Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization.

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.

Developmental Toxicity of Fine Particulate Matter: Multifaceted Exploration from Epidemiological and Laboratory Perspectives.

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.

Tumor necrosis factor α1 decreases mucosal immune and antioxidant response in the midgut of hybrid fish (white crucian carp ♀ × red crucian carp ♂).

Tumor necrosis factor α1 (TNFα) is a pleiotropic cytokine involved in immune regulation and cellular homeostasis, but the crucial role of TNFα in fish gut remained unclear. The current study aimed to evaluate the immunoregulatory function of TNFα1 on gut barrier in a novel hybrid fish (WR), which was produced by crossing white crucian carp (Carassius cuvieri, ♀) with red crucian carp (Carassius auratus red var, ♂). In this study, WR-tnfα1 sequence was identified, and a high-level expression was detected in the intestine. Elevated levels of WR-tnfα1 expressions were detected in immune-related tissues and cultured fish cells on stimulation. The appearance of vacuolization and submucosal rupture was observed in TNFα1-treated midgut of WR, along with elevated levels of goblet cell atrophy, whereas no significant changes were detected in most expressions of tight-junction genes and mucin genes. In contrast, WR receiving gut perfusion with WR-TNFα1 showed a remarkable decrease in antioxidant status in midgut, whereas the expression levels of apoptotic genes and redox responsive genes increased sharply. These results suggested that TNFα1 could exhibit a detrimental effect on antioxidant defense and immune regulation in the midgut of WR.

Role of sleep in asthenospermia induced by di (2-ethyl-hexyl) phthalate.

Di (2-ethyl-hexyl) phthalate (DEHP) mainly enters the human body through the digestive tract, respiratory tract, and skin. At the same time, it has reproductive and developmental toxicity, neurotoxicity, and so on, which can cause the decrease of sperm motility. Asthenospermia is also known as low sperm motility, and the semen quality of men in some areas of China is declining year by year. Interestingly, previous studies have shown that sleep disorders can also lead to asthenospermia. However, the relationship between sleep, DEHP, and asthenospermia is still unclear. Analysis of the National Health and Nutrition Examination Survey (NHANES) population database showed that DEHP was associated with sleep disorders, and subsequent experiments in mice and Drosophila indicated that DEHP exposure had certain effects on sleep and asthenospermia. Furthermore, we analyzed the Comparative Toxicogenomics Database (CTD) to find out the common signaling pathway among the three: hypoxia-inducible factor 1(HIF-1). Then Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to screen out the proteins that DEHP affected the HIF-1 pathway: glyceraldehyde-3-phosphate dehydrogenase (GAPDH), serine/threonine-protein kinase (AKT1), epidermal growth factor receptor (EGFR), and finally Western blot analysis was used to detect the expression levels of the three proteins. Compared with the control group, DEHP decreased the protein expression levels of GAPDH and AKT1 in the HIF-1 pathway, and caused sleep disorders and decreased sperm motility. This study provides preliminary evidence for exploring the mechanism among DEHP, sleep disorders, and asthenospermia.

Cloning, expression and functional characterization of recombinant tumor necrosis factor α1 (TNFα1) from white crucian carp in gut immune regulation.

TNFα is one of important cytokines belonging to TNF superfamily, which can exhibit a pleiotropic effect in immune modulation, homeostasis as well as pathogenesis. However, its immunoregulatory function on mucosal immunity in fish gut are still unclear. In this study, we aimed to investigated the immunoregulatory role of TNFα1 in midgut of white crucian carp (WCC). WCC-TNFα1 sequence and its deduced structure were firstly identified in WCC. Then, tissue-specific analysis revealed that high-level WCC-TNFα1 expression was detected in gill. After Aeromonas hydrophila and lipopolysaccharide (LPS) stimulated, increased trends of WCC-TNFα1 expressions were detected in immune-related tissues and cultured fish cells, respectively. WCC anal-intubated with WCC-TNFα1 fusion protein showed the increased levels of edema and fuzzy appearance in impaired villi, along with atrophy and reduction of goblet cells (GC). Moreover, the expression levels of tight junction (TJ) genes and mucin genes were consistently lower than those of the control (P < 0.05). WCC-TNFα1 treatment could sharply decrease antioxidant status in midgut, while the expression levels of caspase (CASP) genes, unfolded protein response (UPR) genes and redox response genes increased dramatically. Our results suggested that WCC-TNFα1 could exhibit a detrimental effect on antioxidant and mucosal immune regulation in midgut of WCC.

