GSX in Orlando, held just before Hurricane Helene, showcased over 200 educational sessions and 500 exhibitors, emphasizing a shift from traditional product-focused displays to innovative solutions that leverage data for improved efficiency and predictive security management.

Washington – Safety efforts involving unmanned aircraft systems – commonly referred to as drones – must improve to protect airline aircraft, according to the Air Line Pilots Association, International.

Fairfax, VA — Virtual reality could provide a reliable measure in predicting a candidate’s ability to pass a crane certification exam, results of a recent study published by the National Commission for the Certification of Crane Operators indicate.

Whole Foods Market’s summer condiment trends predictions come at a time when customers are turning to condiments to elevate their meals more than ever before. According to Mintel, sales for the condiment, marinade and dressing category are expected to hit $2.9 billion by 2024, showing growth of more than 5% since 2020.

The explosion of plant-base meat and dairy analogs has created a paradigm shift in the ways food product developers are considering fats and oils. There’s plenty of innovation around plant-based meat alternatives, especially as they expand beyond burgers, sausages, and poultry into mimics of bacon, pork, and even such specific items as turkey burgers.

In a few weeks, we will begin publishing predictions for 2025. Prior to sharing our outlook for the year ahead, we wanted to review the predictions we made for 2024 to determine what we got right and what might not have fully taken shape.

Prior to sharing our outlook for 2025, we wanted to review the predictions we made for 2024 to determine what we got right and what might not have fully taken shape. Here, we've collected capsule information for each section of our 2024 Predictions, in areas such as sustainability, foodservice and global flavors. Within each capsule, you will find our 2024 predictions and assessments of their accuracy.

The trend predictions are developed by Kroger's industry-leading team of food experts including Our Brands' product developers, chefs, buyers, culinary specialists and Kroger's retail data science arm, 84.51º, for a well-rounded projection of what customers will be buzzing about for the upcoming year.

Read up on the bits and pieces of flavor trends and product innovation in this installment of Nuts & Bolts

The post Derivation and validation of an algorithm to predict transitions from community to residential long-term care among persons with dementia—A retrospective cohort study was curated by information for practice.

The post Machine Learning Technology in Predicting Relapse and Implementing Peer Recovery Intervention Before Drug Use Occurs was curated by information for practice.

Originally broadcast on November 2, 2012.

As the nation heads into election day on November 6, Basic Black considers the relevance of polls and the persistence of predictions. And what does it say about the candidates and this country that the race is so close?

In conversation:
- Latoyia Edwards, anchor, New England Cable News
- Phillip Martin, senior reporter, 89.7 WGBH Radio
- Peniel Joseph, professor of history Tufts University; Du Bois Fellow, Harvard University
- Robert Fortes, Republican strategist


(Photo: Early voting, Ohio 2012. Source: Associated Press.)

Here are seven people who wrongly predicted that Vice President Kamala Harris was going to win the presidential election. They include a widely respected election predictor, a veteran Democrat strategist, and a conservative columnist.

Here's a list of seven people who predicted that former President Donald Trump was going to win the election. They include a widely respected election predictor, a former adviser to Bill Clinton and a conservative columnist.

World War Three that may put an end t your existence as a human civilisation, will set off on its destructive march from the Middle East. This is what a number of prominent figures, as well as seers and mystics predicted. Perhaps the most famous modern forecast on the subject came from the late leader of the Liberal Democratic Party Vladimir Zhirinovsky, authors of AZ numerology project said while collecting predictions about the Middle East conflict. Speaking on Vladimir Solovyov Live in 2019, Zhirinovsky voiced an opinion that elections in Ukraine were its last, as "such a country simply will not remain on the map by 2024.” Moreover, the crisis in the Middle East will be so intense everyone will completely forget about Ukraine. 

The third Easter that the Orthodox world celebrated on May 5, 2024, is believed to be a victorious one for Russia, Elder Jonah of Odessa predicted. Victorious Easter in 2024 Elder Jonah of Odessa, the confessor of the Holy Dormition Monastery, who died in 2012 at the age of 87, warned long before the coup in Kyiv about the war that would erupt between a "small state” and Russia. The war, he predicted, would sow "chaos” in the world and spread "spiritual sprite” around Ukraine: "Ukraine and Russia do not exist separately — there is one Holy Rus'. Our enemies decided to divide us in order to destroy Orthodoxy in Little Rus'. God will not let that happen.”

