Biology subjects

Nomura, Y.

Publications and source records attributed to Nomura, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Core genome MLST reveals genetic and BafA-associated phenotypic diversities in Bartonella henselae strains

Bartonella henselae is a zoonotic pathogen associated with cat-scratch disease. Although multilocus sequence typing (MLST) has been used for strain classification, its resolution for distinguishing between B. henselae isolates remains limited. We herein developed a B. henselae-specific core genome MLST (cgMLST) scheme based on whole-genome sequencing data and examined the genetic and phenotypic diversities of 80 strains derived from cats, humans, mongooses, and masked palm civets. Using the conventional MLST scheme, the 80 strains were classified into nine sequence types (STs), while cgMLST subdivided them into 72 cgSTs, demonstrating a marked improvement in discriminatory power. The cgMLST scheme comprised 1,183 core genes and showed high applicability across the 80 strains. A phylogenetic analysis revealed that ST1, which has been associated with cat-scratch disease, was further subdivided into three major clusters and two singletons, indicating high genetic heterogeneity within this ST. We also found that the bafA subtypes clustered in a manner that was largely consistent with the cgMLST-based phylogenetic structure, suggesting a close relationship between bafA variations and the genomic background of B. henselae strains. In a human umbilical vein endothelial cell proliferation assay, strains belonging to distinct cgSTs exhibited strain-dependent differences in proliferative capacity, which were associated with the bafA subtype classification. Some strains induced focal cell fragmentation and a reduced cell density at a high multiplicity of infection, indicating strain-dependent differences in endothelial cell injury. Collectively, the present results establish a high-resolution cgMLST framework for B. henselae and demonstrate that genetically distinct strains have diverse endothelial cell phenotypes.

microbiology↗

bioRxiv2025

Year-long transcriptome survey highlights a resource allocation strategy at the late growth stage in a monocarpic plant

Day length and temperature drastically changes along the annual cycle in temperate zone. Temperate plants are thought to adapt to the annual cycle by various physiological responses in appropriate season. In the previous study, the annual transcriptome dynamics of Arabidopsis halleri, which is a polycarpic sister species of A. thaliana, was obtained in natural habitats and annual environments simulated by growth chambers. Here, we performed year-long transcriptome analysis of A. thaliana (Col-0 and FRISf2) under annual environments simulated by growth chambers. Comparing gene expressions in FRISf2 and A. helleri, most genes had similar annual expression pattens. However, in February-March conditions, 2,787 genes, containing LHCB and TGG1, exhibited significantly lower expressions in FRISf2 than those in A. helleri. LHCB and TGG1 were not downregulated in cold treatments for a month. These results suggest that factors other than temperature, such as age, are involved in the down-regulations of LHCB and TGG1. Resource translocation to seeds occurs at the end of the growing season in annual plants. The down-regulation of photosynthesis-related proteins and defense-related proteins, known to be highly abundant in leaves, is possibly an adaptive strategy of monocarpic A. thaliana.

plant biology↗

bioRxiv2025

Parallel Evolution of a Drimenol Biosynthetic Gene Cluster Unique to Marine Bacteria

Natural bacterial communities represent a rich resource for the discovery of enzymes and natural compounds including terpenoids. However, the biosynthetic potential of marine bacteria remains poorly explored due to limitations in studying fastidious microorganisms. In a recent study, we discovered unusual haloacid dehalogenase (HAD)-like drimenol synthases (DMSs) of marine bacterial origin, including Aquimarina spongiae AsDMS and Flavivirga eckloniae FeDMS, which produce drimenol in vitro. In the present study, we conducted an in vivo investigation of the biosynthesis of this drimane-type sesquiterpene in marine bacteria. To verify drimenol accumulation in cultures of F. eckloniae, we mined the drimenol biosynthetic genes encoded in the F. eckloniae genome and found a putative AraC family transcriptional regulator in the drimenol gene cluster. Upon the addition of arabinose, we observed drimenol biosynthesis in F. eckloniae, demonstrating the importance of genomics-driven strategies in investigating natural products. Syntenic comparisons of drimenol gene clusters across bacteria revealed the unique evolution of drimenol biosynthesis in marine bacteria. Moreover, the distinct nature of drimenol pathways and relevant enzymes in bacteria compared to those in plants and fungi suggests parallel evolution. Remarkably, we describe two individual proteins from Sorangium cellulosum, HAD and terpene synthase with {beta} domain architecture, similar to HAD-like and terpene synthase {beta} domains of AsDMS, respectively, suggesting the fusion of monodomain proteins during the evolution of bacterial bifunctional DMSs. Our findings shed light on the evolutionary trajectory of the drimenol gene cluster unique to marine bacteria, while also demonstrating the new bacterial mono-{beta}-domain sesquiterpene synthase. ABSTRACT GRAPHIC O_FIG O_LINKSMALLFIG WIDTH=183 HEIGHT=200 SRC="FIGDIR/small/677545v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1883b5borg.highwire.dtl.DTLVardef@f0eab7org.highwire.dtl.DTLVardef@c1bad1org.highwire.dtl.DTLVardef@cff6e8_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Decoding the emergent properties of microbial community functions through sub-community observations and interpretable machine learning

