Miracle-dz/UniFAR-FTUs huggingface dataset:
Computed Tomography – based Airway Surface Area – to-Volume Ratio for Phenotyping Airway Remodeling in Chronic Obstructive Pulmonary Disease Airway Surface Area–to-Volume Ratio in COPD

Rationale: Airway remodeling in chronic obstructive pulmonary disease (COPD) is due to luminal narrowing and/or loss of airways. Existingcomputedtomographicmetricsofairwaydiseasere ﬂ ectonly components of these processes. With progressive airway narrowing, the ratio of the airway luminal surface area to volume (SA/V) should increase, and with predominant airway loss, SA/V should decrease. Objectives: To phenotype airway remodeling in COPD. Methods: Weanalyzedtheairwaytreesof4,325subjectswith COPD Global Initiative for Chronic Obstructive Lung Disease stages 0 to 4 and 73 nonsmokers enrolled in the multicenter COPDGene (Genetic Epidemiology of COPD) cohort. Surface area and volume measurements were estimated for the subtracheal airway tree to derive SA/V. We performed multivariable regression analyses to test associations between SA/V and lung function, 6-minute-walk distance, St. George ’ s Respiratory Questionnaire, change in FEV 1 , andmortality,adjustingfordemographics, totalairwaycount,airway wall thickness, and emphysema. On the basis of the change in SA/V over 5 years, we categorized subjects into predominant airway narrowing [positive Δ (SA/V) more than 0] and predominant airway loss [negative Δ (SA/V) less than 0] and compared survival between the two groups. Measurements and Main Results: Airway SA/V was independently associated with FEV 1 /FVC ( b = 0.12; 95% con ﬁ dence interval [CI], 0.09 – 0.14; P , 0.001) and FEV 1 % predicted ( b = 20.10; 95% CI, 15.13 – 25.08; P , 0.001). Airway SA/V was also independently associated with 6-minute-walk distance, respiratory quality of life, and lung function decline. Compared with subjects with predominant airway narrowing ( n = 2,914; 66.3%), those with predominant airway loss ( n = 1,484; 33.7%) had worse survival (adjusted hazard ratio for all-cause mortality = 1.58; 95% CI, 1.18 – 2.13; P = 0.002). Conclusions: Computed tomography – based airway SA/V is an imaging biomarker of airway remodeling and provides differential information on predominant airway narrowing and loss in COPD. SA/V is associated with respiratory morbidity, lung function decline, and survival.
In Vivo Layer-Specific Mechanical Characterization of Porcine Stomach Tissue Using a Customized Ultrasound Elastography System

Suvranu De Center for Modeling, Simulation and Imaging in Medicine (CeMSIM), Rensselaer Polytechnic Institute, Troy, NY 12180; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180; Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 In Vivo Layer-Specific Mechanical Characterization of Porcine Stomach Tissue Using a Customized Ultrasound Elastography System
A Single Amino Acid Replacement in Penicillin-Binding Protein 2X in Streptococcus pyogenes Significantly Increases Fitness on Subtherapeutic Benzylpenicillin Treatment in a Mouse Model of NecrotizingMyositis

opyright a 2020 American Society for Inve ttps://doi.org/10.1016/j.ajpath. 2020.04. 014 Invasive strains of Streptococcus pyogenes with significantly reduced susceptibility to b-lactam antibiotics have been recently described. These reports have caused considerable concern in the international infectious disease, medical microbiology, and public health communities because S. pyogenes has remained universally susceptible to b-lactam antibiotics for 70 years. Virtually all analyzed strains had single amino acid replacements in penicillin-binding protein 2X (PBP2X), a major target of b-lactam antibiotics in pathogenic bacteria. We used isogenic strains to test the hypothesis that a single amino acid replacement in PBP2X conferred a fitness advantage in a mouse model of necrotizing myositis. We determined that when mice were administered intermittent subtherapeutic dosing of benzylpenicillin, the strain with a Pro601Leu amino acid replacement in PBP2X that confers reduced b-lactam susceptibility in vitro was more fit, as assessed by the magnitude of colony-forming units recovered from disease tissue. These data provide important pathogenesis information that bears on this emerging global infectious disease problem. (Am J Pathol 2020, 190: 1625e1631; https://doi.org/10.1016/j.ajpath.2020.04.014)
The (still) underappreciated role of allostery in the cellular network

