bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical Relevance of a Balance Training Program on Liver Transplant Patients. A Randomized Controlled Trial
Authors: Moya-Nájera Diego , Moya-Herraiz Ángel , Gargallo Pedro , Calatayud Joaquin , Escrig-Sos Javier , Colado Juan C. 
Published: August 2018

Background.
Although some studies have reported significant improvements in physical function and strength after training programs on liver transplant (LT) recipients, there is a lack of knowledge on how it affects in static and dynamic balance, being an important part of these participants’ tasks development. The aim of the study was to determine the effects of a 6-month multicomponent circuit training program on static and dynamic balance in LT participants.

Methods.
Fifty-four participants were randomized at 6 months after LT into 2 groups: exercise (EXER) group and control (CONTROL) group, with repeat testing at 6 (baseline) and 12 months after LT. The intervention consisted of a multicomponent training, including balance, strength, endurance, and flexibility training, with exercises arranged in a circuit setup and a moderate intensity with high perceived exertion. Training sessions were performed in the hospital facilities with qualified trainers. To determine differences over time between EXER and CONTROL, mixed-regression linear models with subject variable as random factor and variables of treatment duration, type, and interaction as predictors were used.

Results.
The EXER group showed significant differences (P < 0.05) compared with CONTROL in all variables of static and dynamic balance, hip strength (49% versus 13%), agility (−16% versus −1%), and flexibility (78% versus −26%). Adherence to the intervention was 94%, and 80% of the participants continued voluntarily training after the 6 months.

Conclusions.
This study demonstrated that a multicomponent circuit training program at a moderate intensity with high perceived exertion could reduce the probability of injuries because it improves balance on LT recipients.
Intra-abdominal Complications After Pediatric Kidney Transplantation: Incidence and Risk Factors
Authors: Taher Amir , Zhu Benjamin , Ma Sophia , Shun Albert , Durkan Anne Maria 
Published: August 2018

Background.
The incidence and types of intra-abdominal complications after pediatric transplantation are not well established, and specific risk groups have not been clearly identified.

Methods.
A retrospective chart review of all pediatric transplant recipients between 1995 and 2016 was undertaken. Intra-abdominal complications were grouped into 4 categories: fluid collections, gastrointestinal, vascular, and urogenital. Donor, recipient, and transplant characteristics were evaluated using univariate and multivariate logistic regressions.

Results.
There were 146 transplants meeting the inclusion criteria. The mean follow-up time was 4.6 ± 3.7 years (range, 0.3–18 y). The mean weight at transplantation was 31.5 ± 16.5 kg (range, 9–78), with 24 (16%) recipients being <15 kg and 23% younger than 5 years. Thirty-four (23%) patients had previous abdominal surgery. There were 32 complications identified in 27 (18%) transplant recipients. Fluid collections requiring surgical drainage developed in 9 (6.2%), gastrointestinal surgical complications in 12 (8.2%), vascular complications in 5 (3.5%), and urogenital complications in 6 (4.1%). There were only 3 graft losses due to abdominal complications, all after renal vein thrombosis. Weight <15 kg at the time of transplant (P = 0.016), previous abdominal surgery (P = 0.047), and intraperitoneal surgical technique (P = 0.008) were risk factors in the univariate analysis using Cox regression models, whereas only weight <15 kg (P = 0.003) and previous abdominal surgery (P = 0.008) were retained in the multivariate analysis.

Conclusions.
Intraabdominal complications occur in almost 1 in 5 pediatric renal transplant recipients. Weight <15 kg and previous abdominal surgery are risk factors for developing such complications.
Medical Nonadherence, Cannabis Use, and Renal Outcome in Systemic Lupus Erythematosis
Authors: Jalil Basmah A. , Qualls Clifford R. , Cabacungan Romy J. , Sibbitt Wilmer L. , Gibb James I. , Noronha Leonard E. , Fields Roderick A. , Emil N. Suzanne , Fangtham Monthida , Bankhurst Arthur D 
Published: August 2018

