Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, helper T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physico-chemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this paper, we present a comprehensive trans-allelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide-MHC-II interactions. We train the model using a benchmark experimental dataset, and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method. Focusing on the physical bases of peptide-MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. Additionally, we find that binding pockets P 4 and P 5 of HLA-DP, which were not previously considered as primary anchors, do make strong contributions to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide-MHC interactions.

What began as a small U.S. training operation has expanded to an 800-strong force that accompanies the Nigeriens on intelligence gathering and other missions. It includes a $100 million drone base in the central Nigerien city of Agadez which at present only deploys surveillance drones.

The evolution of financial systems has been a long but interesting journey characterised by sudden changes in underlying technology. Retail banking in Africa is far from where it should have been never followed the natural progression any ways. Artificial intelligence is here to reinvent the whole game of banking and transform this hundreds of years old business into new innovative, scalable, dynamic, micro service environment and efficient to the level where its incomparable. We're only at the beginning of this new age of computing which holds the potential to transform the entire working of a bank Financial payments and banking started in a very inefficient and traditional way which was slow but still acceptable to the customers due to the stage in the information age. There are lucrative but under-utilised banking opportunities in Africa and banks in the region need to step up and grasp these opportunities to succeed.

Startups deploying AI can do everything from using drones for medical deliveries to helping lawyers prepare for court. Chen said his firm has invested in Zipline, an AI startup that uses drones to deliver blood to remote places such as western Rwanda. The service is critical to locations that are hard to access by land. "By the time a truck can get there, it may be too late," he said. Medical personnel in the field use an app to order blood by type.

Artificial Intelligence is not just a project for the biggest names in tech. Starting in 2018 1 million US dollars will be invested in the African AI start-up as part of the AI Revolution Challenge.

The fight against malaria has been improving, but there's still lots more work to do. For one thing, anti-larval sprays are both expensive and time-consuming -- you can't always afford to spray an entire area. Thankfully, a mix of technology is making that mosquito battle more practical. Wales' Aberystwyth University and Tanzania's Zanzibar Malaria Elimination Programme have partnered on an initiative that uses drones to survey malaria hot zones and identify the water-laden areas where malaria-carrying mosquitoes are likely to breed. An off-the-shelf drone (in this case, DJI's Phantom 3) can cover a large rice paddy in 20 minutes, and the data can be processed in the space of an afternoon.

We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household. The RL agent learns to determine which survey question to ask next and when to stop to make a prediction about their likelihood of malaria based on their responses hitherto. The agent incurs a small penalty for each question asked, and a large reward/penalty for making the correct/wrong prediction; it thus has to learn to balance the length of the survey with the accuracy of its final predictions. Our RL agent is a Deep Q-network that learns a policy directly from the responses to the questions, with an action defined for each possible survey question and for each possible prediction class. We focus on Kenya, where malaria is a massive health burden, and train the RL agent on a dataset of 6481 households from the Kenya Malaria Indicator Survey 2015. To investigate the importance of having survey questions be adaptive to responses, we compare our RL agent to a supervised learning (SL) baseline that fixes its set of survey questions a priori. We evaluate on prediction accuracy and on the number of survey questions asked on a holdout set and find that the RL agent is able to predict with 80% accuracy, using only 2.5 questions on average. In addition, the RL agent learns to survey adaptively to responses and is able to match the SL baseline in prediction accuracy while significantly reducing survey length.

One major theme that's been running through science fiction recently is the rise of artificial intelligence and the impact that might have on humanity. As we continue to improve upon and refine machine learning, it seems inevitable that the development of a true AI will occur at some point. And consensus is that, once it does, humans will probably be in a bit of trouble. The four books on this list deal with common themes: intelligent robots that are contemplating the nature of their existence, and malevolent AI that seek the destruction of humanity (and the link between the two). When it comes to machine intelligence, we will reap what we sow, as these novels make evidently clear. Thirty years ago, humans lost the war with their servants, robots they created.

Speaking at the Abu Dhabi International Petroleum Exhibition Conference (ADIPEC) on Wednesday, Omar Saleh said technology disruptions over the past three years had been a "wake-up call" for all oil and gas firms.He said AI would be of "massive importance" over the next to five to 10 years, before adding that of any technology, AI would also have the most impact on the oil and gas sector overall.The U.S. shale revolution paved the way for a three-year oil price downturn that sent crude spiraling from more than $100 a barrel in 2014 to about $60 today. That has piled pressure on the oil-dependent economies of OPEC nations and forced a round of production cuts this year. On Tuesday, Baker Hughes GE CEO Lorenzo Simonelli said " " in the oil and gas industry should be viewed positively. Correction: This story has been updated to reflect that Omar Saleh believes AI will have the greatest technological impact on the oil and gas industry over the coming years. Speaking at the Abu Dhabi International Petroleum Exhibition Conference (ADIPEC) on Wednesday, Omar Saleh said technology disruptions over the past three years had been a "wake-up call" for all oil and gas firms.

The Libyan National Army has been battling ISIS in the cities of Sirte and Benghazi. The U.S. military has launched airstrikes this month in Yemen, Somalia, Iraq, Syria, Afghanistan and Friday, for the first time since September, in Libya. According to a defense official, the drone strike in the desert of central Libya Friday killed "several" ISIS militants in a sign the Pentagon may be ramping up pressure on terror groups in Africa. The most recent strike comes a year after the military launched nearly 500 airstrikes against ISIS in the coastal city of Sirte, located halfway between Tripoli and Benghazi. The September strike killed 17 ISIS fighters.