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Evidence showed that N6-methyladenosine (m6A) modification plays a pivotal role in influencing RNA fate and is strongly associated with cell growth and developmental processes in many species. However, no information regarding m6A modification in Eimeria tenella is currently available. In the present study, we surveyed the transcriptome-wide prevalence of m6A in sporulated oocysts and unsporulated oocysts of E. tenella. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) analysis showed that m6A modification was most abundant in the coding sequences, followed by stop codon. There were 3,903 hypermethylated and 3,178 hypomethylated mRNAs in sporulated oocysts compared with unsporulated oocysts. Further joint analysis suggested that m6A modification of the majority of genes was positively correlated with mRNA expression. The mRNA relative expression and m6A level of the selected genes were confirmed by quantitative reverse transcription PCR (RT-qPCR) and MeRIP-qPCR. GO and KEGG analysis indicated that differentially m6A methylated genes (DMMGs) with significant differences in mRNA expression were closely related to processes such as regulation of gene expression, epigenetic, microtubule, autophagy-other and TOR signaling. Moreover, a total of 96 DMMGs without significant differences in mRNA expression showed significant differences at protein level. GO and pathway enrichment analysis of the 96 genes showed that RNA methylation may be involved in cell biosynthesis and metabolism of E. tenella. We firstly present a map of RNA m6A modification in E. tenella, which provides significant insights into developmental biology of E. tenella.

Abstract Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters, divers, etc., as well as scheduling nursing, intervention, and other plans. In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input. Because manatees have unique shape and they often stay in shallow water in groups, water surface reflection, occlusion, camouflage etc. making it difficult to accurately count manatee numbers. To address the challenges, we propose to use Anisotropic Gaussian Kernel (AGK), with tunable rotation and variances, to ensure that density functions can maximally capture shapes of individual manatees in different aggregations. After that, we apply AGK kernel to different types of deep neural networks primarily designed for crowd counting, including VGG, SANet, Congested Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and calculate number of manatees in the scene. By using generic low quality images extracted from surveillance videos, our experiment results and comparison show that AGK kernel based manatee counting achieves minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed method works particularly well for counting manatee aggregations in environments with complex background.

Abstract The success of the business directly contributes towards the growth of the nation. Hence it is important to evaluate and predict whether the business will be successful or not. In this study, we use the company’s dataset which contains information from startups to Fortune 1000 companies to create a machine learning model for predicting business success. The main challenge of business success prediction is twofold: (1) Identifying variables for defining business success; (2) Feature selection and feature engineering based on Investor-Business-Market interrelation to provide a successful outcome of the predictive modeling. Many studies have been carried out using only the available features to predict business success, however, there is still a challenge to identify the most important features in different business angles and their interrelation with business success. Motivated by the above challenge, we propose a new approach by defining a new business target based on the definition of business success used in this study and develop additional features by carrying out statistical analysis on the training data which highlights the importance of investments, business, and market features in forecasting business success instead of using only the available features for modeling. Ensemble machine learning methods as well as existing supervised learning methods were applied to predict business success. The results demonstrated a significant improvement in the overall accuracy and AUC score using ensemble methods. By adding new features related to the Investor-Business-Market entity demonstrated good performance in predicting business success and proved how important it is to identify significant relationships between these features to cover different business angles when predicting business success. Graphical Abstract

Federated Learning (FL), as a new computing framework, has received significant attentions recently due to its advantageous in preserving data privacy in training models with superb performance. During FL learning, distributed sites first learn respective parameters. A central site will consolidate learned parameters, using average or other approaches, and disseminate new weights across all sites to carryout next round of learning. The distributed parameter learning and consolidation repeat in an iterative fashion until the algorithm converges or terminates. Many FL methods exist to aggregate weights from distributed sites, but most approaches use a static node alignment approach, where nodes of distributed networks are statically assigned, in advance, to match nodes and aggregate their weights. In reality, neural networks, especially dense networks, have nontransparent roles with respect to individual nodes. Combined with random nature of the networks, static node matching often does not result in best matching between nodes across sites. In this paper, we propose, FedDNA, a dynamic node alignment federated learning algorithm. Our theme is to find best matching nodes between different sites, and then aggregate weights of matching nodes for federated learning. For each node in a neural network, we represent its weight values as a vector, and use a distance function to find most similar nodes, i.e., nodes with the smallest distance from other sides. Because finding best matching across all sites are computationally expensive, we further design a minimum spanning tree based approach to ensure that a node from each site will have matched peers from other sites, such that the total pairwise distances across all sites are minimized. Experiments and comparisons demonstrate that FedDNA outperforms commonly used baseline, such as FedAvg, for federated learning.

