Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Influence of Human Factors in Adaptive User Interface Design

The increased utilisation of adaptive visualisation techniques in commercial software requires design principles that provide guidance on how to design user interfaces that change their appearance during runtime. To contribute to the ongoing adaptive user interface (AUI) research, the development of a model is proposed that enables the methodological development of design principles for AUIs. The adoption of the conceptual framework of design-science is proposed for that purpose. Following the framework, the literature of AUI research (to identify observed user behaviour) as well as the literature of human factors (to explain the observed behaviour) have been reviewed. Design principles are built based on this knowledge. This thesis can contribute to an quality improvement of AUI systems.
Purification of influenza virus‐like particles using sulfated cellulose membrane adsorbers

ABSTRACT BACKGROUND Vaccines based on virus‐like particles (VLPs) are an alternative to inactivated viral vaccines that combine good safety profiles with strong immunogenicity. In order to be economically competitive, efficient manufacturing is required, in particular downstream processing, which often accounts for major production costs. This study describes the optimization and establishment of a chromatography capturing technique using sulfated cellulose membrane adsorbers (SCMA) for purification of influenza VLPs. RESULTS Using a design of experiments approach, the critical factors for SCMA performance were described and optimized. For optimal conditions (membrane ligand density: 15.4 µmol cm−2, salt concentration of the loading buffer: 24 mmol L‐1 NaCl, and elution buffer: 920 mmol L‐1 NaCl, as well as the corresponding flow rates: 0.24 and 1.4 mL min−1), a yield of 80% in the product fraction was obtained. No loss of VLPs was detected in the flowthrough fraction. Removal of total protein and DNA impurities were higher than 89% and 80%, respectively. CONCLUSION Use of SCMA represents a significant improvement compared with conventional ion exchanger membrane adsorbers. As the method proposed is easily scalable and reduces the number of steps required compared with conventional purification methods, SCMA could qualify as a generic platform for purification of VLP‐based influenza vaccines. © 2017 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Cholesterol-Bearing Fluorescent G-Quadruplex Potassium Probes for Anchoring at the Langmuir Monolayer and Cell Membrane

The purpose of the present work was to design, synthesize and spectrally characterize cholesterol-anchored fluorescent oligonucleotide probes (Ch(F-TBA-T), Ch(py-TBA-py)), based on G-quadruplexes, which were able to incorporate into a lipid structure (Langmuir monolayer, living cell membrane). The probes, based on the thrombin-binding aptamer (TBA) sequence, were labeled with fluorescent dyes which enabled simultaneous monitoring of the formation of G-quadruplex structures and visualization of probe incorporation into the cellular membrane. The combinations of fluorophores used included fluorescence resonance energy transfer (FRET) and excimer emission approaches. The structural changes of the probes upon binding with K+ or Na+ ions were monitored with fluorescence techniques. These systems showed a very high binding preference for K+ over Na+ ions. The use of confocal fluorescence microscopy indicated successful anchoring of the cholesterol-bearing fluorescent probes to the living cell membrane. These structurally simple cholesterol-based fluorescent probes have good potential for opening up new and exciting opportunities in the field of biosensors; e.g., in vivo detection of K+ ions.
Persistent and Transient Airborne MRSA Colonization of Piglets in a Newly Established Animal Model

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) was first found in 2005 and is up to date widespread in animal husbandry reservoir – focusing on pig farming. The regular detectability of MRSA in the air of pigsties as well as in exhaust air of pig farms (mean count: 102 cfu/m3) poses the question whether an airborne spread and, therefore, a MRSA colonization of animals via the airborne route exists. To answer this question, we exposed three groups of nine MRSA-negative tested piglets each to a defined airborne MRSA concentration (102, 104, and 106 cfu/m3) in our aerosol chamber for 24 h. In the following observation period of 21 days, the MRSA status of the piglets was monitored by taking different swab samples (nasal, pharyngeal, skin, conjunctival, and rectal swab). At the end of the experiment, we euthanized the piglets and investigated different tissues and organs for the spread of MRSA. The data of our study imply the presence of an airborne MRSA colonization route: the animals exposed to 106 cfu/m3 MRSA in the air were persistent colonized. The piglets exposed to an airborne MRSA concentration of 104 cfu/m3 were transient, and the piglets exposed to an airborne MRSA concentration of 102 cfu/m3 were not colonized. Consequently, a colonization via the airborne route was proven.
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model.
Significant Enrichment and Diversity of the Staphylococcal Arginine Catabolic Mobile Element ACME in Staphylococcus epidermidis Isolates From Subgingival Peri-implantitis Sites and Periodontal Pockets

