bluuebunny/crossref_metadata_2025_split huggingface dataset:
A plasma metabolite score of three eicosanoids predicts incident type 2 diabetes – a prospective study in three independent cohorts
Authors: Tuomisto Karolina , Palmu Joonatan , Long Tao , Watrous Jeramie D. , Mercader Kysha , Lagerborg Kim A. , Andres Allen , Salmi Marko , Jalkanen Sirpa , Vasan Ramachandran S. , Inouye Michael , Havulinna Aki S. , Jousilahti Pekka , Niiranen Teemu J. , Cheng Susan , Jain Mohit , Salomaa Veikko 
Published: July 2021

ABSTRACTAimPeptide markers of inflammation have been associated with the development of type 2 diabetes. The role of upstream, lipid-derived mediators of inflammation such as eicosanoids, remains less clear. The aim was to examine whether eicosanoids are associated with incident type 2 diabetes.MethodsIn the FINRISK 2002 study, a population-based sample of Finnish men and women aged 25-74 years, we used directed, non-targeted liquid chromatography – mass spectrometry to identify 545 eicosanoids and related oxylipins in the participants’ plasma samples (n=8,292). We used multivariable-adjusted Cox regression to examine associations between eicosanoids and incident type 2 diabetes. The findings were replicated in the Framingham Heart Study (FHS, n=2,886) and DILGOM 2007 (n=3,905). Together, these three cohorts had 1070 cases of incident type 2 diabetes.Results76 eicosanoids were associated individually with incident type 2 diabetes. We identified three eicosanoids independently associated with incident type 2 diabetes using stepwise Cox regression with forward selection and a Bonferroni-corrected inclusion threshold. A three-eicosanoid risk score produced a hazard ratio (HR) of 1.56 (95% confidence interval 1.41-1.72) per one standard deviation (SD) increment for risk of incident diabetes. The HR for comparing the top quartile to the lowest was 2.80 (2.53-3.07). Meta-analysis of the three cohorts yielded a pooled HR per SD of 1.31 (1.05-1.56).ConclusionPlasma eicosanoid profiles predict incident type 2 diabetes and the clearest signals replicate in three independent cohorts. Our findings give new information on the biology underlying type 2 diabetes and suggest opportunities for early identification of people at risk.
Geographical concentration of COVID-19 cases by social determinants of health in 16 large metropolitan areas in Canada – a cross-sectional study
Authors: Xia Yiqing , Ma Huiting , Moloney Gary , Velásquez García Héctor A. , Sirski Monica , Janjua Naveed Z. , Vickers David , Williamson Tyler , Katz Alan , Yu Kristy , Kustra Rafal , Buckeridge David L , Brisson Marc , Baral Stefan D , Mishra Sharmistha , Maheu-Giroux Mathieu 
Published: July 2021

BackgroundThere is a growing recognition that strategies to reduce SARS-CoV-2 transmission should be responsive to local transmission dynamics. Studies have revealed inequalities along social determinants of health, but little investigation was conducted surrounding geographic concentration within cities. We quantified social determinants of geographic concentration of COVID-19 cases across sixteen census metropolitan areas (CMA) in four Canadian provinces.MethodsWe used surveillance data on confirmed COVID-19 cases at the level of dissemination area. Gini (co-Gini) coefficients were calculated by CMA based on the proportion of the population in ranks of diagnosed cases and each social determinant using census data (income, education, visible minority, recent immigration, suitable housing, and essential workers) and the corresponding share of cases. Heterogeneity was visualized using Lorenz (concentration) curves.ResultsGeographic concentration was observed in all CMAs (half of the cumulative cases were concentrated among 21-35% of each city’s population): with the greatest geographic heterogeneity in Ontario CMAs (Gini coefficients, 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32), and Québec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income, education attainment, and suitable housing; and higher proportion of visible minorities, recent immigrants, and essential workers. Although a consistent feature across CMAs was concentration by proportion visible minorities, the magnitude of concentration by social determinants varied across CMAs.InterpretationThe feature of geographical concentration of COVID-19 cases was consistent across CMAs, but the pattern by social determinants varied. Geographically-prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to SARS-CoV-2’s resurgence.
Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach
Authors: Weiss-Lehman Christopher P. , Werner Chhaya M. , Bowler Catherine H. , Hallett Lauren M. , Mayfield Margaret M. , Godoy Oscar , Aoyama Lina , Barabás György , Chu Chengjin , Ladouceur Emma , Larios Loralee , Shoemaker Lauren G. 
Published: July 2021

