bluuebunny/crossref_metadata_2025 huggingface dataset:
A convective–radiative propagation model for wildland fires
Authors: Balbi Jacques Henri , Chatelon François Joseph , Morvan Dominique , Rossi Jean Louis , Marcelli Thierry , Morandini Frédéric
Published: May 2020

The ‘Balbi model’ is a simplified steady-state physical propagation model for surface fires that considers radiative heat transfer from the surface area of burning fuel particles as well as from the flame body. In this work, a completely new version of this propagation model for wildand fires is proposed. Even if, in the present work, this model is confined to laboratory experiments, its purpose is to be used at a larger scale in the field under operational conditions. This model was constructed from a radiative propagation model with the addition of a convective heat transfer term resulting from the impingement of packets of hot reacting gases on unburnt fuel elements located at the base of the flame. The flame inside the fuel bed is seen as the ‘fingers of fire’ described in the literature. The proposed model is physics-based, faster than real time and fully predictive, which means that model parameters do not change from one experiment to another. The predicted rate of spread is applied to a large set of laboratory experiments (through homogeneous pine needles and excelsior fuel beds) and is compared with the predictions of both a very simple empirical model (Catchpole) and a detailed physical model (FireStar2D).
Regulators of nitric oxide signaling triggered by host perception in a plant pathogen
Authors: Ding Yi , Gardiner Donald M. , Xiao Di , Kazan Kemal
Published: May 2020

The rhizosphere interaction between plant roots or pathogenic microbes is initiated by mutual exchange of signals. However, how soil pathogens sense host signals is largely unknown. Here, we studied early molecular events associated with host recognition in
Fusarium graminearum
, an economically important fungal pathogen that can infect both roots and heads of cereal crops. We found that host sensing prior to physical contact with plant roots radically alters the transcriptome and triggers nitric oxide (NO) production in
F. graminearum
. We identified an ankyrin-repeat domain containing protein (FgANK1) required for host-mediated NO production and virulence in
F. graminearum
. In the absence of host plant, FgANK1 resides in the cytoplasm. In response to host signals, FgANK1 translocates to the nucleus and interacts with a zinc finger transcription factor (FgZC1), also required for specific binding to the
nitrate reductase
(
NR
) promoter, NO production, and virulence in
F. graminearum
. Our results reveal mechanistic insights into host-recognition strategies employed by soil pathogens.
“A tiny cog in a large machine”
Authors: Moorkens Joss
Published: May 2020

Translators have worked with the assistance of computers for many years, usually translating whole texts, divided into segments but in sequential order. In order to maximise efficiency and inspired by similar moves in the tech industry and predictions for Industry 4.0, large translation companies have begun to break tasks down into smaller chunks and to rigidly define and monitor translation processes. This is particularly true of platform-mediated work, highly collaborative workflows, and multimedia work that requires near-live turnaround times. This article considers such workflows in the context of measures of job satisfaction and discussion of sustainable work systems, proposing that companies prioritise long-term returns and attempt to balance the needs of all stakeholders in a translation process. Translators and translator trainers also have a role to play in achieving this balance.
‘Time is money’ and the value of translation
Authors: do Carmo Félix
Published: May 2020

This article uses a multi-faceted approach to discuss the relation between time, money and different perspectives that help define the value of professional translation. It challenges the narratives created by the translation industry on post-editing as a revision of pre-translated content, confronting them with the detailed description of the task in industry standards and with the reality of translators’ work. The article also addresses the different roles that time plays as an instrument of analysis and evaluation of translation, and as a fundamental factor in the definition of labour relations in the translation market. The main claim of the article is that translation is increasingly specialised high-value work, requiring translators that are able to make complex and efficient decisions, especially when they are expected to work under time restrictions, with the support of content that has been previously processed by machine translation.
Social groups in machine translationpost-editing
Authors: Sakamoto Akiko , Yamada Masaru
Published: May 2020

We analysed focus group interview data collected from 22 project managers (PMs) working in Japan, covering their experiences of machine translation post-editing (MTPE). A Social Construction of Technology analysis of how PMs describe different social groups in translation enabled us to examine the meanings those groups attach to MTPE, the intricate and complex power structures which exist between them, and the negotiations that take place in their day-to-day operations. The examination discovered that MTPE is still in a fluid and controversial state due to the difficulty of meeting all groups’ interests, which may lead to MTPE’s disappearance as a business model and the eventual dominance of conventional human translation and raw MT. We conclude that establishing ethical and sustainable translation workflows for all social groups will be vital for MTPE’s survival, which will require careful consideration of the complexity of these social groups and negotiations between them.
Machine translation in the news
Authors: Vieira Lucas Nunes
Published: May 2020

