Unsupervised AI reveals insect species-specific genome signatures.

Insects are a highly diverse phylogeny and possess a wide variety of traits, including the presence or absence of wings and metamorphosis. These diverse traits are of great interest for studying genome evolution, and numerous comparative genomic studies have examined a wide phylogenetic range of insects. Here, we analyzed 22 insects belonging to a wide phylogenetic range (Endopterygota, Paraneoptera, Polyneoptera, Palaeoptera, and other insects) by using a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions in their genomic fragments (100-kb or 1-Mb sequences), which is an unsupervised machine learning algorithm that can extract species-specific characteristics of the oligonucleotide compositions (genome signatures).

AI-based search for convergently expanding, advantageous mutations in SARS-CoV-2 by focusing on oligonucleotide frequencies.

Among mutations that occur in SARS-CoV-2, efficient identification of mutations advantageous for viral replication and transmission is important to characterize and defeat this rampant virus. Mutations rapidly expanding frequency in a viral population are candidates for advantageous mutations, but neutral mutations hitchhiking with advantageous mutations are also likely to be included. To distinguish these, we focus on mutations that appear to occur independently in different lineages and expand in frequency in a convergent evolutionary manner.

Comparative genomic analysis of the human genome and six bat genomes using unsupervised machine learning: Mb-level CpG and TFBS islands.

Background: Emerging infectious disease-causing RNA viruses, such as the SARS-CoV-2 and Ebola viruses, are thought to rely on bats as natural reservoir hosts. Since these zoonotic viruses pose a great threat to humans, it is important to characterize the bat genome from multiple perspectives. Unsupervised machine learning methods for extracting novel information from big sequence data without prior knowledge or particular models are highly desirable for obtaining unexpected insights.

Unsupervised explainable AI for molecular evolutionary study of forty thousand SARS-CoV-2 genomes.

Background: Unsupervised AI (artificial intelligence) can obtain novel knowledge from big data without particular models or prior knowledge and is highly desirable for unveiling hidden features in big data. SARS-CoV-2 poses a serious threat to public health and one important issue in characterizing this fast-evolving virus is to elucidate various aspects of their genome sequence changes. We previously established unsupervised AI, a BLSOM (batch-learning SOM), which can analyze five million genomic sequences simultaneously.

AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genome.

In genetics and related fields, huge amounts of data, such as genome sequences, are accumulating, and the use of artificial intelligence (AI) suitable for big data analysis has become increasingly important. Unsupervised AI that can reveal novel knowledge from big data without prior knowledge or particular models is highly desirable for analyses of genome sequences, particularly for obtaining unexpected insights. We have developed a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions that can reveal various novel genome characteristics.

Time-series analyses of directional sequence changes in SARS-CoV-2 genomes and an efficient search method for candidates for advantageous mutations for growth in human cells.

We first conducted time-series analysis of mono- and dinucleotide composition for over 10,000 SARS-CoV-2 genomes, as well as over 1500 Zaire ebolavirus genomes, and found clear time-series changes in the compositions on a monthly basis, which should reflect viral adaptations for efficient growth in human cells. We next developed a sequence alignment free method that extensively searches for advantageous mutations and rank them in an increase level for their intrapopulation frequency. Time-series analysis of occurrences of oligonucleotides of diverse lengths for SARS-CoV-2 genomes revealed seven distinctive mutations that rapidly expanded their intrapopulation frequency and are thought to be candidates of advantageous mutations for the efficient growth in human cells.

Comparative genomics of using unsupervised AI reveals a high CG frequency.

The Japanese wrinkled frog () is unique in having both XX-XY and ZZ-ZW types of sex chromosomes within the species. The genome sequencing and comparative genomics with other frogs should be important to understand mechanisms of turnover of sex chromosomes within one species or during a short period. In this study, we analyzed the newly sequenced genome of using a batch-learning self-organizing map which is unsupervised artificial intelligence for oligonucleotide compositions.

Time-series analyses of directional sequence changes in SARS-CoV-2 genomes and an efficient search method for candidates for advantageous mutations for growth in human cells.

We first conducted time-series analysis of mono- and dinucleotide composition for over 10,000 SARS-CoV-2 genomes, as well as over 1500 Zaire ebolavirus genomes, and found clear time-series changes in the compositions on a monthly basis, which should reflect viral adaptations for efficient growth in human cells. We next developed a sequence alignment free method that extensively searches for advantageous mutations and rank them in an increase level for their intrapopulation frequency. Time-series analysis of occurrences of oligonucleotides of diverse lengths for SARS-CoV-2 genomes revealed seven distinctive mutations that rapidly expanded their intrapopulation frequency and are thought to be candidates of advantageous mutations for the efficient growth in human cells.