1AbstractModeling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species-interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species’ intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighboring species from the diverse community that had non-generic interactions with our focal species. This sparse modeling approach facilitates exploration of species-interactions in diverse communities while maintaining a manageable number of parameters.
Fusion-oncogenes are associated with increased metastatic capacity and persistent disease in pediatric thyroid cancers
Authors: Franco Aime T. , Ricarte-Filho Julio C. , Isaza Amber , Jones Zachary , Jain Neil , Mostoufi-Moab Sogol , Surrey Lea , Laetsch Theodore W. , Li Marilyn M. , DeHart Jessica Clague , Reichenberger Erin , Taylor Deanne , Kazahaya Ken , Scott Adzick N. , Bauer Andrew J. 
Published: July 2021

ABSTRACTBackgroundIn 2014, data from a comprehensive multiplatform analysis of 496 adult papillary thyroid cancer samples reported by The Cancer Genome Atlas project suggested that reclassification of thyroid cancer into molecular subtypes,RAS-like andBRAF-like, better reflects clinical behavior than sole reliance on pathological classification. The aim of this study was to categorize the common oncogenic variants in pediatric differentiated thyroid cancer and investigate if mutation subtype classification correlated with the risk of metastasis and response to initial therapy in pediatric DTC.MethodsSomatic cancer gene panel analysis was completed on DTC from 131 pediatric patients. DTC were categorized intoRAS-mutant (H-K-NRAS),BRAF-mutant (BRAF p.V600E) andRET/NTRKfusion (RET, NTRK1andNTRK3fusions) to determine differences between subtype classification in regard to pathological data (AJCC TNM) as well as response to therapy 1-year after initial treatment had been completed.ResultsMutation-based subtype categories were significant in most variables, including age at diagnosis, metastatic behavior, and the likelihood of remission at 1-year. Patients withRET/NTRKfusions were significantly more likely to have advanced lymph node and distant metastasis and less likely to achieve remission at one year than patients withinRAS- orBRAF-mutsubgroups.ConclusionsOur data supports that genetic subtyping of pediatric DTC more accurately reflects clinical behavior than sole reliance on pathological classification with patients withRET/NTRKfusions having worse outcomes than those withBRAF-mutant disease. Future trials should consider inclusion of molecular subtype into risk stratification.
AlphaPept, a modern and open framework for MS-based proteomics
Authors: Strauss Maximilian T. , Bludau Isabell , Zeng Wen-Feng , Voytik Eugenia , Ammar Constantin , Schessner Julia , Ilango Rajesh , Gill Michelle , Meier Florian , Willems Sander , Mann Matthias 
Published: July 2021

ABSTRACTIn common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making their efficient analysis a principal challenge. There is a plethora of different computational tools that process the raw MS data and derive peptide and protein identification and quantification. During the last decade, there has been dramatic progress in computer science and software engineering, including collaboration tools that have transformed research and industry. To leverage these advances, we developed AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Using Numba for just-in-time machine code compilation on CPU and GPU, we achieve hundred-fold speed improvements while maintaining clear syntax and rapid development speed. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while providing access to the latest advances in machine learning. We provide an easy on-ramp for community validation and contributions through the concept of literate programming, implemented in Jupyter Notebooks of the different modules. A framework for continuous integration, testing, and benchmarking enforces solid software engineering principles. Large datasets can rapidly be processed as shown by the analysis of hundreds of cellular proteomes in minutes per file, many-fold faster than the data acquisiton. The AlphaPept framework can be used to build automated processing pipelines using efficient HDF5 based file formats, web-serving functionality and compatibility with downstream analysis tools. Easy access for end-users is provided by one-click installation of the graphical user interface, for advanced users via a modular Python library, and for developers via a fully open GitHub repository.
Large ecological benefits of small urban greening actions
Authors: Mata Luis , Hahs Amy K. , Palma Estibaliz , Backstrom Anna , King Tyler , Olson Ashley R. , Renowden Christina , Smith Tessa R. , Vogel Blythe 
Published: July 2021

AbstractThe detrimental effects of human-induced environmental change on people and other species are acutely manifested in urban environments. While urban greenspaces are known to mitigate these effects and support functionally diverse ecological communities, evidence of the ecological outcomes of urban greening remains scarce. We use a longitudinal observational design to provide empirical evidence of the putative ecological benefits of greening actions. We show how a small greening action quickly led to large positive changes in the richness, demographic dynamics, and network structure of a depauperate insect community. An increase in the diversity and complexity of the plant community led to, after only three years, a large increase in insect species richness, a greater probability of occurrence of insects within the greenspace, and a higher number and diversity of interactions between insects and plant species. We demonstrate how large ecological benefits may be derived from investing in small greening actions and how these contribute to bring indigenous species back to greenspaces where they have become rare or locally extinct. Our findings provide crucial evidence that support best practice in greenspace design and contribute to re-invigorate policies aimed at mitigating the negative impacts of urbanisation on people and other species.
Capturing variation in metagenomic assembly graphs with MetaCortex
Authors: Martin Samuel , Ayling Martin , Patrono Livia , Caccamo Mario , Murcia Pablo , Leggett Richard M. 
Published: July 2021

