Miracle-dz/UniFAR-FTUs huggingface dataset:
Usefulness of Inhaled Sedation in Patients With Severe ARDS Due to COVID-19

BACKGROUND: Sedation in intensive care is fundamental for optimizing clinical outcomes. For many years the world has been facing high rates of opioid use, and to combat the increasing opioid addiction plans at both national and international level have been implemented. The COVID-19 pandemic posed a major challenge for health systems and also increased the use of sedatives and opioid analgesia for prolonged periods of time, and at high doses, in a significant proportion of patients. In our institutions, the shortage of many drugs for intravenous (IV) analgosedation forces us to alternatives to replace out-of-stock drugs or to seek sedation goals, which are difficult to obtain with traditional drugs at high doses. METHODS: This was an analytical retrospective cohort study evaluating the follow-up of subjects with inclusion criteria from ICU admission to discharge (alive or dead). Five end points were measured: need for high-dose opioids (≥ 200 µg/h), comparison of inhaled versus IV sedation of opioid analgesic doses, midazolam dose, need for muscle relaxant, and risk of delirium. RESULTS: A total of 283 subjects were included in the study, of whom 230 were administered IV sedation and 53 inhaled sedation. In the inhaled sedation group, the relative risks (RRs) were 0.5 (95% CI 0.4–0.8, P = .045) for need of high-dose fentanyl, 0.3 (95% CI 0.20–0.45, P < .001) for need of muscle relaxant, and 0.8 (95% CI 0.61–1.15, P = .25) for risk of delirium. The median difference of fentanyl dose between the inhaled sedation and IV sedation groups was 61 µg/h or 1,200 µg/d (2.2 ampules/d, P < .001), and that of midazolam dose was 5.7 mg/h. CONCLUSIONS: Inhaled sedation was associated with lower doses of opioids, benzodiazepines, and muscle relaxants compared to IV sedation. This therapy should be considered as an alternative in critically ill patients requiring prolonged ventilatory support and where IV sedation is not possible, always under adequate supervision of ICU staff.
High-speed video microscopy in optical tweezers

Optical tweezers have become an invaluable tool for measuring and exerting forces in the pico-Newton regime. Force measurements have in the past concentrated on using only one trapped particle as a probe, partly due to the diﬃculties in tracking more than one par- ticle at high enough frame rate. Recent advances in video camera technology allow the collection of images at several kHz. However, there has been little use of high-speed cameras in optical tweezers, partly due to data management problems and aﬀordability. This the- sis presents seven experiments carried out during my PhD involving the use of several diﬀerent high-speed cameras. Chapter 3 presents the use of a CMOS high-speed camera with in- tegrated particle tracking built by Durham Smart Imaging. The camera was used in a Shack-Hartmann sensor setup to determine rapidly and non-ambiguously the sign and magnitude of the orbital angular momentum of a helically-phased beam light beam, as an alternative to interferometric techniques. Chapter 4 presents a di- rect comparison of a CCD high-speed video camera with a quadrant photodiode to track particle position. Particle tracking was possible at high enough accuracy and bandwidth to allow convenient trap calibration by thermal analysis. Chapter 5 reports an investigation of the resulting change in trap stiﬀness during the update of trap positions in holographic optical tweezers. Chapter 6 presents the re- sults from using a high-speed camera to successfully track multiple particles in a microﬂuidic channel to measure the viscosity at sev- eral points simultaneously. The last three chapters investigate the hydrodynamic interactions between trapped particles under diﬀerent conditions and comparisons were made with theory.
The Multidrug Efflux Regulator AcrR of Escherichia coli Responds to Exogenous and Endogenous Ligands To Regulate Efflux and Detoxification

