Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Replacing Linezolid with OTB-658 in Anti-Tuberculosis Regimens in Murine Models

Linezolid (LZD) was the first oxazolidinone approved for treating drug-resistant tuberculosis. A newly approved regimen combining LZD with bedaquiline (BDQ) and pretomanid (PMD) (BPaL regimen) is the first 6-month oral regimen that is effective against multidrug- and extensively drug-resistant tuberculosis. ABSTRACT Linezolid (LZD) was the first oxazolidinone approved for treating drug-resistant tuberculosis. A newly approved regimen combining LZD with bedaquiline (BDQ) and pretomanid (PMD) (BPaL regimen) is the first 6-month oral regimen that is effective against multidrug- and extensively drug-resistant tuberculosis. However, LZD toxicity, primarily due to mitochondrial protein synthesis inhibition, may undermine the efficacy of LZD regimens, and oxazolidinones with higher efficacy and lower toxicity during prolonged administration are needed. OTB-658 is an oxazolidinone anti-TB candidate derived from LZD that could replace LZD in TB treatment. We previously found that OTB-658 had better anti-TB activity and safety than LZD in vitro and in vivo. In the present work, two murine TB models were used to evaluate replacing LZD with OTB-658 in LZD-containing regimens. In the C3HeB/FeJ murine model, replacing 100 mg/kg LZD with 50 mg/kg OTB-658 in the BDQ + PMD backbone significantly reduced lung and spleen CFU counts (P < 0.05), and there were few relapses at 8 weeks of treatment. Replacing 100 mg/kg LZD with 50 or 100 mg/kg OTB-658 in the pyrifazimine (previously called TBI-166) + BDQ backbone did not change the anti-TB efficacy and relapse rate. In BALB/c mice, replacing 100 mg/kg LZD with 100 mg/kg OTB-658 in the TBI-166 + BDQ backbone resulted in no culture-positive lungs at 4 and 8 weeks of treatment, and there were no significant differences in relapses rate between the groups. In conclusion, OTB-658 is a promising clinical candidate that could replace LZD in the BPaL or TBI-166 + BDQ + LZD regimens and should be studied further in clinical trials.
Microbiome influencers of checkpoint blockade–associated toxicity

The microbiome influences response to immunotherapy, including immune-related adverse events (irAEs) with checkpoint inhibitors. Wang, Jenq, et al. discuss microbiome contributions to irAEs by focusing on major bacterial taxa, potential immune mechanisms, and clinical opportunities for microbiome modulation to treat irAEs.
Dual diagnosis in immigrant patients with alcohol use disorder: 2-years of follow-up treatment

Introduction. There is a high prevalence of comorbid psychiatric disorders in alcohol abuse disorder. The presence of dual diagnosis in patients decreases the maintenance of abstinence and increases the likelihood of relapse, which makes treatment more complicated. The aims of this study are: to investigate the progression along two years of treatment of a sample of migrant patients affected by alcohol abuse disorder associated with psychiatric disorders, comparing it with a sample of migrant patients without dual diagnosis and investigating the diagnoses of comorbid psychiatric pathology with alcohol abuse disorder. Methods. Two populations of migrant patients with alcohol abuse disorder (DSM-5) were compared, one with comorbid psychiatric disorders consisting of 219 patients and the other of 169 patients without dual diagnosis. Results and conclusions. The most frequent psychiatric disorders associated with alcohol use disorder are personality disorders (51,6%), adjustment and depressive disorders (22,8%), anxiety disorders (15,5%), eating disorders and obsessive-compulsive disorders (5,9%), bipolar disorders (5%) and schizophrenia (2,3%). The two-year follow-up treatment shows that 27% of immigrant sample with dual diagnosis remains in abstinence compared to 41,4% of those who only suffer from alcohol use disorder: Worse outcomes are observed in patients with dual diagnosis.
Structural insights into translation regulation by the THF-II riboswitch

