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Abstract Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of molecules, traditional fine-tuning of such pretrained GNNs on the target task can lead to poor generalization. To address this, we explore the adaptation of pretrained GNNs to the target task by jointly training them with multiple auxiliary tasks. This could enable the GNNs to learn both general and task-specific features, which may benefit the target task. However, a major challenge is to determine the relatedness of auxiliary tasks with the target task. To address this, we investigate multiple strategies to measure the relevance of auxiliary tasks and integrate such tasks by adaptively combining task gradients or by learning task weights via bi-level optimization. Additionally, we propose a novel gradient surgery-based approach, Rotation of Conflicting Gradients ( $$\mathop {\texttt{RCGrad}}\limits$$ RCGrad ), that learns to align conflicting auxiliary task gradients through rotation. Our experiments with state-of-the-art pretrained GNNs demonstrate the efficacy of our proposed methods, with improvements of up to 7.7% over fine-tuning. This suggests that incorporating auxiliary tasks along with target task fine-tuning can be an effective way to improve the generalizability of pretrained GNNs for molecular property prediction. Scientific contribution We introduce a novel framework for adapting pretrained GNNs to molecular tasks using auxiliary learning to address the critical issue of negative transfer. Leveraging novel gradient surgery techniques such as $$\mathop {\texttt{RCGrad}}\limits$$ RCGrad , the proposed adaptation framework represents a significant departure from the dominant pretraining fine-tuning approach for molecular GNNs. Our contributions are significant for drug discovery research, especially for tasks with limited data, filling a notable gap in the efficient adaptation of pretrained models for molecular GNNs.

Objective Riveting quality issues cannot be visually judged. By employing the far-field eddy current testing method, internal quality defects in riveted structural components can be detected, providing theoretical and technical support for product quality inspection. Method Riveting quality inspection and classification of clearance types are conducted without damaging the workpiece. Far-field eddy current testing experiments are carried out for the intermediate plate clearance and rivet hole clearance of rail vehicle riveted structural components respectively. Testing research on metro vehicle anti-roll torsion bar seats riveted to the body frame side beams is also conducted. Result & Conclusion From the results of the three sets of experiments, it is evident that the range of phase angles can distinguish between rivet and hole clearances and plate clearances. The phase amplitude range for hole clearances is 60° to 90°, while for plate clearances, it is 0 to 10°.

Abstract Due to cancer’s complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patient makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods’ abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanisms of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balanced reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.

The developmental toxicity and human health risks of triazole fungicides (TFs) have attracted worldwide attention due to the ability to enter the human body in a variety of ways. Nevertheless, the specific mechanism by which TFs exert remains incompletely understood. Given that retinoic acid (RA) signaling pathway are closely related to development, this study aimed to screen and identify developmentally disabled chemicals in commonly used TFs and to reveal the potential effects of TFs on developmental retardation through the RA signaling pathway in mouse embryonic stem cells (mESCs). Specifically, six typical TFs (myclobutanil, tebuconazole, hexaconazole, propiconazole, difenoconazole, and flusilazole) were exposed through the construction of an embryoid bodies (EBs)-based in vitro global differentiation models. Our results clarified that various TFs disturbed lineage commitment during early embryonic development. Crucially, the activation of RA signaling pathway, which alters the expression of key genes and interferes the transport and metabolism of retinol, may be responsible for this effect. Furthermore, molecular docking, molecular dynamics simulations, and experiments using a retinoic acid receptor α inhibitor provide evidence supporting the potential modulatory role of the retinoic acid signaling pathway in developmental injury. The current study offers new insights into the TFs involved in the RA signaling pathway that interfere with the differentiation process of mESCs, which is crucial for understanding the impact of TFs on pregnancy and early development.

