Miracle-dz/UniFAR-FTUs huggingface dataset:
A CNN for homogneous Riemannian manifolds with applications to Neuroimaging

Convolutional neural networks are ubiquitous in Machine Learning applications for solving a variety of problems. They however can not be used in their native form when the domain of the data is commonly encountered manifolds such as the sphere, the special orthogonal group, the Grassmanian, the manifold of symmetric positive definite matrices and others. Most recently, generalization of CNNs to data domains such as the 2-sphere has been reported by some research groups, which is referred to as the spherical CNNs (SCNNs). The key property of SCNNs distinct from CNNs is that they exhibit the rotational equivariance property that allows for sharing learned weights within a layer. In this paper, we theoretically generalize the CNNs to Riemannian homogeneous manifolds, that include but are not limited to the aforementioned example manifolds. Our key contributions in this work are: (i) A theorem stating that linear group equivariance systems are fully characterized by correlation of functions on the domain manifold and vice-versa. This is fundamental to the characterization of all linear group equivariant systems and parallels the widely used result in linear system theory for vector spaces. (ii) As a corrolary, we prove the equivariance of the correlation operation to group actions admitted by the input domains which are Riemannian homogeneous manifolds. (iii) We present the first end-to-end deep network architecture for classification of diffusion magnetic resonance image (dMRI) scans acquired from a cohort of 44 Parkinson Disease patients and 50 control/normal subjects. (iv) A proof of concept experiment involving synthetic data generated on the manifold of symmetric positive definite matrices is presented to demonstrate the applicability of our network to other types of domains.
Fat-to-Muscle Ratio: A New Anthropometric Indicator as a Screening Tool for Metabolic Syndrome in Young Colombian People

Fat-to-muscle ratio has been proposed as an alternative approach for assessing body fat. The objective of this study was to explore fat-to-muscle ratio thresholds in metabolic syndrome (MetS) diagnosis; it was hypothesised that the fat-to-muscle ratio is a good predictive indicator of MetS in a large population of young Colombian adults. For this purpose, a cross-sectional study was conducted on 1416 subjects (66.6% female), aged from 18.1 to 25.1. As part of the study, measurements of the subjects’ anthropometric indicators, serum lipid indices, blood pressure, and fasting plasma glucose were taken. Body composition was measured through bioelectrical impedance analysis (BIA). A new variable (ratio of fat mass to muscle mass, in kg) was calculated. Following the International Diabetes Federation (IDF) definition, MetS includes three or more metabolic abnormalities. Receiver operating characteristic (ROC) curves and logistic regression determined the discriminatory ability of the fat-to-muscle ratio to predict MetS. According to the IDF, the best fat-to-muscle ratio cut-off point for detecting MetS in men was 0.225 kg, with an area under the curve (AUC) of 0.83, sensitivity of 80%, and specificity of 70%. For women, the fat-to-muscle ratio cut-off point was 0.495 kg, the AUC was 0.88, and the sensitivity and specificity were 82% and 80%, respectively. In conclusion, our results showed that the fat-to-muscle ratio cut-off points from ROC analyses demonstrate good discriminatory power for detecting MetS in young Colombian adults.
Clinical Outcomes Associated With Linezolid Resistance in Leukemia Patients With Linezolid-Resistant Staphylococcus epidermidis Bacteremia

Abstract Background Coagulase-negative staphylococci, including Staphylococcus epidermidis, are the most common cause of bloodstream infection in cancer patients. Linezolid resistance is increasingly identified in S. epidermidis, but whether such resistance alters the clinical course of S. epidermidis infections is unknown. The purpose of this study was to assess the clinical impact of linezolid resistance in leukemia patients with S. epidermidis bloodstream infection. Methods This was a retrospective, single-center cohort study of all adult leukemia patients with S. epidermidis bacteremia treated with empiric linezolid between 2012 and 2015. The primary end point was adverse clinical outcome on day 3, defined as a composite of persistent bacteremia, fever, intensive care unit admission, or death. Fourteen- and 30-day mortality were also assessed. Results Eighty-two unique leukemia patients with S. epidermidis were identified. Linezolid resistance was identified in 33/82 (40%). Patients with linezolid-resistant S. epidermidis were significantly more likely to have persistent bacteremia (41% vs 7%; adjusted relative risk [aRR], 5.15; 95% confidence interval [CI], 1.63–16.30; P = .005); however, adverse short-term clinical outcomes overall were not more common among patients with linezolid-resistant S. epidermidis (61% vs 33%; aRR, 1.46; 95% CI, 0.92–2.32; P = .108). No differences were observed in 14- or 30-day mortality. Conclusions Leukemia patients with linezolid-resistant S. epidermidis bacteremia who were treated with linezolid were significantly more likely to have persistent bacteremia compared with those with linezolid-sensitive isolates. Interventions to limit the clinical impact of linezolid-resistant S. epidermidis are warranted.
Global prevalence of resistant hypertension: a meta-analysis of data from 3.2 million patients

