Scientists in many fields got little attention over the last two years as the world focused on the emergency push to develop vaccines and treatments for COVID-19. But labs and researchers remained busy, and this year they've reported a dizzying series of major discoveries and achievements. Scientists at the Lawrence Livermore National Laboratory in California announced in December that they had produced the first fusion reaction that created more energy than was used to start it. The long-elusive achievement marked a major breakthrough in harnessing the process that fuels the sun. "This milestone moves us one significant step closer" to "powering our society" with zero-carbon fusion energy, Energy Secretary Jennifer Granholm said.

From a pig heart being successfully transplanted into a human, to being able to redirect an asteroid on a collision course with Earth, there have been all manner of weird and wonderful scientific discoveries in 2022. They include the human genome finally been mapped after two decades, the unearthing of Africa's oldest known dinosaur, and the release of the first ever image of a supermassive black hole at the heart of our Milky Way galaxy. There was also the alarming discovery that microplastics are everywhere – including in us – and the hugely-anticipated first images from the world's most powerful space telescope James Webb, which will peer back to the dawn of the universe. Here, MailOnline looks at 10 of the most interesting advances this year. The year began with a bang scientifically when just a week into it a dying man became the first patient in the world to get a heart transplant from a genetically-modified pig.

We are at a crossroads in the study of Heliophysics. On one hand we operate in the same paradigm that has guided the field over the past couple of decades, ruled by the triumvirate of data, theory, and simulations. On the other hand, we are beginning to recognize that powerful new opportunities for scientific discovery are possible through increased data volume and sophisticated methods to explore these data. The emergence of the hyperconnected digital society and the massive quantities of data it generates has led to new analysis capabilities that scale well to the solar-terrestrial environment. Heliophysics is squarely positioned to benefit from the emerging field of data science [1].

In the past decade, storied brands like meal-replacement Huel and men's grooming company Harry's built multibillion-dollar retail businesses by using social media and digital-first advertising to sell directly to consumers online, without the need for middlemen. These brands were exemplars of a new form of retail, called direct-to-consumer (DTC). The global pandemic only accelerated this trend, with many high-street stores being forced to close and to keep driving sales by going direct to shoppers online. Some brands successfully navigated the transition, like outdoor pizza oven maker Ooni, whose sales exploded during lockdown, with annual revenue up from £13.7 million ($167 million) in 2019 to £52.7 million in 2020. Shoppers also adapted--around 60 percent purchased from a direct-to-consumer brand at least once in 2021.

If scientific discovery is one of the main driving forces of human progress, insight is the fuel for the engine, which has long attracted behavior-level research to understand and model its underlying cognitive process. However, current tasks that abstract scientific discovery mostly focus on the emergence of insight, ignoring the special role played by domain knowledge. In this concept paper, we view scientific discovery as an interplay between $thinking \ out \ of \ the \ box$ that actively seeks insightful solutions and $thinking \ inside \ the \ box$ that generalizes on conceptual domain knowledge to keep correct. Accordingly, we propose Mindle, a semantic searching game that triggers scientific-discovery-like thinking spontaneously, as infrastructure for exploring scientific discovery on a large scale. On this basis, the meta-strategies for insights and the usage of concepts can be investigated reciprocally. In the pilot studies, several interesting observations inspire elaborated hypotheses on meta-strategies, context, and individual diversity for further investigations.

