Using planet migration and dust drift to weigh protoplanetary discs

ALMA has spatially resolved over 200 annular structures in protoplanetary discs, many of which are suggestive of the presence of planets. Constraining the mass of these putative planets is quite degenerate for it depends on the disc physical properties, and for simplicity a steady-state is often assumed whereby the planet position is kept fixed and there is a constant source of dust at the outer edge of the disc. Here, we argue against this approach by demonstrating how the planet and dust dynamics can lift degeneracies of such steady-state models. We take main disc parameters from the well-known protoplanetary disc HD 163296 with a suspected planet at R ≈ 86 au as an example. By running gas and dust hydrodynamical simulations post-processed with dust radiative transfer calculations, we first find steady-state disc and planet parameters that reproduce ALMA continuum observations fairly well. For the same disc mass, but now allowing the planet to migrate in the simulation, we find that the planet undergoes runaway migration and reaches the inner disc in ∼0.2 Myr. Further, decreasing the disc mass slows down planet migration, but it then also increases the dust's radial drift, thereby depleting the disc dust faster. We find that the opposing constraints of planet migration and dust drift require the disc mass to be at most 0.025 M⊙, must less massive than previously estimated, and for the dust to be porous rather than compact. We propose that similar analysis should be extended to other sources with suspected planetary companions

Principal weighted support vector machines for sufficient dimension reduction in binary classification

© 2017 Biometrika Trust. Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are studied, and an efficient computing algorithm is proposed. Numerical examples demonstrate its performance in binary classification

A general and transferable deep learning framework for predicting phase formation in materials

Machine learning has been widely exploited in developing new materials. However, challenges still exist: small dataset is common for most tasks; new datasets, special descriptors and specific models need to be built from scratch when facing a new task; knowledge cannot be readily transferred between independent models. In this paper we propose a general and transferable deep learning (GTDL) framework for predicting phase formation in materials. The proposed GTDL framework maps raw data to pseudo-images with some special 2-D structure, e.g., periodic table, automatically extracts features and gains knowledge through convolutional neural network, and then transfers knowledge by sharing features extractors between models. Application of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems; for high-entropy alloys the GTDL framework can discriminate five types phases (BCC, FCC, HCP, amorphous, mixture) with accuracy and recall above 94% in fivefold cross-validation. In addition, periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small dataset. This method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials

Performance Study of Cognitive Relay NOMA Networks with Dynamic Power Transmission

The cooperative non-orthogonal multiple access (NOMA) networks with one pair primary user and one pair cognitive user share the same spectrum resource via a common relay is considered in this paper. We propose a dynamic power transmission scheme for both uplink and downlink NOMA transmission in cognitive relay networks, which preserves the quality of service for the primary user. The closed-form expressions of overall outage probability and average sum rate for the proposed dynamic power transmission scheme of cognitive relay NOMA networks are derived. Both developed analytical results and Monte Carlo simulations show that the proposed dynamic power control scheme can dramatically enhance performance gain for the proposed networks, compared to other existing NOMA power allocation schemes

A Social Activity-Based Control Model for Rumor Propagation

Abstract In this paper, we introduce the concept of “social activity” to describe individual behavior on social networks, acknowledging its potential impact on rumor propagation within complex networks. With this in mind, we develop a dynamic model of rumor propagation based on social behavior and analyze the influence of various parameters on the scale of rumors through static comparison. Using this model, we investigate an optimal solution that balances costs and benefits. Numerical simulations and comparative experiments demonstrate the practical value of these findings for strategies aimed at suppressing rumors

Metabolomics of Pregnancy Complications: Emerging Application of Maternal Hair.

In recent years, the study of metabolomics has begun to receive increasing international attention, especially as it pertains to medical research. This is due in part to the potential for discovery of new biomarkers in the metabolome and to a new understanding of the "exposome", which refers to the endogenous and exogenous compounds that reflect external exposures. Consequently, metabolomics research into pregnancy-related issues has increased. Biomarkers discovered through metabolomics may shed some light on the etiology of certain pregnancy-related complications and their adverse effects on future maternal health and infant development and improve current clinical management. The discoveries and methods used in these studies will be compiled and summarized within the following paper. A further focus of this paper is the use of hair as a biological sample, which is gaining increasing attention across diverse fields due to its noninvasive sampling method and the metabolome stability. Its significance in exposome studies will be considered in this review, as well as the potential to associate exposures with adverse pregnancy outcomes. Currently, hair has been used in only two metabolomics studies relating to fetal growth restriction (FGR) and gestational diabetes mellitus (GDM)

TRPM7 in CHBP-induced renoprotection upon ischemia reperfusion-related injury.

