bluuebunny/crossref_metadata_2025_split huggingface dataset:
All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting
Authors: Adiga Aniruddha , Wang Lijing , Hurt Benjamin , Peddireddy Akhil , Porebski Przemyslaw , Venkatramanan Srinivasan , Lewis Bryan , Marathe Madhav 
Published: March 2021

ABSTRACTTimely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model’s performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response.ACM Reference FormatAniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. InProceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 9 pages.https://doi.org/10.1145/nnnnnnn.nnnnnnn
A potential SARS-CoV-2 variant of interest (VOI) harboring mutation E484K in the Spike protein was identified within lineage B.1.1.33 circulating in Brazil
Authors: Resende Paola Cristina , Gräf Tiago , Paixão Anna Carolina Dias , Appolinario Luciana , Lopes Renata Serrano , da Fonseca Mendonça Ana Carolina , da Rocha Alice Sampaio Barreto , Motta Fernando Couto , Neto Lidio Gonçalves Lima , Khouri Ricardo , de Oliveira Camila Indiani , Santos-Muccillo Pedro , Bezerra Joao Felipe , Teixeira Dalane Loudal Florentino , Riediger Irina , do Carmo Debur Maria , Ribeiro-Rodrigues Rodrigo , Leite Anderson Brandao , do Santos Cliomar Alves , Gregianini Tatiana Schäffer , Fernandes Sandra Bianchini , Bernardes André Felipe Leal , Cavalcanti Andrea Cony , Miyajima Fábio , Sachhi Claudio , Mattos Tirza , da Costa Cristiano Fernandes , Delatorre Edson , Wallau Gabriel L , Naveca Felipe G , Bello Gonzalo , Siqueira Marilda Mendonça 
Published: March 2021

SummaryThe SARS-CoV-2 epidemic in Brazil was dominated by two lineages designated as B.1.1.28 and B.1.1.33. Two SARS-CoV-2 variants harboring mutations at the receptor-binding domain of the Spike (S) protein, designated as lineages P.1 and P.2, evolved within lineage B.1.1.28 and are rapidly spreading in Brazil. Lineage P.1 is considered a Variant of Concern (VOC) because of the presence of multiple mutations in the S protein (including K417T, E484K, N501Y), while lineage P.2 only harbors mutation S:E484K and is considered a Variant of Interest (VOI). Here we report the identification of a new SARS-CoV-2 VOI within lineage B.1.1.33 that also harbors mutation S:E484K and was detected in Brazil between November 2020 and February 2021. This VOI displayed four non-synonymous lineage-defining mutations (NSP3:A1711V, NSP6:F36L, S:E484K, and NS7b:E33A) and was designated as lineage N.9. The VOI N.9 probably emerged in August 2020 and has spread across different Brazilian states from the Southeast, South, North and Northeast regions.
16S rRNA Gene Amplicon Profiling of the New Zealand parasitic blowflyCalliphora vicina
Authors: Palevich Nikola , Maclean Paul H. , Carvalho Luis , Jauregui Ruy 
Published: March 2021

ABSTRACTHere, we present a 16S rRNA gene amplicon sequence data set and profiles demonstrating the bacterial diversity of larval and adultCalliphora vicina, collected from Ashhurst, New Zealand (May 2020). The three dominant genera among adult male and femaleC. vicinawereSerratiaandMorganella(phylumProteobacteria) andCarnobacterium(phylumFirmicutes), while the larvae were also dominated by the generaLactobacillus(phylumFirmicutes).
Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults
Authors: Siranosian Benjamin A. , Brooks Erin F. , Andermann Tessa , Rezvani Andrew R. , Banaei Niaz , Tang Hua , Bhatt Ami S. 
Published: March 2021

