bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Fungal Pathogens in Otomycosis and Their Drug Sensitivity: Our Experience
Authors: Ali Khaled , Hamed Mahmood , Hassan Hameda , Esmail Amira , Sheneef Abeer
Published: April 2018

Introduction Otomycosis is a common problem in otolaryngology practice. However, we usually encounter some difficulties in its treatment because many patients show resistance to antifungal agents, and present high recurrence rate.

Objectives To determine the fungal pathogens that cause otomycosis as well as their susceptibility to the commonly used antifungal agents. Additionally, to discover the main reasons for antifungal resistance.

Methods We conducted an experimental descriptive study on 122 patients clinically diagnosed with otomycosis from April 2016 to April 2017. Aural discharge specimens were collected for direct microscopic examination and fungal culture. In vitro antifungal susceptibility testing was performed against the commonly used antifungal drugs. We tested the isolated fungi for their enzymatic activity.

Results Positive fungal infection was found in 102 samples. The most common fungal pathogens were Aspergillus and Candida species, with Aspergillus niger being the predominant isolate (51%). The antifungal susceptibility testing showed that mold isolates had the highest sensitivity to voriconazole (93.48%), while the highest resistance was to fluconazole (100%). For yeast, the highest sensitivity was to nystatin (88.24%), followed by amphotericin B (82.35%), and the highest resistance was to terbinafine (100%), followed by Itraconazole (94.12%). Filamentous fungi expressed a high enzymatic ability, making them more virulent.

Conclusion The Aspergillus and Candida species are the most common fungal isolates in otomycosis. Voriconazole and Nystatin are the medications of choice for the treatment of otomycosis in our community. The high virulence of fungal pathogens is owed to their high enzymatic activity. Empirical use of antifungals should be discouraged.
The Hybrid Arch Bar is a Cost-Beneficial Alternative in the Open Treatment of Mandibular Fractures
Authors: Khelemsky Renata , Powers David , Greenberg Seth , Suresh Visakha , Silver EllenJohnson , Turner Michael
Published: April 2018

Obtaining maxillomandibular fixation (MMF) to achieve fracture reduction and functional occlusion is essential in the management of maxillofacial trauma. The aims of this retrospective review were to compare the total time spent in the operating room (OR) when using the Erich arch bar (EAB) versus the bone anchored hybrid arch bar (HAB) as well as performing a cost–benefit analysis (CBA). The study sample comprised patients older than 18 years who underwent open reduction internal fixation of mandible fractures at two separate institutions over a 5-year period. The primary outcome variable was total surgical time in minutes, defined as the time from incision to the completion of closure. Average operative time was significantly longer for the EAB than for the HAB (186.74 ± 70.73 vs. 135.98 ± 2.69 minutes, p < 0.001). A significant amount of time was saved by using the HAB for unilateral (37.17 ± 13.19 minutes; p = 0.007) and bilateral fractures (55.83 ± 18.89 minutes; p = 0.005). In-depth CBA showed that, for average OR fees of $60 per minute, the HAB produced savings of at least 4.01 and 11.63% of the total cost of surgery for unilateral and bilateral fractures. These results support the hypothesis that the HAB is a time-saving maneuver in the open treatment of mandible fractures. The HAB saves more time in bilateral fracture cases despite the longer overall operative times. This study shows the differential time-saving effect of the HAB regardless of fracture laterality as well as its cost minimization benefit compared with the EAB.
Do Otolaryngology Patients Show Gender Preference When Choosing a Surgeon? — A Quantitative and Qualitative Analysis
Authors: Chitguppi Chandala , Brar Tripti
Published: April 2018

Introduction Otorhinolaryngology has always been considered a gender-neutral specialty, whereas in several other specialties, such as obstetrics, gynecology and urology, gender preference has been consistently shown by patients when choosing their treating surgeon. To date, no study has been performed to analyze whether this practice of gender preference is prevalent in otorhinolaryngology patients too.

Objectives To identify if gender preference exists in the field of otorhinolaryngology, specifically in its four subspecialties, namely otology, pediatric otolaryngology, laryngology and head and neck surgery.

