Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Adaptive Threshold Segmentation of Ultrasound Medical Images

∗ Ultrasound Medical Image (UMI) segmentation has usually been a challenging hassle in the field of image segmentation. Since it is difficult to segment target effectively by manually adjusting parameters in UMI, the aim is to find automatic parameter selection approach for the improved adaptive threshold segmentation. This paperwork is divided into three phases to present an improved segmentation algorithm. In first pre-processing phase, Region of Interest (ROI) is extracted from UMI and converted into integral image using computational method. In second phase, the improved method finds the best optimal parameters using grid search approach for the segmentation of extracted ROI whereas third phase focuses on obtaining contours for automated and manual extracted ROI and computes the overlapping area between contours for the final re-definition of segmented results. This overlapping area is directly proportional to segmented result, more is the overlapping area better is the segmentation result. Comparison table shows that the values of the improved method gives the better segmented results as compared to similar techniques. Experimental results verify the feasibility and effectiveness of the approach in the field of medical imaging alongside computational methods.
Three-dimensional Medical Image Segmentation with SE-VNet Neural Networks

∗ In computer-aided clinical diagnosis, automatic segmentation of medical images collected from computed tomography and magnetic resonance imaging is a key and challenging task. To improve the accuracy of medical image segmentation, this paper proposes a three-dimensional medical image segmentation neural network based on a channel attention mechanism—SE-VNet. This method can realize end-to-end automatic segmentation of three-dimensional medical images and has a high accuracy rate. The experimental results on the prostate MRI data set show that the accuracy of the Dice coefficient reaches 88.7% and the Hausdorff distance reaches 5.491 mm; the experimental results on the femoral CT data set reach 93.2% and 4.175 mm. Compared with the current mainstream three-dimensional medical image segmentation methods, the method proposed in this paper greatly improves the segmentation accuracy.
Human Identification Based on Feature Extraction of Pulse Signal

Authentication methods based on Biometric feature such as fingerprint, face features have the defect of easy to be faked because of non-living detection, but physiological signals are usually not reproducible, so we proposed a method of human identification based on pulse signal in this paper. Using the average amplitude difference function algorithm (AMDF)to calculate the period of the pulse waveform after band-pass filtering. The features including pulse rate, peak points value, energy and sub-band spectrum were extracted. The sub-band spectrum features contain 9 sub-bands which are not uniformly spaced based on STFT. Build Gaussian statistic model using the above four features and classify the samples based on the maximum likelihood of joint probability density. The pulse signals of 46 subjects who ages from 20 to 70 were sampled at 1024 Hz and 20 kHz respectively by General Auto-test Platform System. The experimental results show that: at the sampling rate of 1024Hz, the recognition rate of 46 subjects is 74.19%; considering the family scenario, the average recognition rate of groups of 6 subjects can reach 99.60%.
Using clustering methods to identify different profiles based on similarity in online security and privacy attitudes

This paper examines behavior patterns related to online security and privacy attitudes from individuals across 28 European Union (EU) countries. By using the k-means clustering, the countries were assigned to three different profiles based on similarities in online security and privacy attitudes. The study revealed significant differences in online security and privacy attitudes between individuals from countries assigned to high, low and medium concerned profile. Concerns about online privacy and security of individuals in high concern profile were significantly higher compared to other two profiles, while individuals in low concern profile expressed a significantly lower level of concern about online privacy and security compared to other two profiles. A cross-national EU-based exploration and visual map-ping of attitudes was provided.
Polygenic score accuracy in ancient samples: Quantifying the effects of allelic turnover

