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Abstract Fexuprazan, a novel potassium‐competitive acid blocker, is expected to be used for the prevention of nonsteroidal anti‐inflammatory drugs (NSAIDs) induced ulcer. This study aimed to evaluate pharmacokinetic (PK) interactions between fexuprazan and NSAIDs in healthy subjects. A randomized, open‐label, multicenter, six‐sequence, one‐way crossover study was conducted in healthy male subjects. Subjects randomly received one of the study drugs (fexuprazan 40 mg BID, celecoxib 200 mg BID, naproxen 500 mg BID, or meloxicam 15 mg QD) for 5 or 7 days in the first period followed by the combination of fexuprazan and one of NSAIDs for the same days and the perpetrator additionally administered for 1–2 days in the second period. Serial blood samples for PK analysis were collected until 48‐ or 72‐h post‐dose at steady state. PK parameters including maximum plasma concentration at steady state (Cmax,ss) and area under plasma concentration–time curve over dosing interval at steady state (AUCτ,ss) were compared between monotherapy and combination therapy. The PKs of NSAIDs were not significantly altered by fexuprazan. For fexuprazan, differences in PK parameters (22% in Cmax, 19% in AUCτ,ss) were observed when co‐administered with naproxen, but not clinically significant. The geometric mean ratio (90% confidence interval) of combination therapy to monotherapy for Cmax,ss and AUCτ,ss was 1.22 (1.02–1.46) and 1.19 (1.00–1.43), respectively. There were no significant changes in the systemic exposure of fexuprazan by celecoxib and meloxicam. Fexuprazan and NSAIDs did not show clinically meaningful PK interactions.

The melting of Arctic ice has facilitated the successful navigation of merchant ships through the Arctic route, often requiring icebreakers for assistance. To reduce the risk of accidents between merchant vessels and icebreakers stemming from human errors during operations, this paper introduces an enhanced human reliability assessment approach. This method utilizes the Dynamic Bayesian Network (DBN) model, integrated with the information, decision, and action in crew context (IDAC) framework. First, a qualitative analysis of crew maneuvering behavior in scenarios involving a collision with the preceding vessel during icebreaker assistance is conducted using the IDAC model. Second, the D–S evidence theory and cloud models are integrated to process multi-source subjective data. Finally, the human error probability of crew members is quantified using the DBN. The research results indicate that during convoy operations, the maximum probability that the officer on watch (OOW) chooses an incorrect deceleration strategy is 8.259×10−2 and the collision probability is 4.129×10−3. Furthermore, this study also found that the factors of Team Effectiveness and Knowledge/Abilities during convoy operations have the greatest impact on collision occurrence. This research provides important guidance and recommendations for the safe navigation of merchant ships in the Arctic waters. By reducing human errors and adopting appropriate preventive measures, the risk of collisions between merchant ships and icebreakers can be significantly decreased.

Multi-task learning (MTL) is a field in which a deep neural network simultaneously learns knowledge from multiple tasks. However, achieving resource-efficient MTL remains challenging due to entangled network parameters across tasks and varying task-specific complexity. Existing methods employ network compression techniques while maintaining comparable performance, but they often compress uniformly across all tasks without considering individual complexity. This can lead to suboptimal solutions due to entangled network parameters and memory inefficiency, as the parameters for each task may be insufficient or excessive. To address these challenges, we propose a framework called Dynamic Model Optimization (DMO) that dynamically allocates network parameters to groups based on task-specific complexity. This framework consists of three key steps: measuring task similarity and task difficulty, grouping tasks, and allocating parameters. This process involves the calculation of both weight and loss similarities across tasks and employs sample-wise loss as a measure of task difficulty. Tasks are grouped based on their similarities, and parameters are allocated with dynamic pruning according to task difficulty within their respective groups. We apply the proposed framework to MTL with various classification datasets. Experimental results demonstrate that the proposed approach achieves high performance while taking fewer network parameters than other MTL methods.

