Tag: Japan

The new method can be used to construct copolymers comprising different metal species, which have potential uses in catalysis and drug discovery

Polymers with different metal complexes in their side chains are thought to be promising high-performance materials with a wide variety of applications. However, conventional fabrication methods are not suitable for constructing such polymers because controlling their resulting metal composition is complicated. Recently, scientists from Japan have developed a method to overcome this limitation and successfully produce multimetallic copolymers, which can be used as building blocks to create future hybrid materials.

From plastics to clothes to DNA, polymers are everywhere. Polymers are highly versatile materials that are made of long chains of repeating units called monomers. Polymers containing metal complexes on their side chains have enormous potential as hybrid materials in a variety of fields. This potential only increases with the inclusion of multiple metal species into the polymers. But conventional methods of fabricating polymers with metal complexes are not appropriate for the construction of multimetallic polymers, because controlling the composition of metal species in the resulting polymer is complex.

Recently, a research team, led by Assistant Professor Shigehito Osawa and Professor Hidenori Otsuka from Tokyo University of Science, has proposed a new method of polymerization that can overcome this limitation. Dr. Osawa explains, “The usual method of preparing such complexes is to design a polymer with ligands (molecular ‘backbones’ that join together other chemical species) and then add the metal species to form complexes on it. But each metal has a different binding affinity to the ligand, which makes it complicated to control the resulting structure. By considering polymerizable monomers with complexes of different metal species, we can effectively control the composition of the resulting copolymer.” The study was made available online on April 1, 2022, and published in Volume 58, Issue 34 of Chemical Communications on April 30, 2022.

When the monomers that make up a polymer are polymers themselves, the polymer is called a copolymer. For their study, the scientists designed a dipicolylamine acrylate (DPAAc) monomer. DPA was chosen because it is an excellent metal ligand and has been used in various biochemical applications. They then polymerized DPAAc with zinc (Zn) and platinum (Pt) to form two polymer chains with metal complexes—DPAZn(II)Ac and DPAPt(II)Ac. They then copolymerized the two monomers. They found that they could not only successfully create a copolymer, but that they could also control its metal composition by varying the feeding composition of the monomers.

Then they applied this copolymer as a building block to fabricate nanoparticles using plasmid deoxyribonucleic acid (DNA) as a template. Plasmid DNA was chosen as a template because the two constituent monomers are known to bind to it. The formation of the resulting nanoparticle polymer complexes with DNA (polyplexes) was confirmed using high-resolution scanning tunneling electron microscopy and energy-dispersive X-ray spectroscopy.

This technique—now a patent-pending technology—can be extended to a novel method for fabricating intermetallic nanomaterials. “Intermetallic catalytic nanomaterials are known to have significant advantages over nanomaterials containing only a single metallic species,” says Dr. Osawa.

The polyplexes formed in the study are DNA-binding molecules, which indicates that they could be used to develop anti-cancer drugs and gene carriers. The proposed fabrication method will also lead to advances in catalysis that move away from precious metals like platinum. “These multimetallic copolymers can serve as building blocks for future macromolecular metal complexes of many varieties,” concludes Dr. Osawa.

The findings of this study are sure to have far reaching consequences in the field of polymer chemistry.

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Reference

Title of original paper: Controlled polymerization of metal complex monomers – fabricating random copolymers comprising different metal species and nano-colloids

Journal: Chemical Communications

DOI: https://doi.org/10.1039/D1CC07265J

About The Tokyo University of Science

Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan’s development in science through inculcating the love for science in researchers, technicians, and educators.

With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society”, TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today’s most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.

Website: https://www.tus.ac.jp/en/mediarelations/

About Assistant Professor Shigehito Osawa from Tokyo University of Science

Shigehito Osawa obtained a PhD in Materials Engineering from the University of Tokyo, Japan, in 2016. He worked as a Research Scientist at the Kawasaki Institute of Industrial Promotion from 2016 to 2018. He joined Tokyo University of Science afterwards, where he now serves as Assistant Professor at the Department of Applied Chemistry. His research interests are in the fields of polymer materials and polymer chemistry. He has published 24 peer-reviewed papers and has patent-pending technology currently under review. He is currently a member of the Water Frontier Research Center (WaTUS).

 

Funding information

This work was financially supported by Grants-in-Aids for Early Carrier Scientists (JSPS KAKENHI Grant Number 20K15346 to Shigehito Osawa) from the Japanese Society of the Promotion of Science (JSPS).

The development of new thin semiconductor materials requires a quantitative analysis of a large amount of reflection high-energy electron diffraction (RHEED) data, which is time consuming and requires expertise. To tackle this issue, scientists from Tokyo University of Science identify machine learning techniques that can help automate RHEED data analysis. Their findings could greatly accelerate semiconductor research and pave the way for faster, energy efficient electronic devices.

The semiconductor industry has been growing steadily ever since its first steps in the mid-twentieth century and, thanks to the high-speed information and communication technologies it enabled, it has given way to the rapid digitalization of society. Today, in line with a tight global energy demand, there is a growing need for faster, more integrated, and more energy-efficient semiconductor devices.

However, modern semiconductor processes have already reached the nanometer scale, and the design of novel high-performance materials now involves the structural analysis of semiconductor nanofilms. Reflection high-energy electron diffraction (RHEED) is a widely used analytical method for this purpose. RHEED can be used to determine the structures that form on the surface of thin films at the atomic level and can even capture structural changes in real time as the thin film is being synthesized!

