Miracle-dz/UniFAR-FTUs huggingface dataset:
Periplaneta americana extract ameliorates lipopolysaccharide-induced liver injury by improving mitochondrial dysfunction via the AMPK/PGC-1α signaling pathway

Periplaneta americana (PA) extract acts clinically as a therapeutic treatment in various diseases; it enhances liver function in mouse models and mitigates the pathological condition of liver fibrosis. The present study aimed to investigate the role and potential mechanisms underlying the action of the PA extract, xinmailong (XML), in lipopolysaccharide (LPS)-induced liver injury. Following the treatment of AML12 cells with LPS, the content of cytochrome c in the cytoplasm and mitochondria, and the level of ATP synthesis were detected using corresponding kits. The relative mRNA expression levels of nuclear respiratory factor 1 and transcription factor A, mitochondrial were investigated using reverse transcription-quantitative (RT-q)PCR analysis. The MTT assay was performed to detect the viability of AML12 cells following treatment with XML, in the absence or presence of LPS. Western blot analysis was performed to determine the expression levels of proteins in the AMP-activated protein kinase (AMPK)/proliferator-activated receptor γ coactivator-1α (PGC-1α) pathway. Following treatment with compound C, an inhibitor of AMPK, the expression levels of inflammatory cytokines were determined using ELISA and RT-qPCR analysis. The levels of oxidative stress-related markers were detected using corresponding kits following treatment with compound C. In addition, TUNEL staining was performed to detect the apoptosis of AML12 cells, and western blot analysis was performed to investigate the expression levels of apoptosis-related proteins. Mitochondrial dysfunction was induced by LPS in AML12 cells. LPS stimulation significantly downregulated the expression of proteins in the AMPK/PGC-1α pathway, which was reversed following treatment with XML. In addition, inflammation, oxidative stress and mitochondrial dysfunction induced by LPS were alleviated by XML in AML12 cells. However, the addition of compound C and XML to LPS-induced AML12 cells resulted in the aggravation of cell injury. Collectively, the results of the present study indicated that XML suppressed mitochondrial dysfunction induced by LPS by activating AMPK/PGC-1α signaling. Thus, the results of the present study may contribute to further understanding of the underlying mechanism via which XML alleviates liver injury.
Tutorial on Empirical Mode Decomposition: Basis Decomposition and Frequency Adaptive Graduation in Non-Stationary Time Series

This tutorial explores the class of non-parametric time series basis decomposition methods particularly suited for nonstationary time series known as Empirical Mode Decomposition (EMD). In outlining a statistical perspective of the EMD method, it will be contrasted and combined (for the betterment of both methods) with other existing nonstationary basis decomposition methods. Some such techniques are functional Independent Component Analysis (ICA), Empirical Fourier Decomposition (EFD) (nonstationary extension of the Short-Time Fourier Transform (STFT), Empirical Wavelet Transform (EWT) (nonstationary extension of Morlet Wavelet Transform (MWT)), and Singular Spectrum Decomposition (SSD) (nonstationary extension and refinement of Singular Spectrum Analysis (SSA)). A detailed review of this time series basis decomposition approach is presented that explores 3 core aspects for a statistical audience: 1) the basis functions (Intrinsic Mode Functions (IMFs)) representation and estimation methods including robustness and optimal spline representations including smoothing and knot placements; 2) the computational and numerical robustness of various aspects of the iterative algorithmic design for EMD basis extraction, including treating carefully boundary effects; and 3) the first attempt at a population-based characterisation of EMD that provides a novel stochastic embedding of the EMD method within a stochastic model framework. Furthermore, the basis representations considered will be connected to local frequency graduation smoothing methods, demonstrating that these can be adapted to a local frequency adaptive framework within the EMD context. This will provide new practical insights into the interface between time series basis decomposition and graduation-smoothed representations.
Broken Windows Policing and Crime: Evidence from 80 Colombian Cities

We study the effects of broken windows policing on crime using geo-located crime and arrest reports for 80 Colombian cities. Broadly defined, broken windows policing consists of intensifying arrests—sometimes for minor offenses—to deter potential criminals. To estimate causal effects, we build grids of 200 × 200 meters over the urban perimeter of all cities and produce event studies to look at the effects of shocks in police activity in the periods to follow. We use spikes in the number of arrests with no warrant—which are more likely associated with unplanned police presence—as a proxy for shocks in broken windows policing. As expected, we observe an increase in crimes during the shock period, as each arrest implies at least one crime report. In the following periods, crimes decrease both in the place of the arrests and the surroundings. With many treated grids and many places exposed to spillovers, these effects add up. On aggregate, the crime reduction offsets the observed increase during the shock period. Direct effects are more immediate and precise at low crime grids, but beneficial spillovers seem more relevant at crime hot spots. The effects of broken windows policing circumscribe to cities with low or moderate organized crime, consistent with criminal organizations planning their activities more systematically than disorganized criminals.
OPTH_A_270575 2821..2828

