Alternative Data Sets Research Articles

Cortexa: a comprehensive resource for studying gene expression and alternative splicing in the murine brain

BackgroundGene expression and alternative splicing are strictly regulated processes that shape brain development and determine the cellular identity of differentiated neural cell populations. Despite the availability of multiple valuable datasets, many functional implications, especially those related to alternative splicing, remain poorly understood. Moreover, neuroscientists working primarily experimentally often lack the bioinformatics expertise required to process alternative splicing data and produce meaningful and interpretable results. Notably, re-analyzing publicly available datasets and integrating them with in-house data can provide substantial novel insights. However, such analyses necessitate developing harmonized data handling and processing pipelines which in turn require considerable computational resources and in-depth bioinformatics expertise.ResultsHere, we present Cortexa—a comprehensive web portal that incorporates RNA-sequencing datasets from the mouse cerebral cortex (longitudinal or cell-specific) and the hippocampus. Cortexa facilitates understandable visualization of the expression and alternative splicing patterns of individual genes. Our platform provides SplicePCA—a tool that allows users to integrate their alternative splicing dataset and compare it to cell-specific or developmental neocortical splicing patterns. All standardized gene expression and alternative splicing datasets can be downloaded for further in-depth downstream analysis without the need for extensive preprocessing.ConclusionsCortexa provides a robust and readily available resource for unraveling the complexity of gene expression and alternative splicing regulatory processes in the mouse brain. The data portal is available at https://cortexa-rna.com/

An Enhanced Tunicate Swarm Algorithm with Symmetric Cooperative Swarms for Training Feedforward Neural Networks

The input layer, hidden layer, and output layer are three models of neural processors that comprise feedforward neural networks. In this paper, an enhanced tunicate swarm algorithm based on a differential sequencing alteration operator (ETSA) with symmetric cooperative swarms is presented to train feedforward neural networks. The objective is to accomplish minimum classification errors and the most appropriate neural network layout by regulating the layers’ connection weights and neurons’ deviation thresholds according to the transmission error between the anticipated input and the authentic output. The TSA mimics jet motorization and swarm scavenging to mitigate directional collisions and to maintain the greatest solution that is customized and regional. However, the TSA exhibits the disadvantages of low computational accuracy, a slow convergence speed, and easy search stagnation. The differential sequencing alteration operator has adaptable localized extraction and search screening to broaden the identification scope, enrich population creativity, expedite computation productivity, and avoid search stagnation. The ETSA integrates exploration and exploitation to mitigate search stagnation, which has sufficient stability and flexibility to acquire the finest solution. The ETSA was distinguished from the ETTAO, EPSA, SABO, SAO, EWWPA, YDSE, and TSA by monitoring seventeen alternative datasets. The experimental results confirm that the ETSA maintains profound sustainability and durability to avoid exaggerated convergence, locate the acceptable transmission error, and equalize extraction and prospection to yield a faster convergence speed, superior calculation accuracy, and greater categorization accuracy.

ACP-PDAFF: Pretrained model and dual-channel attentional feature fusion for anticancer peptides prediction

Anticancer peptides(ACPs) have attracted significant interest as a novel method of treating cancer due to their ability to selectively kill cancer cells without damaging normal cells. Many artificial intelligence-based methods have demonstrated impressive performance in predicting ACPs. Nevertheless, the limitations of existing methods in feature engineering include handcrafted features driven by prior knowledge, insufficient feature extraction, and inefficient feature fusion. In this study, we propose a model based on a pretrained model, and dual-channel attentional feature fusion(DAFF), called ACP-PDAFF. Firstly, to reduce the heavy dependence on expert knowledge-based handcrafted features, binary profile features (BPF) and physicochemical properties features(PCPF) are used as inputs to the transformer model. Secondly, aimed at learning more diverse feature informations of ACPs, a pretrained model ProtBert is utilized. Thirdly, for better fusion of different feature channels, DAFF is employed. Finally, to evaluate the performance of the model, we compare it with other methods on five benchmark datasets, including ACP-Mixed-80 dataset, Main and Alternate datasets of AntiCP 2.0, LEE and Independet dataset, and ACPred-Fuse dataset. And the accuracies obtained by ACP-PDAFF are 0.86, 0.80, 0.94, 0.97 and 0.95 on five datasets, respectively, higher than existing methods by 1% to 12%. Therefore, by learning rich feature informations and effectively fusing different feature channels, ACD-PDAFF achieves outstanding performance. Our code and the datasets are available at https://github.com/wongsing/ACP-PDAFF.

