bluuebunny/crossref_metadata_2025_split huggingface dataset:
Excessive Hypoxia‐Inducible Factor‐1α Expression Induces Cardiac Rupture via p53‐Dependent Apoptosis After Myocardial Infarction
Authors: Ikeda Masataka , Ide Tomomi , Tadokoro Tomonori , Miyamoto Hiroko Deguchi , Ikeda Soichiro , Okabe Kosuke , Ishikita Akihito , Sato Midori , Abe Ko , Furusawa Shun , Ishimaru Kosei , Matsushima Shouji , Tsutsui Hiroyuki 
Published: September 2021

Background
Apoptosis plays a pivotal role in cardiac rupture after myocardial infarction (MI), and p53 is a key molecule in apoptosis during cardiac rupture. Hif‐1α (hypoxia‐inducible factor‐1α), upregulated under hypoxia, is a known p53 inducer. However, the role of Hif‐1α in the regulatory mechanisms underlying p53 upregulation, apoptosis, and cardiac rupture after MI is unclear.

Methods and Results
We induced MI in mice by ligating the left anterior descending artery. Hif‐1α and p53 expressions were upregulated in the border zone at day 5 after MI, accompanied by apoptosis. In rat neonatal cardiomyocytes, treatment with cobalt chloride (500 μmol/L), which mimics severe hypoxia by inhibiting PHD (prolyl hydroxylase domain‐containing protein), increased Hif‐1α and p53, accompanied by myocyte death with caspase‐3 cleavage. Silencing Hif‐1α or p53 inhibited caspase‐3 cleavage, and completely prevented myocyte death under PHD inhibition. In cardiac‐specific Hif‐1α hetero‐knockout mice, expression of p53 and cleavage of caspase‐3 and poly (ADP‐ribose) polymerase were reduced, and apoptosis was suppressed on day 5. Furthermore, the cleavage of caspase‐8 and IL‐1β (interleukin‐1β) was also suppressed in hetero knockout mice, accompanied by reduced macrophage infiltration and matrix metalloproteinase/tissue inhibitor of metalloproteinase activation. Although there was no intergroup difference in infarct size, the cardiac rupture and survival rates were significantly improved in the hetero knockout mice until day 10 after MI.

Conclusions
Hif‐1α plays a pivotal role in apoptosis, inflammation, and cardiac rupture after MI, in which p53 is a critical mediator, and may be a prospective therapeutic target for preventing cardiac rupture.
Racial Differences in Blood Pressure Control Following Stroke: The REGARDS Study
Authors: Akinyelure Oluwasegun P. , Jaeger Byron C. , Moore Tony L. , Hubbard Demetria , Oparil Suzanne , Howard Virginia J. , Howard George , Buie Joy N. , Magwood Gayenell S. , Adams Robert J. , Bonilha Leonardo , Lackland Daniel T. , Muntner Paul 
Published: September 2021

Background and Purpose:
In the general population, Black adults are less likely than White adults to have controlled blood pressure (BP), and when not controlled, they are at greater risk for stroke compared with White adults. High BP is a major modifiable risk factor for recurrent stroke, but few studies have examined racial differences in BP control among stroke survivors.

Methods:
We used data from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke) to examine disparities in BP control between Black and White adults, with and without a history of stroke. We studied participants taking antihypertensive medication who did and did not experience an adjudicated stroke (n=306 and 7693 participants, respectively) between baseline (2003–2007) and a second study visit (2013–2016). BP control at the second study visit was defined as systolic BP <130 mm Hg and diastolic BP <80 mm Hg except for low-risk adults ≥65 years of age (ie, those without diabetes, chronic kidney disease, history of cardiovascular disease, and with a 10-year predicted atherosclerotic cardiovascular disease risk <10%) for whom BP control was defined as systolic BP <130 mm Hg.

Results:
Among participants with a history of stroke, 50.3% of White compared with 39.3% of Black participants had controlled BP. Among participants without a history of stroke, 56.0% of White compared with 50.2% of Black participants had controlled BP. After multivariable adjustment, there was a tendency for Black participants to be less likely than White participants to have controlled BP (prevalence ratio, 0.77 [95% CI, 0.59–1.02] for those with a history of stroke and 0.92 [95% CI, 0.88–0.97] for those without a history of stroke).

