Miracle-dz/UniFAR-FTUs huggingface dataset:
Vitamin D modulates inflammatory response of DENV-2-infected macrophages by inhibiting the expression of inflammatory-liked miRNAs

ABSTRACT Dengue disease caused by dengue virus (DENV) infection is the most common vector-borne viral disease worldwide. Currently, no treatment is available to fight dengue symptoms. We and others have demonstrated the antiviral and immunomodulatory properties of VitD3 as a possible therapy for DENV infection. MicroRNAs (miRNAs) are small non-coding RNAs responsible for the regulation of cell processes including antiviral defense. Previous transcriptomic analysis showed that VitD3 regulates the expression of genes involved in stress and immune response by inducing specific miRNAs. Here, we focus on the effects of VitD3 supplementation in the regulation of the expression of inflammatory-liked miR-182-5p, miR-130a-3p, miR125b-5p, miR146a-5p, and miR-155-5p during DENV-2 infection of monocyte-derived macrophages (MDMs). Further, we evaluated the effects of inhibition of these miRNAs in the innate immune response. Our results showed that supplementation with VitD3 differentially regulated the expression of these inflammatory miRNAs. We also observed that inhibition of miR-182-5p, miR-130a-3p, miR-125b-5p, and miR-155-5p, led to decreased production of TNF-α and TLR9 expression, while increased the expression of SOCS-1, IFN-β, and OAS1, without affecting DENV replication. By contrast, over-expression of miR-182-5p, miR-130a-3p, miR-125b-5p, and miR-155-5p significantly decreased DENV-2 infection rates and also DENV-2 replication in MDMs. Our results suggest that VitD3 immunomodulatory effects involve regulation of inflammation-linked miRNAs expression, which might play a key role in the inflammatory response during DENV infection.
Ezrin and CD44 participate in the internalization process of Coxiella burnetii into non‐phagocytic cells

Ezrin protein is involved in the interaction of actin cytoskeleton with membrane receptors such as CD44. It regulates plasma membrane dynamics and intracellular signaling. Coxiella burnetii, the etiologic agent of Q fever, is internalized into host cell through a poorly characterized molecular mechanism. Here we analyzed the role of ezrin and CD44 in the C. burnetii internalization by HeLa cells. The knockdown of ezrin and CD44 inhibited the bacterial uptake. Interestingly, at early stages of C. burnetii internalization, ezrin was recruited to the cell membrane fraction and phosphorylated. Moreover, the overexpression of non‐phosphorylatable and phosphomimetic ezrin mutants decreased and increased the bacterial entry, respectively. A decrease in the internalization of C. burnetii was observed by the overexpression of CD44 truncated forms containing the intracellular or the extracellular domains. Interestingly, the CD44 mutant was unable to interact with ERM proteins decreased the bacterial internalization. These findings demonstrate the participation of ezrin in the internalization process of C. burnetii in non‐phagocytic cells. Additionally, we present evidence that CD44 receptor would be involved in that process.
Unsupervised Domain Adaptation for Medical Image Segmentation by Disentanglement Learning and Self-Training

Unsupervised domain adaption (UDA), which aims to enhance the segmentation performance of deep models on unlabeled data, has recently drawn much attention. In this paper, we propose a novel UDA method (namely DLaST) for medical image segmentation via disentanglement learning and self-training. Disentanglement learning factorizes an image into domain-invariant anatomy and domain-specific modality components. To make the best of disentanglement learning, we propose a novel shape constraint to boost the adaptation performance. The self-training strategy further adaptively improves the segmentation performance of the model for the target domain through adversarial learning and pseudo label, which implicitly facilitates feature alignment in the anatomy space. Experimental results demonstrate that the proposed method outperforms the state-of-the-art UDA methods for medical image segmentation on three public datasets, i.e., a cardiac dataset, an abdominal dataset and a brain dataset. The code will be released soon.
Modulation transfer functions for audiovisual speech