Methods Patients attending our outpatient department were asked to complete a preformed proforma. The pro forma consisted of two parts, demographic details of the subjects and gender preference in the following subspecialties: otology, pediatric otolaryngology, laryngology and head and neck oncosurgery.

Results A total of 1,112 subjects took part in the study, out of which 1,089 subjects were included in the final analysis. Female gender preference was highest in the field of pediatric otolaryngology, while male preference was highest for head and neck oncosurgery.

Conclusion Though otorhinolaryngology and head and neck surgery has been considered a gender-neutral field, subspecialties of this field show considerable gender preference.
Behavior of stress generated in semiconductor chips with high-temperature joints: Influence of mechanical properties of joint materials
Authors: Ito H. , Kuwahara M. , Ohta R. , Usui M.
Published: April 2018

High-temperature joint materials are indispensable to realizing next-generation power modules with high-output performance. However, crack initiation resulting from stress concentration in semiconductor chips joined with high-temperature joint materials remains a critical problem in high-temperature operation. Therefore, clarifying the quantitative influence of joint materials on the stress generated in chips is essential. This study investigates the stress behavior of chips joined by Ni–Sn solid–liquid interdiffusion (SLID), which results in a high-temperature joint material likely to generate cracks after joining or when under thermal cycling. The results are compared with those fabricated using three types of solders, Pb–10%Sn, Sn–0.7%Cu, and Sn–10%Sb (mass %), which are conventional joint materials with different melting points and mechanical properties. Using Ni–Sn SLID results in the generation of high compressive stress (500 MPa) without stress relaxation after the joining process in contrast to the case of solders in which the compressive stresses are low (<300 MPa) and decrease to still lower levels (<250 MPa). In addition, no stress relaxation occurs during thermal cycling when using Ni–Sn SLID, whereas stress relaxation is clearly observed during heating to 200 °C using solders. Different stress behaviors between Ni–Sn SLID and other joint materials are illustrated by their mechanical strength and resistance against plastic and creep deformation. These results suggest that stress relaxation in a chip is key in suppressing crack initiation in highly reliable modules during high-temperature operation.
Autonomous cycling between excitatory and inhibitory coupling in photosensitive chemical oscillators
Authors: Yengi Desmond , Tinsley Mark R. , Showalter Kenneth
Published: April 2018

Photochemically coupled Belousov-Zhabotinsky micro-oscillators are studied in experiments and simulations. The photosensitive oscillators exhibit excitatory or inhibitory coupling depending on the surrounding reaction mixture composition, which can be systematically varied. In-phase or out-of-phase synchronization is observed with predominantly excitatory or inhibitory coupling, respectively, and complex frequency cycling between excitatory and inhibitory coupling is found between these extremes. The dynamical behavior is characterized in terms of the corresponding phase response curves, and a map representation of the dynamics is presented.
Design and implementation of a shearing apparatus for the experimental study of shear displacement in rocks
Authors: Moore Johnathan , Crandall Dustin , Gill Magdalena , Brown Sarah , Tennant Bryan
Published: April 2018

Fluid flow in the subsurface is not well understood in the context of “impermeable” geologic media. This is especially true of formations that have undergone significant stress fluctuations due to injection or withdrawal of fluids that alters the localized pressure regime. When the pressure regime is altered, these formations, which are often already fractured, move via shear to reduce the imbalance in the stress state. While this process is known to happen, the evolution of these fractures and their effects on fluid transport are still relatively unknown. Numerous simulation and several experimental studies have been performed that characterize the relationship between shearing and permeability in fractures; while many of these studies utilize measurements of fluid flow or the starting and ending geometries of the fracture to characterize shear, they do not characterize the intermediate stages during shear. We present an experimental apparatus based on slight modifications to a commonly available Hassler core holder that allows for shearing of rocks, while measuring the hydraulic and mechanical changes to geomaterials during intermediate steps. The core holder modification employs the use of semi-circular end caps and structural supports for the confining membrane that allow for free movement of the sheared material while preventing membrane collapse. By integrating this modified core holder with a computed tomography scanner, we show a new methodology for understanding the interdependent behavior between fracture structure and flow properties during intermediate steps in shearing. We include a case study of this device function which is shown here through shearing of a fractured shale core and simultaneous observation of the mechanical changes and evolution of the hydraulic properties during shearing.
Generation of maximally entangled states and coherent control in quantum dot microlenses
Authors: Bounouar Samir , de la Haye Christoph , Strauß Max , Schnauber Peter , Thoma Alexander , Gschrey Manuel , Schulze Jan-Hindrik , Strittmatter André , Rodt Sven , Reitzenstein Stephan
Published: April 2018

