Food and Dysmorphic Disorder

The purpose of our research is to raise awareness about the conditions of Body Dysmorphic Disorder, and to provide evidence base practices on how to combat this mental illness disorder through therapy.We will be presenting our information on a poster to discuss therapeutic based practices

Biophysics at the coffee shop: lessons learned working with George Oster

Over the past 50 years, the use of mathematical models, derived from physical reasoning, to describe molecular and cellular systems has evolved from an art of the few to a cornerstone of biological inquiry. George Oster stood out as a pioneer of this paradigm shift from descriptive to quantitative biology not only through his numerous research accomplishments, but also through the many students and postdocs he mentored over his long career. Those of us fortunate enough to have worked with George agree that his sharp intellect, physical intuition and passion for scientific inquiry not only inspired us as scientists but also greatly influenced the way we conduct research. We would like to share a few important lessons we learned from George in honor of his memory and with the hope that they may inspire future generations of scientists.Comment: 22 pages, 3 figures, accepted in Molecular Biology of the Cel

Fitness Conferred by BCR-ABL Kinase Domain Mutations Determines the Risk of Pre-Existing Resistance in Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) is the first human malignancy to be successfully treated with a small molecule inhibitor, imatinib, targeting a mutant oncoprotein (BCR-ABL). Despite its successes, acquired resistance to imatinib leads to reduced drug efficacy and frequent progression of disease. Understanding the characteristics of pre-existing resistant cells is important for evaluating the benefits of first-line combination therapy with second generation inhibitors. However, due to limitations of assay sensitivity, determining the existence and characteristics of resistant cell clones at the start of therapy is difficult. Here we combined a mathematical modeling approach using branching processes with experimental data on the fitness changes (i.e., changes in net reproductive rate) conferred by BCR-ABL kinase domain mutations to investigate the likelihood, composition, and diversity of pre-existing resistance. Furthermore, we studied the impact of these factors on the response to tyrosine kinase inhibitors. Our approach predicts that in most patients, there is at most one resistant clone present at the time of diagnosis of their disease. Interestingly, patients are no more likely to harbor the most aggressive, pan-resistant T315I mutation than any other resistance mutation; however, T315I cells on average establish larger-sized clones at the time of diagnosis. We established that for patients diagnosed late, the relative benefit of combination therapy over monotherapy with imatinib is significant, while this benefit is modest for patients with a typically early diagnosis time. These findings, after pre-clinical validation, will have implications for the clinical management of CML: we recommend that patients with advanced-phase disease be treated with combination therapy with at least two tyrosine kinase inhibitors

Application of MBSE to model Hierarchical AI Planning problems in HDDL

The recent improvements of hierarchical AI planning open the path to new and exciting application in different areas of expertise. One domain with daring and complex planning and scheduling problems is the definition of operations for space exploration systems. For this specific application, the Hierarchical Definition Domain Language (HDDL) may be the most suitable AI planning language to be adopted. However , the design and writing of problems and domain files for HDDL is a complex task. They require a skilful designer to write and check the consistency of the syntax. Moreover, sharing and modifying HDDL files can be a complicated task, and it may lack traceability of the modifications, making the overall process prone to errors. On the other hand, planning languages like HDDL and PDDL are hardly ever studied in the university curricula by most space systems engineers, the architects of the concepts of operations of space systems. The work proposed in this paper contributes to filling the gap between space operations engineers and the AI planning potentialities to solve planning and scheduling problems applied to space exploration systems. The problem and domain files typical of HDDL are built up from the formalism of SysML, a general-purpose architecture modelling language for System Engineering. SysML is effectively used as modelling language in Model-Based System Engineering (MBSE) to study and design the mission architecture of a space mission. The methodology presented is applied to an analogue space robotic mission, where a collaborative drone and a rover need to explore an unknown environment. The final aim of the method is to transfer the "human knowledge" in the planning problem and showing the capabilities of MBSE applied to Knowledge Engineering (KE) of AI planning problems

