bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pex14p phosphorylation modulates import of citrate synthase 2 into peroxisomes inSaccharomyces cerevisiae
Authors: Schummer Andreas , Maier Renate , Gabay-Maskit Shiran , Hansen Tobias , Mühlhäuser Wignand W. D. , Suppanz Ida , Fadel Amir , Schuldiner Maya , Girzalsky Wolfgang , Oeljeklaus Silke , Zalckvar Einat , Erdmann Ralf , Warscheid Bettina 
Published: July 2020

AbstractThe peroxisomal biogenesis factor Pex14p is an essential component of the peroxisomal matrix protein import machinery. Together with Pex13p and Pex17p, it is part of the membrane-associated peroxisomal docking complex in yeast, facilitating the binding of cargo-loaded receptor proteins for translocation of cargo proteins into the peroxisome. Furthermore, Pex14p is part of peroxisomal import pores. The central role of Pex14p in peroxisomal matrix protein import processes renders it an obvious target for regulatory mechanisms such as protein phosphorylation. To explore this possibility, we examined the state of Pex14p phosphorylation inSaccharomyces cerevisiae. Phos-tag-SDS-PAGE of Pex14p affinity-purified from solubilized membranes revealed Pex14p as multi-phosphorylated protein. Using mass spectrometry, we identified 16 phosphorylation sites, with phosphorylation hot spots located in the N- and C-terminal regions of Pex14p. Analysis of phosphomimicking and nonphosphorylatable variants of Pex14p revealed a decreased import of GFP carrying a peroxisomal targeting signal type 1, indicating a functional relevance of Pex14p phosphorylation in peroxisomal matrix protein import. We show that this effect can be ascribed to the phosphomimicking mutation at serine 266 of Pex14p (Pex14p-S266D). We further screened the subcellular distribution of 23 native GFP-tagged peroxisomal matrix proteins by high-content fluorescence microscopy. Only Cit2p, the peroxisomal isoform of citrate synthase, was affected in the Pex14p-S266D mutant, showing increased cytosolic localization. Cit2p is part of the glyoxylate cycle, which is required for the production of essential carbohydrates when yeast is grown on non-fermentable carbon sources. Pex14p-S266 phosphosite mutants showed reversed growth phenotypes on oleic acid and ethanol with acetyl-CoA formed in peroxisomes and the cytosol, respectively. Our data point to the control of the peroxisomal import of Cit2p via the state of Pex14p phosphorylation at S266, which may helpS. cerevisiaecells to rapidly adjust their carbohydrate metabolism according to the nutritional conditions.
Endogenous suppression of WNT signalling in human embryonic stem cells leads to low differentiation propensity towards definitive endoderm
Authors: Dziedzicka Dominika , Tewary Mukul , Keller Alexander , Tilleman Laurentijn , Prochazka Laura , Östblom Joel , De Deckersberg Edouard Couvreu , Markouli Christina , Franck Silvie , Van Nieuwerburgh Filip , Spits Claudia , Zandstra Peter W. , Sermon Karen , Geens Mieke 
Published: July 2020

SummaryLow differentiation propensity towards a targeted lineage can significantly hamper the utility of individual human pluripotent stem cell (hPSC) lines in biomedical applications. Here, we use monolayer and micropatterned cell cultures, as well as transcriptomic profiling, to investigate how variability in signalling pathway activity between human embryonic stem cell lines affects their differentiation efficiency towards definitive endoderm (DE). We show that endogenous suppression of WNT signalling in hPSCs at the onset of differentiation prevents the switch from self-renewal to DE specification. Gene expression profiling reveals that this inefficient switch is reflected inNANOGexpression dynamics. Importantly, we demonstrate that higher WNT stimulation or inhibition of the PI3K/AKT signalling can overcome the DE commitment blockage. Our findings highlight that redirection of the activity of Activin/NODAL pathway by WNT signalling towards mediating DE fate specification is a vulnerable spot, as disruption of this process can result in poor hPSC specification towards DE.
FLN-1/Filamin is required to anchor the actomyosin cytoskeleton and for global organization of sub-cellular organelles in a contractile tissue
Authors: Kelley Charlotte A. , Triplett Olivia , Mallick Samyukta , Burkewitz Kristopher , Mair William B. , Cram Erin J. 
Published: July 2020

