The Methyltransferase Smyd1 Mediates LPS-Triggered Up-Regulation of IL-6 in Endothelial Cells

The lysine methyltransferase Smyd1 with its characteristic catalytic SET-domain is highly enriched in the embryonic heart and skeletal muscles, participating in cardiomyogenesis, sarcomere assembly and chromatin remodeling. Recently, significant Smyd1 levels were discovered in endothelial cells (ECs) that responded to inflammatory cytokines. Based on these biochemical properties, we hypothesized that Smyd1 is involved in inflammation-triggered signaling in ECs and therefore, investigated its role within the LPS-induced signaling cascade. Human endothelial cells (HUVECs and EA.hy926 cells) responded to LPS stimulation with higher intrinsic Smyd1 expression. By transfection with expression vectors containing gene inserts encoding either intact Smyd1, a catalytically inactive Smyd1-mutant or Smyd1-specific siRNAs, we show that Smyd1 contributes to LPS-triggered expression and secretion of IL-6 in EA.hy926 cells. Further molecular analysis revealed this process to be based on two signaling pathways: Smyd1 increased the activity of NF-kappa B and promoted the trimethylation of lysine-4 of histone-3 (H3K4me3) within the IL-6 promoter, as shown by ChIP-RT-qPCR combined with IL-6-promoter-driven luciferase reporter gene assays. In summary, our experimental analysis revealed that LPS-binding to ECs leads to the up-regulation of Smyd1 expression to transduce the signal for IL-6 up-regulation via activation of the established NF-κB pathway as well as via epigenetic trimethylation of H3K4

Music Information Technology and Professional Stakeholder Audiences: Mind the Adoption Gap

The academic discipline focusing on the processing and organization of digital music information, commonly known as Music Information Retrieval (MIR), has multidisciplinary roots and interests. Thus, MIR technologies have the potential to have impact across disciplinary boundaries and to enhance the handling of music information in many different user communities. However, in practice, many MIR research agenda items appear to have a hard time leaving the lab in order to be widely adopted by their intended audiences. On one hand, this is because the MIR field still is relatively young, and technologies therefore need to mature. On the other hand, there may be deeper, more fundamental challenges with regard to the user audience. In this contribution, we discuss MIR technology adoption issues that were experienced with professional music stakeholders in audio mixing, performance, musicology and sales industry. Many of these stakeholders have mindsets and priorities that differ considerably from those of most MIR academics, influencing their reception of new MIR technology. We mention the major observed differences and their backgrounds, and argue that these are essential to be taken into account to allow for truly successful cross-disciplinary collaboration and technology adoption in MIR

KOMPARASI HASIL PERENCANAAN RIGID PAVEMENT MENGGUNAKAN METODE AASHTO '93 DAN METODE Pd T-14-2003 PADA RUAS JALAN W. J. LALAMENTIK KOTA KUPANG

Selain perencanaan geometric jalan, perkerasan jalan merupakan bagian dari perencanaan jalan yang harus direncanakan secara efektif dan efisien, karena kebutuhan tingkat pelayanan jalan semakin tinggi. Jenis perkerasan jalan W.J. Lalamentik Kota Kupang adalah perkerasan lentur dengan jens lapis permukaan adalah HRS-WC, dimana jalan tersebut sering mengalami kerusakan berulang sebagai akibat dari beban lalulintas dan kondisi lingkungan. Perlu dicoba penggunaan perkerasan kaku untuk diketahui apakah perkerasan tahan sampai pada masa layannya. Terdapat banyak metode untuk mendesain tebal pelat beton ini, diantaranya menggunakan metode Pd T-14-2003 dan AASHTO 1993. Tulisan ini bertujuan untuk menganalisis alternatif desain tebal perkerasan mengkaji pada parameter perencanaan kedua metode, perencanaan tebal pelat beton, dan melakukan analisa perbandingan hasil kedua metode. Metode ini dimulai dengan pengumpulan data sekunder berupa data lalu lintas, data tanah dan data hidrologi, kemudian dilakukan perhitungan tebal pekerasan dengan menggunakan kedua metode, dan hasil perhitungannya dibandingkan. Parameter input perencanaan tebal perkerasan untuk metode Pd T-14-2003 adalah parameter lalu lintas, tanah dasar, pondasi bawah, pondasi bawah material berbutir, dan kekuatan beton. Parameter input perencanaan tebal perkerasan untuk metode AASHTO 1993 adalah parameter lalu lintas, modulus reaksi tanah dasar, material konstruksi perkerasan, realibility, dan koefisien drainase. Untuk studi kasus jalan W.J Lalamentik Kota kupang tebal pelat beton berdasarkan perhitungan metode Pd T-14-2003 adalah 16 cm, sedangkan berdasarkan metode AASHTO 1993 adalah 19 cm. Selisih yang didapat yaitu 3 cm dikarenakan perbedaan parameter input dari masing-masing metode

Deeply bound ultracold molecules in an optical lattice

We demonstrate efficient transfer of ultracold molecules into a deeply bound rovibrational level of the singlet ground state potential in the presence of an optical lattice. The overall molecule creation efficiency is 25%, and the transfer efficiency to the rovibrational level vertical bar v = 73, J = 2 > is above 80%. We find that the molecules in vertical bar v = 73, J = 2 > are trapped in the optical lattice, and that the lifetime in the lattice is limited by optical excitation by the lattice light. The molecule trapping time for a lattice depth of 15 atomic recoil energies is about 20 ms. We determine the trapping frequency by the lattice phase and amplitude modulation technique. It will now be possible to transfer the molecules to the rovibrational ground state vertical bar v = 0, J = 0 > in the presence of the optical lattice

Development of Knowledge and Attitude Measurement Tools in Disaster Preparedness Schools

Indonesia is the largest archipelago country in the world located at the confluence of four tectonic plates. This condition makes Indonesia potentially and become vulnerable to natural hazard that have a significant impact and reach various sectors. One of the impacts of natural hazard that occurred in Indonesia is the education sector. This natural hazard has an impact on the physical building of schools and also hinders the process of teaching and learning, causing trauma, and even fatalities at school. The Indonesian Institute of Sciences (LIPI) through the Community Preparedness (COMPRESS) program inaugurated disaster preparedness education at the school community level. In 2008, LIPI began implementing disaster risk reduction by developing a model of disaster preparedness schools. Then LIPI published Guidelines for Implementing Disaster Preparedness Schools in 2013. This guide does not yet have a specific Knowledge and Attitude category and can be used as a reference. Therefore, the development of the Disaster Preparedness School Implementation Manual needs to be done. The development of this measuring instrument was analysed by compiling a comparison matrix using the AHP method so as to produce a new development system from the Knowledge and Attitude assessment category. This study produced 3 sub-categories of Knowledge and Attitude assessment, namely (1) Standard Operating Procedure (SOP) for Teaching and Learning Disasters with a weight of 33%, (2) Knowledge about Disasters and Disaster Management with a weight of 43%, and (3) Knowledge Access About Disasters and Disaster Management with a weighting of 24%. Such approach can be used in advancement of others variables of measuring tool for school preparedness

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14