Tag: Japan

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.”

***

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.

***
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).

Fearful events negatively impact the brain. For instance, war veterans often go through post-traumatic stress disorder months after the cessation of the triggering event. Now, in a study led by Tokyo University of Science researchers, the precise mechanism of suppression of such fearful memories has been uncovered. Using a mouse model, the researchers identified the associated biochemical pathways, thus paving the way for the development and clinical evaluation of therapeutic compounds such as KNT-127.

Tragic events like wars, famines, earthquakes, and accidents create fearful memories in our brain. These memories continue to haunt us even after the actual event has passed. Luckily, researchers from Tokyo University of Science (TUS) have recently been able to understand the hidden biochemical mechanisms involved in the selective suppression of fearful memories, which is called fear extinction. The researchers, who had previously demonstrated fear extinction in mice using the chemically synthesized compound “KNT-127,” have now identified the underlying mechanism of this compound’s action. Their findings have been published recently in Frontiers in Behavioral Neuroscience.

Prof. Akiyoshi Saitoh, lead author of the study, and Professor at TUS, muses, “Drugs that treat fear-related diseases like anxiety and posttraumatic stress disorder must be able to help extinguish fear. We previously reported that KNT-127, a selective agonist of the d-opioid receptor or DOP, facilitates contextual fear extinction in mice. However, its site of action in the brain and the underlying molecular mechanism remained elusive. We therefore investigated brain regions and cellular signaling pathways that we assumed would mediate the action of KNT-127 on fear extinction.”

“We investigated the molecular mechanism of KNT-127-mediated suppression of fearful memories. We administered KNT-127 to specific brain regions and identified the brain regions involved in promoting fear extinction via delta receptor activation,” elaborates Dr. Daisuke Yamada, co-author of the study, and Assistant Professor at TUS.

Using a mouse model, the research team performed fear conditioning test on laboratory mice. During fear conditioning, mice learn to associate a particular neutral conditioned stimulus with an aversive unconditioned stimulus (e.g., a mild electrical shock to the foot) and show a conditioned fear response (e.g., freezing).

After the initial fear conditioning, the mice were re-exposed to the conditioning chamber for six minutes as part of the extinction training. Meanwhile, the fear-suppressing therapeutic “KNT-127” was microinjected into various regions of the brain, 30 minutes prior to re-exposure. The treated brain regions included the basolateral nucleus of the amygdala (BLA), the hippocampus (HPC), and the prelimbic (PL) or infralimbic subregions (IL) of the medial prefrontal cortex. The following day, the treated mice were re-exposed to the chamber for six minutes for memory testing. The fear-suppressing “KNT-127” that infused into the BLA and IL, but not HPC or PL, significantly reduced the freezing response during re-exposure. Such an effect was not observed in mice that did not receive the KNT-127 treatment, thus confirming the fear-suppressing potential of this novel compound.

Chemical compounds known to inhibit the actions of key intracellular signaling pathways like PI3K/Akt and MEK/ERK pathways reversed the therapeutic effect, thereby suggesting the key roles of these two pathways in influencing KNT-127-mediated fear extinction.

The first author of the study, Ayako Kawaminami, who is currently pursuing research at TUS, says, “The selective DOP antagonist that we used for pretreatment antagonized the effect of KNT-127 administered into the BLA and IL. Further, local administration of MEK/ERK inhibitor into the BLA and of PI3K/Akt inhibitor into the IL abolished the effect of KNT-127. These findings strongly indicated that the effect of KNT-127 is mediated by MEK/ERK signaling in the BLA, by PI3K/Akt signaling in the IL, and by DOPs in both brain regions. We have managed to show that DOPs play a role in fear extinction via distinct signaling pathways in the BLA and IL.”

PTSD and phobias are thought to be caused by the inappropriate or inadequate control of fear memories. Currently, serotonin reuptake inhibitors and benzodiazepines are prescribed during therapy. However, many patients do not derive significant therapeutic benefits from these drugs. Therefore, there is an urgent need for the development of new therapeutic agents that have a different mechanism of action from existing drugs.