AbstractBackground/ObjectiveNon-adherence to recommended medical therapy has been associated with poorer outcomes in systemic lupus erythematosus (SLE). The present research investigated the association of medical non-adherence and cannabis use on renal outcomes of SLE.MethodsThis was a prospective 5-year longitudinal outcome study of 276 female SLE patients 30.4% who chronically used medical cannabis and 69.5% who did not. Outcomes were determined at 5 years after enrollment in the study.ResultsCannabis use in SLE patients was associated with an increased prevalence of neuropsychiatric SLE (p<0.05), opioid analgesic use (p<0.01), cigarette smoking (p<0.001), and non-adherence to the medical regimen (non-cannabis: 3% non-adherence vs. cannabis use: 95% non-adherence, p<0.001). Within the 5-year period, the cannabis group demonstrated a 53% increase in mortality (p=0.12) and 127% increase in end-stage renal disease requiring dialysis (p<0.001). With logistic regression analysis adjusting for SLE disease activity (SLEDAI-2K), cannabis use was an independent predictor of end-stage renal disease: Odds ratio 2.65 (CI 1.32 – 5.32, p<0.01). Adjusting for SLE disease damage (SLICC/ACR-DI), cannabis use remained an independent predictor of end-stage renal disease: Odds ratio 2.0 (CI 1.26 – 6.23, p<0.01). With multivariable analysis adjusting for non-adherence, the effect of cannabis on end-stage renal disease could be largely attributed to an increase in non-adherence to medical therapy.ConclusionsNon-adherence to recommended therapy and medical cannabis use are associated with a significant increase in the development of end-stage renal disease in SLE.
Fitness Effects of Somatic Mutations Accumulating during Vegetative Growth
Authors: Cruzan Mitchell B. , Streisfeld Matthew A. , Schwoch Jaime A. 
Published: August 2018

AbstractThe unique life form of plants promotes the accumulation of somatic mutations that can be passed to offspring in the next generation, because the same meristem cells responsible for vegetative growth also generate gametes for sexual reproduction. However, little is known about the consequences of somatic mutation accumulation for offspring fitness. We evaluate the fitness effects of somatic mutations inMimulus guttatusby comparing progeny from self-pollinations made within the same flower (autogamy) to progeny from self-pollinations made between stems on the same plant (geitonogamy). The effects of somatic mutations are evident from this comparison, as autogamy leads to homozygosity of a proportion of somatic mutations, but progeny from geitonogamy remain heterozygous for mutations unique to each stem. In two different experiments, we find consistent fitness effects of somatic mutations from individual stems. Surprisingly, several progeny groups from autogamous crosses displayed increases in fitness compared to progeny from geitonogamy crosses, indicating that beneficial somatic mutations were prevalent in some stems. These results support the hypothesis that somatic mutations accumulate during vegetative growth, but they are filtered by different forms of selection that occur throughout development, resulting in the culling of expressed deleterious mutations and the retention of beneficial mutations.
Identification of negative BOLD responses using windkessel models
Authors: Valdés-Hernández Pedro A. , Bernal Byron , Moshkforoush Arash , Dunoyer Catalina , Khoo Hui Ming , Bosch-Bayard Jorge , von-Ellenrieder Nicolas , Gotman Jean , Riera Jorge J. 
Published: August 2018

AbstractAlongside positive BOLD responses (PBR), a variety of negative BOLD responses (NBR) with distinct underlying mechanisms also occur. We identify five mechanisms of NBR: i) local/lateral/contralateral inhibition (LCI), ii) neuronal disruption of network activity (NDA), iii) altered balance of neuro-metabolic/vascular couplings (ANC), iv) arterial blood stealing (ABS), and v) venous blood backpressure (VBB). Detecting and classifying these mechanisms from BOLD signals is pivotal in understanding normal/pathological brain functions. This requires models and parameters with anatomical/functional interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a windkessel model with viscoelastic compliance as well as dynamics of both neuronal and tissue/blood O2 to investigate the generation, detection, classification and interpretation of the BOLD hemodynamic response functions (HRF) of above mechanisms. Firstly, we evaluated the use of the general linear model to detect simulated NBRs. Secondly, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying a new HRF. Crossvalidation indicates NDA and ANC can accurately be classified solely from fMRI BOLD signals; while LCI, ABS and VBB might require additional imaging modalities. Thirdly, we demonstrated that estimators of the model parameters determinant in the NBRs formation are accurate, and precise to certain resolutions. Finally, we successfully applied our detection/classification/estimation methodology to EEG-fMRI data in a clinical situation where several of these mechanisms could coexist. We believe that the proper identification and interpretation of NBR mechanisms have important clinical and cognitive implications in fMRI studies.
SonHi-C: a set of non-procedural approaches for predicting 3D genome organization from Hi-C data
Authors: MacKay Kimberly , Carlsson Mats , Kusalik Anthony 
Published: August 2018