Unfortunately, for all its benefits, RHEED is sometimes hindered by the fact that its output patterns are complex and difficult to interpret. In virtually all cases, a highly skilled experimenter is needed to make sense of the huge amounts of data that RHEED can produce in the form of diffraction patterns. But what if we could make machine learning do most of the work when processing RHEED data?

A team of researchers led by Dr. Naoka Nagamura, a visiting associate professor at Tokyo University of Science (TUS) and a senior researcher of National Institute for Materials Science (NIMS), Japan, has been working on just that. In their latest study, published online on 09 June 2022 in the international journal Science and Technology of Advanced Materials: Methods, the team explored the possibility of using machine learning to automatically analyze RHEED data. This work, which was supported by JST-PRESTO and JST-CREST, was the result of joint research by TUS and NIMS, Japan. It was co-authored by Ms. Asako Yoshinari, Prof. Masato Kotsugi also from TUS, and Dr. Yuma Iwasaki from NIMS.

The researchers focused on the surface superstructures that form on the first atomic layers of clean single-crystal silicon (one of the most versatile semiconductor materials). depending on the amount of indium atoms adsorbed and slight differences in temperature. Surface superstructures are atomic arrangements unique to crystal surfaces where atoms stabilize in different periodic patterns than those inside the bulk of the crystal, depending on differences in the surrounding environment. Because they often exhibit unique physical properties, surface superstructures are the focus of much interest in materials science.

First, the team used different hierarchical clustering methods, which are aimed at dividing samples into different clusters based on various measures of similarity. This approach serves to detect how many different surface superstructures are present. After trying different techniques, the researchers found that Ward’s method could best track the actual phase transitions in surface superstructures.

The scientists then sought to determine the optimal process conditions for synthesizing each of the identified surface superstructures. They focused on the indium deposition time for which each superstructure was most extensively formed. Principal component analysis and other typical methods for dimensionality reduction did not perform well. Fortunately, non-negative matrix factorization, a different clustering and dimensionality reduction technique, could accurately and automatically obtain the optimal deposition times for each superstructure. Excited about these results, Dr. Nagamura remarks, “Our efforts will help automate the work that typically requires time-consuming manual analysis by specialists. We believe our study has the potential to change the way materials research is done and allow scientists to spend more time on creative pursuits.”

Overall, the findings reported in this study will hopefully lead to new and effective ways of using machine learning technique for materials science—a central topic in the field of materials informatics. In turn, this would have implications in our everyday lives as existing devices and technologies are upgraded with better materials. “Our approach can be used to analyze the superstructures grown not only on thin-film silicon single-crystal surfaces, but also metal crystal surfaces, sapphire, silicon carbide, gallium nitride, and various other important substrates. Thus, we expect our work to accelerate the research and development of next-generation semiconductors and high-speed communication devices,” concludes Dr. Nagamura.

We certainly hope to see more such discoveries in the future that can automate complex data analysis and ease the workload of scientists!

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Reference

Title of original paper: Skill-agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning

Journal: Science and Technology of Advanced Materials: Methods

DOI: https://doi.org/10.1080/27660400.2022.2079942

About The Tokyo University of Science

Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan’s development in science through inculcating the love for science in researchers, technicians, and educators.

With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society”, TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today’s most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.

Website: https://www.tus.ac.jp/en/mediarelations/

About Professor Masato Kotsugi from Tokyo University of Science

Dr. Masato Kotsugi graduated from Sophia University, Japan, in 1996 and then received a PhD from the Graduate School of Engineering Science at Osaka University in 2001. He joined the Tokyo University of Science in 2015 as a lecturer and became a full Professor in the Department of Materials Creation Engineering in 2021. Prof. Kotsugi and students at his laboratory conduct cutting edge research on high-performance materials with the aim of creating a green energy society. He has published over 110 refereed papers and is currently interested in solid-state physics, magnetism, synchrotron radiation, and materials informatics.

About Dr. Naoka Nagamura from National Institute for Materials Science

Dr. Naoka Nagamura is a visiting Associate Professor at Tokyo University of Science, Japan and a senior researcher at the Research Center for Advanced Measurement and Characterization at National Institute for Materials Science, Japan. She obtained her Ph.D. from the University of Tokyo, Japan in 2011 and did a postdoctoral stint there from 2011–2013. Her research interests include graphene, synchrotron radiation X-ray analysis, operando analysis, imaging, photoemission spectroscopy, and surface and interface analysis. She has published 34 papers so far with over 500 citations to her credit.

Funding information

This study was supported by JSPS KAKENHI Grant No. 19H02561; JST-CREST Grant No. JPMJCR21O1; and JST-PRESTO Grant Nos. JPMJPR20T7 and JPMJPR17NB.

Hydrogen peroxide (H2O2) is an important chemical, with a wide variety of applications. However, the current method used to manufacture H2O2 is expensive and generates a considerable amount of waste, making it an unsustainable approach. In this study, a group of researchers from Japan produced H2O2 from waste coffee grounds and tea leaves, and then demonstrated its industrial use. Their novel method proved to be simple, cost-effective, and most importantly, sustainable.

Coffee and tea are two of the most popular beverages around the world. The extensive consumption of these drinks produces large amounts of coffee grounds and tea leaves, which are typically discarded as waste. These unused biomass resources, however, have the potential to produce several useful chemicals. Tea and coffee contain a group of compounds called polyphenols, which can produce hydrogen peroxide (H2O2).

