Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Recombination Rate of Yeast Killer Virus

The yeast Saccharomyces cerevisiae is host to a selfish intercellular “Killer” virus. The yeast Killer virus is a double stranded RNA (dsRNA) that encodes a Killer toxin and confers immunity to the toxin. This virus was first described over 40 years ago, however, we still have a limited understanding of many aspects of the viral system, including frequency of viral recombination. Here we perform pairwise crosses between yeast strains containing known killer virus mutants that are unable to produce functional toxin. Successful mating events are then screened for killing ability to identify instances of recombination, which is denoted by reconstitution of killing phenotype. We have been unable to identify any instances of recombination in over 16 unique crosses where functional recombinants could have been generated. Sequencing reveals that both Killer virus variants are stably maintained as a heteroplasmic viral population. Our results suggest that killer virus has a low recombination frequency, which may extend to other encapsulated cytoplasmic RNA viruses as well. Recombination of yeast Killer virus 3
Shapes of Fe nanocrystals encapsulated at the graphite surface

We describe and analyze in detail the shapes of Fe islands encapsulated under the top graphene layers in graphite. Shapes are interrogated using scanning tunneling microscopy. The main outputs of the shape analysis are the slope of the graphene membrane around the perimeter of the island, and the aspect ratio of the central metal cluster. Modeling primarily uses a continuum elasticity (CE) model. As input to the CE model, we use density functional theory to calculate the surface energy of Fe, and the adhesion energies between Fe and graphene or graphite. We use the shaft-loaded blister test (SLBT) model to provide independent stretching and bending strain energies in the graphene membrane. We also introduce a model for the elastic strain in which stretching and bending are treated simultaneously. Measured side slopes agree very well with the CE model, both qualitatively and quantitatively. The fit is optimal for a graphene membrane consisting of 2–3 graphene monolayers, in agreement with experiment. Analysis of contributions to total energy shows that the side slope depends only on the properties of graphene/graphite. This reflects delamination of the graphene membrane from the underlying graphite, caused by upward pressure from the growing metal cluster. This insight leads us to evaluate the delamination geometry in the context of two related, classic models that give analytic results for the slope of a delaminated membrane. One of these, the point-loaded circular blister test model, reasonably predicts the delamination geometry at the edge of an Fe island. The aspect ratio also agrees well with the CE model in the limit of large island size, but not for small islands. Previously, we had speculated that this discrepancy was due to lack of coupling between bending and stretching in the SLBT model, but the new modeling shows that this explanation is not viable.
Volume: I: Issue-2: Aug-Oct -2010 ISSN 0976-4550 MOLECULAR DIVERSITY IN RICE GENOTYPES DIFFERING IN PHYSIOLOGICAL MECHANISMS OF SALT TOLERANCE THROUGH SSR AND ISSR MARKERS

Two different DNA-based techniques viz, simple sequence repeat (SSR) and inter simple sequence repeat (ISSR) markers were used to estimate genetic diversity among 20 rice genotypes possessing different physiological mechanisms contributing to salt tolerance. A total of 11 clear and repeatable bands were amplified from ten selected SSR primers pairs and 43 fragments were detected from nine ISSR primers. The level of polymorphism was 1.1% with SSR compared to 90.7% with ISSRs. Mean genetic similarity of 0.88 based on SSRs and 0.85 using ISSRs was observed. A total of 43 (39 polymorphic) and 11 bands were detected using 9 ISSR primers and 10 well distributed mapped SSR markers, respectively. Estimates of genetic similarity of ISSRs based on the 39 polymorphic markers between 20 rice cultivars ranged from 0.55 for PR108/CSR19 to 0.94 for Pokkali/CSR20 with an average of 0.81. The estimates revealed by the 11 polymorphic SSR bands showed the average value (0.94) and also the range of genetic similarity (from 0.86 to 1.00 for CSR22/CSR18 and CSR24/CSR20, respectively) reflecting their hyper variability and their high resolution power. The findings are likely to expedite breeding new salt tolerant cultivars by involving parents from diverse molecular clusters.
Open Source System for Smart Home Devices Based on Smartphone Virtual Assistant

No doubt, the functionality of smartphones and the internet is now very diverse. Various features and applications in it are present to simplify human life, including the voice assistant feature. This study raises the theme of utilizing voice assistant for the purposes of controlling smart household devices based on the IoT module. The IoT NodeMCU module has an integrated WiFi chip, works with low power settings and supports flexible programming languages using the Arduino IDE. Testing using Android OS 5.0. 2 version and 7.1. 2 version generate four different results, which are 100% & 83.33% accuracy for male voices and 83.33% & 58.33% when tested using female voices. The accuracy is determined by several reasons i.e. system version, the quality of the microphone, noise, and voice articulation when giving commands to the Google Assistant.
Use of Gelatin as Tannic Acid Carrier for Its Sustained Local Delivery

