Materials Discovery With Machine Learning and Knowledge Discovery.

Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation.

Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks.

Bipartite networks are pervasive in modeling real-world phenomena and play a fundamental role in graph theory. Interactive exploratory visualization of such networks is an important problem, and particularly challenging when handling large networks. In this paper we present results from an investigation on using a general multilevel method for this purpose.

Forecasting new diseases in low-data settings using transfer learning.

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak.

An agent-based simulation model to compare different reproductive strategies in cow-calf operations: Economic performance.

In a companion paper, Ojeda-Rojas et al. (2021) [1] describe a stochastic agent-based simulation (ABS) model of a cow-calf operation on a commercial farm in São Paulo, Brazil. The model's parameterization was based on data collected from two sources: a real beef cattle herd and related scientific literature.

The Benefits of an Interdisciplinary Approach to Mathematics Education on Issues Around Computation in School.

We present arguments in favor of an interdisciplinary approach in mathematics education. As an instance, we briefly recall how cognitive neuropsychologists promoted intense finger gnosis acquisition, i.e.

Improved wood species identification based on multi-view imagery of the three anatomical planes.

Background: The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections.

Detection of Low MAP Shedder Prevalence in Large Free-Stall Dairy Herds by Repeated Testing of Environmental Samples and Pooled Milk Samples.

An easy-to-use and affordable surveillance system is crucial for paratuberculosis control. The use of environmental samples and milk pools has been proven to be effective for the detection of subsp. (MAP)-infected herds, but not for monitoring dairy herds certified as MAP non-suspect.

Dose-Response Transcranial Electrical Stimulation Study Design: A Well-Controlled Adaptive Seamless Bayesian Method to Illuminate Negative Valence Role in Tinnitus Perception.

The use of transcranial Electrical Stimulation (tES) in the modulation of cognitive brain functions to improve neuropsychiatric conditions has extensively increased over the decades. tES techniques have also raised new challenges associated with study design, stimulation protocol, functional specificity, and dose-response relationship. In this paper, we addressed challenges through the emerging methodology to investigate the dose-response relationship of High Definition-transcranial Direct Current Stimulation (HD tDCS), identifying the role of negative valence in tinnitus perception.

The rise and fall of countries in the global value chains.

Countries become global leaders by controlling international and domestic transactions connecting geographically dispersed production stages. We model global trade as a multi-layer network and study its power structure by investigating the tendency of eigenvector centrality to concentrate on a small fraction of countries, a phenomenon called localization transition. We show that the market underwent a significant drop in power concentration precisely in 2007 just before the global financial crisis.

Phylogenetic Diversity Rankings in the Face of Extinctions: The Robustness of the Fair Proportion Index.

Planning for the protection of species often involves difficult choices about which species to prioritize, given constrained resources. One way of prioritizing species is to consider their "evolutionary distinctiveness," (ED) that is, their relative evolutionary isolation on a phylogenetic tree. Several evolutionary isolation metrics or phylogenetic diversity indices have been introduced in the literature, among them the so-called Fair Proportion (FP) index (also known as the ED score).

Psychometric investigation of the gamification Hexad user types scale in Brazilian Portuguese.

Gamification has become a significant direction in designing technologies, services, products, organizational structures, and any human activities towards being more game-like and consequently being more engaging and motivating. Albeit its success, research indicates that personal differences exist with regards to susceptibility to gamification at large as well as to different types of gamification designs. As a response, models and measurement instruments of user types when it comes to gamification have been developed.

Smartphone applications for informal caregivers of chronically ill patients: a scoping review.

Mobile-health applications can be used to deliver timely and personalized health information to family and friends of chronically ill adults living in the community. This scoping review aims to investigate the nature and extent of native smartphone applications for informal caregivers. Six databases were searched for articles on applications across ten chronic conditions, namely heart disease, stroke, cancer, chronic obstructive respiratory disease, asthma, diabetes, Alzheimer's disease or other dementia, rheumatoid arthritis, hypertension, and mood or anxiety disorders.