Mean wave direction and wave height in the ERA5 reanalysis dataset: Comparison with measured data in the coastal waters of India

Information on wave direction and height is an important input to the coastal engineers. The availability of measured data at every location in the ocean makes maritime operations smoother. However, the practical impossibility makes it to look for alternative datasets like ERA5 reanalysis data. In this study, we compare the significant wave height and mean wave direction in the ERA5 with the buoy-measured data available at the nearest locations in the coastal waters of India. Even though the ERA5 overestimates the measured significant wave heights at certain instances, they both are in good agreement at most of the locations. The correlation coefficient varies from 0.82 to 0.99, with the RMSE falling between 0.15 and 0.31 m. However, the ERA5 wave direction deviates significantly from the measured buoy data at certain locations due to the substantial difference between the measured and ERA5 mean direction of wind-seas. The ERA5 dataset matches the measured mean wave direction when swell dominates i.e., during the southwest monsoon for the locations in the Arabian Sea and during post-monsoon season for the locations in the western Bay of Bengal.

Sensitivity of joint atmospheric-terrestrial water balance simulations to soil representation: Convection-permitting coupled WRF-Hydro simulations for southern Africa

Regional weather and climate models play a crucial role in understanding and representing the regional water cycle, yet the accuracy of soil data significantly affects their reliability. In this study, we employ the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) to assess how soil hydrophysical properties influence regional land-atmosphere coupling and the water cycle over the southern Africa region. We utilize four widely-used global soil datasets, including default soil data for model from the Food and Agriculture Organization, and alternative datasets from the Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids. By conducting convection-permitting coupled WRF-Hydro simulations with the Noah-MP land surface model using each of the aforementioned soil datasets, our benchmark analysis reveals substantial differences in soil hydrophysical properties and their significant impact on the simulated regional water cycle during the austral summer. Alterations in soil datasets lead to both spatial and temporal variations in surface water and energy fluxes, which in turn profoundly influence the atmospheric thermodynamic structure. Reduced soil water-holding capacity leads to subsequent reduction in soil moisture and latent heat, resulting in significant decreases in convective available potential energy and convective inhibition, signaling potential effects on precipitation distributions. In arid interior regions of southern Africa, shifts towards drier and warmer surface conditions due to soil data discrepancies are found to enhance atmospheric moisture convergence, suggesting a possible localized negative feedback of soil moisture on precipitation. Overall, the results for southern Africa indicate that soil data discrepancies exert more pronounced impact on terrestrial fields in dry subregions and on atmospheric fields in temperate subregions, highlighting the broad uncertainties in the regional water cycle reproduced within the model.

Study of Small Area Estimation when Nighttime Lights as an Auxiliary Information is Measured with Error

The need for accelerated development requires rapid data collection. In today's increasingly advanced technological landscape, the utilization of big data emerges as a highly reliable solution for data collection. One exemplary form of big data is the daily capture of satellite imagery, particularly nighttime lights (NTL). NTL serves as a valuable product derived from satellite imagery and can be employed as an alternative dataset for analysis. This research utilizes Nighttime lights as an auxiliary variable to estimate the average household per capita expenditure in small areas, namely districts, employing the empirical best linear unbiased prediction Fay Herriot (EBLUP FH) method and small area estimation by incorporating measurement error effects on the covariate (SAE-ME). The study demonstrates that Nighttime lights can be employed as an alternative auxiliary variable for estimating the average per capita expenditure in districts, as evidenced by a lower RRMSE compared to direct estimation results. However, the measurement error effects on the NTL covariate should be considered by employing a model that takes into account measurement errors. The SAE-ME method provides estimated average expenditure values at the district level that closely align with BPS publications, with an average RRMSE per district of 7.5 percent.

