Wireless, in-vessel neutron monitor for initial core-loading of advanced breeder reactors

An experimental wireless, in-vessel neutron monitor was developed to measure the reactivity of an advanced breeder reactor as the core is loaded for the first time to preclude an accidental critically incident. The environment is liquid sodium at a temperature of approx. 220 C, with negligible gamma or neutron radiation. With ultrasonic transmission of neutron data, no fundamental limitation was observed after tests at 230 C for 2000 h. The neutron sensitivity was approx. 1 count/s-nv, and the potential data transmission rate was approx. 10,000 counts/s

Test–retest reliability of freesurfer measurements within and between sites: Effects of visual approval process

In the last decade, many studies have used automated processes to analyze magnetic resonance imaging (MRI) data such as cortical thickness, which is one indicator of neuronal health. Due to the convenience of image processing software (e.g., FreeSurfer), standard practice is to rely on automated results without performing visual inspection of intermediate processing. In this work, structural MRIs of 40 healthy controls who were scanned twice were used to determine the test–retest reliability of FreeSurfer‐derived cortical measures in four groups of subjects—those 25 that passed visual inspection (approved), those 15 that failed visual inspection (disapproved), a combined group, and a subset of 10 subjects (Travel) whose test and retest scans occurred at different sites. Test–retest correlation (TRC), intraclass correlation coefficient (ICC), and percent difference (PD) were used to measure the reliability in the Destrieux and Desikan–Killiany (DK) atlases. In the approved subjects, reliability of cortical thickness/surface area/volume (DK atlas only) were: TRC (0.82/0.88/0.88), ICC (0.81/0.87/0.88), PD (0.86/1.19/1.39), which represent a significant improvement over these measures when disapproved subjects are included. Travel subjects’ results show that cortical thickness reliability is more sensitive to site differences than the cortical surface area and volume. To determine the effect of visual inspection on sample size required for studies of MRI‐derived cortical thickness, the number of subjects required to show group differences was calculated. Significant differences observed across imaging sites, between visually approved/disapproved subjects, and across regions with different sizes suggest that these measures should be used with caution. Hum Brain Mapp 36:3472–3485, 2015. © 2015 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113142/1/hbm22856.pd

Arabinose and protocatechuate catabolism genes are important for growth of Rhizobium leguminosarum biovar viciae in the pea rhizosphere

Background and aims: To form nitrogen-fixing nodules on pea roots, Rhizobium leguminosarum biovar viciae must be competitive in the rhizosphere. Our aim was to identify genes important for rhizosphere fitness. Methods: Signature-tagged mutants were screened using microarrays to identify mutants reduced for growth in pea rhizospheres. Candidate mutants were assessed relative to controls for growth in minimal medium, growth in pea rhizospheres and for infection of peas in mixed inoculants. Mutated genes were identified by DNA sequencing and confirmed by transduction. Results: Of 5508 signature-tagged mutants, microarrays implicated 50 as having decreased rhizosphere fitness. Growth tests identified six mutants with rhizosphere-specific phenotypes. The mutation in one of the genes (araE) was in an arabinose catabolism operon and blocked growth on arabinose. The mutation in another gene (pcaM), encoding a predicted solute binding protein for protocatechuate and hydroxybenzoate uptake, decreased growth on protocatechuate. Both mutants were decreased for nodule infection competitiveness with mixed inoculants, but nodulated peas normally when inoculated alone. Other mutants with similar phenotypes had mutations predicted to affect secondary metabolism. Conclusions: Catabolism of arabinose and protocatechuate in the pea rhizosphere is important for competitiveness of R.l. viciae. Other genes predicted to be involved in secondary metabolism are also important

In vivo serotonin 1A receptor hippocampal binding potential in depression and reported childhood adversity

