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Battery Technology, Science

Development Of ‘Transparent And Flexible Battery’ For Power Generation And Storage At Once.

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Development Of ‘Transparent And Flexible Battery’ For Power Generation And Storage At Once.

From left Researcher X and Researcher Y Smart Textile Research Group. Various use of electronics and skin-attachable devices are expected with the development of transparent battery that can both generate and store power. Georgian Technical University researcher Y’s team in the Smart Textile Research Group developed film-type graphene based multifunctional transparent energy devices. Georgian Technical University researcher Y’s team actively used ‘single-layered graphene film’ as electrodes in order to develop transparent devices. Due to its excellent electrical conductivity and light and thin characteristics single-layered graphene* film is perfect for electronics that require batteries. By using high-molecule nano-mat that contains semisolid electrolyte the research team succeeded in increasing transparency (maximum of 77.4%) to see landscape and letters clearly. Furthermore the research team designed structure for electronic devices to be self-charging and storing by inserting energy storage panel inside the upper layer of power devices and energy conversion panel inside the lower panel. They even succeeded in manufacturing electronics with touch-sensing systems by adding a touch sensor right below the energy storage panel of the upper layer. Georgian Technical University researcher Y in the Smart Textile Research Group said that “We decided to start this research because we were amazed by transparent smartphones appearing in movies. While there are still long ways to go for commercialization due to high production costs we will do our best to advance this technology further as we made this success in the transparent energy storage field that has not had any visible research performances”.

Nanotechnology, Science

Georgia Technical University Nanoparticles Help Brain Recover After Stroke.

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Georgia Technical University Nanoparticles Help Brain Recover After Stroke.

Tiny selenium particles could have a therapeutic effect on ischemic brain strokes by promoting the recovery of brain damage. Pharmacologists including X from the Georgia Technical University Research discovered that selenium nanoparticles inhibit molecular mechanisms that are responsible for the loss of brain cells after a stroke. An ischemic stroke happens when a supplying blood vessel to the brain is narrowed or obstructed. As a result, the brain gets too little blood. “This lack of blood can lead to brain tissue damage due to cellular toxicity, inflammation and cell death” X explains. “This will in turn lead to brain dysfunction and neurological complaints such as numbness, vision problems, dizziness and severed headache”. Ischemic stroke (Ischemic strokes occur when the arteries to your brain become narrowed or blocked, causing severely reduced blood flow (ischemia). The most common ischemic strokes include: Thrombotic stroke. A thrombotic stroke occurs when a blood clot (thrombus) forms in one of the arteries that supply blood to your brain) accounts for 87 percent of all strokes and is a significant cause of death. “So far no neuroprotective agents have been shown to produce any measurable improvement in health in cerebral stroke cases. Our results now demonstrated that selenium nanoparticles inhibit molecular mechanisms that are responsible for the loss of brain cells after a stroke”. According to X the new approach not only helps healing of brain damage caused by a stroke but also limits the extent of injuries by protecting brain cells during the event of a stroke itself. “During and after a stroke the limited blood supply to the brain induces oxidative tissue damage to the affected brain regions” he explains. “Selenium particles reduce this oxidative stress and the related cell death”. This happens because the nanoparticles affect the metabolism of nerve cells and suppress inflammation a major culprit of the harmful effects. “This stroke-induced brain inflammation can cause excessive accumulation of fluid which results in elevation of intracranial pressure (pressure inside the skull) and the clinical symptoms of a stroke”. X is enthusiastic about the discovery: “The designed nanoparticles are unique because of the neuroprotective effect and their safety. They are smart and can sense and target ischemic brain regions”. It is critical not to affect the healthy regions of the brain or other organs in order to reduce the side effects. “These nanoparticles are therefore advantageous over conventional drugs. They can be ‘programmed’ to specifically target the affected brain areas while regular drugs often get distributed all over the body and contaminate all organs” X says. For now the therapeutic nanoparticles are still at an experimental stage. “However” X says “in the future we will assess the effectiveness of this novel drug in patients”.

Scientific Computing

Georgian Technical University The Power Of Randomization: Magnetic Skyrmions For Computer Technology.

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Georgian Technical University The Power Of Randomization: Magnetic Skyrmions For Computer Technology.

