bluuebunny/crossref_metadata_2025_split huggingface dataset:
Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time series analysis
Authors: Scortichini Matteo , dos Santos Rochelle Schneider , De’ Donato Francesca , De Sario Manuela , Michelozzi Paola , Davoli Marina , Masselot Pierre , Sera Francesco , Gasparrini Antonio 
Published: July 2020

AbstractBackgroundItaly was the first country outside China to experience the impact of the COVID-19 pandemic, which resulted in a significant health burden. This study presents an analysis of the excess mortality across the 107 Italian provinces, stratified by sex, age group, and period of the outbreak.MethodsThe analysis was performed using a two-stage interrupted time series design using daily mortality data for the period January 2015 – May 2020. In the first stage, we performed province-level quasi-Poisson regression models, with smooth functions to define a baseline risk while accounting for trends and weather conditions and to flexibly estimate the variation in excess risk during the outbreak. Estimates were pooled in the second stage using a mixed-effects multivariate meta-analysis.ResultsIn the period 15 February – 15 May 2020, we estimated an excess of 47,490 (95% empirical confidence intervals: 43,984 to 50,362) deaths in Italy, corresponding to an increase of 29.5% (95%eCI: 26.8 to 31.9%) from the expected mortality. The analysis indicates a strong geographical pattern, with the majority of excess deaths occurring in northern regions, where few provinces experienced up to 800% increase during the peak in late March. There were differences by sex, age, and area both in the overall impact and in its temporal distribution.ConclusionsThis study offers a detailed picture of excess mortality during the first months of the COVID-19 pandemic in Italy. The strong geographical and temporal patterns can be related to implementation of lockdown policies and multiple direct and indirect pathways in mortality risk.Key MessagesThis study evaluated mortality trends in Italy during the COVID-19 pandemic, reporting an excess of 47,490 (95% empirical confidence intervals: 43,984 to 50,362) deaths in the period 15 February – 15 May 2020, corresponding to an increase of 29.5% (95%eCI: 26.8 to 31.9%) from the expected mortality.There is a strong geographical pattern, with 71.0% of the estimated excess deaths occurring in just three northern regions (Lombardy, Veneto, and Emilia-Romagna), and few provinces showing increases in mortality up to 800% during the peak of the pandemic.The impact was slightly higher is men compared to women, with 24,655 and 23,125 excess deaths respectively, and varied by age, with higher mortality in the group 70-79 years old and evidence of a lower but measurable risk even in people less than 60.The analysis by week suggests differential trends, with more delayed impacts in women and elderly, and the risk limited to the early period in Central and Southern Italy, likely related to the implementation of lockdown policies and contributions from direct and indirect risk pathways.
Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis
Authors: DeRamus Thomas , Faghiri Ashkan , Iraji Armin , Agcaoglu Oktay , Vergara Victor , Fu Zening , Silva Rogers , Gazula Harshvardhan , Stephen Julia , Wilson Tony W. , Wang Yu-Ping , Calhoun Vince 
Published: July 2020

AbstractResting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008∼0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies.Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions.While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.
Use of IL-6-elafin genetically modified regulatory macrophages as an immunotherapeutic against acute bacterial infection in the lung
Authors: Kheir S. , Villeret B. , Garcia-Verdugo I. , Sallenave JM 
Published: July 2020

AbstractBackgroundPseudomonas aeruginosa (P.a)infections are a major public health issue in ventilator-associated pneumoniae, cystic fibrosis and chronic obstructive pulmonary disease (COPD) exacerbations. This bacterium is multidrug resistant and there is an urgent need to develop new therapeutic approaches.ObjectiveEvaluate the effect of direct pulmonary transplantation of gene-modified (elafin and IL-6) syngeneic macrophages in a mouse model of acute ofP.ainfection.MethodsWild type (WT) or Elafin-transgenic (eTg) alveolar macrophages (AMs) or bone marrow derived macrophages (BMDMs) were recovered from broncho-alveolar lavage or generated from WT or eTg mice bone marrow. Cells were modified with adenovirus IL-6 (Ad-IL6), characterizedin vitro(RNA expression, protein secretion, surface markers) and transferred by oropharyngeal instillation in the lungs of naïve mice. The protective effect of the transferred macrophages was assessed duringP.aacute infection (survival studies, mechanistic studies of the inflammatory response).ResultsWe show that the transfer in the lung of a single bolus of syngeneic AMs or BMDMs genetically modified with IL6 and Elafin provided protection in our pneumoniaP.a-induced model. Mechanistically, Elafin-modified AM had an IL-6-IL-10-IL-4R-IL-22-antimicrobial molecular signature which, in synergy with IL-6, conferred, post-transfer, a regulatory phenotype to the alveolar unit.ConclusionHere we introduce an immunotherapy approach employing gene-modified syngeneic macrophages to target bacterial airway infections. The absence of adverse events during our experiments suggests that our approach is well tolerated and supports the feasibility of translating this therapy to patients suffering from lung acute bacterial infections.
Coarse, Medium or Fine? A Quantum Mechanics Approach to Single Species Population Dynamics
Authors: Akman Olcay , Arriola Leon , Ghosh Aditi , Schroeder Ryan 
Published: July 2020

