Microbial community composition shapes enzyme patterns in topsoil and subsoil horizons along a latitudinal transect in Western Siberia

Soil horizons below 30 cm depth contain about 60% of the organic carbon stored in soils. Although insight into the physical and chemical stabilization of soil organic matter (SUM) and into microbial community composition in these horizons is being gained, information on microbial functions of subsoil microbial communities and on associated microbially-mediated processes remains sparse. To identify possible controls on enzyme patterns, we correlated enzyme patterns with biotic and abiotic soil parameters, as well as with microbial community composition, estimated using phospholipid fatty acid profiles. Enzyme patterns (i.e. distance-matrixes calculated from these enzyme activities) were calculated from the activities of six extracellular enzymes (cellobiohydrolase, leucine-amino-peptidase, N-acetylglucosaminidase, chitotriosidase, phosphatase and phenoloxidase), which had been measured in soil samples from organic topsoil horizons, mineral topsoil horizons, and mineral subsoil horizons from seven ecosystems along a 1500 km latitudinal transect in Western Siberia. We found that hydrolytic enzyme activities decreased rapidly with depth, whereas oxidative enzyme activities in mineral horizons were as high as, or higher than in organic topsoil horizons. Enzyme patterns varied more strongly between ecosystems in mineral subsoil horizons than in organic topsoils. The enzyme patterns in topsoil horizons were correlated with SUM content (i.e., C and N content) and microbial community composition. In contrast, the enzyme patterns in mineral subsoil horizons were related to water content, soil pH and microbial community composition. The lack of correlation between enzyme patterns and SUM quantity in the mineral subsoils suggests that SOM chemistry, spatial separation or physical stabilization of SUM rather than SUM content might determine substrate availability for enzymatic breakdown. The correlation of microbial community composition and enzyme patterns in all horizons, suggests that microbial community composition shapes enzyme patterns and might act as a modifier for the usual dependency of decomposition rates on SUM content or C/N ratios. (C) 2015 The Authors. Published by Elsevier Ltd.

A future dynamically reconfigurable automotive software system

Embedded software systems in vehicles are of rapidly increasing commercial importance for the automotive industry. Current systems employ a static run-time environment; due to the difficulty and cost involved in the development of dynamic systems in a high-integrity embedded control context. A dynamic system, referring to the system configuration, would greatly increase the flexibility of the offered functionality and enable customised software configuration for individual vehicles, adding customer value through plug-and-play capability, and increased quality due to its inherent ability to adjust to changes in hardware and software. We envisage an automotive system containing a variety of components, from a multitude of organizations, not necessarily known at development time. The system dynamically adapts its configuration to suit the run-time system constraints. This paper presents our vision for future automotive control systems that will be regarded in an EU research project, referred to as DySCAS (Dynamically Self-Configuring Automotive Systems). We propose a self-configuring vehicular control system architecture, with capabilities that include automatic discovery and inclusion of new devices, self-optimisation to best-use the processing, storage and communication resources available, self-diagnostics and ultimately self-healing. Such an architecture has benefits extending to reduced development and maintenance costs, improved passenger safety and comfort, and flexible owner customisation. Specifically, this paper addresses the following issues: The state of the art of embedded software systems in vehicles, emphasising the current limitations arising from fixed run-time configurations; and the benefits and challenges of dynamic configuration, giving rise to opportunities for self-healing, self-optimisation, and the automatic inclusion of users’ Consumer Electronic (CE) devices. Our proposal for a dynamically reconfigurable automotive software system platform is outlined and a typical use-case is presented as an example to exemplify the benefits of the envisioned dynamic capabilities.

Explorando a combinação de visualização de software com clusterização de dados em um processo de reconstrução de arquitetura

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.

