Lyophilized disaccharides – the effect of water during storage

Nearly one fourth of new medicinal molecules are biopharmaceutical (protein, antibody or nucleic acid derivative) based. However, the administration of these compounds is not always that straightforward due to the fragile nature of aforementioned domains in GI-tract. In addition, these molecules often exhibit poor bioavailability when administered orally. As a result, parenteral administration is commonly preferred. In addition, shelf-life of these molecules in aqueous environments is poor, unless stored in low temperatures. Another approach is to bring these molecules to anhydrous form via lyophilization resulting in enhanced stability during storage. Proteins cannot most commonly be freeze dried by themselves so some kind of excipients are nearly always necessary. Disaccharides are commonly utilized excipients in freeze-dried formulations since they provide a rigid glassy matrix to maintain the native conformation of the protein domain. They also act as "sink"-agents, which basically mean that they can absorb some moisture from the environment and still help to protect the API itself to retain its activity and therefore offer a way to robust formulation. The aim of the present study was to investigate how four amorphous disaccharides (cellobiose, melibiose, sucrose and trehalose) behave when they are brought to different relative humidity levels. At first, solutions of each disaccharide were prepared, filled into scintillation vials and freeze dried. Initial information on how the moisture induced transformations take place, the lyophilized amorphous disaccharide cakes were placed in vacuum desiccators containing different relative humidity levels for defined period, after which selected analyzing methods were utilized to further examine the occurred transformations. Affinity to crystallization, water sorption of the disaccharides, the effect of moisture on glass transition and crystallization temperature were studied. In addition FT-IR microscopy was utilized to map the moisture distribution on a piece of lyophilized cake. Observations made during the experiments backed up the data mentioned in a previous study: melibiose and trehalose were shown to be superior over sucrose and cellobiose what comes to the ability to withstand elevated humidity and temperature, and to avoid crystallization with pharmaceutically relevant moisture contents. The difference was made evident with every utilized analyzing method. In addition, melibiose showed interesting anomalies during DVS runs, which were absent with other amorphous disaccharides. Particularly fascinating was the observation made with polarized light microscope, which revealed a possible small-scale crystallization that cannot be observed with XRPD. As a result, a suggestion can safely be made that a robust formulation is most likely obtained by utilizing either melibiose or trehalose as a stabilizing agent for biopharmaceutical freeze-dried formulations. On the other hand, more experiments should be conducted to obtain more accurate information on why these disaccharides have better tolerance for elevating humidities than others.

Fast distributed approximation algorithms for vertex cover and set cover in anonymous networks

We present a distributed algorithm that finds a maximal edge packing in O(Δ + log* W) synchronous communication rounds in a weighted graph, independent of the number of nodes in the network; here Δ is the maximum degree of the graph and W is the maximum weight. As a direct application, we have a distributed 2-approximation algorithm for minimum-weight vertex cover, with the same running time. We also show how to find an f-approximation of minimum-weight set cover in O(f2k2 + fk log* W) rounds; here k is the maximum size of a subset in the set cover instance, f is the maximum frequency of an element, and W is the maximum weight of a subset. The algorithms are deterministic, and they can be applied in anonymous networks.

Analysing local algorithms in location-aware quasi-unit-disk graphs

A local algorithm with local horizon r is a distributed algorithm that runs in r synchronous communication rounds; here r is a constant that does not depend on the size of the network. As a consequence, the output of a node in a local algorithm only depends on the input within r hops from the node. We give tight bounds on the local horizon for a class of local algorithms for combinatorial problems on unit-disk graphs (UDGs). Most of our bounds are due to a refined analysis of existing approaches, while others are obtained by suggesting new algorithms. The algorithms we consider are based on network decompositions guided by a rectangular tiling of the plane. The algorithms are applied to matching, independent set, graph colouring, vertex cover, and dominating set. We also study local algorithms on quasi-UDGs, which are a popular generalisation of UDGs, aimed at more realistic modelling of communication between the network nodes. Analysing the local algorithms on quasi-UDGs allows one to assume that the nodes know their coordinates only approximately, up to an additive error. Despite the localisation error, the quality of the solution to problems on quasi-UDGs remains the same as for the case of UDGs with perfect location awareness. We analyse the increase in the local horizon that comes along with moving from UDGs to quasi-UDGs.

Seasonal variation in nitrification and nitrate-reduction pathways in coastal sediments in the Gulf of Finland, Baltic Sea

The Baltic Sea is one of the most eutrophic marine areas in the world. The role of nitrogen as a eutrophicating nutrient in the Baltic Sea has remained controversial, due to lack of understanding of nitrogen cycling in the area. We investigated the seasonal variation in sediment nitrification, denitrification, anaerobic ammonium oxidation (anammox), and dissimilatory nitrate reduction to ammonium (DNRA) at two coastal sites in the Gulf of Finland. In addition to the in situ rates, we assessed the potential for these processes in different seasons. The nitrification and nitrogen removal processes were maximal during the warm summer months, when the sediment organic content was highest. In colder seasons, the in situ rates of the nitrification and nitrate reduction processes decreased, but the potential for nitrification remained equal to or higher than that during the warm months. The denitrification and nitrification rates were usually higher in the accumulation basin, where the organic content of the sediment was higher, but the transportation area, despite lower denitrification rates and potential, typically had higher potential for nitrification than the accumulation basin. Anammox and DNRA were not significant nitrate sinks in any of the seasons sampled. The results also show that the denitrification rates in the coastal Gulf of Finland sediment have decreased, and that benthic denitrification might be a less important sink for fixed nitrogen than previously assumed.

