Biological Sciences Distinguished Professor BAE, YONG-SOO Distinguished Professor
Until now, most immunological research has focused on lymphatic immunity centered in the primary and secondary lymphoid organs, such as bone marrow, thymus, spleen, and lymph nodes. However, recent sporadic research papers have demonstrated new immune cells discovered in non-lymphoid organs, which were not found in the lymphoid organs or tissues, such as liver, lung, kidney, and intestines. It was also disclosed recently that these non-lymphoid organs are maintaining immunosuppressive environments to prevent or protect themselves from the damage of internal or external inflammatory responses. Professor Yong-Soo Bae, who was dedicated as a SKKU Distinguished Professor this time, has been studying for a long time with innate immune cells and related immune responses in these non-lymphatic organs. He established the <Center for Immune Research on Non-lymphoid Organ (CIRNO)> in 2017 and has been leading the research as the Director of the Center with the support of the Science Leading Research Center (SRC) grant from the National Research Foundation of Korea. He said that he would reserve no effort to identify many immunological challenges that could not be explained by lymphoid organ-restricted immune research, thus, pioneering a new academic field. In the Center, nine professors outstanding in the field of immunology (6 from SKKU and 3 from outside) conduct close joint research and collaboration to identify non-lymphoid organ-specific immune cells and their activity, and their mechanism of action with immune modulatory molecules. By elucidating new immune phenomena in non-lymphoid organs through innovative research, Prof. Bae and his colleagues would like to suggest a new alternative to the existing immunotherapy that was limited by lymphatic immune regulation. In addition, Prof. Bae plans to develop a source technology for target-based disease control that can control cancer or inflammatory diseases by controlling key immune cells or immune modulatory molecules in non-lymphoid organs. This research plan was initiated based on his attention to the fact that the immunosuppressive environment of non-lymphoid organs can rather lead to the intractability of cancer and infectious diseases. Last year, the Center published 20 papers in the top international journals and applied for 10 patents at home and abroad. This year, through joint research with Center-affiliated Professor Hyeyoung Kim of Seoul National University, Siglec F+ novel pathogenic neutrophils were found to induce chronic obstructive pulmonary disease in mice exposed to pollutants and published two papers in the related journals, J Clin Invest (IF14.3) https://pubmed.ncbi.nlm.nih.gov/35482420/ and J Aller Clin Immunol (IF14.3) https://pubmed.ncbi.nlm.nih.gov/34653517/. Through a joint study with Center-affiliated Professor Yoe-Sik Bae, a unique phenotype of neutrophils was found to exist only in the lung and published in the Blood journal (22.11) https://pubmed.ncbi.nlm.nih.gov/35679477/. In addition, through joint research with Center-affiliated Professor Yong-Taik Lim, the immune-enhancing and anti-cancer mechanism of a novel adjuvant that was developed by Prof. Lim was investigated and reported to a top academic journal, now in a revision process. Through joint research with three Center-affiliated professors, a newly developed immune cell population was identified in the spleen of mice when treated with a certain anti-cancer cytokine. We investigated the characteristics and induction mechanism of anti-cancer immunity of the cells and are in the submission process to a top journal.
