Search

Scientific data repository has a main role in science because data can be reused, reproduced, and preserved in a long time. In Indonesia there is no institution that manage scientific data repository, generally they only manage publication such as books, journals and proceedings. This is because, most of research data is still managed by a researcher or research group. By using literature study and survey to the journal publisher, authors want to get an information on how to manage research data by publications. Furthermore, the result of literature study is compared to the survey result that produces an important point i.e journal publisher strongly agree to make a policy to the author to attach research data in every paper submitted. Most of journal publisher use Open Journal System (OJS) in managing journal articles, start from paper acceptance until paper publishing. Through this way, research data that attached will be automatically stored to the scientific data repository system based on Application Programming Interface (API).

The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. The latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (EEG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. Our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. The return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of EEG statistical properties.

Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart’s activity. However, automated medical-aided diagnosis with computers usually requires a large volume of labeled clinical data without patients' privacy to train the model, which is an empirical problem that still needs to be solved. To address this problem, we propose a generative adversarial network (GAN), which is composed of a bidirectional long short-term memory(LSTM) and convolutional neural network(CNN), referred as BiLSTM-CNN,to generate synthetic ECG data that agree with existing clinical data so that the features of patients with heart disease can be retained. The model includes a generator and a discriminator, where the generator employs the two layers of the BiLSTM networks and the discriminator is based on convolutional neural networks. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). The results showed that the loss function of our model converged to zero the fastest. We also evaluated the loss of the discriminator of GANs with different combinations of generator and discriminator. The results indicated that BiLSTM-CNN GAN could generate ECG data with high morphological similarity to real ECG recordings.

The three dimensional organization of genomes remains mostly unknown due to their high degree of condensation. Biophysical studies predict that condensation promotes the topological entanglement of chromatin fibers and the inhibition of function. How organisms balance between functionally active genomes and a high degree of condensation remains to be determined. Here we hypothesize that the Rabl configuration, characterized by the attachment of centromeres and telomeres to the nuclear envelope, helps to reduce the topological entanglement of chromosomes. To test this hypothesis we developed a novel method to quantify chromosome entanglement complexity in 3D reconstructions obtained from Chromosome Conformation Capture (CCC) data. Applying this method to published data of the yeast genome, we show that computational models implementing the attachment of telomeres or centromeres alone are not sufficient to obtain the reduced entanglement complexity observed in 3D reconstructions. It is only when the centromeres and telomeres are attached to the nuclear envelope (i.e. the Rabl configuration) that the complexity of entanglement of the genome is comparable to that of the 3D reconstructions. We therefore suggest that the Rabl configuration is an essential player in the simplification of the entanglement of chromatin fibers.

Introduction: with the definitive consolidation of the Information and Communication Digital Technologies in all human processes in the beginning of the 21st century, daily, it is produced an endless amount of data. This avalanche of data is known as the Big Data phenomenon. In the academic field, new possibilities related to the use of research data are emerging. Researchers from several different areas are paying special attention to the Data Management Plan, which involves the curatorship of the research data during all its life cycle, since the planning stage until its (re)use. Objective: aims to investigate how the information professionals can act on the component stages of the Data Life Cycle – DataONE. Methodology: is an exploratory-descriptive and bibliographical research, assuming the comparative method which associates the competences of the information professionals that can be applied and / or adapted in the accomplishment of the component stages of the data life cycle – DataONE. Results: it has been found that most of the skills of information professionals are aligned with the components of the data life cycle. Conclusions: it was possible to conclude that the competence of the information professionals can be effectively applied and suited to the reality of the research data, more precisely, in the process of the data life cycle. Introducción: con la consolidación de las Tecnologías Digitales de Información y Comunicación en la vasta mayoría de los procesos humanos a principios del siglo XXI, diariamente, producimos una infinidad de datos. En el campo académico, nuevas posibilidades relacionadas al uso de datos de investigación, están emergiendo. Los investigadores de las más variadas áreas están prestando especial atención al Plan de Gestión de Datos, el cual involucra la curaduría de los datos de investigación, durante todo su ciclo de vida, desde su planificación hasta su (re) uso. Objetivo: busca investigar cómo los profesionales de la información pueden actuar en las etapas componentes del Ciclo de vida de los datos propuesto por el DataONE. Metodología: investigación exploratoria-descriptiva, y bibliográfica, asumiendo el método comparado el cual asoció las competencias de los profesionales de la información que pueden ser aplicadas y / o adaptadas en la realización de las etapas componentes del Ciclo de vida de los datos - DataONE. Resultados: se comprobó que la mayoría de las competencias de los profesionales de la información se alinean con los componentes del ciclo de vida de los datos. Conclusiones: Concluimos que las competencias de los profesionales de la información pueden ser efectivamente aplicadas y adecuadas a la realidad de los datos de investigación, más precisamente en el proceso del ciclo de vida de los datos. Introdução: com a consolidação das Tecnologias Digitais de Informação e Comunicação na vasta maioria dos processos humanos no início do século XXI, diariamente, produzimos uma infinidade de dados. Essa avalanche de dados é denominada como o fenômeno Big Data. Na seara acadêmica, novas possibilidades relacionadas ao uso de dados de pesquisas, estão emergindo. Pesquisadores das mais variadas áreas estão dando especial atenção ao Plano de Gestão de Dados, o qual envolve a curadoria dos dados de pesquisa, durante todo o seu ciclo de vida, desde o seu planejamento até o seu (re)uso. Objetivo: busca investigar como os profissionais da informação podem atuar nas etapas componentes do Ciclo de Vida dos Dados – DataONE. Metodologia: configura-se como uma pesquisa exploratória-descritiva, e bibliográfica, assumindo o método comparado o qual associou as competências dos profissionais da informação que podem ser aplicadas e/ou adaptadas na realização das etapas componentes do Ciclo de Vida dos Dados – DataONE. Resultados: verificou-se que a maioria das competências dos profissionais da informação alinham-se às componentes do ciclo de vida dos dados. Conclusões: concluímos que as competências dos profissionais da informação podem ser efetivamente aplicadas e adequadas à realidade dos dados de pesquisa, mais precisamente no processo do ciclo de vida dos dados.

