Miracle-dz/UniFAR-FTUs huggingface dataset:
Swelling Driven Crack Propagation in Large Deformation in Ionized Hydrogel

Stepwise crack propagation is evidently observed in experiments both in geomaterials and in hydrogels. Pizzocolo et al. (2012, "Mode I Crack Propagation in Hydrogels is Step Wise," Eng. Fract. Mech., 97(1), pp. 72-79) show experimental evidence that mode I crack propagation in hydrogel is stepwise. The pattern of the intermittent crack growth is influenced by many factors, such as porosity of the material, the permeability of the fluid, the stiffness of the material, etc. The pause duration time is negatively correlated with the stiffness of the material, while the average propagation length per step is positively correlated. In this paper, we integrate extended finite element method (XFEM) and enhanced local pressure (ELP) method, and incorporate cohesive relation to reproduce the experiments of Pizzocolo et al. in the finite deformation regime. We investigate the stepwise phenomenon in air and in water, respectively, under mode I fracture. Our simulations show that despite the homogeneous material properties, the crack growth under mode I fracture is stepwise, and this pattern is influenced by the hydraulic permeability and the porosity of the material. Simulated pause duration is negatively correlated with stiffness, and the average propagating length is positively correlated with stiffness. In order to eliminate the numerical artifacts, we also take different time increments into consideration. The staccato propagation does not disappear with smaller time increments, and the pattern is approximately insensitive to the time increment. However, we do not observe stepwise crack growth scheme when we simulate fracture in homogeneous rocks.
Arithmetic problem size modulates brain activations in females but not in males

Numerous empirical studies have reported that males and females perform equally well in mathematical achievement. However, still to date, very limited is understood about the brain response profiles that are particularly characteristic of males and females when solving mathematical problems. The present study aimed to tackle this issue by manipulating arithmetic problem size to investigate functional significance using functional magnetic resonance imaging (fMRI) in young adults. Participants were instructed to complete two runs of simple calculation tasks with either large or small problem sizes. Behavioural results suggested that the performance did not differ between females and males. Neuroimaging data revealed that sex/gender‐related patterns of problem size effect were found in the brain regions that are conventionally associated with arithmetic, including the left middle frontal gyrus (MFG), left intraparietal sulcus (IPS) and insula. Specifically, females demonstrated substantial brain responses of problem size effect in these regions, whereas males showed marginal effects. Moreover, the machine learning method implemented over the brain signal levels within these regions demonstrated that sex/gender is discriminable. These results showed sex/gender effects in the activating patterns varying as a function of the distinct math problem size, even in a simple calculation task. Accordingly, our findings suggested that females and males use two complementary brain resources to achieve equally successful performance levels and highlight the pivotal role of neuroimaging facilities in uncovering neural mechanisms that may not be behaviourally salient.
Single‐subject electroencephalography measurement of interhemispheric transfer time for the in‐vivo estimation of axonal morphology

Assessing axonal morphology in vivo opens new avenues for the combined study of brain structure and function. A novel approach has recently been introduced to estimate the morphology of axonal fibers from the combination of magnetic resonance imaging (MRI) data and electroencephalography (EEG) measures of the interhemispheric transfer time (IHTT). In the original study, the IHTT measures were computed from EEG data averaged across a group, leading to bias of the axonal morphology estimates. Here, we seek to estimate axonal morphology from individual measures of IHTT, obtained from EEG data acquired in a visual evoked potential experiment. Subject‐specific IHTTs are computed in a data‐driven framework with minimal a priori constraints, based on the maximal peak of neural responses to visual stimuli within periods of statistically significant evoked activity in the inverse solution space. The subject‐specific IHTT estimates ranged from 8 to 29 ms except for one participant and the between‐session variability was comparable to between‐subject variability. The mean radius of the axonal radius distribution, computed from the IHTT estimates and the MRI data, ranged from 0 to 1.09 μm across subjects. The change in axonal g‐ratio with axonal radius ranged from 0.62 to 0.81 μm−α. The single‐subject measurement of the IHTT yields estimates of axonal morphology that are consistent with histological values. However, improvement of the repeatability of the IHTT estimates is required to improve the specificity of the single‐subject axonal morphology estimates.
SCN1A-deficient excitatory neuronal networks display mutation-specific phenotypes

