bluuebunny/crossref_metadata_2025 huggingface dataset:
Comprehensive Analysis of Indels in Whole-genome Microsatellite Regions and Microsatellite Instability across 21 Cancer Types
Authors: Fujimoto Akihiro , Fujita Masashi , Hasegawa Takanori , Wong Jing Hao , Maejima Kazuhiro , Oku-Sasaki Aya , Nakano Kaoru , Shiraishi Yuichi , Miyano Satoru , Imoto Seiya , Stratton Michael R , Rosen Steven G , Nakagawa Hidewaki ,   ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network
Published: September 2018

Microsatellites are repeats of 1-6bp units and ∼10 million microsatellites have been identified across the human genome. Microsatellites are vulnerable to DNA mismatch errors, and have thus been used to detect cancers with mismatch repair deficiency. To reveal the mutational landscape of the microsatellite repeat regions at the genome level, we analyzed approximately 20.1 billion microsatellites in 2,717 whole genomes of pan-cancer samples across 21 tissue types. Firstly, we developed a new insertion and deletion caller (MIMcall) that takes into consideration the error patterns of different types of microsatellites. Among the 2,717 pan-cancer samples, our analysis identified 31 samples, including colorectal, uterus, and stomach cancers, with higher microsatellite mutation rate (≥ 0.03), which we defined as microsatellite instability (MSI) cancers in genome-wide level. Next, we found 20 highly-mutated microsatellites that can be used to detect MSI cancers with high sensitivity. Third, we found that replication timing and DNA shape were significantly associated with mutation rates of the microsatellites. Analysis of germline variation of the microsatellites suggested that the amount of germline variations and somatic mutation rates were correlated. Lastly, analysis of mutations in mismatch repair genes showed that somatic SNVs and short indels had larger functional impact than germline mutations and structural variations. Our analysis provides a comprehensive picture of mutations in the microsatellite regions, and reveals possible causes of mutations, as well as provides a useful marker set for MSI detection.
Diagnosis and Prognosis of Alzheimer’s Disease Using Brain Morphometry and White Matter Connectomes
Authors: Wang Yun , Xu Chenxiao , Park Ji-Hwan , Lee Seonjoo , Stern Yaakov , Yoo Shinjae , Kim Jong Hun , Kim Hyoung Seop , Cha Jiook ,   The Alzheimer’s Disease Neuroimaging Initiative
Published: September 2018

Accurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and structural connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n=60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparison of classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers show the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.

Highlights

We showed the utility of multimodal MRI, combining morphometry and white matter connectomes, to classify the diagnosis of AD and MCI using machine learning.

In predicting the progression from MCI to AD, the morphometry model showed the best performance.

Two independent clinical datasets were used in this study: one for model building, the other for generalizability testing.
DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images
Authors: Çimen Sibel , Çapar Abdulkerim , Ekinci Dursun Ali , Ayten Umut Engin , Kerman Bilal Ersen , Töreyin Behçet Uğur
Published: September 2018

Oligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors’ knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.
GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data
Authors: Noskova Ekaterina , Ulyantsev Vladimir , Koepfli Klaus-Peter , O’Brien Stephen J. , Dobrynin Pavel
Published: September 2018

The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum or AFS, the distribution of allele frequencies in populations. The joint allele frequency spectrum is commonly used to reconstruct the demographic history of multiple populations and several methods based on diffusion approximation (e.g.,∂a∂i) and ordinary differential equations (e.g.,moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint allele frequency spectrum data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Analysis,https://github.com/ctlab/GADMA). We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer demographic models at different local optima close to the global one, making it is possible to detect more biology corrected model during further research.
Integrated computational and experimental identification of p53, KRAS and VHL mutant selection associated with CRISPR-Cas9 editing
Authors: Sinha Sanju , Guerra Karina Barbosa , Cheng Kuoyuan , Leiserson Mark DM , Wilson David M , Ryan Bríd M. , Ronai Ze’ev A. , Lee Joo Sang , Deshpande Aniruddha J. , Ruppin Eytan
Published: September 2018