1Centre for Ocular Research & Education (CORE), School of Optometry & Vision Science, University of Waterloo, Waterloo, ON, Canada; 2Johnson & Johnson Vision Care, Inc, Jacksonville, FL, USA; 3Centre for Eye & Vision Research (CEVR), Hong Kong, People’s Republic of China Purpose: The deposition profile of cholesteryl ester on the surface and throughout the matrix of silicone hydrogel contact lens (CL) materials was determined under conditions that mimic a daily wear regimen. Methods: In this in vitro study, four SiHy CL materials (senofilcon C, lotrafilcon B, comfilcon A and samfilcon A) were incubated in an artificial tear solution (ATS) for up to 30 days. CL incubation was alternated between the ATS (16 hours) and a multipurpose care regimen (8 hours). The ATS included fluorescently tagged cholesteryl ester (5-cholesten-3ßol 6-[(7-nitro-2-1,3-benzoxadiazol-4-yl)amino]caproate; CE-NBD) and confocal laser scanning microscopy visualized the distribution of the lipid through the CLs. Results: The distribution of CE-NBD was homogenous from the anterior to posterior surface in senofilcon C and comfilcon A, at all time points. For lotrafilcon B and samfilcon A, CE-NBD localization was heterogeneous, with greater amounts on the surfaces on Day 1 and Day 14 compared to the lens matrix; however, differences in concentration between the surface and bulk diminished by Day 30. Conclusion: The distribution of the non-polar lipid CE-NBD varied with lens material chemistry. While some lens materials deposited the lipid primarily on the surface after 16 hours of exposure, all materials exhibited a homogenous distribution after one month.
Spark ignition and early flame development of lean mixtures under high-velocity flow conditions: An experimental study

This study set out to experimentally investigate spark ignition and the subsequent early flame development of lean air–fuel mixtures of A/F = 20–30 under high-velocity flow conditions using a uniquely designed swirl chamber. The swirl chamber realizes a high-velocity flow of 65 m/s at the spark plug gap, as well as internal temperature and pressure histories that are equivalent to those of spark-ignition engines, being equipped with an optically accessible engine. The designed swirl chamber clearly captures the characteristic behavior of the spark channels and flames in the vicinity of the spark plug. The results show that the spark channels stretch downstream following the flow and are subject to short circuits or restrikes. In the case of a high ignition energy of 200 mJ, short circuits of the spark channels occur in the early part of the discharge, while restrikes occur in the later parts. With a decrease in the ignition energy, restrikes occur in the earlier parts of the discharge. With a low ignition energy of 65 mJ, restrikes can occur immediately after the electrical breakdown without any significant spark stretch. At a sufficiently low dilution degree of A/F = 20, flames can hold behind the ground electrode of the spark plug, which significantly suppresses the cycle dispersion while also enhancing the combustion in the early stage. With further air dilution, that is, A/F > 20, flames develop, flowing downstream without flame holding. However, temporal flame attachment to the ground electrode is observed during the discharge duration even at A/F = 30, while the attached flames eventually blow off downstream at the end of the spark discharge.
Learning from Uneven Training Data: Unlabeled, Single Label, and Multiple Labels

Training NLP systems typically assumes access to annotated data that has a single human label per example. Given imperfect labeling from annotators and inherent ambiguity of language, we hypothesize that single label is not sufﬁcient to learn the spectrum of language interpretation. We explore new label annotation distribution schemes, assigning multiple labels per example for a small subset of training examples. Introducing such multi label examples at the cost of annotating fewer examples brings clear gains on natural language inference task and entity typing task, even when we simply ﬁrst train with a single label data and then ﬁne tune with multi label examples. Extending a MixUp data augmentation framework, we propose a learning algorithm that can learn from uneven training examples (with zero, one, or multiple labels). This algorithm efﬁciently combines signals from uneven training data and brings additional gains in low annotation budget and cross domain settings. Together, our method achieves consistent gains in both accuracy and label distribution metrics in two tasks, suggesting training with uneven training data can be beneﬁcial for many NLP tasks. 1
Challenges on the development of a pseudotyping assay for Zika glycoproteins