Abstract In bacteria, expression of folate-related genes is controlled by the tetrahydrofolate (THF) riboswitch in response to specific binding of THF and its derivatives. Recently, a second class of THF riboswitches, named THF-II, was identified in Gram-negative bacteria, which exhibit distinct architecture from the previously characterized THF-I riboswitches found in Gram-positive bacteria. Here, we present the crystal structures of the ligand-bound THF-II riboswitch from Mesorhizobium loti. These structures exhibit a long rod-like fold stabilized by continuous base pair and base triplet stacking across two helices of P1 and P2 and their interconnecting ligand-bound binding pocket. The pterin moiety of the ligand docks into the binding pocket by forming hydrogen bonds with two highly conserved pyrimidines in J12 and J21, which resembles the hydrogen-bonding pattern at the ligand-binding site FAPK in the THF-I riboswitch. Using small-angle X-ray scattering and isothermal titration calorimetry, we further characterized the riboswitch in solution and reveal that Mg2+ is essential for pre-organization of the binding pocket for efficient ligand binding. RNase H cleavage assay indicates that ligand binding reduces accessibility of the ribosome binding site in the right arm of P1, thus down-regulating the expression of downstream genes. Together, these results provide mechanistic insights into translation regulation by the THF-II riboswitch.
Investigations on convergence behaviour of Physics Informed Neural Networks across spectral ranges and derivative orders

An important inference from Neural Tangent Kernel (NTK) theory is the existence of spectral bias (SB), that is, low frequency components of the target function of a fully connected Artificial Neural Network (ANN) being learnt significantly faster than the higher frequencies during training. This is established for Mean Square Error (MSE) loss functions with very low learning rate parameters. Physics Informed Neural Networks (PINNs) are designed to learn the solutions of differential equations (DE) of arbitrary orders; in PINNs the loss functions are obtained as the residues of the conservative form of the DEs and represent the degree of dissatisfaction of the equations. So there has been an open question whether (a) PINNs also exhibit SB and (b) if so, how does this bias vary across the orders of the DEs. In this work, a series of numerical experiments are conducted on simple sinusoidal functions of varying frequencies, compositions and equation orders to investigate these issues. It is firmly established that under normalized conditions, PINNs do exhibit strong spectral bias, and this increases with the order of the differential equation.
Sensory evaluation of concert hall acoustics

Consumer products can be perceived in many ways, and individual taste influences quality judgments. Sensory evaluation techniques have been developed to reveal detailed information about perception of products; recently, sensory evaluation has also been shown to be very useful in subjective evaluation of concert hall acoustics. In particular, individual vocabulary based methods have helped to disentangle the detailed perceptual differences between the different seats within a hall and between different halls. For simultaneous and accurate comparison of acoustics, a symphony orchestra needs to play identically in each hall. Therefore, a symphony orchestra simulator has been developed. It consists of 34 loudspeakers reproducing synchronized recordings of individual musicians playing parts of symphonies in an anechoic chamber. In addition, an advanced spatial sound recording technique via impulse responses from a 3D microphone array is applied to reproduce the acoustics of a concert hall in laboratory condit.
Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning

Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations of one image based on contrastive learning. By utilizing the consistency of two augmentations, the burden of manual annotations can be freed. Contrastive learning exploits instance-level information to learn robust features. However, the learned information is probably confined to different views of the same instance. In this paper, we attempt to leverage the similarity between two distinct images to boost representation in self-supervised learning. In contrast to instance-level information, the similarity between two distinct images may provide more useful information. Besides, we analyze the relation between similarity loss and feature-level cross-entropy loss. These two losses are essential for most deep learning methods. However, the relation between these two losses is not clear. Similarity loss helps obtain instance-level representation, while feature-level cross-entropy loss helps mine the similarity between two distinct images. We provide theoretical analyses and experiments to show that a suitable combination of these two losses can get state-of-the-art results. Code is available at https://github.com/guijiejie/ICCL.
Improved fractal Weyl bounds for convex cocompact hyperbolic surfaces and large resonance-free regions

Let X be a convex cocompact hyperbolic surface and let δ be the dimension of its limit set. Let N X ( σ, T ) be the number of resonances for X inside the box [ σ, δ ] + i [0 , T ]. We prove that for all σ > δ/ 2 we have
Color Me Intrigued: Quantifying Usage of Colors in Fiction

We present preliminary results in quantitative analyses of color usage in selected authors' works from LitBank. Using Glasgow Norms, human ratings on 5000+ words, we measure attributes of nouns dependent on color terms. Early results demonstrate a significant increase in noun concreteness over time. We also propose future research directions for computational literary color analytics.
Measuring Power with a Saturated Photodiode

Accurate measurement of optical power is pivotal in many applications and scientific research. However, traditional power meters are unable to measure power levels beyond a certain saturation point, limiting their usefulness in high-power applications. In this technical note, I discuss how optical power can be measured using a saturated photodiode. I demonstrate that by monitoring both the dc photocurrent and ac noise, it is possible to accurately measure power levels beyond its saturation point.
Wave propagation through an assembly of spheres: II. The density of single-particle eigenstates

