Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses

Cardiorenal syndrome (CRS), defined as acute or chronic damage to the heart or kidney triggering impairment of another organ, has a poor prognosis. However, the molecular mechanisms underlying CRS remain largely unknown. The RNA-sequencing data of the left ventricle tissue isolated from the sham-operated and CRS model rats at different time points were downloaded from the Gene Expression Omnibus (GEO) database. Genomic differences, protein–protein interaction networks, and short time-series analyses, revealed fibronectin 1 (FN1) and periostin (POSTN) as hub genes associated with CRS progression. The transcriptome sequencing data of humans obtained from the GEO revealed that FN1 and POSTN were both significantly associated with many different heart and kidney diseases. Peripheral blood samples from 20 control and 20 CRS patients were collected from the local hospital, and the gene expression levels of FN1 and POSTN were detected by real-time quantitative polymerase chain reaction. FN1 (area under the curve [AUC] = 0.807) and POSTN (AUC = 0.767) could distinguish CRS in the local cohort with high efficacy and were positively correlated with renal and heart damage markers, such as left ventricular ejection fraction. To improve the diagnostic ability, diagnosis models comprising FN1 and POSTN were constructed by logistic regression (F-Score = 0.718), classification tree (F-Score = 0.812), and random forest (F-Score = 1.000). Overall, the transcriptome data of CRS rat models were systematically analyzed, revealing that FN1 and POSTN were hub genes, which were validated in different public datasets and the local cohort.
Phosphoinositide-binding protein TIPE1 promotes alternative activation of macrophages and tumor progression via PIP3/Akt/TGF-β axis.

Macrophages perform key and distinct functions in maintaining tissue homeostasis by finely tuning their activation state. Within the tumor microenvironment, macrophages are reshaped to drive tumor progression. Here we report that tumor necrosis factor α-induced protein 8-like 1 (TIPE1) is highly expressed in macrophages, and that depletion of TIPE1 impedes alternative activation of macrophages. TIPE1 enhanced activation of the PI3K/Akt pathway in macrophages by directly binding with and regulating the metabolism of phosphatidylinositol 4,5-bisphosphate (PIP2) and phosphatidylinositol 3,4,5-trisphosphate (PIP3). Accordingly, inhibition of the PI3K/Akt pathway significantly attenuated the effect of TIPE1 on macrophage alternative activation. Tumor-associated macrophages (TAM) in human liver cancer and melanoma tissues showed significantly upregulated TIPE1 expression that negatively correlated with patient survival. In vitro and in vivo, TIPE1 knockdown in macrophages retarded the growth and metastasis of liver cancer and melanoma. Furthermore, blockade or depletion of TGF-β signaling in macrophages abrogated the effects of TIPE1 on tumor cell growth and migration. Together, these results highlight that the phosphoinositide-related signaling pathway involves reprogramming tumor-associated macrophages to optimize the microenvironment for cancer progression.
Iterative Channel Estimation of Multipath-faded Hybrid Data Transmission for Multimedia Satellite Broadcasting

†Summary Two schemes of iterative feedback channel estimation of the hybrid data transmission technique are proposed for coherent detection in a frequency-selective fast Rayleigh fading channel. Analyzed hybrid data transmission technique for multimedia satellite TV communications is that the main-channel data and subchannel data are simultaneously transmitted using QPSK modulation and 2ASK modulation, respectively, but the latter modulation timing is offset by half the main-channel QPSK symbol length in time. In a frequency-selective fast Rayleigh fading channel, two candidates of the iterative feedback channel estimation are proposed. One of the proposed channel estimation feeds back the re-modulated main-channel symbols only, and another feeds back the re-modulated both main-channel symbols and sub-channel symbols. The BER performances achievable by the use of two candidates are evaluated by means of computer simulations. The channel estimation using only remodulated main-channel symbols can minimize BER performances of the main-channel if Eb/N0 is lower than a change-over point. The channel estimation using both remodulated main-channel and sub-channel symbols can minimize BER performances of the sub-channel and can achieve almost the same main-channel BER performances as the only re-modulated main-channel symbols, if Eb/N0 is higher than a change-over point. Therefore, if Eb/N0 is higher than the point, the iterative channel estimation using both re-modulated main-channel and sub-channel symbols is considered to be the optimum, while if Eb/N0 is lower than the point, the channel estimation using only remodulated main-channel symbols is considered to be the optimum.
The acetyltransferase p300 is recruited in trans to multiple enhancer sites by lncSmad7