The functions of microbial communities, including substrate conversion and pathogen suppression, arise not as a simple sum of individual species capabilities but through complex interspecies interactions. Understanding how such functions arise from individual species and their interactions remains a major challenge, limiting efforts to rationally understand microbial roles in both natural and engineered ecosystems. Because current holistic (meta-omics) and reductionist (isolation- or single-cell-based) approaches struggle to capture these emergent microbial community functions, this study explores an intermediate strategy: analyzing simple sub-community combinations to enable a bottom-up understanding of community-level functions. To examine the validity of this approach, we used a nine-member synthetic microbial community capable of degrading the environmental pollutant aniline, and systematically generated a dataset of 256 sub-community combinations and their associated functions. Analyses using random forest models revealed that the sub-community combinations of just three to four species enabled the quantitative prediction of functions in larger communities (5-9-member; Pearsons r = 0.78-0.80). Prediction performance remained robust even with limited sub-community data, suggesting applicability to more diverse microbial communities where exhaustive sub-community observation is infeasible. Moreover, interpreting models trained on these simple sub-community combinations enabled the identification of key species and interspecies interactions that strongly influence the overall community function. These findings provide a methodological framework for mechanistically dissecting complex microbial community functions through sub-community-based analysis.

microbiology↗

bioRxiv2025

Discovery of evolutionarily extended cis-regulatory overlapping genes expanding the protein universe from animals to humans

A recent proteogenomics-driven approach has uncovered actively translated alternative open reading frames (altORFs) that complement reference protein-coding sequences (refCDSs). Here, we further explored proteogenomic data to identify hidden proteomes, specifically short ORF-encoded polypeptides (SEPs) and longer altORF-encoded proteins (LEPs) previously overlooked in human genomes. We discovered a novel class of proteomes originating from SEP/LEP-coding upstream overlapping altORFs (oORFs) associated with refCDSs, which cis-regulate refCDS translation and have undergone evolutionary selection favoring C-terminal extension for the enhanced cis-regulation. These translatable oORFs occur in signal effectors of the Hippo-YAP/TAZ, p53, Wnt, and TGF-{beta} crosstalk pathways and frequently arise from intragenic frameshift polymorphisms closely linked to human diseases. The intragenic frameshift mutations divide refCDSs and capture the N-terminal regions to form the new oORFs. Consequently, we termed these entities upstream region-usurping repurposed proteins (USURPs). These findings offer new insights into birth and evolution of proteomes, broadening our understanding of the protein universe influenced by genome dynamics. TeaserUnveiling a novel category of proteomes from animals to humans and elucidating its birth, evolution, function, and relation to human diseases

genomics↗

bioRxiv2025

Systematic measurements of dose-dependent responses for combinatorial treatments of SA and JA led to the development of transcriptomic biomarkers