Temporal synchrony between facial motion and acoustic modulations is a hallmark feature of audiovisual speech. The moving face and mouth during natural speech is known to be correlated with low-frequency acoustic envelope fluctuations (below 10 Hz), but the precise rates at which envelope information is synchronized with motion in different parts of the face are less clear. Here, we used regularized canonical correlation analysis (rCCA) to learn speech envelope filters whose outputs correlate with motion in different parts of the speakers face. We leveraged recent advances in video-based 3D facial landmark estimation allowing us to examine statistical envelope-face correlations across a large number of speakers (∼4000). Specifically, rCCA was used to learn modulation transfer functions (MTFs) for the speech envelope that significantly predict correlation with facial motion across different speakers. The AV analysis revealed bandpass speech envelope filters at distinct temporal scales. A first set of MTFs showed peaks around 3-4 Hz and were correlated with mouth movements. A second set of MTFs captured envelope fluctuations in the 1-2 Hz range correlated with more global face and head motion. These two distinctive timescales emerged only as a property of natural AV speech statistics across many speakers. A similar analysis of fewer speakers performing a controlled speech task highlighted only the well-known temporal modulations around 4 Hz correlated with orofacial motion. The different bandpass ranges of AV correlation align notably with the average rates at which syllables (3-4 Hz) and phrases (1-2 Hz) are produced in natural speech. Whereas periodicities at the syllable rate are evident in the envelope spectrum of the speech signal itself, slower 1-2 Hz regularities thus only become prominent when considering crossmodal signal statistics. This may indicate a motor origin of temporal regularities at the timescales of syllables and phrases in natural speech.
Reference values for MRI‐derived psoas and paraspinal muscles and macroscopic fat infiltrations in paraspinal muscles in children

Sarcopenia, defined as loss of skeletal muscle mass, is a novel term associated with adverse outcomes in children. Magnetic Resonance Imaging (MRI) is a safe and precise technique for measuring tissue compartments and is commonly used in most routine paediatric imaging protocols. Currently, there is a lack of MRI‐derived normative data which can help in determining the level of sarcopenia. This study aimed to introduce reference values of total psoas muscle area (tPMA), total paraspinal muscle area (tPSMA), and total macroscopic fat infiltrations of the PSMA (tMFI).
Pharmacokinetics of ivermectin after oral and intravenous administration in Biłgorajska geese (Anser anser domesticus)

ABSTRACT Aims To assess the pharmacokinetic profile of ivermectin in Biłgorajska geese (Anser anser domesticus) after single I/V or oral administration, in order to compare these routes of administration and assess oral bioavailability. Methods Ten healthy male geese were used in a single-dose, two-phase study with a 3-month washout period between phases. In the first phase, all geese were given 0.2 mg/kg I/V ivermectin, while in the second phase they were treated orally with the same dosage. Blood samples were collected at selected time points up to 480 hours after each administration. Samples were purified using protein precipitation and drug concentration was quantified using HPLC. The analytical method was validated on blank goose plasma and was characterised by an optimal linearity and a limit of quantification of 0.025 μg/mL. The pharmacokinetic analysis was carried out using a non-compartmental approach. Results The drug was quantifiable up to 240 hours after I/V administration, while after oral treatment it was quantifiable up to 144 hours in most of the geese. The elimination half-life of ivermectin was approximately 3.8 (95% CI = 1.98–7.92; p = 0.027) times higher after I/V administration compared to oral administration. Moreover, the area under the curve from zero to the last detectable timepoint was 6.4 (95% CI = 4.65–8.74; p < 0.001) hours greater after I/V than oral administration. This difference led to a bioavailability of 20.38 (SD 5.92) %. Conclusions Following oral administration in geese, ivermectin has a bioavailability of approximately 20%. Further research on the action of ivermectin in the gastrointestinal tract is required along with assessment of tissue residues to allow calculation of withdrawal time to ensure consumer safety. Abbreviations AUC: Area under the concentration-time curve; AUClast: Area under the curve from zero to the last detectable timepoint; AUMC: Area under the first moment curve; Cmax: Maximum concentration; Tmax: Time at maximum plasma concentration
Understanding the linear and nonlinear optical responses of few-layer exfoliated MoS2 and WS2 nanoflakes: experimental and simulation studies

