Information flow between resting state networks

The resting brain dynamics self-organizes into a finite number of correlated patterns known as resting state networks (RSNs). It is well known that techniques like independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting state magnetic resonance imaging. After haemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of Transfer Entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k = 1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k greater than one our method calculates the k-multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension-dependent, increasing from k =1 (i.e., the average voxels activity) up to a maximum occurring at k =5 to finally decay to zero for k greater than 10. This suggests that a small number of components (close to 5) is sufficient to describe the IF pattern between RSNs. Our method - addressing differences in IF between RSNs for any generic data - can be used for group comparison in health or disease. To illustrate this, we have calculated the interRSNs IF in a dataset of Alzheimer's Disease (AD) to find that the most significant differences between AD and controls occurred for k =2, in addition to AD showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for publication in Brain Connectivity in its current for

The origin of the Acheulean: the 1.7 million-year-old site of FLK West, Olduvai Gorge (Tanzania)

The appearance of the Acheulean is one of the hallmarks of human evolution. It represents the emergence of a complex behavior, expressed in the recurrent manufacture of large-sized tools, with standardized forms, implying more advance forethought and planning by hominins than those required by the precedent Oldowan technology. The earliest known evidence of this technology dates back to c. 1.7 Ma. and is limited to two sites (Kokiselei [Kenya] and Konso [Ethiopia]), both of which lack fauna. The functionality of these earliest Acheulean assemblages remains unknown. Here we present the discovery of another early Acheulean site also dating to c. 1.7 Ma from Olduvai Gorge. This site provides evidence of the earliest steps in developing the Acheulean technology and is the oldest Acheulean site in which stone tools occur spatially and functionally associated with the exploitation of fauna. Simple and elaborate large-cutting tools (LCT) and handaxes co-exist at FLK West, showing that complex cognition was present from the earliest stages of the Acheulean. Here we provide a detailed technological study and evidence of the use of these tools on the butchery and consumption of fauna, probably by early Homo erectus sensu lato

Late Cretaceous sauropod tooth morphotypes may provide supporting evidence for faunal connections between North Africa and Southern Europe

The Cretaceous Kem Kem beds of Morocco and equivalent beds in Algeria have produced a rich fossil assemblage, yielding, amongst others, isolated sauropod teeth, which can be used in species diversity studies. These Albian-Cenomanian ( approximately 113-93.9 Ma) strata rarely yield sauropod body fossils, therefore, isolated teeth can help to elucidate the faunal assemblages from North Africa, and their relations with those of contemporaneous beds and geographically close assemblages. Eighteen isolated sauropod teeth from three localities (Erfoud and Taouz, Morocco, and Algeria) are studied here, to assess whether the teeth can be ascribed to a specific clade, and whether different tooth morphotypes can be found in the samples. Two general morphotypes are found, based on enamel wrinkling and general tooth morphology. Morphotype I, with mainly rugose enamel wrinkling, pronounced carinae, lemon-shaped to (sub)cylindrical cross-section and mesiodistal tapering towards an apical tip, shows affinities to titanosauriforms and titanosaurs. Morphotype II, characterized by more smooth enamel, cylindrical cross-section, rectangular teeth with no apical tapering and both labial and lingual wear facets, shows similarities to rebbachisaurids. Moreover, similarities are found between these northwest African tooth morphotypes, and tooth morphotypes from titanosaurs and rebbachisaurids from both contemporaneous finds from north and central Africa, as well as from the latest Cretaceous (Campanian-Maastrichtian, 83.6 Ma-66.0 Ma) of the Ibero-Armorican Island. These results support previous hypotheses from earlier studies on faunal exchange and continental connections between North Africa and Southern Europe in the Cretaceous