Recent studies have reported that CRISPR-Cas9 gene editing induces a p53-dependent DNA damage response in primary cells, which may select for cells with oncogenic p53 mutations11,12. It is unclear whether these CRISPR-induced changes are applicable to different cell types, and whether CRISPR gene editing may select for other oncogenic mutations. Addressing these questions, we analyzed genome-wide CRISPR and RNAi screens to systematically chart the mutation selection potential of CRISPR knockouts across the whole exome. Our analysis suggests that CRISPR gene editing can select for mutants of KRAS and VHL, at a level comparable to that reported for p53. These predictions were further validated in a genome-wide manner by analyzing independent CRISPR screens and patients’ tumor data. Finally, we performed a new set of pooled and arrayed CRISPR screens to evaluate the competition between CRISPR-edited isogenic p53 WT and mutant cell lines, which further validated our predictions. In summary, our study systematically charts and points to the potential selection of specific cancer driver mutations during CRISPR-Cas9 gene editing.
Chemical blueprints to identifying fire ants: overview on venom alkaloids
Authors: Paterson Fox Eduardo Gonçalves
Published: September 2018

Solenopsisfire ants are remarkably difficult to identify using morphological characters, particularly from the most abundant minor workers. The present manuscript introduces a set of chemical tools to facilitate species diagnosis from field-collected fire ant samples, herein focusing on minor workers. Fire ants from different localities (native and invaded) were analysed using gas-chromatography. Samples were collected from the field into organic solvents; sampling effort included 14 species, and a suspected hybrid. A total of 32 piperidine alkaloids were spotted and tentatively identified and representative relative chemical proportions for minor workers are presented for the first time for a number of species. It is hoped that the provided info will prove useful to researchers working on fire ants in future studies. Further compounds are being analysed for additional auxiliary tools.
Musashi binding elements in Zika and related Flavivirus 3’UTRs: A comparative studyin silico
Authors: de Bernardi Schneider Adriano , Wolfinger Michael T.
Published: September 2018

Zika virus (ZIKV) belongs to a class of neurotropic viruses that have the ability to cause congenital infection, which can result in microcephaly or fetal demise. Recently, the RNA-binding protein Musashi-1 (Msi1), which mediates the maintenance and self-renewal of stem cells and acts as a translational regulator, has been associated with promoting ZIKV replication, neurotropism, and pathology. Msi1 predominantly binds to single-stranded motifs in the 3’ untranslated region (UTR) of RNA that contain aUAGtrinucleotide in their core. We systematically analyzed the properties of Musashi binding elements (MBEs) in the 3’UTR of flaviviruses with a thermodynamic model for RNA folding. Our results indicate that MBEs in ZIKV 3’UTRs occur predominantly in unpaired, single-stranded structural context, thus corroborating experimental observations by a biophysical model of RNA structure formation. Statistical analysis and comparison with related viruses show that ZIKV MBEs are maximally accessible among mosquito-borne flaviviruses. Our study addresses the broader question of whether other emerging arboviruses can cause similar neurotropic effects through the same mechanism in the developing fetus by establishing a link between the biophysical properties of viral RNA and teratogenicity. Moreover, our thermodynamic model can explain recent experimental findings and predict the Msi1-related neurotropic potential of other viruses.
Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models
Authors: Whiteway Matthew R , Socha Karolina , Bonin Vincent , Butts Daniel A
Published: September 2018

Sensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the information contained in those responses. Such variability is often shared across many neurons, which in principle can allow a decoder to mitigate the effects of such noise, depending on the structure of the shared variability and its relationship to sensory encoding at the population level. Latent variable models offer an approach for characterizing the structure of this shared variability in neural population recordings, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we leverage recent advances in machine learning to introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron’s stimulus selectivity and a set of latent variables that modulate these stimulus responses both additively and multiplicatively. While these approaches can detect general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an “affine model”. In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.
The Aquilegia genome reveals a hybrid origin of core eudicots
Authors: Aköz Gökçe , Nordborg Magnus
Published: September 2018

Background

Whole-genome duplications (WGD) have dominated the evolutionary history of plants. One consequence of WGD is a dramatic restructuring of the genome as it undergoes diploidization, a process under which deletions and rearrangements of various sizes scramble the genetic material, leading to a repacking of the genome and eventual return to diploidy. Here, we investigate the history of WGD in the columbine genus Aquilegia, a basal eudicot, and use it to illuminate the origins of the core eudicots.

Results

Within-genome synteny confirms that columbines are ancient tetraploids, and comparison with the grape genome reveals that this tetraploidy appears to be shared with the core eudicots. Thus, the ancient gamma hexaploidy found in all core eudicots must have involved a two-step process: first tetraploidy in the ancestry of all eudicots, then hexaploidy in the ancestry of core eudicots. Furthermore, the precise pattern of synteny sharing suggests that the latter involved allopolyploidization, and that core eudicots thus have a hybrid origin.

