A Discriminant Analysis Model of Alaskan Biomes Based on Spatial Climatic and Environmental Data

Classification of high-latitude landscapes into their appropriate biomes is important for many climate and global change-related issues. Unfortunately, large-scale, high-spatial-resolution observations of plant assemblages associated with these regions are generally unavailable, so accurate modeling of plant assemblages and biome boundaries is often needed. We built different discriminant analysis models and used them to “convert” various combinations of spatial climatic data (surface temperature and precipitation) and spatial environmental data (topography, soil, permafrost) into a biome-level map of Alaska. Five biomes (alpine tundra and ice fi elds, Arctic tundra, shrublands, boreal forest, and coastal rainforest) and one biome transition zone are modeled. Mean annual values of climatic variables were less useful than their annual extrema in this context. A quadratic discriminant analysis, combined with climate, topography, permafrost, and soil information, produced the most accurate Alaskan biome classification (skill = 74% when compared to independent data). The multivariate alteration detection transformation was used to identify Climatic Transition Zones (CTZs) with large interannual variability, and hence, less climatic consistency than other parts of Alaska. Biome classification was the least accurate in the CTZs, leading to the conclusion that large interannual climatic variability does not favor a unique biome. We interpret the CTZs as “transition biome areas” or ecotones between the five “core biomes” cited above. Both disturbance events (e.g., fires and subsequent plant succession sequences) and the partial intersection of the environmental variables used to characterize Alaskan biomes further complicate biome classification. Alaskan results obtained from the data-driven quadratic discriminant model compare favorably (based on Kappa statistics) with those produced by an equilibrium-based biome model for regions of Canada ecologically similar to the biomes we studied in Alaska. Climatic statistics are provided for each biome studied. Le classement des paysages de hautes latitudes dans les biomes adéquats revêt de l'importance dans le cadre de nombreux enjeux relatifs aux changements climatiques et à d'autres changements d'envergure mondiale. Malheureusement et en règle générale, il n'existe pas d'observations spatiales de haute résolution et à grande échelle pour ce qui est des assemblages de végétaux pour ces régions. C'est pourquoi il faut souvent procéder à la modélisation des assemblages de végétaux et des limites des biomes. Nous avons élaboré différents modèles d'analyses discriminantes dont nous nous sommes servis pour « transformer » divers ensembles de données climatiques spatiales (température de la surface et précipitation) et diverses données sur l'environnement spatial (topographie, sol, pergélisol) en carte des biomes de l'Alaska. La modélisation porte sur cinq biomes (toundra alpine et champs de glace, toundra arctique, arbustaie, forêt boréale et forêt pluviale côtière) et sur une zone de transition de biome. Les valeurs moyennes annuelles des variables climatiques ont été moins utiles que leurs extremas annuels dans ce contexte. Une analyse discriminante quadratique, combinée aux données relatives au climat, à la topographie, au pergélisol et au sol, a permis d'aboutir au classement de biomes alaskiens le plus précis (habileté = 74 % lorsque comparé aux données indépendantes). Nous avons recouru à la transformation de la détection de l'altération à variables multiples (multivariate alteration detection transformation) pour identifi er les zones de transition climatique (ZTC) ayant une importante variabilité interannuelle et, par conséquent, une moins grande uniformité climatique que d'autres parties de l'Alaska. Le classement des biomes était moins précis dans les ZTC, ce qui nous a amenés à conclure que l'importante variabilité climatique interannuelle ne favorise pas un biome unique. Nous interprétons les ZTC comme des « régions de biomes de transition » ou des écotones entre les cinq « biomes principaux » dont il est question ci-dessus. Les deux perturbations (c'est-à-dire les incendies et les séquences subséquentes des végétaux) et l'intersection partielle des variables environnementales utilisées pour caractériser les biomes alaskiens compliquent davantage le classement des biomes. Les résultats alaskiens obtenus à partir du modèle discriminant quadratique dérivant des données se comparent favorablement (en fonction des statistiques kappa) à ceux obtenus par un modèle de biome en équilibre pour des régions du Canada similaires du point de vue écologique aux biomes que nous avons étudiés en Alaska. Des statistiques climatiques sont fournies pour chaque biome étudié