Abstract Eslicarbazepine acetate (ESL) is a prodrug antiseizure medication for the treatment of focal seizures. ESL shows a well‐established pharmacokinetic (PK)‐pharmacodynamic relationship and has similar extrinsic epilepsy‐related factors across ethnicities. This study evaluated and compared ESL safety, tolerability, and PK characteristics between Korean and White subjects. A randomized, double‐blind, placebo‐controlled, single‐ and multiple‐dose escalation study was conducted in healthy Korean and White adults. Participants randomly received a single dose and multiple oral doses of ESL (400–1600 mg) or placebo once daily for 11 days at a ratio of 8:2. Serial blood samples were collected to determine the plasma concentration of ESL and its metabolites (eslicarbazepine, [R‐licarbazepine and oxcarbazepine). Safety and tolerability were assessed throughout the study. A total of 29 Korean and 20 White subjects completed the study. The PK profiles of the metabolites of ESL were similar between Korean and White subjects. The geometric mean ratio (90% confidence interval) of Korean to White subjects for the area under the concentration–time curve within a dosing interval of eslicarbazepine was 1.06 (0.97–1.17) and 0.96 (0.87–1.06) after multiple oral doses of 400 and 1600 mg ESL, respectively. Other PK parameters were also similar between the two ethnic groups. ESL was well‐tolerated in healthy Korean and White subjects, and its PK characteristics were similar between the two ethnic groups. The results of this study support to use the same dosage regimen of ESL in both White and Korean patients with seizures.

Abstract In this work, a new stacked encoder–decoder transformer (SEDT) model is proposed for action segmentation. SEDT is composed of a series of encoder–decoder modules, each of which consists of an encoder with self‐attention layers and a decoder with cross‐attention layers. By adding an encoder with self‐attention before every decoder, it preserves local information along with global information. The proposed encoder–decoder pair also prevents the accumulation of errors that occur when features are propagated through decoders. Moreover, the approach performs boundary smoothing in order to handle ambiguous action boundaries. Experimental results for two popular benchmark datasets, “GTEA” and “50 Salads”, show that the proposed model is more effective in performance than existing temporal convolutional network based models and the attention‐based model, ASFormer.

This paper addresses an incremental learning problem, in which tasks are learned sequentially without access to the previously trained dataset. Catastrophic forgetting is a significant bottleneck to incremental learning as the network performs poorly on previous tasks when it is trained on a new task. We propose an adaptive model search method that uses a different part of the backbone network depending on an input image to mitigate catastrophic forgetting. Our model search method prevents forgetting by minimizing the update of critical parameters for the previous tasks while learning a new task. This model search involves a trainable network that selects the model structure for an input image. We also propose a method for approximating the loss function of previous tasks without the previous dataset. The critical parameters for the previous tasks can be found, considering the relationship between the approximated loss function and the parameters. The proposed framework is the first method of model search that can consider the performance of both current and previous tasks in the incremental learning problem. Empirical studies and theoretical analysis show that the proposed method outperforms other competitors for old and new tasks while requiring less computation.

While human intelligence can easily recognize some characteristics of classes with one or few examples, learning from few examples is a challenging task in machine learning. Recently emerging deep learning generally requires hundreds of thousands of samples to achieve generalization ability. Despite recent advances in deep learning, it is not easy to generalize new classes with little supervision. Few-shot learning (FSL) aims to learn how to recognize new classes with few examples per class. However, learning with few examples makes the model difficult to generalize and is susceptible to overfitting. To overcome the difficulty, data augmentation techniques have been applied to FSL. It is well-known that existing data augmentation approaches rely heavily on human experts with prior knowledge to find effective augmentation strategies manually. In this work, we propose an efficient data augmentation network, called EDANet, to automatically select the most effective augmentation approaches to achieve optimal performance of FSL without human intervention. Our method overcomes the disadvantages of relying on domain knowledge and requiring expensive labor to design data augmentation rules manually. We demonstrate the proposed approach on widely used FSL benchmarks (Omniglot and mini-ImageNet). The experimental results using three popular FSL networks indicate that the proposed approach improves performance over existing baselines through an optimal combination of candidate augmentation strategies.