Plants coordinate defence against pathogens and herbivores through the crosstalk between salicylic acid (SA) and jasmonic acid (JA) signalling pathways. However, little is known about how plants integrate the phytohormone concentration profiles to shape defence states in complex field environments. Here we show that systematically varying combinatorial SA and JA concentrations in Arabidopsis thaliana reveals 43 distinct combination-specific transcriptional signatures beyond SA-JA antagonism. Leveraging these data, we developed and validated machine-learning-based transcriptomic biomarkers that enabled independent quantification of SA and JA response states. By applying these biomarkers to field-grown A. thaliana, we revealed that SA/JA response states were dynamically shaped by abundant and co-occurrence of herbivores on individual plants. Our study provides a strategy to decode hormonal responses to biotic environments from field transcriptomes, bridging the gap between molecular signalling and ecological dynamics.

plant biology↗

bioRxiv2025

RNA Modifications and Prp24 Coordinate Lsm2-8 Binding Dynamics during S. cerevisiae U6 snRNP Assembly

In eukaryotes, the process of intron removal from nuclear pre-mRNA is performed by the spliceosome, a dynamic molecular machine composed of small nuclear ribonucleoproteins (snRNPs; U1, U2, U4, U5, and U6) and dozens of other protein splicing factors. The U6 snRNP contains the U6 snRNA and the proteins Prp24 and Lsm2-8 heteroheptamer. A key feature of the snRNP is a modified U6 snRNA 3 end, which in S. cerevisiae (yeast) contains a 3 phosphate. U6 plays an essential role in splicing, and the U6 snRNP must be completely disassembled for splicing to occur. Once splicing is finished, the snRNP must then be reassembled to participate in a subsequent splicing reaction. While splicing efficiency depends on rapid U6 snRNP assembly, this process has not yet been kinetically characterized. Here, we use colocalization single molecule spectroscopy (CoSMoS) to dissect the kinetic pathways of yeast U6 snRNA association with the Lsm2-8 complex and their dependence on the Prp24 protein and post-transcriptional snRNA modification. In the absence of 3 end processing, Lsm2-8 association with the RNA is highly dependent on Prp24. However, processed RNAs can rapidly recruit Lsm2-8 in Prp24s absence. Post-transcriptional processing facilitates Lsm2-8 association while the presence of Prp24 promotes both recruitment and retention of the complex. This suggests that efficient U6 snRNP assembly could depend on kinetic selection of Lsm2-8 binding to 3-end modified or Prp24 bound U6 snRNAs in order to discriminate against association with other RNAs.

biochemistry↗

bioRxiv2025

Label Noise in Pathological Segmentation is Overlooked, Leading to Potential Overestimation of Artificial Intelligence Models

Artificial intelligence (AI) has transformed medical imaging, driving advancements in radiology and endoscopy. Semantic segmentation, a pixel-level technique crucial for delineating pathological features, has become a cornerstone of digital pathology. Pathology segmentation AI models are often trained using annotations generated by pathologists. Despite the meticulous care typically exercised, these annotations frequently contain empirical label noise. However, the specific types of label noise in pathology data and their impact on AI model training remain inadequately explored. This study systematically investigated the effects of label noise on the performance of pathology segmentation models. Using publicly available datasets and a breast cancer semantic segmentation dataset, modules were developed to simulate four types of artificial label noise at varying intensity levels. These datasets were used to train deep learning models with encoder-decoder architectures, and their performance was evaluated using metrics such as the Dice coefficient, precision, recall, and intersection over union. The results indicated that models were highly susceptible to overfitting label noise, particularly boundary-dependent noise such as dilation and shrinkage. Discrepancies were identified between apparent performance scores obtained under real-world conditions and true performance scores derived using clean test data. This overestimation risk was most pronounced for datasets containing boundary-altering noise. Furthermore, random combinations of noise types and levels significantly impaired model generalization. This study underscores the critical importance of addressing label noise in pathology datasets. It is proposed that future efforts focus on developing standardized methods for quantifying and mitigating label noise, along with creating robust benchmarks using noise-inclusive datasets. Enhancing annotation quality and addressing label noise can improve the reliability and generalizability of AI in pathology, facilitating broader clinical adoption.

bioinformatics↗

bioRxiv2024

Modeling of transcriptomic variation among subgenomes in 25 accessions of common wheat reveals cis- and trans-regulation architectures