AbstractActomyosin networks are organized in space, direction, size, and connectivity to produce coordinated contractions across cells. We use theC. elegansspermatheca, a tube composed of contractile myoepithelial cells, to study how actomyosin structures are organized. FLN-1/filamin is required for the formation and stabilization of a regular array of parallel, contractile, actomyosin fibers in this tissue. Loss offln-1results in the detachment of actin fibers from the basal surface, which then accumulate along the cell junctions and are stabilized by spectrin. In addition, actin and myosin are captured at the nucleus by the linker of nucleoskeleton and cytoskeleton complex (LINC) complex, where they form large foci. Nuclear positioning and morphology, distribution of the endoplasmic reticulum and the mitochondrial network are also disrupted. These results demonstrate that filamin is required to prevent large actin bundle formation and detachment, to prevent excess nuclear localization of actin and myosin, and to ensure correct positioning of organelles.
Cardiomyocyte-specific Srsf3 deletion reveals a mitochondrial regulatory role
Authors: Dumont Audrey-Ann , Dumont Lauralyne , Zhou Delong , Giguère Hugo , Pileggi Chantal , Harper Mary-Ellen , Blondin Denis P , Scott Michelle S , Auger-Messier Mannix 
Published: July 2020

AbstractSrsf3 was recently reported as being necessary to preserve RNA stability via an mTOR mechanism in a cardiac mouse model in adulthood. Here, we demonstrate the link between Srsf3 and mitochondrial integrity in an embryonic cardiomyocyte-specific Srsf3 conditional knockout (cKO) mouse model. Fifteen-day-old Srsf3 cKO mice showed dramatically reduced (below 50%) survival and reduced left ventricular systolic performance, and histological analysis of these hearts revealed a significant increase in cardiomyocyte size, confirming the severe remodelling induced by Srsf3 deletion. RNA-seq analysis of the hearts of 5-day-old Srsf3 cKO mice revealed early changes in expression levels and alternative splicing of several transcripts related to mitochondrial integrity and oxidative phosphorylation. Likewise, the levels of several protein complexes of the electron transport chain decreased, and mitochondrial complex I-driven respiration of permeabilized cardiac muscle fibres from the left ventricle was impaired. Furthermore, transmission electron microscopy analysis showed disordered mitochondrial length and cristae structure. Together with its indispensable role in the physiological maintenance of mouse hearts, these results highlight the previously unrecognized function of Srsf3 in regulating mitochondrial integrity.
Multiple sounds degrade the frequency representation in monkey inferior colliculus
Authors: Willett Shawn M. , Groh Jennifer M. 
Published: July 2020

AbstractHow we distinguish multiple simultaneous stimuli is uncertain, particularly given that such stimuli sometimes recruit largely overlapping populations of neurons. One commonly proposed hypothesis is that the sharpness of tuning curves might change to limit the number of stimuli driving any given neuron when multiple stimuli are present. To test this hypothesis, we recorded the activity of neurons in the inferior colliculus while monkeys made saccades to either one or two simultaneous sounds differing in frequency and spatial location. Although monkeys easily distinguished simultaneous sounds (∼90% correct performance), the frequency selectivity of inferior colliculus neurons on dual sound trials did not improve in any obvious way. Frequency selectivity was degraded on dual sound trials compared to single sound trials: neural response functions broadened, and frequency accounted for less of the variance in firing rate. These changes in neural firing led a maximum-likelihood decoder to perform worse on dual sound trials than on single sound trials. These results fail to support the hypothesis that changes in frequency response functions serve to reduce the overlap in the representation of simultaneous sounds. Instead, these results suggest that alternative possibilities, such as recent evidence of alternations in firing rate between the rates corresponding to each of the two stimuli, offer a more promising approach.Graphic AbstractHow sensory representations encode multiple stimuli despite coarse coding is unknown. Using a maximum likelihood decoder operating on the spike count response patterns of monkey inferior colliculus neurons, we show a marked reduction in decoding accuracy when two sounds are presented compared to one. The decoding was inferior to the behavioral performance of the animals, and thus suggests the presence of alternative coding strategies.
Immune Classification of Clear Cell Renal Cell Carcinoma
Authors: Su Sumeyye , Akbarinejad Shaya , Shahriyari Leili 
Published: July 2020