H2O2 has a lot of industrial value; this chemical plays a critical role in the oxidation of several compounds. The oxidation process is typically catalyzed by an enzyme called P450 peroxygenase, but it can’t occur unless H2O2 is present. These oxidation reactions are used to produce many chemicals of note.

Now, H2O2 is currently produced through an unsustainable method called the anthraquinone process, which is not only energy-intensive but also produces a lot of waste, highlighting the need for a greener, environmentally friendly alternative. While there are other methods which use enzymes or light to produce H2O2, these are expensive because they require catalysts and additional reagents.

Keeping these issues in mind, a group of scientists from Japan—including Associate Professor Toshiki Furuya and Mr. Hideaki Kawana from Tokyo University of Science, and Dr. Yuki Honda from Nara Women’s University, Japan—has found an alternative way to produce H2O2. Their product comes from an unlikely source—the leftovers of brewed tea and coffee, called spent coffee grounds (SCG) or tea leaf residue (TLR)!

“Given their polyphenol content, we predicted that SCG and TLR could be used to produce hydrogen peroxide,” says Dr. Furuya. Proving their prediction to be true, their study—published in ACS Omega on June 1, 2022—details their successful production of H2O2 using these underutilized biomass resources.

The team’s production method involved adding coffee grounds and tea leaves to a sodium phosphate buffer, then incubating this solution while shaking it. In the presence of the buffer, SCG and TLR interacted with molecular oxygen to produce H2O2.

The team also explored the scope of using this H2O2 to synthesize other chemicals of industrial importance. The newly-synthesized H2O2 aided in the production of Russig’s blue. Moreover, in the presence of peroxygenase (an enzyme that catalyzes an oxidation reaction using H2O2), TLR- and SCG-derived H2O2 was allowed to react with a molecule called styrene to produce styrene oxide—which has several applications in medicine—and another useful compound, phenylacetaldehyde.

These results prove that the team’s new approach of using SCG and TLR to produce H2O2 proved to be simple, cost-effective, and environmentally friendly, compared to the traditional anthraquinone process. Hailing these promising results, Dr. Furuya says, “Our method can be used to produce hydrogen peroxide from materials that would otherwise have been discarded. This could further result in new ways to synthesize industrial chemicals like styrene oxide, opening up new applications for these unused biomass resources.”

These findings thus open up a new way towards the sustainable production of H2O2, from the most unexpected sources: tea and coffee waste!

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Reference

Title of original paper: Sustainable Approach for Peroxygenase-Catalyzed Oxidation Reactions Using Hydrogen Peroxide Generated from Spent Coffee Grounds and Tea Leaf Residues

Journal: ACS Omega

DOI: https://doi.org/10.1021/acsomega.2c02186

Chondroitin sulfate proteoglycans (CSPGs), commonly obtained from salmon nasal cartilage, are a key ingredient of various health foods. As the popularity of health foods increases, scientists are searching for alternative sources of CSPGs. Now, researchers from Japan have analyzed the PGs and their CS structures in the head cartilage of 10 edible bony fishes, including sturgeons. Their findings point to several new fishes that can serve as alternatives to salmon as a source of CSPGs.

Aggrecan, a major component of proteoglycan (PG) having chondroitin sulfate (CS) in cartilaginous tissues, has become increasingly popular as an ingredient in health food. In fact, proteoglycans from salmon nasal cartilage demonstrate biological properties such as antiaging, inhibition of angiogenesis, and attenuation of inflammatory responses. Commercially available chondroitin sulfate proteoglycans (CSPGs) have only been prepared from salmon nasal cartilage. Although the head cartilage was found in other edible bony fishes, there is little information on the composition of core proteins and their CS structures in the head cartilage.

Now, in a new study published in the International Journal of Biology Macromolecules, a team of researchers led by Associate Professor Kyohei Higashi of Tokyo University of Science, and Dr. Naoshi Dohmae and Dr. Takehiro Suzuki of the RIKEN Center for Sustainable Resource Science tackles this question. “We found that composition of PGs and their CS structure in the skull of the Siberian sturgeon and Russian sturgeon were similar to that in the salmon nasal cartilage,” reports Dr. Higashi. The fishes for the study were provided by Mr. Atsuhi Nakamura from Miyazaki Prefectural Fisheries Research Institute. This study was made available online on March 23, 2022 and was published in Volume 208 of the journal on May 31, 2022.

All the fishes examined contained abundant CSPGs in the head cartilage. Comprehensive analysis of CS structure in PGs derived from 10 bony fishes revealed that the structure of CS derived from Perciformes were similar to that of CS derived from cartilage of terrestrial animals. On the other hand, the structure of CS from skull of sturgeons was similar to that of CS from salmon nasal cartilage. In addition, they also found that aggrecan, a major CSPG in the cartilaginous tissue, was conserved in 10 bony fishes. In fact, the aggrecan protein from LOC117428125 and LOC117964296 genes registered in the National Center for Biotechnology Information database was found to be abundant in the skull of sturgeons. Furthermore, compositions of other PGs, collagens, and matrix proteins in the skull of sturgeons were similar to that of salmon nasal cartilage.

Elaborating on the findings of this study, Dr. Kyohei Higashi says, “Head cartilage from bony fishes is an underutilized resource and is typically discarded after food processing. The PGs, especially from the sturgeon, are similar in CS structure to the salmon nasal cartilage, showing that the sturgeon has a lot of potential to be an alternative source of CSPGs for health food formulations.”

The researchers hope with further studies to evaluate the biological properties of sturgeon PG, bony fishes could become an important source for CS as well as PGs.

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Reference

Title of original paper: Comprehensive analysis of chondroitin sulfate and aggrecan in the head cartilage of bony fishes: Identification of proteoglycans in the head cartilage of sturgeon

Journal: International Journal of Biological Macromolecules

DOI: https://doi.org/10.1016/j.ijbiomac.2022.03.125

 

 

Animals often use highly specific signals to warn their herd about approaching predators. Surprisingly, similar behaviors are also observed among plants.

Shedding more light on this phenomenon, Tokyo University of Science researchers have discovered one such mechanism. Using Arabidopsis thaliana as a model system, the researchers have shown that herbivore-damaged plants give off volatile chemical “scents” that trigger epigenetic modifications in the defense genes of neighboring plants. These genes subsequently trigger anti-herbivore defense systems.

In the wild, many species of animals, especially those with known predators, signal each other of imminent dangers using a variety of techniques, ranging from scent to sound. Now, thanks to multiple studies on the topic, we have reason to believe that plants, too, can sound an alarm under threat of an attack.

Prior studies have shown that when grown near mint plants, soybean and field mustard (Brassica rapa) plants display heightened defense properties against herbivore pests by activating defense genes in their leaves, as a result of “eavesdropping” on mint volatiles. Put simply, if mint leaves get damaged after a herbivore attack, the plants in their immediate vicinity respond by activating their anti-herbivore defense systems in response to the chemical signals released by the damaged mint plant. To understand this mechanism better, a team of researchers from multiple Japanese research institutes, including Tokyo University of Science, studied these responses in Arabidopsis thaliana, a model plant used widely in biological studies.

“Surrounding undamaged plants exposed to odors emitted from plants eaten by pests can develop resistance to the pests.

Although the induction of the expression of defense genes in odor-responsive plants is key to this resistance, the precise molecular mechanisms for turning the induced state on or off have not been understood. In this study, we hypothesized that histone acetylation, or the so-called epigenetic regulation, is involved in the phenomenon of resistance development,” explains Dr. Gen-ichiro Arimura, Professor at the Tokyo University of Science and one of the authors of the study. Their findings have recently been published in the journal Plant Physiology.

First, they exposed the plants to β-ocimene, a volatile organic compound often released by plants in response to attacks by herbivores like Spodoptera litura. Next, the researchers tried to determine the exact mechanism of action of volatile-chemical-activated plant defense.

The results were interesting—defense traits were induced in Arabidopsis leaves, presumably through “epigenetic” mechanisms, which refer to gene regulation that occurs because of external environmental influences. In this case, the volatile chemicals released by the damaged plants enhanced histone acetylation and the expression of defense gene regulators, including the ethylene response factor genes “ERF8” and “ERF104”. The team found a specific set of histone acetyltransferase enzymes (HAC1, HAC5, and HAM1) were responsible for the induction and maintenance of the anti-herbivore properties.

The researchers are ecstatic about their discovery of the role that epigenetics has to play in plant defense. According to them, the communication between plants via volatile compounds (known as the “talking plants” phenomenon) can potentially be applied to organic cultivation systems. This can increase the pest resistance of plants and effectively reduce our massive dependence on pesticides.

“The effective use of plants’ natural survival strategies in production systems will bring us closer to the realization of a sustainable society that simultaneously solves environmental and food problems,” concludes Dr. Arimura.

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Reference

Title of original paper: Sustained defense response via volatile signaling and its epigenetic transcriptional regulation

Journal: Plant Physiology

DOI: https://doi.org/10.1093/plphys/kiac07About Professor Gen-ichiro Arimura from Tokyo University of Science

Dr. Gen-ichiro Arimura obtained his Ph.D. from Hiroshima University, Japan. He is serving as a Professor at the Tokyo University of Science’s Department of Biological Science and Technology. His primary research interest includes biological communications. His laboratory also conducts research on biological interaction networks and defense response systems in plants. Dr. Arimura has published over 60 refereed papers. He also has three patents to his credit.

Funding information

This work was financially supported in part by a Japan Society for the Promotion of Science (JSPS) KAKENHI (20H02951), JSPS -DST Joint Research Program (JPJSBP120217713), MEXT Grants-in-Aid for Scientific Research on Innovative Areas (20H04786 and 18H04786), and Japan Science and Technology Agency (JST) A-STEP (JPMJTM20D2), and Nagase Science and Technology Foundation to GA.

 

Cells have certain proteins that help them adhere to each other while covering body surfaces and organs. Loss of these identifying proteins could result in cellular progression towards cancer and, subsequently, metastasis.

However, lipids may play a role in maintaining cellular identity as well. Japanese scientists have now identified the role of PIP2, a phospholipid, in maintaining epithelial cell-cell adhesion and cellular identity. Their findings will help develop strategies aimed at suppressing metastasis.

In multicellular organisms, body cells adhere to each other to form tissues that perform various physiological functions. Epithelial cells form our skin and lining surfaces, such as the gut and other ducts, and protect our internal organs. To maintain the integrity of an organism and function properly, it is important for these cells to remain attached to each other.

