Miracle-dz/UniFAR-FTUs huggingface dataset:
Angiopoietin-Like4 Is a Novel Marker of COVID-19 Severity

IMPORTANCE: Vascular dysfunction and capillary leak are common in critically ill COVID-19 patients, but identification of endothelial pathways involved in COVID-19 pathogenesis has been limited. Angiopoietin-like 4 (ANGPTL4) is a protein secreted in response to hypoxic and nutrient-poor conditions that has a variety of biological effects including vascular injury and capillary leak. OBJECTIVES: To assess the role of ANGPTL4 in COVID-19–related outcomes. DESIGN, SETTING, AND PARTICIPANTS: Two hundred twenty-five COVID-19 ICU patients were enrolled from April 2020 to May 2021 in a prospective, multicenter cohort study from three different medical centers, University of Washington, University of Southern California and New York University. MAIN OUTCOMES AND MEASURES: Plasma ANGPTL4 was measured on days 1, 7, and 14 after ICU admission. We used previously published tissue proteomic data and lung single nucleus RNA (snRNA) sequencing data from specimens collected from COVID-19 patients to determine the tissues and cells that produce ANGPTL4. RESULTS: Higher plasma ANGPTL4 concentrations were significantly associated with worse hospital mortality (adjusted odds ratio per log2 increase, 1.53; 95% CI, 1.17–2.00; p = 0.002). Higher ANGPTL4 concentrations were also associated with higher proportions of venous thromboembolism and acute respiratory distress syndrome. Longitudinal ANGPTL4 concentrations were significantly different during the first 2 weeks of hospitalization in patients who subsequently died compared with survivors (p for interaction = 8.1 × 10–5). Proteomics analysis demonstrated abundance of ANGPTL4 in lung tissue compared with other organs in COVID-19. ANGPTL4 single-nuclear RNA gene expression was significantly increased in pulmonary alveolar type 2 epithelial cells and fibroblasts in COVID-19 lung tissue compared with controls. CONCLUSIONS AND RELEVANCE: ANGPTL4 is expressed in pulmonary epithelial cells and fibroblasts and is associated with clinical prognosis in critically ill COVID-19 patients.
Adverse drug event detection using natural language processing: A scoping review of supervised learning methods

To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP methods were extracted and critically appraised. Out of 1,065 articles screened for eligibility, 29 articles met the inclusion criteria. Most frequent tasks included named entity recognition (n = 17; 58.6%) and relation extraction/classification (n = 15; 51.7%). Clinical involvement was reported in nine studies (31%). Multiple NLP modelling approaches seem suitable, with Long Short Term Memory and Conditional Random Field methods most commonly used. Although reported overall performance of the systems was high, it provides an inflated impression given a steep drop in performance when predicting the ADE entity or ADE relation class. When annotating corpora, treating an ADE as a relation between a drug and non-drug entity seems the best practice. Future research should focus on semi-automated methods to reduce the manual annotation effort, and examine implementation of the NLP methods in practice.
Probing the atmosphere of WASP-69 b with low- and high-resolution transmission spectroscopy

Consideration of both low- and high-resolution transmission spectroscopy is key for obtaining a comprehensive picture of exoplanet atmospheres. In studies of transmission spectra, the continuum information is well established with low-resolution spectra, while the shapes of individual lines are best constrained with high-resolution observations. In this work, we aim to merge high- with low-resolution transmission spectroscopy. We present the analysis of three primary transits of WASP-69b in the VIS channel of the CARMENES instrument and perform a combined low- and high-resolution analysis using additional data from HARPS-N, OSIRIS/GTC, and WFC3/HST already available in the literature. During the first CARMENES observing night, we detected the planet Na D$_{2}$ and D$_{1}$ lines at $\sim 7\sigma$ and $\sim 3\sigma$ significance levels, respectively. We measured a D$_{2}$/D$_{1}$ intensity ratio of 2.5$\pm$0.7, which is in agreement with previous HARPS-N observations. Our modelling of WFC3 and OSIRIS data suggests strong Rayleigh scattering, solar to super-solar water abundance, and a highly muted Na feature in the atmosphere of this planet, in agreement with previous investigations of this target. We use the continuum information retrieved from the low-resolution spectroscopy as a prior to break the degeneracy between the Na abundance, reference pressure, and thermosphere temperature for the high-resolution spectroscopic analysis. We fit the Na D$_{1}$ and D$_{2}$ lines individually and find that the posterior distributions of the model parameters agree with each other within 1$\sigma$. Our results suggest that local thermodynamic equilibrium processes can explain the observed D$_{2}$/D$_{1}$ ratio because the presence of haze opacity mutes the absorption features.
Cleistanthins A and B Ameliorate Testosterone-Induced Benign Prostatic Hyperplasia in Castrated Rats by Regulating Apoptosis and Cell Differentiation

