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Dramatic increases in climate data underlie a gradual paradigm shift in knowledge acquisition methods from physically based models to data-based mining approaches. One of the most popular data clustering/mining techniques is k-means, and it has been used to detect hidden patterns in climate systems; k-means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with “structured” data, that is, data in time and space domains, which are dominant in climate science. Here, we propose (i) a novel structural-similarity-recognition-based k-means algorithm called structural k-means or S k-means for climate data mining and (ii) a new clustering uncertainty representation/evaluation framework based on the information entropy concept. We demonstrate that the novel S k-means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we, for the first time, evaluated the “meaningfulness” of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k-means clustering with “structural” input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science.

In this paper, we describe a visible-light positioning system that uses the ambient-light sensor assembly in a smartphone as a receiver for illumination signals from LED-based room lighting. After correcting for missing or inconsistently sampled data captured by the receiver, we can identify the source LEDs contributing to the received signal via Fourier analysis of its frequency distribution. Localization of the smartphone is then achieved by trilateration of the distances to the various LEDs, which are calculated from the received signal strength for each LED. Experiments using multiple LEDs were conducted to evaluate the signal reception and positioning performance of the system. We found that multiple LEDs modulated at frequencies of over 70 Hz could be correctly identified from the received signal pattern. The 90th-percentile 2D positioning errors using low frequency signals (3.951-7.902 Hz) were less than 0.21 meter for a room illuminated by four LEDs arranged in a 1-m square on the ceiling at 2.22 meters height, where the smartphone was placed parallel on the floor from 1.4 meters height. Those using high frequency signals (111.619-114.582 Hz) in the same room setting showed 50th-percentile 2D position errors of less than 0.44 meter. The position calculation was offline and not implemented on real time. Potential applications and limitations of the proposed method are discussed.

Dramatic increases in climate data underlie a gradual paradigm shift in knowledge-acquisition methods from physical-based models to data-based mining techniques. k-Means is one of the most popular data clustering/mining techniques, and it has been used to detect hidden patterns in climate systems. k-Means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with “structured” data that are dominant in climate science, that is, data in time and space domains. Here, we propose (i) a novel structural similarity recognition-based k-means algorithm called structural k-means or S k-means for climate data mining and (ii) a new clustering uncertainty representation/evaluation framework based on the information entropy concept. We demonstrated that the novel S k-means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we for the first time evaluated the “meaningfulness” of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k-means clustering with “structural” input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science.

This study proposes a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data with spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based on a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the locations of highs or lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the S-SOM's superiority compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error. Better performance of S-SOM versus ED is consistent with results from different tests and node-size configurations. S-SOM performs better than a SOM using the Pearson correlation coefficient (or COR-SOM), though the difference is not as clear as it is compared to ED-SOM.

We introduce a massively parallel novel sequencing platform that combines an open flow cell design on a circular wafer with a large surface area and mostly natural nucleotides that allow optical end-point detection without reversible terminators. This platform enables sequencing billions of reads with longer read length (∼300bp) and fast runs times ( 85%), at a low cost of $1/Gb. We establish system performance by whole-genome sequencing of the Genome-In-A-Bottle reference samples HG001-7, demonstrating high accuracy for SNPs (99.6%) and Indels in homopolymers up to length 10 (96.4%) across the vast majority (>98%) of the defined high-confidence regions of these samples. We demonstrate scalability of the whole-genome sequencing workflow by sequencing an additional 224 selected samples from the 1000 Genomes project achieving high concordance with reference data.

In this study, we propose a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data that have spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based upon a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the location of highs of lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the superiority of the S-SOM compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error analysis. The superior performance of the S-SOM compared with an ED-SOM is probably independent of both the input data and SOM configuration.

In our previously reported method for the construction of the IJKLM-ring of ciguatoxin 3C (CTX3C), the lengthy synthetic process for the intermediate C42–C52 (L-ring) segment was problematic. Therefore, a new and improved procedure for the C42–C52 segment, having modified protecting groups, was developed. The new route includes a chirality transferring Ireland-Claisen rearrangement for the construction of the vicinal dimethyl branching at C47–48, a one-pot cyclization process for the establishment of the stereocenters at C45 and C46 as well as the γ-hydroxy δ-lactone framework corresponding to the L-ring, and Brown’s asymmetric crotylboration for the installation of the stereocenters at C43 and C44. The new C42–C52 segment was successfully coupled with the previously reported C32–C41 (I-ring) segment to produce the IJKLM-ring.