Machine translation (MT) is now firmly in the public eye. The media can reflect and influence the public
perception of MT and, by extension, of translation itself, but the news coverage of MT has to date remained largely unexplored.
This study draws on the news framing literature to present an analysis of how MT is described in the written press. Based on a
sample of 284 MT-focused newspaper articles, the news reporting on MT was found to be significantly more positive than negative.
This positive framing was unrelated to the launch of neural MT. Furthermore, the portrayal of MT in the press tended to lack
nuance, with few instances that raised awareness of the technology’s use implications. The study calls for higher standards in the
public discussion and promotion of MT and for more research on non-professional conceptualisations of translation technologies and
their role in communication.
Hdac3 is an epigenetic inhibitor of the cytotoxicity program in CD8 T cells
Authors: Tay Rong En , Olawoyin Olamide , Cejas Paloma , Xie Yingtian , Meyer Clifford A. , Ito Yoshinaga , Weng Qing Yu , Fisher David E. , Long Henry W. , Brown Myles , Kim Hye-Jung , Wucherpfennig Kai W.
Published: May 2020

Cytotoxic T cells play a key role in adaptive immunity by killing infected or cancerous cells. While the transcriptional control of CD8 T cell differentiation and effector function following T cell activation has been extensively studied, little is known about epigenetic regulation of these processes. Here we show that the histone deacetylase HDAC3 inhibits CD8 T cell cytotoxicity early during activation and is required for persistence of activated CD8 T cells following resolution of an acute infection. Mechanistically, HDAC3 inhibits gene programs associated with cytotoxicity and effector differentiation of CD8 T cells including genes encoding essential cytotoxicity proteins and key transcription factors. These data identify HDAC3 as an epigenetic regulator of the CD8 T cell cytotoxicity program.
Finite-size effects in cylindrical topological insulators
Authors: Governale Michele , Bhandari Bibek , Taddei Fabio , Imura Ken-Ichiro , Zülicke Ulrich
Published: May 2020

We present a theoretical study of a nanowire made of a three-dimensional topological insulator. The bulk topological insulator is described by a continuum-model Hamiltonian, and the cylindrical-nanowire geometry is modelled by a hard-wall boundary condition. We provide the secular equation for the eigenergies of the systems (both for bulk and surface states) and the analytical form of the energy eigenfunctions. We describe how the surface states of the cylinder are modified by finite-size effects. In particular, we provide a 1/R expansion for the energy of the surface states up to second order. The knowledge of the analytical form for the wavefunctions enables the computation of matrix elements of any single-particle operators. In particular, we compute the matrix elements of the optical dipole operator, which describe optical absorption and emission, treating intra- and inter-band transition on the same footing. Selection rules for optical transitions require conservation of linear momentum parallel to the nanowire axis, and a change of 0 or ±1 in the total-angular-momentum projection parallel to the nanowire axis. The magnitude of the optical-transition matrix elements is strongly affected by the finite radius of the nanowire.
Interplay of phase separation and itinerant magnetism for correlated few fermions in a double-well
Authors: Koutentakis G M , Mistakidis S I , Schmelcher P
Published: May 2020

We explore the stability of the phase separation phenomenon in few-fermion spin-1/2 systems confined in a double-well potential. It is shown that within the SU(2) symmetric case, where the total spin is conserved, the phase separation cannot be fully stabilized. An interaction regime characterized by metastable phase separation emerges for intermediate interactions which is inherently related with ferromagnetic spin–spin correlations emerging within each of the wells. The breaking of the SU(2) symmetry crucially affects the stability properties of the system as the phase separated state can be stabilized even for weak magnetic potential gradients. Our results imply an intricate relation between the phenomena of phase separation and ferromagnetism that lies beyond the view of the Stoner instability.
Microrheology of filament networks from Brownian dynamics simulations
Authors: Azevedo T. N. , Rizzi L. G.
Published: May 2020

