3D geo-cellular modeling for Oligocene reservoirs: a marginal field in offshore Vietnam

This study focuses on constructing a 3D geo-cellular model by using well-log data and other geological information to enable a deep investigation of the reservoir characteristics and estimation of the hydrocarbon potential in the clastic reservoir of the marginal field in offshore Vietnam. In this study, Petrel software was adopted for geostatistical modeling. First, a sequential indicator simulation (SIS) was adopted for facies modeling. Next, sequential Gaussian simulation (SGS) and co-kriging approaches were utilized for petrophysical modeling. Furthermore, the results of the petrophysical models were verified by a quality control process before determining the in-place oil for each reservoir in the field. Multiple geological realizations were generated to reduce the geological uncertainty of the model assessment for the facies and porosity model. The most consistent one would then be the best candidate for further evaluation. The porosity distribution ranged from 9 to 22%. The original oil place of clastic reservoirs in the marginal field was 50.28 MMbbl. Ultimately, this research found that the marginal field could be considered a potential candidate for future oil and gas development in offshore Vietnam.

Cerebrospinal fluid concentration of complement component 4A is increased in first-episode schizophrenia

Excessive synapse loss is a core feature of schizophrenia and is linked to the complement component 4A gene (C4A). In two independent cohorts, we show that cerebrospinal fluid (CSF) C4A concentration is elevated in first-episode psychosis patients who develop schizophrenia and correlates with CSF measurements of synapse density. Using patient-derived cellular modeling, we find that disease-associated cytokines increase neuronal C4A expression and that IL-1beta associates with C4A in patient-derived CSF.

Functional Studies with Primary Cells Provide a System for Genome-to-Phenome Investigations in Marine Mammals

Synopsis Marine mammals exhibit some of the most dramatic physiological adaptations in their clade and offer unparalleled insights into the mechanisms driving convergent evolution on relatively short time scales. Some of these adaptations, such as extreme tolerance to hypoxia and prolonged food deprivation, are uncommon among most terrestrial mammals and challenge established metabolic principles of supply and demand balance. Non-targeted omics studies are starting to uncover the genetic foundations of such adaptations, but tools for testing functional significance in these animals are currently lacking. Cellular modeling with primary cells represents a powerful approach for elucidating the molecular etiology of physiological adaptation, a critical step in accelerating genome-to-phenome studies in organisms in which transgenesis is impossible (e.g., large-bodied, long-lived, fully aquatic, federally protected species). Gene perturbation studies in primary cells can directly evaluate whether specific mutations, gene loss, or duplication confer functional advantages such as hypoxia or stress tolerance in marine mammals. Here, we summarize how genetic and pharmacological manipulation approaches in primary cells have advanced mechanistic investigations in other non-traditional mammalian species, and highlight the need for such investigations in marine mammals. We also provide key considerations for isolating, culturing, and conducting experiments with marine mammal cells under conditions that mimic in vivo states. We propose that primary cell culture is a critical tool for conducting functional mechanistic studies (e.g., gene knockdown, over-expression, or editing) that can provide the missing link between genome- and organismal-level understanding of physiological adaptations in marine mammals.

Rapidly Assessing the Quality of Targeted Proteomics Experiments Through Monitoring Stable-isotope Labeled Standards

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in clinical biomarker, cellular modeling and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards which are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved MS instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy labeled peptides and flags those that fail quality control metrics. Using four metrics, the tool detects problems both with individual SRM transitions and the collective group of transitions that monitor a single peptide. The program’s speed enables its use at the point of data acquisition and can be ideally run immediately upon the completion of an LC-SRM-MS analysis.

Integrated cellular framework for modeling ecosystems: Theory and applications

We introduce an integrated framework for modeling and simulation of ecosystems based on cellular models. The framework integrates cellular modeling, web-based simulation, and geographic information systems (GISs) for data collection and visualization. In this framework, data extraction from GISs is automated; we use the Cell-DEVS formalism for modeling the ecosystem and the CD++ cellular modeling tool within the RISE (RESTful Interoperability Simulation Environment) middleware for web-based simulation. The simulation results are easily integrated with Google Earth data for visualization. We discuss the design, implementation, and benefits of the integrated approach for modeling and simulation in spatial analysis of ecosystem services. We show different case studies in the area of ecological systems, demonstrating how to apply the framework, its usability, and flexibility. We focus on the use of models available in remote servers, their integration with GIS data for inputs, and georeferenced visualization of the results. We show how the modeling methods based on DEVS and their modular interfaces make it easy to build such an architecture and we discuss its application to the field of environmental systems.