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Background Lymphoid malignancies are common in dogs. However, the limitations of existing chemotherapy highlight the need for alternative therapies. Drug repositioning is a promising approach for discovering new therapies using existing drugs. In this study, we conducted high-throughput screening (HTS) of clinically used drugs to identify candidates with antiproliferative activity against canine lymphoid tumor cells in vitro. Methods A total of 1,824 compounds were screened at 5 µM through HTS using a water-soluble tetrazolium assay in three canine lymphoid tumor cell lines (GL-1, UL-1, CLBL-1) and one non-tumorigenic epithelial cell line (MDCK). Compounds that selectively inhibited tumor cells while sparing the MDCK cells were retained as primary screening candidates. Compounds unsuitable for drug repositioning for cancer treatment, such as anticancer agents or topical formulations, were excluded from the study. The remaining compounds were reviewed based on literature-derived pharmacodynamic or clinical evidence and pharmacokinetic data in dogs. Selected candidates were subjected to secondary screening in which dose-dependent antiproliferative effects were evaluated. Half-maximal inhibitory concentration (IC₅₀) values were determined and compared with reported maximum plasma concentrations (C max ) in dogs to assess the potential for achieving pharmacologically active concentrations in vivo. Results Forty-five compounds were identified in a primary screening that showed tumor-selective inhibitory activity against lymphoid tumor cells. Based on the literature, five compounds (artesunate, niclosamide, pentamidine, itraconazole, and dronedarone) were selected for secondary screening. All the five compounds exhibited dose-dependent antiproliferative effects, and their IC₅₀ values were comparable to or below the reported C max in dogs. Conclusions This exploratory screening study identified clinically approved drugs with available pharmacokinetic data as candidate therapeutic agents for the treatment of canine lymphoid malignancies. Based on this study, further studies are warranted to validate the in vivo efficacy and elucidate the underlying mechanisms of candidate drugs in canine lymphoid malignancies.

Histiocytic sarcoma (HS) is an incurable aggressive tumor, and no consensus has been made on the treatment due to its rare occurrence. Since dogs spontaneously develop the disease and several cell lines are available, they have been advocated as translational animal models. In the present study, therefore, we explored gene mutations and aberrant molecular pathways in canine HS by next generation sequencing to identify molecular targets for treatment. Whole exome sequencing and RNA-sequencing revealed gene mutations related to receptor tyrosine kinase pathways and activation of ERK1/2, PI3K-AKT, and STAT3 pathways. Analysis by quantitative PCR and immunohistochemistry revealed that fibroblast growth factor receptor 1 (FGFR1) is over-expressed. Moreover, activation of ERK and Akt signaling were confirmed in all HS cell lines, and FGFR1 inhibitors showed dose-dependent growth inhibitory effects in two of the twelve canine HS cell lines. The findings obtained in the present study indicated that ERK and Akt signaling were activated in canine HS and drugs targeting FGFR1 might be effective in part of the cases. The present study provides translational evidence that leads to establishment of novel therapeutic strategies targeting ERK and Akt signaling in HS patients.

Background/Objectives: For effective exercise prescription for patients with cardiovascular disease, it is important to determine the target heart rate at the level of the anaerobic threshold (AT-HR). The AT-HR is mainly determined by cardiopulmonary exercise testing (CPET). The aim of this study is to develop a machine learning (ML) model to predict the AT-HR solely from non-exercise clinical features. Methods: From consecutive 21,482 cases of CPET between 2 February 2008 and 1 December 2021, an appropriate subset was selected to train our ML model. Data consisted of 78 features, including age, sex, anthropometry, clinical diagnosis, cardiovascular risk factors, vital signs, blood tests, and echocardiography. We predicted the AT-HR using a ML method called gradient boosting, along with a rank of each feature in terms of its contribution to AT-HR prediction. The accuracy was evaluated by comparing the predicted AT-HR with the target HRs from guideline-recommended equations in terms of the mean absolute error (MAE). Results: A total of 8228 participants included healthy individuals and patients with cardiovascular disease and were 62 ± 15 years in mean age (69% male). The MAE of the AT-HR by the ML-based model was 7.7 ± 0.2 bpm, which was significantly smaller than those of the guideline-recommended equations; the results using Karvonen formulas with the coefficients 0.7 and 0.4 were 34.5 ± 0.3 bpm and 11.9 ± 0.2 bpm, respectively, and the results using simpler formulas, rest HR + 10 and +20 bpm, were 15.9 ± 0.3 and 9.7 ± 0.2 bpm, respectively. The feature ranking method revealed that the features that make a significant contribution to AT-HR prediction include the resting heart rate, age, N-terminal pro-brain natriuretic peptide (NT-proBNP), resting systolic blood pressure, highly sensitive C-reactive protein (hsCRP), cardiovascular disease diagnosis, and β-blockers, in that order. Prediction accuracy with the top 10 to 20 features was comparable to that with all features. Conclusions: An accurate prediction model of the AT-HR from non-exercise clinical features was proposed. We expect that it will facilitate performing cardiac rehabilitation. The feature selection technique newly unveiled some major determinants of AT-HR, such as NT-proBNP and hsCRP.

