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War is an extreme form of collective human behaviour characterized by coordinated violence. We show that this nature of war is substantiated in the temporal patterns of conflict occurrence that obey power law. The focal metric is the interconflict interval (ICI), the interval between the end of a conflict in a dyad (i.e. a pair of states) and the start of the subsequent conflict in the same dyad. Using elaborate statistical tests, we confirmed that ICI samples compiled from the history of interstate conflicts from 1816 to 2014 followed a power-law distribution. We then demonstrate that the power-law properties of ICIs can be explained by a hypothetical model assuming an information-theoretic formulation of the Clausewitz thesis on war: the use of force is a means of interstate communication. Our findings help us to understand the nature of wars between regular states, the significance of which has increased since the Russian invasion of Ukraine in 2022.

Herein, we present a novel topic variation detection method that combines a topic extraction method and a change-point detection method. It extracts topics from time-series text data as the feature of each time and detects change points from the changing patterns of the extracted topics. We applied this method to analyze the valuable, albeit underutilized, text dataset containing the Japanese Prime Minister’s (PM’s) detailed daily activities for over 32 years. The proposed method and data provide novel insights into the empirical analyses of political business cycles, which is a classical issue in economics and political science. For instance, as our approach enables us to directly observe and analyze the PM’s actions, it can overcome the empirical challenges encountered by previous research owing to the unobservability of the PM’s behavior. Our empirical observations are primarily consistent with recent theoretical developments regarding this topic. Despite limitations, by employing a completely novel method and dataset, our approach enhances our understanding and provides new insights into this classic issue.

Risk prediction for heart failure (HF) using machine learning methods (MLM) has not yet been established at practical application levels in clinical settings. This study aimed to create a new risk prediction model for HF with a minimum number of predictor variables using MLM. We used two datasets of hospitalized HF patients: retrospective data for creating the model and prospectively registered data for model validation. Critical clinical events (CCEs) were defined as death or LV assist device implantation within 1 year from the discharge date. We randomly divided the retrospective data into training and testing datasets and created a risk prediction model based on the training dataset (MLM-risk model). The prediction model was validated using both the testing dataset and the prospectively registered data. Finally, we compared predictive power with published conventional risk models. In the patients with HF ( n  = 987), CCEs occurred in 142 patients. In the testing dataset, the substantial predictive power of the MLM-risk model was obtained (AUC = 0.87). We generated the model using 15 variables. Our MLM-risk model showed superior predictive power in the prospective study compared to conventional risk models such as the Seattle Heart Failure Model (c-statistics: 0.86 vs. 0.68, p  < 0.05). Notably, the model with an input variable number ( n  = 5) has comparable predictive power for CCE with the model (variable number = 15). This study developed and validated a model with minimized variables to predict mortality more accurately in patients with HF, using a MLM, than the existing risk scores.

Massively parallel, tag-based sequencing systems, such as the SOLiD system, hold the promise of revolutionizing the study of whole genome gene expression due to the number of data points that can be generated in a simple and cost-effective manner. We describe the development of a 5'-end transcriptome workflow for the SOLiD system and demonstrate the advantages in sensitivity and dynamic range offered by this tag-based application over traditional approaches for the study of whole genome gene expression. 5'-end transcriptome analysis was used to study whole genome gene expression within a colon cancer cell line, HT-29, treated with the DNA methyltransferase inhibitor, 5-aza-2'-deoxycytidine (5Aza). More than 20 million 25-base 5'-end tags were obtained from untreated and 5Aza-treated cells and matched to sequences within the human genome. Seventy three percent of the mapped unique tags were associated with RefSeq cDNA sequences, corresponding to approximately 14,000 different protein-coding genes in this single cell type. The level of expression of these genes ranged from 0.02 to 4,704 transcripts per cell. The sensitivity of a single sequence run of the SOLiD platform was 100-1,000 fold greater than that observed from 5'end SAGE data generated from the analysis of 70,000 tags obtained by Sanger sequencing. The high-resolution 5'end gene expression profiling presented in this study will not only provide novel insight into the transcriptional machinery but should also serve as a basis for a better understanding of cell biology.

Multiple-system atrophy is a rare neurodegenerative disease characterized by autonomic failure. Mutations affecting an enzyme essential for the synthesis of coenzyme Q10 confer susceptibility to the disease in some persons. Multiple-system atrophy is a progressive neurodegenerative disease that is clinically characterized by autonomic failure in addition to various combinations of parkinsonism, cerebellar ataxia, and pyramidal dysfunction. The term multiple-system atrophy was introduced in 1969 to encompass the disease entities of olivopontocerebellar ataxia, striatonigral degeneration, and the Shy–Drager syndrome, on the basis of neuropathological findings in these disorders. 1 Multiple-system atrophy is characterized by the development of cytoplasmic aggregates of α-synuclein, primarily in oligodendroglia. 2 – 7 However, the pathogenic mechanisms underlying this disease remain unknown, making it difficult to develop effective therapies. The disorder is classified into two subtypes: subtype C, characterized predominantly . . .

