Miracle-dz/UniFAR-FTUs huggingface dataset:
U-KIM-1 as a predictor of treatment response in lupus nephritis

Objective Biomarkers of disease activity in lupus nephritis (LN) are in demand. This is because they may be useful in patients who are unable to undergo invasive kidney biopsy, as predictors of renal function, and for early detection of LN recurrence. The focus is on the measurement of urinary chemokines and cytokines, especially in urinary biomarkers, which are non-invasive and simple. In our previous report, we reported that kidney injury molecule-1 (KIM-1) is expressed in injured tubules and that the number of tubular-KIM-expressing positive cells correlates with renal pathology findings and also with urinary (u)-KIM-1 levels. However, there have been no reports examining the effect of u-KIM-1 levels on response to therapy, correlation with renal pathology, and usefulness as a predictor of renal function. Methods U-KIM-1 levels were measured by ELISA in 61 SLE patients. In 38 active LN who underwent renal biopsy, we also examined whether u-KIM-1 levels affected LN disease activity, renal histological findings, and predictors of renal function. Results In SLE patients, proteinuria and u-KIM-1 levels were elevated in active LN compared to inactive LN. U-KIM-1 and proteinuria decreased with intensified treatment. U-KIM-1 levels also correlated with the percentage of glomerular crescent formation in renal pathology. In addition, patients with higher baseline u-KIM-1 levels had significantly higher eGFR and lower LN disease activity at 12 months after treatment intensification. Conclusions These data suggest that u-KIM-1 levels correlate with LN disease activity and renal histopathology findings and may be used as a predictor of treatment response.
Using infinite-volume, continuum QED and lattice QCD for the hadronic light-by-light contribution to the muon anomalous magnetic moment

In our previous work, the connected and leading disconnected hadronic light-by-light contributions to the muon anomalous magnetic moment (g - 2) have been computed using lattice QCD ensembles corresponding to physical pion mass generated by the RBC/UKQCD collaboration. However, the calculation is expected to suffer from a significant finite volume error that scales like $1/L^2$ where $L$ is the spatial size of the lattice. In this paper, we demonstrate that this problem is cured by treating the muon and photons in infinite volume, continuum QED, resulting in a weighting function that is pre-computed and saved with affordable cost and sufficient accuracy. We present numerical results for the case when the quark loop is replaced by a muon loop, finding the expected exponential approach to the infinite volume limit and consistency with the known analytic result. We have implemented an improved weighting function which reduces both discretization and finite volume effects arising from the hadronic part of the amplitude.
Constraining the sources of ultra-high energy cosmic rays with multi-messenger data

Ultra-high energy cosmic rays (UHECRs) are cosmic rays with energy exceeding 10¹⁸ electronvolts. The sources of these particles remain unknown despite decades of research. This thesis presents a series of studies aimed at constraining the sources of UHECRs both directly by studying their observed arrival directions and indirectly through their expected secondary gamma-ray signatures. An analysis of the arrival direction distribution of the highest energy cosmic rays detected at the Pierre Auger Observatory is presented. The aim of the study was to determine whether the arrival directions of observed UHECRs follow the distribution of nearby extragalactic sources, which is expected if UHECRs are light nuclei of extragalactic origin. A departure from isotropy at the 95% level is observed but no clear correlation with the extragalactic matter distribution is found. The sensitivity of upcoming UHECR experiments, with an order of magnitude higher annual exposure than current experiments, to the expected UHECR anisotropy has been investigated through simulations. It is shown, that with five years of data from such a detector an anisotropy should be detectable at the 99% level as long as the composition is proton dominated. In a scenario where the UHECR source distribution is strongly clustered, similar to the distribution of galaxy clusters, an anisotropy at the 99.9% level is expected even if the fraction of protons at the highest energies is as low as 30%. Constraints on the sources of UHECRs may also come from the secondary particles that UHECRs produce during their propagation. A study of the expected secondary gamma-ray signatures of UHECR accelerators embedded in magnetised environments is presented. The secondary gamma-ray emission expected in this model is shown to be consistent with the spectra of a number of extreme blazars. It is shown that this model is more robust to variations of the overall extragalactic magnetic eld strength than other proposed scenarios, which is appealing in view of the large uncertainty surrounding the strength and con guration of extragalactic magnetic fi elds.
Modification in drag of turbulent boundary layers resulting from manipulation of large-scale structures

