The Maternal Personhood of Cattle and Plants at a Hindu Center in the United States

Religious experiences with sacred nonhuman natural beings considered to be “persons” remain only vaguely understood. This essay provides a measure of clarification by engendering a dialogue between psychoanalytic self psychology on one side and, on the other, religious experiences of cattle and Tulsi plants as holy mothers at a Hindu cattle sanctuary in the United States. Ethnographic data from the Hindu center uncover experiences of sacred maternal natural beings that are tensive, liminal, and colored with affective themes of nurturance, respect, and intimacy, much like psychoanalytic maternal selfobjects. Devotees protect cattle and ritually venerate plants because these actions facilitate a limited experiential grounding of religiosity on what is perhaps the most fundamental of all relationships, the relationship with the mother, within a theological worldview that somewhat embraces nonhuman natural beings in both doctrine and practice

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods

Insights on the Neuromagnetic Representation of Temporal Asymmetry in Human Auditory Cortex.

Communication sounds are typically asymmetric in time and human listeners are highly sensitive to this short-term temporal asymmetry. Nevertheless, causal neurophysiological correlates of auditory perceptual asymmetry remain largely elusive to our current analyses and models. Auditory modelling and animal electrophysiological recordings suggest that perceptual asymmetry results from the presence of multiple time scales of temporal integration, central to the auditory periphery. To test this hypothesis we recorded auditory evoked fields (AEF) elicited by asymmetric sounds in humans. We found a strong correlation between perceived tonal salience of ramped and damped sinusoids and the AEFs, as quantified by the amplitude of the N100m dynamics. The N100m amplitude increased with stimulus half-life time, showing a maximum difference between the ramped and damped stimulus for a modulation half-life time of 4 ms which is greatly reduced at 0.5 ms and 32 ms. This behaviour of the N100m closely parallels psychophysical data in a manner that: i) longer half-life times are associated with a stronger tonal percept, and ii) perceptual differences between damped and ramped are maximal at 4 ms half-life time. Interestingly, differences in evoked fields were significantly stronger in the right hemisphere, indicating some degree of hemispheric specialisation. Furthermore, the N100m magnitude was successfully explained by a pitch perception model using multiple scales of temporal integration of auditory nerve activity patterns. This striking correlation between AEFs, perception, and model predictions suggests that the physiological mechanisms involved in the processing of pitch evoked by temporal asymmetric sounds are reflected in the N100m

Estimating gene regulatory networks with pandaR

Abstract PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that begins with a model of transcription factor–target gene interactions and uses message passing to update the network model given available transcriptomic and protein–protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks. Contact: [email protected] or [email protected] Availability and Implementation: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR

Estimating drivers of cell state transitions using gene regulatory network models

Background: Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Results: Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Conclusions: We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0517-y) contains supplementary material, which is available to authorized users

Models of time structures of interest rates and their use in valuation of liabilities of life insurance Company

This master thesis aims to describe problematics of the stochastic modeling of time structures of interest rates with Vasicek, CIR and Hull-White models and the use of these models in valuation of liabilities and time value of options and guaranties in life insurance. In the theoretical part of the thesis there are fundamentals of stochastic calculus, stochastic models of interest rates and introduction to problematics of life insurance defined. Furthermore, the last practical part of the thesis demonstrates impact of particular models on the value of liabilities in relation to clients and on the value of TVOG of real European life insurance Company

Additional file 7: of Transcriptomic network analyses of leaf dehydration responses identify highly connected ABA and ethylene signaling hubs in three grapevine species differing in drought tolerance

kME values of all transcripts in each of the gene modules determined by WGCNA. (XLSX 15417 kb