Mb-level CpG and TFBS islands visualized by AI and their roles in the nuclear organization of the human genome.

Unsupervised machine learning that can discover novel knowledge from big sequence data without prior knowledge or particular models is highly desirable for current genome study. We previously established a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions, which can reveal various novel genome characteristics from big sequence data, and found that transcription factor binding sequences (TFBSs) and CpG-containing oligonucleotides are enriched in human centromeric and pericentromeric regions, which support centromere clustering and form the condensed heterochromatin "chromocenter" in interphase nuclei. The number and size of chromocenters, as well as the type of centromeres gathered in individual chromocenters, vary depending on cell type.

An artificial intelligence approach fit for tRNA gene studies in the era of big sequence data.

Unsupervised data mining capable of extracting a wide range of knowledge from big data without prior knowledge or particular models is a timely application in the era of big sequence data accumulation in genome research. By handling oligonucleotide compositions as high-dimensional data, we have previously modified the conventional self-organizing map (SOM) for genome informatics and established BLSOM, which can analyze more than ten million sequences simultaneously. Here, we develop BLSOM specialized for tRNA genes (tDNAs) that can cluster (self-organize) more than one million microbial tDNAs according to their cognate amino acid solely depending on tetra- and pentanucleotide compositions.

Ability of community pharmacists to promote self-care and self-medication by local residents [I]: Improvements in bone mineral density.

This study was conducted in order to establish a health management method for the elderly in a community through follow-ups of bone mineral density (BMD) measurement results over a 1-year period based on BMD measurements performed by pharmacists and a guidance program. Regarding BMD measurement results, the percent young adult mean (%YAM: mean BMD in healthy persons of the same sex aged between 51 and 82 years old) significantly increased in Period I, during which the intervention by pharmacists was performed (6 months after the start of measurements), but significantly decreased in Period II, during which this intervention was not performed (between 7 and 12 months after the start of measurements). Based on these results, lifestyle improvements were effective in Period I regardless of sex or age; however, it may be important to maintain an improved diet and subject motivation in the future.

Directional and reoccurring sequence change in zoonotic RNA virus genomes visualized by time-series word count.

Ebolavirus, MERS coronavirus and influenza virus are zoonotic RNA viruses, which mutate very rapidly. Viral growth depends on many host factors, but human cells may not provide the ideal growth conditions for viruses invading from nonhuman hosts. The present time-series analyses of short and long oligonucleotide compositions in these genomes showed directional changes in their composition after invasion from a nonhuman host, which are thought to recur after future invasions.

A case of expressive-vocal amusia in a right-handed patient with left hemispheric cerebral infarction.

A 53-year-old right-handed woman had an extensive lesion in the left hemisphere due to an infarction caused by vasospasm secondary to subarachnoid bleeding. She exhibited persistent expressive-vocal amusia with no symptoms of aphasia. Evaluation of the patient's musical competence using the Montreal Battery for Evaluation of Amusia, rhythm reproduction tests, acoustic analysis of pitch upon singing familiar music, Japanese standard language tests, and other detailed clinical examinations revealed that her amusia was more dominantly related to pitch production.

CG-containing oligonucleotides and transcription factor-binding motifs are enriched in human pericentric regions.

Unsupervised data mining capable of extracting a wide range of information from big sequence data without prior knowledge or particular models is highly desirable in an era of big data accumulation for research on genes, genomes and genetic systems. By handling oligonucleotide compositions in genomic sequences as high-dimensional data, we have previously modified the conventional SOM (self-organizing map) for genome informatics and established BLSOM for oligonucleotide composition, which can analyze more than ten million sequences simultaneously and is thus suitable for big data analyses. Oligonucleotides often represent motif sequences responsible for sequence-specific binding of proteins such as transcription factors.

[A case of hepatocellular carcinoma complicated by pleural effusion mixed with bile after radiofrequency ablation].

An 84-year-old Japanese man was admitted with hepatocellular carcinoma (HCC). He underwent transcatheter arterial chemoembolization and percutaneous radiofrequency ablation (RFA). Three weeks later, he developed sudden-onset right pleural effusion mixed with bile.

Evolutionary changes in vertebrate genome signatures with special focus on coelacanth.

With a remarkable increase in genomic sequence data of a wide range of species, novel tools are needed for comprehensive analyses of the big sequence data. Self-organizing map (SOM) is a powerful tool for clustering high-dimensional data on one plane. For oligonucleotide compositions handled as high-dimensional data, we have previously modified the conventional SOM for genome informatics: BLSOM.

Possible autoimmune hepatitis induced after chronic active Epstein-Barr virus infection.

