arXiv subjects

Minghan Chen

Publications and source records attributed to Minghan Chen.

33 records · Page 2Linked to original sources

arXiv2023

Post-processing CHARIS integral field spectrograph data with PyKLIP

We present the pyKLIP-CHARIS post-processing pipeline, a Python library that reduces high contrast imaging data for the CHARIS integral field spectrograph used with the SCExAO project on the Subaru Telescope. The pipeline is a part of the pyKLIP package, a Python library dedicated to the reduction of direct imaging data of exoplanets, brown dwarfs, and discs. For PSF subtraction, the pyKLIP-CHARIS post-processing pipeline relies on the core algorithms implemented in pyKLIP but uses image registration and calibrations that are unique to CHARIS. We describe the pipeline procedures, calibration results, and capabilities in processing imaging data acquired via the angular differential imaging and spectral differential imaging observing techniques. We showcase its performance on extracting spectra of injected synthetic point sources as well as compare the extracted spectra from real data sets on HD 33632 and HR 8799 to results in the literature. The pipeline is a python-based complement to the SCExAO project supported, widely used (and currently IDL-based) CHARIS data post-processing pipeline (CHARIS DPP) and provides an additional approach to reducing CHARIS data and extracting calibrated planet spectra.

astro-ph.IM↗

arXiv2023

Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, we propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term trajectories that capture individual progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.

q-bio.QM↗

arXiv2023

Uncovering the System Vulnerability and Criticality of Human Brain under Dynamical Neuropathological Events in Alzheimer's Disease

Background: Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-\b{eta} (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive. Objectives: To disentangle the massive heterogeneities in AD progressions and identify vulnerable/critical brain regions to AD pathology. Methods: In this work, we characterized the interaction of AT[N] biomarkers and their propagation across brain networks using a novel bistable reaction-diffusion model, which allows us to establish a new systems biology underpinning of Alzheimer's disease (AD) progression. We applied our model to large-scale longitudinal neuroimages from the ADNI database and studied the systematic vulnerability and criticality of brains. Results: Our model yields long term prediction that is statistically significant linear correlated with temporal imaging data, produces clinically consistent risk prediction, and captures the Braak-like spreading pattern of AT[N] biomarkers in AD development. Conclusion: Our major findings include (i) tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed critical brain regions outperform hub nodes in transmitting disease factors across the brain, and (iv) comparing the spread of neuropathological burdens caused by amyloid-\b{eta} and tau diffusions, disruption of metabolic balance is the most determinant factor contributing to the initiation and progression of Alzheimer's disease.

q-bio.QM↗

arXiv2023

Dynamical Masses and Ages of Sirius-like Systems

We measure precise orbits and dynamical masses and derive age constraints for six confirmed and one candidate Sirius-like systems, including the Hyades member HD 27483. Our orbital analysis incorporates radial velocities, relative astrometry, and Hipparcos-Gaia astrometric accelerations. We constrain the main-sequence lifetime of a white dwarf's progenitor from the remnant's dynamical mass and semi-empirical initial-final mass relations and infer the cooling age from mass and effective temperature. We present new relative astrometry of HD 27483 B from Keck/NIRC2 observations and archival HST data, and obtain the first dynamical mass of ${0.798}_{-0.041}^{+0.10}$ $M_{\odot}$, and an age of ${450}_{-180}^{+570}$ Myr, consistent with previous age estimates of Hyades. We also measure precise dynamical masses for HD 114174 B ($0.591 \pm 0.011$ $M_{\odot}$) and HD 169889 B (${0.526}_{-0.037}^{+0.039}$ $M_{\odot}$), but their age precisions are limited by their uncertain temperatures. For HD 27786 B, the unusually small mass of $0.443 \pm 0.012$ $M_{\odot}$ suggests a history of rapid mass loss, possibly due to binary interaction in its progenitor's AGB phase. The orbits of HD 118475 and HD 136138 from our RV fitting are overall in good agreement with Gaia DR3 astrometric two-body solutions, despite moderate differences in the eccentricity and period of HD 136138. The mass of ${0.580}_{-0.039}^{+0.052}$ $M_{\odot}$ for HD 118475 B and a speckle imaging non-detection confirms that the companion is a white dwarf. Our analysis shows examples of a rich number of precise WD dynamical mass measurements enabled by Gaia DR3 and later releases, which will improve empirical calibrations of the white dwarf initial-final mass relation.