The joint research team led by Professor Jae-Young Choi (School of advanced materials science and engineering) and professor Hak Ki Yu at Ajou University (Department of materials science and engineering) has developed Ultrahigh-Porosity MgO Microparticles for thermochemical heat-storage reaction with high stability and exceptional reactant permeability. Professor Choi is also the co-CEO of C&C materials. Regarding paper has been published on Advanced Materials with the title “Ultrahigh-Porosity MgO Microparticles for Heat-Energy Storage”. Research on renewable energy, and waste heat retrieval and conversion, has been the key to carbon neutrality. Among those research retrieval of industrial waste heat has earned significant interest. Naturally, the development of materials that can meet the criteria for industrial waste heat retrieval is now more important than ever. [Figure] Schematic illustration of the strategy for synthesizing porous MgO and images of a porous MgO particle. The research team has introduced ultrahigh porous structure to magnesium oxide (MgO), a highly promising candidate for waste heat storage, to develop high-performance heat energy storing material. This Ultrahigh Porosity MgO has 4 times more surface area than commercial MgO, and therefore is free of swelling during heat storage, enabling heat storage capacity 7.2 times bigger than commercial MgO. This Ultra-high Porosity MgO is expected to serve as the key material for chemically storing industrial waste heat, and the research team will carry out follow-up research to develop new materials and control the structure of existing materials to overcome obstacles of nanomaterials. Funded by the National Research Foundation of Korea (NRF), this work has been published in Advanced Materials (IF=32.086) in July 2022. ※ Title: Ultrahigh-Porosity MgO Microparticles for Heat-Energy Storage ※ Authors: Youngho Kim1, Xue Dong1, Sudong Chae1, Ghulam Asghar, Sungwoong Choi, Bum Jun Kim#, Jae-Young Choi#, Hak Ki Yu# ※ DOI: https://doi.org/10.1002/adma.202204775
A joint research team from SKKU (Prof. Wan Ki Bae), Sogang University (Prof. Moon Sung Kang), and Electronics and Telecommunications Research Institute (Dr. Chan-mo Kang) have developed ultra-high-resolution quantum dot (QD) patterning technology that could be implemented in nearly all QD photonic applications. The study appears in the journal ‘Nature Nanotechnology’ in August. QDs are a new class of luminophores that stand at the forefront of nearly all light-emitting applications. The success of next-generation QD displays demands multicolor QD patterns on desired substrates over a large area with high-precision and high-definition, and most importantly, without compromising on optical or optoelectronic characteristics of QDs. Here, the researchers devise QD materials that can be processed via photolithographic processes without the presence of photoresists and photoinitiators. Specifically, the researchers devise a dual-ligand passivation system comprising photocrosslinkable ligands (PXLs) and dispersing ligands (DLs) to enable QDs to be universally compatible with solution-based patterning techniques. Upon UV irradiation, PXLs create a covalent bond between the ligands of neighboring QDs. The chemically crosslinked QD films are no longer dispersible when a solvent is applied. Hence, the researchers can achieve QD patterns by selective UV irradiation on QD films followed by development with good solvents. The dual-ligand passivation system awards full freedom to choose the solvent, and thus the processing methods. The dual-ligand passivation approach does not demand extra processing steps besides standard microfabrication processes, promising its immediate and practicable use in a range of photonic applications across academia to industry. The highlight of the present work is the demonstration of high-definition, large-area QD patterns via commercialized photolithography (i-line) or inkjet printing techniques at no cost to the optical properties of QDs or optoelectronic performances of devices implementing them. As an ultimate achievement, the researchers exemplify the integration of bright, multicolored QD patterns attained by the present approach into optoelectronic devices. For the “mixed-reality” (i.e., virtual reality and augmented reality) applications, the QD deposition process should enable the patterning of RGB QDs (or RG QDs along with the blank) into a few micrometer sub-pixels over a large area with high-precision and high-fidelity. The advantages of the present approach are well represented by the high-definition QD patterns over a large area achieved by means of standard photolithography equipment (i.e., pixel resolution for primary colors over 15,000 ppi on a 6-inch wafer) Our approach offers a versatile way of creating various structures of luminescent QDs in a cost-effective and non-destructive manner, and could be implemented in nearly all commercial photonics applications where QDs are used. [Figure] Schematic illustration of dual-ligand QDs and multi-colored patterns made of direct patterning of dual-ligand QDs [Reference] Donghyo Hahm, Jaemin Lim, Hyeokjun Kim, Jin-Wook Shin, Sungkwon Hwang, Seunghyun Rhee, Jun Hyuk Chang, Jeehye Yang, Chang Hyeok Lim, Hyunwoo Jo, Beomgyu Choi, Nam Sung Cho, Young-Shin Park, Doh C. Lee, Euyheon Hwang, Seungjun Chung, Chan-mo Kang, Moon Sung Kang, and Wan Ki Bae, Direct patterning of colloidal quantum dots with adaptable dual-ligand surface, Nature Nanotechnology, 10.1038/s41565-022-01182-5 [Main Author] Wan Ki Bae (SKKU), Moon Sung Kang (Sogang University), Chan-mo Kang (Electronics and Telecommunications Research Institute), Donghyo Hahm (SKKU), Jaemin Lim (SKKU), Hyeokjun Kim (Sogang University) * Contact: Prof. Wan Ki Bae (email@example.com), Prof. Moon Sung Kang (firstname.lastname@example.org), Dr. Chan-mo Kang (email@example.com)
