bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plant–Microbe Interactions: From Genes to Ecosystems Using Populus as a Model System
Authors: Cregger Melissa A. , Carper Dana L. , Christel Stephan , Doktycz Mitchel J. , Labbé Jessy , Michener Joshua K. , Dove Nicholas C. , Johnston Eric R. , Moore Jessica A. M. , Vélez Jessica M. , Morrell-Falvey Jennifer , Muchero Wellington , Pelletier Dale A. , Retterer Scott , Tschaplinski Timothy J. , Tuskan Gerald A. , Weston David J. , Schadt Christopher W. 
Published: February 2021

Plant–microbe symbioses span a continuum from pathogenic to mutualistic, with functional consequences for both organisms in the symbiosis. In order to increase sustainable food and fuel production in the future, it is imperative that we harness these symbioses. The tree genus Populus is an excellent model system for studies examining plant–microbe interactions due to the wealth of genomic information available and the molecular tools that have been developed to manipulate Populus–microbe symbioses. In this review, we highlight how Populus can serve as a model system to explore plant–microbe interactions. Specifically, we highlight research linking Populus–microbe interactions from the gene to the ecosystem level. We explore why Populus is an excellent model for perennial plant systems, the molecular underpinnings of Populus–microbe interactions, how host genetics influence microbial community composition, and how microbial communities vary at fine spatial scales and between Populus spp. Furthermore, we explore how changes in the microbiome may affect ecosystem-level functions in managed and natural ecosystems. Understanding and manipulating these interactions in Populus has the potential to improve plant health and affect ecosystem sustainability and processes because Populus trees function as foundational species in many natural ecosystems and are also deployed in managed ecosystems for various agroforestry applications. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
Comparing Methods for Pairing Electrodes Across Ears With Cochlear Implants
Authors: Staisloff Hannah E. , Aronoff Justin M. 
Published: February 2021

Objectives:
Currently, bilateral cochlear implants (CIs) are independently programmed in clinics using frequency allocations based on the relative location of a given electrode from the end of each electrode array. By pairing electrodes based on this method, bilateral CI recipients may have decreased sensitivity to interaural time differences (ITD) and/or interaural level differences (ILD), two cues critical for binaural tasks. There are multiple different binaural measures that can potentially be used to determine the optimal way to pair electrodes across the ears. Previous studies suggest that the optimal electrode pairing between the left and right ears may vary depending on the binaural task used. These studies, however, have only used one reference location or a single bilateral CI user. In both instances, it is difficult to determine if the results that were obtained reflect a measurement error or a systematic difference across binaural tasks. It is also difficult to determine from these studies if the differences between the three cues vary across electrode regions, which could result from differences in the availability of binaural cues across frequency regions. The purpose of this study was to determine if, after experience-dependent adaptation, there are systematic differences in the optimal pairing of electrodes at different points along the array for the optimal perception of ITD, ILD, and pitch.

Design:
Data from seven bilateral Nucleus users was collected and analyzed. Participants were tested with ITD, ILD, and pitch-matching tasks using five different reference electrodes in one ear, spaced across the array. Comparisons were conducted to determine if the optimal bilateral electrode pairs systematically differed in different regions depending on whether they were measured based on ITD sensitivity, ILD sensitivity, or pitch matching, and how those pairs differed from the pairing in the participants’ clinical programs.

Results:
Results indicate that there was a significant difference in the optimal pairing depending on the cue measured, but only at the basal end of the array.

Conclusion:
The results suggest that optimal electrode pairings differ depending on the cue measured to determine optimal pairing, at least for the basal end of the array. This also suggests that the improvements seen when using optimally paired electrodes may be tied to the particular percept being measured both to determine electrode pairing and to assess performance, at least for the basal end of the array.
Prognosis of Symptomatic Pseudarthrosis Observed at 1 Year After Lateral Lumbar Interbody Fusion
Authors: Jung Jong-myung , Chung Chun Kee , Kim Chi Heon , Yang Seung Heon , Ko Young San 
Published: February 2021

Study Design.
Retrospective cohort study.

Objective.
The aim of this study was to investigate the prognosis of symptomatic pseudarthrosis observed at 1 year after lateral lumbar interbody fusion (LLIF) surgery and to analyze the risk factors for persistent pseudarthrosis for 2 years postoperatively.

Summary of Background Data.
Few articles have evaluated the prognosis of symptomatic pseudarthrosis following LLIF surgery.