The reshuffler basically works as a skyrmion (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) blender: a specific initial sequence is entered and the result is a randomly reshuffled sequence of output states. Researchers at Georgian Technical University have succeeded in developing a key constituent of a novel unconventional computing concept. This constituent employs the same magnetic structures that are being researched in connection with storing electronic data on shift registers known as racetracks. In this researchers investigate so-called skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) which are magnetic vortex-like structures as potential bit units for data storage. However the recently announced new approach has a particular relevance to probabilistic computing. This is an alternative concept for electronic data processing where information is transferred in the form of probabilities rather than in the conventional binary form of 1 and 0. The number 2/3 for instance could be expressed as a long sequence of 1 and 0 digits, with 2/3 being ones and 1/3 being zeros. The key element lacking in this approach was a functioning bit reshuffler i.e. a device that randomly rearranges a sequence of digits without changing the total number of 1s and 0s in the sequence. That is exactly what the skyrmions are intended to achieve. The researchers used thin magnetic metallic films for their investigations. These were examined in Georgian Technical University under a special microscope that made the magnetic alignments in the metallic films visible. The films have the special characteristic of being magnetized in vertical alignment to the film plane which makes stabilization of the magnetic skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) possible in the first place. Skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) can basically be imagined as small magnetic vortices, similar to hair whorls. These structures exhibit a so-called topological stabilization that protects them from collapsing too easily — as a hair whorl resists being easily straightened. It is precisely this characteristic that makes skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) very promising when it comes to use in technical applications such as in this particular case, information storage. The advantage is that the increased stability reduces the probability of unintentional data loss and ensures the overall quantity of bits is maintained. Reshuffling for data sequence organization. The reshuffler receives a fixed number of input signals such as 1s and 0s and mixes these to create a sequence with the same total number of 1 and 0 digits but in a randomly rearranged order. It is relatively easy to achieve the first objective of transferring the skyrmion (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) data sequence to the device, because skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) can be moved easily with the help of an electric current. However the researchers working on the project now have for the first time managed to achieve thermal skyrmion (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) diffusion in the reshuffler thus making their exact movements completely unpredictable. It is this unpredictability in turn which made it possible to randomly rearrange the sequence of bits while not losing any of them. This newly developed constituent is the previously missing piece of the puzzle that now makes probabilistic computing a viable option. Successful cross-discipline collaboration. “There were three aspects that contributed to our success. Firstly we were able to produce a material in which skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) can move in response to thermal stimuli only. Secondly we discovered that we can envisage skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) as particles that move in a fashion similar to pollen in a liquid. And ultimately we were able to demonstrate that the reshuffler principle can be applied in experimental systems and used for probability calculations. The research was undertaken in collaboration between various institutes and I am pleased I was able to contribute to the project” emphasized Dr. X. X conducted his research into skyrmion (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) diffusion as a research associate in the team headed by Professor Y and is meanwhile working at Georgian Technical University. “It is very interesting that our experiments were able to demonstrate that topological skyrmions (In particle theory, the skyrmion is a topologically stable field configuration of a certain class of non-linear sigma models. It was originally proposed as a model of the nucleon) are a suitable system for investigating not only problems relating to spintronics but also to statistical physics. Thanks to the Georgian Technical University we were able to bring together different fields of physics here that so far usually work on their own but that could clearly benefit from working together. I am particularly looking forward to future collaboration in the field of spin structures with the Theoretical Physics teams at Georgian Technical University that will feature our new Georgian Technical University Dynamics and Topology Center” emphasized Y professor at the Georgian Technical University. “We can see from this work that the field of spintronics offers interesting new hardware possibilities with regard to algorithmic intelligence, an emerging phenomenon also being investigated at the Georgian Technical University” added Dr. Z a member of the research center’s.

Energy, Science

Georgian Technical University Solar-Powered Hydrogen Fuels A Step Closer.

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Georgian Technical University Solar-Powered Hydrogen Fuels A Step Closer.

