Miracle-dz/UniFAR-FTUs huggingface dataset:
Endosymbiont diversity across native and invasive brown widow spider populations

The invasive brown widow spider, Latrodectus geometricus (Araneae: Theridiidae), has spread in multiple locations around the world and, along with it, brought associated organisms such as endosymbionts. We investigated endosymbiont diversity and prevalence across putative native and invasive populations of this spider, predicting lower endosymbiont diversity across the invasive range compared to the native range. First, we characterized the microbial community in the putative native (South Africa) and invasive (Israel and the United States) ranges via high throughput 16S sequencing of 103 adult females. All specimens were dominated by reads from only 1-3 amplicon sequence variants (ASV), and most individuals were infected with an apparently uniform strain of Rhabdochlamydia. We also found Rhabdochlamydia in spider eggs, indicating that it is a maternally-inherited endosymbiont. Relatively few other ASV were detected, but included two variant Rhabdochlamydia strains and several Wolbachia, Spiroplasma and Enterobacteriaceae strains. We then diagnostically screened 118 adult female spiders from native and invasive populations specifically for Rhabdochlamydia and Wolbachia. We found Rhabdochlamydia in 86% of individuals and represented in all populations, which suggests that it is a consistent and potentially important associate of L. geometricus. Wolbachia was found at lower overall prevalence (14%) and was represented in all countries, but not all populations. In addition, we found evidence for geographic variation in endosymbiont prevalence: spiders from Israel were more likely to carry Rhabdochlamydia than those from the US and South Africa, and Wolbachia was geographically clustered in both Israel and South Africa. Characterizing endosymbiont prevalence and diversity is a first step in understanding their function inside the host and may shed light on the process of spread and population variability in cosmopolitan invasive species.
Tradeoffs in Modeling Context Dependency in Complex Trait Genetics

Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures or social settings. However, it is often unclear if the extent of context dependency, or Gene-by-Environment interaction (GxE), merits more involved models than the additive model typically used to analyze data from genome-wide association studies (GWAS). Here, we suggest considering the utility of GxE models in GWAS as a tradeoff between bias and variance parameters. In particular, We derive a decision rule for choosing between competing models for the estimation of allelic effects. The rule weighs the increased estimation noise when context is considered against the potential bias when context dependency is ignored. In the empirical example of GxSex in human physiology, the increased noise of context-specific estimation often outweighs the bias reduction, rendering GxE models less useful when variants are considered independently. However, we argue that for complex traits, considering patterns of context dependency across many variants jointly can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how considering polygenic trends of GxE may also be important for interpretation, as analyses based on independently ascertained “top hits” alone can be misleading. Significance Statement Genome-wide association studies have shown surprisingly little evidence for genetic effects on complex traits depending on context (e.g., age, sex, environmental exposures). At the same time, we have learned from the last two decades of GWAS that additive trait variation is often due to small contributions from large parts of the genome. This observation shifted our perspective and sparked a methodological effort to analyze these subtler and harder to interpret associations from throughout the genome together with core genes. Here, we argue through theory and empirical example that a similar shift in the consideration of high polygenicity is an important piece missing from our understanding of context-specificity in complex traits.
Genome-wide kinetic profiling of pre-mRNA 3′ end cleavage

Cleavage and polyadenylation is necessary for the formation of mature mRNA molecules. The rate at which this process occurs can determine the temporal availability of mRNA for subsequent function throughout the cell and is likely tightly regulated. Despite advances in high-throughput approaches for global kinetic profiling of RNA maturation, genome-wide 3’ end cleavage rates have never been measured. Here, we describe a novel approach to estimate the rates of cleavage, using metabolic labeling of nascent RNA, high-throughput sequencing, and mathematical modeling. Using in-silico simulations of nascent RNA-seq data, we show that our approach can accurately and precisely estimate cleavage half-lives for both constitutive and alternative sites. We find that 3’ end cleavage is fast on average, with half-lives under a minute, but highly variable across individual sites. Rapid cleavage is promoted by the presence of canonical sequence elements and an increased density of polyadenylation signals near a cleavage site. Finally, we find that cleavage rates are associated with the localization of RNA Polymerase II at the end of a gene and faster cleavage leads to quicker degradation of downstream read-through RNA. Our findings shed light on the features important for efficient 3’ end cleavage and the regulation of transcription termination.
Toward transparent taxonomy: an interactive web-tool for evaluating competing taxonomic arrangements