Sinch, the company developing the way the world communicates through its Customer Communications Cloud, has released its predictions for the 2024 Black Friday and Cyber Monday (BF/CM) shopping season.

Added October 19, 2006:

The results of the seventh blind test of crystal structure prediction are presented, focusing on structure generation methods.

The results of the seventh blind test of crystal structure prediction are presented, focusing on structure ranking methods.

Accurate modeling of conformational energies is key to the crystal structure prediction of conformational polymorphs. Focusing on molecules XXXI and XXXII from the seventh blind test of crystal structure prediction, this study employs various electronic structure methods up to the level of domain-local pair natural orbital coupled cluster singles and doubles with perturbative triples [DLPNO-CCSD(T1)] to benchmark the conformational energies and to assess their impact on the crystal energy landscapes. Molecule XXXI proves to be a relatively straightforward case, with the conformational energies from generalized gradient approximation (GGA) functional B86bPBE-XDM changing only modestly when using more advanced density functionals such as PBE0-D4, ωB97M-V, and revDSD-PBEP86-D4, dispersion-corrected second-order Møller–Plesset perturbation theory (SCS-MP2D), or DLPNO-CCSD(T1). In contrast, the conformational energies of molecule XXXII prove difficult to determine reliably, and variations in the computed conformational energies appreciably impact the crystal energy landscape. Even high-level methods such as revDSD-PBEP86-D4 and SCS-MP2D exhibit significant disagreements with the DLPNO-CCSD(T1) benchmarks for molecule XXXII, highlighting the difficulty of predicting conformational energies for complex, drug-like molecules. The best-converged predicted crystal energy landscape obtained here for molecule XXXII disagrees significantly with what has been inferred about the solid-form landscape experimentally. The identified limitations of the calculations are probably insufficient to account for the discrepancies between theory and experiment on molecule XXXII, and further investigation of the experimental solid-form landscape would be valuable. Finally, assessment of several semi-empirical methods finds r2SCAN-3c to be the most promising, with conformational energy accuracy intermediate between the GGA and hybrid functionals and a low computational cost.

The seventh blind test of crystal structure prediction (CSP) methods substantially increased the level of complexity of the target compounds relative to the previous tests organized by the Cambridge Crystallographic Data Centre. In this work, the performance of density-functional methods is assessed using numerical atomic orbitals and the exchange-hole dipole moment dispersion correction (XDM) for the energy-ranking phase of the seventh blind test. Overall, excellent performance was seen for the two rigid molecules (XXVII, XXVIII) and for the organic salt (XXXIII). However, for the agrochemical (XXXI) and pharmaceutical (XXXII) targets, the experimental polymorphs were ranked fairly high in energy amongst the provided candidate structures and inclusion of thermal free-energy corrections from the lattice vibrations was found to be essential for compound XXXI. Based on these results, it is proposed that the importance of vibrational free-energy corrections increases with the number of rotatable bonds.

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol−1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

The Circular Electron–Positron Collider (CEPC) in China can also work as an excellent powerful synchrotron light source, which can generate high-quality synchrotron radiation. This synchrotron radiation has potential advantages in the medical field as it has a broad spectrum, with energies ranging from visible light to X-rays used in conventional radiotherapy, up to several megaelectronvolts. FLASH radiotherapy is one of the most advanced radiotherapy modalities. It is a radiotherapy method that uses ultra-high dose rate irradiation to achieve the treatment dose in an instant; the ultra-high dose rate used is generally greater than 40 Gy s−1, and this type of radiotherapy can protect normal tissues well. In this paper, the treatment effect of CEPC synchrotron radiation for FLASH radiotherapy was evaluated by simulation. First, a Geant4 simulation was used to build a synchrotron radiation radiotherapy beamline station, and then the dose rate that the CEPC can produce was calculated. A physicochemical model of radiotherapy response kinetics was then established, and a large number of radiotherapy experimental data were comprehensively used to fit and determine the functional relationship between the treatment effect, dose rate and dose. Finally, the macroscopic treatment effect of FLASH radiotherapy was predicted using CEPC synchrotron radiation through the dose rate and the above-mentioned functional relationship. The results show that the synchrotron radiation beam from the CEPC is one of the best beams for FLASH radiotherapy.