Definition of metafounders based on population structure analysis

BackgroundLimitations of the concept of identity by descent in the presence of stratification within a breeding population may lead to an incomplete formulation of the conventional numerator relationship matrix (A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document}). Combining A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document} with the genomic relationship matrix (G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document}) in a single-step approach for genetic evaluation may cause inconsistencies that can be a source of bias in the resulting predictions. The objective of this study was to identify stratification using genomic data and to transfer this information to matrix A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document}, to improve the compatibility of A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document} and G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document}.MethodsUsing software to detect population stratification (ADMIXTURE), we developed an iterative approach. First, we identified 2 to 40 strata (k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}) with ADMIXTURE, which we then introduced in a stepwise manner into matrix A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document}, to generate matrix AΓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{A}}^{{\\varvec{\\Gamma}}}$$\\end{document} using the metafounder methodology. Improvements in consistency between matrix G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document} and AΓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{A}}^{{\\varvec{\\Gamma}}}$$\\end{document} were evaluated by regression analysis and through the comparison of the overall mean and mean diagonal values of both matrices. The approach was tested on genotype and pedigree information of European and North American Brown Swiss animals (85,249). Analyses with ADMIXTURE were initially performed on the full set of genotypes (S1). In addition, we used an alternative dataset where we avoided sampling of closely related animals (S2).ResultsResults of the regression analyses of standard A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document} on G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document} were – 0.489, 0.780 and 0.647 for intercept, slope and fit of the regression. When analysing S1 data results of the regression for AΓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{A}}^{{\\varvec{\\Gamma}}}$$\\end{document} on G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document} corresponding values were – 0.028, 1.087 and 0.807 for k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}=7, while there was no clear optimum k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}. Analyses of S2 gave a clear optimal k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}=24, with − 0.020, 0.998 and 0.817 as results of the regression. For this k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document} differences in mean and mean diagonal values between both matrices were negligible.ConclusionsThe derivation of hidden stratification information based on genotyped animals and its integration into A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{A}$$\\end{document} improved compatibility of the resulting AΓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{A}}^{{\\varvec{\\Gamma}}}$$\\end{document} and G\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbf{G}$$\\end{document} considerably compared to the initial situation. In dairy breeding populations with large half-sib families as sub-structures it is necessary to balance the data when applying population structure analysis to obtain meaningful results.

Radar Outage Costs and the Value of Alternate Datasets

Abstract Quantifying the costs of radar outages allows value to be attributed to the alternate datasets that help mitigate outages. When radars are offline, forecasters rely more heavily on nearby radars, surface reports, numerical weather prediction models, and satellite observations. Monetized radar benefit models allow value to be attributed to individual radars for mitigating the threat to life from tornadoes, flash floods, and severe winds. Eighteen radars exceed $20 million in annual benefits for mitigating the threat to life from these convective hazards. The Jackson, Mississippi, radar (KDGX) provides the most value ($41.4 million), with the vast majority related to tornado risk mitigation ($29.4 million). During 2020–23, the average radar is offline for 2.57% of minutes or 9.27 days per year and experiences an average of 58.9 outages per year lasting 4.32 h on average. Radar outage cost estimates vary by location and convective hazard. Outage cost estimates concentrate at the top, with 8, 2, 4, and 5 radars exceeding $1 million in outage costs during 2020, 2021, 2022, and 2023, respectively. The KDGX radar experiences outage frequencies of 4.92% and 5.50% during 2020 and 2023, resulting in outage cost estimates > $2 million in both years. Combining outage cost estimates for all radars suggests that approximately $29.1 million in annual radar outage costs may be attributable as value to alternative datasets for helping mitigate radar outage impacts. Significance Statement This study combines information on radar status and monetized radar benefit models to attribute value to individual radars, estimate radar outage costs, and quantify the potential value of alternative datasets during outage-induced gaps in coverage. Eighteen radars exceed $20 million in annual benefits for mitigating the combined threat to life from tornadoes, flash floods, and severe winds. The first and third most valuable radars, both in Mississippi, experience outage frequencies twice the national average, accounting for a disproportionate share of the overall outage costs. Our findings suggest that characterizing and mitigating these outages might provide a near-term solution to better protect these communities from convective hazards. Combining outage cost estimates for all radars suggests that approximately $29.1 million in annual radar outage costs may be attributable as value to alternative datasets for helping mitigate the impacts of radar outages.

