The spin Seebeck effect refers to the generation of spin voltage as a result of a temperature gradient in ferromagnetic or ferrimagnetic materials. When a conductor is attached to a magnet under a temperature gradient, the thermally generated spin voltage in the magnet injects a spin current into the conductor, which in turn produces electric voltage owing to the spin-orbit interaction. The spin Seebeck effect is of increasing importance in spintronics, since it enables direct generation of a spin current from heat and appears in a variety of magnets ranging from metals and semiconductors to insulators. Recent studies on the spin Seebeck effect have been conducted mainly in paramagnetic metal/ferrimagnetic insulator junction systems in the longitudinal configuration in which a spin current flowing parallel to the temperature gradient is measured. This 'longitudinal spin Seebeck effect' (LSSE) has been observed in various sample systems and exclusively established by separating the spin-current contribution from extrinsic artefacts, such as conventional thermoelectric and magnetic proximity effects. The LSSE in insulators also provides a novel and versatile pathway to thermoelectric generation in combination of the inverse spin-Hall effects. In this paper, we review basic experiments on the LSSE and discuss its potential thermoelectric applications with several demonstrations.

Energy harvesting technologies, which generate electricity from environmental energy, have been attracting great interest because of their potential to power ubiquitously deployed sensor networks and mobile electronics. Of these technologies, thermoelectric (TE) conversion is a particularly promising candidate, because it can directly generate electricity from the thermal energy that is available in various places. Here we show a novel TE concept based on the spin Seebeck effect, called 'spin-thermoelectric (STE) coating', which is characterized by a simple film structure, convenient scaling capability, and easy fabrication. The STE coating, with a 60-nm-thick bismuth-substituted yttrium iron garnet (Bi:YIG) film, is applied by means of a highly efficient process on a non-magnetic substrate. Notably, spin-current-driven TE conversion is successfully demonstrated under a temperature gradient perpendicular to such an ultrathin STE-coating layer (amounting to only 0.01% of the total sample thickness). We also show that the STE coating is applicable even on glass surfaces with amorphous structures. Such a versatile implementation of the TE function may pave the way for novel applications making full use of omnipresent heat.

Unique secondary metabolites, glucosinolates (S-glucopyranosyl thiohydroximates), are naturally occurring S-linked glucosides found mainly in Brassicaceae plants. They are enzymatically hydrolyzed to produce sulfate ions, D-glucose, and characteristic degradation products such as isothiocyanates. The functions of glucosinolates in the plants remain unclear, but isothiocyanates possessing a pungent or irritating taste and odor might be associated with plant defense from microbes. Isothiocyanates have been studied extensively in experimental in vitro and in vivo carcinogenesis models for their cancer chemopreventive properties. The beneficial isothiocyanates, glucosinolates that are functional for supporting human health, have received attention from many scientists studying plant breeding, plant physiology, plant genetics, and food functionality. This review presents a summary of recent topics related with glucosinolates in the Brassica family, along with a summary of the chemicals, metabolism, and genes of glucosinolates in Brassicaceae. The bioavailabilities of isothiocyanates from certain functional glucosinolates and the importance of breeding will be described with emphasis on glucosinolates.

The spin Seebeck effect (SSE) refers to the generation of a spin current as a result of a temperature gradient in magnetic materials including insulators. The SSE is applicable to thermoelectric generation because the thermally generated spin current can be converted into a charge current via spin-orbit interaction in conductive materials adjacent to the magnets. The insulator-based SSE device exhibits unconventional characteristics potentially useful for thermoelectric applications, such as simple structure, device-design flexibility, and convenient scaling capability. In this article, we review recent studies on the SSE from the viewpoint of thermoelectric applications. Firstly, we introduce the thermoelectric generation process and measurement configuration of the SSE, followed by showing fundamental characteristics of the SSE device. Secondly, a theory of the thermoelectric conversion efficiency of the SSE device is presented, which clarifies the difference between the SSE and conventional thermoelectric effects and the efficiency limit of the SSE device. Finally, we show preliminary demonstrations of the SSE in various device structures for future thermoelectric applications and discuss prospects of the SSE-based thermoelectric technologies.

Radish (Raphanus sativus L., n = 9) is one of the major vegetables in Asia. Since the genomes of Brassica and related species including radish underwent genome rearrangement, it is quite difficult to perform functional analysis based on the reported genomic sequence of Brassica rapa. Therefore, we performed genome sequencing of radish. Short reads of genomic sequences of 191.1 Gb were obtained by next-generation sequencing (NGS) for a radish inbred line, and 76,592 scaffolds of ≥300 bp were constructed along with the bacterial artificial chromosome-end sequences. Finally, the whole draft genomic sequence of 402 Mb spanning 75.9% of the estimated genomic size and containing 61,572 predicted genes was obtained. Subsequently, 221 single nucleotide polymorphism markers and 768 PCR-RFLP markers were used together with the 746 markers produced in our previous study for the construction of a linkage map. The map was combined further with another radish linkage map constructed mainly with expressed sequence tag-simple sequence repeat markers into a high-density integrated map of 1,166 cM with 2,553 DNA markers. A total of 1,345 scaffolds were assigned to the linkage map, spanning 116.0 Mb. Bulked PCR products amplified by 2,880 primer pairs were sequenced by NGS, and SNPs in eight inbred lines were identified.

Tumor necrosis factor-α (TNF-α) is a cytokine produced by monocytes, macrophages, and T cells and is induced by pathogens, endotoxins, or related substances. TNF-α may play a key role in bone metabolism and is important in inflammatory bone diseases such as rheumatoid arthritis. Cells directly involved in osteoclastogenesis include macrophages, which are osteoclast precursor cells, osteoblasts, or stromal cells. These cells express receptor activator of NF-κB ligand (RANKL) to induce osteoclastogenesis, and T cells, which secrete RANKL, promote osteoclastogenesis during inflammation. Elucidating the detailed effects of TNF-α on bone metabolism may enable the identification of therapeutic targets that can efficiently suppress bone destruction in inflammatory bone diseases. TNF-α is considered to act by directly increasing RANK expression in macrophages and by increasing RANKL in stromal cells. Inflammatory cytokines such as interleukin- (IL-) 12, IL-18, and interferon-γ (IFN-γ) strongly inhibit osteoclast formation. IL-12, IL-18, and IFN-γ induce apoptosis in bone marrow cells treated with TNF-α   in vitro, and osteoclastogenesis is inhibited by the interactions of TNF-α-induced Fas and Fas ligand induced by IL-12, IL-18, and IFN-γ. This review describes and discusses the role of cells concerned with osteoclast formation and immunological reactions in TNF-α-mediated osteoclastogenesis in vitro and in vivo.

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Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect. In such nascent scientific field, data-driven approaches relying on statistics and machine learning, instead of more traditional modeling methods, can exhibit their full potential. Here, we use machine learning modeling to establish the key physical parameters controlling STE. Guided by the models, we have carried out actual material synthesis which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation of STE devices.