Allosteric propagation results in communication between distinct sites in the protein structure; it also encodes specific effects on cellular pathways, and in this way it shapes cellular response. One example of long-range effects is binding of morphogens to cell-surface receptors, which initiates a cascade of protein interactions that leads to genome activation and specific cellular action. Allosteric propagation results from combinations of multiple factors; takes place through dynamic shifts of conformational ensembles; and affects the equilibria of macromolecular interactions. Here, we emphasize (1) the well-known, yet still underappreciated role of allostery in conveying explicit signals across large multimolecular assemblies and distances to specify cellular action; (2) the need for quantitation of the allosteric effects; and finally, (3) we propose that each specific combination of allosteric effectors along the pathway spells a distinct function. The challenges are colossal; the inspiring reward will be predicting function, misfunction, and outcomes of drug regimes.
Inhibition of Na1 Transport in Lung Epithelial Cells by Respiratory Syncytial Virus Infection

We investigated the mechanisms by which respiratory syncytial virus (RSV) infection decreases vectorial Na1 transport across respiratory epithelial cells. Mouse tracheal epithelial (MTE) cells from either BALB/c or C57BL/6 mice and human airway H441 cells were grown on semipermeable supports under an air–liquid interface. Cells were infected with RSV-A2 and mounted in Ussing chambers for measurements of short-circuit currents (Isc). Infection with RSV for 24 hours (multiplicity of infection 5 1) resulted in positive immunofluorescence for RSV antigen in less than 10% of MTE or H441 cells. In spite of the limited number of cells infected, RSV reduced both basal and amiloride-sensitive Isc in both MTE and H441 cells by approximately 50%, without causing a concomitant reduction in transepithelial resistance. Agents that increased intracellular cAMP (forskolin, cptCAMP, and IBMX) increased mainly Cl2 secretion in MTE cells and Na1 absorption in H441 cells. RSV infection for 24 hours blunted both variables. In contrast, ouabain sensitive Isc, measured across apically permeabilized H441 monolayers, remained unchanged. Western blot analysis of H441 cell lysates demonstrated reductions in abut not g-ENaC subunit protein levels at 24 hours after RSV infection. The reduction in amiloride-sensitive Isc in H441 cells was prevented by pretreatment with inhibitors of de novo pyrimidine or purine synthesis (A77-1726 and 6-MP, respectively, 50 mM). Our results suggest that infection of both murine and human respiratory epithelial cells with RSV inhibits vectorial Na1 transport via nucleotide release. These findings are consistent with our previous studies showing reduced alveolar fluid clearance after RSV infection of BALB/c mice.
Memory for Intestinal Rotavirus

The integrin a 4 b 7 mediates lymphocyte binding to mucosal addressin cell adhesion molecule-1, and its expression defines lymphocytes capable of trafficking through the intestines and the intestinal lymphoid tissues. We examined the ability of discrete a 4 b 7 hi and a 4 b 7 2 subsets of circulating memory phenotype (CD45RA 2 ) CD4 1 T cells to proliferate in response to rotavirus, a ubiquitous intestinal pathogen. a 4 b 7 hi memory (CD45RA 2 ) CD4 1 T cells displayed much greater reactivity to rotavirus than a 4 b 7 2 memory or naive (CD45RA 1 ) CD4 1 T cells. In contrast, a 4 b 7 2 memory cells were the predominant population responsive to mumps antigen after intramuscular vaccination. Our results are consistent with the conclusion that natural rotavirus infection, an enteric pathogen, results in a specific circulating memory CD4 1 response that is largely limited to the gut-homing a 4 b 7 1 subpopulation. This phenotype is not shared with memory cells elicited by intramuscular immunization (shown here) or by skin contact allergens. The results support the hypothesis that gut trafficking memory CD4 1 T cells comprise cellular memory for intestinal antigens and suggest that regulated expression of a 4 b 7 helps target and segregate intestinal versus systemic immune response. ( J. Clin. Invest. 1997. 100:1204–1208.)
Do we need a vaccine against Moraxella catarrhalis in chronic lung disease? What are the options and opportunities?

