Improving acclimatization through the photoautotrophic culture of coconut (Cocos nucifera) seedlings: an in vitro system for the efficient exchange of germplasm

An in vitro photoautotrophic step based on the supply of CO₂-enriched air (1,600 μmol mol^?1) during the light phase and ambient air (350 μmol mol^?1 CO₂) during the dark phase has been used to promote the ex vitro establishment of coconut (Cocos nucifera L.) seedlings. The introduction of this step into a previously developed in vitro protocol was found to improve the quality of the seedlings (as assessed by fresh weight increase, physical stature, leaf area and thickness, stomatal density, and chlorophyll a content, and primary and secondary root production), the proportion of seedlings successfully transferred to soil (improvement from 40% to 100%) and achieved in a shorter time (reduction from 10 to 6 mo). Best results using this photoautotrophic growth step were obtained when a low medium concentration of sucrose (43.8 mM or lower) was used, when it was applied to seedlings that had already reached 4 or 5 mo of age in the in vitro culture step, and when seedlings were cultured in the photoautotrophic system for 2 mo or more before transfer to soil. Our improved protocol is more efficient and it reduces the cost per plant for the international exchange of coconut germplasm.     

Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs

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MBAT: A scalable informatics system for unifying digital atlasing workflows

Background  

Digital atlases provide a common semantic and spatial coordinate system that can be leveraged to compare, contrast, and correlate data from disparate sources. As the quality and amount of biological data continues to advance and grow, searching, referencing, and comparing this data with a researcher's own data is essential. However, the integration process is cumbersome and time-consuming due to misaligned data, implicitly defined associations, and incompatible data sources. This work addressing these challenges by providing a unified and adaptable environment to accelerate the workflow to gather, align, and analyze the data.     

OGR1/GPR68 Modulates the Severity of Experimental Autoimmune Encephalomyelitis and Regulates Nitric Oxide Production by Macrophages

Ovarian cancer G protein-coupled receptor 1 (OGR1) is a proton-sensing molecule that can detect decreases in extracellular pH that occur during inflammation. Although OGR1 has been shown to have pro-inflammatory functions in various diseases, its role in autoimmunity has not been examined. We therefore sought to determine whether OGR1 has a role in the development of T cell autoimmunity by contrasting the development of experimental autoimmune encephalomyelitis between wild type and OGR1-knockout mice. OGR1-knockout mice showed a drastically attenuated clinical course of disease that was associated with a profound reduction in the expansion of myelin oligodendrocyte glycoprotein 35-55-reactive T helper 1 (Th1) and Th17 cells in the periphery and a reduced accumulation of Th1 and Th17 effectors in the central nervous system. We determined that these impaired T cell responses in OGR1-knockout mice associated with a reduced frequency and number of dendritic cells in draining lymph nodes during EAE and a higher production of nitric oxide by macrophages. Our studies suggest that OGR1 plays a key role in regulating T cell responses during autoimmunity.     

A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.     

Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.