Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Effect of four plant species on soil 15N-access and herbage yield in temporary agricultural grasslands

Positive plant diversity-productivity relationships have been reported for experimental semi-natural grasslands (Cardinale et al. 2006; Hector et al. 1999; Tilman et al. 1996) as well as temporary agricultural grasslands (Frankow-Lindberg et al. 2009; Kirwan et al. 2007; Nyfeler et al. 2009; Picasso et al. 2008). Generally, these relationships are explained, on the one hand, by niche differentiation and facilitation (Hector et al. 2002; Tilman et al. 2002) and, on the other hand, by greater probability of including a highly productive plant species in high diversity plots (Huston 1997). Both explanations accept that diversity is significant because species differ in characteristics, such as root architecture, nutrient acquisition and water use efficiency, to name a few, resulting in composition and diversity being important for improved productivity and resource use (Naeem et al. 1994; Tilman et al. 2002). Plant diversity is generally low in temporary agricultural grasslands grown for ruminant fodder production. Grass in pure stands is common, but requires high nitrogen (N) inputs. In terms of N input, two-species grass-legume mixtures are more sustainable than grass in pure stands and consequently dominate low N input grasslands (Crews and Peoples 2004; Nyfeler et al. 2009; Nyfeler et al. 2011). In temperate grasslands, N is often the limiting factor for productivity (Whitehead 1995). Plant available soil N is generally concentrated in the upper soil layers, but may leach to deeper layers, especially in grasslands that include legumes (Scherer-Lorenzen et al. 2003) and under conditions with surplus precipitation (Thorup-Kristensen 2006). To improve soil N use efficiency in temporary grasslands, we propose the addition of deep-rooting plant species to a mixture of perennial ryegrass and white clover, which are the most widespread forage plant species in temporary grasslands in a temperate climate (Moore 2003). Perennial ryegrass and white clover possess relatively shallow root systems (Kutschera and Lichtenegger 1982; Kutschera and Lichtenegger 1992) with effective rooting depths of <0.7 m on a silt loamy site (Pollock and Mead 2008). Grassland species, such as lucerne and chicory, grow their tap-roots into deep soil layers and exploit soil nutrients and water in soil layers that the commonly grown shallow-rooting grassland species cannot reach (Braun et al. 2010; Skinner 2008). Chicory grown as a catch crop after barley reduced the inorganic soil N down to 2.5 m depth during the growing season, while perennial ryegrass affected the inorganic soil N only down to 1 m depth (Thorup-Kristensen 2006). Further, on a Wakanui silt loam in New Zealand chicory extracted water down to 1.9 m and lucerne down to 2.3 m soil depth, which resulted in greater herbage yields compared with a perennial ryegrass-white clover mixture, especially for dryland plots (Brown et al. 2005). There is little information on both the ability of deep- and shallow-rooting grassland species to access soil N from different vertical soil layers and the relation of soil N-access and herbage yield in temporary agricultural grasslands. Therefore, the objective of the present work was to test the hypotheses 1) that a mixture comprising both shallow- and deep-rooting plant species has greater herbage yields than a shallow-rooting binary mixture and pure stands, 2) that deep-rooting plant species (chicory and lucerne) are superior in accessing soil N from 1.2 m soil depth compared with shallow-rooting plant species, 3) that shallow-rooting plant species (perennial ryegrass and white clover) are superior in accessing soil N from 0.4 m soil depth compared with deep-rooting plant species, 4) that a mixture of deep- and shallow-rooting plant species has greater access to soil N from three soil layers compared with a shallow-rooting two-species mixture and that 5) the leguminous grassland plants, lucerne and white clover, have a strong impact on grassland N acquisition, because of their ability to derive N from the soil and the atmosphere

Why do Particle Clouds Generate Electric Charges?

Grains in desert sandstorms spontaneously generate strong electrical charges; likewise volcanic dust plumes produce spectacular lightning displays. Charged particle clouds also cause devastating explosions in food, drug and coal processing industries. Despite the wide-ranging importance of granular charging in both nature and industry, even the simplest aspects of its causes remain elusive, because it is difficult to understand how inert grains in contact with little more than other inert grains can generate the large charges observed. Here, we present a simple yet predictive explanation for the charging of granular materials in collisional flows. We argue from very basic considerations that charge transfer can be expected in collisions of identical dielectric grains in the presence of an electric field, and we confirm the model's predictions using discrete-element simulations and a tabletop granular experiment