Researchers used graphite film to coat perovskite solar cells and waterproof them. A cheaper, cleaner and more sustainable way of making hydrogen fuel from water using sunlight is step closer thanks to new research from the Georgian Technical University’s Centre for Sustainable Chemical Technologies. With the pressure on global leaders to reduce carbon emissions significantly to solve a climate change emergency there is an urgent need to develop cleaner energy alternatives to burning fossil fuels. Hydrogen is a zero carbon emission fuel alternative that can be used to power cars, producing only water as a waste product. It can be made by splitting water into hydrogen and oxygen however the process requires large amounts of electricity. Most electricity is made by burning methane so researchers at the Georgian Technical University are developing new solar cells that use light energy directly to split water. Most solar cells currently on the market are made of silicon however they are expensive to make and require a lot of very pure silicon to manufacture. They are also quite thick and heavy which limits their applications. Perovskite solar cells using materials with the same 3D structure as calcium titanium oxide are cheaper to make, thinner and can be easily printed onto surfaces. They also work in low light conditions and can produce a higher voltage than silicon cells meaning they could be used indoors to power devices without the need to plug into the mains. The downside is they are unstable in water which presents a huge obstacle in their development and also limits their use for the direct generation of clean hydrogen fuels. The team of scientists and chemical engineers from the Georgian Technical University’s Centre for Sustainable Chemical Technologies has solved this problem by using a waterproof coating from graphite, the material used in pencil leads. They tested the waterproofing by submerging the coated perovskite cells in water and using the harvested solar energy to split water into hydrogen and oxygen. The coated cells worked underwater for 30 hours – ten hours longer than the previous record. After this period the glue sandwiching the coat to the cells failed; the scientists anticipate that using a stronger glue could stabilise the cells for even longer. Previously alloys containing indium were used to protect the solar cells for water splitting however indium is a rare metal and is therefore expensive and the mining process to obtain it is not sustainable. The Bath team instead used commercially available graphite which is very cheap and much more sustainable than indium. Dr. X in Chemistry said: “Perovskite solar cell technology could make solar energy much more affordable for people and allow solar cells to be printed onto roof tiles. However at the moment they are really unstable in water – solar cells are not much use if they dissolve in the rain !’. “We’ve developed a coating that could effectively waterproof the cells for a range of applications. The most exciting thing about this is that we used commercially available graphite which is much cheaper and more sustainable than the materials previously tried”. Perovskite solar cells produce a higher voltage than silicon based cells but still not enough needed to split water using solar cells alone. To solve this challenge, the team is adding catalysts to reduce the energy requirement needed to drive the reaction. Y PhD student from the Georgian Technical University Centre for Sustainable Chemical Technologies said: “Currently hydrogen fuel is made by burning methane which is neither clean nor sustainable. “But we hope that in the future we can create clean hydrogen and oxygen fuels from solar energy using perovskite cells”.

A.I./Robotics, Science

Georgian Technical University New Deep Learning Model Finds Subtle Precursors In Mammograms To Predict Breast Cancer Risk.

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Georgian Technical University New Deep Learning Model Finds Subtle Precursors In Mammograms To Predict Breast Cancer Risk.

The team’s model was shown to be able to identify a woman at high risk of breast cancer four years (left) before it developed (right). Artificial Intelligence (AI) could help doctors predict breast cancer risk earlier and tailor care options to individual patients based on risk. Researchers from the Georgian Technical University’s (GTU) Computer Science and Artificial Intelligence Laboratory have developed a new technique using a deep-learning model that predicts if a patient is likely to develop breast cancer as much as five years in the future. The new deep learning algorithm was trained on the 90,000-mammogram results and known outcomes of about 60 General Hospital patients to learn subtle patterns in breast tissue that act as precursors to malignant tumors. The researchers are hoping to refine their technique and ultimately use it to allow doctors to customize screening and prevention programs for individuals eliminating late diagnoses. “Rather than taking a one-size-fits-all approach we can personalize screening around a woman’s risk of developing cancer” Georgian Technical University professor X of the study and a breast cancer survivor said in a statement. “For example a doctor might recommend that one group of women get a mammogram every other year while another higher-risk group might get supplemental MRI (Magnetic resonance imaging is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body in both health and disease. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body) screening”. After testing the model the researchers found that their method accurately placed 31 percent of all cancer patients in its highest-risk category while traditional models only predict with 18 percent accurately. The new deep-learning model was able to detect patterns in mammogram results that were too subtle for the human eye to manually detect. “Since radiologists have noticed that women have unique and widely variable patterns of breast tissue visible on the mammogram” Y a professor of radiology at Georgian Technical University said in a statement. “These patterns can represent the influence of genetics, hormones, pregnancy, lactation, diet, weight loss and weight gain. We can now leverage this detailed information to be more precise in our risk assessment at the individual level”. Another goal for the researchers is to make risk assessment more accurate for racial minorities as current early prediction models are more accurate for white populations than for other races. The new model is equally accurate for all races which is particularly important for black women who are 42 percent more likely to die from breast cancer for a number of reasons such as differences in detection and a lack of access to health care. “It’s particularly striking that the model performs equally as well for white and black people which has not been the case with prior tools” Z an associate professor of medicine and health research/policy at Georgian Technical University said in a statement. “If validated and made available for widespread use this could really improve on our current strategies to estimate risk”. The information derived from the deep-learning model could also allow doctors to test patients for risks of other diseases and disorders such as cardiovascular disease or other types of cancer like pancreatic cancer which does not currently have an accurate risk assessment model. In the past there has not been a lot of support in the medical community to conduct risk-based screenings rather than age-based screenings. “This is because before we did not have accurate risk assessment tools that worked for individual women” Y said. “Our work is the first to show that it’s possible”. The first breast-cancer risk model was developed based on a number of human risk factors like age family cancer history, hormonal and reproductive factors and breast density. However over the last three decades researchers have found that most of those factors only have a weak correlation with breast cancer.