Edwards' formalism for the density of single-particle eigenstates in a system of random scattering centres is not valid if the true potential is replaced by an energy-dependent pseudo-potential. When the system is such that the scattering potentials do not overlap, the density of states may be expressed in a form which involves the scattering phase shifts and the positions of the scatterers rather than the potential. The resulting formalism for the density of states of such a system is a generalization of the Green function method for determining band structure, and reduces to it for a regular lattice.
Seasonal and intraseasonal climatology of summer monsoon rainfall over East Asia

Abstract This study attempts to bridge a gap in our knowledge and understanding of the East Asian monsoon by presenting a documentation of the rainfall and intraseasonal rainfall climatology of summer monsoon rainfall over East Asia. The rainfall climatology is viewed both in terms of its regional characteristics and in relation to the large-scale circulation with the aim of bringing to light the myriad phenomena associated with the East Asian monsoon and its relevance to current mainstream research in monsoon and climate dynamics. Specially, climatology of 30-day and 10-day rainfall records from East Asian stations are studied and compared with satellite outgoing longwave radiation and the large-scale circulation field. Results show that climatologically two major monsoon rainfall onsets over East Asia are identified during the period from April to September. The first, known as the Mei-yu (or plum rain), occurs over central China around the beginning or the middle of June, and the second over northeast .
Efficient Mutation Testing via Pre-Trained Language Models

—Mutation testing is an established fault-based testing technique. It operates by seeding faults into the programs under test and asking developers to write tests that reveal these faults. These tests have the potential to reveal a large number of faults – those that couple with the seeded ones – and thus are deemed important. To this end, mutation testing should seed faults that are both “natural” in a sense easily understood by developers and strong (have high chances to reveal faults). To achieve this we propose using pre-trained generative language models (i.e. CodeBERT) that have the ability to produce developer-like code that operates similarly, but not exactly, as the target code. This means that the models have the ability to seed natural faults, thereby offering opportunities to perform mutation testing. We realise this idea by implementing µ B ERT , a mutation testing technique that performs mutation testing using CodeBert and empirically evaluated it using 689 faulty program versions. Our results show that the fault revelation ability of µ B ERT is higher than that of a state-of-the-art mutation testing (PiTest), yielding tests that have up to 17% higher fault detection potential than that of PiTest. Moreover, we observe that µ B ERT can complement PiTest, being able to detect 47 bugs missed by PiTest, while at the same time, PiTest can ﬁnd 13 bugs missed by µ B ERT .
Label-free incoherent super-resolution optical microscopy

The photo-kinetics of fluorescent molecules has enabled the circumvention of far-field optical diffraction-limit. Despite its enormous potential, the necessity to label the sample may adversely influence the delicate biology under investigation. Thus, continued development efforts are needed to surpass the far-field label-free diffraction barrier. The coherence of the detected light in label-free mode hinders the application of existing super-resolution methods based on incoherent fluorescence imaging. In this article, we present the physics and propose a methodology to circumvent this challenge by exploiting the photoluminescence of silicon nitride waveguides for near-field illumination of unlabeled samples. The technique is abbreviated EPSLON, Evanescently decaying Photoluminescence Scattering enables Label-free Optical Nanoscopy. We demonstrate that such an illumination has properties that mimics the photo-kinetics of nano-sized fluorescent molecules. This allows to develop a label-free incoherent system that is linear in intensity, and stable with time thereby permitting the application of techniques like structured illumination microscopy (SIM) and intensity-fluctuation based optical nanoscopy (IFON) in label-free mode to circumvent the diffraction limit. We experimentally demonstrate label-free super-resolution imaging of nanobeads (polystyrene and gold), extra-cellular vesicles and human placenta tissue. We believe EPSLON is a step forward within the nascent field of label-free super-resolution microscopy that holds the key to investigate delicate biological systems in its natural state without the need for exogenous labels.
$\Gamma$-convergent LDG method for large bending deformations of bilayer plates

Bilayer plates are slender structures made of two thin layers of different materials. They react to environmental stimuli and undergo large bending deformations with relatively small actuation. The reduced model is a constrained minimization problem for the second fundamental form, with a given spontaneous curvature that encodes material properties, subject to an isometry constraint. We design a local discontinuous Galerkin (LDG) method which imposes a relaxed discrete isometry constraint and controls deformation gradients at barycenters of elements. We prove $\Gamma$-convergence of LDG, design a fully practical gradient flow, which gives rise to a linear scheme at every step, and show energy stability and control of the isometry defect. We extend the $\Gamma$-convergence analysis to piecewise quadratic creases. We also illustrate the performance of the LDG method with several insightful simulations of large deformations, one including a curved crease.
Visual Story Generation Based on Emotion and Keywords

