Planering hur person- och kollektivtrafiken skall ordnas på området : Kaskö - Korsnäs - Kristinestad - Malax - Närpes

Planeringen av hur person- och kollektivtrafiken skall ordnas på området påbörjades i mars 2015. Planen var klar i september 2015. Arbetet syftade till att ge kommunerna och NTM-centralen ett verktyg varmed de i fortsättningen ska kunna säkerställa att planeringen av persontrafiken gemensamt processeras på ett så ändamålsenligt sätt som möjligt så att man därmed kan se till att utbudet är tillräckligt högklassigt och effektivt i framtiden. Under arbetet utreddes nuläget för kollektiv- och persontrafiken inom planeringsområdet genomgående. Dessutom diskuterades kommunernas beredskap för gemensam anskaffning av olika trafiktyper och olika långa avtalsperioder samt utveckling av samarbetet .Kollektivtrafiksexpert Anders Pulkkis på NTM-centralen i Södra Österbotten har deltagit i styrgruppsarbetet. Som utvecklingsåtgärder i arbetet klassificerades kollektivtrafikens turer i fyra klasser enligt lönsamhet. Klassificeringen användes som verktyg i identifieringen av turernas betydelse och planeringen av anskaffningarna. För kommunernas och NTM-centralens anskaffning av trafik fastställdes ett förlopp för åren 2015–2019. Som utvecklingsåtgärder har man även begrundat utveckling av organiseringen av kollektivtrafikplaneringen till exempel genom en regionlogistiker som är gemensam för kommunerna.

Recognition of nucleic acids by metal ion complexes

Metal-ion-mediated base-pairing of nucleic acids has attracted considerable attention during the past decade, since it offers means to expand the genetic code by artificial base-pairs, to create predesigned molecular architecture by metal-ion-mediated inter- or intra-strand cross-links, or to convert double stranded DNA to a nano-scale wire. Such applications largely depend on the presence of a modified nucleobase in both strands engaged in the duplex formation. Hybridization of metal-ion-binding oligonucleotide analogs with natural nucleic acid sequences has received much less attention in spite of obvious applications. While the natural oligonucleotides hybridize with high selectivity, their affinity for complementary sequences is inadequate for a number of applications. In the case of DNA, for example, more than 10 consecutive Watson-Crick base pairs are required for a stable duplex at room temperature, making targeting of sequences shorter than this challenging. For example, many types of cancer exhibit distinctive profiles of oncogenic miRNA, the diagnostics of which is, however, difficult owing to the presence of only short single stranded loop structures. Metallo-oligonucleotides, with their superior affinity towards their natural complements, would offer a way to overcome the low stability of short duplexes. In this study a number of metal-ion-binding surrogate nucleosides were prepared and their interaction with nucleoside 5´-monophosphates (NMPs) has been investigated by 1H NMR spectroscopy. To find metal ion complexes that could discriminate between natural nucleobases upon double helix formation, glycol nucleic acid (GNA) sequences carrying a PdII ion with vacant coordination sites at a predetermined position were synthesized and their affinity to complementary as well as mismatched counterparts quantified by UV-melting measurements.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.