PNAS plus: plasmodium falciparum responds to amino acid starvation by entering into a hibernatory state

The human malaria parasite Plasmodium falciparum is auxotrophic for most amino acids. Its amino acid needs are met largely through the degradation of host erythrocyte hemoglobin; however the parasite must acquire isoleucine exogenously, because this amino acid is not present in adult human hemoglobin. We report that when isoleucine is withdrawn from the culture medium of intraerythrocytic P. falciparum, the parasite slows its metabolism and progresses through its developmental cycle at a reduced rate. Isoleucine-starved parasites remain viable for 72 h and resume rapid growth upon resupplementation. Protein degradation during starvation is important for maintenance of this hibernatory state. Microarray analysis of starved parasites revealed a 60% decrease in the rate of progression through the normal transcriptional program but no other apparent stress response. Plasmodium parasites do not possess a TOR nutrient-sensing pathway and have only a rudimentary amino acid starvation-sensing eukaryotic initiation factor 2α (eIF2α) stress response. Isoleucine deprivation results in GCN2-mediated phosphorylation of eIF2α, but kinase-knockout clones still are able to hibernate and recover, indicating that this pathway does not directly promote survival during isoleucine starvation. We conclude that P. falciparum, in the absence of canonical eukaryotic nutrient stress-response pathways, can cope with an inconsistent bloodstream amino acid supply by hibernating and waiting for more nutrient to be provided

Chaotic oscillations in a map-based model of neural activity

We propose a discrete time dynamical system (a map) as phenomenological model of excitable and spiking-bursting neurons. The model is a discontinuous two-dimensional map. We find condition under which this map has an invariant region on the phase plane, containing chaotic attractor. This attractor creates chaotic spiking-bursting oscillations of the model. We also show various regimes of other neural activities (subthreshold oscillations, phasic spiking etc.) derived from the proposed model

Capgras Syndrome and Unilateral Spatial Neglect in Nonconvulsive Status Epilepticus

Nonconvulsive status epilepticus can manifest as personality changes and psychosis. We report an 87-year-old right-handed male presenting with both Capgras syndrome and severe unilateral spatial neglect during nonconvulsive status epilepticus. After treatment of his seizures, his Capgras syndrome and hemispatial neglect resolved. This case illustrates a report of the confluence of Capgras syndrome and documented hemispatial neglect in nonconvulsive status epilepticus only reported once previously [1]

Cross-continental emergence of Nannizziopsis barbatae disease may threaten wild Australian lizards

Members of the genus Nannizziopsis are emerging fungal pathogens of reptiles that have been documented as the cause of fatal mycoses in a wide range of reptiles in captivity. Cases of severe, proliferative dermatitis, debility and death have been detected in multiple free-living lizard species from locations across Australia, including a substantial outbreak among Eastern water dragons (Intellagama lesueurii) in Brisbane, Queensland. We investigated this disease in a subset of severely affected lizards and identified a clinically consistent syndrome characterized by hyperkeratosis, epidermal hyperplasia, dermal inflammation, necrosis, ulceration, and emaciation. Using a novel fungal isolation method, histopathology, and molecular techniques, we identified the etiologic agent as Nannizziopsis barbatae, a species reported only once previously from captive lizards in Australia. Here we report severe dermatomycosis caused by N. barbatae in five species of Australian lizard, representing the first cases of Nannizziopsis infection among free-living reptiles, globally. Further, we evaluate key pathogen and host characteristics that indicate N. barbatae-associated dermatomycosis may pose a concerning threat to Australian lizards

3',5'-Cyclic Adenosine Monophosphate- and Ca2+-Calmodulin-Dependent Endogenous Protein Phosphorylation Activity in Membranes of the Bovine Chromaffin Secretory Vesicles: Identification of Two Phosphorylated Components as Tyrosine Hydroxylase and Protein Kinase Regulatory Subunit Type II

