Palinoestratigrafia do Jurássico da região de Sagres (Bacia Algarvia) e da Carrapateira: resultados preliminaries

A análise palinoestratigráfica das sucessões jurássicas da região de Sagres (Bacia Algarvia) e do afloramento Mesozóico da Carrapateira permitiu a obtenção de novos dados bioestratigráficos, com base em dinoflagelados e mioesporos. Os novos resultados confirmam, e em alguns casos refinam, as idades atribuídas a estas sucessões com base em macrofaunas.The palinostratigraphic study of the Jurassic successions in the Sagres region (Algarve Basin) and Carrapateira Outlier, has yielded new data based on dinoflagellate cysts and miospores biostratigraphy. The results confirm, and in some cases refine, the existing macrofaunal age determinations of these successions

Middle-Upper Jurassic palynology of the Sagres region and the Carrapateira outlier: southern Portugal

The palynology of the Middle-Upper Jurassic fill of the Sagres region (Algarve Basin) and the Carrapateira outlier, southern Portugal was investigated. Samples were collected from Mareta beach, Cilheta beach and the Carrapateira outlier. Dinoflagellate cysts are confined to the Upper Bajocian to Upper Callovian sedimentary rocks exposed at Mareta and Cilheta beaches and the Lower Kimmeridgian strata of the Carrapateira outlier. The palynostratigraphical study of the Jurassic successions has yielded new biostratigraphical data based on dinoflagellate cysts and miospores. The results confirm, and in some cases refine, the existing macrofaunal age determinations of these successions

The Middle Jurassic palynology of the Sagres region, southern Portugal.

The Algarve Basin corresponds to the southernmost geological province of mainland Portugal. It has an E-W strike and is represented onshore from Cap São Vicente to the Guadiana River on the Portuguese-Spanish border. More than 3000 m of essentially marine sediments accumulated during Mesozoic-Cenozoic times in the Algarve Basin

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO

A Genetic Deconstruction of Neurocognitive Traits in Schizophrenia and Bipolar Disorder

Background: Impairments in cognitive functions are common in patients suffering from psychiatric disorders, such as schizophrenia and bipolar disorder. Cognitive traits have been proposed as useful for understanding the biological and genetic mechanisms implicated in cognitive function in healthy individuals and in the dysfunction observed in psychiatric disorders. Methods: Sets of genes associated with a range of cognitive functions often impaired in schizophrenia and bipolar disorder were generated from a genome-wide association study (GWAS) on a sample comprising 670 healthy Norwegian adults who were phenotyped for a broad battery of cognitive tests. These gene sets were then tested for enrichment of association in GWASs of schizophrenia and bipolar disorder. The GWAS data was derived from three independent single-centre schizophrenia samples, three independent single-centre bipolar disorder samples, and the multi-centre schizophrenia and bipolar disorder samples from the Psychiatric Genomics Consortium. Results: The strongest enrichments were observed for visuospatial attention and verbal abilities sets in bipolar disorder. Delayed verbal memory was also enriched in one sample of bipolar disorder. For schizophrenia, the strongest evidence of enrichment was observed for the sets of genes associated with performance in a colour-word interference test and for sets associated with memory learning slope. Conclusions: Our results are consistent with the increasing evidence that cognitive functions share genetic factors with schizophrenia and bipolar disorder. Our data provides evidence that genetic studies using polygenic and pleiotropic models can be used to link specific cognitive functions with psychiatric disorders

Multi-decadal and ontogenetic trophic shifts inferred from stable isotope ratios of pinniped teeth

This work was supported by National Capability funding from the Natural Environment Research Council to the Sea Mammal Research Unit (grant no. SMRU1001).Identifying and characterizing top predators’ use of trophic resources provides important information about animal ecology and their response to changing conditions. Information from sources such as stable isotopes can be used to infer changes in resource use as direct observations in the wildare difficult to obtain, particularly in the marine environment. Stable carbon and nitrogen isotope values were recovered from the canine teeth of grey seals collected from haul outs in the central North Sea in the 1970/80s (n = 44) and 2000s (n = 25), spanning a period of marked ecosystem changes in the region. Extracting material deposited during juvenile and adult life-stages, we reconstructed a multi-decadal record ofδ15N and δ13C variation. Using established correlations between stable isotope ratios and sea bottom temperature we created a proxy for baseline isotopic variability to account for this source of temporal change. We found(1) a significant long-term decline in juvenile grey seal δ15N values,suggesting trophic position has decreased over time; (2) a decline in adultδ15N values and contraction in stable isotopic niche space after the North Sea regime shift, signifying both a decline in trophic position and change in foraging habits over the 20th century; and (3) evidence for dietary segregation between juvenile and adult animals, showing juvenile individuals feeding at a lower trophic position and in more nearshore areas than adults. Our results demonstrate the efficacy of mining archived biological samples to address ecological questions and imply important ontogenetic and long-term shifts in the feeding ecology of a top predator.Long-term changes in grey seal trophic dynamics may be partly in response to well documented ecosystem changes in the North Sea. Such indirect monitoring of marine predators may have utility when set in the context of ecosystem assessments where paucity of long-term monitoring data is prevalent.Publisher PDFPeer reviewe

Predicting treatment response using EEG in major depressive disorder : a machine-learning meta-analysis

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90–89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747–0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05–88.70), and 84.60% (95% CI: 67.89–92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45–94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45–94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options

Observational insights into chlorophyll distributions of subtropical South Indian Ocean eddies

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