Introduction: The importance of the dissemination of research data has been increasingly debated by the scientific community in the context of Open Science, mainly to maximize the use and reuse of data from scientific research. Data Repositories aim at the storage, organization, dissemination, retrieval and preservation of research data, enhancing communication and scientific collaboration. Studies in the field of Information Science, especially Information Findability, bring significant contributions to the design and implementation of Data Repositories. Objectives: to analyze the DataONE data repository from the viewpoint of Information Findability. Methodology: the observation technique was used with the support of the evaluation tool checklist, which allows the analysis of informational environments from the attributes of Information Findability. Results: positive aspects include the use of specific metadata standards for the Earth Science community, the Ecological Metadata Language (EML), and the attribute of Responsiveness. As a negative aspect, we can notice the lack of Digital Accessibility resources. Conclusion: it is concluded that, in general, DataONE contemplates the main attributes of Information Findability, which facilitates the interaction and findability of data and information by informational subjects.

A Retrieval System requires several components that define its functionality and behavior. In the case of a meta-search engine for the retrieval of scientific data, a schema that defines the way to store such data is considered a necessary element for its evolution. Unified profiles have been developed for the data storage of the entities involved in the scientific data management, generated from the fact of publishing a scientific paper. Such profiles are considered the beginning of the generation of new components for the meta-search engine that, using the proprietary information, can deliver information relevant for the user of the tool. To this end, the use of an intelligent distributed data warehouse is proposed.

Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.

The sudden and unexpected migration flows that reached Europe during the so-called ‘refugee crisis’ of 2015–2016 left governments unprepared, exposing significant shortcomings in the field of migration forecasting. Forecasting asylum-related migration is indeed problematic. Migration is a complex system, drivers are composite, measurement incorporates uncertainty, and most migration theories are either under-specified or hardly actionable. As a result, approaches to forecasting generally focus on specific migration flows, and the results are often inconsistent and difficult to generalise. Here we present an adaptive machine learning algorithm that integrates administrative statistics and non-traditional data sources at scale to effectively forecast asylum-related migration flows. We focus on asylum applications lodged in countries of the European Union (EU) by nationals of all countries of origin worldwide, but the same approach can be applied in any context provided adequate migration or asylum data are available. Uniquely, our approach (a) monitors drivers in countries of origin and destination to detect early onset change; (b) models individual country-to-country migration flows separately and on moving time windows; (c) estimates the effects of individual drivers, including lagged effects; (d) delivers forecasts of asylum applications up to four weeks ahead; (e) assesses how patterns of drivers shift over time to describe the functioning and change of migration systems. Our approach draws on migration theory and modelling, international protection, and data science to deliver what is, to our knowledge, the first comprehensive system for forecasting asylum applications based on adaptive models and data at scale. Importantly, this approach can be extended to forecast other social processes.

Notational analysis is a popular tool for understanding what constitutes optimal performance in traditional sports. However, this approach has been seldom used in esports. The popular esport “Rocket League” is an ideal candidate for notational analysis due to the availability of an online repository containing data from millions of matches. The purpose of this study was to use Random Forest models to identify in-match metrics that predicted match outcome (performance indicators or “PIs”) and/or in-game player rank (rank indicators or “RIs”). We evaluated match data from 21,588 Rocket League matches involving players from four different ranks. Upon identifying goal difference (GD) as a suitable outcome measure for Rocket League match performance, Random Forest models were used alongside accompanying variable importance methods to identify metrics that were PIs or RIs. We found shots taken, shots conceded, saves made, and time spent goalside of the ball to be the most important PIs, and time spent at supersonic speed, time spent on the ground, shots conceded and time spent goalside of the ball to be the most important RIs. This work is the first to use Random Forest learning algorithms to highlight the most critical PIs and RIs in a prominent esport.