The lithospheric structure of Pangea

Lithospheric thickness of continents, obtained from Rayleigh wave tomography, is used to make maps of the lithospheric thickness of Pangea by reconstructing the continental arrangement in the Permian. This approach assumes that lithosphere moves with the overlying continents, and therefore that the arrangement of both can be obtained using the poles of rotation obtained from magnetic anomalies and fracture zones. The resulting reconstruction shows that a contiguous arc of thick lithosphere underlay most of eastern Pangea. Beneath the western convex side of this arc, there is a wide belt of thinner lithosphere underlying what is believed to have been the active margin of Pangea, here named the Pangeides. On the inner side of this arc is another large area of thin lithosphere beneath the Pan-African belts of North Africa and Arabia. The arc of thick lithosphere is crossed by bands of slightly thinner lithosphere that lie beneath the Pan-African and Brasiliano mobile belts of South America, Africa, India, Madagascar, and Antarctica. This geometry suggests that lithospheric thickness has an important influence on continental deformation and accretion

Image-guided fluorescence tomography in head & neck surgical models

Clinical indications for fluorescence-guided surgery continue to expand, and are being spurred by the rapid development of new agents that improve biological targeting.1 There is a corresponding need to develop imaging systems that quantify fluorescence - not only at the tissue surface, but at depth. We have recently described an image-guided fluorescence tomography system that leverages geometric data from intraoperative cone-beam CT and surgical navigation,2 and builds on finite-element method software (NIRFAST) for diffuse optical tomography (DOT).3 DOT systems have most commonly been used for sub-surface inclusions buried within tissue (e.g., breast and neurological tumors). Here, we focus on inclusion models relevant to tumors infiltrating from the mucosal surface (an “iceberg” model), as is most often the case in head and neck cancer, where over 85% of tumors are squamous cell carcinoma.4 This work presents results from simulations, tissue-simulating anatomical phantoms, and animal studies involving infiltrative tumor models. The objective is to characterize system performance across a range of inclusion diameters, depths, and optical properties. For example, Fig. 1 shows a fluorescence reconstruction of a simulated tonsil tumor in an oral cavity phantom. Future clinical studies are necessary to assess in vivo performance and intraoperative workflow. Please click Additional Files below to see the full abstract

Fluorescence depth quantification using spatial frequency imaging and deep learning reconstruction

Please click Additional Files below to see the full abstract

Reproducible model development in the Cardiac Electrophysiology Web Lab

The modelling of the electrophysiology of cardiac cells is one of the most mature areas of systems biology. This extended concentration of research effort brings with it new challenges, foremost among which is that of choosing which of these models is most suitable for addressing a particular scientific question. In a previous paper, we presented our initial work in developing an online resource for the characterisation and comparison of electrophysiological cell models in a wide range of experimental scenarios. In that work, we described how we had developed a novel protocol language that allowed us to separate the details of the mathematical model (the majority of cardiac cell models take the form of ordinary differential equations) from the experimental protocol being simulated. We developed a fully-open online repository (which we termed the Cardiac Electrophysiology Web Lab) which allows users to store and compare the results of applying the same experimental protocol to competing models. In the current paper we describe the most recent and planned extensions of this work, focused on supporting the process of model building from experimental data. We outline the necessary work to develop a machine-readable language to describe the process of inferring parameters from wet lab datasets, and illustrate our approach through a detailed example of fitting a model of the hERG channel using experimental data. We conclude by discussing the future challenges in making further progress in this domain towards our goal of facilitating a fully reproducible approach to the development of cardiac cell models

