bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differential modulation of attentional ERPs in smoked and insufflated cocaine-dependent associated with neuropsychological performance
Authors: Aragón-Daud Agustina , Oberti de Luca Sofía Milagros , Schurmann Vignaga Sofía , Prado Pilar , Figueras Rosario , Lizaso Lucia , González-Gadea María Luz , Manes Facundo , Cetkovich Marcelo , Pallavicini Carla , Torralva Teresa , de la Fuente Laura Alethia 
Published: July 2023

AbstractBackgroundCocaine consumption is associated with reduced attentional event-related potentials (ERPs), namely P3a and P3b, indicating bottom-up and top-down deficits respectively. At cognitive level, these impairments are larger for faster routes of administration (e.g., smoked cocaine [SC]) than slower routes (e.g., insufflated cocaine [IC]). Here we assess these ERPs considering the route of cocaine administration. We hypothesized that SC dependent (SCD) would exhibit reduced modulation of the P3a, while both SCD and IC dependent (ICD) would show reduced modulation of the P3b.MethodsWe examined 25 SCD, 22 ICD matched by poly-consumption profiles, and 25 controls matched by demographic variables. We combined EEG data from the Global-Local task with behavioral data from attentional cognitive tasks.ResultsAt the behavioral level, SCD exhibited attentional deficits in both bottom-up and top-down processes, while ICD only showed a tendency for top-down deficits. Modulation of P3a and P3b was lower in consumer groups. We observed subtle route-based differences, with larger differences in the P3a for SCD and in the P3b for ICD. Neurophysiological and behavioral data converged, with the P3a associated to bottom-up performance and P3b to top-down.ConclusionsDifferent routes of administration lead to distinct modulations of attentional neurocognitive profiles. Specifically, SCD showed greater attentional impairment, mainly at bottom-up/P3a, while ICD showed a trend of top-down/P3b deficits. These findings emphasize the crucial role of considering the route of administration in both clinical and research settings and support the use of attentional ERPs as valid measures for assessing attentional deficits in substance abuse.
Poor Hemorrhagic Stroke Outcomes During the COVID-19 Pandemic Are Driven by Socioeconomic Disparities: Analysis of Nationally Representative Data
Authors: Bako Abdulaziz T. , Potter Thomas , Pan Alan , Borei Karim A. , Prince Taya , Britz Gavin , Vahidy Farhaan S. 
Published: July 2023

AbstractBackgroundNationally representative data demonstrating the impact of the COVID-19 pandemic on hemorrhagic stroke outcomes are lacking.MethodsIn this pooled cross-sectional analysis, we used the National Inpatient Sample (2016-2020) to identify adults (>=18 years) with primary intracerebral hemorrhage (ICH) or subarachnoid hemorrhage (SAH). We fit segmented logistic regression models to evaluate the differences in the rates of in-hospital outcomes (in-hospital mortality, home discharge, and receiving neurosurgical procedures) between the pre-pandemic (January 2016-February 2020) and pandemic periods (March 2020-December 2020). We used multivariable logistic regression models to evaluate the differences in mortality between patients admitted from April to December 2020, with and without COVID-19, and those admitted during a similar period in 2019. Stratified analyses were conducted among patients residing in low and high-income zip codes and among patients with extreme loss of function (E-LoF) and those with minor to major loss of function (MM-LoF).ResultsOverall, 309,965 ICH patients (mean age [SD]: 68[14.8], 47% female, 56% low-income) and 112,210 SAH patients (mean age [SD]: 60.2[15.4], 62% female, 55% low-income) were analyzed. Pre-pandemic, ICH mortality was decreasing by ≈ 1 % per month (adjusted odds ratio, 95% confidence interval: 0.99, 0.99-1.00). However, during the pandemic, the overall ICH mortality rate increased by ≈ 2% per month (1.02, 1.00-1.02) and ≈ 4% per month among low-income patients (1.04, 1.01-1.07). However, there was no change in trend among high-income ICH patients during the pandemic (1.00, 0.97-1.03). Patients with comorbid COVID-19 in 2020 had significantly higher odds of mortality compared to the 2019 comparison cohort, overall (ICH: 1.83, 1.33-2.51; SAH: 2.76, 1.68-4.54), and among patients with MM-LoF (ICH: 2.15, 1.12-4.16; SAH: 5.77, 1.57-21.17). However, patients with E-LoF and comorbid COVID-19 had similar mortality rates with the 2019 cohort.ConclusionSustained efforts are needed to address socioeconomic disparities in healthcare access, quality, and outcomes during public health emergencies.
SPAT: Surface Protein Annotation Tool
Authors: Spinella JF , Theret L , Aubert L , Audemard E , Boucher Geneviève , Pfammatter S , Bonneil E , Bordeleau ME , Thibault P , Hébert J , Roux PP , Sauvageau G 
Published: July 2023