astro-ph.SR↗

arXiv2023

Surveying Nearby Brown Dwarfs with HGCA: Direct Imaging Discovery of a Faint, High-Mass Brown Dwarf Orbiting HD 176535 A

Brown dwarfs with well-measured masses, ages and luminosities provide direct benchmark tests of substellar formation and evolutionary models. We report the first results from a direct imaging survey aiming to find and characterize substellar companions to nearby accelerating stars with the assistance of the Hipparcos-Gaia Catalog of Accelerations (HGCA). In this paper, we present a joint high-contrast imaging and astrometric discovery of a substellar companion to HD 176535 A, a K3.5V main-sequence star aged approximately $3.59_{-1.15}^{+0.87}$ Gyrs at a distance of $36.99 \pm 0.03$ pc. In advance of our high-contrast imaging observations, we combined precision HARPS RVs and HGCA astrometry to predict the potential companion's location and mass. We thereafter acquired two nights of KeckAO/NIRC2 direct imaging observations in the $L'$ band, which revealed a companion with a contrast of $ΔL'_p = 9.20\pm0.06$ mag at a projected separation of $\approx$0.$\!\!''35$ ($\approx$13 AU) from the host star. We revise our orbital fit by incorporating our dual-epoch relative astrometry using the open-source MCMC orbit fitting code $\tt orvara$. HD 176535 B is a new benchmark dwarf useful for constraining the evolutionary and atmospheric models of high-mass brown dwarfs. We found a luminosity of $\rm log(L_{bol}/L_{\odot}) = -5.26\pm0.07$ and a model-dependent effective temperature of $980 \pm 35$ K for HD 176535 B. Our dynamical mass suggests that some substellar evolutionary models may be underestimating luminosity for high-mass T dwarfs. Given its angular separation and luminosity, HD 176535 B would make a promising candidate for Aperture Masking Interferometry with JWST and GRAVITY/KPIC, and further spectroscopic characterization with instruments like the CHARIS/SCExAO/Subaru integral field spectrograph.

astro-ph.SR↗

arXiv2022

Astrometric Accelerations as Dynamical Beacons: Discovery and Characterization of HIP 21152 B, the First T-Dwarf Companion in the Hyades

Benchmark brown dwarf companions with well-determined ages and model-independent masses are powerful tools to test substellar evolutionary models and probe the formation of giant planets and brown dwarfs. Here, we report the independent discovery of HIP~21152~B, the first imaged brown dwarf companion in the Hyades, and conduct a comprehensive orbital and atmospheric characterization of the system. HIP~21152 was targeted in an ongoing high-contrast imaging campaign of stars exhibiting proper motion changes between Hipparcos and Gaia, and was also recently identified by Bonavita et al. (2022) and Kuzuhara et al. (2022). Our Keck/NIRC2 and SCExAO/CHARIS imaging of HIP~21152 revealed a comoving companion at a separation of $0.37^{\prime\prime}$ (16 au). We perform a joint orbit fit of all available relative astrometry and radial velocities together with the Hipparcos-Gaia proper motions, yielding a dynamical mass of $24^{+6}_{-4}\,\mathrm{M_{Jup}}$, which is $1{-}2σ$ lower than evolutionary model predictions. Hybrid grids that include the evolution of cloud properties best reproduce the dynamical mass. We also identify a comoving wide-separation ($1837^{\prime\prime}$ or $7.9 \times 10^4 \, \mathrm{au}$) early-L dwarf with an inferred mass near the hydrogen-burning limit. Finally, we analyze the spectra and photometry of HIP~21152~B using the Saumon & Marley (2008) atmospheric models and a suite of retrievals. The best-fit grid-based models have $f_{\mathrm{sed}}=2$, indicating the presence of clouds, $T_{\mathrm{eff}}=1400 \, \mathrm{K}$, and $\log{g}=4.5 \, \mathrm{dex}$. These results are consistent with the object's spectral type of $\mathrm{T0\pm1}$. As the first benchmark brown dwarf companion in the Hyades, HIP~21152~B joins the small but growing number of substellar companions with well-determined ages and dynamical masses.

