Ugroženi bazofilni cret uz potok Jarak (Park prirode Žumberak-Samoborsko gorje, Hrvatska)

This study presents the results of floristic research into a flat fen along a part of the Jarak stream in Žumberak-Samoborsko gorje Nature Park. This fen belongs to order Caricetalia davallianae Br.-Bl. 1949, alliance Caricio davallianae Klika 1934, association Eriophoro latifolio-Caricetum panicae Horvat ex Trinajstić 2002. The area was researched in 2007/2008 and it was divided into four subareas: fen and three succession stages, Phragmites subarea, transitional stage between common reed (Phragmites australis) and young forest, and young forest. During the winter period of the research the overgrowing woody vegetation on three subareas was cut, because that action should prevent further succession and help the restoration of the fen. The following year, floristic changes were monitored. Recorded plants were taxonomically analyzed and ecological indicator values according to Landolt were calculated. During this study 222 plant species were found, out of which 15 were endangered (according to IUCN categorization) and 59 protected or strictly protected in Croatia. The summarized ecological indicator values according to Landolt showed only minor differences between the subareas, out of which only the young forest subarea stood out. The removal of the plants resulted in low floristic changes, present mostly in the young forest subarea, where species of open habitats enlarged their abundance, but no characteristic fen species were recorded. It is concluded that the fen along the stream Jarak should be protected, because of its rare vegetation type, but also because of the endangered and protected species found in this area. It is also necessary to carry out regular removal of overgrowing vegetation from all the subareas and multiple mowing to protect the fen and even enlarge its surface

Flora and vegetation of the Sopot waterfall and upper flow of the brook Kupčina (Nature park Žumberak – Samoborsko gorje) with proposals for conservation measures

Sopotski slap smješten je oko 3 km sjeveroistočno od sela Sošice u Parku prirode Žumberak – Samoborsko gorje. Strmo se obrušava niz 40 metara kaskada uz stari mlin i sastavni je dio toka Kupčine. Područje Sopotskog slapa i gornjeg toka Kupčine do sada nisu bili floristički i vegetacijski sustavno istraživani. Terenska istraživanja vršena su tijekom 2009. i 2010. godine. Ovim preliminarnim istraživanjem zabilježene su ukupno 204 biljne svojte od kojih su, prema IUCN kategorizaciji, dvije kritično ugrožene vrste (CR) – Eriophorum angustifolium Honck. i Tofieldia calyculata (L.) Wahlenb., jedna ugrožena vrsta (EN) – Eriophorum latifolium, i pet osjetljivih svojti (VU) – Carex panicea L., Ophrys fuciflora Haller, O. insectifera L., Orchis purpurea Huds., Helleborus niger L. subsp. macranthus (Freyn) Schiffner i jedna gotovo ugrožena vrsta (NT) – Cephalanthera damasonium (Mill.) Druce. Na istraživanom području dokumentirana su i dva tipa ugroženih staništa: elementi vegetacije niskog, bazofilnog creta suhoperke (Eriophoro-Caricetum paniceae Horvat 1964), te (polu) suhi kontinentalni travnjaci razreda Festuco-Brometea Br.-Bl. et R. Tx. 1943 (as. Seslerietum kalnikensis Horvat 1942), stanište bogato različitim značajnim vrstama otvorenih staništa, naročito kaćunima. Oba staništa su ugrožena zaraštavanjem i nedostatkom ekstenzivne ispaše i u odmaklom su stadiju sukcesije prema šumskim zajednicama što vodi nestanku mnogih, a posebno navedenih ugroženih vrsta s ovog područja.The Sopot waterfall is the biggest waterfall in the Nature park Žumberak - Samoborsko gorje, situated about 3 km NW from the village Sošice. So far, there were no methodical investigations of flora or vegetation in the area around the Sopot waterfall and along the upper flow of the brook Kupčina. The initial research was carried out in the summer and autumn of the year 2009 and in the spring and early summer of the year 2010. Plant species found on the research area were recorded during every field trip and the herbarium samples were taken for later identification of some plant taxa. For better recommendations how to protect different habitats the study area was divided into 3 subareas; subarea 1 - the waterfall area, subarea 2 – the area of the brook Kupčina, which flows through small canyon and subarea 3 – the area of upper flow of the brook Kupcina, located in small dale, where few water streams appear and form more or less unitary stream of the brook Kupcina. In our preliminary study we recorded in total 204 plant species, out of which according to IUCN categorization, two critically endangered (CR): Eriophorum angustifolium Honck. and Tofieldia calyculata (L.) Wahlenb., one endangered (EN): Eriophorum latifolium Hoppe, one near threatened (NT): Cephalanthera damasonium (Mill.) Druce and four vulnerable (VU): Carex panicea L., Helleborus niger L. subsp. macranthus (Freyn) Schiffner, Ophrys fuciflora Haller and Ophrys insectifera L. In the researched area 44 species were protected and 15 strictly protected by the Croatian law. The research showed that subarea 1 has a different type of habitat than subareas 2 and 3. The subarea 1 is covered with beech and hop hornbeam forest (Ostryo-Fagetum M. Wraber ex Trinajstic 1972) and we assume that it should be left to spontaneous natural development, because it does not show any signs of endangerments. On the subareas 2 and 3 we recorded two endangered habitats: elements of flat, basophilous, rich fen with bog cotton (Eriophoro-Caricetum paniceae Horvat 1964) and (semi)dry continental grasslands from Festuco-Brometea Br.-Bl. et R. Tx. 1943 (as. Seslerietum kalnikensis Horvat 1942) class, which are the habitats rich with many significant species of open habitats, especially with orchids. Both habitats are endangered by overgrowing and lack of extensive pasture and they are in advanced stage of succession towards wood associations, why we consider that it is necessary to try to stop the succession in progress. At first it would be necessary to remove the woody vegetation which endangers the rich fen and grassland vegetation beside the brook, and later on, it is important to periodically organize mowing or intensive pasture in that area