Abstract Background Reported childhood adversity (CA) is associated with development of depression in adulthood and predicts a more severe course of illness. Although elevated serotonin 1A receptor (5-HT1AR) binding potential, especially in the raphe nuclei, has been shown to be a trait associated with major depression, we did not replicate this finding in an independent sample using the partial agonist positron emission tomography tracer [11C]CUMI-101. Evidence suggests that CA can induce long-lasting changes in expression of 5-HT1AR, and thus, a history of CA may explain the disparate findings. Methods Following up on our initial report, 28 unmedicated participants in a current depressive episode (bipolar n = 16, unipolar n = 12) and 19 non-depressed healthy volunteers (HVs) underwent [11C]CUMI-101 imaging to quantify 5-HT1AR binding potential. Participants in a depressive episode were stratified into mild/moderate and severe CA groups via the Childhood Trauma Questionnaire. We hypothesized higher hippocampal and raphe nuclei 5-HT1AR with severe CA compared with mild/moderate CA and HVs. Results There was a group-by-region effect (p = 0.011) when considering HV, depressive episode mild/moderate CA, and depressive episode severe CA groups, driven by significantly higher hippocampal 5-HT1AR binding potential in participants in a depressive episode with severe CA relative to HVs (p = 0.019). Contrary to our hypothesis, no significant binding potential differences were detected in the raphe nuclei (p -value s > 0.05). Conclusions With replication in larger samples, elevated hippocampal 5-HT1AR binding potential may serve as a promising biomarker through which to investigate the neurobiological link between CA and depression

A Comprehensive Examination Of White Matter Tracts And Connectometry In Major Depressive Disorder

Background Major depressive disorder (MDD) is a debilitating disorder characterized by widespread brain abnormalities. The literature is mixed as to whether or not white matter abnormalities are associated with MDD. This study sought to examine fractional anisotropy (FA) in white matter tracts in individuals with MDD using diffusion tensor imaging (DTI). Methods 139 participants with MDD and 39 healthy controls (HC) in a multisite study were included. DTI scans were acquired in 64 directions and FA was determined in the brain using four methods: region of interest (ROI), tract-based spatial statistics (TBSS), and diffusion tractography. Diffusion connectometry was used to identify white matter pathways associated with MDD. Results There were no significant differences when comparing FA in MDD and HC groups using any method. In the MDD group, there was a significant relationship between depression severity and FA in the right medial orbitofrontal cortex, and between age of onset of MDD and FA in the right caudal anterior cingulate cortex using the ROI method. There was a significant relationship between age of onset and connectivity in the thalamocortical radiation, inferior longitudinal fasciculus, and cerebellar tracts using diffusion connectometry. Conclusions The lack of group differences in FA and connectometry analysis may result from the clinically heterogenous nature of MDD. However, the relationship between FA and depression severity may suggest a state biomarker of depression that should be investigated as a potential indicator of response. Age of onset may also be a significant clinical feature to pursue when studying white matter tracts

A Comparison Of Structural Connectivity In Anxious Depression Versus Non-anxious Depression

Background: Major depressive disorder (MDD) and anxiety disorders are highly co-morbid. Research has shown conflicting evidence for white matter alteration and amygdala volume reduction in mood and anxiety disorders. To date, no studies have examined differences in structural connectivity between anxious depressed and non-anxious depressed individuals. This study compared fractional anisotropy (FA) and density of selected white matter tracts and amygdala volume between anxious depressed and non-anxious depressed individuals. Methods: 64- direction DTI and T1 scans were collected from 110 unmedicated subjects with MDD, 39 of whom had a co-morbid anxiety disorder diagnosis. Region of interest (ROI) and tractography methods were performed to calculate amygdala volume and FA in the uncinate fasciculus, respectively. Diffusion connectometry was performed to identify whole brain group differences in white matter health. Correlations were computed between biological and clinical measures. Results: Tractography and ROI analyses showed no significant differences between bilateral FA values or bilateral amygdala volumes when comparing the anxious depressed and non-anxious depressed groups. The diffusion connectometry analysis showed no significant differences in anisotropy between the groups. Furthermore, there were no significant relationships between MRI-based and clinical measures. Conclusion: The lack of group differences could indicate that structural connectivity and amygdalae volumes of those with anxious-depression are not significantly altered by a co-morbid anxiety disorder. Improving understanding of anxiety co-morbid with MDD would facilitate development of treatments that more accurately target the underlying networks