Conclusions:
There was a lower proportion of controlled BP among Black compared with White adults with or without stroke, with no statistically significant differences after multivariable adjustment.
Long-Term Evolution of Functional Limitations in Stroke Survivors Compared With Stroke-Free Controls: Findings From 15 Years of Follow-Up Across 3 International Surveys of Aging
Authors: Gil-Salcedo Andres , Dugravot Aline , Fayosse Aurore , Jacob Louis , Bloomberg Mikaela , Sabia Séverine , Schnitzler Alexis 
Published: September 2021

Background and Purpose:
In the chronic phase 2 to 5 years poststroke, limitations in activities of daily living (ADL) and instrumental ADL (IADL) initially plateau before steady increasing. However, the impact of age and differences in initial levels of disability on the evolution of these limitations remains unclear. As such, this study aims to evaluate differences in long-term evolution of ADL/IADL limitations between stroke survivors and stroke-free population, and how limitations differ by initial level of disability for stroke survivors.

Methods:
Thirty-three thousand six hundred sixty participants (5610 first-ever stroke cases with no recurrence during follow-up and 28 050 stroke-free controls) aged ≥50 from the Health and Retirement Study, Survey of Health, Ageing and Retirement in Europe, and English Longitudinal Study of Ageing were assessed for number of ADL/IADL limitations during the poststroke chronic phase (for cases) and over follow-up years 1996 to 2018 (for controls). Three thousand seven hundred eighteen stroke cases were additionally categorized by disability level using the modified Rankin Scale score of 1 to 2 years poststroke. Evolution of ADL/IADL limitations was assessed in stroke cases and controls and by modified Rankin Scale score (0–1, 2–3, 4–5) using linear mixed models. Models were stratified by age group (50–74 and ≥75 years) and adjusted for baseline characteristics, health behaviors, BMI, and comorbidities.

Results:

Findings showed relative stability of ADL/IADL limitations during 3 to 6 years poststroke followed by an increase for both populations, which was faster for younger stroke cases, suggesting a differential age-effect (
P
<0.001). Disability level at 1 to 2 years poststroke influenced the evolution of limitations over time, especially for severe disability (modified Rankin Scale score, 4–5) associated with a reduction in limitations at 5 to 6 years poststroke.

Conclusions:
Our findings showed that during the poststroke chronic phase functional limitations first plateau and then increase and the evolution differs by disability severity. These results highlight the importance of adaptive long-term health and social care measures for stroke survivors.
Sequence-Level Training for Non-Autoregressive Neural Machine Translation
Authors: Shao Chenze , Feng Yang , Zhang Jinchao , Meng Fandong , Zhou Jie 
Published: September 2021

Abstract
In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregressive mechanism leads to high translation latency of the NMT and restricts its low-latency applications. Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup by generating target words independently and simultaneously. Nevertheless, NAT still takes the word-level cross-entropy loss as the training objective, which is not optimal because the output of NAT cannot be properly evaluated due to the multimodality problem. In this article, we propose using sequence-level training objectives to train NAT models, which evaluate the NAT outputs as a whole and correlates well with the real translation quality. First, we propose training NAT models to optimize sequence-level evaluation metrics (e.g., BLEU) based on several novel reinforcement algorithms customized for NAT, which outperform the conventional method by reducing the variance of gradient estimation. Second, we introduce a novel training objective for NAT models, which aims to minimize the Bag-of-N-grams (BoN) difference between the model output and the reference sentence. The BoN training objective is differentiable and can be calculated efficiently without doing any approximations. Finally, we apply a three-stage training strategy to combine these two methods to train the NAT model. We validate our approach on four translation tasks (WMT14 En↔De, WMT16 En↔Ro), which shows that our approach largely outperforms NAT baselines and achieves remarkable performance on all translation tasks. The source code is available at https://github.com/ictnlp/Seq-NAT.
Task-Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updates
Authors: Zeno Chen , Golan Itay , Hoffer Elad , Soudry Daniel 
Published: September 2021

Abstract
Catastrophic forgetting is the notorious vulnerability of neural networks to the changes in the data distribution during learning. This phenomenon has long been considered a major obstacle for using learning agents in realistic continual learning settings. A large body of continual learning research assumes that task boundaries are known during training. However, only a few works consider scenarios in which task boundaries are unknown or not well defined: task-agnostic scenarios. The optimal Bayesian solution for this requires an intractable online Bayes update to the weights posterior.
We aim to approximate the online Bayes update as accurately as possible. To do so, we derive novel fixed-point equations for the online variational Bayes optimization problem for multivariate gaussian parametric distributions. By iterating the posterior through these fixed-point equations, we obtain an algorithm (FOO-VB) for continual learning that can handle nonstationary data distribution using a fixed architecture and without using external memory (i.e., without access to previous data). We demonstrate that our method (FOO-VB) outperforms existing methods in task-agnostic scenarios. FOO-VB Pytorch implementation is available at https://github.com/chenzeno/FOO-VB.
Flexible Transmitter Network
Authors: Zhang Shao-Qun , Zhou Zhi-Hua 
Published: September 2021