Objective We conducted the first systematic review and meta-analysis to estimate the specific prevalence of apparent treatment-resistant, pseudo-resistant and true-resistant hypertension among treated patients with hypertension globally. Methods We conducted a search in PubMed, EMBASE, Web of Science and Global Index Medicus to identify articles published from inception to 30 September 2017, and searched the reference list of retrieved articles. We used a random-effects model to estimate the prevalence of resistant hypertension across studies and heterogeneity was assessed via the χ² test on Cochran’s Q statistic. Results We included 91 studies published between 1991 and 2017 reporting data of a pooled sample of 3 207 911 patients with hypertension on antihypertensive drugs globally. Most of the studies (n=64, 70%) only used office blood pressure (BP) measurement. In the general, population of treated patients with hypertension, the prevalence of true-resistant, apparent treatment-resistant and pseudo-resistant hypertension were 10.3% (95% CI 7.6% to 13.2%), 14.7% (95% CI 13.1% to 16.3%) and 10.3% (95% CI 6.0% to 15.5%). The prevalence of true-resistant hypertension was 22.9% (95% CI 19.1% to 27.0%), 56.0% (95% CI 52.7% to 59.3%) and 12.3% (95% CI 1.7% to 30.5%) in chronic kidney disease, renal transplant and elderly patients, respectively. Conclusions This study shows a high prevalence of true-resistant hypertension. This prevalence is lower than that of apparent treatment-resistant hypertension, demonstrating the importance to exclude causes of pseudo-resistant hypertension including white-coat hypertension with the use of ambulatory BP measurement. The burden of resistant hypertension is highest in patients with chronic kidney disease. New treatments for resistant hypertension are highly needed, considering the disastrous complications of the disease.
Heterologous Production of Microbial Ribosomally Synthesized and Post-translationally Modified Peptides

Ribosomally synthesized and post-translationally modified peptides, or RiPPs, which have mainly isolated from microbes as well as plants and animals, are an ever-expanding group of peptidic natural products with diverse chemical structures and biological activities. They have emerged as a major category of secondary metabolites partly due to a myriad of microbial genome sequencing endeavors and the availability of genome mining software in the past two decades. Heterologous expression of RiPP gene clusters mined from microbial genomes, which are often silent in native producers, in surrogate hosts such as Escherichia coli and Streptomyces strains can be an effective way to elucidate encoded peptides and produce novel derivatives. Emerging strategies have been developed to facilitate the success of the heterologous expression by targeting multiple synthetic biology levels, including individual proteins, pathways, metabolic flux and hosts. This review describes recent advances in heterologous production of RiPPs, mainly from microbes, with a focus on E. coli and Streptomyces strains as the surrogate hosts.
Hyperacute liver injury following intravenous fluconazole: A rare case of dose-independent hepatotoxicity

Fluconazole is a triazole antifungal medication used in the treatment of various fungal infections. It is available in both oral and parenteral formulations. Liver damage has been reported with fluconazole use, but most commonly it is benign elevated liver transaminases. Acute liver failure (ALF) in fluconazole use is rare, with cases being reported sporadically in literature and large cohorts describing incidence rates of acute liver injury ranging from 0.0 to 31.6/10,000 patients. We present a case of a 45-year-old African-American male with no history of liver disease who presented with superficial candidiasis and superimposed bacterial cellulitis. He was subsequently started on intravenous fluconazole and clindamycin. Shortly after he developed ALF and a drug-induced liver injury (DILI) was suspected. Fluconazole was stopped, and the clinical picture improved shortly afterward, leading to a diagnosis of fluconazole-induced ALF. Patient underwent laboratory and clinical evaluation to exclude competing etiologies of liver injury as well as a standardized assessment for causality and disease severity such as Roussel Uclaf Causality Assessment Method/Council for International Organizations of Medical Sciences score, which concluded a “Highly Probable” DILI, and a Naranjo score identifying adverse drug reaction (ADR) which concluded a “Definite ADR. ” Due to the severity of ALF and the routine use of fluconazole in clinical practice, clinicians should be aware that fluconazole can be a causative agent of ALF, even in low-risk populations.
Blockchain and IoT Integration: A Systematic Survey

The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its “security by design,” Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy.
Word Familiarity Modulated the Effects of Category Familiarity on Memory Performance