Diel variation in movement patterns and habitat use by the near-threatened cabrera vole in mediterranean farmland

As variações circadianas no comportamento animal e o seu impacto nas populações constituem desafios importantes em ecologia e conservação. Nesta tese documentam-se as variações circadianas no uso do habitat e padrões de movimento pelo rato de Cabrera, em habitats Mediterrânicos fragmentados. O estudo baseou-se no radio-seguimento de indivíduos em habitats dominados por herbáceas e arbustos. Os resultados indicaram que a proporção de tempo despendido em deslocações, a distância percorrida, e a selecção do tipo de vegetação, estão fortemente interrelacionados, variando consideravelmente ao longo de diferentes períodos do dia. Os ratos movimentaram-se mais frequentemente e maiores distâncias nos períodos diurnos, durante os quais as áreas dominadas por herbáceas foram usadas mais intensivamente. Durante a estação seca houve alguma tendência para a diminuição dos movimentos durante as horas mais quentes. Estes resultados são discutidos no sentido de mostrar como indicadores comportamentais podem contribuir para melhorar a gestão e conservação da espécie; ABSTRACT: Understanding the circadian variations in species behaviour and its impacts on population is a challenging topic in ecology and conservation. This thesis documents the circadian variations in habitat use and movement patterns by Cabrera voles in fragmented Mediterranean farmland. The study was based on radiotracking data of individuals living in habitat patches dominated by wet grasses and shrubs. Results indicated that the proportion of time animals spent moving, the distance moved and the selection strength of vegetation were closely linked behavioural traits, which varied considerably across the 24 hour cycle. Voles moved more frequently and over larger distances during daytime, which was when wet grasses were used more intensively. During the dry season there was some tendency for a decrease in movement activity during the hottest hours of the day. These results are used to discuss how behavioural indicators may be useful to improve conservation management of the species.

Patterns of sedentary behavior : insights from observational and experimental studies on body composition and energy expenditure

It is recognized that sedentary behavior (SB) has deleterious effects on numerous health outcomes and it appears that physiological mechanisms underlying these harms are distinct from the ones explaining moderate-to-vigorous physical activity (MVPA) benefits. Sedentary behavior represents a large portion of human’s life and is increasing with technological development. A new current of opinion supports the idea that the manner SB is accumulated plays an important role. This dissertation presents six research studies conducted under the scope of SB. In the methodological area, the first study highlighted the magnitude of potential errors in estimating SB and its patterns from common alternative methods (accelerometer and heart rate monitor) compared to ActivPAL. This study presented the accelerometer as a valid method at a group level. Two studies (2 and 5) were performed in older adults (the most sedentary group in the population) to test the associations for SB patterns with abdominal obesity using accelerometry. The findings showed positive graded associations for prolonged sedentary bouts with abdominal obesity and showed that those who interrupted SB more frequently were less likely to present abdominal obesity. Therefore, public health recommendations regarding breaking up SB more often are expected to be relevant. The associations between sedentary patterns and abdominal obesity were independent of MVPA in older adults. However, the low MVPA in this group makes it unclear whether this independent relationship still exists if highly active persons are analysed. Study 3 inovates by examining the association of SB with body fatness in highly trained athletes and found SB to predict total fat mass and trunk fat mass, independently of age and weekly training time. Study 4 also brings novelty to this research field by quantifying the metabolic and energetic cost of the transition from sitting to standing and then sitting back down (a break), informing about the modest energetic costs (0.32 kcal·min−1). Finally, from a successful multicomponent pilot intervention to reduce and break up SB (study 6), an important behavioral resistance to make more sit/stand transitions despite successfully reducing sitting time (~ 1.85 hours·day-1) was found, which may be relevant to inform future behavioral modification programs. The present work provides observational and experimental evidence on the relation for SB patterns with body composition outcomes and energy regulation that may be relevant for public health interventions.

