Identification of Candida glabrata genes involved in pH modulation and modification of the phagosomal environment in macrophages

notes: PMCID: PMC4006850types: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov'tCandida glabrata currently ranks as the second most frequent cause of invasive candidiasis. Our previous work has shown that C. glabrata is adapted to intracellular survival in macrophages and replicates within non-acidified late endosomal-stage phagosomes. In contrast, heat killed yeasts are found in acidified matured phagosomes. In the present study, we aimed at elucidating the processes leading to inhibition of phagosome acidification and maturation. We show that phagosomes containing viable C. glabrata cells do not fuse with pre-labeled lysosomes and possess low phagosomal hydrolase activity. Inhibition of acidification occurs independent of macrophage type (human/murine), differentiation (M1-/M2-type) or activation status (vitamin D3 stimulation). We observed no differential activation of macrophage MAPK or NFκB signaling cascades downstream of pattern recognition receptors after internalization of viable compared to heat killed yeasts, but Syk activation decayed faster in macrophages containing viable yeasts. Thus, delivery of viable yeasts to non-matured phagosomes is likely not triggered by initial recognition events via MAPK or NFκB signaling, but Syk activation may be involved. Although V-ATPase is abundant in C. glabrata phagosomes, the influence of this proton pump on intracellular survival is low since blocking V-ATPase activity with bafilomycin A1 has no influence on fungal viability. Active pH modulation is one possible fungal strategy to change phagosome pH. In fact, C. glabrata is able to alkalinize its extracellular environment, when growing on amino acids as the sole carbon source in vitro. By screening a C. glabrata mutant library we identified genes important for environmental alkalinization that were further tested for their impact on phagosome pH. We found that the lack of fungal mannosyltransferases resulted in severely reduced alkalinization in vitro and in the delivery of C. glabrata to acidified phagosomes. Therefore, protein mannosylation may play a key role in alterations of phagosomal properties caused by C. glabrata.Deutsche ForschungsgemeinschaftNational Institutes for HealthWellcome TrustBBSR

BIBLIOGRAPHIC PAPER REGARDING MONITORING OF SOIL EROSION USING GEOGRAPHIC INFORMATION SYSTEMS TECHNOLOGY

One of the most dangerous forms of ecological imbalance is the problem of soil erosion with large economic effects and impact on the environment. For this reason particular importance should be given to monitoring a territory, monitoring the main purpose best estimate of the possibility of erosion processes and soil loss calculation. This can be achieved by using Geographic Information Systems (GIS) that allows the creation of models on the occurrence of erosion processes, estimation of results by continuously changing the input parameters easy, storage and spatial-temporal data analysis and evaluation. Monitoring and evaluation processes of soil erosion is of particular importance as this may be established ways of preventing and combating erosion processes to eliminate economic, environmental and bringing these lands through the improvement works aside. Following the erosion occurs as a series of consequences: diminishing agricultural production, disrupting the ecological balance, fouling ponds and reservoirs, changing water pathways, changing land to be turned into productive lands, increasing flood risks etc

The Upper Campanian - lower Maastrichtian cephalopod fauna of Botellos, Nuevo León: a key to understand faunal turnover across the Campanian-Maastrichtian boundary in NE Mexico

A new cephalopod collection from the Campanian-Maastrichtian boundary interval of NE Mexico, consisting of 1076 individuals assigned to 29 species and 22 genera is presented. This collection is a mix of ammonoids, one coleoid and one nautilid, which originate from at least three ammonoid biozones: The upper Campanian Exiteloceras jenneyi and Nostoceras (Nostoceras) hyatti zones, and the lower Maastrichtian Pachydiscus (Pachydiscus) neubergicus Zone. The age of the collection is thus middle late Campanian to late early Maastrichtian, and it closes a stratigraphic gap between faunas described formerly from this region. The specimens are nuclei collected from the desert pavement. The abundance of specimens allows for a comparison to other Campanian-Maastrichtian ammonoid records from Mexico, North America and Europe

Demand Forecasting in the Presence of Privileged Information

Predicting the amount of sales in the future is a fundamental problem in the replenishment process of retail companies. Models for forecasting the demand of an item typically rely on influential features and historical sales of the item. However, the values of some influential features (to which we refer as non-plannable features) are only known during model training (for the past), and not for the future at prediction time. Examples of such features include sales in other channels, such as other stores in chain supermarkets. Existing forecasting methods ignore such non-plannable features or wrongly assume that they are also known at prediction time. We identify non-plannable features as privileged information, i.e., information that is available at training time but not at prediction time, and design a neural network to leverage this source of data accordingly. We present a dual branch neural network architecture that incorporates non-plannable features at training time, with a first branch to embed the historical information, and a second branch, the privileged information (PI) branch, to predict demand based on privileged information. Next, we leverage a single branch network at prediction time, which applies a simulation component to mimic the behavior of the PI branch, whose inputs are not available at prediction time. We evaluate our approach on two real-world forecasting datasets, and find that it outperforms state-of-the-art competitors in terms of mean absolute error and symmetric mean absolute percentage error metrics. We further provide visualizations and conduct experiments to validate the contribution of different components in our proposed architecture

Temporal Exceptional Model Mining Using Dynamic Bayesian Networks

The discovery of subsets of data that are characterized by models that differ significantly from the entire dataset, is the goal of exceptional model mining. With the increasing availability of temporal data, this task has clear relevance in discovering deviating temporal subprocesses that can bring insight into industrial processes, medical treatments, etc. As temporal data is often noisy, high-dimensional and has complex statistical dependencies, discovering such temporal subprocesses is challenging for current exceptional model mining methods. In this paper, we introduce Temporal Exceptional Model Mining to capture multiple and complex relationships among temporal variables of a dataset in a principled way. Our contributions are as follows: (i) we define the new task of temporal exceptional model mining; (ii) we characterize the discovery of exceptional temporal submodels using dynamic Bayesian networks by means of a new distance measure, (iii) we introduce a search procedure for exceptional dynamic Bayesian networks optimized by properties of the proposed distance, and (iv) the practical value of the proposed method is demonstrated based on simulated data and process data of funding applications and by comparisons with other exceptional model mining methods