bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning and likelihood approaches for viral phylogeography converge on the same answers whether the inference model is right or wrong
Authors: Thompson Ammon , Liebeskind Benjamin , Scully Erik J. , Landis Michael 
Published: February 2023

AbstractAnalysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among five locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression which we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior intervals (HPI) and greatly overlap with HPIs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.
Ethnic differences of genetic risk and smoking in lung cancer: two prospective cohort studies
Authors: Zhu Meng , Lv Jun , Huang Yanqian , Ma Hongxia , Li Ni , Wei Xiaoxia , Ji Mengmeng , Ma Zhimin , Song Ci , Wang Cheng , Dai Juncheng , Tan Fengwei , Guo Yu , Walters Robin , Millwood Iona Y. , Hung Rayjean J. , Christiani David C. , Yu Canqing , Jin Guangfu , Chen Zhengming , Wei Qingyi , Amos Christopher I. , Hu Zhibin , Li Liming , Shen Hongbing 
Published: February 2023

AbstractBackgroundThe relative risk of smoking on lung cancer have been reported to be much higher in white population than that in East Asians. However, it’s unknown whether genetic background underlies this disparity between ethnic groups. To assess the role of ethnic differences in genetic factors associated with this phenomenon.MethodsWe first constructed ethnic-specific polygenic risk scores (PRSs) to quantify individual genetic risk of lung cancer in Chinese and white populations. Then, we compared genetic risk and smoking as well as their interactions on lung cancer between two cohorts, including the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). We also evaluated the absolute risk reduction over a 5-year period.Results19 SNPs and 23 SNPs were identified to construct the PRSs in Chinese and white populations, and smoking-related loci were only included in white populations. The PRSs were consistently associated with lung cancer risk respectively, but stronger associations were observed in smokers of the UKB (HR 1.26versus1.15,P=0.028). A significant interaction between genetic risk and smoking on lung cancer was observed in the UKB (RERI, 11.39 [95% CI, 7.01-17.94]), but not in the CKB. By comparing heavy smokers with nonsmokers, a greater absolute risk reduction was found in the UKB (10.95versus7.12 per 1000 person-years,P<0.001), especially for those at high genetic risk.ConclusionsIn China, tobacco control alone is not enough to reduce the burden of lung cancer, and comprehensive policies should be made to lower its high incidence.
Forecasting influenza incidence as an ordinal variable using machine learning
Authors: Wang Haowei , Kwok Kin On , Riley Steven 
Published: February 2023

AbstractMany mechanisms contribute to the variation in the incidence of influenza disease, such as strain evolution, the waning of immunity and changes in social mixing. Although machine learning methods have been developed for forecasting, these methods are used less commonly in influenza forecasts than statistical and mechanistic models. In this study, we applied a relatively new machine learning method, Extreme Gradient Boosting (XGBoost), to ordinal country-level influenza disease data. We developed a machine learning forecasting framework by adopting the XGBoost algorithm and training it with surveillance data for over 30 countries between 2010 and 2018 from the World Health Organisation’s FluID platform. We then used the model to predict incidence 1- to 4-week ahead. We evaluated the performance of XGBoost forecast models by comparing them with a null model and a historical average model using mean-zero error (MZE) and macro-averaged mean absolute error (mMAE). The XGBoost models were consistently more accurate than the null and historical models for all forecast time horizons. For 1-week ahead predictions across test sets, the mMAE of the XGBoost model with an extending training window was reduced by 78% on average compared to the null model. Although the mMAE increased with longer prediction horizons, XGBoost models showed a 62% reduction in mMAE compared to the null model for 4-week ahead predictions. Our results highlight the potential utility of machine learning methods in forecasting infectious disease incidence when that incidence is defined as an ordinal variable. In particular, the XGBoost model can be easily extended to include more features, thus capturing complex patterns and improving forecast accuracy. Given that many natural extreme phenomena, such as floods and earthquakes, are often described on an ordinal scale when informing planning and response, these results motivate further investigation of using similar scales for communicating risk from infectious diseases.Author SummaryAccurate and timely influenza forecasting is essential to help policymakers improve influenza preparedness and responses to potential outbreaks and allocate medical resources effectively. Here, we present a machine learning framework based on Extreme Gradient Boosting (XBoost) for forecast influenza activity. We used publicly available weekly influenza-like illness (ILI) incidence data in 32 countries. The predictive performance of the machine learning framework was evaluated using several accuracy metrics and compared with baseline models. XGBoost model was shown to be the most accurate prediction approach, and its accuracy remained stable with increasing prediction time horizons. Our results suggest that the machine learning framework for forecasting ILI has the potential to be adopted as a valuable public health tool globally in the future.
Deficiency for scavenger receptors Stabilin-1 and Stabilin-2 leads to age-dependent renal and hepatic depositions of fasciclin domain proteins TGFBI and Periostin in mice
Authors: Leibing Thomas , Riedel Anna , Xi Yannick , Adrian Monica , Krzistetzko Jessica , Kirkamm Christof , Dormann Christof , Schledzewski Kai , Goerdt Sergij , Géraud Cyrill 
Published: February 2023

