CA-CAS-01-A: A Permissive Cell Line for Isolation and Live Attenuated Vaccine Development Against African Swine Fever Virus.

African swine fever virus (ASFV) is the causative agent of the highly lethal African swine fever disease that affects domestic pigs and wild boars. In spite of the rapid spread of the virus worldwide, there is no licensed vaccine available. The lack of a suitable cell line for ASFV propagation hinders the development of a safe and effective vaccine. For ASFV propagation, primary swine macrophages and monocytes have been widely studied. However, obtaining these cells can be time-consuming and expensive, making them unsuitable for mass vaccine production. The goal of this study was to validate the suitability of novel CA-CAS-01-A (CAS-01) cells, which was identified as a highly permissive cell clone for ASFV replication in the MA-104 parental cell line for live attenuated vaccine development. Through a screening experiment, maximum ASFV replication was observed in the CAS-01 cell compared to other sub-clones of MA-104 with 14.89 and log10 7.5 ± 0.15 Ct value and TCID50/ml value respectively. When CAS-01 cells are inoculated with ASFV, replication of ASFV was confirmed by Ct value for ASFV DNA, HAD50/ml assay, TCID50/ml assay, and cytopathic effects and hemadsoption were observed similar to those in primary porcine alveolar macrophages after 5th passage. Additionally, we demonstrated stable replication and adaptation of ASFV over the serial passage. These results suggest that CAS-01 cells will be a valuable and promising cell line for ASFV isolation, replication, and development of live attenuated vaccines.

Exploring the large chemical space in search of thermodynamically stable and mechanically robust MXenes via machine learning.

To effectively utilize MXenes, a family of two-dimensional materials, in various applications that include thermoelectric devices, semiconductors, and transistors, their thermodynamic and mechanical properties, which are closely related to their stability, must be understood. However, exploring the large chemical space of MXenes and verifying their stability using first-principles calculations are computationally expensive and inefficient. Therefore, this study proposes a machine learning (ML)-based high-throughput MXene screening framework to identify thermodynamically stable MXenes and determine their mechanical properties. A dataset of 23 857 MXenes with various compositions was used to validate this framework, and 48 MXenes were predicted to be stable by ML models in terms of heat of formation and energy above the convex hull. Among them, 45 MXenes were validated using density functional theory calculations, of which 23 MXenes, including Ti2CClBr and Zr2NCl2, have not been previously known for their stability, confirming the effectiveness of this framework. The in-plane stiffness, shear moduli, and Poisson's ratio of the 45 MXenes were observed to vary widely according to their constituent elements, ranging from 90.11 to 198.02 N m-1, 64.00 to 163.40 N m-1, and 0.19 to 0.58, respectively. MXenes with Group-4 transition metals and halogen surface terminations were shown to be both thermodynamically stable and mechanically robust, highlighting the importance of electronegativity difference between constituent elements. Structurally, a smaller volume per atom and minimum bond length were determined to be preferable for obtaining mechanically robust MXenes. The proposed framework, along with an analysis of these two properties of MXenes, demonstrates immense potential for expediting the discovery of stable and robust MXenes.

1.3-µm identical active electro-absorption modulated laser with quantum well intermixed passive waveguide.

Quantum well intermixing (QWI) is an effective method for simple and well-defined monolithic integration of photonic devices. We introduce an identical-active electro-absorption modulated laser (IA-EML) with optimized QWI, which is applied to reduce the absorptive waveguide region. To determine the optimal intermixed IA-EML structure, we conduct a comparative analysis between the cases of an IA-EML with only an intermixed waveguide region and with both intermixed waveguide and electro-absorption modulator (EAM) regions, as well as the case without QWI. The results reveal that the intermixed region effectively inhibits the absorption in the waveguide. In particular, the IA-EML with only waveguide intermixing exhibits superior modulation characteristics with low driving voltages and a high extinction ratio. Our work provides an attractive approach for suppressing the absorptive waveguide region in the IA-EML to enhance modulation performance and to develop photonic integrated circuits with a simplified process.

Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield.

