Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiomimetic In Vitro Human Models for Viral Infection in the Liver

Viral hepatitis is a leading cause of liver morbidity and mortality globally. The mechanisms underlying acute infection and clearance, versus the development of chronic infection, are poorly understood. In vitro models of viral hepatitis circumvent the high costs and ethical considerations of animal models, which also translate poorly to studying the human-specific hepatitis viruses. However, significant challenges are associated with modeling long-term infection in vitro. Differentiated hepatocytes are best able to sustain chronic viral hepatitis infection, but standard two-dimensional models are limited because they fail to mimic the architecture and cellular microenvironment of the liver, and cannot maintain a differentiated hepatocyte phenotype over extended periods. Alternatively, physiomimetic models facilitate important interactions between hepatocytes and their microenvironment by incorporating liver-specific environmental factors such as three-dimensional ECM interactions and co-culture with non-parenchymal cells. These physiologically relevant interactions help maintain a functional hepatocyte phenotype that is critical for sustaining viral hepatitis infection. In this review, we provide an overview of distinct, novel, and innovative in vitro liver models and discuss their functionality and relevance in modeling viral hepatitis. These platforms may provide novel insight into mechanisms that regulate viral clearance versus progression to chronic infections that can drive subsequent liver disease.
Survival rates and outcomes of pregnancies with prenatal diagnosis of trisomy 18: A 16‐year experience from a public hospital in South Africa

Many studies, largely from high‐income countries (HIC), have reported outcomes in babies with trisomy 18 (T18), with a paucity of data from Africa. Knowledge of outcomes is important in counselling women prenatally diagnosed with T18. We aimed to review all prenatally diagnosed cases of T18 between January 2006 and December 2021.
Ambient PM2.5 exposure causes cellular senescence via DNA damage, micronuclei formation, and cGAS activation

Abstract Ambient PM2.5 is one of the environmental risk factors and was correlated with senescence-related diseases based on the epidemiologic investigation. However, little is known about senescence induced by PM2.5 as well as the underlying mechanisms. In this study, we demonstrated that PM2.5 exposure aggravated cellular senescence in vivo and in vitro, and disrupted micronuclei (MN) played a vital role in this process. Our results suggested that the nuclear envelope (NE) of PM2.5-induced MN was ruptured. Subsequently, cGAS was found to localize to approximately 80% of the disrupted MN but few for intact MN. Upon examination of cGAMP and SA-β-Gal, the cGAS-STING pathway was found activated and related to cellular senescence induced by PM2.5. Taken together, we reported a novel finding that PM2.5 exposure causes cellular senescence via DNA damage, MN formation, and cGAS activation. These results revealed the potential toxicity of PM2.5 and its related mechanisms in cellular senescence.
Analytical Data Transformations in Space–Time Region: Three Stories of Space–Time Cube

In this era of abundant space–time geographic information systems (GIS) data, one challenge in GIScience is to adapt and improve current GIS environments to facilitate new ways of space–time thinking and to fully exploit the information in these data. This article focuses on the use of spatiotemporal data transformations using the construct of a space–time cube, which is a space–time coordinate space with geographical horizontal axes and a temporal vertical axis. I have chosen three examples in which the concept of space–time cubes is applied to different themes and on different scales to illustrate how diverse subjects can be interpreted with space–time analysis and transformations of space–time data. The transformation functions, such as space–time overlays, interpolation, and surface operations, can be understood as natural extensions of popular analytical operations already present in current GIS and should be intrinsic in the next generation of GIS to handle the dense space–time information that is available to us.
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 (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 24−4+6MJup , 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″ or 7.9 × 104 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 sed = 2, indicating the presence of clouds, T eff = 1400 K, and logg=4.5dex . These results are consistent with the object’s spectral type of T0 ± 1. 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.
Posterior Predictive $p$-Values

Extending work of Rubin, this paper explores a Bayesian counterpart of the classical $p$-value, namely, a tail-area probability of a "test statistic" under a null hypothesis. The Bayesian formulation, using posterior predictive replications of the data, allows a "test statistic" to depend on both data and unknown (nuisance) parameters and thus permits a direct measure of the discrepancy between sample and population quantities. The tail-area probability for a "test statistic" is then found under the joint posterior distribution of replicate data and the (nuisance) parameters, both conditional on the null hypothesis. This posterior predictive $p$-value can also be viewed as the posterior mean of a classical $p$-value, averaging over the posterior distribution of (nuisance) parameters under the null hypothesis, and thus it provides one general method for dealing with nuisance parameters. Two classical examples, including the Behrens-Fisher problem, are used to illustrate the posterior predictive $p$-value and some of its interesting properties, which also reveal a new (Bayesian) interpretation for some classical $p$-values. An application to multiple-imputation inference is also presented. A frequency evaluation shows that, in general, if the replication is defined by new (nuisance) parameters and new data, then the Type I frequentist error of an $\alpha$-level posterior predictive test is often close to but less than $\alpha$ and will never exceed $2\alpha$.
Deep learning for structural health monitoring: An application to heritage structures

