Theory and Algorithms for Partial Order Based Reduction in Planning

Search is a major technique for planning. It amounts to exploring a state space of planning domains typically modeled as a directed graph. However, prohibitively large sizes of the search space make search expensive. Developing better heuristic functions has been the main technique for improving search efficiency. Nevertheless, recent studies have shown that improving heuristics alone has certain fundamental limits on improving search efficiency. Recently, a new direction of research called partial order based reduction (POR) has been proposed as an alternative to improving heuristics. POR has shown promise in speeding up searches. POR has been extensively studied in model checking research and is a key enabling technique for scalability of model checking systems. Although the POR theory has been extensively studied in model checking, it has never been developed systematically for planning before. In addition, the conditions for POR in the model checking theory are abstract and not directly applicable in planning. Previous works on POR algorithms for planning did not establish the connection between these algorithms and existing theory in model checking. In this paper, we develop a theory for POR in planning. The new theory we develop connects the stubborn set theory in model checking and POR methods in planning. We show that previous POR algorithms in planning can be explained by the new theory. Based on the new theory, we propose a new, stronger POR algorithm. Experimental results on various planning domains show further search cost reduction using the new algorithm

Ganoderma triterpenes Protect Against Hyperhomocysteinemia Induced Endothelial-Mesenchymal Transition via TGF-β Signaling Inhibition

Endothelial dysfunction is one of the most important pathological status in hyperhomocysteinemia (HHcy) related cardiovascular diseases. Whereas, the underlying mechanisms have not been fully elucidated yet, concomitant with the absence of effective treatment. The purpose of this study was to explore the main mechanisms involved in HHcy-induced endothelial injury and identify the protective effect of Ganoderma triterpenes (GT). Bovine aortic endothelial cells (BAECs) were applied as in vitro experimental model. The small molecular inhibitors were used to explore the signalings involved in HHcy-induced endothelial injury. The experimental results provided initial evidence that HHcy led to endothelial-mesenchymal transition (EndMT). Meanwhile, TGF-β/Smad, PI3K/AKT and MAPK pathways were activated in this process, which was demonstrated by pretreatment with TGF-β RI kinase inhibitor VI SB431542, PI3K inhibitor LY294002, p38 inhibitor SB203580, and ERK inhibitor PD98059. Furthermore, it was found that GT restrained the process of HHcy-induced EndMT via reducing oxidative stress and suppressing fore mentioned pathways with further inhibiting the activity of Snail. These results implicate that there is an untapped potential for GT as a novel therapeutic candidate for HHcy-induced EndMT through alleviating oxidative stress and canonical TGF-β/Smad and non-Smad dependent signaling pathways

High-throughput bioprinting of the nasal epithelium using patient-derived nasal epithelial cells.

Progenitor human nasal epithelial cells (hNECs) are an essential cell source for the reconstruction of the respiratory pseudostratified columnar epithelium composed of multiple cell types in the context of infection studies and disease modeling. Hitherto, manual seeding has been the dominant method for creating nasal epithelial tissue models through biofabrication. However, this approach has limitations in terms of achieving the intricate three-dimensional (3D) structure of the natural nasal epithelium. 3D bioprinting has been utilized to reconstruct various epithelial tissue models, such as cutaneous, intestinal, alveolar, and bronchial epithelium, but there has been no attempt to use of 3D bioprinting technologies for reconstruction of the nasal epithelium. In this study, for the first time, we demonstrate the reconstruction of the nasal epithelium with the use of primary hNECs deposited on Transwell inserts via droplet-based bioprinting (DBB), which enabled high-throughput fabrication of the nasal epithelium in Transwell inserts of 24-well plates. DBB of progenitor hNECs ranging from one-tenth to one-half of the cell seeding density employed during the conventional cell seeding approach enabled a high degree of differentiation with the presence of cilia and tight-junctions over a 4 weeks air-liquid interface culture. Single cell RNA sequencing of these cultures identified five major epithelial cells populations, including basal, suprabasal, goblet, club, and ciliated cells. These cultures recapitulated the pseudostratified columnar epithelial architecture present in the native nasal epithelium and were permissive to respiratory virus infection. These results denote the potential of 3D bioprinting for high-throughput fabrication of nasal epithelial tissue models not only for infection studies but also for other purposes, such as disease modeling, immunological studies, and drug screening

Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.Methods: We screened all 162 “kidney transplant”–related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms—LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs—were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan–Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis.Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response–related pathways were found in the C4 group, which had poor prognosis.Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO

Insights into human genetic variation and population history from 929 diverse genomes.

Genome sequences from diverse human groups are needed to understand the structure of genetic variation in our species and the history of, and relationships between, different populations. We present 929 high-coverage genome sequences from 54 diverse human populations, 26 of which are physically phased using linked-read sequencing. Analyses of these genomes reveal an excess of previously undocumented common genetic variation private to southern Africa, central Africa, Oceania, and the Americas, but an absence of such variants fixed between major geographical regions. We also find deep and gradual population separations within Africa, contrasting population size histories between hunter-gatherer and agriculturalist groups in the past 10,000 years, and a contrast between single Neanderthal but multiple Denisovan source populations contributing to present-day human populations.Wellcome grants 098051 and 206194, and S.A.M. and R.D. also by Wellcome grant 207492. A.B. and P.S. were supported by the Francis Crick Institute (FC001595) which receives its core funding from Cancer Research UK, the UK Medical Research Council and the Wellcome Trust. P.S. was also supported by the European Research Council (grant no. 852558) and the Wellcome Trust (217223/Z/19/Z). R.H. was supported by a Gates Cambridge scholarship. P.H. was supported by Estonian Research Council Grant PUT1036. D.R. is an Investigator of the Howard Hughes Medical Institute

Designing Cross-Subsidy Mechanisms for Multi-Modal Transportation Systems

Final ReportDespite large investments in passenger transportation infrastructure, congestion has increased at an alarming pace and at substantial societal costs. Congestion was estimated to cost $124 billion in 2013 and to rise to about$186 billion by 2030. Empirical evidence demonstrates that public transit is effective in alleviating congestion as well as the environmental impacts that result from congestion. What’s more, public transportation is the mode that provides access to jobs, goods, and services that are critical to economic mobility for those on the lower rungs of the economic ladder. Hence, this project focused on the development of modeling tools to support the design of cross-subsidy mechanisms in multi-modal passenger transportation networks by integrating road congestion pricing and multimodal transportation services design.U.S. Department of Transportation 69A355174711

Integrating Congestion Pricing And Transit Investment Planning

This paper develops a mathematical model and solution procedure to identify an optimal zonal pricing scheme for automobile traffic to incentivize the expanded use of transit as a mechanism to stem congestion and the social costs that arise from that congestion. The optimization model assumes that there is a homogenous collection of users whose behavior can be described as utility maximizers and for which their utility function is driven by monetary costs. These monetary costs are assumed to be the tolls in place, the per mile cost to drive, and the value of their time. We assume that there is a system owner who sets the toll prices, collects the proceeds from the tolls, and invests those funds in transit system improvements in the form of headway reductions. This yields a bi-level optimization model which we solve using an iterative procedure that is an integration of a genetic algorithm and the Frank-Wolfe method. The method and solution procedure is applied to an illustrative example