Prof. Jung Kyu Kim (School of Chemical Engineering, SKKU) and Prof. Uk Sim (NEEL Inc. & KENTECH) reported his collaboration research achievements with Prof. Chang Hyuck Choi (Department of Chemistry, POSTECH) and Dr. Heechae Choi (University of Cologne, Germany): development of an environmental friendly biochar electrocatalyst from Camellia japonica flowers for sustainable green hydrogen production and supercapacitor. The versatile use of sulfur self-doped biochar derived from Camellia japonica (camellia) flowers is demonstrated as a multifunctional catalyst for overall water splitting and a supercapacitor. The native sulfur content in the camellia flower facilitates in situ self-doping of sulfur, which highly activates the camellia-driven biochar (SA-Came) as a multifunctional catalyst with the enhanced electron-transfer ability and long-term durability. For water splitting, an SA-Came-based electrode is highly stable and shows reaction activities in both hydrogen and oxygen evolution reactions, with overpotentials of 154 and 362 mV at 10 mA cm−2, respectively. For supercapacitors, SA-Came achieves a specific capacitance of 125.42 F g−1 at 2 A g−1 and high cyclic stability in a three-electrode system in a 1 M KOH electrolyte. It demonstrated a high energy density of 34.54 Wh kg−1 at a power density of 1600 W kg−1 as a symmetric hybrid supercapacitor device with a wide working potential range of 0–1.6 V. This research achievement was selected as the cover art of the journal ‘Carbon Energy’ (DOI: https://doi.org/10.1002/cey2.207) *A sulfur self-doped multifunctional biochar catalyst for overall water splitting and a supercapacitor from Camellia japonica flowers (Journal: Carbon Energy, DOI: https://doi.org/10.1002/cey2.207) A development of an environmental friendly biochar electrocatalyst from Camellia japonica flowers for sustainable green hydrogen production and supercapacitor The cover art of the journal ‘Carbon Energy’
Korea is experiencing a high rate of population aging and a low fertility rate at the same time. The OECD predicts that Korea will enter a “super-aged society” (having more than 20% of the population as the elderly) in 2026, eight years after entering the aging society. The purpose of this study is to examine the potential financial outcomes and the sustainability of implementing the British policy of integrating healthcare and social care in Korea. Furthermore, we predict the size of the integrated funds through this approach and analyze the operational and policy design requirements for successful integration through a case study of an existing financially integrated social care policy. Many countries, including Korea, are spending a lot of money on healthcare and social care due to the rapidly aging population, and the scale is expected to increase in the future. In such a situation, it is important to suggest a direction for reform through institutional analysis and to make a financial estimate based on the analysis of existing cases. Based on this, we find it meaningful to present an alternative policy that can be used for reform in the medical welfare field and to provide a policy direction for other countries that have become aging societies. We first analyze the size and trend of funds used for the care of the elderly out of Korea’s health insurance, long-term care insurance, and national budget. We then analyze the number of financial resources required and the cost-saving effect when the related financial resources are converted into local community care funds. This approach sheds light on the possibility of harmonizing healthcare policy for the elderly and integrated care under the existing insurance system and suggests a direction for reform in policies pertaining to healthcare for the elderly. Given that the same services are provided, we find that combining the finances from the insurance and the national budget would result in a fund of KRW 2.6 trillion to KRW 4.7 trillion. This approach also confirms that health care costs for the elderly can be reduced by 1-5% in the long term, which we estimate to be between KRW 1 trillion to KRW 4 trillion by 2050. We find that by tapping into the national budget to manage the pre-medical stage care, we can utilize an efficient operation mechanism, unlike insurance. We also confirm that information exchange and harmonious operation between the national budget and state-run insurance as well as feedback and incentives through performance management are necessary for these results to become a reality.
