As good as married : a model of long-term cohabitation, learning and marriage

This paper develops a two-sided search-matching model with imperfectly observed types and learning. Since agents do not observe one another’s type accurately, they first engage in a probationary partnership to learn one another’s true type. Using the metaphor ofpremarital cohabitation and marriage, we demonstrate that long-term cohabiting individuals eventually learn each other’s true type. We also demonstrate that singles ofeither sex are partitioned into classes and are matched in the same class in equilibrium. We show that sequential learning reduces signalling errors so that the Bayes estimator of the true type converges almost surely to true type. As noisy information is filtered over time, the mismatch risk disappears and the aggregate matching pattern based on true types is restored.

To Tie the Knot or Not

This paper introduces imperfect information, learning, and risk aversion in a two sided matching model. The model provides a theoretical framework for the commonly occurring phenomenon of cohabitation followed by marriage, and is consistent with empirical findings on these institutions. The paper has three major results. First, individuals set higher standards for marriage than for cohabitation. When the true worth of a cohabiting partner is revealed, some cohabiting unions are converted into marriage while others are not. Second, individuals cohabit within classes. Third, the premium that compensates individuals for the higher risk involved in marriage over a cohabiting partnership is derived. This premium can be decomposed into two parts. The first part is a function of the individual's level of risk aversion, while the second part is a function of the difference in risk between marriage and cohabitation.class partition, imperfect information, risk, stochastic dominance

Controlling a Quadrotor Carrying a Cable-Suspended Load to Pass Through a Window

In this paper, we design an optimal control system for a quadrotor to carry a cable-suspended load flying through a window. As the window is narrower than the length of the cable, it is very challenging to design a practical control system to pass through it. Our solution includes a system identification component, a trajectory generation component, and a trajectory tracking control component. The exact dynamic model that usually derived from the first principles is assumed to be unavailable. Instead, a model identification approach is adopted, which relies on a simple but effective low order equivalent system (LOES) to describe the core dynamical characteristics of the system. After being excited by some specifically designed manoeuvres, the unknown parameters in the LOES are obtained by using a frequency based least square estimation algorithm. Based on the estimated LOES, a numerical optimization algorithm is then utilized for aggressive trajectory generation when relevant constraints are given. The generated trajectory can lead to the quadrotor and load system passing through a narrow window with a cascade PD trajectory tracking controller. Finally, a practical flight test based on an Astec Hummingbird quadrotor is demonstrated and the result validates the proposed approach

A New Analytic Alignment Method for a SINS

Analytic alignment is a type of self-alignment for a Strapdown inertial navigation system (SINS) that is based solely on two non-collinear vectors, which are the gravity and rotational velocity vectors of the Earth at a stationary base on the ground. The attitude of the SINS with respect to the Earth can be obtained directly using the TRIAD algorithm given two vector measurements. For a traditional analytic coarse alignment, all six outputs from the inertial measurement unit (IMU) are used to compute the attitude. In this study, a novel analytic alignment method called selective alignment is presented. This method uses only three outputs of the IMU and a few properties from the remaining outputs such as the sign and the approximate value to calculate the attitude. Simulations and experimental results demonstrate the validity of this method, and the precision of yaw is improved using the selective alignment method compared to the traditional analytic coarse alignment method in the vehicle experiment. The selective alignment principle provides an accurate relationship between the outputs and the attitude of the SINS relative to the Earth for a stationary base, and it is an extension of the TRIAD algorithm. The selective alignment approach has potential uses in applications such as self-alignment, fault detection, and self-calibration

Probe optimization for quantum metrology via closed-loop learning control

Experimentally achieving the precision that standard quantum metrology schemes promise is always challenging. Recently, additional controls were applied to design feasible quantum metrology schemes. However, these approaches generally does not consider ease of implementation, raising technological barriers impeding its realization. In this paper, we circumvent this problem by applying closed-loop learning control to propose a practical controlled sequential scheme for quantum metrology. Purity loss of the probe state, which relates to quantum Fisher information, is measured efficiently as the fitness to guide the learning loop. We confirm its feasibility and certain superiorities over standard quantum metrology schemes by numerical analysis and proof-of-principle experiments in a nuclear magnetic resonance (NMR) system

Pyrimidine metabolism regulator-mediated molecular subtypes display tumor microenvironmental hallmarks and assist precision treatment in bladder cancer