Conclusions

Novel analyses of synteny sharing together with the well-preserved structure of the columbine genome reveal that the gamma hexaploidy at the root of core eudicots is likely a result of hybridization between a tetraploid and a diploid species.
Cell-free gene regulatory network engineering with synthetic transcription factors
Authors: Swank Zoe , Laohakunakorn Nadanai , Maerkl Sebastian J.
Published: September 2018

Gene regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein – protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription factor binding energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene regulatory networks and for studying the biophysics of transcriptional regulation.
A review of the relation between species traits and extinction risk
Authors: Chichorro Filipe , Juslén Aino , Cardoso Pedro
Published: September 2018

Biodiversity is shrinking rapidly, and despite our efforts only a small part of it has been assessed for extinction risk. Identifying the traits that make species vulnerable might help us to predict the outcome for those less known. We gathered information on the relations of traits to extinction risk from 173 publications, across all taxa, spatial scales and biogeographical regions, in what we think it is the most comprehensive compilation to date. Vertebrates and the Palaearctic are the most studied taxon and region because of higher accumulation of data in these groups. Among the many traits that have been suggested to be good predictors, our meta-analyses were successful in identifying two as potentially useful in assessing risk for the lesser-known species: regardless of the taxon, species with small range and habitat breadth are more vulnerable to extinction. On the other hand, body size (the most studied trait) did not present a consistently positive or negative response. In line with recent research, we hypothesize that the relationship between body size and extinction risk is shaped by different aspects, namely body size is a proxy for different phenomena depending on the taxonomic group.
HIV-1 accessory protein Vpr interacts with REAF/RPRD2 to mitigate its antiviral activity
Authors: Gibbons Joseph M , Marno Kelly M , Pike Rebecca , Lee Wing-yiu Jason , Jones Christopher E , Ogunkolade Babatunji W , Pardieu Claire , Bryan Alexander , Fu Rebecca Menhua , Warnes Gary , Rowley Paul A , Sloan Richard D , McKnight Áine
Published: September 2018

The Human Immunodeficiency Virus type 1 (HIV-1) accessory protein Vpr enhances viral replication in both macrophages and in cycling T cells to a lesser extent. Virion packaged Vpr is released in target cells shortly after entry, suggesting its requirement in the early phase of infection. Previously, we described REAF (RNA-associated Early-stage Antiviral Factor, RPRD2), a constitutively expressed protein that potently restricts HIV replication at or during reverse transcription. Here, we show that a virus without intactvpris more highly restricted by REAF and, using delivery by VLPs, that Vpr alone is sufficient for REAF degradation in primary macrophages. REAF is more highly expressed in macrophages than in cycling T cells and we detect, by co-immunoprecipitation assay, an interaction between Vpr protein and endogenous REAF. Vpr acts very quickly during the early phase of replication and induces the degradation of REAF within 30 minutes of viral entry. Using Vpr F34I and Q65R viral mutants, we show that nuclear localisation and interaction with cullin4A-DBB1 (DCAF1) E3 ubiquitin ligase is required for REAF degradation by Vpr. In response to infection, cells upregulate REAF levels. This response is curtailed in the presence of Vpr. These findings support the hypothesis that Vpr induces the degradation of a factor, REAF, which impedes HIV infection in macrophages.

Importance

For at least 30 years, it has been known that HIV-1 Vpr, a protein carried in the virion, is important for efficient infection of primary macrophages. Vpr is also a determinant of the pathogenic effects of HIV-1in vivo. A number of cellular proteins that interact with Vpr have been identified. So far, it has not been possible to associate these proteins with altered viral replication in macrophages, or to explain why Vpr is carried in the virus particle. Here we show that Vpr mitigates the antiviral effects of REAF, a protein highly expressed in primary macrophages and one which inhibits virus replication early during reverse transcription. REAF is degraded by Vpr within 30 minutes of virus entry, in a manner dependent on the nuclear localization of Vpr and its interaction with the cell’s protein degradation machinery.
CORnet: Modeling the Neural Mechanisms of Core Object Recognition
Authors: Kubilius Jonas , Schrimpf Martin , Nayebi Aran , Bear Daniel , Yamins Daniel L. K. , DiCarlo James J.
Published: September 2018

