Role of IL-10-Producing Natural Killer Cells in the Regulatory Mechanisms of Inflammation during Systemic Infection

Natural killer (NK) cells have the dual ability to produce pro-inflammatory (IFNγ) and anti-inflammatory (IL-10) cytokines during systemic infection, which points to their crucial role both as inflammatory effectors for infection clearance and as regulators to counterbalance inflammation to limit immune-mediated damage to the host. In particular, immunosuppressive IL-10 secretion by NK cells has been described to occur in systemic, but not local, infections as a recent immunoregulatory mechanism of inflammation that may be detrimental or beneficial, depending on the timing of release, type of disease, or the infection model. Understanding the factors that drive the production of IL-10 by NK cells and their impact during dualistic inflammatory states, such as sepsis and other non-controlled inflammatory diseases, is relevant for achieving effective therapeutic advancements. In this review, the evidence regarding the immunoregulatory role of IL-10-producing NK cells in systemic infection is summarized and discussed in detail, and the potential molecular mechanisms that drive IL-10 production by NK cells are considered.

Gravity Field Theory

Gravity keep all things on the earth surface on the ground. Gravity method is one of the oldest geophysical methods. It is used to solve many geological problems. This method can be integrated with the other geophysical methods to prepare more accepted geophysical model. Understanding the theory and the principles concepts considered as an important step to improve the method. Chapter one attempt to discuss Newton’s law, potential and attraction gravitational field, Geoid, Spheroid and geodetically figure of the earth, the gravity difference between equator and poles of the earth and some facts about gravity field.

Introduction to Population Models

The chapter “Introduction to Population Models” introduces unstructured population models and shows how model decisions can change model behavior, the different ways that feedbacks can be represented, and how one evaluates the consequences of those feedbacks. The goal here is to show how modeling a single entity, population density, can be done in many different ways, depending on the purpose of the model. Understanding the dynamics of populations remains one of the fundamental goals of ecology. Not surprisingly, many models have contributed to the theory of population dynamics and regulation. The models vary considerably in terms of depth, breadth, intended uses (e.g. prediction vs. generality), and structure. This chapter will largely focus on the behavior of simple models, to see how intrinsic factors can dictate variability in population size. Density-independent and density-dependent models are covered, as well as methods used to understand model behaviors.

Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers

Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key model parameters can lead to significant uncertainty in simulations. Previous work has constrained uncertain parameters in the SCOPE model using coarse-resolution SIF observations from satellites, while few studies have used finer resolution SIF measured from the Orbiting Carbon Observatory-2 (OCO-2) to improve the model. Here, we identified the sensitive parameters to SIF and GPP estimation, and improved the performance of SCOPE in simulating SIF and GPP for temperate forests by constraining the physiological parameters relating to SIF and GPP by combining satellite-based SIF measurements (e.g., OCO-2) with flux tower GPP data. Our study showed that SIF had weak capability in constraining maximum carboxylation capacity (Vcmax), while GPP could constrain this parameter well. The OCO-2 SIF data constrained fluorescence quantum efficiency (fqe) well and improved the performance of SCOPE in SIF simulation. However, the use of the OCO-2 SIF alone cannot significantly improve the GPP simulation. The use of both satellite SIF and flux tower GPP data as constraints improved the performance of the model for simulating SIF and GPP simultaneously. This analysis is useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and improving the estimation of both SIIF and GPP by incorporating satellite SIF products and flux tower data.