Introduction Zika virus (ZIKV) emerged as a public health concern on the American continent during late 2015. As the number of infected grew so did the concerns about its capability to cause long-term damage especially with the appearance of the congenital Zika syndrome (CZS). Proteins from the TAM family of receptor tyrosine kinases (RTKs) were proposed as the cellular receptors, however, due to the ability of the virus to infect a variety of cell lines different strategies to elucidate the tropism of the virus should be investigated. Hypothesis Pseudotyping is a powerful tool to interrogate the ability of the glycoprotein (GP) to permit entry of viruses. Aim We aimed to establish a highly tractable pseudotype model using lenti- and retro-viral backbones to investigate the entry pathway of ZIKV. Methodology We used different glycoprotein constructs and different lenti- or retro-viral backbones, in a matrix of ratios to investigate production of proteins and functional pseudotypes. Results Varying the ratio of backbone and glycoprotein plasmids did not yield infectious pseudotypes. Moreover, the supplementation of the ZIKV protease or the substitution of the backbone had no positive impact on the infectivity. We showed production of the proteins in producer cells implying the lack of infectious pseudotypes is due to a lack of successful glycoprotein incorporation, rather than lack of protein production. Conclusion In line with other reports, we were unable to successfully produce infectious pseudotypes using the variety of methods described. Other strategies may be more suitable in the development of an efficient pseudotype model for ZIKV and other flaviviruses.
Graphine: A Dataset for Graph-aware Terminology Definition Generation

Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intensity curation, further accelerating scientific discovery. Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation. In this paper, we present a large-scale terminology definition dataset Graphine covering 2,010,648 terminology definition pairs, spanning 227 biomedical subdisciplines. Terminologies in each subdiscipline further form a directed acyclic graph, opening up new avenues for developing graph-aware text generation models. We then proposed a novel graph-aware definition generation model Graphex that integrates transformer with graph neural network. Our model outperforms existing text generation models by exploiting the graph structure of terminologies. We further demonstrated how Graphine can be used to evaluate pretrained language models, compare graph representation learning methods and predict sentence granularity. We envision Graphine to be a unique resource for definition generation and many other NLP tasks in biomedicine.
The dispersal of protoplanetary discs – II: photoevaporation models with observationally derived irradiating spectra

Young solar-type stars are known to be strong X-ray emitters and their X-ray spectra have been widely studied. X-rays from the central star may play a crucial role in the thermodynamics and chemistry of the circumstellar material as well as in the atmospheric evolution of young planets. In this paper, we present model spectra based on spectral parameters derived from the observations of young stars in the Orion nebula cluster from the Chandra Orion Ultradeep Project (COUP). The spectra are then used to calculate new photoevaporation prescriptions that can be used in disc and planet population synthesis models. Our models clearly show that disc wind mass loss rates are controlled by the stellar luminosity in the soft ($100\, \mathrm{eV}$ to $1\, \mathrm{keV}$) X-ray band. New analytical relations are provided for the mass loss rates and profiles of photoevaporative winds as a function of the luminosity in the soft X-ray band. The agreement between observed and predicted transition disc statistics moderately improved using the new spectra, but the observed population of strongly accreting large cavity discs can still not be reproduced by these models. Furthermore, our models predict a population of non-accreting transition discs that are not observed. This highlights the importance of considering the depletion of millimetre-sized dust grains from the outer disc, which is a likely reason why such discs have not been detected yet.
Stability of planar rarefaction waves for scalar viscous conservation law under periodic perturbations

Abstract. The large time behavior of the solutions to a multi-dimensional viscous conservation law is considered in this paper. It is shown that the solution timeasymptotically tends to the planar rarefaction wave if the initial perturbations are multi-dimensional periodic. The time-decay rate is also obtained. Moreover, a Gagliardo-Nirenberg type inequality is established in the domain RˆTn ́1pn ě 2q, where Tn ́1 is the n ́ 1-dimensional torus.
Model Explanations via the Axiomatic Causal Lens

Explaining the decisions of black-box models is a central theme in the study of trustworthy ML. Numerous measures have been proposed in the literature; however, none of them take an axiomatic approach to causal explainability. In this work, we propose three explanation measures which aggregate the set of all but-for causes -- a necessary and sufficient explanation -- into feature importance weights. Our first measure is a natural adaptation of Chockler and Halpern's notion of causal responsibility, whereas the other two correspond to existing game-theoretic influence measures. We present an axiomatic treatment for our proposed indices, showing that they can be uniquely characterized by a set of desirable properties. We also extend our approach to derive a new method to compute the Shapley-Shubik and Banzhaf indices for black-box model explanations. Finally, we analyze and compare the necessity and sufficiency of all our proposed explanation measures in practice using the Adult-Income dataset. Thus, our work is the first to formally bridge the gap between model explanations, game-theoretic influence, and causal analysis.
Long-run dynamics of sulphur dioxide emissions , economic growth and energy efficiency in China