Abstract The histone acetyltransferase p300 (also known as KAT3B) is a general transcriptional coactivator that introduces the H3K27ac mark on enhancers triggering their activation and gene transcription. Genome-wide screenings demonstrated that a large fraction of long non-coding RNAs (lncRNAs) plays a role in cellular processes and organ development although the underlying molecular mechanisms remain largely unclear (1,2). We found 122 lncRNAs that interacts directly with p300. In depth analysis of one of these, lncSmad7, is required to maintain ESC self-renewal and it interacts to the C-terminal domain of p300. lncSmad7 also contains predicted RNA-DNA Hoogsteen forming base pairing. Combined Chromatin Isolation by RNA precipitation followed by sequencing (ChIRP-seq) together with CRISPR/Cas9 mutagenesis of the target sites demonstrate that lncSmad7 binds and recruits p300 to enhancers in trans, to trigger enhancer acetylation and transcriptional activation of its target genes. Thus, these results unveil a new mechanism by which p300 is recruited to the genome.
Nonlinear Regression with Residuals: Causal Estimation with Time-varying Treatments and Covariates

Standard regression adjustment gives inconsistent estimates of causal effects when there are time-varying treatment effects and time-varying covariates. Loosely speaking, the issue is that some covariates are post-treatment variables because they may be affected by prior treatment status, and regressing out post-treatment variables causes bias. More precisely, the bias is due to certain non-confounding latent variables that create colliders in the causal graph. These latent variables, which we call phantoms, do not harm the identifiability of the causal effect, but they render naive regression estimates inconsistent. Motivated by this, we ask: how can we modify regression methods so that they hold up even in the presence of phantoms? We develop an estimator for this setting based on regression modeling (linear, log-linear, probit and Cox regression), proving that it is consistent for a reasonable causal estimand. In particular, the estimator is a regression model fit with a simple adjustment for collinearity, making it easy to understand and implement with standard regression software. The proposed estimators are instances of the parametric g-formula, extending the regression-with-residuals approach to several canonical nonlinear models.
Surface Quasigeostrophic Turbulence in Variable Stratification

Numerical and observational evidence indicates that, in regions where mixed-layer instability is active, the surface geostrophic velocity is largely induced by surface buoyancy anomalies. Yet, in these regions, the observed surface kinetic energy spectrum is steeper than predicted by uniformly stratified surface quasigeostrophic theory. By generalizing surface quasigeostrophic theory to account for variable stratification, we show that surface buoyancy anomalies can generate a variety of dynamical regimes depending on the stratification’s vertical structure. Buoyancy anomalies generate longer range velocity fields over decreasing stratification and shorter range velocity fields over increasing stratification. As a result, the surface kinetic energy spectrum is steeper over decreasing stratification than over increasing stratification. An exception occurs if the near surface stratification is much larger than the deep ocean stratification. In this case, we find an extremely local turbulent regime with surface buoyancy homogenization and a steep surface kinetic energy spectrum, similar to equivalent barotropic turbulence. By applying the variable stratification theory to the wintertime North Atlantic, and assuming that mixed-layer instability acts as a narrowband small-scale surface buoyancy forcing, we obtain a predicted surface kinetic energy spectrum between k−4/3 and k−7/3, which is consistent with the observed wintertime k−2 spectrum. We conclude by suggesting a method of measuring the buoyancy frequency’s vertical structure using satellite observations.
Precession of entangled spin and pseudospin in double quantum dots

