bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimating the Global Spread of Epidemic Human Monkeypox with Bayesian Directed Acyclic Graphic Model
Authors: Liao Ling-Chun , Hsu Chen-Yang , Lai Chao-Chih , Chen Tony Hsiu-Hsi 
Published: December 2022

AbstractObjectivesA ‘Public Health Emergency of International Concern (PHEIC)’ monkeypox outbreak was declared by the World Health Organization on June 23, 2022. More than 16,000 monkeypox cases were reported in more than 75 countries across six regions as July 25.DesignA modeling study.Setting and ParticipantsDaily confirmed Monkeypox cases of the global, United States, Spain, Brazil, and United Kingdom were retrieved from the Global health team till September 23,2022. All conducted analyses are at the aggregate level.without involvement of confidential information.Primary and secondary outcome measuresThe Bayesian SIR (Susceptible-Infected-Recovered) model with Directed Acyclic Graphic method was used to estimate the basic/effective reproductive number (R0/ Re) and to assess the epidemic spread of Monkeypox in the globe.ResultsThe maximum estimated R0/ Rewas 1.16 (1.15-1.17), 1.20 (1.20-1.20), 1.34 (1.34-1.35), and 1.33 (1.33-1.33) in United States, Spain, Brazil, and United Kingdom, respectively. The values of R0/ Rewas toward to below 1 after August, 2022. The estimated infectious time before isolation ranged from 2.05 to 2.74 days.ConclusionsThe PHEIC of global spreading of human Monkeypox outside Africa has been contained so as to avoid a pandemic in the light of the reasoning-based epidemic model assessment.Strengths and limitations of this studyWe estimated the epidemiology parameters based on the Bayesian SIR model for the uncertainty transmission of monkeypox under assumptions of homogeneous random mixing population and surveillance systems were the same across countries.This approach can minimize biases between population, different surveillance systems across countries. Estimating results of R0 were limited but consistent between countries and similar to the simulation data by the deterministic SIR model.
Impact of patient habitus and acquisition protocol on iodine quantification in dual source photon-counting CT
Authors: Pua Rizza , Liu Leening P. , Dieckmeyer Michael , Shapira Nadav , Sahbaee Pooyan , Gang Grace J. , Litt Harold I. , Noël Peter B. 
Published: December 2022

AbstractObjectiveEvaluation of iodine quantification accuracy with varying iterative reconstruction level, patient habitus, and acquisition mode on a first-generation dual-source photon-counting computed tomography (PCCT) system.MethodsA multi-energy CT phantom (20 cm diameter/small) was imaged with and without an extension ring (30 by 40 cm/large). It was equipped with various iodine inserts (0.2, 0.5, 1.0, 2.0, 5.0, 10.0, 15.0 mg/ml) and scanned over a range of radiation dose levels (CTDIvol 0.5, 0.8, 1.2, 1.6, 2.0, 4.0, 6.0, 10.0, 15.0 mGy) using four different acquisition modes: single source 120 kVp (SS120), 140 kVp (SS140) and dual-source 120 kVp (DS120), 140 kVp (DS140). Iodine density maps were produced with different levels of iterative reconstruction (QIR 0, 2, 4). To assess the agreement between nominal and measured iodine concentrations, root mean square error (RMSE) and Bland-Altman plots were generated by grouping different radiation dose levels (ultra-low: < 1.5 mGy; low: 1.5 – 5 mGy; medium: 5 – 15 mGy) and iodine concentrations (low: < 5 mg/ml; high: 5 – 15 mg/ml).ResultsOverall, quantification of iodine concentrations was accurate and reliable even at ultra-low radiation dose levels. With low and high iodine concentrations, RMSE ranged from 0.25 to 0.37, 0.20 to 0.38, and 0.25 to 0.37 mg/ml for ultra-low, low, and medium radiation dose levels, respectively. Similarly, for the three acquisition modes (SS120, SS140, DS 120, DS140), RMSE was stable at 0.31, 0.28, 0.33 and 0.30 mg/ml, respectively. Considering all levels of radiation dose, acquisition mode, and iodine concentration, the accuracy of iodine quantification was higher for the phantom without extension ring (RMSE 0.21 mg/ml) and did not vary across different levels of iterative reconstruction.ConclusionsThe first-generation PCCT allows for accurate iodine quantification over a wide range of iodine concentrations and radiation dose levels. Even very small concentrations of iodine can be quantified accurately at different simulated patient sizes. Stable accuracy across iterative reconstruction levels may allow further radiation exposure reductions without affecting quantitative results.SummaryClinical photon-counting CT provides excellent iodine quantification performance for a wide range of parameters (patient habitus, acquisition parameters, and iterative reconstruction modes) due to its excellent ultra-low dose performance.Key ResultsFirst-generation PCCTs are capable of accurately quantifying iodine over a wide range of radiation dose levels and iodine concentrations.Further radiation exposure reductions may be possible given stable accuracy across iterative reconstruction levels.In the future, accurate and precise iodine quantification will allow for the development of spectral-based biomarkers.
CBFA2T3-GLIS2-dependent pediatric acute megakaryoblastic leukemia is driven by GLIS2 and sensitive to Navitoclax
Authors: Neault Mathieu , Lebert-Ghali Charles-Étienne , Fournier Marilaine , Capdevielle Caroline , Garfinkle Elizabeth A.R. , Obermayer Alyssa , Cotton Anitria , Boulay Karine , Sawchyn Christina , Nguyen Kamy H. , Assaf Béatrice , Mercier François E. , Delisle Jean-Sébastien , Drobetsky Elliot A. , Hulea Laura , Shaw Timothy I. , Zuber Johannes , Gruber Tanja A. , Melichar Heather J. , Mallette Frédérick A. 
Published: December 2022

