεί κάποιον αλγόριθμο Μηχανικής Μάθησης. Επομένως, τα εν λόγω δεδομένα συνδυάζονται με τις προβλέψεις του Νευρωνικού Δικτύου με σκοπό την βελτιστοποίηση χωροθέτησης των Μετατροπέων Κυματικής Ενέργειας. Για λόγους πληρότητας και ευελιξίας, δημιουργείται ακόμη ένα Νευρωνικό Δίκτυο πολλαπλών διεργασιών, δηλαδή δύο εξόδων. Το συγκεκριμένο μοντέλο εκτός από την πρόβλεψη της καταλληλότητας μιας περιοχής ανάλογα με την θαλάσσια χλωρίδα και την κυματική ενέργεια, προβλέπει και τις κλάσεις χρήσης γης μέσω της Ταξινόμησης Πολλαπλών Ετικετών. Στην περίπτωση αυτή οι υποψήφιες περιοχές ταξινομούνται ως ιδανικές ή ως μη ιδανικές για την εγκατάσταση Μετατροπέων Κυματικής Ενέργειας αποκλειστικά με την βοήθεια των προβλέψεων του Νευρωνικού Δικτύου. Είναι προφανές πως για την ανάπτυξη και εφαρμογή του προαναφερθέντος συστήματος και του Νευρωνικού Δικτύου απαιτείται μεγάλος όγκος ετερογενών δεδομένων. Για τον λόγο αυτό, δημιουργείται ένα προγραμματιστικό εργαλείο εξαγωγής γεωγραφικής πληροφορίας που αποσκοπεί στην συλλογή γεωαναφερόμενων δεδομένων. Το εργαλείο αυτό καθίσταται υπεύθυνο για την ανάπτυξη δεδομένων εκπαίδευσης και την εφαρμογή του συνολικού συστήματος στην επιθυμητή περιοχή μελέτης μετά το πέρας της εκπαίδευσης. Η προτεινόμενη μεθοδολογία εφαρμόζεται στην θαλάσσια περιοχή της πόλης Σίνες της Πορτογαλίας. Στην συγκεκριμένη γεωγραφική περιοχή εστιάζουν και εργασίες στις οποίες η βέλτιστη χωροθέτηση πραγματοποιείται μέσω των παραδοσιακών μεθόδων. Στην παρούσα εργασία συγκαταλέγονται μεταξύ άλλων και υποψήφιες περιοχές εγκατάστασης κοντά στην ακτή. Γι' αυτόν τον λόγο, η σύγκριση των αποτελεσμάτων μπορεί να πραγματοποιηθεί κυρίως για τα υπεράκτια σημεία. Αν και η αξιολόγηση της κυματικής ενέργειας πραγματοποιείται μέσω διαφορετικών μεθόδων και στην παρούσα εργασία υπολογίζεται στην μελέτη και η θαλάσσια χλωρίδα, υπάρχει συμφωνία των αποτελεσμάτων σε πολύ μεγάλο βαθμό. Το πρώτο Νευρωνικό Δίκτυο επιτυγχάνει απόδοση Δυαδικής Ταξινόμησης 98.7 %, ενώ το Νευρωνικό Δίκτυο πολλαπλών διεργασιών 97.5 % στο αντίστοιχο μέγεθος και 93.5 % στην μετρική F1 της εξόδου Ταξινόμησης Πολλαπλών Ετικετών.Summarization: Nowadays, Renewable Energy Sources are the solution to the problem of ever-increasing climate change. For this reason, several countries are proceeding with the electricity production using alternative sources such as solar and wind farms. This makes it possible to limit the operation of conventional means of production (lignite, coal-fired) gradually. One of the alternative sources of electricity production are the ocean waves. Wave energy Converters are systems that convert wave energy into electrical energy. It is alleged that the percentage of energy produced by renewable sources using wave energy can be increased on a large-scale in the future in regions such as Scandinavia, the Mediterranean, the United Kingdom, Oceania and the maritime region of the American continent. In this work, the optimal siting of the installation of wave energy converters is examined, which is one of the main areas of research of this field. It is essential to take into account geospatial and technical limitations, in order to find the optimal locations. Geospatial constraints depend on both seagrass and the Land Use classes of the closest coastal area. 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. Searching for optimal locations is achieved through the utilization of Machine Learning algorithms. Initially, a Deep Neural Network that is implemented operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of 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 classifier 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 candidate area, a large number of static land use data play an important role, the utilization of which does not require a Machine Learning algorithm. Therefore, 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 marine flora and wave energy, also predicts land use classes through Multi-Label classification process. In this case, the potential regions classified as suitable or as not suitable for the installation of Wave Energy Converter system exclusively with the help of neural network predictions. Obviously, a large amount of heterogeneous data is required for the development and implementation of the aforementioned system and Deep Neural Network. For this reason, a geographic information tool that aims to receive and store georeferenced data is developed. This tool is employed to develop training and validation datasets and to use the overall system in the desired case study. The proposed methodology is applied in the marine area of the city of Sines, Portugal. In this geographical area, they also focus on papers in which optimal zoning is carried out through the traditional methods. The present work includes, among others, potential nearshore areas. For this reason, the comparison of the results can be carried out mainly for the offshore points. Although the evaluation of wave energy is carried out through different methods and in the present work is calculated on the study and seagrass, there is agreement of the results largely. 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

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

The ARMC5 gene shows extensive genetic variance in primary macronodular adrenocortical hyperplasia

OBJECTIVE: Primary macronodular adrenal hyperplasia (PMAH) is a rare type of Cushing’s syndrome (CS) that results in increased cortisol production and bilateral enlargement of the adrenal glands. Recent work showed that the disease may be caused by germline and somatic mutations in the ARMC5 gene, a likely tumor-suppressor gene (TSG). We investigated 20 different adrenal nodules from one patient with PMAH for ARMC5 somatic sequence changes. DESIGN: All of the nodules where obtained from a single patient who underwent bilateral adrenalectomy. DNA was extracted by standard protocols and the ARMC5 sequence was determined by the Sanger method. RESULTS: Sixteen of 20 adrenocortical nodules harbored, in addition to what appeared to be the germline mutation, a second somatic variant. The p.Trp476* sequence change was present in all 20 nodules, as well as in normal tissue from the adrenal capsule, identifying it as the germline defect; each of the 16 other variants were found in different nodules: 6 were frame shift, 4 were missense, 3 were nonsense, and 1 was a splice site variation. Allelic losses were confirmed in 2 of the nodules. CONCLUSION: This is the most genetic variance of the ARMC5 gene ever described in a single patient with PMAH: each of 16 adrenocortical nodules had a second new, “private”, and -in most cases- completely inactivating ARMC5 defect, in addition to the germline mutation. The data support the notion that ARMC5 is a TSG that needs a second, somatic hit, to mediate tumorigenesis leading to polyclonal nodularity; however, the driver of this extensive genetic variance of the second ARMC5 allele in adrenocortical tissue in the context of a germline defect and PMAH remains a mystery