Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological Aspects and Depression in Patients with Symptomatic Keratoconus

Purpose To assess the psychological status of keratoconus sufferers and to determine the relationship between depression and visual impairment in this group of patients. Methods Fifty-six patients with keratoconus and forty-seven age- and gender-matched healthy control subjects were retroprospectively analyzed. Every participant underwent a complete ophthalmological examination. Keratoconus diagnosis was confirmed with corneal topography and tomography. Zung Depression Inventory Questionnaire and Patient Health Questionnaire-9 (PHQ-9) were completed by everyone. Results Visual acuity (logMAR 0.53 ±0.30 versus 0.11 ± 0.16), PHQ-9 score (10.20 ± 4.00 versus 5.40 ± 5.01), and Zung score (46.52 ± 8.70 versus 38.53 ± 8.41) showed a statistically significant difference between keratoconus patients and healthy controls (p < 0.001 for all). Worse visual acuity was strongly correlated with older individuals (rho = 0.339, p=0.011) and higher PHQ-9 (rho = 0.765, p < 0.001) and Zung score (rho = 0.672, p < 0.001). Conclusion Depressive disorders appear to be directly associated with keratoconus, both in frequency and intensity. Worse visual acuity and older age could be identified as predictive factors for their emotional status. Moreover, the disease itself could be recognized as an independent risk factor for depression development, underlying the need for close monitoring and supportive management. To the best of our knowledge, our study is the first in the literature to elaborate the association between keratoconus and depression, by assessing two different questionnaires simultaneously.
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models

Bayesian inference plays an important role in advancing machine learning, but faces computational challenges when applied to complex models such as deep neural networks. Variational inference circumvents these challenges by formulating Bayesian inference as an optimization problem and solving it using gradient-based optimization. In this paper, we argue in favor of natural-gradient approaches which, unlike their gradient-based counterparts, can improve convergence by exploiting the information geometry of the solutions. We show how to derive fast yet simple natural-gradient updates by using a duality associated with exponential-family distributions. An attractive feature of these methods is that, by using natural-gradients, they are able to extract accurate local approximations for individual model components. We summarize recent results for Bayesian deep learning showing the superiority of natural-gradient approaches over their gradient counterparts.
Towards Optimal Concolic Testing

Concolic testing integrates concrete execution (e.g., random testing) and symbolic execution for test case generation. It is shown to be more cost-effective than random testing or symbolic execution sometimes. A concolic testing strategy is a function which decides when to apply random testing or symbolic execution, and if it is the latter case, which program path to symbolically execute. Many heuristics-based strategies have been proposed. It is still an open problem what is the optimal concolic testing strategy. In this work, we make two contributions towards solving this problem. First, we show the optimal strategy can be defined based on the probability of program paths and the cost of constraint solving. The problem of identifying the optimal strategy is then reduced to a model checking problem of Markov Decision Processes with Costs. Secondly, in view of the complexity in identifying the optimal strategy, we design a greedy algorithm for approximating the optimal strategy. We conduct two sets of experiments. One is based on randomly generated models and the other is based on a set of C programs. The results show that existing heuristics have much room to improve and our greedy algorithm often outperforms existing heuristics.
Extraction of Biofilms From Ureteral Stents for Quantification and Cultivation-Dependent and -Independent Analyses

Ureteral stenting is a common surgical procedure, which is associated with a high morbidity and economic burden, but the knowledge on the link between biofilms on these stents, morbidity, and the impact of the involved microbiota is still limited. This is partially due to a lack of methods that allow for a controlled extraction of the biofilms from stents. Development of an appropriate in vitro model to assess prevention of biofilm formation by antimicrobial coatings and biomaterials requires a profound understanding of the biofilm composition, including the involved microbiota. This work describes an analytical pipeline for the extraction of native biofilms from ureteral stents for both cultivation-dependent and -independent analysis, involving a novel mechanical abrasion method of passing stent samples through a tapered pinhole. The efficiency of this novel method was evaluated by quantifying the removed biofilm mass, numbers of cultivable bacteria, calcium content, and microscopic stent analysis after biofilm removal using 30 clinical stent samples. Furthermore, the extraction of in vitro formed Escherichia coli biofilms was evaluated by universal 16S quantitative PCR, a cultivation-independent method to demonstrate efficient biofilm removal by the new approach. The novel method enables effective contamination-free extraction of the biofilms formed on ureteral stents and their subsequent quantification, and it represents a useful tool for comprehensive examinations of biofilms on ureteral stents.
A network-based citation indicator of scientific performance

