Soldiers in a stingless bee: work rate and task repertoire suggest they are an elite force

The differentiation of workers into morphological subcastes (e.g., soldiers) represents an important evolutionary transition and is thought to improve division of labor in social insects. Soldiers occur in many ant and termite species, where they make up a small proportion of the workforce. A common assumption of worker caste evolution is that soldiers are behavioral specialists. Here, we report the first test of the "rare specialist" hypothesis in a eusocial bee. Colonies of the stingless bee Tetragonisca angustula are defended by a small group of morphologically differentiated soldiers. Contrary to the rare specialist hypothesis, we found that soldiers worked more (+34%-41%) and performed a greater variety of tasks (+23%-34%) than other workers, particularly early in life. Our results suggest a "rare elite" function of soldiers in T. angustula, that is, that they perform a disproportionately large amount of the work. Division of labor was based on a combination of temporal and physical castes, but soldiers transitioned faster from one task to the next. We discuss why the rare specialist assumption might not hold in species with a moderate degree of worker differentiation.

Language Learning in Task Management and Task Accomplishment

In this paper we study student interaction in English and Swedish courses at a Finnish university. We focus on language choices made in task-related activities in small group interaction. Our research interests arose from the change in the teaching curriculum, in which content and language courses were integrated at Tampere University of Technology in 2013. Using conversation analysis, we analysed groups of 4-5 students who worked collaboratively on a task via a video conference programme. The results show how language alternation has different functions in 1) situations where students orient to managing the task, e.g., in transitions into task, or where they orient to technical problems, and 2) situations where students accomplish the task. With the results, we aim to show how language alternation can provide interactional opportunities for language learning. The findings will be useful in designing tasks in the future.

Anthropomorphic model observer performance in three-dimensional detection task for low-contrast computed tomography.

X-ray medical imaging is increasingly becoming three-dimensional (3-D). The dose to the population and its management are of special concern in computed tomography (CT). Task-based methods with model observers to assess the dose-image quality trade-off are promising tools, but they still need to be validated for real volumetric images. The purpose of the present work is to evaluate anthropomorphic model observers in 3-D detection tasks for low-contrast CT images. We scanned a low-contrast phantom containing four types of signals at three dose levels and used two reconstruction algorithms. We implemented a multislice model observer based on the channelized Hotelling observer (msCHO) with anthropomorphic channels and investigated different internal noise methods. We found a good correlation for all tested model observers. These results suggest that the msCHO can be used as a relevant task-based method to evaluate low-contrast detection for CT and optimize scan protocols to lower dose in an efficient way.

Predicted decrease in global climate suitability masks regional complexity of invasive fruit fly species response to climate change

Climate change affects the rate of insect invasions as well as the abundance, distribution and impacts of such invasions on a global scale. Among the principal analytical approaches to predicting and understanding future impacts of biological invasions are Species Distribution Models (SDMs), typically in the form of correlative Ecological Niche Models (ENMs). An underlying assumption of ENMs is that species-environment relationships remain preserved during extrapolations in space and time, although this is widely criticised. The semi-mechanistic modelling platform, CLIMEX, employs a top-down approach using species ecophysiological traits and is able to avoid some of the issues of extrapolation, making it highly applicable to investigating biological invasions in the context of climate change. The tephritid fruit flies (Diptera: Tephritidae) comprise some of the most successful invasive species and serious economic pests around the world. Here we project 12 tephritid species CLIMEX models into future climate scenarios to examine overall patterns of climate suitability and forecast potential distributional changes for this group. We further compare the aggregate response of the group against species-specific responses. We then consider additional drivers of biological invasions to examine how invasion potential is influenced by climate, fruit production and trade indices. Considering the group of tephritid species examined here, climate change is predicted to decrease global climate suitability and to shift the cumulative distribution poleward. However, when examining species-level patterns, the predominant directionality of range shifts for 11 of the 12 species is eastward. Most notably, management will need to consider regional changes in fruit fly species invasion potential where high fruit production, trade indices and predicted distributions of these flies overlap.