Common wheat is an allohexaploid plant, thus making it difficult to obtain homoeolog-distinguished transcriptome data. Lasy-Seq, a type of 3 RNA-seq, is efficient for obtaining homoeolog-distinguished transcriptomes and can thus overcome this measurement difficulty. This study obtained transcriptome data from the seedlings, second leaves, and root tips of 25 lines from mainly eastern transmitted area using Lasy-Seq. Roots and seedlings exhibited similar transcriptome profiles; however, they were different from those of the leaves. We determined the effects of subgenomes, lines and their interactions with leaves, roots, and seedlings on the expression levels of each homoeolog triad. Of the 19,805 homoeolog triads, 50.9-55.4%, 24.2-29.5%, and 7.7-9.0% showed significant effects on their expression levels from subgenome, line, and interaction, respectively. 51-55% and 24-30% have genetic variation in the cis- and trans-regulation. Hierarchical clustering and co-trans regulation network analysis of homoeolog triads revealed that the patterns of expression polymorphisms among the lines were shared in different genes. The triads in which the statistical model detected as line effects imply that expression variation between lines is caused by changes in a smaller number of common trans-factors. We assigned gene ontology (GO) terms of the Arabidopsis orthologs to wheat homoeolog triads via reciprocal BLAST between common wheat and Arabidopsis, thus improving the percentage of gene-assigned GO terms to all analyzed GO terms from 19.1% to 90.6%. GO term enrichment analysis revealed that GO terms related to each tissue type function were enriched in genes expressed in the leaves and roots. Our information provides fundamental knowledge for the future breeding of plants possessing complex gene regulatory networks such as common wheat.

plant biology↗

bioRxiv2024

Interhemispheric CA1 projections support spatial cognition and are affected in a mouse model of the 22q11.2 deletion syndrome

Untangling the hippocampus connectivity is critical for understanding the mechanisms supporting learning and memory. However, the function of interhemispheric connections between hippocampal formations is still poorly understood. So far, two major hippocampal commissural projections have been characterized in rodents. Mossy cells from the hilus of the dentate gyrus project to the inner molecular layer of the contralateral dentate gyrus and CA3 and CA2 pyramidal neuron axonal collaterals to contralateral CA3, CA2 and CA1. In contrary, little is known about commissural projection from the CA1 region. Here, we show that CA1 pyramidal neurons from the dorsal hippocampus project to contralateral dorsal CA1 as well as dorsal subiculum. We further demonstrate that the interhemispheric projection from CA1 to dorsal subiculum supports spatial memory and spatial working memory in WT mice, two cognitive functions impaired in male mice from the Df16(A)+/- model of 22q11.2 deletion syndrome (22q11.2DS) associated with schizophrenia. Investigation of the CA1 interhemispheric projections in Df16(A)+/- mice revealed that these projections are disrupted with male mutants showing stronger anatomical defects compared to females. Overall, our results characterize a novel interhemispheric projection from dCA1 to dorsal subiculum and suggest that dysregulation of this projection may contribute to the cognitive deficits associated with the 22q11.2DS.

neuroscience↗

bioRxiv2023

Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space

Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screening strategy that classifies bioactive compounds and plants in semantic space generated by word embedding algorithm. The classifier showed good performance in binary (presence/absence of bioactivity) classification for both compounds and plant genera. Furthermore, the strategy led to the discovery of antimicrobial activity of essential oils from Lindera triloba and Cinnamomum sieboldii against Staphylococcus aureus. The results of this study indicate that machine-learning classification in semantic space can be a highly efficient approach for exploring bioactive plant extracts.

bioinformatics↗

bioRxiv2022

Genetic and phosphoproteomic basis of LysM-mediated immune signaling in Marchantia polymorpha highlights conserved elements and new aspect of pattern-triggered immunity in land plants