The integration of entangled photon emitters in nanophotonic structures designed for the broadband enhancement of photon extraction is a major challenge for quantum information technologies. We study the potential of quantum dot (QD) microlenses as efficient emitters of maximally entangled photons. For this purpose, we perform quantum tomography measurements on InGaAs QDs integrated deterministically into microlenses. Even though the studied QDs show non-zero excitonic fine-structure splitting (FSS), polarization entanglement can be prepared with a fidelity close to unity. The quality of the measured entanglement is only dependent on the temporal resolution of the applied single-photon detectors compared to the period of the excitonic phase precession imposed by the FSS. Interestingly, entanglement is kept along the full excitonic wave-packet and is not affected by decoherence. Furthermore, coherent control of the upper biexcitonic state is demonstrated.
Enhancing electrical conductivity of room temperature deposited Sn-doped In2O3 thin films by hematite seed layers
Authors: Lohaus Christian , Steinert Céline , Deyu Getnet , Brötz Joachim , Jaegermann Wolfram , Klein Andreas
Published: April 2018

Hematite Fe2O3 seed layers are shown to constitute a pathway to prepare highly conductive transparent tin-doped indium oxide thin films by room temperature magnetron sputtering. Conductivities of up to σ = 3300 S/cm are observed. The improved conductivity is not restricted to the interface but related to an enhanced crystallization of the films, which proceeds in the rhombohedral phase.
Note: An absolute X-Y-Θ position sensor using a two-dimensional phase-encoded binary scale
Authors: Kim Jong-Ahn , Kim Jae Wan , Kang Chu-Shik , Jin Jonghan
Published: April 2018

This Note presents a new absolute X-Y-Θ position sensor for measuring planar motion of a precision multi-axis stage system. By analyzing the rotated image of a two-dimensional phase-encoded binary scale (2D), the absolute 2D position values at two separated points were obtained and the absolute X-Y-Θ position could be calculated combining these values. The sensor head was constructed using a board-level camera, a light-emitting diode light source, an imaging lens, and a cube beam-splitter. To obtain the uniform intensity profiles from the vignette scale image, we selected the averaging directions deliberately, and higher resolution in the angle measurement could be achieved by increasing the allowable offset size. The performance of a prototype sensor was evaluated in respect of resolution, nonlinearity, and repeatability. The sensor could resolve 25 nm linear and 0.001° angular displacements clearly, and the standard deviations were less than 18 nm when 2D grid positions were measured repeatedly.
Fractal modeling of fluidic leakage through metal sealing surfaces
Authors: Zhang Qiang , Chen Xiaoqian , Huang Yiyong , Chen Yong
Published: April 2018

This paper investigates the fluidic leak rate through metal sealing surfaces by developing fractal models for the contact process and leakage process. An improved model is established to describe the seal-contact interface of two metal rough surface. The contact model divides the deformed regions by classifying the asperities of different characteristic lengths into the elastic, elastic-plastic and plastic regimes. Using the improved contact model, the leakage channel under the contact surface is mathematically modeled based on the fractal theory. The leakage model obtains the leak rate using the fluid transport theory in porous media, considering that the pores-forming percolation channels can be treated as a combination of filled tortuous capillaries. The effects of fractal structure, surface material and gasket size on the contact process and leakage process are analyzed through numerical simulations for sealed ring gaskets.
An atomic mean-field spin-orbit approach within exact two-component theory for a non-perturbative treatment of spin-orbit coupling
Authors: Liu Junzi , Cheng Lan
Published: April 2018