A precision functional atlas of personalized network topography and probabilities.

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Carrots as Sticks: How Effective Are Foreign Aid Suspensions and Economic Sanctions?

Abstract Existing research shows that economic coercion successfully influences targeted states’ behavior 38 percent of the time. This article integrates research on economic sanctions and foreign aid by assessing the relative effectiveness of two types of economic coercion: economic sanctions and foreign aid suspensions. It argues that suspending aid is more effective than adopting economic sanctions because (1) aid suspensions are economically beneficial for the adopting state, while sanctions are costly, (2) aid suspensions directly affect the targeted government’s budget, (3) market forces undermine sanctions but not aid suspensions, and (4) aid suspensions are less likely to spark adverse behavioral reactions. A quantitative analysis estimates the success rate of imposed aid suspensions to be 44 percent and that of economic sanctions to be 26 percent. The results are robust across two alternative datasets on economic coercion, and qualitative evidence corroborates the outlined mechanisms. The findings suggest that economic sanctions are less effective than previously thought and that large donor states have a higher chance of achieving political goals through economic coercion.

Globally consistent estimates of high-resolution Antarctic ice mass balance and spatially resolved glacial isostatic adjustment

Abstract. A detailed understanding of how the Antarctic ice sheet (AIS) responds to a warming climate is needed because it will most likely increase the rate of global mean sea level rise. Time-variable satellite gravimetry, realized by the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions, is directly sensitive to AIS mass changes. However, gravimetric mass balances are subject to two major limitations. First, the usual correction of the glacial isostatic adjustment (GIA) effect by modelling results is a dominant source of uncertainty. Second, satellite gravimetry allows for a resolution of a few hundred kilometres only, which is insufficient to thoroughly explore causes of AIS imbalance. We have overcome both limitations by the first global inversion of data from GRACE and GRACE-FO, satellite altimetry (CryoSat-2), regional climate modelling (RACMO2), and firn densification modelling (IMAU-FDM). The inversion spatially resolves GIA in Antarctica independently from GIA modelling jointly with changes of ice mass and firn air content at 50 km resolution. We find an AIS mass balance of −144 ± 27 Gt a−1 from January 2011 to December 2020. This estimate is the same, within uncertainties, as the statistical analysis of 23 different mass balances evaluated in the Ice sheet Mass Balance Inter-comparison Exercise (IMBIE; Otosaka et al., 2023b). The co-estimated GIA corresponds to an integrated mass effect of 86 ± 21 Gt a−1 over Antarctica, and it fits better with global navigation satellite system (GNSS) results than other GIA predictions. From propagating covariances to integrals, we find a correlation coefficient of −0.97 between the AIS mass balance and the GIA estimate. Sensitivity tests with alternative input data sets lead to results within assessed uncertainties.

Leveraging AI-Derived Data for Carbon Accounting: Information Extraction from Alternative Sources

Carbon accounting is a fundamental building block in our global path to emissions reduction and decarbonization, yet many challenges exist in achieving reliable and trusted carbon accounting measures. We motivate that carbon accounting not only needs to be more data-driven, but also more methodologically sound. We discuss the need for alternative, more diverse data sources that can play a significant role on our path to trusted carbon accounting procedures and elaborate on not only why, but how Artificial Intelligence (AI) in general and Natural Language Processing (NLP) in particular can unlock reasonable access to a treasure trove of alternative data sets in light of the recent advances in the field that better enable the utilization of unstructured data in this process. We present a case study of the recent developments on real-world data via an NLP-powered analysis using OpenAI's GPT API on financial and shipping data. We conclude the paper with a discussion on how these methods and approaches can be integrated into a broader framework for AI-enabled integrative carbon accounting.