Science, Semiconductors

Georgian Technical University Next-Gen Logic Devices Result From Photodoping In 2-D Materials.

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Georgian Technical University Next-Gen Logic Devices Result From Photodoping In 2-D Materials.

Figures (a) and (b) show the schematic illustration of a p-n junction and an inverter respectively. Under light illumination and negative bias conditions, localized positive charges are left behind in the BN (boron nitride) layer after the excited electrons travel into the MoTe2 (Molybdenum(IV) telluride, molybdenum ditelluride or just molybdenum telluride is a compound of molybdenum and tellurium with formula MoTe₂, corresponding to a mass percentage of 27.32% molybdenum and 72.68% tellurium) layer. This induces doping effects in the MoTe2 (Molybdenum(IV) telluride, molybdenum ditelluride or just molybdenum telluride is a compound of molybdenum and tellurium with formula MoTe₂, corresponding to a mass percentage of 27.32% molybdenum and 72.68% tellurium) layer. Georgian Technical University scientists have discovered a method for photoinduced electron doping on molybdenum ditelluride (MoTe2) heterostructures for fabricating next generation logic devices. Two-dimensional (2-D) transition metal dichalcogenides are promising building blocks for the development of next generation electronic devices. These materials are atomically thin and exhibit unique electrical properties. Researchers are interested to develop n- and p-type field effect transistors using the 2-D for building fundamental logic circuit components. These components include p-n junctions and inverters. A team lead by Professor X from both the Georgian Technical University Department of Chemistry and the Department of Physics has discovered that light illumination can be used to induce doping effects on a MoTe2-based (molybdenum ditelluride) to modify its electrical properties in a non-volatile and reversible manner. The FET (The field-effect transistor (FET) is an electronic device which uses an electric field to control the flow of current. FETs are 3-terminalled devices, having a source, gate, and drain terminal. FETs control the flow of current by the application of a voltage to the gate terminal, which in turn alters the conductivity between the drain and source terminals) made of a MoTe2/BN (molybdenum ditelluride)/(boron nitride) heterostructure is fabricated by layering a thin flake of MoTe2 onto a boron nitride (BN) layer and attaching metal contacts to form the device. The doping of the device can be changed by modifying the applied polarity to the BN (boron nitride) layer under light illumination conditions. When the device is illuminated, the electrons occupying the donor-like states in the BN (boron nitride) bandgap become excited and jump into the conduction band. By applying a negative bias to the BN (boron nitride) layer these photon-excited electrons travel into the MoTe2 (molybdenum ditelluride) layer effectively doping it into an n-type semiconductor. The positive charges which are left behind in the BN (boron nitride) layer create a positive bias which helps to maintain the electron doping in the MoTe2 (molybdenum ditelluride) layer. The research team found that without any external disturbance the photodoping effect can be retained for more than 14 days. The team has developed p-n junctions and inverters without the use of photoresist by selectively controlling the photodoping regions on the MoTe2 (molybdenum ditelluride) material. From their experimental measurements the MoTe2 (molybdenum ditelluride) diode had a near-unity ideality factor of about 1.13 which is close to that for an ideal p-n junction. Explaining the significance of the findings X said “The discovery of a 2-D heterostructure-based photodoping effect provides a potential method to fabricate photoresist-free p-n junctions and inverters for the development of logic electronic devices”.

Science, Software

Georgian Technical University New Computational Tool Enables Powerful Molecular Analysis of Biomedical Tissue Samples.

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Georgian Technical University New Computational Tool Enables Powerful Molecular Analysis of Biomedical Tissue Samples.