OBJECTIVES: Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study was to examine whether meaningful findings could be obtained when negative SBCE videos were reanalyzed with a deep convolutional neural network (CNN) model. METHODS: Clinical data of patients who received SBCE for suspected small-bowel bleeding at two academic hospitals between February 2018 and July 2020 were retrospectively collected. All SBCE videos read as negative were reanalyzed with the CNN algorithm developed in our previous study. Meaningful findings such as angioectasias and ulcers were finally decided after reviewing CNN-selected images by two gastroenterologists. RESULTS: Among 202 SBCE videos, 103 (51.0%) were read as negative by humans. Meaningful findings were detected in 63 (61.2%) of these 103 videos after reanalyzing them with the CNN model. There were 79 red spots or angioectasias in 40 videos and 66 erosions or ulcers in 35 videos. After reanalysis, the diagnosis was changed for 10 (10.3%) patients who had initially negative SBCE results. During a mean follow-up of 16.5 months, rebleeding occurred in 19 (18.4%) patients. The rebleeding rate was 23.6% (13/55) for patients with meaningful findings and 16.1% (5/31) for patients without meaningful findings (P = 0.411). CONCLUSION: Our CNN algorithm detected meaningful findings in negative SBCE videos that were missed by humans. The use of deep CNN for SBCE image reading is expected to compensate for human error.

Comparative genomic analysis of Chryseobacterium species: deep insights into plant-growth-promoting and halotolerant capacities.

Members of the genus Chryseobacterium have attracted great interest as beneficial bacteria that can promote plant growth and biocontrol. Given the recent risks of climate change, it is important to develop tolerance strategies for efficient applications of plant-beneficial bacteria in saline environments. However, the genetic determinants of plant-growth-promoting and halotolerance effects in Chryseobacterium have not yet been investigated at the genomic level. Here, a comparative genomic analysis was conducted with seven Chryseobacterium species. Phylogenetic and phylogenomic analyses revealed niche-specific evolutionary distances between soil and freshwater Chryseobacterium species, consistent with differences in genomic statistics, indicating that the freshwater bacteria have smaller genome sizes and fewer genes than the soil bacteria. Phosphorus- and zinc-cycling genes (required for nutrient acquisition in plants) were universally present in all species, whereas nitrification and sulphite reduction genes (required for nitrogen- and sulphur-cycling, respectively) were distributed only in soil bacteria. A pan-genome containing 6842 gene clusters was constructed, which reflected the general features of the core, accessory and unique genomes. Halotolerant species with an accessory genome shared a Kdp potassium transporter and biosynthetic pathways for branched-chain amino acids and the carotenoid lycopene, which are associated with countermeasures against salt stress. Protein-protein interaction network analysis was used to define the genetic determinants of Chryseobacterium salivictor NBC122 that reduce salt damage in bacteria and plants. Sixteen hub genes comprised the aromatic compound degradation and Por secretion systems, which are required to cope with complex stresses associated with saline environments. Horizontal gene transfer and CRISPR-Cas analyses indicated that C. salivictor NBC122 underwent more evolutionary events when interacting with different environments. These findings provide deep insights into genomic adaptation to dynamic interactions between plant-growth-promoting Chryseobacterium and salt stress.

Deep Learning Model for Predicting Airway Organoid Differentiation.

BACKGROUND: Organoids are self-organized three-dimensional culture systems and have the advantages of both in vitro and in vivo experiments. However, each organoid has a different degree of self-organization, and methods such as immunofluorescence staining are required for confirmation. Therefore, we established a system to select organoids with high tissue-specific similarity using deep learning without relying on staining by acquiring bright-field images in a non-destructive manner. METHODS: We identified four biomarkers in RNA extracted from airway organoids. We also predicted biomarker expression by image-based analysis of organoids by convolution neural network, a deep learning method. RESULTS: We predicted airway organoid-specific marker expression from bright-field images of organoids. Organoid differentiation was verified by immunofluorescence staining of the same organoid after predicting biomarker expression in bright-field images. CONCLUSION: Our study demonstrates the potential of imaging and deep learning to distinguish organoids with high human tissue similarity in disease research and drug screening.

The LBD11-ROS feedback regulatory loop modulates vascular cambium proliferation and secondary growth in Arabidopsis.

