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.

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.

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 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 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).

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.

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.

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.

Unveiling the Future of Prediction and Play

ReductionTech Inc, a pioneering force in climate technology, urgently brings to your attention the grave implications and time-sensitive predictions surrounding climate solutions and finance within the next four-year El Nino period.

Dr. Richard Ruhling Biblical is a prophecy expert and taught Health Science at Loma Linda University. He predicted war with Iraq before 9-11, based on Christ's saying to read the book of Daniel.

Several KM leaders offer predictions for the space in 2023

As a proof of concept, a recurrent neural network (RNN) model was developed using electronic medical record (EMR) data capable of continuously assessing a child's risk of mortality throughout an ICU stay as a proxy measure of illness severity.

tl;dr. Prediction markets give good forecasts because they attract “smart money” that will fix any gaps between current odds and best available information. The “smart money” is in turn motivated by the profits they can take from “dumb money” coming … Continue reading →

This is Jessica. I got through a long string of deadlines and invited talks and now I’m back to thinking about calibration and decision-making. In a previous post I was wondering about the relationship between calibration and Bayesian use of … Continue reading →

With news items such How the Trump Whale Correctly Called the Election and Prediction markets got Trump’s victory right; Betting markets predicted a Trump victory, while traditional polls were showing a tossup, prediction markets are having their coming-out party. Before … Continue reading →

Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer’s disease  Nature.com

Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks  Nature.com

Proteomic signatures improve risk prediction for common and rare diseases  Nature.com

It's time for another round of "Planet Money Predictions!" Economic forecasters square off to predict the future of inflation and explain what's going on in the economy.| Subscribe to our weekly newsletter here.

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It's time for another installment of ... Planet Money Predictions! *air horn*

Last year, we invited two economic forecasters to tell us what they saw coming for jobs, the housing market, and inflation. And now they're back. Which means it's time to find out whose predictions were more on the money, and send the victor to the next round, where they face off against a new forecasting phenom.

Since our last game, housing and inflation have cooled, but the job market keeps going strong. And the possibility of a recession still looms large. Our forecasters tell us what they see in the economy now, and what they expect in the months ahead.

This episode was produced by James Sneed. It was engineered by Katherine Silva. It was fact-checked by Sierra Juarez and edited by Molly Messick. Jess Jiang is our acting executive producer.

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As we enter the second half of 2020, the beginning of the year already feels like a distant memory. So much has changed so quickly, and one might assume the priorities and challenges for staffing and recruiting businesses today are worlds apart from what they were just six months ago. While there's no doubt that the landscape has changed dramatically, the industry is still all about people. In that sense, many ...

Bob Quillin, Vice President of Oracle Cloud Developer Relations, joins guest host Javed Mohammed to explain his tech predictions for 2020, which cover the cloud, open source, Kubernetes, artificial intelligence, machine learning, and more.

 See the complete show notes.

Perceptual objective listening quality prediction

U4SSC - Case study - Crime prediction for more agile policing in cities - Rio de Janeiro, Brazil

Collision avoidance technologies need beefing up to cope with solar storms, says astronomers.

A new brightness forecast for Comet Tsuchinshan-ATLAS whets our hopes for a fine appearance in late September and early October.

The post Update: Comet Tsuchinshan-ATLAS Might Outshine Predictions appeared first on Sky & Telescope.

Location: Electronic Resource- 

President-elect Donald Trump has gone against media expectations by tapping Marco Rubio, Kristi Noem and a number of other Capitol Hill veterans to fill posts in his second administration.

Over the past two decades, Google has hosted two different internal platforms for predictions. Why did the first one fail — and will the other endure?

On any given night, Nuggets center Nikola Jokic or Avalanche center Nathan MacKinnon can be a whole flight of stairs above any competitor on the court or in the rink.

The dust is settling on this year’s Premier League season, and Blues fans are both satisfied and, have a right..

The post Early Prediction: Chelsea’s Chances in the 2024/25 Premier League appeared first on Chelsea FC Blog.

Continuing to set records, Pepe Unchained recently achieved a new milestone after reaching $26 million. The project’s presale has now… Continue reading As Pepe Unchained Launch Date Nears, Analyst Makes ‘Massive’ Prediction For PEPU

The post As Pepe Unchained Launch Date Nears, Analyst Makes ‘Massive’ Prediction For PEPU appeared first on ReadWrite.

