A graphical, rule based robotic interface system

The ability of a human to take control of a robotic system is essential in any use of robots in space in order to handle unforeseen changes in the robot's work environment or scheduled tasks. But in cases in which the work environment is known, a human controlling a robot's every move by remote control is both time consuming and frustrating. A system is needed in which the user can give the robotic system commands to perform tasks but need not tell the system how. To be useful, this system should be able to plan and perform the tasks faster than a telerobotic system. The interface between the user and the robot system must be natural and meaningful to the user. A high level user interface program under development at the University of Alabama, Huntsville, is described. A graphical interface is proposed in which the user selects objects to be manipulated by selecting representations of the object on projections of a 3-D model of the work environment. The user may move in the work environment by changing the viewpoint of the projections. The interface uses a rule based program to transform user selection of items on a graphics display of the robot's work environment into commands for the robot. The program first determines if the desired task is possible given the abilities of the robot and any constraints on the object. If the task is possible, the program determines what movements the robot needs to make to perform the task. The movements are transformed into commands for the robot. The information defining the robot, the work environment, and how objects may be moved is stored in a set of data bases accessible to the program and displayable to the user

Vulnerability-attention analysis for space-related activities

Techniques for representing and analyzing trouble spots in structures and processes are discussed. Identification of vulnerable areas usually depends more on particular and often detailed knowledge than on algorithmic or mathematical procedures. In some cases, machine inference can facilitate the identification. The analysis scheme proposed first establishes the geometry of the process, then marks areas that are conditionally vulnerable. This provides a basis for advice on the kinds of human attention or machine sensing and control that can make the risks tolerable

Using automatic programming for simulating reliability network models

This paper presents the development of an automatic programming system for assisting modelers of reliability networks to define problems and then automatically generate the corresponding code in the target simulation language GPSS/PC

Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker

Molecular susceptibility weighted imaging of the glioma rim in a mouse model

Background: Glioma is the most common and most difficult to treat brain cancer. Despite many efforts treatment, efficacy remains low. As neurosurgical removal is the standard procedure for glioma, a method, allowing for both early detection and exact determination of the location, size and extent of the tumor, could improve a patient's positive response to therapy. New method: We propose application of susceptibility weighted molecular magnetic resonance imaging using, targeted contrast agents, based on superparamagnetic iron oxide nanoparticles, for imaging of the, glioma rim, namely brain-tumor interface. Iron oxide attached to the targeted cells increases, susceptibility differences at the boundary between tumor and normal tissue, providing the opportunity, to utilize susceptibility weighted imaging for improved tumor delineation. We investigated potential, enhancement of the tumor-brain contrast, including tumor core and rim when using susceptibility, weighted MRI for molecular imaging of glioma. Results: There were significant differences in contrast-to-noise ratio before, 12 and 120. min after contrast, agent injection between standard gradient echo pulse sequence and susceptibility weighted molecular, magnetic resonance imaging for the core-brain, tumor rim-core and tumor rim-brain areas. Comparison with existing methods: Currently, the most common MRI contrast agent used for glioma diagnosis is a non-specific, gadolinium-based agent providing T1-weighted enhancement. Susceptibility-weighted magnetic, resonance imaging is much less efficient when no targeted superparamagnetic contrast agents are, used. Conclusion: The improved determination of glioma extent provided by SWI offers an important new tool for, diagnosis and surgical planning.Peer reviewed: YesNRC publication: Ye

Modeling SHH-driven medulloblastoma with patient iPS cell-derived neural stem cells

Medulloblastoma is the most common malignant brain tumor in children. Here we describe a medulloblastoma model using In-duced pluripotent stem (iPS) cell-derived human neuroepithelial stem (NES) cells generated from a Gorlin syndrome patient carry-ing a germline mutation in the sonic hedgehog (SHH) receptor PTCH1. We found that Gorlin NES cells formed tumors in mouse cerebellum mimicking human medulloblastoma. Retransplantation of tumor-isolated NES (tNES) cells resulted in accelerated tumor formation, cells with reduced growth factor dependency, en-hanced neurosphere formation in vitro, and increased sensitivity to Vismodegib. Using our model, we identified LGALS1 to be a GLI target gene that is up-regulated in both Gorlin tNES cells and SHH-subgroup of medulloblastoma patients. Taken together, we dem-onstrate that NES cells derived from Gorlin patients can be used as a resource to model medulloblastoma initiation and progression and to identify putative targets