Multidrug efflux pumps can remove antibiotics and other toxic molecules from cells and are major contributors to antibiotic resistance and bacterial physiology. Therefore, it is essential to better understand their function and regulation. ABSTRACT The transcriptional repressor AcrR is the main regulator of the multidrug efflux pump AcrAB-TolC, which plays a major role in antibiotic resistance and cell physiology in Escherichia coli and other Enterobacteriaceae. However, it remains unknown which ligands control the function of AcrR. To address this gap in knowledge, this study tested whether exogenous and/or endogenous molecules identified as potential AcrR ligands regulate the activity of AcrR. Using electrophoretic mobility shift assays (EMSAs) with purified AcrR and the acrAB promoter and in vivo gene expression experiments, we found that AcrR responds to both exogenous molecules and cellular metabolites produced by E. coli. In total, we identified four functional ligands of AcrR, ethidium bromide (EtBr), an exogenous antimicrobial known to be effluxed by the AcrAB-TolC pump and previously shown to bind to AcrR, and three polyamines produced by E. coli, namely, putrescine, cadaverine, and spermidine. We found that EtBr and polyamines bind to AcrR both in vitro and in vivo, which prevents the binding of AcrR to the acrAB promoter and, ultimately, induces the expression of acrAB. Finally, we also found that AcrR contributes to mitigating the toxicity produced by excess polyamines by directly regulating the expression of AcrAB-TolC and two previously unknown AcrR targets, the MdtJI spermidine efflux pump and the putrescine degradation enzyme PuuA. Overall, these findings significantly expand our understanding of the function of AcrR by revealing that this regulator responds to different exogenous and endogenous ligands to regulate the expression of multiple genes involved in efflux and detoxification. IMPORTANCE Multidrug efflux pumps can remove antibiotics and other toxic molecules from cells and are major contributors to antibiotic resistance and bacterial physiology. Therefore, it is essential to better understand their function and regulation. AcrAB-TolC is the main multidrug efflux pump in the Enterobacteriaceae family, and AcrR is its major transcriptional regulator. However, little is known about which ligands control the function of AcrR or which other genes are controlled by this regulator. This study contributes to addressing these gaps in knowledge by showing that (i) the activity of AcrR is controlled by the antimicrobial ethidium bromide and by polyamines produced by E. coli, and (ii) AcrR directly regulates the expression of AcrAB-TolC and genes involved in detoxification and efflux of excess polyamines. These findings significantly advance our understanding of the biological role of AcrR by identifying four ligands that control its function and two novel targets of this regulator.
Bootstrap percolation on products of cycles and complete graphs

Bootstrap percolation on a graph iteratively enlarges a set of occupied sites by adjoining points with at least $\theta$ occupied neighbors. The initially occupied set is random, given by a uniform product measure, and we say that spanning occurs if every point eventually becomes occupied. The main question concerns the critical probability, that is, the minimal initial density that makes spanning likely. The graphs we consider are products of cycles of $m$ points and complete graphs of $n$ points. The major part of the paper focuses on the case when two factors are complete graphs and one factor is a cycle. We identify the asymptotic behavior of the critical probability and show that, when $\theta$ is odd, there are two qualitatively distinct phases: the transition from low to high probability of spanning as the initial density increases is sharp or gradual, depending on the size of $m$.
Loop Group Methods for Constant Mean Curvature Surfaces

This is an elementary introduction to a method for studying harmonic maps into symmetric spaces, and in particular for studying constant mean curvature (CMC) surfaces, that was developed by J. Dorfmeister, F. Pedit and H. Wu. There already exist a number of other introductions to this method, but all of them require a higher degree of mathematical sophistication from the reader than is needed here. The authors' goal was to create an exposition that would be readily accessible to a beginning graduate student, and even to a highly motivated undergraduate student. Constant mean curvature surfaces in Euclidean 3-space, and also spherical 3-space and hyperbolic 3-space, are described, along with the Lax pair equations that determine their frames. The simplest examples, including Delaunay surfaces and Smyth surfaces, are described in detail.
Smooth embeddings with Stein surface images

