bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical simulation of flow field in a Laval nozzle based on one-dimensional Euler equation
Authors: Cai Han , Jiang Zirun
Published: September 2021

Numerical simulation refers to a computer research method which runs calculation based on a specific mathematical model to simulate actual physical processes. It is a powerful tool to analyse complex engineering problems. In this paper, a numerical method for solving the steady one-dimensional Euler equations using a two-step second-order difference scheme is developed. The method is implemented by a Python code. The method is applied to numerical simulation of flows in a Laval nozzle and used to investigate the influence of shapes of Laval nozzle. It is found that the physical quantities of the nozzle flow show a positive correlation trend with different throat positions. In terms of the temperature, density, and pressure, they increase during the initial evolutions, then reach the maximum point and produce significant fluctuations, indicating the flow flows into the transonic stage. Subsequently, those physical properties gradually tend to a stable value during the supersonic stage. Moreover, it is observed that the closer the throat is to the exit, the lower the Mach number at which it eventually stabilizes. Finally, a suggestion about the best shape of the nozzle which can realize the max efficiency is concluded. Despite using an inviscid flow model, the steady pressures are quite satisfactorily predicted over the range of frequencies studied.
Interpretation of TeV electron and positron data with a single source
Authors: Xie Bohan
Published: September 2021

In recent years, many experiments, for example, like AMS-02, CALET, DAMPE, HESS and Fermi-LAT, have enhanced their precision in detecting the flux of cosmic rays, especially for high energy particles. We aim to interpret the electrons and positrons data above 1 TeV by using a model considering supernova remnants (SNRs) and the single pulsar as the major sources of electrons and positrons. We explain the rationality of applying the continuous scenario model for the single pulsar. We use the data above and choose seven parameters (d, t, γe, Etot, Q0,SNR, γSNR, Ec,SNR
) to perform the best fit for electrons and positrons flux at Earth. We perform the fit for the data above 1 Tev and 10 GeV to analyze how the single pulsar and SNRs contribute to the total flux. We also find that the fit ranges we choose for the parameters have a significant influence on the result. By considering this effect, a further analysis on the best fit by constraining γe
and Ec,SNR
shows that the pulsar PSR B0656+14 can provide most of the flux to the data above 1 TeV.
Research on Furnace Temperature Curve
Authors: Zhang Jieya , Jing Yijun , Chao Yongzheng , Mao Yuanyuan
Published: September 2021

The furnace temperature curve of the reflow furnace records the temperature changes during the soldering process of the printed circuit board components, which is the main factor influencing the quality of component soldering. Given the temperature of each temperature zone and the furnace passing speed, use the conveyor belt model, Fourier’s law and Newton’s law of cooling to establish a mathematical model to find the midpoint temperature of each temperature zone and obtain the furnace temperature curve. A search algorithm is further set up to obtain the maximum conveyor belt passing speed.
Numerical Study on Subsonic-Supersonic Laval Nozzle Using MacCormack Scheme
Authors: Li Boyang , Wu Jingbo , Liu Yuzhou
Published: September 2021

Due to the wide application of the Delaware nozzle in many fields, the flow characteristics of the subsonic-supersonic isentropic flow in the De-Laval nozzle are analyzed numerically. The flow in the nozzle can be simplified to a quasi-one-dimensional flow problem. First, the MacCormack format is employed to discretize the control equations in conservative form. Then, the results with and without artificial viscosity are compared. Grid independence is also discussed. The results show that the numerical solution and the theoretical solution agree very well, indicating that the numerical simulation results are very reliable. In addition, a higher pressure will reduce the peak and valley values of Mach number, pressure, density, temperature and velocity in the nozzle, and these extreme values of subsonic supersonic isentropic nozzle appear earlier. Additionally, the shock wave is accurately captured, and the shock wave is behind the throat. This research is of great significance to understand the flow characteristics in the nozzle.
Research on fracture parameters of asphalt mixture based on XFEM and J Integral method
Authors: Zeng Guowei , Zhou Peng
Published: September 2021

The fracture toughness and load displacement relationship of the three point bending (3pb) asphalt mixture specimens with different notch position were experimentally and numerical investigated. Firstly, the extended finite element was used to simulate the three-point bending and fracture process of asphalt mixture beam, and the load and displacement curves were obtained, which were compared with the test results to verify the accuracy of the XFEM model. Secondly, J contour integral method in ABAQUS software was used to solve the stress intensity factor under the three-point bending of asphalt mixture, and the fracture parameters of asphalt mixture were provided by the numerical calculation results. The results showed that the offset distance increases, the influence of the type II crack in the fracture process increases, but the type I crack still was dominant.
Gödel’s Incompleteness Theorem and Problems Outside Quantum Mechanics
Authors: Yan Yutong
Published: September 2021

