bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel frontier in wildlife monitoring: identification of small rodent species from faecal pellets using Near-Infrared Reflectance Spectroscopy (NIRS)
Authors: Tuomi Maria W. , Murguzur Francisco J.A. , Hoset Katrine S. , Soininen Eeva M. , Vesterinen Eero , Utsi Tove Aa. , Kaino Sissel , Bråthen Kari Anne 
Published: April 2022

ABSTRACTSmall rodents are prevalent and functionally important across world’s biomes, making their monitoring salient for ecosystem management, conservation, forestry and agriculture. Yet, there is a dearth of cost-effective and non-invasive methods for large-scale, intensive sampling. As one such method, fecal pellet counts readily provide relative abundance indices. Given available analytical methods, feces could also allow for determination of multiple ecological and physiological variables, including community composition. We developed calibration models for rodent taxonomic determination using fecal near-infrared reflectance spectroscopy (fNIRS).Our results demonstrate fNIRS as an accurate and robust method for predicting genus and species identity of five co-existing subarctic microtine rodent species. We show that sample exposure to weathering did not reduce accuracy, indicating suitability of the method for samples collected from the field. Diet was not a major determinant of species prediction accuracy in our samples, as diet exhibited large variation and overlap between species. While regional calibration models predicted poorly samples from another region, calibration models including samples from two regions provided a good prediction accuracy for both regions.We propose fNIRS as a fast and cost-efficient high-throughoutput method for rodent taxonomic determination, and highlight its potential for cross-regional calibrations and use on field-collected samples. FNIRS can facilitate rodent population censuses at larger spatial extent than before deemed feasible, if combined with pellet-count based abundance indices. Given the versatility of fNIRS analytics, developing such monitoring schemes can support ecosystem- and interaction-based approaches to monitoring.
Peptidoglycan maturation controls spatiotemporal organisation of outer membrane proteins in Escherichia coli
Authors: Mamou Gideon , Corona Federico , Cohen-Khait Ruth , Sun Dawei , Sridhar Pooja , Knowles Timothy J. , Kleanthous Colin , Vollmer Waldemar 
Published: April 2022

AbstractLinkages between the outer membrane of Gram-negative bacteria and the peptidoglycan layer are crucial to the maintenance of cellular integrity and enable survival in challenging environments1–5. The functionality of the outer membrane relies on outer membrane proteins (OMPs), which are inserted by the β-barrel assembly machine, BAM6, 7. Previous work has shown that growing Escherichia coli cells segregate old OMPs towards the poles by an unknown mechanism8. Here, we demonstrate that peptidoglycan underpins the spatiotemporal organisation of OMPs. Mature, tetrapeptide-rich peptidoglycan binds to BAM components and suppresses OMP foldase activity. Nascent peptidoglycan, which is enriched in pentapeptides and concentrated at septa9, associates with BAM poorly and has little impact on its activity, leading to preferential insertion of OMPs at division sites. Synchronising OMP biogenesis to cell wall growth enables bacteria to replenish their OMPs by binary partitioning. Our study reveals that Gram-negative bacteria coordinate the assembly of two major cell envelope layers by rendering OMP biogenesis responsive to peptidoglycan maturation. This coordination offers new possibilities for the design of antibiotics that disrupt cell envelope integrity.
An adaptive filtering framework for non-specific and inefficient reactions in multiplex digital PCR based on sigmoidal trends
Authors: Miglietta Luca , Xu Ke , Chhaya Priya , Kreitmann Louis , Hill-Cawthorne Kerri , Bolt Frances , Holmes Alison , Georgiou Pantelis , Rodriguez-Manzano Jesus 
Published: April 2022