AbstractStandard heuristic mathematical models of population dynamics are often constructed using ordinary differential equations (ODEs). These deterministic models yield pre-dictable results which allow researchers to make informed recommendations on public policy. A common immigration, natural death, and fission ODE model is derived from a quantum mechanics view. This macroscopic ODE predicts that there is only one stable equilibrium point. We therefore presume that ast→ ∞, the expected value should be. The quantum framework presented here yields the same standard ODE model, however with very unexpected quantum results, namely. The obvious questions are: why isn’t, why are the probabilities ≈ 0.37, and where is the missing probability of 0.26? The answer lies in quantum tunneling of probabilities. The goal of this paper is to study these tunneling effects that give specific predictions of the uncertainty in the population at the macroscopic level. These quantum effects open the possibility of searching for “black–swan” events. In other words, using the more sophisticated quantum approach, we may be able to make quantitative statements about rare events that have significant ramifications to the dynamical system.
Modulation of Human T-type Calcium Channels by Synthetic Cannabinoid Receptor Agonistsin vitro
Authors: Bladen Chris , Mirlohi Somayeh , Santiago Marina , Longworth Mitchell , Kassiou Michael , Banister Sam , Connor Mark 
Published: July 2020

AbstractBACKGROUND AND PURPOSEConsumption of Synthetic Cannabinoid Receptor agonists (SCRAs) is associated with severe adverse reactions including seizures, arrhythmias and death, but the molecular mechanisms surrounding SCRA toxicity are not yet established. These disease-like symptoms are also synonymous with altered T-type calcium channel activity which controls rhythmicity in the heart and brain. This study examined whether SCRAs alter T-type activity and whether this represents a possible mechanism of toxicity.EXPERIMENTAL APPROACHFluorescence-based and electrophysiology assays were used to screen 16 structurally related synthetic cannabinoids for their ability to inhibit human T-type calcium channels expressed in HEK293 cells. The most potent compounds were then further examined using patch clamp electrophysiology.KEY RESULTSMDMB-CHMICA and AMB-CHMINACA potently blocked Cav3.2 with IC50 values of 1.5 and 0.74 μM respectively. Current inhibition increased from 47 to 80% and 45 to 87% respectively when the channel was in slow-inactivated state. Both SCRAs had little effect on steady state inactivation, however MDMB-CHMICA significantly shifted the half activation potential by −7mV. Neither drug produced frequency dependent block, in contrast to the phytocannabinoid Δ9-THC.CONCLUSIONS and IMPLICATIONSSCRAs are potent agonists of CB1 receptors and can be extremely toxic, but observed toxicity also resembles symptoms associated with altered Cav3.2 activity. Many SCRAs tested were potent modulators of Cav3.2, raising the possibility that SC toxicity may be due in part to Cav3.2 modulation. This potent T-type channel modulation suggests the possibility of SCRAs as a new drug class with potential to treat diseases associated with altered T-type channel activity.
Fully unsupervised deep mode of action learning for phenotyping high-content cellular images
Authors: Janssens Rens , Zhang Xian , Kauffmann Audrey , de Weck Antoine , Durand Eric Y. 
Published: July 2020

AbstractThe identification and discovery of phenotypes from high content screening (HCS) images is a challenging task. Earlier works use image analysis pipelines to extract biological features, supervised training methods or generate features with neural networks pretrained on non-cellular images. We introduce a novel fully unsupervised deep learning algorithm to cluster cellular images with similar Mode-of-Action together using only the images’ pixel intensity values as input. The method outperforms existing approaches on the labelled subset of the BBBC021 dataset and achieves an accuracy of 97.09% for correctly classifying the Mode-of-Action (MOA) by nearest neighbors matching. One unique aspect of the approach is that it is able to perform training on the entire unannotated dataset, to correctly cluster similar treatments beyond the annotated subset of the dataset and can be used for novel MOA discovery.
Neural state space alignment for magnitude generalisation in humans and recurrent networks
Authors: Sheahan Hannah , Luyckx Fabrice , Nelli Stephanie , Teupe Clemens , Summerfield Christopher 
Published: July 2020