On the origin of bird’s nest fungi: Phylogenomic analyses of fungi in the Nidulariaceae (Agaricales, Basidiomycota)

Nidulariaceae, also known as bird’s nest fungi, is an understudied group of mushroom-forming fungi. The common name is derived from their nest-like morphology. Bird’s nest fungi are ubiquitous wood decomposers or saprobes on dung. Recent studies showed that species in the Nidulariaceae form a monophyletic group with five sub-clades. However, phylogenetic relationships among genera and placement of Nidulariaceae are still unclear. We present phylogenomic analyses of bird’s nest fungi and related Agaricales fungi to gain insight into the evolution of Nidulariaceae. A species tree with 17 newly generated genomes of bird’s nest fungi and representatives from all major clades of Agaricales was constructed using 1044 single-copy genes to explore the intergeneric relationships and pinpoint the placement of Nidulariaceae within Agaricales. We corroborated the hypothesis that bird’s nest fungi are sister to Squamanitaceae, which includes mushroom-shaped fungi with a stipe and pileus that are saprobes and mycoparasites. Lastly, stochastic character mapping of discrete traits on phylogenies (SIMMAP) suggests that the ancestor of bird’s nest fungi likely possessed an evanescent, globose peridium without strings attaching to the spore packets (funiculi). This analysis suggests that the funiculus was gained twice and that the persistent, cupulate peridium form was gained at least four times and lost once. However, alternative coding schemes and datasets with a wider array of Agaricales produced conflicting results during ancestral state reconstruction, indicating that there is some uncertainty in the number of peridium transitions and that taxon sampling may significantly alter ancestral state reconstructions. Overall, our results suggest that several key morphological characters of Nidulariaceae have been subject to homoplasy.

Deep learning-driven diagnosis of multi-type vertebra diseases based on computed tomography images.

Osteoporotic vertebral compression fractures (OVCFs) are the most common type of fragility fracture. Distinguishing between OVCFs and other types of vertebra diseases, such as old fractures (OFs), Schmorl's node (SN), Kummell's disease (KD), and previous surgery (PS), is critical for subsequent surgery and treatment. Combining with advanced deep learning (DL) technologies, this study plans to develop a DL-driven diagnostic system for diagnosing multi-type vertebra diseases. We established a large-scale dataset based on the computed tomography (CT) images of 1,051 patients with OVCFs from Luhe Hospital and used data of 46 patients from Xuanwu Hospital as alternative hospital validation dataset. Each patient underwent one examination. The dataset contained 11,417 CT slices and 19,718 manually annotated vertebrae with diseases. A two-stage DL-based system was developed to diagnose five vertebra diseases. The proposed system consisted of a vertebra detection module (VDModule) and a vertebra classification module (VCModule). The training and testing dataset for the VDModule consisted of 9,135 and 3,212 vertebrae, respectively. The VDModule using the ResNet18-based Faster region-based convolutional neural network (R-CNN) model achieved an area under the curve (AUC), false-positive (FP) rate, and false-negative (FN) rate of 0.982, 1.52%, and 1.33%, respectively, in the testing dataset. The training dataset for VCModule consisted of 14,584 and 47,604 diseased and normal vertebrae, respectively. The testing dataset consisted of 4,489 and 15,122 diseased and normal vertebrae, respectively. The ResNet50-based VCModule achieved an average sensitivity and specificity of 0.919 and 0.995, respectively, in diagnosing four kinds of vertebra diseases except for SN in the testing dataset. In the alternative hospital validation dataset, the ResNet50-based VCModule achieved an average sensitivity and specificity of 0.891 and 0.989, respectively, in diagnosing four kinds of vertebra diseases except for SN. Our proposed DL system can accurately diagnose four vertebra diseases and has strong potential to facilitate the accurate and rapid diagnosis of vertebral diseases.

