Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting the total electricity production in South Africa: Comparative analysis to improve the predictive modelling accuracy

Electricity plays an important role in the South African economy with the industrial sector consuming the highest proportion followed by the residential and mining sector. Besides the fact that electricity is considered as an important energy sources, an adequate supply of electricity remains an important factor that affects the development and economic growth of a country. Therefore, it becomes even more important to forecast the total electricity production in South Africa. It turns out that the comparison of the predictive performance of different forecasting methods is inevitable. Hybrid forecasting approaches, such as artificial neural network (ANN) based seasonal Autoregressive Integrated Moving Average (sARIMA) model, ANN based multiplicative Holt-Winters (HW) model, ANN based additive HW model, an adaptive neuro-fuzzy inference system (ANFIS) based sARIMA model, ANFIS based multiplicative HW model and ANFIS based additive HW model, are employed as some valuable alternatives compared with the conventional univariate time series models, such as sARIMA model and both multiplicative and additive HW models. The aim of this study is not only to provide evidence on the weakness of the univariate time series models, but also to show that hybrid forecasting method has the superior ability over the univariate time series models, with achieving a higher forecasting accuracy. In addition, random walk model is used as benchmark model, allowing for the fair competition. The results show that the hybrid model, ANN based on multiplicative HW model, is the most fitted for the total electricity production in South Africa. This study presents an empirical framework to guide the field of prediction research by providing a more comprehensive empirical investigation of the total electricity production forecasting by using various hybrid models.
Salience-weighted agreement feature hierarchy modulates language comprehension

The brain establishes relations between elements of an unfolding sentence in order to incrementally build a representation of who is doing what based on various linguistic cues. Many languages systematically mark the verb and/or its arguments to imply the manner in which they are related. A common mechanism to this end is subject-verb agreement, whereby the marking on the verb covaries with one or more of the features such as person, number and gender of the subject argument in a sentence. The cross-linguistic variability of these features would suggest that they may modulate language comprehension differentially based on their relative weightings in a given language. To test this, we investigated the processing of subject-verb agreement in simple intransitive Arabic sentences in a visual event-related brain potential (ERP) study. Specifically, we examined the differences, if any, that ensue in the processing of person, number and gender features during online comprehension, employing sentences in which the verb either showed full agreement with the subject noun (singular or plural) or did not agree in one of the features. ERP responses were measured at the post-nominal verb. Results showed a biphasic negativity−late-positivity effect when the verb did not agree with its subject noun in one of the features, in line with similar findings from other languages. Crucially however, the biphasic effect for agreement violations was systematically graded based on the feature that was violated, which is a novel finding in view of results from other languages. Furthermore, this graded effect was qualitatively different for singular and plural subjects based on the differing salience of the features for each subject-type. These results suggest that agreement features, varying in their salience due to their language-specific weightings, differentially modulate language comprehension. We postulate a Salience-weighted Feature Hierarchy based on our findings and argue that this parsimoniously accounts for the diversity of existing cross-linguistic neurophysiological results on verb agreement processing.
Nanostructures Formed by Custom-Made Peptides Based on Amyloid Peptide Sequences and Their Inhibition by 2-Hydroxynaphthoquinone

Extensive research on amyloid fibril formations shows that certain core sequences within Aβ peptide play an important role in their formation. It is impossible to track these events in vivo. Many proteins and peptides with such core sequences form amyloid fibrils and such Aβ sheet mimics have become excellent tools to study amyloid fibril formation and develop therapeutic strategies. A group of peptides based on amyloid peptide sequences obtained from PDB searches, where glycine residues are substituted with alanine and isoleucine, are tested for aggregation by SEM and ThT binding assay. SEM of different peptide sequences showed morphologically different structures such as nanorods, crystalline needles and nanofibrils. The peptides were co-incubated with HNQ (a quinone) to study its effect on the process of aggregation and/or fibrillation. In conclusion, this group of peptides seem to be Aβ sheet mimics and can be very useful in understanding the different morphologies of amyloid fibrils arising from different peptide sequences and the effective strategies to inhibit or anneal them.
Inference of ploidy by leveraging read depth from amplicon sequencing

