Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional mgrA Influences Genetic Changes within a Staphylococcus aureus Cell Population over Time

Within a bacterial population, a stochastically generated heterogeneity of phenotypes allows continual survival against current and future stressors. The generation of a sub-population of quasi-dormant Small Colony Variants (SCV) in Staphylococcus aureus is such a mechanism, allowing for persistent or relapse of infection despite initial intervention seemingly clearing the infection. ABSTRACT Prolonged survival in the host-bacteria microenvironment drives the selection of alternative cell types in Staphylococcus aureus, permitting quasi-dormant sub-populations to develop. These facilitate antibiotic tolerance, long-term growth, and relapse of infection. Small Colony Variants (SCV) are an important cell type associated with persistent infection but are difficult to study in vitro due to the instability of the phenotype and reversion to the normal cell type. We have previously reported that under conditions of growth in continuous culture over a prolonged culture time, SCVs dominated a heterogenous population of cell types and these SCVs harbored a mutation in the DNA binding domain of the gene for the transcription factor, mgrA. To investigate this specific cell type further, S. aureus WCH-SK2-ΔmgrA itself was assessed with continuous culture. Compared to the wild type, the mgrA mutant strain required fewer generations to select for SCVs. There was an increased rate of mutagenesis within the ΔmgrA strain compared to the wild type, which we postulate is the mechanism explaining the increased emergence of SCV selection. The mgrA derived SCVs had impeded metabolism, altered MIC to specific antibiotics and an increased biofilm formation compared to non-SCV strain. Whole genomic sequencing detected single nucleotide polymorphisms (SNP) in phosphoglucosamine mutase glmM and tyrosine recombinase xerC. In addition, several genomic rearrangements were detected which affected genes involved in important functions such as antibiotic and toxic metal resistance and pathogenicity. Thus, we propose a direct link between mgrA and the SCV phenotype. IMPORTANCE Within a bacterial population, a stochastically generated heterogeneity of phenotypes allows continual survival against current and future stressors. The generation of a sub-population of quasi-dormant Small Colony Variants (SCV) in Staphylococcus aureus is such a mechanism, allowing for persistent or relapse of infection despite initial intervention seemingly clearing the infection. The use of continuous culture under clinically relevant conditions has allowed us to introduce time to the growth system and selects SCV within the population. This study provides valuable insights into the generation of SCV which are not addressed in standard laboratory generated models and reveals new pathways for understanding persistent S. aureus infection which can potentially be targeted in future treatments of persistent S. aureus infection.
Plasma and Liver Pharmacokinetics of the N-Acetylgalactosamine Short Interfering RNA JNJ-73763989 in Recombinant Adeno-Associated – Hepatitis B Virus – Infected Mice S

JNJ-73763989 is an N-acetylgalactosamine conjugated short inter- fering RNA combination product consisting of two triggers in clinical development for chronic hepatitis B virus (HBV) infection treatment that induces a selective degradation of all HBV mRNA transcripts. Our aim is to characterize the plasma and liver pharmacokinetics (PK) of JNJ-73763989 after intravenous and subcutaneous administration in recombinant adeno-associated (rAAV) HBV infected mice. Forty-two male rAAV-HBV infected C57Bl/6 mice received JNJ-73763989 doses of 10 mg/kg i.v. or 1, 3 and 10 mg/kg s.c. Plasma and liver concentrations were analyzed simultaneously using nonlinear mixed-effects modeling with the NONMEM 7.4. A population PK model consisting of a two-compartment disposition model with transporter-mediated drug disposition, including internalization to the liver compartment, linear elimination from plasma and liver, and first-order absorption following subcutaneous administration, was suit- able to describe both plasma and liver PK. After subcutaneous dosing, absolute bioavailability was complete and flip-flop kinetics were observed. JNJ-73763989 distributes from plasma to liver via transporter-mediated liver internalization in less than 24 hours, with sustained ( > 42 days) liver exposure. The saturation of transporter-mediated liver internalization was hypothesized to be due to asialo- glycoprotein receptor saturation. Increasing the dose decreased the relative liver uptake efficiency in mice for intravenously and, to a lesser extent, subcutaneously administered JNJ-73763989. Lower dose levels administered subcutaneously in mice can maximize the proportion of the dose reaching the liver.
A Disk Failure Prediction Method Based on Active Semi-supervised Learning