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed

IEA Bioenergy Task 40 “Sustainable International Bioenergy Trade: Securing supply and demand” Country report of Finland 2008

This study considered the current situation of biofuels markets in Finland. The fact that industry consumes more than half of the total primary energy, widely applied combined heat and power production and a high share of solid biomass fuels in the total energy consumption are specific to the Finnish energy system. Wood is the most important source of bioenergy in Finland, representing 21% of the total energy consumption in 2006. Almost 80% of the wood-based energy is recovered from industrial by-products and residues. Finland has commitment itself to maintaining its greenhouse gas emissions at the 1990 level, at the highest, during the period 2008–2012. The energy and climate policy carried out in recent years has been based on the National Energy and Climate introduced in 2005. The Finnish energy policy aims to achieve the target, and a variety of measures are taken to promote the use of renewable energy sources and especially wood fuels. In 2007, the government started to prepare a new long-term (up to the year 2050) climate and energy strategy that will meet EU’s new targets for the reduction of green house gas emissions and the promotion of renewable energy sources. The new strategy will be introduced during 2008. The international biofuels trade has a substantial importance for the utilisation of bioenergy in Finland. In 2006, the total international trading of solid and liquid biofuels was approximately 64 PJ of which import was 61 PJ. Most of the import is indirect and takes place within the forest industry’s raw wood imports. In 2006, as much as 24% of wood energy was based on foreignorigin wood. Wood pellets and tall oil form the majority of export streams of biofuels. The indirect import of wood fuels increased almost 10% in 2004–2006, while the direct trade of solid and liquid biofuels has been almost constant.

Microstructural Brain Differences Predict Functional Hemodynamic Responses in a Reward Processing Task

Many aspects of human behavior are driven by rewards, yet different people are differentially sensitive to rewards and punishment. In this study, we showthat white matter microstructure inthe uncinate/inferiorfronto-occipitalfasciculus, defined byfractional anisotropy values derived from diffusion tensor magnetic resonance images, correlates with both short-term (indexed by the fMRI blood oxygenation level-dependent response to reward in the nucleus accumbens) and long-term (indexed by the trait measure sensitivity to punishment) reactivityto rewards.Moreover,traitmeasures of reward processingwere also correlatedwith reward-relatedfunctional activation in the nucleus accumbens. The white matter tract revealed by the correlational analysis connects the anterior temporal lobe with the medial and lateral orbitofrontal cortex and also supplies the ventral striatum. The pattern of strong correlations suggests an intimate relationship betweenwhitematter structure and reward-related behaviorthatmay also play a rolein a number of pathological conditions, such as addiction and pathological gambling.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Dynamic patterns of flow in the workplace: characterizing within-individual variability using a complexity science approach

As a result of the growing interest in studying employee well-being as a complex process that portrays high levels of within-individual variability and evolves over time, this present study considers the experience of flow in the workplace from a nonlinear dynamical systems approach. Our goal is to offer new ways to move the study of employee well-being beyond linear approaches. With nonlinear dynamical systems theory as the backdrop, we conducted a longitudinal study using the experience sampling method and qualitative semi-structured interviews for data collection; 6981 registers of data were collected from a sample of 60 employees. The obtained time series were analyzed using various techniques derived from the nonlinear dynamical systems theory (i.e., recurrence analysis and surrogate data) and multiple correspondence analyses. The results revealed the following: 1) flow in the workplace presents a high degree of within-individual variability; this variability is characterized as chaotic for most of the cases (75%); 2) high levels of flow are associated with chaos; and 3) different dimensions of the flow experience (e.g., merging of action and awareness) as well as individual (e.g., age) and job characteristics (e.g., job tenure) are associated with the emergence of different dynamic patterns (chaotic, linear and random).

Market of biomass fuels in Finland : IEA Bioenergy Task 40 and EUBIONET III - Country report of Finland 2009

This study considered the current situation of solid and liquid biomass fuels in Finland. The fact that industry consumes more than half of the total primary energy, widely applied combined heat and power production and a high share of solid biomass fuels in the total energy consumption are specific to the Finnish energy system. Wood is the most important source of bioenergy in Finland, representing 20% of the total energy consumption in 2007. Almost 80% of the woodbased energy is recovered from industrial by-products and residues. As a member of the European Union, Finland has committed itself to the Union’s climate and energy targets, such as reducing its overall emissions of green house gases to at least 20% below 1990 levels by 2020, and increasing the share of renewable energy in the gross final consumption. The renewable energy target approved for Finland is 38%. The present National Climate and Energy Strategy was introduced in November 2008. The strategy covers climate and energy policy measures up to 2020, and in brief thereafter, up to 2050. In recent years, the actual emissions have exceeded the Kyoto commitment and the trend of emissions is on the increase. In 2007, the share of renewable energy in the gross final energy consumption was approximately 25% (360 PJ). Without new energy policy measures, the final consumption of renewable energy would increase to 380 PJ, which would be approximately only 31% of the final energy consumption. In addition, green house gas emissions would exceed the 1990 levels by 20%. Meeting the targets will need the adoption of more active energy policy measures in coming years. The international trade of biomass fuels has a substantial importance for the utilisation of bioenergy in Finland. In 2007, the total international trading of solid and liquid biomass fuels was approximately 77 PJ, of which import was 62 PJ. Most of the import is indirect and takes place within the forest industry’s raw wood imports. In 2007, as much as 21% of wood energy was based on foreign-origin wood. Wood pellets and tall oil form the majority of export streams of biomass fuels. The indirect import of wood fuels peaked in 2006 to 61 PJ. The foreseeable decline in raw wood import to Finland will decrease the indirect import of wood fuels. In 2004– 2007, the direct trade of solid and liquid biomass fuels has been on a moderate growth path. In 2007, the import of palm oil and export of bio-diesel emerged, as a large, 170 000 t/yr biodiesel plant came into operation in Porvoo.