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing.

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

Detection of Cognitive Impairment From eSAGE Metadata Using Machine Learning.

OBJECTIVE: Using the metadata collected in the digital version of the Self-Administered Gerocognitive Examination (eSAGE), we aim to improve the prediction of mild cognitive impairment (MCI) and dementia (DM) by applying machine learning methods. PATIENTS AND METHODS: A total of 66 patients had a diagnosis of normal cognition (NC), MCI, or DM, and eSAGE scores and metadata were used. eSAGE scores and metadata were obtained. Each eSAGE question was scored and behavioral features (metadata) such as the time spent on each test page, drawing speed, and average stroke length were extracted for each patient. Logistic regression (LR) and gradient boosting models were trained using these features to detect cognitive impairment (CI). Performance was evaluated using 10-fold cross-validation, with accuracy, precision, recall, F1 score, and receiver operating characteristic area under the curve (AUC) score as evaluation metrics. RESULTS: LR with feature selection achieved an AUC of 89.51%, a recall of 87.56%, and an F1 of 85.07% using both behavioral and scoring. LR using scores and metadata also achieved an AUC of 84.00% in detecting MCI from NC, and an AUC of 98.12% in detecting DM from NC. Average stroke length was particularly useful for prediction and when combined with 4 other scoring features, LR achieved an even better AUC of 92.06% in detecting CI. The study shows that eSAGE scores and metadata are predictive of CI. CONCLUSIONS: eSAGE scores and metadata are predictive of CI. With machine learning methods, the metadata could be combined with scores to enable more accurate detection of CI.

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing.

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

Upregulation of oxidative stress by triphenyl phosphate (TPhP) exposure causes antioxidant insult and apoptotic process in Epithelioma papulosum cyprini (EPC) cells.

Triphenyl phosphate (TPhP) is the predominant compound of organophosphate flame retardants (OPFRs), which can elicit a toxicological effect on physiological response and tissue development of fish. In this study, we investigated the effect of TPhP exposure on cell viability, antioxidant capacities, and apoptosis in EPC cells. Current study revealed that TPhP exposure could decrease cell viability and promote intracellular oxidative stress in EPC cells. In addition, high-dose TPhP exposure could facilitate antioxidant insults and cause mitochondrial collapse in a dose-dependent manner, along with increased gene expressions involved in apoptosis and unfolded protein response (UPR). These results indicated that reactive oxygen species (ROS)-induced cytotoxic stress and cell death were involved in antioxidant insults and apoptotic activation in TPhP-exposed fish cells.

Several confirmed and probable zoonotic cases of toxigenic Corynebacterium ulcerans, Queensland, Australia.

Background: Toxigenic Corynebacterium ulcerans is an emerging zoonosis globally, causing both cutaneous and respiratory diphtheria-like illness. In Queensland, human infection with toxigenic C. ulcerans is rare, with only three cases reported before October 2015. This case series describes five subsequent cases of toxigenic C. ulcerans in Queensland with links to companion animals. Methods: All data were collected as part of routine public health response, and strains were whole genome sequenced for further characterisation. Household contacts were screened, treated with appropriate antibiotics, and received a diphtheria toxoid-containing vaccine if more than five years had elapsed since their last dose. Findings: No epidemiological or genomic links could be established between any of the five patients, including between the two cases notified from the same locality within eight days of each other. The C. ulcerans strains from Cases Two, Four and Five were closely related to the strains isolated from their respective pets by whole genome sequencing. Domestic dogs were identified as the most likely mode of transmission for Cases One and Three; however, this was unable to be laboratory confirmed, since Case One's dog was treated with antibiotics before it could be tested, and Case Three's dog was euthanised and cremated prior to case notification. Interpretation: These are the first reported Australian cases of this emerging zoonosis with links to companion animals. These cases demonstrate the likely transmission route between companion animals and humans, with no evidence of human-to-human transmission. The existing requirement in the Queensland Health Public Health Management Guidelines, of restrictions on cases and some contacts while awaiting swab results, is currently under review.