Spatio-temporal forecasting is of great importance in a wide range of dynamic systems applications, such as earth science, transport planning, etc. These applications rely on accurate predictions of spatio-temporal structured data reflecting real-world phenomena. A stunning characteristic is that the dynamical system is not only driven by some physics laws but also impacted by the localized factor in spatial and temporal regions. One of the major challenges is to infer the underlying causes, which generate the perceived data stream and propagate the involved causal dynamics through the distributed observing units. Another challenge is that the success of machine learning-based predictive models requires massive annotated data for model training. However, the acquisition of high-quality annotated data is objectively manual and tedious as it needs a considerable amount of human intervention, making it infeasible in fields that require high levels of expertise. To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics. To deal with data-acquisition constraints, an active learning mechanism with Kriging for actively acquiring the most informative data is proposed for ST-PCNN training in a partially observable environment. Our experiments on both synthetic and real-world datasets exhibit that the proposed ST-PCNN with active learning converges to near-optimal accuracy with substantially fewer instances.

Abstract Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.

Abstract In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using feature ranking, a handful of features, such as trial eligibility, trial inclusion/exclusion criteria, sponsor types etc., are found to be related to the clinical trial termination. By using sampling and ensemble learning, we achieve over 67% Balanced Accuracy and over 0.73 AUC (Area Under the Curve) scores to correctly predict clinical trial termination, indicating that machine learning can help achieve satisfactory prediction results for clinical trial study.

Abstract User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming a buying customer. To date, existing methods for Ad click prediction, or click-through rate prediction, mainly consider representing users as a static feature set and train machine learning classifiers to predict clicks. Such approaches do not consider temporal variance and changes in user behaviors, and solely rely on given features for learning. In this paper, we propose two deep learning-based frameworks, $${\hbox {LSTM}}_{\mathrm{cp}}$$ LSTM cp and $${\hbox {LSTM}}_{\mathrm{ip}}$$ LSTM ip , for user click prediction and user interest modeling. Our goal is to accurately predict (1) the probability of a user clicking on an Ad and (2) the probability of a user clicking a specific type of Ad campaign. To achieve the goal, we collect page information displayed to the users as a temporal sequence and use long short-term memory (LSTM) network to learn features that represents user interests as latent features. Experiments and comparisons on real-world data show that, compared to existing static set-based approaches, considering sequences and temporal variance of user requests results in improvements in user Ad response prediction and campaign specific user Ad click prediction.

Toxoplasma gondii (T. gondii) is responsible for severe human and livestock diseases, huge economic losses, and adversely affects the health of the public and the development of animal husbandry. Glycosylation is a common posttranslational modification of proteins in eukaryotes, and N-glycosylation is closely related to the biological functions of proteins. However, glycosylation alterations in the feline small intestine following T. gondii infection have not been reported. In this study, the experimental group was intragastrically challenged with 600 brain cysts of the Prugniuad (Pru) strain that were collected from infected mice. The cats’ intestinal epithelial tissues were harvested at 10 days post-infection and then sent for protein glycosylation analysis. High-performance liquid chromatography coupled to tandem mass spectrometry was used to analyze the glycosylation alterations in the small intestine of cats infected with T. gondii. The results of the present study showed that 56 glycosylated peptides were upregulated and 37 glycosylated peptides were downregulated in the feline small intestine infected by T. gondii. Additionally, we also identified eight N-glycosylated proteins of T. gondii including eight N-glycopeptides and eight N-glycosylation sites. The protein A0A086JND6_TOXGO (eEF2) and its corresponding peptide sequence were identified in T. gondii infection. Some special GO terms (i.e., cellular process and metabolic process, cell and cell part, and catalytic activity) were significantly enriched, and the Clusters of Orthologous Groups of proteins (COG) function prediction results showed that posttranslational modification, protein turnover, and chaperones (11%) had the highest enrichment for T. gondii. Interestingly, eEF2, a protein of T. gondii, is also involved in the significantly enriched T. gondii MAPK pathway. The host proteins ICAM-1 and PPT1 and the endoplasmic reticulum stress pathway may play an important role in the glycosylation of Toxoplasma-infected hosts. This is the first report showing that T. gondii oocysts can undergo N-glycosylation in the definitive host and that eEF2 is involved, which may provide a new target for T. gondii detection to prevent the spread of T. gondii oocysts in the future.