Deep artificial neural networks with spatially repeated processing (a.k.a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in primate ventral visual processing stream. Over the past five years, these ANNs have evolved from a simple feedforward eight-layer architecture in AlexNet to extremely deep and branching NAS-Net architectures, demonstrating increasingly better object categorization performance and increasingly better explanatory power of both neural and behavioral responses. However, from the neuroscientist’s point of view, the relationship between such very deep architectures and the ventral visual pathway is incomplete in at least two ways. On the one hand, current state-of-the-art ANNs appear to be too complex (e.g., now over 100 levels) compared with the relatively shallow cortical hierarchy (4-8 levels), which makes it difficult to map their elements to those in the ventral visual stream and to understand what they are doing. On the other hand, current state-of-the-art ANNs appear to be not complex enough in that they lack recurrent connections and the resulting neural response dynamics that are commonplace in the ventral visual stream. Here we describe our ongoing efforts to resolve both of these issues by developing a “CORnet” family of deep neural network architectures. Rather than just seeking high object recognition performance (as the state-of-the-art ANNs above), we instead try to reduce the model family to its most important elements and then gradually build new ANNs with recurrent and skip connections while monitoring both performance and the match between each new CORnet model and a large body of primate brain and behavioral data. We report here that our current best ANN model derived from this approach (CORnet-S) is among the top models on Brain-Score, a composite benchmark for comparing models to the brain, but is simpler than other deep ANNs in terms of the number of convolutions performed along the longest path of information processing in the model. All CORnet models are available at github.com/dicarlolab/CORnet, and we plan to up-date this manuscript and the available models in this family as they are produced.
Prediction of Deleterious Single Nucleotide Polymorphisms in Human p53 Gene
Authors: Alam Saruar , Sayem Mohammad , Hasan Md. Kamrul , Sharmin Zinat , Pavel Mahmud Arif , Hossain Md. Faruk
Published: September 2018

With a variety of accessible Single Nucleotide Polymorphisms (SNPs) data on human p53 gene, this investigation is intended to deal with detrimental SNPs in p53 gene by executing diverse valid computational tools, including Filter, SIFT, PredictSNP, Fathmm, UTRScan, ConSurf, Phyre, Tm-Adjust, I-Mutant, Task Seek after practical and basic appraisal, dissolvable openness, atomic progression, and analysing the energy minimization. Of 581 p53 SNPs, 420 SNPs are found to be missense or non-synonymous and 435 SNPs are in the 3 prime UTR and 112 SNPs are of every 5 prime UTR from which 16 non synonymous SNPs (nsSNPs) as non-tolerable while PredictSNP package predicted 14 (taking consideration SNP colored green by two or more than 2 analyses is neutral). By concentrating on six bioinformatics tools of various dimensions a combined output is generated where 14 nsSNPs are prone to exert a deleterious effect. By using diverse SNP analysing tools we have found 5 missense SNPs in the 3 crucial amino acids position in the DNA binding domain. The underlying discoveries are fortified by I-Mutant and Project HOPE. The ExPASy-PROSITE tools characterized whether the mutations located in the functional part of the protein or not. This study provides a decisive outcome concluding the accessible SNPs information by recognizing the five harming nsSNPs: rs28934573 (S241F), rs11540652 (R248Q), rs121913342 (R248W), rs121913343 (R273C) and rs28934576 (R273H). The findings of this investigation recognize the detrimental nsSNPs which enhance the danger of various kinds of oncogenesis in patients of different populations’ in genome-wide studies (GWS).
Identification of rare-disease genes in diverse undiagnosed cases using whole blood transcriptome sequencing and large control cohorts
Authors: Frésard Laure , Smail Craig , Smith Kevin S. , Ferraro Nicole M. , Teran Nicole A. , Kernohan Kristin D. , Bonner Devon , Li Xin , Marwaha Shruti , Zappala Zachary , Balliu Brunilda , Davis Joe R. , Liu Boxiang , Prybol Cameron J. , Kohler Jennefer N. , Zastrow Diane B. , Fisk Dianna G. , Grove Megan E. , Davidson Jean M. , Hartley Taila , Joshi Ruchi , Strober Benjamin J. , Utiramerur Sowmithri ,   Care4Rare Canada Consortium,   Undiagnosed Diseases Network, Lind Lars , Ingelsson Erik , Battle Alexis , Bejerano Gill , Bernstein Jonathan A. , Ashley Euan A. , Boycott Kym M. , Merker Jason D. , Wheeler Matthew T. , Montgomery Stephen B.
Published: September 2018