Polygenic scores link the genotypes of ancient individuals to their phenotypes, which are often unobservable, offering a tantalizing opportunity to reconstruct complex trait evolution. In practice, however, interpretation of ancient polygenic scores is subject to numerous assumptions. For one, the genome-wide association (GWA) studies from which polygenic scores are derived, can only estimate effect sizes for loci segregating in contemporary populations. Therefore, a GWA study may not correctly identify all loci relevant to trait variation in the ancient population. In addition, the frequencies of trait-associated loci may have changed in the intervening years. Here, we devise a theoretical framework to quantify the effect of this allelic turnover on the statistical properties of polygenic scores as functions of population genetic dynamics, trait architecture, power to detect significant loci, and the age of the ancient sample. We model the allele frequencies of loci underlying trait variation using the Wright-Fisher diffusion, and employ the spectral representation of its transition density to find analytical expressions for several error metrics, including the correlation between an ancient individual’s polygenic score and true phenotype, referred to as polygenic score accuracy. Our theory also applies to a two-population scenario and demonstrates that allelic turnover alone may explain a substantial percentage of the reduced accuracy observed in cross-population predictions, akin to those performed in human genetics. Finally, we use simulations to explore the effects of recent directional selection, a bias-inducing process, on the statistics of interest. We find that even in the presence of bias, weak selection induces minimal deviations from our neutral expectations for the decay of polygenic score accuracy. By quantifying the limitations of polygenic scores in an explicit evolutionary context, our work lays the foundation for the development of more sophisticated statistical procedures to analyze both temporally and geographically resolved polygenic scores.
Hyperconcentrated Mucus Unifies Submucosal Gland and Superficial Airway Dysfunction in Cystic Fibrosis

Cystic fibrosis (CF) is characterized by abnormal transepithelial ion transport. However, a description of CF lung disease pathophysiology unifying superficial epithelial and submucosal gland (SMG) dysfunctions has remained elusive. We hypothesized that biophysical abnormalities associated with CF mucus hyperconcentration provide a unifying mechanism. Studies of the anion secretion-inhibited pig airway CF model revealed elevated SMG mucus concentrations, osmotic pressures, and SMG mucus accumulation. Human airway studies revealed hyperconcentrated CF SMG mucus with raised osmotic pressures and cohesive forces predicted to limit SMG mucus secretion/release. Utilizing proline-rich protein 4 (PRR4) as a biomarker of SMG secretion, proteomics analyses of CF sputum revealed markedly lower PRR4 levels compared to healthy and bronchiectasis controls, consistent with a failure of CF SMGs to secrete mucus onto airway surfaces. Raised mucus osmotic/cohesive forces, reflecting mucus hyperconcentration, provide a unifying mechanism that describes disease-initiating mucus accumulation on airway surfaces and within SMGs of the CF lung. One Sentence Summary Hyperconcentrated Mucus Unifies Submucosal Gland and Superficial Airway Dysfunction in Cystic Fibrosis
A method to enrich and purify centromeric DNA from human cells

Centromeres are key elements for chromosome segregation. Canonical centromeres are built over long-stretches of tandem repetitive arrays. Despite being quite abundant compared to other loci, centromere sequences overall still represent only 2 to 5% of the human genome, therefore studying their genetic and epigenetic features is a major challenge. Furthermore, sequencing of centromeric regions requires high coverage to fully analyze length and sequence variations, which can be extremely costly. To bypass these issues, we have developed a technique based on selective restriction digestion and size fractionation to enrich for centromeric DNA from human cells. Combining enzymes capable of cutting at high frequency throughout the genome, except within most human centromeres, with size-selection of >20 kb fragments resulted in over 25-fold enrichment in centromeric DNA. Sequencing of the enriched fractions revealed that up to 60% of the enriched material is made of centromeric DNA. This approach has great potential for making sequencing of centromeric DNA more affordable and efficient and for single DNA molecule studies.
An innovative optical context to make honeybees crash repeatedly

To investigate altitude control in honeybees, an optical context was designed to make honeybees crash. It has been widely accepted that honeybees rely on the optic flow generated by the ground to control their altitude. However, identifying an optical context capable of uncorrelating forward speed from altitude in honeybees’ flight was the first step towards enhancing the optical context to better understand altitude control in honeybees. This optical context aims to put honeybees in the same flight conditions as an open sky flight above mirror-smooth water. An optical manipulation, based on a pair of opposed horizontal mirrors, was designed to remove any visual information coming from the floor and ceiling. Such an optical manipulation reproduced quantitatively the seminal experiment of Heran & Lindauer (1963), and revealed that honeybees control their altitude by detecting the optic flow with a visual field that extends to approximately 165°.
Energy efficient convolutional neural networks for arrhythmia detection

Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimisation steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.
Leveling up: improving power in fMRI by moving beyond cluster-level inference

Inference in neuroimaging commonly occurs at the level of “clusters” of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the “tip of the iceberg” of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed “adequate” power (β=80%) to detect an average effect, with almost double the power for network-compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.
Idiosyncratic epistasis leads to global fitness-correlated trends