Shallow convolutional neural network with rank-1 Fourier domain weights for brain signal classification

International audienc

Diffusion MRI fiber orientation distribution function estimation using voxel-wise spherical U-net

International audienceDiffusion Magnetic Resonance Imaging (dMRI) is an imaging technique which enables analysis of the brain tissue at a microscopic scale, particularly the analysis of white matter. Given a high enough angular resolution, a common way to explain the measured signal is via fiber orientation distribution function (fODF). This function describes the orientation and volume fraction of axon bundles within each voxel and is an essential ingredient of tractography. In this work, we have investigated a deep learning approach for the fODF estimation. U-nets enable fast and high resolution inference by combining multi-scale features from contracting and expanding parts of the network. As dMRI signals are most commonly acquired on spheres, we propose a spherical U-net which is adjusted to the properties of the dMRI data, namely its real nature, antipodal symmetry, uniform sampling and axial symmetry of the signals corresponding to individual fibers. We compared our model with another deep learning approach based on a 3D convolutional neural network and a state-of-the-art approach-multi-shell multi-tissue constrained spherical deconvolution, on real data from Human Connectome Project and synthetic data generated using ball and stick model. The methods are compared in terms of mean square error and mean angular error for dMRI signals of different angular resolutions. Provided quantitative analyses show improved performance with our approach even with significantly reduced number of parameters and results obtained on synthetic data indicate its robustness with respect to noise. Qualitative results illustrating the performance of the methods are also presented

Spherical convolutional neural network for diffusion MRI analysis

International audienc

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Réseaux de neurones convolutifs adaptés au domaine pour l'analyse des signaux IRMd et M/EEG

