1,721,279 research outputs found
Biosynthesis of heterocycles: From isolation to gene cluster
No full textThis book describes biosynthetic methods to synthesize heterocyclic compounds, offering a guide for the development of new drugs based on natural products. The authors explain the role of natural products in chemistry and their formation along with important analytical methods and techniques for working with heterocycles. Covers methods and techniques: isotopic labelling, enzymes and mutants, and pathway identification. Provides a thorough resource of information specifically on heterocyclic natural products and their practical biosynthetic relevance. Explains the role of natural products in chemistry and their formation. Discusses gene cluster identification and the use of biogenetic engineering in pharmaceutical application
Shallow Neural Network for Biometrics from the ECG-WATCH
Full text linkApplications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate
‘DNA microarray classification: evolutionary optimization of neural network hyperparameters"
No full textDouble Channel Neural Non Invasive Blood Pressure Prediction
Full text linkCardiovascular Diseases represent the leading cause of deaths in the world. Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention. This work applies the neural network output-error (NNOE) model to ABP forecasting. Three input configurations are proposed based on ECG and PPG for estimating both systolic and diastolic blood pressures. The double channel configuration is the best performing one by means of the mean absolute error w.r.t the corresponding invasive blood pressure signal (IBP); indeed, it is also proven to be compliant with the ANSI/AAMI/ISO 81060-2:2013 regulation for non invasive ABP techniques. Both ECG and PPG correlations to IBP signal are further analyzed using Spearman’s correlation coefficient. Despite it suggests PPG is more closely related to ABP, its regression performance is worse than ECG input configuration one. However, this behavior can be explained looking to human biology and ABP computation, which is based on peaks (systoles) and valleys (diastoles) extraction
WIND SPEED SPATIAL ESTIMATION FOR ENERGY PLANNING IN SICILY: A NEURAL KRIGING APPLICATION
No full textOne of the first steps for the exploitation of any energy source is necessarily represented by its estimation and mapping at the aim of identifying the most suitable areas in terms of energy potential. In the field of renewable energies this is often a very difficult task, because the energy source is in this case characterized by relevant variations over space and time. This implies that any temporal, but also spatial. estimation model has to be able to incorporate this spatial and temporal variability.
The paper deals with the spatial estimation of the wind fields in Sicily (Italy) by following a data-driven approach. Starting front the results of a preliminary study, a novel technique resulting front the integration of neural and geostatistical techniques was developed in order to obtain the wind speed maps for the region at 10 and 50 meters above the ground level. The mean values of the theoretical Weibull distribution function describing the wind regime at each of the available measurement sites were used to train a multi-layer perceptron (MLP) whose goal is to compute the most of the wind spatial trends. Other pieces of information about the territory (altitude, land coverage) were also used as inputs of the network and organized into a geographic information system (GIS) environment. The remaining de-trended linear means have been computed by using a universal kriging (UK) estimator. The results of these steps were then summed Lip and it was thus possible to obtain a map of the estimated wind fields
SYNTHESIS OF 3-(2-PHENYL-1,3-THIAZOL-4-YL)-1H-INDOLES, NEW DERIVATIVES WITH POTENTIAL ANTINEOPLASTIC ACTIVITY
No full textMulti-omics Classification on Kidney Samples Exploiting Uncertainty-Aware Models
No full textDue to the huge amount of available omic data, classifying samples according to various omics is a complex process. One of the most common approaches consists of creating a classifier for each omic and subsequently making a consensus among the classifiers that assigns to each sample the most voted class among the outputs on the individual omics. However, this approach does not consider the confidence in the prediction ignoring that a biological information coming from a certain omic may be more reliable than others. Therefore, it is here proposed a method consisting of a tree-based multi-layer perceptron (MLP), which estimates the class-membership probabilities for classification. In this way, it is not only possible to give relevance to all the omics, but also to label as Unknown those samples for which the classifier is uncertain in its prediction. The method was applied to a