Vascular cambium produces the phloem and xylem, vascular tissues that transport resources and provide mechanical support, making it an ideal target for crop improvement. However, much remains unknown about how vascular cambium proliferates. In this study, through pharmaceutical and genetic manipulation of reactive oxygen species (ROS) maxima, we demonstrate a direct link between levels of ROS and activity of LATERAL ORGAN BOUNDARIES DOMAIN 11 (LBD11) in maintaining vascular cambium activity. LBD11 activates the transcription of several key ROS metabolic genes, including PEROXIDASE 71 and RESPIRATORY BURST OXIDASE HOMOLOGS D and F, to generate local ROS maxima in cambium, which in turn enhance the proliferation of cambial cells. In a negative feedback mechanism, higher ROS levels then repress LBD11 expression and maintain the balance of cambial cell proliferation. Our findings thus reveal the role of a novel LBD11/ROS-dependent feedback regulatory system in maintaining vascular cambium-specific redox homeostasis and radial growth in plants.

DDM1-mediated gene body DNA methylation is associated with inducible activation of defense-related genes in Arabidopsis.

BACKGROUND: Plants memorize previous pathogen attacks and are "primed" to produce a faster and stronger defense response, which is critical for defense against pathogens. In plants, cytosines in transposons and gene bodies are reported to be frequently methylated. Demethylation of transposons can affect disease resistance by regulating the transcription of nearby genes during defense response, but the role of gene body methylation (GBM) in defense responses remains unclear. RESULTS: Here, we find that loss of the chromatin remodeler decrease in DNA methylation 1 (ddm1) synergistically enhances resistance to a biotrophic pathogen under mild chemical priming. DDM1 mediates gene body methylation at a subset of stress-responsive genes with distinct chromatin properties from conventional gene body methylated genes. Decreased gene body methylation in loss of ddm1 mutant is associated with hyperactivation of these gene body methylated genes. Knockout of glyoxysomal protein kinase 1 (gpk1), a hypomethylated gene in ddm1 loss-of-function mutant, impairs priming of defense response to pathogen infection in Arabidopsis. We also find that DDM1-mediated gene body methylation is prone to epigenetic variation among natural Arabidopsis populations, and GPK1 expression is hyperactivated in natural variants with demethylated GPK1. CONCLUSIONS: Based on our collective results, we propose that DDM1-mediated GBM provides a possible regulatory axis for plants to modulate the inducibility of the immune response.

Wafer map failure pattern classification using geometric transformation-invariant convolutional neural network.

Wafer map defect pattern classification is essential in semiconductor manufacturing processes for increasing production yield and quality by providing key root-cause information. However, manual diagnosis by field experts is difficult in large-scale production situations, and existing deep-learning frameworks require a large quantity of data for learning. To address this, we propose a novel rotation- and flip-invariant method based on the labeling rule that the wafer map defect pattern has no effect on the rotation and flip of labels, achieving class discriminant performance in scarce data situations. The method utilizes a convolutional neural network (CNN) backbone with a Radon transformation and kernel flip to achieve geometrical invariance. The Radon feature serves as a rotation-equivariant bridge for translation-invariant CNNs, while the kernel flip module enables the model to be flip-invariant. We validated our method through extensive qualitative and quantitative experiments. For qualitative analysis, we suggest a multi-branch layer-wise relevance propagation to properly explain the model decision. For quantitative analysis, the superiority of the proposed method was validated with an ablation study. In addition, we verified the generalization performance of the proposed method to rotation and flip invariants for out-of-distribution data using rotation and flip augmented test sets.

CPR5-mediated nucleo-cytoplasmic localization of IAA12 and IAA19 controls lateral root development during abiotic stress.