Gödel’s Incompleteness Theorem is considered a significant finding in logic and mathematics and has many indications in physics. This paper analyzes and concludes that Gödel’s Incompleteness theorem potentially parallels problems of reference frame in classical physics and other issues considering self-inclusion with regard to consciousness, which not necessarily relevant to quantum mechanics. It also demonstrates further investigations on Gödel’s Incompleteness theorem, which can add to our understanding of problems in classical physics and philosophy of mind that still require in-depth research.
Study on the Relationship between Quantum Entanglement and Spacetime
Authors: Zhu Peiyu
Published: September 2021

Quantum entanglement and spacetime are strongly associated fields in modern physics. This paper investigated the connection between quantum entanglement and spacetime, particularly focusing on how they mutually form one another. The quantum mechanics and relativity theory cannot peacefully coexist in some cases, so it is difficult to combine them together. This article analyzes studies which suggest quantum entanglement can fabricate the continuous spacetime, and studies that imply spacetime geometry can explain quantum entanglement. These findings may bring us a better vision of the relation between quantum mechanics and relativity, as well as pave a path to the unification of quantum mechanics and relativity.
The challenge of the new theoretical system of physics in the 21st century to the popular views of physics in the world
Authors: Zhou Jiang Hua
Published: September 2021

This paper is based on the new theoretical system of physics in the 21st century published by the British Physical Society IOP Journal of physics in 2020. It is a challenge to some popular views of physics today! It negates the big bang theory, general relativity, epistemology of gravitational wave existence and epistemology of antimatter existence. It denies the research method of relying on the electron Collider to find the smallest particle in the material world! It is put forward that “the state of matter light” is the epistemology of the fifth physical state; stars are not composed of hydrogen and helium, nor are they nuclear reactions; the formation reason of Galactic System and the epistemology of cosmic unit. This paper is a summary and deduction of the new theoretical system of physics in the 21st century.
Five Methods of Exoplanet Detection
Authors: Dai Ziqi , Ni Dong , Pan Lizhuang , Zhu Yiheng
Published: September 2021

The study of exoplanetary systems can help us understand the formation and evolution of the solar system itself and search for terrestrial planets in the habitable and extrasolar lives in exoplanetary systems. Exoplanets have become an important area of astrophysics in the last two decades. This paper reviews five different methods to detect exoplanets, including direct imaging, astrometry, radial velocity, transit event observation, and microlensing. These approaches could expand the sample size of exoplanets and further our understanding of the types, formation and evolution of exoplanets.
Phosphatidylinositol and phosphatidylinositol-3-phosphate activate HOPS to catalyze SNARE assembly, allowing small headgroup lipids to support the terminal steps of membrane fusion
Authors: Torng Thomas , Wickner William
Published: September 2021

Vacuole membranes contain several lipid species. Membrane fusion occurs very poorly with just phosphatidylcholine. We now report that PI, PI3P, and Erg first activate HOPS to catalyze assembly of trans-SNARE complexes, and then PE, PS, PA, and DAG facilitate membrane bilayer rearrangements for complete fusion.
The EnigmaticCulex pipiens(Diptera: Culicidae) Species Complex: Phylogenetic Challenges and Opportunities From a Notoriously Tricky Mosquito Group
Authors: Aardema Matthew L , Olatunji Sarah K , Fonseca Dina M
Published: September 2021

Our understanding of how natural selection and demographic processes produce and maintain biological diversity remains limited. However, developments in high-throughput genomic sequencing coupled with new analytical tools and phylogenetic methods now allow detailed analyses of evolutionary patterns in genes and genomes responding to specific demographic events, ecological changes, or other selection pressures. Here, we propose that the mosquitoes in the Culex pipiens complex, which include taxa of significant medical importance, provide an exceptional system for examining the mechanisms underlying speciation and taxonomic radiation. Furthermore, these insects may shed light on the influences that historical and contemporary admixture have on taxonomic integrity. Such studies will have specific importance for mitigating the disease and nuisance burdens caused by these mosquitoes. More broadly, they could inform predictions about future evolutionary trajectories in response to changing environments and patterns of evolution in other cosmopolitan and invasive species that have developed recent associations with humans.
Losing an Archive: Doing Place-Based History in the Age of the Anthropocene
Authors: Dunlop Catherine Tatiana
Published: September 2021