Automated visual story generation aims to produce stories with corresponding illustrations that exhibit coherence, progression, and adherence to characters' emotional development. This work proposes a story generation pipeline to co-create visual stories with the users. The pipeline allows the user to control events and emotions on the generated content. The pipeline includes two parts: narrative and image generation. For narrative generation, the system generates the next sentence using user-specified keywords and emotion labels. For image generation, diffusion models are used to create a visually appealing image corresponding to each generated sentence. Further, object recognition is applied to the generated images to allow objects in these images to be mentioned in future story development.
Linguistic-style-aware Neural Networks for Fake News Detection

We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal. We first generate the hierarchical linguistic tree of news documents; by doing so, we translate each news document's linguistic style into its writer's usage of words and how these words are recursively structured as phrases, sentences, paragraphs, and, ultimately, the document. By integrating the hierarchical linguistic tree with the neural network, the proposed method learns and classifies the representation of news documents by capturing their locally sequential and globally recursive structures that are linguistically meaningful. It is the first work offering the hierarchical linguistic tree and the neural network preserving the tree information to our best knowledge. Experimental results based on public real-world datasets demonstrate the proposed method's effectiveness, which can outperform state-of-the-art techniques in classifying short and long news documents. We also examine the differential linguistic style of fake news and the truth and observe some patterns of fake news. The code and data have been publicly available.
EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living

In this article, we present electromyography analysis of human activity-database 1 (EMAHA-DB1), a novel dataset of multichannel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 non-disabled subjects while performing 22 activities categorized according to functional arm activity behavioral observation system (FAABOS) (three-full hand gestures, six-open/close office draw, eight-grasping and holding of small office objects, two-flexion and extension of finger movements, two-writing and one-rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using six state-of-the-art machine learning classifiers, including random forest (RF), fine $K$ -nearest neighbor (KNN), ensemble KNN (sKNN), linear discriminant analysis (LDA), support vector machines (SVMs), and a hybrid deep learning architecture of convolutional neural network (CNN) and bidirectional-long short term memory (Bi-LSTM) layers. In the classical methods, ten combinations of time domain and frequency domain feature sets are analyzed. The state-of-the-art classification accuracy on five FAABOS categories is 83.21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75.39% by the same SVM classifier with the log moments in the frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBPs), channel cross correlation and local binary patterns (LBPs) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics.
On Interference-Rejection Using Riemannian Geometry for Direction of Arrival Estimation

We consider the problem of estimating the direction of arrival of desired acoustic sources in the presence of multiple acoustic interference sources. All the sources are located in noisy and reverberant environments and are received by a microphone array. We propose a new approach for designing beamformers and DoA estimation methods based on the Riemannian geometry of the manifold of Hermitian positive definite matrices. Specifically, we show theoretically that incorporating the Riemannian mean of the spatial correlation matrices into frequently-used beamformers gives rise to spatial spectra that reject the directions of interference sources and result in a higher signal-to-interference ratio. We experimentally demonstrate the advantages of our approach in designing several beamformers and a recent DoA estimation method in the presence of simultaneously active multiple interference sources.
Model selection for network data based on spectral information

We introduce a new methodology for model selection in the context of modeling network data. The statistical network analysis literature has developed many different classes of network data models, with notable model classes including stochastic block models, latent position models, and exponential families of random graph models. A persistent question in the statistical network analysis literature lies in understanding how to compare different models for the purpose of model selection and evaluating goodness-of-fit, especially when models have different mathematical foundations. In this work, we develop a novel non-parametric method for model selection in network data settings which exploits the information contained in the spectrum of the graph Laplacian in order to obtain a measure of goodness-of-fit for a defined set of network data models. We explore the performance of our proposed methodology to popular classes of network data models through numerous simulation studies, demonstrating the practical utility of our method through two applications.
Localized Bounded Below Approximate Schauder Frames are Finite Unions of Approximate Riesz Sequences

: Based on the truth of Feichtinger conjecture by Marcus, Spielman and Srivastava [Ann. of Math. (2), 2015] and from the localized version by Gr¨ochenig [Adv. Comput. Math., 2003] , we introduce the notion of localization of approximate Schauder frames (ASFs) and approximate Riesz sequences (ARSs). We show that localized bounded below ASFs are ﬁnite unions of ARSs.