Quantum dot spin valves are characterized by exchange fields which induce spin precession and generate current spin resonances even in absence of spin splitting. Analogous effects have been studied in double quantum dots, in which the orbital degree of freedom, the pseudospin, replaces the spin in the valve configuration. We generalize, now, this setup to allow for arbitrary spin and orbital polarization of the leads, thus obtaining an even richer variety of current resonances, stemming from the precession dynamics of entangled spin and pseudospin. We observe for both vectors a delicate interplay of decoherence, pumping and precession which can only be understood by also considering the dynamics of the spin-pseudospin correlators. The numerical results are obtained in the framework of a generalized master equation within the cotunneling approximation and are complemented by the analytics of a coherent sequential tunneling model.
Optimal Clustering with Bandit Feedback

Junwen Yang Institute of Operations Research and Analytics, National University of Singapore, Singapore 117602, junwen yang@u.nus.edu Zixin Zhong Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Department of Mathematics, National University of Singapore, Singapore 119076, zixin.zhong@u.nus.edu Vincent Y. F. Tan Department of Mathematics, National University of Singapore, Singapore 119076, Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Institute of Operations Research and Analytics, National University of Singapore, Singapore 117602, vtan@nus.edu. sg
Resolving the Hubble tension through hidden sector dynamics in the early universe

The recent analysis from the SH0ES Collaboration has confirmed the existence of a Hubble tension between measurements at high redshift (z > 1000) and at low redshift (z < 1) at the 5σ level with the low redshift measurement giving a higher value. In this work we propose a particle physics model that can resolve the Hubble tension via an out of equilibrium hidden sector coupled to the visible sector. The particles that populate the dark sector consist of a dark fermion, which acts as dark matter, a dark photon, a massive scalar and a massless pseudo-scalar. Assuming no initial population of particles in the dark sector, feeble couplings between the visible and the hidden sectors via kinetic mixing populate the dark sector even though the number densities of hidden sector particles never reach their equilibrium distribution and the two sectors remain at different temperatures. A cosmologically consistent analysis is presented where a correlated evolution of the visible and the hidden sectors with coupled Boltzmann equations involving two temperatures, one for the visible sector and the other for the hidden sector, is carried out. The relic density of the dark matter constituted of dark fermions is computed in this two-temperature formalism. As a consequence, BBN predictions are upheld with a minimal contribution to ∆Neff . However, the out-of-equilibrium decay of the massive scalar to the massless pseudoscalar close to the recombination time causes an increase in ∆Neff to resolve the Hubble tension. ∗aabouibr@uni-muenster.de †michael.klasen@uni-muenster.de ‡p.nath@northeastern.edu 1 ar X iv :2 20 2. 04 45 3v 1 [ as tr oph . C O ] 9 F eb 2 02 2
Probing the Early History of Cosmic Reionization by Future Cosmic Microwave Background Experiments

Cosmic reionization imprints its signature on the temperature and polarization anisotropies of the cosmic microwave background (CMB). Advances in CMB telescopes have already placed a significant constraint on the history of reionization. As near-future CMB telescopes target the maximum sensitivity, or observations limited only by the cosmic variance (CV), we hereby forecast the potential of future CMB observations in constraining the history of reionization. In this study, we perform Markov Chain Monte Carlo analysis for CV-limited E-mode polarization observations such as the Lite (Light) satellite for the studies of B-mode polarization and Inflation from cosmic background Radiation Detection (LiteBIRD), based on a few different methods that vary in the way of sampling reionization histories. We focus especially on estimating the very early history of reionization that occurs at redshifts z > 15, which is quantified by the partial CMB optical depth due to free electrons at z > 15, τ z>15. We find that reionization with τ z>15 ∼ 0.008, which is well below the current upper limit τ z>15 ∼ 0.02, is achievable by reionization models with minihalo domination in the early phase and can be distinguished from those with τ z>15 ≲ 5 × 10−4 through CV-limited CMB polarization observations. An accurate estimation of τ z>15, however, remains somewhat elusive. We investigate whether resampling the E-mode polarization data with limited spherical-harmonic modes may resolve this shortcoming.
Stratospheric balloons as a platform for the next large far infrared observatory