MotivationThe assembly of contiguous sequence from metagenomic samples presents a particular challenge, due to the presence of multiple species, often closely related, at varying levels of abundance. Capturing diversity within species, for example viral haplotypes, or bacterial strain-level diversity, is even more challenging.ResultsWe present MetaCortex, a metagenome assembler that captures intra-species diversity by searching for signatures of local variation along assembled sequences in the underlying assembly graph and outputting these sequences in sequence graph format. We show that MetaCortex produces accurate assemblies with higher genome coverage and contiguity than other popular metagenomic assemblers on mock viral communities with high levels of strain level diversity, and on simulated communities containing simulated strains.Availability and ImplementationSource code is freely available to download fromhttps://github.com/SR-Martin/metacortex, is implemented in C and supported on MacOS and Linux.Contactrichard.leggett@earlham.ac.ukSupplementary informationSupplementary materials are available at the journal’s website. All assemblies, simulated reads, and simulated genomes used in this paper have been deposited online on Zenodo and can be found at DOI 10.5281/zenodo.6616437.
Emotional states as distinct configurations of functional brain networks
Authors: Dan Rotem , Weinstock Marta , Goelman Gadi 
Published: July 2021

AbstractThe conceptualization of emotional states as patterns of interactions between large-scale brain networks has recently gained support. Yet, few studies have directly examined the brain’s network structure during emotional experiences. Here, we investigated the brain’s functional network organization during experiences of sadness, amusement, and neutral states elicited by movies, in addition to a resting-state. We tested the effects of the experienced emotion on individual variability in the brain’s functional connectome. Next, for each state, we defined a community structure of the brain and quantified its segregation and integration. We found that sadness, relative to amusement, was associated with higher modular integration and increased connectivity of cognitive control networks: the salience and fronto-parietal networks. Moreover, in both the functional connectome and the emotional report, the similarity between individuals was dependent on the sex. Our results suggest that the experience of emotion is linked to a reconfiguration of whole-brain distributed, not emotion-specific, functional networks and that the brain’s topological structure carries information about the subjective emotional experience.
Global, Low Amplitude Cortical State Predicts Response Outcomes in a Selective Detection Task
Authors: Marrero Krista , Aruljothi Krithiga , Zareian Behzad , Gao Chengchun , Zhang Zhaoran , Zagha Edward 
Published: July 2021

AbstractSpontaneous neuronal activity strongly impacts stimulus encoding and behavioral responses. We sought to determine the effects of neocortical prestimulus activity on stimulus detection. We trained mice in a selective whisker detection task, in which they learned to respond (lick) to target stimuli in one whisker field and ignore distractor stimuli in the contralateral whisker field. During expert task performance, we used widefield Ca2+ imaging to assess prestimulus and post-stimulus neuronal activity broadly across frontal and parietal cortices. We found that lower prestimulus activity correlated with enhanced stimulus detection: lower prestimulus activity predicted response versus no response outcomes and faster reaction times. The activity predictive of trial outcome was distributed through dorsal neocortex, rather than being restricted to whisker or licking regions. Using principal component analysis, we demonstrate that response trials are associated with a distinct and less variable prestimulus neuronal subspace. For single units, prestimulus choice probability was weak yet distributed broadly, with lower than chance choice probability correlating with stronger sensory and motor encoding. These findings support a low amplitude, low variability, optimal prestimulus cortical state for stimulus detection that presents globally and predicts response outcomes for both target and distractor stimuli.
Necroptosis increases with age in the brain and contributes to age-related neuroinflammation
Authors: Thadathil Nidheesh , Nicklas Evan H , Mohammed Sabira , Lewis Tommy L , Richardson Arlan , Deepa Sathyaseelan S. 
Published: July 2021

