Correlation between parodontal indexes and orthodontic retainers: prospective study in a group of 16 patients

Purpose. Fixed retainers are used to stabilize dental elements after orthodontic treatment. Being it a permanent treatment, it is necessary to instruct patients about a constant and continuous monitoring of their periodontal conditions and a correct oral hygiene. The aim of this study was to highlight the possible adverse effects of bonded retainers on parameters correlated to the health conditions of periodontal tissues. Materials and methods. We selected 16 patients, under treatment in the Orthodontics Department of University of Bari Dental School, who had undergone a lingual retainer insertion at the end of the orthodontic treatment. The patients were then divided into two groups (Control Group and Study Group) and monitored for 3 and 36 months, respectively. The following indexes were taken into consideration: gingival index (GI), plaque index (PI) and the presence of calculus (Calculus Index, CI), the probing depth and the presence of gingival recession on the six inferior frontal dental elements. Results. After the observation was carried out, any of the patients showed periodontal sockets and gingival recession. In the Study Group, only 1 patient had a PI score=3, the 7 left had scores between 0.66 and 2.83. In the Control Group, one patient had score=0, the other ones showed values between 0.5 and 1.66. The mean GI in the Study Group peaked at a score of 2.83, the minimum was 0.66; whereas in the Control Group the maximum value was 2 and the minimum 0.66. The CI in the Group Study was between 1 and 2. In the Control Group it was absent in only 1 patient, whereas in the remaining 7, it had a value between 0.3 and 1. The clinical data were studied by means of the Wilcoxon test. We found a statistically significant difference for what concerns the Plaque Indexes (PI) (P>0.05) and Calculus Indexes (CI) (P>0.1) in both groups, with higher scores in the Study Group, having retainers for 36 months. Any statistically significant difference was calculated for the GI. Conclusions. We can therefore conclude that patients with lingual retainers need periodontal hygiene and treatment as to prevent, in the course of time, periodontal damages non-detectable in short-term

High prevalence of hepatitis C virus infection in patients with B-cell lymphoproliferative disorders in Italy.

Starting from the observation that a number of consecutive patients with non-Hodgkin's lymphoma (NHL) resulted positive for hepatitis C virus (HCV) antibodies on routine testing, we set up a survey for HCV contact prevalence in all patients with lymphoproliferative disorders (LPD) followed in our institution. We searched for HCV antibodies by a thirdgeneration ELISA technique, followed by a confirmation test (RIBA III); serum viral RNA and HCV genotype were investigated by a RT-PCR technique. We screened a total of 315 patients suffering from B-NHL (91), multiple myeloma (56), MGUS (48), chronic lymphocytic leukemia (57), Waldentrom's macroglobulinemia (13), Hodgkin's disease (HD)(43), and T-NHL (9). While only I of 52 patients with a non-B-LPD (HD or T-NHL) had signs of HCV contact (i.e., 1.9%, which is in the range of the normal population in the South of Italy), 59 of 263 patients with a B-LPD (22.4%) had HCV antibodies or RNA, or both, with no major differences among the various types of disorders, except for WM, in which the rate was higher (61.5%). The same prevalence was found for patients tested at diagnosis or during the follow-up, and in transfused or never-transfused patients. Only a few patients were aware of having a liver disease; one-half of HCV-positive patients never had transaminase increase. A review of data from Central and Northern Italy is included, showing similar findings; a report from Japan has confirmed such an association, while limited surveys in England have not revealed any correlation. These findings may have important biological and clinical implications

Central venous catheter insertion: a bedside procedure for haematological patients.

The present management of onco-haematologic patients may require continuous infusion of cytotoxic drugs, use of drugs or concentrated ion solutions which are toxic for the endothelial wall of small vessels, infusion of large amounts of antibiotics or antimycotics, red blood cell and platelet transfusion, and not rarely parenteral nutrition. Such a complex therapy needs a vascular access by a central vein catheter (CVC) insertion. Many types of CVC are available at present: tunnelled Hickman or Hickmanlike catheters, subcutaneous ports, tunnelled catheters with Groshong valve, external untunnelled catheters

Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas.