They do so through specific types of cellular junctions. These junctions are characterized by proteins, which also help in maintaining cellular identity. The loss of these proteins from cell surfaces causes them to lose their identity as epithelial cells, prompting their transformation into mesenchymal cells (through a process known as epithelial-mesenchymal transformation, or EMT), and subsequently, their progression towards cancer and fibrosis.

These cancerous cells are only loosely adherent to each other (given that the proteins that helped maintain cellular adhesion are now lost), so they may separate from each other, migrate into the bloodstream, and cause the cancer to metastasize (spread to other parts of the body).

Now, while the role of proteins in maintaining cellular identity is well-researched, we can’t help but wonder–do lipids (fatty molecules) also play a role in characterizing cells and preventing EMT?

Under the guidance of Dr. Yoshikazu Nakamura and Dr. Kaori Kanemaru, researchers from Tokyo University of Science (TUS), Tokyo University of Pharmacy and Life Sciences, Tokyo Medical and Dental University, Akita University, Hokkaido University, and Kobe University have tried to find an answer to this question.

“We know lipids are an important class of biomolecules, necessary for certain cellular functions. One such lipid, a phosphatidylinositol, forms a phospholipid called phosphatidylinositol bisphosphate (PIP2),” Associate Professor Dr. Nakamura from TUS dives into the topic.

He tells us that PIP2 is important because it is crucial for the formation of signaling molecules that regulate cell proliferation, survival, and migration. “We had evidence that higher amounts of PIP2 were found in the epidermal layer of skin, so we hypothesized that this phospholipid contributed to the properties and characterization of epithelial cells.”

The findings from their study have been published in Nature Communications. The paper describes how the team used a battery of analytical techniques including chromatography, mass spectroscopy, immunofluorescence, retroviral expression, and real-time quantitative PCR to confirm that PIP2 plays a critical role in the determination of epithelial identity.

“We saw that epithelial cells lost their properties when PIP2 was depleted from their cell membranes. On the other hand, osteosarcoma cells (which are cancerous, non-epithelial cells) gained epithelial cell-like properties when PIP2 was produced in their plasma membranes.” says Dr. Nakamura, with a look of excitement. The group was also able to show that PIP2 regulates these epithelial properties by recruiting Par3—a protein which guides vesicles intracellularly—to the plasma membrane.

Once in the plasma membrane, Par3 facilitates the formation of adherens junctions (one of the cellular junctions discussed above) which anchor neighboring cells together. This partially prevents EMT, and hence, progression of cancer.

“So,” Dr. Nakamura explains, “In theory, PIP2’s partial inhibition of EMT could halt cancer progression, making this phospholipid an attractive target molecule for anti-cancer treatment.”

TUS’ research has opened a new avenue for the development of anti-cancer drug development, possibly giving us a solution that will “stick.”

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Reference

Title of original paper: Plasma membrane phosphatidylinositol (4,5)-bisphosphate is critical for determination of epithelial characteristics

Journal: Nature Communications

DOI: https://doi.org/10.1038/s41467-022-30061-9

About Dr. Yoshikazu Nakamura from Tokyo University of Science

Dr. Yoshikazu Nakamura earned his Ph.D. in 2006 from the University of Tokyo and is an Associate Professor in the Department of Applied Biological Science at Tokyo University of Science. His lab conducts basic medical research tied to understanding the role of inositol lipids in skin diseases and cancer. He is currently coordinating research on the role of lipids in epithelial regulation and their importance in skin barrier formation. He is affiliated with the Japanese Biochemical Society and the Japanese Society for Investigative Dermatology and received Young Investigator Award from the Japanese Biochemical Society in 2016.

Funding information

The project was funded by a Grant-in-Aid for Scientific Research (B) 18H02575, the Takeda Science Foundation, the Sumitomo Foundation, the Terumo Life Science Foundation, the Mochida Memorial 1 Foundation for Medical and Pharmaceutical Research, the Ichiro Kanehara Foundation, the Hamaguchi Foundation for the Advancement of Biochemistry and PRIME to Y.N. and a Grant in-Aid for Young Scientists to Kaori Kanemaru.

Bicycle sharing is an attractive zero-carbon transportation option for a world that is being increasingly disrupted by climate change.

But bikes need to be restored at bike ports every now and then. Calculating the optimal way to restore bicycles is time consuming and computationally expensive.

Recently, researchers from Tokyo University of Science have built upon their previous optimization algorithm to propose two strategies to reduce computational costs while maintaining the performance of the algorithm.

Bicycle sharing systems (BSSs) are transport solutions wherein users can rent a bicycle from a depot or ‘port,’ travel, and then return the bike to the same port or different port. BSSs are growing in popularity around the world because they are eco-friendly, reduce traffic congestion, and offer added health benefits to users.

But eventually, a port becomes either full or empty in a BSS. This means that users are no longer able to rent a bike (when empty) or return one (when full). To address this issue, bikes need to be rebalanced among the ports in a BSS so that users are always able to use them. This rebalancing must also be carried out in a way that is beneficial to BSS companies so that they can reduce labor costs, as well as carbon emissions from rebalancing vehicles.

There are several existing approaches to BSS rebalancing, however, most solution algorithms are computationally expensive and take a lot of time to find an ‘exact’ solution in cases where there are a large number of ports. Even finding an approximate solution is computationally expensive.

