Approximate Data Mining Techniques on Clinical Data

The past two decades have witnessed an explosion in the number of medical and healthcare datasets available to researchers and healthcare professionals. Data collection efforts are highly required, and this prompts the development of appropriate data mining techniques and tools that can automatically extract relevant information from data. Consequently, they provide insights into various clinical behaviors or processes captured by the data. Since these tools should support decision-making activities of medical experts, all the extracted information must be represented in a human-friendly way, that is, in a concise and easy-to-understand form. To this purpose, here we propose a new framework that collects different new mining techniques and tools proposed. These techniques mainly focus on two aspects: the temporal one and the predictive one. All of these techniques were then applied to clinical data and, in particular, ICU data from MIMIC III database. It showed the flexibility of the framework, which is able to retrieve different outcomes from the overall dataset. The first two techniques rely on the concept of Approximate Temporal Functional Dependencies (ATFDs). ATFDs have been proposed, with their suitable treatment of temporal information, as a methodological tool for mining clinical data. An example of the knowledge derivable through dependencies may be "within 15 days, patients with the same diagnosis and the same therapy usually receive the same daily amount of drug". However, current ATFD models are not analyzing the temporal evolution of the data, such as "For most patients with the same diagnosis, the same drug is prescribed after the same symptom". To this extent, we propose a new kind of ATFD called Approximate Pure Temporally Evolving Functional Dependencies (APEFDs). Another limitation of such kind of dependencies is that they cannot deal with quantitative data when some tolerance can be allowed for numerical values. In particular, this limitation arises in clinical data warehouses, where analysis and mining have to consider one or more measures related to quantitative data (such as lab test results and vital signs), concerning multiple dimensional (alphanumeric) attributes (such as patient, hospital, physician, diagnosis) and some time dimensions (such as the day since hospitalization and the calendar date). According to this scenario, we introduce a new kind of ATFD, named Multi-Approximate Temporal Functional Dependency (MATFD), which considers dependencies between dimensions and quantitative measures from temporal clinical data. These new dependencies may provide new knowledge as "within 15 days, patients with the same diagnosis and the same therapy receive a daily amount of drug within a fixed range". The other techniques are based on pattern mining, which has also been proposed as a methodological tool for mining clinical data. However, many methods proposed so far focus on mining of temporal rules which describe relationships between data sequences or instantaneous events, without considering the presence of more complex temporal patterns into the dataset. These patterns, such as trends of a particular vital sign, are often very relevant for clinicians. Moreover, it is really interesting to discover if some sort of event, such as a drug administration, is capable of changing these trends and how. To this extent, we propose a new kind of temporal patterns, called Trend-Event Patterns (TEPs), that focuses on events and their influence on trends that can be retrieved from some measures, such as vital signs. With TEPs we can express concepts such as "The administration of paracetamol on a patient with an increasing temperature leads to a decreasing trend in temperature after such administration occurs". We also decided to analyze another interesting pattern mining technique that includes prediction. This technique discovers a compact set of patterns that aim to describe the condition (or class) of interest. Our framework relies on a classification model that considers and combines various predictive pattern candidates and selects only those that are important to improve the overall class prediction performance. We show that our classification approach achieves a significant reduction in the number of extracted patterns, compared to the state-of-the-art methods based on minimum predictive pattern mining approach, while preserving the overall classification accuracy of the model. For each technique described above, we developed a tool to retrieve its kind of rule. All the results are obtained by pre-processing and mining clinical data and, as mentioned before, in particular ICU data from MIMIC III database

DInSAR investigation in the Pärvie endglacial fault region, Lapland, Sweden

Northern Fennoscandia bears witness to the Pleistocene glaciation in the form of a series of large faults that have been shown to have ruptured immediately after the retreat of the ice sheet, about 9500 years ago. The largest one, known as the Pärvie fault, consists of a 155 km long linear series of fault scarps forming north–northeast-trending, that stretch west of Kiruna, Lapland. End-glacial intra-plate faults of this extent are very rare in the continental crust and the Pärvie system represents one of the major fault zone structures of this type in the world. Seismological evidence shows that there is still noticeable seismic activity, roughly one event of magnitude 2 per year that can be attributed to the fault. Nevertheless assessing its state of activity is a difficult task due to the extent and remoteness of the area. This study is aimed at the determination of crustal motion around the Pärvie fault zone using the differential inter-ferometric synthetic aperture radar (DInSAR) technique, based on images acquired with the European Space Agency (ESA) satellites European Remote Sensing (ERS) 1, ERS-2, and the Environmental Satellite (ENVISAT). We present results achieved in terms of deformation of the crystalline bedrock along different sectors of the fault where high levels of coherence were obtained, even from image pairs several years apart. This finding does not exclude deformation in other segments, as observing conditions are not always as favourable in terms of data availability

