Miracle-dz/UniFAR-FTUs huggingface dataset:
Activity of Choline Alphoscerate on Adult-Onset Cognitive Dysfunctions: A Systematic Review and Meta-Analysis

Background: Choline alphoscerate (alpha glyceryl phosphorylcholine, α-GPC) is a choline-containing phospholipid used as a medicine or nutraceutical to improve cognitive function impairment occurring in neurological conditions including adult-onset dementia disorders. Despite its 1985 marketing authorization, there are still discrepancies between countries regarding its approval as a prescription medicine and discussions about its effectiveness. Objective: This study aimed to evaluate the efficacy of the α-GPC compound for treating cognitive impairment in patients with adult-onset neurological disorders. Methods: Relevant studies were identified by searching PubMed, Web of Science, and Embase. Studies that evaluated the effects of α-GPC alone or in combination with other compounds on adult-onset cognitive impairment reporting cognition, function, and behavior were considered. We assessed the risk of bias of selected studies using the Cochrane risk of bias tool. Results: A total of 1,326 studies and 300 full-text articles were screened. We included seven randomized controlled trials (RCTs) and one prospective cohort study that met our eligibility criteria. We found significant effects of α-GPC in combination with donepezil on cognition [4 RCTs, mean difference (MD):1.72, 95% confidence interval (CI): 0.20 to 3.25], functional outcomes [3 RCTs, MD:0.79, 95% CI: 0.34 to 1.23], and behavioral outcomes [4 RCTs; MD: –7.61, 95% CI: –10.31 to –4.91]. We also observed that patients who received α-GPC had significantly better cognition than those who received either placebo or other medications [MD: 3.50, 95% CI: 0.36 to 6.63]. Conclusion: α-GPC alone or in combination with donepezil improved cognition, behavior, and functional outcomes among patients with neurological conditions associated with cerebrovascular injury.
Forecasting Output with the Composite Leading Index: A Real-Time Analysis

Abstract We examine the ability of the composite index of leading economic indicators to predict future movements in aggregate economic activity. Previous examinations of predictive performance have evaluated either the in-sample residual errors from a forecasting equation fitted to the entire sample of data or the out-of-sample forecast errors from an equation fitted to a subsample of the data. Unlike previous evaluations, we perform a real-time analysis, which uses the provisional and partially revised data for the leading index that were actually available historically, along with recursive out-of-sample forecasts. We find a substantial deterioration of forecasting performance in the real-time framework.
The influence of PV inversion on polar‐vortex dynamics and passive‐tracer simulations in atmosphere‐like regimes

Both the stratospheric polar‐vortex edge and the midlatitude tropopause are characterized by strong potential vorticity (PV) gradients on isentropic surfaces, a jet and persistent wave activity. Passive tracers in these regions also develop strong gradients, where contours of mixing ratio lie almost parallel to PV contours. Both sets of contours stretch and fold producing narrow filaments. This behaviour is associated with time‐dependent large‐scale straining flow. The dominance of the large spatial scales in velocity on the tracer evolution depends upon the smoothing effects of PV inversion.
TGF-β/IFN-γ Antagonism in Subversion and Self-Defense of Phase II Coxiella burnetii-Infected Dendritic Cells

Dendritic cells (DCs) belong to the first line of innate defense and come into early contact with invading pathogens, including the zoonotic bacterium Coxiella burnetii, the causative agent of Q fever. However, the pathogen-host cell interactions in C. burnetii-infected DCs, particularly the role of mechanisms of immune subversion beyond virulent phase I lipopolysaccharide (LPS), as well as the contribution of cellular self-defense strategies, are not understood. ABSTRACT Dendritic cells (DCs) belong to the first line of innate defense and come into early contact with invading pathogens, including the zoonotic bacterium Coxiella burnetii, the causative agent of Q fever. However, the pathogen-host cell interactions in C. burnetii-infected DCs, particularly the role of mechanisms of immune subversion beyond virulent phase I lipopolysaccharide (LPS), as well as the contribution of cellular self-defense strategies, are not understood. Using phase II Coxiella-infected DCs, we show that impairment of DC maturation and MHC I downregulation is caused by autocrine release and action of immunosuppressive transforming growth factor-β (TGF-β). Our study demonstrates that IFN-γ reverses TGF-β impairment of maturation/MHC I presentation in infected DCs and activates bacterial elimination, predominantly by inducing iNOS/NO. Induced NO synthesis strongly affects bacterial growth and infectivity. Moreover, our studies hint that Coxiella-infected DCs might be able to protect themselves from mitotoxic NO by switching from oxidative phosphorylation to glycolysis, thus ensuring survival in self-defense against C. burnetii. Our results provide new insights into DC subversion by Coxiella and the IFN-γ-mediated targeting of C. burnetii during early steps in the innate immune response.
Moderate continuous- and high-intensity interval training elicit comparable cardiovascular effect among middle-aged men regardless of recovery mode