RNA sequencing (RNA-seq) is a complementary approach for Mendelian disease diagnosis for patients in whom exome-sequencing is not informative. For both rare neuromuscular and mitochondrial disorders, its application has improved diagnostic rates. However, the generalizability of this approach to diverse Mendelian diseases has yet to be evaluated. We sequenced whole blood RNA from 56 cases with undiagnosed rare diseases spanning 11 diverse disease categories to evaluate the general application of RNA-seq to Mendelian disease diagnosis. We developed a robust approach to compare rare disease cases to existing large sets of RNA-seq controls (N=1,594 external and N=31 family-based controls) and demonstrated the substantial impacts of gene and variant filtering strategies on disease gene identification when combined with RNA-seq. Across our cohort, we observed that RNA-seq yields a 8.5% diagnostic rate. These diagnoses included diseases where blood would not intuitively reflect evidence of disease. We identified RARS2 as an under-expression outlier containing compound heterozygous pathogenic variants for an individual exhibiting profound global developmental delay, seizures, microcephaly, hypotonia, and progressive scoliosis. We also identified a new splicing junction in KCTD7 for an individual with global developmental delay, loss of milestones, tremors and seizures. Our study provides a broad evaluation of blood RNA-seq for the diagnosis of rare disease.
CNVkit-RNA: Copy number inference from RNA-Sequencing data
Authors: Talevich Eric , Shain A. Hunter
Published: September 2018

RNA-sequencing is most commonly used to measure gene expression, but it is possible to extract genotypic information from RNA-sequencing data, too. Point mutations and translocations can be detected when they occur in expressed genes, however, there are few software solutions to infer copy number information from RNA-sequencing data. This is because a gene’s expression is dictated by a number of variables, including, but not limited to, copy number variation. Here, we report new functionalities within the software package CNVkit that enable copy number inference from RNA-sequencing data. First, CNVkit removes technical variation in gene expression associated with GC-content and transcript length. Next, CNVkit assigns a weight, dictated by several variables, to each transcript with the net effect of preferentially inferring copy number from highly and stably expressed genes. We benchmarked our approach on 105 melanomas from The Cancer Genome Atlas project and observed a high degree of concordance (R = 0.739) between our estimates and those from array comparative genomic hybridization (aCGH) on the same samples. After initial configuration, the software requires few inputs, is able to process a batch of up to 100 samples in less than ten minutes, and can be used in conjunction with pre-existing features of CNVkit, including visualization tools. Overall, we present a rapid, user-friendly software solution to infer copy number information from gene expression data.
Hydrogen exchange protection factors can be extracted from sparse HDX-MS data
Authors: Skinner S. , Radou G. , Tuma R. , Houwing-Duistermaat J. J. , Paci E.
Published: September 2018

Hydrogen/deuterium exchange (HDX) monitored by mass spectrometry (MS) is a promising technique for rapidly fingerprinting structural and dynamical properties of proteins. The time dependent change in mass of any fragment of the polypeptide chain depends uniquely on the rate of exchange of its amide hydrogens but determining the latter from the former is generally not possible. Here we show that, if time-resolved measurements are available for a number of overlapping peptides that cover the whole sequence, rate constants for each amide hydrogen exchange (or equivalently, their protection factors) can be predicted. In most cases, the solution may not be unique, so a number of solutions have to be considered. Such analysis always provides meaningful constraints on protection factors and thus can be used in situations where obtaining more refined data is impractical, e.g., high throughput structure and interaction fingerprinting. It also provides a systematic way to improve data collection strategies in order to obtain unambiguous information at single residue level, e.g. for assessing protein structure predictions at atomistic level.
Human genome-edited hematopoietic stem cells phenotypically correct Mucopolysaccharidosis type I
Authors: Gomez-Ospina Natalia , Scharenberg Sam Glynne , Mostrel Nathalie , Bak Rasmus O. , Mantri Sruthi , Quadros Rolen M. , Gurumurthy Channabasavaiah B. , Lee Ciaran , Bao Gang , Aurelian Laure , Porteus Matthew H.
Published: September 2018