Understanding the linear and nonlinear optical (NLO) responses of two-dimensional nanomaterials is essential to effectively utilize them in various optoelectronic applications. Here, few-layer MoS2 and WS2 nanoflakes with lateral size less than 200 nm were prepared by liquid-phase exfoliation, and their linear and NLO responses were studied simultaneously using experimental measurements and theoretical simulations. Finite-difference time-domain (FDTD) simulations confirmed the redshift in the excitonic transitions when the thickness was increased above 10 nm indicating the layer-number dependent bandgap of nanoflakes. WS2 nanoflakes exhibited around 5 times higher absorption to scattering cross-section ratio than MoS2 nanoflakes at various wavelengths. Open aperture Z scan analysis of both the MoS2 and WS2 nanoflakes using 532 nm nanosecond laser pulses reveals strong nonlinear absorption activity with effective nonlinear absorption coefficient (β eff) of 120 cm GW−1 and 180 cm GW−1, respectively, which was attributed to the combined contributions of ground, singlet excited and triplet excited state absorption. FDTD simulation results also showed the signature of strong absorption density of few layer nanoflakes which may be account for their excellent NLO characteristics. Optical limiting threshold values of MoS2 and WS2 nanoflakes were obtained as ∼1.96 J cm−2 and 0.88 J cm−2, respectively, which are better than many of the reported values. Intensity dependent switching from saturable absorption (SA) to reverse SA was also observed for MoS2 nanoflakes when the laser intensity increased from 0.14 to 0.27 GW cm−2. The present study provides valuable information to improve the selection of two-dimensional nanomaterials for the design of highly efficient linear and nonlinear optoelectronic devices.
How to safely use extensionality in Liquid Haskell

Refinement type checkers are a powerful way to reason about functional programs. For example, one can prove properties of a slow, specification implementation and port the proofs to an optimized pure implementation that behaves the same. But to reason about higher-order programs, we must reason about equalities between functions: we need a consistent encoding of functional extensionality. A natural but naive phrasing of the functional extensionality axiom (funExt) is inconsistent in refinement type systems with semantic subtyping and polymorphism: if we assume funExt, then we can prove false. We demonstrate the inconsistency and develop a new approach to equality in Liquid Haskell: we define a propositional equality in a library we call PEq. Using PEq avoids the inconsistency while proving useful equalities at higher types; we demonstrate its use in several case studies. We validate PEq by building a model and developing its metatheory. Additionally, we prove metaproperties of PEq inside Liquid Haskell itself using an unnamed folklore technique, which we dub 'classy induction'.
Turbulent Dust-trapping Rings as Efficient Sites for Planetesimal Formation

Recent observations of protoplanetary disks (PPDs) at submillimeter wavelengths have revealed the ubiquity of annular substructures that are indicative of pebble-sized dust particles trapped in turbulent ringlike gas pressure bumps. This major paradigm shift also challenges the leading theory of planetesimal formation from such pebbles by means of the streaming instability, which operates in a pressure gradient and can be suppressed by turbulence. Here, we conduct 3D local shearing box nonideal magnetohydrodynamic simulations of dust trapping in enforced gas pressure bumps, including dust backreaction. Under a moderate level of turbulence generated by the magnetorotational instability with ambipolar diffusion, which is suitable for outer disk conditions, we achieve quasi-steady states of dust trapping balanced by turbulent diffusion. We find strong dust clumping in all simulations near the gas pressure maxima, reaching a maximum density well above the threshold for triggering gravitational collapse to form planetesimals. A strong pressure bump concentrates dust particles toward the bump’s center. With a weak pressure bump, dust can also concentrate in secondary filaments off the bump’s center, due to dust backreaction, but strong clumping still occurs mainly in the primary ring around the bump’s center. Our results reveal dust-trapping rings to be robust locations for planetesimal formation in outer PPDs, while they may possess diverse observational properties.
Realistic Sources, Receivers and Walls Improve The Generalisability of Virtually-Supervised Blind Acoustic Parameter Estimators