Abstract Background Pattern recognition receptors (PRRs) can recognize pathogen-associated molecular patterns and activate downstream signalling pathways, resulting in modulation of host immunity against pathogens. Here, we investigated whether PRR-mediated recognition is involved in host immune responses to the blood-feeding nematode Haemonchus contortus. Methods During blood-feeding, H. contortus secretes immune-modulating antigens into host blood. Therefore, we stimulated sheep peripheral blood mononuclear cells (PBMCs) with H. contortus soluble extract (HcAg) and performed transcriptional profiling. Results HcAg upregulated two genetically linked CLRs (CLEC2L and KLRG2), two NLRs attenuating inflammation (NLRP12 and NLRC3) and one G protein-coupled receptor with potent anti-inflammatory effects (HCAR2). Furthermore, several Th2-related transcription factors (ATF3, IRF4, BCL3 and NFATC) were also upregulated, which may confer anti-inflammatory type 2 immune responses to HcAg. Conclusions Together, our preliminary studies provide new insights into how the host innate immune system controls type 2 immunity to H. contortus. Further work will be needed to identify H. contortus products recognized by the host innate immune system and determine the Th2 polarization ability of these putative PRR ligands.

As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Clinicaltrial.gov. Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study). On the other hand, 909 trials have been completed (indicating the completion of the study). In this study, we propose to study underlying factors of COVID-19 trial completion vs. cessation, and design predictive models to accurately predict whether a COVID-19 trial may complete or cease in the future. We collect 4,441 COVID-19 trials from ClinicalTrial.gov to build a testbed, and design four types of features to characterize clinical trial administration, eligibility, study information, criteria, drug types, study keywords, as well as embedding features commonly used in the state-of-the-art machine learning. Our study shows that drug features and study keywords are most informative features, but all four types of features are essential for accurate trial prediction. By using predictive models, our approach achieves more than 0.87 AUC (Area Under the Curve) score and 0.81 balanced accuracy to correctly predict COVID-19 clinical trial completion vs. cessation. Our research shows that computational methods can deliver effective features to understand difference between completed vs. ceased COVID-19 trials. In addition, such models can also predict COVID-19 trial status with satisfactory accuracy, and help stakeholders better plan trials and minimize costs.

Eimeria tenella encodes a genome of approximately 8000 genes. To date, however, very few data are available regarding E. tenella rhoptry kinase family proteins. In the present study, the gene fragment encoding the mature peptide of the rhoptry kinase family protein 17 of E. tenella (EtROP17) was amplified by PCR and expressed in E. coli. Then, we generated polyclonal antibodies that recognize EtROP17 and investigated the expression of EtROP17 in the merozoite stage of E. tenella by immunofluorescent staining and Western blot analysis. Meanwhile, the protective efficacy of rEtROP17 against E. tenella was evaluated in chickens. Sequencing analysis showed that a single base difference at sequence position 1901 was observed between the SD-01 strain and the Houghton strain. EtROP17 was expressed in the merozoite stage of E. tenella. The results of the animal challenge experiments demonstrated that vaccination with rEtROP17 significantly reduced cecal lesions and oocyst outputs compared with the challenged control group. Our findings indicate that EtROP17 could serve as a potential candidate for developing a new vaccine against E. tenella.