Fermentazione con Bacillus Stearothermophilus: produzione di 2,3-butandiolo, studio della via metabolica ed applicazioni biocatalitiche

The here presented PhD regarded fermentation with the bacteria Bacillus stearothermophilus ATCC 2027 evaluating its potential production of 2,3-butanediol, a metabolic product with wide applications, evaluating also the metabolic pathway and the involved enzyme. B. steraothermophilus was previously successfully used in our Laboratory in biocatalysis obtaining kinetic resolution of chiral alcohols via oxidation e stereo selective reduction of 1,2-diketons to S,Sdiols. Growing substrate analysis evidenced bacteria capacity to produce significant amounts of 2,3- butanediol (2,3-BD) and its precursor acetoin (AC) using sucrose as carbon source. 2,3-butanediol is widely used in many fields, from alimentary to polymers industries, beside its use as bio-fuel or additive in fuels, increasing the relative importance of its production. The first aim of my PhD activity was to verify real quality of 2,3-BD and AC produced by B. stearothermophilus sucrose’s fermentation, and to flowingly screen other mono- and disaccharides as carbon sources. Experiments were conducted at fixed sucrose concentration (40, 30, 20, 10 gr/l) and results indicated fermentation with 30 gr/l of sucrose as the best in terms of yield (about 100%) and carbon consume (residual about 1 gr/l). This result was compared with those obtained using other carbon sources as glucose, fructose, xylose, maltose, lactose and cellobiose, and cane molasses, chosen in relation to their high production in agroindustrial processes. Obtained results evidenced B. stearothermophilus complete sucrose fermentation and an only partial fermentation of its constituents monosaccharides, fructose and glucose. The other sugars give small amounts of the two metabolites. An important point of the present research activity was the comprehension of biochemical mechanisms allowing microorganism production of the cited metabolites. Gaschromatographic endproducts analysis with a chiral column demonstrated B. stearothermophilus production of high quantity of (R)-acetoin, (2R,3R)-butanediol and meso-butanediol. Consequently, a study on the cited bacteria metabolic pathway was developed adopting methods previously used with other microorganisms. Similarly to other cogeneric bacteria, Bacillus sterarothermophilus presents two metabolic pathways to produce butanediol. The firs, more diffused, “catabolic way” starts from pyruvate derived from glicolysis and by the way of three enzymatic steps (condensation, decarboxylation and reduction) produces 2,3-butanediol. The second, less diffused, indicated as “butanediol cycle” starts from diacetil and produces butanediol by the way of three enzymatic reactions (condensation, reduction, hydrolysis). The present research evidenced a new S-stereo specific acetoin-reductase (AC-reductase) that in association with the enzyme diacetil acetoin reductase (BSDR) previously used in biocatalysis, produces meso-butanediol. “Butanediol cycle” was confirmed by the presence of an acetil acetoin synthetase (AAC-synthetase) capable of acetilacetoin (AAC) production from diacetil. While AC-reductase was partially purified, AAC-synthetase was used raw in biocatalysis. Other 1,2 dichetons, beside diacetil, were considered as possible starting-products to obtain α- hydroxy-dichetons variably substituted. 3,4-hexanedione and 1-phenyl-2,3-propanedione were used obtaining respectively 4-hydroxy-4-ethyl-3,5-heptanedione and 1-phenil-2-hydroxy-2-methyl-1,3- butanedione. Obtained results evidenced AAC-synthetase efficiency in this catalysis showing an almost total conversion in the first case (82%) and a lower product yield in the second one (45%) with optical purity of chiral product about 40%. The present PhD research activity allowed also publications on fermentation1, metabolic pathway2 and the formation of C-C linkage mediated by AAC-synthetase.3 References 1. P. P. GIOVANNINI, M. MANTOVANI, A. MEDICI, P. PEDRINI– Productions of 2,3-butandiol by Bacillus sterothermophilus: fermentation and metabolic pathway. Proceedings of IBIC 2008, Chem. Eng. Transactions, 14, 281-286 (2008). 2. P.P. GIOVANNINI, M. MANTOVANI, M. FOGAGNOLO, S. MAIETTI, A. MEDICI, P. PEDRINI – Bacillus stearothermophilus fermentation: the enzymatic route to 3R-hydroxy-2-butanone and meso-and 2R,3Rbutanediol. Journal of Molecular Catalysis B: enzymatic, in stampa. 3. P.P. GIOVANNINI, M. MANTOVANI, A. MEDICI, P. PEDRINI - Enzymatic Carbon-Carbon Bond Formation: Synthesis of a-Hydroxy-1,3-Diketones from the Corresponding 1,2-Diketones. Organic Letters, in stampa

