bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalence of a Histologic Change of Ocular Adnexal Lymphoma in Patients With a History of Lymphoma
Authors: Sagiv Oded , Thakar Sudip D. , Manning John T. , Kandl Thomas J. , Fayad Luis E. , Fowler Nathan , Hagemeister Fredrick B. , Fanale Michelle A. , Pinnix Chelsea C. , Samaniego Felipe , Esmaeli Bita
Published: August 2018

Purpose:

The authors examined the prevalence of a histologic change of ocular adnexal lymphoma (OAL) grade in patients with a history of lymphoma in nonocular sites.

Methods:

In this retrospective study, the authors reviewed the clinical and pathological data of 209 patients with OAL treated by the senior author during 2000 to 2017.

Results:

Of 209 patients with OAL, 65 (31%) had a history of lymphoma. In 54 of the 65 patients (83%), the original lymphoma and OAL were of the same histologic type. In 8 of the 65 patients (12.3%), the OAL was more indolent than the original lymphoma: 6 patients with a history of diffuse large B-cell lymphoma, one of mantle cell lymphoma, and one of grade 3 follicular lymphoma had biopsy-proven extranodal marginal-zone lymphoma in the orbital area. Two additional patients (3%) with a history of chronic lymphocytic leukemia developed OAL: diffuse large B-cell lymphoma in one patient and extranodal marginal-zone lymphoma in the other. One patient (1.5%) with a history of a low-grade follicular lymphoma relapsed as a different low-grade histology of extranodal marginal-zone lymphoma. Lower-grade OAL than the original lymphoma was more common than higher-grade OAL than the original lymphoma (p = 0.048).

Conclusions:

In this cohort of 209 patients with OAL, the authors found that nearly one third had a history of lymphoma, 17% of whom had a different histologic type of lymphoma in the orbit, more commonly a more indolent type. This underscores the importance of biopsy of OAL even in patients with a known history of lymphoma to determine the histologic subtype of orbital lymphoma and to help guide appropriate treatment.
Predominant Contribution of Superior Rectus–Levator Complex Enlargement to Optic Neuropathy and Inferior Visual Field Defects in Thyroid Eye Disease
Authors: Oropesa Susel , Dunbar Kristen E. , Godfrey Kyle J. , Callahan Alison B. , Campbell Ashley A. , Kazim Michael
Published: August 2018

Purpose:

To compare extraocular muscle volumes in thyroid eye disease patients with and without compressive optic neuropathy.

Methods:

A retrospective review of 44 orbital CT scans (28 orbits without compressive disease and 16 orbits with compressive optic neuropathy) was conducted. The extraocular muscle volumes, summated soft tissue volumes, and optic nerve volumes were calculated at a section in the posterior 1/3 of the orbit. The visual fields of the orbits with compressive optic neuropathy were analyzed.

Results:

The mean combined extraocular muscle/summated soft tissue volume ratio and the mean superior rectus–levator complex/summated soft tissue volume ratio were greater in those with compressive optic neuropathy than in those without compressive optic neuropathy (p = 0.02, 0.008, respectively). The ratio of the mean inferior, medial, or lateral rectus/summated soft tissue volume did not differ significantly between patients with or without compressive optic neuropathy (p values of 0.315, 0.615, and 0.254, respectively). Visual field analysis of the compressive optic neuropathy group demonstrated that 58% of the orbits with visual field defects had inferior field defects.

Conclusions:

When measured at a section near the orbital apex, the mean combined muscle/summated soft tissue volume ratio and the mean superior rectus–levator complex/summated soft tissue volume ratio are greater in those with compressive disease than those without. This suggests that the specific enlargement of the superior rectus–levator complex makes a significant contribution to thyroid eye disease-compressive optic neuropathy and may explain the inferior visual field deficits classically found in this group of patients.
Individual Differences in Working Memory Capacity Predict Topiramate-Related Cognitive Deficits
Authors: Barkley Christopher M. , Hu Zhenhong , Fieberg Ann M. , Eberly Lynn E. , Birnbaum Angela K. , Leppik Ilo E. , Marino Susan E.
Published: August 2018

Purpose/Background

Topiramate (TPM) and lorazepam (LZP) are two examples of frequently prescribed medications that are associated with a high incidence of cognitive impairment; however, the factors that underlie interindividual differences in side effect profiles have not been fully characterized. Our objective was to determine whether working memory capacity (WMC), the amount of information that can be stored and manipulated in memory over short time intervals, is one such factor.

Methods/Procedures

Twenty-nine healthy volunteers completed a double-blind, randomized, placebo-controlled crossover study during which they received placebo (PBO), TPM, and LZP in random order. Four hours after drug administration, a blood draw was taken to establish drug concentrations, and subjects performed a verbal working memory task while the accuracy and reaction time of their responses were recorded. Working memory capacity was calculated based on accuracy rates during the PBO session, and the role of WMC in moderating the severity of drug-related cognitive impairment was assessed by examining drug-related performance changes from PBO as a function of WMC.