ABSTRACTSince the outcome of treatments, particularly immunotherapeutic interventions, depends on the tumor immune micro-environment (TIM), several experimental and computational tools such as flow cytometry, immunohistochemistry, and digital cytometry have been developed and utilized to classify TIM variations. In this project, we identify immune pattern of clear cell renal cell carcinomas (ccRCC) by estimating the percentage of each immune cell type in 526 renal tumors using the new powerful technique of digital cytometry. The results, which are in agreement with the results of a large-scale mass cytometry analysis, show that the most frequent immune cell types in ccRCC tumors are CD8+ T-cells, macrophages, and CD4+ T-cells. Saliently, unsupervised clustering of ccRCC primary tumors based on their relative number of immune cells indicates the existence of four distinct groups of ccRCC tumors. Tumors in the first group consist of approximately the same numbers of CD8+ T-cells, CD4+ T-cells, and macrophages, while tumors in the second group have a significantly high number of macrophages compared to any other immune cell type (P-value< 0.01). The third group of ccRCC tumors have a significantly higher number of CD8+ T-cells than any other immune cell type (P-value< 0.01), while tumors in the group 4 have approximately the same numbers of macrophages and CD4+ T-cells and a significantly smaller number of CD8+ T-cells than CD4+ T-cells (P-value< 0.01). Moreover, there is a high positive correlation between the expression levels of IFNG and PDCD1 and the percentage of CD8+ T-cells, and higher stage and grade of tumors have a substantially higher percentage of CD8+ T-cells. Furthermore, the primary tumors of patients, who are tumor free at the last time of follow up, have a significantly higher percentage of mast cells (P-value< 0.01) compared to the patients with tumors for all groups of tumors except group 3.
A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text
Authors: Slater Luke T , Bradlow William , Motti Dino FA , Hoehndorf Robert , Ball Simon , Gkoutos Georgios V 
Published: July 2020

AbstractBackgroundNegation detection is an important task in biomedical text mining. Particularly in clinical settings, it is of critical importance to determine whether findings mentioned in text are present or absent. Rule-based negation detection algorithms are a common approach to the task, and more recent investigations have resulted in the development of rule-based systems utilising the rich grammatical information afforded by typed dependency graphs. However, interacting with these complex representations inevitably necessitates complex rules, which are time-consuming to develop and do not generalise well. We hypothesise that a heuristic approach to determining negation via dependency graphs could offer a powerful alternative.ResultsWe describe and implement an algorithm for negation detection based on grammatical distance from a negatory construct in a typed dependency graph. To evaluate the algorithm, we develop two testing corpora comprised of sentences of clinical text extracted from the MIMIC-III database and documents related to hypertrophic cardiomyopathy patients routinely collected at University Hospitals Birmingham NHS trust. Gold-standard validation datasets were built by a combination of human annotation and examination of algorithm error. Finally, we compare the performance of our approach with four other rule-based algorithms on both gold-standard corpora.ConclusionsThe presented algorithm exhibits the best performance by f-measure over the MIMIC-III dataset, and a similar performance to the syntactic negation detection systems over the HCM dataset. It is also the fastest of the dependency-based negation systems explored in this study. Our results show that while a single heuristic approach to dependency-based negation detection is ignorant to certain advanced cases, it nevertheless forms a powerful and stable method, requiring minimal training and adaptation between datasets. As such, it could present a drop-in replacement or augmentation for many-rule negation approaches in clinical text-mining pipelines, particularly for cases where adaptation and rule development is not required or possible.
Activation of bone marrow adaptive immunity in type 2 diabetes: rescue by co-stimulation modulator Abatacept
Authors: Santopaolo Marianna , Sullivan Niall , Thomas Anita C. , Alvino Valeria , Nicholson Lindsay , Gu Yue , Spinetti Gaia , Kallikourdis Marinos , Blom Ashley , Madeddu Paolo 
Published: July 2020