The protection of private data is a hot research issue in the era of big data. Differential privacy is a strong privacy guarantees in data analysis. In this paper, we propose DP-MSNM, a parametric density estimation algorithm using Multivariate Skew-Normal Mixtures (MSNM) model to differential privacy. MSNM can solve the asymmetric problem of datasets, and it is could approximate any distribution through Expectation-Maximization (EM) algorithm. In this model,we add two extra steps on the estimated parameters in the M step of each iteration. The first step is adding calibrated noise to the estimated parameters based on Laplacian mechanism. The second step is post-processes those noisy parameters to ensure their intrinsic characteristics based on the theory of vector normalize and positive semi definition matrix. Extensive experiments using both real datasets evaluate the performance of DP-MSNM, and demonstrate that the proposed method outperforms DP-GMM.

Two highly relevant aspects of football, namely forecasting of results and performance analysis by means of performance indicators, are combined in the present study by analysing the value of in-play information in terms of event and positional data in forecasting the further course of football matches. Event and positional data from 50 matches, including more than 300 million datapoints were used to extract a total of 18 performance indicators. Moreover, goals from more than 30,000 additional matches have been analysed. Results suggest that surprisingly goals do not possess any relevant informative value on the further course of a match, if controlling for pre-game market expectation by means of betting odds. Performance indicators based on event and positional data have been shown to possess more informative value than goals, but still are not sufficient to reveal significant predictive value in-play. The present results are relevant to match analysts and bookmakers who should not overestimate the value of in-play information when explaining match performance or compiling in-play betting odds. Moreover, the framework presented in the present study has methodological implications for performance analysis in football, as it suggests that researchers should increasingly segment matches by scoreline and control carefully for general team strength.

Scientific data repositories act as important tools to support data storage, preservation and sharing. In this sense, the TRUST Principles emerge as a set of guidelines that seek to guarantee the reliability of digital data repositories. This research aims to verify the applicability of TRUST Principles as an assessment tool for data repositories, based on the analysis of the “Covid-19 Data Sharing / BR” repository. For this, two instruments were used: first, a set of criteria was developed to assess the repository's adherence to each of the TRUST Principles; second, a scoring system capable of assessing the repository's adherence to each criterion, on a scale graded from one to five, was formulated, the average score obtained in each principle was named trustworthiness degree. The scoring system was modeled using a software developed in the PHP programming language. It was observed that the principle with the highest value in the repository was Responsibility, while a low adherence to the Technology principle was noted. The research found that the TRUST Principles can be used as a useful certification tool for data repositories, as they direct their focus to very specific aspects of the repositories. Os repositórios de dados científicos atuam como importantes ferramentas de suporte ao armazenamento, preservação e compartilhamento de dados. Nesse sentido, os Princípios TRUST emergem como um conjunto de diretrizes que buscam garantir a confiabilidade de repositórios de dados digitais. Esta pesquisa tem como objetivo verificar a aplicabilidade dos Princípios TRUST como uma ferramenta de avaliação de repositórios de dados, a partir da análise do repositório “Covid-19 Data Sharing/BR”. Para isso, contou-se com dois instrumentos: primeiro, elaborou-se um conjunto de critérios para avaliar a aderência do repositório a cada um dos Princípios TRUST; segundo, formulou-se um sistema de pontuação capaz mensurar a aderência do repositório a cada critério, em uma escala de um a cinco, denominando-se de trustworthiness degree a média da pontuação obtida em cada princípio. O sistema de pontuação foi modelado através de um software desenvolvido na linguagem de programação PHP. Observou-se que o princípio com maior valor no repositório foi o de Responsabilidade, ao passo que se notou uma baixa aderência quanto ao princípio Tecnologia. Concluiu-se que os Princípios TRUST podem ser empregados como uma ferramenta útil de certificação de repositórios de dados, visto que direcionam o seu foco para aspectos bastante específicos dos repositórios.

The built environment affects mental health outcomes, but this relationship is less studied and understood. This article proposes a novel multi-level scenario-based predictive analytics framework (MSPAF) to explore the complex relationships between community mental health outcomes and the built environment conditions. The MSPAF combines rigorously validated interpretable machine learning algorithms and scenario-based sensitivity analysis to test various hypotheses on how the built environment impacts community mental health outcomes across the largest metropolitan areas in the US. Among other findings, our results suggest that declining socio-economic conditions of the built environment (e.g., poverty, low income, unemployment, decreased access to public health insurance) are significantly associated with increased reported mental health disorders. Similarly, physical conditions of the built environment (e.g., increased housing vacancies and increased travel costs) are significantly associated with increased reported mental health disorders. However, this positive relationship between the physical conditions of the built environment and mental health outcomes does not hold across all the metropolitan areas, suggesting a mixed effect of the built environment’s physical conditions on community mental health. We conclude by highlighting future opportunities of incorporating other variables and datasets into the MSPAF framework to test additional hypotheses on how the built environment impacts community mental health.