Recruitment patterns and processes of coastal fish species in a temperate rocky reef

Tese de Doutoramento, Ciências do Mar, da Terra e do Ambiente, Ramo: Ciências do Mar, Especialização em Ecologia Marinha, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016

Spatial distribution patterns and movements of Holothuria arguinensis in the Ria Formosa (Portugal)

Holothurian populations are under pressure worldwide because of increasing demand for beche-de-mer, mainly for Asian consumption. Importations to this area from new temperate fishing grounds provide economic opportunities but also raise concerns regarding future over-exploitation. Studies on the habitat preferences and movements of sea cucumbers are important for the management of sea cucumber stocks and sizing of no-take zones, but information on the ecology and behavior of temperate sea cucumbers is scarce. This study describes the small-scale distribution and movement patterns of Holothuria arguinensis in the intertidal zone of the Ria Formosa national park (Portugal).Mark/recapture studieswere performed to record theirmovements over time on different habitats (sand and seagrass). H. arguinensis preferred seagrass habitats and did not show a size or life stage-related spatial segregation. Its density was 563 ind. ha−1 and mean movement speed was 10 m per day. Movement speed did not differ between habitats and the direction of movement was offshore during the day and shoreward during the night. Median home range size was 35 m2 and overlap among home ranges was 84%. H. arguinensis' high abundance, close association with seagrass and easy catchability in the intertidal zone, indicate the importance of including intertidal lagoons in future studies on temperate sea cucumber ecology since those systems might require different management strategies than fully submerged habitats.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Exploring the relationship between patterns of physical activity, sedentary behaviour and cardiometabolic health in middle-aged Irish adults

Background and Study Rationale Being physically active is a major contributor to both physical and mental health. More specifically, being physically active lowers risk of coronary heart disease, high blood pressure, stroke, metabolic syndrome (MetS), diabetes, certain cancers and depression, and increases cognitive function and wellbeing. The physiological mechanisms that occur in response to physical activity and the impact of total physical activity and sedentary behaviour on cardiometabolic health have been extensively studied. In contrast, limited data evaluating the specific effects of daily and weekly patterns of physical behaviour on cardiometabolic health exist. Additionally, no other study has examined interrelated patterns and minute-by-minute accumulation of physical behaviour throughout the day across week days in middle-aged adults. Study Aims The overarching aims of this thesis are firstly to describe patterns of behaviour throughout the day and week, and secondly to explore associations between these patterns and cardiometabolic health in a middle-aged population. The specific objectives are to: 1 Compare agreement between the International Physical Activity Questionnaire-Short Form (IPAQ-SF) and GENEActiv accelerometer-derived moderate-to-vigorous (MVPA) activity and secondly to compare their associations with a range of cardiometabolic and inflammatory markers in middle-aged adults. 2 Determine a suitable monitoring frame needed to reliably capture weekly, accelerometer-measured, activity in our population. 3 Identify groups of participants who have similar weekly patterns of physical behaviour, and determine if underlying patterns of cardiometabolic profiles exist among these groups. 4 Explore the variation of physical behaviour throughout the day to identify whether daily patterns of physical behaviour vary by cardiometabolic health. Methods All results in this thesis are based on data from a subsample of the Mitchelstown Cohort; 475 (46.1% males; mean aged 59.7±5.5 years) middle-aged Irish adults. Subjective physical activity levels were assessed using the IPAQ-SF. Participants wore the wrist GENEActiv accelerometer for 7 consecutive days. Data was collected at 100Hz and summarised into a signal magnitude vector using 60s epochs. Each time interval was categorised based on validated cut-offs. Data on cardiometabolic and inflammatory markers was collected according to standard protocol. Cardiometabolic outcomes (obesity, diabetes, hypertension and MetS) were defined according to internationally recognised definitions by World Health Organisation (WHO) and Irish Diabetes Federation (IDF). Results The results of the first chapter suggest that the IPAQ-SF lacks the sensitivity to assess patterning of activity and guideline adherence and assessing the relationship with cardiometabolic and inflammatory markers. Furthermore, GENEActiv accelerometer-derived MVPA appears to be better at detecting relationships with cardiometabolic and inflammatory markers. The second chapter examined variations in day-to-day physical behaviour levels between- and within-subjects. The main findings were that Sunday differed from all other days in the week for sedentary behaviour and light activity and that a large within-subject variation across days of the week for vigorous activity exists. Our data indicate that six days of monitoring, four weekdays plus Saturday and Sunday, are required to reliably estimate weekly habitual activity in all activity intensities. In the next chapter, latent profile analysis of weekly, interrelated patterns of physical behaviour identified four distinct physical behaviour patterns; Sedentary Group (15.9%), Sedentary; Lower Activity Group (28%), Sedentary; Higher Activity Group (44.2%) and a Physically Active Group (11.9%). Overall the Sedentary Group had poorer outcomes, characterised by unfavourable cardiometabolic and inflammatory profiles. The remaining classes were characterised by healthier cardiometabolic profiles with lower sedentary behaviour levels. The final chapter, which aimed to compare daily cumulative patterns of minute-by-minute physical behaviour intensities across those with and without MetS, revealed significant differences in weekday and weekend day MVPA. In particular, those with MetS start accumulating MVPA later in the day and for a shorted day period. Conclusion In conclusion, the results of this thesis add to the evidence base regards an optimal monitoring period for physical behaviour measurement to accurately capture weekly physical behaviour patterns. In addition, the results highlight whether weekly and daily distribution of activity is associated with cardiometabolic health and inflammatory profiles. The key findings of this thesis demonstrate the importance of daily and weekly physical behaviour patterning of activity intensity in the context of cardiometabolic health risk. In addition, these findings highlight the importance of using physical behaviour patterns of free-living adults observed in a population-based study to inform and aid health promotion activity programmes and primary care prevention and treatment strategies and development of future tailored physical activity based interventions.