Moraxella catarrhalis is the second most common cause of exacerbations in adults with COPD, resulting in enormous morbidity and mortality in this clinical setting. Vaccine development for M. catarrhalis has lagged behind the other two important causes of exacerbations in COPD, nontypeable Haemophilus influenzae and Streptococcus pneumoniae. While no licensed vaccine is currently available for M. catarrhalis, several promising candidate vaccine antigens have been identified and characterized and are close to entering clinical trials. Key steps that are required to advance vaccines for M. catarrhalis along the translational pipeline include standardization of assay systems to assess candidate antigens, identification of a reliable correlate of protection and expansion of partnerships between industry, academia and government to overcome regulatory hurdles. A vaccine to prevent M. catarrhalis infections in COPD would have a major impact in reducing morbidity, mortality and healthcare costs in COPD.
How do influential and non-influential papers spread online?

Social media has become an important channel for publicizing academic research. Employing a dataset of about 10 million tweets of 584,264 scientific papers from 2012 to 2018, this study investigates the differential diffusion of influential and non-influential papers (divided by Average journal impact factor percentile ). We find that non-influential papers shows a diffusion trend with multiple rounds, sparse, short-duration and small-scale bursts. In contrast, the bursts of influential journals are characterized by a small number of persistent, dense and large-scale bursts. Influential papers are generally disseminated to many loosely connected communities, while non-influential papers are diffused to several densely connected communities.
Volumes of parent Hamiltonians for benchmarking quantum simulators

We investigate the relative volume of parent Hamiltonians having a target ground state up to some ﬁxed error ǫ , a quantity which sets a benchmark on the performance of quantum simulators. For vanishing error, this relative volume is of measure zero, whereas for a generic ǫ we show that it increases with the dimension of the Hilbert space. We also address the volume of parent Hamiltonians when they are restricted to be local. For translationally invariant Hamiltonians, we provide an upper bound to their relative volume. Finally, we estimate numerically the relative volume of parent Hamiltonians when the target state is the ground state of the Ising chain in a transverse ﬁeld.
Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and 
Planck
. III. Combined cosmological constraints

We present cosmological constraints from the analysis of two-point correlation functions between galaxy positions and galaxy lensing measured in Dark Energy Survey (DES) Year 3 data and measurements of cosmic microwave background (CMB) lensing from the South Pole Telescope (SPT) and Planck. When jointly analyzing the DES-only two-point functions and the DES cross-correlations with SPT+Planck CMB lensing, we find $\Omega_{\rm m} = 0.344\pm 0.030$ and $S_8 \equiv \sigma_8 (\Omega_{\rm m}/0.3)^{0.5} = 0.773\pm 0.016$, assuming $\Lambda$CDM. When additionally combining with measurements of the CMB lensing autospectrum, we find $\Omega_{\rm m} = 0.306^{+0.018}_{-0.021}$ and $S_8 = 0.792\pm 0.012$. The high signal-to-noise of the CMB lensing cross-correlations enables several powerful consistency tests of these results, including comparisons with constraints derived from cross-correlations only, and comparisons designed to test the robustness of the galaxy lensing and clustering measurements from DES. Applying these tests to our measurements, we find no evidence of significant biases in the baseline cosmological constraints from the DES-only analyses or from the joint analyses with CMB lensing cross-correlations. However, the CMB lensing cross-correlations suggest possible problems with the correlation function measurements using alternative lens galaxy samples, in particular the redMaGiC galaxies and high-redshift MagLim galaxies, consistent with the findings of previous studies. We use the CMB lensing cross-correlations to identify directions for further investigating these problems.
BOSS: A Benchmark for Human Belief Prediction in Object-context Scenarios

Humans with an average level of social cognition can infer the beliefs of others based solely on the nonverbal communication signals (e.g. gaze, gesture, pose and contextual information) exhibited during social interactions [1, 2, 3]. This social cognitive ability to predict human beliefs and intentions is more important than ever for ensuring safe human-robot interaction and collaboration. This paper uses the combined knowledge of Theory of Mind (ToM) and Object-Context Relations to investigate methods for enhancing collaboration between humans and autonomous systems in environments where verbal communication is prohibited. We propose a novel and challenging multimodal video dataset for assessing the capability of artiﬁcial intelligence (AI) systems in predicting human belief states in an object-context scenario. The proposed dataset consists of precise labelling of human belief state ground-truth and multimodal inputs replicating all nonverbal communication inputs captured by human perception. We further evaluate our dataset with existing deep learning models and provide new insights into the effects of the various input modalities and object-context relations on the performance of the baseline models.
Modèle de fragilité et algorithme EM stochastique