How will climate change affect place-based historical research? In this personal essay, author Catherine Dunlop reflects on her experience of researching historical attitudes toward France’s mistral wind while simultaneously feeling the wind’s powerful gusts firsthand. Unsatisfied with the French state’s indoor weather archives, Dunlop develops her research project around the outdoor archives that she encounters on a daily basis. Through her sensory experiences of the mistral, both at sea and on land, Dunlop hones her research questions and ultimately deepens her understanding of the mistral’s role in modern French history. Even as she acknowledges the benefits that came from her firsthand encounters with the mistral, Dunlop recognizes the fragile and tenuous aspects of place-based historical research. Places like Provence, and their distinctive winds, are changing. The Anthropocene, she argues, will alter historians’ ability to connect with and learn from the landscape heritage of the places that they visit. Losing our outdoor archives to climate change will impact historical research in ways that we are just beginning to realize.
Anatomy of a Cyberattack
Authors: Stowman Anne M , Cacciatore Lori S , Cortright Valerie , McConnell John , Wilburn Clayton , Bryant Bronwyn , Frisch Nora , Kalof Alexandra N
Published: September 2021

Objectives

Our institution was subject to a multi-institutional, systemwide cyberattack that led to a complete shutdown of multiple major patient care, operational, and communication systems for more than 25 days. The electronic health record computer system was taken offline, as was the hospital email and authentication systems, internet access, and the laboratory information system. The impact on the hospital and patient care was substantial, and our laboratories were crippled.

Methods

Our laboratory endured challenges in communication because of the loss of connectivity and difficulties in laboratory management, and we recognized a need to restructure leadership to maintain operations during the crisis. As an academic institution, residents and trainees were also significantly affected by the disaster.

Results

We developed an incident command team (ICT), alternative methods of communication, and innovative management strategies to remain operational. Trainees were incorporated into the disaster-relief efforts, with negative impacts on resident education.

Conclusions

This paper focuses on the challenges in communication and lab management as well as the need for an alternative leadership structure during the crisis. We also highlight the unique experience of our trainees during this prolonged downtime, underscoring the importance of incorporating resident trainees into the daily ICT’s administrative activities as an invaluable lab management experience.
Bone Marrow Collection: Comparison of Unassisted vs Assisted Bedside Collections by a Laboratory Technologist
Authors: Ali Saba F , Cloe Adam , Siaghani Parwiz J , Himchak Evan , Cantu David , Gaal Karl , Kim Young S , Afkhami Michelle , Pillai Raju , Chan Wanda , Quirk Elizabeth , Weisenburger Dennis D , Aoun Patricia , Song Joo Y
Published: September 2021

Objectives

Bone marrow collections are often difficult, and creating quality smears and touch preparations at the bedside can prove challenging. The objective of this study is to compare the quality of bone marrow specimens between unassisted and assisted bone marrow collections by a bone marrow technologist.

Methods

Data for this study were collected from 422 hematopathology reports over 14 months. We recorded the bone marrow quality of the different parts (aspirate smears, touch imprints, core biopsy, and clot/particle sections) as adequate, suboptimal, or inadequate. Student t test statistical analysis was performed between the corresponding parts in the two groups.

Results

Our results demonstrate that the quality of assisted bone marrow specimens is significantly better compared with unassisted specimens, particularly for the aspirate smears (P < .0001) and touch imprints (P < .0001). Notably, the quality of aspirate smears was improved, which is important for cytologic evaluation.

Conclusions

We conclude that assistance by a bone marrow technologist resulted in a significant improvement in the quality of bone marrow collection.
Potential for Process Improvement of Clinical Flow Cytometry by Incorporating Real-Time Automated Screening of Data to Expedite Addition of Antibody Panels
Authors: Simonson Paul D , Lee Aaron Y , Wu David
Published: September 2021

Objectives

We desired an automated approach to expedite ordering additional antibody panels in our clinical flow cytometry lab. This addition could improve turnaround times, decrease time spent revisiting cases, and improve consistency.

