Salt stress-accelerated proteasomal degradation of LBD11 suppresses ROS-mediated meristem development and root growth in Arabidopsis.

Roots absorb water and nutrients from the soil, support the plant's aboveground organs, and detect environmental changes, making them crucial targets for improving crop productivity. Roots are particularly sensitive to soil salinity, a major abiotic stress that poses a serious threat to global agriculture. In response to salt stress, plants suppress root meristem size, thus reducing root growth; however, the mechanisms underlying this growth restriction remain unclear.

Development and characterization of high-efficiency cell-adapted live attenuated vaccine candidate against African swine fever.

African swine fever (ASF), a contagious and lethal haemorrhagic disease of domestic pigs and wild boars, poses a significant threat to the global pig industry. Although experimental vaccine candidates derived from naturally attenuated, genetically engineered, or cell culture-adapted ASF virus have been tested, no commercial vaccine is accepted globally. We developed a safe and effective cell-adapted live attenuated vaccine candidate (ASFV-MEC-01) by serial passage of a field isolate in CA-CAS-01-A cells.

Active Learning Framework for Expediting the Search of Thermodynamically Stable MXenes in the Extensive Chemical Space.

MXenes possess a wide range of materials properties owing to their compositional and stoichiometric diversities, facilitating their utilization in various technological applications such as electrodes, catalysts, and supercapacitors. To explore their applicability, identification of thermodynamically stable and synthesizable MXenes should precede. The energy above the convex hull () calculated using the density functional theory (DFT) is a powerful scale to probe the thermodynamic stability.

Impact of Quorum Sensing on the Virulence and Survival Traits of .

Quorum sensing (QS) is a mechanism by which bacteria detect and respond to cell density, regulating collective behaviors. , the causal agent of rice seedling blight, employs the LuxIR-type QS system, common among Gram-negative bacteria, where LuxI-type synthase produces QS signals recognized by LuxR-type regulators to control gene expression. This study aimed to elucidate the QS mechanism in KACC18965.

Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal-Organic Frameworks.

Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability.

Optimizing tenderness of M. Semitendinosus steak for elderly people with the combination of ficin and sous-vide cooking.

This study aimed to evaluate the sous-vide cooking and ficin treatment effects on the tenderness of beef steak and optimize it for the elderly using response surface methodology (RSM). The M. semitendinosus (ST) from Chikso cattle was shaped into 5 × 5 × 2.

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

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.

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.

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

Members of the genus 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 have not yet been investigated at the genomic level.

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.

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.

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.

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.

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.

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.

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 cell‑based 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.

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.

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 emission. 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 material. 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.

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.

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.