The Scitech Journal is a multi disciplinary monthly publication in print and digital format dedicated to science, technology and innovation and aims to promote communication and interaction among, scientists, engineers and public. The mission is to spread awareness about scientific and technological advancements and achievements by publishing the latest scientific discoveries and technological innovations.
Our brains have a basic algorithm that enables us to not just recognize a traditional Thanksgiving meal, but the intelligence to ponder the broader implications of a bountiful harvest as well as good family and friends.
“A relatively simple mathematical logic underlies our complex brain computations,” said Dr. Joe Z. Tsien, neuroscientist at the Medical College of Georgia at Augusta University, co-director of the Augusta University Brain and Behavior Discovery Institute and Georgia Research Alliance Eminent Scholar in Cognitive and Systems Neurobiology.
Tsien is talking about his Theory of Connectivity, a fundamental principle for how our billions of neurons assemble and align not just to acquire knowledge, but to generalize and draw conclusions from it.
For emerging technologies to achieve their full potential to improve human life and address global challenges, action is needed to make sure their use is governed properly. This is the finding of research published by the World Economic Forum.
The research forms part of a survey of nearly 900 experts that is used to compile the Forum’s Global Risks report. When asked which emerging technologies need better governance, two technologies were clear outliers: artificial intelligence and robotics, followed by biotechnologies. The third technology most in need of governance is energy capture, storage and transmission.
Other technologies in the top 10 are blockchain and distributed ledger (4), which has been touted as having a game-changing effect on industries, from banking and financial services to agriculture. Following this is geo-engineering (5), which is often seen as a response to climate change but whose effectiveness and potential negative side effects remain largely unknown.
To “turn off” particular regions of genes or protect them from damage, DNA strands can wrap around small proteins, called histones, keeping out all but the most specialized molecular machinery. Now, new research shows how an enzyme called KDM4B “reads” one and “erases” another so-called epigenetic mark on a single histone protein during the generation of sex cells in mice. The researchers say the finding may one day shed light on some cases of infertility and cancer.
A summary of the work, a collaborative study among researchers at the Johns Hopkins University School of Medicine, Rice University and the University of Wisconsin–Madison, was published online in the journal Nature Communications on Nov. 14.
Wellcome Trust Sanger Institute and University of Cambridge researchers have created sOPTiKO, a more efficient and controllable CRISPR genome editing platform.In the journal Development, they describe how the freely available single-step system works in every cell in the body and at every stage of development. This new approach will aid researchers in developmental biology, tissue regeneration and cancer.
Two complementary methods were developed. sOPiTKO is a knock-out system that turns off genes by disrupting the DNA. sOPTiKD is a knock-down system that silences the action of genes by disrupting the RNA. Using these two methods, scientists can inducibly turn off or silence genes, in any cell type, at any stage of a cell’s development from stem cell to fully differentiated adult cell. These systems will allow researchers world wide to rapidly and accurately explore the changing role of genes as the cells develop into tissues such as liver, skin or heart, and discover how this contributes to health and disease.
MIT biologists have identified a new biomarker that can reveal whether patients with a particularly aggressive type of breast cancer will be helped by paclitaxel (commercially known as Taxol), one of the drugs most commonly used to treat this cancer.
The findings could offer doctors a new way to choose drugs for this type of breast cancer, known as triple-negative because it lacks the three most common breast cancer markers: estrogen receptor, progesterone receptor, and Her2 protein. The biomarker, a protein called Mena, has previously been shown to help cancer cells spread through the body.
The researchers also showed that combining paclitaxel with another drug that interferes with Mena’s effects can kill the cells much more effectively than paclitaxel alone.
“Drugs that target that pathway restore paclitaxel sensitivity to cells expressing Mena,” says Frank Gertler, an MIT professor of biology and a member of the Koch Institute for Integrative Cancer Research. “The study also suggests that during the course of treatment it might be worth monitoring the level of Mena. If the levels begin to increase, it might suggest that switching to another type of therapy could be useful.”
In 1959 renowned physicist Richard Feynman, in his talk “Plenty of Room at the Bottom,” spoke of a future in which tiny machines could perform huge feats. Like many forward-looking concepts, his molecule and atom-sized world remained for years in the realm of science fiction.
And then, scientists and other creative thinkers began to realize Feynman’s nanotechnological visions.
In the spirit of Feynman’s insight, and in response to the challenges he issued as a way to inspire scientific and engineering creativity, electrical and computer engineers at UC Santa Barbara have developed a design for a functional nanoscale computing device. The concept involves a dense, three-dimensional circuit operating on an unconventional type of logic that could, theoretically, be packed into a block no bigger than 50 nanometers on any side.