Previously, a research team led by Prof. Tohru Ikeguchi from Tokyo University of Science proposed a ‘multiple-vehicle bike sharing system routing problem with soft constraints’ (mBSSRP-S) that can find the shortest travel times for multiple bike rebalancing vehicles with the caveat that the optimal solution can sometimes violate the real-world limitations of the problem. Now, in a recent study published in MDPI’s Applied Sciences, the team has proposed two strategies to search for approximate solutions to the mBSSRP-S that can reduce computational costs without affecting performance. The research team also featured PhD student Ms. Honami Tsushima of Tokyo University of Science and Prof. Takafumi Matsuura of Nippon Institute of Technology.

Describing their research, Prof. Ikeguchi says, “Earlier, we had proposed the mBSSRP-S and that offered improved performance as compared to our original mBSSRP, which did not allow the violation of constraints. But the mBSSRP-S also increased the overall computational cost of the problem because it had to calculate both the feasible and infeasible solutions of the mBSSRP. Therefore, we have now proposed two consecutive search strategies to address this problem.”

The proposed search strategies look for feasible solutions in a much shorter period of time as compared to the one originally proposed with mBSSRP-S. The first strategy focuses on reducing the number of ‘neighboring’ solutions (solutions that are numerically close to a solution to the optimization problem) before finding a feasible solution. The strategy employs two well-known algorithms called ‘Or-opt’ and ‘CROSS-exchange,’ to reduce the overall time taken to compute a solution. The feasible solution here refers to values that satisfy the constraints of mBSSRP.

The second strategy changes the problem to be solved based on the feasible solution to either the mBSSRP problem or the mBSSRP-S problem and then searches for good near-optimal solutions in a short time by either Or-opt or CROSS-exchange.

The research team then performed numerical experiments to evaluate the computational cost and performance of their algorithms. “With the application of these two strategies, we have succeeded in reducing computational time while maintaining performance,” reveals Prof. Ikeguchi. “We also found that once we calculated the feasible solution, we could find short travel times for the rebalancing vehicles quickly by solving the hard constraint problem, mBSSRP, instead of mBSSRP-S.”

The popularity of BSSs is only expected to grow in the future. The new solution-search strategies proposed here will go a long way towards realizing convenient and comfortable BSSs that benefit users, companies, and the environment.

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Reference

Title of original paper: Searching Strategies with Low Computational Costs for Multiple-Vehicle Bike Sharing System Routing Problem

Journal: Applied Sciences

DOI: https://doi.org/10.3390/app12052675

Physical reservoir computing (PRC), which relies on the transient response of physical systems, is an attractive machine learning framework that can perform high-speed processing of time-series signals at low power.

However, PRC systems have low tunability, limiting the signals it can process. Now, researchers from Japan present ionic liquids as an easily tunable physical reservoir device that can be optimized to process signals over a broad range of timescales by simply changing their viscosity.

Artificial Intelligence (AI) is fast becoming ubiquitous in the modern society and will feature a broader implementation in the coming years. In applications involving sensors and internet-of-things devices, the norm is often edge AI, a technology in which the computing and analyses are performed close to the user (where the data is collected) and not far away on a centralized server. This is because edge AI has low power requirements as well as high-speed data processing capabilities, traits that are particularly desirable in processing time-series data in real time.

In this regard, physical reservoir computing (PRC), which relies on the transient dynamics of physical systems, can greatly simplify the computing paradigm of edge AI. This is because PRC can be used to store and process analog signals into those edge AI can efficiently work with and analyze. However, the dynamics of solid PRC systems are characterized by specific timescales that are not easily tunable and are usually too fast for most physical signals. This mismatch in timescales and their low controllability make PRC largely unsuitable for real-time processing of signals in living environments.

To address this issue, a research team from Japan involving Professor Kentaro Kinoshita and Sang-Gyu Koh, a PhD student, from the Tokyo University of Science, and senior researchers Dr. Hiroyuki Akinaga, Dr. Hisashi Shima, and Dr. Yasuhisa Naitoh from the National Institute of Advanced Industrial Science and Technology, proposed, in a new study published in Scientific Reports, the use of liquid PRC systems instead.

“Replacing conventional solid reservoirs with liquid ones should lead to AI devices that can directly learn at the time scales of environmentally generated signals, such as voice and vibrations, in real time,” explains Prof. Kinoshita. “Ionic liquids are stable molten salts that are completely made up of free-roaming electrical charges. The dielectric relaxation of the ionic liquid, or how its charges rearrange as a response to an electric signal, could be used as a reservoir and is holds much promise for edge AI physical computing.”

In their study, the team designed a PRC system with an ionic liquid (IL) of an organic salt, 1-alkyl-3-methylimidazolium bis(trifluoromethane sulfonyl)imide ([Rmim+] [TFSI-] R = ethyl (e), butyl (b), hexyl (h), and octyl (o)), whose cationic part (the positively charged ion) can be easily varied with the length of a chosen alkyl chain. They fabricated gold gap electrodes, and filled in the gaps with the IL. “We found that the timescale of the reservoir, while complex in nature, can be directly controlled by the viscosity of the IL, which depends on the length of the cationic alkyl chain.

Changing the alkyl group in organic salts is easy to do, and presents us with a controllable, designable system for a range of signal lifetimes, allowing a broad range of computing applications in the future,” says Prof. Kinoshita. By adjusting the alkyl chain length between 2 and 8 units, the researchers achieved characteristic response times that ranged between 1 – 20 ms, with longer alkyl sidechains leading to longer response times and tunable AI learning performance of devices.