AbstractHypothesisType 2 diabetes (T2D) is characterized by low-grade inflammation. Here, we investigated the state of adaptive immunity in bone marrow (BM) of patients and mice with T2D. We also tested if inhibition T cell co-stimulation by Abatacept could rescue the immune profile of T2D mice.MethodsFlow-cytometry and cytokine analyses were performed on BM samples from patients with or without T2D. Moreover, we studied the immune profile of db/db and control wt/db mice. A cohort of db/db mice was randomized to receive Abatacept or vehicle for 4 weeks, with endpoints being immune cell profile and indexes of insulin sensitivity and heart performance.ResultsT2D patients showed increased frequencies of BM CD4+(2.8-fold, p=0.001) and CD8+T cells (1.8-fold, p=0.01), with upregulation of the activation marker CD69 and homing receptor CCR7 in CD4+(1.64-fold, p=0.003 and 2.27-fold, p=0.01, respectively) and CD8+fractions (1.79-fold, p=0.05 and 1.69-fold, p=0.02, respectively). CCL19 (CCR7 receptor ligand) and CXCL10/11 (CXCR3 receptor ligands), implicated in T cell migration and activation, were the most differentially modulated chemokines. Studies in mice confirmed the activation of adaptive immunity in T2D. Abatacept reduced the activation of T cells and levels of pro-inflammatory chemokines and cytokines. Additionally, Abatacept improved indexes of cardiac systolic function, but not insulin sensitivity.ConclusionsThese novel findings support the concept of BM adaptive immune activation in T2D. Modulation of T cell co-stimulation could represent an attractive and immediately available modality to dampen inappropriate activation of adaptive immune response and protect from target organ damage.
Pygmaeomycetaceae, a new root associated family in Mucoromycotina from the pygmy pine plains
Authors: Walsh Emily , Luo Jing , Khiste Swapneel , Scalera Adam , Sajjad Sana , Zhang Ning 
Published: July 2020

ABSTRACTA new genus, Pygmaeomyces, and two new species are described based on phylogenetic analyses, phenotypic and ecological characters. The species delimitation was based on concordance of gene genealogies. The Pygmaeomyces cultures were isolated from the roots of mountain laurel (Kalmia latifolia) and pitch pine (Pinus rigida) from the acidic and oligotrophic New Jersey Pygmy Pine Plains; however, they likely have a broader distribution because their internal transcribed spacer (ITS) sequences have high similarity with a number of environmental sequences from multiple independent studies. Based on the phylogeny and phenotypical characters, a new family Pygmaeomycetaceae is proposed to accommodate this new lineage in Mucoromycotina. Pygmaeomycetaceae corresponds to Clade GS23, which was identified based on a sequence-only soil fungal survey and was believed to be a distinct new class. Compared to the culture-based methods, we observed that sequence-only analyses tend to over-estimate the taxonomic level. Results from this work will facilitate ecological and evolutionary studies on root-associated fungi.
Deep learning and computer vision will transform entomology
Authors: Høye Toke T. , Ärje Johanna , Bjerge Kim , Hansen Oskar L. P. , Iosifidis Alexandros , Leese Florian , Mann Hjalte M. R. , Meissner Kristian , Melvad Claus , Raitoharju Jenni 
Published: July 2020

ABSTRACTMost animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse. Insect populations are challenging to study and most monitoring methods are labour intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors that can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to the big data outputs to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) Validation of image-based taxonomic identification, 2) generation of sufficient training data, 3) development of public, curated reference databases, and 4) solutions to integrate deep learning and molecular tools.Significance statementInsect populations are challenging to study, but computer vision and deep learning provide opportunities for continuous and non-invasive monitoring of biodiversity around the clock and over entire seasons. These tools can also facilitate the processing of samples in a laboratory setting. Automated imaging in particular can provide an effective way of identifying and counting specimens to measure abundance. We present examples of sensors and devices of relevance to entomology and show how deep learning tools can convert the big data streams into ecological information. We discuss the challenges that lie ahead and identify four focal areas to make deep learning and computer vision game changers for entomology.
Optimal sample pooling: an efficient tool against SARS-CoV-2
Authors: Goyal Saurabh , Bist Priti , Sharma Rakesh 
Published: July 2020