Locally checkable proofs

This work studies decision problems from the perspective of nondeterministic distributed algorithms. For a yes-instance there must exist a proof that can be verified with a distributed algorithm: all nodes must accept a valid proof, and at least one node must reject an invalid proof. We focus on locally checkable proofs that can be verified with a constant-time distributed algorithm. For example, it is easy to prove that a graph is bipartite: the locally checkable proof gives a 2-colouring of the graph, which only takes 1 bit per node. However, it is more difficult to prove that a graph is not bipartite—it turns out that any locally checkable proof requires Ω(log n) bits per node. In this work we classify graph problems according to their local proof complexity, i.e., how many bits per node are needed in a locally checkable proof. We establish tight or near-tight results for classical graph properties such as the chromatic number. We show that the proof complexities form a natural hierarchy of complexity classes: for many classical graph problems, the proof complexity is either 0, Θ(1), Θ(log n), or poly(n) bits per node. Among the most difficult graph properties are symmetric graphs, which require Ω(n2) bits per node, and non-3-colourable graphs, which require Ω(n2/log n) bits per node—any pure graph property admits a trivial proof of size O(n2).

Compression of Weighted Graphs

We propose to compress weighted graphs (networks), motivated by the observation that large networks of social, biological, or other relations can be complex to handle and visualize. In the process also known as graph simplication, nodes and (unweighted) edges are grouped to supernodes and superedges, respectively, to obtain a smaller graph. We propose models and algorithms for weighted graphs. The interpretation (i.e. decompression) of a compressed, weighted graph is that a pair of original nodes is connected by an edge if their supernodes are connected by one, and that the weight of an edge is approximated to be the weight of the superedge. The compression problem now consists of choosing supernodes, superedges, and superedge weights so that the approximation error is minimized while the amount of compression is maximized. In this paper, we formulate this task as the 'simple weighted graph compression problem'. We then propose a much wider class of tasks under the name of 'generalized weighted graph compression problem'. The generalized task extends the optimization to preserve longer-range connectivities between nodes, not just individual edge weights. We study the properties of these problems and propose a range of algorithms to solve them, with dierent balances between complexity and quality of the result. We evaluate the problems and algorithms experimentally on real networks. The results indicate that weighted graphs can be compressed efficiently with relatively little compression error.

Jakojuuriviljelyn vaikutus kasvihuonekurkun (Cucumis sativus L.) kasvuun ja kehitykseen

Kasvit ottavat vettä parhaiten kasteluravinneliuoksesta, jonka ravinnepitoisuus on pieni. Intensiivisessä kasvihuonetuotannossa käytetään silti kastelulannoituksessa usein korkeita ravinnepitoisuuksia ravinnepuutosten ja satotappioiden välttämiseksi. Jakojuuriviljelyssä kasvin juuriston annetaan kasvaa kahteen erilliseen kasvualustaosioon. Tällöin toiselle puolelle annetaan johtokyvyltään väkevää ja toiselle puolelle laimeaa ravinneliuosta. Erityisesti kasvihuonekurkun, joka on herkkä kasvualustan suolaisuuden aiheuttamille vedensaantiongelmille, on todettu hyötyvän tästä tekniikasta, mikä näkyy kasvaneina satoina. Tämän MTT Piikkiössä toteutetun kasvihuonekurkun jakojuuriviljelytutkimuksen tavoitteena oli tarkentaa tekniikkaa erityisesti kasteluliuosten johtokyvyn osalta. Yhtenäisjuuriviljelyn ja perinteisen jakojuuriviljelyn lisäksi kokeessa oli kaksi jakojuuriviljelykäsittelyä, joissa ravinneliuosväkevyyksiä vaihdettiin väliajoin juuriston toimintakyvyn parantamiseksi. Erillisessä osakokeessa tutkittiin erilaisten johtokyky-yhdistelmien vaikutusta kasvihuonekurkun vegetatiiviseen kasvuun maanpäällisten ja -alaisten kasvinosien välillä sekä juurten morfologiaan ja anatomiaan. Tulokset osoittivat, että jakojuuriviljely lisäsi kasvihuonekurkun sadontuottoa jopa 16 %, mutta ei vaikuttanut koko viljelykauden veden tai ravinteiden ottoon. Yhtenäisjuuriviljelyssä muodostui eniten piikkikärkisiä hedelmiä, mikä viittaa vedensaantiongelmiin haihdutustarpeen ollessa suurin. Viljelytekniikalla ei ollut vaikutusta kasvien vegetatiiviseen kasvuun tai kasvuston rakenteeseen. Lehtiruodeista tehdyt nitraatti- ja kaliummittaukset osoittivat, ettei kasteluliuosten ravinnepitoisuuksilla ollut vaikutusta juurten ravinteiden ottoon. Erilaisilla johtokyky-yhdistelmillä oli huomattavampi vaikutus kasvihuonekurkun juurten painoon kuin verson painoon tai varren pituuskasvuun. Lehtiruotianalyysit viittasivat ravinteiden erilaiseen allokointiin eri johtokyky-yhdistelmissä. Korkeiden johtokykyjen aiheuttama osmoottinen stressi johti muutoksiin juurten morfologiassa ja anatomiassa. Tulosten perusteella jakojuuriviljely paransi kehittyvien hedelmien kohdevahvuutta suhteessa muihin kohteisiin vaikuttamatta vegetatiiviseen kasvuun. Kun laimean ja väkevän ravinneliuoksen puolia vaihdettiin, juuristo otti joustavasti vettä ja ravinteita olosuhteiden määräämästä edullisemmasta johtokyvystä, jolloin kasvihuonekurkun viljelyssä saavutettiin merkittävä satoetu. Juuriston jakaminen vaikuttanee kasvien hormoniaineenvaihduntaan ja voi heikentää juuriston kasvua heikentämättä sen toimintakykyä, jolloin yhteyttämistuotteita kohdennetaan tehokkaammin maanpäällisten osien kasvuun.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.