Plasticity of the root system architecture (RSA) is essential in enabling plants to cope with various environmental stresses and is mainly controlled by the phytohormone auxin. Lateral root development is a major determinant of RSA. Abiotic stresses reduce auxin signaling output, inhibiting lateral root development; however, how abiotic stress translates into a lower auxin signaling output is not fully understood. Here, we show that the nucleo-cytoplasmic distribution of the negative regulators of auxin signaling AUXIN/INDOLE-3-ACETIC ACID INDUCIBLE 12 (AUX/IAA12 or IAA12) and IAA19 determines lateral root development under various abiotic stress conditions. The cytoplasmic localization of IAA12 and IAA19 in the root elongation zone enforces auxin signaling output, allowing lateral root development. Among components of the nuclear pore complex, we show that CONSTITUTIVE EXPRESSOR OF PATHOGENESIS-RELATED GENES 5 (CPR5) selectively mediates the cytoplasmic translocation of IAA12/19. Under abiotic stress conditions, CPR5 expression is strongly decreased, resulting in the accumulation of nucleus-localized IAA12/19 in the root elongation zone and the suppression of lateral root development, which is reiterated in the cpr5 mutant. This study reveals a regulatory mechanism for auxin signaling whereby the spatial distribution of AUX/IAA regulators is critical for lateral root development, especially in fluctuating environmental conditions.

Deep learning predicts the differentiation of kidney organoids derived from human induced pluripotent stem cells.

BACKGROUND: Kidney organoids derived from human pluripotent stem cells (hPSCs) contain multilineage nephrogenic progenitor cells and can recapitulate the development of the kidney. Kidney organoids derived from hPSCs have the potential to be applied in regenerative medicine as well as renal disease modeling, drug screening, and nephrotoxicity testing. Despite biotechnological advances, individual differences in morphological and growth characteristics among kidney organoids need to be addressed before clinical and commercial application. In this study, we hypothesized that an automated noninvasive method based on deep learning of bright-field images of kidney organoids can predict their differentiation status. METHODS: Bright-field images of kidney organoids were collected on day 18 after differentiation. To train convolutional neural networks (CNNs), we utilized a transfer learning approach. CNNs were trained to predict the differentiation of kidney organoids on bright-field images based on the messenger RNA expression of renal tubular epithelial cells as well as podocytes. RESULTS: The best prediction model was DenseNet121 with a total Pearson correlation coefficient score of 0.783 on a test dataset. W classified the kidney organoids into two categories: organoids with above-average gene expression (Positive) and those with below-average gene expression (Negative). Comparing the best-performing CNN with human-based classifiers, the CNN algorithm had a receiver operating characteristic-area under the curve (AUC) score of 0.85, while the experts had an AUC score of 0.48. CONCLUSION: These results confirmed our original hypothesis and demonstrated that our artificial intelligence algorithm can successfully recognize the differentiation status of kidney organoids.

Predicting multipotency of human adult stem cells derived from various donors through deep learning.

Adult stem cell-based therapeutic approaches have great potential in regenerative medicine because of their immunoregulatory properties and multidifferentiation capacity. Nevertheless, the outcomes of stem cell­based therapies to date have shown inconsistent efficacy owing to donor variation, thwarting the expectation of clinical effects. However, such donor dependency has been elucidated by biological consequences that current research could not predict. Here, we introduce cellular morphology-based prediction to determine the multipotency rate of human nasal turbinate stem cells (hNTSCs), aiming to predict the differentiation rate of keratocyte progenitors. We characterized the overall genes and morphologies of hNTSCs from five donors and compared stemness-related properties, including multipotency and specific lineages, using mRNA sequencing. It was demonstrated that transformation factors affecting the principal components were highly related to cell morphology. We then performed a convolutional neural network-based analysis, which enabled us to assess the multipotency level of each cell group based on their morphologies with 85.98% accuracy. Surprisingly, the trend in expression levels after ex vivo differentiation matched well with the deep learning prediction. These results suggest that AI­assisted cellular behavioral prediction can be utilized to perform quantitative, non-invasive, single-cell, and multimarker characterizations of live stem cells for improved quality control in clinical cell therapies.

Ultra-bright, efficient and stable perovskite light-emitting diodes.