AbstractGiven the particular attractivity of antibody-based immunotherapies,in vitroexperimental approaches aiming to identify and quantify proteins directly located at the cell surface, such as the surfaceome, have been recently developed and improved. However, the “surface” enriched, yet noisy output obtained from available methods makes it challenging to accurately evaluate which proteins are more likely to be located at the surface of the plasma membrane and which are simple contaminants. To that purpose, we developed thein silicoSurface Protein Annotation Tool (SPAT), which unifies established annotations to grade proteins according to the chance they have to be located at the cell surface. SPAT accuracy was tested using in-house acute myeloid leukemia data, as well as public datasets, and despite using publicly available annotations, showed good performances when compared to more complex surfaceome predictors. Given its simple input requirement, SPAT is easily usable for the annotation of any gene/protein lists. Its output, in addition to the “surface” score, provides additional annotations including a “secretion” flag, references to verified antibodies targeting annotated proteins, as well as expression data and protein levels in essential human organs, making it a user-friendly tool for the community.
Molecular recognition of an aversive odorant by the murine trace amine-associated receptor TAAR7f
Authors: Gusach Anastasiia , Lee Yang , Khoshgrudi Armin Nikpour , Mukhaleva Elizaveta , Ma Ning , Koers Eline J. , Chen Qingchao , Edwards Patricia C. , Huang Fanglu , Kim Jonathan , Mancia Filippo , Verprintsev Dmitry B. , Vaidehi Nagarajan , Weyand Simone N. , Tate Christopher G. 
Published: July 2023

AbstractThere are two main families of G protein-coupled receptors that detect odours in humans, the odorant receptors (ORs) and the trace amine-associated receptors (TAARs). Their amino acid sequences are distinct, with the TAARs being most similar to the aminergic receptors such as those activated by adrenaline, serotonin and histamine. To elucidate the structural determinants of ligand recognition by TAARs, we have determined the cryo-EM structure of a murine receptor, mTAAR7f, coupled to the heterotrimeric G protein Gsand bound to the odorant N,N-dimethylcyclohexylamine (DMCH) to an overall resolution of 2.9 Å. DMCH is bound in a hydrophobic orthosteric binding site primarily through van der Waals interactions and a strong charge-charge interaction between the tertiary amine of the ligand and an aspartic acid residue. This site is distinct and non-overlapping with the binding site for the odorant propionate in the odorant receptor OR51E2. The structure, in combination with mutagenesis data and molecular dynamics simulations suggests that the activation of the receptor follows a similar pathway to that of the β-adrenoceptors, with the significant difference that DMCH interacts directly with one of the main activation microswitch residues.
NECing goes: flexibility of the herpesvirus nuclear egress complex
Authors: Pražák Vojtěch , Mironova Yuliia , Vasishtan Daven , Hagen Christoph , Laugks Ulrike , Jensen Yannick , Sanders Saskia , Heumann John M. , Bosse Jens B. , Klupp Barbara , Mettenleiter Thomas C. , Grange Michael , Grünewald Kay 
Published: July 2023

AbstractThe nuclear egress complex (NEC) allows herpesvirus capsids to escape from the nucleus without breaking the nuclear envelope barrier. It assembles into a lattice on the inner nuclear membrane enveloping newly assembled nucleocapsids, which bud into the perinuclear space. The primary virion envelope subsequently fuses with the outer nuclear membrane, releasing the capsid into the cytosol. Here we interrogated the NEC in the context of intact cells infected with pseudorabies or herpes simplex virus using focused-ion beam milling and electron cryo- tomography. We determined the structure of NEC in different conformations and show that it consists of a flexible hexameric lattice that generates curvature through a combination of ordered and disordered domains. After interrogating the intermediate stages of capsid formation, we show that capsid vertex binding may initiate envelopment but does not directly induce curvature formation. These data and many examples of the intermediate stages of nuclear egress paint a detailed holistic view of a versatile transport system.
Emergent digital bio-computation through spatial diffusion and engineered bacteria
Authors: Fedorec Alex J.H. , Treloar Neythen J. , Wen Ke Yan , Dekker Linda , Ong Qing Hsuan , Jurkeviciute Gabija , Lyu Enbo , Rutter Jack , Rosa Luca , Zaikin Alexey , Barnes Chris P. 
Published: July 2023

