bluuebunny/crossref_metadata_2025_split huggingface dataset:
Random forest modeling can predict infectious complications following trauma laparotomy
Authors: Gelbard Rondi B. , Hensman Hannah , Schobel Seth , Khatri Vivek , Tracy Brett M. , Dente Christopher J. , Buchman Timothy , Kirk Allan , Elster Eric 
Published: August 2019

BACKGROUND
Identifying clinical and biomarker profiles of trauma patients may facilitate the creation of models that predict postoperative complications. We sought to determine the utility of modeling for predicting severe sepsis (SS) and organ space infections (OSI) following laparotomy for abdominal trauma.

METHODS
Clinical and molecular biomarker data were collected prospectively from patients undergoing exploratory laparotomy for abdominal trauma at a Level I trauma center between 2014 and 2017. Machine learning algorithms were used to develop models predicting SS and OSI. Random forest (RF) was performed, and features were selected using backward elimination. The SS model was trained on 117 records and validated using the leave-one-out method on the remaining 15 records. The OSI model was trained on 113 records and validated on the remaining 19. Models were assessed using areas under the curve.

RESULTS
One hundred thirty-two patients were included (median age, 30 years [23–42 years], 68.9% penetrating injury, median Injury Severity Score of 18 [10–27]). Of these, 10.6% (14 of 132) developed SS and 13.6% (18 of 132) developed OSI. The final RF model resulted in five variables for SS (Penetrating Abdominal Trauma Index, serum epidermal growth factor, monocyte chemoattractant protein-1, interleukin-6, and eotaxin) and four variables for OSI (Penetrating Abdominal Trauma Index, serum epidermal growth factor, monocyte chemoattractant protein-1, and interleukin-8). The RF models predicted SS and OSI with areas under the curve of 0.798 and 0.774, respectively.

CONCLUSION
Random forests with RFE can help identify clinical and biomarker profiles predictive of SS and OSI after trauma laparotomy. Once validated, these models could be used as clinical decision support tools for earlier detection and treatment of infectious complications following injury.

LEVEL OF EVIDENCE
Prognostic, level III.
The Small RNA Repertoire of Small Extracellular Vesicles Isolated From Donor Kidney Preservation Fluid Provides a Source for Biomarker Discovery for Organ Quality and Posttransplantation Graft Function
Authors: Gremmels Hendrik , de Jong Olivier G. , Toorop Raechel J. , Michielsen Laura , van Zuilen Arjan D. , Vlassov Alexander V. , Verhaar Marianne C. , van Balkom Bas W.M. 
Published: August 2019

Background.
Delayed graft function (DGF) after kidney transplantation is negatively associated with long-term graft function and survival. Kidney function after transplantation depends on multiple factors, both donor- and recipient-associated. Prediction of posttransplantation graft function would allow timely intervention to optimize patient care and survival. Currently, graft-based predictions can be made based on histological and molecular analyses of 0-hour biopsy samples. However, such analyses are currently not implemented, as biopsy samples represent only a very small portion of the entire graft and are not routinely analyzed in all transplantation centers. Alternatives are thus required.

Methods.
We analyzed whether donor organ preservation fluid contain small extracellular vesicles (sEV) and whether the RNA content of these vesicles could be used as a source for potential biomarkers for posttransplantation kidney function.

Results.
We provide proof of principle that sEVs are present in preservation fluid, which contain RNAs associated with donor origin. Furthermore, sEV micro RNA profiles could be associated with graft function during the first 7 days posttransplantation, but no significant correlation with DGF could be established based on the current dataset.