AbstractPediatric acute megakaryoblastic leukemia (AMKL) is an aggressive, uncurable blood cancer associated with poor therapeutic response and high mortality. We developed CBFA2T3-GLIS2-driven mouse models of AMKL that recapitulate the phenotypic and transcriptional signatures of the human disease. We show that an activating Ras mutation, which occurs in human AMKL, increased the penetrance and decreased the latency of CBF2AT3-GLIS2-driven AMKL. CBFA2T3-GLIS2 and GLIS2 modulate similar transcriptional networks. We uncover the dominant oncogenic properties of GLIS2, which trigger AMKL in cooperation with oncogenic Ras. We find that both CBFA2T3-GLIS2 and GLIS2 alter the expression of numerous BH3-only proteins, causing AMKL cell sensitivity to the BCL-2 inhibitor navitoclax bothin vitroandin vivo, suggesting a novel therapeutic option for pediatric patients suffering from CBFA2T3-GLIS2-driven AMKL.Key pointsGLIS2 cooperates with activated Nras to promote the development of acute megakaryoblastic leukemia.CBFA2T3-GLIS2 and GLIS2 alter the expression of BCL2 family members rendering AMKL cells sensitive to navitoclax.
Characterization of fluorescence lifetime of organic fluorophores for molecular imaging in the SWIR window
Authors: Chavez Luis , Gao Shan , Intes Xavier 
Published: December 2022

AbstractSignificanceFluorescence lifetime imaging in the short-wave infrared (SWIR) is expected to enable high resolution multiplexed molecular imaging in highly scattering tissue.AimTo characterize the brightness and fluorescence lifetime of commercially available organic SWIR fluorophores and benchmark them against the tail emission of conventional NIR-excited probes.ApproachCharacterization was performed through our established Time-domain Mesoscopic Fluorescence Molecular Tomography (TD-MFMT) system integrated around a TCSPC-SPAD array. Brightness and fluorescence lifetime was measured for NIR and SWIR probes above 1000 nm. Simultaneous probe imaging was then performed to assess their potential for multiplexed studies.ResultsNIR probes outperformed SWIR probes in brightness while the mean fluorescence lifetimes of the SWIR probes were extremely short. The phantom study demonstrated the feasibility of lifetime multiplexing in the SWIR window with both NIR and SWIR probes.ConclusionsLong tail emission of NIR probes outperformed the SWIR probes in brightness beyond 1000 nm. Fluorescence lifetime was readily detectable in the SWIR window, where the SWIR probes showed shorter lifetimes compared to the NIR probes. We demonstrate the feasibility of lifetime multiplexing in the SWIR window, which paves the way for in vivo multiplexed studies of intact tissues at improved resolution.
Age but not menopausal status is linked to lower resting energy expenditure
Authors: Karppinen Jari E. , Wiklund Petri , Ihalainen Johanna K. , Juppi Hanna-Kaarina , Isola Ville , Hyvärinen Matti , Ahokas Essi K. , Kujala Urho M. , Laukkanen Jari , Hulmi Juha J. , Ahtiainen Juha P. , Cheng Sulin , Laakkonen Eija K. 
Published: December 2022