ObjectiveExisting literature has not clearly elucidated whether SARS-CoV-2 infection increases the incidence of Parkinson’s disease or if Parkinson’s disease patients are more susceptible to the effects of SARS-CoV-2 infection. To clarify the issue, this study employs a genetic epidemiological approach to investigate the association.MethodsThis study utilizes a two-sample Mendelian randomization analysis. The primary analysis employs the inverse variance-weighted (IVW) method, supplemented by secondary analyses including MR-Egger regression, weighted median, IVW radial method, and weighted mode, to evaluate the bidirectional causal relationship between Parkinson’s disease and SARS-CoV-2 infection.ResultsIVW results showed no genetic causality between SARS-CoV-2 susceptibility, hospitalization rate and severity and Parkinson’s disease. (IVW method: p = 0.408 OR = 1.10 95% CI: 0.87 ~ 1.39; p = 0.744 OR = 1.11 95% CI: 0.94 ~ 1.09; p = 0.436 OR = 1.05 95% CI: 0.93 ~ 1.17). Parkinson’s disease was not genetically associated with susceptibility to new crown infections, hospitalization rates, and severity (IVW method: p = 0.173 OR = 1.01 95% CI: 0.99 ~ 1.03; p = 0.109 OR = 1.05 95% CI: 0.99 ~ 1.12; p = 0.209 OR = 1.03 95% CI: 0.99 ~ 1.07). MR-Egger regression, weighted median, IVW radial method, and weighted mode results are consistent with the results of the IVW method.ConclusionThis study does not support a genetic link between Parkinson’s disease and SARS-CoV-2 infection, and the association observed in previous cohort studies and observational studies may be due to other confounding factors.

Abstract Background Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. Methods We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. Results The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. Conclusion We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

Abstract Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper,we develop a generative framework G 2 Retro for one-step retrosynthesis prediction. G 2 Retro imitates the reversed logic of synthetic reactions. It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants. G 2 Retro defines a comprehensive set of reaction center types, and learns from the molecular graphs of the products to predict potential reaction centers. To complete synthons into reactants, G 2 Retro considers all the involved synthon structures and the product structures to identify the optimal completion paths, and accordingly attaches small substructures sequentially to the synthons. Here we show that G 2 Retro is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods.

Objective: To investigate the performance of machine learning (ML) methods for predicting outcomes from inpatient rehabilitation for subjects with TBI using a dataset with a large number of predictor variables. Our second objective was to identify top predictive features selected by the ML models for each outcome and to validate the interpretability of the models. Design: Secondary analysis using computational modeling of relationships between patients, injury and treatment activities and 6 outcomes, applied to the large multi-site, prospective, longitudinal observational dataset collected during the traumatic brain injury inpatient rehabilitation study. Setting: Acute inpatient rehabilitation. Participants: 1946 patients aged 14 years or older, who sustained a severe, moderate, or complicated mild TBI, and were admitted to 1 of 9 US inpatient rehabilitation sites between 2008 and 2011 (N=1946). Main Outcome Measures: Rehabilitation length of stay, discharge to home, FIM cognitive and FIM motor at discharge and at 9-months post discharge. Results: Advanced ML models, specifically gradient boosting tree model, performed consistently better than all other models, including classical linear regression models. Top ranked predictive features were identified for each of the 6 outcome variables. Level of effort, days to rehabilitation admission, age at rehabilitation admission, and advanced mobility activities were the most frequently top ranked predictive features. The highest-ranking predictive feature differed across the specific outcome variable. Conclusions: Identifying patient, injury, and rehabilitation treatment variables that are predictive of better outcomes will contribute to cost-effective care delivery and guide evidence-based clinical practice. ML methods can contribute to these efforts.

Particulate matter of size ≤ 2.5 μm (PM2.5) is a critical environmental threat that considerably contributes to the global disease burden. However, accompanied by the rapid research progress in this field, the existing research on developmental toxicity is still constrained by limited data sources, varying quality, and insufficient in-depth mechanistic analysis. This review includes the currently available epidemiological and laboratory evidence and comprehensively characterizes the adverse effects of PM2.5 on developing individuals in different regions and various pollution sources. In addition, this review explores the effect of PM2.5 exposure to individuals of different ethnicities, genders, and socioeconomic levels on adverse birth outcomes and cardiopulmonary and neurological development. Furthermore, the molecular mechanisms involved in the adverse health effects of PM2.5 primarily encompass transcriptional and translational regulation, oxidative stress, inflammatory response, and epigenetic modulation. The primary findings and novel perspectives regarding the association between public health and PM2.5 were examined, highlighting the need for future studies to explore its sources, composition, and sex-specific effects. Additionally, further research is required to delve deeper into the more intricate underlying mechanisms to effectively prevent or mitigate the harmful effects of air pollution on human health.