Scientists are embedded in social and information networks that influence and are influenced by the quality of their scientific work, its impact, and the recognition they receive. Here we quantify the systematic relationship between a scientist's position in the network of scientific collaborations and the citations they receive. As expected, we find that authors closer to others in this network are, on average, more highly cited than those further away from others. We construct a novel indicator, the s-index, that explicitly captures performance linked to network position along two complimentary dimensions: performance expected due to network position and performance relative to this position. The basis of our approach is to represent an author's network position through their distribution of distances to other authors. The s-index then ranks (1) the citation potential of an individual's network position relative to all other authors, and (2) the citations they accrue relative to authors that have a comparable network position. Characterizing scientists through these two complimentary dimensions can be used to make more informed evaluations in a networked environment. For example, it can identify individuals that play an important role in diffusing scientific ideas. It also sheds a new light on central debates in the Science of Science, namely the impact of author teams and comparisons of impact across scientific fields.
Evolution of Therapeutic Antibodies, Influenza Virus Biology, Influenza, and Influenza Immunotherapy

This narrative review article summarizes past and current technologies for generating antibodies for passive immunization/immunotherapy. Contemporary DNA and protein technologies have facilitated the development of engineered therapeutic monoclonal antibodies in a variety of formats according to the required effector functions. Chimeric, humanized, and human monoclonal antibodies to antigenic/epitopic myriads with less immunogenicity than animal-derived antibodies in human recipients can be produced in vitro. Immunotherapy with ready-to-use antibodies has gained wide acceptance as a powerful treatment against both infectious and noninfectious diseases. Influenza, a highly contagious disease, precipitates annual epidemics and occasional pandemics, resulting in high health and economic burden worldwide. Currently available drugs are becoming less and less effective against this rapidly mutating virus. Alternative treatment strategies are needed, particularly for individuals at high risk for severe morbidity. In a setting where vaccines are not yet protective or available, human antibodies that are broadly effective against various influenza subtypes could be highly efficacious in lowering morbidity and mortality and controlling unprecedented epidemic/pandemic. Prototypes of human single-chain antibodies to several conserved proteins of influenza virus with no Fc portion (hence, no ADE effect in recipients) are available. These antibodies have high potential as a novel, safe, and effective anti-influenza agent.
Towards meaningful distance-preserving encryption

Mining complex data is an essential and at the same time challenging task. Therefore, organizations pass on their encrypted data to service providers carrying out such analyses. Thus, encryption must preserve the mining results. Many mining algorithms are distance-based. Thus, we investigate how to preserve the results for such algorithms upon encryption. To this end, we propose the notion of distance-preserving encryption (DPE). This notion has just the right strictness - we show that we cannot relax it, using formal arguments as well as experiments. Designing a DPE scheme is challenging, as it depends both on the data set and the specific distance measure in use. We propose a procedure to engineer DPE-schemes, dubbed DisPE. In a case study, we instantiate DisPE for SQL query logs, a type of data containing valuable information about user interests. In this study, we design DPE schemes for all SQL query distance measures from the scientific literature. We formally show that one can use a combination of existing secure property-preserving encryption schemes to this end. Finally, we discuss on the generalizability of our findings using two other data sets as examples.
Generalized Adaptive Possibilistic C-Means Clustering Algorithm

In this paper a generalized possibilistic c-means clustering algorithm, called Generalized Adaptive Possibilistic C-Means (GAPCM), is presented. The algorithm extents the abilities of its ancestor, Adaptive Possibilistic C-Means (APCM), allowing the study of cases where the data form compact and hyper-ellipsoidally shaped clusters, whose points may lie around certain subspaces in the feature space. In addition, these clusters may be located very close, or even intersect each other. More specifically, a proper definition and an adaptivity concept of the parameters that GAPCM involves, during its execution, renders the algorithm able to unravel on its own the actual hyper-ellipsoidal shape of the clusters formed by the data. The performance of the algorithm is assessed through its comparison with other related algorithms on the basis of both simulated and real data sets.
Enriching Lexicons with Ephemeral Words for Sentiment Analysis in Social Streams