Dr. Hiroshi Nagase, a Professor at University of Tsukuba and a coauthor of the study, concludes, “We have succeeded in creating KNT-127 by successfully separating convulsion- and catalepsy-inducing actions, which has so far been extremely difficult. Our findings will provide useful and important information for the development of evidence-based therapeutics with a new mechanism of action, that is targeting DOP.”

Fighting fear with the right therapeutic is the need of the hour, as anxiety and stress increase globally, and the findings of this study could help us achieve this objective. We have our fingers crossed.

***

Reference

Title of original paper: Selective δ-Opioid Receptor Agonist, KNT-127, Facilitates Contextual Fear Extinction via Infralimbic Cortex and Amygdala in Mice

Journal: Frontiers in Behavioral Neuroscience

DOI: https://doi.org/10.3389/fnbeh.2022.808232

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 Akiyoshi Saitoh from Tokyo University of Science

Dr. Akiyoshi Saitoh is serving as a Professor in the Department of Pharmacy, at the Tokyo University of Science, Japan. His research work primarily focuses on the role of the amygdala in the rodent fear extinction memory as well as on the development of novel opioid delta receptor agonists for combating depression and anxiety. Prof. Saitoh has published over 100 refereed papers so far. He also has a patent to his credit.

Certain types of bacteria are unable to survive and thrive outside host organisms. This makes their isolation and identification technically challenging. Recently, a researcher from Tokyo University of Science successfully isolated a new bacterial strain of the candidate bacterial group, Candidatus phylum Dependentiae, from a pond in the Noda campus of the university. This study marks the first time such a novel strain has been isolated from a Japanese environment.

The development of the field of metagenomics—the study of genetic material from environmental samples—has revolutionized how we observe and discover new species. Many bacteria cannot be independently cultivated in the lab. Sometimes this is because the medium they are grown in is not suitable, sometimes it is because these bacteria thrive only

in multispecies communities (such as many bacteria in our gut!) and sometimes this is because they can only grow in relation to another larger organism. A group of bacteria belonging to the final category are Candidatus phylum Dependentiae. Not much is known about this group because thus far, only three strains belonging to it have been isolated. But in a recent study, published in Microbiology Resource Announcements, Professor Masaharu Takemura from Tokyo University of Science (TUS) has succeeded in isolating the fourth such strain—Noda2021.

“Initially we sampled Risoukai Park in the Noda Campus of TUS with the aim of isolating a giant virus by screening it using a common laboratory host ‘Vermamoeba vermiformis.’ However, in the process of doing so we accidentally discovered this rare bacterium that also infects Vermamoeba,” says Dr. Takemura.

To isolate the new strain, Dr. Takemura first cultured a sample obtained from the pond in Risoukai Park and then added it to a culture of Vermamoeba. After growing the Vermamoeba for a few days, he extracted Noda2021 from this and then performed an analysis of its genetic material.

“We found that the Noda2021 strain consists of 1,222,284 base pairs with approximately 38.3% guanine and cytosine (GC) content and 1,287 genes. We then performed a 16S rRNA molecular phylogenetic analysis of the strain and found that it is relatively close to one of the other Candidatus phylum Dependentiae strains isolated so far, ‘Vermiphilus pyriformis,’” explains Dr. Takemura. He also examined the infected Vermamoeba cells under an electron microscope and found that Noda2021 sometimes exhibited a connected cellular structure within its host cells.

“This discovery is evidence that the pond in the Noda campus is microbiologically diverse and ecologically exciting,” says Dr. Takemura. This is also the first time such a strain has been isolated in Japan.

The isolation of this new strain of Candidatus phylum Dependentiae is sure to further our understanding of this curious bacterial group. According to Dr. Takemura, “This bacterium is located in the border region between giant viruses and microbacteria, so we expect it to provide some useful and unique information on the origin and ecological position of both these groups.”