The availability of highly accurate protein structure predictions from AlphaFold2 (AF2) and similar tools has hugely expanded the applicability of molecular replacement (MR) for crystal structure solution. Many structures can be solved routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here, this question is addressed using a large set of PDB depositions that were solved by SAD. A large majority (87%) could be solved using unedited or minimally edited AF2 predictions. A further 18 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using Slice'N'Dice, although different splitting methods succeeded on slightly different sets of cases. It is also found that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set, did not yield to any form of MR tested here, offering valuable hints as to the number and the characteristics of cases where experimental phasing remains essential for macromolecular structure solution.

Highly accurate protein structure prediction can generate accurate models of protein and protein–protein complexes in X-ray crystallography. However, the question of how to make more effective use of predicted models for completing structure analysis, and which strategies should be employed for the more challenging cases such as multi-helical structures, multimeric structures and extremely large structures, both in the model preparation and in the completion steps, remains open for discussion. In this paper, a new strategy is proposed based on the framework of direct methods and dual-space iteration, which can greatly simplify the pre-processing steps of predicted models both in normal and in challenging cases. Following this strategy, full-length models or the conservative structural domains could be used directly as the starting model, and the phase error and the model bias between the starting model and the real structure would be modified in the direct-methods-based dual-space iteration. Many challenging cases (from CASP14) have been tested for the general applicability of this constructive strategy, and almost complete models have been generated with reasonable statistics. The hybrid strategy therefore provides a meaningful scheme for X-ray structure determination using a predicted model as the starting point.

This paper uses deep learning to present a proof-of-concept for data-driven chemistry in single-molecule magnets (SMMs). Previous discussions within SMM research have proposed links between molecular structures (crystal structures) and single-molecule magnetic properties; however, these have only interpreted the results. Therefore, this study introduces a data-driven approach to predict the properties of SMM structures using deep learning. The deep-learning model learns the structural features of the SMM molecules by extracting the single-molecule magnetic properties from the 3D coordinates presented in this paper. The model accurately determined whether a molecule was a single-molecule magnet, with an accuracy rate of approximately 70% in predicting the SMM properties. The deep-learning model found SMMs from 20 000 metal complexes extracted from the Cambridge Structural Database. Using deep-learning models for predicting SMM properties and guiding the design of novel molecules is promising.

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules (e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

Ultra-intense, ultra-fast X-ray free-electron lasers (XFELs) enable the imaging of single protein molecules under ambient temperature and pressure. A crucial aspect of structure reconstruction involves determining the relative orientations of each diffraction pattern and recovering the missing phase information. In this paper, we introduce a predicted model-aided algorithm for orientation determination and phase retrieval, which has been tested on various simulated datasets and has shown significant improvements in the success rate, accuracy and efficiency of XFEL data reconstruction.

Stimulated by informal conversations at the XVII International Small Angle Scattering (SAS) conference (Traverse City, 2017), an international team of experts undertook a round-robin exercise to produce a large dataset from proteins under standard solution conditions. These data were used to generate consensus SAS profiles for xylose isomerase, urate oxidase, xylanase, lysozyme and ribonuclease A. Here, we apply a new protocol using maximum likelihood with a larger number of the contributed datasets to generate improved consensus profiles. We investigate the fits of these profiles to predicted profiles from atomic coordinates that incorporate different models to account for the contribution to the scattering of water molecules of hydration surrounding proteins in solution. Programs using an implicit, shell-type hydration layer generally optimize fits to experimental data with the aid of two parameters that adjust the volume of the bulk solvent excluded by the protein and the contrast of the hydration layer. For these models, we found the error-weighted residual differences between the model and the experiment generally reflected the subsidiary maxima and minima in the consensus profiles that are determined by the size of the protein plus the hydration layer. By comparison, all-atom solute and solvent molecular dynamics (MD) simulations are without the benefit of adjustable parameters and, nonetheless, they yielded at least equally good fits with residual differences that are less reflective of the structure in the consensus profile. Further, where MD simulations accounted for the precise solvent composition of the experiment, specifically the inclusion of ions, the modelled radius of gyration values were significantly closer to the experiment. The power of adjustable parameters to mask real differences between a model and the structure present in solution is demonstrated by the results for the conformationally dynamic ribonuclease A and calculations with pseudo-experimental data. This study shows that, while methods invoking an implicit hydration layer have the unequivocal advantage of speed, care is needed to understand the influence of the adjustable parameters. All-atom solute and solvent MD simulations are slower but are less susceptible to false positives, and can account for thermal fluctuations in atomic positions, and more accurately represent the water molecules of hydration that contribute to the scattering profile.

