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.

HAM: Hybrid Associations Models for Sequential Recommendation.

Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations. using three factors: 1) users' long-term preferences, 2) sequential, high-order and low-order association patterns in the users' most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings. with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods. and are able to achieve significant speedup as much as 139.7 folds.

Graph embedding on biomedical networks: methods, applications and evaluations.

MOTIVATION: Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. RESULTS: We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks. AVAILABILITY AND IMPLEMENTATION: As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combined segmental spinal epidural for major spine surgery: a case report.

A woman in her mid-50s, hesitant about general anaesthesia due to a difficult airway, opted for neuraxial anaesthesia for L4 laminectomy with pedicle screw fixation (L3-L5). Preoperatively, she received 150 µg buprenorphine and 1 mg midazolam. In lateral position, a T8-T9 epidural catheter was placed, followed by segmental spinal anaesthesia (2.5 mL 0.5% hyperbaric bupivacaine+30 µg clonidine) at T10-T11. Prone positioning was executed using standard techniques. During the 6-7 hours surgery, three 7 mL epidural top-ups (2% lignocaine epinephrine) were administered at 90 min intervals. Haemodynamics remained stable with 2.5 L crystalloids, 350 mL packed red cells and three ephedrine doses (6 mg each). Sedation included 150 µg buprenorphine and two 1 mg midazolam doses. Postoperatively, she received epidural 0.25% bupivacaine for 2 days, systemic analgesics and was discharged on the sixth day.

Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning.

Adaptive optics-optical coherence tomography (AO-OCT) allows for the three-dimensional visualization of retinal ganglion cells (RGCs) in the living human eye. Quantitative analyses of RGCs have significant potential for improving the diagnosis and monitoring of diseases such as glaucoma. Recent advances in machine learning (ML) have made possible the automatic identification and analysis of RGCs within the complex three-dimensional retinal volumes obtained with such imaging. However, the current state-of-the-art ML approach relies on fully supervised training, which demands large amounts of training labels. Each volume requires many hours of expert manual annotation. Here, two semi-supervised training schemes are introduced, (i) cross-consistency training and (ii) cross pseudo supervision that utilize unlabeled AO-OCT volumes together with a minimal set of labels, vastly reducing the labeling demands. Moreover, these methods outperformed their fully supervised counterpart and achieved accuracy comparable to that of human experts.

Belief-consistent information is most shared despite being the least surprising.

In the classical information theoretic framework, information "value" is proportional to how novel/surprising the information is. Recent work building on such notions claimed that false news spreads faster than truth online because false news is more novel and therefore surprising. However, another determinant of surprise, semantic meaning (e.g., information's consistency or inconsistency with prior beliefs), should also influence value and sharing. Examining sharing behavior on Twitter, we observed separate relations of novelty and belief consistency with sharing. Though surprise could not be assessed in those studies, belief consistency should relate to less surprise, suggesting the relevance of semantic meaning beyond novelty. In two controlled experiments, belief-consistent (vs. belief-inconsistent) information was shared more despite consistent information being the least surprising. Manipulated novelty did not predict sharing or surprise. Thus, classical information theoretic predictions regarding perceived value and sharing would benefit from considering semantic meaning in contexts where people hold pre-existing beliefs.

Identifying functional modules in interaction networks through overlapping Markov clustering.

MOTIVATION: In recent years, Markov clustering (MCL) has emerged as an effective algorithm for clustering biological networks-for instance clustering protein-protein interaction (PPI) networks to identify functional modules. However, a limitation of MCL and its variants (e.g. regularized MCL) is that it only supports hard clustering often leading to an impedance mismatch given that there is often a significant overlap of proteins across functional modules. RESULTS: In this article, we seek to redress this limitation. We propose a soft variation of Regularized MCL (R-MCL) based on the idea of iteratively (re-)executing R-MCL while ensuring that multiple executions do not always converge to the same clustering result thus allowing for highly overlapped clusters. The resulting algorithm, denoted soft regularized Markov clustering, is shown to outperform a range of extant state-of-the-art approaches in terms of accuracy of identifying functional modules on three real PPI networks. AVAILABILITY: All data and codes are freely available upon request. CONTACT: srini@cse.ohio-state.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A single source k-shortest paths algorithm to infer regulatory pathways in a gene network.

MOTIVATION: Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene. RESULTS: In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source k-shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines. AVAILABILITY: All data and codes are freely available upon request.

Evaluation of age-based local anaesthetic dosing of bupivacaine for popliteal sciatic nerve block in children undergoing foot and ankle surgery: A prospective single arm interventional study.

Background and Aims: Recommendations on paediatric single-injection local anaesthetic (LA) dosing for peripheral nerve blocks (PNBs) are based on the children's weight and limited by weight-based toxicity concerns. In this study, we assessed the extent of circumferential spread and block characteristics following the injection of an age-based volume (age in years = LA volume) of 0.25% bupivacaine following popliteal sciatic nerve block (PSNB). Methods: Thirty children aged between 2 and 12 years with the American Society of Anesthesiologists (ASA) physical status I and II and undergoing foot and ankle surgical procedures were given single-injection ultrasound-guided subparaneural PSNB using 0.25% bupivacaine at age-based LA volume after the administration of anaesthesia. The circumferential pattern of LA spread (primary objective) was assessed along the nerve (both cephalad and caudal) using ultrasound from the point of administration and the block characteristics in terms of duration of sensory block. Results: The mean [standard deviation (SD)] cephalic circumferential LA spread distance was 2.52 (0.68) [95% confidence interval (CI): 2.27-2.76] cm. The mean (SD) caudal circumferential LA spread distance was 2.27 (0.48) [95% CI: 2.09-2.44] cm. The mean (SD) duration of the sensory block was 9.03 (0.97) [95% CI: 8.67-9.38] h. Conclusion: The age-based LA volume of bupivacaine for ultrasound-guided PSNB resulted in a longitudinal circumferential spread of around 4.7 cm (adding both cephalic and caudal spread) and provided adequate analgesia for nine postoperative hours.