Staphylococcus aureus and Staphylococcus epidermidis are frequent commensals of the nares and skin and are considered transient oral residents. Reports on their prevalence in the oral cavity, periodontal pockets and subgingivally around infected oral implants are conflicting, largely due to methodological limitations. The prevalence of these species in the oral cavities, periodontal pockets and subgingival sites of orally healthy individuals with/without implants and in patients with periodontal disease or infected implants (peri-implantitis) was investigated using selective chromogenic agar and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Staphylococcus epidermidis was predominant in all participant groups investigated. Its prevalence was significantly higher (P = 0.0189) in periodontal pockets (30%) than subgingival sites of healthy individuals (7.8%), and in subgingival peri-implantitis sites (51.7%) versus subgingival sites around non-infected implants (16.1%) (P = 0.0057). In contrast, S. aureus was recovered from subgingival sites of 0-12.9% of the participant groups, but not from periodontal pockets. The arginine catabolic mobile element (ACME), thought to enhance colonization and survival of S. aureus, was detected in 100/179 S. epidermidis and 0/83 S. aureus isolates screened using multiplex PCR and DNA microarray profiling. Five distinct ACME types, including the recently described types IV and V (I; 14, II; 60, III; 10, IV; 15, V; 1) were identified. ACME-positive S. epidermidis were significantly (P = 0.0369) more prevalent in subgingival peri-implantitis sites (37.9%) than subgingival sites around non-infected implants (12.9%) and also in periodontal pockets (25%) compared to subgingival sites of healthy individuals (4.7%) (P = 0.0167). To investigate the genetic diversity of ACME, 35 isolates, representative of patient groups, sample sites and ACME types underwent whole genome sequencing from which multilocus sequence types (STs) were identified. Sequencing data permitted ACME types II and IV to be subdivided into subtypes IIa-c and IVa-b, respectively, based on distinct flanking direct repeat sequences. Distinct ACME types were commonly associated with specific STs, rather than health/disease states or recovery sites, suggesting that ACME types/subtypes originated amongst specific S. epidermidis lineages. Ninety of the ACME-positive isolates encoded the ACME-arc operon, which likely contributes to oral S. epidermidis survival in the nutrient poor, semi-anaerobic, acidic and inflammatory conditions present in periodontal disease and peri-implantitis.
Akkermansia muciniphila as a Model Case for the Development of an Improved Quantitative RPA Microbiome Assay

Changes in the population levels of specific bacterial species within the gut microbiome have been linked to a variety of illnesses. Most assays that determine the relative abundance of specific taxa are based on amplification and sequencing of stable phylogenetic gene regions. Such lab-based analysis requires pre-analytical sample preservation and storage that have been shown to introduce biases in the characterization of microbial profiles. Recombinase polymerase amplification (RPA) is an isothermal nucleic acid amplification method that employs commercially available, easy-to-use freeze-dried enzyme pellets that can be used to analyze specimens rapidly in the field or clinic, using a portable fluorometer. Immediate analysis of diverse bacterial communities can lead to a more accurate quantification of relative bacterial abundance. In this study, we discovered that universal bacterial 16S ribosomal DNA primers give false-positive signals in RPA analysis because manufacturing host Escherichia coli DNA is present in the RPA reagents. The manufacturer of RPA reagents advises against developing an RPA assay that detects the presence of E. coli due to the presence of contaminating E. coli DNA in the reaction buffer (www.twistdx.co.uk/). We, therefore, explored four strategies to deplete or fragment extraneous DNA in RPA reagents while preserving enzyme activity: metal-chelate affinity chromatography, sonication, DNA cleavage using methylation-dependent restriction endonucleases, and DNA depletion using anti-DNA antibodies. Removing DNA with anti-DNA antibodies enabled the development of a quantitative RPA microbiome assay capable of determining the relative abundance of the physiologically-important bacterium Akkermansia muciniphila in human feces.
An Inguinal Perivascular Epithelioid Cell Tumor Metastatic to the Orbit

Malignant PEComas are rare mesenchymal neoplasms. These tumors harbor distinct myomelanocytic phenotype. The PEComa family of tumors includes lymphangioleiomyomatosis, angiomyolipoma, clear cell sugar tumor of the lung, and myomelanocytic tumor of the falciparum ligament/ligamentum teres. PEComas have no known normal cell counterpart. Majority of PEComas are benign and occur predominantly in the middle-age women. These tumors are commonly encountered in the uterus. Herein, we report a 20-year-old woman with a left inguinal mass metastatic to orbit, brain, lumbar spine, and skin at presentation. To our knowledge, this is the first case of metastatic PEComa to the orbit. This is the third case of primary PEComa of the inguinal area.
Methodology and cohort profile for the Hangzhou Lumbar Spine Study: a study focusing on back health in a Chinese population