astro-ph.SR↗

arXiv2022

The Dynamical Mass of the Coma Cluster from Deep Learning

In 1933, Fritz Zwicky's famous investigations of the mass of the Coma cluster led him to infer the existence of dark matter \cite{1933AcHPh...6..110Z}. His fundamental discoveries have proven to be foundational to modern cosmology; as we now know such dark matter makes up 85\% of the matter and 25\% of the mass-energy content in the universe. Galaxy clusters like Coma are massive, complex systems of dark matter in addition to hot ionized gas and thousands of galaxies, and serve as excellent probes of the dark matter distribution. However, empirical studies show that the total mass of such systems remains elusive and difficult to precisely constrain. Here, we present new estimates for the dynamical mass of the Coma cluster based on Bayesian deep learning methodologies developed in recent years. Using our novel data-driven approach, we predict Coma's $\mthc$ mass to be $10^{15.10 \pm 0.15}\ \hmsun$ within a radius of $1.78 \pm 0.03\ h^{-1}\mathrm{Mpc}$ of its center. We show that our predictions are rigorous across multiple training datasets and statistically consistent with historical estimates of Coma's mass. This measurement reinforces our understanding of the dynamical state of the Coma cluster and advances rigorous analyses and verification methods for empirical applications of machine learning in astronomy.

astro-ph.CO↗

arXiv2022

Precise Dynamical Masses of Epsilon Indi Ba and Bb: Evidence of Slowed Cooling at the L/T Transition

We report individual dynamical masses of $66.92 \pm 0.36 \; M_{Jup}$ and $53.25 \pm 0.29 \; M_{Jup}$ for the binary brown dwarfs $\varepsilon$ Indi Ba and Bb, measured from long term ($\approx 10$ yr) relative orbit monitoring and absolute astrometry monitoring data on the VLT. Relative astrometry with NACO fully constrains the Keplerian orbit of the binary pair, while absolute astrometry with FORS2 measures the system's parallax and mass ratio. We find a parallax consistent with the Hipparcos and Gaia values for $\varepsilon$ Indi A, and a mass ratio for $\varepsilon$ Indi Ba to Bb precise to better than $0.2\%$. $\varepsilon$ Indi Ba and Bb have spectral types T1-1.5 and T6, respectively. With an age of $3.5^{+0.8}_{-1.0}$ Gyr from $\varepsilon$ Indi A's activity, these brown dwarfs provide some of the most precise benchmarks for substellar cooling models. Assuming coevality, the very different luminosities of the two brown dwarfs and our moderate mass ratio imply a steep mass-luminosity relationship $L \propto M^{5.37 \pm 0.08}$ that can be explained by a slowed cooling rate in the L/T transition, as previously observed for other L/T binaries. Finally, we present a periodogram analysis of the near-infrared photometric data, but find no definitive evidence of periodic signals with a coherent phase.