SummaryA prerequisite for intelligent behaviour is to understand how stimuli are related and to generalise this knowledge across contexts. Generalisation can be challenging when relational patterns are shared across contexts but exist on different physical scales. Here, we studied neural representations in humans and recurrent neural networks performing a magnitude comparison task, for which it was advantageous to generalise concepts of “more” or “less” between contexts. Using multivariate analysis of human brain signals and of neural network hidden unit activity, we observed that both systems developed parallel neural “number lines” for each context. In both model systems, these number state spaces were aligned in a way that explicitly facilitated generalisation of relational concepts (more and less). These findings suggest a previously overlooked role for neural normalisation in supporting transfer of a simple form of abstract relational knowledge (magnitude) in humans and machine learning systems.
DeepG4 : A deep learning approach to predict active G-quadruplexes from DNA
Authors: Rocher Vincent , Genais Matthieu , Nassereddine Elissar , Mourad Raphael 
Published: July 2020

AbstractDNA is a complex molecule carrying the instructions an organism needs to develop, live and reproduce. In 1953, Watson and Crick discovered that DNA is composed of two chains forming a double-helix. Later on, other structures of DNA were discovered and shown to play important roles in the cell, in particular G-quadruplex (G4). Following genome sequencing, several bioinformatic algorithms were developed to map G4s in vitro based on a canonical sequence motif, G-richness and G-skewness or alternatively sequence features including k-mers, and more recently machine/deep learning. Here, we propose a novel convolutional neural network (DeepG4) to map active G4s (forming both in vitro and in vivo). DeepG4 is very accurate to predict active G4s, while most state-of-the-art algorithms fail. Moreover, DeepG4 identifies key DNA motifs that are predictive of G4 activity. We found that active G4 motifs do not follow a very flexible sequence pattern as current algorithms seek for. Instead, active G4s are determined by numerous specific motifs. Moreover, among those motifs, we identified known transcription factors (TFs) which could play important roles in G4 activity by contributing either directly to G4 structures themselves or indirectly by participating in G4 formation in the vicinity. Moreover, we showed that specific TFs might explain G4 activity depending on cell type. Lastly, variant analysis suggests that SNPs altering predicted G4 activity could affect transcription and chromatin,e.g. gene expression, H3K4me3 mark and DNA methylation. Thus, DeepG4 paves the way for future studies assessing the impact of known disease-associated variants on DNA secondary structure by providing a mechanistic interpretation of SNP impact on transcription and chromatin.Availability:https://github.com/morphos30/DeepG4.Author summaryDNA is a molecule carrying genetic information and found in all living cells. In 1953, Watson and Crick found that DNA has a double helix structure. However, other DNA structures were later identified, and most notably, G-quadruplex (G4). In 2000, the Human Genome Project revealed the widespread presence of G4s in the genome using algorithms. To date, all G4 mapping algorithms were developed to map G4s on naked DNA, without knowing if they could be formed in the cell. Here, we designed a novel artificial intelligence algorithm that could map G4s active in the cell from the DNA sequence. We showed its better accuracy compared to existing algorithms. Moreover, we identified key transcriptional factor motifs that could explain G4 activity depending on cell type. Lastly, we demonstrated the existence of mutations that could alter G4 activity and therefore impact molecular processes, such as transcription, in the cell. Such results could provide a novel mechanistic interpretation of known disease-associated mutations.
Dynamics and functional roles of splicing factor autoregulation
Authors: Ding Fangyuan , Su Christina , Chow Ke-Huan Kuo , Elowitz Michael B. 
Published: July 2020

AbstractNon-spliceosomal splicing factors are essential, conserved regulators of alternative splicing. They provide concentration-dependent control of diverse pre-mRNAs. Many splicing factors direct unproductive splicing of their own pre-mRNAs through negative autoregulation. However, the impact of such feedback loops on splicing dynamics at the single cell level remains unclear. We developed a system to dynamically, quantitatively analyze negative autoregulatory splicing by the SF2 splicing factor in response to perturbations in single HEK293 cells. Here, we show that negative autoregulatory splicing provides critical functions for gene regulation, establishing a ceiling of SF2 protein concentration, reducing cell-cell heterogeneity in SF2 levels, and buffering variation in SF2 transcription. Most importantly, it adapts SF2 splicing activity to variations in demand from other pre-mRNA substrates. A minimal mathematical model of autoregulatory splicing explains these experimentally observed features, and provides values for effective biochemical parameters. These results reveal the unique functional roles that splicing negative autoregulation plays in homeostatically regulating transcriptional programs.
Overweight/obesity as the potentially most important lifestyle factor associated with signs of pneumonia in COVID-19
Authors: Sacco Vanessa , Rauch Barbara , Gar Christina , Haschka Stefanie , Potzel Anne L. , Kern-Matschilles Stefanie , Banning Friederike , Benz Irina , Meisel Mandy , Seissler Jochen , Lechner Andreas 
Published: July 2020