Computer chips in development at the University of Wisconsin–Madison could make future computers more efficient and powerful by combining tasks usually kept separate by design.
Jing Li, an assistant professor of electrical and computer engineering at UW–Madison, is creating computer chips that can be configured to perform complex calculations and store massive amounts of information within the same integrated unit — and communicate efficiently with other chips. She calls them “liquid silicon.”
“Liquid means software and silicon means hardware. It is a collaborative software/hardware technique,” says Li. “You can have a supercomputer in a box if you want. We want to target a lot of very interesting and data-intensive applications, including facial or voice recognition, natural language processing, and graph analytics.”
The high-speed number-crunching of processors and the data warehousing of big storage memory in modern computers usually fall to two entirely different types of hardware.
An international team of computer scientists has for the first time developed a method to find antibiotics hidden in huge but still unexplored mass spectrometry datasets. They detailed their new method, called DEREPLICATOR, in the Oct. 31 issue of Nature Chemical Biology.
Each year more than 2 million people develop antibiotic resistance in the United States, and researchers hope their work will help identify new antibiotics to effectively treat diseases.
“This is the first time that we are using Big Data to look into microbial chemistry and characterize antibiotics and other drug candidates,” said Hosein Mohimani, a computer scientist at the University of California San Diego and the paper’s first author. “Although proteomics researchers have been routinely using huge spectral datasets to find important peptides, all traditional proteomics tools fail when it comes to new drug discovery. “
The algorithms the researchers developed scour mass-spectrometry data to discover so-called peptidic natural products (PNPs)—widely used bioactive compounds that include many antibiotics.
Researchers from the Moscow Institute of Physics and Technology (MIPT), Technological Institute for Superhard and Novel Carbon Materials (TISNCM), Lomonosov Moscow State University (MSU), and the National University of Science and Technology MISiS have shown that an ultrastrong material can be produced by “fusing” multiwall carbon nanotubes together. The research findings have been published in Applied Physics Letters.
According to the scientists, a material of that kind is strong enough to endure very harsh conditions, making it useful for applications in the aerospace industry, among others.
The authors of the paper performed a series of experiments to study the effect of high pressure on multiwall carbon nanotubes (MWCNTs). In addition, they simulated nanotube behavior in high pressure cells, finding that the shear stress strain in the outer walls of the MWCNTs causes them to connect to each other as a result of the structural rearrangements on their outer surfaces. The inner concentric nanotubes, however, retain their structure completely: they simply shrink under pressure and restore their shape once the pressure is released.
EPFL scientists have developed a new perovskite material with unique properties that can be used to build next-generation hard drives.
As we generate more and more data, we need storage systems, e.g. hard drives, with higher density and efficiency. But this also requires materials whose magnetic properties can be quickly and easily manipulated in order to write and access data on them. EPFL scientists have now developed a perovskite material whose magnetic order can be rapidly changed without disrupting it due to heating. The work, which describes the first ever magnetic photoconductor, is published in Nature Communications.
The lab of László Forró at EPFL, in a project led by postdoc Bálint Náfrádi, synthesized a ferromagnetic photovoltaic material. Perovskite photovoltaics are gradually becoming a cheaper alternative to current silicon systems, drawing much interest from energy scientists. But this particular material, which is a modified version of perovskite, exhibits some unique properties that make it particularly interesting as a material to build next-generation digital storage systems.
A new method for producing conductive cotton fabrics using graphene-based inks opens up new possibilities for flexible and wearable electronics, without the use of expensive and toxic processing steps.
Turning cotton fibres into functional electronic components can open to an entirely new set of applications from healthcare and wellbeing to the Internet of Things
Wearable, textiles-based electronics present new possibilities for flexible circuits, healthcare and environment monitoring, energy conversion, and many others. Now, researchers at the Cambridge Graphene Centre (CGC) at the University of Cambridge, working in collaboration with scientists at Jiangnan University, China, have devised a method for depositing graphene-based inks onto cotton to produce a conductive textile. The work, published in the journal Carbon, demonstrates a wearable motion sensor based on the conductive cotton.
Cotton fabric is among the most widespread for use in clothing and textiles, as it is breathable and comfortable to wear, as well as being durable to washing. These properties also make it an excellent choice for textile electronics. A new process, developed by Dr Felice Torrisi at the CGC, and his collaborators, is a low-cost, sustainable and environmentally-friendly method for making conductive cotton textiles by impregnating them with a graphene-based conductive ink.
The composer Johann Sebastian Bach left behind an incomplete fugue upon his death, either as an unfinished work or perhaps as a puzzle for future composers to solve.
A classical music dataset released by University of Washington researchers — which enable machine learning algorithms to learn the features of classical music from scratch — raises the likelihood that a computer could expertly finish the job.
