Tumor growth reduction is regulated at the gene level in Walker 256 tumor-bearing rats supplemented with fish oil rich in EPA and DHA

We investigated the effect of fish oil (FO) supplementation on tumor growth, cyclooxygenase 2 (COX-2), peroxisome proliferator-activated receptor gamma (PPARγ), and RelA gene and protein expression in Walker 256 tumor-bearing rats. Male Wistar rats (70 days old) were fed with regular chow (group W) or chow supplemented with 1 g/kg body weight FO daily (group WFO) until they reached 100 days of age. Both groups were then inoculated with a suspension of Walker 256 ascitic tumor cells (3×107 cells/mL). After 14 days the rats were killed, total RNA was isolated from the tumor tissue, and relative mRNA expression was measured using the 2-ΔΔCT method. FO significantly decreased tumor growth (W=13.18±1.58 vsWFO=5.40±0.88 g, P<0.05). FO supplementation also resulted in a significant decrease in COX-2 (W=100.1±1.62 vsWFO=59.39±5.53, P<0.001) and PPARγ (W=100.4±1.04vs WFO=88.22±1.46, P<0.05) protein expression. Relative mRNA expression was W=1.06±0.022 vsWFO=0.31±0.04 (P<0.001) for COX-2, W=1.08±0.02vs WFO=0.52±0.08 (P<0.001) for PPARγ, and W=1.04±0.02 vs WFO=0.82±0.04 (P<0.05) for RelA. FO reduced tumor growth by attenuating inflammatory gene expression associated with carcinogenesis.

Low-level red laser therapy alters effects of ultraviolet C radiation on Escherichia coli cells

Low-level lasers are used at low power densities and doses according to clinical protocols supplied with laser devices or based on professional practice. Although use of these lasers is increasing in many countries, the molecular mechanisms involved in effects of low-level lasers, mainly on DNA, are controversial. In this study, we evaluated the effects of low-level red lasers on survival, filamentation, and morphology of Escherichia colicells that were exposed to ultraviolet C (UVC) radiation. Exponential and stationary wild-type and uvrA-deficientE. coli cells were exposed to a low-level red laser and in sequence to UVC radiation. Bacterial survival was evaluated to determine the laser protection factor (ratio between the number of viable cells after exposure to the red laser and UVC and the number of viable cells after exposure to UVC). Bacterial filaments were counted to obtain the percentage of filamentation. Area-perimeter ratios were calculated for evaluation of cellular morphology. Experiments were carried out in duplicate and the results are reported as the means of three independent assays. Pre-exposure to a red laser protected wild-type and uvrA-deficient E. coli cells against the lethal effect of UVC radiation, and increased the percentage of filamentation and the area-perimeter ratio, depending on UVC fluence and physiological conditions in the cells. Therapeutic, low-level red laser radiation can induce DNA lesions at a sub-lethal level. Consequences to cells and tissues should be considered when clinical protocols based on this laser are carried out.

Structural and Computational Existence Results for Multidimensional Subshifts

Symbolic dynamics is a branch of mathematics that studies the structure of infinite sequences of symbols, or in the multidimensional case, infinite grids of symbols. Classes of such sequences and grids defined by collections of forbidden patterns are called subshifts, and subshifts of finite type are defined by finitely many forbidden patterns. The simplest examples of multidimensional subshifts are sets of Wang tilings, infinite arrangements of square tiles with colored edges, where adjacent edges must have the same color. Multidimensional symbolic dynamics has strong connections to computability theory, since most of the basic properties of subshifts cannot be recognized by computer programs, but are instead characterized by some higher-level notion of computability. This dissertation focuses on the structure of multidimensional subshifts, and the ways in which it relates to their computational properties. In the first part, we study the subpattern posets and Cantor-Bendixson ranks of countable subshifts of finite type, which can be seen as measures of their structural complexity. We show, by explicitly constructing subshifts with the desired properties, that both notions are essentially restricted only by computability conditions. In the second part of the dissertation, we study different methods of defining (classes of ) multidimensional subshifts, and how they relate to each other and existing methods. We present definitions that use monadic second-order logic, a more restricted kind of logical quantification called quantifier extension, and multi-headed finite state machines. Two of the definitions give rise to hierarchies of subshift classes, which are a priori infinite, but which we show to collapse into finitely many levels. The quantifier extension provides insight to the somewhat mysterious class of multidimensional sofic subshifts, since we prove a characterization for the class of subshifts that can extend a sofic subshift into a nonsofic one.

