Miracle-dz/UniFAR-FTUs huggingface dataset:
A Compendium of Age-Related PheWAS and GWAS Traits for Human Genetic Association Studies, Their Networks and Genetic Correlations

The rich data from the genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) offer an unprecedented opportunity to identify the biological underpinnings of age-related disease (ARD) risk and multimorbidity. Surprisingly, however, a comprehensive list of ARDs remains unavailable due to the lack of a clear definition and selection criteria. We developed a method to identify ARDs and to provide a compendium of ARDs for genetic association studies. Querying 1,358 electronic medical record-derived traits, we first defined ARDs and age-related traits (ARTs) based on their prevalence profiles, requiring a unimodal distribution that shows an increasing prevalence after the age of 40 years, and which reaches a maximum peak at 60 years of age or later. As a result, we identified a list of 463 ARDs and ARTs in the GWAS and PheWAS catalogs. We next translated the ARDs and ARTs to their respective 276 Medical Subject Headings diseases and 45 anatomy terms. The most abundant disease categories are neoplasms (48 terms), cardiovascular diseases (44 terms), and nervous system diseases (27 terms). Employing data from a human symptoms-disease network, we found 6 symptom-shared disease groups, representing cancers, heart diseases, brain diseases, joint diseases, eye diseases, and mixed diseases. Lastly, by overlaying our ARD and ART list with genetic correlation data from the UK Biobank, we found 54 phenotypes in 2 clusters with high genetic correlations. Our compendium of ARD and ART is a highly useful resource, with broad applicability for studies of the genetics of aging, ARD, and multimorbidity.
Involvement of RpoN in Regulating Motility, Biofilm, Resistance, and Spoilage Potential of Pseudomonas fluorescens

Pseudomonas fluorescens is a typical spoiler of proteinaceous foods, and it is characterized by high spoilage activity. The sigma factor RpoN is a well-known regulator controlling nitrogen assimilation and virulence in many pathogens. However, its exact role in regulating the spoilage caused by P. fluorescens is unknown. Here, an in-frame deletion mutation of rpoN was constructed to investigate its global regulatory function through phenotypic and RNA-seq analysis. The results of phenotypic assays showed that the rpoN mutant was deficient in swimming motility, biofilm formation, and resistance to heat and nine antibiotics, while the mutant increased the resistance to H2O2. Moreover, the rpoN mutant markedly reduced extracellular protease and total volatile basic nitrogen (TVB-N) production in sterilized fish juice at 4°C; meanwhile, the juice with the rpoN mutant showed significantly higher sensory scores than that with the wild-type strain. To identify RpoN-controlled genes, RNA-seq-dependent transcriptomics analysis of the wild-type strain and the rpoN mutant was performed. A total of 1224 genes were significantly downregulated, and 474 genes were significantly upregulated by at least two folds at the RNA level in the rpoN mutant compared with the wild-type strain, revealing the involvement of RpoN in several cellular processes, mainly flagellar mobility, adhesion, polysaccharide metabolism, resistance, and amino acid transport and metabolism; this may contribute to the swimming motility, biofilm formation, stress and antibiotic resistance, and spoilage activities of P. fluorescens. Our results provide insights into the regulatory role of RpoN of P. fluorescens in food spoilage, which can be valuable to ensure food quality and safety.
Effects of Social Distancing During the COVID-19 Pandemic on Anxiety and Eating Behavior—A Longitudinal Study

As social animals, humans need to live in groups. This contact with conspecifics is essential for their evolution and survival. Among the recommendations to reduce transmission of the new coronavirus (SARS-CoV-2) responsible for COVID-19 are social distancing and home confinement. These measures may negatively affect the social life and, consequently, the emotional state and eating behavior of individuals. We assessed the impact of the COVID-19 pandemic on the anxiety, premenstrual symptoms, and eating behavior of young women. Data collection was conducted in person (prepandemic—from March to December 2019) and online (during the pandemic—August 2020). A total of 71 participants, average age of 21.26 years (SD = 0.41), took part in the study. Trait anxiety during the pandemic was significantly lower than in the prepandemic period. Investigation of the “anxiety/stress” symptom of the Premenstrual Symptoms Screening Tool (PSST) revealed that this symptom was more severe before the pandemic. There was a decline in the desire for sweet and fatty foods during the pandemic. However, craving for traditional foods rose significantly in the same period. Uncontrolled and emotional eating were significantly lower during the pandemic. The results suggest that the pandemic may have had a positive impact on anxiety and eating behavior of the participants, which may be due to differences between urban and rural populations and the latter living with their families. These findings are important for raising a discussion regarding the effects of the current environment on the regulation of cognitive and dietary adaptations.
Dual Capability EAM for Agility in Business Capability Building: A Systems Theoretical View