The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets.

Tebuconazole, one of the most widely used triazole fungicides, is reported to potentially pose a risk of inducing neurological disorders in human beings. Considering the increasing exposure, whether it could influence cognitive function remains to be elucidated. Herein, we used a mouse model to evaluate the potential cognitive risks and possible mechanisms from the continuous edible application of tebuconazole at low concentrations. Our study revealed that tebuconazole deteriorated spatial learning and memory and downregulated the expression of glutamate receptor subunits. Importantly, metagenomic analysis indicated that tebuconazole not only led to significant shifts in the composition and diversity of the gut microbiota but also changed intestinal homeostasis. Specifically, after exposure, tebuconazole circulated in the bloodstream and largely entered the gut–brain axis for disruption, including disturbing the Firmicutes/Bacteroidetes ratio, interrelated neurotransmitters and systemic immune factors. Moreover, pretreatment with probiotics improved immune factor expression and restored the deterioration of synaptic function and spatial learning and memory. The current study provides novel insights concerning perturbations of the gut microbiome and its functions as a potential new mechanism by which tebuconazole exposes cognitive function-related human health.

Abstract Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as $$\mathop {\mathtt {CTKG}}\limits$$ CTKG . $$\mathop {\mathtt {CTKG}}\limits$$ CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of $$\mathop {\mathtt {CTKG}}\limits$$ CTKG in various applications such as drug repurposing and similarity search, among others.

Abstract Introduction Applied health informatics infrastructure is a requirement for learning health systems and it is imperative that we train a workforce that can support this infrastructure. Our department offers courses in several interdisciplinary programs with topics ranging from bioinformatics to population health informatics. Due to changes in the field and our faculty members, we sought to assess our courses relevant to applied health informatics. Methods In this paper, we discuss the three‐phase evaluation of our program and include the survey we developed to identify the skills and knowledge base of our faculty. Results We show how this assessment allowed us to identify gaps and develop strategies for program expansion. Conclusions A focus on workforce development can help to guide and focus curricular review in an interdisciplinary graduate program.

Summary: In this work, we propose a new deep-learning model, MHCrank, to predict the probability that a peptide will be processed for presentation by MHC class I molecules. We find that the performance of our model is significantly higher than that of two previously published baseline methods: MHCflurry and netMHCpan. This improvement arises from utilizing both cleavage site-specific kernels and learned embeddings for amino acids. By visualizing site-specific amino acid enrichment patterns, we observe that MHCrank’s top-ranked peptides exhibit enrichments at biologically relevant positions and are consistent with previous work. Furthermore, the cosine similarity matrix derived from MHCrank’s learned embeddings for amino acids correlates highly with physiochemical properties that have been experimentally demonstrated to be instrumental in determining a peptide’s favorability for processing. Altogether, the results reported in this work indicate that MHCrank demonstrates strong performance compared with existing methods and could have vast applicability in aiding drug and vaccine development. Motivation: More than binding affinity, designing effective drugs and vaccines to stimulate the adaptive immune response requires accurate predictions regarding which antigens will be produced through processing and ultimately presented by MHC class I molecules. Constructing a model that is more acutely aware of biologically relevant features for processing and presentation will improve these predictions. However, future predictions will benefit from considering amino acid structure in addition to the antigen sequence.

Abstract Background Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer’s Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method We adapt a newly developed learning-to-rank approach $${\mathtt {PLTR}}$$ PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend $${\mathtt {PLTR}}$$ PLTR to better separate the most effective cognitive assessments and the less effective ones. Results Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.

Abstract In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9–11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this supplement issue, covering four major areas, namely computational method development, genomics analysis, network-based analysis and biomarker prediction. The selected papers perform cutting edge computational research applied to a broad range of human diseases such as cancer, neural degenerative and chronic inflammatory disease. They also proposed solutions for fundamental medical genomics problems range from basic data processing and quality control to functional interpretation, biomarker and drug prediction, and database releasing.

