Photooxidation of Polyolefins to Produce Materials with In-chain Ketones and Improved Materials Properties.

Herein, we report a selective photooxidation of commodity postconsumer polyolefins to produce polymers with in-chain ketones. The reaction does not involve the use of catalyst, metals, or expensive oxidants, and selectively introduces ketone functional groups. Under mild and operationally simple conditions, yields up to 1.23 mol% of in-chain ketones were achieved. Installation of in-chain ketones resulted in materials with improved adhesion of the materials and miscibility of mixed plastics relative to the unfunctionalized plastics. The introduction of ketone groups into the polymer backbone allows these materials to react with diamines, forming dynamic covalent polyolefin networks. This strategy allows for the upcycling of mixed plastic waste into reprocessable materials with enhanced performance properties compared to polyolefin blends. Mechanistic studies support the involvement of photoexcited nitroaromatics in consecutive hydrogen and oxygen atom transfer reactions.

Enantioconvergent Cross-Nucleophile Coupling: Copper-Catalyzed Deborylative Cyanation.

Organoboron compounds are widely utilized in organic synthesis for their diverse reactivity, modular preparation, and stability compared to other classes of organometallic reagents. While organoboron species are commonly employed as nucleophiles in cross-coupling reactions, their potential as racemic building blocks in enantioconvergent transformations remains largely untapped. Herein, we demonstrate the direct utilization of alkylboronic pinacol esters in intermolecular enantioconvergent transformations. Specifically, this work describes the development and mechanistic study of an enantioconvergent deborylative cyanation enabled by Cu catalysis. This method imparts a high degree of enantioselectivity and tolerates a wide range of common functional groups and heterocycles. The reaction is proposed to proceed through a radical-relay mechanism. Aniline-assisted homolysis of the carbon-boron bond results in prochiral alkyl radicals that are functionalized by in situ generated Cu(II)(CN)2 species in an enantioselective fashion. The Cu(II)(CN)2 intermediate was characterized by electron paramagnetic resonance (EPR) spectroscopy, and its electronic structure was probed using density functional theory (DFT) calculations. Computational studies were carried out to corroborate the proposed radical-relay mechanism.

Modular synthesis of aryl amines from 3-alkynyl-2-pyrones.

The synthesis of aryl amines from 3-alkynyl-2-pyrones and various amines is described. Mechanistically, the aryl amines are proposed to arise from the 3-alkynyl-2-pyrone substrates through their selective opening in a 1,6-fashion by secondary amines followed by decarboxylation and an unexpected rearrangement. The proposed mechanism is supported by quantum chemical transition-state calculations, which are consistent with the regiochemical outcome. The scope of this transformation spans a variety of 3-alkynyl-2-pyrones and a range of secondary amines. The influence of the secondary amine coupling partners on reaction efficiency was elucidated through data-driven modeling as well as scope exploration. These latter studies revealed that the steric bulk of the secondary amine coupling partner under the reaction conditions serves as a strong indicator of overall reaction efficiency.

Modelling the temperature dependent extrinsic incubation period of West Nile Virus using Bayesian time delay models.

West Nile Virus (WNV) is a mosquito-borne pathogen that primarily infects birds. Infections can spillover to humans and cause a spectrum of clinical symptoms, including WNV neuroinvasive disease. The extrinsic incubation period (EIP) is the time taken for a mosquito to become infectious following the ingestion of an infected blood meal. Characterising how the EIP varies with temperature is an essential part of predicting the impact and transmission dynamics of WNV. We re-analyse existing experimental data using Bayesian time delay models, allowing us to account for variation in how quickly individual mosquitoes developed disseminated WNV infections. In these experiments, cohorts of Culex pipiens mosquitoes were infected with WNV and kept under different temperature conditions, being checked for disseminated infection at defined timepoints. We find that EIPs are best described with a Weibull distribution and become shorter log-linearly with temperature. Under 18°C, less than 1% of infected Cx. pipiens had a disseminated infection after 5 days, compared to 9.73% (95% CrI: 7.97 to 11.54) at 25°C and 42.20% (95% CrI: 38.32 to 46.60) at 30°C. In the hottest experimental temperature treatment (32°C), the EIP50 was estimated at 3.78 days (CrI: 3.42 to 4.15) compared to over 100 days in the coolest treatment (15°C). The variance of EIPs was found to be much larger at lower temperatures than higher temperatures, highlighting the importance of characterising the time delay distribution associated with the EIP. We additionally demonstrate a competitive advantage of WNV strain WN02 over NY99, where the former infects mosquitoes more quickly at colder temperatures than the latter. This research contributes crucial parameters to the WNV literature, providing essential insights for modellers and those planning interventions.