Abstract Dravet syndrome is a severe epileptic encephalopathy, characterized by (febrile) seizures, behavioural problems and developmental delay. Eighty per cent of patients with Dravet syndrome have a mutation in SCN1A, encoding Nav1.1. Milder clinical phenotypes, such as GEFS+ (generalized epilepsy with febrile seizures plus), can also arise from SCN1A mutations. Predicting the clinical phenotypic outcome based on the type of mutation remains challenging, even when the same mutation is inherited within one family. This clinical and genetic heterogeneity adds to the difficulties of predicting disease progression and tailoring the prescription of anti-seizure medication. Understanding the neuropathology of different SCN1A mutations may help to predict the expected clinical phenotypes and inform the selection of best-fit treatments. Initially, the loss of Na+-current in inhibitory neurons was recognized specifically to result in disinhibition and consequently seizure generation. However, the extent to which excitatory neurons contribute to the pathophysiology is currently debated and might depend on the patient clinical phenotype or the specific SCN1A mutation. To examine the genotype-phenotype correlations of SCN1A mutations in relation to excitatory neurons, we investigated a panel of patient-derived excitatory neuronal networks differentiated on multi-electrode arrays. We included patients with different clinical phenotypes, harbouring various SCN1A mutations, along with a family in which the same mutation led to febrile seizures, GEFS+ or Dravet syndrome. We hitherto describe a previously unidentified functional excitatory neuronal network phenotype in the context of epilepsy, which corresponds to seizurogenic network prediction patterns elicited by proconvulsive compounds. We found that excitatory neuronal networks were affected differently, depending on the type of SCN1A mutation, but did not segregate according to clinical severity. Specifically, loss-of-function mutations could be distinguished from missense mutations, and mutations in the pore domain could be distinguished from mutations in the voltage sensing domain. Furthermore, all patients showed aggravated neuronal network responses at febrile temperatures compared with controls. Finally, retrospective drug screening revealed that anti-seizure medication affected GEFS+ patient- but not Dravet patient-derived neuronal networks in a patient-specific and clinically relevant manner. In conclusion, our results indicate a mutation-specific excitatory neuronal network phenotype, which recapitulates the foremost clinically relevant features, providing future opportunities for precision therapies.
Antigenic and molecular characterization of isolates of the Brazilian genotype BR-I (GI-11) of infectious bronchitis virus supports its recognition as BR-I serotype

ABSTRACT The antigenic and molecular characteristics of BR-I infectious bronchitis viruses (IBVs) isolated from Brazil are reported. IBVs isolated from commercial flocks with different clinical manifestations between 2003 and 2019 were submitted to antigenic and molecular characterization. The complete S1 glycoprotein gene of 11 field isolates was amplified and sequenced. The virus neutralization (VN) test showed 94.75% neutralization with a BR-I isolate and 30% or less against other worldwide reference strains. The nucleotide and amino acid sequence analyses revealed 84.3–100% and 83.5–100% identity among them, respectively. The identity values ranged from 57.1 to 82.6% for nucleotides and from 46.6–84.4% for amino acids compared with those of other genotypes. By phylogenetic tree analysis, the Brazilian isolates were branched into the BR-I genotype (lineage GI-11), which was differentiated from foreign reference strains. Selective pressure analyses of BR-I IBVs revealed evolution under purifying selection (negative pressure) for the complete S1 gene but four specific sites (87, 121, 279, and 542) under diversifying selection (positive pressure). Profiles of cleavage sites and potential N-glycosylation sites differed from those of other genotypes. The low molecular relationship among the Brazilian viruses and foreign serotypes was concordant with the VN test results. The low antigenic relatedness (ranging from 5.3–30% between Brazilian genotype BR-I and reference IBV serotypes of North America, Europe, and Asia) indicates that the BR-I genotype is a different serotype, referred to for the first time and hereafter as serotype BR-I. RESEARCH HIGHLIGHTS Strains of the BR-I genotype presented robust antigenic and molecular similarity. BR-I strains evolved under purifying selection mode (negative pressure). The BR-I genotype originated in Brazil and dispersed to other countries. BR-I genotype viruses can be referred to as the BR-I serotype.
Analysis and Prediction of the Audio Feature Space when Mixing Raw Recordings into Individual Stems

Processing individual stems from raw recordings is one of the ﬁrst steps of multitrack audio mixing. In this work, we explore which set of low-level audio features are sufﬁcient to design a prediction model for this transformation. We extract a large set of audio features from bass, guitar, vocal and keys raw recordings and stems. We show that a procedure based on random forests classiﬁers can lead us to reduce signiﬁcantly the number of features and we use the selected audio features to train various multi-output regression models. Thus, we investigate stem processing as a content-based transformation, where the inherent content of raw recordings leads us to predict the change of feature values that occurred within the transformation.
Structure preserving clustering-object tracking via subgroup motion pattern segmentation