ABSTRACTReal-time digital PCR (qdPCR) coupled with artificial intelligence has shown the potential of unlocking scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One of the most promising applications is the use of machine learning (ML) methods to enable single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves. However, the robustness of such methods can be affected by the presence of undesired amplification events and nonideal reaction conditions. Therefore, here we proposed a novel framework to filter non-specific and low efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported ML-based Amplification Curve Analysis (ACA), using available data from a previous publication where the ACA method was used to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named Adaptive Mapping Filter (AMF), to consider the variability of positive counts in digital PCR. Over 152,000 amplification events were analyzed. For the positive reactions, filtered and unfiltered amplification curves were evaluated by comparing against melting peak distribution, proving that abnormalities (filtered out data) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to compare classification accuracies before and after AMF, showing an improved sensitivity of 1.18% for inliers and 20% for outliers (p-value < 0.0001). This work explores the correlation between kinetics of amplification curves and thermodynamics of melting curves and it demonstrates that filtering out non-specific or low efficient reactions can significantly improve the classification accuracy for cutting edge multiplexing methodologies.
Rational design by structural biology of industrializable, long-acting antihyperglycemic GLP-1 receptor agonists
Authors: Sun Lei , Zheng Zhi-Ming , Shao Chang-Sheng , Li Ming-Wei , Wang Li , Wang Han , Zhao Gen-Hai , Wang Peng 
Published: April 2022

SummaryGlucagon-like peptide-1 (GLP-1) is easily degraded by dipeptidyl peptidase-4 (DPP-4) in the human body, limiting its therapeutic effect on type II diabetes. Therefore, improving GLP-1 receptor agonist (GLP-1RA) stability is a major obstacle for drug development. We analyzed human GLP-1, DPP-4, and GLP-1 receptor structures and designed three GLP-1RAs, which were introduced into fusion protein fragments and changed in the overall conformation. This modification effectively prevented GLP-1RAs from entering DPP-4 active center without affecting GLP-1Ras ability to bind to GLP-1R, the new GLP-1RA hypoglycemic effect lasting for >24 h. Through molecular modeling, molecular dynamics calculation, and simulation, possible tertiary structure models of GLP-1RAs were obtained; molecular docking with DPP-4 and GLP-1R showed access to the fusion protein. The overall conformational change of GLP-1RAs prevented DPP-4 binding, without affecting GLP-1Ras affinity to GLP-1R. This study provides important drug design ideas for GLP-1RA development and a new example for application of structural biology-based protein design in drug development.
Strong and consistent associations of waterbird community composition with HPAI H5 occurrence in European wild birds
Authors: Huang Zheng Y.X. , Yin Shenglai , Zhang Yong , de Boer Willem F. , Mundkur Taej , Artois Jean , Velckers Francisca , Tian Huaiyu , Takekawa John Y. , Si Yali , Han Guan-Zhu , Zhang Huimin , Chen Yuyang , Chai Hongliang , Xu Chi 
Published: April 2022

AbstractSince 2014, highly pathogenic avian influenza (HPAI) H5 viruses of clade 2.3.4.4 have been dominating the outbreaks across Europe, causing massive deaths among poultry and wild birds. However, the factors shaping its broad-scale outbreak patterns remain unclear. With extensive waterbird survey datasets of about 7,000 sites across Europe, we here demonstrated that H5N8 occurrence in wild birds in the 2016/17 and 2020/21 epidemics as well as H5N1 occurrence in 2005/06 epidemic were strongly associated with very similar waterbird community attributes, pointing to the possibility of similar interspecific transmission processes between different epidemics. A simple extrapolation of the model constructed from the 2016/17 epidemic can well predict the H5N8 pattern in wild birds in 2020/21 epidemic. We also found a dilution effect of phylogenetic diversity that was always negatively correlated with H5 occurrence in wild birds. In contrast, H5N8 occurrence in poultry was subject to different risk factors between the two epidemics. In general, waterbird community composition play a much more important role in determining the spatial pattern of H5N8 in wild birds than in poultry. Our work contributes to reveal the factors driving H5N8 patterns, and highlights the value of waterbird community factors in future HPAI surveillance and prediction.
Variation in subcortical anatomy: relating interspecies differences, heritability, and brain-behavior relationships
Authors: Blostein Nadia , Devenyi Gabriel A. , Patel Sejal , Patel Raihaan , Tullo Stephanie , Plitman Eric , Costantino Manuela , Markello Ross , Parent Olivier , Bedford Saashi A. , Sherwood Chet C. , Hopkins William D , Seidlitz Jakob , Raznahan Armin , Chakravarty M. Mallar 
Published: April 2022