1AbstractBackgroundMany computational methods have been developed that leverage the results from biological experiments (such as Hi-C) to infer the 3D organization of the genome. Formally, this is referred to as the 3D genome reconstruction problem (3D-GRP). None of the existing methods for solving the 3D-GRP have utilized a non-procedural programming approach (such as constraint programming or integer programming) despite the established advantages and successful applications of such approaches for predicting the 3D structure of other biomolecules. Our objective was to develop a set of mathematical models and corresponding non-procedural implementations for solving the 3D-GRP to realize the same advantages.ResultsWe present a set of non-procedural approaches for predicting 3D genome organization from Hi-C data (collectively referred to as SonHi-C and pronounced “sonic”). Specifically, this set is comprised of three mathematical models based on constraint programming (CP), graph matching (GM) and integer programming (IP). All of the mathematical models were implemented using non-procedural languages and tested with Hi-C data from Schizosaccharomyces pombe (fission yeast). The CP implementation could not optimally solve the problem posed by the fission yeast data after several days of execution time. The GM and IP implementations were able to predict a 3D model of the fission yeast genome in 1.088 and 294.44 seconds, respectively. These 3D models were then biologically validated through literature search which verified that the predictions were able to recapitulate key documented features of the yeast genome.ConclusionsOverall, the mathematical models and programs developed here demonstrate the power of non-procedural programming and graph theoretic techniques for quickly and accurately modelling the 3D genome from Hi-C data. Additionally, they highlight the practical differences observed when differing non-procedural approaches are utilized to solve the 3D-GRP.
A Bacterial Expression Vector Archive(BEVA) for flexible modular assembly of Golden Gate-compatible vectors
Authors: Geddes Barney A. , Mendoza-Suárez Marcela A. , Poole Philip S. 
Published: August 2018

ABSTRACTWe present a Bacterial Expression Vector Archive (BEVA) for the modular assembly of bacterial vectors compatible with both traditional and Golden Gate cloning, utilizing the Type IIS restriction enzyme Esp3I. Ideal for synthetic biology and other applications, this modular system allows a rapid, low-cost assembly of new vectors tailored to specific tasks. To demonstrate the potential of the system three example vectors were constructed and tested. Golden Gate level 1 vectors; pOGG024, with a broad-host range and high copy number was used for gene expression in laboratory-culturedRhizobium leguminosarum, and pOGG026, with a broad-host range a lower copy number and excellent stability, even in the absence of antibiotic selection. The application of pOGG026 is demonstrated in environmental samples by bacterial gene expression in nitrogen-fixing nodules on pea plants roots formed byR. leguminosarum. Finally, the level 2 cloning vector pOGG216 is a broad-host range, medium copy number, for which we demonstrate an application by constructing a dual reporter plasmid expressing green and red fluorescent proteins.IMPORTANCEModular assembly is powerful as it allows easy combining of different components from a library of parts. In designing a modular vector assembly system, the key constituent parts (and modules) are; an origin of plasmid replication, antibiotic resistance marker(s), cloning site(s), together with additional accessory modules as required. In an ideal vector, the size of each module would be minimized, and this we have addressed. We have designed such a vector assembly system by utilizing the Type IIS restriction enzyme Esp3I and have demonstrated its use for Golden Gate cloning inEscherichia coli. An important attribute of this modular vector assembly is that using the principles outlined here, new modules for specific applications, e.g. origin of replication for plasmids in other bacteria, can easily be designed. It is hoped that this vector construction system will be expanded by the scientific community over time by creation of novel modules through an open source approach.
Droplet rather than Aerosol Mediated Dispersion is the Primary Mechanism of Bacterial transmission from Contaminated Hand Washing Sink Traps
Authors: Kotay Shireen , Donlan Rodney M. , Ganim Christine , Barry Katie , Christensen Bryan E. , Mathers Amy J. 
Published: August 2018