AbstractBacterial bloodstream infections are a major cause of morbidity and mortality among patients undergoing hematopoietic cell transplantation (HCT). Although previous research has demonstrated that pathogenic organisms may translocate from the gut microbiome into the bloodstream to cause infections, the mechanisms by which HCT patients acquire pathogens in their microbiome have not yet been described. We hypothesized that patient-patient transmission may be responsible for pathogens colonizing the microbiome of HCT patients, and that patients who share time and space in the hospital are more likely to share bacterial strains.Here, we used linked-read and short-read metagenomic sequencing to analyze 401 stool samples collected from 149 adults undergoing HCT and hospitalized in the same unit over five years. We used metagenomic assembly and strain-specific comparison methods to investigate transmission of gut microbiota between individuals. While patients who shared time and space in the hospital did not converge in overall microbiome composition, we did observe four pairs of patients who harbor identical or nearly identicalE. faeciumstrains in their microbiome. These strains may be the result of transmission between patients who shared a room and bathroom, acquisition from a common source in the hospital or transmission from an unsampled source.We also observed identicalAkkermansia muciniphilaandHungatella hathewayistrains in two pairs of patients. In both cases, the patients were roommates for at least one day, the strain was absent in the putative recipient’s microbiome prior to the period of roommate overlap and the putative recipient had a microbiome perturbed by antibiotic treatment for a bloodstream infection. Finally, we identified multiple patients who harbored identical strains of several species commonly found in commercial probiotics and dairy products, includingLactobacillus rhamnosus,Lactobacillus gasseriandStreptococcus thermophilus. Overall, our findings indicate that pathogenic organisms from a single source are not frequently colonizing the gut microbiome of multiple patients. However, the potential transmission of commensal microbes with immunomodulatory properties raises questions about the recovery of microbiome diversity after HCT, and indicates that patients in this setting may acquire new microbes by sharing space with others.
Yersinia pestislipopolysaccharide remodeling confers resistance to aXenopsylla cheopiscecropin
Authors: Mathew Basil , Aoyagi Kari L. , Fisher Mark A. 
Published: March 2021

AbstractFleas are major vectors ofYersinia pestis,the causative agent of plague. It has been proposed thatY. pestishas developed the ability to overcome the innate immune responses of fleas. Despite the fact that they transmit a number of bacterial infections, very little is known about the immune responses in fleas. In this study, we describe the antimicrobial activities of cecropin fromXenopsylla cheopis(cheopin), a major vector forY. pestisin the wild. This is the first cecropin-class antimicrobial peptide described from Siphonaptera insects. Cheopin showed potent activity against Gram-negative bacteria, but little activity against wild-typeY. pestisKIM6+. Deletion of the aminoarabinose operon, which is responsible for the 4-amino-4-deoxy-L-arabinose (Ara4N) modification of LPS, renderedY. pestishighly susceptible to cheopin. Confocal microscopy and whole cell binding assays indicated that Ara4N modification reduces the affinity of cheopin forY. pestis.Further, cheopin only permeabilized bacterial membranes in the absence of Ara4N-modified LPS, which was correlated with bacterial killing. This study provides insights into innate immunity of the flea and evidence for the crucial role of Ara4N modification ofY. pestisLPS in conferring resistance against flea antimicrobial peptides.
Clinical Knowledge Extraction via Sparse Embedding Regression (KESER) with Multi-Center Large Scale Electronic Health Record Data
Authors: Hong Chuan , Rush Everett , Liu Molei , Zhou Doudou , Sun Jiehuan , Sonabend Aaron , Castro Victor M. , Schubert Petra , Panickan Vidul A. , Cai Tianrun , Costa Lauren , He Zeling , Link Nicholas , Hauser Ronald , Gaziano J. Michael , Murphy Shawn N. , Ostrouchov George , Ho Yuk-Lam , Begoli Edmon , Lu Junwei , Cho Kelly , Liao Katherine P. , Cai Tianxi ,   with the VA Million Veteran Program
Published: March 2021

ABSTRACTObjectiveThe increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Even with a working knowledge of EHR, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions to establish a cooperative and integrated knowledge network. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease or condition of interest.MethodWe constructed large-scale code embeddings for a wide range of codified concepts, including diagnosis codes, medications, procedures, and laboratory tests from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis based on the trained code embeddings. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions.ResultsThe features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Additionally, features identified automatically via KESER used in the development of phenotype algorithms resulted in comparable performance to those built upon features selected manually or identified via existing feature selection methods with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data.ConclusionAnalysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among diseases, treatment, procedures, and laboratory measurement. This approach automates the grouping of clinical features facilitating studies of the condition. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.
How a sticky fluid facilitates prey retention in a carnivorous pitcher plant (Nepenthes rafflesiana)
Authors: Kang Victor , Isermann Hannah , Sharma Saksham , Wilson D Ian , Federle Walter 
Published: March 2021