BackgroundBladder cancer (BLCA) is a common urinary system malignancy with a significant morbidity and death rate worldwide. Non-muscle invasive BLCA accounts for over 75% of all BLCA cases. The imbalance of tumor metabolic pathways is associated with tumor formation and proliferation. Pyrimidine metabolism (PyM) is a complex enzyme network that incorporates nucleoside salvage, de novo nucleotide synthesis, and catalytic pyrimidine degradation. Metabolic reprogramming is linked to clinical prognosis in several types of cancer. However, the role of pyrimidine metabolism Genes (PyMGs) in the BLCA-fighting process remains poorly understood.MethodsPredictive PyMGs were quantified in BLCA samples from the TCGA and GEO datasets. TCGA and GEO provided information on stemness indices (mRNAsi), gene mutations, CNV, TMB, and corresponding clinical features. The prediction model was built using Lasso regression. Co-expression analysis was conducted to investigate the relationship between gene expression and PyM.ResultsPyMGs were overexpressed in the high-risk sample in the absence of other clinical symptoms, demonstrating their predictive potential for BLCA outcome. Immunological and tumor-related pathways were identified in the high-risk group by GSWA. Immune function and m6a gene expression varied significantly between the risk groups. In BLCA patients, DSG1, C6orf15, SOST, SPRR2A, SERPINB7, MYBPH, and KRT1 may participate in the oncology process. Immunological function and m6a gene expression differed significantly between the two groups. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all showed significant gene connections.ConclusionsBLCA-associated PyMGs are available to provide guidance in the prognostic and immunological setting and give evidence for the formulation of PyM-related molecularly targeted treatments. PyMGs and their interactions with immune cells in BLCA may serve as therapeutic targets

Proteomic analysis for phenanthrene-elicited wheat chloroplast deformation

The exposure of polycyclic aromatic hydrocarbons (PAHs) can cause wheat leaf chlorosis. Thus, we hypothesize that chloroplast inner structure damage is the reason for leaf chlorosis. This study was conducted with the wheat seedlings exposed to Hoagland nutrient solution containing 1.0 mg L−1 phenanthrene for 9 days. Subcellular observation showed that chloroplast turns round and loses its structural integrity. Herein, iTRAQ (isobaric tag for relative and absolute quantification) was applied to analyze the changes of protein profile in chloroplast exposed to phenanthrene. A total of 517 proteins are identified, 261 of which are up-regulated. Eight proteins related with thylakoid (the structural component of chloroplast) are down-regulated and the expression of related genes further confirms the proteomic results through real-time PCR under phenanthrene treatment, suggesting that the thylakoid destruction is the reason for chloroplast deformation. Four proteins related with envelope and stroma are up-regulated, and this is the reason why chloroplast remains round. This study is useful in discussing the carcinogenic and teratogenic effects of PAHs in plant cells in the environment, and provides necessary knowledge for improving crop resistance to PAH pollution

Construction and validation of prognostic signatures related to mitochondria and macrophage polarization in gastric cancer

BackgroundIncreasing evidence reveals the involvement of mitochondria and macrophage polarisation in tumourigenesis and progression. This study aimed to establish mitochondria and macrophage polarisation-associated molecular signatures to predict prognosis in gastric cancer (GC) by single-cell and transcriptional data.MethodsInitially, candidate genes associated with mitochondria and macrophage polarisation were identified by differential expression analysis and weighted gene co-expression network analysis. Subsequently, candidate genes were incorporated in univariateCox analysis and LASSO to acquire prognostic genes in GC, and risk model was created. Furthermore, independent prognostic indicators were screened by combining risk score with clinical characteristics, and a nomogram was created to forecast survival in GC patients. Further, in single-cell data analysis, cell clusters and cell subpopulations were yielded, followed by the completion of pseudo-time analysis. Furthermore, a more comprehensive immunological analysis was executed to uncover the relationship between GC and immunological characteristics. Ultimately, expression level of prognostic genes was validated through public datasets and qRT-PCR.ResultsA risk model including six prognostic genes (GPX3, GJA1, VCAN, RGS2, LOX, and CTHRC1) associated with mitochondria and macrophage polarisation was developed, which was efficient in forecasting the survival of GC patients. The GC patients were categorized into high-/low-risk subgroups in accordance with median risk score, with the high-risk subgroup having lower survival rates. Afterwards, a nomogram incorporating risk score and age was generated, and it had significant predictive value for predicting GC survival with higher predictive accuracy than risk model. Immunological analyses revealed showed higher levels of M2 macrophage infiltration in high-risk subgroup and the strongest positive correlation between risk score and M2 macrophages. Besides, further analyses demonstrated a better outcome for immunotherapy in low-risk patients. In single-cell and pseudo-time analyses, stromal cells were identified as key cells, and a relatively complete developmental trajectory existed for stromal C1 in three subclasses. Ultimately, expression analysis revealed that the expression trend of RGS2, GJA1, GPX3, and VCAN was consistent with the results of the TCGA-GC dataset.ConclusionOur findings demonstrated that a novel prognostic model constructed in accordance with six prognostic genes might facilitate the improvement of personalised prognosis and treatment of GC patients