Characterisation and processing of amorphous binary mixtures with low glass transition temperature

The number of drug substances in formulation development in the pharmaceutical industry is increasing. Some of these are amorphous drugs and have glass transition below ambient temperature, and thus they are usually difficult to formulate and handle. One reason for this is the reduced viscosity, related to the stickiness of the drug, that makes them complicated to handle in unit operations. Thus, the aim in this thesis was to develop a new processing method for a sticky amorphous model material. Furthermore, model materials were characterised before and after formulation, using several characterisation methods, to understand more precisely the prerequisites for physical stability of amorphous state against crystallisation. The model materials used were monoclinic paracetamol and citric acid anhydrate. Amorphous materials were prepared by melt quenching or by ethanol evaporation methods. The melt blends were found to have slightly higher viscosity than the ethanol evaporated materials. However, melt produced materials crystallised more easily upon consecutive shearing than ethanol evaporated materials. The only material that did not crystallise during shearing was a 50/50 (w/w, %) blend regardless of the preparation method and it was physically stable at least two years in dry conditions. Shearing at varying temperatures was established to measure the physical stability of amorphous materials in processing and storage conditions. The actual physical stability of the blends was better than the pure amorphous materials at ambient temperature. Molecular mobility was not related to the physical stability of the amorphous blends, observed as crystallisation. Molecular mobility of the 50/50 blend derived from a spectral linewidth as a function of temperature using solid state NMR correlated better with the molecular mobility derived from a rheometer than that of differential scanning calorimetry data. Based on the results obtained, the effect of molecular interactions, thermodynamic driving force and miscibility of the blends are discussed as the key factors to stabilise the blends. The stickiness was found to be affected glass transition and viscosity. Ultrasound extrusion and cutting were successfully tested to increase the processability of sticky material. Furthermore, it was found to be possible to process the physically stable 50/50 blend in a supercooled liquid state instead of a glassy state. The method was not found to accelerate the crystallisation. This may open up new possibilities to process amorphous materials that are otherwise impossible to manufacture into solid dosage forms.

Problems and Algorithms for Sequence Segmentations

The analysis of sequential data is required in many diverse areas such as telecommunications, stock market analysis, and bioinformatics. A basic problem related to the analysis of sequential data is the sequence segmentation problem. A sequence segmentation is a partition of the sequence into a number of non-overlapping segments that cover all data points, such that each segment is as homogeneous as possible. This problem can be solved optimally using a standard dynamic programming algorithm. In the first part of the thesis, we present a new approximation algorithm for the sequence segmentation problem. This algorithm has smaller running time than the optimal dynamic programming algorithm, while it has bounded approximation ratio. The basic idea is to divide the input sequence into subsequences, solve the problem optimally in each subsequence, and then appropriately combine the solutions to the subproblems into one final solution. In the second part of the thesis, we study alternative segmentation models that are devised to better fit the data. More specifically, we focus on clustered segmentations and segmentations with rearrangements. While in the standard segmentation of a multidimensional sequence all dimensions share the same segment boundaries, in a clustered segmentation the multidimensional sequence is segmented in such a way that dimensions are allowed to form clusters. Each cluster of dimensions is then segmented separately. We formally define the problem of clustered segmentations and we experimentally show that segmenting sequences using this segmentation model, leads to solutions with smaller error for the same model cost. Segmentation with rearrangements is a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. We formulate the problem of segmentation with rearrangements and we show that it is an NP-hard problem to solve or even to approximate. We devise effective algorithms for the proposed problem, combining ideas from dynamic programming and outlier detection algorithms in sequences. In the final part of the thesis, we discuss the problem of aggregating results of segmentation algorithms on the same set of data points. In this case, we are interested in producing a partitioning of the data that agrees as much as possible with the input partitions. We show that this problem can be solved optimally in polynomial time using dynamic programming. Furthermore, we show that not all data points are candidates for segment boundaries in the optimal solution.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.