The tunability of the system was demonstrated using an AI image identification task. The AI was presented a handwritten image as the input, which was represented by 1 ms width rectangular pulse voltages. By increasing the side chain length, the team made the transient dynamics approach that of the target signal, with the discrimination rate improving for higher chain lengths. This is because, compared to [emim+] [TFSI-], in which the current relaxed to its value in about 1 ms, the IL with a longer side chain and, in turn, longer relaxation time retained the history of the time series data better, improving identification accuracy. When the longest sidechain of 8 units was used, the discrimination rate reached a peak value of 90.2%.

These findings are encouraging as they clearly show that the proposed PRC system based on the dielectric relaxation at an electrode-ionic liquid interface can be suitably tuned according to the input signals by simply changing the IL’s viscosity. This could pave the way for edge AI devices that can accurately learn the various signals produced in the living environment in real time.

Computing has never been more flexible!

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Reference

Authors: Sang-Gyu Koh1, 2, Hisashi Shima2, Yasuhisa Naitoh2, Hiroyuki Akinaga2, and Kentaro Kinoshita1

Title of original paper: Reservoir computing with dielectric relaxation at an electrode– ionic liquid interface

Journal: Scientific Reports

DOI: https://doi.org/10.1038/s41598-022-10152-9

Affiliations:

1Department of Applied Physics, Tokyo University of Science

2Device Technology Research Institute, National Institute of Advanced Industrial Science and Technology

About Professor Kinoshita Kentaro from Tokyo University of Science

Kinoshita Kentaro is a Professor at the Department of Applied Physics at Tokyo University of Science, Japan. His area of interest is device physics, with a focus on memory devices, AI devices, and functional materials. He has published 105 papers with over 1600 citations to his credit and holds a patent to his name. For more information, visit: https://www.tus.ac.jp/en/fac/p/index.php?6e52&ls=gk

We often encounter nonlinear dynamical systems that behave unpredictably, such as the earth’s climate and the stock market. To analyze them, measurements taken over time are used to reconstruct the state of the system. However, this depends on the quality of the data. Now, researchers from Japan have proposed an all-new method for determining the necessary parameters that results in an accurate reconstruction. Their new technique has far-reaching implications for the field of data science.

Many frequently observed real-world phenomena are nonlinear in nature. This means that their output does not change in a manner that is proportional to their input. These models have a degree of unpredictability, where it is unclear how the system will respond to any changes in its input. This is especially important in the case of dynamical systems, where the output of the model changes with time. For such systems, the time series data, or the measurements from the system over time, have to be analyzed to determine how the system changes or evolves with time.

Due to the commonality of the problem, many solutions have been proposed to analyze time-series data to gain an understanding of the system. One method of reconstructing the state of a system based on time series data is state space reconstruction, which can be used to reconstruct those states where the system remains stable or unchanged with time. Such states are known as “attractors.” However, the accuracy of the reconstructed attractors depends on the parameters used for reconstruction, and due to the finite nature of the data, such parameters are difficult to ascertain, resulting in inaccurate reconstructions.

Now, in a new study to be published on April 1, 2022, in Nonlinear Theory and Its Applications, IEICE, Professor Tohru Ikeguchi from Tokyo University of Science, his PhD student Mr. Kazuya Sawada from Tokyo University of Science, and Prof. Yutaka Shimada from Saitama University, Japan, have used the geometric structure of the attractor to estimate the reconstruction parameters.

“To reconstruct the state space using time-delay coordinate systems, two parameters, the dimension of the state space and the delay time, must be set appropriately, which is an important issue that is still being actively studied in this field. We discuss how to set these parameters optimally by focusing on the geometric structure of the attractor as one way to solve this problem,” explains Prof. Ikeguchi.

To obtain the optimal values of the parameters, the researchers used five three-dimensional nonlinear dynamical systems and maximized the similarity of the inter-point distance distributions between the reconstructed attractor and the original attractor. As a result, the parameters were obtained in a way that produced a reconstructed attractor which was geometrically as close as possible to the original.

While the method was able to generate the appropriate reconstruction parameters, the researchers did not factor in the noise that is normally encountered in real-world data, which can significantly affect the reconstruction. “Mathematically, this method has been proven to be a good one, but there are many considerations that need to be made before applying this method to real-world data analysis. This is because real-world data contains noise, and the length and accuracy of the observed data is finite,” explains Prof. Ikeguchi.

Despite this, the method resolves one of the limitations involved in determining the state of nonlinear dynamical systems that are encountered in various fields of science, economics, and engineering. “This research has yielded an important analysis technique in the current data science field, and we believe that it is important for handling a wide variety of data in the real world,” concludes Prof. Ikeguchi.

***

Reference

Title of original paper: Similarities of inter-point distance distributions on original and

reconstructed attractors

Journal: Nonlinear Theory and Its Applications, IEICE

DOI: https://doi.org/10.1587/nolta.13.385

About The Tokyo University of Science

Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan’s development in science through inculcating the love for science in researchers, technicians, and educators.

With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society”, TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today’s most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.