There is an increasing need in our society to achieve faster advances in Science to tackle urgent problems, such as climate changes, environmental hazards, sustainable energy systems, pandemics, among others. In certain domains like chemistry, scientific discovery carries the extra burden of assessing risks of the proposed novel solutions before moving to the experimental stage. Despite several recent advances in Machine Learning and AI to address some of these challenges, there is still a gap in technologies to support end-to-end discovery applications, integrating the myriad of available technologies into a coherent, orchestrated, yet flexible discovery process. Such applications need to handle complex knowledge management at scale, enabling knowledge consumption and production in a timely and efficient way for subject matter experts (SMEs). Furthermore, the discovery of novel functional materials strongly relies on the development of exploration strategies in the chemical space. For instance, generative models have gained attention within the scientific community due to their ability to generate enormous volumes of novel molecules across material domains. These models exhibit extreme creativity that often translates in low viability of the generated candidates. In this work, we propose a workbench framework that aims at enabling the human-AI co-creation to reduce the time until the first discovery and the opportunity costs involved. This framework relies on a knowledge base with domain and process knowledge, and user-interaction components to acquire knowledge and advise the SMEs. Currently,the framework supports four main activities: generative modeling, dataset triage, molecule adjudication, and risk assessment.

A vast amount of expert and domain knowledge is captured by causal structural priors, yet there has been little research on testing such priors for generalization and data synthesis purposes. We propose a novel model architecture, Causal Structural Hypothesis Testing, that can use nonparametric, structural causal knowledge and approximate a causal model's functional relationships using deep neural networks. We use these architectures for comparing structural priors, akin to hypothesis testing, using a deliberate (non-random) split of training and testing data. Extensive simulations demonstrate the effectiveness of out-of-distribution generalization error as a proxy for causal structural prior hypothesis testing and offers a statistical baseline for interpreting results. We show that the variational version of the architecture, Causal Structural Variational Hypothesis Testing can improve performance in low SNR regimes. Due to the simplicity and low parameter count of the models, practitioners can test and compare structural prior hypotheses on small dataset and use the priors with the best generalization capacity to synthesize much larger, causally-informed datasets. Finally, we validate our methods on a synthetic pendulum dataset, and show a use-case on a real-world trauma surgery ground-level falls dataset. Our code is available on GitHub.

In computational imaging, hardware for signal sampling and software for object reconstruction are designed in tandem for improved capability. Examples of such systems include computed tomography (CT), magnetic resonance imaging (MRI), and superresolution microscopy. In contrast to more traditional cameras, in these devices, indirect measurements are taken and computational algorithms are used for reconstruction. This allows for advanced capabilities such as super-resolution or 3-dimensional imaging, pushing forward the frontier of scientific discovery. However, these techniques generally require a large number of measurements, causing low throughput, motion artifacts, and/or radiation damage, limiting applications. Data-driven approaches to reducing the number of measurements needed have been proposed, but they predominately require a ground truth or reference dataset, which may be impossible to collect. This work outlines a self-supervised approach and explores the future work that is necessary to make such a technique usable for real applications. Light-emitting diode (LED) array microscopy, a modality that allows visualization of transparent objects in two and three dimensions with high resolution and field-of-view, is used as an illustrative example. We release our code at https://github.com/vganapati/LED_PVAE and our experimental data at https://doi.org/10.6084/m9.figshare.21232088 .

Schmidt Futures, a philanthropic initiative co-founded by former Google CEO and Chairman Eric ... [ ] Schmidt and his wife Wendy, is expanding its investment in artificial intelligence research. Schmidt Futures announced today that it was investing $148 million to fund the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Futures. With this newest funding, Schmidt Futures, a philanthropic initiative co-founded by former Google CEO and Chairman Eric Schmidt and his wife Wendy, has now committed a total of $400 million to support the development of artificial intelligence (AI) for scientific discovery for other advances in technology and engineering fields. According to the announcement, the new funding will initially support about 160 postdoctoral fellows at nine universities around the world to learn and apply AI methods to their research. The fellowship is expected to expand to more institutions and countries in the future.

When you picture a scientist, do you see a white coat-clad PhD-holder pipetting away at a lab bench? Or maybe a skygazer with a different day job who goes out on clear nights for a good view of the stars? Historically speaking, both of those examples fit the bill. German-British astronomer William Herschel was originally an amateur who observed the night sky using homemade telescopes. He discovered Uranus in 1781, working alongside his sister, Caroline Herschel, who made multiple discoveries herself.