AbstractNepenthespitcher plants live in nutrient-poor soils and produce large pitfall traps to obtain additional nutrients from animal prey. Previous research has shown that the digestive secretion inN. rafflesianais a sticky viscoelastic fluid that is much more effective at retaining insects than water, even after significant dilution. Although the physical properties of the fluid are important for its retentive function, it is unclear how the fluid interacts with insect cuticle and how its sticky nature affects struggling insects. In this study, we investigated the mechanisms behind the efficient prey retention inN. rafflesianapitcher fluid. By measuring the attractive forces exerted on insect body parts moving in and out of test fluids, we show that it costs insects significantly more energy to separate from pitcher fluid than from water. Moreover, both the maximum force and the energy required for retraction increase after the first contact with the pitcher fluid. We found that insects sink more easily into pitcher fluid than water and, accordingly, the surface tension ofN. rafflesianapitcher fluid was significantly lower than that of water (60.2 vs. 72.3 mN/m). By analysing the pitcher fluid dewetting behaviour, we demonstrate that it strongly resists dewetting from all surfaces tested, leaving behind residual films and filaments that can facilitate re-wetting. This inhibition of dewetting may be a further consequence of the fluid’s viscoelastic nature and likely represents a key mechanism underlying prey retention inNepenthespitcher plants.
A robust voice-selective response in the human brain as revealed by electrophysiological recordings and fast periodic auditory stimulation
Authors: Barbero Francesca M. , Calce Roberta P. , Talwar Siddharth , Rossion Bruno , Collignon Olivier 
Published: March 2021

AbstractVoices are arguably among the most relevant sounds in humans’ everyday life, and several studies have suggested the existence of voice-selective regions in the human brain. Despite two decades of research, defining the human brain regions supporting voice recognition remains challenging. Moreover, whether neural selectivity to voices is merely driven by acoustic properties specific to human voices (e.g. spectrogram, harmonicity), or whether it also reflects a higher-level categorization response is still under debate. Here, we objectively measured rapid automatic categorization responses to human voices with Fast Periodic Auditory Stimulation (FPAS) combined with electroencephalography (EEG). Participants were tested with stimulation sequences containing heterogeneous non-vocal sounds from different categories presented at 4 Hz (i.e., 4 stimuli/second), with vocal sounds appearing every 3 stimuli (1.333 Hz). A few minutes of stimulation are sufficient to elicit robust 1.333 Hz voice-selective focal brain responses over superior temporal regions of individual participants. This response is virtually absent for sequences using frequency-scrambled sounds, but is clearly observed when voices are presented among sounds from musical instruments matched for pitch and harmonicity-to-noise ratio. Overall, our FPAS paradigm demonstrates that the human brain seamlessly categorizes human voices when compared to other sounds including matched musical instruments and that voice-selective responses are at least partially independent from low-level acoustic features, making it a powerful and versatile tool to understand human auditory categorization in general.Significance statementVoices are arguably among the most relevant sounds we hear in our everyday life, and several studies have corroborated the existence of regions in the human brain that respond preferentially to voices. However, whether this preference is driven by specific acoustic properties of voices or if it rather reflects a higher-level categorization response to voices is still under debate. We propose a new approach to objectively identify rapid automatic voice-selective responses with frequency tagging and electroencephalographic recordings. In four minutes of recording only, we recorded robust voice-selective responses independent from low-level acoustic cues, making this approach highly promising for studying auditory perception in children and clinical populations.
Symmetry processing in the macaque visual cortex
Authors: Audurier Pauline , Héjja-Brichard Yseult , De Castro Vanessa , Kohler Peter J. , Norcia Anthony M. , Durand Jean-Baptiste , Cottereau Benoit R. 
Published: March 2021