Indeed, Tokyo University of Science’s Noda campus seems to have plenty of hidden treasures for budding microbiologists. We for one, cannot wait for the next discovery— accidental or otherwise!

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Reference

DOI: https://doi.org/10.1128/mra.01123-21

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 Masaharu Takemura from Tokyo University of Science

Dr. Masaharu Takemura is a Professor at the Tokyo University of Science. His research interests include giant virus biology, evolutionary cell biology, and the origin of the eukaryotic nucleus. Dr. Takemura also has a deep interest in biology education. He completed his Ph.D. from Nagoya University in 1998. He has published over 100 papers in internationally renowned journals thus far.

Though bicycle sharing systems (BSSs) are popular in many big cities, it is necessary to actively rebalance the number of bicycles across the various ports with optimization algorithms. In a recent study, Tokyo University of Science researchers statistically analyzed the bicycle usage patterns in four real-world BSSs to obtain realistic benchmarks for testing these algorithms. Their findings can make BSS rebalancing more efficient through an understanding of the social dynamics of human movement.

Bicycle sharing systems (BSSs) are a popular transport system in many of the world’s big cities. Not only do BSSs provide a convenient and eco-friendly mode of travel, they also help reduce traffic congestion. Moreover, bicycles can be rented at one port and returned at a different port. Despite these advantages, however, BSSs cannot rely solely on its users to maintain the availability of bicycles at all ports at all times. This is because many bicycle trips only go in one direction, causing excess bicycles at some ports and a lack of bicycles in others.

This problem is generally solved by rebalancing, which involves strategically dispatching special trucks to relocate excess bicycles to other ports, where they are needed. Efficient rebalancing, however, is an optimization problem of its own, and Professor Tohru Ikeguchi and his colleagues from Tokyo University of Science, Japan, have devoted much work to finding optimal rebalancing strategies. In a study from 2021, they proposed a method for optimally rebalancing tours in a relatively short time. However, the researchers only checked the performance of their algorithm using randomly generated ports as benchmarks, which may not reflect the conditions of BSS ports in the real world.

Addressing this issue, Prof. Ikeguchi has recently led another study, together with PhD student Ms. Honami Tsushima, to find more realistic benchmarks. In their paper published in Nonlinear Theory and Its Applications, IEICE, the researchers sought to create these benchmarks by statistically analyzing the actual usage history of rented and returned bicycles in real BSSs. “Bike sharing systems could become the preferred public transport system globally in the future. It is, therefore, an important issue to address in our societies,” Prof. Ikeguchi explains.

The researchers used publicly available data from four real BSSs located in four major cities in USA: Boston, Washington DC, New York City, and Chicago. Save for Boston, these cities have over 560 ports each, for a total number of bicycles in the thousands.

First, a preliminary analysis revealed that an excess and lack of bicycles occurred across all four BSSs during all months of the year, verifying the need for active rebalancing. Next, the team sought to understand the temporal patterns of rented and returned bicycles, for which they treated the logged rent and return events as “point processes.”

The researchers independently analyzed both point processes using three approaches, namely raster plots, coefficient of variation, and local variation. Raster plots helped them find periodic usage patterns, while coefficient of variation and local variation allowed them to measure the global and local variabilities, respectively, of the random intervals between consecutive bicycle rent or return events.

The analyses of raster plots yielded useful insights about how the four BSSs were used in their respective cities. Most bicycles were used during daytime and fewer overnight, producing a periodic pattern. Interestingly, from the analyses of the local variation, the team found that usage patterns were similar between weekdays and weekends, contradicting the results of previous studies. Finally, the results indicated that the statistical characteristics of the temporal patterns of rented and returned bikes followed a Poisson process—a widely studied random distribution—only in New York City. This was an important find, given the original objective of the research team. “We can now create realistic benchmark instances whose temporal patterns of rented and returned bicycles follow the Poisson process. This, in turn, can help improve the bicycle rebalancing model we proposed in our earlier work,” explains Prof. Ikeguchi.

Overall, this study paves the way to a deeper understanding of how people use BSSs. Moreover, through further detailed analyses, it should be possible to gain insight into more complex aspects of human life, as Prof. Ikeguchi remarks: “We believe that the analysis of BSS data will lead not only to efficient bike sharing but also to a better understanding of the social dynamics of human movement.”

In any case, making BSSs a more efficient and attractive option will, hopefully, make a larger percentage of people choose the bicycle as their preferred means of transportation.

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Reference

Title of original paper: Statistical analysis of usage history of bicycle sharing systems

Journal: Nonlinear Theory and Its Applications, IEICE

DOI: https://doi.org/10.1587/nolta.13.355

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.

Funding information

This study was partially supported by JSPS KAKENHI Grant Numbers JP20H000596 and JP21H03514.

Organic molecular interfaces with minimized structural mismatch and spontaneous electron transfer could open doors to high-efficiency optoelectronics

Organic semiconductors have garnered much attention in optoelectronics owing to their flexibility, which is allowed by weak interaction forces. However, this also makes for poor charge carrier mobility. In a new study, researchers from Japan combined organic semiconductor molecules with similar structures to produce interfaces with better crystal quality and charge transport efficiency, paving the way for the realization of high-mobility organic optoelectronics.

Semiconductor electronic devices can be made of either inorganic crystals, formed by the strong bonding of atoms and ions, or organic crystals, which demonstrate weaker bonds held together by van der Waals forces (weak electric forces of attraction between neutral atoms or molecules that do not share a chemical bond). These weak bonds make organic semiconductors viable for flexible optoelectronics applications such as wearable electronic devices and flexible solar cells. However, this very characteristic also lends them a disadvantage: organic semiconductors typically exhibit poor charge carrier mobility and, therefore, do not conduct electricity well.

It is well-known that single-crystalline semiconductors can conduct electricity much better compared to their non-crystalline forms. Moreover, crystals composed of organic molecules can be grown to have interfaces with little structural mismatch even when their structures are quite different. Is there a way to leverage these properties to improve the charge transport in organic semiconductors?

This is where researchers from Tokyo University of Science, Japan decided to step in. In a new study led by Associate Professor Yasuo Nakayama, the researchers attempted to enhance the charge transport efficiency by minimizing the crystal structure mismatch between the growing crystal layer and the substrate. “I wanted to confirm whether the quality of the crystals at the interface would be better if we combined materials with similar structures so that we could create a crystalline interface even with inorganic materials,” says Dr. Nakayama, speaking of his personal motivation for the research. The paper was made available online on 18 November 2021, and published in Volume 12, Issue 46 of The Journal of Physical Chemistry Letters on 25 November 2021.

The team designed a high-quality crystalline interface using a technique called “quasi-homo-epitaxial growth” to grow bis(trifluoromethyl)dimethylrubrene on a single crystal surface of rubrene. They used surface X-ray diffraction measurements to characterize the interface and demonstrated its high crystallinity resulting from minimized structure mismatch. This eliminated the mobility issue. Additionally, they probed its electronic structure using ultraviolet photoelectron spectroscopy, which revealed an abrupt step in the electronic energy levels across the interface. This allowed for spontaneous electron transfer across the interface, validating their strategy.

With these results, the team is now excited about the potential applications their findings could entail. “Our work could potentially open up an untested route for the realization of high-mobility organic semiconductor optoelectronics. Additionally, since organic semiconductors can be made into thin and light crystals, it is possible to print semiconductor devices on transparent films and fabrics for carrying and wearing,” speculates Dr. Nakayama. “Furthermore, it could also lead to highly efficient flexible solar cells with better performance than those of existing technologies.”

Those certainly are some fascinating consequences to look forward to!

 

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Reference

Title of original paper: Quasi-Homoepitaxial Junction of Organic Semiconductors: A Structurally Seamless but Electronically Abrupt Interface between Rubrene and Bis(trifluoromethyl)dimethylrubrene

Journal: The Journal of Physical Chemistry Letters

DOI: https://doi.org/10.1021/acs.jpclett.1c03094