Thanks to recent advancements in numerical methods, computer power, and monitoring technology, seismic ambient noise provides precious information about the structural behavior of old buildings. The measurement of the vibrations produced by anthropic and environmental sources and their use for dynamic identification and structural health monitoring of buildings initiated an emerging, cross-disciplinary field engaging seismologists, engineers, mathematicians, and computer scientists. In this work, we employ recent deep learning techniques for time-series forecasting to inspect and detect anomalies in the large dataset recorded during a long-term monitoring campaign conducted on the San Frediano bell tower in Lucca. We frame the problem as an unsupervised anomaly detection task and train a Temporal Fusion Transformer to learn the normal dynamics of the structure. We then detect the anomalies by looking at the differences between the predicted and observed frequencies.
The photon-index–time-lag correlation in black hole X-ray binaries

NDK acknowledges a useful discussion with Emrah Kalemci regarding the correlation of time lag with spectral index in his PhD Thesis. This discussion sparked the research reported in this paper. This work was supported in part by the 'AGNQUEST' project, which is implemented under the 'Aristeia II' Action of the 'Education and Lifelong Learning' operational programme of the GSRT, Greece.
Kondo insulator description of spin state transition in FeSb2

The thermal expansion and heat capacity of $\mathrm{Fe}{\mathrm{Sb}}_{2}$ at ambient pressure agrees with a picture of a temperature induced spin state transition within the Fe ${t}_{2g}$ multiplet. However, high pressure powder diffraction data show no sign of a structural phase transition up to 7 GPa. A bulk modulus $\mathrm{B}=84(3)$ GPa has been extracted and the temperature dependence of the Gr\"uneisen parameter has been determined. We discuss here the relevance of a Kondo insulator description for this material.
The Solar Neighborhood. XII. Discovery of New High Proper Motion Stars with μ ≥ 04 yr−1 between Declinations −90° and −47°

We report the discovery of 141 new high proper motion systems (μ ≥ 0.″4 yr-1) in the southern sky (δ = -90° to -47°) brighter than UKST plate R59F = 16.5 via our SuperCOSMOS-RECONS search. When combined with the nine systems having μ ≥ 1. ″0 yr-1 and/or late spectral type from the initial phases of this effort, we find that 73 of the 150 total systems are moving faster than 0. ″5 yr-1 and are therefore new members of the classic Luyten Half-Second sample. These constitute a 21% increase in the sample of stars with μ ≥ 0. ″5 yr-1 in the declination region searched, thereby comprising an important addition to this long-neglected region of the sky. Distance estimates are provided for the entire sample, based on a combination of photographic plate magnitudes and Two Micron All Sky Survey photometry, using the relations recently presented by Hambly et al. for the presumed main-sequence stars. Three systems are anticipated to be within 10 pc, and an additional 15 are within 25 pc. Nine of these 18 nearby systems have proper motions falling between 0. ″4 and 0. ″6 yr-1, hinting at a large population of nearby stars with fast, but not extremely high, proper motions that have not been thoroughly investigated.
High-resolution Radio Study of the Dragonfly Pulsar Wind Nebula Powered by PSR J2021+3651

The Dragonfly Nebula (G75.2+0.1) powered by the young pulsar J2021+3651 is a rare pulsar wind nebula (PWN) that shows double tori and polar jets enclosed by a bow-shock structure in X-rays. We present new radio observations of this source taken with the Very Large Array at 6 GHz. The radio PWN has an overall size about two times as large as the X-ray counterpart, consisting of a bright main-body region in the southwest, a narrow and fainter bridge region in the northeast, and a dark gap in between. The nebula shows a radio spectrum much softer than that of a typical PWN. This could be resulting from compression by the ram pressure as the system travels mildly supersonically in the interstellar medium. Our polarization maps reveal a highly ordered and complex B-field structure. This can be explained by a toroidal field distorted by the pulsar motion.
Group Theoretical Quantization of a Phase Space S^1 x R^+ and the Mass Spectrum of Schwarzschild Black Holes in D Space-Time Dimensions