Abstract Background Cervids used to be considered the only animal intermediate hosts of the G10 genotype of Echinococcus canadensis. Yaks are often herded in the Qinghai-Tibet Plateau, China, where echinococcosis remains prevalent. However, no E. canadensis G10 cases have been recorded in yaks until now. The aim of our study was to identify causative agents of echinococcosis in yaks in this region. Methods Total genomic DNA was extracted from the germinal layer of one hydatid using a Blood and Tissue Kit. Full-length mitochondrial (mt) cytochrome c oxidase subunit 1 (cox1) and NADH dehydrogenase subunit 1 (nad1) genes were amplified by PCR. All purified PCR products were directly sequenced in both directions. Then seven pairs of overlap primers were designed to amplify the entire mt genome sequence of a suspected E. canadensis G10 isolate. Phylogenetic analyses were performed based on concatenated nucleotides from the 12 protein-coding genes of mt genomes of Echinococcus species in a Bayesian framework using MrBayes v3.1 and implementing the GTR + I + G model. Results Hydatids were found in yaks (n = 129) when organs were inspected at the slaughterhouse in Maqu county, Gannan Tibetan Autonomous Prefecture, Gansu Province, China in October 2016. Of these, 33 (25.6%) harbored up to a dozen hydatid cysts. One cyst from each yak was characterized by sequencing its mitochondrial (mt) cox1 and nad1 genes. On the basis of these sequence data, 32 cysts were identified as Echinococcus granulosus (sensu stricto) (G1-G3) and the remaining one was identified as the G10 genotype of E. canadensis. Its mt genome was then fully sequenced and compared with that of the G10 genotype in GenBank (AB745463). Phylogenetic analysis using complete mt genomes confirmed the Chinese cyst as belonging to the G10 genotype. Conclusions To our knowledge, this is the first report globally of E. canadensis (G10) from yaks in China, which suggests that the G10 genotype has a wider geographical distribution and broader host range than previously believed. This genotype has therefore potential risks to human health and animal husbandry.

Abstract Background Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that affects airflow to the lungs. Discovering the co-occurrence of COPD with other diseases, symptoms, and medications is invaluable to medical staff. Building co-occurrence indexes and finding causal relationships with COPD can be difficult because often times disease prevalence within a population influences results. A method which can better separate occurrence within COPD patients from population prevalence would be desirable. Large hospital systems may potentially have tens of millions of patient records spanning decades of collection and a big data approach that is scalable is desirable. The presented method, Co-Occurring Evidence Discovery (COED), presents a methodology and framework to address these issues. Methods Natural Language Processing methods are used to examine 64,371 deidentified clinical notes and discover associations between COPD and medical terms. Apache cTAKES is leveraged to annotate and structure clinical notes. Several extensions to cTAKES have been written to parallelize the annotation of large sets of clinical notes. A co-occurrence score is presented which can penalize scores based on term prevalence, as well as a baseline method traditionally used for finding co-occurrence. These scoring systems are implemented using Apache Spark. Dictionaries of ground truth terms for diseases, medications, and symptoms have been created using clinical domain knowledge. COED and baseline methods are compared using precision, recall, and F1 score. Results The highest scoring diseases using COED are lung and respiratory diseases. In contrast, baseline methods for co-occurrence rank diseases with high population prevalence highest. Medications and symptoms evaluated with COED share similar results. When evaluated against ground truth dictionaries, the maximum improvements in recall for symptoms, diseases, and medications were 0.212, 0.130, and 0.174. The maximum improvements in precision for symptoms, diseases, and medications were 0.303, 0.333, and 0.180. Median increase in F1 score for symptoms, diseases, and medications were 38.1%, 23.0%, and 17.1%. A paired t-test was performed and F1 score increases were found to be statistically significant, where p < 0.01. Conclusion Penalizing terms which are highly frequent in the corpus results in better precision and recall performance. Penalizing frequently occurring terms gives a better picture of the diseases, symptoms, and medications co-occurring with COPD. Using a mathematical and computational approach rather than purely expert driven approach, large dictionaries of COPD related terms can be assembled in a short amount of time.