AbstractBackgroundStabilin-1 and Stabilin-2 are two major scavenger receptors of liver sinusoidal endothelial cells that mediate removal of diverse molecules from the plasma. Double-knockout mice (Stab-DKO) develop impaired kidney function and a decreased lifespan, while single Stabilin deficiency or therapeutic inhibition ameliorates atherosclerosis and Stab1-inhibition is subject of clinical trials in immuno-oncology. Although POSTN and TFGBI have recently been described as novel Stabilin ligands, the dynamics and functional implications of these ligands have not been comprehensively studied.MethodsImmunofluorescence, Western Blotting and Simple Western™ as well as in situ hybridization (RNAScope™) und qRT-PCR were used to analyze transcription levels and tissue distribution of POSTN and TGFBI in Stab-KO mice. Stab-POSTN-Triple deficient mice were generated to assess kidney and liver fibrosis and function in young and aged mice.ResultsTGFBI and POSTN protein accumulated in kidney and liver tissue in Stab-DKO mice during aging despite unchanged transcriptional levels. Stab-POSTN-Triple KO mice showed glomerulofibrosis and a reduced lifespan comparable to Stab-DKO mice. However, alterations of the glomerular diameter and vascular density were partially normalized in Stab-POSTN-Triple KO.ConclusionTGFBI and POSTN are Stabilin-ligands that are deposited in an age-dependent manner in the kidneys and liver due to insufficient scavenging in the liver. Functionally, POSTN appears to partially contribute to the observed renal phenotype in Stab-DKO mice. This study provides details on downstream effects how how Stabilin dysfunction affects distant organs on a molecular and functional level.
Red2Flpe-SCON: A Versatile, Multicolor Strategy for Generating Mosaic Conditional Knockout Mice
Authors: Wu Szu-Hsien Sam , Kim Somi , Lee Heetak , Lee Ji-Hyun , Colozza Gabriele , Park So-Yeon , Bakonyi Réka , Teriyapirom Isaree , Hallay Natalia , Pilat-Carrota Sandra , Theussl Hans-Christian , Kim Jihoon , Lee Joo-Hyeon , Simons Benjamin D. , Kim Jong Kyoung , Koo Bon-Kyoung 
Published: February 2023

AbstractImage-based lineage tracing enables tissue turnover kinetics and lineage potentials of different adult cell populations to be investigated. Previously, we reported a genetic mouse model system,Red2Onco, which ectopically expressed mutated oncogenes together with red fluorescent proteins (RFP). This system enabled the expansion kinetics and neighboring effects of oncogenic clones to be dissected. We now report Red2Flpe-SCON: a new mosaic knockout system that uses multicolor reporters to label both mutant and wild-type cells. We have developed theRed2Flpemouse line for red clone-specific Flpe expression, as well as the FRT-based SCON (ShortConditional IntrON) method to facilitate tunable conditional mosaic knockouts in mice. We used the Red2Flpe-SCON method to study Sox2 mutant clonal analysis in the esophageal epithelium of adult mice which revealed that the stem cell gene, Sox2, is not essential for adult stem cell maintenance itself, but rather for stem cell proliferation and differentiation.
Metabolomic and transcriptomic analyses ofFmo5-/-mice reveal roles for flavin-containing monooxygenase 5 (FMO5) in NRF2-mediated oxidative stress, the unfolded protein response, lipid homeostasis, and carbohydrate and one-carbon metabolism
Authors: Phillips Ian R. , Veeravalli Sunil , Khadayate Sanjay , Shephard Elizabeth A. 
Published: February 2023