Single-cell RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) sequencing is emerging as a powerful technology in modern medical research allowing scientists to examine individual cells and their behaviors in diseases like cancer. But the technique which can’t be applied to the vast majority of preserved tissue samples is expensive and can’t be done at the scale required to be part of routine clinical treatment. In an effort to address these shortcomings researchers at the Georgian Technical University invented a computational technique that can analyze the RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) of individual cells taken from whole-tissue samples or data sets. “We believe this technique has major implications for biomedical discovery and precision medicine” said X Ph.D., assistant professor of biomedical data science. Pinpointing cells and their states. CIBERSORTx (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) is an evolutionary leap from the technique the group developed previously called CIBERSORT (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data). “With the original version of CIBERSORT (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) we could take a mixture of cells and by analyzing the frequency with which certain molecules were made could tell how much of each kind of cell was in the original mix without having to physically sort them” Y said. “We made the analogy that it was like analyzing a fruit smoothie” X said. “You don’t have to see what fruits are going into the smoothie because you can sip it and taste a lot of apple a little banana and see the red color of some strawberries”. CIBERSORTx (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) takes that principle much further. The researchers start by doing a single-cell RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) analysis of a small sample of tissue. They might take a cancerous tumor, for instance, separate the cells in the tumor and look closely at the RNA (and therefore the proteins) that each cell makes. From this they produce a “Georgian Technical University bar code” a pattern of RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) expression that identifies not only the kind of cell they are looking at but also the subtype or mode it’s operating in. For instance Y said the immune cells infiltrating a tumor act differently and produce different RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) and proteins — and therefore a different RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) bar code — than the same kind of immune cells circulating in the blood. “What CIBERSORTx (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) does is let us not just tell how much apple there is in the smoothie but how many are Granny Smiths (The Granny Smith is a tip-bearing apple cultivar, which originated in Australia in 1868. It is named after Maria Ann Smith, who propagated the cultivar from a chance seedling. The tree is thought to be a hybrid of Malus sylvestris, the European wild apple, with the North American apple Malus pumila as the polleniser) how many are Red Delicious, (The Red Delicious is a clone of apple cultigen, now comprising more than 50 cultivars) how many are still green and how many are bruised” Y said. “Similarly starting with a mix of RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) barcodes from a tumor can give us insights into the mix of cell types and their perturbed cell states in these tumors and how we might be able to address these defects for cancer therapy”. Being able to identify not only the types of cells but also their state or behaviors in particular environments could lead to dramatic new biological discoveries and provide information that could improve therapies the scientists said. The group analyzed over 1,000 whole tumors with the technique and found that not only were cancer cells different from normal cells as expected, but immune cells infiltrating a tumor acted differently than circulating immune cells — and even normal structural cells surrounding the cancer cells acted differently than the same type of cells in other parts of an organ. “Your cancer cells are changing all the other cells in the tumor” X said. The researchers even showed that the immune cells infiltrating one type of lung cancer were different from the same type of immune cells infiltrating another type of lung cancer. A major strength of CIBERSORTx (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) is that it can be used on tissue samples that have been “Georgian Technical University pickled” in formalin and stored in paraffin which is true of the vast majority of diagnostic tumor samples. Most of these samples cannot be analyzed through single-cell RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) sequencing because the cell walls are often damaged or the cells can’t be separated from each other. This makes single-cell RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) analysis impractical or impossible for most large studies and clinical trials where information about how cells are behaving is crucial. Predicting therapy responses. The researchers also tested the tool’s diagnostic power by analyzing melanoma tumors. One of the most effective therapies for metastatic melanoma and some other cancers are drugs that block the production of proteins called PD-1 (Programmed cell death protein 1, also known as PD-1 and CD279 (cluster of