The identification of structural damages takes a more and more important role within the modern economy, where often the monitoring of an infrastructure is the last approach to keep it under public use. Conventional monitoring methods require specialized engineers and are mainly time-consuming. This research paper considers the ability of neural networks to recognize the initial or alteration of structural properties based on the training processes. The presented model, a spatially asymmetric encoder–decoder network, is based on 1D-Convolutional Neural Networks (CNN) for wave field pattern recognition, or more specifically the wave field change recognition. The proposed model is used to identify the change within propagating wave fields after a crack initiation within the structure. The paper describes the implemented method and the required training procedure to get a successful crack detection accuracy, where the training data are based on the dynamic lattice model. Although the training of the model is still time-consuming, the proposed new method has an enormous potential to become a new crack detection or structural health monitoring approach within the conventional monitoring methods.

Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model’s performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.

Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain conclusive data sets. To automate this process, we developed Gold Digger, a software tool that uses a modified pix2pix deep learning network capable of detecting and annotating colloidal gold particles in biological EM images obtained from both freeze-fracture replicas and plastic sections prepared with the post-embedding method. Gold Digger performs at near-human-level accuracy, can handle large images, and includes a user-friendly tool with a graphical interface for proof reading outputs by users. Manual error correction also helps for continued re-training of the network to improve annotation accuracy over time. Gold Digger thus enables rapid high-throughput analysis of immunogold-labeled EM data and is freely available to the research community.

Identifying structure underlying high-dimensional data is a common challenge across scientific disciplines. We revisit correspondence analysis (CA), a classical method revealing such structures, from a network perspective. We present the poorly-known equivalence of CA to spectral clustering and graph-embedding techniques. We point out a number of complementary interpretations of CA results, other than its traditional interpretation as an ordination technique. These interpretations relate to the structure of the underlying networks. We then discuss an empirical example drawn from ecology, where we apply CA to the global distribution of Carnivora species to show how both the clustering and ordination interpretation can be used to find gradients in clustered data. In the second empirical example, we revisit the economic complexity index as an application of correspondence analysis, and use the different interpretations of the method to shed new light on the empirical results within this literature.

Gait rehabilitation therapies provide adjusted sensory inputs to modify and retrain walking patterns in an impaired gait. Asymmetric walking is a common gait abnormality, especially among stroke survivors. Physical therapy interventions using adaptation techniques (such as treadmill training, auditory stimulation, visual biofeedback, etc.) train gait toward symmetry. However, a single rehabilitation therapy comes up short of affecting all aspects of gait performance. Multiple-rehabilitation therapy applies simultaneous stimuli to affect a wider range of gait parameters and create flexible training regiments. Understanding gait responses to individual and jointly applied stimuli is important for developing improved and efficient therapies. In this study, 16 healthy subjects participated in a four-session experiment to study gait kinetics and spatiotemporal outcomes under training. Each session consisted of two stimuli, treadmill training and auditory stimulation, with symmetric or asymmetric ratios between legs. The study hypothesizes a linear model for gait response patterns. We found that the superposition principle largely applies to the gait response under two simultaneous stimuli. The linear models developed in this study fit the actual data from experiments with the r-squared values of 0.95 or more.

Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.

Geomagnetic substorms are a global magnetospheric reconfiguration, during which energy is abruptly transported to the ionosphere. Central to this are the auroral electrojets, large-scale ionospheric currents that are part of a larger three-dimensional system, the substorm current wedge. Many, often conflicting, magnetospheric reconfiguration scenarios have been proposed to describe the substorm current wedge evolution and structure. SuperMAG is a worldwide collaboration providing easy access to ground based magnetometer data. Here we show application of techniques from network science to analyze data from 137 SuperMAG ground-based magnetometers. We calculate a time-varying directed network and perform community detection on the network, identifying locally dense groups of connections. Analysis of 41 substorms exhibit robust structural change from many small, uncorrelated current systems before substorm onset, to a large spatially-extended coherent system, approximately 10 minutes after onset. We interpret this as strong indication that the auroral electrojet system during substorm expansions is inherently a large-scale phenomenon and is not solely due to many meso-scale wedgelets., During geomagnetic substorms, the energy accumulated from solar wind is abruptly transported to ionosphere. Here, the authors show application of community detection on the time-varying networks constructed from all magnetometers collaborating with the SuperMAG initiative.

Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening `in phases'. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. {We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population., 5 figures

Group A Streptococcus (GAS) is a globally significant bacterial pathogen. The GAS genotyping gold standard characterises the nucleotide variation of emm, which encodes a surface-exposed protein that is recombinogenic and under immune-based selection pressure. Within a supervised learning methodology, we tested three random forest (RF) algorithms (Guided, Ordinary, and Regularized) and 53 GAS response regulator (RR) allele types to infer six genomic traits (emm-type, emm-subtype, tissue and country of sample, clinical outcomes, and isolate invasiveness). The Guided, Ordinary, and Regularized RF classifiers inferred the emm-type with accuracies of 96.7%, 95.7%, and 95.2%, using ten, three, and four RR alleles in the feature set, respectively. Notably, we inferred the emm-type with 93.7% accuracy using only mga2 and lrp. We demonstrated a utility for inferring emm-subtype (89.9%), country (88.6%), invasiveness (84.7%), but not clinical (56.9%), or tissue (56.4%), which is consistent with the complexity of GAS pathophysiology. We identified a novel cell wall-spanning domain (SF5), and proposed evolutionary pathways depicting the ‘contrariwise’ and ‘likewise’ chimeric deletion-fusion of emm and enn. We identified an intermediate strain, which provides evidence of the time-dependent excision of mga regulon genes. Overall, our workflow advances the understanding of the GAS mga regulon and its plasticity.

Empirical diagnosis of stability has received considerable attention, mostly focused on variance metrics for early warning signals of abrupt system change. Despite this, the theoretical foundation and application has been limited to relatively simple system changes such as bifurcating fixed points where variability is extrinsic to the steady state. There is currently no foundation and associated metric for empirically exploring stability in wide ranging systems that contain variability in both internal steady state dynamics and in response to external perturbations. Utilizing connections between stability, dissipation, and phase space flow, we show that stability correlates with temporal asymmetry in a measure of phase space flow contraction. Our method is general as it reveals stability variation independent of assumptions about the nature of system variability or attractor shape. After showing efficacy in a variety of model systems, we apply our technique for measuring stability to monthly returns of the S&P 500 index in the time periods surrounding the global stock market crash of October 1987. Market stability is shown to be higher in the several years preceding and subsequent to the 1987 market crash. We anticipate our technique will have wide applicability in climate, ecological, financial, and social systems where stability is a pressing concern., 8 pages, 7 figures

Spaceborne Earth observation is a key technology for flood response, offering valuable information to decision makers on the ground. Very large constellations of small, nano satellites— ’CubeSats’ are a promising solution to reduce revisit time in disaster areas from days to hours. However, data transmission to ground receivers is limited by constraints on power and bandwidth of CubeSats. Onboard processing offers a solution to decrease the amount of data to transmit by reducing large sensor images to smaller data products. The ESA’s recent PhiSat-1 mission aims to facilitate the demonstration of this concept, providing the hardware capability to perform onboard processing by including a power-constrained machine learning accelerator and the software to run custom applications. This work demonstrates a flood segmentation algorithm that produces flood masks to be transmitted instead of the raw images, while running efficiently on the accelerator aboard the PhiSat-1. Our models are trained on WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations, which we make available in a common format. We test the system on independent locations, demonstrating that it produces fast and accurate segmentation masks on the hardware accelerator, acting as a proof of concept for this approach.

This study aims to improve our understanding of gait initiation mechanisms and the lower-limb joint mechanical energy contributions. Healthy subjects were instructed to initiate gait on an instrumented track to reach three self-selected target velocities: slow, normal and fast. Lower-limb joint kinematics and kinetics of the first five strides were analyzed. The results show that the initial lateral weight shift is achieved by hip abduction torque on the lifting leg (leading limb). Before the take-off of the leading limb, the forward body movement is initiated by decreasing ankle plantarflexion torque, which results in an inverted pendulum-like passive forward fall. The hip flexion/extension joint has the greatest positive mechanical energy output in the first stride of the leading limb, while the ankle joint contributes the most positive mechanical energy in the first stride of the trailing limb (stance leg). Our results indicate a strong correlation between control of the frontal plane and the sagittal plane joints during gait initiation. The identified mechanisms and the related data can be used as a guideline for improving gait initiation with wearable robots such as exoskeletons and prostheses.

The spread of COVID-19 is closely related to the structure of human social networks. Based on 237 cases, by using epidemiological retrospective statistics, data visualization, and social network analysis methods, this paper summarized characteristics of patients with COVID-19 in Shaanxi, China, and analyzed these patients’ dynamic contact network structure. The study found that there are many clustered infections through strong ties, about one-third of cases are caused by relatives' infection. In early stages of the epidemic, imported cases were the most, and in the later stages, local infection cases were the most. The infected people were mostly middle-aged men. Symptoms of imported cases occurred on average of 3 days after they arrived, and medical measures were taken 5 days later on average. All cases showed symptoms in less than 2 days on average and were then taken to medical treatment. The contact network can be divided into multiple disconnected components. The largest component has 12 patients. The average degree centrality in the network is 0.987, average betweenness degree is 0, average closeness degree is 0.452, and average PageRank index is 0.0042. The number of contacts of patients is unevenly distributed in the network.

