Structural biology experiments benefit significantly from state-of-the-art synchrotron data collection. One can acquire macromolecular crystallography (MX) diffraction data on large-area photon-counting pixel-array detectors at framing rates exceeding 1000 frames per second, using 200 Gbps network connectivity, or higher when available. In extreme cases this represents a raw data throughput of about 25 GB s−1, which is nearly impossible to deliver at reasonable cost without compression. Our field has used lossless compression for decades to make such data collection manageable. Many MX beamlines are now fitted with DECTRIS Eiger detectors, all of which are delivered with optimized compression algorithms by default, and they perform well with current framing rates and typical diffraction data. However, better lossless compression algorithms have been developed and are now available to the research community. Here one of the latest and most promising lossless compression algorithms is investigated on a variety of diffraction data like those routinely acquired at state-of-the-art MX beamlines.

During beam time at a research facility, alignment and optimization of instrumentation, such as spectrometers, is a time-intensive task and often needs to be performed multiple times throughout the operation of an experiment. Despite the motorization of individual components, automated alignment solutions are not always available. In this study, a novel approach that combines optimisers with neural network surrogate models to significantly reduce the alignment overhead for a mobile soft X-ray spectrometer is proposed. Neural networks were trained exclusively using simulated ray-tracing data, and the disparity between experiment and simulation was obtained through parameter optimization. Real-time validation of this process was performed using experimental data collected at the beamline. The results demonstrate the ability to reduce alignment time from one hour to approximately five minutes. This method can also be generalized beyond spectrometers, for example, towards the alignment of optical elements at beamlines, making it applicable to a broad spectrum of research facilities.

Transition-metal nitro­gen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co–N–C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co–N–C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co–N–C catalysts, and further optimization of this class of electrocatalytic systems.

The extraction and purification procedures, crystallization and crystal structure refinement (single-crystal X-ray data) of germacrone type II, C15H22O, are presented. The structural results are compared with a previous powder X-ray synchrotron study [Kaduk et al. (2022). Powder Diffr. 37, 98–104], revealing significant improvements in terms of accuracy and precision. Hirshfeld atom refinement (HAR), as well as Hirshfeld surface analysis, give insight into the inter­molecular inter­actions of germacrone type II.

The highly cytotoxic macrocyclic trichothecene Isororidin A (C29H40O9) was isolated from the fungus Myrothesium verrucaria endophytic on the wild medicinal plant `Datura' (Datura stramonium L.) and was characterized by one- (1D) and two-dimensional (2D) NMR spectroscopy. The three-dimensional structure of Isororidin A has been confirmed by X-ray crystallography at 0.81 Å resolution from crystals grown in the ortho­rhom­bic space group P212121, with one mol­ecule per asymmetric unit. Isororidin A is the epimer of previously described (by X-ray crystallography) Roridin A at position C-13' of the macrocyclic ring.

This article describes the High-Pressure Freezing Laboratory for Macromolecular Crystallography (HPMX) at the ESRF, and highlights new and complementary research opportunities that can be explored using this facility. The laboratory is dedicated to investigating interactions between macromolecules and gases in crystallo, and finds applications in many fields of research, including fundamental biology, biochemistry, and environmental and medical science. At present, the HPMX laboratory offers the use of different high-pressure cells adapted for helium, argon, krypton, xenon, nitrogen, oxygen, carbon dioxide and methane. Important scientific applications of high pressure to macromolecules at the HPMX include noble-gas derivatization of crystals to detect and map the internal architecture of proteins (pockets, tunnels and channels) that allows the storage and diffusion of ligands or substrates/products, the investigation of the catalytic mechanisms of gas-employing enzymes (using oxygen, carbon dioxide or methane as substrates) to possibly decipher intermediates, and studies of the conformational fluctuations or structure modifications that are necessary for proteins to function. Additionally, cryo-cooling protein crystals under high pressure (helium or argon at 2000 bar) enables the addition of cryo-protectant to be avoided and noble gases can be employed to produce derivatives for structure resolution. The high-pressure systems are designed to process crystals along a well defined pathway in the phase diagram (pressure–temperature) of the gas to cryo-cool the samples according to the three-step `soak-and-freeze method'. Firstly, crystals are soaked in a pressurized pure gas atmosphere (at 294 K) to introduce the gas and facilitate its inter­actions within the macromolecules. Samples are then flash-cooled (at 100 K) while still under pressure to cryo-trap macromolecule–gas complexation states or pressure-induced protein modifications. Finally, the samples are recovered after depressurization at cryo-temperatures. The final section of this publication presents a selection of different typical high-pressure experiments carried out at the HPMX, showing that this technique has already answered a wide range of scientific questions. It is shown that the use of different gases and pressure conditions can be used to probe various effects, such as mapping the functional internal architectures of enzymes (tunnels in the haloalkane dehalogenase DhaA) and allosteric sites on membrane-protein surfaces, the interaction of non-inert gases with proteins (oxygen in the hydrogenase ReMBH) and pressure-induced structural changes of proteins (tetramer dissociation in urate oxidase). The technique is versatile and the provision of pressure cells and their application at the HPMX is gradually being extended to address new scientific questions.