Flood damage in urbanized watersheds may be assessed by combining the flood depth–damage curves and the outputs of urban flood models. The complexity of the physical processes that must be simulated and the limited amount of data available for model calibration may lead to high uncertainty in the model results and consequently in damage estimation. Moreover depth–damage functions are usually affected by significant uncertainty related to the collected data and to the simplified structure of the regression law that is used. The present paper carries out the analysis of the uncertainty connected to the flood damage estimate obtained combining the use of hydraulic models and depth–damage curves. A Bayesian inference analysis was proposed along with a probabilistic approach for the parameters estimating. The analysis demonstrated that the Bayesian approach is very effective considering that the available databases are usually short

HIERARCHICAL ENSEMBLE METHODS FOR ONTOLOGY-BASED PREDICTIONS IN COMPUTATIONAL BIOLOGY

L'annotazione standardizzata di entit\ue0 biologiche, quali geni e proteine, ha fortemente promosso l'organizzazione dei concetti biologici in vocabolari controllati, cio\ue8 ontologie che consentono di indicizzare in modo coerente le relazioni tra le diverse classi funzionali organizzate secondo una gerarchia predefinita. Esempi di ontologie biologiche in cui i termini funzionali sono strutturati secondo un grafo diretto aciclico (DAG) sono la Gene Ontology (GO) e la Human Phenotype Ontology (HPO). Tali tassonomie gerarchiche vengono utilizzate dalla comunit\ue0 scientifica rispettivamente per sistematizzare le funzioni proteiche di tutti gli organismi viventi dagli Archea ai Metazoa e per categorizzare le anomalie fenotipiche associate a malattie umane. Tali bio-ontologie, offrendo uno spazio di classificazione ben definito, hanno favorito lo sviluppo di metodi di apprendimento per la predizione automatizzata della funzione delle proteine e delle associazioni gene-fenotipo patologico nell'uomo. L'obiettivo di tali metodologie consiste nell'\u201cindirizzare\u201d la ricerca \u201cin-vitro\u201d per favorire una riduzione delle spese ed un uso pi\uf9 efficace dei fondi destinati alla ricerca. Dal punto di vista dell'apprendimento automatico il problema della predizione della funzione delle proteine o delle associazioni gene-fenotipo patologico nell'uomo pu\uf2 essere modellato come un problema di classificazione multi-etichetta strutturato, in cui le predizioni associate ad ogni esempio (i.e., gene o proteina) sono sotto-grafi organizzati secondo una determinata struttura (albero o DAG). A causa della complessit\ue0 del problema di classificazione, ad oggi l'approccio di predizione pi\uf9 comunemente utilizzato \ue8 quello \u201cflat\u201d, che consiste nell'addestrare un classificatore separatamente per ogni termine dell'ontologia senza considerare le relazioni gerarchiche esistenti tra le classi funzionali. L'utilizzo di questo approccio \ue8 giustificato non soltanto dal fatto di ridurre la complessit\ue0 computazionale del problema di apprendimento, ma anche dalla natura \u201cinstabile\u201d dei termini che compongono l'ontologia stessa. Infatti tali termini vengono aggiornati mensilmente mediante un processo curato da esperti che si basa sia sulla letteratura scientifica biomedica che su dati sperimentali ottenuti da esperimenti eseguiti \u201cin-vitro\u201d o \u201cin-silico\u201d. In questo contesto, in letteratura sono stati proposti due classi generali di classificatori. Da una parte, si collocano i metodi di apprendimento automatico che predicono le classi funzionali in modo \u201cflat\u201d, ossia senza esplorare la struttura intrinseca dello spazio delle annotazioni. Dall'altra parte, gli approcci gerarchici che, considerando esplicitamente le relazioni gerarchiche fra i termini funzionali dell'ontologia, garantiscono che le annotazioni predette rispettino la \u201ctrue-path-rule\u201d, la regola biologica che governa le ontologie. Nell'ambito dei metodi gerarchici, in letteratura sono stati proposti due diverse categorie di approcci. La prima si basa su metodi kernelizzati per predizioni con output