山形県「年間三隣亡」の経済面への影響についての一考察《論文》

山形県では, 三隣亡の年は1年間を通して家を建てることを避けるべきであるという迷信が, 現時点においても広く浸透している｡この地域的なアノマリーについて分析すると, 山形県の年間三隣亡は住宅投資(持家の新設住宅着工) への負の効果が認められ, 簡単な計量分析を行えば, 三隣亡の年には住宅着工が平均的に15～20％程度減少している｡また, その影響は, 庄内地域だけではなく, 山形県全体に及んでいる一方で, 近隣他県においてはみられない｡さらに, 三隣亡という迷信が住宅着工に影響を与えるというアノマリーは, プロスペクト理論を使えば, ある程度説明できる｡ In Yamagata prefecture, we can find the anomaly that most people avoid building residential houses in the Sanrinbou year which exists three times in 12 years (one circle of the signs of the zodiac in Chinese astrology). Econometric analyses show that the dummy variable of “Annual Sanrinbou” of the estimation equation has a 15～20％ negative effect on residential investment. This negative effect spreads to all areas of Yamagata prefecture, but the phenomenon can’t be found in other prefectures. The mechanism of this anomaly can be explained mostly by the Cumulative Prospect Theory.textapplication/pdfdepartmental bulletin pape

泥棒の速度が異なるCop and Robbersの格子状における戦略

電気通信大学修士2022Cops and Robbersとはグラフ上で行われる鬼ごっこのようなゲームである。プレイヤーは警官プレイヤーと泥棒プレイヤーに分かれる。それぞれのプレイヤーは警官と泥棒をグラフ上で移動させ、警官は泥棒を追いかけて捕まえること、泥棒は警官から逃げることが目的である。このゲームについて多くの研究が行われている。警官が勝利するグラフの性質の解析や、グラフ上で強盗を捕まえることができる警官の最小数を求めることのNP困難性、また、ケイリーグラフ、木構造をもつグラフ2つの直積を取ったグラフなどの様々なグラフ上での研究が行われている。さらに、ルールの様々な変種が提案され、研究が行われている。例えば、泥棒が警官より早く移動できるルールや警官が今いる頂点に留まれないルール、泥棒が警官を攻撃して、警官を取り除くことができるルールなどが挙げられる。 本研究では、泥棒のみ1回の手番で、2頂点分移動できるというルールを考える。そのうえで、警官のみ泥棒の位置する頂点を認識できず、その代わりに、泥棒の位置する頂点への最短経路に含まれて、かつ警官の位置する頂点と隣接している頂点を認識できる、という変種ルールに変更した場合と警官が泥棒の頂点を認識できるという通常ルールの2通りのルールで三角格子と正方格子上における警官と泥棒の各々の戦略を提案する。泥棒の速度が2で、警官の認識条件が通常のルールにおいてn=10の正方格子上で警官2人の場合の泥棒が必勝であることを示した。泥棒の速度が2で、警官は方向のみ認識できるルールにおいて、n≤4の三角格子と正方格子上での警官2人で警官の必勝である事を示した。また、泥棒の速度が2で、警官の認識条件が通常のルールと警官が方向のみ認識できるルールにおいて、n-正方格子上で[(n+1)/2]+1人の警官が存在する場合、警官の必勝であることを示した。thesi

Observation of a Resonancelike Structure in the π^+-Ψ' Mass Distribution in Exclusive B → Kπ^+-Ψ' Decays

A distinct peak is observed in the π^±Ψ'invariant mass distribution near 4.43 GeV in B→ Kπ^±Ψ' decays. A fit using a Breit-Wigner resonance shape yields a peak mass and width of M = 4433± 4(stat)± 2(syst) MeV and Γ= 45\begin+18\-13\end(stat)\begin+30\-13\end(syst)MeV.The product branching fraction is determined to be B(B^0→ K^∓Z^±(4430)→ π^±Ψ')= (4.1±1.0(stat)±1.4(syst))×10^{-5}, where Z^±(4430)is used to denote the observed structure. The statistical significance of the observed peak is 6.5σ. These results are obtained from a 605 fb^{-1} data sample that contains 657 ×10^6 B\bar{B} pairs collected near the Υ(4S) resonance with the Belle detector at the KEKB asymmetric energy e^+e^- collider.journal articl

Search for CP Violation in the Decay B0→D*±D∓

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In situ preparation of highly fluorescent pyrene-dyes from non-luminous precursors upon photoirradiation

The non-luminous precursor, 2-(1-pyrenyl)-9,10-dihydro-9,10-ethanoanthracene-11,12-dione, was photochemically converted to highly-fluorescent 2-(1-pyrenyl)anthracene quantitatively in solution and in the PMMA film and the fluorescence quantum yield of the acene in benzonitrile was as high as 0.99.journal articl

Peripartum changes in serum activities of three major alkaline phosphatase isoenzymes in Holstein dairy cows

application/pdfPolish Journal of Veterinary Sciences. 2020, 23 (3), P.457-459journal articl

A statistical framework for joint genotype calls.