Catastrophic forgetting is a well-known tendency in continual learning of a deep neural network to forget previously learned knowledge when optimizing for sequentially incoming tasks. To address the issue, several methods have been proposed in research on continual learning. However, these methods cannot preserve the previously learned knowledge when training for a new task. Moreover, these methods are susceptible to negative interference between tasks, which may lead to catastrophic forgetting. It even becomes increasingly severe when there exists a notable gap between the domains of tasks. This paper proposes a novel method of controlling gates to select a subset of parameters learned for old tasks, which are then used to optimize a new task while avoiding negative interference efficiently. The proposed approach executes the subset of old parameters that provides positive responses by evaluating the effect when the old and new parameters are used together. The execution or skipping of old parameters through the gates is based on several responses across the network. We evaluate the proposed method in different continual learning scenarios involving image classification datasets. The proposed method outperforms other competitive methods and requires fewer parameters than the state-of-the-art methods during inference by applying the proposed gating mechanism that selectively involves a set of old parameters that provides positive prior knowledge to newer tasks. Additionally, we further prove the effectiveness of the proposed method through various analyses.

Multi-task learning (MTL) improves learning efficiency compared to the single-task counterpart in that it performs multiple tasks at the same time. Due to the nature, it can achieve generalized performance as well as alleviate overfitting. However, it does not efficiently perform resource-aware inference from a single trained architecture. To address the issue, we aim to build a learning framework that minimizes the cost to infer tasks under different memory budgets. To this end, we propose a multi-path network with a self-auxiliary learning strategy. The multi-path structure contains task-specific paths in a backbone network, where a lower-level path predicts earlier with a smaller number of parameters. To alleviate the performance degradation from earlier predictions, a self-auxiliary learning strategy is presented. The self-auxiliary tasks convey task-specific knowledge to the main tasks to compensate for the performance leak. We evaluate the proposed method on an extensive set of multi-task learning scenarios, including multiple tasks learning, hierarchical learning, and curriculum learning. The proposed method outperforms existing multi-task learning competitors for most scenarios about by a margin of 1% ~ 2% accuracy on average while consuming 30% ~ 60% smaller computational cost.

Abstract Metformin is a major treatment for type 2 diabetes. This study was conducted to investigate the impact of gut microbiome dysbiosis on the pharmacokinetics and antihyperglycemic effects of metformin. Healthy adult males aged 19–45 years with no defecation abnormalities were recruited for this 4‐period clinical study: baseline; post‐metformin (i.e., multiple oral doses of 1000 mg metformin on days 1–4); post‐vancomycin (i.e., multiple oral doses of 500 mg vancomycin on days 11–17 inducing gut microbiome changes); and post‐metformin + vancomycin (i.e., multiple oral doses of 1000 mg metformin on days 16–19). In each period, serum glucose and insulin concentrations following an oral glucose tolerance test, fecal samples for gut microbiome composition, and safety data were obtained. Following metformin dosing, plasma and urine samples for pharmacokinetics were collected. Nine subjects completed the study. The pharmacokinetics of metformin remained unchanged, and the antihyperglycemic effect was significantly decreased after vancomycin administration (p value = 0.039), demonstrating the weak relationship between the pharmacokinetics and pharmacodynamics of metformin. Relative abundances of some genus were changed after vancomycin administration, and tended to correlate with the antihyperglycemic effects of metformin (p value = 0.062 for Erysipelatoclostridium; p value = 0.039 for Enterobacter; and p value = 0.086 for Faecalibacterium). Adverse events occurred in all subjects and were resolved without sequelae. In conclusion, a decrease in the antihyperglycemic effect of metformin was observed after concomitant administration with vancomycin, without changes in metformin pharmacokinetics. The antihyperglycemic effect was tended to correlate with the relative abundance of several genus, suggesting that the effect of metformin is partly attributable to the gut microbiome (ClinicalTrials.gov, NCT03809260).