Lysosomal enzyme deficiencies comprise a large group of genetic disorders that generally lack effective treatments. A potential treatment approach is to engineer the patient’s own hematopoietic system to express high levels of the deficient enzyme, thereby correcting the biochemical defect and halting disease progression. Here, we present an efficient ex vivo genome editing approach using CRISPR/Cas9 that targets the lysosomal enzyme iduronidase to the CCR5 safe harbor locus in human CD34+ hematopoietic stem and progenitor cells. The modified cells secrete supra-endogenous enzyme levels, maintain long-term repopulation and multi-lineage differentiation potential, and can correct biochemical and phenotypic abnormalities in an immunocompromised mouse model of Mucopolysaccharidosis type I. Our studies provide support for the development of human, genome-edited CD34+ hematopoietic stem and progenitor cells for the treatment of a multi-systemic lysosomal storage disorder. Our safe harbor approach constitutes a flexible platform for the expression of lysosomal enzymes, exemplifying a potential new paradigm for the treatment of these diseases.
Signaling pathways and steroid receptors modulating estrogen receptor α function in breast cancer
Authors: Siersbæk Rasmus , Kumar Sanjeev , Carroll Jason S.
Published: September 2018

Estrogen receptor α (ER) is the major driver of ∼75% of breast cancers, and multiple ER targeting drugs are routinely used clinically to treat patients with ER+breast cancer. However, many patients relapse on these targeted therapies and ultimately develop metastatic and incurable disease, and understanding the mechanisms leading to drug resistance is consequently of utmost importance. It is now clear that, in addition to estrogens, ER function is modulated by other steroid receptors and multiple signaling pathways (e.g., growth factor and cytokine signaling), and many of these pathways affect drug resistance and patient outcome. Here, we review the mechanisms through which these pathways impact ER function and drug resistance as well as discuss the clinical implications.
Increased chromosomal mobility after DNA damage is controlled by interactions between the recombination machinery and the checkpoint
Authors: Smith Michael J. , Bryant Eric E. , Rothstein Rodney
Published: September 2018

During homologous recombination, cells must coordinate repair, DNA damage checkpoint signaling, and movement of chromosomal loci to facilitate homology search. In Saccharomyces cerevisiae, increased movement of damaged loci (local mobility) and undamaged loci (global mobility) precedes homolog pairing in mitotic cells. How cells modulate chromosome mobility in response to DNA damage remains unclear. Here, we demonstrate that global chromosome mobility is regulated by the Rad51 recombinase and its mediator, Rad52. Surprisingly, rad51Δ rad52Δ cells display checkpoint-dependent constitutively increased mobility, indicating that a regulatory circuit exists between recombination and checkpoint machineries to govern chromosomal mobility. We found that the requirement for Rad51 in this circuit is distinct from its role in recombination and that interaction with Rad52 is necessary to alleviate inhibition imposed by mediator recruitment to ssDNA. Thus, interplay between recombination factors and the checkpoint restricts increased mobility until recombination proteins are assembled at damaged sites.
Surgical Artificial Insemination in Mice
Authors: De Repentigny Yves , Kothary Rashmi
Published: September 2018

Artificial insemination can be achieved by directly adding sperm from a particular male into the oviduct of a female successfully bred with a vasectomized male by a surgical procedure. Those who are comfortable performing oviduct embryo transfers might find this approach much easier than delivering the sperm into the vagina. Multiple females can be inseminated with sperm from a single male to rescue the line, expand the line quickly, or generate relatively synchronous embryos.
Hermeneutics as innovative method to design the brand identity of a nanotechnology company
Authors: Settembre-Blundo Davide , Fernández del Hoyo Alfonso Pedro , García-Muiña Fernando Enrique
Published: September 2018

Purpose

The purpose of this paper is to develop an innovative branding method based on the hermeneutical approach and interpretive theory, to respond to the need of a simple and effective tool to build corporate identity through an industrial brand identity design and, being a new business, has been considered the brand perception of their stakeholders as the main input to analyze.

Design/methodology/approach

The case study of a small- and medium-sized enterprise (SME), that produces nanomaterials for the business-to-business (B2B) industrial market, is used to design the new hermeneutic branding method. The methodology process has been structured in four phases that have marked the investigation and that correspond to four different levels of knowledge that, in succession, between them, constituted the so-called hermeneutic circle.

Findings

This new approach allowed us to understand the social phenomenon related to the brand: its characteristic, context and the brand itself. Though hermeneutic analysis has confirmed that business strategy is only part of a more complex system of brand management, it must also consider the competitive environment and the views of the stakeholders.

