Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients.

Cardiovascular magnetic resonance (CMR) is an important cardiac imaging tool for assessing the prognostic extent of myocardial injury after myocardial infarction (MI). Within the context of clinical trials, CMR is also useful for assessing the efficacy of potential cardioprotective therapies in reducing MI size and preventing adverse left ventricular (LV) remodelling in reperfused MI. However, manual contouring and analysis can be time-consuming with interobserver and intraobserver variability, which can in turn lead to reduction in accuracy and precision of analysis. There is thus a need to automate CMR scan analysis in MI patients to save time, increase accuracy, increase reproducibility and increase precision. In this regard, automated imaging analysis techniques based on artificial intelligence (AI) that are developed with machine learning (ML), and more specifically deep learning (DL) strategies, can enable efficient, robust, accurate and clinician-friendly tools to be built so as to try and improve both clinician productivity and quality of patient care. In this review, we discuss basic concepts of ML in CMR, important prognostic CMR imaging biomarkers in MI and the utility of current ML applications in their analysis as assessed in research studies. We highlight potential barriers to the mainstream implementation of these automated strategies and discuss related governance and quality control issues. Lastly, we discuss the future role of ML applications in clinical trials and the need for global collaboration in growing this field

Phylogenetic origins and family classification of typhuloid fungi, with emphasis on Ceratellopsis, Macrotyphula and Typhula (Basidiomycota)

Typhuloid fungi are a very poorly known group of tiny clavarioid homobasidiomycetes. The phylogenetic position and family classification of the genera targeted here, Ceratellopsis, Macrotyphula, Pterula sensu lato and Typhula, are controversial and based on unresolved phylogenies. Our six-gene phylogeny with an expanded taxon sampling shows that typhuloid fungi evolved at least twice in the Agaricales (Pleurotineae, Clavariineae) and once in the Hymenochaetales. Macrotyphula, Pterulicium and Typhula are nested within the Pleurotineae. The type of Typhula (1818) and Sclerotium (1790), T. phacorrhiza and S. complanatum (synonym T. phacorrhiza), are encompassed in the Macrotyphula clade that is distantly related to a monophyletic group formed by species usually assigned to Typhula. Thus, the correct name for Macrotyphula (1972) and Typhula is Sclerotium and all Typhula species but those in the T. phacorrhiza group need to be transferred to Pistillaria (1821). To avoid undesirable nomenclatural changes, we suggest to conserve Typhula with T. incarnata as type. Clavariaceae is supported as a separate, early diverging lineage within Agaricales, with Hygrophoraceae as a successive sister taxon to the rest of the Agaricales. Ceratellopsis s. auct. is polyphyletic because C. acuminata nests in Clavariaceae and C. sagittiformis in the Hymenochaetales. Ceratellopsis is found to be an earlier name for Pterulicium, because the type, C. queletii, represents Pterulicium gracile (synonym Pterula gracilis), deeply nested in the Pterulicium clade. To avoid re-combining a large number of names in Ceratellopsis we suggest to conserve it with C. acuminata as type. The new genus Bryopistillaria is created to include C. sagittiformis. The families Sarcomyxaceae and Phyllotopsidaceae, and the suborder Clavariineae, are described as new. Six new combinations are proposed and 15 names typified

Learning features for tissue classification with the classification restricted Boltzmann machine

__Abstract__ Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convolutional classification RBM, a combination of the existing convolutional RBM and classification RBM, and use it for discriminative feature learning. We evaluate the classification accuracy of convolutional and non-convolutional classification RBMs on two lung CT problems. We find that RBM-learned features outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy

The influence of soil communities on the temperature sensitivity of soil respiration

Soil respiration represents a major carbon flux between terrestrial ecosystems and the atmosphere, and is expected to accelerate under climate warming. Despite its importance in climate change forecasts, however, our understanding of the effects of temperature on soil respiration (RS) is incomplete. Using a metabolic ecology approach we link soil biota metabolism, community composition and heterotrophic activity, to predict RS rates across five biomes. We find that accounting for the ecological mechanisms underpinning decomposition processes predicts climatological RS variations observed in an independent dataset (n = 312). The importance of community composition is evident because without it RS is substantially underestimated. With increasing temperature, we predict a latitudinal increase in RS temperature sensitivity, with Q10 values ranging between 2.33 ±0.01 in tropical forests to 2.72 ±0.03 in tundra. This global trend has been widely observed, but has not previously been linked to soil communities

Contribution of Cystine-Glutamate Antiporters to the Psychotomimetic Effects of Phencyclidine