Over the last decade, the development of time-resolved serial crystallography (TR-SX) at X-ray free-electron lasers (XFELs) and synchrotrons has allowed researchers to study phenomena occurring in proteins on the femtosecond-to-minute timescale, taking advantage of many technical and methodological breakthroughs. Protein crystals of various sizes are presented to the X-ray beam in either a static or a moving medium. Photoactive proteins were naturally the initial systems to be studied in TR-SX experiments using pump–probe schemes, where the pump is a pulse of visible light. Other reaction initiations through small-molecule diffusion are gaining momentum. Here, selected examples of XFEL and synchrotron time-resolved crystallography studies will be used to highlight the specificities of the various instruments and methods with respect to time resolution, and are compared with cryo-trapping studies.

Diffuse scattering is a promising method to gain additional insight into protein dynamics from macromolecular crystallography experiments. Bragg intensities yield the average electron density, while the diffuse scattering can be processed to obtain a three-dimensional reciprocal-space map that is further analyzed to determine correlated motion. To make diffuse scattering techniques more accessible, software for data processing called mdx2 has been created that is both convenient to use and simple to extend and modify. mdx2 is written in Python, and it interfaces with DIALS to implement self-contained data-reduction workflows. Data are stored in NeXus format for software interchange and convenient visualization. mdx2 can be run on the command line or imported as a package, for instance to encapsulate a complete workflow in a Jupyter notebook for reproducible computing and education. Here, mdx2 version 1.0 is described, a new release incorporating state-of-the-art techniques for data reduction. The implementation of a complete multi-crystal scaling and merging workflow is described, and the methods are tested using a high-redundancy data set from cubic insulin. It is shown that redundancy can be leveraged during scaling to correct systematic errors and obtain accurate and reproducible measurements of weak diffuse signals.

Radiation damage remains one of the major impediments to accurate structure solution in macromolecular crystallography. The artefacts of radiation damage can manifest as structural changes that result in incorrect biological interpretations being drawn from a model, they can reduce the resolution to which data can be collected and they can even prevent structure solution entirely. In this article, we discuss how to identify and mitigate against the effects of radiation damage at each stage in the macromolecular crystal structure-solution pipeline.

The formation of a vitrified thin film embedded with randomly oriented macromolecules is an essential prerequisite for cryogenic sample electron microscopy. Most commonly, this is achieved using the plunge-freeze method first described nearly 40 years ago. Although this is a robust method, the behaviour of different macromolecules shows great variation upon freezing and often needs to be optimized to obtain an isotropic, high-resolution reconstruction. For a macromolecule in such a film, the probability of encountering the air–water interface in the time between blotting and freezing and adopting preferred orientations is very high. 3D reconstruction using preferentially oriented particles often leads to anisotropic and uninterpretable maps. Currently, there are no general solutions to this prevalent issue, but several approaches largely focusing on sample preparation with the use of additives and novel grid modifications have been attempted. In this study, the effect of physical and chemical factors on the orientations of macromolecules was investigated through an analysis of selected well studied macromolecules, and important parameters that determine the behaviour of proteins on cryo-EM grids were revealed. These insights highlight the nature of the interactions that cause preferred orientations and can be utilized to systematically address orientation bias for any given macromolecule and to provide a framework to design small-molecule additives to enhance sample stability and behaviour.

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction-quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of the home laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example.