Practical implications

This paper contributes to research on industrial branding by adopting the hermeneutical approach in managerial practice. This paper is the first of its kind in detail modelling the design phases of a B2B brand, providing an operational tool for marketing specialists.

Originality/value

There is a lack of research studies in the methods for designing industrial brands. The contribution of this paper lies in proposing a new interpretative approach that, acknowledging the different expectations of the stakeholder in the supply chain, allows to draw a B2B brand that communicates the system values of the product and company.
Efficiency analysis for nonprofit organizations using DEA
Authors: Kim Hyunsoo , Lee Chang Won
Published: September 2018

Purpose

The purpose of this study is to provide models to analyze the efficiency of programs and efficiency of fundraising to apply the models to non-profit organizations (NPOs) in Korea and to draw out improvement points of inefficiency using data envelopment analysis (DEA).

Design/methodology/approach

Using DEA, this study analyzed the program efficiency and fundraising efficiency of 22 Korean NPOs in the field of humanitarian assistance.

Findings

Of 22 NPOs, 15 were identified as being efficient in the program efficiency and 7 of 15 NPOs were found efficient in the fundraising efficiency. In all, four organizations were found efficient in both the program and the fundraising efficiency. Using CCR and BCC model, this study proposed the cause of inefficiency and state of returns of scale.

Practical implications

This study presents non-profit efficiency evaluation models regarding program efficiency and fundraising efficiency. This study provides the inefficient DMUs with their reference set of efficient DMUs to improve efficiency and the cause of inefficiency, whether the inefficiency is because of the pure technical inefficiency or the scale inefficiency. This study also indicates the state of variable returns to scale to propose the way of improving inefficiency by controlling the scale of inputs. The methods and the results of this study can serve as a model for researchers and practitioners to follow when evaluating efficiency in the NPOs.

Originality/value

This study has the value of performing the empirical studies of efficiency analysis of Korean NPOs and providing non-profits with the model of efficiency analysis in programs and fundraising activities and basis for establishing strategies to improve both efficiencies.
Innovative capability, strategic goals and financial performance of SMEs in Ghana
Authors: Donkor Jacob , Donkor George Nana Agyekum , Kankam-Kwarteng Collins , Aidoo Eunice
Published: September 2018

Purpose

This paper aims to investigate the moderating role of innovation capability and strategic goals in the financial performance of small- and medium-scale enterprises (SMEs) in Ghana.

Design/methodology/approach

Innovative capabilities and strategic goals in SMEs and their influence on financial performance were recognized and briefly debated according to the existing literature. Hypotheses were tested on research data on 340 SMEs in Ghana, which were conveniently selected. Finally, quantitative analysis was done, followed by a discussion of the research findings.

Findings

Results from the study have proved that strategic goals have a strong positive relationship with financial performance. Also, there is a strong, positive and highly significant impact innovative capacity has on financial performance. Finally, the study found that innovative capability moderates the relationship between strategic goals and financial performance. It showed that at high levels of innovative capacity, high levels of strategic goals boost financial performance massively.

Research limitations/implications

The findings are limited to SMEs in Ghana. Researchers should study why SMEs may not pursue any innovation capability activities as they have positive impact on their financial performance. They may also focus on strategic goals and financial performance.

Practical implications

The study shows a necessity for longer-term innovation perspectives and a higher level of the importance of the application and assessment of strategic goals. Business owners and caretakers need greater awareness about the importance of innovation capability and strategic goals and their influence on the overall financial performance of SMEs. This will help them to adopt right innovate procedures for their businesses.

Originality/value

One of few research works to examine innovation capability and strategic goals on the financial performance of SMEs in a developing country.
Farm size and production efficiency in Chinese agriculture: output and profit
Authors: Yan Jiao , Chen Chunlai , Hu Biliang
Published: September 2018

Purpose

The purpose of this paper is to analyze the relationship between farm size and agricultural production efficiency from the aspects of output and profit in order to find an optimal farm size that achieves both output and profit efficiency in agricultural production in China.

Design/methodology/approach

This study uses the 2012 China Family Panel Studies survey data and employs the stochastic frontier analysis (SFA) models to investigate empirically the relationship between farm size and agricultural production efficiency.

Findings

The study finds that there is an inverted-U curve relationship between farm size and output efficiency and a U-shaped curve relationship between farm size and profit efficiency in agricultural production in China. Based on the empirical results, the study estimates that the appropriate farm size is around 10–40 mu and the optimal farm size is around 20–40 mu both in terms of output efficiency and profit efficiency in Chinese agricultural production under the current agricultural technology and land management system.