Microrheology is a technique that have been largely used to investigate viscoelasticity in biological systems. For example, it revealed that filament networks, which are the main component of the citoskeleton of eukaryote cells, show an interesting semisolid viscoelastic response that is characterized by a hardening behaviour at high frequencies. Here, we adopt a computational approach based on microrheology to study the relationship between the Brownian motion of probe particles immersed in a filament network and its viscoelastic response. In particular, we consider a simple model for the filament networks and perform Brownian dynamics simulations to obtain the mean-squared displacement of probe particles, which is used to evaluate the shear moduli G’ and G” of the networks. Our numerical results indicate that the proposed numerical approach can reproduce several features observed in experiments, including the sol-gel transition observed when varying the density of filaments, and the aforementioned hardening behaviour at high frequencies.
A Simple Monte Carlo Simulation For the Two Dimensional Attractive Hubbard Model
Authors: Correa Eberth , Rodrigues R. G. M. , Mól L. A. S. , Costa B. V.
Published: May 2020

We use the Auxiliary Field Quantum Monte Carlo technique to perform a simple study of the two dimensional attractive Hubbard model and calculate the total ground state energy to compare with another technique in the literature for U = –4t. Our finite size extrapolated result corroborate the approximated lattice density-functional theory method for this value of U at half-filling. To stabilize the Auxiliary Field Quantum Monte Carlo algorithm one must resort to one of the two decomposition methods: the modified Grand-Schmidt decomposition and the singular value decomposition. Two characteristics like accuracy and total execution time were considered to compare the decomposition algorithms, and the results are presented bellow.
Nanowire Growth Simulation
Authors: de Souza G.B.G. , Costa B.V.
Published: May 2020

In this work we use a Computational Monte Carlo experiment for modeling the far from equilibrium growth of nanowires from a mixing of two particles species MBE experiment. We observed three distinct phases. There is a low temperature region where nanowires are formed. An intermediate phase, at a higher temperature where the solute floats over the solvent. Above a critical temperature a phase, similar to a paramagnetic phase in magnetic materials shows up. In this region solvent and solute are completely mixed up.
A New Universality at a First-Order Phase Transition: The Spin-flop Transition in an Anisotropic Heisenberg Antiferromagnet
Authors: Xu Jiahao , Tsai Shan-Ho , Landau David P. , Binder Kurt
Published: May 2020

A great triumph of statistical physics in the latter part of the 20th century was the understanding of critical behavior and universality at second-order phase transitions. In contrast, first-order transitions were believed to have no common features. However, we argue that the classic, first-order “spin-flop” transition (between the antiferromagnetic and the rotationally degenerate, canted state) in an anisotropic antiferromagnet in a magnetic field exhibits a new kind of universality. We present a finite-size scaling theory for a first-order phase transition where a continuous symmetry is broken using an approximation of Gaussian probability distributions with a phenomenological degeneracy factor “q” included, where “q” characterizes the relative degeneracy of the ordered phases. Predictions are compared with high resolution Monte Carlo simulations of the three-dimensional, XXZ Heisenberg antiferromagnet in a field to study the finite-size behavior for L×L×L simple cubic lattices. The field dependence of all moments of the order parameters exhibit universal intersections at the spin-flop transition. Our Monte Carlo data agree with theoretical predictions for asymptotic large L behavior. Our theory yields q = π, and we present numerical evidence that is compatible with this prediction. The agreement between the theory and simulation implies a heretofore unknown universality.
The impact of fluctuations on the zeros of the energy probability distribution
Authors: Rodrigues R G M , Mól L A S
Published: May 2020

In this communication we explore the possible effects of statistical fluctuations on the use of the Energy Probability Distribution (EPD) zeros to study phase transitions. In the EPD zeros technique one has to find the roots of a polynomial whose coefficients are given by the EPD - a histogram of energy values obtained in a Monte Carlo simulation, for example. Phase transitions are signalized by the presence of a zero that approaches the point (1,0) in the complex plane. Once the EPD estimations are usually subject to statistical fluctuations and polynomial roots are known to be sensitive to modifications in its coefficients, we have compared the roots of a given polynomial with the roots of a perturbed one, searching for possible impacts on the method. Our results show that although the overall map of zeros is modified, the location of the dominant zero, the one that indicates the presence of a phase transition, is not affected. Indeed, even for 30% perturbation only small modifications in the dominant zero location is observed.
Genetic Algorithm to search for exotically shaped fullerenes
Authors: Ferreira L. R. N. , Mendonça J. P. A. , Sato F.
Published: May 2020