astro-ph.SR↗

arXiv2022

Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data

Motivation: Lung cancer is one of the leading causes for cancer-related death, with a five-year survival rate of 18%. It is a priority for us to understand the underlying mechanisms that affect the implementation and effectiveness of lung cancer therapeutics. In this study, we combine the power of Bioinformatics and Systems Biology to comprehensively uncover functional and signaling pathways of drug treatment using bioinformatics inference and multiscale modeling of both scRNA-seq data and proteomics data. The innovative and cross-disciplinary approach can be further applied to other computational studies in tumorigenesis and oncotherapy. Results: A time series of lung adenocarcinoma-derived A549 cells after DEX treatment were analysed. (1) We first discovered the differentially expressed genes in those lung cancer cells. Then through the interrogation of their regulatory network, we identified key hub genes including TGF-\b{eta}, MYC, and SMAD3 varied underlie DEX treatment. Further enrichment analysis revealed the TGF-\b{eta} signaling pathway as the top enriched term. Those genes involved in the TGF-\b{eta} pathway and their crosstalk with the ERBB pathway presented a strong survival prognosis in clinical lung cancer samples. (2) Based on biological validation and further curation, a multiscale model of tumor regulation centered on both TGF-\b{eta}-induced and ERBB-amplified signaling pathways was developed to characterize the dynamics effects of DEX therapy on lung cancer cells. Our simulation results were well matched to available data of SMAD2, FOXO3, TGF\b{eta}1, and TGF\b{eta}R1 over the time course. Moreover, we provided predictions of different doses to illustrate the trend and therapeutic potential of DEX treatment.

q-bio.QM↗

arXiv2021

Improved Dynamical Masses for Six Brown Dwarf Companions Using Hipparcos and Gaia EDR3

We present comprehensive orbital analyses and dynamical masses for the substellar companions Gl~229~B, Gl~758~B, HD~13724~B, HD~19467~B, HD~33632~Ab, and HD~72946~B. Our dynamical fits incorporate radial velocities, relative astrometry, and most importantly calibrated Hipparcos-Gaia EDR3 accelerations. For HD~33632~A and HD~72946 we perform three-body fits that account for their outer stellar companions. We present new relative astrometry of Gl~229~B with Keck/NIRC2, extending its observed baseline to 25 years. We obtain a $<$1\% mass measurement of $71.4 \pm 0.6\,M_{\rm Jup}$ for the first T dwarf Gl~229~B and a 1.2\% mass measurement of its host star ($0.579 \pm 0.007\,M_{\odot}$) that agrees with the high-mass-end of the M dwarf mass-luminosity relation. We perform a homogeneous analysis of the host stars' ages and use them, along with the companions' measured masses and luminosities, to test substellar evolutionary models. Gl~229~B is the most discrepant, as models predict that an object this massive cannot cool to such a low luminosity within a Hubble time, implying that it may be an unresolved binary. The other companions are generally consistent with models, except for HD~13724~B that has a host-star activity age 3.8$σ$ older than its substellar cooling age. Examining our results in context with other mass-age-luminosity benchmarks, we find no trend with spectral type but instead note that younger or lower-mass brown dwarfs are over-luminous compared to models, while older or higher-mass brown dwarfs are under-luminous. The presented mass measurements for some companions are so precise that the stellar host ages, not the masses, limit the analysis.

astro-ph.SR↗

arXiv2021

Analyzing the Spatiotemporal Interaction and Propagation of ATN Biomarkers in Alzheimer's Disease using Longitudinal Neuroimaging Data

Three major biomarkers: beta-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimer's disease. While there are extensive studies on cerebrospinal fluids biomarkers (amyloid, tau), the spatial propagation pattern across brain is missing and their interactive mechanisms with neurodegeneration are still unclear. To this end, we aim to analyze the spatiotemporal associations between ATN biomarkers using large-scale neuroimaging data. We first investigate the temporal appearances of amyloid plaques, tau tangles, and neuronal loss by modeling the longitudinal transition trajectories. Second, we propose linear mixed-effects models to quantify the pathological interactions and propagation of ATN biomarkers at each brain region. Our analysis of the current data shows that there exists a temporal latency in the build-up of amyloid to the onset of tau pathology and neurodegeneration. The propagation pattern of amyloid can be characterized by its diffusion along the topological brain network. Our models provide sufficient evidence that the progression of pathological tau and neurodegeneration share a strong regional association, which is different from amyloid.

