Operational Psychology Perspective

This slide presentation reviews the history of long duration spaceflight, and the changes in the International Space Station crew and the effect that this has had on the psychology of astronaut selection and training

NsrR from Streptomyces coelicolor is a nitric oxide-sensing [4Fe-4S] cluster protein with a specialized regulatory function

The Rrf2 family transcription factor NsrR controls expression of genes in a wide range of bacteria in response to nitric oxide (NO). The precise form of the NO-sensing module of NsrR is the subject of controversy because NsrR proteins containing either [2Fe-2S] or [4Fe-4S] clusters have been observed previously. Optical, Mössbauer, resonance Raman spectroscopies and native mass spectrometry demonstrate that Streptomyces coelicolor NsrR (ScNsrR), previously reported to contain a [2Fe-2S] cluster, can be isolated containing a [4Fe-4S] cluster. ChIP-seq experiments indicated that the ScNsrR regulon is small, consisting of only hmpA1, hmpA2, and nsrR itself. The hmpA genes encode NO-detoxifying flavohemoglobins, indicating that ScNsrR has a specialized regulatory function focused on NO detoxification and is not a global regulator like some NsrR orthologues. EMSAs and DNase I footprinting showed that the [4Fe-4S] form of ScNsrR binds specifically and tightly to an 11-bp inverted repeat sequence in the promoter regions of the identified target genes and that DNA binding is abolished following reaction with NO. Resonance Raman data were consistent with cluster coordination by three Cys residues and one oxygen-containing residue, and analysis of ScNsrR variants suggested that highly conserved Glu-85 may be the fourth ligand. Finally, we demonstrate that some low molecular weight thiols, but importantly not physiologically relevant thiols, such as cysteine and an analogue of mycothiol, bind weakly to the [4Fe-4S] cluster, and exposure of this bound form to O2 results in cluster conversion to the [2Fe-2S] form, which does not bind to DNA. These data help to account for the observation of [2Fe-2S] forms of NsrR

NASA's Astronant Family Support Office

During the NASAMir program of the 1990s and due to the challenges inherent in the International Space Station training schedule and operations tempo, it was clear that a special focus on supporting families was a key to overall mission success for the ISS crewmembers pre, in and postflight. To that end, in January 2001 the first Family Services Coordinator was hired by the Behavioral Health and Performance group at NASA JSC and matrixed from Medical Operations into the Astronaut Office's organization. The initial roles and responsibilities were driven by critical needs, including facilitating family communication during training deployments, providing missionspecific and other relevant trainings for spouses, serving as liaison for families with NASA organizations such as Medical Operations, NASA management and the Astronaut Office, and providing assistance to ensure success of an Astronaut Spouses Group. The role of the Family Support Office (FSO) has modified as the ISS Program matured and the needs of families changed. The FSO is currently an integral part of the Astronaut Office's ISS Operations Branch. It still serves the critical function of providing information to families, as well as being the primary contact for US and international partner families with resources at JSC. Since crews launch and return on Russian vehicles, the FSO has the added responsibility for coordinating with Flight Crew Operations, the families, and their guests for Soyuz launches, landings, and Direct Return to Houston postflight. This presentation will provide a summary of the family support services provided for astronauts, and how they have changed with the Program and families the FSO serves. Considerations for future FSO services will be discussed briefly as NASA proposes one year missions and beyond ISS missions. Learning Objective: 1) Obtain an understanding of the reasons a Family Support Office was important for NASA. 2) Become familiar with the services provided for astronauts and their families and how they changed with the Program and family needs

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Human Inborn Errors of Immunity: 2019 Update on the Classification from the International Union of Immunological Societies Expert Committee.

We report the updated classification of Inborn Errors of Immunity/Primary Immunodeficiencies, compiled by the International Union of Immunological Societies Expert Committee. This report documents the key clinical and laboratory features of 430 inborn errors of immunity, including 64 gene defects that have either been discovered in the past 2 years since the previous update (published January 2018) or were characterized earlier but have since been confirmed or expanded upon in subsequent studies. The application of next-generation sequencing continues to expedite the rapid identification of novel gene defects, rare or common; broaden the immunological and clinical phenotypes of conditions arising from known gene defects and even known variants; and implement gene-specific therapies. These advances are contributing to greater understanding of the molecular, cellular, and immunological mechanisms of disease, thereby enhancing immunological knowledge while improving the management of patients and their families. This report serves as a valuable resource for the molecular diagnosis of individuals with heritable immunological disorders and also for the scientific dissection of cellular and molecular mechanisms underlying inborn errors of immunity and related human diseases

Training the 8-Balls: Psychological Readiness Preparation for the 2013 US Astronaut Class

No abstract availabl

NXFit: A program for simultaneously fitting X-ray and neutron diffraction pair-distribution functions to provide optimized structural parameters

NXFit is a program for obtaining optimized structural parameters from amorphous materials by simultaneously fitting X-ray and neutron pair-distribution functions. Partial correlation functions are generated in Q space, summed and Fourier transformed for comparison with the experimental data in r space. NXFit uses the Nelder-Mead method to vary a set of 'best guess' parameters to achieve a fit to experimentally derived data. The output parameters from NXFit are coordination number, atomic separation and disorder parameter for each atomic correlation used in the fitting process. The use of NXFit has been demonstrated by fitting both X-ray and neutron diffraction data from two quite different amorphous materials: a melt-quenched (Na2O) 0.5(P2O5)0.5glass and a (TiO2)0.18(SiO2)0.82sol-gel

Hands-On Parameter Search for Neural Simulations by a MIDI-Controller

Computational neuroscientists frequently encounter the challenge of parameter fitting – exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems