bluuebunny/crossref_metadata_2025_split huggingface dataset:
Right-Angled Traction Bronchiectasis in Differentiating Idiopathic Pulmonary Fibrosis Without Honeycombing From Idiopathic Nonspecific Interstitial Pneumonia
Authors: Kim Heekyung , Yoon Soon Ho , Lee Hyun-Ju , Song Seung Geun , Koh Jaemoon , Jeon Yoon Kyung , Chae Kum Ju , Jin Gong Yong , Jin Kwang Nam , Lee Ki Yeol , Goo Jin Mo 
Published: February 2020

Objectives
The aim of this study was to conduct a radiopathologic evaluation of right-angled traction bronchiectasis to differentiate idiopathic pulmonary fibrosis (IPF) without honeycombing from idiopathic nonspecific interstitial pneumonia (NSIP).

Materials and Methods
The derivation cohort included 78 consecutive patients with idiopathic NSIP (n = 39) or IPF (n = 39) without honeycombing who underwent preoperative thin-section computed tomography scans at a single tertiary hospital. The validation cohort comprised 22 patients (14 IPF and 8 NSIP) from another institution. We assessed conventional computed tomography findings, right-angled traction bronchiectasis on minimum intensity projection (MinIP) images, and pathologic features associated with right-angled bronchiectasis. Right-angled traction bronchiectasis was defined as abrupt kinking of a single bronchus by over 90 degrees or an abrupt angle close to 180 degrees of branching bronchi in the background of fibrosis. In the validation cohort, we evaluated the proportion of correct IPF diagnoses and interobserver agreement of 4 radiologists before and after reviewing MinIP images.

Results
A probable usual interstitial pneumonia (UIP) pattern (odds ratio [OR], 6.948; 95% confidence interval [CI], 1.525–31.654; P = 0.012) and right-angled traction bronchiectasis (OR, 6.004; 95% CI, 1.980–18.209; P = 0.002) were independently associated with IPF. Patients with right-angled traction bronchiectasis were more likely to have extensive reticular opacity (OR, 1.149; 95% CI, 1.077–1.225; P < 0.001) and pathologically were more likely to have a broad extent of subpleural fibrosis (OR, 4.000; 95% CI, 1.457–10.987; P = 0.007) and relatively thick fibrosis (OR, 7.750; 95% CI, 2.504–23.991; P < 0.001). After reviewing MinIP images, the proportion of correct diagnoses increased from 40.9% to 54.5% to 50.0% to 77.3%. The mean kappa value for right-angled traction bronchiectasis was 0.489 ± 0.192.

Conclusions
Right-angled traction bronchiectasis pathologically reflected a subpleural predominance of fibrosis and partly supported the radiologic differentiation of IPF without honeycombing from idiopathic NSIP.
Climate and urbanization drive mosquito preference for humans
Authors: Rose Noah H. , Sylla Massamba , Badolo Athanase , Lutomiah Joel , Ayala Diego , Aribodor Ogechukwu B. , Ibe Nnenna , Akorli Jewelna , Otoo Sampson , Mutebi John-Paul , Kriete Alexis L. , Ewing Eliza G. , Sang Rosemary , Gloria-Soria Andrea , Powell Jeffrey R. , Baker Rachel E. , White Bradley J. , Crawford Jacob E. , McBride Carolyn S. 
Published: February 2020

SummaryThe majority of mosquito-borne illness is spread by a few mosquito species that have evolved to specialize in biting humans, yet the precise causes of this behavioral shift are poorly understood. We address this gap in the arboviral vectorAedes aegypti. We first characterize the behaviour of mosquitoes from 27 sites scattered across the species’ ancestral range in sub-Saharan Africa, revealing previously unrecognized diversity in female preference for humanversusanimal odor. We then use modelling to show that this diversity can be almost fully predicted by two ecological factors – dry season intensity and human population density. Finally we integrate this information with whole genome sequence data from 345 individual mosquitoes to identify a single underlying ancestry component linked to human preference, with genetic changes concentrated in a few key chromosomal regions. Our findings strongly suggest that human-biting in this important disease vector originally evolved as a by-product of breeding in human-stored water in areas where doing so provided the only means to survive the long, hot dry season. Our model also predicts that changes in human population density are likely to drive future mosquito evolution. Rapid urbanization may drive a shift to human-biting in many cities across Africa by 2050.
Modeling reveals human-rodent differences in h-current kinetics influencing resonance in cortical layer 5 neurons
Authors: Rich Scott , Chameh Homeira Moradi , Sekulic Vladislav , Valiante Taufik A. , Skinner Frances K. 
Published: February 2020

AbstractWhile our understanding of human neurons is often inferred from rodent data, inter-species differences between neurons can be captured by building cellular models specifically from human data. This includes understanding differences at the level of ion channels and their implications for human brain function. Thus, we here present a full spiking, biophysically-detailed multi-compartment model of a human layer 5 (L5) cortical pyramidal cell. Model development was primarily based on morphological and electrophysiological data from the same human L5 neuron, avoiding confounds of experimental variability. Focus was placed on describing the behavior of the hyperpolarization-activated cation (h-) channel, given increasing interest in this channel due to its role in pacemaking and differentiating cell types. We ensured that the model exhibited post-inhibitory rebound (PIR) spiking considering its relationship with the h-current, along with other general spiking characteristics. The model was validated against data not used in its development, which highlighted distinctly slower kinetics of the human h-current relative to the rodent setting. We linked the lack of subthreshold resonance observed in human L5 neurons to these human-specific h-current kinetics. This work shows that it is possible and necessary to build human-specific biophysical neuron models in order to understand human brain dynamics.
A mathematical model of ctDNA shedding predicts tumor detection size
Authors: Avanzini Stefano , Kurtz David M. , Chabon Jacob J. , Moding Everett J. , Hori Sharon Seiko , Gambhir Sanjiv Sam , Alizadeh Ash A. , Diehn Maximilian , Reiter Johannes G. 
Published: February 2020

AbstractEarly cancer detection aims to find tumors before they progress to an incurable stage. We developed a stochastic mathematical model of tumor evolution and circulating tumor DNA (ctDNA) shedding to determine the potential and the limitations of cancer early detection tests. We inferred normalized ctDNA shedding rates from 176 early stage lung cancer subjects and calculated that a 15 mL blood sample contains on average 1.7 genome equivalents of ctDNA for lung tumors with a volume of 1 cm3. For annual screening, the model predicts median detection sizes between 3.8 and 6.6 cm3corresponding to lead times between 310 and 450 days compared to current lung tumor sizes at diagnosis. For monthly cancer relapse testing based on 20 a priori known mutations, the model predicts a median detection size of 0.26 cm3corresponding to a lead time of 150 days. This mechanistic framework can help to optimize early cancer detection approaches.
Widespread selection against deleterious mutations in theDrosophilagenome
Authors: Khromov Pavel , Morozov Alexandre V. 
Published: February 2020

AbstractWe have developed a computational approach to simultaneous genome-wide inference of key population genetics parameters: selection strengths, mutation rates rescaled by the effective population size and the fraction of viable genotypes, solely from an alignment of genomic sequences sampled from the same population. Our approach is based on a generalization of the Ewens sampling formula, used to compute steady-state probabilities of allelic counts in a neutrally evolving population, to populations subjected to selective constraints. Patterns of polymorphisms observed in alignments of genomic sequences are used as input to Approximate Bayesian Computation, which employs the generalized Ewens sampling formula to infer the distributions of population genetics parameters. After carrying out extensive validation of our approach on synthetic data, we have applied it to the evolution of theDrosophila melanogastergenome, where an alignment of 197 genomic sequences is available for a single ancestral-range population from Zambia, Africa. We have divided theDrosophilagenome into 100-bp windows and assumed that sequences in each window can exist in either low- or high-fitness state. Thus, the steady-state population in our model is subject to a constant influx of deleterious mutations, which shape the observed frequencies of allelic counts in each window. Our approach, which focuses on deleterious mutations and accounts for intra-window linkage and epistasis, provides an alternative description of background selection. We find that most of theDrosophilagenome evolves under selective constraints imposed by deleterious mutations. These constraints are not confined to known functional regions of the genome such as coding sequences and may reflect global biological processes such as the necessity to maintain chromatin structure. Furthermore, we find that inference of mutation rates in the presence of selection leads to mutation rate estimates that are several-fold higher than neutral estimates widely used in the literature. Our computational pipeline can be used in any organism for which a sample of genomic sequences from the same population is available.
Predicting molecular subtypes of breast cancer using pathological images by deep convolutional neural network from public dataset
Authors: Phan Nam Nhut , Huang Chi-Cheng , Chuang Eric Y 
Published: February 2020

AbstractBreast cancer is a heterogeneously complex disease. A number of molecular subtypes with distinct biological features lead to different treatment responses and clinical outcomes. Traditionally, breast cancer is classified into subtypes based on gene expression profiles; these subtypes include luminal A, luminal B, basal like, HER2-enriched, and normal-like breast cancer. This molecular taxonomy, however, could only be appraised through transcriptome analyses. Our study applies deep convolutional neural networks and transfer learning from three pre-trained models, namely ResNet50, InceptionV3 and VGG16, for classifying molecular subtypes of breast cancer using TCGA-BRCA dataset. We used 20 whole slide pathological images for each breast cancer subtype. The results showed that our scale training reached about 78% of accuracy for validation. This outcomes suggested that classification of molecular subtypes of breast cancer by pathological images are feasible and could provide reliable results
The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support
Authors: Irmisch Anja , Bonilla Ximena , Chevrier Stéphane , Lehmann Kjong-Van , Singer Franziska , Toussaint Nora C , Esposito Cinzia , Mena Julien , Milani Emanuela S , Casanova Ruben , Stekhoven Daniel J , Wegmann Rebekka , Jacob Francis , Sobottka Bettina , Goetze Sandra , Kuipers Jack , del Castillo Jacobo Sarabia , Prummer Michael , Tuncel Mustafa , Menzel Ulrike , Jacobs Andrea , Engler Stefanie , Sivapatham Sujana , Frei Anja , Gut Gabriele , Ficek Joanna , Dummer Reinhard ,   Tumor Profiler Consortium, Aebersold Rudolf , Bacac Marina , Beerenwinkel Niko , Beisel Christian , Bodenmiller Bernd , Koelzer Viktor H , Moch Holger , Pelkmans Lucas , Snijder Berend , Tolnay Markus , Wollscheid Bernd , Rätsch Gunnar , Levesque Mitchell 
Published: February 2020

AbstractRecent technological advances allow profiling of tumor samples to an unparalleled level with respect to molecular and spatial composition as well as treatment response. We describe a prospective, observational clinical study performed within the Tumor Profiler (TuPro) Consortium that aims to show the extent to which such comprehensive information leads to advanced mechanistic insights of a patient’s tumor, enables prognostic and predictive biomarker discovery, and has the potential to support clinical decision making. For this study of melanoma, ovarian carcinoma, and acute myeloid leukemia tumors, in addition to the emerging standard diagnostic approaches of targeted NGS panel sequencing and digital pathology, we perform extensive characterization using the following exploratory technologies: single-cell genomics and transcriptomics, proteotyping, CyTOF, imaging CyTOF, pharmacoscopy, and 4i drug response profiling (4i DRP). In this work, we outline the aims of the TuPro study and present preliminary results on the feasibility of using these technologies in clinical practice showcasing the power of an integrative multi-modal and functional approach for understanding a tumor’s underlying biology and for clinical decision support.
Erosion of representativeness in a cohort study
Authors: Christodoulou M.D. , Brettschneider J.A. , Steinsaltz D. 
Published: February 2020

SUMMARYBackgroundThe National Child and Development Study (1958 British Birth Cohort) follows the lives of over 17 000 people born in a single week in England, Scotland, and Wales. Since the initial recruitment there have been nine sweeps to gather subsequent life-course data, and a Biomedical Sweep (Age 44) – between Sweeps 6 (Age 42) and 7 (Age 46) – that has found widespread application in genetic studies. Due to its non-selective recruitment, the survey is frequently used as a representative proxy for the British population in demographic, epidemiological, and medical studies. We examine the effect of attrition on representativeness of female fertility and education length.MethodsWe compare numbers and timings of fertility-related events of female cohort members with national estimates. Spline approximation was used to link records with different aggregation intervals. Participants present in the Biomedical Sweep (Age 44) were compared to those who were not.ResultsWe established that both timings and counts of maternities and terminations in the cohort diverge from the patterns of their contemporaries. For women who participated in the Biomedical Sweep (Age 44), we noted positive correlations of study continuation with years spent in full time education, and with age at first birth. We determined that women who did not participate in the Biomedical Sweep (Age 44) reported different fertility patterns from those who did.ConclusionsIt is possible to use National Statistics to quantify various forms of selection bias that inevitably creep into even the most meticulously sampled longitudinal study, and the misreporting that affects particular questions. While the 1958 Birth Cohort has reasonably been described as “broadly representative” of the British population, such a characterisation becomes questionable when the data are to be applied to questions about female fertility.KEY MESSAGESFertility patterns of women reported in later sweeps of the 1958 British Birth Cohort diverge from those of their contemporaries as estimated from national statistics using spline approximation. Both maternities and terminations are underreported.Female participants who dropped out earlier follow national maternity trends more closely than those who participated in the study longer, but they still report slightly fewer maternities than national statistics suggest.Female participants who persisted through later sweeps experienced first births later and left education at a later age than those who dropped out earlier.Although the 1958 British Birth Cohort has been judged as representative of the British population for some research questions, the cohort population presents increasingly biased fertility patterns in female participants over time. Studies related to fertility using data from this cohort may require adjustment.
Machine Boss: Rapid Prototyping of Bioinformatic Automata
Authors: Silvestre-Ryan J. , Wang Y. , Sharma M. , Lin S. , Shen Y. , Dider S. , Holmes I. 
Published: February 2020

ABSTRACTMotivationMany C++ libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting, and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone.ResultsWe present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line “recipes” for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage, and nanopore sequence analysis.Availability and ImplementationMachine Boss is released under the BSD-3 open source license and is available fromhttp://machineboss.org/.ContactIan Holmes,ihh@berkeley.edu
Validating the potential of double-stranded RNA targeting Colorado potato beetlemeshgene in laboratory and field trials
Authors: Petek Marko , Coll Anna , Ferenc Rok , Razinger Jaka , Gruden Kristina 
Published: February 2020

ABSTRACTColorado potato beetle (CPB) is an agricultural pest of solanaceous crops, notorious for its rapid resistance development to chemical pesticides. Foliar spraying of dsRNA formulations is a promising innovative technology providing highly specific and environmentally acceptable option for CPB management.We designed dsRNA to silence CPBmeshgene (dsMESH) and performed laboratory feeding trials to assess impacts on beetle survival and development. We compared the effectiveness ofin-vivoandin-vitroproduced dsRNA in a series of laboratory experiments. We additionally performed a field trial in which the efficacy of dsRNA sprayed onto potato foliage was compared to a spinosad-based insecticide.We showed that dsMESH ingestion consistently and significantly impaired larval growth and decreased larval survival in laboratory feeding experiments.In-vivoproduced dsRNA performed similarly asin-vitrosynthesised dsRNA in laboratory settings. In the field trial, dsMESH was as effective in controlling CPB larvae as a commercial spinosad insecticide, its activity was however slower. We discuss limitations and benefits of a potential dsMESH-based CPB management strategy and list some important RNAi based CPB research topics, which will have to be addressed in future.
Defective heart chamber growth and myofibrillogenesis after knockout ofadprhl1gene function by targeted disruption of the ancestral catalytic active site
Authors: Smith Stuart J. , Towers Norma , Demetriou Kim , Mohun Timothy J. 
Published: February 2020