Transient receptor potential melastatin 7 (TRPM7) is a membrane ion channel and kinase. TRPM7 was abundantly expressed in the kidney, and up-regulated by ischemia reperfusion (IR) injury. Our previous studies showed that cyclic helix B peptide (CHBP) improved renal IR-related injury, but its underlying mechanism is not well defined. IR-related injury was established in renal tubular epithelial cells (TCMK-1 and HK-2) via 12 to 24-h hypoxia (H) followed by 2-24 h reoxygenation (R), and in mouse kidneys subjected to 30-min ischemia and 12-h to 7-day reperfusion. TRPM7-like current in TCMK-1 cells, TRPM7 mRNA and protein in the in vitro and in vivo models were increased, but reversed by CHBP. TRPM7 was also positively associated with LDH, HMGB1, caspase-3, Bax/Bcl-2, inflammation, apoptosis, tubulointerstitial damage and renal function respectively. Furthermore, silencing TRPM7 improved injury parameters, renal histology and function in the both models. Specific TRPM7 agonist, bradykinin, exaggerated HR induced injury in TCMK-1 cells, and partially blocked the renoprotection of CHBP as well. In conclusion, TRPM7 is involved not only in IR-related injury, but also CHBP-induced renoprotection, which are through its ion channel and subsequent affects inflammation and apoptosis. Therefore, TRPM7 could be a potential biomarker for IR-induced acute kidney injury

miR21 modulates the Hippo signaling pathway via interference with PP2A Bβ to inhibit trophoblast invasion and cause preeclampsia

Preeclampsia (PE) is a pregnancy-specific disorder attributed to deficient extravillous trophoblast (EVT) invasion into the uterus, but the mechanism of EVT invasion remains unclear. In this study, we found significantly elevated expression of microRNA 21 (miR21), which negatively regulates trophoblast invasion and migration, in preeclamptic placentae. Whole-genome RNA sequencing revealed that PPP2R2B, which encodes PP2A Bβ, and the Hippo pathway are downstream targets of miR21. The effects of miR21 on trophoblast mobility were abolished in LATS1T1079A/S909A and YAP-5SA mutants. Moreover, we found that PP2A Bβ dephosphorylates LATS1 via direct protein-protein interactions and thus modulates the phosphorylation and subcellular distribution of YAP. PPP2R2B overexpression ameliorated the miR21-induced LATS1-YAP phosphorylation and cytoplasmic sequestration of YAP, which resulted in the rescue of compromised trophoblast invasion and migration. The upregulation of placental miR21 abundance by placenta-specific nanoparticles loaded with agomir-miR21 during placentation interfered with PPP2R2B and activated the Hippo pathway in the placenta, leading to a PE-like phenotype. Thus, aberrant elevation of miR21 impairs EVT mobility by modulating the PP2A Bβ/Hippo axis, which is one of the causes of PE.</p

The Metabolic Signatures of Surviving Cotwins in Cases of Single Intrauterine Fetal Death During Monochorionic Diamniotic Pregnancy: A Prospective Case-Control Study

Introduction: Single intrauterine fetal death (sIUFD) in monochorionic diamniotic (MCDA) twin pregnancy may be associated with adverse clinical outcomes and possible metabolic changes in the surviving co-twin. Metabolomic profiling has not been undertaken before in these complex twin pregnancies. Methods: In this prospectively collected case-control study, three cross-cohort comparisons were made between sIUFD MCDA (n = 16), uncomplicated MCDA (n = 16, eight pairs), and uncomplicated singleton pregnancies (n = 8). To identify major sources of variation within the sIUFD MCDA cohort, a secondary comparison was conducted between spontaneous sIUFD (n = 8) and sIUFD in MCDA twins due to selective termination of a single abnormal fetus by radiofrequency ablation (RFA) (n = 8). Metabolomics analysis of placental tissue and umbilical cord plasma was performed using LC-MS profiling. The underlying metabolic networks and pathways were analyzed by web-based platforms. Associations and statistical correlations of all identified differential metabolites with neonatal birthweight and birth length were assessed by multivariable linear regression, adjusted for maternal age and gestation. Results: Across four comparisons, 131 and 111 differential metabolites were identified in placental tissue and cord plasma, respectively, with the highest variation seen between the spontaneous vs. single-induced IUFD in MCDA twins by RFA in the cord plasma. Conversely, the number of viable fetuses and the presence of sIUFD in MCDA twins had the highest impact on metabolite variation in placental tissue. Compounds correlated with fetal growth including placental acylcarnitines and gangliosides, along with specific amino acids (e.g., histidinyl-hydroxyproline), xenobiotics and biliverdin in cord plasma. Conclusion: sIUFD in MCDA twin pregnancy correlates with distinctive metabolic signatures, mostly in fatty acyls and complex lipids, in placental tissue and cord plasma of the surviving cotwin. Some metabolites are also associated with fetal growth

Proton transport enabled by a field-induced metallic state in a semiconductor heterostructure

Copyright © 2020 The Authors. Tuning a semiconductor to function as a fast proton conductor is an emerging strategy in the rapidly developing field of proton ceramic fuel cells (PCFCs). The key challenge for PCFC researchers is to formulate the proton-conducting electrolyte with conductivity above 0.1 siemens per centimeter at low temperatures (300 to 600°C). Here we present a methodology to design an enhanced proton conductor by means of a NaxCoO2/CeO2 semiconductor heterostructure, in which a field-induced metallic state at the interface accelerates proton transport. We developed a PCFC with an ionic conductivity of 0.30 siemens per centimeter and a power output of 1 watt per square centimeter at 520°C. Through our semiconductor heterostructure approach, our results provide insight into the proton transport mechanism, which may also improve ionic transport in other energy applications