Altered glutamate signaling contributes to a myriad of neural disorders, including schizophrenia. While synaptic levels are intensely studied, nonvesicular release mechanisms, including cystine–glutamate exchange, maintain high steady-state glutamate levels in the extrasynaptic space. The existence of extrasynaptic receptors, including metabotropic group II glutamate receptors (mGluR), pose nonvesicular release mechanisms as unrecognized targets capable of contributing to pathological glutamate signaling. We tested the hypothesis that activation of cystine–glutamate antiporters using the cysteine prodrug N-acetylcysteine would blunt psychotomimetic effects in the rodent phencyclidine (PCP) model of schizophrenia. First, we demonstrate that PCP elevates extracellular glutamate in the prefrontal cortex, an effect that is blocked by N-acetylcysteine pretreatment. To determine the relevance of the above finding, we assessed social interaction and found that N-acetylcysteine reverses social withdrawal produced by repeated PCP. In a separate paradigm, acute PCP resulted in working memory deficits assessed using a discrete trial t-maze task, and this effect was also reversed by N-acetylcysteine pretreatment. The capacity of N-acetylcysteine to restore working memory was blocked by infusion of the cystine–glutamate antiporter inhibitor (S)-4-carboxyphenylglycine into the prefrontal cortex or systemic administration of the group II mGluR antagonist LY341495 indicating that the effects of N-acetylcysteine requires cystine–glutamate exchange and group II mGluR activation. Finally, protein levels from postmortem tissue obtained from schizophrenic patients revealed significant changes in the level of xCT, the active subunit for cystine–glutamate exchange, in the dorsolateral prefrontal cortex. These data advance cystine–glutamate antiporters as novel targets capable of reversing the psychotomimetic effects of PCP

AdS5AdS_{5} black hole at N=2 supergravity

In this paper, we consider the charged non-extremal black hole at five dimensional N = 2 supergravity. We study thermodynamics of AdS_{5} black hole with three equal charges (q_{1} = q_{2} = q_{3} = q). We obtain Schrodinger like equation and discuss the effective potential. Then, we consider the case of the perturbed dilaton field background and find presence of odd coefficients of the wave function. Also we find that the higher derivative corrections have no effect on the first and second even coefficients of the wave function.Comment: 17 pages, 4 figures. Published versio

Mind-body interventions for vasomotor symptoms in healthy menopausal women and breast cancer survivors. A systematic review

Mind–body therapies are commonly recommended to treat vasomotor symptoms, such as hot flushes and night sweats (HFNS). The purpose of this systematic review was to evaluate the available evidence to date for the efficacy of different mind–body therapies to alleviate HFNS in healthy menopausal women and breast cancer survivors. Randomized controlled trials (RCTs) were identified using seven electronic search engines, direct searches of specific journals and backwards searches through reference lists of related publications. Outcome measures included HFNS frequency and/or severity or self-reported problem rating at post-treatment. The methodological quality of all studies was systematically assessed using predefined criteria. Twenty-six RCTs met the inclusion criteria. Interventions included yoga (n = 5), hypnosis (n = 3), mindfulness (n = 2), relaxation (n = 7), paced breathing (n = 4), reflexology (n = 1) and cognitive behavioural therapy (CBT) (n = 4). Findings were consistent for the effectiveness of CBT and relaxation therapies for alleviating troublesome vasomotor symptoms. For the remaining interventions, although some trials indicated beneficial effects (within groups) at post-treatment and/or follow up, between group findings were mixed and overall, methodological differences across studies failed to provide convincing supporting evidence. Collectively, findings suggest that interventions that include breathing and relaxation techniques, as well as CBT, can be beneficial for alleviating vasomotor symptoms. Additional large, methodologically rigorous trials are needed to establish the efficacy of interventions on vasomotor symptoms, examine long-term outcomes and understand how they work

Effects of Chitin and Contact Insecticide Complexes on Rove Beetles in Commercial Orchards

A five-year research project was performed to explore the potential effects of contact insecticide applications on the change of abundance and species richness of predatory rove beetles (Coleoptera: Staphylinidae) in conventionally managed orchards. Twelve blocks of nine orchards were used for this study in Central Europe. High sensitivity atomic force microscopic examination was carried out for chitin structure analyses as well as computer simulation for steric energy calculation between insecticides and chitin. The species richness of rove beetles in orchards was relatively high after insecticide application. Comparing the mean abundance before and after insecticide application, a higher value was observed before spraying with alphacypermethrin and lambda-cyhalothrin, and a lower value was observed in the cases of diflubenzuron, malathion, lufenuron, and phosalone. The species richness was higher only before chlorpyrifos-methyl application. There was a negative correlation between abundance and stability value of chitin-insecticides, persistence time, and soil absorption coefficients. Positive correlation was observed with lipo- and water solubility