bluuebunny/crossref_metadata_2025_split huggingface dataset:
Disparities in Case Frequency and Mortality of Coronavirus Disease 2019 (COVID-19) Among Various States in the United States
Authors: Loomba Rohit S. , Aggarwal Gaurav , Aggarwal Saurabh , Flores Saul , Villarreal Enrique G. , Farias Juan S. , Lavie Carl J. 
Published: July 2020

AbstractObjectiveTo utilize publicly reported, state-level data to identify factors associated with the frequency of cases, tests, and mortality in the US.Materials & MethodsRetrospective study using publicly reported data collected included the number of COVID-19 cases, tests, and mortality from March 14ththrough April 30th, 2020. Publicly available state-level data was collected which included: demographics comorbidities, state characteristics and environmental factors. Univariate and multivariate regression analyses were performed to identify the significantly associated factors with percent mortality, case and testing frequency. All analyses were state-level analyses and not patient-level analyses.ResultsA total of 1,090,500 COVID-19 cases were reported during the study period. The calculated case and testing frequency were 3,332 and 19,193 per 1,000,000 patients. There were 63,642 deaths during this period which resulted in a mortality of 5.8%. Factors including to but not limited to population density (beta coefficient 7.5, p< 0.01), transportation volume (beta coefficient 0.1, p< 0.01), tourism index (beta coefficient -0.1, p=0.02) and older age (beta coefficient 0.2, p=0.01) are associated with case frequency and percent mortality.ConclusionsThere were wide variations in testing and case frequencies of COVID-19 among different states in the US. States with higher population density had a higher case and testing rate. States with larger population of elderly and higher tourism had a higher mortality.Key MessagesThere were wide variations in testing and case frequencies of COVID-19 among different states in the US.States with higher population density had a higher case and testing rate.States with larger population of elderly and higher tourism had a higher mortality.
SARS-CoV-2 detection with de novo designed synthetic riboregulators
Authors: Köksaldi İlkay Çisil , Ahan Recep Erdem , Köse Sila , Haciosmanoğlu Nedim , Şahin Kehribar Ebru , Güngen Murat Alp , Özkul Aykut , Özgür Şafak Şeker Urartu 
Published: July 2020

AbstractSars-CoV-2 is a human pathogen and is the main cause of COVID-19 disease. COVID-19 is announced as a global pandemic by World Health Organization. COVID-19 is characterized by severe conditions and early diagnosis can make dramatic changes both for personal and public health. In order to increase the reach for low cost equipment which requires a very limited technical knowledge can be beneficial to diagnose the viral infection. Such diagnostic capabilities can have a very critical role to control the transmission of the disease. Here we are reporting a state-of-the-art diagnostic tool developed by using an in vitro synthetic biology approach by employing engineered de novo riboregulators. Our design coupled with a home-made point-of-care device setting can detect and report presence of Sars-CoV-2 specific genes. The presence of Sars-CoV-2 related genes triggers translation of sfGFP mRNAs, resulting in green fluorescence output. The approach proposed here has the potential of being a game changer in Sars-COV-2 diagnostics by providing an easy-to-run, low-cost-demanding diagnostic capability.
Models of SIV rebound after treatment interruption that involve multiple reactivation events
Authors: van Dorp Christiaan H. , Conway Jessica M. , Whitney James B. , Barouch Dan H. , Perelson Alan S. 
Published: July 2020

AbstractIn order to assess the efficacy of novel HIV-1 treatments leading to a functional cure, the time to viral rebound is frequently used as a surrogate endpoint. The longer the time to viral rebound, the more efficacious the therapy. In support of such an approach, mathematical models serve as a connection between the size of the latent reservoir and the time to HIV-1 rebound after treatment interruption. The simplest of such models assumes that a single successful latent cell reactivation event leads to observable viremia after a period of exponential viral growth. Here we consider a generalization developed by Pinkevychet al.and Hillet al.of this simple model in which multiple reactivation events can occur, each contributing to the exponential growth of the viral load. We formalize and improve the previous derivation of the dynamics predicted by this model, and use the model to estimate relevant biological parameters from SIV rebound data. We confirm a previously described effect of very early antiretroviral therapy (ART) initiation on the rate of recrudescence and the viral load growth rate after treatment interruption. We find that every day ART initiation is delayed results in a 39% increase in the recrudescence rate, and a 11% decrease of the viral growth rate. We show that when viral rebound occurs early relative to the viral load doubling time, a model with multiple successful reactivation events fits the data better than a model with only a single successful reactivation event.Author SummaryHIV-1 persists during suppressive antiretroviral therapy (ART) due to a reservoir of latently infected cells. When ART is stopped, HIV generally rebounds within a few weeks. However, there is a small fraction of patients that do not rebound over a period of months or years. A variety of treatments are being tested for their ability to reduce the size of the latent reservoir, to induce effective immune responses against the virus, or to prevent or prolong the time to viral rebound after ART interruption. These novel treatments are typically first tested in SIV infected macaques, and the efficacy of the treatment assessed by interrupting ART and measuring the time to viral rebound. Here, we develop and test a mathematical and statistical model that describes the process of viral rebound. The model can be used for statistical inference of the efficacy of newly developed treatments. Importantly, the model takes into account that multiple recrudescence events can precede rebound. We test the model using data from early treated SIV infected macaques.
Patient-tailored design of AML cell subpopulation-selective drug combinations
Authors: Ianevski Aleksandr , Lahtela Jenni , Javarappa Komal K. , Sergeev Philipp , Ghimire Bishwa R. , Gautam Prson , Vähä-Koskela Markus , Turunen Laura , Linnavirta Nora , Kuusanmäki Heikki , Kontro Mika , Porkka Kimmo , Heckman Caroline A. , Mattila Pirkko , Wennerberg Krister , Giri Anil K. , Aittokallio Tero 
Published: July 2020