Methods

We trained a machine learning classifier to use our screening B-cell panel to predict whether we should order an additional panel to distinguish chronic lymphocytic lymphoma from mantle cell lymphoma. We used data from 2016 to 2018 for training and validation, and cases were restricted to the first case per patient (9,635 cases, 887 with the additional panel). We applied the model in real time over approximately 2.5 months in 2020 to 376 sequential cases, with automated email notifications for positive predictions.

Results

Using 80% of the data from 2016 to 2018 to train and 20% for validation, we achieved 95% area under the receiving operating characteristic curve (AUROC) and 94% accuracy in the validation set. Applying the classifier in real time achieved 89% AUROC and 94% real-time prediction accuracy (precision [positive predictive value] = 51%, recall [sensitivity] = 78%, and F1 score = 0.62). Fourteen of the 17 false positives had prior diagnoses to which the algorithm was not privy.

Conclusions

As an observational, not interventional study, our system performed well on testing within our laboratory for identifying cases to be flagged but cannot be used without laboratory-specific modifications.
Insulin-Like Growth Factor 1 in the Early Postoperative Assessment of Acromegaly
Authors: Donegan Diane , Algeciras-Schimnich Alicia , Ashrafzadeh-Kian Susan , Erickson Dana
Published: September 2021

Objectives

Assessment of surgical outcome in acromegaly is typically recommended at 3 to 6 months following surgery. The purpose of this study was to determine if insulin-like growth factor 1 (IGF-1) concentrations at 6 weeks were equally predictive of surgical outcomes compared with IGF-1 concentrations at 3 to 6 months postoperatively applying newer IGF-1 assays.

Methods

Retrospective review of patients with newly diagnosed acromegaly who had surgery between 2013 and 2020 and had postoperative IGF-1 measured by 6 weeks and 3 to 6 months.

Results

At 6 weeks, 20 (35%) of the total 57 had normal IGF-1 and became abnormal in 1 at 3 to 6 months, whereas 37 (65%) of 57 had abnormal IGF-1 concentrations at 6 weeks, which normalized in 1 patient by 3 to 6 months. In patients who changed clinical status, IGF-1 at 6 weeks was within ±0.1-fold of normal. Although a difference was seen between median IGF-1 concentrations (286 vs 267 ng/mL, P = .009) at 6 weeks and 3 to 6 months, the mean reduction was small (–19.9 ng/mL).

Conclusions

Compared with 3 to 6 months, use of IGF-1 at 6 weeks was associated with a change in clinical status in 3.5% of patients. Therefore, in most patients, IGF-1 at 6 weeks can be used to assess clinical outcome via newer assays.
Distinct Mutation Landscapes Between Acute Myeloid Leukemia With Myelodysplasia-Related Changes and De Novo Acute Myeloid Leukemia
Authors: Gao Yajuan , Jia Mingnan , Mao Yueying , Cai Hao , Jiang Xianyong , Cao Xinxin , Zhou Daobin , Li Jian
Published: September 2021

Objectives

To explore the distinct mutation profiles between acute myeloid leukemia with myelodysplasia-related changes (AML-MRC) and de novo AML and their relationships with prognosis.

Methods

Next-generation sequencing of 42 myeloid neoplasm-related genes in 293 newly diagnosed patients with AML.

Results

Eighty-four patients had AML-MRC, and 161 patients had de novo AML. The mutation rates of ASXL1 (25% vs 8.7%, P = .001), NRAS (17.9% vs 8.1%, P = .022), PTPN11 (11.9% vs 5%, P = .048), SETBP1 (6% vs 0.6%, P = .033), SRSF2 (11.9% vs 5.6%, P = .08), TP53 (16.7% vs 1.2%, P < .001), and U2AF1 (17.9% vs 7.5%, P = .014) in AML-MRC were higher than those in de novo AML, while the rates of FLT3-ITD (3.6% vs 15.5%, P = .005), KIT (0% vs 6.2%, P = .046), WT1 (3.6% vs 9.9%, P = .077), NPM1 (1.2% vs 21.7%, P < .001), and CEBPA (4.8% vs 24.2%, P < .001) mutation were lower. The appearance of ASXL1, TP53, U2AF1, SRSF2, and SETBP1 mutation could predict AML-MRC–like features in de novo AML, which was related to older age (60 vs 51 years, P = .001), low WBC counts (4.7 × 109/L vs 11.6 × 109/L, P = .001), and inferior outcomes (median overall survival, 15 months vs not reached, P = .003).