AbstractBuilding computationally capable biological systems has long been an aim of synthetic biology. The potential utility of biocomputing devices ranges from biosafety and environmental applications to diagnosis and personalised medicine. Here we present work for the design of bacterial computers which use spatial patterning to process information. Our computers are composed of bacterial colonies which, inspired by patterning in embryo development, receive information in the form of diffusible morphogen-like signals. A computation is encoded by the physical locations of the input sources of morphogen and the output receiver colonies. We demonstrate, mathematically and with engineeredEscherichia coli, the simple digital logic capability of single bacterial colonies and show how additional colonies are required to build complex functions. Inspired by electronic design automation, an algorithm for designing optimal spatial circuits computing two-level digital functions is presented. This enhances the capability of our system to complex digital functions without increasing the biological complexity. We extend our experimental system to incorporate biosensing colonies as morphogen sources, demonstrating how a diagnostic device might be constructed. Our approach will open up new ways to perform biological computation, with applications in bioengineering, biomaterials and biosensing. Ultimately, these computational bacterial communities will help us explore information processing in natural biological systems.
Sea lamprey enlightens the origin of the coupling of retinoic acid signaling to vertebrate hindbrain segmentation
Authors: Bedois Alice M. H. , Parker Hugo J. , Bronner Marianne E. , Krumlauf Robb 
Published: July 2023

AbstractRetinoic acid (RA) is involved in antero-posterior patterning of the chordate body axis and, in jawed vertebrates, has been shown to play a major role at multiple levels of the gene regulatory network (GRN) regulating hindbrain segmentation. Knowing when and how RA became coupled to the core hindbrain GRN is important for understanding how ancient signaling pathways and patterning genes can evolve and generate diversity. Hence, we investigated the link between RA signaling and hindbrain segmentation in the sea lampreyPetromyzon marinus, an important jawless vertebrate model providing clues to decipher ancestral vertebrate features. Combining genomics, gene expression, and functional analyses of major components involved in RA synthesis (Aldh1as) and degradation (Cyp26s), we demonstrate that RA signaling is coupled to hindbrain segmentation in lamprey. Thus, the link between RA signaling and hindbrain segmentation is a pan vertebrate feature of the hindbrain and likely evolved at the base of vertebrates.
Mitochondrial-Derived Compartments are Multilamellar Domains that Encase Membrane Cargo and Cytosol
Authors: Wilson Zachary N. , West Matt , English Alyssa M. , Odorizzi Greg , Hughes Adam L. 
Published: July 2023

ABSTRACTPreserving the health of the mitochondrial network is critical to cell viability and longevity. To do so, mitochondria employ several membrane remodeling mechanisms, including the formation of mitochondrial-derived vesicles (MDVs) and compartments (MDCs) to selectively remove portions of the organelle. In contrast to well-characterized MDVs, the distinguishing features of MDC formation and composition remain unclear. Here we used electron tomography to observe that MDCs form as large, multilamellar domains that generate concentric spherical compartments emerging from mitochondrial tubules at ER-mitochondria contact sites. Time-lapse fluorescence microscopy of MDC biogenesis revealed that mitochondrial membrane extensions repeatedly elongate, coalesce, and invaginate to form these compartments that encase multiple layers of membrane. As such, MDCs strongly sequester portions of the outer mitochondrial membrane, securing membrane cargo into a protected domain, while also enclosing cytosolic material within the MDC lumen. Collectively, our results provide a model for MDC formation and describe key features that distinguish MDCs from other previously identified mitochondrial structures and cargo-sorting domains.SUMMARYWilson and colleagues use electron tomography and time-lapse fluorescence microscopy to observe that mitochondrial-derived compartments (MDCs) are generated from outer mitochondrial membrane extensions that repeatedly elongate, coalesce, and invaginate to secure membrane cargo and cytosol within a distinct, protected domain.
Improving genome-scale metabolic models of incomplete genomes with deep learning
Authors: Boer Meine D. , Melkonian Chrats , Zafeiropoulos Haris , Haas Andreas F. , Garza Daniel , Dutilh Bas E. 
Published: July 2023