Abstract
Current neural networks are mostly built on the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. This letter proposes the flexible transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. The FT model employs a pair of parameters to model the neurotransmitters between neurons and puts up a neuron-exclusive variable to record the regulated neurotrophin density. Thus, the FT model can be formulated as a two-variable, two-valued function, taking the commonly used MP neuron model as its particular case. This modeling manner makes the FT model biologically more realistic and capable of handling complicated data, even spatiotemporal data. To exhibit its power and potential, we present the flexible transmitter network (FTNet), which is built on the most common fully connected feedforward architecture taking the FT model as the basic building block. FTNet allows gradient calculation and can be implemented by an improved backpropagation algorithm in the complex-valued domain. Experiments on a broad range of tasks show that FTNet has power and potential in processing spatiotemporal data. This study provides an alternative basic building block in neural networks and exhibits the feasibility of developing artificial neural networks with neuronal plasticity.
Dynamic Spatiotemporal Pattern Recognition with Recurrent Spiking Neural Network
Authors: Shen Jiangrong , Liu Jian K. , Wang Yueming 
Published: September 2021

Abstract
Our real-time actions in everyday life reflect a range of spatiotemporal dynamic brain activity patterns, the consequence of neuronal computation with spikes in the brain. Most existing models with spiking neurons aim at solving static pattern recognition tasks such as image classification. Compared with static features, spatiotemporal patterns are more complex due to their dynamics in both space and time domains. Spatiotemporal pattern recognition based on learning algorithms with spiking neurons therefore remains challenging. We propose an end-to-end recurrent spiking neural network model trained with an algorithm based on spike latency and temporal difference backpropagation. Our model is a cascaded network with three layers of spiking neurons where the input and output layers are the encoder and decoder, respectively. In the hidden layer, the recurrently connected neurons with transmission delays carry out high-dimensional computation to incorporate the spatiotemporal dynamics of the inputs. The test results based on the data sets of spiking activities of the retinal neurons show that the proposed framework can recognize dynamic spatiotemporal patterns much better than using spike counts. Moreover, for 3D trajectories of a human action data set, the proposed framework achieves a test accuracy of 83.6% on average. Rapid recognition is achieved through the learning methodology–based on spike latency and the decoding process using the first spike of the output neurons. Taken together, these results highlight a new model to extract information from activity patterns of neural computation in the brain and provide a novel approach for spike-based neuromorphic computing.
Parametric UMAP Embeddings for Representation and Semisupervised Learning
Authors: Sainburg Tim , McInnes Leland , Gentner Timothy Q. 
Published: September 2021

Abstract
UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data. The UMAP algorithm consists of two steps: (1) computing a graphical representation of a data set (fuzzy simplicial complex) and (2) through stochastic gradient descent, optimizing a low-dimensional embedding of the graph. Here, we extend the second step of UMAP to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding. We first demonstrate that parametric UMAP performs comparably to its nonparametric counterpart while conferring the benefit of a learned parametric mapping (e.g., fast online embeddings for new data). We then explore UMAP as a regularization, constraining the latent distribution of autoencoders, parametrically varying global structure preservation, and improving classifier accuracy for semisupervised learning by capturing structure in unlabeled data.
A Correspondence Between Normalization Strategies in Artificial and Biological Neural Networks
Authors: Shen Yang , Wang Julia , Navlakha Saket 
Published: September 2021

Abstract
A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods such as batch normalization, weight normalization, and their many variants help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this article, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neuron's activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent—that is, both push activation patterns of hidden units toward a homeostatic state, where all neurons are equally used—and we argue that such representations can improve coding capacity, discrimination, and regularization. As a proof of concept, we develop an algorithm, inspired by a neural normalization technique called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several data sets. Overall, we hope this bidirectional connection will inspire neuroscientists and machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.
Dead Enough to Bury
Authors: Smith Russell 
Published: September 2021

Abstract
This essay examines the concepts of life and afterlife as they appear across Beckett’s trilogy, through focussing on representations of the act of burial, an act which draws attention to a caesura between biotic and abiotic conceptions of both life and afterlife. As the worlds of the trilogy become progressively less biotic, The Unnamable might be thought of as a laboratory in which the ‘lives’ of its characters are subjected to various biological experiments, experiments which suggest that narrative fiction, like the act of burial, is a kind of prophylactic against the fundamental processual nature of biotic life.
A Framework for Simulating the Tropical-Cyclone Boundary Layer Using Large-Eddy Simulation and Its Use in Evaluating PBL Parameterizations
Authors: Chen Xiaomin , Bryan George H. , Zhang Jun A. , Cione Joseph J. , Marks Frank D. 
Published: September 2021