Epistasis can dramatically affect evolutionary trajectories. In recent decades, protein-level fitness landscapes have revealed extensive idiosyncratic epistasis among specific mutations. In contrast, other work has found ubiquitous and apparently non-specific patterns of global diminishing-returns and increasing-costs epistasis among mutations across the genome. Here, we use a hierarchical CRISPR gene drive system to construct all combinations of 10 missense mutations from across the genome in budding yeast, and measure their fitness in six environments. We show that the resulting fitness landscapes exhibit global fitness-correlated trends, but that these trends emerge from specific idiosyncratic interactions. This provides the first experimental validation of recent theoretical work that has argued that fitness-correlated trends can emerge as the generic consequence of idiosyncratic epistasis. One-Sentence Summary A genome-spanning fitness landscape reveals how idiosyncratic genetic interactions lead to global epistatic patterns.
Personalizing the control law of an upper-limb exoskeleton using EMG signal

Implementing an intuitive control law for an upper-limb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The goal of this study is to adapt an EMG-based control system to a user based on individual caracteristics. To this aim, a method has been designed to tune the parameters of control using objective criteria, improving user’s feedback. The user’s response time is used as an objective value to adapt the gain of the controller. The proposed approach was tested on 10 participants during a lifting task. Two different conditions have been used to control the exoskeleton: with a generic gain and with a personalized gain. EMG signals was captured on five muscles to evaluate the efficiency of the conditions and the user’s adaptation. Results showed a statistically significant reduction of mean muscle activity of the deltoid between the beginning and the end of each situation (28.6 ± 13.5% to 17.2 ± 7.3% of Relative Maximal Contraction for the generic gain and from 24.9 ± 8.5% to 18.0 ± 6.8% of Relative Maximal Contraction for the personalized gain). When focusing on the first assisted movements, the personalized gain induced a mean activity of the deltoïd significantly lower (29.0 ± 8.0% of Relative Maximal Contraction and 37.4 ± 9.5% of Relative Maximal Contraction, respectively). Subjective evaluation showed that the system with a personalized gain was perceived as more intuitive, and required less concentration when compared to the system with a generic gain.
Pericyte-mediated constriction of renal capillaries evokes no-reflow and kidney injury following ischemia

Acute kidney injury is common, with ∼13 million cases and 1.7 million deaths/year worldwide. A major cause is renal ischemia, typically following cardiac surgery, renal transplant or severe hemorrhage. We examined the cause of the sustained reduction in renal blood flow (“no-reflow”), which exacerbates kidney injury even after an initial cause of compromised blood supply is removed. After 60 min kidney ischemia and 30-60 min reperfusion, renal blood flow remained reduced, especially in the medulla, and kidney tubule damage was detected as Kim-1 expression. Constriction of the medullary descending vasa recta and cortical peritubular capillaries occurred near pericyte somata, and led to capillary blockages, yet glomerular arterioles and perfusion were unaffected, implying that the long-lasting decrease of renal blood flow contributing to kidney damage was generated by pericytes. Blocking Rho kinase to decrease pericyte contractility from the start of reperfusion increased the post-ischemic diameter of the descending vasa recta capillaries at pericytes, reduced the percentage of capillaries that remained blocked, increased medullary blood flow and reduced kidney injury. Thus, post-ischemic renal no-reflow, contributing to acute kidney injury, reflects pericytes constricting the descending vasa recta and peritubular capillaries. Pericytes are therefore an important therapeutic target for treating acute kidney injury.
Reconstitution and use of highly active human CDK1:Cyclin-B:CKS1 complexes

As dividing cells transition into mitosis, hundreds of proteins are phosphorylated by a complex of cyclin-dependent kinase 1 (CDK1) and Cyclin-B, often at multiple sites. CDK1:Cyclin-B phosphorylation patterns alter conformations, interaction partners, and enzymatic activities and need to be recapitulated in vitro for the structural and functional characterization of the mitotic protein machinery. This requires a pure and active recombinant kinase complex. The kinase activity of CDK1 critically depends on the phosphorylation of a Threonine residue in its activation loop by a CDK1 activating kinase (CAK). We developed protocols to activate CDK1:Cyclin-B either in vitro with purified CDK1 activating kinases (CAK) or in insect cells through CDK-CAK co-expression. To boost kinase processivity, we reconstituted a tripartite complex consisting of CDK1, Cyclin-B, and CKS1. In this work, we provide and compare detailed protocols to obtain and use highly active CDK1:Cyclin-B (CC) and CDK1:Cyclin-B:CKS1 (CCC).
Characterization of Cervical-Cranial Muscle Network in Correlation with Vocal Features