Disk failure has always been a major problem for data centers, leading to data loss. Current disk failure prediction approaches are mostly offline and assume that the disk labels required for training learning models are available and accurate. However, these offline methods are no longer suitable for disk failure prediction tasks in large-scale data centers. Behind this explosive amount of data, most methods do not consider whether it is not easy to get the label values during the training or the obtained label values are not completely accurate. These problems further restrict the development of supervised learning and offline modeling in disk failure prediction. In this article, Active Semi-supervised Learning Disk-failure Prediction (ASLDP), a novel disk failure prediction method is proposed, which uses active learning and semi-supervised learning. According to the characteristics of data in the disk lifecycle, ASLDP carries out active learning for those clear labeled samples, which selects valuable samples with the most significant probability uncertainty and eliminates redundancy. For those samples that are unclearly labeled or unlabeled, ASLDP uses semi-supervised learning for pre-labeled by calculating the conditional values of the samples and enhances the generalization ability by active learning. Compared with several state-of-the-art offline and online learning approaches, the results on four realistic datasets from Backblaze and Baidu demonstrate that ASLDP achieves stable failure detection rates of 80–85% with low false alarm rates. In addition, we use a dataset from Alibaba to evaluate the generality of ASLDP. Furthermore, ASLDP can overcome the problem of missing sample labels and data redundancy in large data centers, which are not considered and implemented in all offline learning methods for disk failure prediction to the best of our knowledge. Finally, ASLDP can predict the disk failure 4.9 days in advance with lower overhead and latency.
Impacto de Ofuscadores e Otimizadores de Código na Acurácia de Classificadores de Programas

O problema de detecção de clones consiste em determinar, dado um programa inicial e classes de problemas, qual problema o programa resolve. A detecção de clones é útil em vários contextos diferentes, incluindo a identificação de código malicioso e a identificação de plágio. Uma vez que a solução exata para detecção de clones é uma impossibilidade teórica, esse problema é resolvido via heurísticas; a maior parte delas de natureza estatística. Este trabalho analisa o impacto de técnicas de transformação de código sobre essas heurísticas. Mostramos que otimizações de código padrão são tão efetivas quanto ofuscadores para enganar as heurísticas mais populares para detecção de clones, gerando, contudo, código ordens de magnitude mais eficientes. Mostramos também que histogramas de instruções são tão efetivos quanto representações mais complexas ao executar as heurísticas. E por fim mostramos que otimizações de código podem funcionar como um normalizador de programas, em alguns casos, revertendo o efeito da ofuscação, e recuperando assim a capacidade de um detector estatístico de encontrar clones.
Creating More Personas Improves Representation of Demographically Diverse Populations: Implications Towards Interactive Persona Systems

Personas represent distinct user types. However, while online user data can be demographically and behaviorally heterogeneous, most studies generate less than ten personas, regardless of how heterogeneous the data is. Because all persona creation efforts need to assign a number of personas to create, assigning this number evokes a fundamental question, How many personas to create? To address this question, we apply data-driven persona creation in a dataset with 250 million YouTube views from a global news and media organization. We focus on a statistically optimal number of personas, namely, how the distribution of demographic persona attributes deviates from the baseline user data. Altering the number of generated personas, ranging from 5 to 160 personas per set, we find that more personas cover more age groups and countries, thus improving the statistical correspondence with the raw user data, and increasing the representation of demographic diversity by including more fringe user segments. While the user representation continuously improved with more personas, the relative diversity gain was maximal with 40 personas, implying that, using our data, one ought to create more than 4 times more personas than generally advocated. The results imply that organizations with heterogeneous online audiences benefit from many personas in terms of more inclusive user representation. We further demonstrate how an interactive persona system can help stakeholders navigate many personas with possibly smaller cognitive effort.
Correlating Historical Events and Cinematic Releases Using Web Information

Mimesis is a term created by Aristotle and Plato in which art imitates life. Mimesis has been studied since ancient Greece and governed the theatrical and sculptural creations of the time. In this context, our work aims to study the effect of mimesis in the current cinematographic scenario, correlating historical events of the 20th and 21st centuries to the great cinematographic productions that follow. The question that guides our work is “Is there an increase in the release of films with a certain theme after a historical event? ”. To answer this, we propose a methodology that uses two distinct data sources: one related to descriptions of historical facts from the 20th and 21st centuries extracted from Wikipedia and another with descriptions of films extracted from TMDb. Using topic modeling strategies, we automatically find the main themes related to historical events, and later, we evaluate how the description of a film is associated with the themes found. Temporal analysis is done to assess the popularity of each of the themes over time. In the results obtained by our methodology, there was a significant increase in the popularity of films that addressed themes related to historical events that occurred in an immediately preceding moment in time, corroborating the concept of mimesis.
Four Challenges of Designing Digital Services to Support Participatory Design

