Miracle-dz/UniFAR-FTUs huggingface dataset:
Definition of Mucosal Breaks in the Era of Magnifying Endoscopy with Narrow-Band Imaging

Background Gastroesophageal reflux disease is diagnosed endoscopically based on the presence of mucosal breaks. However, mucosal breaks can be judged differently depending on the endoscopist, even in the same image. We investigated how narrow-band imaging (NBI) and magnified endoscopy affect the judgment of mucosal breaks. Methods A total of 43 consecutive patients were enrolled who had suspected mucosal breaks on white-light images (WLI) and underwent nonmagnified NBI (N-NBI) and magnified NBI (M-NBI) by a single endoscopist. From WLI, N-NBI, and M-NBI, 129 image files were created. Eight endoscopists reviewed the image files and judged the presence of mucosal breaks. Results The 8 endoscopists determined mucosal breaks were present in 79.4 ± 9.5% (67.4%–93.0%) on WLI, and 76.7 ± 12.7% (53.5%–90.7%) on N-NBI. However, the percentage of mucosal breaks on M-NBI was significantly lower at 48.8 ± 17.0% (18.6%–65.1%) (p < 0.05). Intraclass correlation between observers was 0.864 (95% CI 0.793–0.918) for WLI and 0.863 (95% CI 0.791–0.917) for N-NBI but was lower for M-NBI at 0.758 (95% CI 0.631–0.854). Conclusion Rates of detection and agreement for mucosal breaks on WLI and N-NBI were high among endoscopists. However, these rates were lower on M-NBI.
The Beneficial Effects of Two Polysaccharide Fractions from Sargassum fusiform against Diabetes Mellitus Accompanied by Dyslipidemia in Rats and Their Underlying Mechanisms

The current study aimed to assess the anti-diabetic effects and potential mechanisms of two Sargassum fusiform polysaccharide fractions (SFPs, named SFP-1 and SFP-2). The carbohydrate-loading experiment revealed that SFP-2 could control postprandial hyperglycemia by inhibiting the activity of digestive enzymes in rats. The analysis of diabetic symptoms and serum profiles indicated that SFPs could mitigate diabetes accompanied by dyslipidemia, and SFP-2 showed better regulatory effects on body weight, food intake and the levels of total cholesterol (TC), triglycerides (TG), low density lipoprotein-cholesterol (LDL-C) and free fatty acid (FFA) in diabetic rats. Intestinal bacterial analysis showed that SFP treatment could reshape the gut flora of diabetic rats, and SFP-2 possessed a greater regulatory effect on the growth of Lactobacillus and Blautia than SFP-1. RT-qPCR analysis revealed that SFPs could regulate the genes involved in the absorption and utilization of blood glucose, hepatic glucose production and lipid metabolism, and the effects of SFP-2 on the relative expressions of Protein kinase B (Akt), Glucose-6-phosphatase (G-6-Pase), Glucose transporter 2 (GLUT2), AMP-activated protein kinase-α (AMPKα), Peroxisome proliferator-activated receptor γ (PPARγ) and Cholesterol 7-alpha hydroxylase (CYP7A1) were greater than SFP-1. All above results indicated that SFPs could be exploited as functional foods or pharmaceutical supplements for the treatment of diabetes and its complications.
in Mice Infections Salmonella Protein in Resistance to The Role of Lipopolysaccharide Binding

Polymorphonuclear leukocytes (PMN) and LPS-binding protein (LBP) are both components of the innate immune system. LBP is a plasma protein that binds to lipid A and enhances the biological activity of LPS 100- to 1000-fold. Recently it was reported that LBP-deﬁcient mice are more susceptible to Salmonella typhimurium infection. Here we report that LBP KO mice are more susceptible to Salmonella peritonitis, but not to oral or i.v. infection. LBP knockout (KO) mice responded normally to i.p. injections of Staphylococcus aureus and casein, but not to i.p. injection of S. typhimurium or Salmonella LPS. Mice with a mutation in Toll-like receptor 4 (C3H/HeJ) have a similar defect in PMN chemotaxis. In normal mice S. typhimurium stimulated production of the CXC chemokines macrophage inﬂammatory protein-2 and cytokine-induced neutrophil chemoattractant, but levels of cytokine-induced neutrophil chemoattractant and macrophage inﬂammatory protein-2 were greatly reduced in the LBP KO mice. LBP KO mice pretreated with casein to attract PMN in an LBP-independent manner were more resistant to Salmonella infection, but neutropenic mice were not protected by casein. Splenic TNF- (cid:1) mRNA levels were also lower in LBP KO than in control mice infected with Salmonella . Since TNF- (cid:1) can activate PMN, LBP KO mice may have both fewer and less active PMN in the ﬁrst few hours after Salmonella are injected, making LBP KO mice more susceptible. This work conﬁrms the importance of PMN in resistance to Salmonella infections and shows that this is facilitated by LBP. The Journal of Immunology, 2002, 168: 6396–6403.
Scientific Literacy Assessment Instrument on Addictive Substances Ethnoscience Integrated Inquiry Based-Learning