Triazole fungicides exert neural differentiation alteration through H3K27me3 modifications: In vitro and in silico study.

Considering that humans are unavoidably exposed to triazole fungicides through the esophagus, respiratory tract, and skin contact, revealing the developmental toxicity of triazole fungicides is vital for health risk assessment. This study aimed to screen and discriminate neural developmental disorder chemicals in commonly used triazole fungicides, and explore the underlying harmful impacts on neurogenesis associated with histone modification abnormality in mouse embryonic stem cells (mESCs). The triploblastic and neural differentiation models were constructed based on mESCs to expose six typical triazole fungicides (myclobutanil, tebuconazole, hexaconazole, propiconazole, difenoconazole, and flusilazole). The result demonstrated that although no cytotoxicity was observed, different triazole fungicides exhibited varying degrees of alterations in neural differentiation, including increased ectodermal differentiation, promoted neurogenesis, increased intracellular calcium ion levels, and disturbance of neurotransmitters. Molecular docking, cluster analysis, and multiple linear regressions demonstrated that the binding affinities between triazole fungicides and the Kdm6b-ligand binding domain were the dominant determinants of the neurodevelopmental response. This partially resulted in the reduced enrichment of H3K27me3 at the promoter region of the serotonin receptor 2 C gene, finally leading to disturbed neural differentiation. The data suggested potential adverse outcomes of triazole fungicides on embryonic neurogenesis even under sublethal doses through interfering histone modification, providing substantial evidence on the safety control of fungicides.

New insights into triazole fungicide-caused hematopoietic abnormality in zebrafish based on GRα screening developmental toxicity.

Triazole fungicides (TFs) are known to be common environmental contaminants that can be toxic to aquatic animals, but their developmental toxicity is not fully understood. To address this gap, we first used a glucocorticoid receptor α (GRα)-mediated dual luciferase reporter gene system to explore the possible development toxicity of ten TFs and found that flusilazole (FLU) exhibited stronger agonistic activity against GRα. Subsequent transcriptome sequencing showed that FLU exposure affected GRα activation and hematopoiesis associated with a variety of biological processes, including responses to corticosteroid release, embryonic hematopoiesis, erythroid differentiation, and the development of hematopoietic or lymphoid organs. Furthermore, based on in situ hybridization and staining techniques, we clarified that FLU decreased the expression of the primitive hematopoietic marker genes gata1 and pu.1. and caused the defects in the posterior blood island (PBI), thereby impacting intermediate hematopoietic processes. Also, FLU significantly reduced the expression of the crucial hematopoietic gene cmyb and disrupted the production of erythrocytes and bone marrow cells during definitive hematopoiesis. Consistently, we found that FLU induced lesions in the kidney, a hematopoietic organ, including the infiltration of inflammatory cells, tubular collapse, reduced tubular filtration area, and interstitial hydronephrosis. We also found that FLU increased aberrant red blood cells in the peripheral blood of zebrafish. These findings provide new insights into the developmental toxicity and ecotoxicological risk of TFs.

Potential toxicity of landfill leachate to zebrafish and mung beans.