ABSTRACT To assess the effect of active and passive intra-interval recovery modes in time-efficient high-intensity interval training (HIT) on cardiorespiratory fitness, autonomic function, and endothelial function in sedentary middle-aged men. Participants (n = 62; age: 49.5 ± 5.8 y; BMI: 29.7 ± 3.7 kg·m−2) completed the assessments of cardiorespiratory fitness, flow-mediated dilation (FMD) and heart rate variability before being randomly allocated to control (CON; n = 14), moderate intensity continuous training (MICT; n = 15), HIT with passive (P-HIT; n-15), or active recovery (A-HIT; n = 15). Participants performed thrice weekly exercise sessions for 12 weeks. MICT completed 50–60 min of continuous cycling at 60–70% heart rate (HR) maximum. HIT completed 30-s work intervals (∼85% HR) interspaced with 2.5 min of active or passive recovery. All exercise modalities increased oxygen uptake (V̇O2) (MD: ≥ 3.1 ml·kg−1·min−1, 95%CI: 1.5–4.7 ml·kg−1·min−1; P < 0.001), power output (MD: ≥ 26 W, 95%CI: 15–37 W; P < 0.001) and cycle duration (MD: ≥ 62 s, 95%CI: 36–88 s; P < 0.001) at 85% HRM. Significant pre-to-post differences were observed among all exercise groups for FMD (MD: ≥ 3.4%, 95%CI: 0.3–6.5%; P < 0.05), while MICT and P-HIT significantly increased the standard deviation of all NN intervals (SDNN) pre-to-post intervention (MD: ≥ 7 ms, 2–13 ms; P ≤ 0.05). Time-efficient HIT elicits significant improvements in cardiorespiratory fitness, FMD and autonomic modulation following a thrice weekly 12-week exercise intervention among sedentary middle-aged men. Active recovery between successive high-intensity intervals provided no additional benefit among this deconditioned cohort.
Dense RGB SLAM with Neural Implicit Maps

There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder. This design effectively fuses shape cues across different scales to facilitate map reconstruction. Our method simultaneously solves the camera motion and the neural implicit map by matching the rendered and input video frames. To facilitate optimization, we further propose a photometric warping loss in the spirit of multi-view stereo to better constrain the camera pose and scene geometry. We evaluate our method on commonly used benchmarks and compare it with modern RGB and RGB-D SLAM systems. Our method achieves favorable results than previous methods and even surpasses some recent RGB-D SLAM methods.The code is at poptree.github. io/DIM-SLAM/.
Deep Reinforcement Learning for Concentric Tube Robot Path Planning

—As surgical interventions trend towards minimally invasive approaches, Concentric Tube Robots (CTRs) have been explored for various interventions such as brain, eye, fetoscopic, lung, cardiac and prostate surgeries. Arranged concentrically, each tube is rotated and translated independently to move the robot end-effector position, making kinematics and control challenging. Classical model-based approaches have been previously investigated with developments in deep learning based approaches outperforming more classical approaches in both forward kinematics and shape estimation. We propose a deep reinforcement learning approach to control where we generalise across two to four systems, an element not yet achieved in any other deep learning approach for CTRs. In this way we explore the likely robustness of the control approach. Also investigated is the impact of rotational constraints applied on tube actuation and the effects on error metrics. We evaluate inverse kinematics errors and tracking error for path following tasks and compare the results to those achieved using state of the art methods. Additionally, as current results are performed in simulation, we also investigate a domain transfer approach known as domain randomization and evaluate error metrics as an initial step towards hardware implementation. Finally, we compare our method to a Jacobian approach found in literature.
ABS: Adaptive Bounded Staleness Converges Faster and Communicates Less

