Modular continuous crystallizer: operation, experimental arrangement and identification of process parameters

Crystallization is employed in different industrial processes. The method and operation can differ depending on the nature of the substances involved. The aim of this study is to examine the effect of various operating conditions on the crystal properties in a chemical engineering design window with a focus on ultrasound assisted cooling crystallization. Batch to batch variations, minimal manufacturing steps and faster production times are factors which continuous crystallization seeks to resolve. Continuous processes scale-up is considered straightforward compared to batch processes owing to increase of processing time in the specific reactor. In cooling crystallization process, ultrasound can be used to control the crystal properties. Different model compounds were used to define the suitable process parameters for the modular crystallizer using equal operating conditions in each module. A final temperature of 20oC was employed in all experiments while the operating conditions differed. The studied process parameters and configuration of the crystallizer were manipulated to achieve a continuous operation without crystal clogging along the crystallization path. The results from the continuous experiment were compared with the batch crystallization results and analysed using the Malvern Morphologi G3 instrument to determine the crystal morphology and CSD. The modular crystallizer was operated successfully with three different residence times. At optimal process conditions, a longer residence time gives smaller crystals and narrower CSD. Based on the findings, at a constant initial solution concentration, the residence time had clear influence on crystal properties. The equal supersaturation criterion in each module offered better results compared to other cooling profiles. The combination of continuous crystallization and ultrasound has large potential to overcome clogging, obtain reproducible and narrow CSD, specific crystal morphologies and uniform particle sizes, and exclusion of milling stages in comparison to batch processes.

The identification and differentiation of the Candida parapsilosis complex species by polymerase chain reaction-restriction fragment length polymorphism of the internal transcribed spacer region of the rDNA

Currently, it is accepted that there are three species that were formerly grouped under Candida parapsilosis : C. parapsilosis sensu stricto, Candida orthopsilosis , and Candida metapsilosis . In fact, the antifungal susceptibility profiles and distinct virulence attributes demonstrate the differences in these nosocomial pathogens. An accurate, fast, and economical identification of fungal species has been the main goal in mycology. In the present study, we searched sequences that were available in the GenBank database in order to identify the complete sequence for the internal transcribed spacer (ITS)1-5.8S-ITS2 region, which is comprised of the forward and reverse primers ITS1 and ITS4. Subsequently, an in silico polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was performed to differentiate the C. parapsilosis complex species. Ninety-eight clinical isolates from patients with fungaemia were submitted for analysis, where 59 isolates were identified as C. parapsilosis sensu stricto, 37 were identified as C. orthopsilosis, and two were identified as C. metapsilosis. PCR-RFLP quickly and accurately identified C. parapsilosis complex species, making this method an alternative and routine identification system for use in clinical mycology laboratories.

Search for Higgs Boson Production Beyond the Standard Model Using the Razor Kinematic Variables in pp Collisions at √s=8 TeV and Optimization of Higgs Boson Identification Using a Quantum Annealer

In the first part of this thesis we search for beyond the Standard Model physics through the search for anomalous production of the Higgs boson using the razor kinematic variables. We search for anomalous Higgs boson production using proton-proton collisions at center of mass energy √s=8 TeV collected by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to an integrated luminosity of 19.8 fb^-1.

In the second part we present a novel method for using a quantum annealer to train a classifier to recognize events containing a Higgs boson decaying to two photons. We train that classifier using simulated proton-proton collisions at √s=8 TeV producing either a Standard Model Higgs boson decaying to two photons or a non-resonant Standard Model process that produces a two photon final state.

The production mechanisms of the Higgs boson are precisely predicted by the Standard Model based on its association with the mechanism of electroweak symmetry breaking. We measure the yield of Higgs bosons decaying to two photons in kinematic regions predicted to have very little contribution from a Standard Model Higgs boson and search for an excess of events, which would be evidence of either non-standard production or non-standard properties of the Higgs boson. We divide the events into disjoint categories based on kinematic properties and the presence of additional b-quarks produced in the collisions. In each of these disjoint categories, we use the razor kinematic variables to characterize events with topological configurations incompatible with typical configurations found from standard model production of the Higgs boson.