The effects of placing a parallel-plate turbulence manipulator in a boundary layer are documented through flow visualization and hot wire measurements. The boundary layer manipulator was designed to manage the large scale structures of turbulence leading to a reduction in surface drag. The differences in the turbulent structure of the boundary layer are summarized to demonstrate differences in various flow properties. The manipulator inhibited the intermittent large scale structure of the turbulent boundary layer for at least 70 boundary layer thicknesses downstream. With the removal of the large scale, the streamwise turbulence intensity levels near the wall were reduced. The downstream distribution of the skin friction was also altered by the introduction of the manipulator.
Self-consistent semi-analytic models of the first stars

We have developed a semi-analytic framework to model the large-scale evolution of the first Population III (Pop III) stars and the transition to metal-enriched star formation. Our model follows dark matter halos from cosmological N-body simulations, utilizing their individual merger histories and three-dimensional positions, and applies physically motivated prescriptions for star formation and feedback from Lyman-Werner (LW) radiation, hydrogen ionizing radiation, and external metal enrichment due to supernovae winds. This method is intended to complement analytic studies, which do not include clustering or individual merger histories, and hydrodynamical cosmological simulations, which include detailed physics, but are computationally expensive and have limited dynamic range. Utilizing this technique, we compute the cumulative Pop III and metal-enriched star formation rate density (SFRD) as a function of redshift at $z \geq 20$. We find that varying the model parameters leads to significant qualitative changes in the global star formation history. The Pop III star formation efficiency and the delay time between Pop III and subsequent metal-enriched star formation are found to have the largest impact. The effect of clustering (i.e. including the three-dimensional positions of individual halos) on various feedback mechanisms is also investigated. The impact of clustering on LW and ionization feedback is found to be relatively mild in our fiducial model, but can be larger if external metal enrichment can promote metal-enriched star formation over large distances.
Does misremembering drive false alarms for emotional lures? A diffusion model investigation

Previous evidence has shown that, in a recognition memory task, emotion leads participants to make more false alarms and decreases response times (RTs) for false alarm responses. This pattern could arise because participants adopt more liberal responding for emotional stimuli and/or because emotional lures are more likely than neutral lures to produce misleading memory retrieval. Recently, Starns et al. designed a new recognition memory paradigm and found that the speed of memory errors shows the influence of misleading information resulting in unavoidable memory errors. This study investigates the basis of false alarms to emotional lures by testing predictions of the diffusion model for a recognition paradigm similar to that by Starns et al. Participants studied lists of emotional words and then completed an old–new recognition memory test. After each old–new decision, participants were asked to make a forced-choice recognition decision that provided a chance to correct possible errors on the preceding old–new decision. Under the assumption that emotion promotes misremembering, the diffusion model predicts that forced-choice accuracy should be lower for pairs with emotional versus neutral lures and that faster old–new errors should be associated with lower forced-choice accuracy. This study tested these predictions, providing theoretical insights into how emotion affects memory retrieval and further developing a new methodology for measuring recognition performance.
Synergistic deterioration of prognosis associated with decreased grip strength and hyporesponse to erythropoiesis-stimulating agents in patients undergoing hemodialysis

Abstract Introduction We examined the combined effect of erythropoietin (EPO) hyporesponsiveness and low handgrip strength (HGS) on the prognosis of patients undergoing hemodialysis (HD). Methods We recruited patients with chronic kidney disease (CKD) Stage 5, who were undergoing HD at our dialysis clinic between January 2015 and March 2015 (n = 182). Patients of ≥20 years of age and who had been undergoing HD for ≧3 months at enrollment were eligible for inclusion. Seven patients treated with epoetin-β pegol were excluded. First, the erythropoietin resistance index (ERI) and HGS were measured. The patients were stratified by the ERI of 9.44 (U/kg/week/g/dL), and by the HGS of 28 kg for men and 18 kg for women. We then observed death and cardiovascular disease (CVD), composite endpoint (deaths or CVD) for a median of 2 years. Results A total of 175 patients (male, n = 122; female, n = 53; age, 34–92 years) were included in the analysis. During the observation period of 24 months, 57 events (14 deaths and 43 CVD) were observed. High ERI and low HGS were associated with a high incidence of endpoints compared to low ERI and high HGS. Among the four groups classified by ERI and HGS values, the highest risk group was the high ERI/low HGS group (HR: 4.20 95% CI 2.12–8.33). Conclusions EPO hyporesponsiveness combined with low HGS were found to be significant predictors of a poor outcome, and the synergistic effects of the two factors had stronger predictive ability than either single factor.
A Benchmark Dataset of Endoscopic Images and Novel Deep Learning Method to Detect Intestinal Metaplasia and Gastritis Atrophy