Advances in structural biology have relied heavily on synchrotron cryo-crystallography and cryogenic electron microscopy to elucidate biological processes and for drug discovery. However, disparities between cryogenic and room-temperature (RT) crystal structures pose challenges. Here, Cryo2RT, a high-throughput RT data-collection method from cryo-cooled crystals that leverages the cryo-crystallography workflow, is introduced. Tested on endothiapepsin crystals with four soaked fragments, thaumatin and SARS-CoV-2 3CLpro, Cryo2RT reveals unique ligand-binding poses, offers a comparable throughput to cryo-crystallography and eases the exploration of structural dynamics at various temperatures.

AlphaFold2 has revolutionized structural biology by offering unparalleled accuracy in predicting protein structures. Traditional methods for determining protein structures, such as X-ray crystallography and cryo-electron microscopy, are often time-consuming and resource-intensive. AlphaFold2 provides models that are valuable for molecular replacement, aiding in model building and docking into electron density or potential maps. However, despite its capabilities, models from AlphaFold2 do not consistently match the accuracy of experimentally determined structures, need to be validated experimentally and currently miss some crucial information, such as post-translational modifications, ligands and bound ions. In this paper, the advantages are explored of collecting X-ray anomalous data to identify chemical elements, such as metal ions, which are key to understanding certain structures and functions of proteins. This is achieved through methods such as calculating anomalous difference Fourier maps or refining the imaginary component of the anomalous scattering factor f''. Anomalous data can serve as a valuable complement to the information provided by AlphaFold2 models and this is particularly significant in elucidating the roles of metal ions.

The success of experimental phasing in macromolecular crystallography relies primarily on the accurate locations of heavy atoms bound to the target crystal. To improve the process of substructure determination, a modified phase-retrieval algorithm built on the framework of the relaxed alternating averaged reflection (RAAR) algorithm has been developed. Importantly, the proposed algorithm features a combination of the π-half phase perturbation for weak reflections and enforces the direct-method-based tangent formula for strong reflections in reciprocal space. The proposed algorithm is extensively demonstrated on a total of 100 single-wavelength anomalous diffraction (SAD) experimental datasets, comprising both protein and nucleic acid structures of different qualities. Compared with the standard RAAR algorithm, the modified phase-retrieval algorithm exhibits significantly improved effectiveness and accuracy in SAD substructure determination, highlighting the importance of additional constraints for algorithmic performance. Furthermore, the proposed algorithm can be performed without human intervention under most conditions owing to the self-adaptive property of the input parameters, thus making it convenient to be integrated into the structural determination pipeline. In conjunction with the IPCAS software suite, we demonstrated experimentally that automatic de novo structure determination is possible on the basis of our proposed algorithm.

Single-crystal X-ray diffraction analysis of small molecule active pharmaceutical ingredients is a key technique in the confirmation of molecular connectivity, including absolute stereochemistry, as well as the solid-state form. However, accessing single crystals suitable for X-ray diffraction analysis of an active pharmaceutical ingredient can be experimentally laborious, especially considering the potential for multiple solid-state forms (solvates, hydrates and polymorphs). In recent years, methods for the exploration of experimental crystallization space of small molecules have undergone a `step-change', resulting in new high-throughput techniques becoming available. Here, the application of high-throughput encapsulated nanodroplet crystallization to a series of six di­hydro­pyridines, calcium channel blockers used in the treatment of hypertension related diseases, is described. This approach allowed 288 individual crystallization experiments to be performed in parallel on each molecule, resulting in rapid access to crystals and subsequent crystal structures for all six di­hydro­pyridines, as well as revealing a new solvate polymorph of nifedipine (1,4-dioxane solvate) and the first known solvate of nimodipine (DMSO solvate). This work further demonstrates the power of modern high-throughput crystallization methods in the exploration of the solid-state landscape of active pharmaceutical ingredients to facilitate crystal form discovery and structural analysis by single-crystal X-ray diffraction.