Conclusions

The presence of AML-MRC–related mutations can reveal a subset of patients with de novo AML similar to patients with AML-MRC.
Gabapentin Reduces Alcohol Intake in Rats by Regulating NF-κB Signaling Pathway Via PPAR γ
Authors: Li Jing , Xu Kewei , Ding Hao , Xi Qiaozhen
Published: September 2021

Aims

Increasing preclinical and clinical reports have demonstrated the efficacy of gabapentin (GBP) in treating alcohol use disorder (AUD). However, the mechanism of the effects of GBP in AUD is largely unknown. Herein, we sought to investigate the effect of GBP in a rat model of AUD and explore the underlying mechanism.

Methods

The intermittent access to 20% ethanol in a 2-bottle choice (IA2BC) procedure was exploited to induce high voluntary ethanol consumption in rats. The rats were treated daily for 20 days with different doses of GBP, simultaneously recording ethanol/water intake. The locomotor activity and grooming behavior of rats were also tested to evaluate the potential effects of GBP on confounding motor in rats. The levels of IL-1β and TNF-α in serum and hippocampus homogenate from the rats were detected by using ELISA. The expressions of peroxisome proliferator-activated-receptor γ (PPAR-γ) and nuclear factor-κB (NF-κB) in the hippocampus were determined by immunofluorescence and western blot.

Results

GBP reduced alcohol consumption, whereas increased water consumption and locomotor activity of rats. GBP was also able to decrease the levels of IL-1β and TNF-α in both serum and hippocampus, in addition to the expression of NF-κB in the hippocampus. Furthermore, these effects attributed to GBP were observed to disappear in the presence of bisphenol A diglycidyl ether (BADGE), a specific inhibitor of PPAR-γ.

Conclusions

Our findings revealed that GBP could activate PPAR-γ to suppress the NF-κB signaling pathway, contributing to the decrease of ethanol consumption and ethanol-induced neuroimmune responses.
Racialized Trajectories to Catalan Higher Education: Language, Anti-Racism and the ‘Politics of Listening’
Authors: Khan Kamran , Gallego-Balsà LíDIA
Published: September 2021

This paper examines two forms of listening in relation to racism and the minoritylanguage context in Catalonia. The research is based on a collaboration between twoacademics and RC (Racialised Catalans) which started with initial interviews on theireducational experiences and trajectories in Catalonia. From there, a collaboration betweenthe academics and a inter-cultural, anti-racism group (Junts a pseudonym) emerged .First, we analyse the role of the white listening subject in which RC are discriminatedagainst for a perceived lack of Catalan. Second, we describe how the group made otherslisten through their work. Thus, listening results in a form of 'undecideability' which requireswider society for the possibilities of racial equality.
Faux Pas Recognition and Executive Processes in Patients with Alcohol Use Disorder: Toward an Investigation of Interindividual Heterogeneity
Authors: Schmid Franca , Moreau Franklin , Benzerouk Farid , Raucher-Chéné Delphine , Kaladjian Arthur , Gierski Fabien , Henry Audrey
Published: September 2021

Objective

Impairment of executive functions (EF) has been documented for decades in patients with alcohol use disorders (AUD), while more recent studies have also reported impaired theory of mind. Both have been associated with negative outcomes, particularly a high risk of relapse. However, the interrelatedness of EF and theory of mind impairments remains subject to debate.

Method

About 19 AUD outpatients and 20 healthy controls (HC) were asked to complete measures of motor inhibition, mental flexibility, and updating to assess EF, and the faux pas test to assess theory of mind.

Results

As expected, patients’ mean performances on EF and faux pas measures were poorer than those of HC. Correlational analyses revealed that executive processes were differentially related to faux pas subscores. Additional single-case analyses corroborated the strong association between EF and faux pas interpretation, as patients with AUD mostly had congruent performances (i.e., both EF and faux pas impaired or both EF and faux pas preserved).

Conclusions

This study highlights the interrelatedness of EF and faux pas performances in AUD, but also emphasizes the incomplete overlap of the cognitive processes involved in these tasks, with heterogeneous patterns of association. Based on these findings, tailored cognitive rehabilitation programs that simultaneously target EF and faux pas recognition could be developed to favor patients’ social inclusion and reduce the risk of relapse. Results also argue in favor of systematic screening for EF and theory of mind impairments among AUD patients.
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
Authors: Bommert Andrea , Welchowski Thomas , Schmid Matthias , Rahnenführer Jörg
Published: September 2021

Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies for data with a survival outcome are rare. We compare 14 filter methods for feature selection based on 11 high-dimensional gene expression survival data sets. The aim is to provide guidance on the choice of filter methods for other researchers and practitioners. We analyze the accuracy of predictive models that employ the features selected by the filter methods. Also, we consider the run time, the number of selected features for fitting models with high predictive accuracy as well as the feature selection stability. We conclude that the simple variance filter outperforms all other considered filter methods. This filter selects the features with the largest variance and does not take into account the survival outcome. Also, we identify the correlation-adjusted regression scores filter as a more elaborate alternative that allows fitting models with similar predictive accuracy. Additionally, we investigate the filter methods based on feature rankings, finding groups of similar filters.
Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power
Authors: Yu Tzu-Hui , Su Bo-Han , Battalora Leo Chander , Liu Sin , Tseng Yufeng Jane
Published: September 2021

The trade-off between a machine learning (ML) and deep learning (DL) model’s predictability and its interpretability has been a rising concern in central nervous system-related quantitative structure–activity relationship (CNS-QSAR) analysis. Many state-of-the-art predictive modeling failed to provide structural insights due to their black box-like nature. Lack of interpretability and further to provide easy simple rules would be challenging for CNS-QSAR models. To address these issues, we develop a protocol to combine the power of ML and DL to generate a set of simple rules that are easy to interpret with high prediction power. A data set of 940 market drugs (315 CNS-active, 625 CNS-inactive) with support vector machine and graph convolutional network algorithms were used. Individual ML/DL modeling methods were also constructed for comparison. The performance of these models was evaluated using an additional external dataset of 117 market drugs (42 CNS-active, 75 CNS-inactive). Fingerprint-split validation was adopted to ensure model stringency and generalizability. The resulting novel hybrid ensemble model outperformed other constituent traditional QSAR models with an accuracy of 0.96 and an F1 score of 0.95. With the power of the interpretability provided with this protocol, our model laid down a set of simple physicochemical rules to determine whether a compound can be a CNS drug using six sub-structural features. These rules displayed higher classification ability than classical guidelines, with higher specificity and more mechanistic insights than just for blood–brain barrier permeability. This hybrid protocol can potentially be used for other drug property predictions.
How much can deep learning improve prediction of the responses to drugs in cancer cell lines?
Authors: Chen Yurui , Zhang Louxin
Published: September 2021

The drug response prediction problem arises from personalized medicine and drug discovery. Deep neural networks have been applied to the multi-omics data being available for over 1000 cancer cell lines and tissues for better drug response prediction. We summarize and examine state-of-the-art deep learning methods that have been published recently. Although significant progresses have been made in deep learning approach in drug response prediction, deep learning methods show their weakness for predicting the response of a drug that does not appear in the training dataset. In particular, all the five evaluated deep learning methods performed worst than the similarity-regularized matrix factorization (SRMF) method in our drug blind test. We outline the challenges in applying deep learning approach to drug response prediction and suggest unique opportunities for deep learning integrated with established bioinformatics analyses to overcome some of these challenges.
Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing
Authors: Wang Ting , Lu Haojie , Zeng Ping
Published: September 2021

Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection–union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations
Authors: Wang Jinxian , Liu Xuejun , Shen Siyuan , Deng Lei , Liu Hui
Published: September 2021

Motivation

Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound–protein interactions, but it has not been applied to the screening of drug combinations.

Results

In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug–Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation.

Availability and implementation

Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master
Stratified neural networks in a time-to-event setting
Authors: Kuruc Fabrizio , Binder Harald , Hess Moritz
Published: September 2021

Deep neural networks are frequently employed to predict survival conditional on omics-type biomarkers, e.g., by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations in clinical studies, combining different data sets has been proposed to improve learning of network parameters. However, if baseline hazards differ between the studies, the assumptions of Cox proportional hazards model are violated. Based on high dimensional transcriptome profiles from different tumor entities, we demonstrate how using a stratified partial likelihood as loss function allows for accounting for the different baseline hazards in a deep learning framework. Additionally, we compare the partial likelihood with the ranking loss, which is frequently employed as loss function in machine learning approaches due to its seemingly simplicity. Using RNA-seq data from the Cancer Genome Atlas (TCGA) we show that use of stratified loss functions leads to an overall better discriminatory power and lower prediction error compared to their non-stratified counterparts. We investigate which genes are identified to have the greatest marginal impact on prediction of survival when using different loss functions. We find that while similar genes are identified, in particular known prognostic genes receive higher importance from stratified loss functions. Taken together, pooling data from different sources for improved parameter learning of deep neural networks benefits largely from employing stratified loss functions that consider potentially varying baseline hazards. For easy application, we provide PyTorch code for stratified loss functions and an explanatory Jupyter notebook in a GitHub repository.
Prediction of RNA secondary structure including pseudoknots for long sequences
Authors: Sato Kengo , Kato Yuki
Published: September 2021