Methods.
One hundred fifty-two patients with a minimum follow-up of 2 years were screened. Fusion status was assessed at 1 year postoperatively, and unfused segments were reevaluated at 2 years postoperatively. Dynamic x-rays and computed tomography images were acquired to evaluate the fusion status. Demographic data were evaluated to identify the risk factors associated with persistent pseudarthrosis. Clinical outcomes, including the visual analog scale (VAS) for back/leg pain and the Oswestry disability index (ODI), were evaluated preoperatively and at 1 and 2 years postoperatively.

Results.
Symptomatic pseudarthrosis was detected in 42 patients at 1 year postoperatively. Among them, 23 patients (54.8%) exhibited solid bony fusion 2 years postoperatively without further intervention. Fourteen patients (33.3%) showed asymptomatic pseudarthrosis, and the remaining five patients (11.9%) showed symptomatic pseudarthrosis. Multivariable analysis showed that diabetes (adjusted odds ratio [OR]: 2.817, P = 0.007), smoking (adjusted OR: 6.497, P = 0.008), and fusion at more than three levels (adjusted OR: 2.525, P = 0.031) were risk factors for persistent pseudarthrosis. Improvements in the VAS scores for back pain and ODI scores were significantly lower in the persistent pseudarthrosis group than in the final solid fusion group at 2 years postoperatively.

Conclusion.
It is not necessary to intervene for all patients in whom symptomatic pseudarthrosis is detected at 1 year postoperatively because only 11.9% of them will show persistent symptomatic pseudarthrosis. However, early revision surgery should be considered when severe symptomatic pseudarthrosis associated with diabetes, smoking, and fusion at more than three levels is present.

Level of Evidence: 4
The need for polygenic score reporting standards in evidence-based practice: lipid genetics use case
Authors: Wand Hannah , Knowles Joshua W. , Clarke Shoa L. 
Published: February 2021

Purpose of review
Polygenic scores (PGS) are used to quantify the genetic predisposition for heritable traits, with hypothesized utility for personalized risk assessments. Lipid PGS are primed for clinical translation, but evidence-based practice changes will require rigorous PGS standards to ensure reproducibility and generalizability. Here we review applicable reporting and technical standards for dyslipidemia PGS translation along phases of the ACCE (Analytical validity, Clinical validity, Clinical utility, Ethical considerations) framework for evaluating genetic tests.

Recent findings
New guidance suggests existing standards for study designs incorporating the ACCE framework are applicable to PGS and should be adopted. One recent example is the Clinical Genomics Resource (ClinGen) and Polygenic Score Catalog's PRS reporting standards, which define minimal requirements for describing rationale for score development, study population definitions and data parameters, risk model development and application, risk model evaluation, and translational considerations, such as generalizability beyond the target population studied.

Summary
Lipid PGS are likely to be integrated into clinical practice in the future. Clinicians will need to be prepared to determine if and when lipid PGS is useful and valid. This decision-making will depend on the quality of evidence for the clinical use of PGS. Establishing reporting standards for PGS will help facilitate data sharing and transparency for critical evaluation, ultimately benefiting the efficiency of evidence-based practice.
Reduction techniques for intramedullary nailing of tibial shaft fractures: a comparative study
Authors: Behlmer Richard J. , Whiting Paul S. , Kliethermes Stephanie A. , Wendt Linder , Simske Natasha M. , Sato Eleanor H. , Doro Christopher J. , Goodspeed David C. , Lang Gerald J. 
Published: February 2021

Abstract

Objectives:
To assess the impact of various reduction techniques on postoperative alignment following intramedullary nail (IMN) fixation of tibial shaft fractures.

Design:
Retrospective comparative study.

Setting:
Level I trauma center.

Patients:
Four hundred twenty-eight adult patients who underwent IMN fixation of a tibial shaft fracture between 2008 and 2017.

Intervention:
IMN fixation with use of one or more of the following reduction techniques: manual reduction, traveling traction, percutaneous clamps, provisional plating, or blocking screws.

Main outcome measures:
Immediate postoperative coronal and sagittal plane alignment, measured as deviation from anatomic axis (DFAA); coronal and sagittal plane malalignment (defined as DFAA >5° in either plane).

Results:
Four hundred twenty-eight patients met inclusion criteria. Manual reduction (MR) alone was used in 11% of fractures, and adjunctive reduction aids were used for the remaining 89%. After controlling for age, BMI, and fracture location, the use of traveling traction (TT) with or without percutaneous clamping (PC) resulted in significantly improved coronal plane alignment compared to MR alone (TT: 3.4°, TT+PC: 3.2°, MR: 4.5°, P = .007 and P = .01, respectively). Using TT+PC resulted in the lowest rate of coronal plane malalignment (13% vs 39% with MR alone, P = .01), and using any adjunctive reduction technique resulted in decreased malalignment rates compared to MR (24% vs 39%, P = .02). No difference was observed in sagittal plane alignment between reduction techniques. Intraclass correlation coefficient (ICC) results indicated excellent intraobserver reliability on both planes (both ICC>0.85), good inter-observer reliability in the coronal plane (ICC = 0.7), and poor inter-observer reliability in the sagittal plane (ICC = 0.05).