Medaka genome The medaka fish ( Oryzias latipes ) is a popular pet in Japan and more recently a laboratory model organism for developmental genetics and evolutionary biology. Now the medaka's genome has been sequenced and analysed by a large Japanese consortium. Cichlids and stickleback, which are emerging model systems for understanding the genetic basis of vertebrate speciation, are evolutionarily closer to medaka than zebrafish, so the medaka's genome sequence will yield valuable insights into 400 million years of vertebrate genome evolution. The medaka fish ( Oryzias latipes ) has long been a popular pet in Japan and more recently a laboratory model organism; it now has its genome sequenced and analysed by a Japanese consortium. Teleosts comprise more than half of all vertebrate species and have adapted to a variety of marine and freshwater habitats 1 . Their genome evolution and diversification are important subjects for the understanding of vertebrate evolution. Although draft genome sequences of two pufferfishes have been published 2 , 3 , analysis of more fish genomes is desirable. Here we report a high-quality draft genome sequence of a small egg-laying freshwater teleost, medaka ( Oryzias latipes ). Medaka is native to East Asia and an excellent model system for a wide range of biology, including ecotoxicology, carcinogenesis, sex determination 4 , 5 , 6 and developmental genetics 7 . In the assembled medaka genome (700 megabases), which is less than half of the zebrafish genome, we predicted 20,141 genes, including ∼2,900 new genes, using 5′-end serial analysis of gene expression tag information. We found single nucleotide polymorphisms (SNPs) at an average rate of 3.42% between the two inbred strains derived from two regional populations; this is the highest SNP rate seen in any vertebrate species. Analyses based on the dense SNP information show a strict genetic separation of 4 million years (Myr) between the two populations, and suggest that differential selective pressures acted on specific gene categories. Four-way comparisons with the human, pufferfish ( Tetraodon ), zebrafish and medaka genomes revealed that eight major interchromosomal rearrangements took place in a remarkably short period of ∼50 Myr after the whole-genome duplication event in the teleost ancestor and afterwards, intriguingly, the medaka genome preserved its ancestral karyotype for more than 300 Myr.

Might DNA sequence variation reflect germline genetic activity and underlying chromatin structure? We investigated this question using medaka (Japanese killifish, Oryzias latipes), by comparing the genomic sequences of two strains (Hd-rR and HNI) and by mapping ~37.3 million nucleosome cores from Hd-rR blastulae and 11,654 representative transcription start sites from six embryonic stages. We observed a distinctive ~200-base pair (bp) periodic pattern of genetic variation downstream of transcription start sites; the rate of insertions and deletions longer than 1 bp peaked at positions of approximately +200, +400, and +600 bp, whereas the point mutation rate showed corresponding valleys. This ~200-bp periodicity was correlated with the chromatin structure, with nucleosome occupancy minimized at positions 0, +200, +400, and +600 bp. These data exemplify the potential for genetic activity (transcription) and chromatin structure to contribute to molding the DNA sequence on an evolutionary time scale.

Posterior column ataxia with retinitis pigmentosa (PCARP) is an autosomal recessive neurodegenerative disorder characterized by retinitis pigmentosa and sensory ataxia. Previous studies of PCARP in two families showed a linkage to 1q31–q32. However, detailed investigations on the clinical presentations as well as molecular genetics of PCARP have been limited. Here, we describe a Japanese consanguineous family with PCARP. Two affected siblings suffered from childhood-onset retinitis pigmentosa and slowly progressive sensory ataxia. They also showed mild mental retardation, which has not been described in patients with PCARP. Parametric linkage analysis using high-density single nucleotide polymorphism arrays supported a linkage to the same locus. Target capture and high-throughput sequencing technologies revealed a novel homozygous c.1477G>C (G493R) mutation in FLVCR1 , which cosegregated with the disease. A recent study has identified three independent mutations in FLVCR1 in the original and other families. Our results further confirmed that PCARP is caused by mutations in FLVCR1 .

The severity of war, measured by battle deaths, follows a power-law distribution. Here, we demonstrate that power law also holds in the temporal aspects of interstate conflicts. A critical quantity is the inter-conflict interval (ICI), the interval between the end of a conflict in a dyad and the start of the subsequent conflict in the same dyad. Using elaborate statistical tests, we confirmed that the ICI samples compiled from the history of interstate conflicts from 1816 to 2014 followed a power-law distribution. We propose an information-theoretic model to account for the power-law properties of ICIs. The model predicts that a series of ICIs in each dyad is independently generated from an identical power-law distribution. This was confirmed by statistical examination of the autocorrelation of the ICI series. Our findings help us understand the nature of wars between normal states, the significance of which has increased since the Russian invasion of Ukraine in 2022.

The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the \\emph{typical set} have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.