1. Informative and consistent taxonomy above the species level is essential to communication about evolution, biodiversity, and conservation, and yet the practice of taxonomy is considered opaque and subjective by non-taxonomist scientists and the public alike. While various proposals have tried to make the basis for ranking and inclusiveness of taxa more transparent and objective, widespread adoption of these ideas has lagged. 2. Here, we present TaxonomR, an interactive online decision-support tool to evaluate alternative taxonomic classifications. This tool implements an approach that quantifies the criteria commonly used in taxonomic treatments and allows the user to interactively manipulate weightings for different criteria to compare scores for taxonomic groupings under those weights. 3. We use the butterfly taxon Argynnis to demonstrate how different weightings applied to common taxonomic criteria result in fundamentally different genus-level classifications that are predominantly used in different continents and geographic regions. These differences are objectively compared and quantified using TaxonomR to evaluate the kinds of criteria that have been emphasized in earlier classifications, and the nature of the support for current alternative taxonomic arrangements. 4. The main role of TaxonomR is to make taxonomic decisions transparent via an explicit prioritization scheme. TaxonomR is not a prescriptive application. Rather, it aims to be a tool for facilitating our understanding of alternative taxonomic classifications that can, in turn, potentially support global harmony in biodiversity assessments through evidence-based discussion and community-wide resolution of historically entrenched taxonomic tensions.
Astrocyte Ca2+ signalling mediates long-distance metaplasticity in the hippocampal CA1

Astrocytes play an increasingly recognised role in regulating synaptic plasticity, but their contribution to metaplasticity is poorly understood. We have previously described a long-distance form of metaplasticity whereby priming stimulation in stratum oriens inhibits subsequent LTP in the neighbouring stratum radiatum of the hippocampal CA1 region of both rats and mice. Using genetic and pharmacological strategies to manipulate astrocytic Ca2+ signalling, we now show this form of metaplasticity requires inositol triphosphate receptor-dependent Ca2+ release in these cells. Blocking Ca2+signalling or inositol triphosphate receptors in single radiatum astrocytes abolishes the metaplasticity at nearby synapses. We also show the relevant Ca2+release in astrocytes is driven by adenosine A2B receptors, and stimulation of these receptors elicits the metaplasticity effect both in vitro and in vivo. Further, the metaplasticity requires signalling via tumor necrosis factor, but this cytokine is required to act on astrocytes, not neurons. Instead, glutamate, acting on GluN2B-containing NMDA receptors, is the likely gliotransmitter that signals to neurons to inhibit LTP. Together these data reveal a novel role for astrocytes in hippocampal LTP regulation across broader spatiotemporal scales than previously recognised. Main points In hippocampal CA1, “priming” activity inhibits subsequent LTP at synapses hundreds of microns away. This effect requires astrocytic Ca2+signaling, and a molecular cascade involving adenosine A2B receptors, tumor necrosis factor and GluN2B-containing NMDA receptors. The metaplasticity effect is evident in vitro and in vivo. Long-distance astrocyte signaling is a mechanism for regulating neural activity over broad spatiotemporal scales.
Enrichment of hard sweeps on the X chromosome compared to autosomes in six Drosophila species

The X chromosome, being hemizygous in males, is exposed one third of the time increasing the visibility of new mutations to natural selection, potentially leading to different evolutionary dynamics than autosomes. Recently, we found an enrichment of hard selective sweeps over soft selective sweeps on the X chromosome relative to the autosomes in a North American population of Drosophila melanogaster. To understand whether this enrichment is a universal feature of evolution on the X chromosome, we analyze diversity patterns across six commonly studied Drosophila species. We find an increased proportion of regions with steep reductions in diversity and elevated homozygosity on the X chromosome compared to autosomes. To assess if these signatures are consistent with positive selection, we simulate a wide variety of evolutionary scenarios spanning variations in demography, mutation rate, recombination rate, background selection, hard sweeps, and soft sweeps, and find that the diversity patterns observed on the X are most consistent with hard sweeps. Our findings highlight the importance of sex chromosomes in driving evolutionary processes and suggest that hard sweeps have played a significant role in shaping diversity patterns on the X chromosome across multiple Drosophila species.
Design and Evaluation of an Affordable Hypoxic Chamber with Comprehensive Environmental Control for Research Applications

Purpose In-vivo Hypoxia chamber is sought after for research on sleep apnea, among others. Recently hypoxia chambers are utilized to create intermittent hypoxia, thereby utilizing it as a treatment strategy. The commonly used Invivo hypoxic chambers only monitor oxygen levels and fail to read up on vital factors like CO2 buildup, and humidity among others. In addition, these devices are expensive and make them unaffordable for many labs. Methods Here we report a lab-designed and assembled Arduino microcontroller-based hypoxic chamber with automatic maintenance of O2 level, CO2 level, and Humidity. The entire 650$ setup is inclusive of an Arduino module with sensors for oxygen, CO2, humidity, pressure, and temperature. A detailed software program was also written to help efficiently carry out the parameters set out for use. Results The equipment is capable of automatically regulating the inner environment based on the set parameters. The combined cluster of regulators, efficiently controlled the oxygen levels, CO2 levels, and humidity levels within the experimental parameters. Conclusion This equipment thus acts as one of the easily reproducible simple assembly units with the integration of complex parameters, thus monitoring and controlling the inner environment with high precision.
Multimodule imaging of the hierarchical equine hoof wall porosity and structure