Introduction Tracing letters is recognized in occupational therapy as an effective method for improving writing skills with the non-dominant hand. Additionally, peripheral sensory nerve electrical stimulation (PES) increases corticospinal tract excitability and enhances the acquisition and retention of motor skills. This study aimed to investigate whether combining character tracing with PES can improve non-dominant handwriting ability in adults. Methods The participants were randomly assigned to one of three groups: handwriting with PES (PES group), handwriting only (non-PES group), and a control group. All participants were instructed to copy a sample character using a ballpoint pen with their non-dominant hand at baseline and five days after the intervention. The primary outcome was character quality assessed using computer-based character recognition software and human-rated global legibility scales. Writing speed during the copying task was the secondary outcome. The intervention groups practiced character tracing for 15 minutes per day for five consecutive days. In the PES group, stimulation was applied for 40 minutes before and 15 minutes during each handwriting session. Outcome data were tested for normality, and non-normally distributed data were log-transformed before analysis. Analysis of covariance (ANCOVA) was used to adjust for the writing speed. Results ANCOVA revealed a significant improvement in character quality in the PES group compared with that in the control group after the intervention. However, no significant differences were observed between the PES and non-PES groups or between the non-PES and control groups. Conclusion When adjusted for writing speed, handwriting practice combined with PES significantly improved non-dominant handwriting quality compared with no intervention. However, PES alone did not demonstrate a clear additional benefit over handwriting practice.

Recently proposed DNN-based stereo matching methods that learn priors directly from data are known to suffer a drastic drop in accuracy in new environments. Although supervised approaches with ground truth disparity maps often work well, collecting them in each deployment environment is cumbersome and costly. For this reason, many unsupervised domain adaptation methods based on image-to-image translation have been proposed, but these methods do not preserve the geometric structure of a stereo image pair because the image-to-image translation is applied to each view separately. To address this problem, in this paper, we propose an attention mechanism that aggregates features in the left and right views, called Stereoscopic Cross Attention (SCA). Incorporating SCA to an image-to-image translation network makes it possible to preserve the geometric structure of a stereo image pair in the process of the image-to-image translation. We empirically demonstrate the effectiveness of the proposed unsupervised domain adaptation based on the image-to-image translation with SCA.

Monocular 3D object detection poses a significant challenge in 3D scene understanding due to its inherently ill-posed nature in monocular depth estimation. Existing methods heavily rely on supervised learning using abundant 3D labels, typically obtained through expensive and labor-intensive annotation on LiDAR point clouds. To tackle this problem, we propose a novel weakly supervised 3D object detection framework named VSRD (Volumetric Silhouette Rendering for Detection) to train 3D object detectors without any 3D supervision but only weak 2D supervision. VSRD consists of multi-view 3D auto-labeling and subsequent training of monocular 3D object detectors using the pseudo labels generated in the auto-labeling stage. In the auto-labeling stage, we represent the surface of each instance as a signed distance field (SDF) and render its silhouette as an instance mask through our proposed instance-aware volumetric silhouette rendering. To directly optimize the 3D bounding boxes through rendering, we decompose the SDF of each instance into the SDF of a cuboid and the residual distance field (RDF) that represents the residual from the cuboid. This mechanism enables us to optimize the 3D bounding boxes in an end-to-end manner by comparing the rendered instance masks with the ground truth instance masks. The optimized 3D bounding boxes serve as effective training data for 3D object detection. We conduct extensive experiments on the KITTI-360 dataset, demonstrating that our method outperforms the existing weakly supervised 3D object detection methods. The code is available at https://github.com/skmhrk1209/VSRD.