ABSTRACTAn alarming rise in hospital outbreaks implicating hand-washing sinks has led to widespread acknowledgement that sinks are a major reservoir of antibiotic resistant pathogens in patient-care areas. An earlier study using a GFP-expressing Escherichia coli (GFP-E. coli) as a model organism demonstrated dispersal from drain biofilm in contaminated sinks. The present study further characterizes the dispersal of microorganisms from contaminated sinks. Replicate hand-washing sinks were inoculated with GFP-E. coli, and dispersion was measured using qualitative (settle plates) and quantitative (air sampling) methods. Dispersal caused by faucet water was captured with settle plates and air sampling methods when bacteria were present on the drain. In contrast, no dispersal was captured without or in between faucet events amending earlier theory that bacteria aerosolize from P-trap and disperse. Numbers of dispersed GFP-E. coli diminished substantially within 30 minutes after faucet usage, suggesting that the organisms were associated with larger droplet-sized particles that are not suspended in the air for long periods.IMPORTANCEAmong the possible environmental reservoirs in a patient care environment, sink drains are increasingly recognized as potential reservoir of multidrug resistant healthcare-associated pathogens to hospitalized patients. With increasing antimicrobial resistance limiting therapeutic options for patients, better understanding of how pathogens disseminate from sink drains is urgently needed. Once this knowledge gap has decreased, interventions can be engineered to decrease or eliminate transmission from hospital sink drains to patients. The current study further defines the mechanisms of transmission for bacteria colonizing sink drains.
Limits to anatomical accuracy of diffusion tractography using modern approaches
Authors: Schilling Kurt G , Nath Vishwesh , Hansen Colin , Parvathaneni Prasanna , Blaber Justin , Gao Yurui , Neher Peter , Aydogan Dogu Baran , Shi Yonggang , Ocampo-Pineda Mario , Schiavi Simona , Daducci Alessandro , Girard Gabriel , Barakovic Muhamed , Rafael-Patino Jonathan , Romascano David , Rensonnet Gaёtan , Pizzolato Marco , Bates Alice , Fischi Elda , Thiran Jean-Philippe , Canales-Rodríguez Erick J. , Huang Chao , Zhu Hongtu , Zhong Liming , Cabeen Ryan , Toga Arthur W , Rheault Francois , Theaud Guillaume , Houde Jean-Christophe , Sidhu Jasmeen , Chamberland Maxime , Westin Carl-Fredrik , Dyrby Tim B. , Verma Ragini , Rathi Yogesh , Irfanoglu M Okan , Thomas Cibu , Pierpaoli Carlo , Descoteaux Maxime , Anderson Adam W , Landman Bennett A 
Published: August 2018

AbstractDiffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the ISBI 2018 3D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography’s inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
Identification of candidate genes underlying nodulation-specific phenotypes in Medicago truncotula through integration of genome-wide association studies and co-expression networks
Authors: Michno Jean-Michel , Burghardt Liana T. , Liu Junqi , Jeffers Joseph R. , Tiffin Peter , Stupar Robert M. , Myers Chad L. 
Published: August 2018

ABSTRACTGenome-wide association studies (GWAS) have proven to be a valuable approach for identifying genetic intervals associated with phenotypic variation in Medicago truncatula. These intervals can vary in size, depending on the historical local recombination near each significant interval. Typically, significant intervals span numerous gene models, limiting the ability to resolve high-confidence candidate genes underlying the trait of interest. Additional genomic data, including gene co-expression networks, can be combined with the genetic mapping information to successfully identify candidate genes. Co-expression network analysis provides information about the functional relationships of each gene through its similarity of expression patterns to other well-defined clusters of genes. In this study, we integrated data from GWAS and co-expression networks to pinpoint candidate genes that may be associated with nodule-related phenotypes in Medicago truncatula. We further investigated a subset of these genes and confirmed that several had existing evidence linking them nodulation, including MEDTR2G101090 (PEN3-like), a previously validated gene associated with nodule number.
Randomized lasso associates freshwater lake-system specific bacterial taxa with heterotrophic production through flow cytometry
Authors: Rubbens Peter , Schmidt Marian L. , Props Ruben , Biddanda Bopaiah A. , Boon Nico , Waegeman Willem , Denef Vincent J. 
Published: August 2018

AbstractHigh-(HNA) and low-nucleic acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. HNA cell density often correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R2= 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels, with the highest performance at the OTU level. Selected OTUs ranged from low to high relative abundance and were mostly lake system-specific (89.5%-99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5%-33.3%) suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of systems-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships.ImportanceA major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Research is limited by the ability to readily culture most bacteria present in the environment and the difference in bacterial physiologyin situcompared to in laboratory culture. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system-specific, regularly rare members of the community, and that some could switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production.