An atomic mean-field (AMF) spin-orbit (SO) approach within exact two-component theory (X2C) is reported, thereby exploiting the exact decoupling scheme of X2C, the one-electron approximation for the scalar-relativistic contributions, the mean-field approximation for the treatment of the two-electron SO contribution, and the local nature of the SO interactions. The Hamiltonian of the proposed SOX2CAMF scheme comprises the one-electron X2C Hamiltonian, the instantaneous two-electron Coulomb interaction, and an AMF SO term derived from spherically averaged Dirac-Coulomb Hartree-Fock calculations of atoms; no molecular relativistic two-electron integrals are required. Benchmark calculations for bond lengths, harmonic frequencies, dipole moments, and electric-field gradients for a set of diatomic molecules containing elements across the periodic table show that the SOX2CAMF scheme offers a balanced treatment for SO and scalar-relativistic effects and appears to be a promising candidate for applications to heavy-element containing systems. SOX2CAMF coupled-cluster calculations of molecular properties for bismuth compounds (BiN, BiP, BiF, BiCl, and BiI) are also presented and compared with experimental results to further demonstrate the accuracy and applicability of the SOX2CAMF scheme.
Measurements of fireball onset
Authors: Scheiner Brett , Barnat Edward V. , Baalrud Scott D. , Hopkins Matthew M. , Yee Benjamin T.
Published: April 2018

Laser-based measurements of the characteristic features of fireball onset and stabilization in response to a stepped voltage applied to an anode immersed in a low pressure (100 mTorr) helium afterglow are reported. These include spatial and temporal evolution of metastable species, electron density, and electric field magnitude as measured by planar laser induced fluorescence, laser-collision induced fluorescence, and laser-induced fluorescence-dip spectroscopy, respectively. These measurements are found to be in qualitative agreement with recent particle-in-cell simulations and theoretical models [Scheiner et al., Phys. Plasmas 24, 113520 (2017)]. The measurements validate the simulations and models in which fireball onset was predicted to follow from the trapping of electrons born from electron impact ionization within a potential well created by a buildup of ions in the sheath. The experimental measurements also demonstrate transient features following the onset that were not present in previous simulations. New simulation results are presented which demonstrate that these features are associated with the abruptness of the voltage step used to initiate fireball onset. An abrupt step in the anode bias causes rapid displacement of ions and an associated plasma potential response following the sheath and fireball expansion.
The effect of silage type on animal performance, energy utilisation and enteric methane emission in lactating dairy cows
Authors: Günal M. , McCourt A. , Zhao Y. , Yan Z. G. , Yan T.
Published: April 2018

The present study aimed to investigate the effect of silage type on dry matter (DM) intake, nutrient digestibility, energy utilisation and methane (CH4) emission. Six late lactating Holstein dairy cows were used in a replicated 3 × 3 Latin square design study with three treatments (grass silage (GS), maize silage (MS) and whole-crop wheat silage (WCWS)) and three periods (3 weeks/period). All animals were offered forage ad libitum and 5.55 kg/day of a concentrate supplement, which contained (DM basis) 66.0% rapeseed meal, 28.3% soyabean meal and 5.7% a mineral/vitamin supplement. During each period, animals were subject to digestibility, CH4 and heat production measurements during the final 6 days using calorimeter chambers. The results demonstrated that total DM intake for MS and WCWS diets were higher (P < 0.001) than for the GS diet. Faecal energy and heat production loss for the GS diet were lower (P < 0.01) than for MS and WCWS diets. In contrast, cows fed the GS diet had higher (P < 0.05) urine energy loss compared with MS and WCWS diets. In comparison with the GS and MS diets, WCWS diet produced a lower CH4 loss per kg DM and organic matter intake (P < 0.01), and CH4 energy output as a proportion of gross energy and metabolisable energy intake (P < 0.05). The present study demonstrates that choice of forage types affects energy utilisation and CH4 emission in dairy cows.
A new perspective on managing the onset of puberty and early reproductive performance in ewe lambs: a review
Authors: Rosales Nieto C. A. , Thompson A. N. , Martin G. B.
Published: April 2018