The versatility of carbon-carbon bonds is in charge of various carbon-based structures including numerous possibilities for building fullerenes. Theoretically, it is possible to make any closed surface consisting of C atoms in a number of ways. However, the generation of possible arrangements and, furthermore, calculating the corresponding energetics is a great challenge even for a small molecule. In this context, we develop a genetic-algorithm-based code that can search for exotically shaped fullerenes. Furthermore, we discuss the construction and optimization of the algorithm assisted by some test results.
Phase Transitions in the Two-Dimensional Ising Model from the Microcanonical Perspective
Authors: Sitarachu Kedkanok , Bachmann Michael
Published: May 2020

The continuous ferromagnetic-paramagnetic phase transition in the two-dimensional Ising model has already been excessively studied by conventional canonical statistical analysis in the past. We use the recently developed generalized microcanonical inflection-point analysis method to investigate the least-sensitive inflection points of the microcanonical entropy and its derivatives to identify transition signals. Surprisingly, this method reveals that there are potentially two additional transitions for the Ising system besides the critical transition.
Determination of the Phase Diagram for the 3d Spin Glass ± J Model Using the Zeros of the Energy Probability Distribution
Authors: Figueiredo T. P. , Costa B.V.
Published: May 2020

In this work we use the zeros of the energy probability distribution to obtain the critical diagram of the three dimensional (L
3) ±J Ising model. Starting with a complete ferromagnetic system we introduce a number of p × L
3 antiferromagnetic bonds in the system. The phase diagram, temperature against antiferromagnetic bond concentration (p), is estimated for several values of p. The goal of this preliminary study is to show how it is possible to determine the phase diagram of such a complex model without the need to define any order parameter.
Strained ultra-thin films of BaO: a molecular dynamics investigation
Authors: Nascimento V. B. , Rino J.P. , Costa B.V.
Published: May 2020

A recent theoretical work by Bousquet and collaborators have predicted that ferroelectric ordering could be induced in the rocksalt oxides of alkaline earth metals (BaO, MgO, CaO and SrO) by strain. The expected functional properties present in these strained binary oxides, like polarization, dielectric constant and piezoelectric response, would be comparable to those of typical ferroelectric perovskites. Consequently, the strained binary oxides would be promising materials for fabrication of devices like ferroelectric memories and sensors. One possible way to explore the potential underlying these theoretical predictions is to grow thin and ultra-thin films of these binary oxides under epitaxial strain by choosing an adequate substrate. In such systems the interplay between epitaxial strain and the lack of translational symmetry (limited film thickness) may lead to the formation of interesting (anti)ferroelectric phases. Our goal in this work is to explore the potential structural and functional phase diagram of BaO ultra-thin films (thickness of only 8 BaO layers, ≈ 20 nm) obtained for different values of compressive epitaxial strain and temperature by performing a molecular dynamics investigation. A polarized phase (antipolar) is observed at a compressive strain of -9.0%, wich resists till a melting temperature around 1500 K, which is indeed high for such an ultra-thin film.
What drives public support for policies to enhance electric vehicle adoption?
Authors: Brückmann Gracia , Bernauer Thomas
Published: May 2020

Decarbonizing the transportation sector is crucial to limiting global warming, but faces severe political feasibility challenges due to widespread opposition by those who incur the costs. With respect to private motorized vehicles, which account for the largest share of emissions from transportation, various studies show that pull measures, such as subsidies for electric vehicles (EVs) and charging infrastructure, attract more public support than push measures, such as carbon taxes or regulation to phase out fossil fuel cars. Based on a choice experiment with a large, representative sample (N = 5325) of car holders in Switzerland, we reassess and add to these findings. We empirically focus on Switzerland because its newly registered cars have the worst emissions record in Europe. First, we reassess the presumably stronger support for pull measures by studying whether such support is (negatively) affected by revealing the cost implications in terms of means for funding these policy measures. Second, a unique feature of our study is that we examine support for policies to promote EVs both amongst non-EV and EV holders. Our hypothesis is that EV holders are likely to be more supportive of such policies, even when cost implications become apparent. Our key finding is that support for pull measures, which is high amongst non-EV holders, and even higher among technology adopters (EV holders), remains stable even when policy funding is revealed. This suggests that more ambitious pull measures in this area are politically feasible, even more so as the share of EV-adopters increases. Our research also provides a methodological template for similar research in other countries.
The effect of moisture and reinforcement on the self-sensing properties of hybrid-fiber-reinforced concrete
Authors: Maier M
Published: May 2020