Through two cases of IT-enabled business capability building in large enterprises, this paper elucidates how the systems theory approach can explain the enterprise architecture management (EAM) challenge to support business agility. The observation in both of these cases is that a legacy EAM approach does not adapt to a business development scenario involving agility. This leads to a study of the nature of the challenges in EAM when enabling strategic business moves involving new technologies, at the business unit level. For the type of projects as in these cases, we do not find a fitting paradigm in the EAM literature. Suggested solutions are IT bimodality, or Two Speed IT. However, its combination with EAM is scarce in earlier research. To be able to provide guiding ideas for the further development of a dual capability EAM approach, with an evident need, we develop a systems theoretical starting point to examine the cases. Complex Adaptive System (CAS) characteristics appear to give the necessary explanations to build on. Supported by this theoretical development, the study results in principles of a dual capability EAM, for agile strategic business capability building involving enterprise re-structuring.
On Computing the Diameter of (Weighted) Link Streams

A weighted link stream is a pair (V, 𝔼) comprising V, the set of nodes, and 𝔼, the list of temporal edges (u,v,t,λ) , where u,v are two nodes in V, t is the starting time of the temporal edge, and λ is its travel time. By making use of this model, different notions of diameter can be defined, which refer to the following distances: earliest arrival time, latest departure time, fastest time, and shortest time. After proving that any of these diameters cannot be computed in time sub-quadratic with respect to the number of temporal edges, we propose different algorithms (inspired by the approach used for computing the diameter of graphs) that allow us to compute, in practice very efficiently, the diameter of quite large real-world weighted link stream for several definitions of the diameter. In the case of the fastest time distance and of the shortest time distance, we introduce the notion of pivot-diameter, to deal with the fact that temporal paths cannot be concatenated in general. The pivot-diameter is the diameter restricted to the set of pair of nodes connected by a path that passes through a pivot (that is, a node at a given time instant). We prove that the problem of finding an optimal set of pivots, in terms of the number of pairs connected, is NP-hard, and we propose and experimentally evaluate several simple and fast heuristics for computing “good” pivot sets. All the proposed algorithms (for computing either the diameter or the pivot-diameter) require very often a very low number of single source (or target) best path computations. We verify the effectiveness of our approaches by means of an extensive set of experiments on real-world link streams. We also experimentally prove that the temporal version of the well-known 2-sweep technique, for computing a lower bound on the diameter of a graph, is quite effective in the case of weighted link stream, by returning very often tight bounds.
De-identification of Medical Information for Forming Multimodal Datasets to Train Neural Networks

An important source of medical information for forming multimodal datasets to train neural networks is electronic patient records. In order to process data from electronic health records with a specified purpose, the number of requirements must be met first of all, de-identification. This paper discusses the first stage of this process searching for named entities in medical texts (which should be replaced or encrypted afterwards). The problem is solved by an example of semi-structured EHRs in Russian as a fusional, grammatically complex language. The structure and specificity of EMC typical for Russia is analyzed in detail. A problem-oriented comparison of approaches to solving the NER problem is carried out. We developed a pipeline for processing of HER and experimentally showed the advantages of the rule-based method over using specialized libraries. The achieved Recall and Precision values were 0.990 and 0.980 respectively.
PlaceProfile: Employing Visual and Cluster Analysis to Profile Regions based on Points of Interest

: Understanding how commercial and social activities and points of interest are located in a city is essential to plan efﬁcient cities in smart mobility. Over the years, the growth of data sources from distinct online social networks has enabled new perspectives to applications that provide mechanisms to aid in comprehension of how people displaces between different regions within a city. To support enterprises and governments better understand and compare distinct regions of a city, this work proposes a web application called PlaceProﬁle to perform visual proﬁling of city areas based on iconographic visualization and to label areas based on clustering algorithms. The visualization results are overlayered on Google Maps to enrich the map layout and aid analyst in understanding region proﬁling at a glance. Besides, PlaceProﬁle coordinates a radar chart with areas selected by the user to enable detailed inspection of the frequency of categories of points of interest (POIs). This linked views approach also supports clustering algorithms’ explainability by providing inspections of the attributes used to compute similarities. We employed the proposed approach in a case study in the S˜ao Paulo city, Brazil.
Database-Conscious End-to-End Testing for Reactive Systems using Containerization