Green Service Level Agreement under Sustainability Lens in IT Industry

Nowadays, when most of the business are moving forward to sustainability by providing or getting different services from different vendors, Service Level Agreement (SLA) becomes very important for both the business providers/vendors and as well as for users/customers. There are many ways to inform users/customers about various services with its inherent execution functionalities and even non-functional/Quality of Services (QoS) aspects through negotiating, evaluating or monitoring SLAs. However, these traditional SLA actually do not cover eco-efficient green issues or IT ethics issues for sustainability. That is why green SLA (GSLA) should come into play. GSLA is a formal agreement incorporating all the traditional commitments as well as green issues and ethics issues in IT business sectors. GSLA research would survey on different traditional SLA parameters for various services like as network, compute, storage and multimedia in IT business areas. At the same time, this survey could focus on finding the gaps and incorporation of these traditional SLA parameters with green issues for all these mentioned services. This research is mainly points on integration of green parameters in existing SLAs, defining GSLA with new green performance indicators and their measurable units. Finally, a GSLA template could define compiling all the green indicators such as recycling, radio-wave, toxic material usage, obsolescence indication, ICT product life cycles, energy cost etc for sustainable development. Moreover, people’s interaction and IT ethics issues such as security and privacy, user satisfaction, intellectual property right, user reliability, confidentiality etc could also need to add for proposing a new GSLA. However, integration of new and existing performance indicators in the proposed GSLA for sustainable development could be difficult for ICT engineers. Therefore, this research also discovers the management complexity of proposed green SLA through designing a general informational model and analyses of all the relationships, dependencies and effects between various newly identified services under sustainability pillars. However, sustainability could only be achieved through proper implementation of newly proposed GSLA, which largely depends on monitoring the performance of the green indicators. Therefore, this research focuses on monitoring and evaluating phase of GSLA indicators through the interactions with traditional basic SLA indicators, which would help to achieve proper implementation of future GSLA. Finally, this newly proposed GSLA informational model and monitoring aspects could definitely help different service providers/vendors to design their future business strategy in this new transitional sustainable society.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Socially shared metacognitive regulation during collaborative learning processes in student dyads and small groups