Adipose Tissue Fatty Acid Patterns and Changes in Anthropometry: A Cohort Study

INTRODUCTION: Diets rich in n-3 long chain polyunsaturated fatty acids (LC-PUFA), but low in n-6 LC-PUFA and 18:1 trans-fatty acids (TFA), may lower the risk of overweight and obesity. These fatty acids have often been investigated individually. We explored associations between global patterns in adipose tissue fatty acids and changes in anthropometry. METHODS: 34 fatty acid species from adipose tissue biopsies were determined in a random sample of 1100 men and women from a Danish cohort study. We used sex-specific principal component analysis and multiple linear regression to investigate the associations of adipose tissue fatty acid patterns with changes in weight, waist circumference (WC), and WC controlled for changes in body mass index (WC(BMI)), adjusting for confounders. RESULTS: 7 principal components were extracted for each sex, explaining 77.6% and 78.3% of fatty acid variation in men and women, respectively. Fatty acid patterns with high levels of TFA tended to be positively associated with changes in weight and WC for both sexes. Patterns with high levels of n-6 LC-PUFA tended to be negatively associated with changes in weight and WC in men, and positively associated in women. Associations with patterns with high levels of n-3 LC-PUFA were dependent on the context of the rest of the fatty acid pattern. CONCLUSIONS: Adipose tissue fatty acid patterns with high levels of TFA may be linked to weight gain, but patterns with high n-3 LC-PUFA did not appear to be linked to weight loss. Associations depended on characteristics of the rest of the pattern

Effects of semaglutide on stroke subtypes in type 2 diabetes: post hoc analysis of the randomised SUSTAIN 6 & PIONEER 6

This is the final version. Available on open access from Lippincott, Williams & Wilkins via the DOI in this record Background: Glucagon-like peptide-1 receptor agonists, including semaglutide, may reduce stroke risk in people with type 2 diabetes (T2D). This post hoc analysis examined the subcutaneous and oral semaglutide effects, versus placebo, on stroke and its subtypes in people with T2D at high cardiovascular (CV) risk. Methods: SUSTAIN 6 and PIONEER 6 were randomised CV outcome trials of subcutaneous and oral semaglutide in people with T2D at high CV risk, respectively. Time to first stroke and stroke subtypes were analysed using a Cox proportional hazards model stratified by trial with pooled treatment as a factor. The impact of prior stroke, prior myocardial infarction or stroke, age, sex, systolic blood pressure, estimated glomerular filtration rate, and prior atrial fibrillation on treatment effects was assessed using interaction p-values. Risk of major adverse CV event (MACE) was analysed according to prior stroke. Results: 106/6480 participants had a stroke (1.0 event/100 patient-years of observation [PYO]). Semaglutide reduced incidence of any stroke versus placebo (0.8 vs 1.1 events/100 PYO; HR 0.68, 95%CI 0.46–1.00;p=0.048), driven by significant reductions in risk of small-vessel occlusion (0.3 vs 0.7 events/100 PYO; HR 0.51, 95%CI 0.29–0.89;p=0.017). HRs for risk of any stroke with semaglutide versus placebo were 0.60 (95%CI 0.37–0.99; 0.5 vs 0.9 events/100 PYO) and 0.89 (95%CI 0.47–1.69; 2.7 vs 3.0 events/100 PYO) in those without and with prior stroke, respectively. Except for prior atrial fibrillation (pinteraction=0.025), no significant interactions were observed between treatment effects on risk of any stroke and subgroups investigated, or between treatment effects on risk of MACE and prior stroke (pinteraction>0.05 for all). Conclusions: Semaglutide reduced incidence of any first stroke during the trials versus placebo in people with T2D at high CV risk, primarily driven by small-vessel occlusion prevention. Semaglutide treatment, versus placebo, lowered the risk of stroke irrespective of prior stroke at baseline. Clinical Trial Registration Information: SUSTAIN 6: NCT01720446 (https://clinicaltrials.gov/ct2/show/NCT01720446); PIONEER 6: NCT02692716 (https://clinicaltrials.gov/ct2/show/NCT02692716).Novo Nordisk A/S (Søborg, Denmark

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Spontaneous and deliberate future thinking: A dual process account

