Addernet: Do we really need multiplications in deep learning?

Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been applied. However, these ML models generally do not incorporate the experimental measurements directly, such as X-ray crystallographic diffraction data. To this end, we explore an approach that more tightly couples these traditional crystallographic and recent ML-based methods, by training a hybrid 3-d vision transformer and convolutional network on inputs from both domains. We make use of two distinct input constructs / Patterson maps, which are directly obtainable from crystallographic data, and "partial structure" template maps derived from predicted structures deposited in the AlphaFold Protein Structure Database with subsequently omitted residues. With these, we predict electron density maps that are then post-processed into atomic models through standard crystallographic refinement processes. Introducing an initial dataset of small protein fragments taken from Protein Data Bank entries and placing them in hypothetical crystal settings, we demonstrate that our method is effective at both improving the phases of the crystallographic structure factors and completing the regions missing from partial structure templates, as well as improving the agreement of the electron density maps with the ground truth atomic structures. This work has been accepted in Acta Crystallographic section D.

The CMAP (Cultural Mapping and Pattern Analysis) visualization toolkit is an open-source suite for analyzing and visualizing text data--from qualitative fieldnotes and in-depth interview transcripts to historical documents and web-scraped data such as message board posts or blogs. The toolkit is designed for scholars integrating pattern analysis, data visualization, and explanation in qualitative and/or computational social science (CSS). Despite the existence of off-the-shelf commercial qualitative data analysis software, there remains a shortage of highly scalable open-source options capable of handling large datasets and supporting advanced statistical and language modeling. The foundation of the toolkit is a pragmatic approach that aligns research tools with social science project goals--empirical explanation, theory-guided measurement, comparative design, or evidence-based recommendations--guided by the principle that research paradigms and questions should determine methods. Consequently, the CMAP visualization toolkit offers a wide range of possibilities through the adjustment of a relatively small number of parameters and allows seamless integration with other Python tools.

A team of researchers at Rice University and Baylor College of Medicine has developed a new strategy for identifying hazardous pollutants in soil, even ones that have never been isolated or studied in a lab. The new approach, described in a study published in Proceedings of the National Academy of Sciences, uses light-based imaging, theoretical predictions of compounds' light signatures and machine learning (ML) algorithms to detect toxic compounds like polycyclic aromatic hydrocarbons (PAHs) and their derivative compounds (PACs) in soil. A common by-product of combustion, PAHs and PACs have been linked to cancer, developmental issues and other serious health problems. Identifying pollutants in soil usually requires advanced laboratories and standard physical reference samples of the suspected contaminants. However, for many environmental pollutants that pose a public health risk, there is no experimental data available that can be used to detect them.

Keeping count of nostril hairs and investigating the promiscuity of anchovies may seem completely unrelated. But these studies are among 10 others to win this year's spoof'Ig Nobels', thanks to their ability to make scientists chuckle. Traditionally hosted at Harvard University, this ceremony is the 33rd of its kind, and sees genuine Nobel laureates handing out awards to lucky academics. The prize is ten trillion Zimbabwean dollars, which might sound like a huge amount, but is actually only the equivalent of 30p in the UK (40 cents in the US). MailOnline spoke with some of the wackiest prize winners of 2023.

An ongoing project intends to enable military and other scientists to monitor and even enhance the ways in which a soldier's brain sleeps and, importantly, attains rest and repair. The effort – a collaboration between the U.S. Army Medical Research and Development Command (USAMRDC) Military Operational Medicine Research Program (MOMRP) and scientists and engineers at Rice University (Houston, Texas) – is only one of a group of sensor-driven military projects seeking to create wearable technology to track and improve soldier performance and outcomes. Scientists at the Houston-based university are developing a noninvasive "sleeping cap" that analyzes the glymphatic system, the flow of fluid that is thought to cleanse and rid the brain of common metabolic waste during sleep. The cap will be used to further understand how the human brain deals with that waste, and if that function actually prepares and refreshes people for the next day. A team at Rice University's NeuroEngineering Initiative – together with teams from Rice's Institute of Biosciences and Bioengineering (IBB) and physicians from Houston Methodist Hospital and Baylor College of Medicine in Houston – are developing a lightweight skullcap that can analyze the wearer's glymphatic function and stimulate proper flow to treat sleep disorders and improve wakefulness and day-to-day function.

Why bother to design your own robots when you can just reuse what nature created? This was the thought process behind a research project from engineers at Rice University who successfully transformed dead spiders into robotic gripping claws. The scientists have dubbed their new area of research "necrobotics" and say it could create cheap, effective, and biodegradable alternatives to current robotic systems. Well, while humans move their limbs using pairs of antagonistic muscles, like biceps and triceps, spiders' legs contain only a single flexor muscle that draws the leg inward. This is opposed by a hydraulic system: a chamber in the center of the spider's body (known as a prosoma) pushes out fluid to open the leg, with separate valves allowing the animal to control each limb independently.

While we've seen scientists find novel ways to use insects after they're dead, it's hard to imagine any group of researchers topping the work of a team from Rice University that turned lifeless wolf spiders into "necrobotic" grippers. Yes, you read that right – and, no, you're not the only one with a sudden phantom itch. How did we get here? Well, I'm glad you asked. Let's start with an anatomy lesson.

Arachnophobes look away now; engineers have found a way to turn dead spiders into mechanical gripping robots straight out of your nightmares. Researchers from Rice University in Texas pumped wolf spider cadavers with air to get their legs to unfurl and clasp around objects. They discovered that the arachnids were able to lift 130 per cent of their own body weight, and could manipulate a circuit board. It is hoped the delicate gripper could be used in microelectronics, or that its natural camouflage could be helpful if capturing small insects for study. Daniel Preston, assistant professor in mechanical engineering, said: 'It happens to be the case that the spider, after it's deceased, is the perfect architecture for small scale, naturally derived grippers.

You are free to share this article under the Attribution 4.0 International license. Researchers have come up with a new strategy that allows humans to help robots "see" their environments and carry out tasks. Just like us, robots can't see through walls. Sometimes they need a little help to get where they're going. The strategy called Bayesian Learning IN the Dark--BLIND, for short--is a new solution to the long-standing problem of motion planning for robots that work in environments where not everything is clearly visible all the time.