Observations that require large physical instrument dimensions and/or a considerable amount of cryogens, as it is the case for high spatial resolution far infrared (FIR) astronomy, currently still face technological limits for their execution from space. Due to these limits, the FIR domain in particular is lagging behind other wavelength regimes in terms of angular resolution and available observational capabilities, especially after the retirement of the Herschel Space Observatory. Balloon-based platforms promise to complement the existing observational capabilities by offering means to deploy comparably large telescopes with comparably little effort, including other advantages such as the possibility to regularly refill cryogens and to change and/or update instruments. The planned European Stratospheric Balloon Observatory (ESBO), currently under preparation by a consortium of European research institutes and industry, aims at providing these additional large aperture FIR capabilities, exceeding the spatial resolution of Herschel, in the long term. In particular, the plans focus on reusable platforms performing regular flights and an operations concept that provides researchers with proposal-based access to observations as also practiced on space-based observatories. It thereby aims at offering a complement to other airborne, ground-based and space-based observatories in terms of access to wavelength regions, spatial resolution capability, and photometric stability. In order to fully exploit the potential offered by regularly flying balloon platforms, ESBO foresees the option to exchange instruments and telescopes in between flights. While the FIR capabilities are a main long-term objective, ESBO will offer benefits in other wavelength regimes along the way. Within the recently initiated ESBO Design Study (ESBO DS), financed within the European Union’s Horizon 2020 Programme, a prototype platform carrying a 0.5 m telescope for ultraviolet (UV) and visible (VIS) light observations is being built and a platform concept for a next-generation FIR telescope is being studied. A flight of the UV/VIS prototype platform is currently foreseen for 2021. In this paper we will outline the scientific and technical motivation for a large aperture balloon-based FIR observatory and the ESBO DS approach towards such an infrastructure. Secondly, we will present the technical motivation, science case, and instrumentation of the 0.5 m UV/VIS platform.
Pressure-induced reemergence of superconductivity in BaIr2Ge7 and Ba3Ir4Ge16 with cage structures

Clathrate-like or caged compounds have attracted great interest owing to their structural flexibility, as well as their fertile physical properties. Here, we report the pressure-induced reemergence of superconductivity in BaIr2Ge7 and Ba3Ir4Ge16, two new caged superconductors with two-dimensional building blocks of cage structures. After suppression of the ambient-pressure superconducting (SC-I) states, new superconducting (SC-II) states emerge unexpectedly, with Tc increased to a maximum of 4.4 and 4.0 K for BaIr2Ge7 and Ba3Ir4Ge16, respectively. Combined with high-pressure synchrotron x-ray diffraction and Raman measurements, we propose that the reemergence of superconductivity in these caged superconductors can be ascribed to a pressure-induced phonon softening linked to cage shrinkage.
Counting problems from the viewpoint of ergodic theory: from primitive integer points to simple closed curves

In her thesis, Mirzakhani showed that the number of simple closed geodesics of length ≤ L on a closed, connected, oriented hyperbolic surface X of genus g is asymptotic to L6g−6 times a constant depending on the geometry of X. In this survey we give a detailed account of Mirzakhani’s proof of this result aimed at non-experts. We draw inspiration from classic primitive lattice point counting results in homogeneous dynamics. The focus is on understanding how the general principles that drive the proof in the case of lattices also apply in the setting of hyperbolic surfaces.
Parsimonious Learning-Augmented Caching