AbstractFlavin-containing monooxygenase 5 (FMO5) is a member of the FMO family of proteins best known for their roles in the detoxification of foreign chemicals and more recently in endogenous metabolism. We have previously shown thatFmo5-/-mice display an age-related lean phenotype, with much reduced weight gain from 20 weeks of age. The phenotype is characterized by decreased fat deposition, lower plasma concentrations of glucose and cholesterol, higher glucose tolerance and insulin sensitivity, and resistance to diet-induced obesity. In the present study we report the use of metabolomic and transcriptomic analyses of livers ofFmo5-/-and wild-type mice to identify factors underlying the lean phenotype ofFmo5-/-mice and gain insights into the function of FMO5. Disruption of theFmo5gene has wide-ranging effects on the abundance of metabolites and expression of genes in the liver. The results reveal that FMO5 is involved in upregulating the NRF2-mediated oxidative stress response, the unfolded protein response and response to hypoxia and cellular stress, indicating a role for the enzyme in adaptation to oxidative and metabolic stress. FMO5 also plays a role in stimulating a wide range of metabolic pathways and processes, particularly ones involved in the regulation of lipid homeostasis, the uptake and metabolism of glucose, the generation of cytosolic NADPH, and in one-carbon metabolism. The results predict that FMO5 acts by stimulating the NRF2, XBP1, PPARA and PPARG regulatory pathways, while inhibiting STAT1 and IRF7 pathways.
Logistics of Bone Mineralization in the Chick Embryo Studied by 3D Cryo FIB-SEM Imaging
Authors: Raguin Emeline , Weinkamer Richard , Schmitt Clemens , Curcuraci Luca , Fratzl Peter 
Published: February 2023

AbstractDuring skeletal development, bone growth and mineralization require transport of substantial amounts of calcium, while maintaining very low concentration. How an organism overcomes this major logistical challenge remains mostly unexplained. To shed some light on the dynamics of this process, we use cryogenic Focused Ion Beam-Scanning Electron Microscopy (cryo-FIB/SEM) to image forming bone tissue at day 13 of a chick embryo femur. We visualize both cells and matrix in 3D and observe calcium-rich intracellular vesicular structures. Counting the number of these vesicles per unit volume and measuring their calcium content based on the electron back-scattering signal, we are able to estimate the intracellular velocity at which these vesicles need to travel to transport all the calcium required for the mineral deposited in one day within the collagenous tissue. We estimate this velocity at 0.27 μm/s, which is too large for a diffusion process and rather suggests active transport through the cellular network. We conclude that calcium logistics is hierarchical and based on several transport mechanisms: first through the vasculature using calcium-binding proteins and the blood flow, then active transport over tens of micrometers through the network of osteoblasts and osteocytes and, finally, diffusive transport over the last one or two microns.
Diols and sugar substitutes in attractive toxic sugar baits targetingAedes aegyptiandAedes albopictus(Diptera: Culicidae) mosquitoes
Authors: Pullmann-Lindsley Heidi , Bartlett-Miller Ava , Pitts R Jason 
Published: February 2023

AbstractAround the world, mosquitoes continue to transmit disease-causing pathogens while also developing resistance to insecticides. We previously discovered that a generally regarded as safe (GRAS) compound, 1,2-propanediol, reduces adult mosquito survivorship when ingested. In this study, we assess and compare five more chemically related compounds for mosquito lethality and eight GRAS sugar substitutes to determine toxic effects. We conducted a series of feeding assays to determine if ingesting the compounds influenced mosquito mean survivorship in locally collected lab-reared populations ofAedes aegypti(Linnaeus, 1762) andAedes albopictus(Skuse, 1894) mosquitoes. Our results indicate that 1,2-propanediol, 1,3-propanediol, 1,5-pentanediol, 1,6-hexanediol, 2-methyl-1,3-propanediol, DL-dithiothreitol, acesulfame potassium, allulose, erythritol, sodium saccharin, stevia, and sucralose significantly reduced the mean survivorship of one or both species. Short-term trials with the most toxic compounds revealed that they could substantially affect survivorship after 24 hours. We also found that many of the compounds yielded different responses in the two species and that male mosquitoes expired to a greater extent than female mosquitoes. These findings indicate that several of the compounds may be highly effective for local mosquito vector population and disease control through functioning as components in attractive toxic sugar bait systems (ATSBs)
Atrophic astrocytes in aged marmosets present tau hyperphosphorylation, RNA oxidation, and DNA fragmentation
Authors: Rodríguez-Callejas Juan D. , Fuchs Eberhard , Perez-Cruz Claudia 
Published: February 2023