Managing pressure: patterns of appraisals and coping strategies of non-elite and elite athletes during competition

The purpose of this study was to compare moment-to-moment appraisals and coping strategies of 4 non-elite and 2 elite male trap shooters during competitions and in particular during periods of competition perceived as critical to performance. Appraisals and coping patterns of trap shooters were captured via verbal reports of thinking provided between sets of shots during major competitions. Verbal reports were coded according to an appraisal and coping typology. Coded data as well as shooting performance data were subjected to a sequential analysis of probabilities of pairs of events. Fewer reports of negative appraisals (NEGAs) and more frequent reports of problem-focused coping (PFC) were observed among both elite athletes compared to non-elite athletes. After making a NEGA, non-elite shooters often progressed to the next target without attempting to cope, whereas elite shooters used both PFC and emotion-focused coping (EFC) before proceeding to the next target. After missing a target, the non-elite athletes used more EFC than expected. These results indicate that elite athletes are more likely to cope with NEGAs than non-elite athletes using a wider variety of coping strategies. Athletes might benefit from increased awareness of the potentially detrimental impact of NEGAs on performance and by integrating coping strategies within preparatory routines.

Data, Software, and Advanced Computational Usage of University of Washington Research Leaders

Scientific research is increasingly data-intensive, relying more and more upon advanced computational resources to be able to answer the questions most pressing to our society at large. This report presents findings from a brief descriptive survey sent to a sample of 342 leading researchers at the University of Washington (UW), Seattle, Washington in 2010 and 2011 as the first stage of the larger National Science Foundation project “Interacting with Cyberinfrastructure in the Face of Changing Science.” This survey assesses these researcher’s use of advanced computational resources, data, and software in their research. We present high-level findings that describe UW researchers’: demographics, interdisciplinarity, research groups, data use, software and computational use—including software development and use, data storage and transfer activities, and collaboration tools, and computing resources. These findings offer insights into the state of computational resources in use during this time period as well as offering a look at the data intensiveness of UW researchers.