MBSE approach applied to lunar surface exploration elements

The last two decades have shown that among the new drivers of the design of space systems the level of autonomy is a key element to ensure the success of a mission. The final aim is to monitor and direct the operations or counteract unforeseen events as efficiently as possible, even without the man in the loop. To effectively accomplish these new tasks, the decision making layer of the spacecraft should be able to evaluate the available resources and the overall state of health of the system. The Model-Based System Engineering (MBSE) framework can help to understand the general behavior of a complex system as it is an autonomous space platform. The MBSE scheme exhibits the links and the interdependency between the different phases of mission analysis and between the components. The study proposed in this paper follows the MBSE methodology to design an autonomous guidance, navigation, and control (GNC) subsystem of a planetary exploration rover and its collaborative drone. The study starts from the high-level requirements of a lunar exploration mission and ends with the preliminary design of a state-machine, that describes the behavior of an autonomous GNC. To ensure a high level of autonomy, the decision-making layer of the GNC takes into account the outputs of the failure detection, identification, and recovery (FDIR) subsystem and the overall health state of the rover. The FDIR subsystem embodies the idea of a multidisciplinary design where different inputs should be managed to ensure the safety of the overall system under study. The novelty of this analysis lays in using the MBSE to define the design box of the autonomous GNC. The logic behind the MBSE enables the designer to keep track of the effects of the high-level mission-related decisions and of the FDIR on the overall behavior of an autonomous GNC subsystem. In the application presented in this paper, the preferred mean to study the mission and behavioral analysis is MBSE software Genesys 7.0 of Vitech Corporation. While the state machine and the related artificial intelligence algorithms are designed in Robot Operating System (ROS). The described approach is applied to the case study of a collaborative rover and drone on the lunar surface. The mission is designed as a "precursor mission" to assess the safety of the lunar lava tubes as possible future human settlement

Towards the assessment of quality of life in patients with disorders of consciousness

© 2019, Springer Nature Switzerland AG. Purpose: To generate foundational knowledge in the creation of a quality-of-life instrument for patients who are clinically diagnosed as being in a vegetative or minimally conscious state but are able to communicate by modulating their brain activity (i.e., behaviourally nonresponsive and covertly aware). The study aimed to identify a short list of key domains that could be used to formulate questions for an instrument that determines their self-reported quality of life. Methods: A novel two-pronged strategy was employed: (i) a scoping review of quality-of-life instruments created for patient populations sharing some characteristics with patients who are behaviourally nonresponsive and covertly aware was done to compile a set of potentially relevant domains of quality of life; and (ii) a three-round Delphi consensus process with a multidisciplinary panel of experts was done to determine which of the identified domains of quality of life are most important to those who are behaviourally nonresponsive and covertly aware. Five expert groups were recruited for this study including healthcare workers, neuroscientists, bioethicists, quality-of-life methodologists, and patient advocates. Results: Thirty-five individuals participated in the study with an average response rate of 95% per round. Over the three rounds, experts reached consensus on 34 of 44 domains (42 domains were identified in the scoping review and two new domains were added based on suggestions by experts). 22 domains were rated as being important for inclusion in a quality-of-life instrument and 12 domains were deemed to be of less importance. Participants agreed that domains related to physical pain, communication, and personal relationships were of primary importance. Based on subgroup analyses, there was a high degree of consistency among expert groups. Conclusions: Quality of life should be a central patient-reported outcome in all patient populations regardless of patients’ ability to communicate. It remains to be determined how covertly aware patients perceive their circumstances and quality of life after suffering a life-altering injury. Nonetheless, it is important that any further dialogue on what constitutes a life worth living should not occur without direct patient input

From pandemic response to portable population health: A formative evaluation of the Detroit mobile health unit program

This article describes our experience developing a novel mobile health unit (MHU) program in the Detroit, Michigan, metropolitan area. Our main objectives were to improve healthcare accessibility, quality and equity in our community during the novel coronavirus pandemic. While initially focused on SARS-CoV-2 testing, our program quickly evolved to include preventive health services. The MHU program began as a location-based SARS-CoV-2 testing strategy coordinated with local and state public health agencies. Community needs motivated further program expansion to include additional preventive healthcare and social services. MHU deployment was targeted to disease “hotspots” based on publicly available SARS-CoV-2 testing data and community-level information about social vulnerability. This formative evaluation explores whether our MHU deployment strategy enabled us to reach patients from communities with heightened social vulnerability as intended. From 3/20/20-3/24/21, the Detroit MHU program reached a total of 32,523 people. The proportion of patients who resided in communities with top quartile Centers for Disease Control and Prevention Social Vulnerability Index rankings increased from 25% during location-based “drive-through” SARS-CoV-2 testing (3/20/20-4/13/20) to 27% after pivoting to a mobile platform (4/13/20-to-8/31/20; p = 0.01). The adoption of a data-driven deployment strategy resulted in further improvement; 41% of the patients who sought MHU services from 9/1/20-to-3/24/21 lived in vulnerable communities (Cochrane Armitage test for trend, p\u3c0.001). Since 10/1/21, 1,837 people received social service referrals and, as of 3/15/21, 4,603 were administered at least one dose of COVID-19 vaccine. Our MHU program demonstrates the capacity to provide needed healthcare and social services to difficult-to-reach populations from areas with heightened social vulnerability. This model can be expanded to meet emerging pandemic needs, but it is also uniquely capable of improving health equity by addressing longstanding gaps in primary care and social services in vulnerable communities