Wire edge dependent magnetic domain wall creep

open13While edge pinning is known to play an important role in sub-μm wires, we demonstrate that strong deviations from the universal creep law can occur in 1 to 20 μm wide wires. Magnetic imaging shows that edge pinning translates into a marked bending of domain walls at low drive and is found to depend on the wire fabrication process and aging. Edge pinning introduces a reduction of domain wall velocity with respect to full films which increasingly dominates the creep dynamics as the wire width decreases. We show that the deviations from the creep law can be described by a simple model including a counter magnetic field which links the width of the wire to the edge dependent pinning strength. This counter field defines a key nonuniversal contribution to creep motion in patterned structures.openHerrera Diez, L.; Jeudy, V.; Durin, G.; Casiraghi, A.; Liu, Y. T.; Voto, M.; Agnus, G.; Bouville, D.; Vila, L.; Langer, J.; Ocker, B.; Lopez-Diaz, L.; Ravelosona, D.Herrera Diez, L.; Jeudy, V.; Durin, G.; Casiraghi, A.; Liu, Y. T.; Voto, M.; Agnus, G.; Bouville, D.; Vila, L.; Langer, J.; Ocker, B.; Lopez-Diaz, L.; Ravelosona, D

Characterization of Patients with Chronic Diseases and Complex Care Needs: A New High-Risk Emergent Population

Background: To analyze the prevalence and main epidemiological, clinical and outcome features of in-Patients with Complex Chronic conditions (PCC) in internal medicine areas, using a pragmatic working definition. Methods: Prospective study in 17 centers from Spain, with 97 in-hospital, monthly prevalence cuts. A PCC was considered when criteria of polypathological patient (two or more major chronic diseases) were met, or when a patient suffered one major chronic disease plus one or more of nine predefined complexity criteria like socio-familial risk, alcoholism or malnutrition among others (PCC without polypathology). A complete set of baseline features as well as 12-months survival were collected. Then, we compared clinical, outcome variables, and PROFUND index accuracy between polypathological patients and PCC without polypathology. Results: The global prevalence of PCC was 61% (40% of them were polypathological patients, and 21% PCC withouth polypathology) out of the 2178 evaluated patients. Their median age was 82 (59.5% men), suffered 2.3 ± 1.1 major diseases (heart diseases (70.5%), neurologic (41.5%), renal (36%), and lung diseases (26%)), 5.5 ± 2.5 other chronic conditions, met 2.5 ± 1.5 complexity criteria, and presented functional decline (Barthel index 55 (25-90)). Compared to polypathological patients, the subgroup of PCC without polypathology were younger, with a different pattern of major diseases and comorbidities, a better functional status, and lower 12-months mortality rates ((36.2% vs 46.8%; p = .003; OR 0.7(0.48-0.86). The PROFUND index obtained adequate calibration and discrimination power (AUC-ROC 0.67 (0.63-0.69)) in predicting 12-month mortality of PCC. Conclusion: Patients with complex chronic conditions are highly prevalent in internal medicine areas; their clinical pattern has changed in parallel to socio-epidemiological modifications, but their death-risk is still adequately predicted by PROFUND index

Plasma MicroRNA Signature Validation for Early Detection of Colorectal Cancer

OBJECTIVES: Specific microRNA (miRNA) signatures in biological fluids can facilitate earlier detection of the tumors being then minimally invasive diagnostic biomarkers. Circulating miRNAs have also emerged as promising diagnostic biomarkers for colorectal cancer (CRC) screening. In this study, we investigated the performance of a specific signature of miRNA in plasma samples to design a robust predictive model that can distinguish healthy individuals from those with CRC or advanced adenomas (AA) diseases. METHODS: Case control study of 297 patients from 8 Spanish centers including 100 healthy individuals, 101 diagnosed with AA, and 96 CRC cases. Quantitative real-time reverse transcription was used to quantify a signature of miRNA (miRNA19a, miRNA19b, miRNA15b, miRNA29a, miRNA335, and miRNA18a) in plasma samples. Binary classifiers (Support Vector Machine [SVM] linear, SVM radial, and SVM polynomial) were built for the best predictive model. RESULTS: Area under receiving operating characteristic curve of 0.92 (95% confidence interval 0.871-0.962) was obtained retrieving a model with a sensitivity of 0.85 and specificity of 0.90, positive predictive value of 0.94, and negative predictive value of 0.76 when advanced neoplasms (CRC and AA) were compared with healthy individuals. CONCLUSIONS: We identified and validated a signature of 6 miRNAs (miRNA19a, miRNA19b, miRNA15b, miRNA29a, miRNA335, and miRNA18a) as predictors that can differentiate significantly patients with CRC and AA from those who are healthy. However, large-scale validation studies in asymptomatic screening participants should be conducted

The plant traits that drive ecosystems: Evidence from three continents.