Abstract. Tracking clustering objects with similar appearances simultaneously in collective scenes is a challenging task in the field of collective motion analysis. Recent work on clustering-object tracking often suffers from poor tracking accuracy and terrible real-time performance due to the neglect or the misjudgment of the motion differences among objects. To address this problem, we propose a subgroup motion pattern segmentation framework based on a multilayer clustering structure and establish spatial constraints only among objects in the same subgroup, which entails having consistent motion direction and close spatial position. In addition, the subgroup segmentation results are updated dynamically because crowd motion patterns are changeable and affected by objects’ destinations and scene structures. The spatial structure information combined with the appearance similarity information is used in the structure preserving object tracking framework to track objects. Extensive experiments conducted on several datasets containing multiple real-world crowd scenes validate the accuracy and the robustness of the presented algorithm for tracking objects in collective scenes.
Melanoma in infants, caused by a gene fusion involving the anaplastic lymphoma kinase (ALK)

We describe the first cases of pediatric melanoma with ALK fusion gene arising within giant congenital melanocytic nevi. Two newborn boys presented with large pigmented nodular plaques and numerous smaller satellite nevi. Additional expansile nodules developed within both nevi and invasive melanomas were diagnosed before 10 months of age in both boys. Oncogenic driver mutations in NRAS and BRAF were absent in both cases. Instead, oncogenic ZEB2::ALK fusion genes were identified in both the nevus and melanoma developing within the nevus. In both cases, tumors were noted by ultrasound in utero, demonstrated significant nodularity at birth, and progressed to melanoma in the first year of life suggesting that congenital nevi with ALK fusion genes may behave more aggressively than those with other mutations. As ALK kinase inhibitors are effective against a range of tumors with similar ALK fusion kinases, identifying ALK fusion genes in congenital melanocytic nevi may provide an opportunity for targeted therapy.
Fractal complexity alternations in paroxysmal atrial fibrillation patients with and without recurrence after pulmonary vein isolation

Pulmonary vein isolation (PVI) is a cornerstone therapy for paroxysmal atrial fibrillation (PAF). The variations in nonlinear heart rate variability (HRV) between patients with and without recurrences remain unclear. We aimed to characterize the nonlinear HRV before and after PVI in patients with and without recurrence.
Functional magnetic resonance imaging study during resting state and visual oddball task in mild cognitive impairment

Abstract Background Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and dementia, and identifying early biomarkers is crucial for disease detection and intervention. Functional magnetic resonance imaging (fMRI) has the potential to identify changes in neural activity in MCI. Methods We investigated neural activity changes in the visual network of the aMCI patients (n:20) and healthy persons (n:17) using resting‐state fMRI and visual oddball task fMRI. We used independent component analysis to identify regions of interest and compared the activity between groups using a false discovery rate correction. Results Resting‐state fMRI revealed increased activity in the areas that have functional connectivity with the visual network, including the right superior and inferior lateral occipital cortex, the right angular gyrus and the temporo‐occipital part of the right middle temporal gyrus (p‐FDR = 0.008) and decreased activity in the bilateral thalamus and caudate nuclei, which are part of the frontoparietal network in the aMCI group (p‐FDR = 0.002). In the visual oddball task fMRI, decreased activity was found in the right frontal pole, the right frontal orbital cortex, the left superior parietal lobule, the right postcentral gyrus, the right posterior part of the supramarginal gyrus, the right superior part of the lateral occipital cortex, and the right angular gyrus in the aMCI group. Conclusion Our results suggest the alterations in the visual network are present in aMCI patients, both during resting‐state and task‐based fMRI. These changes may represent early biomarkers of aMCI and highlight the importance of assessing visual processing in cognitive impairment. However, future studies with larger sample sizes and longitudinal designs are needed to confirm these findings.
Challenges and current advances in in vitro biofilm characterization

Biofilms are structured communities of bacterial cells encased in a self‐produced polymeric matrix, which develop over time and exhibit temporal responses to stimuli from internal biological processes or external environmental changes. They can be detrimental, threatening public health and causing economic loss, while they also play beneficial roles in ecosystem health, biotechnology processes, and industrial settings. Biofilms express extreme heterogeneity in their physical properties and structural composition, resulting in critical challenges in understanding them comprehensively. The lack of detailed knowledge of biofilms and their phenotypes has deterred significant progress in developing strategies to control their negative impacts and take advantage of their beneficial applications. A range of in vitro models and characterization tools have been developed and used to study biofilm growth and, specifically, to investigate the impact of environmental and growth factors on their development. This review article discusses the existing knowledge of biofilm properties and explains how external factors, such as flow condition, surface, interface, and host factor, may impact biofilm growth. The limitations of current tools, techniques, and in vitro models that are currently used for biofilms are also presented.
EGFR/IGF1R Signaling Modulates Relaxation in Hypertrophic Cardiomyopathy