Variation in ploidy occurs naturally in select plant and animal species. Ploidy variation can also occur spontaneously or be induced during artificial propagation of fish and shellfish. Studying species and systems that have variable ploidy requires techniques to infer ploidy of individuals. Massively parallel sequencing of biallelic SNPs has been used to infer ploidy, but existing techniques have several drawbacks. These include being limited to only comparing a fixed number of ploidies (diploidy, triploidy, and tetraploidy) and requiring that heterozygous genotypes in an individual be identified prior to ploidy inference. We describe a method of inferring ploidy from sequencing of biallelic SNPs based on beta-binomial mixture models. This method is generalized to apply to any ploidy and does not require prior identification of heterozygous genotypes. We demonstrate efficacy of this method for comparing ancestral octoploidy, decaploidy, and dodecaploidy (tetraploidy, pentaploidy, and hexaploidy for the sequenced SNPs) in white sturgeon and diploidy and triploidy in Chinook salmon with amplicon sequencing (GT-seq) data. Results indicated that ploidy could be reliably estimated for individuals based on distinct distribution of log-likelihood ratios (LLR) for known ploidy samples of both species that were tested. Confidence in ploidy estimates increased with sequencing depth. We encourage users to explore the sequencing depths and LLR critical values that provide reliable estimates of ploidy for a given organism and set of SNPs. We expect that the R package provided will empower studies of genetic variation and inheritance in organisms that vary in ploidy naturally or as a result of artificial propagation practices.
Bats dynamically change echolocation parameters in response to acoustic playback

Animals extract behaviorally relevant signals from “noisy” environments. To investigate signal extraction, echolocating provides a rich system testbed. For orientation, bats broadcast calls and assign each echo to the corresponding call. When orienting in acoustically enriched environments or when approaching targets, bats change their spectro-temporal call design. Thus, to assess call adjustments that are exclusively meant to facilitate signal extraction in “noisy” environments, it is necessary to control for distance-dependent call changes. By swinging bats in a pendulum, we tested the influence of acoustic playback on the echolocation behavior of Carollia perspicillata. This paradigm evokes reproducible orientation behavior and allows a precise definition of the influence of the acoustic context. Our results show that bats dynamically switch between different adaptations to cope with sound-based navigation in acoustically contaminated environments. These dynamics of echolocation behavior may explain the large variety of adaptations that have been reported in the bat literature. Summary statement The frugivorous bat Carollia perspicillata dynamically switch between different adaptations when echolocating in acoustically contaminated environments.
Regional variation in maturation of sandeels in the North Sea

Boulcott, P., Wright, P. J., Gibb, F. M. , Jensen, H. , and Gibb, I. M. 2007. Regional variation in maturation of sandeels in the North Sea. - ICES Journal of Marine Science, 64: 369-376.The current assessment of lesser sandeel (Ammodytes marinus) in the North Sea assumes a single stock and a knife-edge maturity ogive. However, there is evidence that the North Sea stock consists of several reproductively isolated components, raising the possibility of demographic differences among regional aggregations. We examine regional variation in size- and age-at-maturity in four components of the North Sea stock. Surveys in 1999 indicated pronounced regional differences in length- and weight-at-age, implying a disparity in growth rate across the North Sea. Logistic regression revealed that the onset of maturity was significantly related to regional distribution, in addition to length and age, with a tendency for fish off the UK's northeast coast to mature later and smaller than elsewhere. No significant effect of year on either growth or length-at-maturity was revealed from a comparison with other data collected in 2004. The results show that important regional phenotypic variation not currently represented in stock assessments could have implications for the local sustainability of sandeel aggregations.
Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks

Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework, WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.
Automated generation of context-specific Gene Regulatory Networks with a weighted approach in D. melanogaster

Motivation The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors. Results We developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Availability Fly T-WEoN, together with its step-by-step guide is available at https://weon.readthedocs. io Contact alberto.martin@umayor.cl
New Insights Into the Influence of Plant and Microbial Diversity on Denitrification Rates in a Salt Marsh

Coastal salt marshes are some of the most productive ecosystems on Earth, providing numerous services such as soil carbon storage, flood protection and nutrient filtering, several of which are mediated by the sediment microbiome associated with marsh vegetation. Here, nutrient filtering (nitrate removal through denitrification) was examined by determining microbial (bacteria and archaea) community structure (16S rRNA gene iTag sequencing), diversity, denitrification rates and metabolic potential (assembled metagenomic sequences) in collocated patches of Spartina alterniflora ( Spartina ) and Juncus roemerianus ( Juncus ) sediments. The iTag data showed that diversity and richness in Spartina and Juncus sediment microbial communities were highly similar. However, microbial community evenness differed significantly, with the most even communities observed in Juncus sediments. Further, denitrification rates were significantly higher in Juncus compared to Spartina , suggesting oscillations in microbial abundances and in particular the core microbiome identified herein, along with plant diversity influence marsh nitrogen (N) removal. Amplicon and assembled metagenome sequences pointed to a potentially important, yet unappreciated Planctomycetes role in N removal in the salt marsh. Thus, ecosystem perturbations that alter marsh vegetation distribution could impact microbial diversity and may ultimately influence the ecologically important ecosystem functions the marsh sediment microbiome provides.
The Mechanics of Mitotic Cell Rounding