Global changes in industry and society have led us to reassess the numerous factors that combine to influence the time of onset of puberty and the efficiency of reproduction in young sheep. Age and weight have long been considered the dominant factors that influence the onset of puberty and, for many years, it has been accepted that these relationships are mediated by the hormone, leptin, produced by body fat. However, recent studies showing that muscle mass also plays a role have challenged this dogma and also presented new options for our understanding of metabolic inputs into the brain control of reproduction. Moreover, the possibility that an improvement in meat production will simultaneously advance puberty is exciting from an industry perspective. An industry goal of strong reproductive performance in the first year of life is becoming possible and, with it, a major step upwards in the lifetime reproductive performance of ewes. The concept of early puberty is not well accepted by producers for a variety of reasons, but the new data show clear industry benefits, so the next challenge is to change that perception and encourage producers to manage young ewes so they produce their first lamb at 1 year of age.
Brain size/body weight in the dingo (Canis dingo): comparisons with domestic and wild canids
Authors: Smith Bradley P. , Lucas Teghan A. , Norris Rachel M. , Henneberg Maciej
Published: April 2018

Endocranial volume was measured in a large sample (n = 128) of free-ranging dingoes (Canis dingo) where body size was known. The brain/body size relationship in the dingoes was compared with populations of wild (Family Canidae) and domestic canids (Canis familiaris). Despite a great deal of variation among wild and domestic canids, the brain/body size of dingoes forms a tight cluster within the variation of domestic dogs. Like dogs, free-ranging dingoes have paedomorphic crania; however, dingoes have a larger brain and are more encephalised than most domestic breeds of dog. The dingo’s brain/body size relationship was similar to those of other mesopredators (medium-sized predators that typically prey on smaller animals), including the dhole (Cuon alpinus) and the coyote (Canis latrans). These findings have implications for the antiquity and classification of the dingo, as well as the impact of feralisation on brain size. At the same time, it highlights the difficulty in using brain/body size to distinguish wild and domestic canids.
Formation of Fe-rich intermetallics in Al–5.0Cu–0.5 Fe alloys with different Mn additions
Authors: Lin Bo , Xu Rui , Li Haoyu , Zhang Weiwen
Published: April 2018

ABSTRACT

The formation of Fe-rich intermetallics in gravity die cast and squeeze cast Al–5.0 wt-% Cu–0.5 wt-% Fe alloys with different Mn contents was investigated. In gravity die cast alloys, the predominant Fe-rich intermetallics are AlmFe and β-Fe with lower Mn contents, but α-Fe and Al6(FeMn) are predominant when the amount of Mn increases. A lower amount of added Mn is required in the squeeze cast alloys because the applied pressure is favourable for the formation of Chinese script Fe-rich intermetallics and for preventing precipitation of the needle-like intermetallics. The formation temperature increases for α-Fe and AlmFe but decreases for β-Fe with an increase in the applied pressure.
Improving radiative recombination efficiency of green light-emitting diodes
Authors: Ding Boning
Published: April 2018

Although the solid-state lighting industry has achieved huge successes in both red and blue part of the visible spectrum during the last 40 years, light-emitting diodes (LEDs) that emit green light consistently exhibit inferior efficiencies. Thanks to the use of down-conversion phosphors, white LEDs have been commercialised without using green LEDs. However, the efficiency problem of green LEDs still hinders many potential applications of solid-state lighting and limits the overall system efficiency. This review first attempts to conclude and comment on the complex factors that limit the performance of green LEDs with recent research progresses. Then the article focuses on reviewing various strategies to improve green light LED radiative recombination efficiencies.

This review was chosen as a runner up of the 2018 Materials Literature Review Prize of the Institute of Materials, Minerals and Mining, run by the Editorial Board of MST. Sponsorship of the prize by TWI Ltd is gratefully acknowledged.
Mechanotransduction via the LINC complex regulates DNA replication in myonuclei
Authors: Wang Shuoshuo , Stoops Elizabeth , CP Unnikannan , Markus Barak , Reuveny Adriana , Ordan Elly , Volk Talila
Published: April 2018

Nuclear mechanotransduction has been implicated in the control of chromatin organization; however, its impact on functional contractile myofibers is unclear. We found that deleting components of the linker of nucleoskeleton and cytoskeleton (LINC) complex in Drosophila melanogaster larval muscles abolishes the controlled and synchronized DNA endoreplication, typical of nuclei across myofibers, resulting in increased and variable DNA content in myonuclei of individual myofibers. Moreover, perturbation of LINC-independent mechanical input after knockdown of β-Integrin in larval muscles similarly led to increased DNA content in myonuclei. Genome-wide RNA-polymerase II occupancy analysis in myofibers of the LINC mutant klar indicated an altered binding profile, including a significant decrease in the chromatin regulator barrier-to-autointegration factor (BAF) and the contractile regulator Troponin C. Importantly, muscle-specific knockdown of BAF led to increased DNA content in myonuclei, phenocopying the LINC mutant phenotype. We propose that mechanical stimuli transmitted via the LINC complex act via BAF to regulate synchronized cell-cycle progression of myonuclei across single myofibers.
Household economic modelsof gill net fishermen at Madura strait
Authors: Primyastanto M
Published: April 2018