Conclusions.
Overall, the predictive potential of sEV RNA biomarkers together with relatively easy and noninvasive sample collection and analysis methods could pave the way towards universal screening of donor kidney-associated risk for DGF, optimized patient treatment, and subsequently improved short- and long-term graft function and survival.
Medal: a patient similarity metric using medication prescribing patterns
Authors: Pineda Arturo Lopez , Pourshafeie Armin , Ioannidis Alexander , Leibold Collin McCloskey , Chan Avis , Frankovich Jennifer , Bustamante Carlos D. , Wojcik Genevieve L. 
Published: August 2019

AbstractPatient similarity offers an exciting potential to improve our understanding of treatment patterns. Medication history is a valuable source of information given the clinical considerations taken during the physician’s prescription process. However, available similarity methods do not consider timeliness of event occurrence in the longitudinal care of patients.In this observational cohort study, we propose an event alignment algorithm, Medal, which uses a dynamic programming approach for pairwise alignment of medication histories. To test our algorithm, we obtained the medication usage from a cohort of patients with pediatric acute-onset neuropsychiatric syndrome (PANS). After calculating a distance metric with Medal, we compute a hierarchical clustering and explore the most appropriate number of clusters.We identified four clusters in PANS with distinct medication usage histories, driven primarily by penicillin. We foresee that our algorithm could be used to identify clusters in other syndromes treated with multiple medication regimens.
Rationalising neurosurgical head injury referrals: The development and implementation of the Liverpool Head Injury Tomography Score (Liverpool HITS) for mild traumatic brain injury
Authors: Gillespie Conor SN , Mcleavy Christopher M , Islim Abdurrahman I , Prescott Sarah , McMahon Catherine J 
Published: August 2019

AbstractObjectivesTo develop and implement a radiological scoring system to define a ‘surgically significant’ mild Traumatic Brain Injury (TBI), stratify neurosurgical referrals and improve communication between referral centres and neurosurgical units.DesignRetrospective single centre case-control analysis of ten continuous months of mild TBI referrals.SettingA major tertiary neurosurgery centre in England, UK.ParticipantsAll neurosurgical referrals with a mild TBI (GCS 13-15) during the period of 1st January to 30th October 2017 were eligible for the study. 1248 patients were identified during the study period, with 1144 being included in the final analysis.InterventionsAll patients’ CT head results from the referring centres were scored retrospectively using the scoring system and stratified according to their mean score, and if they were accepted for transfer to the neurosurgical centre or managed locally.Main outcome measureDetermine the discriminatory and diagnostic power, sensitivity and specificity of the scoring system for predicting a ‘surgically significant’ mild TBI.ResultsMost patients referred were male (59.4%, N=681), with a mean age of 69 years (SD=21.1). Of the referrals to the neurosurgical centre, 17% (n=195) were accepted for transfer and 83% (n=946) were not accepted. The scoring system was 99% sensitive and 51.9% specific for determining a surgically significant TBI. Diagnostic power of the model was fair with an area under the curve of 0.79 (95% CI 0.76 to 0.82). The score identified 495 (52.2%) patients in ten months of referrals that could have been successfully managed locally without neurosurgical referral if the scoring system was correctly used at the time of injury.ConclusionThe Liverpool Head Injury Tomography Score (HITS) score is a CT based scoring system that can be used to define a surgically significant mild TBI. The scoring system can be easily used by multiple healthcare professionals, has high sensitivity, will reduce neurosurgical referrals, and could be incorporated into local, regional and national head injury guidance.
The Implementation, Diagnostic Yield and Clinical Outcome of Genetic Testing on an Inpatient Child and Adolescent Psychiatry Service
Authors: Besterman Aaron D. , Sadik Joshua , Enenbach Michael J. , Quintero-Rivera Fabiola ,   UCLA Clinical Genomics Center, DeAntonio Mark , Martinez-Agosto Julian A. 
Published: August 2019