Methicillin-resistant Staphylococcus aureus in Finland: recent changes in the epidemiology, long-term facility aspects, and phenotypic and molecular detection of isolates

Staphylococcus aureus is one of the most important bacteria that cause disease in humans, and methicillin-resistant S. aureus (MRSA) has become the most commonly identified antibiotic-resistant pathogen in many parts of the world. MRSA rates have been stable for many years in the Nordic countries and the Netherlands with a low MRSA prevalence in Europe, but in the recent decades, MRSA rates have increased in those low-prevalence countries as well. MRSA has been established as a major hospital pathogen, but has also been found increasingly in long-term facilities (LTF) and in communities of persons with no connections to the health-care setting. In Finland, the annual number of MRSA isolates reported to the National Infectious Disease Register (NIDR) has constantly increased, especially outside the Helsinki metropolitan area. Molecular typing has revealed numerous outbreak strains of MRSA, some of which have previously been associated with community acquisition. In this work, data on MRSA cases notified to the NIDR and on MRSA strain types identified with pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST), and staphylococcal cassette chromosome mec (SCCmec) typing at the National Reference Laboratory (NRL) in Finland from 1997 to 2004 were analyzed. An increasing trend in MRSA incidence in Finland from 1997 to 2004 was shown. In addition, non-multi-drug resistant (NMDR) MRSA isolates, especially those resistant only to methicillin/oxacillin, showed an emerging trend. The predominant MRSA strains changed over time and place, but two internationally spread epidemic strains of MRSA, FIN-16 and FIN-21, were related to the increase detected most recently. Those strains were also one cause of the strikingly increasing invasive MRSA findings. The rise of MRSA strains with SCCmec types IV or V, possible community-acquired MRSA was also detected. With questionnaires, the diagnostic methods used for MRSA identification in Finnish microbiology laboratories and the number of MRSA screening specimens studied were reviewed. Surveys, which focused on the MRSA situation in long-term facilities in 2001 and on the background information of MRSA-positive persons in 2001-2003, were also carried out. The rates of MRSA and screening practices varied widely across geographic regions. Part of the NMDR MRSA strains could remain undetected in some laboratories because of insufficient diagnostic techniques used. The increasing proportion of elderly population carrying MRSA suggests that MRSA is an emerging problem in Finnish long-term facilities. Among the patients, 50% of the specimens were taken on a clinical basis, 43% on a screening basis after exposure to MRSA, 3% on a screening basis because of hospital contact abroad, and 4% for other reasons. In response to an outbreak of MRSA possessing a new genotype that occurred in a health care ward and in an associated nursing home of a small municipality in Northern Finland in autumn 2003, a point-prevalence survey was performed six months later. In the same study, the molecular epidemiology of MRSA and methicillin-sensitive S. aureus (MSSA) strains were also assessed, the results to the national strain collection compared, and the difficulties of MRSA screening with low-level oxacillin-resistant isolates encountered. The original MRSA outbreak in LTF, which consisted of isolates possessing a nationally new PFGE profile (FIN-22) and internationally rare MLST type (ST-27), was confined. Another previously unrecognized MRSA strain was found with additional screening, possibly indicating that current routine MRSA screening methods may be insufficiently sensitive for strains possessing low-level oxacillin resistance. Most of the MSSA strains found were genotypically related to the epidemic MRSA strains, but only a few of them had received the SCCmec element, and all those strains possessed the new SCCmec type V. In the second largest nursing home in Finland, the colonization of S. aureus and MRSA, and the role of screening sites along with broth enrichment culture on the sensitivity to detect S. aureus were studied. Combining the use of enrichment broth and perineal swabbing, in addition to nostrils and skin lesions swabbing, may be an alternative for throat swabs in the nursing home setting, especially when residents are uncooperative. Finally, in order to evaluate adequate phenotypic and genotypic methods needed for reliable laboratory diagnostics of MRSA, oxacillin disk diffusion and MIC tests to the cefoxitin disk diffusion method at both +35°C and +30°C, both with or without an addition of sodium chloride (NaCl) to the Müller Hinton test medium, and in-house PCR to two commercial molecular methods (the GenoType® MRSA test and the EVIGENETM MRSA Detection test) with different bacterial species in addition to S. aureus were compared. The cefoxitin disk diffusion method was superior to that of oxacillin disk diffusion and to the MIC tests in predicting mecA-mediated resistance in S. aureus when incubating at +35°C with or without the addition of NaCl to the test medium. Both the Geno Type® MRSA and EVIGENETM MRSA Detection tests are usable, accurate, cost-effective, and sufficiently fast methods for rapid MRSA confirmation from a pure culture.