Lexical approaches for sentiment analysis like SentiWordNet rely upon a fixed dictionary of words with fixed sentiment, i.e., sentiment that does not change. With the rise of Web 2.0 however, what we observe more and more often is that words that are not sentimental per se, are often associated with positive/negative feelings, for example, "refugees", "Trump", "iphone". Typically, those feelings are temporary as responses to external events; for example, "iphone" sentiment upon latest iphone version release or "Trump" sentiment after USA withdraw from Paris climate agreement. In this work, we propose an approach for extracting and monitoring what we call ephemeral words from social streams; these are words that convey sentiment without being sentimental and their sentiment might change with time. Such sort of words cannot be part of a lexicon like SentiWordNet since their sentiment has an ephemeral character, however detecting such words and estimating their sentiment can significantly improve the performance of lexicon-based approaches, as our experiments show.
Machine Learning – Based Detection of Water Contamination in Water Distribution Systems

- Accurate detection of natural or intentional contamination events in water distribution pipes is critical to drinking water safety. Efficient early warning systems that can detect contamination events require detection algorithms that can accurately predict the occurrence of such events. This paper presents the development of adaptive neuro-fuzzy inference system (ANFIS) models for detecting the safety condition of water in pipe networks when concentrations of water quality variables in the pipes exceed their maximum thresholds. The event detection is based on time series data composed of pH, turbidity, color and bacteria count measured at the effluent of a drinking water utility and nine different locations of sensors in the distribution network in the city of Ålesund, Norway. The proposed ANFIS models correctly detected between 92% and 96% of the safety condition of the water in the pipe network, with approximately 1% false alarm rate during the testing stage. The models also achieved high rates of specificity and precision, with very high correlations between the predictions and actual conditions of the water in the pipes. The accuracy of the models achieved in this study suggests that the models can be useful in real time contamination event detection in the pipe networks.
Retrobulbar hemodynamics and aqueous humor levels of endothelin-1 in exfoliation syndrome and exfoliation glaucoma

Background Endothelin-1 (ET-1) is the most potent vasoconstrictor in the body. There are reports in the literature correlating plasma levels of ET-1 and its impact on retrobulbar hemodynamics. This study evaluates aqueous humor levels of ET-1 and retrobulbar hemodynamics in patients with exfoliation syndrome (XFS) and exfoliation glaucoma (XFG). Patients and methods Patients scheduled for cataract surgery were included. Patients were allocated to non-exfoliation non-glaucoma group (controls), XFG and XFS groups. Peak systolic velocity (PSV), end diastolic velocity, and resistivity index of the ophthalmic artery (OA), short posterior ciliary arteries, and central retinal artery (CRA) were determined preoperatively using color Doppler imaging. Aqueous humor samples obtained at the beginning of cataract surgery were analyzed with enzyme-linked immunosorbent assay. Results Sixty-nine participants of similar age were included (controls: n=24, XFG: n=22, XFS: n=23). Multiple regression analysis showed that ET-1, OA-PSV, OA-resistivity index, CRA-PSV, and CRA-end diastolic velocity were influenced by group but not by sex or age. ET-1 for the XFG group (15.93±2.8 pg/mL) was significantly higher than for the XFS (8.92±2.7 pg/mL) and control (8.44±2.6 pg/mL) groups. The difference in ET-1 titers between the control and XFS groups was not statistically significant. All four hemodynamic parameters affected by group status significantly correlated with ET-1 levels in eyes with XFS. In eyes with XFG, two of the four hemodynamic parameters significantly correlated with ET-1 levels. In control eyes, no correlation between ET-1 and hemodynamic parameters affected by group status was detected. Conclusion ET-1 aqueous levels in eyes with XFG were significantly higher than those of age-matched controls. Reduced blood flow and increased vascular resistivity were detected in the OA and the CRA in eyes with XFG/XFS. ET-1 levels in eyes with XFG/XFS exhibit a significant correlation with hemodynamic parameters that indicate reduced perfusion.
PACELA: A Neural Framework for User Visitation in Location-based Social Networks

Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as well as the latent attributes of both users and venues. More specifically, we use a probabilistic matrix factorization-based technique to infer the latent attributes specific to users and locations in location-based social networks (LBSNs), considering the user visitation decisions that could be affected by area attraction, neighborhood competition, and social homophily. PACELA also includes a deep learning neural network to combine both embedding and latent features to predict if a user performs check-in on a location. Our experiments on three different real world datasets show that PACELA yields the best check-in prediction accuracy against several baseline methods.
Who is Your Best Friend?: Ranking Social Network Friends According to Trust Relationship