AbstractAstrocytes perform multiple essential functions in the brain showing morphological changes. Hypertrophic astrocytes are commonly observed in cognitively healthy aged animals, implying a functional defense mechanism without losing neuronal support. In neurodegenerative diseases, astrocytes show morphological alterations, such as decreased process length and reduced number of branch points, known asastroglial atrophy, with detrimental effects on neuronal cells. The common marmoset (Callithrix jacchus) is a non-human primate that, with age, develops several features that resemble neurodegeneration. In this study, we characterize the morphological alterations in astrocytes of adolescent (mean 1.75 y), adult (mean 5.33 y), old (mean 11.25 y), and aged (mean 16.83 y) male marmosets. We observed a significantly reduced arborization in astrocytes of aged marmosets compared to younger animals in the hippocampus and entorhinal cortex. These astrocytes also show oxidative damage to RNA and increased nuclear pTau (AT100). Astrocytes lacking S100A10 protein show a more severe atrophy and DNA fragmentation. Our results demonstrate the presence of atrophic astrocytes in the brains of aged marmosets.
IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets
Authors: Fakouri Baygi Sadjad , Kumar Yashwant , Barupal Dinesh Kumar 
Published: February 2023

AbstractPoor chemical annotation of high-resolution mass spectrometry data limit applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics – Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of LC/HRMS peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R CRAN repository athttps://cran.r-project.org/package=IDSL.CSA. Detailed documentation and tutorials are provided athttps://github.com/idslme/IDSL.CSA.For Table of Contents Only
Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution
Authors: Dimayacyac Jose Rafael , Wu Shanyun , Jiang Daohan , Pennell Matt 
Published: February 2023

AbstractPhylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.
The evolutionary history of spines – a Cenozoic arms race with mammals
Authors: Gélin Uriel , Charles-Dominique Tristan , Davies T. Jonathan , Svenning Jens-Christian , Bond William J. , Tomlinson Kyle W. 
Published: February 2023

AbstractThe role of mammal herbivory in plant evolution is largely unrecognised. Spines on stems are a common and important feature found in ∼9% of eudicot woody plant species worldwide. Spines evolved independently multiple times during the Cenozoic. The timing and extent of spiny plant diversification varied among continents, pointing towards continental rather than global drivers. Spine evolution is closely related to radiation of extant ungulates and extinct ground sloths, rather than climate variation. Diversification began in the Paleogene in herbivore species-rich Eurasia and North America, emerging later in the Neogene in species-poorer South America, Africa and Australia. Spiny lineages expanded their ecological footprint over non-spiny plants, mainly through intercontinental migrations, indicating that spines likely provided a competitive advantage with increasing, and novel, mammal herbivory pressure.
Trial Averaging for Deep EEG Classification
Authors: Williams Jacob M. , Samal Ashok , Johnson Matthew R. 
Published: February 2023

AbstractMany signals, particularly of biological origin, suffer from a signal-to-noise ratio sufficiently low that it can be difficult to classify individual examples reliably, even with relatively sophisticated machine-learning techniques such as deep learning. In some cases, the noise can be high enough that it is even difficult to achieve convergence during training. We considered this problem for one data type that often suffers from such difficulties, namely electroencephalography (EEG) data from cognitive neuroscience studies in humans. One solution to increase signal-to-noise is, of course, to perform averaging among trials, which has been employed before in other studies of human neuroscience but not, to our knowledge, investigated rigorously, particularly not in deep learning. Here, we parametrically studied the effects of different amounts of trial averaging during training and/or testing in a human EEG dataset, and compared the results to that of a related algorithm, Mixup. Broadly, we found that even a small amount of averaging could significantly improve classification, particularly when both training and testing data were subjected to averaging. Simple averaging clearly outperformed Mixup, although the benefits of averaging differed across classification categories. Overall, our results confirm the value of averaging during training and testing when single-trial classification is not strictly necessary for the application in question.HighlightsAveraging trials can dramatically improve performance in classification of EEG dataThe benefits can be seen when averaging on both training and test datasetsSimple trial averaging outperformed a popular related algorithm, MixupHowever, effects of averaging differed across different stimulus categories