Findings/Results

Both TPM and LZP had a negative impact on task performance, although only TPM-related deficits were modulated by WMC; high WMC was associated with more severe impairments and heightened sensitivity to increasing TPM concentrations.

Implications/Conclusions

We have identified a potential clinical risk factor, high WMC, which is associated with drug-related adverse cognitive events. These data provide objective evidence in support of clinical observations that high-functioning patients are more likely to experience severe cognitive impairments.
Outcomes of Primary Radiosurgery Treatment of Glomus Jugulare Tumors: Systematic Review With Meta-analysis
Authors: Shapiro Scott , Kellermeyer Brian , Ramadan Jad , Jones Garret , Wiseman Brian , Cassis Adam
Published: August 2018

Objective:

1) Determine tumor control, symptomatic control, and complication rates of primary radiosurgery (PRS) for treatment of glomus jugulare tumors (GJTs) via systematic review and meta-analysis. 2) Identify risk factors for these outcomes.

Data Sources:

1) Search of English articles in PubMed, Web of Science, Cochrane, and EBSCOhost databases from January 1950 to August 2017.

Study Selection:

Inclusion criteria: 1) treated GJT patients who had no previous treatment with radiosurgery, 2) follow-up with magnetic resonance imaging for at least 12 months, 3) reported pre and posttreatment symptoms, tumor control, or complications for individual PRS patients or for PRS patients as a cohort.

Data Extraction:

The following were extracted: number of patients, level of evidence, mean age, mean pretreatment tumor volume, tumor control rate, criteria for change in tumor size, symptom control rate, and complications. At the individual patient level the following were extracted: age, number of treatment fractions, total radiation dose to tumor margin, pretreatment tumor volume, Fisch or Glasscock–Jackson stage, pre- and posttreatment symptoms, tumor control, symptom control/improvement, length of follow-up, and complications.

Data Synthesis:

Fifteen studies encompassing 91 patients met criteria. Tumor control was achieved in 92% of patients, symptom control in 93%, and complications occurred in 8%. There was one major complication. Smaller tumor volume predicted improvement in symptoms with PRS.

Conclusions:

In the short term, PRS is safe and effective at controlling growth and clinical symptoms for patients with GJTs, though there exists significant selection bias, inconsistent reporting, and clinical heterogeneity among existing studies.
Chinese Version of Pediatric Patient-Reported Outcomes Measurement Information System Short Form Measures
Authors: Liu Yanyan , Yuan Changrong , Wang Jichuan , Shen Nanping , Shen Min , Hinds Pamela S.
Published: August 2018

Background

The Pediatric Patient-Reported Outcomes Measurement Information System (PROMIS) was developed to measure symptoms and functions of children with a variety of chronic diseases. As the Chinese version of pediatric PROMIS (C-Ped-PROMIS) measures was developed, the measurement properties of C-Ped-PROMIS have not been demonstrated.

Objective

The aim of this study was to examine the reliability, validity, and factorial structure of the C-Ped-PROMIS measures in children with cancer in China.

Methods

A total of 272 children and adolescents were recruited from 3 hospitals in China. The 8 C-Ped-PROMIS measures and Pediatric Quality of Life Inventory General Core Module and Cancer Module were administered in a cross-sectional design. Known-group validity, concurrent validity, and item and scale reliability of these 8 measures were examined by using SPSS 21.0, and factorial structures were tested by using confirmatory factor analysis with Mplus 7.1.

Results

All 8 C-Ped-PROMIS measures showed good known-group validity as hypothesized (P < .05) and good concurrent validity measured by significant correlations with the Pediatric Quality of Life Inventory General Core Module and Cancer Module; the correlation coefficients ranged from r = 0.519 to r = 0.655, except for peer relationship with r = 0.255 and r = 0.136, respectively. The Cronbach's α of C-Ped-PROMIS ranged from .758 to .910, and model-estimated scale reliabilities ranged from 0.740 to 0.905. The confirmatory factor analysis models of each measure fit data very well.

Conclusions

All 8 C-Ped-PROMIS measures have a valid factorial structure as theoretically defined with good reliability and validity.