This paper estimates the linkages among total Sulphur dioxide (SO2) emissions, total GDP and energy efficiency using China’s provincial panel data from 2002 to 2015. We investigate total emissions rather than per capita emissions or ambient concentrations, since it is total emissions that the environment cares about. Energy efficiency is estimated using stochastic frontier analysis and decomposed into both persistent and transient efficiency. We then investigate the long-run dynamics among SO2 emissions, economic growth and energy efficiency by employing the panel-based error correction model and taking the effects of cyclical variations into account. Our analysis shows that GDP has a positive impact on total SO2 emissions in the short run and gains in energy efficiency have a significant negative effect on emissions in the long run. By controlling the effects of business cycle, the effects of GDP on emissions remain positive in both short and long run. Cross-sectional analysis provides similar insights. We argue that economic growth itself is an emission generator. Therefore, the government needs to establish a long-run strategy to curb the emissions by improving energy efficiency.
Nonlinear Pricing in the Transport Industry and the Gains from Trade

Recent empirical researches documented that there exists nonlinear pricing phenomenon in the shipping industry. This paper strives to show how this empirical regularity would alter conventional results in trade literature. This paper shows that when nonlinear pricing in shipping industry is considered, while the average productivity is higher conducive to higher welfare level, the gains from trade is generally lower than the situation without. In addition, the model built in this paper offers micro foundations for additive trade cost and features endogenous response of shipping charges to iceberg trade cost, an empirical finding emphasized in Hummels et al. (2009). In a much broader sense, this paper argues that heterogeneous firm model offers a lens through which traditional results on some interesting objects, e.g. gains from trade, could be altered.
On the Utility Maximization of the Discrepancy between a Perceived and Market Implied Risk Neutral Distribution

A method is developed to determine the portfolio that maximizes the expected utility of an agent that trades the difference between a perceived future price distribution of an asset and the associated market implied risk neutral density. Exact results to construct and price such a portfolio are presented under the assumption that the underlying asset price evolves according to a geometric Brownian motion. Integer programming optimization techniques are applied to the general case where one first calibrates the asset price risk neutral density directly from option market data using Gatheral’s SVI parameterization. Several numerical examples approximating the optimal payoff function with liquid securities are given.
BMS: Secure Decentralized Reconfiguration for Blockchain and BFT Systems

Reconfiguration of long-lived blockchain and Byzantine fault-tolerant (BFT) systems poses fundamental security challenges. In case of state-of-the-art Proof-of-Stake (PoS) blockchains, stake reconfiguration enables so-called long-range attacks, which can lead to forks. Similarly, permissioned blockchain systems, typically based on BFT, reconfigure internally, which makes them susceptible to a similar"I still work here"attack. In this work, we propose BMS (Blockchain/BFT Membership Service) offering a secure and dynamic reconfiguration service for BFT and blockchain systems, preventing long-range and similar attacks. In particular: (1) we propose a root BMS for permissioned blockchains, implemented as an Ethereum smart contract and evaluate it reconfiguring the recently proposed Mir-BFT protocol, (2) we discuss how our BMS extends to PoS blockchains and how it can reduce PoS stake unbonding time from weeks/months to the order of minutes, and (3) we discuss possible extensions of BMS to hierarchical deployments as well as to multiple root BMSs.
Active Multiobject Exploration and Recognition via Tactile Whiskers

Robotic exploration under uncertain environments is challenging when optical information is not available. In this article, we propose an autonomous solution of exploring an unknown task space based on tactile sensing alone. We first designed a whisker sensor based on MEMS barometer devices. This sensor can acquire contact information by interacting with the environment nonintrusively. This sensor is accompanied by a planning technique to generate exploration trajectories by using mere tactile perception. This technique relies on a hybrid policy for tactile exploration, which includes a proactive informative path planner for object searching, and a reactive Hopf oscillator for contour tracing. Results indicate that the hybrid exploration policy can increase the efficiency of object discovery. Last, scene understanding was facilitated by segmenting objects and classification. A classifier was developed to recognize the object categories based on the geometric features collected by the whisker sensor. Such an approach demonstrates the whisker sensor, together with the tactile intelligence, can provide sufficiently discriminative features to distinguish objects.
Popularity Adjusted Block Models are Generalized Random Dot Product Graphs

Abstract We connect two random graph models, the Popularity Adjusted Block Model (PABM) and the Generalized Random Dot Product Graph (GRDPG), by demonstrating that the PABM is a special case of the GRDPG in which communities correspond to mutually orthogonal subspaces of latent vectors. This insight allows us to construct new algorithms for community detection and parameter estimation for the PABM, as well as improve an existing algorithm that relies on Sparse Subspace Clustering. Using established asymptotic properties of Adjacency Spectral Embedding for the GRDPG, we derive asymptotic properties of these algorithms. In particular, we demonstrate that the absolute number of community detection errors tends to zero as the number of graph vertices tends to infinity. Simulation experiments illustrate these properties. Supplementary materials for this article are available online.