Abstract Background Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs. Methods We proposed an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining, and further developed a novel visualization method to organize and present the high-order directional DDI effects involving more than three drugs in an interactive, concise and comprehensive manner. We demonstrated its performance by mining the directional DDIs associated with myopathy using a publicly available FAERS dataset. Results Directional effects of DDIs involving up to seven drugs were reported. Our analysis confirmed previously reported myopathy associated DDIs including interactions between fusidic acid with simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs leading to increased risk for myopathy, such as the co-administration of zoledronate with different types of drugs including antibiotics (ciprofloxacin, levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). Finally, we visualized directional DDI findings via the proposed tool, which allows one to interactively select any drug combination as the baseline and zoom in/out to obtain both detailed and overall picture of interested drugs. Conclusions We developed a more efficient data mining strategy to identify high-order directional DDIs, and designed a scalable tool to visualize high-order DDI findings. The proposed method and tool have the potential to contribute to the drug interaction research and ultimately impact patient health care. Availability and implementation http://lishenlab.com/d3i/explorer.html

Abstract The goal of this editorial is to summarize the 2019 International Conference on Intelligent Biology and Medicine (ICIBM 2019) conference that took place on June 9–11, 2019 in The Ohio State University, Columbus, OH, and to provide an introductory summary of the seven articles presented in this supplement issue. ICIBM 2019 hosted four keynote speakers, four eminent scholar speakers, five tutorials and workshops, twelve concurrent sessions and a poster session, totaling 23 posters, spanning state-of-the-art developments in bioinformatics, genomics, next-generation sequencing (NGS) analysis, scientific databases, cancer and medical genomics, and computational drug discovery. A total of 105 original manuscripts were submitted to ICIBM 2019, and after careful review, seven were selected for this supplement issue. These articles cover methods and applications for functional annotations of miRNA targeting, clonal evolution of bacterial cells, gene co-expression networks that describe a given phenotype, functional binding site analysis of RNA-binding proteins, normalization of genome architecture mapping data, sample predictions based on multiple NGS data types, and prediction of an individual’s genetic admixture given exonic single nucleotide polymorphisms data.

BackgroundA new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. ObjectiveThe objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. MethodsWe constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. ResultsOur pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. ConclusionsOur study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses.

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network-based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope Database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles.

Triplophysa yarkandensis is a specific cobitidae species that is endemic to Xinjiang Tarim River basin, China. The complete mitochondrial genome sequence of T. yarkandensis from Bosten Lake was determined in this study (Accession number MN821008). The mitogenome (16,552 bp) consists of 22 tRNA genes, 2 ribosomal RNA genes, 13 protein-coding genes, and 1 control region (D-loop region). The complete mitochondrial genome sequence of the T. yarkandensis provides an important data set for further study in genetic mechanism and classification.

Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records to physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, for prioritizing information based on various similarities among physicians, patients and information items. We tested this new method using electronic health record data from the Indiana Network for Patient Care, a large, inter-organizational clinical data repository maintained by the Indiana Health Information Exchange. Our experimental results demonstrated that, for top-5 recommendations, our method was able to correctly predict the information in which physicians were interested in 46.7% of all test cases. For top-1 recommendations, the corresponding figure was 24.7%. In addition, the new method was 22.3% better than the conventional Markov model for top-1 recommendations.