On June 11th, the research team led by Prof. Kotiba Hamad at the school of advanced materials science and engineering (AMSE) published a paper titled “Brittle and ductile characteristics of intermetallic compounds in magnesium alloys: A large-scale screening guided by machine learning” in the Journal of Magnesium and Alloys (IF =11.8) which is ranked the 1st in the category of metallurgy & metallurgical engineering according to Clarivate’s Journal Citation Reports’ (JCR) ranking. This study is one of the works conducted by this group to investigate the applicability and the potential of AI techniques in the field of materials discovery and design. The findings of this work showed that, by machine learning (ML), a technique of AI, the brittle-ductile characteristics of intermetallic compounds that form in magnesium-based alloys are reliably, accurately, and quickly predicted. The ML results were validated by theoretical calculations done by density functional theory (DFT), shown in the figure below. The results can facilitate the designing of magnesium alloys with high performance for structural applications. This led to say that, due to the exploding computational capabilities, artificial intelligence, in its machine learning subcategory, has been utilized heavily in the field of material discovery and design for its ability to construct data-driven models that are magnitude faster than conventional experimentation or even physics-driven modeling and simulation. The present research group; Kotiba Hamad (Professor), Russlan Jaafreh (Ph.D. candidate), Yoo Seong Kang (Graduate collaborator/Currently working in ‘Computer Systems and Intelligence Laboratory’), and Santiago Pereznieto (Masters Student), have been utilizing the capabilities of AI in the field of material science & engineering, and have published multiple papers regarding this topic in high-tier journals such as ACS Applied Materials & Interfaces, Journal of Materiomics and many more. Related Links: -Russlan Jaafreh, Yoo Seong Kang, Kotiba Hamad, Journal of Magnesium and Alloys 2022, DOI: doi.org/10.1016/j.jma.2022.05.006. -Russlan Jaafreh, Yoo Seong Kang, and Kotiba Hamad, ACS Applied Materials & Interfaces 2021 13 (48), 57204-57213, DOI: doi.org/10.1021/acsami.1c17378 -Professor Kotiba’s Website: kotibahamad995.wixsite.com/aem-skku
Prof. Balachandran Manavalan, Department of Integrative Biotechnology, is interested in investigating, developing, and deploying cutting-edge bioinformatics techniques using AI-based machine learning techniques in order to better understand and address a range of open and challenging problems in genomics and molecular biology. Recently, his group proposed three different novel computational methods in Molecular Therapy (Impact Factor 12.91, 2022), Briefings in Bioinformatics (Impact Factor 13.994), and Briefings in Bioinformatics (Impact Factor 13.994), respectively with him as the corresponding author. 1. Human RNA m5C Site Identification Using Stacking Strategy N5-methylcytosine (m5C) is one of the most prevalent post-transcriptional epigenetic modifications that plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important to accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms. Prof. Balachandran Manavalan and Prof. Hong-Wen Deng (Tulane University) team developed a novel strategy to overcome limitations of the existing methods. The team constructed an up-to-date benchmarking dataset and extracted different properties from the sequences that included novel contextual one-hot encoding. Various encodings were used to construct both conventional and deep learning baseline models. A stacking approach was then utilized to combine important models for the final prediction – Deepm5C. The results show that Deepm5C significantly outperformed existing predictors for identifying m5C sites, further demonstrating the efficiency of the proposed hybrid framework. This research was conducted with the support of NRF- 2021R1A2C1014338 and the result was published online on May 06 in Molecular Therapy (Impact factor 12.91) journal, (Cell Press). 2. Human lncRNA Subcellular Localization Prediction Using Tree-Based Algorithms Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which is responsible for their molecular functions, including cell cycle regulation and genome rearrangements. In the past, several ML-based methods have been developed to identify lncRNA subcellular localization, but relevant work for identifying cell-specific localization of human lncRNA remains limited. Prof. Balachandran Manavalan and Prof. Young-Jun Jeon (Department of Integrative Biotechnology) team proposed the first application of tree-based stacking approach named TACOS (Figure 1) to allow users to identify subcellular localization of human lncRNA for ten different cell types. This team conducted comprehensive evaluations of six tree-based classifiers with ten different feature descriptors using a newly constructed balanced training dataset for each cell type. Subsequently, AdaBoost baseline model’s strengths were integrated with an appropriate tree-based classifier for the final prediction. [Figure 1] An overview of TACOS. It involves the following steps: dataset construction, feature extraction, baseline model construction, and final model construction. This research was conducted with the support of NRF (2021R1A2C1014338 and 2021R1C1C1007833) and the result was published online on June 27 in Briefings in Bioinformatics (Impact factor 13.994; JCR=1) journal. 3. Novel Algorithm to Predict Anti-coronavirus Peptides Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not easily susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying ACVPs are needed. Prof. Balachandran Manavalan and Prof. Hiroyuki Kurata (Kyushu Institute of Technology, Japan) team developed a tool called iACVP (Figure 2). Based on an exhaustive analysis of five different classifiers and conventional features, the team concluded that the random forest classifier and the word-embedding word2vec (W2V) achieved the best performance, regardless of the dataset. The two main controlling factors in iACVP were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets rather than using the Uniprot proteome and (ii) the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. [Figure 2] Workflow of iACVP development. (A) Construction, evaluation and analysis of the ML methods with W2V encoding and BE. (B) Word2vec encoding of k-mer consecutive amino acid (AA) sequences and the sandwich structure of the training and test datasets. This research was conducted with the support of NRF- 2021R1A2C1014338 and the result was published online on July 1 in Briefings in Bioinformatics (Impact factor 13.994; JCR=1) journal. Prof. Balachandran Manavalan has developed several bioinformatics tools that have been widely used by researchers worldwide. He has published several articles in top-tier journals and is continuing to produce highly renowned research. His research interests can be found at: https://balalab-skku.org/. Currently, he is looking for talented and motivated graduate/undergraduate students with backgrounds in biological or biomedical sciences, statistics, chemistry, engineering, or computer science. Interested candidates can directly contact him at firstname.lastname@example.org.