The Architecture of the Persian Period in the Levant

The earliest scholars were not concerned about preparing extensive investigations linking the Persian-period building remains excavated in the entire Levant together. Moreover, the research interests of scholars caused some impediments to the study of this period viz in the last decades; the Achaemenid period has been neglected by the scholars who -in turn- focused on the earlier and later periods for religious reasons. Too, while some regions have been studied abundantly, but it was not the case in other areas, which makes our knowledge is incomplete. From the explanation side, some scholars try to interpret the architectural remains from an ethnic perspective or unsubstantiated personal fancies, so their arguments were utterly lacking any objectivity. This thesis explores what are the Persian architectural and ornamental impacts on the Levantine architecture and the relations between Persian-period sites in Syria-Palestine region. Too, the architectural remains and their contents benefited us to clarify the settlement patterns in the regions being discussed. The author analyzed the ground plans of the buildings and their architectural features and ornamental motifs by conducting a descriptive, analytical, and interpretative study. He also conducted comparisons with similar buildings outside the Levant, especially in Fars to obtain a more comprehensive and systematic study, and then extracting any direct or indirect Persian influences. This has given us a better understanding of the nature of the social, political, and religious life in the entire Levant and the knowledge gap has been bridged to a satisfying extent. This study has demonstrated a few of the Achaemenid impacts, especially on the northern coastline of the Levant.

Employment of Real-Time/FPGA Architectures for Test and Control of Automotive Engines

Nowadays the production of increasingly complex and electrified vehicles requires the implementation of new control and monitoring systems. This reason, together with the tendency of moving rapidly from the test bench to the vehicle, leads to a landscape that requires the development of embedded hardware and software to face the application effectively and efficiently. The development of application-based software on real-time/FPGA hardware could be a good answer for these challenges: FPGA grants parallel low-level and high-speed calculation/timing, while the Real-Time processor can handle high-level calculation layers, logging and communication functions with determinism. Thanks to the software flexibility and small dimensions, these architectures can find a perfect collocation as engine RCP (Rapid Control Prototyping) units and as smart data logger/analyser, both for test bench and on vehicle application. Efforts have been done for building a base architecture with common functionalities capable of easily hosting application-specific control code. Several case studies originating in this scenario will be shown; dedicated solutions for protype applications have been developed exploiting a real-time/FPGA architecture as ECU (Engine Control Unit) and custom RCP functionalities, such as water injection and testing hydraulic brake control.

Hybrid Artificial Intelligence to Extract Patterns and Rules from Argumentative and Legal Texts

This Thesis is composed of a collection of works written in the period 2019-2022, whose aim is to find methodologies of Artificial Intelligence (AI) and Machine Learning to detect and classify patterns and rules in argumentative and legal texts. We define our approach “hybrid”, since we aimed at designing hybrid combinations of symbolic and sub-symbolic AI, involving both “top-down” structured knowledge and “bottom-up” data-driven knowledge. A first group of works is dedicated to the classification of argumentative patterns. Following the Waltonian model of argument and the related theory of Argumentation Schemes, these works focused on the detection of argumentative support and opposition, showing that argumentative evidences can be classified at fine-grained levels without resorting to highly engineered features. To show this, our methods involved not only traditional approaches such as TFIDF, but also some novel methods based on Tree Kernel algorithms. After the encouraging results of this first phase, we explored the use of a some emerging methodologies promoted by actors like Google, which have deeply changed NLP since 2018-19 — i.e., Transfer Learning and language models. These new methodologies markedly improved our previous results, providing us with best-performing NLP tools. Using Transfer Learning, we also performed a Sequence Labelling task to recognize the exact span of argumentative components (i.e., claims and premises), thus connecting portions of natural language to portions of arguments (i.e., to the logical-inferential dimension). The last part of our work was finally dedicated to the employment of Transfer Learning methods for the detection of rules and deontic modalities. In this case, we explored a hybrid approach which combines structured knowledge coming from two LegalXML formats (i.e., Akoma Ntoso and LegalRuleML) with sub-symbolic knowledge coming from pre-trained (and then fine-tuned) neural architectures.

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.