In the process of correction.The analysis of neuroimaging data is essential for the interpretation of the functional or structural characteristics of the human brain. New machine learning algorithms usually require a high amount of data often infeasible to acquire in clinical and practical conditions. This requirement is a consequence of significant data variability arising from numerous factors (various recording procedures, subjects and sessions, presence of high levels of noise). To address this problem, in this thesis, we have investigated and proposed convolutional machine learning models adapted to the properties and well grounded assumptions about the acquired data. Therefore, the models are endowed with valuable knowledge and consequently more efficiently learn to perform certain inferences. In particular, we have studied models for the analysis of non-invasive and in-vivo structural and functional neuroimaging data, namely diffusion Magnetic Resonance Imaging (dMRI) and magneto- and electro-encephalography (M/EEG) signals.Diffusion MRI is a nuclear imaging modality which captures micro-structural properties of the examined tissue. As q-space sampling has been the most widely used high angular resolution diffusion imaging protocol (HARDI) over the last decade, we have studied spherical rotation equivariant convolutional neural networks (CNNs) for dMRI local modeling. As a first contribution, we have proposed a spherical U-net for the estimation of fiber orientation distribution functions (fODFs) with convolutions and non-linearities realized in the spectral and signal domains, respectively. To avoid aliasing, our second contribution proposes a Fourier domain CNN for micro-structure parameter estimation, where non-linearities are defined in the spectral domain.M/EEG are functional imaging techniques which measure magnetic field strength and electric field potential caused by neural electric activities in the cerebral cortex. Measured signals can be explained by Maxwell's equations with quasi-static approximations. Consequently, we can assume that cortical brain activities spread instantaneously and linearly over the measuring sensors, thus a multivariate M/EEG signal can be represented as a sum of rank-1 multivariate signals corresponding to individual sources in the cortex and noise. Considering this assumption, the second part of the thesis firstly investigates an M/EEG spatial and temporal dictionary learning approach with an $L_0$ constraint. A second contribution is a CNN classifier with rank-1 spatio-temporal kernels regularized in the spectral domain, where the spatial components of the kernels are represented in terms of spherical harmonics basis, while the temporal components are represented in terms of discrete cosine basis.L'analyse des données de neuroimagerie est essentielle pour l'interprétation des caractéristiques fonctionnelles ou structurelles du cerveau humain. Les algorithmes d’apprentissage automatique récents requièrent généralement une grande quantité de données souvent impossibles à acquérir dans des conditions cliniques et pratiques. Une telle exigence est une conséquence de la variabilité importante des données résultant de nombreux facteurs (différentes procédures d'enregistrement, sujets et sessions, présence de niveaux élevés de bruit). Pour résoudre ce problème, dans cette thèse, nous avons étudié et proposé des modèles convolutifs d'apprentissage automatique adaptés aux propriétés et aux hypothèses bien fondées sur les données acquises. Par conséquent, les modèles sont dotés de connaissances précieuses et apprennent plus efficacement à effectuer certaines inférences. En particulier, nous avons étudié des modèles d'analyse des données de neuroimagerie structurelle et fonctionnelle non-invasives et in-vivo pour de l'imagerie par résonance magnétique de diffusion (IRMd) et des signaux de magnéto et d'électro-encéphalographie (M/EEG).L'IRM de diffusion est une modalité d'imagerie nucléaire qui capture les propriétés microstructurales des tissus examinés. Comme l'échantillonnage de q-space est le protocole d'imagerie de diffusion à haute résolution angulaire (HARDI) le plus largement utilisé au cours de la dernière décennie, nous avons étudié les réseaux de neurones convolutionnels (CNN) sphériques équivariants par rotation pour la modélisation locale de l'IRMd. Comme première contribution, nous avons proposé un U-net sphérique pour l'estimation des fonctions de distribution d'orientation des fibres (fODF) avec des convolutions et des non-linéarités réalisées respectivement dans les domaines spectral et signal. Pour éviter l'aliasing, la deuxième contribution propose un CNN travaillant entièrement dans le domain spectral -- y compris pour les non-linéarités -- pour l'estimation des paramètres de microstructure.La M/EEG est une technique d'imagerie fonctionnelle qui mesure l'intensité du champ magnétique et le potentiel du champ électrique provoqués par les activités électriques neurales dans le cortex cérébral. Les signaux mesurés peuvent être expliqués par les équations de Maxwell avec des approximations quasi-statiques. Par conséquent, nous pouvons supposer que les activités cérébrales corticales se propagent instantanément et linéairement sur les capteurs de mesure, ainsi un signal M/EEG multivarié peut être représenté comme une somme de signaux multivariés de rang 1 correspondant à des sources individuelles dans le cortex et le bruit. Partant de cette hypothèse, la deuxième partie de la thèse étudie une approche d'apprentissage de dictionnaire spatio-temporel M/EEG sous contrainte $L_0$. Une deuxième contribution dans cette partie est un classificateur CNN à noyaux spatio-temporels de rang 1 régularisés dans le domaine spectral, où les composantes spatiales et temporelles des noyaux sont représenteés respectivement en termes d'éléments de base d'harmoniques sphériques et de base de cosinus discrets