ABSTRACTContextIt remains uncertain whether aging before late adulthood and menopause are associated with fat-free mass and fat mass–adjusted resting energy expenditure (REEadj).ObjectivesWe investigated whether REEadjdiffers between middle-aged and younger women and between middle-aged women with different menopausal statuses. We repeated the age group comparison between middle-aged mothers and their daughters to partially control for genotype. We also explored whether serum estradiol and follicle-stimulating hormone concentrations explain REEadjin midlife.MethodsWe divided 120 women, including 16 mother–daughter pairs, into age groups; group I (n= 26) consisted of participants aged 17–21, group II (n= 35) of those aged 22–38 and group III (n= 59) of those aged 41–58 years. The women in group III were further categorized as pre- or perimenopausal (n= 19), postmenopausal (n= 30) or postmenopausal hormone therapy users (n= 10). REE was assessed using indirect calorimetry, body composition using dual-energy X-ray absorptiometry and hormones using immunoassays.ResultsThe REEadjof group I was 126 kcal/d (95% CI: 93–160) higher than that of group III, and the REEadjof group II was 88 kcal/d (95% CI: 49–127) higher. Furthermore, daughters had a 100 kcal/d (95% CI: 63–138 kcal/d) higher REEadjthan their middle-aged mothers (allP< 0.001). In group III, REEadjwas not lower in postmenopausal women and did not vary by sex hormone concentrations.ConclusionsWe demonstrated that REEadjdeclines with age in women before late adulthood, also when controlling partially for genetic background, and that menopause may not contribute to this decline.
Consistent typing of plasmids with the mge-cluster pipeline
Authors: Arredondo-Alonso Sergio , Gladstone Rebecca A. , Pöntinen Anna K. , Gama João A. , Schürch Anita C. , Lanza Val F. , Johnsen Pål Jarle , Samuelsen Ørjan , Tonkin-Hill Gerry , Corander Jukka 
Published: December 2022

AbstractExtrachromosomal elements of bacterial cells such as plasmids are notorious for their importance in evolution and adaptation to changing ecology. However, high-resolution population-wide analysis of plasmids has only become accessible recently with the advent of scalable long-read sequencing technology. Current typing methods for the classification of plasmids remain limited in their scope which motivated us to develop a computationally efficient approach to simultaneously recognize novel types and classify plasmids into previously identified groups. Our method can easily handle thousands of input sequences which are compressed using a unitig representation in a de Bruijn graph. We provide an intuitive visualization, classification and clustering scheme that users can explore interactively. This provides a framework that can be easily distributed and replicated, enabling a consistent labelling of plasmids across past, present, and future sequence collections. We illustrate the attractive features of our approach by the analysis of population-wide plasmid data from the opportunistic pathogenEscherichia coliand the distribution of the colistin resistance genemcr-1.1in the plasmid population.
MultiGML: Multimodal Graph Machine Learning for Prediction of Adverse Drug Events
Authors: Krix Sophia , DeLong Lauren Nicole , Madan Sumit , Domingo-Fernández Daniel , Ahmad Ashar , Gul Sheraz , Zaliani Andrea , Fröhlich Holger 
Published: December 2022

SummaryAdverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources and thus neglect a wealth of information that is uncovered by fusion of different data sources, including biological protein function, gene expression, chemical compound structure, cell-based imaging, etc. In this work we propose an integrative and explainable Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event.MotivationAdverse drug events are a major risk for failure of late-stage clinical trials. Attempts to prevent adverse drug events in preclinical drug development include experimental procedures for measuring liver-toxicity, cardio-toxicity, etc. Yet these procedures are costly and cannot fully guarantee success in later clinical studies, specifically in situations without a reliable animal model. Computational approaches developed for adverse event prediction have shown to be valuable, but are mostly limited to single data sources. Our approach successfully integrates various data sources on protein functions, gene expression, chemical compound structures and more, into the prediction of adverse events. A main distinguishing characteristic is the explainability of our model predictions which allow further insight into biological mechanisms.
Decoupled neoantigen cross-presentation in tumors with high intratumor heterogeneity reduces dendritic cell activation to limit anti-tumor immunity
Authors: Nguyen Kim B. , Copeland Christopher J. , Backlund Coralie M. , Klop-Packel Nory G. , Remba Tanaka , Kim Byungji , Singh Nishant K. , Birnbaum Michael E. , Irvine Darrell J. , Spranger Stefani 
Published: December 2022

AbstractCancer immunotherapies, in particular checkpoint blockade immunotherapy (CBT), can induce control of cancer growth, with a fraction of patients experiencing durable responses. However, the majority of patients currently do not respond to CBT and the molecular determinants of resistance have not been fully elucidated. Mounting clinical evidence suggests that the clonal status of neoantigens (NeoAg) impacts the anti-tumor T cell response. High intratumor heterogeneity (ITH), where the majority of NeoAgs are expressed subclonally, is correlated with poor clinical response to CBT and poor infiltration with tumor-reactive T cells. However, the mechanism by which ITH blunts tumor-reactive T cells is unclear. We developed a transplantable murine lung cancer model to characterize the immune response against a defined set of NeoAgs expressed either clonally or subclonally to model low or high ITH, respectively. Here we show that clonal expression of a weakly immunogenic NeoAg with a relatively strong NeoAg increased the immunogenicity of tumors with low but not high ITH. Mechanistically we determined that clonal NeoAg expression allowed cross-presenting dendritic cells to acquire and present both NeoAgs. Dual NeoAg presentation by dendritic cells was associated with a more mature DC phenotype and a higher stimulatory capacity. These data suggest that clonal NeoAg expression can induce more potent anti-tumor responses due to more stimulatory dendritic cell: T cell interactions. Therapeutic vaccination targeting subclonally expressed NeoAgs could be used to boost anti-tumor T cell responses.
Unraveling MLL1-fusion Leukemia: Epigenetic Revelations from an iPS Cell Point Mutation
Authors: Kobrossy Laila , Xu Weiyi , Zhang Chunling , Feng Wenyi , Turner Christopher E. , Cosgrove Michael S. 
Published: December 2022

AbstractOur understanding of acute leukemia pathology is heavily dependent on 11q23 chromosomal translocations involving the mixed lineage leukemia-1 (MLL1) gene, a key player in histone H3 lysine 4 (H3K4) methylation. These translocations result in distinct MLL1-fusion (MLL1F) proteins that are thought to drive leukemogenesis. However, the mechanism behind increased H3K4 trimethylation in MLL1F-leukemic stem cells (MLL1F-LSCs), following loss of catalytic SET domain of MLL1 (known for H3K4 mono- and dimethylation), remains unclear. In our investigation, we introduced a homozygous loss-of-function point mutation in MLL1 within human induced pluripotent stem cells. Remarkably, this mutation mimics the histone methylation, gene expression, and EMT phenotypes of MLL1F-LSCs- without the need for a translocation or functional wild-type MLL1. This observation underscores the essential role of MLL1’s enzymatic activity in restraining the cascade of epigenetic changes associated with the gene activating H3K4 trimethylation mark, which we show is catalyzed by mislocalized SETd1a H3K4 trimethyltransferase in the absence of MLL1’s enzymatic activity. Challenging existing models, our findings imply that MLL1F-induced leukemias arise from a dominant-negative impact on MLL1’s histone methyltransferase activity. We advocate for a therapeutic paradigm shift, targeting SETd1a for precision medicine. This work opens new avenues for addressing the complexities of MLL1-associated leukemias and improving targeted therapies.SummaryEpigenomic and gene expression changes in iPS cells with a mutated MLL1 histone methyltransferase suggest an oncogenic mechanism for MLL1-fusion leukemias.
Comprehensive profiling of wastewater viromes by genomic sequencing
Authors: Wyler Emanuel , Lauber Chris , Manukyan Artür , Deter Aylina , Quedenau Claudia , Teixeira Alves Luiz Gustavo , Seitz Stefan , Altmüller Janine , Landthaler Markus 
Published: December 2022