Ultrafast, high-intensity X-ray free-electron lasers can perform diffraction imaging of single protein molecules. Various algorithms have been developed to determine the orientation of each single-particle diffraction pattern and reconstruct the 3D diffraction intensity. Most of these algorithms rely on the premise that all diffraction patterns originate from identical protein molecules. However, in actual experiments, diffraction patterns from multiple different molecules may be collected simultaneously. Here, we propose a predicted model-aided one-step classification–multireconstruction algorithm that can handle mixed diffraction patterns from various molecules. The algorithm uses predicted structures of different protein molecules as templates to classify diffraction patterns based on correlation coefficients and determines orientations using a correlation maximization method. Tests on simulated data demonstrated high accuracy and efficiency in classification and reconstruction.

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. A novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold2 has previously been established. It is particularly well suited to the detection of register errors. Importantly, this new approach is orthogonal to traditional methods based on stereochemistry or map–model agreement, and is resolution independent. Here, thousands of likely register errors are identified by scanning 3–5 Å resolution structures in the PDB. Unlike most methods, the application of this approach yields suggested corrections to the register of affected regions, which it is shown, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterized, but this approach is expected to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping to ensure the accuracy of future depositions.

Predicting crystal symmetry simply from chemical composition has remained challenging. Several machine-learning approaches can be employed, but the predictive value of popular crystallographic databases is relatively modest due to the paucity of data and uneven distribution across the 230 space groups. In this work, virtually all crystallographic information available to science has been compiled and used to train and test multiple machine-learning models. Composition-driven random-forest classification relying on a large set of descriptors showed the best performance. The predictive models for crystal system, Bravais lattice, point group and space group of inorganic compounds are made publicly available as easy-to-use software downloadable from https://gitlab.com/vishsoft/cosy.

The graphics in my post about R^2 were produced by an updated version of a sixty-year old program involving the U.S. census. Originally, the program was based on census data from 1900 to 1960 and sought to predict the population in 1970. The software back then was written in Fortran, the predominate technical programming language a half century ago. I have updated the MATLAB version of the program so that it now uses census data from 1900 to 2020.... read more >>

A new report from the National Academies of Sciences, Engineering, and Medicine calls for an international, multi-institutional comprehensive campaign of research, observation, and analysis activities that would help improve understanding and prediction of the Gulf of Mexico’s Loop Current System (LCS).

The emergence of inexpensive small unmanned aircraft systems (sUASs) that operate without a human pilot, commonly known as drones, has led to adversarial groups threatening deployed U.S. forces, especially infantry units.

The Gulf Research Program (GRP) of the National Academies of Sciences, Engineering, and Medicine today announced a new funding opportunity to provide up to $2.5 million in grants to foster innovative approaches that support its ongoing efforts to improve understanding and prediction of the Gulf of Mexico Loop Current System (LCS).

Urban flooding is a complex and distinct kind of flooding, compounded by land use and high population density, and it requires a different approach to assess and manage, says Framing the Challenge of Urban Flooding in the United States, a new report from the National Academies of Sciences, Engineering, and Medicine.

International thought leaders will join the next generation of engineers in London from Sept. 16 to 18 for the Global Grand Challenges Summit 2019. The summit aims to help inspire and equip future engineering leaders to address the rapidly evolving challenges of an unpredictable world.

The Gulf Research Program (GRP) of the National Academies of Sciences, Engineering, and Medicine today announced $2 million in grant awards for seven new projects through its Understanding Gulf Ocean Systems (UGOS) Grants 2 competition.

Sixteen years after Hurricane Katrina, communities across the Southeast are recovering from the catastrophic aftermath of Hurricane Ida. Learn more about advice that the National Academies have developed on managing evacuations during COVID, predicting storms and flooding, and preparing infrastructure for disasters.

The Gulf Research Program (GRP) of the National Academies of Sciences, Engineering, and Medicine announced $4.6 million in awards to support three project teams undertaking research to improve the forecasting of sea level rise along the Gulf Coast of the United States.

Snack Food & Wholesale Bakery was recently able to talk to Jonathan Davis, SVP of innovation, La Brea Bakery, Los Angeles, about upcoming bakery trends for 2020.

Domino Sugar is sharing an inside look into the hottest 2024 baking trends.

Puratos Serbia has utilized flour and dough analysis technology to help develop a game-changing quality assurance database for its extensive product line.

As 2023 comes to a close it is now time to review my predictions from last year as well as predict ten more trends I see as we enter 2024.

Report says plastic substitution and environmentally friendly packaging trends should provide a tailwind for carton growth over the forecast period.