Implications for Practice

The C-Ped-PROMIS can be readily used to measure symptoms and functions of children and adolescents with cancer in China.
Smoking and Risk for Alzheimer Disease
Authors: Niu Huikun , Qu Yangming , Li Zhijun , Wang Rui , Li Lu , Li Meiqi , Lv Xin , Gao Chunshi , Song Yuanyuan , Li Bo
Published: August 2018

This study was performed to identify the association between smoking and Alzheimer's disease (AD). To perform this meta-analysis based on case-control and cohort studies, PubMed, Google Scholar, and the CNKI electronic databases were searched through April 30, 2017. Our meta-analysis included 27 studies, including 16 that reported odds ratios (ORs) and 11 that reported hazard ratios (HRs) or ratio risks. No significant association was found between smoking and AD among the studies that reported ORs (1.020, 95% confidence interval [CI] = 0.812–1.281, I
2 = 67.9%, random model, p < 0.001). A subgroup analysis revealed no significant difference between different smoking statuses. The pooled HRs revealed a significant association between smoking and AD (HR = 1.520, 95% CI = 1.194–1.934, I
2 = 83.6%, random model, p < 0.001). Cumulative meta-analysis of the HRs revealed that the effect of smoking on AD tended to be stable over time. Smoking may confer an increased risk of AD, and this effect has tended to be stable over time.
Full Neurological Recovery From Escherichia coli Meningitis Associated With Near-Death Experience
Authors: Khanna Surbhi , Moore Lauren E. , Greyson Bruce
Published: August 2018

A 54-year-old otherwise healthy man presented with altered mental status. On admission, the patient was confused and agitated, with a Glasgow Coma Scale (GCS) score of 11, suggesting moderate brain injury. He was sedated, placed on a ventilator, and started on tobramycin and ceftazidime for presumed bacterial meningitis, but switched to ceftriaxone once cultures returned as Escherichia coli. During his 8-day hospitalization, his mental status fluctuated from confused to nonresponsive, with GCS scores between 6 and 11. Although E. coli meningitis has a high rate of neurological complications and death, this patient recovered completely without any deficits, and recalled an elaborate near-death experience that occurred during his coma. This case highlights the importance of studying near-death experiences occurring during compromised brain function to further our understanding of the brain and consciousness.
Overexpression of Gilz Protects Mice Against Lethal Septic Peritonitis
Authors: Ballegeer Marlies , Vandewalle Jolien , Eggermont Melanie , Van Isterdael Gert , Dejager Lien , De Bus Liesbet , Decruyenaere Johan , Vandenbroucke Roosmarijn E. , Libert Claude
Published: August 2018

Sepsis in humans and experimental animals is characterized by an acute inflammatory response. glucocorticoids (GCs) are widely used for the treatment of many inflammatory disorders, yet their effectiveness in sepsis is debatable. One of the major anti-inflammatory proteins induced by GCs is glucocorticoid-induced leucine zipper (GILZ, coded by the TSC22D3 gene). We found that TSC22D3 mRNA expression is downregulated in white blood cells of human sepsis patients. Interestingly, transgenic GILZ-overexpressing mice (GILZ-tg) showed better survival rates in the cecal ligation and puncture (CLP) model of mouse sepsis. To our surprise, GILZ had only mild anti-inflammatory effects in this model, as the systemic proinflammatory response was not significantly reduced in GILZ-tg mice compared with control mice. During CLP, we observed reduced bacterial counts in blood of GILZ-tg mice compared with control mice. We found increased expression of Tsc22d3 mRNA specifically in peritoneal exudate cells in the CLP model, as well as increased capacity for bacterial phagocytosis of CD45+ GILZ-tg cells compared with CD45+ GILZ-wt cells. Hence, we believe that the protective effects of GILZ in the CLP model can be linked to a more efficient phagocytosis.
Expression based biomarkers and models to classify early and late stage samples of Papillary Thyroid Carcinoma
Authors: Bhalla Sherry , Kaur Harpreet , Kaur Rishemjit , Sharma Suresh , Raghava Gajendra P. S.
Published: August 2018

In this study, we describe the key transcripts and machine learning models developed for classifying the early and late stage samples of Papillary Thyroid Cancer (PTC), using transcripts’ expression data from The Cancer Genome Atlas (TCGA). First, we rank all the transcripts on the basis of area under receiver operating characteristic curve, (AUROC) value to discriminate the early and late stage, based on an expression threshold. With the expression of a single transcript DCN, we can classify the stage samples with a 68.5% accuracy and AUROC of 0.66. Then we implemented various combination of multiple gene panels, selected using various gold standard feature selection techniques. The model based on the expression of 36 multiple transcripts (protein coding and non-coding) selected using SVC-L1 achieves the maximum accuracy of 74.51% with AUROC of 0.75 on independent validation dataset with balanced sensitivity and specificity. Further, these signatures also performed well on external microarray data obtained from GEO, predicting nearly 70% (12 samples out of 17 samples) early stage samples correctly. Further, multiclass model, classifying the normal, early and late stage samples achieves the accuracy of 75.43% with AUROC of 0.80 on independent validation dataset. With correlation analysis, we found that transcripts with maximum change in correlation of their expression in both the stages are significantly enriched in neuroactive ligand receptor interaction pathway. We also propose a panel of five protein coding transcripts, which on the basis of their expression, can segregate cancer and normal samples with 97.32% accuracy and AUROC of 0.99 on independent validation dataset. All the models and dataset used in this study are available from the web server CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).
New Statistical Methods for Constructing Robust Differential Correlation Networks
Authors: Yu Danyang , Zhang Zeyu , Glass Kimberly , Su Jessica , DeMeo Dawn L. , Tantisira Kelan , Weiss Scott T. , Qiu Weiliang
Published: August 2018

The interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fisher’s Z-transformation test is usually used. However, the Fisher’s Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fisher’s Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.
Therapeutic potential of TAF1 bromodomains for cancer treatment
Authors: Garcia-Carpizo Veronica , Ruiz-Llorente Sergio , Sarmentero Jacinto , Barrero Maria J.
Published: August 2018

The discovery of the antiproliferative effects of BRD4 bromodomain inhibitors prompted us to investigate additional bromodomains that might be involved in supporting cellular proliferation. TAF1 is a general transcription factor with two bromodomains that is likely to play important roles in cell viability by supporting transcription. Our work shows that knock down of TAF1 caused antiproliferative effects in several cancer cell lines. Using CRISPR-Cas9 editing techniques we demonstrate that the bromodomains of TAF1 are essential to maintain proliferation of K562 and H322 cells. BAY-299, the best TAF1 bromodomain inhibitor developed so far, also showed antiproliferative effects. BAY-299 caused discrete transcriptional changes that were likely on target but did not correlate with strong effects in cell cycle distribution suggesting that these effects might be, at least in part, mediated by another target. Compared to the TAF1 knock down, BAY-299 specifically downregulated the expression of genes with high levels of TAF1 and histone acetylation at their promoters, suggesting that targeting the bromodomains has distinct transcriptional effects than targeting the whole protein. This might help to provide a therapeutic window to target specifically cancer cells with high levels of histone acetylation at proliferation-related genes.
Low-cost and clinically applicable copy number profiling using repeat DNA
Authors: Abujudeh S , Zeki SS , van Lanschot MCV , Pusung M , Weaver JMJ , Li X , Noorani A , Metz AJ , Bornschein J , Bower L , Miremadi A , Fitzgerald RC , Morrissey ER , Lynch AG ,   Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS)Consortium, Fitzgerald Rebecca C. , Noorani Ayesha , Edwards Paul A.W. , Grehan Nicola , Nutzinger Barbara , Hughes Caitriona , Fidziukiewicz Elwira , Bornschein Jan , MacRae Shona , Crawte Jason , Northrop Alex , Contino Gianmarco , Li Xiaodun , la Rue Rachel de , O’Donovan Maria , Miremadi Ahmad , Malhotra Shalini , Tripathi Monika , Tavaré Simon , Lynch Andy G. , Eldridge Matthew , Secrier Maria , Bower Lawrence , Devonshire Ginny , Perner Juliane , Jammula Sriganesh , Davies Jim , Crichton Charles , Carroll Nick , Safranek Peter , Hindmarsh Andrew , Sujendran Vijayendran , Hayes Stephen J. , Ang Yeng , Preston Shaun R. , Oakes Sarah , Bagwan Izhar , Save Vicki , Skipworth Richard J.E. , Hupp Ted R. , O’Neill J. Robert , Tucker Olga , Beggs Andrew , Taniere Philippe , Puig Sonia , Underwood Timothy J. , Noble Fergus , Owsley Jack , Barr Hugh , Shepherd Neil , Old Oliver , Lagergren Jesper , Gossage James , Davies Andrew , Chang Fuju , Zylstra Janine , Mahadeva Ula , Goh Vicky , Ciccarelli Francesca D. , Sanders Grant , Berrisford Richard , Harden Catherine , Lewis Mike , Cheong Ed , Kumar Bhaskar , Parsons Simon L , Soomro Irshad , Kaye Philip , Saunders John , Lovat Laurence , Haidry Rehan , Igali Laszlo , Scott Michael , Sothi Sharmila , Suortamo Sari , Lishman Suzy , Hanna George B. , Peters Christopher J. , Grabowska Anna , Turkington Richard
Published: August 2018

Large-scale cancer genome studies suggest that tumors are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Due to the low-cost, the clinical use of genomics assays is biased towards targeted gene panels, which identify SNVs. There is a need for a comparably low-cost and simple assay for high-resolution SCNA profiling. Here we present our method, conliga, which infers SCNA profiles from a low-cost and simple assay.
rawMSA: End-to-end Deep Learning Makes Protein Sequence Profiles and Feature Extraction obsolete
Authors: Mirabello Claudio , Wallner Björn
Published: August 2018

In the last few decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and about their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed in the hope of taking advantage of these new architectures. On the other hand, most methods are still based on heavy pre-processing of the input data, as well as the extraction and integration of multiple hand-picked, manually designed features. Since Multiple Sequence Alignments (MSA) are almost always the main source of information in de novo prediction methods, it should be possible to develop Deep Networks to automatically refine the data and extract useful features from it. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering sequence profiles and other pre-calculated features obsolete. We developed rawMSA in three different flavors to predict secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and benchmarked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on a par with the top ranked CASP12 methods in the inter-residue contact map prediction category. We believe that rawMSA represents a promising, more powerful approach to protein structure prediction that could replace older methods based on protein profiles in the coming years.