Background The aging male population is at higher risk for benign prostatic hyperplasia (BPH) wherein increased proliferation of stromal and epithelial cells of the prostate is observed. In this study, we investigated the effect of cleistanthins A and B on the inhibition of testosterone-induced BPH in castrated rats. Methodology Male Wistar rats were divided into eight groups (n = 6) and surgical castration was performed. BPH was induced by the administration of testosterone propionate in corn oil at 5 mg/kg for four weeks. The control group received corn oil, and the model group received testosterone propionate. The standard treatment group received finasteride orally along with testosterone. Cleistanthins A and B at 0.3, 1, and 3 mg/kg were administered by oral gavage along with testosterone. After four weeks, rats were sacrificed, and prostates were weighed and assessed for histomorphological, inflammatory, apoptotic, and proliferative markers. Results Cleistanthins A and B decreased prostatic enlargement and histopathological abnormalities. Elevated serum dihydrotestosterone levels were lowered significantly in both the cleistanthin A and cleistanthin B groups compared to the BPH model group. Cleistanthins A and B significantly lowered the serum interleukin (IL)-1β and tumor necrosis factor-alpha inflammatory markers in the test groups. Western blot analysis revealed cleistanthin A downregulated the IL-6, signal transducer and activator of transcription 3/cyclin D1 signaling pathway. Both cleistanthins A and B upregulated the apoptotic markers caspase-3 and cleaved caspase-3, whereas the cell proliferation markers cyclin D1 and proliferating cell nuclear antigen were found to be downregulated. Conclusions Both cleistanthins A and B inhibited BPH in a rat model by apoptotic induction and impeded cell proliferation.
3D myofibril imaging in live cardiomyocytes via hybrid SHG-TPEF microscopy

We developed a hybrid SHG-TPEF polarization imaging system that allowed the excitation beam from an fs Ti:Sappire laser being bi-directionally raster scanned across the focal plane using a pair of orthogonal galvanometers. To implement high-speed scanning, the turning regions of the triangular waves were smoothed by a custom-designed waveform. The SHG and TPEF signals from samples were recorded by two PMTs in the forward and backward direction. Using this imaging system, we obtained 3D images of the sarcomere structure via SHG and DiO-stained lipid membrane via TPEF in live cardiomyocytes isolated from neonatal and adult rats. The results demonstrated the potential applications of SHG and TPEF in the research of myofibrillogensis.
Fabrication and characterization of an indium tin oxide-octadecanethiol-aluminum junction for molecular electronics

In order to create a different avenue for molecular electronics, metal-molecule-metal junctions are reported for 1-octadecanethiol (ODT) self-assembled monolayers on indium tin oxide surface using thermal/electron beam evaporated aluminum as top contact. The bottom electrode is patterned to 10μm dumbbell patterns followed by self-assembly of the molecule and evaporation of Al through a 270μm dumbbell patterned shadow mask to form a 230μm Al top contact. Current-voltage measurements show that the junction is resistive with ODT resistance estimated to be ∼2×1013Ω∕molecule.
Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking

A supervised approach to online-learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a robust and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multiclassifier jointly using iterative reweighed least squares algorithm. Combined with robust sparse coding, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between robust sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy, and robustness.
Influence of Road Wetness on Tire-Pavement Rolling Resistance

Rolling resistance of tires is one of the most important factors influencing energy consumption of road vehicles, especially on rural roads. For practical reasons, most of rolling resistance measurements are usually performed for dry road conditions. Based on the fact that roads are wet during a considerable time over the year and as part of the projects MIRIAM, ROLRES and ROSANNE, the TUG (Technical University of Gdansk) in Poland and VTI (Swedish National Road and Transport Research Institute) in Sweden carried out trailer rolling resistance measurements on wet road surfaces to investigate water film influence on rolling resistance on different pavements. A specially-designed trailer to measure rolling resistance has been used. The test sections were both rural roads and an abandoned airfield equipped with water film sensors mounted in the pavement. Results indicate strong influence of test speed and water film depth, as well as influence of surface texture. The increase of rolling resistance on wet surfaces is caused by both hydrodynamic phenomena and cooling effect of water that decreases tire temperature thus increasing rolling resistance.
Relaxations of Envy-Freeness Over Graphs

