Evaluation of hemosponge in promoting dental socket healing after 3rd mandibular premolar extraction in a feline model

Aim: To investigate the healing process following use of collagen sponges in the dental socket after extraction. Wound complications during the study were also evaluated. Methods: 32 cats were included in this study. IV administration of the combination of diazepam (0.22 mg/kg) and ketamine (10 mg/kg) was used to induce general anesthesia. Surgical extraction of both 3rd mandibular premolars was performed. The open dental sockets were divided in two groups. In Group A, the open dental socket on the left side was closed using 4-0 Monocryl in simple interrupted pattern. In Group B, the right dental socket was filled with lyophilized hydrolyzed collagen and the buccal and lingual flaps were sutured using 4-0 Monocryl and simple interrupted pattern. Meloxicam (0.2 mg/kg) was used to manage the post-extraction pain in all cats. Ampicilline 20 mg/kg was used as prophylaxis. The wounds were observed during the study to evaluate any signs of inflammation or dehiscence. Radiographs were taken to compare healing of the socket 3 weeks after the procedure. A 1 mm biopsy punch sample was taken from sockets in all cats for comparison of the healing in both groups. Results: Hemorrhage occurred only in the sockets of Group A. Remission of radiolucent area occurred in both groups. Mean score of inflammation was lower and mean scores of fibrotic reaction and fibroplasia were higher in Group B (p<0.05). Conclusions: Use of hemosponge in alveolar socket may accelerate fibroplasia and formation of the connective tissue and reduce inflammation after tooth extraction. Therefore, post-extraction use of the hemostatic agent in the dental socket is recommended.

IONIC LIQUID EXTRACTION UNVEILS PREVIOUSLY OCCLUDED HUMICBOUND IRON IN PEAT POREWATER

Extracellular iron reduction has been suggested as a candidate metabolic pathway that may explain a large proportion of carbon respiration in temperate peatlands. However, the o-phenanthroline colorimetric method commonly employed to quantitate iron and partition between redox species is known to be unreliable in the presence of humic and fulvic acids, both of which represent a considerable proportion of peatland dissolved organic matter. We propose ionic liquid extraction as a more accurate iron quantitation and redox speciation method in humic-rich peat porewater. We evaluated both o-phenanthroline and ionic liquid extraction in four distinct peatland systems spanning a gradient of physico-chemical conditions to compare total iron recovery and Fe2+:Fe3+ ratios determined by each method. Ionic liquid extraction was found to provide more accurate iron quantitation and speciation in the presence of dissolved organic matter. A multivariate approach utilizing fluorescence- and UV-Vis spectroscopy was used to identify dissolved organic matter characteristics in peat porewater that lead to poor performance of the o-phenanthroline method. Where these interferences are present, we offer an empirical correction factor for total iron quantitation by o-phenanthroline, as verified by ionic liquid extraction. The written work presented in this thesis is in preparation for submission to Soil Biology and Biochemisrty by T.J. Veverica, E.S. Kane, A.M. Marcarelli, and S.A. Green.

Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction

Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants.

Jointly event extraction and visualization on Twitter via probabilistic modelling

Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. In this paper, we propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tweets where both tasks benefit from each other. We model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tweets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tweets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization.

Topic extraction from microblog posts using conversation structures

Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model.

Function, Purpose, Predication, and Context of Information Organization Frameworks

This paper outlines the purposes, predications, functions, and contexts of information organization frameworks; including: bibliographic control, information retrieval, resource discovery, resource description, open access scholarly indexing, personal information management protocols, and social tagging in order to compare and contrast those purposes, predications, functions, and contexts. Information organization frameworks, for the purpose of this paper, consist of information organization systems (classification schemes, taxonomies, ontologies, bibliographic descriptions, etc.), methods of conceiving of and creating the systems, and the work processes involved in maintaining these systems. The paper first outlines the theoretical literature of these information organization frameworks. In conclusion, this paper establishes the first part of an evaluation rubric for a function, predication, purpose, and context analysis.

