Walverine: A Walrasian Trading Agent

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments.trading agent, trading competition, tatonnement, competitive equilibrium

Where does stress happen? Ecological momentary assessment of daily stressors using a mobile phone app. [Journal article]

Despite the importance of daily stress to individuals' health and wellbeing, few studies have explored where stress happens in real time, that is, dynamic stress processes in different spaces. As such, stress interventions rarely account for the environment in which stress occurs. We used mobile phone based ecological momentary assessment (EMA) to collect daily stress data. Thirty-three participants utilized a mobile-phone-based EMA app to self-report stressors as they went about their daily lives. Geographic coordinates were automatically collected with each stress report. Data from thematic analysis of stressors by location (home, work, work from home, other) were used to determine whether certain stressors were more prevalent in certain environments. Nine daily stressors significantly differed by location. Work-related stress was reported more often at work. Pets, household chores, sleep, and media-related stressors were reported most at home. Physical illnesses, vehicle issues, and safety/security stressors occurred most often while participants were "working from home." Traffic-related stress was experienced more commonly in "other" environments. Other 18 stressors were generated regardless of location, suggesting that these stressors were persistent and without respect to location. Study findings expand the understanding of environments in which specific stressors occur, providing baseline data for potential targeted "just-in-time" stress interventions tailored to unique stressors in specific environments. We also provide findings related to the "work from home" phenomenon. Further work is needed to better understand the unique stressors among the large number of individuals who transitioned to working from home during and after the COVID-19 pandemic

Intrusive Traumatic Re-Experiencing Domain (ITRED) – Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background Intrusive Traumatic Re-Experiencing Domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods Data was collected from nine sites taking part in the ENIGMA-PTSD Consortium (n=584) and included itemized PTSD symptoms scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and Trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. Random forest classification model was built on a training set using cross-validation (CV), and the averaged CV model performance for classification was evaluated using area-under-the-curve (AUC). The model was tested using a fully independent portion of the data (test dataset), and the test AUC was evaluated. Results RsFC signatures differentiated TE-only participants from PTSD and from ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and from ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontal-parietal network, differentiated TE-only participants from one group (PTSD or ITRED-only), but to a lesser extent from the other. Conclusion Neural network connectivity supports ITRED as a novel neurobiologically-based approach to classifying post-trauma psychopathology

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

An NF-κB and Slug Regulatory Loop Active in Early Vertebrate Mesoderm

BACKGROUND: In both Drosophila and the mouse, the zinc finger transcription factor Snail is required for mesoderm formation; its vertebrate paralog Slug (Snai2) appears to be required for neural crest formation in the chick and the clawed frog Xenopus laevis. Both Slug and Snail act to induce epithelial to mesenchymal transition (EMT) and to suppress apoptosis. METHODOLOGY & PRINCIPLE FINDINGS: Morpholino-based loss of function studies indicate that Slug is required for the normal expression of both mesodermal and neural crest markers in X. laevis. Both phenotypes are rescued by injection of RNA encoding the anti-apoptotic protein Bcl-xL; Bcl-xL's effects are dependent upon IκB kinase-mediated activation of the bipartite transcription factor NF-κB. NF-κB, in turn, directly up-regulates levels of Slug and Snail RNAs. Slug indirectly up-regulates levels of RNAs encoding the NF-κB subunit proteins RelA, Rel2, and Rel3, and directly down-regulates levels of the pro-apopotic Caspase-9 RNA. CONCLUSIONS/SIGNIFICANCE: These studies reveal a Slug/Snail–NF-κB regulatory circuit, analogous to that present in the early Drosophila embryo, active during mesodermal formation in Xenopus. This is a regulatory interaction of significance both in development and in the course of inflammatory and metastatic disease

Comprehensive Molecular Characterization of Pheochromocytoma and Paraganglioma

SummaryWe report a comprehensive molecular characterization of pheochromocytomas and paragangliomas (PCCs/PGLs), a rare tumor type. Multi-platform integration revealed that PCCs/PGLs are driven by diverse alterations affecting multiple genes and pathways. Pathogenic germline mutations occurred in eight PCC/PGL susceptibility genes. We identified CSDE1 as a somatically mutated driver gene, complementing four known drivers (HRAS, RET, EPAS1, and NF1). We also discovered fusion genes in PCCs/PGLs, involving MAML3, BRAF, NGFR, and NF1. Integrated analysis classified PCCs/PGLs into four molecularly defined groups: a kinase signaling subtype, a pseudohypoxia subtype, a Wnt-altered subtype, driven by MAML3 and CSDE1, and a cortical admixture subtype. Correlates of metastatic PCCs/PGLs included the MAML3 fusion gene. This integrated molecular characterization provides a comprehensive foundation for developing PCC/PGL precision medicine