MusicNet is the first publicly available large-scale classical music dataset with curated fine-level annotations. It’s designed to allow machine learning researchers and algorithms to tackle a wide range of open challenges — from note prediction to automated music transcription to offering listening recommendations based on the structure of a song a person likes, instead of relying on generic tags or what other customers have purchased.
“At a high level, we’re interested in what makes music appealing to the ears, how we can better understand composition, or the essence of what makes Bach sound like Bach. It can also help enable practical applications that remain challenging, like automatic transcription of a live performance into a written score,” said Sham Kakade, a UW associate professor of computer science and engineering and of statistics.
Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions — about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results — may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming.
Information extraction — or automatically classifying data items stored as plain text — is thus a major topic of artificial-intelligence research. Last week, at the Association for Computational Linguistics’ Conference on Empirical Methods on Natural Language Processing, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head.
Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators. For instance, humans might label parts of speech in a set of texts, and the machine-learning system will try to identify patterns that resolve ambiguities — for instance, when “her” is a direct object and when it’s an adjective.
Typically, computer scientists will try to feed their machine-learning systems as much training data as possible. That generally increases the chances that a system will be able to handle difficult problems.
In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook.
But recognition of natural sounds — such as crowds cheering or waves crashing — has lagged behind. That’s because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications.
Sound recognition may be catching up, however, thanks to researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). At the Neural Information Processing Systems conference next week, they will present a sound-recognition system that outperforms its predecessors but didn’t require hand-annotated data during training.
Instead, the researchers trained the system on video. First, existing computer vision systems that recognize scenes and objects categorized the images in the video. The new system then found correlations between those visual categories and natural sounds.
Spinach is no longer just a superfood: By embedding leaves with carbon nanotubes, MIT engineers have transformed spinach plants into sensors that can detect explosives and wirelessly relay that information to a handheld device similar to a smartphone.
This is one of the first demonstrations of engineering electronic systems into plants, an approach that the researchers call “plant nanobionics.”
“The goal of plant nanobionics is to introduce nanoparticles into the plant to give it non-native functions,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT and the leader of the research team
In this case, the plants were designed to detect chemical compounds known as nitroaromatics, which are often used in landmines and other explosives. When one of these chemicals is present in the groundwater sampled naturally by the plant, carbon nanotubes embedded in the plant leaves emit a fluorescent signal that can be read with an infrared camera. The camera can be attached to a small computer similar to a smartphone, which then sends an email to the user.
“This is a novel demonstration of how we have overcome the plant/human communication barrier,” says Strano, who believes plant power could also be harnessed to warn of pollutants and environmental conditions such as drought.
Scientists at Rice University and at the University of Graz, Austria, are driving three-wheeled, single-molecule “nanoroadsters” with light and, for the first time, seeing how they move.
The Rice lab of nanocar inventor and chemist James Tour synthesized light-driven nanocars six years ago, but with the aid of experimental physicists in Austria, they’re now able to drive fleets of single-molecule vehicles at once.
A report on the work appears in the American Chemical Society journal ACS Nano.
“It is exciting to see that motorized nanoroadsters can be propelled by their light-activated motors,” said Tour, who introduced nanocars in 2005 and motorized them a year later. “These three-wheelers are the first example of light-powered nanovehicles being observed to propel across a surface by any method, let alone by scanning tunneling microscopy.”
Rather than drive them chemically or with the tip of a tunneling microscope, as they will do with other vehicles in the upcoming international NanoCar Race in Toulouse, France, the researchers used light at specific wavelengths to move their nanoroadsters along a copper surface. The vehicles have rear-wheel molecular motors that rotate in one direction when light hits them. The rotation propels the vehicle much like a paddle wheel on water.
In earlier communications we had discussed the botany, uses of the fruits of Phyllanthus emblica (Emblica officinalis) or Amla such as ; Metabolism, Weight-loss, Traditional-medicine, common ailments, Acidity, Anti-Inflammatory; beneficial to the reproductive systems; helpful to pregnant and lactating mothers; as a prophylactic to cancer; as an opthalmintic for eyes care: used in cosmetics in hair care; the home and commercial uses and production in India,(Shah,2016 )
In this part we would discuss the important folk-uses of Bundelkhanda by Saksena(1999) and by Darshanchandra(2003) from all over India and cultivation and its production of Amla in India..
In Ayurveda: In Ayurveda it is known pharmacologically as a Vrishya herb, which means that it enhances all the seven tissues (dhatus), including the reproductive tissue. It is considered useful in treating skin diseases. it has sheet virya in potency. It inhibits pitta and thus helps in getting relief from all the skin disorders caused by pitta