Factores de riesgo preoperatorios asociados a conversión a técnica abierta en colecistectomía laparoscópica de urgencia

Introducción: la colecistectomía laparoscópica es la técnica de elección en pacientes con indicación de extracción quirúrgica de la vesícula; sin embargo, en promedio 20% de éstos requieren conversión a técnica abierta. En este estudio se evaluaron los factores de riesgo preoperatorios para conversión en colecistectomía laparoscópica de urgencia. Metodología: se realizó un estudio de casos y controles no pareado. Se obtuvo información sociodemográfica y de variables de interés de los registros de historias clínicas de pacientes operados entre el 2013 y 2016. Se identificaron los motivos de conversión de técnica quirúrgica. Se caracterizó la población de estudio y se estimaron asociaciones según la naturaleza de las variables. Mediante un análisis de regresión logística se ajustaron posibles variables de confusión. Resultados: se analizaron los datos de 444 pacientes (111 casos y 333 controles). La causa de conversión más frecuente fue la dificultad técnica (50,5%). Se encontró que la mayor edad, el sexo masculino, el antecedente de cirugía abierta en hemiabdomen superior, el signo de Murphy clínico positivo, la dilatación de la vía biliar, la leucocitosis y la mayor experiencia del cirujano, fueron factores de riesgo para conversión. Se encontró un área bajo la curva ROC= 0,743 (IC95% 0,692–0,794, p= <0,001). Discusión: existen unos factores que se asocian a mayor riesgo de conversión en colecistectomía laparoscópica. La mayoría se relacionan con un proceso inflamatorio más severo, por lo que se debe evitar la prolongación del tiempo de espera entre el inicio de los síntomas y la extirpación quirúrgica de la vesícula.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Connecting works of art within the semantic web of symbolic meanings

My doctoral research is about the modelling of symbolism in the cultural heritage domain, and on connecting artworks based on their symbolism through knowledge extraction and representation techniques. In particular, I participated in the design of two ontologies: one models the relationships between a symbol, its symbolic meaning, and the cultural context in which the symbol symbolizes the symbolic meaning; the second models artistic interpretations of a cultural heritage object from an iconographic and iconological (thus also symbolic) perspective. I also converted several sources of unstructured data, a dictionary of symbols and an encyclopaedia of symbolism, and semi-structured data, DBpedia and WordNet, to create HyperReal, the first knowledge graph dedicated to conventional cultural symbolism. By making use of HyperReal's content, I showed how linked open data about cultural symbolism could be utilized to initiate a series of quantitative studies that analyse (i) similarities between cultural contexts based on their symbologies, (ii) broad symbolic associations, (iii) specific case studies of symbolism such as the relationship between symbols, their colours, and their symbolic meanings. Moreover, I developed a system that can infer symbolic, cultural context-dependent interpretations from artworks according to what they depict, envisioning potential use cases for museum curation. I have then re-engineered the iconographic and iconological statements of Wikidata, a widely used general-domain knowledge base, creating ICONdata: an iconographic and iconological knowledge graph. ICONdata was then enriched with automatic symbolic interpretations. Subsequently, I demonstrated the significance of enhancing artwork information through alignment with linked open data related to symbolism, resulting in the discovery of novel connections between artworks. Finally, I contributed to the creation of a software application. This application leverages established connections, allowing users to investigate the symbolic expression of a concept across different cultural contexts through the generation of a three-dimensional exhibition of artefacts symbolising the chosen concept.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).

Quantum information and quantum communication theory in relation to black hole idealized mechanical models

Oggigiorno il concetto di informazione è diventato cruciale in fisica, pertanto, siccome la migliore teoria che abbiamo per compiere predizioni riguardo l'universo è la meccanica quantistica, assume una particolare importanza lo sviluppo di una versione quantistica della teoria dell'informazione. Questa centralità è confermata dal fatto che i buchi neri hanno entropia. Per questo motivo, in questo lavoro sono presentati elementi di teoria dell'informazione quantistica e della comunicazione quantistica e alcuni sono illustrati riferendosi a modelli quantistici altamente idealizzati della meccanica di buco nero. In particolare, nel primo capitolo sono forniti tutti gli strumenti quanto-meccanici per la teoria dell'informazione e della comunicazione quantistica. Successivamente, viene affrontata la teoria dell'informazione quantistica e viene trovato il limite di Bekenstein alla quantità di informazione chiudibile entro una qualunque regione spaziale. Tale questione viene trattata utilizzando un modello quantistico idealizzato della meccanica di buco nero supportato dalla termodinamica. Nell'ultimo capitolo, viene esaminato il problema di trovare un tasso raggiungibile per la comunicazione quantistica facendo nuovamente uso di un modello quantistico idealizzato di un buco nero, al fine di illustrare elementi della teoria. Infine, un breve sommario della fisica dei buchi neri è fornito in appendice.