Practical implications

The findings of this study suggest that appropriate land consolidation will bring more benefits to farmer households and agricultural production efficiency. There are some policy implications. First, governments should give long term and more stable land using rights to farmers through extending the period of land contract and verifying land using rights. Second, governments should encourage transfers of land using rights and promote land consolidation. But the implementation of this policy should consider regional differences and not be used for blindly pursuing increasing land size. Third, land consolidation should be accompanied with the development of specialized agricultural services.

Originality/value

The paper makes two major contributions to the literature. First, the authors use the SFA model to investigate the relationship between land size and agricultural production efficiency. Second, the authors establish two SFA models – the stochastic frontier output analysis model and the stochastic frontier profit analysis model – to estimate the optimal land size to achieve both output and profit efficiency of agricultural production in China.
Hybrid simulation approaches for induction machine calculation
Authors: von Pfingsten Georg , Nell Martin Marco , Hameyer Kay
Published: September 2018

Purpose

Induction machines for traction applications are operated at working points of high ferromagnetic saturation. Depending on the working point, a broad spectrum of harmonic frequencies appears in the magnetic flux density of induction machines. Detailed loss analysis therefore requires local and temporal highly resolved nonlinear field computation. This loss analysis can be performed in the post processing of nonlinear transient finite element simulations of the magnetic circuit. However, it takes a large number of transient simulation time steps to build up the rotor flux of the machine.

Design/methodology/approach

In this paper, hybrid simulation approaches that couple static FEA, transient FEA and analytic formulations to significantly decrease the number of simulation time steps to calculate the magnetic field in steady state are discussed, analyzed and compared.

Findings

The proposed hybrid simulation approaches drastically decrease the simulation time by shortening the transient build-up of the rotor flux. Depending on the maximum error of the rotor flux linkage amplitude compared to the steady state value, a reduction of simulation time steps in the range of 55.5 to 98 per cent is found.

Originality/value

The presented hybrid simulation approaches allow efficient performing of the transient FE magnetic field simulations of induction machines operated as traction drives.
Change in the library: the viewpoint of a columnist
Authors: Massis Bruce
Published: September 2018

Purpose

The purpose of this study is to reflect on the author’s decade as a columnist for New Library World and Information and Learning Science.

Design/methodology/approach

This paper provides literature review and commentary on this topic that has been addressed by professionals, researchers and practitioners.

Findings

When a patron approaches a librarian with a question, and the resources are discovered that can aid and support that patron in finding a solution to his or her dilemma, the stock of the librarian rises, reminding the public that with all the changes around the profession since the advent of the internet, the human touch, that face-to-face interaction and personal communication skill, still remains a significant instrument in the librarian’s customer services toolkit.

Originality/value

The value in exploring this topic is for others to acknowledge that which has been gained over time by an experienced library colleague.
Biophysical dependences among functional wood traits
Authors: Dlouhá Jana , Alméras Tancrède , Beauchêne Jacques , Clair Bruno , Fournier Meriem
Published: September 2018

Wood properties and especially wood density have been used as functional traits organized along major axes of species life history and strategy. Beyond statistical analyses, a better mechanistic understanding of relationships among wood traits is essential for ecologically relevant interpretation of wood trait variations.

A set of theoretical relationships mechanistically linking wood basic density with some other wood traits is derived from cellular material physics. These theoretical models picture basic physical constraints and thus provide null hypotheses for further ecological studies. Analysis is applied to data from two original datasets and several datasets extracted from the literature.

Results emphasize the strong physical constraint behind the link between basic density and maximal storable water on the one hand, and elastic modulus on the other hand. Beyond these basic physical constraints, the developed framework reveals physically less expected trends: the amount of free water available for physiological needs increases in less dense wood of fast‐growing species, and the cell wall stiffness decreases with density in temperate hardwoods and is higher in sapling stages in the rainforest understorey where competition for light is associated with high mechanical risk.

We emphasize the use of theoretically independent traits derived from models of cellular material physics to investigate the functional variation of wood traits together with their environmental and phylogenetic variations. Although the current study is limited to basic density, green wood lumen saturation and wood specific modulus, we further emphasize the identification of complementary independent wood traits representing other biomechanical functions, nutrient storage, hydraulic conductance and resistance to drought.

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