When animal cells enter mitosis, they round up to become spherical. This shape change is accompanied by changes in mechanical properties. Multiple studies using different measurement methods have revealed that cell surface tension, intracellular pressure and cortical stiffness increase upon entry into mitosis. These cell-scale, biophysical changes are driven by alterations in the composition and architecture of the contractile acto-myosin cortex together with osmotic swelling and enable a mitotic cell to exert force against the environment. When the ability of cells to round is limited, for example by physical confinement, cells suffer severe defects in spindle assembly and cell division. The requirement to push against the environment to create space for spindle formation is especially important for cells dividing in tissues. Here we summarize the evidence and the tools used to show that cells exert rounding forces in mitosis in vitro and in vivo, review the molecular basis for this force generation and discuss its function for ensuring successful cell division in single cells and for cells dividing in normal or diseased tissues.
Heritability of individualized cortical network topography

Significance The widespread use of population-average cortical parcellations has provided important insights into broad properties of human brain organization. However, the size, location, and spatial arrangement of regions comprising functional brain networks can vary substantially across individuals. Here, we demonstrate considerable heritability in both the size and spatial organization of individual-specific network topography across cortex. Genetic factors had a regionally variable influence on brain organization, such that heritability in network size, but not topography, was greater in unimodal relative to heteromodal cortices. These data suggest individual-specific network parcellations may provide an avenue to understand the genetic basis of variation in human cognition and behavior. Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2: M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.
A conservation strategy for dugongs : implications of Australian research

The dugong (Dugong dugon) is listed as vulnerable to extinction at a global scale. It has a large range that spans some forty countries and includes tropical and subtropical coastal and island waters from east Africa to Vanuatu. A significant proportion of the world's dugongs is found in northern Australian waters where most modern dugong research has been conducted. Dugongs are long-lived animals with a low repro- ductive rate, long generation time, and a high investment in each offspring. Population simulations indicate that even with the most optimistic combinations of life-history parameters (e.g. low natural mortality and no human-induced mortality) a dugong population is unlikely to increase by more than 5% per year. Dugongs are vulnerable to anthropogenic impacts because of their life history and their dependence on seagrasses that are restricted to coastal habitats. Even a slight reduction in adult survivorship as a result of habitat loss, disease, hunting or incidental drowning in nets can cause a chronic decline in a dugong population. The optimum management strategy is to identify areas that consistently support large numbers of dugongs and to set these aside as dugong sanctuaries in which dugong mortality is minimized and their habitat protected.
Using Single Protein/Ligand Binding Models to Predict Active Ligands for Unseen Proteins

Machine learning models that predict which small molecule ligands bind a single protein target report high levels of accuracy for held-out test data. An important challenge is to extrapolate and make accurate predictions for new protein targets. Improvements in drug-target interaction (DTI) models that address this challenge would have significant impact on drug discovery by eliminating the need for high-throughput screening experiments against new protein targets. Here we propose a data augmentation strategy that addresses this challenge to enable accurate prediction in cases where no experimental data is available. To proceed, we first build single protein-ligand binding models and use these models to predict whether additional ligands bind to each protein. We then use these predictions to augment the experimental data, train standard DTI models, and predict interactions between unseen test proteins and ligands. This approach achieves Area Under the Receiver Operator Characteristic (AUC) > 0.9 consistently on test sets consisting exclusively of proteins and ligands for which the model is given no experimental data. We verify that performance improvements extend to held-out test proteins distant from the training set. Our data augmentation framework can be applied to any DTI model, and enhances performance on a range of simple models.
Corilagin Ameliorates Atherosclerosis in Peripheral Artery Disease via the Toll-Like Receptor-4 Signaling Pathway in vitro and in vivo

We investigated if corilagin can ameliorate or reverse atherosclerotic development via the toll-like receptor 4 (TLR4) signaling pathway in vitro and in vivo. Ana-1 cells or mouse peritoneal macrophages (MPMs) were stimulated with oxidized low-density lipoprotein followed by corilagin treatment. TLR4 expression in Ana-1 cells was upregulated by lentiviral transduction and downregulated by small interfering RNA. Peripheral blood mononuclear cells (PBMCs), plasma samples, and femoral arteries were collected from rats exhibiting peripheral artery disease (PAD). mRNA and protein expression of TLR4 and downstream molecules were decreased significantly by corilagin treatment in Ana-1 cells, MPMs, and rat PBMCs, and the reduction remained irrespective of downregulation or upregulation of TLR4 expression in Ana-1 cells. Corilagin also exerted a prominent effect on changes in plasma levels of cytokines and the pathologic manifestation of atherosclerosis in femoral arteries. Corilagin could ameliorate the development of atherosclerotic plaques by inhibiting the TLR4 signaling pathway in monocyte/macrophages and reduce the release of proinflammatory cytokines. This study provides a new therapeutic target and new niche targeting drug to oppose atherosclerosis and reveals the enormous potential of corilagin for control of PAD in humans.