BACKGROUND: Diastolic dysfunction is central to diseases such as heart failure with preserved ejection fraction and hypertrophic cardiomyopathy (HCM). However, therapies that improve cardiac relaxation are scarce, partly due to a limited understanding of modulators of cardiomyocyte relaxation. We hypothesized that cardiac relaxation is regulated by multiple unidentified proteins and that dysregulation of kinases contributes to impaired relaxation in patients with HCM. METHODS: We optimized and increased the throughput of unloaded shortening measurements and screened a kinase inhibitor library in isolated adult cardiomyocytes from wild-type mice. One hundred fifty-seven kinase inhibitors were screened. To assess which kinases are dysregulated in patients with HCM and could contribute to impaired relaxation, we performed a tyrosine and global phosphoproteomics screen and integrative inferred kinase activity analysis using HCM patient myocardium. Identified hits from these 2 data sets were validated in cardiomyocytes from a homozygous MYBPC3c.2373insG HCM mouse model. RESULTS: Screening of 157 kinase inhibitors in wild-type (N=33) cardiomyocytes (n=24 563) resulted in the identification of 17 positive inotropes and 21 positive lusitropes, almost all of them novel. The positive lusitropes formed 3 clusters: cell cycle, EGFR (epidermal growth factor receptor)/IGF1R (insulin-like growth factor 1 receptor), and a small Akt (α-serine/threonine protein kinase) signaling cluster. By performing phosphoproteomic profiling of HCM patient myocardium (N=24 HCM and N=8 donors), we demonstrated increased activation of 6 of 8 proteins from the EGFR/IGFR1 cluster in HCM. We validated compounds from this cluster in mouse HCM (N=12) cardiomyocytes (n=2023). Three compounds from this cluster were able to improve relaxation in HCM cardiomyocytes. CONCLUSIONS: We showed the feasibility of screening for functional modulators of cardiomyocyte relaxation and contraction, parameters that we observed to be modulated by kinases involved in EGFR/IGF1R, Akt, cell cycle signaling, and FoxO (forkhead box class O) signaling, respectively. Integrating the screening data with phosphoproteomics analysis in HCM patient tissue indicated that inhibition of EGFR/IGF1R signaling is a promising target for treating impaired relaxation in HCM.
Data Classification Based on GEPSVM using Backtracking Search Algorithm

Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) is an extremely fast and simple algorithm for generating linear and nonlinear classifiers. Kernel functions are essential in fitting GEPSVM. Usually a single kernel is used by most researchers in their studies, but the real world applications may require a combination of multiple kernel functions. There are two kind of kernels which known as global and local kernels. Global kernel functions have good generalization ability, but low learning ability. Local kernel functions have good learning ability with weak generalization. The presented approach constructs a mixed kernel function with better performance by fully combining local kernel function for strong learning ability and global kernel function for strong generalization. The Backtracking Search Algorithm (BSA) is used for determining the best value of the weight parameter between the two kernels. To evaluate the performance of the proposed approach, we applied it to public datasets from UCI repository.
The added value of abnormal regional myocardial function for risk prediction in arrhythmogenic right ventricular cardiomyopathy

Abstract Aims A risk calculator for individualized prediction of first-time sustained ventricular arrhythmia (VA) in arrhythmogenic right ventricular cardiomyopathy (ARVC) patients has recently been developed and validated (www.ARVCrisk.com). This study aimed to investigate whether regional functional abnormalities, measured by echocardiographic deformation imaging, can provide additional prognostic value. Methods and results From two referral centres, 150 consecutive patients with a definite ARVC diagnosis, no prior sustained VA, and an echocardiogram suitable for deformation analysis were included (aged 41 ± 17 years, 50% female). During a median follow-up of 6.3 (interquartile range 3.1–9.8) years, 37 (25%) experienced a first-time sustained VA. All tested left and right ventricular (LV and RV) deformation parameters were univariate predictors for first-time VA. While LV function did not add predictive value in multivariate analysis, two RV deformation parameters did; RV free wall longitudinal strain and regional RV deformation patterns remained independent predictors after adjusting for the calculator-predicted risk [hazard ratio 1.07 (95% CI 1.02–1.11); P = 0.004 and 4.45 (95% CI 1.07–18.57); P = 0.040, respectively] and improved its discriminative value (from C-statistic 0.78 to 0.82 in both; Akaike information criterion change > 2). Importantly, all patients who experienced VA within 5 years from the echocardiographic assessment had abnormal regional RV deformation patterns at baseline. Conclusions This study showed that regional functional abnormalities measured by echocardiographic deformation imaging can further refine personalized arrhythmic risk prediction when added to the ARVC risk calculator. The excellent negative predictive value of normal RV deformation could support clinicians considering the timing of implantable cardioverter defibrillator implantation in patients with intermediate arrhythmic risk.