A skill needed in the 21st-century learning is scientific literacy (SL). Ethnoscience integrated inquiry-based learning (IBL) is expected to train the SL. The SL ability can be measured using an instrument. The purposes of the research were i) to produce a valid and reliable instrument on the ability to measure the SL using ethnoscience integrated IBL and ii) to know the item analysis results of the item test. The research method used was research and development (R&D) with instrument development. Data collection techniques were questionnaires and tests. The instruments of data collection were validation sheets and assessment instruments on SL ability. The data analysis technique used was both descriptive qualitative and quantitative techniques. The qualitative analysis was aimed to determine the product quality using Aiken's V theory. The quantitative analysis aimed to determine the product quality empirically using Rasch model. A valid and reliable instrument was produced with 15 test items for additive substances topic with integrated ethnoscience in learning.
Caregivers’ perception of risk for malaria, helminth infection and malaria-helminth co-infection among children living in urban and rural settings of Senegal: A qualitative study

Malaria parasites and worms frequently co-exist in children living in low-and middle-income countries where existing vertical control programmes for the control of these two conditions are not operating at optimal levels. This gap necessitates the development and implementation of strategic interventions to achieve effective control and eventual elimination of these co-infections. Central to the successful implementation of any intervention is its acceptance and uptake by caregivers whose perception about the risk for malaria-helminth co-infection has been little documented. Therefore, we conducted a qualitative study to understand the caregivers perspectives about the risk as well as the behavioural and social risk factors promoting malaria-helminth co-infection among pre-school and school-aged children living in endemic rural and urban communities in Senegal. In June and December 2021, we conducted individual and group interviews, and participant observations, among 100 primary caregivers of children recruited from Saraya villages in southeast Senegal and among leaders and teachers of Koranic schools in Diourbel, western Senegal. Our findings showed that a majority of the study participants in the two settings demonstrated a high level of perception of risk for malaria and acceptable awareness about handwashing practices, but had misconceptions that malaria-helminth co-infection was due to a combination of excessive consumption of sugary food and mosquito bites. Our observations revealed many factors in the house structures, toilet practices and handwashing with ashes and sands, which the caregivers did not consider as risks for malaria-helminth co-infections. These findings underscore the need to promote caregivers awareness about the existence and risk of malaria-helminth co-infection in children. This approach would assist in addressing the caregivers misconceptions about the occurrence of the co-infection and could enhance their uptake of the strategic interventions targeted at achieving control and subsequent elimination of malaria and helminth co-infection.
What is an equivariant neural network?

We explain equivariant neural networks, a notion underlying breakthroughs in machine learning from deep convolutional neural networks for computer vision to AlphaFold 2 for protein structure prediction, without assuming knowledge of equivariance or neural networks. The basic mathematical ideas are simple but are often obscured by engineering complications that come with practical realizations. We extract and focus on the mathematical aspects, and limit ourselves to a cursory treatment of the engineering issues at the end.
A Relaxation Approach to Feature Selection for Linear Mixed Effects Models

Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult than for linear regression because LME models are nonlinear. In this work we propose a relaxation strategy and optimization methods that enable a wide range of variable selection methods for LMEs using both convex and nonconvex regularizers, including $\ell_1$, Adaptive-$\ell_1$, CAD, and $\ell_0$. The computational framework only requires the proximal operator for each regularizer to be available, and the implementation is available in an open source python package pysr3, consistent with the sklearn standard. The numerical results on simulated data sets indicate that the proposed strategy improves on the state of the art for both accuracy and compute time. The variable selection techniques are also validated on a real example using a data set on bullying victimization.
A comparison of PINN approaches for drift-diffusion equations on metric graphs

In this paper we focus on comparing machine learning approaches for quantum graphs, which are metric graphs, i.e., graphs with dedicated edge lengths, and an associated differential operator. In our case the differential equation is a drift-diffusion model. Computational methods for quantum graphs require a careful discretization of the differential operator that also incorporates the node conditions, in our case Kirchhoff-Neumann conditions. Traditional numerical schemes are rather mature but have to be tailored manually when the differential equation becomes the constraint in an optimization problem. Recently, physics informed neural networks (PINNs) have emerged as a versatile tool for the solution of partial differential equations from a range of applications. They offer flexibility to solve parameter identification or optimization problems by only slightly changing the problem formulation used for the forward simulation. We compare several PINN approaches for solving the drift-diffusion on the metric graph.
Calibration of TES bolometer arrays with application to CLASS