<p>We developed a statistical framework to jointly estimate sample genotypes from array intensities, sequence reads, and haplotype phasing. The framework first estimates genotype likelihoods independently for each SNP from array and sequence data, given initial parameters for genotype cluster locations and sequence read error rates. It then multiplies the likelihoods for each SNP to obtain joint likelihoods, inputs these to haplotype phasing and imputation, and then uses the output likelihoods to re-cluster intensities for all SNPs. The process is iterated, and upon termination genotype likelihoods are converted to posterior genotype probabilities. The framework can estimate genotypes given only sequence data or array data as well as with or without imputation — many of these special cases are similar in principle to previously described genotyping algorithms (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002604#pcbi.1002604.s027" target="_blank">Text S1</a>).</p

A novel next-generation sequencing error mode.

<p>(<b>a</b>) We identified a novel error mode based on visual examination of disputed SNPs. As shown in the cluster plot, one of the samples is called homozygous reference (Hom-ref) based on analysis of array data but homozygote non-reference (Hom-var) based on analysis of sequence data (shown by the sample outlined in green within the red cluster). This unusual error mode contrasts with the more common error mode, due to low sequence coverage, of samples called heterozygous (Het) based on array data but homozygous reference or non-reference based on sequence data (shown by samples outlined in pink or green within the blue cluster). (<b>b</b>) Inspection of the sequence reads in the Integrated Genomics Viewer (IGV) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002604#pcbi.1002604-Robinson1" target="_blank">[54]</a> shows that the sample in question has only two reads that cover this SNP, and these reads are pairs sequenced from the same underlying DNA fragment. (<b>c</b>) This error mode is introduced in the shearing and library preparation stage of next-generation sequencing and as a result is reflected in both reads from the same DNA fragment. Depending on protocol details, the error rate is around 1/10,000. During genotype calling, independent treatment of reads (read-based) results in much more confident (here 100×) non-reference genotype calls than analysis at the fragment level (fragment-based). (<b>d</b>) To account for these effects, which can be large for low coverage sequencing projects like the 1000G Project, we implemented a fragment based genotyping algorithm in the Unified Genotyper of the Genome Analysis Toolkit (GATK). Use of this new caller has a significant impact on SNP call quality, shown by a smaller number of novel SNP calls and a higher Transition∶Transversion ratio (proxies for accuracy <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002604#pcbi.1002604-DePristo1" target="_blank">[27]</a>). The effect is pronounced for populations such as MXL and ASW, which have a higher fraction of newer Illumina sequencing data with longer reads (e.g., AWS data is reads, while YRI has less than ), which results in greatly increased rate of overlapping reads and associated errors. Abbreviations are as defined in the 1000G Project.</p

Reduction in errors from joint genotype calls.

<p>(<b>a</b>) To assess the improvement in imputation quality afforded by joint genotype calls with a SNP array (relative to calls based on sequence data alone), we measured sensitivity and specificity at sites absent from the array; errors at these sites can be reduced only through improved imputation. The Metabochip is absent from this plot, as it is not a genome-wide array. Plotted are and , the sum of which equals the number of sites where (1) the gold-standard or called genotype is non-reference and (2) the gold-standard and called genotypes disagree. Normalized values (defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002604#s4" target="_blank">Materials and Methods</a>) are plotted to show visual trends; actual values are given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002604#pcbi.1002604.s016" target="_blank">Figure S16</a>(<b>b</b>) To assess the reduction in erroneous genotype cluster locations afforded by joint genotype calls with sequence data (relative to calls based on array data alone), we measured sensitivity and specificity at sites on the array. Red bars correspond to and , measured from calls without haplotype phasing; blue bars correspond to and , measured from joint calls. As described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002604#s4" target="_blank">Materials and Methods</a>, these experiments used 82 additional unrelated samples, absent from our other experiments, to inform cluster locations.</p

Sensitivity and specificity of data collection strategies.

<p>For different combinations of array and sequence data, we produced joint genotype calls on chromosome 20 for 382 European samples from the 1000G project. For a single test sample, we obtained “gold-standard” genotypes from high coverage multi-technology sequencing published by the 1000G project. We then measured non-reference site sensitivity and specificity with imputation (, ) and without (, ). (<b>a</b>) (left) and (right) of calls from five array densities and four sequence coverages. The first row of each table contains results for strategies with only sequence data, and the first column contains results for strategies with only array data. A common color scheme is used across all tables, with white corresponding to 100%, red corresponding to , and yellow corresponding to 80%. (<b>b</b>) of calls; is given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002604#pcbi.1002604.s009" target="_blank">Figure S9</a>. (<b>c</b>) for three variant frequency ranges, with frequency estimated from the non-test samples. Private variants have frequency 0% in the non-test samples. (<b>d</b>) for four sequence coverages, with separate lines that correspond to joint calls made with each SNP array. (<b>e</b>) for four array densities, with separate lines that correspond to joint calls made with each sequence coverage. No Array: from sequence data alone; 0×: from array data alone; .5×-4×: mean number of sequence reads per genomic position; array abbreviations are defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002604#s4" target="_blank">Materials and Methods</a>.</p