Learning-augmented algorithms -- in which, traditional algorithms are augmented with machine-learned predictions -- have emerged as a framework to go beyond worst-case analysis. The overarching goal is to design algorithms that perform near-optimally when the predictions are accurate yet retain certain worst-case guarantees irrespective of the accuracy of the predictions. This framework has been successfully applied to online problems such as caching where the predictions can be used to alleviate uncertainties. In this paper we introduce and study the setting in which the learning-augmented algorithm can utilize the predictions parsimoniously. We consider the caching problem -- which has been extensively studied in the learning-augmented setting -- and show that one can achieve quantitatively similar results but only using a sublinear number of predictions.
A Lagrangian Duality Approach to Active Learning

We consider the pool-based active learning problem, where only a subset of the training data is labeled, and the goal is to query a batch of unlabeled samples to be labeled so as to maximally improve model performance. We formulate the problem using constrained learning, where a set of constraints bounds the performance of the model on labeled samples. Considering a primal-dual approach, we optimize the primal variables, corresponding to the model parameters, as well as the dual variables, corresponding to the constraints. As each dual variable indicates how significantly the perturbation of the respective constraint affects the optimal value of the objective function, we use it as a proxy of the informativeness of the corresponding training sample. Our approach, which we refer to as Active Learning via Lagrangian dualitY, or ALLY, leverages this fact to select a diverse set of unlabeled samples with the highest estimated dual variables as our query set. We demonstrate the benefits of our approach in a variety of classification and regression tasks and discuss its limitations depending on the capacity of the model used and the degree of redundancy in the dataset. We also examine the impact of the distribution shift induced by active sampling and show that ALLY can be used in a generative mode to create novel, maximally-informative samples.
The Galactic high mass X-ray binary population with Fermi -LAT

We search for γ-ray emission from 114 Galactic high mass X-ray binaries, including 4 well studied catalogued sources, in 12.5 years of Fermi -LAT data in conjunction with the 10-year point source catalogue. Where a γ-ray excess appears to be spatially coincident with an X-ray binary, further investigation is performed to ascertain whether this excess is the product of physical processes within the binary system itself. We identify γ-ray excesses coincident with 20 high mass X-ray binaries where there is little or no prior evidence for γ-ray emission. However, we find that many of these are false positives caused by source confusion or the γ-ray background. Nonetheless, tentative but promising indicators of γ-ray emission are identified for several new systems, notably including 1A 0535+262, RX J2030.5+4751 and SAX J1324.4-6200.
An Experimental Proof of Concept for Integrated Sensing and Communications Waveform Design

The integration of sensing and communication (ISAC) functionalities have recently gained significant research interest as a hardware-, power-, spectrum- and cost- efficient solution. This experimental work implements a dual-functional sensing and communication framework where a single radiation waveform, either omnidirectional or directional, can realize both sensing and communication functions. We design an orthogonal frequency division multiplexing (OFDM) based multi-user multiple input multiple output (MIMO) software-defined radio (SDR) testbed to validate the dual-functional model. We carry out over-the-air experiments to investigate the optimal trade-off factor to balance the performance for both functions. On the communication side, we obtain bit error rate (BER) results from the testbed to show the communication performance using the dual-functional waveform. On the sensing performance, we measure the output beampatterns of our transmission to examine their similarity to simulation based beampatterns. We also implement a sensing experiment to realize activity detection functions. Our experiment reveals that the dual-functional approach can achieve comparable BER performance with pure communication-based solutions while achieving fine sensing beampatterns and realistic sensing functionality simultaneously.
Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs

Many problems in RL, such as meta-RL, robust RL, generalization in RL, and temporal credit assignment, can be cast as POMDPs. In theory, simply augmenting model-free RL with memory-based architectures, such as recurrent neural networks, provides a general approach to solving all types of POMDPs. However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs. This paper revisits this claim. We find that careful architecture and hyperparameter decisions can often yield a recurrent model-free implementation that performs on par with (and occasionally substantially better than) more sophisticated recent techniques. We compare to 21 environments from 6 prior specialized methods and find that our implementation achieves greater sample efficiency and asymptotic performance than these methods on 18/21 environments. We also release a simple and efficient implementation of recurrent model-free RL for future work to use as a baseline for POMDPs.