Building complexity in O2-binding copper complexes : site-selective metalation and intermolecular O2-binding at dicopper and heterometallic complexes derived from an unsymmetric ligand

A novel unsymmetric dinucleating ligand (LN3N4) combining a tridentate and a tetradentate binding sites linked through a m-xylyl spacer was synthesized as ligand scaffold for preparing homo- and dimetallic complexes, where the two metal ions are bound in two different coordination environments. Site-selective binding of different metal ions is demonstrated. LN3N4 is able to discriminate between CuI and a complementary metal (M′ = CuI, ZnII, FeII, CuII, or GaIII) so that pure heterodimetallic complexes with a general formula [CuIM′(LN3N4)]n+ are synthesized. Reaction of the dicopper(I) complex [CuI 2(LN3N4)]2+ with O2 leads to the formation of two different copper-dioxygen (Cu2O2) intermolecular species (O and TP) between two copper atoms located in the same site from different complex molecules. Taking advantage of this feature, reaction of the heterodimetallic complexes [CuM′(LN3N4)]n+ with O2 at low temperature is used as a tool to determine the final position of the CuI center in the system because only one of the two Cu2O2 species is formed

Blocking and unblocking in a navigation task

Rodrigo, Chamizo, McLaren, & Mackintosh (1997) demonstrated the blocking effect in a navigational task using a swimming pool: rats initially trained to use three landmarks (ABC) to find an invisible platform learned less about a fourth landmark (X) added later than did rats trained from the outset with these four landmarks (ABCX). The aim of the experiment reported here was to demonstrate unblocking using a similar procedure as in the previous work. Three groups of rats were initially trained to find an invisible platfom in the presence of three landmarks: ABC for the Blocking and Unblocking groups and LMN for the Control group. Then, all animals were trained to find the platform in the presence of four landmarks, ABCX. In this second training, unlike animals in the Blocking group to which only a new landmark (X) was added in comparison to the first training, the animals in the Unblocking group also had a change in the platform position. In the Control group, both the four landmarks and the platform position were totally new at the beginning of this second training. As in Rodrigo et al. (1997) a blocking effect was found: rats in the Blocking group learned less with respect to the added landmark (X) than did animals in the Control group. However, rats in the Unblocking group learned about the added landmark (X) as well as did animals in the Control group. The results are interpreted as an unblocking effect due to a change in the platform position between the two phases of training, similarly to what is normal in classical conditioning experiments, in which a change in the conditions of reinforcement between the two training phases of a blocking design produce an attenuation or elimination of this effect. These results are explained within an error-correcting connectionist account of spatial navigation (McLaren, 2002).

Argument omissions in preschool Catalan and Spanish speaking children with SLI

El presente artículo describe tres estudios sobre la producción del verbo y la estructura argumental en niños con Trastorno Específico del Lenguaje (TEL) usando diferentes metodologías. El primero es un estudio observacional que usa una muestra de habla espontánea. El segundo usa una tarea experimental de denominación de oraciones como resultado de la observación de videos de acciones. El tercero comprende la tarea de denominación de oraciones con imágenes estáticas en eventos con diferente complejidad argumental. Aunque los datos concretos varían en función de la metodología usada, hay una clara evidencia de que los niños de habla catalana y española con TEL presentan especiales dificultades en la producción de verbos con una alta complejidad en relación a la estructura argumental y cometen errores en la especificación de los argumentos obligatorios. Se concluye quetanto limitaciones en el procesamiento como déficits en la representación semántica de los verbos pueden estar implicados en estas dificultades