This study presents the piezoresistive properties of a hybrid fiber-reinforced concrete (HyFRC) for structural health monitoring. This HyFRC mixture incorporates micro PVA and macro steel fibers to control the crack propagation. Investigations on self-sensing concrete (SSC) focus mainly on mixtures containing highly conductive fillers such as carbon nanotubes, graphite or nickel powder which results in a highly engineered cost-intensive mix composition for structural applications. This paper investigates the sensing ability of a cost-effective structural concrete mixture by considering the environmental conditions of engineering structures e.g. the moisture content as well as the influence of reinforcement on the electrical resistance measurement. The sensing parameters were evaluated by performing simultaneously bending tests and Electrochemical Impedance Spectroscopy (EIS) on beams with and without steel reinforcement. Furthermore, the influence of the pore solution on the EIS was investigated on specimens with varying moisture contents. The sensing behavior was compared to a control mixture without steel and PVA fibers. Results showed a significant lower initial resistivity for the HyFRC mixture compared to the ordinary concrete as well as the activation of the self-sensing properties due to the incorporated steel fibers. Finally, the influence of the pore solution on the EIS measurement is addressed and emphasizes the imperative need to consider the moisture content of self-sensing materials.
Guaranteed a posteriori bounds for eigenvalues and eigenvectors: Multiplicities and clusters
Authors: Cancès Eric , Dusson Geneviève , Maday Yvon , Stamm Benjamin , Vohralík Martin
Published: May 2020

This paper presents a posteriori error estimates for conforming numerical approximations of eigenvalue clusters of second-order self-adjoint elliptic linear operators with compact resolvent. Given a cluster of eigenvalues, we estimate the error in the sum of the eigenvalues, as well as the error in the eigenvectors represented through the density matrix, i.e., the orthogonal projector on the associated eigenspace. This allows us to deal with degenerate (multiple) eigenvalues within the framework. All the bounds are valid under the only assumption that the cluster is separated from the surrounding smaller and larger eigenvalues; we show how this assumption can be numerically checked. Our bounds are guaranteed and converge with the same speed as the exact errors. They can be turned into fully computable bounds as soon as an estimate on the dual norm of the residual is available, which is presented in two particular cases: the Laplace eigenvalue problem discretized with conforming finite elements, and a Schrödinger operator with periodic boundary conditions of the form

−
Δ
+
V

-\Delta + V

discretized with planewaves. For these two cases, numerical illustrations are provided on a set of test problems.
Effects of pharmacy interventions at transitions of care on patient outcomes
Authors: Fosnight Susan , King Philip , Ewald Jacqueline , Feucht John , Lamtman Angela , Kropp Denise , Dittmer Alison , Sampson Jordan , Shah Morali
Published: May 2020

Purpose

An interdisciplinary group developed a care transitions process with a prominent pharmacist role.

Methods

The new transitions process was initiated on a 32-bed medical/surgical unit. Demographics, reconciliation data, information on medication adherence barriers, medication recommendations, and time spent performing interventions were prospectively collected for 284 consecutive patients over 54 days after the pharmacy participation was completely implemented. Outcome data, including 30-day readmission rates and length of stay, were retrospectively collected.

Results

When comparing metrics for all intervention patients to baseline metrics from the same months of the previous year, the readmission rate was decreased from 21.0% to 15.3% and mean length of stay decreased from 5.3 days to 4.4 days. Further improvement to a 10.2% readmission rate and a 3.6-day average length of stay were observed in the subgroup of intervention patients who received all components of the pharmacy intervention. Additionally, greater improvements were observed in intervention-period patients who received the full pharmacy intervention, as compared to those receiving only parts of the pharmacy intervention, with a 10.2-percentage-point lower readmission rate (10.2% vs 20.4%, P = 0.016) and a 1.7-day shorter length of stay (3.6 days vs 5.3 days; 95% confidence interval, 0.814-2.68 days; P = 0.0003). For patients receiving any component of the pharmacy intervention, an average of 9.56 medication recommendations were made, with a mean of 0.89 change per patient deemed to be required to avoid harm and/or increased length of stay.