AbstractSymmetry is a highly salient feature of the natural world that is perceived by many species of the animal kingdom and that impacts a large array of behaviours such as partner selection or food choice. In humans, the cerebral areas processing symmetry are now well identified from neuroimaging measurements. However, we currently lack an animal model to explore the underlying neural mechanisms. Macaque is a potentially good candidate, but a previous comparative study (1) found that functional magnetic resonance imaging (fMRI) responses to mirror symmetry in this species were substantially weaker than those observed in humans under similar experimental conditions. Here, we re-examined symmetry processing in macaques from a broader perspective, using both rotation (experiment 1) and reflection (experiment 2) symmetry. Our experimental design was directly derived from that of a previous human fMRI study (2), in order to facilitate the comparison between the two primate species. Highly consistent responses to symmetry were found in a large network of areas (notably V3, V3A, V4, V4A and PITd), in line with what has been observed in humans. Within this network, response properties in areas V3 and V4 (notably their dependency on the rotation symmetry order) were strikingly similar to those observed in their human counterparts. Our results suggest that the cortical networks that process symmetry in humans and macaques are much more similar than previously reported and point toward macaque as a relevant model for understanding symmetry processing.Significance statementSymmetry processing is an important aspect of human visual perception. We currently lack an animal model for characterizing the neural mechanisms that underlie it at the microscopic scale. Here, we use fMRI measurements in macaques to demonstrate that the cortical responses to symmetry in this species are comparable to those observed in humans under similar experimental conditions to a much higher extent than previously documented. Our results call for a re-examination of the relevance of the macaque model for symmetry processing in humans and open the door to an exploration of the underlying neural mechanisms at the single-cell level, notably in V3, an area often neglected in most current models of visual processing.
Clomipramine suppresses ACE2-mediated SARS-CoV-2 entry
Authors: Kato Yuri , Yamada Shigeru , Nishiyama Kazuhiro , Satsuka Ayano , Re Suyong , Tomokiyo Daiki , Lee Jae Man , Tanaka Tomohiro , Nishimura Akiyuki , Yonemitsu Kenzo , Asakura Hiroshi , Ibuki Yuko , Imai Yumiko , Kamiya Noriho , Mizuguchi Kenji , Kusakabe Takahiro , Kanda Yasunari , Nishida Motohiro 
Published: March 2021

AbstractMyocardial damage caused by the newly emerged coronavirus (SARS-CoV-2) infection is one of key determinants of COVID-19 severity and mortality. SARS-CoV-2 entry to host cells are initiated by binding with its receptor, angiotensin converting enzyme (ACE) 2, and the ACE2 abundance is thought to reflect the susceptibility to infection. Here, we found that clomipramine, a tricyclic antidepressant, potently inhibits SARS-CoV-2 infection and metabolic disorder in human iPS-derived cardiomyocytes. Among 13 approved drugs that we have previously identified as potential inhibitor of doxorubicin-induced cardiotoxicity, clomipramine showed the best potency to inhibit SARS-CoV-2 spike glycoprotein pseudovirus-stimulated ACE2 internalization. Indeed, SARS-CoV-2 infection to human iPS-derived cardiomyocytes (iPS-CMs) and TMPRSS2-expressing VeroE6 cells were dramatically suppressed even after treatment with clomipramine. Furthermore, the combined use of clomipramine and remdesivir was revealed to synergistically suppress SARS-CoV-2 infection. Our results will provide the potentiality of clomipramine for the breakthrough treatment of severe COVID-19.
Using recent baselines as benchmarks for megafauna restoration places an unfair burden on the Global South
Authors: Monsarrat Sophie , Svenning Jens-Christian 
Published: March 2021

AbstractThe potential for megafauna restoration is unevenly distributed across the world, along with the socio-political capacity of countries to support these restoration initiatives. We show that choosing a recent baseline to identify species’ indigenous range puts a higher burden for megafauna restoration on countries in the Global South, which also have less capacity to support these restoration initiatives. We introduce the Megafauna Index, which considers large mammal’s potential species richness and range area at country-level, to explore how the responsibility for megafauna restoration distributes across the world according to four scenarios using various temporal benchmarks to define species’ indigenous range – current, historical (1500AD), mid-Holocene and Pleistocene. We test how the distribution of restoration burden across the world correlates to indicators of conservation funding, human development, and governance. Using a recent or historical baseline as a benchmark for restoration puts a higher pressure on African and southeast Asian countries while lifting the responsibility from the Global North, where extinctions happened a long time ago. When using a mid-Holocene or Pleistocene baseline, new opportunities arise for megafauna restoration in Europe and North America respectively, where countries have a higher financial and societal capacity to support megafauna restoration. These results contribute to the debate around benchmarks in rewilding initiatives and the ethical implications of using recent baselines to guide restoration efforts. We suggest that countries from the Global North should reflect on their responsibility in supporting global restoration efforts, by increasing their support for capacity building in the South and taking responsibility for restoring lost biodiversity at home.
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding
Authors: Zeng Min , Wu Yifan , Lu Chengqian , Zhang Fuhao , Wu Fang-Xiang , Li Min 
Published: March 2021

AbstractMotivationLong non-coding RNAs (IncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features.ResultsWe proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences, and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared to traditional machine learning models with k-mer features and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also provided a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks.AvailabilityThe DeepLncLoc web server, source code and datasets are freely available athttp://bioinformatics.csu.edu.cn/DeepLncLoc/, andhttps://github.com/CSUBioGroup/DeepLncLoc.Contactlimin@mail.csu.edu.cn