RNA structural elements called pseudoknots are involved in various biological phenomena including ribosomal frameshifts. Because it is infeasible to construct an efficiently computable secondary structure model including pseudoknots, secondary structure prediction methods considering pseudoknots are not yet widely available. We developed IPknot, which uses heuristics to speed up computations, but it has remained difficult to apply it to long sequences, such as messenger RNA and viral RNA, because it requires cubic computational time with respect to sequence length and has threshold parameters that need to be manually adjusted. Here, we propose an improvement of IPknot that enables calculation in linear time by employing the LinearPartition model and automatically selects the optimal threshold parameters based on the pseudo-expected accuracy. In addition, IPknot showed favorable prediction accuracy across a wide range of conditions in our exhaustive benchmarking, not only for single sequences but also for multiple alignments.
scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding
Authors: Wu Hao , Wu Yingfu , Jiang Yuhong , Zhou Bing , Zhou Haoru , Chen Zhongli , Xiong Yi , Liu Quanzhong , Zhang Hongming
Published: September 2021

Single-cell Hi-C data are a common data source for studying the differences in the three-dimensional structure of cell chromosomes. The development of single-cell Hi-C technology makes it possible to obtain batches of single-cell Hi-C data. How to quickly and effectively discriminate cell types has become one hot research field. However, the existing computational methods to predict cell types based on Hi-C data are found to be low in accuracy. Therefore, we propose a high accuracy cell classification algorithm, called scHiCStackL, based on single-cell Hi-C data. In our work, we first improve the existing data preprocessing method for single-cell Hi-C data, which allows the generated cell embedding better to represent cells. Then, we construct a two-layer stacking ensemble model for classifying cells. Experimental results show that the cell embedding generated by our data preprocessing method increases by 0.23, 1.22, 1.46 and 1.61$\%$ comparing with the cell embedding generated by the previously published method scHiCluster, in terms of the Acc, MCC, F1 and Precision confidence intervals, respectively, on the task of classifying human cells in the ML1 and ML3 datasets. When using the two-layer stacking ensemble framework with the cell embedding, scHiCStackL improves by 13.33, 19, 19.27 and 14.5 over the scHiCluster, in terms of the Acc, ARI, NMI and F1 confidence intervals, respectively. In summary, scHiCStackL achieves superior performance in predicting cell types using the single-cell Hi-C data. The webserver and source code of scHiCStackL are freely available at http://hww.sdu.edu.cn:8002/scHiCStackL/ and https://github.com/HaoWuLab-Bioinformatics/scHiCStackL, respectively.
Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data
Authors: Song Wenjing , Wang Weiwen , Dai Dao-Qing
Published: September 2021

The discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have certain guiding significance for cancer subtyping. It is a challenge to effectively utilize the valuable information of distinct subtypes to produce more accurate and reliable subtypes. A weighted ensemble sparse latent representation (subtype-WESLR) is proposed to detect cancer subtypes on heterogeneous omics data. Using a weighted ensemble strategy to fuse base clustering obtained by distinct methods as prior knowledge, subtype-WESLR projects each sample feature profile from each data type to a common latent subspace while maintaining the local structure of the original sample feature space and consistency with the weighted ensemble and optimizes the common subspace by an iterative method to identify cancer subtypes. We conduct experiments on various synthetic datasets and eight public multi-view datasets from The Cancer Genome Atlas. The results demonstrate that subtype-WESLR is better than competing methods by utilizing the integration of base clustering of exist methods for more precise subtypes.
Characterizing viral circRNAs and their application in identifying circRNAs in viruses
Authors: Niu Mengting , Ju Ying , Lin Chen , Zou Quan
Published: September 2021