Solution structure of the N-terminal extension domain of a Schistosoma japonicum asparaginyl-tRNA synthetase

Several secreted proteins from helminths (parasitic worms) have been shown to have immunomodulatory activities. Asparaginyl-tRNA synthetases are abundantly secreted in the filarial nematode Brugia malayi (BmAsnRS) and the parasitic flatworm Schistosoma japonicum (SjAsnRS), indicating a possible immune function. The suggestion is supported by BmAsnRS alleviating disease symptoms in a T-cell transfer mouse model of colitis. This immunomodulatory function is potentially related to an N-terminal extension domain present in eukaryotic AsnRS proteins but few structure/function studies have been done on this domain. Here we have determined the three-dimensional solution structure of the N-terminal extension domain of SjAsnRS. A protein containing the 114 N-terminal amino acids of SjAsnRS was recombinantly expressed with isotopic labelling to allow structure determination using 3D NMR spectroscopy, and analysis of dynamics using NMR relaxation experiments. Structural comparisons of the N-terminal extension domain of SjAsnRS with filarial and human homologues highlight a high degree of variability in the β-hairpin region of these eukaryotic N-AsnRS proteins, but similarities in the disorder of the C-terminal regions. Limitations in PrDOS-based intrinsically disordered region (IDR) model predictions were also evident in this comparison. Empirical structural data such as that presented in our study for N-SjAsnRS will enhance the prediction of sequence-homology based structure modelling and prediction of IDRs in the future

Differential Neural Responses to Food Images in Women with Bulimia versus Anorexia Nervosa

BACKGROUND: Previous fMRI studies show that women with eating disorders (ED) have differential neural activation to viewing food images. However, despite clinical differences in their responses to food, differential neural activation to thinking about eating food, between women with anorexia nervosa (AN) and bulimia nervosa (BN) is not known. METHODS: We compare 50 women (8 with BN, 18 with AN and 24 age-matched healthy controls [HC]) while they view food images during functional Magnetic Resonance Imaging (fMRI). RESULTS: In response to food (vs non-food) images, women with BN showed greater neural activation in the visual cortex, right dorsolateral prefrontal cortex, right insular cortex and precentral gyrus, women with AN showed greater activation in the right dorsolateral prefrontal cortex, cerebellum and right precuneus. HC women activated the cerebellum, right insular cortex, right medial temporal lobe and left caudate. Direct comparisons revealed that compared to HC, the BN group showed relative deactivation in the bilateral superior temporal gyrus/insula, and visual cortex, and compared to AN had relative deactivation in the parietal lobe and dorsal posterior cingulate cortex, but greater activation in the caudate, superior temporal gyrus, right insula and supplementary motor area. CONCLUSIONS: Women with AN and BN activate top-down cognitive control in response to food images, yet women with BN have increased activation in reward and somatosensory regions, which might impinge on cognitive control over food consumption and binge eating

Yoga and Emotion Regulation in High School Students: A Randomized Controlled Trial

Middle adolescents (15–17 years old) are prone to increased risk taking and emotional instability. Emotion dysregulation contributes to a variety of psychosocial difficulties in this population. A discipline such as yoga offered during school may increase emotion regulation, but research in this area is lacking. This study was designed to evaluate the impact of a yoga intervention on the emotion regulation of high school students as compared to physical education (PE). In addition, the potential mediating effects of mindful attention, self-compassion, and body awareness on the relationship between yoga and emotion regulation were examined. High school students were randomized to participate in a 16-week yoga intervention (n=19) or regular PE (n=18). Pre-post data analyses revealed that emotion regulation increased significantly in the yoga group as compared to the PE group (F (1,32) = 7.50, p=.01, and eta2 = .19). No significant relationship was discovered between the changes in emotion regulation and the proposed mediating variables. Preliminary results suggest that yoga increases emotion regulation capacities of middle adolescents and provides benefits beyond that of PE alone