SummaryThere has been an immense research focus on the topic of cortical reorganization in human evolution, but much less is known regarding the reorganization of subcortical circuits which are intimate working partners of the cortex. Here, by combining advanced image analysis techniques with comparative neuroimaging data, we systematically map organizational differences in striatal, pallidal and thalamic anatomy between humans and chimpanzees. We relate interspecies differences, a proxy for evolutionary changes, to genetics and behavioral correlates. We show that highly heritable morphological measures are significantly expanded across species, in contrast to previous findings in the cortex. The identified morphological-cognitive latent variables were associated with striatal expansion, and affective latent variables were associated with more evolutionarily-conserved areas in the thalamus and globus pallidus. These findings provide new insight into the architecture of these subcortical hubs and can provide greater information on the role of these structures in health and illness.
Human peroxiredoxin 6 is essential for malaria parasites and provides a host-based drug target
Authors: Wagner Matthias Paulus , Formaglio Pauline , Gorgette Olivier , Dziekan Jerzy Michal , Huon Christèle , Berneburg Isabell , Rahlfs Stefan , Barale Jean-Christophe , Feinstein Sheldon I. , Fisher Aron B. , Ménard Didier , Bozdech Zbynek , Amino Rogerio , Touqui Lhousseine , Chitnis Chetan E. 
Published: April 2022

SUMMARYThe uptake and digestion of host hemoglobin by malaria parasites during blood stage growth leads to significant oxidative damage of membrane lipids. Repair of lipid peroxidation damage is crucial for parasite survival. Here, we demonstrate that Plasmodium falciparum imports a host antioxidant enzyme, peroxiredoxin 6 (PRDX6), during hemoglobin uptake from the red blood cell cytosol. PRDX6 is a lipid peroxidation repair enzyme with phospholipase A2 (PLA2) activity. Inhibition of PRDX6 with a PLA2 inhibitor, Darapladib, increases lipid peroxidation damage in the parasite and disrupts transport of hemoglobin-containing vesicles to the food vacuole, causing parasite death. Furthermore, inhibition of PRDX6 synergistically reduces the survival of artemisinin-resistant parasites following co-treatment of parasite cultures with artemisinin and Darapladib. Thus, PRDX6 is a unique host-derived drug target for development of antimalarial drugs that could help overcome artemisinin resistance.GRAPHICAL ABSTRACT
METTL3 regulates breast cancer-associated alternative splicing switches
Authors: Achour Cyrinne , Groza Paula , Bhattarai Devi Prasad , Román Ángel-Carlos , Aguilo Francesca 
Published: April 2022

ABSTRACTAlternative splicing (AS) enables differential inclusion of exons from a given transcript, thereby contributing to the transcriptome and proteome diversity. Aberrant AS patterns play major roles in the development of different pathologies, including breast cancer.N6-methyladenosine (m6A), the most abundant internal modification of eukaryotic mRNA, influences tumor progression and metastasis of breast cancer, and it has been recently linked to AS regulation. Here, we identify a specific AS signature associated with breast tumorigenesisin vitro. We characterize for the first time the role of METTL3 in modulating breast cancer-associated AS programs, expanding the role of the m6A-methyltransferase in tumorigenesis. Specifically, we find that both m6A deposition in splice site boundaries and in splicing and transcription factor transcripts, such asPHF5AandMYC, direct AS switches of specific breast cancer-associated transcripts. Finally, we show that five of the AS events validatedin vitroare associated with a poor overall survival rate for patients with breast cancer, suggesting the use of these AS events as a novel potential prognostic biomarker.
Neural mass modelling for the masses: Democratising access to whole-brain biophysical modelling with FastDMF
Authors: Herzog Rubén , Mediano Pedro A.M. , Rosas Fernando E. , Luppi Andrea I. , Perl Yonatan Sanz , Tagliazucchi Enzo , Kringelbach Morten , Cofré Rodrigo , Deco Gustavo 
Published: April 2022