Background: Polyphenols are macromolecules that play a pivotal role in plant protection against external aggression from microorganisms or radiations. Their antimicrobial properties have raised interest in the medical community for several years now. Among them, tannic acid is one of the cheapest polyphenols extracted from plants. However, its broad use is impeded because it is unstable, with rapid hydrolysis leading to byproducts such as epigallocatechin with less activity. In this work, we developed a porous material to deliver locally tannic acid. Methods: The preparation of this matrix relied on the interaction between gelatin and tannic acid in an aqueous medium. Structure properties of the developed matrix were investigated through rheology measurement and by scanning electron microscopy (SEM). Tannic acid quantity and release over time were measured. Antimicrobial activity was tested in vitro on E. coli and S. aureus. Results: The presence of two tannic acid populations, one free to diffuse across the scaffold and the second stabilized by gelatin with a more structural activity, was demonstrated. We showed that the formulation is possible above a molar ratio of 1:15 (gelatin:tannic acid). Gelatin was used here as a carrier for the tannic acid. Tannic acid content could be modulated depending on its initial concentration during the preparation. Tannic acid was released from these matrices over a period of four days at a concentration that was above the minimum inhibitory concentration (MIC). Antimicrobial activity was confirmed on E. coli and S. aureus. The porous material could be conserved by lyophilization without loss of activity. Its handiness is good enough to consider its use as a wound dressing. Conclusions: We demonstrate herein an easy and cost-effective protocol to locally deliver tannic acid.
Time Constrained MDPs 16.420-Project Report

In some planning problems, there are time dependent constraints over the trajectory of states. For example consider a scenario where an unmanned underwater vehicle (AUV) is exploring deep-sea areas. As Figure 1 highlights, the AUV is dropped at a certain area and is going to be fished in another location within a timeline. While mapping the area along the way is a static desired behavior, in certain periods of time, interesting phenomena going to happen, and the research team requires the AUV to be present and captures such incidents. While in some cases such information are not available beforehand, through this work we assume that the agent has prior access to such timelines. In particular, we are interested in the problem of finding the trajectory which satisfies such constraints while maximizing the reward along the way. We begin by providing the formal problem formulation in the next section. Then, we introduce Soft Dynamic Programing (Soft-DP) and DP as two algorithms to deal with such problems and discuss their space and time complexity. Further we extend these methods to tackle the problem of planning under uncertain transition model. We empirically examine the quality and speed of the proposed algorithms. Finally we conclude.
Having Two Conflicting Goals in Mind: The Tension Between IS Security and Privacy when Avoiding Threats

Despite users of personal IT devices perceive high risks of losing their personal data if their devices get lost or damaged, many are reluctant to use userfriendly online services (i.e., online backups) to recover from such incidents. We suggest that the reason for this denial are information privacy concerns because users need to disclose their personal files to the safeguard provider. As safeguarding services promise to reduce the IS security threat of losing data, individuals are subsequently tensed between two goals: protecting their data against loss (IS security) and their information privacy. To shed light on this goal conflict, our work builds on the theory of goal-directed behavior. Based on a quantitative online survey among 446 participants, we show that privacy concerns impede threat avoidance to prevent data loss. Comparing current users and non-users of online backup services, our results confirm that provider-related privacy concerns are significantly higher for non-users.
Demigods of Technology Use - How Beating the Overconfidence Bias Can Prevent Medical Errors

The healthcare domain faces considerable challenges due to the digitization of medical processes and routines. Information technologies are designed to enable physicians to treat more patients and to increase service quality and patient safety. Despite acknowledging the rapid digital transformation of healthcare, research often neglects whether physicians are actually able to effectively decide which technology to use in which setting and whether their technology use thus effectively enhances quality and safety. Literature on cognitive biases already looked broadly at related errors in judgment and action and questioned rational behavior. Nevertheless, overconfidence, being one of the most common cognitive biases, has barely been linked to the accurate adoption and use of technology by physicians. Against this background, this research-in-progress paper proposes a framework for conducting a mixed-methods study based on the particularities of overconfidence in healthcare. We invite future research to compare our approach with established theoretical frameworks in IS research.
Understanding the Mood of Social Media Messages