A simple characterization is given of open subsets of a complex surface that smoothly perturb to Stein open subsets. As applications, ℂ2 contains domains of holomorphy (Stein open subsets) that are exotic smoothings of ℝ4, and others homotopy equivalent to S2 but cut out by smooth, compact 3‐manifolds. Pseudoconvex embeddings of Brieskorn spheres and other 3‐manifolds into complex surfaces are constructed, as are pseudoconcave holomorphic fillings (with disagreeing contact and boundary orientations). Pseudoconcave complex structures on Milnor fibers are found. A byproduct of this construction is a simple polynomial expression for the signature of the (p, q, npq−1) Milnor fiber. Akbulut corks in complex surfaces can always be chosen to be pseudoconvex or pseudoconcave submanifolds. The main theorem is expressed via Stein handlebodies (possibly infinite), which are defined holomorphically in all dimensions by extending Stein theory to manifolds with non‐compact boundary.
Doping control analysis of trimetazidine in dried blood spot.

Dried blood spot (DBS) analysis has been an inherent part of sports drug testing through the technological advancements of the past decade. Trimetazidine, a non-threshold banned substance, is excreted into urine after a dose of the permitted drug lomerizine. Therefore, a lomerizine-specific metabolite (M6) is analyzed to confirm the origin of trimetazidine in traditional urine analysis. Application studies were conducted to develop an analytical method for trimetazidine applicable to DBS. These studies comprise (1) the effect of different sampling sites on the detection of trimetazidine, (2) the determination of the appropriate trimetazidine level required for DBS analysis, and (3) differentiating between trimetazidine and lomerizine use. A high-resolution mass spectrometric method for detecting trimetazidine in DBS was validated. After oral administration of trimetazidine (n = 7), venous and capillary blood (fingertip and upper arm) were spotted on cellulose paper. Trimetazidine could be identified in DBS in all subjects up to 60 h after administration. The limit of detection was 0.05 ng/mL, and the limit of identification was 0.06 ng/mL, suggesting the minimum required performance level of 0.2 ng/mL. In the fingertip capillary blood, biases of 9.7% (vs. upper arm) and 13.0% (vs. vein) were observed in the trimetazidine intensity; however, there were no concerns in the qualitative analysis. After administering lomerizine (n = 10), the intact lomerizine has a strong peak intensity in blood compared to trimetazidine. Contrary to urine analysis, the M6 was less detectable in blood. Laboratories should confirm intact lomerizine whenever trimetazidine is identified in DBS.
Electronic conduction in GaN nanowires

Conductivity mechanisms in unintentionally doped GaN nanowires (NWs) are studied. Gated current-voltage measurements and threshold voltage modeling demonstrate the unique impact of device parameters on NW field-effect transistors as compared to conventional systems. Temperature-dependent resistivity results, acquired with a scanning tunneling microscope equipped with multiple tips, reveal only mild temperature dependence at higher temperatures, with temperature-independent resistivity observed below ∼100K indicating impurity band conduction. The likely origins and implications of these results are discussed.
Mountain Torques Caused by Normal-Mode Global Rossby Waves, and the Impact on Atmospheric Angular Momentum

Abstract Planetary-scale free Rossby waves present in the earth’s atmosphere propagate toward the west. Pressure torques varying in time then arise as a consequence of unequal pressure on the eastern and western sides of mountains and small-scale topographic features. These torques, referred to as mountain torques, have an influence on the exchange of angular momentum between the atmosphere and the earth. The authors investigated the impact of all identified planetary-scale free Rossby waves on atmospheric angular momentum by computing the contribution from mountain torques to the rate of change of total atmospheric angular momentum for each wave. Comparing contributions from individual waves, the authors found that for the average wave amplitudes the maximum torque for a particular wave is around 2 Hadleys, and that considering all meridional wavenumbers, zonal wavenumber 2 causes the largest global torques. Changes in angular momentum depend on both the amplitude of the changing torque and on its period.
Orbit closures of unipotent flows for hyperbolic manifolds with Fuchsian ends