AbstractThe extensive primary and secondary drug resistance in acute myeloid leukemia (AML) requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to optimally target disease-driving AML cell subpopulations. However, the large number of AML-relevant drug combinations makes the testing impossible in scarce primary patient cells. This combinatorial problem is further exacerbated by the translational challenge of how to design such personalized and selective drug combinations that do not only show synergistic effect in overall AML cell killing but also result in minimal toxic side effects on non-malignant cells. To solve these challenges, we implemented a systematic computational-experimental approach for identifying potential drug combinations that have a desired synergy-efficacy-toxicity balance. Our mechanism-agnostic approach combines single-cell RNA-sequencing (scRNA-seq) withex vivosingle-agent viability testing in primary patient cells. The data integration and predictive modelling are carried out at a single-cell resolution by means of a machine learning model that makes use of compound-target interaction networks to narrow down the massive search space of potentially effective drug combinations. When applied to two diagnostic and two refractory AML patient cases, each having a different genetic background, our integrated approach predicted a number of patient-specific combinations that were shown to result not only in synergistic cancer cell inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Overall, 53% of the 59 predicted combinations were experimentally confirmed to show synergy, and 83% were non-antagonistic, as validated with viability assays, which is a significant improvement over the success rate of randomly guessing a synergistic drug combination (5%). Importantly, 67% of the predicted combinations showed low toxicity to non-malignant cells, as validated with flow-based population assays, suggesting their selective killing of AML cell populations. Our data-driven approach provides an unbiased means for systematic prioritization of patient-specific drug combinations that selectively inhibit AML cells and avoid co-inhibition of non-malignant cells, thereby increasing their likelihood for clinical translation. The approach uses only a limited number of patient primary cells, and it is widely applicable to hematological cancers that are accessible for scRNA-seq profiling andex vivocompound testing.
Stem girth changes in response to soil water potential in lowland dipterocarp forest in Borneo: a phenomenological and individualistic time-series analysis
Authors: Newbery D. M. , Lingenfelder M. 
Published: July 2020

AbstractTime series data offer a way of investigating the causes driving ecological processes as phenomena. To test for possible differences in water relations between species of different forest structural guilds at Danum (Sabah, NE Borneo), daily stem girth increments (gthi), of 18 trees across six species were regressed individually on soil moisture potential (SMP) and temperature (TEMP), accounting for temporal autocorrelation (in GLS-arima models), and compared between a wet and a dry period. The best-fitting significant variables were SMP the day before and TEMP the same day. The first resulted in a mix of positive and negative coefficients, the second largely positive ones. An adjustment for dry-period showers was applied. Interactions were stronger in dry than wet period. Negative relationships for overstorey trees can be interpreted in a reversed causal sense: fast transporting stems depleted soil water and lowered SMP. Positive relationships for understorey trees meant they took up most water at high SMP. The unexpected negative relationships for these small trees may have been due to their roots accessing deeper water supplies (if SMP was inversely related to that of the surface layer), and this was influenced by competition with larger neighbour trees. A tree-soil flux dynamics manifold may have been operating. Patterns of mean diurnal girth variation were more consistent among species, and time-series coefficients were negatively related to their maxima. Expected differences in response to SMP in the wet and dry periods did not clearly support a previous hypothesis differentiating drought and non-drought tolerant understorey guilds. Trees within species showed highly individual responses when tree size was standardized. Data on individual root systems and SMP at several depths are needed to get closer to the mechanisms that underlie the tree-soil water phenomena in these tropical forests. Neighborhood stochasticity importantly creates varying local environments experienced by individual trees.
CHK2 inhibition provides a strategy to suppress hematological toxicity from PARP inhibitors
Authors: Xu Zhen , Vandenberg Cassandra J , Lieschke Elizabeth , Scott Clare L , Majewski Ian J 
Published: July 2020