dataset composed of 909 kidney cancer samples for which these three omics were available: gene expression (mRNA), microRNA expression (miRNA) and methylation profiles (meth) data. The method is valid also for other tissues and on other omics (e.g. proteomics, copy number alterations data, single nucleotide polymorphism data). The accuracy and weighted average f1-score of the model are both higher than 95%. This tool can therefore be particularly useful in clinical practice, allowing physicians to focus on the most interesting and challenging samples. Data availability: the code is freely accessible at https://github.com/Bontempogianpaolo1/Consunsus-on-multi-omics, while mRNA, miRNA and meth data can be obtained from the GDC database [2] or upon request to the authors
Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor
Full text linkWind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor - GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region. First, the topographic surface of the island was modelled using two different neural techniques and by exploiting the information extracted from a digital elevation model of the region. Then, GMR was used for automatic modelling of the terrain roughness. Afterwards, a statistical analysis of the wind data allowed for the estimation of the parameters of the Weibull wind probability distribution function. In the last sections of the paper, the expected values of the Weibull distributions were regionalized using the GMR neural networ
Neural Biclustering in Gene Expression Analysis
Full text linkClustering in high dimensional spaces is a very difficult task. Dealing with DNA microarrays is even more difficult because gene subsets are coregulated and coexpressed only under specific conditions. Biclusterng addresses the problem of finding such submanifolds by exploiting both gene and condition (tissue) clustering. The paper proposes a self-organizing neural network, GH EXIN, which builds a hierarchical tree by adapting its architecture to data. It is integrated in a framework in which gene and tissue clustering are alternated and controlled by the quality of the bicluster. Examples of the approach and a biological validation of results are also given
La valutazione di un percorso di "bilancio di risorse"
No full textPer le loro finalità generali, le azioni di orientamento possono senza dubbio considerarsi interventi in ambito psicosociale, e come tali per esse può considerarsi imprescindibile il tema della valutazione (Fraccaroli e Vergani, 2004). Attraverso la verifica della qualità delle attività offerte e degli effetti che esse hanno sull’utenza, si può comprendere se tali interventi abbiano funzionato o meno, in che misura, in che modo e, con adeguate riflessioni a margine, perché. La valutazione di una azione di orientamento dovrebbe assumere come oggetto di analisi diverse dimensioni, non necessariamente convergenti (Ferrieux e Carayon, 1998; Tronti, 2002). Un serio problema nasce dal fatto che non risulta ancora ben chiaro quali siano le variabili da prendere in considerazione nella valutazione degli effetti e dei risultati di una pratica orientativa. La difficoltà principale sta nella definizione stessa di tali risultati e nell’individuazione dei parametri (Gaudron, Bernaud e Lemoine, 2001). A tal proposito possono essere utili gli studi longitudinali, che permettono di comprendere se gli effetti positivi dell’orientamento tendono a rimanere stabili nel tempo, o se invece tendono a scomparire e, inoltre, se l’intervento può avere ricadute a medio e lungo termine, anche diverse da quelle immediate (Pombeni, 2004, Masdonati e Dauwalder, 2010). Il presente lavoro prende in esame gli effetti a lungo termine di un intervento di orientamento realizzato presso il Centro di Orientamento e Tutorato (COT) dell'Università degli Studi di Palermo denominato "Scelta e progetto di carriera", ed ispirato al modello di Bilancio delle Competenze (Pace, Ciaccio, Di Bernardo, Governale, Messana e Pupillo, 2007). La finalità è stata quella di verificare se, a distanza di anni, gli studenti che hanno partecipato alle attività abbiano avuto risultati positivi in termini di successo formativo e professionale, rispetto a coloro che non hanno intrapreso tale percorso, e quali siano le implicazioni in termini di soddisfazione rispetto al percorso universitario ed alla eventuale professione intrapresa. La ricerca, a disegno longitudinale, ha preso in esame complessivamente 211 studenti (suddivisi tra gruppo sperimentale e di controllo) ad 8 anni dall'intervento. I risultati mostrano, in linea con la letteratura, che l’effetto di un intervento di orientamento ha marcati effetti sugli aspetti motivazionali e di soddisfazione formativa e professionale, mentre dal punto di vista della performance formativa esso non mostra una incidenza specifica
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