© 2019 Springer Nature.This is the final published version of an article published in Psychological Research, licensed under a Creative Commons Attri-bution 4.0 International License. Available online at: https://doi.org/10.1007/s00426-019-01262-7.In this article, we address an apparent paradox in the literature on mental time travel and mind-wandering: How is it possible that future thinking is both constructive, yet often experienced as occurring spontaneously? We identify and describe two ‘routes’ whereby episodic future thoughts are brought to consciousness, with each of the ‘routes’ being associated with separable cognitive processes and functions. Voluntary future thinking relies on controlled, deliberate and slow cognitive processing. The other, termed involuntary or spontaneous future thinking, relies on automatic processes that allows ‘fully-fledged’ episodic future thoughts to freely come to mind, often triggered by internal or external cues. To unravel the paradox, we propose that the majority of spontaneous future thoughts are ‘pre-made’ (i.e., each spontaneous future thought is a re-iteration of a previously constructed future event), and therefore based on simple, well-understood, memory processes. We also propose that the pre-made hypothesis explains why spontaneous future thoughts occur rapidly, are similar to involuntary memories, and predominantly about upcoming tasks and goals. We also raise the possibility that spontaneous future thinking is the default mode of imagining the future. This dual process approach complements and extends standard theoretical approaches that emphasise constructive simulation, and outlines novel opportunities for researchers examining voluntary and spontaneous forms of future thinking.Peer reviewe

Classification and clinical features of headache patients: an outpatient clinic study from China

This study aimed to analyze and classify the clinical features of headache in neurological outpatients. A cross-sectional study was conducted consecutively from March to May 2010 for headache among general neurological outpatients attending the First Affiliated Hospital of Chongqing Medical University. Personal interviews were carried out and a questionnaire was used to collect medical records. Diagnosis of headache was according to the International classification of headache disorders, 2nd edition (ICHD-II). Headache patients accounted for 19.5% of the general neurology clinic outpatients. A total of 843 (50.1%) patients were defined as having primary headache, 454 (27%) secondary headache, and 386 (23%) headache not otherwise specified (headache NOS). For primary headache, 401 (23.8%) had migraine, 399 (23.7%) tension-type headache (TTH), 8 (0.5%) cluster headache and 35 (2.1%) other headache types. Overall, migraine patients suffered (1) more severe headache intensity, (2) longer than 6 years of headache history and (3) more common analgesic medications use than TTH ones (p < 0.001).TTH patients had more frequent episodes of headaches than migraine patients, and typically headache frequency exceeded 15 days/month (p < 0.001); 22.8% of primary headache patients were defined as chronic daily headache. Almost 20% of outpatient visits to the general neurology department were of headache patients, predominantly primary headache of migraine and TTH. In outpatient headaches, more attention should be given to headache intensity and duration of headache history for migraine patients, while more attention to headache frequency should be given for the TTH ones

Phosphorylation and Activation of the Plasma Membrane Na+/H+ Exchanger (NHE1) during Osmotic Cell Shrinkage

The Na+/H+ Exchanger isoform 1 (NHE1) is a highly versatile, broadly distributed and precisely controlled transport protein that mediates volume and pH regulation in most cell types. NHE1 phosphorylation contributes to Na+/H+ exchange activity in response to phorbol esters, growth factors or protein phosphatase inhibitors, but has not been observed during activation by osmotic cell shrinkage (OCS). We examined the role of NHE1 phosphorylation during activation by OCS, using an ideal model system, the Amphiuma tridactylum red blood cell (atRBC). Na+/H+ exchange in atRBCs is mediated by an NHE1 homolog (atNHE1) that is 79% identical to human NHE1 at the amino acid level. NHE1 activity in atRBCs is exceptionally robust in that transport activity can increase more than 2 orders of magnitude from rest to full activation. Michaelis-Menten transport kinetics indicates that either OCS or treatment with the phosphatase inhibitor calyculin-A (CLA) increase Na+ transport capacity without affecting transport affinity (Km = 44 mM) in atRBCs. CLA and OCS act non-additively to activate atNHE1, indicating convergent, phosphorylation-dependent signaling in atNHE1 activation. In situ 32P labeling and immunoprecipitation demonstrates that the net phosphorylation of atNHE1 is increased 4-fold during OCS coinciding with a more than 2-order increase in Na+ transport activity. This is the first reported evidence of increased NHE1 phosphorylation during OCS in any vertebrate cell type. Finally, liquid chromatography and mass spectrometry (LC-MS/MS) analysis of atNHE1 immunoprecipitated from atRBC membranes reveals 9 phosphorylated serine/threonine residues, suggesting that activation of atNHE1 involves multiple phosphorylation and/or dephosphorylation events