ABSTRACTDifferent whole-brain models constrained by neuroimaging data have been developed during the last years to investigate causal hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is a particularly attractive model, combining a biophysically realistic single-neuron model that is scaled up via a mean-field approach and multimodal imaging data. Despite these favourable features, an important barrier for a widespread usage of the DMF model is that current implementations are computationally expensive – to the extent that the model often becomes unfeasible when no high-performance computing infrastructure is available. Furthermore, even when such computing structure is available, current implementations can only support simulations on brain parcellations that consider less than 100 brain regions. To remove these barriers, here we introduce a user-friendly and computationally-efficient implementation of the DMF model, which we call FastDMF, with the goal of making biophysical whole-brain modelling accessible to neuroscientists worldwide. By leveraging a suit of analytical and numerical advances – including a novel estimation of the feedback inhibition control parameter, and a Bayesian optimisation algorithm – the FastDMF circumvents various computational bottlenecks of previous implementations. An evaluation of the performance of the FastDMF showed that it can attain a significantly faster performance than previous implementations while reducing the memory consumption by several orders of magnitude. Thanks to these computational advances, FastDMF makes it possible to increase the number of simulated regions by one order of magnitude: we found good agreement between empirical and simulated functional MRI data parcellated at two different spatial scales (N=90 and N=1000 brain regions). These advances open the way to the widespread use of biophysically grounded whole-brain models for understanding the interplay among anatomy, function and brain dynamics in health and disease, and to provide mechanistic explanations of recent results obtained from empirical fine-grained neuroimaging data sets, such as turbulence or connectome harmonics.HighlightsWe present the FastDMF, a user-friendly and computationally efficient implementation of the Dynamic Mean Field model for simulations of whole-brain dynamics.Using analytical and numerical tools, we develop a novel estimation of the feedback inhibition control based on the structural connectivity, bypassing an important computational bottleneck.The FastDMF is coupled with a Bayesian Optimization algorithm significantly reducing the number of simulations required to fit the FastDMF to empirical neuroimaging data.Our advances open the possibility of simulating thousands of brain regions in a biophysically grounded whole-brain model.
Trascriptome meta-analysis of microalga Dunaliella tertiolecta under stress condition
Authors: Panahi Bahman , Farhadian Mohammad , Mohammadi Seyyed Abolghasem , Hejazi Mohammad Amin 
Published: April 2022

AbstractMicroalgae are photosynthetic organisms, which are considered as a potential source for sustainable metabolite production. Furthermore, stress conditions can affect metabolite production. In this study, a meta-analysis of RNA-seq experiments was performed to evaluate the response of metabolite biosynthesis pathways in Dunaliella tertiolecta to abiotic stress conditions, including high light, nitrogen deficiency, and high salinity. The results indicated down-regulation of light reaction, photorespiration, tetrapyrrole, and lipid-related pathways in salt stress. In comparison to salt stress, nitrogen deficiency mostly induced light reaction and photorespiration metabolisms. The up-regulation of phosphoenolpyruvate carboxylase, phosphoglucose isomerase, bisphosphoglycerate mutase, and glucose-6-phosphate-1-dehydrogenase (involved in central carbon metabolism) was observed under salt, high light, and nitrogen stress conditions. Interestingly, the results indicated that the meta-genes (i.e., modules of genes strongly correlated) tended to be located in a hub of stress-specific PPI (Protein-Protein Interaction) networks. Module enrichment of meta-genes PPI networks highlighted the cross talk between photosynthesis, fatty acids, starch, and sucrose metabolism under multiple stress conditions. Moreover, it was observed that the coordinated expression of the tetrapyrrole intermediated with meta-genes involved in starch biosynthesis. The results of the present study also showed that some pathways such as vitamin B6 metabolism, methane metabolism, ribosome biogenesis, and folate biosynthesis responded to different stress factors specifically. In conclusion, the results of this study revealed the main pathways underlying the abiotic stress responses for optimized metabolite production by the microalga Dunaliella in future studies. PRISMA check list was also included in the study.
Independent acquisition of short insertions at the RIR1 site in the spike N-terminal domain of the SARS-CoV-2 BA.2 lineage
Authors: Greco Samuele , Gerdol Marco 
Published: April 2022