Availability

datasets, dataset generation code, evaluation code and models are available at: https://bitbucket.org/clami66/rawmsa
Biomarker-based assessment of the muscle maintenance and energy status of anurans from an extremely seasonal semi-arid environment, the Brazillian Caatinga
Authors: Madelaire Carla Bonetti , Gomes Fernando Ribeiro , Sokolova Inna
Published: August 2018

Strongly seasonal environments pose challenges for performance and survival of animals, especially when resource abundance seasonally fluctuates. We investigated the seasonal variation of expression of key metabolic biomarkers in the muscles of three species of anurans from the drastically seasonal Brazilian semi-arid area, Caatinga. The three studied anuran species (Rhinella jimi, R. granulosaandPleurodema diplolister) differ in their seasonal activity patterns. We examined the expression of proteins regulating energy turnover (AMP-activated protein kinase [AMPK] and protein kinase B [AKT]), protein synthesis and homeostasis (total and phosphorylated eukaryotic initiation factor 2α [eIF2α and p-eIF2α] and chaperone proteins [HSP 60, 70, and 90]) in muscles related to reproduction and locomotion. Cytochrome c oxidase (COX) activity was also assessed as an index of the muscle aerobic capacity. Our results point to the importance of metabolic regulators mediating the muscular function during the drastic seasonal variation. The toads that remain active during the drought appear to maintain muscles through more energy extensive pathways including elevated protein synthesis, while the aestivating species employs energy conservation strategy suppressing protein synthesis, decreasing chaperone expression and increasing expression of AMPK. All three studied species activate cell survival pathways during the drought likely to prevent muscle atrophy, and maintain the muscle capacity throughout the year, despite the resource limitation. These strategies are important considering the unpredictability of the reproductive event and high demand on muscular activity during the reproductive season in these amphibians.

Summary statement

We studied seasonal variation of key metabolic regulators in the muscles of anurans that experience drastic variation in environmental conditions and differ in the seasonal activity patterns.
Perivascular space fluid contributes to diffusion tensor imaging changes in white matter
Authors: Sepehrband Farshid , Cabeen Ryan P , Choupan Jeiran , Barisano Giuseppe , Law Meng , Toga Arthur W ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: August 2018

Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.

Highlights

Perivascular space (PVS) fluid significantly contributes to diffusion tensor imaging metrics

Increased PVS fluid results in increased mean diffusivity and decreased fractional anisotropy

PVS contribution to diffusion signal is overlooked and demands further investigation
A 7-member SNP Assay on the iPlex MassARRAY Platform Provides a Rapid and Affordable Alternative to Typing Major AfricanStaphylococcus aureusTypes
Authors: Nyasinga Justin , Kyany’a Cecilia , Okoth Raphael , Oundo Valerie , Matano Daniel , Wacira Simon , Sang Willie , Musembi Susan , Musila Lillian
Published: August 2018

Background

Data on the clonal distribution ofStaphylococcus aureusin Africa is scanty, partly due to high costs and long turnaround times imposed by conventional genotyping methods such asspaand multilocus sequence typing (MLST) warranting the need for alternative typing approaches. This study applied and evaluated the accuracy, cost and time of using iPlex massARRAY genotyping method on Kenyan staphylococcal isolates.

Methods

Fifty four clinicalS. aureusisolates from three counties were characterized using iPlex massARRAY,spaand MLST typing methods. Ten Single Nucleotide Polymorphisms (SNPs) from theS. aureusMLST database were assessed by iPlex massARRAY.

Results

The iPlex massARRAY assay grouped the isolates into 14 SNP genotypes with 9/10 SNPs interrogated showing high detection rates (average 89%). spaand MLST typing revealed 22spatypes and 21 STs that displayed unique regional distribution.spatype t355 (ST152) was the dominant type and t2029 and t037 (ST 241) were observed among MRSA strains. MassARRAY showed 83% and 82% accuracy againstspaand MLST typing respectively in isolate classification. Moreover, massARRAY identified all MRSA strains and a novelspatype. MassARRAY had reduced turnaround time (<12 hrs) compared tospa(3 days) and MLST (20 days) typing. The iPlex massARRAY cost approximately 18 USD compared tospa(30 USD) and MLST (126 USD) typing based on consumable costs/isolate.