Pattern-recognition receptor (PRR)-triggered immunity (PTI) wards off a wide range of pathogenic microbes, playing a pivotal role in plant immunity. The model liverwort Marchantia polymorpha is emerging as a popular model for investigating the evolution of plant-microbe interactions. M. polymorpha triggers defense-related gene expression upon sensing components of bacterial and fungal extracts, suggesting the existence of PTI in this plant model. However, the molecular components of the putative PTI in M. polymorpha have not yet been described. We show that, in M. polymorpha, which has four LysM receptor homologs, lysin motif (LysM) receptor-like kinase (LYK) MpLYK1 and LYK-related (LYR) MpLYR are required for sensing chitin and peptidoglycan fragments, triggering a series of characteristic immune responses. Comprehensive phosphoproteomic analysis of M. polymorpha in response to chitin treatment identified regulatory proteins that potentially shape LysM-mediated PTI. The identified proteins included homologs of well-described PTI components in angiosperms as well as proteins whose roles in PTI are not yet determined, including the blue-light receptor phototropin MpPHOT. We revealed that MpPHOT is required for a negative feedback of defense-related gene expression during PTI. Taken together, this study outlines the basic framework of LysM-mediated PTI in M. polymorpha and demonstrates the utility of M. polymorpha as a plant model for discovering novel or fundamental molecular mechanisms underlying PRR-triggered immune signaling in plants.

plant biology↗

bioRxiv2022

Corticotropin-releasing hormone signaling from prefrontal cortex to lateral septum supports social novelty preference

Social preference, the decision to interact with one member of the same species over another, is a key feature of optimizing social interactions. In rodents, social preference relies on both extrinsic factors, such as sex, strain and kinship, and intrinsic ones, such as the memory of previous encounters, which favors interactions with novel compared to familiar animals (social novelty preference). At present, it is unclear which neuronal circuits guide social preferences and whether such circuits promote social interactions with the preferred individuals or suppress interactions with the non-preferred ones. Although both the infra-limbic area of the pre-frontal cortex (ILA) and the lateral septum (LS) have been shown to support social novelty preference, the neuronal circuits and molecular mechanisms by which these brain regions interact to regulate social interactions are unknown. Here, we identify a population of inhibitory neurons in ILA that express the neuropeptide corticotropin releasing hormone (CRH) and project to the rostro-dorsal region of LS (rdLS). Release of CRH from ILA in rdLS during interactions with familiar mice disinhibits rdLS neurons, thereby suppressing interactions with familiar mice and contributing to social novelty preference. We further demonstrate how the maturation of CRH expression during the first two post-natal weeks enables the developmental shift from a preference for littermates in juveniles to a preference for novel mice in adults.

neuroscience↗

bioRxiv2022

Autism in a dish: ES cell models of autism with copy number variations reveal cell-type-specific vulnerability

Human genetics has identified numerous single nucleotide variations (SNVs) and copy number variations (CNVs) associated with autism spectrum disorders (ASD) and other psychiatric disorders. However, the lack of standardized biological resources impedes understanding of the common pathophysiology of ASD. Here, using next-generation chromosome engineering based on the CRISPR/Cas9 system, we established a biological resource including 65 genetically modified mouse embryonic stem cell (mESC) lines as genetic models of human SNVs and CNVs. To illustrate cell-type and CNV specific molecular features of ASD, we performed single-cell RNA sequencing (37,397 cells in total), morphological, and physiological analyses using 12 representative cell lines with CNVs highly associated with ASD. These results uncover gene ontology (GO) terms, canonical pathways, upstream regulators, and related neuropsychiatric disorders in a cell-type and CNV specific manner.

neuroscience↗

bioRxiv2021

An Atypical RNA Quadruplex Marks RNAs as Vectors for Gene Silencing

The addition of poly(UG) ("pUG") repeats to 3' termini of mRNAs drives gene silencing and trans-generational epigenetic inheritance in the metazoan C. elegans.1 pUG tails promote silencing by recruiting an RNA-dependent RNA Polymerase (RdRP) that synthesizes small interfering (si)RNAs.1 Here we show that active pUG tails require a minimum of 11.5 repeats and adopt a quadruplex (G4)2 structure we term the pUG fold. The pUG fold differs from known G4s in that it has a left-handed backbone similar to Z-RNA3,4, no consecutive guanosines in its sequence, and three G quartets and one U quartet stacked non-sequentially. Its biological importance is emphasized by our observations that porphyrin molecules bind to the pUG fold and inhibit both gene silencing and binding of RdRP. Moreover, specific N7-deaza RNA substitutions that do not adopt the pUG fold neither bind RdRP nor induce RNA silencing. These data define the pUG fold as a previously unrecognized RNA secondary structure motif that drives gene silencing. The pUG fold can also form internally within larger RNA molecules. Approximately 20,000 pUG-fold sequences are found in non-coding regions of human RNAs, suggesting the fold likely has biological roles beyond gene silencing.