Domain specific convolutional neural networks for dMRI and M/EEG signal analysis

L'analyse des données de neuroimagerie est essentielle pour l'interprétation des caractéristiques fonctionnelles ou structurelles du cerveau humain. Les algorithmes d'apprentissage automatique récents requièrent généralement une grande quantité de données souvent impossibles à acquérir dans des conditions cliniques et pratiques. Une telle exigence est une conséquence de la variabilité importante des données résultant de nombreux facteurs (différentes procédures d'enregistrement, sujets et sessions, présence de niveaux élevés de bruit). Pour résoudre ce problème, dans cette thèse, nous avons étudié et proposé des modèles convolutifs d'apprentissage automatique adaptés aux propriétés et aux hypothèses bien fondées sur les données acquises. Par conséquent, les modèles sont dotés de connaissances précieuses et apprennent plus efficacement à effectuer certaines inférences. En particulier, nous avons étudié des modèles d'analyse des données de neuroimagerie structurelle et fonctionnelle non-invasives et in-vivo pour de l'imagerie par résonance magnétique de diffusion (IRMd) et des signaux de magnéto et d'électro-encéphalographie (M/EEG).L'IRM de diffusion est une modalité d'imagerie nucléaire qui capture les propriétés microstructurales des tissus examinés. Comme l'échantillonnage de q-space est le protocole d'imagerie de diffusion à haute résolution angulaire (HARDI) le plus largement utilisé au cours de la dernière décennie, nous avons étudié les réseaux de neurones convolutionnels (CNNs) sphériques équivariants par rotation pour la modélisation locale de l'IRMd. Comme première contribution, nous avons proposé un U-net sphérique pour l'estimation des fonctions de distribution d'orientation des fibres (fODFs) avec des convolutions et des non-linéarités réalisées respectivement dans les domaines spectral et signal. Pour éviter l'aliasing, la deuxième contribution propose un CNN travaillant entièrement dans le domain spectral -- y compris pour les non-linéarités -- pour l'estimation des paramètres de microstructure.La M/EEG est une technique d'imagerie fonctionnelle qui mesure l'intensité du champ magnétique et le potentiel du champ électrique provoqués par les activités électriques neurales dans le cortex cérébral. Les signaux mesurés peuvent être expliqués par les équations de Maxwell avec des approximations quasi-statiques. Par conséquent, nous pouvons supposer que les activités cérébrales corticales se propagent instantanément et linéairement sur les capteurs de mesure, ainsi un signal M/EEG multivarié peut être représenté comme une somme de signaux multivariés de rang 1 correspondant à des sources individuelles dans le cortex et le bruit. Partant de cette hypothèse, la deuxième partie de la thèse étudie une approche d'apprentissage de dictionnaire spatio-temporel M/EEG sous contrainte L0. Une deuxième contribution dans cette partie est un classificateur CNN à noyaux spatio-temporels de rang 1 régularisés dans le domaine spectral, où les composantes spatiales et temporelles des noyaux sont représenteés respectivement en termes d'éléments de base d'harmoniques sphériques et de base de cosinus discrets.The analysis of neuroimaging data is essential for the interpretation of the functional or structural characteristics of the human brain. New machine learning algorithms usually require a high amount of data often infeasible to acquire in clinical and practical conditions. This requirement is a consequence of significant data variability arising from numerous factors (various recording procedures, subjects and sessions, presence of high levels of noise). To address this problem, in this thesis, we have investigated and proposed convolutional machine learning models adapted to the properties and well grounded assumptions about the acquired data. Therefore, the models are endowed with valuable knowledge and consequently more efficiently learn to perform certain inferences. In particular, we have studied models for the analysis of non-invasive and in-vivo structural and functional neuroimaging data, namely diffusion Magnetic Resonance Imaging (dMRI) and magneto- and electro-encephalography (M/EEG) signals.Diffusion MRI is a nuclear imaging modality which captures micro-structural properties of the examined tissue. As q-space sampling has been the most widely used high angular resolution diffusion imaging protocol (HARDI) over the last decade, we have studied spherical rotation equivariant convolutional neural networks (CNNs) for dMRI local modeling. As a first contribution, we have proposed a spherical U-net for the estimation of fiber orientation distribution functions (fODFs) with convolutions and non-linearities realized in the spectral and signal domains, respectively. To avoid aliasing, our second contribution proposes a Fourier domain CNN for micro-structure parameter estimation, where non-linearities are defined in the spectral domain.M/EEG are functional imaging techniques which measure magnetic field strength and electric field potential caused by neural electric activities in the cerebral cortex. Measured signals can be explained by Maxwell's equations with quasi-static approximations. Consequently, we can assume that cortical brain activities spread instantaneously and linearly over the measuring sensors, thus a multivariate M/EEG signal can be represented as a sum of rank-1 multivariate signals corresponding to individual sources in the cortex and noise. Considering this assumption, the second part of the thesis firstly investigates an M/EEG spatial and temporal dictionary learning approach with an L0 constraint. A second contribution is a CNN classifier with rank-1 spatio-temporal kernels regularized in the spectral domain, where the spatial components of the kernels are represented in terms of spherical harmonics basis, while the temporal components are represented in terms of discrete cosine basis

Internal structure of an alternative measure of burnout

This study evaluates the factorial validity and reliability of the Slovenian adaptation of the Oldenburg Burnout Inventory (OLBI) in a sample of 1436 Slovenian employees of various occupations. Confirmatory factor analyses were used to evaluate alternative structural models of OLBI, and reliability of variant scales was estimated. The results reveal a different structure of the Slovenian adaptation compared with the original one and a very notable difference in reliability between positively and negatively framed items. The results could be explained with a response bias or the specific nature of burnout and work engagement that OLBI promises to assess simultaneously. Therefore, we believe that the internal structure of the original inventory needs to be reconsidered