Abstract Background Sugarcane (Saccharum officinarum L.) is an economically important crop, mainly due to the production of sugar and biofuel (Azevedo RA, Carvalho RF, Cia MC, & Gratão PL, Trop Plant Biol 4:42-51, 2011). Grown mainly in tropical and subtropical countries, sugarcane is a highly polyploid plant with up to ten copies of each chromosome, which increases the difficulties of genome assembly and genetic, physiologic and biochemical analyses. The increasing demands of sugar and the increasing cost of sugarcane harvest require sugarcane varieties which can shed their leaves during the maturity time, so it is important to study the mechanism of leaf abscission in sugarcane. Results To improve the understanding of miRNA roles in sugarcane leaf abscission, we reported the genome-wide characterization of miRNAs and their putative targets in sugarcane using deep sequencing for six small RNA libraries. In total, 93 conserved miRNAs and 454 novel miRNAs were identified in sugarcane using previously reported transcriptome as reference. Among them, 25 up-regulated and 13 down-regulated miRNAs were identified in leaf abscission sugarcane plants (LASP) compared to leaf packaging sugarcane plants (LPSP). Target prediction revealed several miRNA-mRNA modules including miR156-SPL, miR319-TPR2, miR396-GRF and miR408-LAC3 might be involved in the sugarcane leaf abscission. KEGG pathway enrichment analysis showed differentially expressed miRNAs may regulate pathways like “plant hormone signal transduction” and “plant-pathogen interaction”, which is consistent with previous transcriptome study. In addition, we identified 96 variant miRNAs with 135 single nucleotide polymorphisms (SNPs). The expression of sugarcane miRNAs and variant miRNAs were confirmed by qRT-PCR. We identified a possible poaceae specific miRNA called miR5384 for the first time in sugarcane. Conclusions We not only reported miR5384, a possible poaceae specific miRNA, for the first time in sugarcane but also presented some miRNA-mRNA modules including miR156-SPL, miR319-TPR2, miR396-GRF and miR408-LAC in sugarcane. These modules might be involved in the regulation of sugarcane leaf abscission during the maturity time. All of these findings may lay ground work for future application of sugarcane breeding program and benefit research studies of sugarcane miRNAs.

More than 10 harbour and estuarine sediment reference materials have been developed in the United States, Canada and other countries, but most of them focus on organic pollutants and radionuclide, and lack certified values. Three sediment reference materials of the Yellow river delta have been prepared in China in 2007. In order to meet the needs of offshore marine sediment geochemical surveys and resource exploration, two sediment standard materials of the Yangtze river delta were prepared according to ISO guidelines and technical standards for reference standards at the national level. Two samples were collected according to different particle sizes, and were dried and ball milled to 200 mesh. Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) were used to test the homogeneity. Results show that the F values for the variance test were less than the threshold, which indicates good homogeneity. X-ray Fluorescence Spectroscopy was used for a four-time stability test in two years. No statistically significant changes were observed and the stability of the samples was good. A total of 13 accurate and reliable analytical methods were used in 12 technologically significant laboratories to finalize the contents of 53 elements. The major elements show gradient distribution with 16.42% and 11.48% of Al2O3. These two reference materials have multiple elements with determined values by accurate and reliable methods, which can provide quality assurance for the analysis and testing during geological and environmental surveys in the Delta region.

Abstract In this editorial, we briefly summarize the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9–11, 2019 at Columbus, Ohio, USA. Then, we introduce the two research articles included in this supplement issue. These two research articles were selected after careful review of 105 articles that were submitted to the conference, and cover topics on deep learning for drug-target interaction prediction and data mining and visualization of high-order drug-drug interactions.

With the progress and development of society, the power supply quality of power system is required to be higher and higher. It is necessary to locate the fault and remove it quickly. Therefore, it is necessary to install fault indicator on distribution line to improve the efficiency of finding fault location. As an important part of distribution network, the 10kV overhead line has the characteristics of many branches, wide coverage area, time-consuming and labor-consuming in line inspection and maintenance. The fault indicators currently used have problems such as complex structure, high cost of installation and deployment, and inaccurate fault detection. In this paper, a new type of fault indicator is proposed, which uses the comprehensive fault detection method. DSP processor is used to collect, calculate and process the voltage and current information of power grid. Through the embedded programming language, the comprehensive fault detection and identification is realized. Finally, the acquisition accuracy and fault judgment accuracy of the fault indicator are tested by simulating the fault signal in the laboratory. The experimental results show that the proposed fault indicator has high accuracy and can meet the requirements of fault indication, location and alarm.

An amendment to this paper has been published and can be accessed via the original article.

After publication of this supplement article [1], it is requested the grant ID in the Funding section should be corrected from NSF grant IIS-7811367 to NSF grant IIS-1902617.