A research collaboration team led by Prof. Seongpil An (co-first author: Dogun Park) and Prof. Ki Hyun Kim (co-first author: Joo-Hyun Hong) reported that they have developed a environmentally friendly wood-derived triboelectric nanogenerator (wood-TENG) composed of mechanically durable and biocompatible nanofibers. TENGs have attracted great attention because they can harness ubiquitous kinetic energies, such as vibration, friction, and impact, originated from unlimited natural resources. Not only this eco-friendly working principle, but the TENGs are also cost effective and can show high efficiency even at low operation frequency (meaning that they can harness human kinetic motions). On the other hand, in order to use these TENGs as a power source for next-generation self-powered wearable electronic devices, there are two important requirements should be considered when one fabricates TENGs. First, to harness human kinetic motion most efficiently, a geometrical structure of TENG should be efficiently designed, considering various human motions, such as bending at joints, and friction and impact during walk or running. Second, materials used for TENGs should be biocompatible, non-toxic, and skin-friendly so that they can be attached to human skin. [Figure 1] Schematic of the fabrication process and application of the wood-TENG developed. Considering these issues the research team has developed and employed the electrospun nanofibers composed of nature-derived biomaterials, in which a nontoxic biopolymer (i.e., polycaprolactone) and wood-derived extract (i.e., the root bark of Ulmus davidiana var. japonica) were included, as a tribopostive material. The wood-TENG based on these nature-derived biomaterials could generate a maximum output voltage of 80 V and also show stable cyclic energy harvesting performance during 100,000 cycles. In particular, the insole that consisted of the wood-TENG was able to generate electrical energy when one was walking and running along with showing antifungal activity against fungi existing in the foot. This research result was reported in Nano Energy (IF=19.069) at June 2022.
It has been reported that lung cancer development and progression are induced by genetic mutations and various external factors. Recently, lung cancer genetic data are being used to identify novel factors capable of regulating cancer development and progression, thereby providing a therapeutic strategy for the intervention of lung cancers. Various extrinsic and intrinsic factors from the tumor microenvironment (TEM) influence lung cancer progression. Recent studies have shown that toll-like receptors (TLRs) are expressed in lung cancers, suggesting that TLRs may be implicated in lung cancer progression. Although several studies have shown that stratifin (SFN, 14-3-3 sigma) facilitates lung cancer development and progression, the molecular and cellular mechanisms by which SFN is functionally involved in lung cancer progression, and the role of SFN in lung cancer progression in response to extrinsic stimulation, such as TLR agonist, are largely unknown. In this study, we show that SFN expression remarkably up-regulates in lung cancer tissues through the analyses of The Cancer Genome Atlas (TCGA) data and primary non-small cell lung cancers (n = 31 of our cohort patients). Moreover, SFN positively regulates lung cancer progression through autophagy induction by facilitating the TRAF6-Vps34-BECN1 complex in response to an extrinsic TLR4 agonist (Figure 1). Together, our clinically comparative results and functional investigations of SFN expression in lung cancer will potentially contribute to translational approaches for the development of lung cancer therapeutic agents. This study was carried out in collaboration with Dr. Eunyoung Chun's team in the R&D center at CHA Vaccine. Ji-Young Kim, a Ph.D. student at SKKU College of Medicine, contributed to this study as the first author. Article: Kim, J. Y., Kim, M. J., Lee, J. S., Son, J., Kim, D. H., Lee, J. S., Jeong, S. K., Chun, E., & Lee, K. Y. (2022). Stratifin (SFN) regulates lung cancer progression via nucleating the Vps34-BECN1-TRAF6 complex for autophagy induction. Clinical and translational medicine (IF: 11.492), 12(6), e896. https://doi.org/10.1002/ctm2.896.
