Common Variants in a Novel Gene, FONG on Chromosome 2q33.1 Confer Risk of Osteoporosis in Japanese

Osteoporosis is a common disease characterized by low bone mass, decreased bone quality and increased predisposition to fracture. Genetic factors have been implicated in its etiology; however, the specific genes related to susceptibility to osteoporosis are not entirely known. To detect susceptibility genes for osteoporosis, we conducted a genome-wide association study in Japanese using ∼270,000 SNPs in 1,747 subjects (190 cases and 1,557 controls) followed by multiple levels of replication of the association using a total of ∼5,000 subjects (2,092 cases and 3,114 controls). Through these staged association studies followed by resequencing and linkage disequilibrium mapping, we identified a single nucleotide polymorphism (SNP), rs7605378 associated with osteoporosis. (combined P = 1.51×10−8, odds ratio = 1.25). This SNP is in a previously unknown gene on chromosome 2q33.1, FONG. FONG is predicted to encode a 147 amino-acid protein with a formiminotransferase domain in its N-terminal (FTCD_N domain) and is ubiquitously expressed in various tissues including bone. Our findings would give a new insight into osteoporosis etiology and pathogenesis

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

Deriving safety constraints for integration of unmanned aircraft systems into the national airspace by application of STECA

Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 129-132).Unmanned aircraft systems (UAS) have been used for years especially in the military. However, the operation of UAS in civil aviation has been limited since there are a lot of uncertainties: a regulatory scheme needs to be established and associated technologies need to be developed. This thesis contributes to both technology development and establishing a regulatory scheme for UAS by generating safety constraints using the new methodology developed by Professor Leveson and Dr. Fleming. This methodology is called "'Systems-Theoretic Early Concept Analysis" (STECA) and is based on Systems-Theoretic Accident Model and Processes (STAMP) analysis, which is also developed by the professor. STECA has potential to generate more safety constraints that have not been considered otherwise in the early stage of development and this allows the producer to redesign the entire system with potentially less cost. This thesis illustrates why and how STECA can be powerful to support integration of UAS into NAS. In addition, this thesis actually demonstrates how STECA derives safety constraints as a case study and shows how the safety constraints should be integrated in the system development.by Yusuke Urano.S.M. in Technology and Polic

Light Driven CO₂ Fixation by Using Cyanobacterial Photosystem I and NADPH-Dependent Formate Dehydrogenase

<div><p>The ultimate goal of this research is to construct a new direct CO₂ fixation system using photosystems in living algae. Here, we report light-driven formate production from CO₂ by using cyanobacterial photosystem I (PS I). Formate, a chemical hydrogen carrier and important industrial material, can be produced from CO₂ by using the reducing power and the catalytic function of formate dehydrogenase (FDH). We created a bacterial FDH mutant that experimentally switched the cofactor specificity from NADH to NADPH, and combined it with an <i>in vitro</i>-reconstituted cyanobacterial light-driven NADPH production system consisting of PS I, ferredoxin (Fd), and ferredoxin-NADP⁺-reductase (FNR). Consequently, light-dependent formate production under a CO₂ atmosphere was successfully achieved. In addition, we introduced the NADPH-dependent FDH mutant into heterocysts of the cyanobacterium <i>Anabaena</i> sp. PCC 7120 and demonstrated an increased formate concentration in the cells. These results provide a new possibility for photo-biological CO₂ fixation.</p> </div

Advancement of fluorescent aminopeptidase probes for rapid cancer detection–current uses and neurosurgical applications

Surgical resection is considered for most brain tumors to obtain tissue diagnosis and to eradicate or debulk the tumor. Glioma, the most common primary malignant brain tumor, generally has a poor prognosis despite the multidisciplinary treatments with radical resection and chemoradiotherapy. Surgical resection of glioma is often complicated by the obscure border between the tumor and the adjacent brain tissues and by the tumor's infiltration into the eloquent brain. 5-aminolevulinic acid is frequently used for tumor visualization, as it exhibits high fluorescence in high-grade glioma. Here, we provide an overview of the fluorescent probes currently used for brain tumors, as well as those under development for other cancers, including HMRG-based probes, 2MeSiR-based probes, and other aminopeptidase probes. We describe our recently developed HMRG-based probes in brain tumors, such as PR-HMRG, combined with the existing diagnosis approach. These probes are remarkably effective for cancer cell recognition. Thus, they can be potentially integrated into surgical treatment for intraoperative detection of cancers

Formate production by pAM505-fdh exoconjugant cyanobacteria.

<p>The wild-type and exoconjugant <i>Anabaena</i> cultures (BG11, OD₇₀₀ ~3) were inoculated into BG11₀ and cultivated for 36 h under a light of 15 µmol photon m^-2 s^-1 and under air atmosphere. The media were then degassed and bubbled with argon + 10% CO₂ for 12 h under a light of 150 µmol photon m^-2 s^-1. The cells anaerobically harvested were homogenized in 1N HCl. The resulting lysate was analyzed by ion-chromatography and the chromatograms of <i>Anabaena</i>-WT (A) and <i>Anabaena</i>-fdh (B) are shown. The lysate of <i>Anabaena</i>-fdh was also treated with <i>Candida</i><i>boidinii</i> FDH (Sigma-Aldrich, Missouri, USA) and NAD⁺ overnight and was then analyzed (C). The chromatograms of formate solutions without and with the treatment with <i>Candida</i><i>boidinii</i> FDH and NAD⁺ are shown as controls (D and E, respectively).</p

Substrate dependence and light sensitivity of PsFDH(QN).

<p>The formate production in the presence of 1 mM NADPH (circles) were compared with that in the presence of 10 mM NADPH (diamonds), that under visible light at an intensity of 1000 µmol photon m^-2 s^-1 (squares), and that in which NaHCO₃ was used instead of CO₂ (crosses). These experiments were performed as mentioned in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071581#pone-0071581-g002" target="_blank">Figure 2</a>, except as described above.</p