Social media is a valuable source of information when seeking to understand community opinion and sentiment about issues of public interest. Such analysis is usually based on sentiment or emotion processing using machine learning techniques or references a curated lexicon of words to measure the emotive intensity being expressed. The lexicon approach can be limited by the sparsity problem, where the lexicon words are not present in the text being processed, and context issues, where the lexicon words have different meanings in the domain under investigation. We have developed a novel technique based on word embeddings to mitigate these issues and present a case study showing its application, where the mood expressed by the community on social media about the Centenary of Armistice in Australia was determined in near real-time.
My Virtual Colleague: A State-of-the-Art Analysis of Conversational Agents for the Workplace

Conversational interfaces at the workplace are not a new idea, but it is only the recent technological advancements that turned what was once a vision into near-future reality. Improved reliability and accuracy enable conversational systems to be used in higher stake environments, such as the workplace. In this work, we perform a literature review on concepts proposed to incorporate Conversational Agents (CA) into the workplace. We found 29 workplace CAs designed for workers that contribute to eight different application domains. Based on the studies of these CAs, we compiled a list of aspects to be considered when designing such CAs and identified starting points for further research.
Facebook Marketing Intelligence

Facebook marketing is becoming an increasingly important tool for companies to influence consumer decision-making. However, there is currently little empirical knowledge about the extent of influence of Facebook marketing on the decision-making process of consumers. This study contributes to these gaps in the literature and investigates the influence of Facebook marketing activities on the decision-making process of consumers. The theory revealed four Facebook marketing activities that affected the first two phases of the decision-making process. These Facebook marketing activities were advertisements, recommend/share, likes and reviews. Whether they actually had an impact has been tested with the help of survey among 112 respondents. The results of the regression analysis showed that all four Facebook marketing activities had a positive influence on the decision-making process.
"Thank you, next": A Call for Intentional Design

As a social network science axiom, homophily informs the current design of Web 2.0 platforms, like Spotify. As a result, sociotechnical systems propagate current hegemonic structures such as historically male dominated markets like the music industry. To understand how the current design of sociotechnical systems promote existing power structures this investigation performed an empirical social network comparison between the organic 2018 Hip-Hop collaboration network and Spotify's automated related Hip-Hop artist network. This study produced several interesting findings including, (1) organic network tie formation differs from automated networks, (2) homophilous and heterophilous connections were positively correlated with artists’ gender, and (3) statistically significant homophilous male connection were observed in Spotify’s related Hip Hop artist network but not in the organic network. By and large, these findings suggest that Spotify’s sociotechnical architecture and affordances promote the existing patriarchal structure.
Capturing the Forest or the Trees: Designing for Granularity in Data Crowdsourcing

Crowdsourcing is a method of completing a task by engaging a large group of heterogeneous contributors. Data crowdsourcing is crowdsourcing of data collection. In this paper, we demonstrate how data crowdsourcing projects can be differentiated along five dimensions: (1) the extent to which tasks are well-defined; (2) the duration of the task; (3) the type of value generated by the consumers of crowdsourcing data; (4) the variety of contribution allowed when completing the task; and (5) the relative value of each contribution. We argue that the quality of information created by a crowd depends on the granularity of contributions contributors are able to make. Finally, we propose a set of principles for designing crowdsourcing system to align the level of granularity of contributions with project objectives.
Metrics for Analyzing Social Documents to Understand Joint Work

Social Collaboration Analytics (SCA) aims at measuring and interpreting communication and joint work on collaboration platforms and is a relatively new topic in the discipline of Information Systems. Previous applications of SCA are largely based on transactional data (event logs). In this paper, we propose a novel approach for the examination of collaboration based on the structure of social documents. Guided by the ontology for social business documents (SocDOnt) we develop metrics to measure collaboration around documents that provide traces of collaborative activity. For the evaluation, we apply these metrics to a large-scale collaboration platform. The findings show that group workspaces that support the same use case are characterized by a similar richness of their social documents (i.e. the number of components and contributing authors). We also show typical differences in the “col-laborativity” of functional modules (containers).
Rethinking Infrastructure Resilience Assessment with Human Sentiment Reactions on Social Media in Disasters

The objective of this study is to propose and test a theoretical framework which integrates the human sentiment reactions on social media in disasters into infrastructure resilience assessment. Infrastructure resilience assessment is important for reducing adverse consequences of infrastructure failures and promoting human well-being in natural disasters. Integrating societal impacts of infrastructure disruptions can enable a better understanding of infrastructure performance in disasters and human capacities under the stress of disruptions. However, the consideration of societal impacts of infrastructure disruptions is limited in existing studies for infrastructure resilience assessment. That is because an integrative theoretical framework for connecting the societal impacts to infrastructure resilience is missing. This study proposed a theoretical framework to examine the relationship between the societal impacts and infrastructure performance in disasters using social media data. Sentiments of human messages related to infrastructure systems are adopted as an indicator of societal impacts of infrastructure disruptions. A case study for electricity and transportation systems in Houston during the 2017 Hurricane Harvey was conducted to illustrate the application of the proposed framework. We find a relation between human sentiment and infrastructure status and validate it by comparing situational information from relevant tweets with official public sources. The findings enable a better understanding of societal expectations and collective sentiments regarding the infrastructure disruptions and improve the visibility of infrastructure performance in Smart City Digital Twin in the context of disasters.
Pathogenesis of Coronary Occlusion