molecular biology↗

bioRxiv2021

Reciprocal differentiation via GABAergic components and ASD-related phenotypes in hES with 1q21.1 CNV

Copy number variations (CNVs) in the distal 1q21.1 region, both deletion (1q del) and duplication (1q dup), are associated with autism spectrum disorder, epilepsy and schizophrenia. Besides common phenotypes, 1q del and 1q dup manifest opposite clinical phenotypes--e.g., microcephaly in 1q del and macrocephaly in 1q dup. However, molecular and cellular mechanisms are still elusive. We generate isogenic human ES (hES) cell lines with reciprocal 1q21.1 CNVs using CRISPR/Cas9 system and differentiate them into 2-dimensional (2-D) neurons and 3-D cortical organoids. Our study recapitulates opposite organoid size and shows dosage-dependent differentiation changes i.e., more mature and GABAergic components in 1q del and more proliferative state in 1q dup. In contrast, both CNVs show hyperexcitability and altered expressions of glutamate system as common features. These results demonstrate that 1q21.1 CNVs dramatically affect cell fate in the early neurodevelopmental periods. This is the first isogenic model of hES CNVs and our findings provide new insights into the underlying mechanisms of neurodevelopmental disorders.

genomics↗

bioRxiv2021

Fillable and Unfillable Gaps in Rice Transcriptome under Field and Controlled Environments

The differences between plants grown in field and controlled environments have long been recognised; however, few studies have addressed the underlying molecular mechanisms. Here, we show fillable and unfillable gaps in the transcriptomes of rice grown in field and controlled environments by utilising SmartGC, a high-performance growth chamber that reproduces the fluctuating irradiance, temperature, and humidity of field environments. Rice transcriptome dynamics in SmartGC mimicked those in the field, particularly during the morning and evening; those in conventional growth chamber conditions did not. Further analysis revealed that fluctuation of irradiance affects transcriptome dynamics in the morning and evening, while fluctuation of temperature only affects transcriptome dynamics in the morning. We found upregulation of genes related to biotic and abiotic stress, whose expression was affected by environmental factors that cannot be mimicked by SmartGC. Our results accelerate the understanding of plant responses to field environments for both field and laboratory studies.

plant biology↗

bioRxiv2020

The Phosphorylation Status of NPH3 Affects Photosensory Adaptation During the Phototropic Response

Photosensory adaptation, which can be classified as sensor or effector adaptation, optimizes the light sensing of living organisms by tuning their sensitivity to changing light conditions. During the phototropic response in Arabidopsis (Arabidopsis thaliana), the light-dependent expression controls of blue-light photoreceptor phototropin1 (phot1) and its modulator ROOT PHOTOTROPISM2 (RPT2) are known as the molecular mechanisms underlying sensor adaptation. However, little is known about effector adaption in plant phototropism. Here we show that control of the phosphorylation status of NONPHOTOTROPIC HYPOCOTYL3 (NPH3) leads to effector adaptation in hypocotyl phototropism. We identified seven phosphorylation sites of NPH3 proteins in the etiolated seedlings of Arabidopsis and generated unphosphorable and phosphomimetic NPH3 proteins on those sites. Unphosphorable NPH3 showed a shortening of its subcellular localization in the cytosol and caused an inability to adapt to very low fluence rates of blue light ([~]10-5 {micro}mol m-2 s-1) during the phototropic response. In contrast, the phosphomimetic NPH3 proteins had a lengthened subcellular localization in the cytosol and could not lead to the adaptation for blue light at fluence rates of 10-3 {micro}mol m-2 s-1 or more. Our results suggest that the activation levels of phot1 and the corresponding phosphorylation levels of NPH3 determine the rate of plasma membrane-cytosol shuttling of NPH3, which moderately maintains the active state of phot1 signaling across a broad range of blue-light intensities and contributes to the photosensory adaptation of phot1 signaling during the phototropic response in hypocotyls. One sentence summaryThe phosphorylation status of NON-PHOTOTROPIC HYPOCOTYL3 proteins affects their subcellular localization and the photosensory adaptation of phot1 signaling.

plant biology↗