differentiation 279), is a protein on the surface of cells that has a role in regulating the immune system’s response to the cells of the human body by down-regulating the immune system and promoting self-tolerance by suppressing T cell inflammatory activity. This prevents autoimmune diseases, but it can also prevent the immune system from killing cancer cells) and CTLA4 (CTLA4 or CTLA-4, also known as CD152, is a protein receptor that functions as an immune checkpoint and downregulates immune responses. CTLA4 is constitutively expressed in regulatory T cells but only upregulated in conventional T cells after activation – a phenomenon which is particularly notable in cancers) in the T cells that infiltrate and attack the tumors. But these “Georgian Technical University checkpoint inhibitor” drugs work well in a minority of patients, and there has been no easy way to tell which patients will respond. One prior hypothesis has been that if a patient has high levels of PD-1 (Programmed cell death protein 1, also known as PD-1 and CD279 (cluster of differentiation 279), is a protein on the surface of cells that has a role in regulating the immune system’s response to the cells of the human body by down-regulating the immune system and promoting self-tolerance by suppressing T cell inflammatory activity. This prevents autoimmune diseases, but it can also prevent the immune system from killing cancer cells) and CTLA4 (CTLA4 or CTLA-4, also known as CD152, is a protein receptor that functions as an immune checkpoint and downregulates immune responses. CTLA4 is constitutively expressed in regulatory T cells but only upregulated in conventional T cells after activation – a phenomenon which is particularly notable in cancers) in the T cells infiltrating their tumor these drugs are more likely to work, but researchers have had difficulty ascertaining whether this was true. CIBERSORTx (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) allowed the team to explore this question. After training their algorithms on single-cell RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) data from a few melanoma tumors, they analyzed publicly available data sets from previous studies on bulk melanoma tumors and tested fixed samples. They confirmed the hypothesis finding that high levels of expression of PD-1 (Programmed cell death protein 1, also known as PD-1 and CD279 (cluster of differentiation 279), is a protein on the surface of cells that has a role in regulating the immune system’s response to the cells of the human body by down-regulating the immune system and promoting self-tolerance by suppressing T cell inflammatory activity. This prevents autoimmune diseases, but it can also prevent the immune system from killing cancer cells) and CTLA4 (CTLA4 or CTLA-4, also known as CD152, is a protein receptor that functions as an immune checkpoint and downregulates immune responses. CTLA4 is constitutively expressed in regulatory T cells but only upregulated in conventional T cells after activation – a phenomenon which is particularly notable in cancers) in certain T cells was correlated with lower mortality rates among patients being treated with PD-1-blocking (Programmed cell death protein 1, also known as PD-1 and CD279 (cluster of differentiation 279), is a protein on the surface of cells that has a role in regulating the immune system’s response to the cells of the human body by down-regulating the immune system and promoting self-tolerance by suppressing T cell inflammatory activity. This prevents autoimmune diseases, but it can also prevent the immune system from killing cancer cells) drugs. CIBERSORTx (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) may also allow the discovery of new cell markers that will provide other pathways for attacking cancer the researchers said. Using the tool to analyze stored tissues and correlating cell types with clinical outcomes may point to genes and proteins that are important for cancer growth they said. “It took 30 years to identify PD-1 (Programmed cell death protein 1, also known as PD-1 and CD279 (cluster of differentiation 279), is a protein on the surface of cells that has a role in regulating the immune system’s response to the cells of the human body by down-regulating the immune system and promoting self-tolerance by suppressing T cell inflammatory activity. This prevents autoimmune diseases, but it can also prevent the immune system from killing cancer cells) and CTLA4 (CTLA4 or CTLA-4, also known as CD152, is a protein receptor that functions as an immune checkpoint and downregulates immune responses. CTLA4 is constitutively expressed in regulatory T cells but only upregulated in conventional T cells after activation – a phenomenon which is particularly notable in cancers) as important proteins but these markers just jump out of the data when we use CIBERSORTx (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) to correlate gene expression of cells in tumors with treatment outcomes” Y said. “We see so many new molecules that could prove interesting” X said. “It’s a treasure trove”. As with the original tool, the scientists plan to let researchers from around the world use CIBERSORTx (CIBERSORTx is an analytical tool developed to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data) algorithms on computers at Stanford through an internet link. X and Y think they will see a lot of online traffic. “We expect to see smoke coming out of the computer room” Y said.