Neospora caninum is an obligate intracellular apicomplexan parasite, the etiologic agent of neosporosis, and a major cause of reproductive loss in cattle. There is still a lack of effective prevention and treatment measures. The 14-3-3 protein is a widely expressed acidic protein that spontaneously forms dimers within apicomplexan parasites. This protein has been isolated and sequenced in many parasites; however, there are few reports about the N. caninum 14-3-3 protein. Here, we successfully expressed and purified a recombinant fusion protein of Nc14-3-3 (rNc14-3-3) and prepared a polyclonal antibody. Immunofluorescence and immunogold electron microscopy studies of tachyzoites or N. caninum-infected cells suggested that 14-3-3 was localized in the cytosol and the membrane. Western blotting analysis indicated that rNc14-3-3 could be recognized by N. caninum-infected mouse sera, suggesting that 14-3-3 may be an infection-associated antigen that is involved in the host immune response. We demonstrated that rNc14-3-3 induced cytokine expression by activating the MAPK and AKT signaling pathways, and inhibitors of p38, ERK, JNK, and AKT could significantly decrease the production of IL-6, IL-12p40, and TNF-α. In addition, phosphorylated nuclear factor-κB (NF-κB/p65) was observed in wild-type peritoneal macrophages (PMs) treated with rNc14-3-3, and the protein level of NF-κB/p65 was reduced in the cytoplasm but increased correspondingly in the nucleus after 2 h of treatment. These results were also observed in deficient in TLR2-/- PMs. Taken together, our results indicated that the N. caninum 14-3-3 protein can induce effective immune responses and stimulate cytokine expression by activating the MAPK, AKT, and NF-κB signaling pathways but did not dependent TLR2, suggesting that Nc14-3-3 is a novel vaccine candidate against neosporosis.

During host-parasite interactions, binding of excretory/secretory proteins (ESPs) on the host immune cells is considered the fundamental phase for regulation of immune responses. In this study, gene encoding Haemonchus contortus tropomyosin (Hc-TpMy), was successfully cloned and expressed, and the recombinant protein after host cell surface attachment was evaluated for immune functional analysis with goat peripheral blood mononuclear cells (PBMCs) in vitro. The isopropyl-β-D-thiogalactopyranoside (IPTG)-induced recombinant protein was successfully recognized by the sera of rat experimentally infected with rHc-TpMy. The immunofluorescence assay detected attachment of rHc-TpMy on the surface of host PBMCs. Furthermore, immunoregulatory roles of rHc-TpMy on cytokines expression, PBMC proliferation, migration, nitric oxide (NO) production, apoptosis and monocytes phagocytosis were observed. The results showed that expression of IL-4 and IFN-γ cytokines, cell proliferation, NO production and PBMC migration were significantly suppressed by goat PBMCs after co-incubation with rHc-TpMy protein. However, the productions of IL-10, IL-17 and TGF-β1 cytokines, PBMCs apoptosis and monocytes phagocytosis were elevated at dose dependent manner. Our findings indicated that rHc-TpMy is an important ES binding protein exhibit distinct immuno-suppressive roles on goat PBMCs which might be a potential molecular target to control haemonchosis in future.

Hepatocyte-based metacestode culture is an attractive method to study alveolar echinococcosis (AE), but it is limited by the relatively short lifespan of cultured hepatocytes in maintaining their normal function.We describe a three-dimensional (3D) hepatic culture system developed from co-cultured hepatocytes and mesenchymal stem cells using a collagen scaffold to study the development of Echinococcus multilocularis larvae. This 3D culture system preserved the function of hepatocytes for a longer period of time than their monolayer counterparts, with albumin secretion, 7-ethoxyresorufin O-deethylation activity, urea synthesis, CYP3A4 and CYP2D6 activity being highly preserved for 21-28 days. The expression levels of hepatocyte-specific genes including CLDN-3, Bsep, AFP, G6P, A1AT, CYP3A4 and NR1I3 were significantly higher in the 3D cultured system compared with their monolayer counterparts after 14-days in culture. Additionally, in the presence of 3D cultured hepatocytes, 81.2% of E. multilocularis protoscoleces rapidly de-differentiated into infective vesicles within eight weeks. Transcriptomic analyses revealed 807 differentially expressed genes between cultured vesicles and protoscoleces, including 119 genes uniquely expressed in protoscoleces, and 242 genes uniquely expressed in vesicles. These differentially expressed genes were mainly involved in parasite growth relating to the G-protein coupled receptor activity pathway, substrate-specific transmembrane transporter activity, cell-cell adhesion process, and potentially with neuroactive ligand-receptor interaction.This culture system provides a valuable advance in prolonging hepatocyte functionality, a foundation for future in-depth analysis of the host-parasite interaction in AE, and a useful model to evaluate potential therapeutic strategies to treat AE.