AbstractObjectiveDiagnostic genetic testing is recommended for children with neurodevelopmental disorders (NDDs). However, many children with NDDs do not receive genetic testing. One approach to improve access to genetic services for these patients is to offer testing on the inpatient child and adolescent psychiatry (CAP) service.MethodsWe implemented systematic genetic testing on an inpatient CAP service by providing medical genetics education to CAP fellows. We compared the genetic testing rates pre- and post-education. We compared the diagnostic yield to previously published studies and the demographics of our cohort to inpatients who received genetic testing on other clinical services. We assessed rates of outpatient genetics follow-up post-discharge.ResultsThe genetic testing rate on the inpatient CAP service was 1.6% (2/125) before the educational intervention and 10.7% (21/197) afterwards (OR = 0.13, 95% CI = 0.015- 0.58, p = 0.0015). Diagnostic yield for patients on the inpatient service was 4.3% (1/23), lower than previously reported. However, 34.8% (8/23) of patients had variants of unknown significance (VUSs). 39.1% (9/23) of children who received genetic testing while inpatients were underrepresented minorities, compared to 7.7% (1/13) of patients who received genetic testing on other clinical services (OR = 7.35, CI = 0.81-365.00, p = 0.057). 43.5% of patients were lost to outpatient genetics follow-up.ConclusionMedical genetics education for fellows on an inpatient CAP service can improve genetic testing rates. Genetic testing for inpatients may primarily identify VUSs instead of well-known NDD risk variants. Genetic testing on the inpatient CAP service may improve access to genetic services for underrepresented minorities, but assuring outpatient follow-up can be challenging.
A gene-diet interaction-based score predicts response to dietary fat in the Women’s Health Initiative
Authors: Westerman Kenneth , Liu Qing , Liu Simin , Parnell Laurence D. , Sebastiani Paola , Jacques Paul , DeMeo Dawn L. , Ordovás José M. 
Published: August 2019

AbstractWhile diet response prediction for cardiometabolic risk factors (CRFs) has been demonstrated using single SNPs and main-effect genetic risk scores, little investigation has gone into the development of genome-wide diet response scores. We sought to leverage the multi-study setup of the Women’s Health Initiative cohort to generate and test genetic scores for the response of six CRFs (body mass index, systolic blood pressure, LDL-cholesterol, HDL-cholesterol, triglycerides, and fasting glucose) to dietary fat. A genome-wide interaction study was undertaken for each CRF in women (n ∼ 10000) not participating in the Dietary Modification (DM) trial, which focused on the reduction of dietary fat. Genetic scores based on these analyses were developed using a pruning-and-thresholding approach and tested for the prediction of one-year CRF changes as well as long-term chronic disease development in DM trial participants (n ∼ 5000). One of these genetic scores, for LDL-cholesterol (LDL-C), predicted changes in the associated CRF. This 1760-variant score explained 3.4% of the variance in one-year LDL-C changes in the intervention arm, but was unassociated with changes in the control arm. In contrast, a main-effect genetic risk score for LDL-C was not useful for predicting dietary fat response. Further investigation of this score with respect to downstream disease outcomes revealed suggestive differential associations across DM trial arms, especially with respect to coronary heart disease and stroke subtypes. These results lay the foundation for the combination of many genome-wide gene-diet interactions for diet response prediction while highlighting the need for further research and larger samples in order to achieve robust biomarkers for use in personalized nutrition.
Comparison of risk factors for coronary heart disease morbidity versus mortality
Authors: Batty G. David , Kivimäki Mika , Bell Steven 
Published: August 2019

AbstractOwing to the often prohibitively high costs of medical examinations, or an absence of infrastructure for linkage of study members to morbidity registries, much aetiological research in the field of cardiovascular research relies on death records. Because they are regarded as being more distal to risk factor assessment than morbidity endpoints, mortality data are generally maligned in this context for seemingly providing less clear insights into aetiology. The relative utility of mortality versus morbidity registries is, however, untested. In a pooling of data from three large cohort studies whose participants had been linked to both death and morbidity registries for coronary heart disease, we related a range of established and emerging risk factors to these two methods of ascertainment. A mean duration of study member surveillance of 10.1 years (mortality) and 9.9 years (morbidity) for a maximum of 20,956 study members (11,868 women) in the analytical sample yielded 289 deaths from coronary heart disease and 770 hospitalisations for this condition. The direction of the age- and sex-adjusted association was the same for 21 of the 24 risk factor– morbidity/mortality combinations. The only marked discordance in effect estimates, such that different conclusions about the association could be drawn, was for social support, total cholesterol, and fruit/vegetable consumption whereby null effects were evident for selected outcomes. In conclusion, variation in disease definition typically did not have an impact on the direction of the association of an array of risk factors for coronary heart disease.
A Transient Intermediate Populated in Prion Folding Leads to Domain Swapping
Authors: Mondal Balaka , Reddy Govardhan 
Published: August 2019