Shape Memorable Collagen-Biocomposite Scaffold for the Biomedical Engineering Field Scaffold-based tissue engineering aims to develop biocompatible scaffolds for the recovery of defected tissues or organs exceeding the self-healing capacity. Collagen, one of the main structural proteins in human tissues, has been widely used for the fabrication of scaffolds. However, collagen-based scaffolds prepared in vitro have limitations in that they are easily deformed by external forces. The research team led by Prof. GeunHyung Kim (first author, researcher JiUn Lee) introduced a fabrication process utilizing collagen properties for the four different types of collagen hydrogels preparation. Among the structures, two types of them showed shape memory hydrogel (SMH) properties. In the case of SMH (Cryo-gel) manufactured through a low-temperature process, the deformed structure recovered immediately after the water immersion, but low cellular activities are a limitation. On the other hand, in the case of SMH (F-gel) produced via collagen fibrillogenesis, the structure recovered slowly, but it was shown that high cell activities were promoted due to the nanofibrous structure. [Figure 1] Schematic of various types of collagen-based hydrogel In addition to reporting these characteristics, the research team developed a process to fabricate a composite hydrogel that fused the advantages of two different types of SMHs (Cryo-gel and F-gel). The fabricated shape memorable biocomposite hydrogels not only possess the fast shape recovery properties of Cryo-gel, but also have a nanofiber network similar to that of F-gel in the structure. These unique structures induced enhanced cellular activities than Cryo-gel. Furthermore, to further add the functionality of the shape of memorable biocomposite hydrogels, heparin, which is used to delay the release of growth factors, or hydroxyapatite, a major component of bone tissue, were mixed. The research team confirmed that even if these various materials were mixed, all of the produced hydrogels maintain the shape memory properties. In addition, by combining the manufacturing process with 3D printing technology, it was shown that a 3D structure could be printed. The deformed 3D printed scaffold could be recovered to its initial structure by immersion in water. The research team also reported the possibility of its use as an injectable scaffold that can be used for minimally invasive operations. [Figure 2] Optical images of shape recovery of the 3D printed collagen biocomposite scaffold Prof. Kim said, “The result of this study is expected to be a technology that overcomes the limitations of being easily damaged and deformed in the existing collagen-based scaffolds and broadens the scope of application of the collagen to various medical fields.” In addition, Prof Kim’s research group used bioprinting technology to fabricate a muscle-ligament complex artificial tissue model (myotendinous junction) (first author, researcher WonJin Kim) and a collagen/bioceramic porous structure containing adipose-derived stem cells (first author, researcher Youngwon Koo). All the fabricated structures showed outstanding tissue regeneration ability compared to a conventional 3D cell structure. The research was supported by a grant from the National Research Foundation of Korea grant funded by the Ministry of Science and ICT for the Bioinspired Innovation Technology Development Project. The results were published in Applied Physics Reviews (IF = 19.2, 2022. Jun.) as a feature article and Bioengineering & Translational Medicine (IF = 10.7, 2022. Mar., Apr.), respectively. ※ Article titles - Collagen-based shape-memory biocomposites (Applied Physics Reviews) - A bioprinted complex tissue model for myotendinous junctionwith biochemical and biophysical cues (Bioengineering & Translational Medicine) - Bioprinted hASC-laden collagen/HA constructs with meringue-like macro/micropores (Bioengineering & Translational Medicine) ※ Feature article: https://aip.scitation.org/doi/10.1063/10.0011692
Last June, Benedetto Vigna became the new CEO of Ferrari NV, joining the company from semiconductor manufacturer STMicroelectronics NV. The chairman of Ferrari noted Vigna’s “deep understanding of the technologies driving much of the change in our industry,” and the subsequent press release stressed Vigna’s experience “at the heart of the semiconductor industry that is rapidly transforming the automotive sector.” The emphasis on the new CEO’s technological background was emblematic of an important yet underexplored development: the growing impact of technological expertise on the executive labor market. In a new paper, we examine whether the degree to which companies share an expertise in technology drives competition for managers and, hence, compensation. Our focus is based on the notion that firms with similar technologies are likely to value similar managerial attributes. As CEOs gain experience with and knowledge of the businesses they run, they are also likely to gain expertise in technological domains associated with managing firms in certain technological areas. Therefore, managers’ expertise in certain technological domains is valuable not only to their firm but also to other firms that focus on similar technology. This will ultimately be manifested in CEO compensation policies. Studies show that a manager’s technological expertise plays an important role