: Reactive systems are a relatively new paradigm in computer science architecture with important implications for computer science. While much attention has been paid to effectively running end-to-end (E2E) tests on these architectures, little work has considered the implications of tests which modify the database. We propose a framework to group and orchestrate E2E tests based on data qualities across a series of parallel containerized application instances. The framework is designed to run completely independent tests in parallel while being mindful of system costs. We present a conceptual version of the framework and discuss future directions with this work.
NEWRITER: A Text Editor for Boosting Scientific Paper Writing

: Nowadays, in the scientiﬁc ﬁeld, text production is required from scientists as means of sharing their research and contribution to science. Scientiﬁc text writing is a task that demands time and formal writing skills and can be speciﬁcally slow and challenging for inexperienced researchers. In addition, scientiﬁc texts must be written in English, follow a speciﬁc style and terminology, which can be a difﬁcult task specially for researchers that aren’t native English speakers or that don’t know the speciﬁc writing procedures required by some publisher. In this article, we propose N E W RITER , a neural network based approach to address scientiﬁc text writing assistance. In particular, it enables users to feed related scientiﬁc text as an input to train a scientiﬁc-text base language model into a user-customized, specialized one. Then, the user is presented with real-time text suggestions as they write their own text. Experimental results show that our user-customized language model can be effectively used for scientiﬁc text writing assistance when compared to state-of-the-art pre-trained models.
Towards the Automation of Industrial Data Science: A Meta-learning based Approach

In context of the fourth industrial revolution (industry 4.0), the industrial big data is subject to grow rapidly to respond the agile industrial computing and manufacturing technologies. This data evolution can be captured using ubiquitous integrated sensors and multiple smart machines. We believe the use of data science methodologies, for the selection of models and configuration of hyper-parameters, may help to better control such data evolution. But, at the same time, the industrial practitioners and researchers often lack machine-learning expertise to directly retrieve the benefit from valuable manufacturing big data. Such a lack poses the major obstacle to yield value from even-though familiar data. In this case, a collaboration with data scientists may become an exigence along with the extensive machine learning knowledge which presumably may result to pursue further delays and effort. Multiple approaches for automating machine learning (AutoML) have been proposed for the past recent years in order to alleviate this deficiency. These approaches are expected to perform better along with accomplishment of computing resources which are mostly not readily accessible. To address this research challenge, in this paper, we propose a meta-learning based approach that may serve an effective decision support system for the AutoML process.
Faceshield HUD: Extended Usage of Wearable Computing on the COVID-19 Frontline

Wearable Computing brings up novel methods and appliances to solve various problems in society’s routine tasks. Also, it brings the possibility of enhancing human abilities and perception throughout the execution of specialist activities. Finally, the flexibility and modularity of wearable devices allow the idealization of multiple appliances. In 2020, the world faced a global threat from the COVID-19 pandemic. Healthcare professionals are directly exposed to contamination and therefore require attention. In this work, we propose a novel wearable appliance to aid healthcare professionals working on the frontline of pandemic control. This new approach aids the professional in daily tasks and monitors his health for early signs of contamination. Our results display the system feasibility and constraints using a prototype and indicate initial restrictions for this appliance. This proposal also works as a benchmark for the aid in health monitoring in general hazardous situations. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
Lessons Learned from a Lean R&D Project

In a partnership between academia and industry, we report our experience in applying the Lean R&D approach in the IAGO project, an oil and gas machine-learning-based dashboard. The approach is grounded in continuous experimentation through agile development, beginning with a Lean Inception workshop to define a Minimal Viable Product. Then, after technical feasibility assessments and conception phases, Scrum-based development begins, continually testing business hypotheses. We discuss our experiences in following the approach, and report developers’ perceptions gathered through interviews and user evaluations of the final product. We found that the Lean Inception works well for aligning expectations and objectives among stakeholders, but it is not enough to level the domain knowledge among developers. The participation of end users in the workshop and throughout the project, as well as constant communication among all stakeholders, is very important to deliver appropriate solutions.
Multi-object Tracking for Urban and Multilane Traffic: Building Blocks for Real-World Application

Visual object detection and tracking is a fundamental research topic in computer vision with multiple applications ranging from object classification to multi-object tracking in heavy urban traffic scenarios. While object detection and tracking tasks, especially multi-object tracking, have multiple solutions, it is still unclear how to build the real-world applications using different building blocks like algorithms, filters, base neuron networks, etc. The issue becomes more sophisticated as most of the recently proposed solutions are based on existing methodologies, frameworks and applicable technologies; however, some are showing promising results using contradictory realization. This paper addresses issues and research trends of multi-object tracking, while depicting its building blocks and currently best solutions. In result, a potential building blocks for realworld application in the framework of Jelgava city in Latvia is presented.
Systematic Selection and Prioritization of Communication Channels in the Healthcare Sector