Traditionally metacognition has been theorised, methodologically studied and empirically tested from the standpoint mainly of individuals and their learning contexts. In this dissertation the emergence of metacognition is analysed more broadly. The aim of the dissertation was to explore socially shared metacognitive regulation (SSMR) as part of collaborative learning processes taking place in student dyads and small learning groups. The specific aims were to extend the concept of individual metacognition to SSMR, to develop methods to capture and analyse SSMR and to validate the usefulness of the concept of SSMR in two different learning contexts; in face-to-face student dyads solving mathematical word problems and also in small groups taking part in inquiry-based science learning in an asynchronous computer-supported collaborative learning (CSCL) environment. This dissertation is comprised of four studies. In Study I, the main aim was to explore if and how metacognition emerges during problem solving in student dyads and then to develop a method for analysing the social level of awareness, monitoring, and regulatory processes emerging during the problem solving. Two dyads comprised of 10-year-old students who were high-achieving especially in mathematical word problem solving and reading comprehension were involved in the study. An in-depth case analysis was conducted. Data consisted of over 16 (30–45 minutes) videotaped and transcribed face-to-face sessions. The dyads solved altogether 151 mathematical word problems of different difficulty levels in a game-format learning environment. The interaction flowchart was used in the analysis to uncover socially shared metacognition. Interviews (also stimulated recall interviews) were conducted in order to obtain further information about socially shared metacognition. The findings showed the emergence of metacognition in a collaborative learning context in a way that cannot solely be explained by individual conception. The concept of socially-shared metacognition (SSMR) was proposed. The results highlighted the emergence of socially shared metacognition specifically in problems where dyads encountered challenges. Small verbal and nonverbal signals between students also triggered the emergence of socially shared metacognition. Additionally, one dyad implemented a system whereby they shared metacognitive regulation based on their strengths in learning. Overall, the findings suggested that in order to discover patterns of socially shared metacognition, it is important to investigate metacognition over time. However, it was concluded that more research on socially shared metacognition, from larger data sets, is needed. These findings formed the basis of the second study. In Study II, the specific aim was to investigate whether socially shared metacognition can be reliably identified from a large dataset of collaborative face-to-face mathematical word problem solving sessions by student dyads. We specifically examined different difficulty levels of tasks as well as the function and focus of socially shared metacognition. Furthermore, the presence of observable metacognitive experiences at the beginning of socially shared metacognition was explored. Four dyads participated in the study. Each dyad was comprised of high-achieving 10-year-old students, ranked in the top 11% of their fourth grade peers (n=393). Dyads were from the same data set as in Study I. The dyads worked face-to-face in a computer-supported, game-format learning environment. Problem-solving processes for 251 tasks at three difficulty levels taking place during 56 (30–45 minutes) lessons were video-taped and analysed. Baseline data for this study were 14 675 turns of transcribed verbal and nonverbal behaviours observed in four study dyads. The micro-level analysis illustrated how participants moved between different channels of communication (individual and interpersonal). The unit of analysis was a set of turns, referred to as an ‘episode’. The results indicated that socially shared metacognition and its function and focus, as well as the appearance of metacognitive experiences can be defined in a reliable way from a larger data set by independent coders. A comparison of the different difficulty levels of the problems suggested that in order to trigger socially shared metacognition in small groups, the problems should be more difficult, as opposed to moderately difficult or easy. Although socially shared metacognition was found in collaborative face-to-face problem solving among high-achieving student dyads, more research is needed in different contexts. This consideration created the basis of the research on socially shared metacognition in Studies III and IV. In Study III, the aim was to expand the research on SSMR from face-to-face mathematical problem solving in student dyads to inquiry-based science learning among small groups in an asynchronous computer-supported collaborative learning (CSCL) environment. The specific aims were to investigate SSMR’s evolvement and functions in a CSCL environment and to explore how SSMR emerges at different phases of the inquiry process. Finally, individual student participation in SSMR during the process was studied. An in-depth explanatory case study of one small group of four girls aged 12 years was carried out. The girls attended a class that has an entrance examination and conducts a language-enriched curriculum. The small group solved complex science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry during 22 lessons (á 45–minute). Students’ network discussion were recorded in written notes (N=640) which were used as study data. A set of notes, referred to here as a ‘thread’, was used as the unit of analysis. The inter-coder agreement was regarded as substantial. The results indicated that SSMR emerges in a small group’s asynchronous CSCL inquiry process in the science domain. Hence, the results of Study III were in line with the previous Study I and Study II and revealed that metacognition cannot be reduced to the individual level alone. The findings also confirm that SSMR should be examined as a process, since SSMR can evolve during different phases and that different SSMR threads overlapped and intertwined. Although the classification of SSMR’s functions was applicable in the context of CSCL in a small group, the dominant function was different in the asynchronous CSCL inquiry in the small group in a science activity than in mathematical word problem solving among student dyads (Study II). Further, the use of different analytical methods provided complementary findings about students’ participation in SSMR. The findings suggest that it is not enough to code just a single written note or simply to examine who has the largest number of notes in the SSMR thread but also to examine the connections between the notes. As the findings of the present study are based on an in-depth analysis of a single small group, further cases were examined in Study IV, as well as looking at the SSMR’s focus, which was also studied in a face-to-face context. In Study IV, the general aim was to investigate the emergence of SSMR with a larger data set from an asynchronous CSCL inquiry process in small student groups carrying out science activities. The specific aims were to study the emergence of SSMR in the different phases of the process, students’ participation in SSMR, and the relation of SSMR’s focus to the quality of outcomes, which was not explored in previous studies. The participants were 12-year-old students from the same class as in Study III. Five small groups consisting of four students and one of five students (N=25) were involved in the study. The small groups solved ill-defined science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry over a total period of 22 hours. Written notes (N=4088) detailed the network discussions of the small groups and these constituted the study data. With these notes, SSMR threads were explored. As in Study III, the thread was used as the unit of analysis. In total, 332 notes were classified as forming 41 SSMR threads. Inter-coder agreement was assessed by three coders in the different phases of the analysis and found to be reliable. Multiple methods of analysis were used. Results showed that SSMR emerged in all the asynchronous CSCL inquiry processes in the small groups. However, the findings did not reveal any significantly changing trend in the emergence of SSMR during the process. As a main trend, the number of notes included in SSMR threads differed significantly in different phases of the process and small groups differed from each other. Although student participation was seen as highly dispersed between the students, there were differences between students and small groups. Furthermore, the findings indicated that the amount of SSMR during the process or participation structure did not explain the differences in the quality of outcomes for the groups. Rather, when SSMRs were focused on understanding and procedural matters, it was associated with achieving high quality learning outcomes. In turn, when SSMRs were focused on incidental and procedural matters, it was associated with low level learning outcomes. Hence, the findings imply that the focus of any emerging SSMR is crucial to the quality of the learning outcomes. Moreover, the findings encourage the use of multiple research methods for studying SSMR. In total, the four studies convincingly indicate that a phenomenon of socially shared metacognitive regulation also exists. This means that it was possible to define the concept of SSMR theoretically, to investigate it methodologically and to validate it empirically in two different learning contexts across dyads and small groups. In-depth micro-level case analysis in Studies I and III showed the possibility to capture and analyse in detail SSMR during the collaborative process, while in Studies II and IV, the analysis validated the emergence of SSMR in larger data sets. Hence, validation was tested both between two environments and within the same environments with further cases. As a part of this dissertation, SSMR’s detailed functions and foci were revealed. Moreover, the findings showed the important role of observable metacognitive experiences as the starting point of SSMRs. It was apparent that problems dealt with by the groups should be rather difficult if SSMR is to be made clearly visible. Further, individual students’ participation was found to differ between students and groups. The multiple research methods employed revealed supplementary findings regarding SSMR. Finally, when SSMR was focused on understanding and procedural matters, this was seen to lead to higher quality learning outcomes. Socially shared metacognition regulation should therefore be taken into consideration in students’ collaborative learning at school similarly to how an individual’s metacognition is taken into account in individual learning.