in determining how much the manager and his firm complement each other. Our focus on the role of technological-expertise similarities in shaping firms’ compensation policies is also consistent with how many proxy statements treat technological considerations as important in choosing peer groups for benchmarking executive compensation. We use patent technology classifications to measure firms’ technological expertise and their similarities. Using compensation-benchmarking peer-firm data and the technological overlap measure, we begin by showing that similarity in technological expertise is a significant determinant of whether a certain firm is used as a compensation benchmarking peer. Moreover, even within the same industry and size groups, we show that a firm’s choice of peer firms is determined by its technological similarity to those firms. Our results suggest that technological similarity plays a crucial role in firms’ choice of peer group and that considering the role of technological fit is critical for demonstrating the efficiency of the labor market and the composition of the peer group. We then present evidence consistent with compensation benchmarking being an efficient approach to estimating the market wage for human capital, as opposed to its use reflecting managerial opportunism. We show that higher technological similarity with benchmarking peer firms increases the likelihood that a CEO who received above-median (below-median) pay in the previous year received at or below-median (above-median) pay in the following year. This result is obtained even after controlling for other important compensation determinants from previous studies, and is consistent with the market-based theory of CEO compensation in that firms set CEO pay to remain competitive with firms that are competing for similar managerial talent. After establishing that technological similarity has an important effect on CEO compensation benchmarking patterns, we provide evidence that its use reflects CEOs’ outside options. In particular, we show that an increase in technological similarity increases the likelihood of the CEO joining a similar firm. Our finding reflects the notion that the marketability of CEOs’ technological expertise is at least partly reflected in firms’ technological similarity and that firms prefer to hire CEOs with a better technological fit. Furthermore, we show that the CEO compensation levels of technologically similar peer firms are positively associated with CEO pay at the focal firm. We thus provide evidence that technological similarity plays a crucial role in the market for CEO talent and that the labor market consistently reflects CEOs’ outside opportunities. Overall, our study contributes to the literature focusing on the effect of similarities in technological expertise on corporate policies, shows that technological expertise is a distinct and previously overlooked aspect of transferable CEO skill, adds to the literature on optimal contracting for CEO compensation, and shows that technological similarity plays an important role in determining compensation benchmarking peers – consistent with an efficient contracting motivation. This paper is forthcoming in the one of the leading journals in the finance field, the Journal of Financial and Quantitative Analysis, and is available at the following link: https://doi.org/10.1017/S0022109022000229
Professor Seyoung Park in the department of Statistics recently proposed the novel clustering method by integrating multi-dimensional data for clustering analysis. Advances in high-throughput genomic technologies coupled with large-scale study projects have generated rich resources of diverse types of omics data to better understand disease etiology and treatment responses. Clustering patients into subtypes with similar disease etiologies and/or treatment responses using multiple omics data has the potential to improve the precision of clustering than using a single type of omics data. However, in practice patient clustering is still mostly based on a single omics data type or ad hoc integration of clustering results from each data type, leading to potential loss of information. By treating each omic data type as a different informative representation from patients, this research proposes a novel multi-view spectral clustering framework to integrate different omics data types measured from the same subject. The proposed method learns the weight of each data type as well as a similarity measure between patients via a non-convex optimization framework. When the proposed method is applied to the TCGA data, the patient clusters inferred by the proposed method show more significant differences in survival times between clusters than those between clusters inferred from existing clustering methods. Professor Park said “The main contribution of this research is to conduct clustering analysis using multiple high-dimensional data by considering the heterogeneity of different data and learning importance of data. We expect to apply the same idea to the different statistical frameworks using multiple high-dimensional data. “ This research is published in the “Journal of the American Statistical Association”, which is the top journal in Statistics. ※ Title: Integrating multidimensional data for clustering analysis with applications to cancer patient data ※ Source: https://doi.org/10.1080/01621459.2020.1730853