Il regolamento (UE) 2283/2015 sui novel food rappresenta un’evoluzione nella protezione dei dati scienti ci relativi ai nuovi prodotti alimentari. La normativa, sviluppata sul modello offerto dal regolamento (UE) 1924/2006 in materia di indicazioni nutrizionali e sulla salute, garantisce una protezione quinquennale dei dati scienti ci e delle prove pro- dotte dal richiedente in sede di domanda di autorizzazione di un nuovo prodotto ex art. 10 del regolamento (UE) 2283/2015. La previsione di un termine quinquennale di tutela se, secondo taluni, può risultare troppo breve per rappresentare un vero incentivo per compiere investimenti in ricerca e sviluppo da parte delle imprese; d’altra parte può risultare un tempo idoneo a limitare la concorrenza, facendo, altresì, emergere criticità.

We have further extended our compartmental model describing the spread of the infection in Italy. As in our previous work, the model assumes that the time evolution of the observable quantities (number of people still positive to the infection, hospitalized and fatalities cases, healed people, and total number of people that has contracted the infection) depends on average parameters, namely people diffusion coefficient, infection cross-section, and population density. The model provides information on the tight relationship between the variation of the reported infection cases and a well-defined observable physical quantity: the average number of people that lie within the daily displacement area of any single person. With respect to our previous paper, we have extended the analyses to several regions in Italy, characterized by different levels of restrictions and we have correlated them to the diffusion coefficient. Furthermore, the model now includes self-consistent evaluation of the reproduction index, effect of immunization due to vaccination, and potential impact of virus variants on the dynamical evolution of the outbreak. The model fits the epidemic data in Italy, and allows us to strictly relate the time evolution of the number of hospitalized cases and fatalities to the change of people mobility, vaccination rate, and appearance of an initial concentration of people positives for new variants of the virus.

Structural identifiabilityis a binary property that determines whether or notuniqueparameter values can, in principle, be estimated from error-free input–output data. The many papers that have been written on this topic collectively stress the importance of this a priori analysis in the model development process. The story however, often ends with a structurally unidentifiable model. This may leave a model developer with no plan of action on how to address this potential issue. We continue this model exploration journey by identifying one of the possible sources of a model’s unidentifiability: problematic initial conditions. It is well-known that certaininitial valuesmay result in the loss of local structural identifiability. Nevertheless, literature on this topic has been limited to the analysis of small toy models. Here, we present a systematic approach to detect problematic initial conditions of real-world systems biology models, that are usually not small. A model’s identifiability can often be reinstated by changing the value of such problematic initial conditions. This provides modellers an option to resolve the “unidentifiable model” problem. Additionally, a good understanding of which initial values should rather be avoided can be very useful during experimental design. We show how our approach works in practice by applying it to five models. First, two small benchmark models are studied to get the reader acquainted with the method. The first one shows the effect of a zero-valued problematic initial condition. The second one illustrates that the approach also yields correct results in the presence of input signals and that problematic initial conditions need not be zero-values. For the remaining three examples, we set out to identify key initial values which may result in the structuralunidentifiability. The third and fourth examples involve a systems biology Epo receptor model and a JAK/STAT model, respectively. In the final Pharmacokinetics model, of which its global structural identifiability has only recently been confirmed, we indicate that there are still sets of initial values for which this property does not hold.

The arbitrary order Hilbert spectral analyses are applied to study the intermittency and multifractality of Global Horizonal Irradiation (GHI) based on one available high sampling rate of 1-year GHI records located at Saint-Denis (Moufia) over Reunion Island. The scaling exponents $$\xi \left( q \right)$$ ξ q is estimated through the arbitrary order Hilbert spectral analyses, and three parameters: Hurst exponent (H), the fractal co-dimension (C1), and Lévy parameter ($$\alpha$$ α ) are taken to study the multifractal process of the GHI in the sub-daily, daily fluctuations and also in seasonal variations. A power law behaviour with a spectral exponent β = 1.68 close to the Kolmogorov spectrum is detected through Fourier spectrum analysis, which indicates that the sub-daily fluctuations of GHI are nonstationary. The scaling exponent ζ(q) is then estimated by the arbitrary order Hilbert spectral analysis and the multifractal properties is detected. The log-stable model parameters $$H,{ }C_{1}$$ H , C 1 and $$\alpha$$ α characterize the concavity of the scaling exponent ζ(q) for analysing the intermittency of GHI. The classification method is applied to the daily GHI for analysing the distribution of the daily intermittency process and five classes with GHI and Kb are obtained.