Conclusion

Upon validation with a larger collection of isolates, iPlex massARRAY could provide a faster, more affordable and fairly accurate method of resolving AfricanS.aureusisolates especially in large surveillance studies.
The Evolution of Phenotypic Plasticity when Environments Fluctuate in Time and Space
Authors: King Jessica G. , Hadfield Jarrod D.
Published: August 2018

Most studies have explored the evolution of plasticity when the environment, and there-fore the optimal trait, varies in time or space. When the environment varies in time and space we show that genetic adaptation to temporal fluctuations depends on the between-generation autocorrelation in the environment in exactly the same way that genetic adaptation to spatial fluctuations depends on the probability of philopatry. This is because both measure the correlation in parent-offspring environments and therefore the effectiveness of a genetic response to selection. If the capacity to genetically respond to selection is stronger in one dimension (e.g. space) then plasticity mainly evolves in response to fluctuations in the other dimension (e.g. time). If the relationship between the environments of development and selection are the same in time and space then the evolved plastic response to temporal fluctuations is useful in a spatial context and genetic differentiation in space is reduced. However, if the relationship between the environments of development and selection are different then the optimal level of plasticity is different in the two dimensions. In this case the plastic response that evolves to cope with temporal fluctuations may actually be maladaptive in space. This can result in the evolution of hyperplasticity or negative plasticity, the effects of which are mitigated by spatial genetic differentiation. However, genetic differentiation acts in opposition to plasticity resulting in counter-gradient variation. These results highlight the difficulty of making space-for-time substitutions in empirical work but identify the key parameters which need to be measured in order to test whether space-for-time substitutions are likely to be valid.
Independent component analysis provides clinically relevant insights into the biology of melanoma patients
Authors: Nazarov Petr V. , Wienecke-Baldacchino Anke K. , Zinovyev Andrei , Czerwińska Urszula , Muller Arnaud , Nashan Dorothée , Dittmar Gunnar , Azuaje Francisco , Kreis Stephanie
Published: August 2018

The integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) – an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. Cancer subtypes, patient survival and activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation were characterized. Additionally, through integration of transcriptomes and miRNomes, the method identified biological functions of miRNAs, which would otherwise not be possible.
Clustering of the structures of protein kinase activation loops: A new nomenclature for active and inactive kinase structures
Authors: Modi Vivek , Dunbrack Roland L.
Published: August 2018

Targeting protein kinases is an important strategy for intervention in cancer. Inhibitors are directed at the active conformation or a variety of inactive conformations. While attempts have been made to classify these conformations, a structurally rigorous catalogue of states has not been achieved. The kinase activation loop is crucial for catalysis and begins with the conserved DFGmotif (Asp-Phe-Gly). This motif is observed in two major classes of conformations, DFGin - a set of active and inactive conformations where the Phe residue is in contact with the C-helix of the N-terminal lobe, and DFGout - an inactive form where Phe occupies the ATP site exposing the C-helix pocket. We have developed a clustering of kinase conformations based on the location of the Phe side chain (DFGin, DFGout, and DFGinter or intermediate) and the backbone dihedral angles of the sequence X-D-F, where X is the residue before the DFGmotif, and the DFG-Phe side-chain rotamer, utilizing a density-based clustering algorithm. We have identified 8 distinct conformations and labeled them based on their Ramachandran regions (A=alpha, B=beta, L=left) and the Phe rotamer (minus, plus, trans). Our clustering divides the DFGin group into six clusters including ‘BLAminus,’ which contains active structures, and two common inactive forms, ‘BLBplus’ and ‘ABAminus.’ DFGout structures are predominantly in the ‘BBAminus’ conformation, which is essentially required for binding Type II inhibitors. The inactive conformations have specific features that make them unable to bind ATP, magnesium ion, and/or substrates. Our structurally intuitive nomenclature will aid in understanding the conformational dynamics of these proteins and structure-based development of kinase drugs.

Significance statement

Protein kinases play important roles in signaling pathways and are widely studied as drug targets. Their active site exhibits remarkable structural variation as observed in the large number of available crystal structures. We have developed a clustering scheme and nomenclature to categorize and label all the observed conformations in human protein kinases. This has enabled us to clearly define the geometry of the active state and to distinguish closely related inactive states which were previously not characterized. Our classification of kinase conformations will help in better understanding the conformational dynamics of these proteins and the development of inhibitors against them.
MultiPLIER: a transfer learning framework for transcriptomics reveals systemic features of rare disease
Authors: Taroni Jaclyn N. , Grayson Peter C. , Hu Qiwen , Eddy Sean , Kretzler Matthias , Merkel Peter A. , Greene Casey S.
Published: August 2018

