Accurate detection and analysis of traces of persistent organic pollutants in water is important in many areas, including environmental monitoring and food quality control, due to their long environmental stability and potential bioaccumulation. While conventional analysis of organic pollutants requires expensive equipment, surface enhanced Raman spectroscopy (SERS) has demonstrated great potential for accurate detection of these contaminants. However, SERS analytical difficulties, such as spectral preprocessing, denoising, and substrate-based spectral variation, have hindered widespread use of the technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from messy, unprocessed Raman data using machine learning. Frequency domain transform methods, including the Fourier and Walsh Hadamard transforms, are applied to sets of Raman spectra of three model micropollutants in water (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of sample pollutants are predicted with more than 80 percent cross-validation accuracy from raw Raman data. cross-validation accuracy of 85 percent was achieved using deep learning for a moderately sized dataset (100 spectra), and 70 to 80 percent cross-validation accuracy was achieved even for very small datasets (50 spectra). Additionally, standard models were shown to accurately identify characteristic peaks via analysis of their importance scores. The approach shown here has the potential to be applied to facilitate accurate detection and analysis of persistent organic pollutants by surface-enhanced Raman spectroscopy.

An enormous figure looms over scientists searching for new drugs: the estimated US$2.6-billion price tag of developing a treatment. A lot of that effectively goes down the drain, because it includes money spent on the nine out of ten candidate therapies that fail somewhere between phase I trials and regulatory approval. Few people in the field doubt the need to do things differently. Leading biopharmaceutical companies believe a solution is at hand. Pfizer is using IBM Watson, a system that uses machine learning, to power its search for immuno-oncology drugs.

An enormous figure looms over scientists searching for new drugs: the estimated US$2.6-billion price tag of developing a treatment. A lot of that effectively goes down the drain, because it includes money spent on the nine out of ten candidate therapies that fail somewhere between phase I trials and regulatory approval. Few people in the field doubt the need to do things differently. Leading biopharmaceutical companies believe a solution is at hand. Pfizer is using IBM Watson, a system that uses machine learning, to power its search for immuno-oncology drugs.

Malaria causes about half a million deaths each year, and about half the world's population is at risk of contracting it. The disease is caused by the Plasmodium parasite and is transmitted by the bite of the infected mosquito. The big problem is that this organism is becoming increasingly resistant to the remedies that fight it. Therefore, the risk of this worsening condition has become much greater if new drugs are not developed. But it seems there are already signs of a light at the end of the tunnel.

Around half of the world's population is at risk of contracting malaria and it causes around half a million deaths each year. However, the parasites that cause malaria are becoming more resistant to the drugs we currently use to combat them, meaning the global malaria risk stands to increase if we don't develop new drugs quickly enough. Well new research published recently in Scientific Reports finds that a common chemical used in everything from soap and toothpaste to clothing and furniture might be an effective treatment, and it was done with the help of AI. Many popular antimalarial drugs target a specific enzyme found in malaria-causing parasites, an enzyme important for the parasites' growth. So researchers used AI-powered Robot Scientist Eve to screen a slew of FDA-approved compounds to see how well they were able to inhibit that enzyme and it found that triclosan was able to inhibit the enzyme from two different species of malaria-causing parasites, including variants that had developed resistance to common malaria treatments. The researchers then tested triclosan against the enzyme in a number of different ways in order to confirm its effectiveness, and that, combined with previous research showing that the chemical can also inhibit an additional enzyme found in these parasites, led the researchers to conclude that triclosan may be a useful therapeutic with multiple targets.

A laboratory robot powered by artificial intelligence (AI) has discovered that a compound commonly found in toothpaste could be used to combat drug-resistant malaria parasites. Triclosan could be deployed against strains of plasmodium malaria parasites that have evolved resistance to the widely used drug pyrimethamine, according to the University of Cambridge. Pyrimethamine works by inhibiting a particular enzyme called DHFR and scientists have known for some time that triclosan can be employed to target another enzyme, ENR. The fast-moving AI routines of the robot "Eve", however, which formulate, test and re-evaluate hypotheses in quick succession, discovered that the common toothpaste chemical also attacks DHFR – even in parasites resistant to pyrimethamine. It has led researchers to hope that triclosan could be developed for use in a two-pronged attack on plasmodium in the liver and in the blood.

SAN FRANCISCO, Jan. 18 (Xinhua) -- An artificial intelligence (AI) robot made by a British university has become a big hero after it helped scientists find a malaria killer in a common toothpaste ingredient, a new study revealed Thursday. Scientists at the university of Cambridge in Britain used the "Robot Scientist," Eve, in a high-throughput screen and discovered that triclosan, an ingredient found in many toothpastes, may help fight against strains of a malaria parasite that have grown resistant to one of the currently-used drugs to treat the disease. The findings of the study by the Cambridge researchers were published in the journal Scientific Reports on Thursday. With the help of the AI-powered Eve, the researchers discovered that triclosan inhibits the spread of a kind of enzyme of the malaria parasite, called DHFR, thus stopping the growth of the parasite in the blood. The discovery challenged a previous assumption that triclosan inhibits the growth in culture of the malaria parasite Plasmodium during the blood-stage, because it is targeting an enzyme known as enoyl reductase (ENR) found in the liver.

London, Jan 18 (PTI) An artificially-intelligent'robot scientist' has helped identify a common toothpaste ingredient that can fight strains of malaria parasite that have grown resistant currently-used drugs. Malaria kills over half a million people each year, predominantly in Africa and south-east Asia. While a number of medicines are used to treat the disease, malaria parasites are growing increasingly resistant to these drugs, raising the spectre of untreatable malaria in the future. The study, published in the journal Scientific Reports, employed the robot scientist'Eve' in a high-throughput screen and discovered that triclosan, an ingredient found in many toothpastes, may help the fight against drug-resistance. When used in toothpaste, triclosan prevents the build-up of plaque bacteria by inhibiting the action of an enzyme known as enoyl reductase (ENR), which is involved in the production of fatty acids.

When a mosquito infected with malaria parasites bites someone, it transfers the parasites into their bloodstream via its saliva. These parasites work their way into the liver, where they mature and reproduce. After a few days, the parasites leave the liver and hijack red blood cells, where they continue to multiply, spreading around the body and causing symptoms, including potentially life-threatening complications. Malaria kills over half a million people each year, predominantly in Africa and south-east Asia. While a number of medicines are used to treat the disease, malaria parasites are growing increasingly resistant to these drugs, raising the spectre of untreatable malaria in the future.