AbstractBoundary layer turbulent processes affect tropical cyclone (TC) structure and intensity change. However, uncertainties in the parameterization of the planetary boundary layer (PBL) under high-wind conditions remain challenging, mostly due to limited observations. This study presents and evaluates a framework of numerical simulation that can be used for a small-domain [O(5 km)] large-eddy simulation (LES) and single-column modeling (SCM) to study the TC boundary layer. The framework builds upon a previous study that uses a few input parameters to represent the TC vortex and adds a simple nudging term for temperature and moisture to account for the complex thermodynamic processes in TCs. The reference thermodynamic profiles at different wind speeds are retrieved from a composite analysis of dropsonde observations of mature hurricanes. Results from LES show that most of the turbulence kinetic energy and vertical momentum flux is associated with resolved processes when horizontal grid spacing is O(10 m). Comparison to observations of turbulence variables such as momentum flux, effective eddy viscosity, and turbulence length scale show that LES produces reasonable results but highlight areas where further observations are necessary. LES results also demonstrate that compared to a classic Ekman-type boundary layer, the TC boundary layer is shallower, develops steady conditions much quicker, and exhibits stronger wind speed near the surface. The utility of this framework is further highlighted by evaluating a first-order PBL parameterization, suggesting that an asymptotic turbulence length scale of 40 m produces a good match to LES results.
The Influence of WENO Schemes on Large-Eddy Simulations of a Neutral Atmospheric Boundary Layer
Authors: Wang Aaron , Pan Ying , Markowski Paul M. 
Published: September 2021

AbstractThis work explores the influence of Weighted Essentially Non-Oscillatory (WENO) schemes on Cloud Model 1 (CM1) large-eddy simulations (LES) of a quasi-steady, horizontally homogeneous, fully developed, neutral atmospheric boundary layer (ABL). An advantage of applying WENO schemes to scalar advection in compressible models is the elimination of acoustic waves and associated oscillations of domain-total vertical velocity. Applying WENO schemes to momentum advection in addition to scalar advection yields no further advantage, but has an adverse effect on resolved turbulence within LES. As a tool designed to reduce numerically generated spurious oscillations, WENO schemes also suppress physically realistic instability development in turbulence-resolving simulations. Thus, applying WENO schemes to momentum advection reduces vortex stretching, suppresses the energy cascade, reduces shear-production of resolved Reynolds stress, and eventually amplifies the differences between the surface-layer mean wind profiles in the LES and the mean wind profiles expected in accordance with the filtered law of the wall (LOTW). The role of WENO schemes in adversely influencing surface-layer turbulence has inspired a concept of anti-WENO (AWENO) schemes to enhance instability development in regions where energy-containing turbulent motions are inadequately resolved by LES grids. The success in reproducing the filtered LOTW via AWENO schemes suggests that improving advection schemes is a critical component toward faithfully simulating near-surface turbulence and dealing with other "Terra Incognita" problems.
Competition between baroclinic instability and Ekman transport under varying buoyancy forcings in upwelling systems: An idealized analog to the Southern Ocean.
Authors: Thomsen Soeren , Capet Xavier , Echevin Vincent 
Published: September 2021

AbstractCoastal upwelling rates are classically determined by the intensity of the upper-ocean offshore Ekman transport. But (sub-)mesoscale turbulence modulates offshore transport, hence the net upwelling rate. Eddy effects generally oppose the Ekman circulation, resulting in so-called “eddy cancellation”, a process well studied in the Southern Ocean. Here we investigate how air-sea heat/buoyancy fluxes modulate eddy cancellation in an idealized upwelling model. We run CROCO simulations with constant winds but varying heat fluxes with and without submesoscale-rich turbulence. Eddy cancellation is consistently evaluated with three different methods that all account for the quasi-isopycnal nature of ocean circulation away from the surface. For zero heat fluxes the release of available potential energy by baroclinic instabilities is strongest and leads, near the coast, to nearly full cancellation of the Ekman cross-shore circulation by eddy effects,i.e., zero net mean upwelling flow. With increasing heat fluxes eddy cancellation is reduced and the transverse flow progressively approaches the classical Ekman circulation. Sensitivity of the eddy circulation to synoptic changes in air-sea heat fluxes is felt down to 125 m depth despite short experiments of tens of days. Mesoscale dynamics dominate the cancellation effect in our simulations which might also hold for the real ocean as the relevant processes act below the surface boundary layer. Although the idealized setting overemphasis the role of eddies and thus studies with more realistic settings should follow, our findings have important implications for the overall understanding of upwelling system dynamics.