Endoscopy has been routinely used to diagnose stomach diseases including intestinal metaplasia (IM) and gastritis atrophy (GA). Such routine examination usually demands highly skilled radiologists to focus on a single patient with substantial time, causing the following two key challenges: 1) the dependency on the radiologist's experience leading to inconsistent diagnosis results across different radiologists; 2) limited examination efficiency due to the demanding time and energy consumption to the radiologist. This paper proposes to address these two issues in endoscopy using novel machine learning method in three-folds. Firstly, we build a novel and relatively big endoscopy dataset of 21,420 images from the widely used White Light Imaging (WLI) endoscopy and more recent Linked Color Imaging (LCI) endoscopy, which were annotated by experienced radiologists and validated with biopsy results, presenting a benchmark dataset. Secondly, we propose a novel machine learning model inspired by the human visual system, named as local attention grouping, to effectively extract key visual features, which is further improved by learning from multiple randomly selected regional images via ensemble learning. Such a method avoids the significant problem in the deep learning methods that decrease the resolution of original images to reduce the size of input samples, which would remove smaller lesions in endoscopy images. Finally, we propose a dual transfer learning strategy to train the model with co-distributed features between WLI and LCI images to further improve the performance. The experiment results, measured by accuracy, specificity, sensitivity, positive detection rate and negative detection rate, on IM are 99.18<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 98.90<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 99.45<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 99.45<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 98.91<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, respectively, and on GA are 97.12<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 95.34<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 98.90<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 98.86<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 95.50<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, respectively, achieving state of the art performance that outperforms current mainstream deep learning models.
A sequence‐based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder continuum

Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are—and stay—fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the hydrophobic cluster analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe [soluble domains] and OPM [transmembrane domains]). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well‐folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder. We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold protein structure database. Cases of low‐confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by AlphaFold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.
Heroin and fentanyl in Dallas County: A 5‐year retrospective review of toxicological, seized drug, and demographical data

The opioid epidemic resulted in an increase in identifications of fentanyl, fentanyl analogs, and heroin, among other opioids, in the toxicology and seized drug analysis fields over the past several years. The Dallas County Southwestern Institute of Forensic Sciences, comprised of the Toxicology Laboratory and Drug Analysis Laboratory, observed a rise in fentanyl‐positive analyses in both laboratories. Fentanyl positivity increased from 1.1% to 3.4% in the Toxicology Laboratory over the five years of this study, whereas 6‐monoacetylmorphine (6‐MAM) positivity remained relatively stable at approximately 2%. Similarly, in the Drug Analysis Laboratory, the fentanyl positivity rate changed from 0.03% to 0.60%, whereas heroin identification remained stable at approximately 8–9%. Based on data obtained from toxicology submissions, a typical fentanyl or heroin user was a white male in their late 30s. The average concentration of fentanyl in postmortem cases, antemortem cases, and 6‐MAM in postmortem specimens was 9.7 ng/ml, 5.5 ng/ml, and 68.4 ng/ml, respectively. Poly‐drug use was evident, with benzodiazepines being the most commonly co‐administered drug with fentanyl (49.4%). Multiple drugs were identified via analysis of clandestine tablets submitted to the Drug Analysis Laboratory, including fentanyl and/or heroin in combination with compounds such as cocaine, etizolam, and acetaminophen. The most frequently identified clandestine tablet was “M367,” typically manufactured to contain acetaminophen and hydrocodone, but was found to contain fentanyl or heroin. The results from this study aid in the understanding of the current opioid trends in Dallas County and provides an opportunity for comparison to other populations and geographical regions.
Discrete Painlevé system for the partition function of Nf   =  2 SU(2) supersymmetric gauge theory and its double scaling limit

We continue to study the matrix model of the Nf = 2 case that represents the irregular conformal block. What provides us with the Painlevé system is not the instanton partition function per se but rather a finite analog of its Fourier transform that can serve as a generating function. The system reduces to the extension of the Gross–Witten–Wadia unitary one-matrix model by the logarithmic potential while keeping the planar critical behavior intact. The double scaling limit to this critical point is a constructive way to study Argyres–Douglas type theory from IR. We elaborate upon the method of orthogonal polynomials and its relevance to these problems, developing it further for the case of a generic unitary matrix model and that of a special case with the logarithmic potential.
The causal consciousness: presynaptic beta-neurexin promotes neuromediator release via vibrational multidimensional tunneling