Wall-clock convergence time and communication rounds are critical performance metrics in distributed learning with parameter-server setting. While synchronous methods converge fast but are not robust to stragglers; and asynchronous ones can reduce the wall-clock time per round but suffers from degraded convergence rate due to the staleness of gradients, it is natural to combine the two methods to achieve a balance. In this work, we develop a novel asynchronous strategy that leverages the advantages of both synchronous methods and asynchronous ones, named adaptive bounded staleness (ABS). The key enablers of ABS are two-fold. First, the number of workers that the PS waits for per round for gradient aggregation is adaptively selected to strike a straggling-staleness balance. Second, the workers with relatively high staleness are required to start a new round of computation to alleviate the negative effect of staleness. Simulation results are provided to demonstrate the superiority of ABS over state-of-the-art schemes in terms of wall-clock time and communication rounds.
Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images

The variation in histologic staining between different medical centers is one of the most profound challenges in the ﬁeld of computer-aided diagnosis. The appearance disparity of pathological whole slide images causes algorithms to become less reliable, which in turn impedes the wide-spread applicability of downstream tasks like cancer diagnosis. Furthermore, different stainings lead to biases in the training which in case of domain shifts negatively affect the test performance. Therefore, in this paper we propose MultiStain-CycleGAN, a multi-domain approach to stain normalization based on CycleGAN. Our modiﬁcations to CycleGAN allow us to normalize images of different origins without retraining or using different models. We perform an extensive evaluation of our method using various metrics and compare it to commonly used methods that are multi-domain capable. First, we evaluate how well our method fools a domain classiﬁer that tries to assign a medical center to an image. Then, we test our normalization on the tumor classiﬁcation performance of a downstream classiﬁer. Furthermore, we evaluate the image quality of the normalized images using the Structural similarity index and the ability to reduce the domain shift using the Fréchet inception distance. We show that our method proves to be multi-domain capable, provides the highest image quality among the compared methods, and can most reliably fool the domain classiﬁer while keeping the tumor classiﬁer performance high. By reducing the domain inﬂuence, biases in the data can be removed on the one hand and the origin of the whole slide image can be disguised on the other, thus enhancing patient data privacy.
JWST/MIRI Spectroscopy of the Disk of the Young Eruptive Star EX Lup in Quiescence

EX Lup is a low-mass pre-main-sequence star that occasionally shows accretion-related outbursts. Here, we present JWST/MIRI medium-resolution spectroscopy obtained for EX Lup 14 yr after its powerful outburst. EX Lup is now in quiescence and displays a Class II spectrum. We detect a forest of emission lines from molecules previously identified in infrared spectra of classical T Tauri disks: H2O, OH, H2, HCN, C2H2, and CO2. The detection of organic molecules demonstrates that they are back after disappearing during the large outburst. Spectral lines from water and OH are for the first time deblended and will provide a much-improved characterization of their distribution and density in the inner disk. The spectrum also shows broad emission bands from warm, submicron-size amorphous silicate grains at 10 and 18 μm. During the outburst, in 2008, crystalline forsterite grains were annealed in the inner disk within 1 au, but their spectral signatures in the 10 μm silicate band later disappeared. With JWST we rediscovered these crystals via their 19.0, 20.0, and 23.5 μm emission, the strength of which implies that the particles are at ∼3 au from the star. This suggests that crystalline grains formed in 2008 were transported outwards and now approach the water snowline, where they may be incorporated into planetesimals. Containing several key tracers of planetesimal and planet formation, EX Lup is an ideal laboratory to study the effects of variable luminosity on the planet-forming material and may provide an explanation for the observed high crystalline fraction in solar system comets.
A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017--2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis.
Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks

The image-based diagnosis is now a vital aspect of modern automation as-sisted diagnosis. To enable models to produce pixel-level diagnosis, pixel-level ground-truth labels are essentially required. However, since it is often not straight forward to obtain the labels in many application domains such as in medical image, classiﬁcation-based approaches have become the de facto standard to perform the diagnosis. Though they can identify class-salient regions, they may not be useful for diagnosis where capturing all of the evi-dences is important requirement. Alternatively, a counterfactual explanation (CX) aims at providing explanations using a casual reasoning process of form ”If X has not happend, Y would not heppend”. Existing CX approaches, however, use classiﬁer to explain features that can change its predictions. Thus, they can only explain class-salient features, rather than entire object of interest. This hence motivates us to propose a novel CX strategy that is not reliant on image classiﬁcation. This work is inspired from the recent developments in generative adversarial networks (GANs) based image-to-image domain translation, and leverages to translate an abnormal image to counterpart normal image (i.e. counterfactual instance CI) to ﬁnd discrepancy maps between the two. Since it is generally not possible to obtain abnormal and normal image pairs, we leverage Cycle-Consistency principle (a.k. a Cy-∗
A Simple Tropical Atmosphere Model of Relevance to Short-Term Climate Variations