The purposes of this research was to analyze household economic models of gill net fishermen at Madura strait. 30 families of gillnet fishermenwere used for purposive sampling. Data analysis used descriptive qualitative and quantitative (regression analysis). Quantitative descriptive analysis was used to analyze research and compare to factors that affecting household economic models of gill net fishermen family. Research results showed tha thousehold economic models of gill net fishermen at Madura strait was production value level or fishermen revenue at sea was strongly influenced byp roduction asset production, education level, fuel, and work flow. Work flow rate of fishermen families affected by asset production, non fisheries workflow and number of male workforce. Non fishing income level was strongly influenced by non-fishery business assets, number of family members owned andnon-fishing work flow. Spending levels of gill net fishermen at Madura strait was affected by fishing income, non-fishing income, fishermen wife education and fishermen family members.
Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering
Authors: Ballnus Benjamin , Schaper Steffen , Theis Fabian J , Hasenauer Jan
Published: April 2018

Motivation

Mathematical models have become standard tools for the investigation of cellular processes and the unraveling of signal processing mechanisms. The parameters of these models are usually derived from the available data using optimization and sampling methods. However, the efficiency of these methods is limited by the properties of the mathematical model, e.g. non-identifiabilities, and the resulting posterior distribution. In particular, multi-modal distributions with long valleys or pronounced tails are difficult to optimize and sample. Thus, the developement or improvement of optimization and sampling methods is subject to ongoing research.

Results

We suggest a region-based adaptive parallel tempering algorithm which adapts to the problem-specific posterior distributions, i.e. modes and valleys. The algorithm combines several established algorithms to overcome their individual shortcomings and to improve sampling efficiency. We assessed its properties for established benchmark problems and two ordinary differential equation models of biochemical reaction networks. The proposed algorithm outperformed state-of-the-art methods in terms of calculation efficiency and mixing. Since the algorithm does not rely on a specific problem structure, but adapts to the posterior distribution, it is suitable for a variety of model classes.

Availability and implementation

The code is available both as Supplementary Material and in a Git repository written in MATLAB.

Supplementary information

Supplementary data are available at Bioinformatics online.
Training for translation between disciplines: a philosophy for life and data sciences curricula
Authors: Anton Feenstra K , Abeln Sanne , Westerhuis Johan A , Brancos dos Santos Filipe , Molenaar Douwe , Teusink Bas , Hoefsloot Huub C J , Heringa Jaap
Published: April 2018

Motivation

Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other.

Results

Here we present our experiences with shaping and running a masters’ programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula.

Supplementary information

Supplementary data are available at Bioinformatics online.
Covariate-dependent negative binomial factor analysis of RNA sequencing data
Authors: Zamani Dadaneh Siamak , Zhou Mingyuan , Qian Xiaoning
Published: April 2018

Motivation

High-throughput sequencing technologies, in particular RNA sequencing (RNA-seq), have become the basic practice for genomic studies in biomedical research. In addition to studying genes individually, for example, through differential expression analysis, investigating co-ordinated expression variations of genes may help reveal the underlying cellular mechanisms to derive better understanding and more effective prognosis and intervention strategies. Although there exists a variety of co-expression network based methods to analyze microarray data for this purpose, instead of blindly extending these methods for microarray data that may introduce unnecessary bias, it is crucial to develop methods well adapted to RNA-seq data to identify the functional modules of genes with similar expression patterns.