Epiphenomenalism is shown to be absurd because the development of consciousness must be explainable through natural selection. A detailed neuromolecular basis of the neuromediator release is given and it is stressed on the possible key point where the quantum mind could act, namely presynaptic scaffold protein dynamics and detachment of the calcium sensor v-SNARE synaptotagmin-1. The β -neurexin molecules are tuned via fast propagating solitons by the quantum coherent microtubule network so that the β -neurexin molecule thermal vibrations could promote or suppress conformational changes via vibrational multidimensional tunneling, which drives synaptotagmin-1 detachment from the SNARE complex under calcium ion binding. Following the synaptotagmin-1 detachment membrane fusion takes place in SNARE dependent fashion and the presynaptic vesicle spills neuromediator in the synaptic cleft. Thus the quantum transfer of information causally affects the neuromediator release. alpha-helical minimal binding domain the H3 domain syntaxin
Periodic orbits around a spherically symmetric naked singularity

The motion of time-like test particles in the Fisher/Janis-Newman-Winicour (F/JNW) spacetime is studied with the Hamiltonian formulation of the geodesic equations. The spacetime is characterised by its mass parameter $r_g$ and scalar field parameter $\nu$. The innermost bound and stable circular orbits are calculated and the effective potential is analysed. Consistent with numerical results in earlier literature, for $\nu<1/2$, particles with non-zero angular momentum encounter an infinite potential barrier, preventing them from reaching the naked singularity at $r=r_g$. Periodic orbits in the spacetime are also obtained. Compared to the periodic orbits around the Schwarzschild black hole, it is found that typically lower energies are required for the same orbits in the F/JNW spacetime.
Motor Function Characteristics of Adults With Late-Onset Pompe Disease

Background and Objectives Pompe disease is a rare neuromuscular disease caused by a deficiency of the lysosomal enzyme acid α-glucosidase. The late-onset Pompe disease (LOPD) in adults is characterized by weakness of ventilatory, axial, and proximal extremity muscles. These muscle impairments progressively impair various motor functions such as locomotion and postural control. Nearly 87% of adults with LOPD (aLOPD) report walking problems, and more than 80% report instability and falls. Knowledge of these motor functions is now sufficient to provide a clear and comprehensive overview of motor function in aLOPD. Therefore, this scoping review aimed to summarize current knowledge about motor function in aLOPD. It specifically targeted neuromuscular performance, locomotion, and postural control. Methods A systematic search in MEDLINE (through PubMed), EMBASE, and Cochrane databases was conducted until May 2021. We included studies providing primary data on at least 4 participants, exploring neuromuscular performance, locomotion, and/or postural control in aLOPD. Risk of bias analysis was assessed using tools appropriate to the study designs; the risk of bias 2 (Cochrane tool) for randomized controlled trials, risk of bias in Nonrandomized Studies - of Interventions (Cochrane tool) for nonrandomized interventional trials, and the Newcastle-Ottawa Scale for cohort studies and case-control studies. Results The search identified 2,885 articles. After screening, 58 articles were included in the analysis. In these studies, 88% explored locomotion, 83% neuromuscular performance, and 3% postural control. This review showed that aLOPD experience symmetrical weakness, concerning especially the hip and lumbar muscles. Locomotor activities are limited with a distance reduction, spatiotemporal gait parameter modification, and an increased pelvic drop and tilt. Balance disorders are also observed especially in the anteroposterior direction. Discussion We performed the first review on motor function characteristics in aLOPD. Although a significant amount of knowledge was synthesized in this review, our study also highlighted the lack of current research on this topic. Maximal muscle strength was the only neuromuscular performance studied, and gait biomechanics and postural control were poorly explored in LOPD. Relationships between the degree of muscle weakness and motor function alterations also remain to be determined in aLOPD.
Termination of the phase of quintessence by gravitational back reaction

We study the effects of gravitational back reaction in models of quintessence. The effective energy-momentum tensor with which cosmological fluctuations back react on the background metric will in some cases lead to a termination of the phase of acceleration. The fluctuations we make use of are the perturbations in our present Universe. Their amplitude is normalized by recent measurements of anisotropies in the cosmic microwave background; their slope is taken to be either scale invariant or characterized by a slightly blue tilt. In the latter case, we find that the back-reaction effect of fluctuations whose present wavelength is smaller than the Hubble radius but which are stretched beyond the Hubble radius by the accelerated expansion during the era of quintessence domination can become large. Since the back-reaction effects of these modes oppose acceleration, back reaction will lead to a truncation of the period of quintessence domination. This result impacts on the recent discussions of the potential incompatibility between string theory and quintessence.