AbstractAlthough the SARS-CoV-2 variants BA.1 and BA.2 share over 30 non-synonymous substitutions in the spike glycoprotein, they show several unique mutations that were likely acquired after the split between these two major omicron lineages. One of the most intriguing mutations associated with BA.1 is the presence of the inserted tripeptide Glu-Pro-Glu within the N-terminal domain. While the functional implications of this insertion are still unclear, several other SARS-CoV-2 lineages had previously independently acquired similarly short insertions at the very same site, named RIR1. We have previously identified this site, located approximately between codon 212 and codon 216, as a hotspot of insertions, which usually involve small nucleotide sequences including three or four codons.Here we show that similar insertion events have independently occurred at least 13 times in early 2022 within the BA.2 lineage, being occasionally associated with significant community transmission. One of these omicron sublineages, characterized by a Ser-Gly-Arg insertion in position 212, is responsible of over 2% of all SARS-CoV-2 cases recorded in Denmark, as of early April 2022. Molecular surveillance data highlight a slow but steady growth compared with the parental BA.2 lineage in all Danish regions, suggesting that the RIR1 insertion may confer a selective advantage. We report the identification of other currently circulating BA.2 sublineages showing similar insertions, whose spread should be therefore carefully monitored in the upcoming months.
Expansive Linguistic Representations to Predict Interpretable Odor Mixture Discriminability
Authors: Dhurandhar Amit , Cecchi Guillermo A. , Meyer Pablo 
Published: April 2022

AbstractLanguage is often thought as being poorly adapted to precisely describe or quantify smell and olfactory attributes. In this work, we show that semantic descriptors of odors can be implemented in a model to successfully predict odor mixture discriminability, an olfactory attribute. We achieved this by taking advantage of the structure-to-percept model we previously developed for monomolecular odorants, using chemical descriptors to predict pleasantness, intensity and 19 semantic descriptors such as ‘fish’, ‘cold’, ‘burnt’, ‘garlic’, ‘grass’ and ‘sweet’ for odor mixtures, followed by a metric learning to obtain odor mixture discriminability. Through this expansion of the representation of olfactory mixtures, our Semantic model outperforms state of the art methods by taking advantage of the intermediary semantic representations learned from human perception data to enhance and generalize the odor discriminability/similarity predictions. As 10 of the semantic descriptors were selected to predict discriminability/similarity, our approach meets the need of rapidly obtaining interpretable attributes of odor mixtures as illustrated by the difficulty of finding olfactory metamers. More fundamentally, it also shows that language can be used to establish a metric of discriminability in the everyday olfactory space.
An electro-optical bead-nanochip technology for the ultrasensitive and multi-dimensional detection of small extracellular vesicles and their markers
Authors: Dias Tomás , Figueiras Ricardo , Vagueiro Susana , Domingues Renato , Hung Yu-Hsien , Persia Elnaz , Arsène Pierre 
Published: April 2022

ABSTRACTSmall extracellular vesicles (sEVs), including exosomes, are enriched in multiomics information mirroring their parental cells. They have been investigated in health and disease and utilised in several applications from drug discovery to diagnostics. In disease diagnostics, sEVs can be sampled via a blood draw, enabling the convenient liquid biopsy of the tissue they originate from. However, few applications with sEVs have been translated into clinical practice.We developed a Nanoparticle EXOsome Sensing (NEXOS) technology, for the ultrasensitive and multi-dimensional detection of sEVs. NEXOS comprises two methods: a novel nanoelectronics method, E-NEXOS, and a high-throughput optical detection method, O-NEXOS. Both methods share the same steps for the immunocapture and antibody-labelling of sEVs and can be combined to derive differentiated detection parameters.As a proof of concept, we show the analytical detection and sensitivity of these methods in detecting pre-prepared cancer cell-derived CD9+CD81+and CD9+HER2+sEVs. Both sEV populations were diluted in PBS and spiked in processed plasma. We also provide a novel approach for the determination of target sEVs (TEVs), target epitopes in sEVs (TEPs), and epitopes per target sEV, as yet unseen from current and emerging technologies.Further, we demonstrate the higher sensitivity of O-NEXOS compared to the gold standard techniques, as well as demonstrating that E-NEXOS possesses commensurate sensitivity whilst only being powered by 36 nanogap-based sensors per nanochip.Finally, this manuscript lays the groundwork for a scalable electronics miniaturization of E-NEXOS nanochip with millions of nanogap-based sensors for the translation of NEXOS into standard clinical practice.