Website: https://www.tus.ac.jp/en/mediarelations/

About Professor Tohru Ikeguchi from Tokyo University of Science

Tohru Ikeguchi received M.E. and Ph.D degrees from Tokyo University of Science, Japan. After working for nearly a decade as Full Professor at Saitama University, Japan, he worked at Tokyo University of Science as Full Professor at the Department of Management Science from 2014 to 2016. Since then, he has been a Full Professor at the Department of Information and Computer Technology in Tokyo University of Science. His research interests include nonlinear time series analysis, computational neuroscience, application of chaotic dynamics to solving combinatorial optimization problems, and complex network theory. He has published over 230 papers and proceedings.

A new study found that disulfiram, a drug used to treat chronic alcoholism, can safely reduce anxiety levels in rodents.

Disulfiram is a drug used to treat chronic alcoholism. However, studies suggest that it also inhibits chemokine receptor signaling pathways that are associated with the regulation of anxiety in rodents. Now, a team of researchers from the Tokyo University of Science show that disulfiram can effectively reduce anxiety without causing any of the adverse effects that are linked to other anxiolytic drugs. Thus, disulfiram could potentially become a safe and effective anti-anxiety drug.

Alcoholism, if left untreated, could have dangerous repercussions. Thus, it is no surprise that there are a range of drugs developed to treat this condition. Of these drugs, disulfiram (DSF) is approved by the Food and Drug Agency (FDA) for the treatment of alcoholism. DSF primarily inhibits the enzyme aldehyde dehydrogenase (ALDH), which is responsible for the metabolism of alcohol.

Could the inhibitory effects of DSF extend to signaling molecules as well?

According to recent studies, DSF in fact inhibits a cytoplasmic protein known as FROUNT, which controls the direction in which certain immune cells migrate. DSF blocks FROUNT from interacting with two chemokine receptors known as CCR2 and CCR5, which are involved in important cellular signaling pathways.

A few studies suggest that chemokine receptors may be involved in the regulation of emotional behaviors in rodents. However, there is a lack of data on the exact association between FROUNT-chemokine signaling and DSF. To clarify this link, a team comprising Prof. Akiyoshi Saitoh from Tokyo University of Science and other researchers from institutes across Japan conducted a study examining the pharmacological properties of DSF. The study, which was published online on March 7, 2022 in Frontiers in Pharmacology, describes how the research team used an elevated plus-maze (EPM) test—which is used to screen for anxiolytic drugs—to study the effects of DSF in mice.

The EPM apparatus consists of four arms set in a cross pattern, connected to a central square. Two arms are protected by vertical boundaries, whereas two have unprotected edges. Usually, mice with anxiety prefer to spend time in the closed arms.

In this case, some mice were administered diazepam (a drug commonly used to treat anxiety) and others, DSF. These mice were then placed in the EPM apparatus, and their activity was monitored. To their surprise, the team found that mice treated with DSF spent significantly more time in the open arms of the apparatus, which indicates that they were less anxious. The team also tested the anxiolytic effects of a more potent FROUNT inhibitor, known as DSF-41, and observed similar results.

What’s interesting is that these behavioral changes were similar to those observed in mice treated with diazepam. How exactly did DSF achieve this?

The team had previously discovered that increased extracellular glutamate (which is an important amino acid and neurotransmitter) levels are associated with increased anxiety in mice.

“We propose that DSF inhibits FROUNT protein and the chemokine signaling pathways under its influence, which may suppress presynaptic glutamatergic transmission in the brain,” says Prof. Saitoh. “This, in turn, attenuates the levels of glutamate in the brain, reducing overall anxiety.”

The team was also pleasantly surprised to find that in contrast with diazepam, DSF treatment did not lead to adverse effects such as amnesia, coordination disorders, or sedation.

According to Prof. Saitoh, “These results indicate that DSF can be used safely by elderly patients suffering from anxiety and insomnia and has the potential to become a breakthrough psychotropic drug.”

What are the long-term implications of these results? Dr. Saitoh explains, “We plan to further clarify how DSF exerts its pharmaceutical actions. Hopefully, we will also be able to elucidate the exact role of the FROUNT molecule in the central nervous system.”

This is one of the first studies to reveal that DSF exhibits anti-anxiety properties comparable to those of existing benzodiazepines without exhibiting any side effects observed with benzodiazepines. Hopefully, DSF’s inhibitory activity against FROUNT functioning could be explored for successful anxiolytic drug development.

***

Reference

Title of original paper: Disulfiram Produces Potent Anxiolytic-Like Effects Without Benzodiazepine Anxiolytics-Related Adverse Effects in Mice

Journal: Frontiers in Pharmacology

DOI: https://doi.org/10.3389/fphar.2022.826783

About The Tokyo University of Science

Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan’s development in science through inculcating the love for science in researchers, technicians, and educators.

With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society”, TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today’s most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.

Website: https://www.tus.ac.jp/en/mediarelations/

About Prof. Akiyoshi Saitoh from Tokyo University of Science

Dr. Akiyoshi Saitoh is a Professor at the Faculty of Pharmaceutical Sciences, Tokyo University of Science. He is a senior researcher with more than 20 years of experience in the fields of medicinal pharmacology, behavioral pharmacology, and neuroscience. His research also focuses on the role of the amygdala in the extinction of fear memory in rodents, and the development of a novel opioid delta receptor agonist for antidepressants/anxiolytics. Dr. Saitoh has contributed to more than 100 research publications and is the first author of this study.

Funding information

This study was partially supported by the Tsukuba Clinical Research and Development Organization (T-CReDO) from the Japan Agency for Medical Research and Development (AMED).