Unsupervised machine learning methods provide a promising means to analyze and interpret large datasets. However, most gene expression datasets generated by individual researchers remain too small to fully benefit from these methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. We trained a Pathway Level Information ExtractoR (PLIER) model using on a large public data compendium comprised of multiple experiments, tissues, and biological conditions. We then transferred the model to small rare disease datasets in an approach we term MultiPLIER. Models constructed from large, diverse public data i) included features that aligned well to important biological factors; ii) were more comprehensive than those constructed from individual datasets or conditions; iii) transferred to rare disease datasets where the models describe biological processes related to disease severity more effectively than models trained on specifically those datasets.
GBAT: a gene-based association method for robust trans-gene regulation detection
Authors: Liu Xuanyao , Mefford Joel A , Dahl Andrew , Subramaniam Meena , Battle Alexis , Price Alkes L , Zaitlen Noah
Published: August 2018

Identification of trans-eQTLs has been limited by a heavy multiple testing burden, read-mapping biases, and hidden confounders. To address these issues, we developed GBAT, a powerful gene-based method that allows robust detection of trans gene regulation. Using simulated and real data, we show that GBAT drastically increases detection of trans-gene regulation over standard trans-eQTL analyses.
Reduction in CD11c+microglia correlates with clinical progression in chronic experimental autoimmune demyelination
Authors: Mayrhofer Florian , Dariychuk Zhanna , Zhen Anthony , Daugherty Daniel J. , Bannerman Peter , Hanson Angela M. , Pleasure David , Soulika Athena , Deng Wenbin , Chechneva Olga V.
Published: August 2018

Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease with high variability of clinical symptoms. In most cases MS appears as a relapsing-remitting disease course that at a later stage transitions into irreversible progressive decline of neurologic function. The mechanisms underlying MS progression remain poorly understood. Experimental autoimmune encephalomyelitis (EAE) is an animal model of MS. Here we demonstrate that mice that develop mild EAE after immunization with myelin oligodendrocyte glycoprotein 35-55 are prone to undergo clinical progression around 30 days after EAE induction. EAE progression was associated with reduction in CD11c+microglia and dispersed coalescent parenchymal infiltration. We found sex-dependent differences mediated by p38α signaling, a key regulator of inflammation. Selective reduction of CD11c+microglia in female mice with CD11c-promoter drivenp38αknockout (KO)correlated with increased rate of EAE progression. In protected animals, we found CD11c+microglia forming contacts with astrocyte processes at the glia limitans and immune cells retained within perivascular spaces. Together, our study provides evidence on the protective role of CD11c+microglia in controlling CNS immune cell parenchymal infiltration in autoimmune demyelination.
Full title: Label free quantitative proteomics ofQualea grandifloraMart. (Vochysiaceae) roots indicates an aluminium requirement for growth and development
Authors: Cury Natalia F. , Silva Renata C. C. , Melo Jessica R. , Andre Michelle S. F. , Fontes Wagner , Ricart Carlos A. O. , Castro Mariana S. , Silveira Conceição E. S. , de Sousa Marcelo V. , Pereira Luiz A. R.
Published: August 2018

Aluminium in acid soils is a hindrance to crop growth. Nonetheless, Brazilian Cerrado possesses many species such asQualea grandiflora, which are adapted to acid soils with large amounts of Al and accumulate this metal in its tissues and organs. Nonetheless, the mechanisms involved in these processes are poorly understood, mainly at molecular level. Thus, a root proteomic analysis was accomplished to identify Al-responsive proteins inQ. grandifloraplants. Concomitantly, a root growth analysis of plants grown with and without Al supplementation was conducted to determine the effects of Al on the whole plant. Subsequently, proteins from both treatments were identified and quantified by LC-MS/MS in a label-free fashion. From the 2,520 identified proteins, 410 were differentially abundant between the two treatments, which were associated with carbohydrate metabolism, redox activity, stress response and catabolism of organic compounds. Furthermore, Al was crucial for the growth and development ofQ. grandiflora. In fact, this species may have an Al-dependent metabolism. Moreover, it was possible to correlate plant growth to Al-upregulated proteins that were directly involved in cell wall synthesis, oxidative phosphorylation, genetic information processing, and amino acid metabolism. Additionally, this work provides an extensive dataset of Al-regulated protein inQ. grandiflora, which will be crucial to understanding the functions of Al in this species.
Regeneration-Associated Cells Improve Recovery from Myocardial Infarction through Enhanced Vasculogenesis, Anti-inflammation, and Cardiomyogenesis
Authors: Salybekov Amankeldi A. , Kawaguchi Akira T. , Masuda Haruchika , Vorateera Kosit , Okada Chisa , Asahara Takayuki
Published: August 2018

Background

Considering the impaired function of regenerative cells in patients with comorbidities and associated risk factors, cell therapy to enhance the regenerative microenvironment was designed using regeneration-associated cells (RACs), including endothelial progenitor cells (EPCs) and anti-inflammatory cells.

