Miracle-dz/UniFAR-FTUs huggingface dataset:
An Interacting Wasserstein Gradient Flow Strategy to Robust Bayesian Inference

Model Updating is frequently used in Structural Health Monitoring to determine structures' operating conditions and whether maintenance is required. Data collected by sensors are used to update the values of some initially unknown physics-based model's parameters. Bayesian Inference techniques for model updating require the assumption of a prior distribution. This choice of prior may affect posterior predictions and subsequent decisions on maintenance requirements, specially under the typical case in engineering applications of little informative data. Therefore, understanding how the choice of prior may affect the posterior prediction is of great interest. In this paper, a Robust Bayesian Inference technique evaluates the optimal and worst-case prior in the vicinity of a chosen nominal prior, and their corresponding posteriors. This technique employs an interacting Wasserstein gradient flow formulation. Two numerical case studies are used to showcase the proposed algorithm: a double-banana-posterior and a double beam structure. Optimal and worst-case prior are modelled by specifying an ambiguity set containing any distribution at a statistical distance to the nominal prior, less or equal to the radius. Examples show how particles flow from an initial assumed Gaussian distribution to the optimal worst-case prior distribution that lies inside the defined ambiguity set, and the resulting particles from the approximation to the posterior. The resulting posteriors may be used to yield the lower and upper bounds on subsequent calculations used for decision-making. If the metric used for decision-making is not sensitive to the resulting posteriors, it may be assumed that decisions taken are robust to prior uncertainty.
Visualization Generation with Large Language Models: An Evaluation

Analysts frequently need to create visualizations in the data analysis process to obtain and communicate insights. To reduce the burden of creating visualizations, previous research has developed various approaches for analysts to create visualizations from natural language queries. Recent studies have demonstrated the capabilities of large language models in natural language understanding and code generation tasks. The capabilities imply the potential of using large language models to generate visualization specifications from natural language queries. In this paper, we evaluate the capability of a large language model to generate visualization specifications on the task of natural language to visualization (NL2VIS). More specifically, we have opted for GPT-3.5 and Vega-Lite to represent large language models and visualization specifications, respectively. The evaluation is conducted on the nvBench dataset. In the evaluation, we utilize both zero-shot and few-shot prompt strategies. The results demonstrate that GPT-3.5 surpasses previous NL2VIS approaches. Additionally, the performance of few-shot prompts is higher than that of zero-shot prompts. We discuss the limitations of GPT-3.5 on NL2VIS, such as misunderstanding the data attributes and grammar errors in generated specifications. We also summarized several directions, such as correcting the ground truth and reducing the ambiguities in natural language queries, to improve the NL2VIS benchmark.
Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey

Colorectal polyp segmentation (CPS), an essential problem in medical image analysis, has garnered growing research attention. Recently, the deep learning-based model completely overwhelmed traditional methods in the field of CPS, and more and more deep CPS methods have emerged, bringing the CPS into the deep learning era. To help the researchers quickly grasp the main techniques, datasets, evaluation metrics, challenges, and trending of deep CPS, this paper presents a systematic and comprehensive review of deep-learning-based CPS methods from 2014 to 2023, a total of 115 technical papers. In particular, we first provide a comprehensive review of the current deep CPS with a novel taxonomy, including network architectures, level of supervision, and learning paradigm. More specifically, network architectures include eight subcategories, the level of supervision comprises six subcategories, and the learning paradigm encompasses 12 subcategories, totaling 26 subcategories. Then, we provided a comprehensive analysis the characteristics of each dataset, including the number of datasets, annotation types, image resolution, polyp size, contrast values, and polyp location. Following that, we summarized CPS's commonly used evaluation metrics and conducted a detailed analysis of 40 deep SOTA models, including out-of-distribution generalization and attribute-based performance analysis. Finally, we discussed deep learning-based CPS methods' main challenges and opportunities.
Semi-supervised segmentation of land cover images using nonlinear canonical correlation analysis with multiple features and t-SNE

Image segmentation is a clustering task whereby each pixel is assigned a cluster label. Remote sensing data usually consists of multiple bands of spectral images in which there exist semantically meaningful land cover subregions, co-registered with other source data such as LIDAR (LIght Detection And Ranging) data, where available. This suggests that, in order to account for spatial correlation between pixels, a feature vector associated with each pixel may be a vectorized tensor representing the multiple bands and a local patch as appropriate. Similarly, multiple types of texture features based on a pixel's local patch would also be beneficial for encoding locally statistical information and spatial variations, without necessarily labelling pixel-wise a large amount of ground truth, then training a supervised model, which is sometimes impractical. In this work, by resorting to label only a small quantity of pixels, a new semi-supervised segmentation approach is proposed. Initially, over all pixels, an image data matrix is created in high dimensional feature space. Then, t-SNE projects the high dimensional data onto 3D embedding. By using radial basis functions as input features, which use the labelled data samples as centres, to pair with the output class labels, a modified canonical correlation analysis algorithm, referred to as RBF-CCA, is introduced which learns the associated projection matrix via the small labelled data set. The associated canonical variables, obtained for the full image, are applied by k-means clustering algorithm. The proposed semi-supervised RBF-CCA algorithm has been implemented on several remotely sensed multispectral images, demonstrating excellent segmentation results.
Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources

The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogs of astrophysical sources. Classification is useful for the study of individual objects, statistics for population studies, as well as for anomaly detection, i.e., the identification of new unexplored phenomena, including transients and spectrally extreme sources. Despite the importance of this task, classification remains challenging in X-ray astronomy due to the lack of optical counterparts and representative training sets. We develop an alternative methodology that employs an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources with a limited number of labeled sources, and without ancillary information from optical and infrared catalogs. We provide a catalog of probabilistic classes for 8,756 sources, comprising a total of 14,507 detections, and demonstrate the success of the method at identifying emission from young stellar objects, as well as distinguishing between small-scale and large-scale compact accretors with a significant level of confidence. We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses such as the unified AGN model. This provides interpretability to the probabilistic classifier. Code and tables are available publicly through GitHub. We provide a web playground for readers to explore our final classification at https://umlcaxs-playground.streamlit. app.
Modern approaches to improving phase contrast electron microscopy

Although defocus can be used to generate partial phase contrast in transmission electron microscope images, cryo-electron microscopy (cryo-EM) can be further improved by the development of phase plates which increase contrast by applying a phase shift to the unscattered part of the electron beam. Many approaches have been investigated, including the ponderomotive interaction between light and electrons. We review the recent successes achieved with this method in high-resolution, single-particle cryo-EM. We also review the status of using pulsed or near-field enhanced laser light as alternatives, along with approaches that use scanning transmission electron microscopy (STEM) with a segmented detector rather than a phase plate.
Antioxidant and Cosmeceutical Activities of Agarum cribrosum Phlorotannin Extracted by Ultrasound Treatment

An ultrasonic process for the effective extraction of phlorotannins from Agarum cribrosum with high total phlorotannin content (TPC) was optimized using response surface methodology (RSM). Optimal conditions for ultrasound extraction consisted of temperature: 30°C; time: 5.75 h; ratio of solvent to solid: 35 mL/g, where the TPC was 19.59 mg PGE/g. The crude extract was further partitioned using hexane, dichloromethane, ethyl acetate (EtOAc), and water, in that order. The EtOAc fraction with the highest TPC exhibited the strongest radical-scavenging activity against DPPH, ABTS, hydrogen peroxide, and reducing power, which was similar to that of ascorbic acid. Furthermore, the IC50 values of the EtOAc fraction against collagenase, elastase, and tyrosinase inhibitory activity were 29.63, 16.13, and 6.17 μg/mL, respectively, whereas epigallocatechin gallate (EGCG), EGCG, and kojic acid, the counterpart positive controls, were 68.90, 23.34, and 43.29 μg/mL, respectively. The EtOAc fraction was further fractionated using Sephadex LH-20 column chromatography, which yielded two major and four minor subfractions. The structure of the SF5 and SF6 subfractions were further determined through HPLC-DAD-QMS analyses and 1H and 13C NMR, in which only trifuhalol A was identified. The SF5 and SF6 subfractions exhibited significantly higher radical-scavenging activity against DPPH and reducing power than the EtOAc fraction, but no statistically significant differences were detected for ABTS, hydrogen peroxide, collagenase, elastase, and tyrosinase inhibitory activity. Based on the results, we propose that A. cribrosum phlorotannins could be a good source for natural antioxidants and cosmeceutical.
On partial endomorphisms of a star graph

In this paper we consider the monoids of all partial endomorphisms, of all partial weak endomorphisms, of all injective partial endomorphisms, of all partial strong endomorphisms and of all partial strong weak endomorphisms of a star graph with a finite number of vertices. Our main objective is to determine their ranks. We also describe their Green's relations, calculate their cardinalities and study their regularity.
Large deviations of the empirical spectral measure of supercritical sparse Wigner matrices