Modelos de redes neurais artificiais para apoiar a gestão de resíduos urbanos: um recurso tecnológico que impulsiona a concretização dos Objetivos de Desenvolvimento Sustentável

ABSTRACT Waste management is crucial to achieving the Sustainable Development Goals (SDGs) established by the United Nations. However, traditional on-site waste characterization techniques require specialized professionals, who are exposed to biological, chemical, and physical risks. In this sense, the use of artificial neural networks (ANN) in models for characterizing municipal solid waste has been discussed, especially after the advent of the COVID-19 pandemic. Predictions made by ANN can be carried out with little or no handling of waste, making the process faster, cleaner, and safer. However, ANN models rely on datasets often provided by third parties, so they require diligent monitoring to ensure that an updated dataset is available at the appropriate regularity. This study presented two standard ANN models that were not available due to a lack of up-to-date datasets and demonstrated that dataset interchangeability may be critical for the long-term use of ANN developed to achieve SDG. Furthermore, interchangeability led to the formulation of a hypothesis about the relevance of the variable associated with basic sanitation in the greater assertiveness of one of the models during the pandemic period, resulting in the identification of abnormal patterns relating to the disposal of textiles and sanitary papers in the years 2020 and 2021. Additionally, this study can be the starting point for the development of more sophisticated interchangeable models developed with alternative datasets, meticulously chosen to reduce the effective error of the desired predictions by reducing the amplitude of the intersection set formed by different models.

Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.

Gene regulatory networks are powerful tools for modeling genetic interactions that control the expression of genes driving cell differentiation, and single-cell sequencing offers a unique opportunity to build these networks with high-resolution genomic data. There are many proposed computational methods to build these networks using single-cell data, and different approaches are used to benchmark these methods. However, a comprehensive discussion specifically focusing on benchmarking approaches is missing. In this article, we lay the GRN terminology, present an overview of common gold-standard studies and data sets, and define the performance metrics for benchmarking network construction methodologies. We also point out the advantages and limitations of different benchmarking approaches, suggest alternative ground truth data sets that can be used for benchmarking, and specify additional considerations in this context.

Epigenetic Approaches to Identifying Asthma Endotypes.

The prevalence of asthma escalated rapidly in the late 20th century. In 2019, the World Health Organization estimated the global number of people affected by the condition to be approximately 260 million, causing 450,000 deaths during that year. While there have been advances in therapeutics with the emergence of biologics targeting T2-high asthma, there is still little clarity on the mechanisms underlying the origins of both the condition and all of its endotypes. Several biomarkers for particular asthma phenotypes have been documented. These are generally identified from transcriptomics and proteomics protocols and tend to be biased to T2-high phenotypes. In this review, we summarize some suggestions that analysis of epigenomes may provide alternative datasets that inform of broader asthma endotypes and might highlight pathways amenable for therapeutic intervention.

Moving beyond the political trust crisis debate: Residual analyses to understand trends in political trust

AbstractFor decades, political scientists have hotly debated longitudinal trends in political trust rates. An important undercurrent in the debate is that any decline in political trust might signal a legitimacy crisis. Yet, descriptive figures are unable to distinguish between two interpretations of these downward trends: (i) declines that can reasonably be expected as a reflection of declining political trustworthiness (i.e., procedural or output performance) and thereby reflect critical citizens who monitor their democratic institutions; and (ii) downward trends that are not warranted by democratic performance and thereby suggest a more fundamental disconnect between citizens and their democratic institutions.This research note argues that residuals to multilevel models of political trust allow us to distinguish between these two types, and thereby provide a better understanding of trends in political trust. These residuals do not only reveal short‐term aberrations to the explanatory model (often reflecting short‐lived, country‐specific events), but also the extent to which a country's trust rate systematically underperforms in the middle‐ to long‐term. To the extent that declining trust rates are lower than explanatory models predict, the residuals express excessive distrust. To the extent that declining trust rates are in line with the explanatory models, the residuals reflect critical, monitoring citizenship.We outline the approach of residual analyses as a tool to better understand trends in political trust. We illustrate the use of these residual analyses on a cross‐national, longitudinal data set (the Eurobarometer), covering 15 Western and Southern European countries between 1999 and 2019. While political trust rates fluctuate in all these countries, we only find evidence for a structural decline in two of these countries. In France and Spain political trust failed to recover in line with improving economic and institutional performance after the Great Recession. We then test the versatility of the tool to different conditions, including retests on an alternative set of countries (11 Central and Eastern European countries between 2004 and 2019) and an alternative dataset with different measures and time points (the European Social Survey).Finally, we elaborate on the two main conditions under which residual analyses offer a useful tool to the trend debate in political trust research: (1) a firm understanding of the object‐driven determinants of political trust, and (2) a detailed coverage of country‐wave combinations to separate structural trends from short‐term fluctuations.