AbstractGenomic material in wastewater provides a rich source of data for detection and surveillance of microbes. Used for decades to monitor poliovirus and other pathogens, the SARS-CoV-2 pandemic and the falling costs of high-throughput sequencing have substantially boosted the interest in and the usage of wastewater monitoring. We have longitudinally collected over 100 samples from a wastewater treatment plant in Berlin/Germany, from March 2021 to July 2022, in order to investigate three aspects. First, we conducted a full metagenomic analysis and exemplified the depth of the data by temporal tracking strains and to a certain extent also variants of human astroviruses and enteroviruses. Second, targeting respiratory pathogens, a broad enrichment panel enabled us to detect waves of RSV, influenza, or common cold coronaviruses in high agreement with clinical data. Third, by applying a profile Hidden Markov Model-based search for novel viruses, we identified more than 100 thousand novel transcript assemblies likely not belonging to known virus species, thus substantially expanding our knowledge of virus diversity. Taken together, we present a longitudinal and deep investigation of the viral genomic information in wastewater that underlines the value of sewage surveillance for both public health purposes and planetary virome research.
Mediator subunit MDT-15 promotes expression of propionic acid breakdown genes to prevent embryonic lethality inCaenorhabditis elegans
Authors: Goh Grace Ying Shyen , Beigi Arshia , Yan Junran , Doering Kelsie R. S. , Taubert Stefan 
Published: December 2022

AbstractThe micronutrient vitamin B12 is an essential cofactor for two enzymes: methionine synthase, which plays a key role in the one-carbon cycle; and methylmalonyl-CoA mutase, an enzyme in a pathway that breaks down branched-chain amino acids and odd-chain fatty acids. A second, vitamin B12-independent pathway that degrades methylmalonyl-CoA and its upstream metabolite propionic acid was recently described inCaenorhabditis elegans, the propionate shunt pathway. Activation of five shunt pathway genes in response to low vitamin B12 availability or high propionic acid levels is accomplished by a transcriptional regulatory mechanism involving two nuclear hormone receptors, NHR-10 and NHR-68. Here, we report that theC. elegansMediator subunitmdt-15is also essential for the activation of the propionate shunt pathway genes, likely by acting as a transcriptional coregulator for NHR-10.C. elegans mdt-15mutants fed a low vitamin B12 diet have transcriptomes resembling those of wild-type worms fed a high vitamin B12 diet, with low expression of the shunt genes. Phenotypically, the embryonic lethality ofmdt-15mutants is specifically rescued by diets high in vitamin B12, but not by dietary polyunsaturated fatty acids, which rescue many other phenotypes of themdt-15mutants. Finally, NHR-10 binds to MDT-15 in yeast-two-hybrid assays, and the transcriptomes ofnhr-10mutants resemble those ofmdt-15mutants. Our data show that MDT-15 is a key coregulator for an NHR regulating propionic acid detoxification, adding to roles played by NHR:MDT-15 partnerships in metabolic regulation and pinpointing vitamin B12 availability as a requirement formdt-15dependent embryonic development.
Reproducible And Clinically Translatable Deep Neural Networks For Cervical Screening
Authors: Ahmed Syed Rakin , Befano Brian , Lemay Andreanne , Egemen Didem , Rodriguez Ana Cecilia , Angara Sandeep , Desai Kanan , Jeronimo Jose , Antani Sameer , Campos Nicole , Inturrisi Federica , Perkins Rebecca , Kreimer Aimee , Wentzensen Nicolas , Herrero Rolando , Pino Marta del , Quint Wim , de Sanjose Silvia , Schiffman Mark , Kalpathy-Cramer Jayashree 
Published: December 2022

ABSTRACTCervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. Published AI reports have exhibited overfitting, lack of portability, and unrealistic, near-perfect performance estimates. To surmount recognized issues, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-institutional dataset of 9,462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.