q-bio.QM↗

arXiv2020

A Network-Guided Reaction-Diffusion Model of AT[N] Biomarkers in Alzheimer's Disease

Currently, many studies of Alzheimer's disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages. However, a system-level mechanism of how these neuropathological burdens promote neurodegeneration and why AD exhibits characteristic progression is largely elusive. In this study, we combined the power of systems biology and network neuroscience to understand the dynamic interaction and diffusion process of AT[N] biomarkers from an unprecedented amount of longitudinal Amyloid PET scan, MRI imaging, and DTI data. Specifically, we developed a network-guided biochemical model to jointly (1) model the interaction of AT[N] biomarkers at each brain region and (2) characterize their propagation pattern across the fiber pathways in the structural brain network, where the brain resilience is also considered as a moderator of cognitive decline. Our biochemical model offers a greater mathematical insight to understand the physiopathological mechanism of AD progression by studying the system dynamics and stability. Thus, an in-depth system-level analysis allows us to gain a new understanding of how AT[N] biomarkers spread throughout the brain, capture the early sign of cognitive decline, and predict the AD progression from the preclinical stage.

q-bio.NC↗

arXiv2020

Finding Acceptable Parameter Regions of Stochastic Hill functions for Multisite Phosphorylation Mechanism

Multisite phosphorylation plays an important role in regulating switchlike protein activity and has been used widely in mathematical models. With the development of new experimental techniques and more molecular data, molecular phosphorylation processes emerge in many systems with increasing complexity and sizes. These developments call for simple yet valid stochastic models to describe various multisite phosphorylation processes, especially in large and complex biochemical networks. To reduce model complexity, this work aims to simplify the multisite phosphorylation mechanism by a stochastic Hill function model. Further, this work optimizes regions of parameter space to match simulation results from the stochastic Hill function with the distributive multisite phosphorylation process. While traditional parameter optimization methods have been focusing on finding the best parameter vector, in most circumstances modelers would like to find a set of parameter vectors that generate similar system dynamics and results. This paper proposes a general $α$-$β$-$γ$ rule to return an acceptable parameter region of the stochastic Hill function based on a quasi-Newton stochastic optimization (QNSTOP) algorithm. Different objective functions are investigated characterizing different features of the simulation-based empirical data, among which the approximate maximum log-likelihood method is recommended for general applications. Numerical results demonstrate that with an appropriate parameter vector value, the stochastic Hill function model depicts the multisite phosphorylation process well except the initial (transient) period.

q-bio.MN↗

arXiv2020

Design and Implementation of A Novel Precision Irrigation Robot Based on An Intelligent Path Planning Algorithm

The agricultural irrigation system is closely related to agricultural production. There are some problems in nowadays agricultural irrigation system, such as poor mobility, imprecision and high price. To address these issues, an intelligent irrigation robot is designed and implemented in this work. The robot achieves precise irrigation by the irrigation path planning algorithm which is improved by Bayesian theory. In the proposed algorithm, we utilize as much information as possible to achieve full coverage irrigation in the complex agricultural environment. Besides, we propose the maximum risk to avoid the problem of lack of inspection in certain areas. Finally, We carried out simulation experiments and field experiments to verify the robot and the algorithm. The experimental results indicate that the robot is capable of fulfilling the requirements of various agricultural irrigation tasks.

cs.RO↗

arXiv2020

Encryption Algorithm for TCP Session Hijacking

Distributed network of the computer and the design defects of the TCP protocol are given to the network attack to be multiplicative. Based on the simple and open assumptions of the TCP protocol in academic and collaborative communication environments, the protocol lacks secure authentication. In this paper, by adding RSA-based cryptography technology, RSA-based signature technology, DH key exchange algorithm, and HAMC-SHA1 integrity verification technology to the TCP protocol, and propose a security strategy which can effectively defend against TCP session hijacking.

cs.CR↗