Current and future cosmic microwave background (CMB) experiments ﬁelding kilo-pixel arrays of transition-edge sensor (TES) bolometers require accurate and robust gain calibration methods. We simplify and refactor the standard TES model to directly relate the detector responsivity calibration and optical time constant to the measured TES current 𝐼 and the applied bias current 𝐼 b . The calibration method developed for the Cosmology Large Angular Scale Surveyor (CLASS) TES bolometer arrays relies on current versus voltage ( 𝐼 - 𝑉 ) measurements acquired daily prior to CMB observations. By binning Q-band (40 GHz) 𝐼 - 𝑉 measurements by optical loading, we ﬁnd that the gain calibration median standard error within a bin is 0.3%. We test the accuracy of this “ 𝐼 - 𝑉 bin” detector calibration method by using the Moon as a photometric standard. The ratio of measured Moon amplitudes between detector pairs sharing the same feedhorn indicates a TES calibration error of 0.5%. We also ﬁnd that for the CLASS Q-band TES array, calibrating the response of individual detectors based solely on the applied TES bias current accurately corrects TES gain variations across time but introduces a bias in the TES calibration from data counts to power units. Since the TES current bias value is set and recorded before every observation, this calibration method can always be applied to raw TES data and is not subject to 𝐼 - 𝑉 data quality or processing errors.
Modeling the Optical to Ultraviolet Polarimetric Variability from Thomson Scattering in Colliding-wind Binaries

Massive-star binaries are critical laboratories for measuring masses and stellar wind mass-loss rates. A major challenge is inferring viewing inclination and extracting information about the colliding-wind interaction (CWI) region. Polarimetric variability from electron scattering in the highly ionized winds provides important diagnostic information about system geometry. We combine for the first time the well-known generalized treatment of Brown et al. for variable polarization from binaries with the semianalytic solution for the geometry and surface density CWI shock interface between the winds based on Cantó et al. Our calculations include some simplifications in the form of inverse-square law wind densities and the assumption of axisymmetry, but in so doing they arrive at several robust conclusions. One is that when the winds are nearly equal (e.g., O+O binaries) the polarization has a relatively mild decline with binary separation. Another is that despite Thomson scattering being a gray opacity, the continuum polarization can show chromatic effects at ultraviolet wavelengths but will be mostly constant at longer wavelengths. Finally, when one wind dominates the other, as, for example, in WR+OB binaries, the polarization is expected to be larger at wavelengths where the OB component is more luminous and generally smaller at wavelengths where the WR component is more luminous. This behavior arises because, from the perspective of the WR star, the distortion of the scattering envelope from spherical is a minor perturbation situated far from the WR star. By contrast, the polarization contribution from the OB star is dominated by the geometry of the CWI shock.
Performance Analysis of Irregular Repetition Slotted Aloha with Multi-Cell Interference

Irregular repetition slotted aloha (IRSA) is a massive random access protocol in which users transmit several replicas of their packet over a frame to a base station. Existing studies have analyzed IRSA in the single-cell (SC) setup. In this work, we analyze multi-cell (MC) IRSA, accounting for pilot contamination and multi-user/MC interference. We find that, in practical settings, the throughput of MC IRSA can be up to 70% lower than SC IRSA. Further, MC IRSA requires a significantly higher training length (about 4 − 5x compared to SC IRSA) to support the same user density and achieve the same throughput. We provide insights into the impact of the pilot length, number of antennas, and SNR on the performance of MC IRSA.
Confidence Intervals for the Number of Components in Factor Analysis and Principal Components Analysis via Subsampling

Factor analysis (FA) and principal component analysis (PCA) are popular statistical methods for summarizing and explaining the variability in multivariate datasets. By default, FA and PCA assume the number of components or factors to be known \emph{a priori}. However, in practice the users first estimate the number of factors or components and then perform FA and PCA analyses using the point estimate. Therefore, in practice the users ignore any uncertainty in the point estimate of the number of factors or components. For datasets where the uncertainty in the point estimate is not ignorable, it is prudent to perform FA and PCA analyses for the range of positive integer values in the confidence intervals for the number of factors or components. We address this problem by proposing a subsampling-based data-intensive approach for estimating confidence intervals for the number of components in FA and PCA. We study the coverage probability of the proposed confidence intervals and provide non-asymptotic theoretical guarantees concerning the accuracy of the confidence intervals. As a byproduct, we derive the first-order \emph{Edgeworth expansion} for spiked eigenvalues of the sample covariance matrix when the data matrix is generated under a factor model. We also demonstrate the usefulness of our approach through numerical simulations and by applying our approach for estimating confidence intervals for the number of factors of the genotyping dataset of the Human Genome Diversity Project.
Average-Case Hardness of Proving Tautologies and Theorems