Blind acoustic parameter estimation consists in inferring the acoustic properties of an environment from recordings of unknown sound sources. Recent works in this area have utilized deep neural networks trained either partially or exclusively on simulated data, due to the limited availability of real annotated measurements. In this paper, we study whether a model purely trained using a fast image-source room impulse response simulator can generalize to real data. We present an ablation study on carefully crafted simulated training sets that account for different levels of realism in source, receiver and wall responses. The extent of realism is controlled by the sampling of wall absorption coefficients and by applying measured directivity patterns to microphones and sources. A state-of-the-art model trained on these datasets is evaluated on the task of jointly estimating the room’s volume, total surface area, and octave-band reverberation times from multiple, multichannel speech recordings. Results reveal that every added layer of simulation realism at train time significantly improves the estimation of all quantities on real signals.
A Coherence Parameter Characterizing Generative Compressed Sensing With Fourier Measurements

In Bora et al. (2017), a mathematical framework was developed for compressed sensing guarantees in the setting where the measurement matrix is Gaussian and the signal structure is the range of a generative neural network (GNN). The problem of compressed sensing with GNNs has since been extensively analyzed when the measurement matrix and/or network weights follow a subgaussian distribution. We move beyond the subgaussian assumption, to measurement matrices that are derived by sampling uniformly at random rows of a unitary matrix (including subsampled Fourier measurements as a special case). Specifically, we prove the first known restricted isometry guarantee for generative compressed sensing (GCS) with subsampled isometries, and provide recovery bounds with nearly order-optimal sample complexity, addressing an open problem of (Scarlett et al., 2022, p. 10). Recovery efficacy is characterized by the coherence, a new parameter, which measures the interplay between the range of the network and the measurement matrix. Our approach relies on subspace counting arguments and ideas central to high-dimensional probability. Furthermore, we propose a regularization strategy for training GNNs to have favourable coherence with the measurement operator. We provide compelling numerical simulations that support this regularized training strategy: our strategy yields low coherence networks that require fewer measurements for signal recovery. This, together with our theoretical results, supports coherence as a natural quantity for characterizing GCS with subsampled isometries.
Isolation and frequency reconfigurable compact MIMO antenna for wireless local area network applications

In this study, to design a compact four-port multiple-input-multiple-output (MIMO) antenna for dual-band wireless local area network applications, both frequency and isolation between antennas are made as reconfigurable. Both antenna element and decoupling network shown in this study are designed using meander line concept. The frequency reconfigurable capability of the antenna is achieved by incorporating positive-intrinsic-negative (PIN) diodes in the meander line resonator. By making PIN diodes ON and OFF, four antenna elements switch their operating frequency in between 2.4-2.5 and 5.1-5.8 GHz bands. Isolation between the antenna elements also made as reconfigurable by placing PIN diodes in the decoupling network. In ON state, isolation is found in 2.4-2.5 GHz band, whereas in OFF state, isolation is achieved in 5.1-5.8 GHz band. Overall, in both the bands, excellent isolation (more than 15 dB) between the antenna elements has been achieved. The proposed antenna design is printed on the FR-4 substrate and its overall area is 38 × 38 mm 2 . Each antenna element is in polarisation diversity with the adjacent antenna element which makes the proposed design suitable for achieving good MIMO performance.
Adversarial Training Improves Joint Energy-Based Generative Modelling