We establish an analogue of Ratner's orbit closure theorem for any connected closed subgroup generated by unipotent elements in $\operatorname{SO}(d,1)$ acting on the space $\Gamma\backslash \operatorname{SO}(d,1)$, assuming that the associated hyperbolic manifold $M=\Gamma\backslash \mathbb H^d$ is a convex cocompact manifold with Fuchsian ends. For $d=3$, this was proved earlier by McMullen, Mohammadi and Oh. In a higher dimensional case, the possibility of accumulation on closed orbits of intermediate groups causes serious new issues, but in the end, all orbit closures of unipotent flows are relatively homogeneous. Our results imply the following: for any $k\ge 1$, (1) the closure of any $k$-horosphere in $M$ is a properly immersed submanifold; (2) the closure of any geodesic $(k+1)$-plane in $M$ is a properly immersed submanifold; (3) any infinite sequence of maximal properly immersed geodesic $(k+1)$-planes intersecting $\operatorname{core} M$ becomes dense in $M$.
New navigation algorithm based on the entropy image

Optical flow is a critical component of video processing applications, e.g. for tasks such as object tracking, segmentation, and navigation. In this paper, we present a novel concept entropy flow based on the visual mechanism of bee. It not only generates motion fields form real sequences but also speed up the calculation of optical flow. The introduction of entropy image has shown the information distribution for the given image, and the optical flow algorithm is utilized to get the entropy flow. Global motion estimation and an auto-selecting algorithm of assessment threshold are employed to give a motion image and then the accuracy is improved.
Width effects of broad new resonances in loop observables and application to 
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In the phenomenology of strong interactions most physical states acquire a substantial width, and thus can only be defined in a model-independent way by pole positions and residues of the $S$-matrix. This information is incorporated in the K\"all\'en-Lehmann representation, whose spectral function characterizes the shape of the resonance and can be constrained by the dominant decay channels. Here, we argue that similar effects become important whenever beyond-the-Standard-Model particles possess a sizable decay width - as possible for instance in cases with a large branching fraction to a dark sector or strongly coupled scenarios - and show how their widths can be incorporated in the calculation of loop observables. As an application, we consider the anomalous magnetic moment of the muon, including both the direct effect of new physics and the possible indirect impact of a broad light $Z'$ on $e^+e^-\to\text{hadrons}$ cross sections. Throughout, we provide results for a general spectral function and its reconstruction from the one-loop imaginary part, where the latter captures the leading two-loop effects.
In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing

Assistance during eating is essential for those with severe mobility issues or eating risks. However, dependence on traditional human caregivers is linked to malnutrition, weight loss, and low self-esteem. For those who require eating assistance, a semi-autonomous robotic platform can provide independence and a healthier lifestyle. We demonstrate an essential capability of this platform: safe, comfortable, and effective transfer of a bite-sized food item from a utensil directly to the inside of a person's mouth. Our system uses a force-reactive controller to safely accommodate the user's motions throughout the transfer, allowing full reactivity until bite detection then reducing reactivity in the direction of exit. Additionally, we introduce a novel dexterous wrist-like end effector capable of small, unimposing movements to reduce user discomfort. We conduct a user study with 11 participants covering 8 diverse food categories to evaluate our system end-to-end, and we find that users strongly prefer our method to a wide range of baselines. Appendices and videos are available at our website: https://tinyurl.com/btICRA.
Deep Neural Mel-Subband Beamformer for in-Car Speech Separation