Microbial activities in boreal soils: Biodegradation of organic contaminants at low temperature and ammonia oxidation

This thesis deals with the response of biodegradation of selected anthropogenic organic contaminants and natural autochthonous organic matter to low temperature in boreal surface soils. Furthermore, the thesis describes activity, diversity and population size of autotrophic ammonia-oxidizing bacteria (AOB) in a boreal soil used for landfarming of oil-refinery wastes, and presents a new approach, in which the particular AOB were enriched and cultivated in situ from the landfarming soil onto cation exchange membranes. This thesis demonstrates that rhizosphere fraction of natural forest humus soil and agricultural clay loam soil from Helsinki Metropolitan area were capable of degrading of low to moderate concentrations (0.2 50 µg cm-3) of PCP, phenanthrene and 2,4,5-TCP at temperatures realistic to boreal climate (-2.5 to +15 °C). At the low temperatures, the biodegradation of PCP, phenanthrene and 2,4,5-TCP was more effective (Q10-values from 1.6 to 7.6) in the rhizosphere fraction of the forest soil than in the agricultural soil. Q10-values of endogenous soil respiration (carbon dioxide evolution) and selected hydrolytic enzyme activities (acetate-esterase, butyrate-esterase and β-glucosidase) in acid coniferous forest soil were 1.6 to 2.8 at temperatures from -3 to +30 °C. The results indicated that the temperature dependence of decomposition of natural autochthonous soil organic matter in the studied coniferous forest was only moderate. The numbers of AOB in the landfarming (sandy clay loam) soil were determined with quantitative polymerase chain reaction (real-time PCR) and with Most Probable Number (MPN) methods, and potential ammonium oxidation activity was measured with the chlorate inhibition technique. The results indicated presence of large and active AOB populations in the heavily oil-contaminated and urea-fertilised landfarming soil. Assessment of the populations of AOB with denaturing gradient gel electrophoresis (DGGE) profiling and sequence analysis of PCR-amplified 16S rRNA genes showed that Nitrosospira-like AOB in clusters 2 and 3 were predominant in the oily landfarming soil. This observation was supported by fluorescence in situ hybridization (FISH) analysis of the AOB grown on the soil-incubated cation-exchange membranes. The results of this thesis expand the suggested importance of Nitrosospira-like AOB in terrestrial environments to include chronically oil-contaminated soils.

Very Low Birth Weight Infants as Young Adults : Focus on aspects of cognition, behavior and sleep