AbstractAggregation of misfolded prion proteins causes fatal neurodegenerative disorders in both humans and animals. There is an extensive effort to identify the elusive aggregation-prone conformations (N*) of prions, which are early stage precursors to aggregation. Ve studied temperature and force induced unfolding of the structured C-terminal domain of mouse (moPrP) and human prion proteins (hPrP) using molecular dynamics simulations and coarse-grained protein models. Ve find that these proteins sparsely populate intermediate states bearing the features of N* and readily undergo domain-swapped dimerization by swapping the short β-strands present at the beginning of the C-terminal domain. Structure of the N* state is similar for both moPrP and hPrP, indicating a common pathogenic precursor across diferent species. Interestingly, disease-resistant hPrP (G127V) showed a drastic reduction in the population of N* state further hinting a pathogenic connection to these partially denatured conformations. This study proposes a plausible runaway domain swapping mechanism to describe the onset of prion aggregation.
Evolution of Specialization in Dynamic Fluids
Authors: Uppal Gurdip , Vural Dervis Can 
Published: August 2019

AbstractPreviously we found mechanical factors involving diffusion and fluid shear promote evolution of social behavior in microbial populationsUppal and Vural (2018). Here, we extend this model to study the evolution of specialization using realistic physical simulations of bacteria that secrete two public goods in a dynamic fluid. Through this first principles approach, we find physical factors such as diffusion, flow patterns, and decay rates are as influential as fitness economics in governing the evolution of community structure, to the extent that when mechanical factors are taken into account, (1) Generalist communities can resist becoming specialists, despite the invasion fitness of specialization (2) Generalist and specialists can both resist cheaters despite the invasion fitness of free-riding. (3) Multiple community structures can coexist despite the opposing force of competitive exclusion. Our results emphasize the role of spatial assortment and physical forces on niche partitioning and the evolution of diverse community structures.
deSAMBA: fast and accurate classification of metagenomics long reads with sparse approximate matches
Authors: Li Gaoyang , Liu Bo , Wang Yadong 
Published: August 2019

AbstractSummaryLong read sequencing technologies are promising to metagenomics studies. However, there is still lack of read classification tools to fast and accurately identify the taxonomies of noisy long reads, which is a bottleneck to the use of long read sequencing. Herein, we propose deSAMBA, a tailored long read classification approach that uses a novel sparse approximate match block (SAMB)-based pseudo alignment algorithm. Benchmarks on real datasets demonstrate that deSAMBA enables to simultaneously achieve fast speed and good classification yields, which outperforms state-of-the-art tools and has many potentials to cutting-edge metagenomics studies.Availability and Implementationhttps://github.com/hitbc/deSAMBA.Supplementary information:
Nucleated transcriptional condensates amplify gene expression
Authors: Wei Ming-Tzo , Chang Yi-Che , Shimobayashi Shunsuke F. , Shin Yongdae , Brangwynne Clifford P. 
Published: August 2019

AbstractLiquid-liquid phase separation is thought to underly gene transcription, through the condensation of the large-scale nucleolus, or in smaller assemblies known as transcriptional hubs or condensates. However, phase separation has not yet been directly linked with transcriptional output, and our biophysical understanding of transcription dynamics is poor. Here, we utilize an optogenetic approach to control condensation of key FET-family transcriptional regulators, particularly TAF15. We show that amino acid sequence-dependent phase separation of TAF15 is enhanced significantly due to strong nuclear interactions with the C-terminal domain (CTD) of RNA Pol II. Nascent CTD clusters at primed genomic loci lower the energetic barrier for nucleation of TAF15 condensates, which in turn further recruit RNA Pol II to drive transcriptional output. These results suggest a model in which positive feedback between key transcriptional components drives intermittent dynamics of localized phase separation, to amplify gene expression.
Entry by multiple picornaviruses is dependent on a pathway that includes TNK2, WASL and NCK1
Authors: Jiang Hongbing , Leung Christian , Tahan Stephen , Wang David 
Published: August 2019