We propose the novel framework for generative modelling using hybrid energy-based models. In our method we combine the interpretable input gradients of the robust classiﬁer and Langevin Dynamics for sampling. Using the adversarial training we improve not only the training stability, but robustness and generative modelling of the joint energy-based models.
Multi-parametric Analysis for Mixed Integer Linear Programming: An Application to Transmission Planning and Congestion Control

: Enhancing existing transmission lines is a useful tool to combat transmission congestion and guarantee transmission security with increasing demand and boosting the renewable energy source. This study concerns the selection of lines whose capacity should be expanded and by how much from the perspective of independent system operator (ISO) to minimize the system cost with the consideration of transmission line constraints and electricity generation and demand balance conditions, and incorporating ramp-up and startup ramp rates, shutdown ramp rates, ramp-down rate limits and minimum up and minimum down times. For that purpose, we develop the ISO unit commitment and economic dispatch model and show it as a right-hand side uncertainty multiple parametric analysis for the mixed integer linear programming (MILP) problem. We first relax the binary variable to continuous variables and employ the Lagrange method and Karush-Kuhn-Tucker conditions to obtain optimal solutions (optimal decision variables and objective function) and critical regions associated with active and inactive constraints. Further, we extend the traditional branch and bound method for the large-scale MILP problem by determining the upper bound of the problem at each node, then comparing the difference between the upper and lower bounds and reaching the approximate optimal solution within the decision makers' tolerated error range. In additional, the objective function's first derivative on the parameters of each line is used to inform the selection of lines to ease congestion and maximize social welfare. Finally, the amount of capacity upgrade will be chosen by balancing the cost-reduction rate of the objective function on parameters and the cost of the line upgrade. Our findings are supported by numerical simulation and provide transmission line planners with decision-making guidance.
MRCLens: an MRC Dataset Bias Detection Toolkit

Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-sample data. While many other approaches have been proposed to address this issue from the computation perspective such as new architectures or training procedures, we believe a method that allows researchers to discover biases, and adjust the data or the models in an earlier stage will be beneficial. Thus, we introduce MRCLens, a toolkit that detects whether biases exist before users train the full model. For the convenience of introducing the toolkit, we also provide a categorization of common biases in MRC.
Prior-Guided Adversarial Initialization for Fast Adversarial Training

Fast adversarial training (FAT) effectively improves the efficiency of standard adversarial training (SAT). However, initial FAT encounters catastrophic overfitting, i.e.,the robust accuracy against adversarial attacks suddenly and dramatically decreases. Though several FAT variants spare no effort to prevent overfitting, they sacrifice much calculation cost. In this paper, we explore the difference between the training processes of SAT and FAT and observe that the attack success rate of adversarial examples (AEs) of FAT gets worse gradually in the late training stage, resulting in overfitting. The AEs are generated by the fast gradient sign method (FGSM) with a zero or random initialization. Based on the observation, we propose a prior-guided FGSM initialization method to avoid overfitting after investigating several initialization strategies, improving the quality of the AEs during the whole training process. The initialization is formed by leveraging historically generated AEs without additional calculation cost. We further provide a theoretical analysis for the proposed initialization method. We also propose a simple yet effective regularizer based on the prior-guided initialization,i.e., the currently generated perturbation should not deviate too much from the prior-guided initialization. The regularizer adopts both historical and current adversarial perturbations to guide the model learning. Evaluations on four datasets demonstrate that the proposed method can prevent catastrophic overfitting and outperform state-of-the-art FAT methods. The code is released at https://github.com/jiaxiaojunQAQ/FGSM-PGI.
Kodaira-type Vanishings via Non-abelian Hodge Theory

In this paper, we use non-abelian Hodge Theory to study Kodaira type vanishings and its generalizations. In particular, we generalize Saito vanishing using Mixed Twistor D-modules. We also generalize it to a Kawamata-Viehweg type vanishing using Q-divisors, and we also prove a relative version for a projective morphism.