Conclusion

A comprehensive pharmacy intervention added to a transitions intervention resulted in an average of nearly 10 medication recommendations per patient, improved length of stay, and reduced readmission rates.
MetaFS: Performance assessment of biomarker discovery in metaproteomics
Authors: Tang Jing , Mou Minjie , Wang Yunxia , Luo Yongchao , Zhu Feng
Published: May 2020

Metaproteomics suffers from the issues of dimensionality and sparsity. Data reduction methods can maximally identify the relevant subset of significant differential features and reduce data redundancy. Feature selection (FS) methods were applied to obtain the significant differential subset. So far, a variety of feature selection methods have been developed for metaproteomic study. However, due to FS’s performance depended heavily on the data characteristics of a given research, the well-suitable feature selection method must be carefully selected to obtain the reproducible differential proteins. Moreover, it is critical to evaluate the performance of each FS method according to comprehensive criteria, because the single criterion is not sufficient to reflect the overall performance of the FS method. Therefore, we developed an online tool named MetaFS, which provided 13 types of FS methods and conducted the comprehensive evaluation on the complex FS methods using four widely accepted and independent criteria. Furthermore, the function and reliability of MetaFS were systematically tested and validated via two case studies. In sum, MetaFS could be a distinguished tool for discovering the overall well-performed FS method for selecting the potential biomarkers in microbiome studies. The online tool is freely available at https://idrblab.org/metafs/.
Landscape of drug-resistance mutations in kinase regulatory hotspots
Authors: Kim Pora , Li Hanyang , Wang Junmei , Zhao Zhongming
Published: May 2020

More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. So far, a systematic investigation of kinase mutations on DR hotspots has not been done yet. In this study, we systematically investigated four types of representative mutation hotspots (gatekeeper, G-loop, αC-helix and A-loop) associated with DR in 538 human protein kinases using large-scale cancer data sets (TCGA, ICGC, COSMIC and GDSC). Our results revealed 358 kinases harboring 3318 mutations that covered 702 drug resistance hotspot residues. Among them, 197 kinases had multiple genetic variants on each residue. We further computationally assessed and validated the epidermal growth factor receptor mutations on protein structure and drug-binding efficacy. This is the first study to provide a landscape view of DR-associated mutation hotspots in kinase’s secondary structures, and its knowledge will help the development of effective next-generation KIs for better precision medicine.
rScudo: an R package for classification of molecular profiles using rank-based signatures
Authors: Ciciani Matteo , Cantore Thomas , Lauria Mario
Published: May 2020

The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods.

Availability and implementation

The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo.
PEDL: extracting protein–protein associations using deep language models and distant supervision
Authors: Weber Leon , Thobe Kirsten , Migueles Lozano Oscar Arturo , Wolf Jana , Leser Ulf
Published: May 2020

Motivation

A significant portion of molecular biology investigates signalling pathways and thus depends on an up-to-date and complete resource of functional protein–protein associations (PPAs) that constitute such pathways. Despite extensive curation efforts, major pathway databases are still notoriously incomplete. Relation extraction can help to gather such pathway information from biomedical publications. Current methods for extracting PPAs typically rely exclusively on rare manually labelled data which severely limits their performance.

Results

We propose PPA Extraction with Deep Language (PEDL), a method for predicting PPAs from text that combines deep language models and distant supervision. Due to the reliance on distant supervision, PEDL has access to an order of magnitude more training data than methods solely relying on manually labelled annotations. We introduce three different datasets for PPA prediction and evaluate PEDL for the two subtasks of predicting PPAs between two proteins, as well as identifying the text spans stating the PPA. We compared PEDL with a recently published state-of-the-art model and found that on average PEDL performs better in both tasks on all three datasets. An expert evaluation demonstrates that PEDL can be used to predict PPAs that are missing from major pathway databases and that it correctly identifies the text spans supporting the PPA.

Availability and implementation

PEDL is freely available at https://github.com/leonweber/pedl. The repository also includes scripts to generate the used datasets and to reproduce the experiments from this article.

Supplementary information

Supplementary data are available at Bioinformatics online.
An ultra-sensitive T-cell receptor detection method for TCR-Seq and RNA-Seq data
Authors: Chen Si-Yi , Liu Chun-Jie , Zhang Qiong , Guo An-Yuan
Published: May 2020

Motivation

T-cell receptors (TCRs) function to recognize antigens and play vital roles in T-cell immunology. Surveying TCR repertoires by characterizing complementarity-determining region 3 (CDR3) is a key issue. Due to the high diversity of CDR3 and technological limitation, accurate characterization of CDR3 repertoires remains a great challenge.