Abstract The tropical atmosphere model presented here is suitable for modeling both the annual cycle and short-term (monthly to decadal time scale) climate fluctuations in sole response to the thermal forcing from the underlying surface, especially the ocean surface. The present model consists of a well-mixed planetary boundary layer and a free troposphere represented by the gravest baroclinic mode. The model dynamics involves active interactions between the boundary-layer flow driven by the momentum forcing associated with sea surface temperature (SST) gradient and the free tropospheric flow stimulated by diabatic heating that is controlled by the thermal effects of SST. This process is demonstrated to be essential for modeling Pacific basinwide low-level circulations. The convective heating is parameterized by a SST-dependent conditional heating scheme based upon the proposition that the potential convective instability increases with SST in a nonlinear fashion. The present model integrates the virtue o.
Monitoring of the heart movements using a FMCW radar and correlation with an ECG

—Monitoring the heart activity is an important task to prevent and diagnose cardiovascular diseases. An electrocardiogram (ECG) is the gold standard for such task. It monitors the heart electrical activity, and while the later is highly correlated to the cardiac mechanical activity, it does not provide all the information. Other sensors such as echo-cardiograph allow to monitor the heart movements, but such tools are hard to operate and expensive. Therefore, contact-less monitoring of the heart using RF sensing has gained interest over the past years. In this paper, we provide a process to extract the movement of the heart with a high accuracy from a millimeter wave radar, i.e. we describe a non invasive and affordable way to monitor cardiac movements. We then demonstrate the correlation between the observed movements and the ECG. Furthermore, we propose an algorithm to synchronize the ECG signal and the processed signal from the radar sensor. The results we obtained provide insights on the mechanical activity of the heart, which could assist cardiologists in their diagnosis.
On the spectrum of Random Simplicial Complexes in Thermodynamic Regime

Linial-Meshulam complex is a random simplicial complex on $n$ vertices with a complete $(d-1)$-dimensional skeleton and $d$-simplices occurring independently with probability p. Linial-Meshulam complex is one of the most studied generalizations of the Erdos-Renyi random graph in higher dimensions. In this paper, we discuss the spectrum of adjacency matrices of the Linial-Meshulam complex when $np \rightarrow \lambda$. We prove the existence of a non-random limiting spectral distribution(LSD) and show that the LSD of signed and unsigned adjacency matrices of Linial-Meshulam complex are reflections of each other. We also show that the LSD is unsymmetric around zero, unbounded and under the normalization $1/\sqrt{\lambda d}$, converges to standard semicircle law as $\lambda \rightarrow \infty$. In the later part of the paper, we derive the local weak limit of the line graph of the Linial-Meshulam complex and study its consequence on the continuous part of the LSD.
Informing policy via dynamic models: Cholera in Haiti

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference

We study a normalizing flow in the latent space of a top-down generator model, in which the normalizing flow model plays the role of the informative prior model of the generator. We propose to jointly learn the latent space normalizing flow prior model and the top-down generator model by a Markov chain Monte Carlo (MCMC)-based maximum likelihood algorithm, where a short-run Langevin sampling from the intractable posterior distribution is performed to infer the latent variables for each observed example, so that the parameters of the normalizing flow prior and the generator can be updated with the inferred latent variables. We show that, under the scenario of non-convergent short-run MCMC, the finite step Langevin dynamics is a flow-like approximate inference model and the learning objective actually follows the perturbation of the maximum likelihood estimation (MLE). We further point out that the learning framework seeks to (i) match the latent space normalizing flow and the aggregated posterior produced by the short-run Langevin flow, and (ii) bias the model from MLE such that the short-run Langevin flow inference is close to the true posterior. Empirical results of extensive experiments validate the effectiveness of the proposed latent space normalizing flow model in the tasks of image generation, image reconstruction, anomaly detection, supervised image inpainting and unsupervised image recovery.