We observe an excess of events with di-photon invariant mass compatible with the Higgs boson mass and localized in a small region of the razor plane. We observe 5 events with a predicted background of 0.54 ± 0.28, which observation has a p-value of 10^-3 and a local significance of 3.35σ. This background prediction comes from 0.48 predicted non-resonant background events and 0.07 predicted SM higgs boson events. We proceed to investigate the properties of this excess, finding that it provides a very compelling peak in the di-photon invariant mass distribution and is physically separated in the razor plane from predicted background. Using another method of measuring the background and significance of the excess, we find a 2.5σ deviation from the Standard Model hypothesis over a broader range of the razor plane.

In the second part of the thesis we transform the problem of training a classifier to distinguish events with a Higgs boson decaying to two photons from events with other sources of photon pairs into the Hamiltonian of a spin system, the ground state of which is the best classifier. We then use a quantum annealer to find the ground state of this Hamiltonian and train the classifier. We find that we are able to do this successfully in less than 400 annealing runs for a problem of median difficulty at the largest problem size considered. The networks trained in this manner exhibit good classification performance, competitive with the more complicated machine learning techniques, and are highly resistant to overtraining. We also find that the nature of the training gives access to additional solutions that can be used to improve the classification performance by up to 1.2% in some regions.

Application of dental morphological characteristics for medical-legal identification: sexual diagnosis in a portuguese population

Licenced under a Creative Commons Attribution 3.0.

Identification of metabolites of kurarinone from Sophora flavescens Ait in rat urine by ultra-performance liquid chromatography with linear ion trap orbitrap mass spectrometry

Purpose: To study the in vivo metabolism of kurarinone, a lavandulyl flavanone which is a major constituent of Kushen and a marker compound with many biological activities, using ultra-performance liquid chromatography coupled with linear ion trap Orbitrap mass spectrometry (UPLC-LTQ-Orbitrap- MS). Methods: Six male Sprague-Dawley rats were randomly divided into two groups. First, kurarinone was suspended in 0.5 % carboxymethylcellulose sodium (CMC-Na) aqueous solution, and was given to rats (n = 3, 2 mL for each rat) orally at 50 mg/kg. A 2 mL aliquot of 0.5 % CMC-Na aqueous solution was administered to the rats in the control group. Next, urine samples were collected over 0-24 h after the oral administrations and all urine samples were pretreated by a solid phase extraction (SPE) method. Finally, all samples were analyzed by a UPLC-LTQ-Orbitrap mass spectrometry coupled with an electrospray ionization source (ESI) that was operated in the negative ionization mode. Results: A total of 11 metabolites, including the parent drug and 10 phase II metabolites in rat urine, were first detected and interpreted based on accurate mass measurement, fragment ions, and chromatographic retention times. The results were based on the assumption that kurarinone glucuronidation was the dominant metabolite that was excreted in rat urine. Conclusion: The results from this work indicate that kurarinone in vivo is typically transformed to nontoxic glucuronidation metabolites, and these findings may help to characterize the metabolic profile of kurarinone.

Identification of novel drought-tolerant-associated SNPs in common bean (Phaseolus vulgaris)

Common bean (Phaseolus vulgaris L.) is a leguminous in high demand for human nutrition and a very important agricultural product. Production of common bean is constrained by environmental stresses such as drought. Although conventional plant selection has been used to increase production yield and stress tolerance, drought tolerance selection based on phenotype is complicated by associated physiological, anatomical, cellular, biochemical, and molecular changes. These changes are modulated by differential gene expression. A common method to identify genes associated with phenotypes of interest is the characterization of Single Nucleotide Polymorphims (SNPs) to link them to specific functions. In this work, we selected two drought-tolerant parental lines from Mesoamerica, Pinto Villa, and Pinto Saltillo. The parental lines were used to generate a population of 282 families (F3:5) and characterized by 169 SNPs. We associated the segregation of the molecular markers in our population with phenotypes including flowering time, physiological maturity, reproductive period, plant, seed and total biomass, reuse index, seed yield, weight of 100 seeds, and harvest index in three cultivation cycles. We observed 83 SNPs with significant association (p < 0.0003 after Bonferroni correction) with our quantified phenotypes. Phenotypes most associated were days to flowering and seed biomass with 58 and 44 associated SNPs, respectively. Thirty-seven out of the 83 SNPs were annotated to a gene with a potential function related to drought tolerance or relevant molecular/biochemical functions. Some SNPs such as SNP28 and SNP128 are related to starch biosynthesis, a common osmotic protector; and SNP18 is related to proline biosynthesis, another well-known osmotic protector.