Previous studies have shown that prior knowledge can have both enhancing and detrimental effects on memory for relevant information. Few studies have explored the boundary conditions under which prior knowledge facilitates or interferes with memory processes. In addition, to what extent the effects of prior knowledge change over time is unclear. In this study, we addressed this question by separating category familiarity (i.e., prior conceptual knowledge) and stimulus familiarity at different retention intervals. Participants were tested with a recognition task after they learned four types of words, that is. , familiar words from familiar categories (FwordFcate) and unfamiliar categories (FwordUcate) as well as unfamiliar words from familiar (UwordFcate) and unfamiliar categories (UwordUcate). The results showed a significant interaction between category familiarity and word familiarity, that is, unfamiliar words, but not familiar words, from familiar categories were remembered better than those from unfamiliar categories. The enhancing effect of category familiarity depended on the recollection process and remained stable over time. This study suggested that stimulus familiarity modulates the effects of category familiarity on memory performance, and clarified the boundary conditions for the effects of prior knowledge.
Rethinking PCA for Modern Data Sets: Theory, Algorithms, and Applications

The papers in this special issue introduce the reader to the theory, algorithms, and applications of principal component analysis (PCA) and its many extensions. The aim of PCA is to reduce the dimensionality of multivariate data while preserving as much of the relevant information as possible. It is often the first step in various types of exploratory data analysis, predictive modeling, and classification and clustering tasks, and finds applications in biomedical imaging, computer vision, process fault detection, recommendation systems’ design, and many more domains.
Feeding regimen modulates zebrafish behavior

Here we show that the feeding regimen modulates zebrafish (Danio rerio) behavior. With regard to the time elapsed between feeding and behavioral evaluation, fish fed 3 h before behavioral evaluation in the novel tank test (NTT) showed decreased activity and a trend toward an anxiolytic reaction (increased use of the upper section of the aquarium) in comparison to fish fed 0.5, 6, 12, 24 or 48 h before testing, although differences were not statistically significant for all comparisons. Activity and use of the upper section of the aquarium did not differ significantly among the other treatments. Regarding feeding frequency, fish fed once a day showed higher anxiety-like behavior (decreased use of the upper section of the aquarium) in comparison to fish fed twice a day, but feeding four or six times per day or only every second day did not result in differences from feeding twice a day. Feeding frequency had no effect on activity level. Metabolically, fish fed once a day presented decreased levels of glucose and glycogen and increased lactate when compared to the regular feeding (fish fed twice a day), suggesting that feeding regimen may modulate carbohydrate metabolism. Mechanistically, we suggest that the metabolic changes caused by the feeding regimen may induce behavioral changes. Our results suggest that the high variability of the results among different laboratories might be related to different feeding protocols. Therefore, if issues pertaining to the feeding regimen are not considered during experiments with zebrafish, erroneous interpretations of datasets may occur.
Substantial Epigenetic Variation Causing Flower Color Chimerism in the Ornamental Tree Prunus mume Revealed by Single Base Resolution Methylome Detection and Transcriptome Sequencing

Epigenetic changes caused by methylcytosine modification participate in gene regulation and transposable element (TE) repression, resulting in phenotypic variation. Although the effects of DNA methylation and TE repression on flower, fruit, seed coat, and leaf pigmentation have been investigated, little is known about the relationship between methylation and flower color chimerism. In this study, we used a comparative methylomic–transcriptomic approach to explore the molecular mechanism responsible for chimeric flowers in Prunus mume “Danban Tiaozhi”. High-performance liquid chromatography-electrospray ionization mass spectrometry revealed that the variation in white (WT) and red (RT) petal tissues in this species is directly due to the accumulation of anthocyanins, i.e., cyanidin 3,5-O-diglucoside, cyanidin 3-O-glucoside, and peonidin 3-O-glucoside. We next mapped the first-ever generated methylomes of P. mume, and found that 11.29–14.83% of the genomic cytosine sites were methylated. We also determined that gene expression was negatively correlated with methylcytosine level in general, and uncovered significant epigenetic variation between WT and RT. Furthermore, we detected differentially methylated regions (DMRs) and DMR-related genes between WT and RT, and concluded that many of these genes, including differentially expressed genes (DEGs) and transcription factor genes, are critical participants in the anthocyanin regulatory pathway. Importantly, some of the associated DEGs harbored TE insertions that were also modified by methylcytosine. The above evidence suggest that flower color chimerism in P. mume is induced by the DNA methylation of critical genes and TEs.
Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning †

In the field of indoor pedestrian positioning, the improved Quasi-Static magnetic Field (iQSF) method has been proposed to estimate gyroscope biases in magnetically perturbed environments. However, this method is only effective when a person walks along straight-line paths. For other curved or more complex path patterns, the iQSF method would fail to detect the quasi-static magnetic field. To address this issue, a novel approach is developed for quasi-static magnetic field detection in foot-mounted Inertial Navigation System. The proposed method detects the quasi-static magnetic field using the rate of change in differences between the magnetically derived heading and the heading derived from gyroscope. In addition, to eliminate the distortions caused by system platforms and shoes, a magnetometer calibration method is developed and the calibration is transformed from three-dimensional to two-dimensional coordinate according to the motion model of a pedestrian. The experimental results demonstrate that the proposed method can provide superior performance in suppressing the heading errors with the comparison to iQSF method.
Effect of the level of effort during resistance training on intraocular pressure

Abstract Background: We aimed to evaluate the influence of the level of effort during four basic resistance exercises leading to muscular failure on intraocular pressure (IOP) and ocular perfusion (OPP), as well as the role of exercise type and sex. Methods: Twenty-five young adults (12 women) performed 10 repetitions against their 10-RM (repetition maximum) load in the squat, military press, biceps curl and calf raise exercises. IOP was measured before, during and after exercise, whereas OPP was indirectly assessed before and after each exercise. Results: There was a progressive IOP rise during exercise (p < 0.001, η² = 0.531), which was dependent on exercise type (p = 0.020, η² = 0.125). The squat exercise induced higher IOP increments in comparison to the other exercises (corrected p-values < 0.001), and higher IOP values were found for the military press and biceps curl than for the calf raise exercise (corrected p-values = 0.012 and 0.002). OPP exhibited a significant reduction when leading to muscular failure (p = 0.001, η² = 0.364), being statistically significant for the squat and military press exercises (corrected p-values = 0.037 and 0.047). No effect of sex was found for IOP and OPP (p > 0.05). Conclusions: A single set of resistance training leading to muscular failure causes an instantaneous and progressive IOP rise in healthy young individuals. These IOP rises depend on exercise type (squat > military press = biceps curl > calf raise), but not on participant´s sex. OPP diminished as a consequence of performing resistance training exercise, being statistically significant for the squat and military press exercises. Future studies should include glaucoma patients aiming to corroborate the generalizability of our findings.
Experimental design modulates variance in BOLD activation: The variance design general linear model

Typical fMRI studies have focused on either the mean trend in the blood‐oxygen‐level‐dependent (BOLD) time course or functional connectivity (FC). However, other statistics of the neuroimaging data may contain important information. Despite studies showing links between the variance in the BOLD time series (BV) and age and cognitive performance, a formal framework for testing these effects has not yet been developed. We introduce the variance design general linear model (VDGLM), a novel framework that facilitates the detection of variance effects. We designed the framework for general use in any fMRI study by modeling both mean and variance in BOLD activation as a function of experimental design. The flexibility of this approach allows the VDGLM to (a) simultaneously make inferences about a mean or variance effect while controlling for the other and (b) test for variance effects that could be associated with multiple conditions and/or noise regressors. We demonstrate the use of the VDGLM in a working memory application and show that engagement in a working memory task is associated with whole‐brain decreases in BOLD variance.
Probabilistic Causal Analysis of Social Influence

Mastering the dynamics of social influence requires separating, in a database of information propagation traces, the genuine causal processes from temporal correlation, i.e., homophily and other spurious causes. However, most studies to characterize social influence, and, in general, most data-science analyses focus on correlations, statistical independence, or conditional independence. Only recently, there has been a resurgence of interest in "causal data science,'' e.g., grounded on causality theories. In this paper we adopt a principled causal approach to the analysis of social influence from information-propagation data, rooted in the theory of probabilistic causation. Our approach consists of two phases. In the first one, in order to avoid the pitfalls of misinterpreting causation when the data spans a mixture of several subtypes ("Simpson's paradox''), we partition the set of propagation traces into groups, in such a way that each group is as less contradictory as possible in terms of the hierarchical structure of information propagation. To achieve this goal, we borrow the notion of "agony'' and define the Agony-bounded Partitioning problem, which we prove being hard, and for which we develop two efficient algorithms with approximation guarantees. In the second phase, for each group from the first phase, we apply a constrained MLE approach to ultimately learn a minimal causal topology. Experiments on synthetic data show that our method is able to retrieve the genuine causal arcs w.r. t. a ground-truth generative model. Experiments on real data show that, by focusing only on the extracted causal structures instead of the whole social graph, the effectiveness of predicting influence spread is significantly improved.