Let $\Xi$ be the adjacency matrix of an Erd\H{o}s-R\'enyi graph on $n$ vertices and with parameter $p$ and consider $A$ a $n\times n$ centered random symmetric matrix with bounded i.i. d. entries above the diagonal. When the mean degree $np$ diverges, the empirical spectral measure of the normalized Hadamard product $(A \circ \Xi)/\sqrt{np}$ converges weakly in probability to the semicircle law. In the regime where $p\ll 1$ and $ np \gg \log n$, we prove a large deviations principle for the empirical spectral measure with speed $n^2p$ and with a good rate function solution of a certain variational problem. The rate function reveals in particular that the only possible deviations at the exponential scale $n^2p$ are around measures coming from Quadratic Vector Equations. As a byproduct, we obtain a large deviations principle for the empirical spectral measure of supercritical Erd\H{o}s-R\'enyi graphs.
Measurement of the double-differential inclusive jet cross section in proton-proton collisions at $\sqrt{s}$ = 5.02 TeV

The inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$ $\lt$ 2, and across the kinematic range 0.06 $\lt$ $p_\mathrm{T}$ $\lt$ 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling $\alpha_\mathrm{S}$.
Discordant effects of ex-vivo JAK inhibition on inflammatory responses in colonic compared to ileal mucosa

Background & aims Janus kinase (JAK) inhibitors modulating JAK-STAT (signal transducers and activators of transcription) signaling pathway, are used for the treatment of patients with inflammatory bowel diseases (IBD). We aimed to identify the molecular effects of JAK inhibition in the human intestinal mucosa, considering the IBD location and phenotype. Methods Colonic and ileal explants from patients with ulcerative colitis (UC), Crohn’s disease (CD), or non-IBD controls (NC) were treated ex-vivo with the JAK inhibitor, tofacitinib. Phosphorylated STAT (p-STAT) levels were assessed by Western blot and Immunofluorescence. Inflammatory genes expression was assessed with Nanostring nCounter system. Human intestinal organoids were used to assess JAK inhibitors’ effects on p-STATs and iNOS expression. Results Explants were collected from 68 patients (NC=28; UC=20; CD=20). JAK inhibition reduced p-STAT1/3/5 expression in all explants. While p-STAT inhibition rates varied among patients (10%-88%), higher inhibition rates were observed in colonic compared to ileal explants. Significant alterations in 120 of 255 inflammatory genes were observed in colonic explants, while only 30 were observed in ileal NC explants. In colonic explants from UC, significant alterations were observed in 5 genes, including STAT1 and NOS2. Various JAK inhibitors reduced IFN-γ-induced increase in p-STAT1 and iNOS expression in organoids. Conclusions A site-specific anti-inflammatory effect of JAK inhibition by tofacitinib was noticed, whereby the colon was more robustly affected than the ileum. Ex-vivo response to tofacitinib is individual. JAK inhibition may attenuate inflammation by decreasing iNOS expression. Ex-vivo mucosal platforms may be a valuable resource for studying drug impact and evaluating personalized treatment effects.
Thermodynamic analog of integrate-and-fire neuronal networks by maximum entropy modelling

Relying on maximum entropy arguments, certain aspects of time-averaged experimental neuronal data have been recently described using Ising-like models, allowing the study of neuronal networks under an analogous thermodynamical framework. Here, we apply for the first time the Maximum Entropy method to an Integrate-and-fire (IF) model that can be tuned at criticality, offering a controlled setting for a systematic study of criticality and finite-size effects in spontaneous neuronal activity, as opposed to experiments. We show that generalized Ising models that accurately predict the average local activities and correlation functions between neurons of the IF model networks in the critical state exhibit a spin glass phase for low temperatures, having mostly negative intrinsic fields and a bimodal distribution of interaction constants that tends to become unimodal for larger networks. Results appear to be affected by sample-to-sample connectivity variations and subsampling. Furthermore, we also found that networks with higher percentage of inhibitory neurons lead to Ising-like systems with reduced thermal fluctuations. Finally, considering only neuronal pairs associated with the largest correlation functions allows the study of larger system sizes. Author summary Brain activity, either stimulated or spontaneous, in vivo or in vitro, exhibits complex spatiotemporal behavior. Trying to make sense of it, several research groups have analyzed time-averaged experimental neuronal data using maximum entropy arguments, mapping the neuronal dynamics into a generalized Ising-like model and allowing to study neuronal data using tools typical of critical phenomena. However, the intricacy of real biological networks in experimental settings pose challenges in the precision and reliability of the neuronal measurements. Here, we apply for the first time the Maximum Entropy Method to an Integrate-and-fire model with synaptic plasticity, providing a foundation for a more systematic and comprehensive study of spontaneous brain activity. We show that generalized Ising models are able to reproduce the numerical time-averaged data of local activities and correlation functions of integrate-and-fire neurons and predict qualitatively higher-order quantities such as the three-point correlation functions across triplets of neurons. We show that subsampling affects the efficiency of the mapping and that the analogous thermodynamics functions of the Ising-like models depend on sample-to-sample network variations and on the presence of inhibition in the neural network.
Sensory choices as logistic classification

Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum them, and use the result to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions). In fact, data from multiple studies in multiple species shows that logistic classification can describe a variety of choices. These include sensory choices influenced by stimuli of other modalities (multisensory integration) or by non-sensory factors such as expected value and recent choices. Logistic classification is the optimal strategy if factors are independent of each other, and a useful heuristic in other conditions. Using it to describe sensory choices can characterize not only normal brain function but also the effects of brain inactivations.
PI3 kinase inhibitor PI828 uncouples aminergic GPCRs and Ca2+ mobilization irrespectively of its primary target

The phosphoinositide 3-kinase (PI3K) is involved in regulation of multiple intracellular processes. Although the inhibitory analysis is generally employed for validating a physiological role of PI3K, increasing body of evidence suggests that PI3K inhibitors can exhibit PI3K-unrelated activity as well. Here we studied Ca2+ signaling initiated by aminergic agonists in a variety of different cells and analyzed effects of the PI3K inhibitor PI828 on cell responsiveness. It turned out that PI828 inhibited Ca2+ transients elicited by acetylcholine (ACh), histamine, and serotonin, but did not affect Ca2+ responses to norepinephrine and ATP. Another PI3K inhibitor wortmannin negligibly affected Ca2+ signaling initiated by any one of the tested agonists. Using the genetically encoded PIP3 sensor PH(Akt)-Venus, we confirmed that both PI828 and wortmannin effectively inhibited PI3K and ascertained that this kinase negligibly contributed to ACh transduction. These findings suggested that PI828 inhibited Ca2+ responses to aminergic agonists tested, involving an unknown cellular mechanism unrelated to the PI3K inhibition. Complementary physiological experiments provided evidence that PI828 could inhibit Ca2+ signals induced by certain agonists, by acting extracellularly, presumably, through their surface receptors. For the muscarinic M3 receptor, this possibility was verified with molecular docking and molecular dynamics. As demonstrated with these tools, wortmannin could be bound in the extracellular vestibule at the muscarinic M3 receptor but this did not preclude binding of ACh to the M3 receptor followed by its activation. In contrast, PI828 could sterically block the passage of ACh into the allosteric site, preventing activation of the muscarinic M3 receptor.
Neural space-time model for dynamic scene recovery in multi-shot computational imaging systems

Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics. Hence, we can both remove motion artifacts and resolve sample dynamics. We demonstrate NSTM in three computational imaging systems: differential phase contrast microscopy, 3D structured illumination microscopy, and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.
Label-free biosensor assay decodes the dynamics of Toll-like receptor signaling

The discovery of Toll-like receptors (TLRs) represented a significant breakthrough that paved the way for the study of hostpathogen interactions in innate immunity. However, there are still major gaps in understanding TLR function, especially the early dynamics of downstream TLR pathways remains less clear. Here, we present a label-free optical biosensor-based assay as a powerful method for detecting TLR activation in a native and label-free environment and defining the dynamics of TLR pathway activation. This technology is sufficiently sensitive to detect TLR signaling and readily discriminates between different TLR signaling pathways. We define pharmacological modulators of cell surface and endosomal TLRs and downstream signaling molecules and uncover previously unrecognized TLR signaling signatures, including biased receptor signaling. These findings highlight that optical biosensor assays complement traditional assays that use a single endpoint and have the potential to facilitate the future design of selective drugs targeting TLRs and their downstream effector cascades.
The landscape of immune dysregulation in pediatric sepsis at a single-cell resolution

Recognizing immune dysregulation as a hallmark of sepsis pathophysiology, leukocytes have attracted major attention of investigation. While adult and pediatric sepsis are clinically distinct, their immunological delineation remains limited. Breakthrough of single cell technologies facilitated the characterization of immune signatures. We tackled to delineate immunological profiles of pediatric sepsis at a single-cell level by analyzing blood samples from six septic children, at both acute and recovery phases, and four healthy children. 16 single-cell transcriptomic datasets (96,156 cells) were analyzed and compared to adult sepsis dataset. We showed a unique shift in neutrophil subpopulations and functions between acute and recovery phases, along with examining the regulatory role of resistin. Neutrophil signatures were comparable between adult and pediatric sepsis. Innate-like CD4 T cells were predominantly and uniquely observed in acute phase of pediatric sepsis. Our study provides a thorough and comprehensive understanding of immune dysregulation in pediatric sepsis.