Rupture of an atherosclerotic plaque, with or without superimposed thrombus formation and vasoconstriction, may be the common anatomic and pathogenetic denominator for unstable angina, myocardial infarction, and ischemic sudden death. Outcome seems to be determined by the suddenness of coronary occlusion, the degree of perfusion insufficiency, and—most important—the duration of ischemia.
A novel model for edge aware sea-land segmentation

Sea-land segmentation is one of important research domains in the remote sensing image processing. Edge aware of sealand segmentation is one of hot-points. Edge information is used as an auxiliary learning to provide more information for the segmentation. In this paper, we propose a novel model for the sea-land segmentation with an edge detection in the lower layers and segmentation in higher layers, which is proved as an effective way to fuse the different tasks. We exploit pre-trained VGG16 model to initial the backbone. We use F-score to assess the segment output. Land accuracy is 0.9929 of F-score and sea accuracy score is 0.9937 of F-score in our own test dataset in the sea-land segmentation, which is the highest score among the five methods we take in the comparisons.
An indoor co-positioning algorithm using WLAN and monocular vision

Simultaneous localization and mapping (SLAM) had been developed in the robotics community initially, and gradually applied to indoor positioning. The monocular visual SLAM lacks metric distances information due to scale ambiguity, so cannot be used in many application scenarios. This paper proposed a precision co-positioning and mapping algorithm based on WLAN and mono vision. This method can estimate the scale factor of monocular VSLAM and get the position in real scale space. Furthermore, the WLAN localization and monocular vision is fused by factor graph model to realize accurate positioning and sparse mapping. Experiment results show that the algorithm can estimate scale factor well and reached positioning accuracy of decimeter, which has good theoretical research and practical value in the field of mobile robot navigation in indoor and outdoor environment
Enhancing DW images spatial resolution using correlated gradient information

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging technique commonly used to infer tissue microstructure, however, acquisition time requirements affect the effective spatial resolution for DWI high quality. This paper presents a novel super-resolution strategy to reconstruct high-resolution DW images by linearly combining information from different gradient acquisitions. The strategy comprises two main stages, a representation learning and a high-resolution mapping. In the former stage, information from different gradients is grouped by patch-wise statistical similarities. Representative coefficients are then estimated to represent each group. In the latter stage, adapted patch coefficients predict the high-resolution image while a regularization method eliminates possible reconstruction overlapping effects. Several tests evaluate the method ability to pre- dict high resolution information, PSNR and SSIM metrics were applied to quantitatively measure the quality improvement. Results demonstrate that quality reconstruction outperforms state of art methods in about 0.3 dB for PSNR and 1 % for SSIM.
Morphometric Gaussian process for landmarking on grey matter tetrahedral models

High-dimensional manifold modeling increases the precision and performance of cortical morphometry analysis by densely sampling on the grey matters. But this also brings redundant information and increased computational burden. Gaussian process regression has been used to tackle this problem by learning a mapping to a low-dimensional subspace. However, current methods may not take relevant morphometric properties, usually measured by geometric features, into account, and as a result, may generate morphometrically insignificant selections. In this paper, we propose a morphometric Gaussian process (M-GP) as a novel Bayesian model on the gray matter tetrahedral meshes. We also implement an M-GP regression landmarking algorithm as a manifold learning method for non-linear dimensionality reduction. The definition of M-GP involves a scale-invariant wave kernel signature distance map measuring the local similarities of geometric features, and a heat flow entropy which implicitly embeds the global curvature flow. With such a design, the prior knowledge fully encodes the geometric information so that a posterior predictive inference is morphometrically significant. In experiments, we use 518 grey matter tetrahedral meshes generated from structural magnetic resonance images of a publicly available Alzheimer’s disease imaging cohort to empirically and numerically evaluate our method. The results verify that our method is theoretically and experimentally valid in selecting a representative subset from the original massive data. Our work may benefit any studies involving large-scale or iterative computations on extensive manifold-valued data, including morphometry analyses and general medical data processing.