When allocating a set of indivisible items among agents, the ideal condition of envy-freeness cannot always be achieved. Envy-freeness up to any good (EFX), and envy-freeness with $k$ hidden items (HEF-$k$) are two very compelling relaxations of envy-freeness, which remain elusive in many settings. We study a natural relaxation of these two fairness constraints, where we place the agents on the vertices of an undirected graph, and only require that our allocations satisfy the EFX (resp. HEF) constraint on the edges of the graph. We refer to these allocations as graph-EFX (resp. graph-HEF) or simply $G$-EFX (resp. $G$-HEF) allocations. We show that for any graph $G$, there always exists a $G$-HEF-$k$ allocation of goods, where $k$ is the size of a minimum vertex cover of $G$, and that this is essentially tight. We show that $G$-EFX allocations of goods exist for three different classes of graphs -- two of them generalizing the star $K_{1, n-1}$ and the third generalizing the three-edge path $P_4$. Many of these results extend to allocations of chores as well. Overall, we show several natural settings in which the graph structure helps obtain strong fairness guarantees. Finally, we evaluate an algorithm using problem instances from Spliddit to show that $G$-EFX allocations appear to exist for paths $P_n$, pointing the way towards showing EFX for even broader families of graphs.
Improved CNN Prediction Based Reversible Data Hiding

 Abstract — This letter proposes an improved CNN predictor (ICNNP) for reversible data hiding (RDH) in images, which consists of a feature extraction module, a pixel prediction module, and a complexity prediction module. Due to predicting the complexity of each pixel with the ICNNP during the embedding process, the proposed method can achieve superior performance than the CNN predictor-based method. Specifically, an input image does be first split into two different sub-images, i.e., the “Dot” image and the “Cross” image. Meanwhile, each sub-image is applied to predict another one. Then, the prediction errors of pixels are sorted with the predicted pixel complexities. In light of this, some sorted prediction errors with less complexity are selected to be efficiently used for low-distortion data embedding with a traditional histogram shift scheme . Experimental results demonstrate that the proposed method can achieve better embedding performance than that of the CNN predictor with the same histogram shifting strategy.
Unpacking the "Black Box" of AI in Education

: Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations—many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers. In this paper, we seek to clarify what “AI” is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition. We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks. We conclude with a set of questions that educationalists may ask as they encounter AI in their research and practice. Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human-centered AI in education.
Cost-Sensitive Stacking: an Empirical Evaluation

Many real-world classification problems are cost-sensitive in nature, such that the misclassification costs vary between data instances. Cost-sensitive learning adapts classification algorithms to account for differences in misclassification costs. Stacking is an ensemble method that uses predictions from several classifiers as the training data for another classifier, which in turn makes the final classification decision. While a large body of empirical work exists where stacking is applied in various domains, very few of these works take the misclassification costs into account. In fact, there is no consensus in the literature as to what cost-sensitive stacking is. In this paper we perform extensive experiments with the aim of establishing what the appropriate setup for a cost-sensitive stacking ensemble is. Our experiments, conducted on twelve datasets from a number of application domains, using real, instance-dependent misclassification costs, show that for best performance, both levels of stacking require cost-sensitive classification decision.
Optical patterns using vortex beams

We demonstrate theoretically and experimentally that modulation instability leading to optical pattern formation can arise by using non conventional counterpropagating beams carrying an orbital angular momentum (optical vortices). Such a vortex beam is injected into a nonlinear single feedback system. We evidence different complex patterns with peculiar phase singularities and rotating dynamics. We prove that the dynamics is induced by the vortex angular momentum and the rotation velocity depends non linearly on both the vortex topological charge and the intensity of the input beam.
Magnetic light amplification by stimulated emission of radiation in subwavelength systems of a dielectric cavity and magnetic quantum emitters

We propose a magnetic laser in a subwavelength system consisting of a high-refractive-index dielectric cavity and an active medium formed by magnetic quantum emitters. Stimulated emissions of magnetic quantum emitters induced by their coherent interactions with quantized magnetic fields of a cavity are theoretically considered. The condition to archive such a magnetic laser is obtained. Numerical results show that magnetic lasers are feasible in some realistic systems, for example, a silicon disk of high-quality whispering gallery modes with embedded emitters. Furthermore, the competitions between the electric interaction and magnetic one in terms of their Purcell factors are also considered in some magnetic laser achievable systems. In a wavelength-scale silicon block of a high-order magnetic mode, the ratio of magnetic Purcell factor to the electric one can reach more than ~10^3 large. Our results open up ways to enhanced magnetic light-matter interactions.
Decentralized Gradient Tracking with Local Steps

