Is the Złoczew lignite deposit geologically suitable for the first underground gasification installation in Poland?

The present study focuses on alternative methods of exploiting lignite in comparison to conventional opencast mining and combustion in power plants for the generation of electricity. In Poland, opencast lignite pits cover large areas, creating social and environmental conflicts. In order to stabilise the production level of electricity and reduce the negative effects of opencast mining, alternative ways of exploiting lignite are suggested, one of these being underground gasification in situ. The Złoczew lignite deposit, which will most likely be exploited in the near future, provides an opportunity to discuss the unconventional method of underground coal gasification (UCG). On the basis of technological and geological criteria that have been established to determine the suitability of Polish lignite for underground gasification, resources to be used this way have been estimated. Through gasification, over 15 million tonnes of lignite can be utilised, which is about 2.5 per cent of resources of the Złoczew deposit intended for opencast mining. With this in mind, we suggest to take action by starting a pilot installation, to be followed by a commercial one for underground gasification after completion of superficial mining. Naturally, any future application of this method will be preceded by assessment of geological conditions at the Złoczew opencast pit

Polimorfizm C242T genu kodującego cytochrom b-245 alfa nie jest związany z udarem niedokrwiennym mózgu u dzieci : analiza wewnątrzrodzinna i badanie kliniczno-kontrolne

Background and purpose: Reactive oxygen species play an important role in the physiology and pathology of cerebral arteries, including ischaemic stroke. The cytochrome b-245 alpha gene (CYBA) encodes cytochrome b-245 alpha light chain (p22phox peptide), a critical element of NAD(P)H oxidases, the most important source of superoxide anion in the cerebral arteries. To search for genetic factors associated with paediatric ischaemic stroke, the possible association between CYBA gene C242T polymorphism and the disease was evaluated. Material and methods: The study group consisted of 238 in - dividuals: children with ischaemic stroke (n = 70), their biological parents (n = 118) and children without any symptoms of stroke (n = 50). The C242T polymorphism was genotyped using polymerase chain reaction – restriction fragment length methodology. To evaluate the possible association between polymorphism and stroke, the transmission disequilibrium test and the case-control method were applied. Results: The C242 allele was transmitted more frequently than 242T (62.2% vs. 37.8%) but observed frequencies did not differ significantly from expected (p = 0.10). There were also no significant differences in allele and genotype distribution between patients and control subjects (patients: CC – 50.0%, CT – 38.6%, TT – 11.4% vs. controls: CC – 52.0%, CT – 36.0%, TT – 12.0%). Conclusions: The study did not show that the C242T polymorphism of the CYBA gene is a risk factor of ischaemic stroke in childre

Polimorfizm C242T genu kodującego cytohrom b-245 alfa nie jest związany z udarem niedokrwiennym mózgu u dzieci: analiza wewnątrzrodzinna i badanie kliniczno-kontrolne

Background and purpose Reactive oxygen species play an important role in the physiology and pathology of cerebral arteries, including ischaemic stroke. The cytochrome b-245 alpha gene (CYBA) encodes cytochrome b-245 alpha light chain (p22phox peptide), a critical element of NAD(P)H oxidases, the most important source of superoxide anion in the cerebral arteries. To search for genetic factors associated with paediatric ischaemic stroke, the possible association between CYBA gene C242T polymorphism and the disease was evaluated. Material and methods The study group consisted of 238 individuals: children with ischaemic stroke (n = 70), their biological parents (n = 118) and children without any symptoms of stroke (n = 50). The C242T polymorphism was genotyped using polymerase chain reaction – restriction fragment length methodology. To evaluate the possible association between polymorphism and stroke, the transmission disequilibrium test and the case-control method were applied. Results The C242 allele was transmitted more frequently than 242T (62.2% vs. 37.8%) but observed frequencies did not differ significantly from expected (p = 0.10). There were also no significant differences in allele and genotype distribution between patients and control subjects (patients: CC – 50.0%, CT – 38.6%, TT – 11.4% vs. controls: CC – 52.0%, CT – 36.0%, TT – 12.0%). Conclusions The study did not show that the C242T polymorphism of the CYBA gene is a risk factor of ischaemic stroke in children.Wstęp i cel pracy Reaktywne formy tlenu pełnią istotną funkcję zarówno w fizjologii, jak i patologii tętnic mózgowych, także w patogenezie udaru niedokrwiennego mózgu. Gen cytochromu b-245 alfa (gen CYBA) koduje lekki łańcuch cytochromu b-245 (białko p22phox), kluczowy składnik oksydaz NAD(P)H, najważniejszego źródła anionorodnika ponadtlenkowego w obrębie tętnic mózgowych. W niniejszej pracy, poszukując genetycznych czynników ryzyka predysponujących do udaru mózgu u dzieci, oceniano możliwe związki pomiędzy polimorfizmem C242T genu CYBA i chorobą. Materiał i metody W badaniu wzięło udział 238 osób: 70 dzieci z udarem niedokrwiennym mózgu, 118 ich biologicznych rodziców oraz 50 dzieci bez żadnych objawów udaru. Polimorfizm C242T genu CYBA genotypowano metodą polimorfizmu długości fragmentów restrykcyjnych w reakcji łańcuchowej polimerazy. Do oceny możliwych związków pomiędzy polimorfizmem i udarem zastosowano dwie niezależne metody: wewnątrzrodzinny test transmisji i badanie kliniczno-kontrolne. Wyniki Allel C242 był częściej przekazywany chorym dzieciom przez heterozygotycznych rodziców niż allel 242T (62,2% w porównaniu z 37,8%), jednak obserwowane częstości nie odbiegały znamiennie od oczekiwanych (p = 0,10). Nie wykazano także znaczących różnic w rozkładzie alleli i genotypów pomiędzy pacjentami i dziećmi z grupy kontrolnej (pacjenci: CC – 50,0%, CT – 38,6%, TT – 11,4%; grupa kontrolna: CC – 52,0%, CT – 36,0%, TT – 12,0%). Wnioski Wyniki badań nie wykazały, aby polimorfizm C242T genu CYBA był czynnikiem ryzyka udaru niedokrwiennego u dzieci