Major advances in the treatment of preterm infants have occurred during the last three decades. Survival rates have increased, and the first generations of preterm infants born at very low birth weight (VLBW; less than 1500 g) who profited from modern neonatal intensive care are now in young adulthood. The literature shows that VLBW children achieve on average lower scores on cognitive tests, even after exclusion of individuals with obvious neurosensory deficits. Evidence also exists for an increased risk in VLBW children for various neuropsychiatric disorders such as attention-deficit hyperactivity disorder (ADHD) and related behavioral symptoms. Up till now, studies extending into adulthood are sparse, and it remains to be seen whether these problems persist into adulthood. The aim of this thesis was to study ADHD-related symptoms and cognitive and executive functioning in young adults born at VLBW. In addition, we aimed to study sleep disturbances, known to adversely affect both cognition and attention. We hypothesized that preterm birth at VLBW interferes with early brain development in a way that alters the neuropsychological phenotype; this may manifest itself as ADHD symptoms and impaired cognitive abilities in young adulthood. In this cohort study from a geographically defined region, we studied 166 VLBW adults and 172 term-born controls born from 1978 through 1985. At ages 18 to 27 years, the study participants took part in a clinic study during which their physical and psychological health was assessed in detail. Three years later, 213 of these individuals participated in a follow-up. The current study is part of a larger research project (The Helsinki Study of Very Low Birth Weight Adults), and the measurements of interest for this particular study include the following: 1) The Adult Problem Questionnaire (APQ), a self-rating scale of ADHD-related symptoms in adults; 2) A computerized cognitive test battery designed for population studies (CogState®) which measures core cognitive abilities such as reaction time, working memory, and visual learning; 3) Sleep assessment by actigraphy, the Basic Nordic Sleep Questionnaire, and the Morningness-Eveningness Questionnaire. Actigraphs are wrist-worn accelerometers that separate sleep from wakefulness by registering body movements. Contrary to expectations, VLBW adults as a group reported no more ADHD-related behavioral symptoms than did controls. Further subdivision of the VLBW group into SGA (small for gestational age) and AGA (appropriate for gestational age) subgroups, however, revealed more symptoms on ADHD subscales pertaining to executive dysfunction and emotional instability among those born SGA. Thus, it seems that intrauterine growth retardation (for which SGA served as a proxy) is a more essential predictor for self-perceived ADHD symptoms in adulthood than is VLBW birth as such. In line with observations from other cohorts, the VLBW adults reported less risk-taking behavior in terms of substance use (alcohol, smoking, and recreational drugs), a finding reassuring for the VLBW individuals and their families. On the cognitive test, VLBW adults free from neurosensory deficits had longer reaction times than did term-born peers on all tasks included in the test battery, and lower accuracy on the learning task, with no discernible effect of SGA status over and above the effect of VLBW. Altogether, on a group level, even high-functioning VLBW adults show subtle deficits in psychomotor processing speed, visual working memory, and learning abilities. The sleep studies provided no evidence for differences in sleep quality or duration between the two groups. The VLBW adults were, however, at more than two-fold higher risk for sleep-disordered breathing (in terms of chronic snoring). Given the link between sleep-disordered breathing and health sequelae, these results suggest that VLBW individuals may benefit from an increased awareness among clinicians of this potential problem area. An unexpected finding from the sleep studies was the suggestion of an advanced sleep phase: The VLBW adults went to bed earlier according to the actigraphy registrations and also reported earlier wake-up times on the questionnaire. In further study of this issue in conjunction with the follow-up three years later, the VLBW group reported higher levels of morningness propensity, further corroborating the preliminary findings of an advanced sleep phase. Although the clinical implications are not entirely clear, the issue may be worth further study, since circadian rhythms are closely related to health and well-being. In sum, we believe that increased understanding of long-term outcomes after VLBW, and identification of areas and subgroups that are particularly vulnerable, will allow earlier recognition of potential problems and ultimately lead to improved prevention strategies.

Unrealized expectations of jumps in volatility: An explanation to the low and time-varying predictive power of implied volatility

The low predictive power of implied volatility in forecasting the subsequently realized volatility is a well-documented empirical puzzle. As suggested by e.g. Feinstein (1989), Jackwerth and Rubinstein (1996), and Bates (1997), we test whether unrealized expectations of jumps in volatility could explain this phenomenon. Our findings show that expectations of infrequently occurring jumps in volatility are indeed priced in implied volatility. This has two important consequences. First, implied volatility is actually expected to exceed realized volatility over long periods of time only to be greatly less than realized volatility during infrequently occurring periods of very high volatility. Second, the slope coefficient in the classic forecasting regression of realized volatility on implied volatility is very sensitive to the discrepancy between ex ante expected and ex post realized jump frequencies. If the in-sample frequency of positive volatility jumps is lower than ex ante assessed by the market, the classic regression test tends to reject the hypothesis of informational efficiency even if markets are informationally effective.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.