strutturato, mentre la seconda su metodi di ensemble gerarchici. Entrambi questi metodi presentano alcuni svantaggi. I primi sono computazionalmente pesanti e non scalano bene se applicati ad ontologie biologiche. I secondi sono stati per la maggior parte concepiti per tassonomie strutturate ad albero, e quei pochi approcci specificatamente progettati per ontologie strutturate secondo un DAG, sono nella maggioranza dei casi incapaci di migliorare le performance di predizione dei metodi \u201cflat\u201d. Per superare queste limitazioni, nel presente lavoro di tesi si sono proposti dei nuovi metodi di ensemble gerarchici capaci di fornire predizioni consistenti con la struttura gerarchica dell'ontologia. Tali approcci, da un lato estendono precedenti metodi originariamente sviluppati per ontologie strutturate ad albero ad ontologie organizzate secondo un DAG e dall'altro migliorano significativamente le predizioni rispetto all'approccio \u201cflat\u201d indipendentemente dalla scelta del tipo di classificatore utilizzato. Nella loro forma pi\uf9 generale, gli approcci di ensemble gerarchici sono altamente modulari, nel senso che adottano una strategia di apprendimento a due passi. Nel primo passo, le classi funzionali dell'ontologia vengono apprese in modo indipendente l'una dall'altra, mentre nel secondo passo le predizioni \u201cflat\u201d vengono combinate opportunamente tenendo conto delle gerarchia fra le classi ontologiche. I principali contributi introdotti nella presente tesi sono sia metodologici che sperimentali. Da un punto di vista metodologico, sono stati proposti i seguenti nuovi metodi di ensemble gerarchici: a) HTD-DAG (Hierarchical Top-Down per tassonomie DAG strutturate); b) TPR-DAG (True-Path-Rule per DAG) con diverse varianti algoritmiche; c) ISO-TPR (True-Path-Rule con Regressione Isotonica), un nuovo algoritmo gerarchico che combina la True-Path-Rule con metodi di regressione isotonica. Per tutti i metodi di ensemble gerarchici \ue8 stato dimostrato in modo formale la coerenza delle predizioni, cio\ue8 \ue8 stato provato come gli approcci proposti sono in grado di fornire predizioni che rispettano le relazioni gerarchiche fra le classi. Da un punto di vista sperimentale, risultati a livello dell'intero genoma di organismi modello e dell'uomo ed a livello della totalit\ue0 delle classi incluse nelle ontologie biologiche mostrano che gli approcci metodologici proposti: a) sono competitivi con gli algoritmi di predizione output strutturata allo stato dell'arte; b) sono in grado di migliorare i classificatori \u201cflat\u201d, a patto che le predizioni fornite dal classificatore non siano casuali; c) sono in grado di predire nuove associazioni tra geni umani e fenotipi patologici, un passo cruciale per la scoperta di nuovi geni associati a malattie genetiche umane e al cancro; d) scalano bene su dataset costituiti da decina di migliaia di esempi (i.e., proteine o geni) e su tassonomie costituite da migliaia di classi funzionali. Infine, i metodi proposti in questa tesi sono stati implementati in una libreria software scritta in linguaggio R, HEMDAG (Hierarchical Ensemble Methods per DAG), che \ue8 pubblica, liberamente scaricabile e disponibile per i sistemi operativi Linux, Windows e Macintosh.The standardized annotation of biomedical related objects, often organized in dedicated catalogues, strongly promoted the organization of biological concepts into controlled vocabularies, i.e. ontologies by which related terms of the underlying biological domain are structured according to a predefined hierarchy. Indeed large ontologies have been developed by the scientific community to structure and organize the gene and protein taxonomy of all the living organisms from Archea to Metazoa, i.e. the Gene Ontology, or human specific ontologies, such as the Human Phenotype Ontology, that provides a structured taxonomy of the abnormal human phenotypes associated with diseases. These ontologies, offering a coded and well-defined classification space for biological entities such as genes and proteins, favor the development of machine learning methods able to predict features of biological objects like the association between a