Onchocerca volvulus causes blindness, onchocerciasis, skin infections and devastating neurological diseases such as nodding syndrome. New treatments are needed because the currently used drug, ivermectin, is contraindicated in pregnant women and those co-infected with Loa loa. The Seattle Structural Genomics Center for Infectious Disease (SSGCID) produced, crystallized and determined the apo structure of N-terminally hexahistidine-tagged O. volvulus macrophage migration inhibitory factor-1 (His-OvMIF-1). OvMIF-1 is a possible drug target. His-OvMIF-1 has a unique jellyfish-like structure with a prototypical macrophage migration inhibitory factor (MIF) trimer as the `head' and a unique C-terminal `tail'. Deleting the N-terminal tag reveals an OvMIF-1 structure with a larger cavity than that observed in human MIF that can be targeted for drug repurposing and discovery. Removal of the tag will be necessary to determine the actual biological oligomer of OvMIF-1 because size-exclusion chomatographic analysis of His-OvMIF-1 suggests a monomer, while PISA analysis suggests a hexamer stabilized by the unique C-terminal tails.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

With the emergence of ultrafast X-ray sources, interest in following fast processes in small molecules and macromolecules has increased. Most of the current research into ultrafast structural dynamics of macromolecules uses X-ray free-electron lasers. In parallel, small-scale laboratory-based laser-driven ultrafast X-ray sources are emerging. Continuous development of these sources is underway, and as a result many exciting applications are being reported. However, because of their low flux, such sources are not commonly used to study the structural dynamics of macromolecules. This article examines the feasibility of time-resolved powder diffraction of macromolecular microcrystals using a laboratory-scale laser-driven ultrafast X-ray source.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

The first Federation of European Biochemical Societies Advanced Course on macromolecular crystallization was launched in the Czech Republic in October 2004. Over the past two decades, the course has developed into a distinguished event, attracting students, early career postdoctoral researchers and lecturers. The course topics include protein purification, characterization and crystallization, covering the latest advances in the field of structural biology. The many hands-on practical exercises enable a close interaction between students and teachers and offer the opportunity for students to crystallize their own proteins. The course has a broad and lasting impact on the scientific community as participants return to their home laboratories and act as nuclei by communicating and implementing their newly acquired knowledge and skills.

VMXm joins the suite of operational macromolecular crystallography beamlines at Diamond Light Source. It has been designed to optimize rotation data collections from protein crystals less than 10 µm and down to below 1 µm in size. The beamline has a fully focused beam of 0.3 × 2.3 µm (vertical × horizontal) with a tuneable energy range (6–28 keV) and high flux (1.6 × 1012 photons s−1 at 12.5 keV). The crystals are housed within a vacuum chamber to minimize background scatter from air. Crystals are plunge-cooled on cryo-electron microscopy grids, allowing much of the liquid surrounding the crystals to be removed. These factors improve the signal-to-noise during data collection and the lifetime of the microcrystals can be prolonged by exploiting photoelectron escape. A novel in vacuo sample environment has been designed which also houses a scanning electron microscope to aid with sample visualization. This combination of features at VMXm allows measurements at the physical limits of X-ray crystallography on biomacromolecules to be explored and exploited.

The lineup from the "Justice League" animated series.; Credit: Warner Bros.

Bruce Timm's DC Comics animated universe, beginning with "Batman: The Animated Series" and continuing with "Superman," "Batman Beyond," "Justice League," "Justice League Unlimited" and more, remains one of the most beloved and critically acclaimed animated runs in existence. The run was so idenified with the producer that it was sometimes called the Timmverse, but the last show in that continuity ended in 2006 and Timm officially stepped down from working with DC animation in 2013.

Now Timm is back. He's providing a darker take than the optimistic world he became known for in "Justice League: Gods and Monsters," a three-part digital series launching spring 2015 that will be tied in with a full-length animated film that comes out later that year, according to a press release.

Timm's also re-teaming with Alan Burnett, who worked with Timm on "Batman: The Animated Series." It's part of DC Comics' efforts to set up their new film "Batman v Superman: Dawn of Justice," which hits in 2016, with the full Justice League film set for 2018.

DC Comics as a whole has been moving in a darker direction with Christopher Nolan's Batman trilogy, the "Man of Steel" reboot of Superman and a more serious direction in many of its comic books. The company has followed in its tradition of epic storytelling, passing on the quips Marvel has popularized in films from "Iron Man" to "Guardians of the Galaxy."

It's yet to be seen if Timm can recapture any of the magic from his classic cartoons, but there's reason to be optimistic for the creator of the series that introduced fan favorite Joker sidekick Harley Quinn, created a new origin for Mr. Freeze that cemented the character in the Batman mythos and led the team reimagining numerous characters in an iconic, broadly appealing way.

If you want to catch up on Timm's legacy, his previous two Justice League series are available on Netflix and Amazon Prime, along with "Batman Beyond," while the Batman and Superman animated series are available on Amazon Prime.