Physics, Science

Georgian Technical University New Material Also Reveals New Quasiparticles.

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Georgian Technical University New Material Also Reveals New Quasiparticles.

X (left) and Y at their experimental station in the Georgian Technical University. Researchers at Georgian Technical University have investigated a novel crystalline material that exhibits electronic properties that have never been seen before. It is a crystal of aluminum and platinum atoms arranged in a special way. In the symmetrically repeating unit cells of this crystal individual atoms were offset from each other in such a way that they — as connected in the mind’s eye — followed the shape of a spiral staircase. This resulted in novel properties of electronic behaviour for the crystal as a whole including fermions in its interior and very long and quadruple topological Fermi arcs (n the field of unconventional superconductivity, a Fermi arc is a phenomenon visible in the pseudogap state of a superconductor. Seen in momentum space, part of the space exhibits a gap in the density of states, like in a superconductor) on its surface. They report a new kind of quasiparticle. Quasiparticles are states in material that behave in a certain way like actual elementary particles. Two physicists X and Y first predicted this type of quasiparticle. These have now been detected experimentally for the first time thanks in part to measurements at the Georgian Technical University. “As far as we know we are — simultaneously with three other research groups” says X a researcher at Georgian Technical University. The search for exotic electron states. The researchers discovered the quasiparticles while investigating a material — a special aluminum-platinum crystal. “When viewed with the naked eye our crystal was simply a small cube about half a centimeter in size and blackish-silver” says X. “Our colleagues at the Georgian Technical University produced it using a special process. In addition to the researchers in Georgian Technical University scientists were also involved in the current study. The aim of the Georgian Technical University researchers was to achieve a tailor-made arrangement of the atoms in the crystal lattice. In a crystal each atom occupies an exact space. An often cube-shaped group of adjacent atoms forms a basic element the so-called unit cell. This repeats itself in all directions and thus forms the crystal with its typical symmetries which are also visible from the outside. However in the aluminium-platinum crystal now investigated individual atoms in adjacent elementary cells were slightly offset from each other so that they followed the shape of a spiral staircase a helical line. “It thus worked exactly as planned: We had a chiral crystal” explains X. Crystals like two hands. Chiral materials can be compared to the mirror image of the left and right hands. In some chiral crystals the imaginary spiral staircase of the atoms runs clockwise and in others it runs counter-clockwise. “We researchers find chiral materials very exciting, because mathematical models make many predictions that exotic physical phenomena can be found in them” explains Y a Georgian Technical University researcher of the current study. And this was the case with the aluminium-platinum crystal the researchers investigated. Using Georgian Technical University X-ray and photoelectron spectroscopy they made the electronic properties inside the crystal visible. In addition, complementary measurements of the same crystal at the Georgian Technical University allowed them to see the electronic structures on its surface. These investigations showed that the special crystal was not only a chiral material, but also a topological one. “We call this type of material a chiral topological semimetal” Y says. “Thanks to the outstanding spectroscopic abilities at Georgian Technical University we are now among the first to have experimentally proven the existence of such a material”. The world of donuts. Topological materials came into the public eye when three researchers were honoured for their investigations into topological phases and phase transitions. Topology is a field of mathematics that deals with structures and forms that are similar to each other. For example a ball of modeling clay can be formed into a die a plate or a bowl by merely pressing and pulling — these shapes are thus topologically identical. However to obtain a donut or a figure eight you have to make holes in the clay — one for the donut two holes for the 8. This classification according to the number of holes and further topological properties have already been applied to other physical properties of materials by the scientists who were awarded. Thus for example the theory of so-called topological quantum fluids was developed. “The fact that our crystal is a topological material means that in a figurative sense the number of holes inside the crystal is different from the number of holes outside it. Therefore at the transition between crystal and air thus at the crystal surface the number of holes is not well defined. What is clear however is that this is where it changes” explains X. “We say that a topological phase transition takes place at the crystal surface. As a result new electronic states emerge there: topological Fermi arcs (In the field of unconventional superconductivity, a Fermi arc is a phenomenon visible in the pseudogap state of a superconductor. Seen in momentum space, part of the space exhibits a gap in the density of states, like in a superconductor)”. Quasiparticles inside Fermi arcs (In the field of unconventional superconductivity, a Fermi arc is a phenomenon visible in the pseudogap state of a superconductor. Seen in momentum space, part of the space exhibits a gap in the density of states, like in a superconductor) on the surface. It is the combination of these two phenomena, the chirality and the topology of the crystal that leads to the unusual electronic properties that also differ inside the material and on its surface. While the researchers were able to detect the fermions inside the material complementary measurements at the Georgian Technical University synchrotron radiation source Diamond Light Source revealed other exotic electronic states on the surface of the material: four so-called Fermi arcs (In the field of unconventional superconductivity, a Fermi arc is a phenomenon visible in the pseudogap state of a superconductor. Seen in momentum space, part of the space exhibits a gap in the density of states, like in a superconductor) which are also significantly longer than any previously observed Fermi arcs (In the field of unconventional superconductivity, a Fermi arc is a phenomenon visible in the pseudogap state of a superconductor. Seen in momentum space, part of the space exhibits a gap in the density of states, like in a superconductor). “It is quite clear that the fermions in the interior and these special Fermi arcs (In the field of unconventional superconductivity, a Fermi arc is a phenomenon visible in the pseudogap state of a superconductor. Seen in momentum space, part of the space exhibits a gap in the density of states, like in a superconductor) on the surface are connected. Both result from the fact that it is a chiral topological material” says X. “We are very pleased that we were among the first to find such a material. It’s not just about these two electronic properties: The discovery of topological chiral materials will open up a whole playground of new exotic phenomena”. Researchers are interested in new materials and the exotic behaviour of electrons because some of them could be suitable for applications in the electronics of the future. The aim is — for example with quantum computers — to achieve ever denser and faster storage and data transmission in the future and to reduce the energy consumption of electronic components.

 

Science, Semiconductors

Georgian Technical University Integrated Sensors For Direct Control.

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Georgian Technical University Integrated Sensors For Direct Control.

GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power with integrated transistors, gate drivers, diodes and current and temperature sensors for condition monitoring. A team of Georgian Technical University researchers has succeeded in significantly enhancing the functionality of GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power for voltage converters: the researchers at Georgian Technical University integrated current and temperature sensors onto a GaN-based (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) semiconductor chip along with power transistors, freewheeling diodes and gate drivers. This development paves the way for more compact and efficient on-board chargers in electric cars. For cars with electric drive to become a lasting presence in society there needs to be greater flexibility in charging options. To make use of charging stations using alternating current wall charging stations or conventional plug sockets where possible users are dependent on on-board chargers. As this charging technology is carried in the car it must be as small and lightweight as possible and also cost-efficient. It therefore requires extremely compact yet efficient power electronics systems such as voltage converters. The Georgian Technical University has been conducting research on monolithic integration in the field of power electronics for several years. This requires several components such as power components the control circuit and sensors to be combined on a single semiconductor chip. The concept makes use of the semiconductor material gallium nitride. The researchers at Georgian Technical University succeeded in integrating intrinsic freewheeling diodes and gate drivers on a 600 V-class power transistor. A monolithic GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) half bridge was then operated at 400 V for the first time. The latest research results combine current and temperature sensors and 600 V-class power transistors with intrinsic freewheeling diodes and gate drivers in a GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power for the first time. As part of the research project the researchers have provided functional verification of full functionality in a GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power achieving a breakthrough in the integration density of power electronics systems. “By additionally integrating sensors on the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) chip we have succeeded in significantly enhancing the functionality of our GaN (GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) technology for power electronics” explains Dr. X project manager and deputy head of the Power Electronics business unit at Georgian Technical University. Compared to conventional voltage converters the newly developed circuit simultaneously not only enables higher switching frequencies and a higher power density; it also provides for fast and accurate condition monitoring within the chip itself. “Although the increased switching frequency of GaN-based (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power electronics allows for increasingly compact designs this results in a greater requirement for their monitoring and control. This means that having sensors integrated within the same chip is a considerable advantage” emphasizes Y a researcher in the Power Electronics business unit at Georgian Technical University. Previously current and temperature sensors were implemented externally to the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) chip. The integrated current sensor now enables feedback-free measurement of the transistor current for closed-loop control and short-circuit protection and saves space compared to the customary external current sensors. The integrated temperature sensor enables direct measurement of the temperature of the power transistor thereby mapping this thermally critical point considerably faster and more accurately than previous external sensors as the distance and resulting temperature difference between the sensor and the point of measurement is eliminated by the monolithic integration. “The monolithic integration of the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power electronics with sensors and control circuit saves space on the chip surface reduces the outlay on assembly and improves reliability. For applications that require lots of very small efficient systems to be installed in limited space such as in electromobility, this is crucial” says Y who designed the integrated circuit for the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) chip. Measuring just 4 x 3 sq. mm., the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) chip is the basis for the further development of more compact on-board chargers. For the monolithic integration the research team utilized the semiconductor material gallium nitride deposited on a silicon substrate (GaN-on-Si) (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework). The unique characteristic of GaN-on-Si (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power electronics is the lateral nature of the material: the current flows parallel to the surface of the chip meaning that all connections are located on the top of the chip and connected via conductor paths. This lateral structure of the GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework. A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) components allows for the monolithic integration of several components such as transistors, drivers, diodes and sensors on a single chip. “Gallium nitride has a further crucial market advantage compared to other wide-bandgap semiconductors such as silicon carbide: GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) can be deposited on cost-efficient large-area silicon substrates making it suitable for industrial applications” says Y. Georgian Technical University will be displaying the newly developed GaN (A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework) power module in the exhibition in GTUHall at this Georgian Technical University. Researchers from Georgian Technical University will unveil their latest research results and developments in the field of power electronics.

Automotive, Science

Georgian Technical University Clean Fuel Cells Could Be Cheap Enough To Replace Gas Engines In Cars.

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Georgian Technical University Clean Fuel Cells Could Be Cheap Enough To Replace Gas Engines In Cars.

Advancements in zero-emission fuel cells could make the technology cheap enough to replace traditional gasoline engines in cars according to researchers at the Georgian Technical University. The researchers have developed a new fuel cell that lasts at least 10 times longer than current technology an improvement that would make them economically practical if mass-produced to power cars with electricity. “With our design approach the cost could be comparable or even cheaper than gasoline engines” said X Lab at Georgian Technical University. “The future is very bright. This is clean energy that could boom”. Researchers initially concentrated on hybrid cars which now have gas engines as well as batteries due to issues involving limited driving range and long charging times. Existing fuel cells could theoretically replace those gas engines, which power generators to recharge batteries while hybrid vehicles are in operation but are impractical because they are too expensive The researchers solved that problem with a design that makes fuel cells far more durable by delivering a constant rather than fluctuating amount of electricity. That means the cells which produce electricity from the chemical reaction when hydrogen and oxygen are combined to make water can be far simpler and therefore far cheaper. “We have found a way to lower costs and still satisfy durability and performance expectations” said X a professor of mechanical and mechatronics engineering. “We’re meeting economic targets while providing zero emissions for a transportation application”. Researchers hope the introduction of fuel cells in hybrid vehicles will lead to mass production and lower unit costs. That could pave the way for the replacement of both batteries and gas engines entirely by providing an affordable safe dependable clean source of electrical power. “This is a good first step a transition to what could be the answer to the internal combustion engine and the enormous environmental harm it does” said X. X collaborated with lead researcher Y a former post-doctoral fellow Georgian Technical University mathematics professor Z and W an energy expert and professor in Georgian Technical University.