Le modele de Cox (1972) joue un role essentiel en analyse de survie. Toutefois, il est souvent peu realiste du fait de l'heterogeneite intrinseque a la population observee. Cette heterogeneite est prise en compte via un effet aleatoire dans le modele de fragilite introduit dans l'article de Vaupel et al. (1979). Estimer les parametres d'un modele de fragilite repose principalement sur la vraisemblance observee et sur l'estimation du maximum de vraisemblance associe. Mais le calcul direct est souvent difficile, voir impossible. Puisque les variables aleatoires de fragilite ne sont pas observees, on se situe dans le cadre plus general des modeles a variables latentes. Pour approcher l'estimateur du maximum de vraisemblance observee dans de tels modeles, on a generalement recours a l'algorithme Expectation Maximization (EM) propose dans l'article de Dempster et al. (1977). Cependant, il n'est pas applicable dans beaucoup de cas pratiques et doit souvent faire l'objet d'approximations. Notre objectif est de proposer un algorithme convergent pour l'estimation par maximum de vraisemblance dans les modeles de fragilite. A cet effet, nous considerons une approximation stochastique de l'algorithme EM couplee a une methode de Monte Carlo par chaines de Markov (SAEM-MCMC) introduite dans l'article de Kuhn et Lavielle (2004). La suite generee par l'algorithme converge presque surement vers un maximum local de la vraisemblance observee sous des hypotheses peu contraignantes. Bien que faisant appel a de la simulation, du fait de l'imbrication astucieuse des deux methodes SAEM et MCMC, cet algorithme est rapide. Nous comparons nos resultats a ceux obtenus par d'autres algorithmes dans la litterature. Mots cles : Donnees de survie et donnees censurees - Medecine - epidemiologie
Percolation of strongly correlated Gaussian fields II. Sharpness of the phase transition

We establish the sharpness of the phase transition for a wide class of Gaussian percolation models, on $\mathbb{Z}^d$ or $\mathbb{R}^d$, $d \ge 2$, with correlations decaying at least algebraically with exponent $\alpha>0$, including the discrete Gaussian free field ($d \ge 3, \alpha = d-2$), the discrete Gaussian membrane model ($d \ge 5, \alpha = d - 4$), and many other examples both discrete and continuous. In particular we do not assume positive correlations. This result is new for all strongly correlated models (i.e. $\alpha \in (0,d]$) in dimension $d \ge 3$ except the Gaussian free field, for which sharpness was proven in a recent breakthrough by Duminil-Copin, Goswami, Rodriguez and Severo; even then, our proof is simpler and yields new near-critical information on the percolation density. For planar fields which are continuous and positively-correlated, we establish sharper bounds on the percolation density by exploiting a new `weak mixing' property for strongly correlated Gaussian fields. As a byproduct we establish the box-crossing property for the nodal set, of independent interest. This is the second in a series of two papers studying level-set percolation of strongly correlated Gaussian fields, which can be read independently.
Representing design alternatives

Artificial intelligence systems are complex designed artifacts. Techniques used in Al systems to describe structures and to represent alternatives can be used to support the design of the systems themselves. PIE is an experimental personal information environment which provides users with descriptive structures for programs and documents. In PIE, alternative designs for programs and documents are simultaneously viewable in the system through the use of a context structured database. This short paper gives an overview of how the use of these facilities improves the design environment for builders of software systems.
Low-latency Imaging and Inference from LoRa-enabled CubeSats

Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth orbit, primarily for research, education, and Earth observation. The vast majority of these CubeSats experience significant latency (several hours) from the time an image is captured to the time it is available on the ground. This is primarily due to the limited availability of dedicated satellite ground stations that tend to be bulky to deploy and expensive to rent. This paper ex-plores using LoRa radios in the ISM band for low-latency downlink communication from CubeSats, primarily due to the availability of extensive ground LoRa infrastructure and minimal interference to terrestrial communication. However, the limited bandwidth of LoRa precludes rich satellite Earth images to be sent – instead, the CubeSats can at best send short messages ( ∼ a few hundred bytes). This paper details our experience in communicating with a LoRa-enabled CubeSat launched by our team. We present Vista, a communication system that makes software modifications to LoRa encoding onboard a CubeSat and decoding on commercial LoRa ground stations to allow for satellite imagery to be communicated, as well as wide-ranging machine learning inference on these images. This is achieved through a LoRa-channel-aware image encoding that is informed by the structure of satellite images, the tasks performed on it, as well as the Doppler variation of satellite signals. A detailed evaluation of Vista that builds upon wireless channel measurements from the LoRa-CubeSat launch (in 2021) shows 4.56 dB improvement in LoRa image PSNR and 1.38 × improvement in land-use classification over those images.
AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment

How to quickly and automatically mine effective information and serve investment decisions has attracted more and more attention from academia and industry. And new challenges have arisen with the global pandemic. This paper proposes a two-phase AlphaMLDigger that effectively finds excessive returns in a highly fluctuated market. In phase 1, a deep sequential natural language processing (NLP) model is proposed to transfer Sina Microblog blogs to market sentiment. In phase 2, the predicted market sentiment is combined with social network indicator features and stock market history features to predict the stock movements with different Machine Learning models and optimizers. The results show that the ensemble models achieve an accuracy of 0.984 and significantly outperform the baseline model. In addition, we find that COVID-19 brings data shift to China's stock market.
Functional Epistemology "Nullifies" Dyson's Rebuttal of Perturbation Theory

Functional epistemology is about ways to access functional objects by using varieties of methods and procedures. Not all such means are equally capable of reproducing these functions in the desired consistency and resolution. Dyson’s argument against the perturbative expansion of quantum field theoretic terms, in a radical form (never pursued by Dyson), is an example of epistemology taken as ontology.
Open Vocabulary Object Detection with Proposal Mining and Prediction Equalization

Open-vocabulary object detection (OVD) aims to scale up vocabulary size to detect objects of novel categories beyond the training vocabulary. Recent work resorts to the rich knowledge in pre-trained vision-language models. However, existing methods are ineffective in proposal-level vision-language alignment. Meanwhile, the models usually suffer from confidence bias toward base categories and perform worse on novel ones. To overcome the challenges, we present MEDet, a novel and effective OVD framework with proposal mining and prediction equalization. First, we design an online proposal mining to refine the inherited vision-semantic knowledge from coarse to fine, allowing for proposal-level detection-oriented feature alignment. Second, based on causal inference theory, we introduce a class-wise backdoor adjustment to reinforce the predictions on novel categories to improve the overall OVD performance. Extensive experiments on COCO and LVIS benchmarks verify the superiority of MEDet over the competing approaches in detecting objects of novel categories, e.g., 32.6% AP50 on COCO and 22.4% mask mAP on LVIS.
1RXH J082623.6–505741: A New Long-period Cataclysmic Variable with an Evolved Donor and a Low Mass-transfer Rate

We report the discovery of 1RXH J082623.6−505741, a 10.4 hr orbital period compact binary. Modeling extensive optical photometry and spectroscopy reveals a ∼0.4 M ⊙ K-type secondary transferring mass through a low-state accretion disk to a nonmagnetic ∼0.8 M ⊙ white dwarf. The secondary is overluminous for its mass and dominates the optical spectra at all epochs and must be evolved to fill its Roche Lobe at this orbital period. The X-ray luminosity L X ∼ 1–2 × 1032 erg s−1 derived from both new XMM-Newton and archival observations, although high compared to most CVs, still only requires a modest accretion rate onto the white dwarf of Ṁ ∼ 3 × 10−11 to 3 × 10−10 M ⊙ yr−1, lower than expected for a cataclysmic variable with an evolved secondary. No dwarf nova outbursts have yet been observed from the system, consistent with the low derived mass-transfer rate. Several other cataclysmic variables with similar orbital periods also show unexpectedly low mass-transfer rates, even though selection effects disfavor the discovery of binaries with these properties. This suggests the abundance and evolutionary state of long-period, low mass-transfer rate cataclysmic variables are worthy of additional attention.