AbstractThe SARS-CoV-2 pandemic situation has presented multiple imminent challenges to the nations around the globe. While health agencies around the world are exploring various options to contain the spread of this fatal viral infection, multiple strategies and guidelines are being issued to boost the fight against the disease. Identifying and isolating infected individuals at an early phase of the disease has been a very successful approach to stop the chain of transmission. But this approach faces a practical challenge of limited resources. Sample pooling solves this enigma by significantly improving the testing capacity and result turn around time while using no extra resources. However, the general sample pooling method also has the scope of significant improvements. This article describes a process to further optimize the resources with optimal sample pooling. This is a user-friendly technique, scalable on a national or international scale. A mathematical model has been built and validated for its performance using clinical data.
Determinants of quality of life in Latin American people with drug-resistant epilepsy: A cross-sectional, correlational study
Authors: Díaz-Torres Marco Antonio , Buzo-Jarquín Edith Giselle , Rodríguez-Martínez Aime Carolina , De León-Altamira Diana Laura , Padilla-Rivas Gerardo , Castillo-Torres Sergio Andrés , Olivas-Reyes Jaime Enrique Giovann , Cisneros-Franco J. Miguel 
Published: July 2020

AbstractOne third of people with epilepsy (PWE) continue to have seizures despite adequate antiepileptic drug treatment. This condition, known as drug-resistant epilepsy (DRE) significantly impairs their social, family and work environment. The aims of this study were to assess the quality of life (QoL) in PWE with DRE and to investigate which factors are associated with a better QoL. This was a cross-sectional observational study of 133 Latin American PWE. QoL was assessed with the Spanish version of the Quality of Life with Epilepsy questionnaire (QOLIE-10). Independent clinical variables were analyzed with non-parametric statistics and their association with QoL was investigated with multiple linear regression. Poor quality of life was found in 25.8% of PWE. A low number of antiepileptic drugs (AEDs) was the major factor associated with better quality of life, closely followed by seizure frequency. We conclude that careful selection of AED treatment may contribute to improving both seizure control and QoL.
Age Matters: COVID-19 Prevalence in a Vaping Adolescent Population – An Observational Study
Authors: Tandan Nitin , Regmi Manjari Rani , Maini Ruby , Ibrahim Abdisamad M. , Koester Cameron , Lara Garcia Odalys Estefania , Parajuli Priyanka , Al-Akchar Mohammad , Bhattarai Mukul , Kulkarni Abhishek , Robinson Robert 
Published: July 2020

AbstractBackgroundCurrently, there is limited or no data demonstrating that vaping is associated with increased transmission or prevalence of coronavirus disease-2019 (COVID-19). Our study aims to investigate the relationship of vaping with the prevalence of COVID-19 infection across the United States and in the District of Columbia.MethodsCOVID-19 case counts by state and the District of Columbia were obtained via the Worldometers website on 04/30/2020. Prevalence of COVID-19 cases per 100,000 residents were calculated using estimated 2019 population data from the US Census Department. Age ranges analyzed were: high school age children, Ages 18-24, Ages 25-44, and Ages 45-65. Spearman correlation analysis was conducted to determine if the rate of vaping was correlated with a higher prevalence of COVID-19 cases per 100,000 population.FindingsThe Spearman correlation analysis demonstrated that persons vaping between 18 years and 24 years of age had a correlation coefficient of 0.278 with prevalence of COVID-19 infection (p=0.048). Vaping high school students had a correlation coefficient of 0.153 with prevalence of COVID-19 (p=0.328). Persons vaping in the age group 25-45 years had a correlation coefficient of 0.101 in association to COVID-19 prevalence (p=0.478). And finally, persons vaping between the age 45-65 years old had a correlation coefficient 0.130 with respect to COVID-19 prevalence (p=0.364).InterpretationIncreased COVID-19 prevalence is associated with vaping in the adolescent population between ages 18 and 24. Further prospective studies need to be performed in order investigate the severity of outcomes of vaping in association with COVID-19 infection.FundingNothing to disclose.