Regulation of Spermatogenesis: Differentiation of GFP-labeled Stem Cells and the Function of Cytoplasmic Bridges

During spermatogenesis, different genes are expressed in a strictly coordinated fashion providing an excellent model to study cell differentiation. Recent identification of testis specific genes and the development of green fluorescence protein (GFP) transgene technology and an in vivo system for studying the differentiation of transplanted male germ cells in infertile testis has opened new possibilities for studying the male germ cell differentiation at molecular level. We have employed these techniques in combination with transillumination based stage recognition (Parvinen and Vanha-Perttula, 1972) and squash preparation techniques (Parvinen and Hecht, 1981) to study the regulation of male germ cell differentiation. By using transgenic mice expressing enhanced-(E)GFP as a marker we have studied the expression and hormonal regulation of beta-actin and acrosin proteins in the developmentally different living male germ cells. Beta-actin was demonstrated in all male germ cells, whereas acrosin was expressed only in late meiotic and in postmeiotic cells. Follicle stimulating hormone stimulated b-actin-EGFP expression at stages I-VI and enhanced the formation of microtubules in spermatids and this way reduced the size of the acrosomic system. When EGFP expressing spermatogonial stem cells were transplanted into infertile mouse testis differentiation and the synchronized development of male germ cells could be observed during six months observation time. Each colony developed independently and maintained typical stage-dependent cell associations. Furthermore, if more than two colonies were fused, each of them was adjusted to one stage and synchronized. By studying living spermatids we were able to demonstrate novel functions for Golgi complex and chromatoid body in material sharing between neighbor spermatids. Immunosytochemical analyses revealed a transport of haploid cell specific proteins in spermatids (TRA54 and Shippo1) and through the intercellular bridges (TRA54). Cytoskeleton inhibitor (nocodazole) demonstrated the importance of microtubules in material sharing between spermatids and in preserving the integrity of the chromatoid body. Golgi complex inhibitor, brefeldin A, revealed the great importance of Golgi complex i) in acrosomic system formation ii) TRA54 translation and in iii) granule trafficking between spermatids.

A conspectus on estimating function theory and its applicability to recurrent modeling issues in forest biometry

Summary

On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Design for Customer Needs: Utilization of Quality Function Deployment in Product Development

Kansainvälisen kaupan kiristyessä yrityksien kyky täyttää asiakasketjunsa lailliset, sosiaaliset ja toiminnalliset asiakastarpeet tulee punnituksi. Globaalisuuden lisääntyessä asiakasketju voi sisältää toimintoja samanaikaisesti yli sadassa maassa. Jotta asiakasketjun tarpeet voidaan sisällyttää tuotteeseen tehokkaasti yhä useammat yritykset ovat siirtyneet käyttämään Quality Function Deployment nimistä projektijohto- ja laatutyökalua. Quality Function Deployment työkalu auttaa yritystä muuntamaan sisäisten ja ulkoisten asiakkaittensa tarpeet, tuotefunktioiksi ja tuotespesifikaatioiksi. Näin tehdessä voidaan uuden tuotteen kehitysaikaa ja hintaa alentaa merkittävästi suunnittelmalla tuote alunalkaen paremmin. QFD:tä on käytetty useissa yrityksissä Aasiassa, Pohjois-Amerikassa ja Euroopassa, sen kehittämisen jälkeen Japanissa 1960 luvulla. Tämä diplomityö antaa teoreettisen ja käytännön kuvauksen siitä miten QFD:tä kannatta käyttää ja mitä sen avulla voidaan saavuttaa vastaten kysymykseen "miten minä, ja yritykseni hyötyy jos käytän QFD:tä".