Despite the continuous development of convolutional neural networks, it remains a challenge to achieve performance improvement with fewer parameters and floating point operations (FLOPs) as a light-weight model. In particular, excessive expressive power on a module is a crucial cause of skyrocketing the computational cost of the entire network. We argue that it is necessary to optimize the entire network by optimizing single modules or blocks of the network. Therefore, we propose GhostNeXt, a promising alternative to GhostNet, by adjusting the module configuration inside the Ghost block. We introduce a controller to select channel operations of the module dynamically. It holds a plug-and-play component that is more useful than the existing approach. Experiments on several classification tasks demonstrate that the proposed method is a better alternative to convolution layers in baseline models. GhostNeXt achieves competitive recognition performance compared to GhostNet and other popular models while reducing computational costs on the benchmark datasets.

Sparse representation has been widely used over the past decade in computer vision and signal processing to model a wide range of natural phenomena. For computational convenience and robustness against noises, the optimization problem for sparse representation is often relaxed using convex or nonconvex surrogates instead of using the l0-norm, the ideal sparsity penalty function. In this paper, we pose the following question for nonconvex sparsity-promoting surrogates: What is a good sparsity surrogate for general nonconvex systems? As an answer to this question, we suggest that the difficulty of handling the l0-norm does not only come from the nonconvexity but also from its gradient being zero or not well-defined. Accordingly, we propose desirable criteria to be a good nonconvex surrogate and suggest a corresponding family of surrogates. The proposed family of surrogates allows a simple regularizer, which enables efficient computation. The proposed surrogate embraces the benefits of both l0 and l1-norms, and most importantly, its gradient vanishes slowly, which allows stable optimization. We apply the proposed surrogate to wellknown sparse representation problems and benchmark datasets to demonstrate its robustness and efficiency.

Multi-task learning (MTL) is a learning strategy for solving multiple tasks simultaneously while exploiting commonalities and differences between tasks for improved learning efficiency and prediction performance. Despite its potential, there remain several major challenges to be addressed. First of all, the task performance degrades when the number of tasks to solve increases or the tasks are less related. In addition, finding the prediction model for each task is typically laborious and can be suboptimal. This nature of manually designing the architecture further aggravates the problem when it comes to solving multiple tasks under different computational budgets. In this work, we propose a novel MTL approach to address these issues. The proposed method learns to search in a finely modularized base network dynamically and to discover an optimal prediction model for each instance of a task on the fly while taking the computational costs of the discovered models into account. We evaluate our learning framework on a diverse set of MTL scenarios comprising standard benchmark datasets. We achieve significant improvements in performance for all tested cases compared with existing MTL alternatives.

Demonstration of bioequivalence (BE) is mandatory while developing generic drugs. The scientific concept of BE applies equally to different regulatory agencies. However, the application of the concept may differ for each agency, which can affect the design of BE studies. To evaluate the study practices in terms of the BE concept in South Korea, we retrospectively analyzed BE study reports available from Ministry of Food and Drug Safety between 2013 and 2019. Statistical estimation of the pharmacokinetic parameters, including peak concentration and area under the concentration–time curve to the last measurable concentration, as well as study design, number of subjects in a study, study duration, fasting status, and formulation of specific drugs were obtained. The drugs were classified per World Health Organization Anatomical Therapeutic Chemical Classification and Biopharmaceutics Classification System. Post-hoc intrasubject coefficient of variation and corresponding sample sizes were calculated from the 90% confidence intervals of pharmacokinetic parameters. A total of 143 generic drugs in 588 BE studies were analyzed. The largest number of studies were performed in the area of Cardiovascular system (172 studies), followed by Nervous system (143 studies) and Alimentary tract and metabolism (92 studies). Overall, BE studies in South Korea were conducted in accordance with the global guideline despite the differences in details. BE studies were focused on the several therapeutic areas and conducted in a similar manner. The number of subjects was generally larger than that estimated with 90% power.