Metal halide perovskites are attracting a lot of attention as next-generation light-emitting materials owing to their excellent emission properties, with narrow band emission1-4. However, perovskite light-emitting diodes (PeLEDs), irrespective of their material type (polycrystals or nanocrystals), have not realized high luminance, high efficiency and long lifetime simultaneously, as they are influenced by intrinsic limitations related to the trade-off of properties between charge transport and confinement in each type of perovskite material5-8. Here, we report an ultra-bright, efficient and stable PeLED made of core/shell perovskite nanocrystals with a size of approximately 10 nm, obtained using a simple in situ reaction of benzylphosphonic acid (BPA) additive with three-dimensional (3D) polycrystalline perovskite films, without separate synthesis processes. During the reaction, large 3D crystals are split into nanocrystals and the BPA surrounds the nanocrystals, achieving strong carrier confinement. The BPA shell passivates the undercoordinated lead atoms by forming covalent bonds, and thereby greatly reduces the trap density while maintaining good charge-transport properties for the 3D perovskites. We demonstrate simultaneously efficient, bright and stable PeLEDs that have a maximum brightness of approximately 470,000 cd m-2, maximum external quantum efficiency of 28.9% (average = 25.2 ± 1.6% over 40 devices), maximum current efficiency of 151 cd A-1 and half-lifetime of 520 h at 1,000 cd m-2 (estimated half-lifetime >30,000 h at 100 cd m-2). Our work sheds light on the possibility that PeLEDs can be commercialized in the future display industry.

Deep Learning Enhances Multiparametric Dynamic Volumetric Photoacoustic Computed Tomography In Vivo (DL-PACT).

Photoacoustic computed tomography (PACT) has become a premier preclinical and clinical imaging modality. Although PACT's image quality can be dramatically improved with a large number of ultrasound (US) transducer elements and associated multiplexed data acquisition systems, the associated high system cost and/or slow temporal resolution are significant problems. Here, a deep learning-based approach is demonstrated that qualitatively and quantitively diminishes the limited-view artifacts that reduce image quality and improves the slow temporal resolution. This deep learning-enhanced multiparametric dynamic volumetric PACT approach, called DL-PACT, requires only a clustered subset of many US transducer elements on the conventional multiparametric PACT. Using DL-PACT, high-quality static structural and dynamic contrast-enhanced whole-body images as well as dynamic functional brain images of live animals and humans are successfully acquired, all in a relatively fast and cost-effective manner. It is believed that the strategy can significantly advance the use of PACT technology for preclinical and clinical applications such as neurology, cardiology, pharmacology, endocrinology, and oncology.

Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder.

Discrimination of ovarian tumors is necessary for proper treatment. In this study, we developed a convolutional neural network model with a convolutional autoencoder (CNN-CAE) to classify ovarian tumors. A total of 1613 ultrasound images of ovaries with known pathological diagnoses were pre-processed and augmented for deep learning analysis. We designed a CNN-CAE model that removes the unnecessary information (e.g., calipers and annotations) from ultrasound images and classifies ovaries into five classes. We used fivefold cross-validation to evaluate the performance of the CNN-CAE model in terms of accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) was applied to visualize and verify the CNN-CAE model results qualitatively. In classifying normal versus ovarian tumors, the CNN-CAE model showed 97.2% accuracy, 97.2% sensitivity, and 0.9936 AUC with DenseNet121 CNN architecture. In distinguishing malignant ovarian tumors, the CNN-CAE model showed 90.12% accuracy, 86.67% sensitivity, and 0.9406 AUC with DenseNet161 CNN architecture. Grad-CAM showed that the CNN-CAE model recognizes valid texture and morphology features from the ultrasound images and classifies ovarian tumors from these features. CNN-CAE is a feasible diagnostic tool that is capable of robustly classifying ovarian tumors by eliminating marks on ultrasound images. CNN-CAE demonstrates an important application value in clinical conditions.

Development of optimized protocol for culturing African swine fever virus field isolates in MA104 cells.

The goal of this study was to identify a candidate commercial cell line for the replication of African swine fever virus (ASFV) by comparing several available cell lines with various medium factors. In the sensitivity test of cells, MA104 and MARC-145 had strong potential for ASFV replication. Next, MA104 cells were used to compare the adaptation of ASFV obtained from tissue homogenates and blood samples in various infectious media. At the 10th passage, the ASFV obtained from the blood sample had a significantly higher viral load than that obtained from the tissue sample (P = 0.000), exhibiting a mean cycle threshold (Ct) value = 20.39 ± 1.99 compared with 25.36 ± 2.11. For blood samples, ASFV grew on infectious medium B more robustly than on infectious medium A (P = 0.006), corresponding to a Ct value = 19.58 ± 2.10 versus 21.20 ± 1.47. African swine fever virus originating from blood specimens continued to multiply gradually and peaked in the 15th passage, exhibiting a Ct value = 14.36 ± 0.22 in infectious medium B and a Ct value = 15.42 ± 0.14 in infectious medium A. When ASFV was cultured from tissue homogenates, however, there was no difference (P = 0.062) in ASFV growth between infectious media A and B. A model was developed to enhance ASFV replication through adaptation to MA104 cells. The lack of mutation at the genetic segments encoding p72, p54, p30, and the central hypervariable region (CVR) in serial culture passages is important in increasing the probability of maintaining immunogenicity when developing a vaccine candidate.