The equine hoof wall has a complex, hierarchical structure that can inspire designs of impact-resistant materials. In this study, we utilized micro-computed tomography (μ-CT) and serial block-face scanning electron microscopy (SBF-SEM) to image the microstructure and nanostructure of the hoof wall. We quantified the morphology of tubular medullary cavities by measuring equivalent diameter, surface area, volume, and sphericity. High-resolution μ-CT revealed that tubules are partially or fully filled with tissue near the exterior surface and become progressively empty towards the inner part of the hoof wall. Thin bridges were detected within the medullary cavity, starting in the middle section of the hoof wall and increasing in density and thickness towards the inner part. Porosity was measured using three-dimensional (3D) μ-CT, two-dimensional (2D) μ-CT, and a helium pycnometer, with the highest porosity obtained using the helium pycnometer (8.07%), followed by 3D (3.47%) and 2D (2.98%) μ-CT. SBF-SEM captured the 3D structure of the hoof wall at the nanoscale, showing that the tubule wall is not solid, but has nano-sized pores, which explains the higher porosity obtained using the helium pycnometer. The results of this investigation provide morphological information on the hoof wall for the future development of hoof-inspired materials and offer a novel perspective on how various measurement methods can influence the quantification of porosity.
Soil carbon mineralization and microbial community dynamics in response to PyOM addition

Wildfires can either negatively impact soil carbon (C) stocks through combustion or increase soil carbon stocks through the production of pyrogenic organic matter (PyOM), which is highly persistent and can affect non-pyrogenic soil organic carbon (SOC) mineralization rates. In this study, we used fine-resolution 13CO2 flux tracing to investigate PyOM-C mineralization, soil priming effects, and their impacts on soil microbial communities in a Californian mixed conifer forest Xerumbrept soil burned in the 2014 King Fire. We added PyOM produced from pine biomass at 350 °C and 550 °C to the soil and separately traced the mineralization of 13C-labeled water-extractable and non-water-extractable PyOM-C fractions in a short-term incubation. Our results indicate that the water-extractable fraction is 10-50x more mineralizable in both 350 °C and 550 °C PyOM treatments than the SOC or non-water-extractable PyOM fraction. 350 °C PyOM addition led to a short-term positive priming effect, likely due to co-metabolism of easily mineralizable PyOM-C and the SOC, whereas 550 °C PyOM addition induced negative priming, potentially due to physical protection of SOC. We observed significant shifts in bacterial community composition in response to both 350 °C and 550 °C PyOM, with positive PyOM responders belonging to the genera Noviherbaspirillum, Pseudonocardia, and Gemmatimonas. In contrast, fungal communities were less responsive to PyOM additions. Our findings expand our understanding of the post-fire cycling of PyOM and SOC, providing insights into the microbial mineralization of different PyOM-C fractions and their influence on soil C dynamics in fire-affected ecosystems.
Is ‘Cutting-Edge’ Good? Assessing Product Newness Factors in Technologically Turbulent Environments

Abstract Overview: Firms are constantly faced with the dilemma of developing products that are both cutting edge and also easily understood by customers. Customers may reject products that diverge too much from prior generations of products, but products that are too similar to prior products may not stand out. We analyzed 423 firms to better understand how both product newness to the firm and product newness to the customer impact NPD performance in varying environments. Our findings highlight that firms are generally effective at innovating on their own, but customers need some turbulence to adopt new innovations that are dissimilar to their prior usage experiences. Firms should continue to innovate and be ready when a turbulent environment provides innovative opportunities.
Enhancing building extraction from remote sensing images through UNet and transfer learning

Performing accurate extraction of buildings from remote sensing (RS) images is a crucial process with widespread applications in urban planning, disaster management, and urban monitoring. However, this task remains challenging due to the diversity and complexity of building shapes, sizes, and textures, as well as variations in lighting and weather conditions. These difficulties motivate our research to propose an improved approach for building extraction using UNet and transfer learning to address these challenges. In this work, we tested seven different backbone architectures within the UNet encoder and found that combining UNet with ResNet101 or ResNet152 yielded the best results. Based on these findings, we combined the superior performance of these base models to create a novel architecture, which achieved significant improvements over previous methods. Specifically, our method achieved a 1.33% increase in Intersection over Union (IoU) compared to the baseline UNet model. Furthermore, it demonstrated a superior performance with a 1.21% increase in IoU over UNet-ResNet101 and a 1.60% increase in IoU over UNet-ResNet152. We evaluated our proposed approach on the INRIA Aerial Image dataset and demonstrated its superiority. Our research addresses a critical need for accurate building extraction from RS images and overcomes the challenges posed by diverse building characteristics.
The Design Space of Kirchhoff Rods