Many industries are using multi-channel approaches to bring users and organizations closer. The services provided through these channels can be leveraged by using chatbots, allowing users to have simpler and more natural interactions. However, this entails designing architectures that make services available through multiple channels, while making the users’ experiences coherent, as they switch between them. Media Richness Theory introduces a way to classify the richness of communication channels, resorting to objective factors, however it does not provide a systematic channel selection and prioritization process. To address these needs, this work proposes a systematic approach to select and prioritize communication channels based on six factors: feedback, multiple cues, personal focus, language variety, accessibility and cost. To validate this approach, the systematic process is applied to three use cases in the healthcare domain.
Malicious Activity Detection using Smart Contracts in IoT

Internet of Things (IoT) is a unique element in the realm of Cybersecurity. It constitutes countless applications, including defense, health, agriculture, finance, amongst other industries. The majority of existing studies focus on various developments of IoT products and services essential to our day-to-day activities, with little emphasis on the security of developed systems. This has led to the proliferation of IoT solutions acquired through rapid development and overlooking the need for a structured security framework during the systems’ development stages. IoT security capability can be improved by using complementary technologies. This paper explores applying Risk-Based Access Control Model using Blockchain to control access to IoT devices. Although current access control models provide efficient security measures to control who can access the system resources, there is no way to detect and prevent malicious attacks after granting access. The proposed solution utilizes smart contracts under the Hyperledger Fabric (HLF) Blockchain Framework to create access permissions and measure the security risks associated with any event in the IoT system and create access permissions to determine what processes may be performed. This will allow the detection of any malicious activity at the early stages of the attack and grant or deny access based on the risk associated with
Detection of Malicious Binaries by Deep Learning Methods

Modern day cyberattacks are complex in nature. These attacks have adverse effects like loss of privacy, intellectual property and revenue on the victim institutions. These attacks have sophisticated payloads like ransom-ware for money extortion, distributed denial of service(DDOS) malware for service disruptions and advanced persistent threat(APT) malware to posses complete control over the victims computing resources. These malware are metamorphic and polymorphic in nature and contains root-kit components to maintain stealth and hide their malicious activity. So conventional defence mechanisms like rule-based and signature based mechanisms fail to detect these malware. Modern approaches use behavioural analysis(static analysis, dynamic analysis) to identity this kind of malware. However behavioural analysis process is hindered by factors like execution environment detection, code obfuscation, anti virtualization, anti-debugging, analysis environment detection etc. Behavioural analysis also requires domain expert to review the large amount of logs produced by it to decide on the nature of the binary which is complex, time consuming and expensive. To deal with these problems we proposed deep learning methods, where convolutional neural network model is trained on the image representation of the binary to decide the binary nature as malicious or benign. In this work we have encoded the binaries into images in a unique way. Deep convolution neural network is trained on these images to learn the features to identify the binary as malicious or normal. The malware and benign samples for the dataset creation are collected from online sources and windows operating system along with compatible third party application software respectively.
Estimated Costs for Single Tuition Fee (STF) using Naïve Bayes Method

The difference in the amount of single tuition fee (STF) paid by students with middle and upper economic backgrounds causes an injustice gap. This is partly due to the instability of the system used, especially in terms of STF determination methods. Other additional shortcomings include the criteria entered the system that is still not enough to be considered in determining STF. Therefore, this study aims to build a web based STF payment system using the Naïve Bayes, probability, and statistical methods for students to determine the cost of an institution's tuition fee easily. System testing is carried out by comparing the output and verification results. This showed that the estimated determination of the cost of STF payments is suitable, with 83.3%. a https://orcid.org/0000-0000-5700-2766 b https://orcid.org/0000-0001-8716-7062 c https://orcid.org/0000-0002-5653-2298 d https://orcid.org/0000-0002-3984-9242 e https://orcid.org/0000-0002-0662-3175 f https://orcid.org/0000-0001-9659-4890 g https://orcid.org/0000-0002-0075-6911 h https://orcid.org/0000-0003-1180-9625 i https://orcid.org/0000-0001-8334-3030 j https://orcid.org/0000-0002-8009-4603 k https://orcid.org/0000-0002-1162-2660 l https://orcid.org/0000-0002-7963-4852 m https://orcid.org/0000-0003-2383-9944 n https://orcid.org/0000-0002-9831-7465