Objective evaluation of physiological responses using non-invasive methods has attracted great interest regarding the assessment of vocal performance and disorders. This paper, for the first time, demonstrates that the topographical features of the cervical-cranial intermuscular coherence network generated using surface electromyography (sEMG) have a strong potential for detecting subtle changes in vocal performance. For this purpose, in this paper, 12 sEMG signals were collected from six cervical and cranial muscles bilaterally. Data were collected from four subjects without a history of a voice disorder performing a series of vocal tasks. The vocal tasks were varied phonation (an /a/ sustained for the maximal duration with combinations of two levels of loudness and two levels of pitch), a pitch glide from low to high, singing a familiar song, spontaneous speech, and reading with different loudness levels. The varied phonation tasks showed the median degree, and weighted clustering coefficient of the coherence-based intermuscular network ascends monotonically, with a high effect size (|rrb| = 0.52). The set of tasks, including pitch glide, singing, and speech, was significantly distinguishable using the network features as both degree and weighted clustering coefficient had a very high effect size (|rrb| > 0.83) across these tasks. Also, pitch glide has the highest degree and weighted clustering coefficient among all tasks (degree > 0.6, weighted clustering coefficient > 0.6). Spectrotemporal features performed far less effective than the proposed functional muscle network metrics to differentiate the vocal tasks. The highest effect size for spectrotemporal features was only |rrb| = 0.19. In this paper, for the first time, the power of a cervical-cranial muscle network has been demonstrated as a neurophysiological window to vocal performance. The results also shed light on the tasks with the highest network involvement, which may be potentially used in monitoring vocal disorders and tracking rehabilitation progress.
FSTruct: An F ST‐based tool for measuring ancestry variation in inference of population structure

In model-based inference of population structure from individual-level genetic data, individuals are assigned membership coefficients in a series of statistical clusters generated by clustering algorithms. Distinct patterns of variability in membership coefficients can be produced for different groups of individuals, for example, representing different predefined populations, sampling sites, or time periods. Such variability can be difficult to capture in a single numerical value; membership coefficient vectors are multivariate and potentially incommensurable across groups, as the number of clusters over which individuals are distributed can vary among groups of interest. Further, two groups might share few clusters in common, so that membership coefficient vectors are concentrated on different clusters. We introduce a method for measuring the variability of membership coefficients of individuals in a predefined group, making use of an analogy between variability across individuals in membership coefficient vectors and variation across populations in allele frequency vectors. We show that in a model in which membership coefficient vectors in a population follow a Dirichlet distribution, the measure increases linearly with a parameter describing the variance of a specified component of the membership vector. We apply the approach, which makes use of a normalized FST statistic, to data on inferred population structure in three example scenarios. We also introduce a bootstrap test for equivalence of two or more groups in their level of membership coefficient variability. Our methods are implemented in the R package FSTruct.
Elevated alpha diversity in disturbed sites obscures regional decline and homogenization of amphibian diversity

Loss of natural habitat is one of the major threats for biodiversity worldwide. Habitat conversion not only changes diversity and species composition locally (alpha diversity) but might also lead to large-scale homogenization of species communities and decrease in regional species richness (gamma diversity). We investigated the effect of farmland conversion on amphibian communities in Rwanda and compared local and regional (country-wide) taxonomic, functional and phylogenetic diversity between natural and farmland sites (agricultural marais). Alpha diversity was higher in the disturbed farmland than in natural sites. However, species turnover among farmland sites was much lower than among natural sites, resulting in highly homogenized amphibian communities and much lower country-wide taxonomic, functional and phylogenetic gamma diversity in farmland compared to natural sites. The few frog species found in farmland were mostly disturbance-tolerant species that are widespread in Eastern Africa and beyond. In contrast, most of the regionally endemic frog species that make this region a continent-scale hotspot of amphibian diversity were found only in the natural habitats. Ongoing farmland conversion might lead to a loss of regional endemism and a widespread homogenization of species communities across sub-Saharan Africa.