Visual Abstract

Kailun Fang
Nov 30, 2020; 0:RA120.002081v1-mcp.RA120.002081
Research

The molecular mechanism associated with mammalian meiosis has yet to be fully explored, and one of the main reasons for this lack of exploration is that some meiosis-essential genes are still unknown. The profiling of gene expression during spermatogenesis has been performed in previous studies, yet few studies have aimed to find new functional genes. Since there is a huge gap between the number of genes that are able to be quantified and the number of genes that can be characterized by phenotype screening in one assay, an efficient method to rank quantified genes according to phenotypic relevance is of great importance. We proposed to rank genes by the probability of their function in mammalian meiosis based on global protein abundance using machine learning. Here, nine types of germ cells focusing on continual substages of meiosis prophase I were isolated, and the corresponding proteomes were quantified by high-resolution mass spectrometry. By combining meiotic labels annotated from the MGI mouse knockout database and the spermatogenesis proteomics dataset, a supervised machine learning package, FuncProFinder, was developed to rank meiosis-essential candidates. Of the candidates whose functions were unannotated, four of ten genes with the top prediction scores, Zcwpw1, Tesmin, 1700102P08Rik and Kctd19, were validated as meiosis-essential genes by knockout mouse models. Therefore,  mammalian meiosis-essential genes could be efficiently predicted based on the protein abundance dataset, which provides a paradigm for other functional gene mining from a related abundance dataset.

Artificial Intelligence Prediction and Counterterrorism Research paper sysadmin 6 August 2019

The use of AI in counterterrorism is not inherently wrong, and this paper suggests some necessary conditions for legitimate use of AI as part of a predictive approach to counterterrorism on the part of liberal democratic states.

Summary

• The use of predictive artificial intelligence (AI) in countering terrorism is often assumed to have a deleterious effect on human rights, generating spectres of ‘pre-crime’ punishment and surveillance states. However, the well-regulated use of new capabilities may enhance states’ abilities to protect citizens’ right to life, while at the same time improving adherence to principles intended to protect other human rights, such as transparency, proportionality and freedom from unfair discrimination. The same regulatory framework could also contribute to safeguarding against broader misuse of related technologies.
• Most states focus on preventing terrorist attacks, rather than reacting to them. As such, prediction is already central to effective counterterrorism. AI allows higher volumes of data to be analysed, and may perceive patterns in those data that would, for reasons of both volume and dimensionality, otherwise be beyond the capacity of human interpretation. The impact of this is that traditional methods of investigation that work outwards from known suspects may be supplemented by methods that analyse the activity of a broad section of an entire population to identify previously unknown threats.
• Developments in AI have amplified the ability to conduct surveillance without being constrained by resources. Facial recognition technology, for instance, may enable the complete automation of surveillance using CCTV in public places in the near future.
• The current way predictive AI capabilities are used presents a number of interrelated problems from both a human rights and a practical perspective. Where limitations and regulations do exist, they may have the effect of curtailing the utility of approaches that apply AI, while not necessarily safeguarding human rights to an adequate extent.
• The infringement of privacy associated with the automated analysis of certain types of public data is not wrong in principle, but the analysis must be conducted within a robust legal and policy framework that places sensible limitations on interventions based on its results.
• In future, broader access to less intrusive aspects of public data, direct regulation of how those data are used – including oversight of activities by private-sector actors – and the imposition of technical as well as regulatory safeguards may improve both operational performance and compliance with human rights legislation. It is important that any such measures proceed in a manner that is sensitive to the impact on other rights such as freedom of expression, and freedom of association and assembly.