human gene and a disease, with the aim to drive wet lab research allowing a reduction of the costs and a more effective usage of the available research funds. Despite the soundness of the aforementioned objectives, the resulting multi-label classification problems raise so complex machine learning issues that until recently the far common approach was the \u201cflat\u201d prediction, i.e. simply training a classifier for each term in the controlled vocabulary and ignoring the relationships between terms. This approach was not only justified by the need to reduce the computational complexity of the learning task, but also by the somewhat \u201cunstable\u201d nature of the terms composing the controlled vocabularies, because they were (and are) updated on a monthly basis in a process performed by expert curators and based on biomedical literature, and wet and in-silico experiments. In this context, two main general classes of classifiers have been proposed in literature. On the one hand, \u201chierarchy-unaware\u201d learning methods predict labels in a \u201cflat\u201d way without exploiting the inherent structure of the annotation space. On the other hand, \u201chierarchy-aware\u201d learning methods can improve the accuracy and the precision of the predictions by considering the hierarchical relationships between ontology terms. Moreover these methods can guarantee the consistency of the predicted labels according to the \u201ctrue path rule\u201d, that is the biological and logical rule that governs the internal coherence of biological ontologies. To properly handle the hierarchical relationships linking the ontology terms, two main classes of structured output methods have been proposed in literature: the first one is based on kernelized methods for structured output spaces, the second on hierarchical ensemble methods for ontology-based predictions. However both these approaches suffer of significant drawbacks. The kernel-based methods for structured output space are computationally intensive and do not scale well when applied to complex multi-label bio-ontologies. Most hierarchical ensemble methods have been conceived for tree-structured taxonomies and the few ones specifically developed for the prediction in DAG-structured output spaces are, in most cases, unable to improve prediction performances over flat methods. To overcome these limitations, in this thesis novel \u201contology-aware\u201d ensemble methods have been developed, able to handle DAG-structured ontologies, leveraging previous results obtained with \u201ctrue-path-rule\u201d-based hierarchical learning algorithms. These methods are highly modular in the sense that they adopt a \u201ctwo-step\u201d learning strategy: in the first step they learn separately each term of the ontology using flat methods, and in the second they properly combine the flat predictions according to the hierarchy of the classes. The main contributions of this thesis are both methodological and experimental. From a methodological standpoint, novel hierarchical ensemble methods are proposed, including: a) HTD (Hierarchical Top-Down algorithm for DAG structured ontologies); b) TPR-DAG (True Path Rule ensemble for DAG) with several variants; c) ISO-TPR, a novel ensemble method that combines the True Path Rule approach with Isotonic Regression. For all these methods a formal proof of their consistency, i.e. the guarantee of providing predictions that \u201crespect\u201d the hierarchical relationships between classes, is provided. From an experimental standpoint, extensive genome and ontology-wide results show that the proposed methods: a) are competitive with state-of-the-art prediction algorithms; b) are able to improve flat machine learning classifiers, if the base learners can provide non random predictions; c) are able to predict new associations between genes and human abnormal phenotypes, a crucial step to discover novel genes associated with human diseases ranging from genetic disorders to cancer; d) scale nicely with large datasets and bio-ontologies. Finally HEMDAG, a novel R library implementing the proposed hierarchical ensemble methods has been developed and publicly delivered