Timm also recently produced a short for the 75th anniversary of Batman called "Strange Days," setting the character in the retro world of the serialized pulp storytelling from the time Batman was originally created. You can watch that below:

Batman anniversary short

Watch the classic opening to "Batman: The Animated Series":

Batman: The Animated Series opening

And, a personal favorite joke from when Lex Luthor and the Flash trade bodies on "Justice League Unlimited":

Flash/Luthor body swap

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How do galaxies such as our Milky Way come into existence? How do they grow and change over time? The science behind galaxy formation has long been a puzzle, but a University of Arizona-led team of scientists is one step closer to finding answers, thanks to supercomputer simulations. Observing real galaxies in space can only provide snapshots in time, so researchers who study how galaxies evolve over billions of years need to use computer simulations. Traditionally, astronomers have used simulations to invent theories of galaxy formation and test them, but they have had to proceed one galaxy at a time. Peter Behroozi of the university's Steward Observatory and colleagues overcame this hurdle by generating millions of different universes on a supercomputer, each according to different physical theories for how galaxies form. The findings challenge fundamental ideas about the role dark matter plays in galaxy formation, the evolution of galaxies over time and the birth of stars. The study is the first to create self-consistent universes that are exact replicas of the real ones -- computer simulations that each represent a sizeable chunk of the actual cosmos, containing 12 million galaxies and spanning the time from 400 million years after the Big Bang to the present day. The results from the "UniverseMachine," as the authors call their approach, have helped resolve the long-standing paradox of why galaxies cease to form new stars even when they retain plenty of hydrogen gas, the raw material from which stars are forged. The research is partially funded by NSF's Division of Physics through grants to UC Santa Barbara's Kavli Institute for Theoretical Physics and the Aspen Center for Physics.

Image credit: NASA/ESA/J. Lotz and the HFF Team/STScI

SEB Embedded has selected Thought Machine’s cloud-native core banking system, Vault Core, as the foundation for its latest service offering.

At the Cascades Volcano Observatory, staff use technical skills and creativity to solve complex problems and innovate for the future. Maciej’s experiments are too large for the observatory, so he travels 3 hours southeast of CVO to the HJ Andrews Experimental Forest in Blue River, Oregon to study debris flows. 

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Machine learning algorithms can sometimes do a great job with a little help from human expertise, at least in the field of materials science. In many specialized areas of science, engineering and medicine, researchers are turning to machine learning algorithms to analyze data sets that have grown too large for humans to understand. In materials science, success with this effort could accelerate the design of next-generation advanced functional materials, where development now usually depends on old-fashioned trial and error. By themselves, however, data analytics techniques borrowed from other research areas often fail to provide the insights needed to help materials scientists and engineers choose which of many variables to adjust -- and the techniques can't account for dramatic changes such as the introduction of a new chemical compound into the process. In a new study, researchers explain a technique known as dimensional stacking, which shows that human experience still has a role to play in the age of machine intelligence. The machines gain an edge at solving a challenge when the data to be analyzed are intelligently organized based on human knowledge of what factors are likely to be important and related. "When your machine accepts strings of data, it really does matter how you are putting those strings together," said Nazanin Bassiri-Gharb, the paper's corresponding author and a scientist at the Georgia Institute of Technology. "We must be mindful that the organization of data before it goes to the algorithm makes a difference. If you don't plug the information in correctly, you will get a result that isn't necessarily correlated with the reality of the physics and chemistry that govern the materials."

Image credit: Rob Felt/Georgia Tech

A demonstrator holds a sign in support of the Affordable Care Act in front of the U.S. Supreme Court last November. On Thursday, the justices did just that.; Credit: Alex Brandon/AP

The U.S. Supreme Court upheld the Affordable Care Act for the third time on Thursday, leaving in place the broad provisions of the law enacted by Congress in 201o. The vote was 7 to 2.

The opinion was authored by Justice Stephen Breyer who was joined by Chief Justice John Roberts and Justices Clarence Thomas, Sonia Sotomayor, Elena Kagan, Brett Kavanaugh and Amy Coney Barrett. Justices Samuel Alito and Neil Gorsuch dissented.

The decision threw out the challenge to the law on the grounds that Texas and other objecting GOP-dominated states were not required to pay anything under the mandate provision and thus had no standing to bring the challenge to court.