Transcriptome Analysis of the Hippocampal CA1 Pyramidal Cell Region after Kainic Acid-Induced Status Epilepticus in Juvenile Rats

Molecular mechanisms involved in epileptogenesis in the developing brain remain poorly understood. The gene array approach could reveal some of the factors involved by allowing the identification of a broad scale of genes altered by seizures. In this study we used microarray analysis to reveal the gene expression profile of the laser microdissected hippocampal CA1 subregion one week after kainic acid (KA)-induced status epilepticus (SE) in 21-day-old rats, which are developmentally roughly comparable to juvenile children. The gene expression analysis with the Chipster software generated a total of 1592 differently expressed genes in the CA1 subregion of KA-treated rats compared to control rats. The KEGG database revealed that the identified genes were involved in pathways such as oxidative phosporylation (26 genes changed), and long-term potentiation (LTP; 18 genes changed). Also genes involved in Ca2+ homeostasis, gliosis, inflammation, and GABAergic transmission were altered. To validate the microarray results we further examined the protein expression for a subset of selected genes, glial fibrillary protein (GFAP), apolipoprotein E (apo E), cannabinoid type 1 receptor (CB1), Purkinje cell protein 4 (PEP-19), and interleukin 8 receptor (CXCR1), with immunohistochemistry, which confirmed the transcriptome results. Our results showed that SE resulted in no obvious CA1 neuronal loss, and alterations in the expression pattern of several genes during the early epileptogenic phase were comparable to previous gene expression studies of the adult hippocampus of both experimental epileptic animals and patients with temporal lobe epilepsy (TLE). However, some changes seem to occur after SE specifically in the juvenile rat hippocampus. Insight of the SE-induced alterations in gene expression and their related pathways could give us hints for the development of new target-specific antiepileptic drugs that interfere with the progression of the disease in the juvenile age group

Development And Evaluation Of A Multimodal Marker Of Major Depressive Disorder

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques—penalized logistic regression, random forest, and support vector machine (SVM)—were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses—two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results

An ecological future for weed science to sustain crop production and the environment. A review

Sustainable strategies for managing weeds are critical to meeting agriculture's potential to feed the world's population while conserving the ecosystems and biodiversity on which we depend. The dominant paradigm of weed management in developed countries is currently founded on the two principal tools of herbicides and tillage to remove weeds. However, evidence of negative environmental impacts from both tools is growing, and herbicide resistance is increasingly prevalent. These challenges emerge from a lack of attention to how weeds interact with and are regulated by the agroecosystem as a whole. Novel technological tools proposed for weed control, such as new herbicides, gene editing, and seed destructors, do not address these systemic challenges and thus are unlikely to provide truly sustainable solutions. Combining multiple tools and techniques in an Integrated Weed Management strategy is a step forward, but many integrated strategies still remain overly reliant on too few tools. In contrast, advances in weed ecology are revealing a wealth of options to manage weedsat the agroecosystem levelthat, rather than aiming to eradicate weeds, act to regulate populations to limit their negative impacts while conserving diversity. Here, we review the current state of knowledge in weed ecology and identify how this can be translated into practical weed management. The major points are the following: (1) the diversity and type of crops, management actions and limiting resources can be manipulated to limit weed competitiveness while promoting weed diversity; (2) in contrast to technological tools, ecological approaches to weed management tend to be synergistic with other agroecosystem functions; and (3) there are many existing practices compatible with this approach that could be integrated into current systems, alongside new options to explore. Overall, this review demonstrates that integrating systems-level ecological thinking into agronomic decision-making offers the best route to achieving sustainable weed management