Noncommutative Quantum Field Theory : Problem of Time and Some Applications

In this thesis the current status and some open problems of noncommutative quantum field theory are reviewed. The introduction aims to put these theories in their proper context as a part of the larger program to model the properties of quantized space-time. Throughout the thesis, special focus is put on the role of noncommutative time and how its nonlocal nature presents us with problems. Applications in scalar field theories as well as in gauge field theories are presented. The infinite nonlocality of space-time introduced by the noncommutative coordinate operators leads to interesting structure and new physics. High energy and low energy scales are mixed, causality and unitarity are threatened and in gauge theory the tools for model building are drastically reduced. As a case study in noncommutative gauge theory, the Dirac quantization condition of magnetic monopoles is examined with the conclusion that, at least in perturbation theory, it cannot be fulfilled in noncommutative space.

Interfacial Phenomena in Pharmaceutical Low Moisture Content Powder Processing

Powders are essential materials in the pharmaceutical industry, being involved in majority of all drug manufacturing. Powder flow and particle size are central particle properties addressed by means of particle engineering. The aim of the thesis was to gain knowledge on powder processing with restricted liquid addition, with a primary focus on particle coating and early granule growth. Furthermore, characterisation of this kind of processes was performed. A thin coating layer of hydroxypropyl methylcellulose was applied on individual particles of ibuprofen in a fluidised bed top-spray process. The polymeric coating improved the flow properties of the powder. The improvement was strongly related to relative humidity, which can be seen as an indicator of a change in surface hydrophilicity caused by the coating. The ibuprofen used in the present study had a d50 of 40 μm and thus belongs to the Geldart group C powders, which can be considered as challenging materials in top-spray coating processes. Ibuprofen was similarly coated using a novel ultrasound-assisted coating method. The results were in line with those obtained from powders coated in the fluidised bed process mentioned above. It was found that the ultrasound-assisted method was capable of coating single particles with a simple and robust setup. Granule growth in a fluidised bed process was inhibited by feeding the liquid in pulses. The results showed that the length of the pulsing cycles is of importance, and can be used to adjust granule growth. Moreover, pulsed liquid feed was found to be of greater significance to granule growth in high inlet air relative humidity. Liquid feed pulsing can thus be used as a tool in particle size targeting in fluidised bed processes and in compensating for changes in relative humidity of the inlet air. The nozzle function of a two-fluid external mixing pneumatic nozzle, typical for small scale pharmaceutical fluidised bed processes, was studied in situ in an ongoing fluidised bed process with particle tracking velocimetry. It was found that the liquid droplets undergo coalescence as they proceed away from the nozzle head. The coalescence was expected to increase droplet speed, which was confirmed in the study. The spray turbulence was studied, and the results showed turbulence caused by the event of atomisation and by the oppositely directed fluidising air. It was concluded that particle tracking velocimetry is a suitable tool for in situ spray characterisation. The light transmission through dense particulate systems was found to carry information on particle size and packing density as expected based on the theory of light scattering by solids. It was possible to differentiate binary blends consisting of components with differences in optical properties. Light transmission showed potential as a rapid, simple and inexpensive tool in characterisation of particulate systems giving information on changes in particle systems, which could be utilised in basic process diagnostics.

A linear sequence of extant families and genera of lycophytes and ferns

Throughout the history of the classification of extant ferns (monilophytes) and lycophytes, familial and generic concepts have been in great flux. For the organisation of lycophytes and ferns in herbaria, books, checklists, indices and spore banks and on the internet, this poses a problem, and a standardized linear sequence of these plants is therefore in great need. We provide here a linear classification to the extant lycophytes and ferns based on current phylogenetic knowledge; this provides a standardized guide for organisation of fern collections into a more natural sequence. Two new families, Diplaziopsidaceae and Rhachidosoraceae, are here introduced.

Planar subgraphs without low-degree nodes

We study the following problem: given a geometric graph G and an integer k, determine if G has a planar spanning subgraph (with the original embedding and straight-line edges) such that all nodes have degree at least k. If G is a unit disk graph, the problem is trivial to solve for k = 1. We show that even the slightest deviation from the trivial case (e.g., quasi unit disk graphs or k = 1) leads to NP-hard problems.