Results

We propose a computational method named CATT for ultra-sensitive and precise TCR CDR3 sequences detection. CATT can be applied on TCR sequencing, RNA-Seq and single-cell TCR(RNA)-Seq data to characterize CDR3 repertoires. CATT integrated de Bruijn graph-based micro-assembly algorithm, data-driven error correction model and Bayesian inference algorithm, to self-adaptively and ultra-sensitively characterize CDR3 repertoires with high performance. Benchmark results of datasets from in silico and experimental data demonstrated that CATT showed superior recall and precision compared with existing tools, especially for data with short read length and small size and single-cell sequencing data. Thus, CATT will be a useful tool for TCR analysis in researches of cancer and immunology.

Availability and implementation

http://bioinfo.life.hust.edu.cn/CATT or https://github.com/GuoBioinfoLab/CATT.

Supplementary information

Supplementary data are available at Bioinformatics online.
Improved survival analysis by learning shared genomic information from pan-cancer data
Authors: Kim Sunkyu , Kim Keonwoo , Choe Junseok , Lee Inggeol , Kang Jaewoo
Published: May 2020

Motivation

Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning.

Results

We pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples.

Availability and implementation

Our implementation of VAECox is available at https://github.com/dmis-lab/VAECox.

Supplementary information

Supplementary data are available at Bioinformatics online.
REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets
Authors: Marchet Camille , Iqbal Zamin , Gautheret Daniel , Salson Mikaël , Chikhi Rayan
Published: May 2020

Motivation

In this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets.

Results

We used REINDEER to index the abundances of sequences within 2585 human RNA-seq experiments in 45 h using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of ∼4 billion distinct k-mers across 2585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph of each dataset, then conceptually merges those de Bruijn graphs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances.

Availability and implementation

https://github.com/kamimrcht/REINDEER.

Supplementary information

Supplementary data are available at Bioinformatics online.
Basic polar and hydrophobic properties are the main characteristics that affect the binding of transcription factors to methylation sites
Authors: Shen Zijie , Zou Quan
Published: May 2020

Motivation

Methylation and transcription factors (TFs) are part of the mechanisms regulating gene expression. However, the numerous mechanisms regulating the interactions between methylation and TFs remain unknown. We employ machine-learning techniques to discover the characteristics of TFs that bind to methylation sites.

Results

The classical machine-learning analysis process focuses on improving the performance of the analysis method. Conversely, we focus on the functional properties of the TF sequences. We obtain the principal properties of TFs, namely, the basic polar and hydrophobic Ile amino acids affecting the interaction between TFs and methylated DNA. The recall of the positive instances is 0.878 when their basic polar value is >0.1743. Both basic polar and hydrophobic Ile amino acids distinguish 74% of TFs bound to methylation sites. Therefore, we infer that basic polar amino acids affect the interactions of TFs with methylation sites. Based on our results, the role of the hydrophobic Ile residue is consistent with that described in previous studies, and the basic polar amino acids may also be a key factor modulating the interactions between TFs and methylation.

Supplementary information

Supplementary data are available at Bioinformatics online.
Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach
Authors: Liang Siqi , Yu Haiyuan
Published: May 2020

Motivation

In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases.

Results

Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug–target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes.

Supplementary information

Supplementary data are available at Bioinformatics online.
DNA-based species delimitation reveals cryptic and incipient species in synchronous flashing fireflies (Coleoptera: Lampyridae) of Southeast Asia
Authors: Jusoh Wan F A , Ballantyne Lesley , Chan Kin Onn
Published: May 2020

Synchronous flashing fireflies of the genus Pteroptyx are ubiquitous throughout Southeast Asia, yet fundamental knowledge about their biodiversity is lacking. Recent studies have revealed notable population-level phylogeographical structure within the Pteroptyx tener and P. bearni groups in Malaysia, suggesting that cryptic species may exist. Additionally, morphological and genetic similarities between P. balingiana and P. malaccae have raised questions about the former’s validity as a distinct species. We collected samples from previously unsampled populations and assembled the most comprehensive genetic dataset for Pteroptyx to date, to characterize species boundaries within the P. tener, P. bearni and P. malaccae groups. Using a suite of species delimitation analyses, we show that P. tener along the west coast of Peninsular Malaysia (PM) is distinct from populations from the east coast and Borneo despite the absence of morphological differentiation. However, analyses could not conclusively differentiate P. bearni from Borneo and eastern PM, nor identify P. balingiana and P. malaccae as distinct species, indicating that these populations may be conspecific or represent incipient species. This study underlines the need to increase geographical, taxonomic and genetic sampling of Southeast Asian fireflies to provide a better understanding of their biodiversity.
ATN status in amnestic and non-amnestic Alzheimer’s disease and frontotemporal lobar degeneration
Authors: Cousins Katheryn A Q , Irwin David J , Wolk David A , Lee Edward B , Shaw Leslie M J , Trojanowski John Q , Da Re Fulvio , Gibbons Garrett S , Grossman Murray , Phillips Jeffrey S
Published: May 2020