Conclusions:
The use of adjunctive reduction techniques during IMN fixation of tibia fractures is associated with a lower incidence of coronal plane malalignment when compared to manual reduction alone.

Level of evidence:
Therapeutic Level III.
Toward Computer-Assisted Triaging of Magnetic Resonance Imaging‐Guided Biopsy in Preoperative Breast Cancer Patients
Authors: Wang Hui , van der Velden Bas H.M. , Ragusi Max A.A. , Veldhuis Wouter B. , Viergever Max A. , Verburg Erik , Gilhuijs Kenneth G.A. 
Published: February 2021

Objectives
Incidental MR-detected breast lesions (ie, additional lesions to the index cancer) pose challenges in the preoperative workup of patients with early breast cancer. We pursue computer-assisted triaging of magnetic resonance imaging (MRI)‐guided breast biopsy of additional lesions at high specificity.

Materials and Methods
We investigated 316 consecutive female patients (aged 26 to 76 years; mean, 54 years) with early breast cancer who received preoperative multiparametric breast MRI between 2013 and 2016. In total, 82 (26%) of 316 patients had additional breast lesions on MRI. These 82 patients had 101 additional lesions in total, 51 were benign and 50 were malignant. We collected 4 clinical features and 46 MRI radiomic features from T1-weighted dynamic contrast-enhanced imaging, high-temporal-resolution dynamic contrast-enhanced imaging, T2-weighted imaging, and diffusion-weighted imaging. A multiparametric computer-aided diagnosis (CAD) model using 10-fold cross-validated ridge regression was constructed. The sensitivities were calculated at operating points corresponding to 98%, 95%, and 90% specificity. The model calibration performance was evaluated by calibration plot analysis and goodness-of-fit tests. The model was tested in an independent testing cohort of 187 consecutive patients from 2017 and 2018 (aged 35 to 76 years; mean, 59 years). In this testing cohort, 45 (24%) of 187 patients had 55 additional breast lesions in total, 23 were benign and 32 were malignant.

Results
The multiparametric CAD model correctly identified 48% of the malignant additional lesions with a specificity of 98%. At specificity 95% and 90%, the sensitivity was 62% and 72%, respectively. Calibration plot analysis and goodness-of-fit tests indicated that the model was well fitted.
In the independent testing cohort, the specificity was 96% and the sensitivity 44% at the 98% specificity operating point of the training set. At operating points 95% and 90%, the specificity was 83% at 69% sensitivity and the specificity was 78% at 81% sensitivity, respectively.

Conclusions
The multiparametric CAD model showed potential to identify malignant disease extension with near-perfect specificity in approximately half the population of preoperative patients originally indicated for a breast biopsy. In the other half, patients would still proceed to MRI-guided biopsy to confirm absence of malignant disease. These findings demonstrate the potential to triage MRI-guided breast biopsy.
SARS-CoV-2 Seroprevalence in Tamil Nadu in October-November 2020
Authors: Malani Anup , Ramachandran Sabareesh , Tandel Vaidehi , Parasa Rajeswari , Imad Sofia , Sudharshini S. , Prakash V. , Yogananth Y. , Raju S. , Selvavinayagam T.S. 
Published: February 2021

AbstractA population-representative serological study was conducted in all districts of the state of Tamil Nadu (population 72 million), India, in October-November 2020. State-level seroprevalence was 31.6%. However, this masks substantial variation across the state. Seroprevalence ranged from just 11.1% in The Nilgris to 51.0% in Perambalur district. Seroprevalence in urban areas (36.9%) was higher than in rural areas (26.9%). Females (30.8%) had similar seroprevalence to males (30.3%). However, working age populations (age 40-49: 31.6%) have significantly higher seroprevalence than the youth (age 18-29: 30.7%) or elderly (age 70+: 25.8%). Estimated seroprevalence implies that at least 22.6 million persons were infected by the end of November, roughly 36 times the number of confirmed cases. Estimated seroprevalence implies an infection fatality rate of 0.052%.
Modeling Influenza Seasonality in the Tropics and Subtropics
Authors: Yuan Haokun , Kramer Sarah C. , Lau Eric H. Y. , Cowling Benjamin J. , Yang Wan 
Published: February 2021