AbstractChronic inflammation of the central nervous system (CNS), termed neuroinflammation, is a hallmark of aging and a proposed mediator of cognitive decline associated with aging. Neuroinflammation is characterized by the persistent activation of microglia, the innate immune cells of the CNS, with damage-associated molecular patterns (DAMPs) being one of the well-known activators of microglia. Because necroptosis is a cell death pathway that induce inflammation through the release of DAMPs, we hypothesized that an age-associated increase in necroptosis contributes to increased neuroinflammation with age. The marker of necroptosis, phosphorylated form of MLKL (P-MLKL), and kinases in the necroptosis pathway (RIPK1, RIPK3, and MLKL) showed a region-specific increase in the brain with age, specifically in the cortex layer V and the CA3 region of the hippocampus of mice. Similarly, MLKL-oligomers, which causes membrane binding and permeabilization were significantly increased in the cortex and hippocampus of old mice relative to young mice. Nearly 70 to 80% of P-MLKL immunoreactivity was localized to neurons and less than 10% was localized to microglia, whereas no P-MLKL was detected in astrocytes. P-MLKL expression in neurons was detected in the soma, not in the processes. Blocking necroptosis using Mlkl-/- mice reduced markers (Iba-1 and GFAP) of neuroinflammation in the brains of old mice and short-term treatment with the necroptosis inhibitor, necrostatin-1s, reduced expression of proinflammatory cytokines, IL-6 and IL-1β, in the hippocampus of old mice. Thus, our data demonstrate for the first time that brain necroptosis increases with age and contributes to age-related neuroinflammation in mice.
Assessing donor-to-donor variability in human intestinal organoid cultures
Authors: Mohammadi Sina , Morell-Perez Carolina , Wright Charles W. , Wyche Thomas P. , White Cory H. , Sana Theodore R. , Lieberman Linda A. 
Published: July 2021

SummaryDonor-to-donor variability in primary human organoid cultures has not been well characterized. As these cultures contain multiple cell types, there is greater concern that variability could lead to increased noise. In this work we investigated donor-to-donor variability in human gut adult stem cell (ASC) organoids. We examined intestinal developmental pathways during culture differentiation in ileum- and colon-derived cultures established from multiple donors, showing that differentiation patterns were consistent among cultures. This finding indicates that donor-to-donor variability in this system remains at a manageable level. Intestinal metabolic activity was evaluated by targeted analysis of central carbon metabolites and by analyzing hormone production patterns. Both experiments demonstrated similar metabolic functions among donors. Importantly, this activity reflected intestinal biology, indicating that these ASC organoid cultures are appropriate for studying metabolic processes. This work establishes a framework for generating high confidence data using human primary cultures through thorough characterization of variability.
Indomethacin Use for Mild & Moderate hospitalised Covid-19 patients: An open label randomized clinical trial
Authors: Ravichandran Rajan , Mohan Surapaneni Krishna , Sukumaran Suresh Kumar , Kamaraj Devakumar , Daivasuga Sumetha Suga , Samuel Ravi Samson Oliver Abraham , Vijayaraghavalu Sivakumar , Kumar Ramarathnam Krishna 
Published: July 2021

ABSTRACTIntroductionIndomethacin, a non-steroidal anti-inflammatory drug (NSAID), has been presented as a broad-spectrum antiviral agent. This randomised clinical trial in a hospital setting evaluated the efficacy and safety of this drug in RT-PCR-positive coronavirus disease 2019 (COVID-19) patients.Materials & MethodsA total of 210 RT-PCR-positive COVID-19 patients, who provided consent were allotted, to control or case arm, based on block randomisation. The control arm received standard of care comprising paracetamol, ivermectin, and other adjuvant therapies. The patients in the case arm received indomethacin instead of paracetamol, with other medications retained. The primary endpoint was the development of hypoxia/desaturation with SpO2≤ 93, while time to become afebrile and time for cough and myalgia resolution were the secondary endpoints.ResultsThe results of 210 patients were available, with 103 and 107 patients in the indomethacin and paracetamol arms, respectively. We monitored patient profiles along with everyday clinical parameters. Blood chemistry at the time of admission and discharge was assessed.As no one in either of the arms required high-flow oxygen, desaturation with SpO2level of 93 and below was an important goal. In the indomethacin group, none of the 103 patients developed desaturation. On the other hand, 20 of the 107 patients in the paracetamol arm developed desaturation. Patients who received indomethacin also experienced more rapid symptomatic relief than those in the paracetamol arm, with most symptoms disappearing in half the time. 56 patients out of 107 in the paracetamol arm had fever on the seventh day, while no patient in the indomethacin group had fever. Neither arm reported any adverse event. The fourteenth-day follow-up revealed that the paracetamol arm patients had faced several discomforts, including myalgia, joint pain, and tiredness; indomethacin arm patients mostly complained only of tiredness.ConclusionIndomethacin is a safe and effective drug for treating patients with mild and moderate covid-19.