The purpose of this study is to explore the information retrieval process in scientific data and to better understand the concepts and internal relationships of metadata and relevance criteria. Qualitative and quantitative analyses were performed using interview and eye movement data from 36 subjects. The results show that users paid attention to 45 types of metadata and used nine relevance criteria to judge the relevance of scientific data. There was a complex relationship between the metadata and criteria, mainly manifesting as one stimulus–multiple responses and multiple stimuli–one response. Metadata associated with the relevance criterion of topicality is the most complex, which includes common metadata and subject-related metadata. Metadata associated with the other relevance criteria (such as quality and authority) has no obvious professional characteristics. What’s more, because of the essential difference between scientific data and documents, users use different criteria. When retrieving data, users pay more attention to the availability of data and whether they can be further analyzed and processed. This study clarifies the concepts of metadata and relevance criteria as well as their roles in relevance judgments. In addition, this study deepens the understanding of the scientific data relevance judgments and their cognitive process and provides a theoretical basis for improving scientific data-sharing platforms.

Climate simulations often need to be adjusted (i.e., corrected) before any climate change impacts studies. However usual bias correction approaches do not differentiate the bias from the different uncertainties of the climate simulations: scenario uncertainty, model uncertainty and internal variability. In particular, in the case of a multi-run ensemble of simulations (i.e., multiple runs of one model), correcting, as usual, each member separately, would mix up the model biases with its internal variability. In this study, two ensemble bias correction approaches preserving the internal variability of the initial ensemble are proposed. These “Ensemble bias correction” (EnsBC) approaches are assessed and compared to the approach where each ensemble member is corrected separately, using precipitation and temperature series at two locations in North America from a multi-member regional climate ensemble. The preservation of the internal variability is assessed in terms of monthly mean and hourly quantiles. Besides, the preservation of the internal variability in a changing climate is evaluated. Results show that, contrary to the usual approach, the proposed ensemble bias correction approaches adequately preserve the internal variability even in changing climate. Moreover, the climate change signal given by the original ensemble is also conserved by both approaches.

Treatment of choice in patients with unilateral aldosterone producing adenoma (APA) is adrenalectomy. Following surgery, most patients retain normal adrenal function, while some develop adrenal insufficiency (AI). To facilitate early detection and treatment of AI, we aimed to identify variables measured pre-operatively that are associated with post-operative AI. Variables obtained from 66 patients before and after surgery included anthropometrical data, clinical chemistry, endocrine work-up. LC–MS/MS steroid hormone profiles from tests before surgery (ACTH-stimulation, saline infusion, dexamethasone suppression) were obtained. Based on 78 variables, machine-learning methods were used in model fitting for classification and regression to predict ACTH-stimulated cortisol after surgery. Among the 78 variables, insulin concentration during pre-operative oral glucose tolerance test (OGTT) correlated positively, and dexamethasone suppressed glucocorticoids correlated negatively with ACTH-stimulated cortisol after surgery. Inclusion of LC–MS/MS measurements allowed construction of better models associated with the occurrence of AI in the training data, but did not allow reliable prediction in cross-validation. Our results suggest that glucocorticoid co-secretion (low insulin during pre-operative OGTT and insufficient suppression of glucocorticoids following dexamethasone) are correlated with the development of post-operative AI. Addition of steroid profiles improved the accuracy of prediction, but cross validation revealed lack of reliability in the prediction of AI.

Predicting if a set of mushrooms is edible or not corresponds to the task of classifying them into two groups—edible or poisonous—on the basis of a classification rule. To support this binary task, we have collected the largest and most comprehensive attribute based data available. In this work, we detail the creation, curation and simulation of a data set for binary classification. Thanks to natural language processing, the primary data are based on a text book for mushroom identification and contain 173 species from 23 families. While the secondary data comprise simulated or hypothetical entries that are structurally comparable to the 1987 data, it serves as pilot data for classification tasks. We evaluated different machine learning algorithms, namely, naive Bayes, logistic regression, and linear discriminant analysis (LDA), and random forests (RF). We found that the RF provided the best results with a five-fold Cross-Validation accuracy and F2-score of 1.0 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =1$$\end{document}μ=1, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0$$\end{document}σ=0), respectively. The results of our pilot are conclusive and indicate that our data were not linearly separable. Unlike the 1987 data which showed good results using a linear decision boundary with the LDA. Our data set contains 23 families and is the largest available. We further provide a fully reproducible workflow and provide the data under the FAIR principles.

Otwarte dane badawcze stają się cennym źródłem informacji. Wzorem bibliotek cyfrowych, gromadzących publikacje w wersji elektronicznej, obecnie powstają różnego rodzaju inicjatywy mające na celu publikowanie danych badawczych. Jednym z najczęściej stosowanych sposobów publikowania danych są repozytoria. Podobnie jak wcześniej w bibliotekach cyfrowych, również w repozytoriach danych ich pełne wykorzystanie nie jest możliwe bez odpowiednich metadanych. Metadane dla danych badawczych muszą jednak spełniać dodatkowe wymogi funkcjonalne, które wpływają na modelowanie i projektowanie nowych schematów metadanych. Open research data is becoming a valuable source of information. As it was previously the case with digital libraries that collect publications in electronic version, various initiatives are emerging nowadays for publishing research data. One of the most frequently used methods of publishing data are repositories. By analogy with digital libraries, also in data repositories their full use is not possible without appropriate metadata. However, the metadata for research data has to meet additional functional requirements that influence the modeling and design of new metadata schemas.