Gradient tracking (GT) is an algorithm designed for solving decentralized optimization problems over a network (such as training a machine learning model). A key feature of GT is a tracking mechanism that allows to overcome data heterogeneity between nodes. We develop a novel decentralized tracking mechanism, $K$-GT, that enables communication-efficient local updates in GT while inheriting the data-independence property of GT. We prove a convergence rate for $K$-GT on smooth non-convex functions and prove that it reduces the communication overhead asymptotically by a linear factor $K$, where $K$ denotes the number of local steps. We illustrate the robustness and effectiveness of this heterogeneity correction on convex and non-convex benchmark problems and on a non-convex neural network training task with the MNIST dataset.
Brightest Cluster Galaxy Formation in the z=4.3 Protocluster SPT2349-56: Discovery of a Radio-Loud AGN

We have observed the z=4.3 protocluster SPT2349-56 with ATCA with the aim of detecting radio-loud active galactic nuclei (AGN) amongst the ~30 submillimeter galaxies identified in the structure. We detect the central complex of SMGs at 2.2\,GHz with a luminosity of L_2.2=(4.42pm0.56)x10^{25} W/Hz. The ASKAP also detects the source at 888 MHz, constraining the radio spectral index to alpha=-1.6pm0. 3, consistent with ATCA non-detections at 5.5 and 9GHz, and implying L_1. 4(rest)=(2.4pm0.3)x10^{26}W/Hz. This radio luminosity is about 100 times higher than expected from star formation, assuming the usual FIR-radio correlation, which is a clear indication of an AGN driven by a forming brightest cluster galaxy (BCG). None of the SMGs in SPT2349-56 show signs of AGN in any other diagnostics available to us (notably 12CO out to J=16, OH163um, CII/IR, and optical spectra), highlighting the radio continuum as a powerful probe of obscured AGN in high-z protoclusters. No other significant radio detections are found amongst the cluster members, consistent with the FIR-radio correlation. We compare these results to field samples of radio sources and SMGs, along with the 22 SPT-SMG gravitational lenses also observed in the ATCA program, as well as powerful radio galaxies at high redshifts. Our results allow us to better understand the effects of this gas-rich, overdense environment on early supermassive black hole (SMBH) growth and cluster feedback. We estimate that (3.3pm0.7)x10^{38} W of power are injected into the growing ICM by the radio-loud AGN, whose energy over 100Myr is comparable to the binding energy of the gas mass of the central halo. The AGN power is also comparable to the instantaneous energy injection from supernova feedback from the 23 catalogued SMGs in the core region of 120kpc projected radius. The SPT2349-56 radio-loud AGN may be providing strong feedback on a nascent ICM.
Extended Lyman-$\alpha$ emission towards the SPT2349-56 protocluster at $z=4.3$

Context. Deep spectroscopic surveys with the Atacama Large Millimeter/submillimeter Array (ALMA) have revealed that some of the brightest infrared sources in the sky correspond to concentrations of dusty star-forming galaxies (DSFG) at high redshift. Among these, the SPT2349-56 protocluster system at z = 4.304 is amongst the most extreme examples due to its high source density and integrated star formation rate. Aims. We conducted a deep Lyman-$\alpha$ line emission survey around SPT2349-56 using the Multi-Unit Spectroscopic Explorer (MUSE) at Very Large Telescope (VLT) in order to characterize this uniquely dense environment. Methods. Taking advantage of the deep three-dimensional nature of this survey, we performed a sensitive search for Lyman-$\alpha$ emitters (LAEs) toward the core and northern extension of the protocluster, which correspond to the brightest infrared regions in this field. Using a smoothed narrowband image extracted from the MUSE datacube around the protocluster redshift, we searched for possible extended structures. Results. We identify only three LAEs at z = 4.3 in this field, in concordance with expectations for blank-fields, and an extended Lyman-$\alpha$ structure spatially associated with core of the protocluster. All the previously-identified DSFGs in this field are undetected in Lyman-$\alpha$ emission, consistent with the conspicuous dust obscuration in these systems. We find an extended Lyman-$\alpha$ structure, about $60 \times 60$ kpc$^{2}$ in size, and located 56 kpc west of the protocluster core. Three DSFGs coincide spatially with the location of this structure. We conclude that either the three co-spatial DSFGs or the protocluster core itself are feeding ionizing photons to the Lyman-$\alpha$ structure.
The use of the decomposition procedure of Adomian for solving a delay differential equation arising in electrodynamics

The pantograph equation is a delay differential equation which arises in quite different fields of pure and applied mathematics, such as number theory, dynamical systems, probability, mechanics and electrodynamics. In this work, the pantograph equation is investigated using the Adomian decomposition method and the convergence of the approach for this equation is established. The decomposition procedure of Adomian is based on the search for a solution in the form of a series with easily computed components. Application of the Adomian decomposition technique to this problem shows the rapid convergence of the sequence constructed by this method to the exact solution. Moreover, this technique reduces the volume of calculations by not requiring discretization of the variables, linearization or small perturbations.