Back pain is a worldwide health problem, adding a tremendous burden to modern societies. However, little information on back health is available in China, even though a quarter of the world’s population is Chinese. To enhance knowledge in this area, we designed and initiated the Hangzhou Lumbar Spine Study, which is a cross-sectional study of a general sample of mainland Chinese with focusing on disc degeneration, Modic changes, endplate lesions, and back pain. The study consists of a structured questionnaire to measure back pain history and lifetime exposure to suspected risk factors, magnetic resonance imaging of the lumbar spine, bone mineral density study of the spine and hip, and DNA sample analysis. Here we briefly introduce the study methodology, report the test-retest reliability of the questionnaire, and describe the cohort profile to date. Since May 2014, 301 randomly selected subjects (male/female, 122/179; mean age, 51.0 years; range, 20–87 years) have been recruited. Tests-retests of the questionnaire, completed by 40 participants, revealed good reliability. To our knowledge, the Hangzhou Lumbar Spine Study is the first population-based epidemiological study conducted to characterize lumbar spinal phenotypes and back pain, their interaction, and their associations with lifetime environmental exposure, in mainland Chinese. Epidemiological information obtained from a reliable questionnaire, magnetic resonance (MR) imaging data, dual energy X-ray absorptiometry (DXA) measurements, and DNA analysis may serve as a valuable reference for future studies on back health, particularly for mainland Chinese.
LandmarkBoost: Efficient visualContext Classifiers for Robust Localization

The growing popularity of autonomous systems creates a need for reliable and efficient metric pose retrieval algorithms. Currently used approaches tend to rely on nearest neighbor search of binary descriptors to perform the 2D-3D matching and guarantee realtime capabilities on mobile platforms. These methods struggle, however, with the growing size of the map, changes in viewpoint or appearance, and visual aliasing present in the environment. The rigidly defined descriptor patterns only capture a limited neighborhood of the keypoint and completely ignore the overall visual context. We propose LandmarkBoost - an approach that, in contrast to the conventional 2D-3D matching methods, casts the search problem as a landmark classification task. We use a boosted classifier to classify landmark observations and directly obtain correspondences as classifier scores. We also introduce a formulation of visual context that is flexible, efficient to compute, and can capture relationships in the entire image plane. The original binary descriptors are augmented with contextual information and informative features are selected by the boosting framework. Through detailed experiments, we evaluate the retrieval quality and performance of Landmark-Boost, demonstrating that it outperforms common state-of-the-art descriptor matching methods.
Data Dimension Reduction and Network Sparsification Based on Minimal Algorithmic Information Loss

We introduce a family of unsupervised, domain-free, and (asymptotically) model-independent algorithms based on the principles of algorithmic information theory designed to minimize the loss of algorithmic information. The method coarse-grains data in an algorithmic fashion by collapsing regions that can be procedurally regenerated from the compressed version. We show that the method can preserve the salient properties of objects and structures in the process of data dimension reduction and denoising. Using suboptimal approximations of efficient (polynomial) estimations to algorithmic complexity by recent numerical methods of algorithmic probability we demonstrate how these algorithms can preserve structure properties, outperforming other algorithms in e.g. the area of network dimension reduction. As a case study, we report that the method preserves all the graph-theoretic indices measured on a well-known set of synthetic and real-world networks of very different nature, ranging from degree distribution and clustering coefficient to edge betweenness and degree and eigenvector centralities, achieving equal or significantly better results than other data reduction and some of the leading network sparsification methods.
Multi-Target Strategy for Pan/Foot-and-Mouth Disease Virus (FMDV) Detection: A Combination of Sequences Analysis, in Silico Predictions and Laboratory Diagnostic Evaluation