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism, which play an important role in a variety of biological activities. Viruses can encode circRNA, and viral circRNAs have been found in multiple single-stranded and double-stranded viruses. However, the characteristics and functions of viral circRNAs remain unknown. Sequence alignment showed that viral circRNAs are less conserved than circRNAs in animal, indicating that the viral circRNAs may evolve rapidly. Through the analysis of the sequence characteristics of viral circRNAs and circRNAs in animal, it was found that viral circRNAs and animals circRNAs are similar in nucleic acid composition, but have obvious differences in secondary structure and autocorrelation characteristics. Based on these characteristics of viral circRNAs, machine learning algorithms were employed to construct a prediction model to identify viral circRNA. Additionally, analysis of the interaction between viral circRNA and miRNAs showed that viral circRNA is expected to interact with 518 human miRNAs, and preliminary analysis of the role of viral circRNA. And it has been also found that viral circRNAs may be involved in many KEGG pathways related to nervous system and cancer. We curated an online server, and the data and code are available: http://server.malab.cn/viral-CircRNA/.
Heterogeneous graph attention network based on meta-paths for lncRNA–disease association prediction
Authors: Zhao Xiaosa , Zhao Xiaowei , Yin Minghao
Published: September 2021

Motivation: Discovering long noncoding RNA (lncRNA)–disease associations is a fundamental and critical part in understanding disease etiology and pathogenesis. However, only a few lncRNA–disease associations have been identified because of the time-consuming and expensive biological experiments. As a result, an efficient computational method is of great importance and urgently needed for identifying potential lncRNA–disease associations. With the ability of exploiting node features and relationships in network, graph-based learning models have been commonly utilized by these biomolecular association predictions. However, the capability of these methods in comprehensively fusing node features, heterogeneous topological structures and semantic information is distant from optimal or even satisfactory. Moreover, there are still limitations in modeling complex associations between lncRNAs and diseases. Results: In this paper, we develop a novel heterogeneous graph attention network framework based on meta-paths for predicting lncRNA–disease associations, denoted as HGATLDA. At first, we conduct a heterogeneous network by incorporating lncRNA and disease feature structural graphs, and lncRNA–disease topological structural graph. Then, for the heterogeneous graph, we conduct multiple metapath-based subgraphs and then utilize graph attention network to learn node embeddings from neighbors of these homogeneous and heterogeneous subgraphs. Next, we implement attention mechanism to adaptively assign weights to multiple metapath-based subgraphs and get more semantic information. In addition, we combine neural inductive matrix completion to reconstruct lncRNA–disease associations, which is applied for capturing complicated associations between lncRNAs and diseases. Moreover, we incorporate cost-sensitive neural network into the loss function to tackle the commonly imbalance problem in lncRNA–disease association prediction. Finally, extensive experimental results demonstrate the effectiveness of our proposed framework.
Learning representation for multiple biological networks via a robust graph regularized integration approach
Authors: Zhang Xiwen , Wang Weiwen , Ren Chuan-Xian , Dai Dao-Qing
Published: September 2021

Learning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network-based inference tasks, including drug–target interaction prediction, gene function identification and fine-grained species categorization, are conducted using representation features learned from our method. Biological networks at different scales and levels of sparsity are involved. Experimental results on real-world data show that the proposed method has robust performance compared with alternatives. Overall, by eliminating noise and integrating effectively, the proposed method is able to learn useful representations from multiple biological networks.
Hypergraph-based logistic matrix factorization for metabolite–disease interaction prediction
Authors: Ma Yingjun , Ma Yuanyuan
Published: September 2021

Motivation

Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships.

Results

In this study, we presented a novel approach with hypergraph-based logistic matrix factorization, HGLMF, to predict the potential interactions between metabolites and disease. First, the molecular structures and gene associations of metabolites and the hierarchical structures and GO functional annotations of diseases were extracted to build various similarity measures of metabolites and diseases. Next, the kernel neighborhood similarity of metabolites (or diseases) was calculated according to the completed interactive network. Second, multiple networks of metabolites and diseases were fused, respectively, and the hypergraph structures of metabolites and diseases were built. Finally, a logistic matrix factorization based on hypergraph was proposed to predict potential metabolite–disease interactions. In computational experiments, HGLMF accurately predicted the metabolite–disease interaction, and performed better than other state-of-the-art methods. Moreover, HGLMF could be used to predict new metabolites (or diseases). As suggested from the case studies, the proposed method could discover novel disease-related metabolites, which has been confirmed in existing studies.

Availability and implementation

The codes and dataset are available at: https://github.com/Mayingjun20179/HGLMF.

Supplementary information

Supplementary data are available at Bioinformatics online.