In online social networks (OSNs), e.g. Facebook, the relationship between users is binary, i.e., either friend (trust) or stranger (distrust). However, in real-world life, people always have different trust relationships with others (e.g., best friend, acquaintance, frenemy). For various applications such as social recommendation and semantic web, it is more worthwhile to know the trust strength between users. In this work, via a unique dataset obtained from a Facebook app and a carefully designed user study, we map trust values with users' online interactions, and thus build personalized trust models. For each individual, we learn her trust model via optimization on a ranking-oriented loss function. Experimental results demonstrate the superior of the proposed approach over state-of-the-art method and the good generalization ability of the approach.
Immune Regulation of Tissue Repair and Regeneration via miRNAs—New Therapeutic Target

The importance of immunity in tissue repair and regeneration is now evident. Thus, promoting tissue healing through immune modulation is a growing and promising field. Targeting microRNAs (miRNAs) is an appealing option since they regulate immunity through post-transcriptional gene fine-tuning in immune cells. Indeed, miRNAs are involved in inflammation as well as in its resolution by controlling immune cell phenotypes and functions. In this review, we first discuss the immunoregulatory role of miRNAs during the restoration of tissue homeostasis after injury, focusing mainly on neutrophils, macrophages and T lymphocytes. As tissue examples, we present the immunoregulatory function of miRNAs during the repair and regeneration of the heart, skeletal muscles, skin and liver. Secondly, we discuss recent technological advances for designing therapeutic strategies which target miRNAs. Specifically, we highlight the possible use of miRNAs and anti-miRNAs for promoting tissue regeneration via modulation of the immune system.
iSEE: Interactive SummarizedExperiment Explorer

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.
Equilibrium State of Interaction: Maximizing User Experience through Optimum Adaptivity States

Nowadays, the role of the computer and the Internet has been upgraded in the lives of people, witnessing a paradigm shift on how users communicate with each other or with their service providers. Main characteristics of this reality include dynamic strategies and conditions, multivariate random interactions with instant feedback, and holistic activities with no clear origins across mixed realities. Thereupon, it seems that traditional human-computer interaction methods and adaptivity practices lack the appropriate dynamicity to fully address these aspects and to explicitly offer holistic solutions for enhancing user experience. In this paper, we propose an alternative model and formalization by combining concepts like equilibria and refocusing the core of investigation into the actual single objective interactions that are generated by the interplay of two (or more) participatory ends over a time interval and which cannot be recognized a priori. The resulting interactions consist of dynamic suboptimal adaptivity states of the two that provide an Equilibrium State of Interaction and a motivational engagement.
DetReduce: Minimizing Android GUI Test Suites for Regression Testing

In recent years, several automated GUI testing techniques for Android apps have been proposed. These tools have been shown to be effective in achieving good test coverage and in finding bugs without human intervention. Being automated, these tools typically run for a long time (say, for several hours), either until they saturate test coverage or until a testing time budget expires. Thus, these automated tools are not good at generating concise regression test suites that could be used for testing in incremental development of the apps and in regression testing. We propose a heuristic technique that helps create a small regression test suite for an Android app from a large test suite generated by an automated Android GUI testing tool. The key insight behind our technique is that if we can identify and remove some common forms of redundancies introduced by existing automated GUI testing tools, then we can drastically lower the time required to minimize a GUI test suite. We have implemented our algorithm in a prototype tool called DetReduce. We applied DetReduce to several Android apps and found that DetReduce reduces a test-suite by an average factor of16.9× in size and14.7× in running time. We also found that for a test suite generated by running SwiftHand and a randomized test generation algorithm for 8 hours, DetReduce minimizes the test suite in an average of 14.6 hours.
Quantitative Analysis of the Effect of an Ectopic Beat on the Heart Rate Variability in the Resting Condition

The purpose of this study is to quantitatively analyze the effect of an ectopic beat on heart rate variability (HRV) in the time domain, frequency domain, and in a non-linear analysis. A quantitative analysis was carried out by generating artificial ectopic beats that probabilistically contained a missed beat or a false-detected beat, and the statistical significance was evaluated though a comparison with an ectopic-free HRV by increasing the ratio of the ectopic beat in 0.1% increments from 0 to 50%. The effect of the interpolation on the ectopic HRV was also investigated by applying nearest-neighbor interpolation, linear interpolation, and cubic spline interpolation. The results confirmed a statistically significant difference (P < 0.05) even in the less-than-1% ectopic HRV in every domain. When interpolation was applied, there were differences according to the interpolation method used, but statistical significance was secured for an ectopic beat ratio from 1 to 2% to several tens of a percent. In the effect, linear interpolation, and spline interpolation were confirmed to have a higher effect on the high-frequency related HRV variables, and nearest-neighbor interpolation had a higher effect on low-frequency related variables.