Question: A set of easily‐measured (‘soft’) plant traits has been identified as potentially useful predictors of ecosystem functioning in previous studies. Here we aimed to discover whether the screening techniques remain operational in widely contrasted circumstances, to test for the existence of axes of variation in the particular sets of traits, and to test for their links with ‘harder’ traits of proven importance to ecosystem functioning. Location: central‐western Argentina, central England, northern upland Iran, and north‐eastern Spain. Recurrent patterns of ecological specialization: Through ordination of a matrix of 640 vascular plant taxa by 12 standardized traits, we detected similar patterns of specialization in the four floras. The first PCA axis was identified as an axis of resource capture, usage and release. PCA axis 2 appeared to be a size‐related axis. Individual PCA for each country showed that the same traits remained valuable as predictors of resource capture and utilization in all of them, despite their major differences in climate, biogeography and land‐use. The results were not significantly driven by particular taxa: the main traits determining PCA axis 1 were very similar in eudicotyledons and monocotyledons and Asteraceae, Fabaceae and Poaceae. Links between recurrent suites of ‘soft’ traits and ‘hard’ traits: The validity of PCA axis 1 as a key predictor of resource capture and utilization was tested by comparisons between this axis and values of more rigorously established predictors (‘hard’ traits) for the floras of Argentina and England. PCA axis 1 was correlated with variation in relative growth rate, leaf nitrogen content, and litter decomposition rate. It also coincided with palatability to model generalist herbivores. Therefore, location on PCA axis 1 can be linked to major ecosystem processes in those habitats where the plants are dominant. Conclusion: We confirm the existence at the global scale of a major axis of evolutionary specialization, previously recognised in several local floras. This axis reflects a fundamental trade‐off between rapid acquisition of resources and conservation of resources within well‐protected tissues. These major trends of specialization were maintained across different environmental situations (including differences in the proximate causes of low productivity, i.e. drought or mineral nutrient deficiency). The trends were also consistent across floras and major phylogenetic groups, and were linked with traits directly relevant to ecosystem processes.Fil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Hodgson, J.G.. The University. Department of Animal and Plant Sciences. Unit of Comparative Plant Ecology; Reino UnidoFil: Thompson, K.. The University. Department of Animal and Plant Sciences. Unit of Comparative Plant Ecology; Reino UnidoFil: Cabido, Marcelo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Cornelissen, Johannes H. C.. Free University. Faculty Earth and Life Sciences. Department of Systems Ecology; Países BajosFil: Funes, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Pérez Harguindeguy, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Vendramini, Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Falczuk, Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Zak, Marcelo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Khoshnevi, M.. Research Institute of Forests and Rangelands; IránFil: Pérez Rontomé, M. C.. Instituto Pirenaico de Ecología; EspañaFil: Shirvani, F. A.. Research Institute of Forests and Rangelands; IránFil: Yazdani, S.. Research Institute of Forests and Rangelands; IránFil: Abbas Azimi, R. Research Institute of Forests and Rangelands; IránFil: Bogaard, A. The University. Department of Archaeology and Prehistory; Reino UnidoFil: Boustani, S.. Research Institute of Forests and Rangelands; IránFil: Charles, M.. The University. Department of Archaeology and Prehistory; Reino UnidoFil: Dehghan, M.. Research Institute of Forests and Rangelands; IránFil: de Torres Espuny, L.. Instituto Pirenaico de Ecología; EspañaFil: Guerrero Campo, J.. Instituto Pirenaico de Ecología; EspañaFil: Hynd, A.. The University. Department of Archaeology and Prehistory; Reino UnidoFil: Jones, G.. The University. Department of Archaeology and Prehistory; Reino UnidoFil: Kowsary, E.. Research Institute of Forests and Rangelands; Irán. Instituto Pirenaico de Ecología; EspañaFil: Kazemi Saeed, F.. Research Institute of Forests and Rangelands; IránFil: Maestro Martínez, M.. Instituto Pirenaico de Ecología; EspañaFil: Romo Diez, A.. Instituto Botanico de Barcelona; EspañaFil: Shaw, S.. Research Institute of Forests and Rangelands; Irán. The University. Department of Animal and Plant Sciences; Reino UnidoFil: Siavash, B.. Research Institute of Forests and Rangelands; IránFil: Villar Salvador, P.. Instituto Pirenaico de Ecología; Españ