AbstractComprehensive knowledge of the host factors required for picornavirus infection would facilitate antiviral development. Here we demonstrate roles for three human genes, TNK2, WASL, and NCK1, in infection by multiple picornaviruses. CRISPR deletion of TNK2, WASL or NCK1 reduced encephalomyocarditis virus (EMCV), coxsackievirus B3 (CVB3), poliovirus and enterovirus D68 infection, and chemical inhibitors of TNK2 and WASL decreased EMCV infection. Reduced EMCV lethality was observed in mice lacking TNK2. TNK2, WASL and NCK1 were important in early stages of the viral lifecycle, and genetic epistasis analysis demonstrated that the three genes function in a common pathway. Mechanistically, reduced internalization of EMCV was observed in TNK2 deficient cells demonstrating that TNK2 functions in EMCV entry. Domain analysis of WASL demonstrated that its actin nucleation activity was necessary to facilitate viral infection. Together, these data support a model wherein TNK2, WASL, and NCK1 comprise a pathway critical for multiple picornaviruses.
Testosterone-mediated behavior shapes the emergent properties of social networks
Authors: Dakin Roslyn , Moore Ignacio T. , Horton Brent M. , Vernasco Ben J. , Ryder T. Brandt 
Published: August 2019

AbstractSocial networks can vary in their organization and dynamics, with implications for ecological and evolutionary processes. Understanding the mechanisms that drive social network dynamics requires integrating individual-level biology with comparisons across multiple social networks.Testosterone is a key mediator of vertebrate social behavior and can influence how individuals interact with social partners. Although the effects of testosterone on individual behavior are well established, no study has examined whether hormone-mediated behavior can scale up to shape the emergent properties of social networks.We investigated the relationship between testosterone and social network dynamics in the wire-tailed manakin, a lekking bird species in which male-male social interactions form complex social networks. We used an automated proximity system to longitudinally monitor several leks and we quantified the social network structure at each lek. Our analysis examines three emergent properties of the networks: social specialization (the extent to which a network is partitioned into exclusive partnerships), network stability (the overall persistence of partnerships through time), and behavioral assortment (the tendency for like to associate with like). All three properties are expected to promote the evolution of cooperation. As the predictor, we analyzed the collective testosterone of males within each social network.Social networks that were composed of high-testosterone dominant males were less specialized, less stable, and had more negative behavioral assortment, after accounting for other factors. These results support our main hypothesis that individual-level hormone physiology can predict group-level network dynamics. We also observed that larger leks with more interacting individuals had more positive behavioral assortment, suggesting that small groups may constrain the processes of homophily and behavior-matching.Overall, these results provide evidence that hormone-mediated behavior can shape the broader architecture of social groups. Groups with high average testosterone exhibit social network properties that are predicted to impede the evolution of cooperation.
Organization of fast and slow chromatin revealed by single-nucleosome dynamics
Authors: Ashwin S. S. , Nozaki Tadasu , Maeshima Kazuhiro , Sasai Masaki 
Published: August 2019

ABSTRACTUnderstanding chromatin organization and dynamics is important since they crucially affect DNA functions. In this study, we investigate chromatin dynamics by statistically analyzing single-nucleosome movement in living human cells. Bi-modal nature of the mean squared displacement distribution of nucleosomes allows for a natural categorization of the nucleosomes as fast and slow. Analyses of the nucleosome-nucleosome correlation functions within these categories along with the density of vibrational modes show that the nucleosomes form dynamically correlated fluid regions, i.e., dynamic domains of fast and slow nucleosomes. Perturbed nucleosome dynamics by global histone acetylation or cohesin inactivation indicate that nucleosome-nucleosome interactions along with tethering of chromatin chains organize nucleosomes into fast and slow dynamic domains. A simple polymer model is introduced, which shows the consistency of this dynamic domain picture. Statistical analyses of single-nucleosome movement provide rich information on how chromatin is dynamically organized in a fluid manner in living cells.Significan StatementRecent live cell imaging has revealed that chromatin is not a static, rigid structure but is dynamically fluctuating in cells. We study chromatin motion by using single-nucleosome tracking data in living human cells. The distribution of single-nucleosome movement shows a distinct two-peak feature: fast and slow fractions. Nucleosome movement is investigated using statistical analyses and a polymer model to elucidate the mechanism of dynamic organization of fast and slow chromatin.