A crucial issue in Participatory Design is to bring together and ensure knowledge dissemination among large groups of diverse stakeholders. This can be especially hard to accomplish if co-located activities are not feasible. Digital services could potentially remedy this. Although Participatory Design as a methodology for systems development has resulted in numerous systems to support collaborative work, we lack systems that directly support Participatory Design processes in their own right. In order to examine the potentials and limitations of how digital systems can better support Participatory Design, we present and analyze a case-study of Ideate, a participatory online platform for communities to develop, discuss and present best-practice examples of solutions for how to rethink and reopen society in the aftermath of COVID-19. We analyse core insights from the design process and the use of the system in three different cases. On this basis, we present and critically discuss four challenges for designing digital systems to support Participatory Design: I) lowering the barrier for participation is necessary but not enough, II) urgency and relevance is not enough, participation requires alignment with the stakeholders’ immediate tasks, III) participation requires negotiation of time and place, IV) digitally mediated participatory design processes require lead figures.
It’s Long and Complicated! Enhancing One-Pager Privacy Policies in Smart Home Applications

With the proliferation of smart home devices, domains where users’ data is processed are becoming an important issue for data protection authorities. Regulations such as the EU-GDPR obligate smart home providers to inform users about the processing of personal data. However, due to the long and complex format of the privacy policies, users often overlook reading them. There have been attempts to make privacy policies more user-friendly. The one-pager approach has shown to be effective in gathering users’ attention. We present an empirical study of one-pager privacy policy templates in a smart home application. Three different versions are compared in a within-group study: a list, a tab-based, and a device-based. Our results show that the tab-based condition outperformed the others in terms of usability, workload, and user preference. Furthermore, we discuss the potentials and challenges of designing one-pager privacy policy representation formats in the context of smart home applications.
Traffic Flow Forecasting in the COVID-19: A Deep Spatial-temporal Model Based on Discrete Wavelet Transformation

Traffic flow prediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into high-dimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.
A Learning-Based Framework for Depth Perception using Dense Light Fields

The rapid development of optical sensors technology has accompanied a growing demand for visual measurement systems in emerging areas that need to interpret the real three-dimensional physical world, such as self-driving cars, mobile robotics, Advanced Driver Assistance Systems (ADAS), and medical diagnostic in 3D imaging. In these systems, for modeling the physical world, it is necessary to unify visual information with depth measurements. Light Field cameras have the potential to be used in such systems as a versatile hypersensor. Since Light Fields represent the scene’s visual information from multiple viewpoints, it is possible to calculate the depth information through trigonometric operations. This paper proposes a learning-based framework that allows unifying scene depth with visual information obtained from Light Fields. The structure of the proposed framework is composed of four main modules. The deep learning modules consist of (i) a depth map estimation using a siamese convolutional neural network and (ii) an instance segmentation employing region-based convolutional neural network. The others two modules apply linear transformations: (iii) a module which applies the matrix transformations with camera intrinsic parameters to generated a new depth map of absolute distances and (iv) a module to return the distance of the selected objects. For the depth map estimation module this framework proposal a siamese neural network called EPINET-FAST, which allows for generating depth maps in less than half the time of the original EPINET. A case study is presented using Dense Light Fields captured by a Lytro Illum camera (plenotic 1.0). The case study seeks to exemplify the processing time of each module, allowing researchers to isolate critical points and propose changes in the future, seeking a processing that can be applied in real time.
Trip Reinforcement Recommendation with Graph-based Representation Learning