Exploring atmospheric radon with airborne gamma-ray spectroscopy

$^{222}$Rn is a noble radioactive gas produced along the $^{238}$U decay chain, which is present in the majority of soils and rocks. As $^{222}$Rn is the most relevant source of natural background radiation, understanding its distribution in the environment is of great concern for investigating the health impacts of low-level radioactivity and for supporting regulation of human exposure to ionizing radiation in modern society. At the same time, $^{222}$Rn is a widespread atmospheric tracer whose spatial distribution is generally used as a proxy for climate and pollution studies. Airborne gamma-ray spectroscopy (AGRS) always treated $^{222}$Rn as a source of background since it affects the indirect estimate of equivalent $^{238}$U concentration. In this work the AGRS method is used for the first time for quantifying the presence of $^{222}$Rn in the atmosphere and assessing its vertical profile. High statistics radiometric data acquired during an offshore survey are fitted as a superposition of a constant component due to the experimental setup background radioactivity plus a height dependent contribution due to cosmic radiation and atmospheric $^{222}$Rn. The refined statistical analysis provides not only a conclusive evidence of AGRS $^{222}$Rn detection but also a (0.96 $\pm$ 0.07) Bq/m$^{3}$ $^{222}$Rn concentration and a (1318 $\pm$ 22) m atmospheric layer depth fully compatible with literature data.Comment: 17 pages, 8 figures, 2 table

Discovering Evolving Temporal Information: Theory and Application to Clinical Databases

Functional dependencies (FDs) allow us to represent database constraints, corresponding to requirements as \u201cpatients having the same symptoms undergo the same medical tests.\u201d Some research eforts have focused on extending such dependencies to consider also temporal constraints such as \u201cpatients having the same symptoms undergo in the next period the same medical tests.\u201d Temporal functional dependencies are able to represent such kind of temporal constraints in relational databases. Another extension for FDs allows one to represent approximate functional dependencies (AFDs), as \u201cpatients with the same symptoms generally undergo the same medical tests.\u201d It enables data to deviate from the defned constraints according to a user-defned percentage. Approximate temporal functional dependencies (ATFDs) merge the concepts of temporal functional dependency and of approximate functional dependency. Among the diferent kinds of ATFD, the Approximate Pure Temporally Evolving Functional Dependencies (APE-FDs for short) allow one to detect patterns on the evolution of data in the database and to discover dependencies as \u201cFor most patients with the same initial diagnosis, the same medical test is prescribed after the occurrence of same symptom.\u201d Mining ATFDs from large databases may be computationally expensive. In this paper, we focus on APE-FDs and prove that, unfortunately, verifying a single APE-FD over a given database instance is in general NP-complete. In order to cope with this problem, we propose a framework for mining complex APE-FDs in real-world data collections. In the framework, we designed and applied sound and advanced model-checking techniques. To prove the feasibility of our proposal, we used real-world databases from two medical domains (namely, psychiatry and pharmacovigilance) and tested the running prototype we developed on such databases

A Design Strategy Based on Topology Optimization Techniques for an Additive Manufactured High Performance Engine Piston

In this paper, a methodology for the design of a motorcycle piston is presented, based on topology optimization techniques. In particular, a design strategy is preliminary investigated aiming at replacing the standard aluminum piston, usually manufactured by forging or casting, with an alternative one made of steel and manufactured via an Additive Manufacturing process. In this methodology, the minimum mass of the component is considered as the objective function and a target stiffness of important parts of the piston is employed as a design constraint. The results demonstrate the general applicability of the methodology presented for obtaining the geometrical layout and thickness distribution of the structure

Training Future Engineers to Be Ghostbusters: Hunting for the Spectral Environmental Radioactivity

Although environmental radioactivity is all around us, the collective public imagination often associates a negative feeling to this natural phenomenon. To increase the familiarity with this phenomenon we have designed, implemented, and tested an interdisciplinary educational activity for pre-collegiate students in which nuclear engineering and computer science are ancillary to the comprehension of basic physics concepts. Teaching and training experiences are performed by using a 4" x 4" NaI(Tl) detector for in-situ and laboratory {\gamma}-ray spectroscopy measurements. Students are asked to directly assemble the experimental setup and to manage the data-taking with a dedicated Android app, which exploits a client-server system that is based on the Bluetooth communication protocol. The acquired {\gamma}-ray spectra and the experimental results are analyzed using a multiple-platform software environment and they are finally shared on an open access Web-GIS service. These all-round activities combining theoretical background, hands-on setup operations, data analysis, and critical synthesis of the results were demonstrated to be effective in increasing students' awareness in quantitatively investigating environmental radioactivity. Supporting information to the basic physics concepts provided in this article can be found at http://www.fe.infn.it/radioactivity/educational