Results

We have developed a fully Bayesian covariate-dependent negative binomial factor analysis (dNBFA) method—dNBFA—for RNA-seq count data, to capture coordinated gene expression changes, while considering effects from covariates reflecting different influencing factors. Unlike existing co-expression network based methods, our proposed model does not require multiple ad-hoc choices on data processing, transformation, as well as co-expression measures and can be directly applied to RNA-seq data. Furthermore, being capable of incorporating covariate information, the proposed method can tackle setups with complex confounding factors in different experiment designs. Finally, the natural model parameterization removes the need for a normalization preprocessing step, as commonly adopted to compensate for the effect of sequencing-depth variations. Efficient Bayesian inference of model parameters is derived by exploiting conditional conjugacy via novel data augmentation techniques. Experimental results on several real-world RNA-seq datasets on complex diseases suggest dNBFA as a powerful tool for discovering the gene modules with significant differential expression and meaningful biological insight.

Availability and implementation

dNBFA is implemented in R language and is available at https://github.com/siamakz/dNBFA.
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion
Authors: Jokinen Emmi , Heinonen Markus , Lähdesmäki Harri
Published: April 2018

Motivation

Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins’ properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability is necessary to facilitate efficient protein design. However, accuracy of predictive models is ultimately constrained by the limited availability of experimental data.

Results

We have developed mGPfusion, a novel Gaussian process (GP) method for predicting protein’s stability changes upon single and multiple mutations. This method complements the limited experimental data with large amounts of molecular simulation data. We introduce a Bayesian data fusion model that re-calibrates the experimental and in silico data sources and then learns a predictive GP model from the combined data. Our protein-specific model requires experimental data only regarding the protein of interest and performs well even with few experimental measurements. The mGPfusion models proteins by contact maps and infers the stability effects caused by mutations with a mixture of graph kernels. Our results show that mGPfusion outperforms state-of-the-art methods in predicting protein stability on a dataset of 15 different proteins and that incorporating molecular simulation data improves the model learning and prediction accuracy.

Availability and implementation

Software implementation and datasets are available at github.com/emmijokinen/mgpfusion.

Supplementary information

Supplementary data are available at Bioinformatics online.
LONGO: an R package for interactive gene length dependent analysis for neuronal identity
Authors: McCoy Matthew J , Paul Alexander J , Victor Matheus B , Richner Michelle , Gabel Harrison W , Gong Haijun , Yoo Andrew S , Ahn Tae-Hyuk
Published: April 2018

Motivation

Reprogramming somatic cells into neurons holds great promise to model neuronal development and disease. The efficiency and success rate of neuronal reprogramming, however, may vary between different conversion platforms and cell types, thereby necessitating an unbiased, systematic approach to estimate neuronal identity of converted cells. Recent studies have demonstrated that long genes (>100 kb from transcription start to end) are highly enriched in neurons, which provides an opportunity to identify neurons based on the expression of these long genes.

Results

We have developed a versatile R package, LONGO, to analyze gene expression based on gene length. We propose a systematic analysis of long gene expression (LGE) with a metric termed the long gene quotient (LQ) that quantifies LGE in RNA-seq or microarray data to validate neuronal identity at the single-cell and population levels. This unique feature of neurons provides an opportunity to utilize measurements of LGE in transcriptome data to quickly and easily distinguish neurons from non-neuronal cells. By combining this conceptual advancement and statistical tool in a user-friendly and interactive software package, we intend to encourage and simplify further investigation into LGE, particularly as it applies to validating and improving neuronal differentiation and reprogramming methodologies.

Availability and implementation

LONGO is freely available for download at https://github.com/biohpc/longo.

Supplementary information

Supplementary data are available at Bioinformatics online.
Bayesian networks for mass spectrometric metabolite identification via molecular fingerprints
Authors: Ludwig Marcus , Dührkop Kai , Böcker Sebastian
Published: April 2018

Motivation

Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Recently, we presented CSI:FingerID for searching in molecular structure databases using tandem mass spectrometry data. CSI:FingerID predicts a molecular fingerprint that encodes the structure of the query compound, then uses this to search a molecular structure database such as PubChem. Scoring of the predicted query fingerprint and deterministic target fingerprints is carried out assuming independence between the molecular properties constituting the fingerprint.