Methods

RACs were prepared by quality and quantity control culture of blood mononuclear cells (QQMNCs). The peripheral blood mononuclear cells (PBMNCs) were isolated from Lewis rats and conditioned for 5 days using a medium containing stem cell factors, thrombopoietin, Flt-3 ligand, vascular endothelial growth factor, and interleukin-6 to generate QQMNCs.

Results

In vitro EPC colony forming assays demonstrated a 5.3-fold increase in the definitive colony-forming EPCs and vasculogenic EPCs, in comparison to naïve PBMNCs. Flow cytometry analysis revealed that QQMNCs were enriched with RACs, such as EPCs (28.9-fold, P<0.0019) and M2 macrophages (160.3-fold, P<0.0002). Cell transcriptome analysis revealed that angiogenesis (angpt1, angpt2, and vegfb), stem/progenitor (c-kit and sca-1) and anti-inflammation related (arg-1, erg-2, tgfb, and foxp3) genes were highly expressed in QQMNCs. For in vivo cell transplantation experiments, 1×105 cells were administered via the tail vein into syngeneic rat models of myocardial infarction (MI). Echocardiographic data showed that QQMNCs-transplanted group (QQ-Tx) preserved cardiac function and fraction shortening (45.5±4.6%) at 28-days post-MI in comparison with PBMNCs-transplanted (PB-Tx) (30.9±6.4%, P<0.0001) and Control (32.2±7.7%, P<0.0008) groups. Histological analysis revealed that QQ-Tx showed enhanced angiogenesis and reduced interstitial left ventricular fibrosis, along with a decrease in neutrophils and an increase in M2 macrophages in the acute phase of MI. Cell tracing studies revealed that intravenously administered QQMNCs preferentially homed to ischemic tissues via blood circulation, while PBMNCs did not. QQ-Tx showed markedly upregulated early cardiac transcriptional cofactors (Nkx2-5, 29.8-fold, and Gata-4, 5.2- fold) as well as c-kit (4.5-fold) while these markers were downregulated in PB-Tx. In QQ-Tx animals, de novo blood vessels formed a “Biological Bypass” as observed macroscopically and microscopically, while PB-Tx and Control-Tx groups never developed epicardial blood vessels but showed severe fibrotic adhesion to the surrounding tissues.

Conclusion

QQMNCs, as RACs derived from rat PBMNCs, conferred potent angiogenic and anti-inflammatory properties to the regenerative microenvironment, enhancing myocardiogenesis and functional recovery of rat MI hearts.
Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE
Authors: Theorell Axel , Bryceson Yenan Troi , Theorell Jakob
Published: August 2018

Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make this highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi-and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better as the best performing state-of-the-art clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. An open source R implementation of DEPECHE is available athttps://github.com/theorell/DepecheR.

Author summary

DEPECHE-a data-mining algorithm for mega-variate data

Modern experimental technologies facilitate an array of single cells measurements,e.g.at the RNA-level, generating enormous datasets with thousands of annotated biological markers for each of thousands of cells. To analyze such datasets, researchers routinely apply automated or semi-automated techniques to order the cells into biologically relevant groups. However, even after such groups have been generated, it is often difficult to interpret the biological meaning of these groups since the definition of each group often dependends on thousands of biological markers. Therefore, in this article, we introduce DEPECHE, an algorithm designed to simultaneously group cells and enhance interpretability of the formed groups. DEPECHE defines groups only with respect to biological markers that contribute significantly to differentiate the cells in the group from the rest of the cells, yielding more succinct group definitions. Using the open source R software DepecheR on RNA sequencing data and mass cytometry data, the number of defining markers were reduced up to 1000-fold, thereby increasing interpretability vastly, while maintaining or improving the biological relevance of the groups formed compared to state-of-the-art algorithms.
Implicit and explicit learning in reactive and voluntary saccade adaptation
Authors: Es D.M. van , Knapen T.
Published: August 2018

Saccades can either be elicited automatically by salient peripheral stimuli or can additionally depend on explicit cognitive goals. Similarly, it is thought that motor adaptation is driven by the combination of a more automatic, implicit process and a more explicit, cognitive process. However, the degree to which such implicit and explicit learning contribute to the adaptation of more reactive and voluntary saccades remains elusive. To study this question, we employed a global saccadic adaptation paradigm with both increasing and decreasing saccade amplitudes. We assessed the resulting adaptation using a dual state model of motor adaptation. This model decomposes learning into a fast and slow process, which are thought to constitute the explicit and implicit learning, respectively. Our results show that adaptation of reactive saccades is equally driven by fast and slow learning, while fast learning is nearly absent when adapting voluntary (i.e. scanning) saccades. This pattern of results was present both when saccade gain was increased or decreased. Our results suggest that the increased cognitive demands associated with voluntary compared to reactive saccade planning interfere specifically with explicit learning.