Chronic active Epstein-Barr virus infection (CAEBV) can be manifested in a variety of systemic conditions, including interstitial pneumonia, malignant lymphoma, and coronary aneurysm. Sometimes it may be associated with hepatic failure, although the mechanism underlying CAEBV-related hepatotoxicity remains unclear. We encountered a case of autoimmune hepatitis (AIH) associated with CAEBV.

A Novel Bioinformatics Strategy to Analyze Microbial Big Sequence Data for Efficient Knowledge Discovery: Batch-Learning Self-Organizing Map (BLSOM).

With the remarkable increase of genomic sequence data of microorganisms, novel tools are needed for comprehensive analyses of the big sequence data available. The self-organizing map (SOM) is an effective tool for clustering and visualizing high-dimensional data, such as oligonucleotide composition on one map. By modifying the conventional SOM, we developed batch-learning SOM (BLSOM), which allowed classification of sequence fragments (e.

Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains.

Background: With the remarkable increase of microbial and viral sequence data obtained from high-throughput DNA sequencers, novel tools are needed for comprehensive analysis of the big sequence data. We have developed "Batch-Learning Self-Organizing Map (BLSOM)" which can characterize very many, even millions of, genomic sequences on one plane. Influenza virus is one of zoonotic viruses and shows clear host tropism.

Notable clustering of transcription-factor-binding motifs in human pericentric regions and its biological significance.

Since oligonucleotide composition in the genome sequence varies significantly among species even among those possessing the same genome G + C%, the composition has been used to distinguish a wide range of genomes and called as "genome signature". Oligonucleotides often represent motif sequences responsible for sequence-specific protein binding (e.g.

KRN633, an inhibitor of vascular endothelial growth factor receptor tyrosine kinase, induces intrauterine growth restriction in mice.

We previously reported that treatment with KRN633, a vascular endothelial growth factor receptor tyrosine kinase inhibitor, during mid-pregnancy caused intrauterine growth restriction resulting from impairment of blood vessel growth in the labyrinthine zone of the placenta and fetal organs. However, the relative sensitivities of blood vessels in the placenta and fetal organs to vascular endothelial growth factor (VEGF) inhibitors have not been determined. In this study, we aimed to examine the effects of KRN633 on the vasculatures of organs in mother mice and their newborn pups by immunohistochemical analysis.

Role of vascular endothelial growth factor in maintenance of pregnancy in mice.

It is well known that withdrawal of progesterone from the maternal circulation is a critical stimulus to parturition in rodents, such as rats and mice. However, mechanisms that determine the timing of progesterone withdrawal are not completely understood. In the present study, we examined whether the vascular endothelial growth factor (VEGF) system in the corpus luteum (CL) contributes to the regulation of circulating progesterone levels and acts as a determinant of the timing of parturition in mice.

Extracellular calcium influx activates adenylate cyclase 1 and potentiates insulin secretion in MIN6 cells.

Intracellular cAMP and Ca(2+) are important second messengers that regulate insulin secretion in pancreatic β-cells; however, the molecular mechanism underlying their mutual interaction for exocytosis is not fully understood. In the present study, we investigated the interplay between intracellular cAMP and Ca(2+) concentrations ([cAMP](i) and [Ca(2+)](i) respectively) in the pancreatic β-cell line MIN6 using total internal reflection fluorescence microscopy. For measuring [cAMP](i), we developed a genetically encoded yellow fluorescent biosensor for cAMP [Flamindo (fluorescent cAMP indicator)], which changes fluorescence intensity with cAMP binding.

Effects of KRN633, an inhibitor of vascular endothelial growth factor receptor-2 tyrosine kinase, on vascular development of placenta and fetus of mid-pregnancy in mice.

Inhibition of the vascular endothelial growth factor (VEGF) signaling pathway during pregnancy contributes to several pathologic pregnancies, such as hypertension, preeclampsia, and intrauterine growth restriction, but its effects on the fetus have not been fully examined. To determine how inhibition of the VEGF signaling pathway affects the fetal vascular development of mid pregnancy, we treated pregnant mice daily with either the VEGF receptor-2 (VEGFR-2) tyrosine kinase inhibitor KRN633 (300 mg/kg, p.o.

Relationship between the tautomeric structures of curcumin derivatives and their Abeta-binding activities in the context of therapies for Alzheimer's disease.

Curcumin, which can exist in an equilibrium between keto and enol tautomers, binds to beta-amyloid (Abeta) fibrils/aggregates. The aim of this study was to assess the relationship between the tautomeric structures of curcumin derivatives and their Abeta-binding activities. Curcumin derivatives with keto-enol tautomerism showed high levels of binding to Abeta aggregates but not to Abeta monomers.