Foot-and-mouth disease (FMD) is a highly contagious viral disease affecting cloven-hoofed animals that causes severe economic losses. The disease is characterized by a vesicular condition and it cannot be differentiated from other vesicular diseases. Therefore, laboratory confirmation of any suspected FMD case is compulsory. Despite viral isolation in cell cultures has been considered for many years as the gold standard for FMD diagnosis, the advantages of real-time reverse transcription polymerase chain reaction (rRT-PCR) technology have motivated its use directly in clinical specimens for FMD diagnosis. The current work was aimed to develop and validate a molecular multi-check strategy using rRT-PCR (mMulti-rRT-PCR) based on SYBR-Green I for pan/foot-and-mouth disease virus (pan/FMDV) diagnosis. From in silico approaches, different primer pairs previously reported were selected and modified to reduce the likelihood of viral escape as well as potential failures in the pan/FMDV detection. The analytical parameters were evaluated using a high number of representative viral strains. The repeatability of the assay and its performance on field samples were also assessed. The mMulti-rRT-PCR was able to detect emergent FMDV strains that circulated in South America between the years 2006–2010 and on which the single rRT-PCRs failed when they were applied independently. The results obtained here showed that the proposed system is an accurate and rapid diagnosis method for sensitive and specific detection of FMDV. Thus, a validated mMulti-rRT-PCR assay based on SYBR-Green I detection coupled to melting curves resolution for pan/FMDV diagnosis on clinical samples is proposed. This study also highlights the need to incorporate the multi-target detection principle in the diagnosis of highly variable agents, specially, of those listed by OIE like FMDV.
Learning Implicit Generative Models by Teaching Explicit Ones

Implicit generative models are difficult to train as no explicit density functions are defined. Generative adversarial nets (GANs) present a minimax framework to train such models, which however can suffer from mode collapse due to the nature of the JS-divergence. This paper presents a learning by teaching (LBT) approach to learning implicit models, which intrinsically avoids the mode collapse problem by optimizing a KL-divergence rather than the JS-divergence in GANs. In LBT, an auxiliary explicit model is introduced to fit the distribution defined by the implicit model while the later one teaches the explicit model to match the data distribution. LBT is formulated as a bilevel optimization problem, whose optimal generator matches the true data distribution. LBT can be naturally integrated with GANs to derive a hybrid LBT-GAN that enjoys complimentary benefits. %implies that we obtain the maximum likelihood estimation of the implicit model. Finally, we present a stochastic gradient ascent algorithm with unrolling to solve the challenging learning problems. Experimental results demonstrate the effectiveness of our method.
Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarms Using Embodied Evolution

Abruptions to the communication infrastructure might occur occasionally, where manual intervention by dedicated personnel is needed to fix the interruptions, restoring communication abilities. However, sometimes this can be dangerous to the personnel carrying out the task, which can be the case in environmental disasters like earthquakes or toxic spills or in the occurrence of fire. Therefore, personal risks can be minimised if autonomous robots are deployed to achieve the same outcome: to establish a communication link between two previously connected but distant sites. In this paper, we investigate the deployment of mobile ad hoc robots which relay traffic between two sites. In order to get the robots to locate themselves appropriately, we take inspiration from self-organisation and emergence in artificial life, where a common overall goal may be achieved if the correct local rules on the agents in the system are invoked. We integrate the aspect of connectivity between two sites into the multirobot simulation platform known as JBotEvolver. The robot swarm is composed of Thymio II robots. In addition, we compare three heuristics, of which one uses neuroevolution (evolution of neural networks) to show how self-organisation and embodied evolution can be used within the integration. Our use of embodiment in robotic controllers shows promising results and provides solid knowledge and guidelines for further investigations.
Topic-Guided Attention for Image Captioning

Attention mechanisms have attracted considerable interest in image captioning because of its powerful performance. Existing attention-based models use feedback information from the caption generator as guidance to determine which of the image features should be attended to. A common defect of these attention generation methods is that they lack a higher-level guiding information from the image itself, which sets a limit on selecting the most informative image features. Therefore, in this paper, we propose a novel attention mechanism, called topic-guided attention, which integrates image topics in the attention model as a guiding information to help select the most important image features. Moreover, we extract image features and image topics with separate networks, which can be fine-tuned jointly in an end-to-end manner during training. The experimental results on the benchmark Microsoft COCO dataset show that our method yields state-of-art performance on various quantitative metrics.
Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0

According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a method to connect people to CPS. Deep learning has shown surpassing performance in HAR. Data preprocessing is an important part of deep learning projects and takes up a large part of the whole analytical pipeline. Data segmentation and data transformation are two critical steps of data preprocessing. This study analyzes the impact of segmentation methods on deep learning model performance, and compares four data transformation approaches. An experiment with HAR based on acceleration data from multiple wearable devices was conducted. The multichannel method, which treats the data for the three axes as three overlapped color channels, produced the best performance. The highest overall recognition accuracy achieved was 97.20% for eight daily activities, based on the data from seven wearable sensors, which outperformed most of the other machine learning techniques. Moreover, the multichannel approach was applied to three public datasets and produced satisfying results for multi-source acceleration data. The proposed method can help better analyze workers’ activities and help to integrate people into CPS.