Influence of the moisture at harvest and drying process of the grains on the level of carotenoids in maize (Zea mays)

AbstractMaize is considered a source of carotenoids; however, these compounds are highly unstable, degraded by high temperatures, exposure to light and presence of oxygen. The objective of this work was to evaluate the influence of the moisture and type of drying applied to grains on the level of carotenoids in yellow maize. The experiment was conducted in a completely randomized design (2 × 4 factorial), two levels of initial moisture at the harvest (22 and 19%) and three types of drying (in the sun; in the shade and in a dryer) and control (no drying). The samples of grains after drying with 12% of final moisture were analyzed by concentration of total carotenoids, carotenes (α-carotene + β-carotene), monohydroxilated carotenoids (β-cryptoxanthin), and xanthophylls (lutein + zeaxanthin). Initial moisture, type of drying and the interaction between moisture versus drying influence (p≤0.05) the levels of carotenoids in grains. This is the first report about the drying conditions and harvest’s initial moisture as influence on the profile and content of carotenoids in maize grains. Based on the results, this work suggested that the harvest be carried out preferably when the grains present 22% humidity, with drying in a dryer or in shade for further use or storage.

Extreme Observables and Optimal Measurements in Quantum Theory

Optimization of quantum measurement processes has a pivotal role in carrying out better, more accurate or less disrupting, measurements and experiments on a quantum system. Especially, convex optimization, i.e., identifying the extreme points of the convex sets and subsets of quantum measuring devices plays an important part in quantum optimization since the typical figures of merit for measuring processes are affine functionals. In this thesis, we discuss results determining the extreme quantum devices and their relevance, e.g., in quantum-compatibility-related questions. Especially, we see that a compatible device pair where one device is extreme can be joined into a single apparatus essentially in a unique way. Moreover, we show that the question whether a pair of quantum observables can be measured jointly can often be formulated in a weaker form when some of the observables involved are extreme. Another major line of research treated in this thesis deals with convex analysis of special restricted quantum device sets, covariance structures or, in particular, generalized imprimitivity systems. Some results on the structure ofcovariant observables and instruments are listed as well as results identifying the extreme points of covariance structures in quantum theory. As a special case study, not published anywhere before, we study the structure of Euclidean-covariant localization observables for spin-0-particles. We also discuss the general form of Weyl-covariant phase-space instruments. Finally, certain optimality measures originating from convex geometry are introduced for quantum devices, namely, boundariness measuring how ‘close’ to the algebraic boundary of the device set a quantum apparatus is and the robustness of incompatibility quantifying the level of incompatibility for a quantum device pair by measuring the highest amount of noise the pair tolerates without becoming compatible. Boundariness is further associated to minimum-error discrimination of quantum devices, and robustness of incompatibility is shown to behave monotonically under certain compatibility-non-decreasing operations. Moreover, the value of robustness of incompatibility is given for a few special device pairs.

The effects of mutated cationic amino acid transporter y+LAT1 at the cellular and systemic level

y+LAT1 is a transmembrane protein that, together with the 4F2hc cell surface antigen, forms a transporter for cationic amino acids in the basolateral plasma membrane of epithelial cells. It is mainly expressed in the kidney and small intestine, and to a lesser extent in other tissues, such as the placenta and immunoactive cells. Mutations in y+LAT1 lead to a defect of the y+LAT1/4F2hc transporter, which impairs intestinal absorbance and renal reabsorbance of lysine, arginine and ornithine, causing lysinuric protein intolerance (LPI), a rare, recessively inherited aminoaciduria with severe multi-organ complications. This thesis examines the consequences of the LPI-causing mutations on two levels, the transporter structure and the Finnish patients’ gene expression profiles. Using fluorescence resonance energy transfer (FRET) confocal microscopy, optimised for this work, the subunit dimerisation was discovered to be a primary phenomenon occurring regardless of mutations in y+LAT1. In flow cytometric and confocal microscopic FRET analyses, the y+LAT1 molecules exhibit a strong tendency for homodimerisation both in the presence and absence of 4F2hc, suggesting a heterotetramer for the transporter’s functional form. Gene expression analysis of the Finnish patients, clinically variable but homogenic for the LPI-causing mutation in SLC7A7, revealed 926 differentially-expressed genes and a disturbance of the amino acid homeostasis affecting several transporters. However, despite the expression changes in individual patients, no overall compensatory effect of y+LAT2, the sister y+L transporter, was detected. The functional annotations of the altered genes included biological processes such as inflammatory response, immune system processes and apoptosis, indicating a strong immunological involvement for LPI.