The symplectic reduction of pure spherically symmetric (Schwarzschild) classical gravity in D space-time dimensions yields a 2-dimensional phase space of observables consisting of the Mass M (>0) and a canonically conjugate (Killing) time variable T. Imposing (mass-dependent) periodic boundary conditions in time on the associated quantum mechanical plane waves which represent the Schwarzschild system in the period just before or during the formation of a black hole, yields an energy spectrum of the hole which realizes the old Bekenstein postulate that the quanta of the horizon A_{D-2} are multiples of a basic area quantum. In the present paper it is shown that the phase space of such a Schwarzschild black hole in D space-time dimensions is symplectomorphic to a symplectic manifold S={(phi in R mod 2 pi, p = A_{D-2} >0)} with the symplectic form d phi wedge d p. As the action of the group SO_+(1,2) on that manifold is transitive, effective and Hamiltonian, it can be used for a group theoretical quantization of the system. The area operator p for the horizon corresponds to the generator of the compact subgroup SO(2) and becomes quantized accordingly: The positive discrete series of the irreducible unitary representations of SO_+(1,2) yields an (horizon) area spectrum proportional k+n, where k =1,2,. characterizes the representation and n = 0,1,2,. the number of area quanta. If one employs the unitary representations of the universal covering group of SO_+(1,2) the number k can take any fixed positive real value (theta-parameter). The unitary representations of the positive discrete series provide concrete Hilbert spaces for quantum Schwarzschild black holes.
JWST/NIRCam Probes Young Star Clusters in the Reionization Era Sunrise Arc

Star cluster formation in the early universe and its contribution to reionization remains largely unconstrained to date. Here we present JWST/NIRCam imaging of the most highly magnified galaxy known at z ∼ 6, the Sunrise arc. We identify six young massive star clusters (YMCs) with measured radii spanning from ∼20 down to ∼1 pc (corrected for lensing magnification), estimated stellar masses of ∼106–7 M ⊙, and ages of 1–30 Myr based on SED fitting to photometry measured in eight filters extending to rest frame 7000 Å. The resulting stellar mass surface densities are higher than 1000 M ⊙ pc−2 (up to a few 105 M ⊙ pc−2), and their inferred dynamical ages qualify the majority of these systems as gravitationally bound stellar clusters. The star cluster ages map the progression of star formation along the arc, with two evolved systems (≳10 Myr old) followed by very young clusters. The youngest stellar clusters (<5 Myr) show evidence of prominent Hβ+[O iii] emission based on photometry with equivalent widths larger than >1000 Å rest frame and are hosted in a 200 pc sized star-forming complex. Such a region dominates the ionizing photon production with a high efficiency log(ξion[Hzerg−1])∼25.7 . A significant fraction of the recently formed stellar mass of the galaxy (10%–30%) occurred in these YMCs. We speculate that such sources of ionizing radiation boost the ionizing photon production efficiency, which eventually carves ionized channels that might favor the escape of Lyman continuum radiation. The survival of some of the clusters would make them the progenitors of massive and relatively metal-poor globular clusters in the local universe.
State dependent delay maps: numerical algorithms and dynamics of projections

This work concerns the dynamics of a certain class of delay diﬀerential equations (DDEs) which we refer to as state dependent delay maps. These maps are generated by delay diﬀerential equations where the derivative of the current state depends only on delayed variables, and not on the un-delayed state. However, we allow that the delay is itself a function of the state variable. A delay map with constant delays can be rewritten explicitly as a discrete time dynamical system on an appropriate function space, and a delay map with small state dependent terms can be viewed as a “non-autonomous” perturbation. We develop a ﬁxed point formulation for the Cauchy problem of such perturbations, and under appropriate assumptions obtain the existence of forward iterates of the map. The proof is constructive and leads to numerical procedures which we implement for illustrative examples, including the cubic Ikeda and Mackey-Glass systems with constant and state-dependent delays. After proving a local convergence result for the method, we study more quali-tative/global convergence issues using data analytic tools for time series analysis (dimension and topological measures derived from persistent ho-mology). Using these tools we quantify the convergence of the dynamics in the ﬁnite dimensional projections to the dynamics of the inﬁnite dimensional system. median and interquartile ranges of the 1-Wasserstein distance between H 0 , H 1 , and H 2 persistence diagrams between the approximate systems ( q < 17) and the ground truth system ( q = 17), with baseline median and interquratile ranges measuring 1-Wasserstein distances between diaagrams corresponding to diﬀerent random samplings of the ground truth ( q = 17) attractor. Missing values due to numerical instabilities for these choices of q .
Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement

Deploying environmental measurement stations can be a costly and time-consuming procedure, especially in remote regions that are difﬁcult to access, such as Antarctica. Therefore, it is crucial that sensors are placed as efﬁciently as possible, maximising the informativeness of their measurements. This can be tackled by ﬁtting a probabilistic model to existing data and identifying placements that would maximally reduce the model’s uncertainty. The models most widely used for this purpose are Gaussian processes (GPs; Williams and Rasmussen, 2006). However, designing a GP covariance which captures the complex behaviour of non-stationary spatiotemporal data is a difﬁcult task. Further, the computational cost of GPs makes them challenging to scale to large environmental datasets. In this work, we explore using a convolutional Gaussian neural process (ConvGNP; Bruinsma et al., 2021; Markou et al., 2022) to address these issues. A ConvGNP is a meta-learning model that uses neural networks to parameterise a GP predictive. Our model is data-driven, ﬂexible, efﬁcient, and permits multiple input predictors of gridded or scattered modalities. Using simulated surface air temperature ﬁelds over Antarctica as ground truth, we show that a ConvGNP signiﬁcantly out-performs a non-stationary GP baseline in terms of predictive performance. We then use the ConvGNP in an Antarctic sensor placement toy experiment, yielding promising results.
Periodic Change of Solar Differential Rotation

The periodic oscillation of the inhomogeneous rotation of the Sun is studied by use of the MHD dynamo theory. There exists a turbulent electromotive force that is driven by the vorticity of the flow (i.e., the γ dynamo). In addition, its counterpart exists in the vorticity equation, that is, the rotation is induced by an inhomogeneous magnetic field in turbulent plasmas through the γ-dynamo process. Based on this dynamo theory, a periodic change of solar differential rotation with a period of 11 yr is theoretically explained under the prescribed solar magnetic cycle. The predicted amplitude is compared with observations.
Robust DNN Surrogate Models with Uncertainty Quantification via Adversarial Training

For computational efficiency, surrogate models have been used to emulate mathematical simulators for physical or biological processes. High-speed simulation is crucial for conducting uncertainty quantification (UQ) when the simulation is repeated over many randomly sampled input points (aka, the Monte Carlo method). In some cases, UQ is only feasible with a surrogate model. Recently, Deep Neural Network (DNN) surrogate models have gained popularity for their hard-to-match emulation accuracy. However, it is well-known that DNN is prone to errors when input data are perturbed in particular ways, the very motivation for adversarial training. In the usage scenario of surrogate models, the concern is less of a deliberate attack but more of the high sensitivity of the DNN's accuracy to input directions, an issue largely ignored by researchers using emulation models. In this paper, we show the severity of this issue through empirical studies and hypothesis testing. Furthermore, we adopt methods in adversarial training to enhance the robustness of DNN surrogate models. Experiments demonstrate that our approaches significantly improve the robustness of the surrogate models without compromising emulation accuracy.
Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM

Air pollution is a worldwide issue that affects the lives of many people in urban areas. It is considered that the air pollution may lead to heart and lung diseases. A careful and timely forecast of the air quality could help to reduce the exposure risk for affected people. In this paper, we use a data-driven approach to predict air quality based on historical data. We compare three popular methods for time series prediction: Exponential Smoothing (ES), Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM). Considering prediction accuracy and time complexity, our experiments reveal that for short-term air pollution prediction ES performs better than ARIMA and LSTM.
Invasion of Optimal Social Contracts

The stag-hunt game is a prototype for social contracts. Adopting a new and better social contract is usually challenging because the current one is already well established and stable due to sanctions imposed on non-conforming members. Thus, how does a population shift from the current social contract to a better one? In other words, how can a social system leave a locally optimum configuration to achieve a globally optimum state? Here, we investigate the effect of promoting diversity on the evolution of social contracts. We consider group-structured populations where individuals play the stag-hunt game in all groups. We model the diversity incentive as a snowdrift game played in a single focus group where the individual is more prone to adopting a deviant norm. We show that a moderate diversity incentive is sufficient to change the system dynamics, driving the population over the stag-hunt invasion barrier that prevents the global optimum being reached. Thus, an initial fraction of adopters of the new, better norm can drive the system toward the optimum social contract. If the diversity incentive is not too large, the better social contract is the new equilibrium and remains stable even if the incentive is turned off. However, if the incentive is large, the population is trapped in a mixed equilibrium and the better social norm can only be reached if the incentive is turned off after the equilibrium is reached. The results are obtained using Monte Carlo simulations and analytical approximation methods.
Saddlepoint approximations to sensitivities of tail probabilities of random sums and comparisons with Monte Carlo estimators

This article proposes computing sensitivities of upper tail probabilities of random sums by the saddlepoint approximation. The considered sensitivity is the derivative of the upper tail probability with respect to the parameter of the summation index distribution. Random sums with Poisson or Geometric distributed summation indices and Gamma or Weibull distributed summands are considered. The score method with importance sampling is considered as an alternative approximation. Numerical studies show that the saddlepoint approximation and the method of score with importance sampling are very accurate. But the saddlepoint approximation is substantially faster than the score method with importance sampling. Thus, the suggested saddlepoint approximation can be conveniently used in various scientific problems.