Landfill leachate has become a major public health concern due to its adverse health effects. However, its toxicological effects have not been thoroughly determined because of its complex composition. To address this issue, two model organisms were used in this study, including mung beans and zebrafish. Bean seedlings were exposed to different concentrations of landfill leachate (1%, 5%, 10%, 15%, and 20%, v/v, leachate/deionized water) for 7 days. Low concentrations (1%) of landfill leachate increased the growth of mung beans, whereas high concentrations (15% and 20%) of landfill leachate inhibited the growth and development of seedlings. Furthermore, landfill leachate reduced chlorophyll levels but increased malondialdehyde levels, leading to an increased rate of root-tip micronuclei. Zebrafish embryos were exposed to different concentrations of landfill leachate (0.5%, 1.0%, 1.2%, and 1.5%, v/v, leachate/E3 medium) for 120 h. The results showed that landfill leachate significantly decreased lower levels of hatching rate and heart rate but increased the mortality and malformation rates of embryos. Moreover, 1.0% landfill leachate reduced the frequency of spontaneous movement and the light stimulation reaction of embryos. Embryos exposed to leachate showed less exploratory behavior and fewer mirror attacks in the black and white areas. Our results suggest that exposure to landfill leachate could cause developmental toxicity and genotoxicity in plants and fish. The findings can improve our understanding of the environmental toxicity of landfill leachate and provide additional evidence for its risk assessment and management.

Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks.

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.

[Network Meta-analysis of oral Chinese patent medicines in treatment of acute exacerbation of chronic obstructive pulmonary disease].

This study aimed to evaluate the effectiveness and safety of eight oral Chinese patent medicines in the treatment of acute exacerbation of chronic obstructive pulmonary disease(AECOPD) by network Meta-analysis. Randomized controlled trial(RCT) on the treatment of AECOPD with eight oral Chinese patent medicines was retrieved from databases including CNKI, Wanfang, VIP, SinoMed, PubMed, Web of Science, EMbase, and Cochrane Library from database inception to August 6, 2022. The information was extracted from the included literature and the quality of the included studies was evaluated using the Cochrane risk of bias assessment tool. The data were analyzed using Stata SE 15.1 and ADDIS 1.16.8 software. Finally, 53 RCTs were included, with 5 289 patients involved, including 2 652 patients in the experimental group and 2 637 patients in the control group. Network Meta-analysis showed that Lianhua Qingwen Capsules+conventional western medicine were optimal in improving clinical effective rate, Shufeng Jiedu Capsules+conventional western medicine in improving FEV1/FVC, Qingqi Huatan Pills+conventional western medicine in improving FEV1%pred, Feilike Mixture(Capsules)+conventional western medicine in improving PaO_2, Lianhua Qingwen Capsules+conventional western medicine in reducing PaCO_2, and Qingqi Huatan Pills+conventional western medicine in reducing C-reactive protein(CRP). In terms of safety, most of them were gastrointestinal symptoms, and no serious adverse reactions were reported. When the clinical effective rate was taken as the comprehensive index of efficacy evaluation, Lianhua Qingwen Capsules+conventional western medicine were the most likely to be the best treatment for AECOPD. There are some limitations in the conclusion of this study. It only provides references for clinical medication.

G2Retro as a two-step graph generative models for retrosynthesis prediction.

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 G2Retro for one-step retrosynthesis prediction. G2Retro 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. G2Retro 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, G2Retro 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 G2Retro is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods.

Alteration of synergistic immune response in gut-liver axis of white crucian carp (Carassius cuvieri) after gut infection with Aeromonas hydrophila.

Aeromonas hydrophila can pose a great threat to the survival of farmed fish. In current study, we investigated the pathological characteristics and immune response in gut-liver axis of white crucian carp (WCC) upon gut infection. WCC anally intubated with A. hydrophila exerted a tissue deformation in damaged midgut with elevated levels of goblet cells along with a significant decrease in tight junction proteins and villi length-to-width ratios. In addition, immune-related gene expressions and antioxidant properties increased dramatically in gut-liver axis of WCC following gut infection with A. hydrophila. These results highlighted the immune modulation and redox alteration in gut-liver axis of WCC in response to gut infection.

M2: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation.

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.