Several classes of antibiotics have reduced the mortality caused by infectious diseases; however, orally administered antibiotics alter the composition of gut microbiota, leading to dysbiosis-related disease. Therefore, in this study, we used 16S rRNA gene sequencing- and metabolomics-based approaches to investigate the effects of oral vancomycin on gut bacterial microbiota and the metabolome in biospecimens collected from healthy men. Samples collected from 11 healthy men were analyzed using 16S rRNA gene sequencing and metabolomics. 16S rRNA gene sequencing was performed to analyze the gut bacterial microbiota, and GC-TOFMS-based untargeted metabolomics was performed to analyze fecal, urine, and plasma metabolomics. Spearman’s rank correlation was utilized to explore the associations between gut bacterial microbiota and metabolome. Fecal 16S rRNA gene sequencing analysis showed decreased relative abundance of genera belonging to the phyla Bacteroidetes and Firmicutes, and increased relative abundance of genera of the phyla Proteobacteria and Fusobacteria. Fecal metabolomics analysis showed that levels of uracil, L-aspartic acid, lithocholic acid, and deoxycholic acid were significantly higher at baseline, whereas that of dihydrouracil was significantly higher after vancomycin administration. No significant metabolic markers were selected from urine and plasma metabolomics analysis. This study demonstrates that oral vancomycin administration induces alterations in gut bacterial microbiota and metabolome. Correlation analysis between our two datasets shows that alteration of the gut bacterial microbiota, induced by oral vancomycin, potentially affected the systemic activity of dihydropyrimidine dehydrogenase. This correlation should be further examined in future studies to define the effects of gut bacterial microbiota on drug-metabolizing enzymes, thereby contributing to the development of personalized therapy.

In this study, the synthesis of a novel catalyst WO3/MoCl5 was carried out by the thermal method. The method gave an entirely different product compared to previous studies that doped Mo on the surface of semiconductor metal oxides. The degradation reaction of crystal violet (CV) and rhodamine B (RB) dye were done without any energy source. The results showed an incomparably superior result for degradation, with a reaction rate constant of 1.74 s−1 for 30 ppm CV, 1.08 s−1 for 30 ppm RB, and a higher value than 1 s−1 for both cases of 50 ppm dye solution. To the author’s knowledge, this catalyst has the highest reaction rate compared to other studies that targeted CV and RB, with an immense reaction rate increase of more than 100 times. Reusability of the three trials was verified, and the only process required was washing the catalyst after the reaction. One of the drawbacks of the advanced oxidation process (AOP), which has a degradation percent limit, has been solved, since 100% mineralization of the dye was available using this catalyst. FT-IR spectroscopy revealed that W-O-Mo linkage was successfully processed while Mo-Cl linkage has retained. 1H-NMR spectroscopy results confirmed that the degradation product of the dye treated by simple MoCl5 and WO3/MoCl5 was different. Deep inspection of specific regions of NMR fields gave necessary information about the degradation product using WO3/MoCl5.

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

Bone loss from the kerf of the sawblade may influence the final outcomes when employing three-dimensional-printed surgical guides. However, no studies have systematically addressed saw blade-induced bone loss. This study aims to quantify bone loss and propose a reduction guide to minimize the fracture gap. The postoperative gap tended to decrease as the amount of gap compensation increased. Osteotomy gaps can be attributed to the thickness of the saw blade, and the proposed methodology addresses this surgical error. Surgeons can proactively plan and design reduction guides with applied compensation using the method described in this study. [ABSTRACT FROM AUTHOR]