Here we apply hyperspectral bright field imaging to collect computed tomographic images with excellent energy resolution (800 eV), applying it for the first time to map the distribution of stain in a fixed biological sample through its characteristic K-edge. Conventionally, because the photons detected at each pixel are distributed across as many as 200 energy channels, energy-selective images are characterised by low count-rates and poor signal-to-noise ratio. This means high X-ray exposures, long scan times and high doses are required to image unique spectral markers. Here, we achieve high quality energy-dispersive tomograms from low dose, noisy datasets using a dedicated iterative reconstruction algorithm. This exploits the spatial smoothness and inter-channel structural correlation in the spectral domain using two carefully chosen regularisation terms. For a multi-phase phantom, a reduction in scan time of 36 times is demonstrated. Spectral analysis methods including K-edge subtraction and absorption step-size fitting are evaluated for an ex vivo, single (iodine)-stained biological sample, where low chemical concentration and inhomogeneous distribution can affect soft tissue segmentation and visualisation. The reconstruction algorithms are available through the open-source Core Imaging Library. Taken together, these tools offer new capabilities for visualisation and elemental mapping, with promising applications for multiply-stained biological specimens., Comment: 13 pages, 5 figures

Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions. Design type Data integration objective Measurement(s) Coronavirus infectious disease, viral epidemiology Technology type(s) Digital curation Factor types(s) Sample characteristic(s) Homo sapiens

With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first “Large Scale Vertebrae Segmentation Challenge” (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae, collected across multiple centres from four different scanner manufacturers, enriched with cases that exhibit anatomical variants such as enumeration abnormalities (n = 77) and transitional vertebrae (n = 161). Metadata includes vertebral labelling information, voxel-level segmentation masks obtained with a human-machine hybrid algorithm and anatomical ratings, to enable the development and benchmarking of robust and accurate segmentation algorithms., Measurement(s)vertebraTechnology Type(s)computed tomographyFactor Type(s)imaging centre • scanner manufacturerSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716968

Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in capillary network representations designed to overcome such limitations. Performed with an unprecedented level of accuracy in geometric approximation at microscale, the pore scale flow simulations predict experimental permeabilities measured at lab scale in the same rock sample without the need for calibration or correction. By applying the method to a broader class of representative geological samples, with permeability values covering two orders of magnitude, we obtain scaling relationships that reveal how mesoscale permeability emerges from microscopic capillary diameter and fluid velocity distributions., Comment: Main article: 11 pages and 4 figures. Supplementary Information: 6 pages and 4 figures. Version 2 includes DOI for microCT dataset

We investigate the degree to which neurons are fractal, the origin of this fractality, and its impact on functionality. By analyzing three-dimensional images of rat neurons, we show the way their dendrites fork and weave through space is unexpectedly important for generating fractal-like behavior well-described by an ‘effective’ fractal dimension D. This discovery motivated us to create distorted neuron models by modifying the dendritic patterns, so generating neurons across wide ranges of D extending beyond their natural values. By charting the D-dependent variations in inter-neuron connectivity along with the associated costs, we propose that their D values reflect a network cooperation that optimizes these constraints. We discuss the implications for healthy and pathological neurons, and for connecting neurons to medical implants. Our automated approach also facilitates insights relating form and function, applicable to individual neurons and their networks, providing a crucial tool for addressing massive data collection projects (e.g. connectomes).

Mobility restrictions are successfully used to contain the diffusion of epidemics. In this work we explore their effect on the epidemic growth by investigating an extension of the Susceptible-Infected-Removed (SIR) model in which individual mobility is taken into account. In the model individual agents move on a chessboard with a Lévy walk and, within each square, epidemic spreading follows the standard SIR model. These simple rules allow to reproduce the sub-exponential growth of the epidemic evolution observed during the Covid-19 epidemic waves in several countries and which cannot be captured by the standard SIR model. We show that we can tune the slowing-down of the epidemic spreading by changing the dynamics of the agents from Lévy to Brownian and we investigate how the interplay among different containment strategies mitigate the epidemic spreading. Finally we demonstrate that we can reproduce the epidemic evolution of the first and second COVID-19 waves in Italy using only 3 parameters, i.e , the infection rate, the removing rate, and the mobility in the country. We provide an estimate of the peak reduction due to imposed mobility restrictions, i. e., the so-called flattening the curve effect. Although based on few ingredients, the model captures the kinetic of the epidemic waves, returning mobility values that are consistent with a lock-down intervention during the first wave and milder limitations, associated to a weaker peak reduction, during the second wave.

We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Interestingly we observe a general decrease in the infection rate occurring two to five weeks after the onset of mobility reduction for the European countries and the American states., Comment: 10 pages, 2 figures, supplementary material included (4 figures and 4 tables)