Results

We present a scoring that takes into account dependencies between molecular properties. As before, we predict posterior probabilities of molecular properties using machine learning. Dependencies between molecular properties are modeled as a Bayesian tree network; the tree structure is estimated on the fly from the instance data. For each edge, we also estimate the expected covariance between the two random variables. For fixed marginal probabilities, we then estimate conditional probabilities using the known covariance. Now, the corrected posterior probability of each candidate can be computed, and candidates are ranked by this score. Modeling dependencies improves identification rates of CSI:FingerID by 2.85 percentage points.

Availability and implementation

The new scoring Bayesian (fixed tree) is integrated into SIRIUS 4.0 (https://bio.informatik.uni-jena.de/software/sirius/).
Association mapping in biomedical time series via statistically significant shapelet mining
Authors: Bock Christian , Gumbsch Thomas , Moor Michael , Rieck Bastian , Roqueiro Damian , Borgwardt Karsten
Published: April 2018

Motivation

Most modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of many syndromes. However, most biological biomarkers suffer from either poor predictive performance or weak explanatory power. Recent developments in time series classification focus on discovering shapelets, i.e. subsequences that are most predictive in terms of class membership. Shapelets have the advantage of combining a high predictive performance with an interpretable component—their shape. Currently, most shapelet discovery methods do not rely on statistical tests to verify the significance of individual shapelets. Therefore, identifying associations between the shapelets of physiological biomarkers and patients that exhibit certain phenotypes of interest enables the discovery and subsequent ranking of physiological signatures that are interpretable, statistically validated and accurate predictors of clinical endpoints.

Results

We present a novel and scalable method for scanning time series and identifying discriminative patterns that are statistically significant. The significance of a shapelet is evaluated while considering the problem of multiple hypothesis testing and mitigating it by efficiently pruning untestable shapelet candidates with Tarone’s method. We demonstrate the utility of our method by discovering patterns in three of a patient’s vital signs: heart rate, respiratory rate and systolic blood pressure that are indicators of the severity of a future sepsis event, i.e. an inflammatory response to an infective agent that can lead to organ failure and death, if not treated in time.

Availability and implementation

We make our method and the scripts that are required to reproduce the experiments publicly available at https://github.com/BorgwardtLab/S3M.

Supplementary information

Supplementary data are available at Bioinformatics online.
Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge
Authors: Sundin Iiris , Peltola Tomi , Micallef Luana , Afrabandpey Homayun , Soare Marta , Mamun Majumder Muntasir , Daee Pedram , He Chen , Serim Baris , Havulinna Aki , Heckman Caroline , Jacucci Giulio , Marttinen Pekka , Kaski Samuel
Published: April 2018

Motivation

Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large.

Results

We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach.

Availability and implementation

Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine.

Supplementary information

Supplementary data are available at Bioinformatics online.
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
Authors: Smaili Fatima Zohra , Gao Xin , Hoehndorf Robert
Published: April 2018

Motivation

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications.

Results

We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein–protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved.

Availability and implementation

https://github.com/bio-ontology-research-group/onto2vec

Supplementary information

Supplementary data are available at Bioinformatics online.
Versatile genome assembly evaluation with QUAST-LG
Authors: Mikheenko Alla , Prjibelski Andrey , Saveliev Vladislav , Antipov Dmitry , Gurevich Alexey
Published: April 2018

Motivation

The emergence of high-throughput sequencing technologies revolutionized genomics in early 2000s. The next revolution came with the era of long-read sequencing. These technological advances along with novel computational approaches became the next step towards the automatic pipelines capable to assemble nearly complete mammalian-size genomes.

Results

In this manuscript, we demonstrate performance of the state-of-the-art genome assembly software on six eukaryotic datasets sequenced using different technologies. To evaluate the results, we developed QUAST-LG—a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference.

Availability and implementation

http://cab.spbu.ru/software/quast-lg

Supplementary information

Supplementary data are available at Bioinformatics online.
DisruPPI: structure-based computational redesign algorithm for protein binding disruption
Authors: Choi Yoonjoo , Furlon Jacob M , Amos Ryan B , Griswold Karl E , Bailey-Kellogg Chris
Published: April 2018

Motivation

Disruption of protein–protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein–Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.

Results

Two existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins.

Availability and implementation

DisruPPI is implemented in the EpiSweep package, freely available under an academic use license.

Supplementary information

Supplementary data are available at Bioinformatics online.