Tourism is an important industry and a popular leisure activity involving billions of tourists per annum. One challenging problem tourists face is identifying attractive Places-of-Interest (POIs) and planning the personalized trip with time constraints. Most of the existing trip recommendation methods mainly consider POI popularity and user preferences, and focus on the last visited POI when choosing the next POI. However, the visit patterns and their asymmetry property have not been fully exploited. To this end, in this article, we present a GRM-RTrip (short for Graph-based Representation Method for Reinforce Trip Recommendation) framework. GRM-RTrip learns POI representations from incoming and outgoing views to obtain asymmetric POI-POI transition probability via POI-POI graph networks, and then fuses the trained POI representation into a user-POI graph network to estimate user preferences. Finally, after formulating the personalized trip recommendation as a Markov Decision Process (MDP), we utilize a reinforcement learning algorithm for generating a personalized trip with maximal user travel experience. Extensive experiments are performed on the public datasets and the results demonstrate the superiority of GRM-RTrip compared with the state-of-the-art trip recommendation methods.
Interactive Mycelium Composites: Material Exploration on Combining Mushroom with Off-the-shelf Electronic Components

Using organic materials for interactive products has gained attention during the past few years. Among them, mycelium, the vegetative part of the mushroom, is a promising material and explored by only distinct examples in HCI. This paper adopts a systematic approach to combining mycelium with LEDs, optical fibers, servo motors, micro-vibration motors, thermochromic inks, conductive threads and fabrics. We created 10 material samples utilizing input (touch) and/or output (light, movement and color-change) features. We describe our material exploration process, as well as the results of an online survey (N=30) and two design workshops (N=10) which examined the experiences evoked by different interactive elements and collected potential use-cases. Our work contributes insights about the technology inclusion to the natural feel of the mycelium, the material’s suitability for tangible interaction and suggests future applications for the composites as a sustainable alternative for interactive interior design elements and ambient displays.
Meaningful Telerobots in Informal Care: A Conceptual Design Case

While telerobots offer potentially unique ways to shape human-human relationships, current concepts often imitate existing practices, such as face-to-face conversations. Using the example of informal care, we explored whether the explicit use of the unique possibilities provided by telerobots can lead to meaningful extended or unique care practices. Initial in-depth conversations with five caregivers and care recipients about their care practices revealed three types of “care visits” (i.e., dedicated, short/spontaneous, reciprocal) as well as what makes them positive (e.g., mutuality, routine, autonomy). We combined this with the unique opportunities telerobots offer, such as masking the feelings of the caregiver. These were further condensed into three video prototypes of potential, robot-mediated care practices and further critically reflected together with the five participants. All in all, telerobots offer opportunities to strengthen relational autonomy, to disentangle roles and to establish less demanding or more activity-based forms of sociability in informal care relations.
KnitxCode: Exploring a Craftsmanship-driven Approach to Computational Thinking

In recent years, computational thinking (CT) has focused on how children and citizens develop basic computing skills which empower them to participate in a digitised society and broaden participation and engagement with CT. We present the exploration of a craftsmanship-driven approach to engage people in CT. To explore this approach a team of knitters, a computer scientist and a computing education expert iteratively designed the KnitxCode workshop. KnitxCode utilises knitting in combination with CT concepts as the foundation for the activities. The workshop was evaluated by 12 participants with no programming experience and with knitting experience ranging from 2-25 years (M = 6). From the evaluation we found that the craftsmanship-driven approach seemed to provide the participants with confidence to learning CT, as shared concepts and practices between knitting and computing supported transitioning from knitting craftsmanship to computing and in this way served to demystify computing.
Toward Fast and Scalable Random Walks over Disk-Resident Graphs via Efficient I/O Management

Traditional graph systems mainly use the iteration-based model, which iteratively loads graph blocks into memory for analysis so as to reduce random I/Os. However, this iteration-based model limits the efficiency and scalability of running random walk, which is a fundamental technique to analyze large graphs. In this article, we first propose a state-aware I/O model to improve the I/O efficiency of running random walk, then we develop a block-centric indexing and buffering scheme for managing walk data, and leverage an asynchronous walk updating strategy to improve random walk efficiency. We implement an I/O-efficient graph system, GraphWalker, which is efficient to handle very large disk-resident graphs and also scalable to run tens of billions of random walks with only a single commodity machine. Experiments show that GraphWalker can achieve more than an order of magnitude speedup when compared with DrunkardMob, which is tailored for random walks based on the classical graph system GraphChi, as well as two state-of-the-art single-machine graph systems, Graphene and GraFSoft. Furthermore, when compared with the most recent distributed system KnightKing, GraphWalker still achieves comparable performance with only a single machine, thereby making it a more cost-effective alternative.