Abstract: Membranes of the secretory vesicles from bovine adrenal medulla were investigated for the presence of the endogenous protein phosphorylation activity. Seven phosphoprotein bands in the molecular weight range of 250,000 to 30,000 were observed by means of the sodium dodecyl sulphate electrophoresis and autoradiography. On the basis of the criteria of molecular weight, selective stimulation of the phosphorylation by cyclic AMP (as compared with cyclic GMP) and immunoprecipitation by specific antibodies, band 5 (molecular weight 60,300) was found to represent the phosphorylated form of the secretory vesicle-bound tyrosine hydroxylase. The electrophoretic mobility, the stimulatory and inhibitory effects of cyclic AMP in presence of Mg2+ and Zn,2+ respectively, and immunoreactivity toward antibodies showed band 6 to contain two forms of the regulatory subunits of the type II cyclic AMP-dependent protein kinase, distinguishable by their molecular weights (56,000 and 52,000, respectively). Phosphorylation of band 7 (molecular weight 29,800) was stimulated about 2 to 3 times by Ca2+ and calmodulin in the concentration range of both agents believed to occur in the secretory tissues under physiological conditions

Implementing Dempster-Shafer Theory for property similarity in Conceptual Spaces modeling

Previous work has shown that the Complex Conceptual Spaces − Single Observation Mathematical framework is a useful tool for event characterization. This mathematical framework is developed on the basis of Conceptual Spaces and uses integer linear programming to find the needed similarity values. The work of this paper is focused primarily on space event characterization. In particular, the focus is on the ranking of threats for malicious space events such as a kinetic kill. To make the Conceptual Spaces framework work, the similarity values between the contents of observations on the one hand and the properties of the entities observed on the other needs to be found. This paper shows how to exploit Dempster-Shafer theory to implement a statistical approach for finding these similarities values. This approach will allow a user to identify the uncertainty involved in similarity value data, which can later be propagated through the developed mathematical model in order for the user to know the overall uncertainty in the observation-to-concept mappings needed for space event characterization

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets

The Case for Dynamic Models of Learners' Ontologies in Physics

In a series of well-known papers, Chi and Slotta (Chi, 1992; Chi & Slotta, 1993; Chi, Slotta & de Leeuw, 1994; Slotta, Chi & Joram, 1995; Chi, 2005; Slotta & Chi, 2006) have contended that a reason for students' difficulties in learning physics is that they think about concepts as things rather than as processes, and that there is a significant barrier between these two ontological categories. We contest this view, arguing that expert and novice reasoning often and productively traverses ontological categories. We cite examples from everyday, classroom, and professional contexts to illustrate this. We agree with Chi and Slotta that instruction should attend to learners' ontologies; but we find these ontologies are better understood as dynamic and context-dependent, rather than as static constraints. To promote one ontological description in physics instruction, as suggested by Slotta and Chi, could undermine novices' access to productive cognitive resources they bring to their studies and inhibit their transition to the dynamic ontological flexibility required of experts.Comment: The Journal of the Learning Sciences (In Press

An Introduction to Hard and Soft Data Fusion via Conceptual Spaces Modeling for Space Event Characterization

This paper describes an AFOSR-supported basic research program that focuses on developing a new framework for combining hard with soft data in order to improve space situational awareness. The goal is to provide, in an automatic and near real-time fashion, a ranking of possible threats to blue assets (assets trying to be protected) from red assets (assets with hostile intentions). The approach is based on Conceptual Spaces models, which combine features from traditional associative and symbolic cognitive models. While Conceptual Spaces are revolutionary, they lack an underlying mathematical framework. Several such frameworks have attempted to represent Conceptual Spaces, but by far the most robust is the model developed by Holender. His model utilizes integer linear programming in order to obtain an overall similarity value between observations and concepts that support the formation of hypotheses. This paper will describe a method for building Conceptual Spaces models for threats that utilizes ontologies as a means to provide a clear semantic foundation for this inferencing process; in particular threat ontologies and space domain ontologies are developed and employed in this approach. A space situational awareness use-case is presented involving a kinetic kill scenario and results are shown to assess the performance of this fusion-based inferencing framework