The Kirchhoff rod model describes the bending and twisting of slender elastic rods in three dimensions and has been widely studied to enable the prediction of how a rod will deform, given its geometry and boundary conditions. In this work, we study a number of inverse problems with the goal of computing the geometry of a straight rod that will automatically deform to match a curved target shape after attaching its endpoints to a support structure. Our solution lets us finely control the static equilibrium state of a rod by varying the cross-sectional profiles along its length. We also show that the set of physically realizable equilibrium states admits a concise geometric description in terms of linear line complexes, which leads to very efficient computational design algorithms. Implemented in an interactive software tool, they allow us to convert three-dimensional hand-drawn spline curves to elastic rods and give feedback about the feasibility and practicality of a design in real time. We demonstrate the efficacy of our method by designing and manufacturing several physical prototypes with applications to interior design and soft robotics.
Deep Learning techniques for stock market forecasting: Recent trends and challenges

Stock market forecasting has been a very intensive area of research in recent years due to the highly uncertain and volatile nature of stock data which makes this task challenging. By accurately predicting a particular stock's price investors can gain maximum profit out of their investment. With the great success of Deep Learning methods in various domains, it has attracted the research community to apply these models for financial domain also. These DL methods have been proven to achieve better accuracy and predictions compared to econometric and traditional ML methods. This work reviews recent papers according to various Deep Learning models which included: Artificial Neural Networks, Convolution Neural Networks, Sequence to Sequence models, Generative Adversarial Networks, Graph Neural Networks and Transformers applied for stock market forecasting. Furthermore this work also reviews datasets, features, evaluation parameters and results of various methods. From the analysis done on various DL models we found that Graph Neural Networks and Transformer models have potential to interpret dynamic and non-linear patterns of financial time series data with greater accuracy. In addition to this, correlation among various stock indices and investors sentiment along with historical data has great influence on the prediction accuracy. We also identified the benchmark datasets for stock market forecasting based on market capitalization value of an economy. The aim of this paper is to provide insight into most recent work done in the finance domain and identify future directions for more accurate predictions.
Cryptocurrency Market Volatility Forecasting

Although cryptocurrencies are catching the fancy of investors for various benefits such as decentralization, low transaction costs, and inflation hedging, their extreme volatility is sometimes keeping many away. Consequently, modeling and forecasting cryptocurrency market volatility are essential to investors’ investment decisions and risk management. However, most previous studies have been limited to Bitcoin volatility, disregarding cryptocurrency market performance as a whole. This study estimates realized volatility of cryptocurrency market with a variety of algorithms employing a portfolio-style technique. After comparison, LSTM networks surpass the conventional GARCH-type models; meanwhile, the hybrid GARCH neural network models perform the worst. This study provides an impetus for a significant number of academics interested in the extreme volatility of cryptocurrencies. Additionally, it illustrates that more sophisticated models may not always lead to better predictive performance.
An Intelligent Report Generator for Chemical Documents

Context: Scientific articles and patents contain academic, industrial, and scientific information. Automatically retrieving information from these documents is necessary for supporting upcoming scientific research development. Problem: Difficulties in manually identifying and analyzing the chemical information in documents make it nearly impossible to access specific contents of chemical investigations and generate reports to support ongoing research. Solution: In this article, we present a system that recognizes chemical entities (elements, classes, compounds, methods, and equipment) and generates intelligent reports from free texts. IS Theory: We developed this work under the support of Soft Systems Theory. Method: This research was evaluated through proof of concept. We used 30 chemical patents from Brazilian National Institute of Industrial Property and 20 scientific articles from Revista Virtual de Química (RVq). For validation, we extracted the texts and recognized the named entities through, for instance, the hybrid method Conditional Random Field (CRF) + Local Grammar (LG). We then apply rules to generate intelligent reports. Summary of Results: The system can generate seven types of intelligent reports, two of which are customized by the user. For datasetPat our model obtained mean values of 98.96% for Precision, 91.12% for Recall, and 94.17% for F-Score. The datasetArt reached average values of 97.31%, 86.94%, and 91.29% for Precision, Recall, and F-Score, respectively. Contributions and Impact in the IS Area: This research presents as the main contribution the availability of an Information System for the generation of intelligent reports from documents based on the recognition of named entities in the chemical area. In addition the hybrid method CRF+LG can contribute to the evolution of Information Systems, helping people and organizations. The model is described throughout the paper and can be replicated in other contexts.