AbstractClimate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist. In addition, multiple influenza viruses could cocirculate and shape epidemic dynamics. Here we construct seven mechanistic epidemic models to test the effect of two major climate drivers (humidity and temperature) and multi-strain co-circulation on influenza transmission in Hong Kong, an influenza epidemic center located in the subtropics. Based on model fit to long-term influenza surveillance data from 1998 to 2018, we found that a simple model incorporating the effect of both humidity and temperature best recreated the influenza epidemic patterns observed in Hong Kong. The model quantifies a bimodal effect of absolute humidity on influenza transmission where both low and very high humidity levels facilitate transmission quadratically; the model also quantifies the monotonic but nonlinear relationship with temperature. In addition, model results suggest that, at the population level, a shorter immunity period can approximate the co-circulation of influenza virus (sub)types. The basic reproductive number R0 estimated by the best-fit model is also consistent with laboratory influenza survival and transmission studies under various combinations of humidity and temperature levels. Overall, our study has developed a simple mechanistic model capable of quantifying the impact of climate drivers on influenza transmission in (sub)tropical regions. This model can be applied to improve influenza forecasting in the (sub)tropics in the future.
NeuroSCORE: A Genome-wide Omics-Based Model to Identify Candidate Disease Genes of the Central Nervous System
Authors: Davis Kyle W. , Bilancia Colleen G. , Martin Megan , Vanzo Rena , Rimmasch Megan , Hom Yolanda , Uddin Mohammed , Serrano Moises 
Published: February 2021

AbstractTo identify and prioritize candidate disease genes of the central nervous system (CNS) we created the Neurogenetic Systematic Correlation of Omics-Related Evidence (NeuroSCORE). We used five genome-wide metrics highly associated with neurological phenotypes to score 19,598 protein-coding genes. Genes scored one point per metric, resulting in a range of scores from 0-5. Approximately 13,000 genes were then paired with phenotype data from the Online Mendelian Inheritance in Man (OMIM) database. We used logistic regression to determine the odds ratio of each metric and compared genes scoring 1+ to cause a known CNS-related phenotype compared to genes that scored zero. We tested NeuroSCORE using microarray copy number variants (CNVs) in case-control cohorts, mouse model phenotype data, and gene ontology (GO) and pathway analyses. NeuroSCORE identified 8,296 genes scored ≥1, of which 1,580 are “high scoring” genes (scores ≥3). High scoring genes are significantly associated with CNS phenotypes (OR=5.5,p<2×10−16), enriched in case CNVs, and enriched in mouse ortholog genes associated with behavioral and nervous system abnormalities. GO and pathway analyses showed high scoring genes were enriched in chromatin remodeling, mRNA splicing, dendrite development, and neuron projection. OMIM has no phenotype for 1,062 high scoring genes (67%). Top scoring genes includeANKRD17, CCAR1, CLASP1, DOCK9, EIF4G2, G3BP2, GRIA1, MAP4K4, MARK2, PCBP2, RNF145, SF1, SYNCRIP, TNPO2, andZSWIM8. NeuroSCORE identifies and prioritizes CNS-disease candidate genes, many not yet associated with any phenotype in OMIM. These findings can help direct future research and improve molecular diagnostics for individuals with neurological conditions.
Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function
Authors: Gardiner Laura-Jayne , Rusholme-Pilcher Rachel , Colmer Josh , Rees Hannah , Crescente Juan Manuel , Carrieri Anna Paola , Duncan Susan , Pyzer-Knapp Edward O. , Krishna Ritesh , Hall Anthony 
Published: February 2021

AbstractThe circadian clock is an important adaptation to life on earth. Here, we use machine learning to predict complex temporal circadian gene expression patterns inArabidopsis. Most significantly, we classify circadian genes using DNA sequence features generated from public genomic resources, with no experimental work or prior knowledge needed. We use model explanation to rank DNA sequence features, observing transcript-specific combinations of potential circadian regulatory elements that discriminate temporal phase of expression. Model interpretation/explanation provides the backbone of our methodological advances, giving insight into biological processes and experimental design. Next, we use model interpretation to optimize sampling strategies when we predict circadian transcripts using reduced numbers of transcriptomic timepoints, saving both time and money. Finally, we predict the circadian time from a single transcriptomic timepoint, deriving novel marker transcripts that are most impactful for accurate prediction, this could facilitate the identification of altered clock function from existing datasets.
FINDER: An automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences
Authors: Banerjee Sagnik , Bhandary Priyanka , Woodhouse Margaret , Sen Taner Z. , Wise Roger P. , Andorf Carson M. 
Published: February 2021

AbstractBackgroundGene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.ResultsWe present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.ConclusionsFINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision – ideal for bench researchers with limited experience in handling computational tools.