Type I multivariate zero-inflated COM-Poisson regression model.

In this paper, we present the Type I multivariate zero-inflated Conway-Maxwell-Poisson distribution, whose development is based on the extension of the Type I multivariate zero-inflated Poisson distribution. We developed important properties of the distribution and present a regression model. The AIC and BIC criteria are used to select the best fitted model.

Analysis of Spatially Distributed Data in Internet of Things in the Environmental Context.

The Internet of Things consists of "things" made up of small sensors and actuators capable of interacting with the environment. The combination of devices with sensor networks and Internet access enables the communication between the physical world and cyberspace, enabling the development of solutions to many real-world problems. However, most existing applications are dedicated to solving a specific problem using only private sensor networks, which limits the actual capacity of the Internet of Things.

Computer Vision for Detection of Body Posture and Behavior of Red Foxes.

The behavior of animals is related to their health and welfare status. The latter plays a particular role in animal experiments, where continuous monitoring is essential for animal welfare. In this study, we focus on red foxes in an experimental setting and study their behavior.

Fast inference for robust nonlinear mixed-effects models.

The interest for nonlinear mixed-effects models comes from application areas as pharmacokinetics, growth curves and HIV viral dynamics. However, the modeling procedure usually leads to many difficulties, as the inclusion of random effects, the estimation process and the model sensitivity to atypical or nonnormal data. The scale mixture of normal distributions include heavy-tailed models, as the Student-, slash and contaminated normal distributions, and provide competitive alternatives to the usual models, enabling the obtention of robust estimates against outlying observations.

CTCFL regulates the PI3K-Akt pathway and it is a target for personalized ovarian cancer therapy.

High-grade serous ovarian carcinoma (HGSC) is the most lethal gynecologic malignancy due to the lack of reliable biomarkers, effective treatment, and chemoresistance. Improving the diagnosis and the development of targeted therapies is still needed. The molecular pathomechanisms driving HGSC progression are not fully understood though crucial for effective diagnosis and identification of novel targeted therapy options.

A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement.

Low-intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation, applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional MRI (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability.

Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.

Background: Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g.

Standards recommendations for the Earth BioGenome Project.

A global international initiative, such as the Earth BioGenome Project (EBP), requires both agreement and coordination on standards to ensure that the collective effort generates rapid progress toward its goals. To this end, the EBP initiated five technical standards committees comprising volunteer members from the global genomics scientific community: Sample Collection and Processing, Sequencing and Assembly, Annotation, Analysis, and IT and Informatics. The current versions of the resulting standards documents are available on the EBP website, with the recognition that opportunities, technologies, and challenges may improve or change in the future, requiring flexibility for the EBP to meet its goals.

Influence of a Darcy-Forchheimer porous medium on the flow of a radiative magnetized rotating hybrid nanofluid over a shrinking surface.

In this paper, the heat transfer properties in the three-dimensional (3D) magnetized with the Darcy-Forchheimer flow over a shrinking surface of the [Formula: see text] water hybrid nanofluid with radiation effect were studied. Valid linear similarity variables convert the partial differential equations (PDEs) into the ordinary differential equations (ODEs). With the help of the shootlib function in the Maple software, the generalized model in the form of ODEs is numerically solved by the shooting method.

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases.

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking.

A systematic comparison of novel and existing differential analysis methods for CyTOF data.

Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist.

TSEBRA: transcript selector for BRAKER.

Background: BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins.

The draft chromosome-level genome assembly of tetraploid ground cherry (Prunus fruticosa Pall.) from long reads.

Cherries are stone fruits and belong to the economically important plant family of Rosaceae with worldwide cultivation of different species. The ground cherry, Prunus fruticosa Pall., is an ancestor of cultivated sour cherry, an important tetraploid cherry species.