Radiology Associates Medical Scan Forecasting

After discontinuing their subscription with Shinyapps and relying on a manual forecasting process, Radiology Associates needs a new method to forecast the number and types of scans that will be executed at each site location. Radiology Associates utilizes Quinsite, which incorporates a live link to their database, as a host for all their Tableau dashboards. This project will create an accurate forecasting model utilizing complex forecasting methods to be hosted by Quinsite which is accessible by all management within Radiology Associates. To begin this process, an exponential smoothing model was created in Tableau to solidify dashboard and storyboard design. Additionally, ARIMA and SARIMAX models were built using TabPy and RServe. Using a MASE score each forecasting method was tested. Using the MASE score and feedback from Radiology Associates during a mock forecasting meeting, exponential smoothing was selected as the most accurate to be in the final design. With the final forecasting method selected the dashboard went through several rounds of slight alterations based on feedback from project stakeholders before being officially handed over to Radiology Associates

Speeding up node label learning in unbalanced biomolecular networks through a parallel and sparse GPU­based Hopfield model

"Motivation" - In network biology and medicine several problems can be modeled as node label inference in partially labeled networks. Nodes are biomedical entities (e.g. genes, patients) and connections represent a notion of functional similarity between entities. Usually, the class being predicted is represented through a labeling vector highly unbalanced towards negatives: that is only few positive instances (those associated with the class) are available. This fosters the adoption of imbalance­aware methodologies to accurately predict node labels. In addition, input data can be large­sized, since we may have millions of instances (e.g. in multi­species protein networks), thus requiring the design of efficient and scalable methodologies. To address these problems, a parametric neural algorithm based on the Hopfield model, COSNet [1,2,3], has been proposed, leveraging the minimization of a Hopfield network energy through the usual sequential dynamics to achieve an asymptotically stable attractor representing a valuable prediction. In this study, we propose a sparse and partially parallel implementation of COSNet, for sparse networks, which decomposes the input net in independent sets of neurons, each processed concurrently by hardware accelerators, like modern GPUs, while still keeping the overall dynamics sequential. "Methods" - The Hopfield dynamics is decomposed in independent tasks by solving the graph coloring problem, that is assigning colors to the graph vertices so that adjacent vertices receive different colors. Thus, the units of the neural network are split into clusters of independent neurons, which are sequentially updated, whereas the single units within each cluster are updated simultaneously. We simulate the algorithm on GPUs achieving a significant speed up with respect to the original sequential implementation and, at the same time, lowering memory requirements thanks to compressed memorization strategies, thus opening the possibility to face with prediction issues on big size instances. Also, a cooperative CPU multithreading – GPU model have been implemented, where the computations over different functional classes are carried independently by assigning each class to a different CPU thread. "Results" - We tested both COSNet and COSNet­GPU on partially labeled networks containing genes belonging to D. melanogaster and Homo sapiens organisms for predicting respectively the Gene Ontology (GO) and the Human Phenotype Ontology (HPO) terms with 10­50 annotated genes. The algorithm behavior has been measured in terms of execution time and memory consumption. Table 1 summarizes the results in term of speed­up and memory usage, when performing a 3­fold cross validation procedure. The results show significant reductions in both execution times and memory consumption, and interestingly the improvement factors increases more than linearly with the number of nodes/genes. This also corroborates the fact that the proposed implementation nicely scales on big data

Improvements in BepiColombo and JUICE radio science experiments with a multi-station tracking configuration for the reduction of Doppler noise

Radio science experiments for planetary geodesy mostly rely on measurements of the Doppler shift of microwave signals sent to a spacecraft by an Earth station, and retransmitted back coherently in phase to the same antenna (two-way link). The retransmitted signal can also be received by a different station in a listen-only configuration (three-way link). In state-of-the-art tracking systems, such as the ones will be used on the future ESA's missions JUICE and BepiColombo, the Doppler error budget is dominated by local noise sources arising at the ground-station, in particular tropospheric scintillation and unmodeled motions of the antenna's structure. In this work, a novel technique aimed at reducing these disturbances is analyzed, with particular emphasis on its benefits to BepiColombo's and JUICE's radio science experiments. The method, referred to as Time-Delay Mechanical-noise Cancellation (TDMC), relies on simultaneous two-way and three-way spacecraft tracking, the latter employing a stiffer listen-only antenna with better mechanical stability and located in a favorable dry region more immune to tropospheric noise. In fact, a proper linear combination of time-shifted observables from the two-way and three-way links can replace local noises of the two-way ground-station with those coming from the listen-only antenna, translating into increased accuracy of the final measurements, while preserving the original Doppler content. We show the results of covariance analyses performed with a multi-arc weighted least square estimator for the entire BepiColombo's Hermean phase and JUICE's flybys of Callisto. We compare the two solutions obtained with and without the application of the TDMC technique. For BepiColombo and JUICE radio science experiments, the two-way links are baselined from the 35-m DSA-3 (Malargüe, Argentina) and the 34-m DSS 25 (Goldstone, California). For the three-way link, we select the 12-m Large Latin American Millimeter Array (LLAMA) antenna for three reasons: 1) its mechanical rigidity with respect to large beam-waveguide antennas, 2) its unique position in the extremely dry Puna de Atacama desert, that assures low tropospheric noise, and 3) its limited longitudinal separation from the two other ground-stations, granting sufficient common visibility time to perform the requested combination of the observables. Besides its noise-reduction effect, enabling unprecedented levels of accuracy on Doppler measurements, TDMC provides also a back-up for unique events: a crucial satellite flyby or a specific passage over a site of particular geophysical interest. Indeed, measurements become virtually independent of unfavorable meteorological conditions at the transmitting station