A Single Deformed Bow Shock for Titan-Saturn System

During periods of high solar wind pressure, Saturn's bow shock is pushed inside Titan's orbit exposing the moon and its ionosphere to the solar wind. The Cassini spacecraft's T96 encounter with Titan occurred during such a period and showed evidence for shocks associated with Saturn and Titan. It also revealed the presence of two foreshocks: one prior to the closest approach (foreshock 1) and one after (foreshock 2). Using electromagnetic hybrid (kinetic ions and fluid electrons) simulations and Cassini observations, we show that the origin of foreshock 1 is tied to the formation of a single deformed bow shock for the Titan‐Saturn system. We also report the observations of a structure in foreshock 1 with properties consistent with those of spontaneous hot flow anomalies formed in the simulations and previously observed at Earth, Venus, and Mars. The results of hybrid simulations also show the generation of oblique fast magnetosonic waves upstream of the outbound Titan bow shock in agreement with the observations of large‐amplitude magnetosonic pulsations in foreshock 2. We also discuss the implications of a single deformed bow shock for new particle acceleration mechanisms and also Saturn's magnetopause and magnetosphere

Probabilistic reconstruction of the tumor progression process in gene regulatory networks in the presence of uncertainty

<p>Abstract</p> <p>Background</p> <p>Accumulation of gene mutations in cells is known to be responsible for tumor progression, driving it from benign states to malignant states. However, previous studies have shown that the detailed sequence of gene mutations, or the steps in tumor progression, may vary from tumor to tumor, making it difficult to infer the exact path that a given type of tumor may have taken.</p> <p>Results</p> <p>In this paper, we propose an effective probabilistic algorithm for reconstructing the tumor progression process based on partial knowledge of the underlying gene regulatory network and the steady state distribution of the gene expression values in a given tumor. We take the BNp (Boolean networks with pertubation) framework to model the gene regulatory networks. We assume that the true network is not exactly known but we are given an uncertainty class of networks that contains the true network. This network uncertainty class arises from our partial knowledge of the true network, typically represented as a set of local pathways that are embedded in the global network. Given the SSD of the cancerous network, we aim to simultaneously identify the true normal (healthy) network and the set of gene mutations that drove the network into the cancerous state. This is achieved by analyzing the effect of gene mutation on the SSD of a gene regulatory network. At each step, the proposed algorithm reduces the uncertainty class by keeping only those networks whose SSDs get close enough to the cancerous SSD as a result of additional gene mutation. These steps are repeated until we can find the best candidate for the true network and the most probable path of tumor progression.</p> <p>Conclusions</p> <p>Simulation results based on both synthetic networks and networks constructed from actual pathway knowledge show that the proposed algorithm can identify the normal network and the actual path of tumor progression with high probability. The algorithm is also robust to model mismatch and allows us to control the trade-off between efficiency and accuracy.</p

Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks

Background: A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference; however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric; hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters. Results: It was observed that beyond a certain number of time-points (i.e., a specific size) of micro-array data, the performance of the algorithms measured in terms of the recall-to-precision ratio saturated due to the saturation in the calculated pair-wise MI metric with increasing data size. The proposed algorithms were compared to existing approaches on four different biological networks. The resulting networks were evaluated based on the benchmark precision and recall metrics and the results favour our approach. Conclusions: To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes

Pathway-based analyses of gene expression profiles at low doses of ionizing radiation

Radiation exposure poses a significant threat to human health. Emerging research indicates that even low-dose radiation once believed to be safe, may have harmful effects. This perception has spurred a growing interest in investigating the potential risks associated with low-dose radiation exposure across various scenarios. To comprehensively explore the health consequences of low-dose radiation, our study employs a robust statistical framework that examines whether specific groups of genes, belonging to known pathways, exhibit coordinated expression patterns that align with the radiation levels. Notably, our findings reveal the existence of intricate yet consistent signatures that reflect the molecular response to radiation exposure, distinguishing between low-dose and high-dose radiation. Moreover, we leverage a pathway-constrained variational autoencoder to capture the nonlinear interactions within gene expression data. By comparing these two analytical approaches, our study aims to gain valuable insights into the impact of low-dose radiation on gene expression patterns, identify pathways that are differentially affected, and harness the potential of machine learning to uncover hidden activity within biological networks. This comparative analysis contributes to a deeper understanding of the molecular consequences of low-dose radiation exposure

2013 Wild Blueberry Project Reports

The 2013 edition of the Wild Blueberry Project Reports was prepared for the Wild Blueberry Commission of Maine and the Wild Blueberry Advisory Committee by researchers at the University of Maine, Orono. Projects in this report include: 1. Development of effective intervention measures to maintain and improve food safety for wild blueberries 2. Do wild blueberries alleviate risk factors related to the Metabolic Syndrome? 3. Wild Blueberry consumption and exercise-induced Oxidative Stress: Inflammatory Response and DNA damage 4. Control tactics for blueberry pest insects, 2013 5. Pesticide residues on wild blueberry, 2013 6. Biology of pest insects and IPM, 2013 7. Biology of blueberry, beneficial insects, and blueberry pollination 8. Biology of spotted wing drosophila, 2013 9. Maine wild blueberry –mummy berry research and extension 10. Evaluation of fungicides for control of mummy berry on lowbush blueberry (2013) 11. Wild blueberry Extension Education Program in 2013 INPUT SYSTEMS STUDY: 12. Systems approach to improving the sustainability of wild blueberry production, Year Four of a four-year study – experimental design 13. Food safety- Prevalence study of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella spp. on lowbush blueberries (Vaccinium angustifolium) 14. Agronomic input effects on sensory quality and chemical composition of wild Maine blueberries 15. Systems approach to improving the sustainability of wild blueberry production, Year four of a four-year study – reports from Frank Drummond 16. Systems approach to improving the sustainability of wild blueberry production, Year 4 of a four-year study, disease management results 17. Systems approach to improving the sustainability of wild blueberry production, Year Four of a four-year study, weed management results 18. Phosphorus and organic matter interactions on short-range ordered minerals in acidic barren soils 19. Systems approach to improving the sustainability of wild blueberry production, preliminary economic comparison for 2012-13 20. Ancillary projects in disease research (ancillary study) 21. Systems approach to improving the sustainability of wild blueberry production – Ancillary land-leveling study, Year Three of a four-year study (ancillary study) 22. Pre-emergent combinations of herbicides for weed control in wild blueberry fields – 2013 results from the 2012 trial (ancillary study) 23. Evaluation of herbicides for 2012 prune year control of fineleaf sheep fescue in wild blueberries – 2013 crop year results (ancillary study) 24. 2012 pre-emergence application timing and rate of Alion and Sandea in combination with Velpar or Sinbar – 2013 yields (ancillary study) 25. Pre-emergence Sinbar combinations for weed control in a non-crop wild blueberry field – 2012-2014 (ancillary study) 26. Evaluation of three pre-emergence herbicides alone and in combination with Velpar or Sinbar for effects on wild blueberry productivity and weed control (ancillary study) 27. Post-harvest control of red sorrel in a non-crop blueberry field, 2012-2014 (ancillary study) 28. Compost and mulch effects on soil health and nutrient dynamics in wild blueberry (ancillary study) 29. Evaluation of conventional and organic fertilizers on blueberry growth and yield (ancillary study