Stock-recruitment relationships for cod (Gadus morhua callarias L.) in the central Baltic Sea incorporating environmental variability

Recruitment of central/eastern Baltic cod critically depends on favourable oceanographic conditions in the deeper basins of the Baltic Sea creating a suitable habitat for the development of early life stages. The decline in the size of the spawning stock since the mid-1980s initiated a series of investigations on recruitment, which were continued through a partial recovery of the stock in the mid-1990s. The principal factors influencing recruitment and recognized at present are: (i) the volume of water with temperature, oxygen and salinity conditions which meet the minimum requirements for successful egg development ('reproductive volume'); (ii) the age-structure of the spawning stock; (iii) the timing of spawning; and (iv) predation mortality on eggs due to sprat (Sprattus sprattus) and herring (Clupea harengus), as well as cod cannibalism. We relate recruitment at age 2 to parent stock size using updated time series of these variables, comprising the period 1966 to 1994. Spawning stock biomass and egg production are compared as measures of parent stock size. The influence of wind energy and zooplankton abundance on cod recruitment are discussed. A modified Ricker model is outlined explicity accounting for environmentally-induced oscillations around the two observed levels of cod stock size

Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort

BACKGROUND:Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers has been evaluated mostly in the artificial setting of research datasets.OBJECTIVE:Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic.METHODS:We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader$^{TM}$); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria.RESULTS:Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM.CONCLUSION:In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis

Real-time spatial characterization of micrometer-sized X-ray free-electron laser beams focused by bendable mirrors

A real-time and accurate characterization of the X-ray beam size is essential to enable a large variety of different experiments at free-electron laser facilities. Typically, ablative imprints are employed to determine shape and size of μm-focused X-ray beams. The high accuracy of this state-of-the-art method comes at the expense of the time required to perform an ex-situ image analysis. In contrast, diffraction at a curved grating with suitably varying period and orientation forms a magnified image of the X-ray beam, which can be recorded by a 2D pixelated detector providing beam size and pointing jitter in real time. In this manuscript, we compare results obtained with both techniques, address their advantages and limitations, and demonstrate their excellent agreement. We present an extensive characterization of the FEL beam focused to ≈1 μm by two Kirkpatrick-Baez (KB) mirrors, along with optical metrology slope profiles demonstrating their exceptionally high quality. This work provides a systematic and comprehensive study of the accuracy provided by curved gratings in real-time imaging of X-ray beams at a free-electron laser facility. It is applied here to soft X-rays and can be extended to the hard X-ray range. Furthermore, curved gratings, in combination with a suitable detector, can provide spatial properties of μm-focused X-ray beams at MHz repetition rate

Free and Cued Selective Reminding Test – accuracy for the differential diagnosis of Alzheimer's and neurodegenerative diseases: A large-scale biomarker-characterized monocenter cohort study (ClinAD)

International audienceIntroduction: The International Working Group recommended the Free and Cued Selective Reminding Test (FCSRT) as a sensitive detector of the amnesic syndrome of the hippocampal type in typical Alzheimer's disease (AD). But does it differentiate AD from other neurodegenerative diseases? Methods: We assessed the FCSRT and cerebrospinal fluid (CSF) AD biomarkers in 992 cases. Experts , blinded to biomarker data, attributed in 650 cases a diagnosis of typical AD, frontotemporal dementia, posterior cortical atrophy, Lewy body disease, progressive supranuclear palsy, corticobasal syndrome, primary progressive aphasias, " subjective cognitive decline, " or depression. Results: The FCSRT distinguished typical AD from all other conditions with a sensitivity of 100% and a specificity of 75%. Non-AD neurodegenerative diseases with positive AD CSF biomarkers (" atypical AD ") did not have lower FCSRT scores than those with negative biomarkers. Discussion: The FCSRT is a reliable tool for diagnosing typical AD among various neurodegener-ative diseases. At an individual level, however, its specificity is not absolute. Our findings also widen the spectrum of atypical AD to multiple neurodegenerative conditions

Preclinical Alzheimer's disease: a systematic review of the cohorts underlying the concept

International audiencePreclinical Alzheimer's disease (AD) is a relatively recent concept describing an entity characterized by the presence of a pathophysiological biomarker signature characteristic for AD in the absence of specific clinical symptoms. There is rising interest in the scientific community to define such an early target population mainly due to failures of all recent clinical trials despite evidence of biological effects on brain amyloidosis for some compounds. A conceptual framework has recently been proposed for this preclinical phase of AD. However, few data exist on this silent stage of AD. We performed a systematic review in order to investigate how the concept is defined across studies. The review highlights the substantial heterogeneity concerning the three main determinants of preclinical AD: " normal cognition " , " cognitive decline " and " AD pathophysiological signature ". We emphasize the need for a harmonized nomenclature of the preclinical AD concept and standardized population-based and case-control studies using unified operationalized criteria

Accuracy of MRI classification algorithms in a tertiary memory center clinical routine cohort

International audienceBackgroundMRI computational tools represent promising instruments to improve the diagnostic accuracy of dementias including Alzheimer’s disease (AD). The Automated Segmentation Softwares (ASS) measure various regions of interest (ROI) volumes, thus supporting physicians in clinical decision-making processes. However, their accuracy has mainly been evaluated for research. Recent developments in Support Vector Machine (SVM) classifier might increase the diagnostic value of MRI indexes compared with ASS. Our objective was to investigate the classification rate based on two-class classifiers.MethodsOur study evaluated two ASS (VolBrain and NeuroReader) and two SVM approaches in a monocentric memory center routine clinical cohort of 263 patients with various dementia etiologies (Early and Late-Onset AD (EOAD, LOAD), Cortico-Basal Degeneration, Lewy Body Dementia, Fronto-Temporal Dementia (FTD), Logopenic Variant of Primary Progressive Aphasia and Semantic Dementia) and depression. All patients had a routine MRI at 1, 1.5 or 3 Tesla. We first entered all ROI volumes from ASS in a univariate analysis. Then, we entered volumes obtained from each ASS separately in an SVM classifier. Finally, results where compared to a classifier based on whole brain gray matter (GM) segmentation maps using SPM12.ResultsIn the univariate classification paradigm, the diagnostic accuracy ranged from 50% to 70%, Frontal and Temporal Lobe providing the most accurate scores and hippocampal volumetry only distinguishing LOAD and EOAD from FTD with a respectively 50% and 60% accuracy. SVM classification provided similar accuracy for both ASS ranging from 60 to 80%. Nonetheless, classification using whole brain GM improved the accuracies ranging from 65 to 85% (FTD vs EAOD: 82%, EOAD vs Depression: 83%, FTD vs Depression: 82%).ConclusionsNovel computational tools can be useful in clinical practice and provide comprehensive information supporting clinicians in decision-making processes. ASS analyzed in a univariate way was moderately adequate, with poor accuracy compared with its implementation in an SVM classifier. SVM using whole brain segmentation yielded the highest diagnostic accuracies. Furthermore, SVM performed as well as published accuracies of pathophysiological markers of AD to distinguish this etiology from other dementias and depression. Implementation of whole brain SVM classification in clinical routine could represent a valuable diagnostic tool