AbstractCancer patients treated with poly (ADP-ribose) polymerase inhibitors (PARPi) experience various side effects, with hematological toxicity being most common. Short term treatment of mice with olaparib resulted in depletion of reticulocytes, B cell progenitors and immature thymocytes, whereas longer treatment induced broader myelosuppression. We performed a CRISPR/Cas9 screen targeting DNA repair genes to identify strategies to suppress hematological toxicity. The screen revealed that sgRNAs targeting the serine/threonine kinase CHK2 were enriched following olaparib treatment. Genetic or pharmacological inhibition of CHK2 blunted PARPi response in lymphoid and myeloid cell lines, and in primary pre-B/pro-B cells. Using a Cas9 base editor, we found that blocking CHK2-mediated phosphorylation of p53 also impaired olaparib response. Our results identify the p53 pathway as a major determinant of the acute response to PARPi in normal blood cells and demonstrate that targeting CHK2 can short-circuit this response. Cotreatment with a CHK2 inhibitor did not antagonise olaparib response in ovarian cancer cells. Selective inhibition of CHK2 may spare blood cells from the toxic influence of PARPi and broaden the utility of these drugs.
Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations
Authors: Spiers Helen , Songhurst Harry , Nightingale Luke , de Folter Joost , Hutchings Roger , Peddie Christopher J , Weston Anne , Strange Amy , Hindmarsh Steve , Lintott Chris , Collinson Lucy M , Jones Martin L 
Published: July 2020

AbstractAdvancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.
A Portable Impedance Microflow Cytometer for Measuring Cellular Response to Hypoxia
Authors: Dieujuste Darryl , Qiang Yuhao , Du E 
Published: July 2020

AbstractThis paper presents the development and testing of a low-cost, portable microflow cytometer based on electrical impedance sensing, for single cell analysis under controlled oxygen microenvironment. The cytometer system is based on an AD5933 impedance analyzer chip, a microfluidic chip, and an Arduino microcontroller operated by a custom Android application. A representative case study on human red blood cells (RBCs) affected by sickle cell disease is conducted to demonstrate the capability of the cytometry system. Equivalent circuit model of a suspending biological cell is used to interpret the electrical impedance of single flowing RBCs. In normal blood, cytoplasmic resistance and membrane capacitance do not change significantly with the change in oxygen tension. In contrast, RBCs affected by sickle cell disease show that upon hypoxia treatment, the cytoplasmic resistance decrease from 11.6 MΩ to 23.4 MΩ, and membrane capacitance decrease from 1.1 pF to 0.8 pF. Strong correlations are identified between the changes in these subcellular electrical components of single cells and the cell sickling process induced by hypoxia treatment. The representative results reported in this paper suggest that single cell electrical impedance can be used as a sensitive biophysical marker for quantifying cellular response to change in oxygen concentration. The developed flow cytometry system and the methodology can also be extended to analysis of cellular response to hypoxia in other cell types.
Lipidome profiles of plasma microvesicles differ in experimental cerebral malaria, compared to malaria without neurological complications
Authors: Batarseh Amani M , Vafaee Fatemeh , Hosseini-Beheshti Elham , Chen Alex , Cohen Amy , Juillard Annette , Hunt Nicholas Henry , Mariani Michael , Mitchell Todd , Raymond Grau Georges Emile 
Published: July 2020

ABSTRACTCerebral malaria (CM), a fatal complication of Plasmodium infection that affects children in sub-Saharan Africa and adults in South-East Asia, results from incompletely understood pathogenetic mechanisms, which include an excessive release of microvesicles (MV). Plasma MV levels have been found elevated in CM patients and in the experimental mouse model.We compared lipid profiles in circulating MV purified from CBA mice infected withP. bergheiANKA (PbA), which causes CM, to those fromP. yoelii(Py), which does not. Here we show that plasma MV produced at the time of CM differed dramatically from those from non-CM mice, in spite of identical levels of parasitaemia. Using high-resolution LCMS, we identified over 300 lipid species within 12 lipid classes. Total lysophosphatidylethanolamine (LPE) levels were significantly lower in PbA infection compared to uninfected mice, while they were unchanged in Py MV, and lysophosphatidylcholine (LPC) was more significantly reduced in PbA mice compared to the other two groups. These results suggest, for the time, that experimental CM is characterised by specific changes in lipid composition of circulating MV, pointing towards triglycerides (TG) especially docosahexaenoic acid (DHA 22:6) containing species, phosphatidylethanolamine (PE), LPC, LPE, and diacylglycerol (DG) as potential important players in CM pathogenesis.
Deep Texture Representations as a Universal Encoder for Pan-cancer Histology
Authors: Komura Daisuke , Kawabe Akihiro , Fukuta Keisuke , Sano Kyohei , Umezaki Toshikazu , Koda Hirotomo , Suzuki Ryohei , Tominaga Ken , Konishi Hiroki , Nishida Shu , Furuya Genta , Katoh Hiroto , Ushiku Tetsuo , Fukayama Masahi , Ishikawa Shumpei 
Published: July 2020

SummaryCancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented direct comparison and accumulation of large-scale datasets. Here we show that deep texture representations (DTRs) produced by a bilinear Convolutional Neural Network, express cancer morphology well in an unsupervised manner, and work as a universal encoder for cancer histology. DTRs are useful for content-based image retrieval, enabling quick retrieval of histologically similar images from optimally area selected datasets of 7,175 cases from The Cancer Genome Atlas. Via comprehensive comparison with driver and clinically actionable gene mutations, we have successfully predicted 309 combinations of genomic features and cancer types from hematoxylin and eosin-stained images at high accuracy (AUC > 0.70 and q < 0.02). With its mounting capabilities on accessible devices such as smartphones, DTR-based encoding for cancer histology has a potentially strong impact on global equalization for cancer diagnosis and targeted therapies.
Disturbance of Information in Superior Parietal Lobe during Dual-task Interference in a Simulated Driving Task
Authors: Abbaszadeh Mojtaba , Hossein-Zadeh Gholam-Ali , Seyed-Allaei Shima , Vaziri-Pashkam Maryam 
Published: July 2020

AbstractPerforming a secondary task while driving causes a decline in driving performance. This phenomenon, called dual-task interference, can have lethal consequences. Previous fMRI studies have looked at the changes in the average brain activity to uncover the neural correlates of dual-task interference. From these results, it is unclear whether the overall modulations in brain activity result from general effects such as task difficulty, attentional modulations, and mental effort or whether it is caused by a change in the responses specific to each condition due to dual-task interference. To overcome this limitation, here, we used multi-voxel pattern analysis (MVPA) to interrogate the change in the information content in multiple brain regions during dual-task interference in simulated driving. Participants performed a lane change task in a simulated driving environment, along with a tone discrimination task with either short or long-time onset difference (Stimulus Onset Asynchrony, SOA) between the two tasks. Behavioral results indicated a robust dual-task effect on driving RT. MVPA revealed regions that carry information about the driving direction, including the superior parietal lobe (SPL), visual, and motor regions. Comparing the decoding accuracies across short and long SOA conditions, we showed lower accuracies in the SPL region in short than long SPA conditions. This change in accuracy was not observed in the visual and motor regions. In addition, the classification accuracy in the SPL was inversely correlated with participants’ reaction time in the driving task. These findings suggest that the dual-task interference in driving may be related to the disturbance of information processing in the SPL region.Significance StatementDuring real-world driving, when a driver wants to make a turn at an intersection and simultaneously respond to a cell phone call, his reaction time slows down. This effect is called dual-task interference. Here, we aimed to examine its neural mechanisms using a paradigm that consisted of a driving turn task and a tone discrimination task in a simulated environment. Results showed that the information for the driving turn was disturbed in the superior parietal lobe (SPL) during dual-task interference. We suggest that the driving performance decline in the presence of the secondary task might be related to the disturbance of information in the SPL.
Pseudomonas aeruginosareverse diauxie is an optimized, resource utilization strategy
Authors: McGill S. Lee , Yung Yeni , Hunt Kristopher A. , Henson Michael A. , Hanley Luke , Carlson Ross P. 
Published: July 2020

AbstractPseudomonas aeruginosais a globally-distributed bacterium often found in medical infections. The opportunistic pathogen uses a different, carbon catabolite repression (CCR) strategy than many, model microorganisms. It does not utilize a classic diauxie phenotype, nor does it follow common systems biology assumptions including preferential consumption of glucose with an ‘overflow’ metabolism. Despite these contradictions,P. aeruginosais competitive in many, disparate environments underscoring knowledge gaps in microbial ecology and systems biology. Physiological, omics, andin silicoanalyses were used to quantify theP. aeruginosaCCR strategy known as ‘reverse diauxie’. An ecological basis of reverse diauxie was identified using a genome-scale, metabolic model interrogated within vitroomics data. Reverse diauxie preference for lower energy, nonfermentable carbon sources, such as acetate or succinate over glucose, was predicted using a multidimensional strategy which minimized resource investment into central metabolism while completely oxidizing substrates. Application of a common,in silicooptimization criterion, which maximizes growth rate, did not predict the reverse diauxie phenotypes. This study quantifiesP. aeruginosametabolic strategies foundational to its wide distribution and virulence.