We consolidate two widely believed conjectures about tautologies -- no optimal proof system exists, and most require superpolynomial size proofs in any system -- into a $p$-isomorphism-invariant condition satisfied by all paddable $\textbf{coNP}$-complete languages or none. The condition is: for any Turing machine (TM) $M$ accepting the language, $\textbf{P}$-uniform input families requiring superpolynomial time by $M$ exist (equivalent to the first conjecture) and appear with positive upper density in an enumeration of input families (implies the second). In that case, no such language is easy on average (in $\textbf{AvgP}$) for a distribution applying non-negligible weight to the hard families. The hardness of proving tautologies and theorems is likely related. Motivated by the fact that arithmetic sentences encoding"string $x$ is Kolmogorov random"are true but unprovable with positive density in a finitely axiomatized theory $\mathcal{T}$ (Calude and J{\"u}rgensen), we conjecture that any propositional proof system requires superpolynomial size proofs for a dense set of $\textbf{P}$-uniform families of tautologies encoding"there is no $\mathcal{T}$ proof of size $\leq t$ showing that string $x$ is Kolmogorov random". This implies the above condition. The conjecture suggests that there is no optimal proof system because undecidable theories help prove tautologies and do so more efficiently as axioms are added, and that constructing hard tautologies seems difficult because it is impossible to construct Kolmogorov random strings. Similar conjectures that computational blind spots are manifestations of noncomputability would resolve other open problems.
An Interference Isolation Method for Wireless Power and Signal Parallel Transmissions on CPT Systems

A novel interference isolation method is proposed by using several designed coils in capacitive power transfer systems as isolation impedances. For each designed coil, its stray parameters such as the inter-turn capacitance, coil resistance and capacitance between the coil and the core, etc. are taken into account. An equivalent circuit model of the designed coil is established. According to this equivalent circuit, the impedance characteristic of the coil is calculated. In addition, the maximum impedance point and the corresponding excitation frequency of the coil are obtained. Based on this analysis, six designed coils are adopted to isolate the interference from power delivery. The proposed method is verified through experiments with a power carrier frequency of 1MHz and a data carrier frequency of 8.7MHz. The power and data are transferred parrallelly with a data carrier attenuation lower than -5dB and a power attenuation on the sensing resistor higher than -45dB.
BackLink: Supervised Local Training with Backward Links

Empowered by the backpropagation (BP) algorithm, deep neural networks have dominated the race in solving various cognitive tasks. The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory cost and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacle by completely cutting off the backward path between modules and isolating their gradients to reduce memory cost and accelerate the training process. These methods prevent errors from flowing between modules and hence information exchange, resulting in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces inter-module backward dependency and allows errors to flow between modules. The algorithm facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, in ResNet32 with 16 local modules, our method surpasses the conventional greedy local training method by 4.00\% and a recent work by 1.83\% in accuracy on CIFAR10, respectively. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79\% reduction in memory cost and 52\% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms towards better efficiency and biological plausibility.
Differentiable programming: Generalization, characterization and limitations of deep learning

In the past years, deep learning models have been successfully applied in several cognitive tasks. Originally inspired by neuroscience, these models are specific examples of differentiable programs. In this paper we define and motivate differentiable programming, as well as specify some program characteristics that allow us to incorporate the structure of the problem in a differentiable program. We analyze different types of differentiable programs, from more general to more specific, and evaluate, for a specific problem with a graph dataset, its structure and knowledge with several differentiable programs using those characteristics. Finally, we discuss some inherent limitations of deep learning and differentiable programs, which are key challenges in advancing artificial intelligence, and then analyze possible solutions
Trajectory phase transitions in non-interacting systems: all-to-all dynamics and the random energy model

We study the fluctuations of time-additive random observables in the stochastic dynamics of a system of N non-interacting Ising spins. We mainly consider the case of all-to-all dynamics where transitions are possible between any two spin configurations with uniform rates. We show that the cumulant generating function of the time-integral of a normally distributed quenched random function of configurations, i.e. the energy function of the random energy model (REM), has a phase transition in the large N limit for trajectories of any time extent. We prove this by determining the exact limit of the scaled cumulant generating function. This is accomplished by connecting the dynamical problem to a spectral analysis of the all-to-all quantum REM. We also discuss finite N corrections as observed in numerical simulations. This article is part of the theme issue ‘Quantum annealing and computation: challenges and perspectives’.
Mask CycleGAN: Unpaired Multi-modal Domain Translation with Interpretable Latent Variable

We propose Mask CycleGAN, a novel architecture for unpaired image domain translation built based on CycleGAN [6], with an aim to address two issues: 1) uni-modality in image translation and 2) lack of interpretability of latent variables. Our innovation in the technical approach is comprised of three key components: masking scheme, generator and objective. Experimental results demonstrate that this architecture is capable of bringing variations to generated images in a controllable manner and is reasonably robust to different masks.