AbstractDeciphering the metabolism of microbial species is crucial for understanding their function within complex ecosystems. Genome-scale metabolic models (GSMMs), which predict metabolic traits based on the enzymes encoded in a genome, are promising tools to study microbial ecosystems when genome sequences can be obtained. However, constructing GSMMs for uncultured bacteria is challenging, as metagenome-assembled genomes are typically incomplete, leading to metabolic reconstructions with numerous gaps. Existing methodologies often fill these gaps with the minimum set of reactions necessary to simulate an objective function such as growth. Here we introduce an artificial intelligence-based alternative: the Deep Neural Network Guided Imputation Of Reactomes (DNNGIOR). The DNNGIOR neural network learns weights for missing reactions in incomplete GSMMs from patterns in the presence and absence of metabolic reactions in genomes spanning the bacterial domain. We identified two important factors contributing to prediction accuracy: (1) the frequency of reaction across all bacteria, and (2) the phylogenetic distance between the query and the genomes in the training dataset. Reactions that occur in > 30% of the training genomes can be accurately predicted (Mean F1 score = 0.85). The weights generated by the DNNGIOR network improved the gap-filling of incomplete GSMMs, when assessed on a large and phylogenetically diverse testing dataset and a small set of high-quality manually curated models. The accuracy of DNNGIOR was on average 14 times greater than the standard unweighted gap-filling for draft reconstructions, and 2-9 times greater for manually curated models. DNNGIOR models could also simulate experimentally measured carbon usage profiles with similar accuracy as CarveMe. DNNGIOR is available athttps://github.com/MGXlab/DNNGIORor as a pip package (https://pypi.org/project/dnngior/).
An error correction strategy for image reconstruction by DNA sequencing microscopy
Authors: Kloosterman Alexander , Baars Igor , Högberg Björn 
Published: July 2023

AbstractBy pairing adjacent molecules in-situ and then mapping these pairs, DNA microscopy could significantly reduce workload in spatial omics methods by directly inferring geometry from sequencing data alone. However, experimental artefacts can lead to errors in the adjacency data which distort the spatial reconstruction. Here, we describe a method to correct two such errors: spurious crosslinks formed between any two nodes, and fused nodes that are formed out of multiple molecules. We build on the principle that spatially close molecules should be connected and show that these errors violate this principle, allowing for their detection and correction. Our method corrects errors in simulated data, even in the presence of up to 20% errors, and proves to be more efficient at removing errors from experimental data than a read count filter. Integrating this method in DNA microscopy will significantly improve the accuracy of spatial reconstructions with lower data loss.
Characterization and Expression Analysis ofNitrate Reductase 6-1ABDGene in Hexaploid Bread Wheat Under Different Nitrogen Regime
Authors: Gayatri  , Ravi Megavath , Chauhan Harsh , Mulani Ekta , Phogat Sachin , Venkatesh Karnam , Mandal Pranab Kumar 
Published: July 2023

AbstractNitrate reductase (NR) is the key rate-limiting enzyme of the nitrogen (N) assimilation process in plants, which has not been characterized in bread wheat under nitrogen stress, especially with respect to their homeologues. Total 9NRs were identified and classified into 3 groups, which showed a close relationship with different wheat ancestors. The occurrence of N-responsivecis-acting regulatory elements like MYB, MYC, G-Box and GATA-motif confirmed their N-responsiveness. Expression of all the three groups ofNRunder N-stress revealedNR 6-1ABDgroup to be the most N-responsive, which was characterized further in detail. The study was carried out in two genotypes contrasting for their N-responsiveness (HD 2967: Highly responsive to applied N, and Choti Lerma: Less responsive to applied N) selected on the basis of field evaluation. Homeologous differences within a genotype were found much more than the genotypic differences of a specific homeologue coding sequence. Among the three homeologues, thoughNR 6-1Dhomeologue was found most responsive to N-stress, the contribution was maximum for this homeologue followed byNR 6-1Aand least byNR 6-1B.We found that the expression of homeologues was linked to the presence of N-responsivecis-elements. All the homeologues ofNR 6-1in Choti Lerma were found less responsive to N-stress, in comparison to HD 2967, which might also be linked to N-use efficiency. Homeologous expression ofNR 6-1ABDrevealed negligible contribution ofB-homeologue to N-stress. Homeologous differences ofNR 6-1ABDwas found much more than the genotypic differences. Hence, our study on wheatNRwill be helpful in manipulating the specific homeologue of theNRgene in the future.
Metastatic prognostic ability of lung cancer stromal cells from a single-cell RNA-seq perspective
Authors: Moon Hyunjin , Kim Jonghwan , Jeon Hajin , Yang Jin Ok 
Published: July 2023

