Thermal modelling of gas generation and retention in the Jurassic organic-rich intervals in the Darquain field, Abadan Plain, SW Iran

The petroleum system with Jurassic source rocks is an important part of the hydrocarbons discovered in the Middle East. Limited studies have been done on the Jurassic intervals in the 26,500 km2 Abadan Plain in south-west Iran, mainly due to the deep burial and a limited number of wells that reach the basal Jurassic successions. The goal of this study was to evaluate the Jurassic organic-rich intervals and shale gas play in the Darquain field using organic geochemistry, organic petrography, biomarker analysis, and basin modelling methods. This study showed that organic-rich zones present in the Jurassic intervals of Darquain field could be sources of conventional and unconventional gas reserves. The organic matter content of samples from the organic-rich zones corresponds to medium-to-high-sulphur kerogen Type II-S marine origin. The biomarker characteristics of organic-rich zones indicate carbonate source rocks that contain marine organic matter. The biomarker results also suggest a marine environment with reducing conditions for the source rocks. The constructed thermal model for four pseudo-wells indicates that, in the kitchen area of the Jurassic gas reserve, methane has been generated in the Sargelu and Neyriz source rocks from Early Cretaceous to recent times and the transformation ratio of organic matter is more than 97%. These organic-rich zones with high initial total organic carbon (TOC) are in the gas maturity stage [1.5–2.2% vitrinite reflectance in oil (Ro)] and could be good unconventional gas reserves and gas source rocks. The model also indicates that there is a huge quantity of retained gas within the Jurassic organic-rich intervals

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

A learning strategy for developing neural networks using repetitive observations

Neural networks can model system behaviors by learning past system observations. As system observations are usually collected by human judgments, physical experiments or sensor measures, they can be inherently imprecise and inconsistent over time. System behaviors can be learned more completely from repetitive observations. However, repetitive observations can be very different due to system or measurement uncertainty. If abnormal observations are used for developing neural networks, spurious behaviors can be learnt and the neural networks are likely to generate spurious prediction. If abnormal observations are excluded, important system behaviors can partially be ignored. In this paper, a novel strategy is proposed to develop neural networks by learning repetitive observations. Numerous neural networks are developed individually based on either abnormal or normal observations. The predictions generated based on the individual neural networks are integrated to a single prediction. Analytical proof indicates that the overall observation uncertainty involved on the proposed learning strategy is less than the uncertainty involved on the general ones. As less uncertainty is involved, more effective learning can be performed on the proposed strategy. Two case studies are conducted in order to evaluate the effectiveness of the proposed learning strategy, where the two case studies are involved data collection from either sensor measures or human evaluations. Numerical results indicate that the proposed strategy can generate better neural networks which have higher fitting capability to captured observations and higher generalization capability to uncaptured samples

Hybrid denture acrylic composites with nanozirconia and electrospun polystyrene fibers

The processing and characterization of hybrid PMMA resin composites with nano-zirconia (ZrO2) and electrospun polystyrene (PS) polymer fibers were presented in this study. Reinforcement was selected with the intention to tune the physical and mechanical properties of the hybrid composite. Surface modification of inorganic particles was performed in order to improve the adhesion of reinforcement to the matrix. Fourier transform infrared spectroscopy (FTIR) provided successful modification of zirconia nanoparticles with 3-Methacryloxypropyltrimethoxysilane (MEMO) and bonding improvement between incompatible inorganic nanoparticles and PMMA matrix. Considerable deagglomeration of nanoparticles in the matrix occurred after the modification has been revealed by scanning electron microscopy (SEM). Microhardness increased with the concentration of modified nanoparticles, while the fibers were the modifier that lowers hardness and promotes toughness of hybrid composites. Impact test displayed increased absorbed energy after the PS electrospun fibers had been embedded. The optimized composition of the hybrid was determined and a good balance of thermal and mechanical properties was achieved