"To have standing, a plaintiff must 'allege personal injury fairly traceable to the defendant's allegedly unlawful conduct and likely to be redressed by the requested relief,' " the majority wrote. "No plaintiff has shown such an injury 'fairly traceable' to the 'allegedly unlawful conduct' challenged here."

The mandate, the most controversial provision of the law, required that people either buy health insurance or pay a penalty. In 2012, it was upheld by a 5-4 vote, with Chief Justice John Roberts casting the decisive fifth vote, on the grounds that the penalty fell within the taxing power of Congress.

In 2017, Congress got rid of the penalty after the Congressional Budget Office concluded that the law would continue to function effectively without it. That prompted the challengers to go back to court, contending that because the penalty had been zeroed out, it was no longer a tax or a mandate. What's more, they contended, because the mandate was so interwoven with the rest of the ACA, the whole law must be struck down.

Over 31 million Americans have access health insurance through the ACA — a record high since the law's inception, the White House said last week. In addition, the Urban Institute reported in May that ACA premiums have gone down each of the last three years.

Many of the provisions of the ACA are now taken for granted. Up to 135 million people are covered by the ban on discrimination against those with preexisting conditions.

Young adults are now permitted to stay on their parents' insurance until age 26; copays are not permitted for preventive care; and insurance companies can no longer put lifetime caps on benefits, are required to spend 80% of premiums on medical coverage and are barred from discrimination based on factors like gender.

In addition, Medicaid coverage was greatly expanded after all but a dozen states took advantage of the ACA to expand federally subsidized coverage under the program. Among those who have benefited are many who lost their health insurance when they lost their jobs in the COVID-19 pandemic.

This content is from Southern California Public Radio. View the original story at SCPR.org.

Research by NAM Pharmacy Fellow Dima M. Qato has shed light on “pharmacy deserts” and closures that reduce people’s access to medications.

A new roundtable will hold its first public meeting on Jan. 23 to discuss challenges associated with incorporating climate change into the macroeconomic analyses used for federal policymaking. The roundtable’s activities will inform a new White House interagency working group.

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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This summer, consumers can elevate their snacking game with Kraft Mac & Cheese Popcorn.

In celebration of World Macadamia Nut Day on September 4, 2021, the Australian and South African Marquis Group donated macadamia trees for planting at local schools in the world’s biggest macadamia producing regions.

The newly formed World Macadamia Organisation (WMO) has officially launched and incorporated in Singapore, with a mandate to drive demand by creating a greater understanding and appreciation of this understated wholefood. 

As a delicious source of plant-based protein, macadamias tick a lot of boxes when it comes to how modern consumers want to eat.

Snak King looks to optimize its efficiency in throughput, while also seeking to alleviate labor shortages at its manufacturing facility in Freeport, Illinois.

The pineapple chips are part of the brand's new Crispy Crunchy Dried Fruit Chip collection, and the chocolate-covered macadamias are part of a revamp to its chocolate portfolio.

Machining and handling of dough prior to the cutting action requires several steps/actions, as they are critical to the textural characteristics of the finished product.

The selection features eight flavors that capture the essence of autumn, per the brand.

The new bulk weigh filling machine is the first to integrate an in-line metal detector between the filling process and weigh bucket. 

The new seasonal treat is a blend of traditional French patisserie and the flavors of fall, and is available October 1 to December 1 while supplies last.

The advanced MULTIMATIC dough divider and moulding machine is now suitable for an even more versatile range of applications.

Automation, advances in sanitary design, greater worker safety and additional flexibility in packaging have been among the top requests that manufacturers of cutting, slicing and portioning equipment say they've been fielding from their customers this year.

This high-production ultrasonic round cake cutting machine is capable of cutting a variety of round products. 

Skout Organic is launching its newest snack, Vanilla Macaroon Soft-Baked Cookies, as part of the brand’s limited-time Small Batch line of cookies and bars this month.

Sweet Diplomacy now has a brick-and-mortar location in Los Altos, CA, a commercial kitchen in Boulder, CO, and an online menu shipping across the U.S.

Siemens recently announced a limited-time offer, effective until May 31, 2017. Under this program, users of any machine tool with SINUMERIK CNC onboard will receive a variety of additional services, plus training and a 10% discount on spare parts orders. 

The Beckhoff Automation booth (S-6302) will be the center of automation technology (AT) and IT convergence at Pack Expo 2017 in Las Vegas.