AbstractSingle-cell RNA sequencing (scRNA-seq) has been widely studied and analyzed to understand cancer heterogeneity. Metastasis and invasion through communication with immune cells have been widely studied; stromal cells are known to change during cancer progression and cause metastasis, but little is known about their inherent metastasis prognostic abilities. This study investigated the abilities of stromal cells by analyzing the scRNA-seq data of adjacent, tumor, and metastasized tissues, biopsied from 15 patients. We considered fibroblast and smooth muscle cells as cell subtypes of stromal cells. We detected decorin (DCN) and insulin-like growth factor-binding protein 7 (IGFBP7) as sub-cell type markers conserved in tumor and metastasis. We found an organic relationship that affects metastasis by assessing the interaction between the expression and related pathways among the assigned stromal cell subtypes. In addition to the interactions of sub-cell-types within stromal cells, we also studied communication between stromal cells and the five assigned lung cancer cell types, and observed its relation with migration and metastasis; the role ofDCNas a mediating factor was also studied. Results of our study indicated thatDCNandIGFBP7are factors that can be monitored in the follow-up of prognostic metastasis factors in patients with lung cancer. Therefore,DCNandIGFBP7, which are the assigned sub-cell types marker in lung cancer stromal cells, can be used as potential biomarkers for follow-up in lung cancer metastasis. Our study assigned stromal cell subtypes of lung cancer and detected markers that can be used as monitoring factors during metastasis of lung cancer. This suggests thatDCNandIGFBP7are potential biomarkers to evaluate metastatic ability and follow-up as metastatic prognostic factors in lung cancer patients.Author SummaryLung cancer is known to be a disease with high heterogeneity. In particular, it has been studied that stromal cells of lung cancer are involved in metastasis in cancer. Finding specific markers of stromal cell subtypes can lead to more accurate cancer markers. In this study, we assigned stromal cell subtypes as FB and SMC through scRNA-seq data analysis.DCNandIGFBP7, which are markers specifically expressed in stromal cell sub-cell types of NSCLC and metastatic NSCLC, were found. WhetherDCNandIGFBP7can be used as prognostic factors for metastasis was verified through co-expression analysis of FB and SMC and cell-cell communication. Specific expression as a marker was verified by confirming the role in the hub-gene network and the target genes of communication. Our findings suggest thatDCNandIGFBP7are potential biomarkers for evaluating the metastasis prognostic ability of stromal cells and can be monitored in the follow-up of metastasis prognostic factors in lung cancer patients.
Robust Phylogenetic Tree-based Microbiome Association Test using Repeatedly Measured Data for Composition Bias
Authors: Kim Kangjin , Won Sungho 
Published: July 2023

AbstractMotivationThe effects of microbiota on the host phenotypes can substantially differ depending on his/her age. Longitudinally measured microbiome data allows us to detect the age modification effect and are useful for the detection of microorganisms related to the progression of disease which change identification over time. Moreover, longitudinal analysis enables the estimation of within-subject covariate effect, is robust against the between-subject confounders, and provides better evidence for the causal relationship than cross-sectional studies. However, they suffer from compositional bias, and few statistical methods can estimate their effect on host diseases with repeatedly measured 16S rRNA gene data. In this article, we proposed mTMAT which can be applied to longitudinal microbiome data and is robust against compositional bias.ResultsmTMAT normalized the microbial abundance and utilized the ratio of the pooled abundances for association analysis. mTMAT is based on generalized estimating equations with a robust variance estimator and can be applied to repeatedly measured microbiome data. With extensive simulation studies, we showed that mTMAT is statistically more powerful and is robust against compositional bias. mTMAT enables detection of microbial taxa associated with host diseases using repeatedly measured 16S rRNA gene data and can provide deeper insight into bacterial pathology.AvailabilityThe 16S rRNA amplicon sequencing metagenomics datasets for Korea Association REsource cohort is available from the NCBI Sequence Read Archive database under project accession number PRJNA716550. mTMAT was implemented in the R package. Detailed information is available athttps://healthstat.snu.ac.kr/software/mtmat.Contactwon1@snu.ac.krSupplementary informationSupplementary data are available atBioinformaticsonline.