L'objectif de cette étude était d'identifier une lignée cellulaire commerciale candidate pour la réplication du virus de la peste porcine africaine (ASFV) en comparant plusieurs lignées cellulaires disponibles et différents milieux. Lors du test de sensibilité des cellules, MA104 et MARC-145 présentaient un fort potentiel pour la réplication d'AFSV. Par la suite, les cellules MA104 ont été utilisées pour comparer l'adaptation d'ASFV obtenu d'homogénats de tissus et d'échantillons de sang dans différents milieux. Au dixième passage, l'ASFV obtenu de l'échantillon de sang avait une charge virale significativement plus élevée que celle obtenue de l'échantillon de tissu (P = 0,000), avec une valeur seuil moyenne de cycles (Ct) de 20,39 ± 1,99 comparativement à 25,36 ± 2,11. Pour les échantillons sanguins, l'ASFV a poussé sur le milieu B de manière plus robuste que sur le milieu A (P = 0,006), ce qui correspond à une valeur Ct de 19,58 ± 2,10 versus 21,20 ± 1,47. L'ASFV provenant des échantillons sanguins continua de se multiplier graduellement et atteignit un pic au 15e passage, avec une valeur Ct de 14,36 ± 0,22 dans le milieu B et une valeur Ct de 15,42 ± 0,14 dans le milieu A. Toutefois, lorsque l'ASFV fut cultivé à partir des homogénats de tissus, il n'y avait pas de différence (P = 0,062) dans la croissance d'ASFV entre les milieux A et B. Un modèle a été développé pour augmenter la réplication d'ASFV par adaptation aux cellules MA104. L'absence de mutation au segment génétique codant pour p72, p54, p30, et la région hypervariable centrale (CVR) dans des passages en série en culture est importante en augmentant la probabilité de maintenir une immunogénicité lors du développement d'un vaccin candidat.(Traduit par Docteur Serge Messier).

High throughput screening of mesenchymal stem cell lines using deep learning.

Mesenchymal stem cells (MSCs) are increasingly used as regenerative therapies for patients in the preclinical and clinical phases of various diseases. However, the main limitations of such therapies include functional heterogeneity and the lack of appropriate quality control (QC) methods for functional screening of MSC lines; thus, clinical outcomes are inconsistent. Recently, machine learning (ML)-based methods, in conjunction with single-cell morphological profiling, have been proposed as alternatives to conventional in vitro/vivo assays that evaluate MSC functions. Such methods perform in silico analyses of MSC functions by training ML algorithms to find highly nonlinear connections between MSC functions and morphology. Although such approaches are promising, they are limited in that extensive, high-content single-cell imaging is required; moreover, manually identified morphological features cannot be generalized to other experimental settings. To address these limitations, we propose an end-to-end deep learning (DL) framework for functional screening of MSC lines using live-cell microscopic images of MSC populations. We quantitatively evaluate various convolutional neural network (CNN) models and demonstrate that our method accurately classifies in vitro MSC lines to high/low multilineage differentiating stress-enduring (MUSE) cells markers from multiple donors. A total of 6,120 cell images were obtained from 8 MSC lines, and they were classified into two groups according to MUSE cell markers analyzed by immunofluorescence staining and FACS. The optimized DenseNet121 model showed area under the curve (AUC) 0.975, accuracy 0.922, F1 0.922, sensitivity 0.905, specificity 0.942, positive predictive value 0.940, and negative predictive value 0.908. Therefore, our DL-based framework is a convenient high-throughput method that could serve as an effective QC strategy in future clinical biomanufacturing processes.