AbstractADP-ribosylhydrolase-like 1 (Adprhl1) is a pseudoenzyme expressed in the developing heart myocardium of all vertebrates. In the amphibianXenopus laevis, knockdown of the two cardiac Adprhl1 protein species (40 and 23 kDa) causes failure of chamber outgrowth but this has only been demonstrated using antisense morpholinos that interfere with RNA-splicing. Transgenic production of 40 kDa Adprhl1 provides only part rescue of these defects. CRISPR/Cas9 technology now enables targeted mutation of theadprhl1gene in G0-generation embryos with routine cleavage of all alleles. Testing multiple gRNAs distributed across the locus reveals exonic locations that encode critical amino acids for Adprhl1 function. The gRNA recording the highest frequency of a specific ventricle outgrowth phenotype directs Cas9 cleavage of an exon 6 sequence, where microhomology mediated end-joining biases subsequent DNA repairs towards three small in-frame deletions. Mutant alleles encode discrete loss of 1, 3 or 4 amino acids from a di-arginine (Arg271-Arg272) containing peptide loop at the centre of the ancestral ADP-ribosylhydrolase site. Thus despite lacking catalytic activity, it is the modified (adenosine-ribose) substrate binding cleft of Adprhl1 that fulfils an essential role during heart formation. Mutation results in striking loss of myofibril assembly in ventricle cardiomyocytes. The defects suggest Adprhl1 participation from the earliest stage of cardiac myofibrillogenesis and are consistent with previous MO results and Adprhl1 protein localization to actin filament Z-disc boundaries. A single nucleotide change to the gRNA sequence renders it inactive. Mice lackingAdprhl1exons 3-4 are normal but production of the smaller ADPRHL1 species is unaffected, providing further evidence that cardiac activity is concentrated at the C-terminal protein portion.HighlightsComparison ofadprhl1morpholinos. Knockdown of the twoXenopuscardiac Adprhl1 protein species (40 and 23 kDa) causes failure of ventricle outgrowth.CRISPR/Cas9 targeted gene mutation ofadprhl1with multiple gRNAs reveals exonic locations that encode critical amino acids for Adprhl1 function.Repair of DSBs at exon 6 yields small in-frame deletions that cause specific ventricle myofibril assembly defects.The deletions disturb a conserved di-arginine containing peptide loop at the centre of the ancestral substrate binding cleft/ADP-ribosylhydrolase site of this pseudoenzyme.Mice lackingAdprhl1exons 3-4 are normal but production of the smaller ADPRHL1 species is unaffected, providing further evidence that cardiac activity is concentrated at the C-terminal protein portion.
Prior expectations evoke stimulus templates in the deep layers of V1
Authors: Aitken Fraser , Menelaou Georgios , Warrington Oliver , Koolschijn Renée S. , Corbin Nadège , Callaghan Martina F. , Kok Peter 
Published: February 2020

AbstractThe way we perceive the world is strongly influenced by our expectations. In line with this, much recent research has revealed that prior expectations strongly modulate sensory processing. However, the neural circuitry through which the brain integrates external sensory inputs with internal expectation signals remains unknown. In order to understand the computational architecture of the cortex, we need to investigate the way these signals flow through the cortical layers. This is crucial because the different cortical layers have distinct intra- and interregional connectivity patterns, and therefore determining which layers are involved in a cortical computation can inform us on the sources and targets of these signals. Here, we used ultra-high field (7T) functional magnetic resonance imaging (fMRI) to reveal that prior expectations evoke stimulus templates selectively in the deep layers of the primary visual cortex. These results shed light on the neural circuit underlying perceptual inference.