Under the ATN framework, CSF analytes provide evidence of the presence or absence of Alzheimer’s disease pathological hallmarks: amyloid plaques (A), phosphorylated tau (T), and accompanying neurodegeneration (N). Still, differences in CSF levels across amnestic and non-amnestic variants or due to co-occurring pathologies might lead to misdiagnoses. We assess the diagnostic accuracy of CSF markers for amyloid, tau, and neurodegeneration in an autopsy cohort of 118 Alzheimer’s disease patients (98 amnestic; 20 non-amnestic) and 64 frontotemporal lobar degeneration patients (five amnestic; 59 non-amnestic). We calculated between-group differences in CSF concentrations of amyloid-β1–42 peptide, tau protein phosphorylated at threonine 181, total tau, and the ratio of phosphorylated tau to amyloid-β1–42. Results show that non-amnestic Alzheimer’s disease patients were less likely to be correctly classified under the ATN framework using independent, published biomarker cut-offs for positivity. Amyloid-β1–42 did not differ between amnestic and non-amnestic Alzheimer’s disease, and receiver operating characteristic curve analyses indicated that amyloid-β1–42 was equally effective in discriminating both groups from frontotemporal lobar degeneration. However, CSF concentrations of phosphorylated tau, total tau, and the ratio of phosphorylated tau to amyloid-β1–42 were significantly lower in non-amnestic compared to amnestic Alzheimer’s disease patients. Receiver operating characteristic curve analyses for these markers showed reduced area under the curve when discriminating non-amnestic Alzheimer’s disease from frontotemporal lobar degeneration, compared to discrimination of amnestic Alzheimer’s disease from frontotemporal lobar degeneration. In addition, the ATN framework was relatively insensitive to frontotemporal lobar degeneration, and these patients were likely to be classified as having normal biomarkers or biomarkers suggestive of primary Alzheimer’s disease pathology. We conclude that amyloid-β1–42 maintains high sensitivity to A status, although with lower specificity, and this single biomarker provides better sensitivity to non-amnestic Alzheimer’s disease than either the ATN framework or the phosphorylated-tau/amyloid-β1–42 ratio. In contrast, T and N status biomarkers differed between amnestic and non-amnestic Alzheimer’s disease; standard cut-offs for phosphorylated tau and total tau may thus result in misclassifications for non-amnestic Alzheimer’s disease patients. Consideration of clinical syndrome may help improve the accuracy of ATN designations for identifying true non-amnestic Alzheimer’s disease.
A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies
Authors: Power Jonathan D , Lynch Charles J , Adeyemo Babatunde , Petersen Steven E
Published: May 2020

This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like “Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties”. Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.
Coronaviruses and the Chemical Senses: Past, Present, and Future
Authors: Pellegrino Robert , Cooper Keiland W , Di Pizio Antonella , Joseph Paule V , Bhutani Surabhi , Parma Valentina
Published: May 2020

A wealth of rapidly evolving reports suggests that olfaction and taste disturbances may be manifestations of the novel COVID-19 pandemic. While otolaryngological societies worldwide have started to consider chemosensory evaluation as a screening tool for COVID-19 infection, the true nature of the relationship between the changes in chemosensory ability and COVID-19 is unclear. Our goal with this review is to provide a brief overview of published and archived literature, as well as the anecdotal reports and social trends related to this topic up to April 29, 2020. We also aim to draw parallels between the clinical/chemosensory symptomology reported in association to past coronavirus pandemics (such as SARS and MERS) and the novel COVID-19. This review also highlights current evidence on persistent chemosensory disturbances after the infection has resolved. Overall, our analysis pinpoints the need for further studies: (1) to better quantify olfaction and taste disturbances associated with SARS-CoV-2 infection, compared to those of other viral and respiratory infections, (2) to understand the relation between smell, taste, and chemesthesis disturbances in COVID-19, and (3) to understand how persistent are these disturbances after the infection has resolved.