Our objective is to explore quantitative imaging markers for early prediction of treatment response in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) undergoing [¹⁷⁷Lu]Lu-DOTATATE therapy. By doing so, we aim to enable timely switching to more effective therapies in order to prevent time-resource waste and minimize toxicities. Methods: Patients diagnosed with unresectable or metastatic, progressive, well-differentiated, receptor-positive GEP-NETs who received 4 sessions of [¹⁷⁷Lu]Lu-DOTATATE were retrospectively selected. Using SPECT/CT images taken at the end of treatment sessions, we counted all visible tumors and measured their largest diameters to calculate the tumor burden score (TBS). Up to 4 target lesions were selected and semiautomatically segmented. Target lesion peak counts and spleen peak counts were measured, and normalized peak counts were calculated. Changes in TBS (TBS) and changes in normalized peak count (nPC) throughout treatment sessions in relation to the first treatment session were calculated. Treatment responses were evaluated using third-month CT and were binarized as progressive disease (PD) or non-PD. Results: Twenty-seven patients were included (7 PD, 20 non-PD). Significant differences were observed in TBS_second-first, TBS_third-first, and TBS_fourth-first (where second-first, third-first, and fourth-first denote scan number between the second and first, third and first, and fourth and first [¹⁷⁷Lu]Lu-DOTATATE treatment cycles), respectively) between the PD and non-PD groups (median, 0.043 vs. –0.049, 0.08 vs. –0.116, and 0.109 vs. –0.123 [P = 0.023, P = 0.002, and P < 0.001], respectively). nPC_second-first showed significant group differences (mean, –0.107 vs. –0.282; P = 0.033); nPC_third-first and nPC_fourth-first did not reach statistical significance (mean, –0.122 vs. –0.312 and –0.183 vs. –0.405 [P = 0.117 and 0.067], respectively). At the optimal threshold, TBS_fourth-first exhibited an area under the curve (AUC) of 0.957, achieving 100% sensitivity and 80% specificity. TBS_second-first and TBS_third-first reached AUCs of 0.793 and 0.893, sensitivities of 71.4%, and specificities of 85% and 95%, respectively. nPC_second-first, nPC_third-first, and nPC_fourth-first showed AUCs of 0.764, 0.693, and 0.679; sensitivities of 71.4%, 71.4%, and 100%; and specificities of 75%, 70%, and 35%, respectively. Conclusion: TBS and nPC can predict [¹⁷⁷Lu]Lu-DOTATATE response by the second treatment session.

Unspecific bone uptake (UBU) related to [¹⁸F]PSMA-1007 PET/CT imaging represents a clinical challenge. We aimed to assess whether a combination of clinical, biochemical, and imaging parameters could predict skeletal metastases in patients with [¹⁸F]PSMA-1007 bone focal uptake, aiding in result interpretation. Methods: We retrospectively analyzed [¹⁸F]PSMA-1007 PET/CT performed in hormone-sensitive prostate cancer (PCa) patients at 3 tertiary-level cancer centers. A fourth center was involved in performing an external validation. For each, a volume of interest was drawn using a threshold method to extract SUV_max, SUV_mean, PSMA tumor volume, and total lesion PSMA. The same volume of interest was applied to CT images to calculate the mean Hounsfield units (HU_mean) and maximum Hounsfield units. Clinical and laboratory data were collected from electronic medical records. A composite reference standard, including follow-up histopathology, biochemistry, and imaging data, was used to distinguish between PCa bone metastases and UBU. PET readers with less (n = 2) or more (n = 2) experience, masked to the reference standard, were asked to visually rate a subset of focal bone uptake (n = 178) as PCa metastases or not. Results: In total, 448 bone [¹⁸F]PSMA-1007 focal uptake specimens were identified in 267 PCa patients. Of the 448 uptake samples, 188 (41.9%) corresponded to PCa metastases. Ongoing androgen deprivation therapy at PET/CT (P < 0.001) with determination of SUV_max (P < 0.001) and HU_mean (P < 0.001) independently predicted bone metastases. A composite prediction score, the bone uptake metastatic probability (BUMP) score, achieving an area under the receiver-operating-characteristic curve (AUC) of 0.87, was validated through a 10-fold internal and external validation (n = 89 bone uptake, 51% metastatic; AUC, 0.92). The BUMP score’s AUC was significantly higher than that of HU_mean (AUC, 0.62) and remained high among lesions with HU_mean in the first tertile (AUC, 0.80). A decision-curve analysis showed a higher net benefit with the score. Compared with the visual assessment, the BUMP score provided added value in terms of specificity in less-experienced PET readers (88% vs. 54%, P < 0.001). Conclusion: The BUMP score accurately distinguished UBU from bone metastases in PCa patients with [¹⁸F]PSMA-1007 focal bone uptake at PET imaging, offering additional value compared with the simple assessment of the osteoblastic CT correlate. Its use could help clinicians interpret imaging results, particularly those with less experience, potentially reducing the risk of patient overstaging.