Item-level life-cycle model for maintenance network – from cost to additional value

The importance of industrial maintenance has been emphasized during the last decades; it is no longer a mere cost item, but one of the mainstays of business. Market conditions have worsened lately, investments in production assets have decreased, and at the same time competition has changed from taking place between companies to competition between networks. Companies have focused on their core functions and outsourced support services, like maintenance, above all to decrease costs. This new phenomenon has led to increasing formation of business networks. As a result, a growing need for new kinds of tools for managing these networks effectively has arisen. Maintenance costs are usually a notable part of the life-cycle costs of an item, and it is important to be able to plan the future maintenance operations for the strategic period of the company or for the whole life-cycle period of the item. This thesis introduces an itemlevel life-cycle model (LCM) for industrial maintenance networks. The term item is used as a common definition for a part, a component, a piece of equipment etc. The constructed LCM is a working tool for a maintenance network (consisting of customer companies that buy maintenance services and various supplier companies). Each network member is able to input their own cost and profit data related to the maintenance services of one item. As a result, the model calculates the net present values of maintenance costs and profits and presents them from the points of view of all the network members. The thesis indicates that previous LCMs for calculating maintenance costs have often been very case-specific, suitable only for the item in question, and they have also been constructed for the needs of a single company, without the network perspective. The developed LCM is a proper tool for the decision making of maintenance services in the network environment; it enables analysing the past and making scenarios for the future, and offers choices between alternative maintenance operations. The LCM is also suitable for small companies in building active networks to offer outsourcing services for large companies. The research introduces also a five-step constructing process for designing a life-cycle costing model in the network environment. This five-step designing process defines model components and structure throughout the iteration and exploitation of user feedback. The same method can be followed to develop other models. The thesis contributes to the literature of value and value elements of maintenance services. It examines the value of maintenance services from the perspective of different maintenance network members and presents established value element lists for the customer and the service provider. These value element lists enable making value visible in the maintenance operations of a networked business. The LCM added with value thinking promotes the notion of maintenance from a “cost maker” towards a “value creator”.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.

Tolerance level of green seed in soybean seed lots after storage

The occurrence of green soybean seed due to forced maturation or premature plant death caused by drought or foliar and/or root diseases has been common in several Brazilian production areas. Physiological quality of seed lots with green seed may have their germination and vigor potentials affected and therefore discarded by the grain industry. The objective of this experiment was to determine the maximum tolerated level of green seed in soybean seed lots, which is information of major importance for seed producers when taking the decision whether to sell these lots. Soybean seed of the cultivars CD 206, produced in Ubirata, Parana, and ‘FMT Tucunare,’ produced in Alto Garças, Mato Grosso, were used in the study. Green seed and yellow seed of both cultivars were mixed in the following proportions: 0%, 3%, 6%, 9%, 12%, 15%, 20%, 30%, 40%, 50%, 75% and 100%. Seed quality was evaluated by the germination, accelerated aging, tetrazolium and electrical conductivity tests. The contents of a, b and total chlorophyll in the seed were also determined. A complete randomized block design in a factorial scheme (two cultivars x 12 levels of green seed) was used. Seed quality was negatively affected and chlorophyll contents incremented with the increase in the percentage of green seed. Seed germination, viability and vigor, measured by the accelerated aging test, were not reduced with levels of up to 3% green seed, for both cultivars. Levels above 6% green seed significantly reduced the quality of the seed. The quality of seed lots with 9% or more green seed was significantly reduced to the point that their commercialization is not recommended.

Future operational-tactical level warfare : challenges and opportunities for the defence of Finland

Finnish Defence Studies is published under the auspices of the National Defence College, and the contributions reflect the fields of research and teaching of the College. Finnish Defence Studies will occasionally feature documentation on Finnish Security Policy. Views expressed are those of the authors and do not necessarily imply endorsement by the National Defence College.