Image classification based on machine learning is being commonly used. However, a classification result given by an advanced method, including deep learning, is often hard to interpret. This problem of interpretability is one of the major obstacles in deploying a trained model in safety-critical systems. Several techniques have been proposed to address this problem; one of which is RISE, which explains a classification result by a heatmap, called a saliency map, which explains the significance of each pixel. We propose MC-RISE (Multi-Color RISE), which is an enhancement of RISE to take color information into account in an explanation. Our method not only shows the saliency of each pixel in a given image as the original RISE does, but the significance of color components of each pixel; a saliency map with color information is useful especially in the domain where the color information matters (e.g., traffic-sign recognition). We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.

Nakanishi, T.; Ohyama, T.; Hagiwara, H., and Sakuma, K., 2021. Impact of extreme typhoon events on the fluvial discharge of particulate radiocesium in Fukushima prefecture. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 310–314. Coconut Creek (Florida), ISSN 0749-0208. Two huge typhoons that occurred in October 2019, Hagibis and Bualoi, caused considerable flood damage in Fukushima Prefecture. There is concern about the radiocesium discharge to the coastal area related to such flood events from the river systems near the Fukushima nuclear power plant. In this study, we quantitatively evaluated the sediment and 137Cs discharges from the Ukedo River catchment based on the field observations conducted over 6 years. In 2019, approximately 90% of the annual sediment and 137Cs discharges occurred during the typhoon events (Hagibis and Bualoi). This sediment discharge was almost twice that related to the largest ever flood event since the Fukushima nuclear accident, which was caused by the typhoon Etau in September 2015. However, the 137Cs discharge related to the Hagibis and Bualoi events was two-thirds of the Etau event; the particulate 137Cs concentration in river water decreased during the observation period, showing an effective half-life of 2.2–3.9 years. Moreover, the 137Cs discharge during the two typhoon events in 2019 accounted for only 0.1% of the 137Cs deposition in the catchment, and the impact of radiocesium on the coastal area was extremely limited.

The egg coat including mammalian zona pellucida (ZP) and the avian equivalent, i.e., inner-perivitelline layer (IPVL), is a specialized extracellular matrix being composed of the ZP glycoproteins and surrounds both pre-ovulatory oocytes and ovulated egg cells in vertebrates. The egg coat is well known for its potential importance in both the reproduction and early development, although the underlying molecular mechanisms remain to be fully elucidated. Interestingly, ZP3, one of the ZP-glycoprotein family members forming scaffolds of the egg-coat matrices with other ZP glycoproteins, exhibits extreme but distinctive microheterogeneity to form a large number of isoelectric-point isoforms at least in the chicken IPVL. In the present study, we performed three-dimensional confocal imaging and two-dimensional polyacrylamide-gel electrophoresis (2D-PAGE) of chicken IPVLs that were isolated from the ovarian follicles at different growth stages before ovulation. The results suggest that the relative proportions of the ZP3 isoforms are differentially altered during the structural maturation of the egg-coat matrices. Furthermore, tandem mass spectrometry (MS/MS) analyses and ZP1 binding assays against separated ZP3 isoforms demonstrated that each ZP3 isoform contains characteristic modifications, and there are large differences among ZP3 isoforms in the ZP1 binding affinities. These results suggest that the microheterogeneity of chicken ZP3 might be regulated to be associated with the formation of egg-coat matrices during the structural maturation of chicken IPVL. Our findings may provide new insights into molecular mechanisms of egg-coat assembly processes.

Recently, microfluidic bioreactors that trap injected megakaryocytes (MKs) by application of fluid force to them have been proposed as small test benches to evaluate the in vitro platelet production process. However, making a flow rate constant after trapping MKs remains a challenge and bottleneck because the cross-sectional area of the microchannel decreases due to the trapped MKs. Therefore, we present a microfluidic bioreactor containing a three-dimensional microchannel that has been designed based on the cell size distribution of immortalized megakaryocyte cell lines (imMKCLs). As results, we succeeded in trapping imMKCLs with small variations in the cross-sectional area along the flow path. Through experiments on on-chip production of platelet-like particles (PLPs) for 12 h using imMKCLs derived from human-induced pluripotent stem cells, we found that the average number of the total produced PLPs per imMKCLs was 23, 24, 16, and 14 when the applied pressures was 10, 50, 100 and 200 kPa, respectively. From these results, we confirmed that the proposed microfluidic bioreactor can be applied as a test bench for evaluating of the on-chip PLP production.