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.Comment: 22 pages, 12 figures, 10 table

Automated design of energy functions for protein structure prediction by means of genetic programming and improved structure similarity assessment

The process of protein structure prediction is a crucial part of understanding the function of the building blocks of life. It is based on the approximation of a protein free energy that is used to guide the search through the space of protein structures towards the thermodynamic equilibrium of the native state. A function that gives a good approximation of the protein free energy should be able to estimate the structural distance of the evaluated candidate structure to the protein native state. This correlation between the energy and the similarity to the native is the key to high quality predictions. State-of-the-art protein structure prediction methods use very simple techniques to design such energy functions. The individual components of the energy functions are created by human experts with the use of statistical analysis of common structural patterns that occurs in the known native structures. The energy function itself is then defined as a simple weighted sum of these components. Exact values of the weights are set in the process of maximisation of the correlation between the energy and the similarity to the native measured by a root mean square deviation between coordinates of the protein backbone. In this dissertation I argue that this process is oversimplified and could be improved on at least two levels. Firstly, a more complex functional combination of the energy components might be able to reflect the similarity more accurately and thus improve the prediction quality. Secondly, a more robust similarity measure that combines different notions of the protein structural similarity might provide a much more realistic baseline for the energy function optimisation. To test these two hypotheses I have proposed a novel approach to the design of energy functions for protein structure prediction using a genetic programming algorithm to evolve the energy functions and a structural similarity consensus to provide a reference similarity measure. The best evolved energy functions were found to reflect the similarity to the native better than the optimised weighted sum of terms, and therefore opening a new interesting area of research for the machine learning techniques.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Automated design of energy functions for protein structure prediction by means of genetic programming and improved structure similarity assessment

The process of protein structure prediction is a crucial part of understanding the function of the building blocks of life. It is based on the approximation of a protein free energy that is used to guide the search through the space of protein structures towards the thermodynamic equilibrium of the native state. A function that gives a good approximation of the protein free energy should be able to estimate the structural distance of the evaluated candidate structure to the protein native state. This correlation between the energy and the similarity to the native is the key to high quality predictions. State-of-the-art protein structure prediction methods use very simple techniques to design such energy functions. The individual components of the energy functions are created by human experts with the use of statistical analysis of common structural patterns that occurs in the known native structures. The energy function itself is then defined as a simple weighted sum of these components. Exact values of the weights are set in the process of maximisation of the correlation between the energy and the similarity to the native measured by a root mean square deviation between coordinates of the protein backbone. In this dissertation I argue that this process is oversimplified and could be improved on at least two levels. Firstly, a more complex functional combination of the energy components might be able to reflect the similarity more accurately and thus improve the prediction quality. Secondly, a more robust similarity measure that combines different notions of the protein structural similarity might provide a much more realistic baseline for the energy function optimisation. To test these two hypotheses I have proposed a novel approach to the design of energy functions for protein structure prediction using a genetic programming algorithm to evolve the energy functions and a structural similarity consensus to provide a reference similarity measure. The best evolved energy functions were found to reflect the similarity to the native better than the optimised weighted sum of terms, and therefore opening a new interesting area of research for the machine learning techniques

Is the Złoczew lignite deposit geologically suitable for the first underground gasification installation in Poland?

The present study focuses on alternative methods of exploiting lignite in comparison to conventional opencast mining and combustion in power plants for the generation of electricity. In Poland, opencast lignite pits cover large areas, creating social and environmental conflicts. In order to stabilise the production level of electricity and reduce the negative effects of opencast mining, alternative ways of exploiting lignite are suggested, one of these being underground gasification in situ. The Złoczew lignite deposit, which will most likely be exploited in the near future, provides an opportunity to discuss the unconventional method of underground coal gasification (UCG). On the basis of technological and geological criteria that have been established to determine the suitability of Polish lignite for underground gasification, resources to be used this way have been estimated. Through gasification, over 15 million tonnes of lignite can be utilised, which is about 2.5 per cent of resources of the Złoczew deposit intended for opencast mining. With this in mind, we suggest to take action by starting a pilot installation, to be followed by a commercial one for underground gasification after completion of superficial mining. Naturally, any future application of this method will be preceded by assessment of geological conditions at the Złoczew opencast pit