Evaluation of Cryptocurrency Price Prediction Using LSTM and CNNs Models

Cryptocurrencies created by Nakamoto in 2009 have gained significant interest due to their potential for high returns. However, the cryptocurrency market's unpredictability makes it challenging to forecast prices accurately. To tackle this issue, a deep learning model has been developed that utilizes Long Short-Term Memory (LSTM) neural networks and Convolutional Neural Networks (CNNs) to predict cryptocurrency prices. LSTMs, a type of recurrent neural network, are well-suited for analyzing time series data and have been successful in various prediction applications. Additionally, CNNs, primarily used for image analysis tasks, can be employed to extract relevant patterns and characteristics from input data in Bitcoin price prediction applications. This study contributes to the existing related works on cryptocurrency price prediction by exploring various predictive models and techniques, which involve a machine learning model, deep learning model, time series analysis, and as well as a hybrid model that combines deep learning methods to predict cryptocurrency prices as well as enhance the accuracy and reliability of the price predictions. To ensure accurate predictions in this study, a trustworthy dataset from investing.com was sought. The dataset, sourced from investing.com, consists of 1826 time series data samples. The dataset covers the time frame from January 1, 2018, to December 31, 2022, providing data for a period of 5 years. Subsequently, pre-processing was conducted on the dataset to guarantee the quality of the input. As a result of absent values and concerns regarding the dataset's obsolescence, an alternative dataset was sourced to avoid these issues. The performance of the LSTM and CNN models was evaluated using root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE) and R-squared (R2). It was observed that they outperformed each other to a certain degree in short-term forecasts compared to long-term predictions, where the R2Â values for LSTM range from 0.973 to 0.986, while for CNNs, they range from 0.972 to 0.988 for 1 day, 3 days and 7 days windows length. Nevertheless, the LSTM model demonstrated the most favorable performance with the lowest error rate. The RMSE values for the LSTM model ranged from 1203.97 to 1645.36, whereas the RMSE values for the CNNs model ranged from 1107.77 to 1670.93. As a result, the LSTM model exhibited a lower error rate in RMSE and achieved the highest accuracy in R2Â compared to the CNNs model. Considering these comparative outcomes, the LSTM model can be deemed as the most suitable model for this specific case

Assessing the Impact of Energy-related Sanctions on Russia and Their Consequences for Europe

This research paper investigates the impact of energy-related sanctions on the European energy sector in light of the Russian-Ukrainian military conflict. The study analyzes the S600ENP index returns using data from June 2018 to July 2023, divided into three distinct periods: pre-invasion, during sanctions, and post-sanctions. Descriptive statistics and distribution analysis reveal distinct market behaviors and volatility clustering during each period. The analysis of variance (ANOVA) test does not show significant differences in mean returns across the periods, indicating relatively consistent index performance. The stationarity test suggests non-stationarity in the index returns. The ARCH model demonstrates the persistence of volatility shocks influenced by geopolitical events like Russian sanctions. The findings underscore the importance of understanding market dynamics during geopolitical uncertainty and its implications for the European energy sector. Though the representation of the S600ENP index limits the research, future studies can explore alternative datasets and periods for more comprehensive insights.