Assessment of MALDI-TOF MS as Alternative Tool for Streptococcus suis Identification

The accuracy of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) for identifying Streptococcus suis isolates obtained from pigs, wild animals, and humans was evaluated using a PCR-based identification assay as the gold standard. In addition, MALDI-TOF MS was compared with the commercial multi-tests Rapid ID 32 STREP system. From the 129 S. suis isolates included in the study and identified by the molecular method, only 31 isolates (24.03%) had score values ≥2.300 and 79 isolates (61.24%) gave score values between 2.299 and 2.000. After updating the currently available S. suis MALDI Biotyper database with the spectra of three additional clinical isolates of serotypes 2, 7, and 9, most isolates had statistically significant higher score values (mean score: 2.65) than those obtained using the original database (mean score: 2.182). Considering the results of the present study, we suggest using a less restrictive threshold score of ≥2.000 for reliable species identification of S. suis. According to this cut-off value, a total of 125 S. suis isolates (96.9%) were correctly identified using the updated database. These data indicate an excellent performance of MALDI-TOF MS for the identification of S. suis.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

Production Diseases in European Organic Dairy Farms – Current Status and Identification of Drivers for Improvement by Means of a Systemic Approach

The aims of this thesis were to determine the animal health status in organic dairy farms in Europe and to identify drivers for improving the current situation by means of a systemic approach. Prevalences of production diseases were determined in 192 herds in Germany, France, Spain, and Sweden (Paper I), and stakeholder consultations were performed to investigate potential drivers to improve animal health on the sector level (ibid.). Interactions between farm variables were assessed through impact analysis and evaluated to identify general system behaviour and classify components according to their outgoing and incoming impacts (Paper II-III). The mean values and variances of prevalences indicate that the common rules of organic dairy farming in Europe do not result in consistently low levels of production diseases. Stakeholders deemed it necessary to improve the current status and were generally in favour of establishing thresholds for the prevalence of production diseases in organic dairy herds as well as taking actions to improve farms below that threshold. In order to close the gap between the organic principle of health and the organic farming practice, there is the need to formulate a common objective of good animal health and to install instruments to ensure and prove that the aim is followed by all dairy farmers in Europe who sell their products under the organic label. Regular monitoring and evaluation of herd health performance based on reference values are considered preconditions for identifying farms not reaching the target and thus in need of improvement. Graph-based impact analysis was shown to be a suitable method for modeling and evaluating the manifold interactions between farm factors and for identifying the most influential components on the farm level taking into account direct and indirect impacts as well as impact strengths. Variables likely to affect the system as a whole, and the prevalence of production diseases in particular, varied largely between farms despite some general tendencies. This finding reflects the diversity of farm systems and underlines the importance of applying systemic approaches in health management. Reducing the complexity of farm systems and indicating farm-specific drivers, i.e. areas in a farm, where changes will have a large impact, the presented approach has the potential to complement and enrich current advisory practice and to support farmers’ decision-making in terms of animal health.

DNA barcoding: an effective tool to overcome morphological identification constraints in the assessment of the ecological quality

DNA barcoding has the potential to overcome taxonomic challenges in biological community assessments. However, fulfilling that potential requires successful amplification of a large and unbiased portion of the community. In this study, we attempted to identify mitochondrial gene cytochrome c oxidase I (COI) barcodes from 1024 benthic invertebrate specimens belonging to 54 taxa from low salinity environments of the Mira estuary and Torgal riverside (SW Portugal). Up to 17 primer pairs and several reaction conditions were attempted among specimens from all taxa, with amplification success defined as a single band of approximately 658 bp visualized on a pre-cast agarose gel, starting near the 5' end of the COI gene and suitable for sequencing. Amplification success was achieved for 99.6% of the 54 taxa, though no single primer was successful for more than 88.9% of the taxa. However, only 68.5% of the specimens within these taxa successfully amplified. Inhibition factors resulting from a non-purified DNA extracted and inexistence of species-specific primers for COI were pointed as the main reasons for an unsuccessful amplification. These results suggest that DNA barcoding can be an effective tool for application in low salinity environments where taxa such as chironomids and oligochaetes are challenging for morphological identification. Nevertheless, its implementation is not simple, as methods are still being standardized and multiple species

Vector Fitting based Automatic Circuit Identification

This paper describes a method to automatically obtain, from a set of impedance measurements at different frequencies, an equivalent circuit composed of lumped elements based on the vector fitting algorithm. The method starts from the impedance measurement of the circuit and then, through the recursive use of vector fitting, identifies the circuit topology and the component values of lumped elements. The method can be expanded to include other components usually used in impedance spectroscopy. The method is firstly described and then two examples highlight the robustness of the method and showcase its applicability.

Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition

Subtle structural differencescan be observed in the islets of Langer-hans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic(glucose intolerant)situa-tions. This paper proposes a way to automatically segment the islets of Langer-hans region fromthe histological image of rat's pancreas cell and on the basis of some morphological feature extracted from the segmented region the images are classified as normal and pre-diabetic.The experiment is done on a set of 134 images of which 56 are of normal type and the rests 78 are of pre-diabetictype. The work has two stages: primarily,segmentationof theregion of interest (roi)i.e. islets of Langerhansfrom the pancreatic cell and secondly, the extrac-tion of the morphological featuresfrom the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentationof the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM etc.are used for evaluation among which MLP performed the best.

Quantum integrability as a new method for exact results on N=2 supersymmetric gauge theories and black holes

In this thesis I show a triple new connection we found between quantum integrability, N=2 supersymmetric gauge theories and black holes perturbation theory. I use the approach of the ODE/IM correspondence between Ordinary Differential Equations (ODE) and Integrable Models (IM), first to connect basic integrability functions - the Baxter’s Q, T and Y functions - to the gauge theory periods. This fundamental identification allows several new results for both theories, for example: an exact non linear integral equation (Thermodynamic Bethe Ansatz, TBA) for the gauge periods; an interpretation of the integrability functional relations as new exact R-symmetry relations for the periods; new formulas for the local integrals of motion in terms of gauge periods. This I develop in all details at least for the SU(2) gauge theory with Nf=0,1,2 matter flavours. Still through to the ODE/IM correspondence, I connect the mathematically precise definition of quasinormal modes of black holes (having an important role in gravitational waves’ obervations) with quantization conditions on the Q, Y functions. In this way I also give a mathematical explanation of the recently found connection between quasinormal modes and N=2 supersymmetric gauge theories. Moreover, it follows a new simple and effective method to numerically compute the quasinormal modes - the TBA - which I compare with other standard methods. The spacetimes for which I show these in all details are in the simplest Nf=0 case the D3 brane in the Nf=1,2 case a generalization of extremal Reissner-Nordström (charged) black holes. Then I begin treating also the Nf=3,4 theories and argue on how our integrability-gauge-gravity correspondence can generalize to other types of black holes in either asymptotically flat (Nf=3) or Anti-de-Sitter (Nf=4) spacetime. Finally I begin to show the extension to a 4-fold correspondence with also Conformal Field Theory (CFT), through the renowned AdS/CFT correspondence.

Identification of sunflower genotypes suitable for organic agriculture and development of analytical tools for their traceability

Thanks to the development and combination of molecular markers for the genetic traceability of sunflower varieties and a gas chromatographic method for the determination of the FAs composition of sunflower oil, it was possible to implement an experimental method for the verification of both the traceability and the variety of organic sunflower marketed by Agricola Grains S.p.A. The experimental activity focused on two objectives: the implementation of molecular markers for the routine control of raw material deliveries for oil extraction and the improvement and validation of a gas chromatographic method for the determination of the FAs composition of sunflower oil. With regard to variety verification and traceability, the marker systems evaluated were the following: SSR markers (12) arranged in two multiplex sets and SCAR markers for the verification of cytoplasmic male sterility (Pet1) and fertility. In addition, two objectives were pursued in order to enable a routine application in the industrial field: the development of a suitable protocol for DNA extraction from single seeds and the implementation of a semi-automatic capillary electrophoresis system for the analysis of marker fragments. The development and validation of a new GC/FID analytical method for the determination of fatty acids (FAME) in sunflower achenes to improve the quality and efficiency of the analytical flow in the control of raw and refined materials entering the Agricola Grains S.p.A. production chain. The analytical performances being validated by the newly implemented method are: linearity of response, limit of quantification, specificity, precision, intra-laboratory precision, robustness, BIAS. These parameters are used to compare the newly developed method with the one considered as reference - Commission Regulation No. 2568/91 and Commission Implementing Regulation No. 2015/1833. Using the combination of the analytical methods mentioned above, the documentary traceability of the product can be confirmed experimentally, providing relevant information for subsequent marketing.

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.