AbstractObjectiveThe occurrence of pneumonia separates severe cases of COVID-19 from the majority of cases with mild disease. However, the factors determining whether or not pneumonia develops remain to be fully uncovered. We therefore explored the associations of several lifestyle factors with signs of pneumonia in COVID-19.MethodsBetween May and July 2020, we conducted an online survey of 201 adults in Germany who had recently gone through COVID-19, predominantly as outpatients. Of these, 165 had a PCR-based diagnosis and 36 had a retrospective diagnosis by antibody testing. The survey covered demographic information, eight lifestyle factors, comorbidities and medication use. We defined the main outcome as the presence vs. the absence of signs of pneumonia, represented by dyspnea, the requirement for oxygen therapy or intubation.ResultsSigns of pneumonia occurred in 39 of the 165 individuals with a PCR-based diagnosis of COVID-19 (23.6%). Among the lifestyle factors examined, only overweight/obesity associated with signs of pneumonia (odds ratio 2.68 (1.29 - 5.59) p=0.008). The observed association remained significant after multivariate adjustment, with BMI as a metric variable, and also after including the antibody-positive individuals into the analysis.ConclusionsThis exploratory study finds an association of overweight/obesity with signs of pneumonia in COVID-19. This finding suggests that a signal proportional to body fat mass, such as the hormone leptin, impairs the body’s ability to clear SARS-CoV-2 before pneumonia develops. This hypothesis concurs with previous work and should be investigated further to possibly reduce the proportion of severe cases of COVID-19.
Rhythmic components of COVID-19 daily cases in various countries
Authors: Drozdov V.A. , Zenchenko T.A. 
Published: July 2020

Not only does COVID-19 pandemic encourage scientists to look for remedies and treatment schemes, but also identify the drivers of pathogenicity and spread of the virus.The scope of this research consisted in identifying recurrence patterns and comparing the number of daily cases between various countries. Data for countries where at least 500 daily cases were recorded at least once (17 in Europe, 3 in North America, 7 in South America, 3 in Central America, 17 in Asia and 3 in Africa).According to our evaluation, the dynamics recorded for 25 countries includes a 7-day statistically significant component. This statistically significant weekly component has been identified in 76% of the countries examined in Europe, 66% in North America, 71% in South America, and 18% in Asia. The range of this rhythmic component is low at the growth stage and increases at the stabilization and decrease stages.The weekly phases feature shifting peaks depending on the country. In some cases, the phases shift, i.e. they are not limited strictly to certain days of the week. Due to range and phase variation, its explanation cannot be limited to strictly medical and social factors. In some cases, national incidence dynamics includes 3, 4, 6, 8 and 10-day periods.Understanding the factors of recurrence patterns in COVID-19 incidence dynamics may help in the pandemic response.
Transcriptome alterations are enriched for synapse-associated genes in the striatum of subjects with obsessive-compulsive disorder
Authors: Piantadosi Sean C. , McClain Lora L. , Klei Lambertus , Wang Jiebiao , Chamberlain Brittany L. , Springer Sara A , Devlin Bernie , Lewis David A. , Ahmari Susanne E. 
Published: July 2020

ABSTRACTBackgroundObsessive compulsive disorder (OCD) is a chronic and severe psychiatric disorder for which effective treatment options are limited. Structural and functional neuroimaging studies have consistently implicated the orbitofrontal cortex (OFC) and striatum in the pathophysiology of the disorder. Recent genetic evidence points to involvement of components of the excitatory synapse in the etiology of OCD. However, the transcriptional alterations that could link genetic risk to known structural and functional abnormalities remain mostly unknown.MethodsTo assess potential transcriptional changes in the OFC and two striatal regions (caudate nucleus and nucleus accumbens) of OCD subjects relative to unaffected comparison subjects, we sequenced messenger RNA transcripts from these brain regions.ResultsIn a joint analysis of all three regions, 904 transcripts were differentially expressed between 7 OCD versus 8 unaffected comparison subjects. Region-specific analyses highlight a smaller number of differences, which concentrate in caudate and nucleus accumbens. Pathway analyses of the 904 differentially expressed transcripts showed enrichment for genes involved in synaptic signaling, with these synapse-associated genes displaying lower expression in OCD subjects relative to unaffected comparison subjects. Finally, we estimate that cell type fractions of medium spiny neurons are lower whereas vascular cells and astrocyte fractions are higher in tissue of OCD subjects.ConclusionsTogether, these data provide the first unbiased examination of differentially expressed transcripts in both OFC and striatum of OCD subjects. These transcripts encode synaptic proteins more often than expected by chance, and thus implicate the synapse as a vulnerable molecular compartment for OCD.