Dna Extraction From Filter-paper Spots Of Vaginal Samples Collected After Sexual Violence.

To detect the presence of male DNA in vaginal samples collected from survivors of sexual violence and stored on filter paper. A pilot study was conducted to evaluate 10 vaginal samples spotted on sterile filter paper: 6 collected at random in April 2009 and 4 in October 2010. Time between sexual assault and sample collection was 4-48hours. After drying at room temperature, the samples were placed in a sterile envelope and stored for 2-3years until processing. DNA extraction was confirmed by polymerase chain reaction for human β-globin, and the presence of prostate-specific antigen (PSA) was quantified. The presence of the Y chromosome was detected using primers for sequences in the TSPY (Y7/Y8 and DYS14) and SRY genes. β-Globin was detected in all 10 samples, while 2 samples were positive for PSA. Half of the samples amplified the Y7/Y8 and DYS14 sequences of the TSPY gene and 30% amplified the SRY gene sequence of the Y chromosome. Four male samples and 1 female sample served as controls. Filter-paper spots stored for periods of up to 3years proved adequate for preserving genetic material from vaginal samples collected following sexual violence.

Transmission Of Intra-cellular Genetic Information: A System Proposal.

One of the great challenges of the scientific community on theories of genetic information, genetic communication and genetic coding is to determine a mathematical structure related to DNA sequences. In this paper we propose a model of an intra-cellular transmission system of genetic information similar to a model of a power and bandwidth efficient digital communication system in order to identify a mathematical structure in DNA sequences where such sequences are biologically relevant. The model of a transmission system of genetic information is concerned with the identification, reproduction and mathematical classification of the nucleotide sequence of single stranded DNA by the genetic encoder. Hence, a genetic encoder is devised where labelings and cyclic codes are established. The establishment of the algebraic structure of the corresponding codes alphabets, mappings, labelings, primitive polynomials (p(x)) and code generator polynomials (g(x)) are quite important in characterizing error-correcting codes subclasses of G-linear codes. These latter codes are useful for the identification, reproduction and mathematical classification of DNA sequences. The characterization of this model may contribute to the development of a methodology that can be applied in mutational analysis and polymorphisms, production of new drugs and genetic improvement, among other things, resulting in the reduction of time and laboratory costs.

Polyphenol Extraction Optimisation From Ceylon Gooseberry (dovyalis Hebecarpa) Pulp.

Originally from Asia, Dovyalis hebecarpa is a dark purple/red exotic berry now also produced in Brazil. However, no reports were found in the literature about phenolic extraction or characterisation of this berry. In this study we evaluate the extraction optimisation of anthocyanins and total phenolics in D. hebecarpa berries aiming at the development of a simple and mild analytical technique. Multivariate analysis was used to optimise the extraction variables (ethanol:water:acetone solvent proportions, times, and acid concentrations) at different levels. Acetone/water (20/80 v/v) gave the highest anthocyanin extraction yield, but pure water and different proportions of acetone/water or acetone/ethanol/water (with >50% of water) were also effective. Neither acid concentration nor time had a significant effect on extraction efficiency allowing to fix the recommended parameters at the lowest values tested (0.35% formic acid v/v, and 17.6 min). Under optimised conditions, extraction efficiencies were increased by 31.5% and 11% for anthocyanin and total phenolics, respectively as compared to traditional methods that use more solvent and time. Thus, the optimised methodology increased yields being less hazardous and time consuming than traditional methods. Finally, freeze-dried D. hebecarpa showed high content of target phytochemicals (319 mg/100g and 1,421 mg/100g of total anthocyanin and total phenolic content, respectively).