The synthesis of the ABCDEF-ring of ciguatoxin 3C was achieved via a route that included anion coupling of a dimethyldithioacetal mono-S-oxide derivative corresponding to the AB-ring and an aldehyde corresponding to the EF-ring, followed by cyclization using reductive etherification. The AB-ring was synthesized from a known d -glucose derivative based on ring-closing olefin metathesis, and the EF-ring was prepared by a process employing chirality-transferring Ireland-Claisen rearrangement and ring-closing olefin metathesis.

Mutect2 is a somatic variant caller that uses local assembly and realignment to detect SNVs and indels. Assembly implies whole haplotypes and read pairs, rather than single bases, as the atomic units of biological variation and sequencing evidence, improving variant calling. Beyond local assembly and alignment, Mutect2 is based on several probabilistic models for genotyping and filtering that work well with and without a matched normal sample and for all sequencing depths.

In meteorology, high-resolution simulations using large eddy simulation (LES) models have attracted strong attention. In these LES models, the output data become large, because the spatio-temporal resolution is higher than that of conventional meteorological simulations. Therefore, I/O requirements become much more demanding. In addition, it becomes challenging to analyze the results of these simulations, owing to a large data set size. To overcome these problems, it is necessary to consider hardware and software architectures for in-situ data analysis, for accommodating these I/O requirements. In this study, we propose a large-scale fluid analysis system combining a meteorological LES model and dynamic mode decomposition (DMD). We propose to use a burst buffer as a temporary storage space for storing large-scale output of LES models, and to read these data from DMD. The burst buffer is an all-flash storage system between compute nodes and a parallel file system. The I/O performance of the LES output and DMD input of this workflow is analyzed, and it is shown that the proposed burst buffer is an effective medium for in-situ data analysis.

The syntheses of ganbajunins D and E and of the structure proposed for thelephantin D, as well as its regioisomer, were achieved in a divergent manner via a common monoester intermediate. The proposed structure of thelephantin D had NMR chemical shifts different from those of natural thelephantin D. This supported Takahashi's suggestion that the true structure of natural thelephantin D should be the same as that of terrestrin C, as was deduced from the similarities in their NMR spectra. The presence of isomerization equilibria between 2′,5′- and 2′,6′-diacyloxy-p-terphenyl derivatives in solution, which are the cause of the inseparability of these derivatives, was also demonstrated by monitoring the dynamic transition from a 2′,6′-diacyloxy-p-terphenyl-rich mixture of the derivatives to an equilibrium mixture. The results of biological and chemical property tests – such as cytotoxicity against cancer cells, affinity towards Fe2+ ion, and antioxidative activity – with the synthesized compounds are also described.

Palm region extraction is one of the most important processes in palmprint recognition, since the accuracy of extracted palm regions has a significant impact on recognition performance. Especially in contactless recognition systems, a palm region has to be extracted froma palm image by taking into consideration a variety of hand poses. Most conventional methods of palm region extraction assume that all the fingers are spread and a palm faces to a camera. This assumption forces users to locate his/her hand with limited pose and position, resulting in impairing the flexibility of the contactless palmprint recognition system. Addressing the above problem, this paper proposes a novel palm region extraction method robust against hand pose. Through a set of experiments using our databases which contains palm images with different hand pose and the public database, we demonstrate that the proposed method exhibits efficient perfomance compared with conventional methods.

A novel 2',3'-dihydroxy-p-terphenyl derivative, thelephantin O (TO), which has cancer-selective cytotoxicity, was isolated. This study investigated the underlying basis of the cytotoxicity of 2',3'-dihydroxy-p-terphenyl compounds in view of their ability to chelate metal ions. FeCl(2) significantly reduced TO-induced cytotoxicity, whereas several other salts of transition metals and alkaline-earth metals did not. A structure-activity relationship study using newly synthesized p-terphenyl derivatives revealed that o-dihydroxy substitution of the central benzene ring was necessary for both the cytotoxicity and Fe(2+) chelation of the compounds. Real-time PCR array and cell cycle analysis revealed that the TO-induced cytotoxicity was attributed to cell cycle arrest at the G1 phase via well-known cell cycle-mediated genes. The TO-induced changes in the cell cycle and gene expression were completely reversed by the addition of FeCl(2). Thus, it was concluded that Fe(2+) chelation occurs upstream in the pivotal pathway of 2',3'-dihydroxy-p-terphenyl-induced inhibition of cancer cell proliferation.

The first total synthesis of natural, unsymmetrical 2',3'-diacyloxy-p-terphenyls, thelephantin O (1) and terrestrins C and D (2 and 3, respectively), was achieved via a practical route which was also applicable to the synthesis of the symmetrical diesters vialinin A/terrestrin A (4) and terrestrin B (5). Compounds 1-5 exhibited cytotoxicity against cancer cells (HepG2 and Caco2) with IC(50) values of 13.6-26.7 μmol/L.