While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use. In this paper, we propose DL-based mel-subband spatio-temporal beamformer to perform speech separation in a car environment with reduced computation cost and inference time. As opposed to conventional subband (SB) approaches, our framework uses a mel-scale based subband selection strategy which ensures a fine-grained processing for lower frequencies where most speech formant structure is present, and coarse-grained processing for higher frequencies. In a recursive way, robust frame-level beamforming weights are determined for each speaker location/zone in a car from the estimated subband speech and noise covariance matrices. Furthermore, proposed framework also estimates and suppresses any echoes from the loudspeaker(s) by using the echo reference signals. We compare the performance of our proposed framework to several NB, SB, and full-band (FB) processing techniques in terms of speech quality and recognition metrics. Based on experimental evaluations on simulated and real-world recordings, we find that our proposed framework achieves better separation performance over all SB and FB approaches and achieves performance closer to NB processing techniques while requiring lower computing cost.
Comparison of Motion Encoding Frameworks on Human Manipulation Actions

Movement generation, and especially generalisation to unseen situations, plays an important role in robotics. Different types of movement generation methods exist such as spline based methods, dynamical system based methods, and methods based on Gaussian mixture models (GMMs). Using a large, new dataset on human manipulations, in this paper we provide a highly detailed comparison of five fundamentally different and widely used movement encoding and generation frameworks: dynamic movement primitives (DMPs), time based Gaussian mixture regression (tbGMR), stable estimator of dynamical systems (SEDS), Probabilistic Movement Primitives (ProMP) and Optimal Control Primitives (OCP). We compare these frameworks with respect to their movement encoding efficiency, reconstruction accuracy, and movement generalisation capabilities. The new dataset consists of nine object manipulation actions performed by 12 humans: pick and place, put on top/take down, put inside/take out, hide/uncover, and push/pull with a total of 7,652 movement examples. Our analysis shows that for movement encoding and reconstruction DMPs and OCPs are the most efficient with respect to the number of parameters and reconstruction accuracy, if a sufficient number of kernels is used. In case of movement generalisation to new start- and end-point situations, DMPs, OCPs and task parameterized GMM (TP-GMM, movement generalisation framework based on tbGMR) lead to similar performance, which ProMPs only achieve when using many demonstrations for learning. All models outperform SEDS, which additionally proves to be difficult to fit. Furthermore we observe that TP-GMM and SEDS suffer from problems reaching the end-points of generalizations.These different quantitative results will help selecting the most appropriate models and designing trajectory representations in an improved task-dependent way in future robotic applications.
TorchScale: Transformers at Scale

Large Transformers have achieved state-of-the-art performance across many tasks. Most open-source libraries on scaling Transformers focus on improving training or inference with better parallelization. In this work, we present TorchScale, an open-source toolkit that allows researchers and developers to scale up Transformers efficiently and effectively. TorchScale has the implementation of several modeling techniques, which can improve modeling generality and capability, as well as training stability and efficiency. Experimental results on language modeling and neural machine translation demonstrate that TorchScale can successfully scale Transformers to different sizes without tears. The library is available at https://aka.ms/torchscale.
A Latent Shrinkage Position Model for Binary and Count Network Data

Interactions between actors are frequently represented using a network. The latent position model is widely used for analysing network data, whereby each actor is positioned in a latent space. Inferring the dimension of this space is challenging. Often, for simplicity, two dimensions are used or model selection criteria are employed to select the dimension, but this requires choosing a criterion and the computational expense of fitting multiple models. Here the latent shrinkage position model (LSPM) is proposed which intrinsically infers the effective dimension of the latent space. The LSPM employs a Bayesian nonparametric multiplicative truncated gamma process prior that ensures shrinkage of the variance of the latent positions across higher dimensions. Dimensions with non-negligible variance are deemed most useful to describe the observed network, inducing automatic inference on the latent space dimension. While the LSPM is applicable to many network types, logistic and Poisson LSPMs are developed here for binary and count networks respectively. Inference proceeds via a Markov chain Monte Carlo algorithm, where novel surrogate proposal distributions reduce the computational burden. The LSPM's properties are assessed through simulation studies, and its utility is illustrated through application to real network datasets. Open source software assists wider implementation of the LSPM.