A comparative study of influenza surveillance systems and administrative data in England during the 2022-2023 season.

Accurate and representative surveillance is essential for understanding the impact of influenza on healthcare systems. During the 2022-2023 influenza season, the Northern Hemisphere experienced its most significant epidemic wave since the onset of the COVID-19 pandemic in 2020. Concurrently, new surveillance systems, developed in response to the pandemic, became available within health services. In this study, we analysed per capita admission rates from National Health Service hospital Trusts across four surveillance systems in England during the winter of 2022-2023. We examined differences in reporting timeliness, data completeness, and regional coverage, modelling key epidemic metrics including the maximum admission rates, cumulative seasonal admissions, and growth rates by fitting generalised additive models at national and regional levels. From modelling the admission rates per capita, we find that different surveillance systems yield varying estimates of key epidemiological metrics, both spatially and temporally. While national data from these systems generally align on the maximum admission rate and growth trends, discrepancies emerge at the subnational level, particularly in the cumulative admission rate estimates, with notable issues observed in London and the East of England. The rapid growth and decay phases of the epidemic contributed to higher uncertainty in these estimates, especially in regions with variable data quality. The study highlights that the choice of surveillance system can significantly influence the interpretation of influenza trends, especially at the subnational level, where regional disparities may mask true epidemic dynamics. Comparing multiple data sources enhances our understanding of the impact of seasonal influenza epidemics and highlights the limitations of relying on a single system.

Organocatalyzed Carbonylation of Alkyl Halides Driven by Visible Light.

Herein, we describe a new strategy for the carbonylation of alkyl halides with different nucleophiles to generate valuable carbonyl derivatives under visible light irradiation. This method is mild, robust, highly selective, and proceeds under metal-free conditions to prepare a range of structurally diverse esters and amides in good to excellent yields. In addition, we highlight the application of this activation strategy for 13C isotopic incorporation. We propose that the reaction proceeds by a photoinduced reduction to afford carbon-centered radicals from alkyl halides, which undergo subsequent single electron-oxidation to form a carbocationic intermediate. Carbon monoxide is trapped by the carbocation to generate an acylium cation, which can be attacked by a series of nucleophiles to give a range of carbonyl products.

Radical Chlorination of Non-Resonant Heterobenzylic C-H Bonds and High-Throughput Diversification of Heterocycles.

Site-selective functionalization of the heterobenzylic C(sp3)-H bonds of pyridines and related heteroaromatic compounds presents challenges associated with the basic nitrogen atom and the variable reactivity among different positions on the heteroaromatic ring. Methods for functionalization of 2- and 4-alkylpyridines are increasingly available through polar pathways that leverage resonance stabilization of charge build-up at these positions. In contrast, functionalization of 3-alkylpyridines is largely inaccessible. Here, we report a photochemically promoted method for chlorination of non-resonant heterobenzylic C(sp3)-H sites in 3-alkylpyridines and related alkylheteroaromatics. Density functional theory calculations show that the optimal reactivity reflects a balance between the energetics of the two radical-chain propagation steps, with the preferred reagent consisting of an N-chlorosulfonamide. The operationally simple chlorination protocol enables access to heterobenzylic chlorides which serve as versatile intermediates in C-H cross-coupling reactions between heteroaromatic building blocks and diverse oxidatively sensitive nucleophiles using high-throughput experimentation.

Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters.

The ability to relate substituent electronic effects to chemical reactivity is a cornerstone of physical organic chemistry and Linear Free Energy Relationships. The computation of electronic parameters is increasingly attractive since they can be obtained rapidly for structures and substituents without available experimental data and can be applied beyond aromatic substituents, for example, in studies of transition metal complexes and aliphatic and radical systems. Nevertheless, the description of "top-down" macroscopic observables, such as Hammett parameters using a "bottom-up" computational approach, poses several challenges for the practitioner. We have examined and benchmarked the performance of various computational charge schemes encompassing quantum mechanical methods that partition charge density, methods that fit charge to physical observables, and methods enhanced by semiempirical adjustments alongside NMR values. We study the locations of the atoms used to obtain these descriptors and their correlation with empirical Hammett parameters and rate differences resulting from electronic effects. These seemingly small choices have a much more significant impact than previously imagined, which outweighs the level of theory or basis set used. We observe a wide range of performance across the different computational protocols and observe stark and surprising differences in the ability of computational parameters to capture para- vs meta-electronic effects. In general, σm predictions fare much worse than σp. As a result, the choice of where to compute these descriptors-for the ring carbons or the attached H or other substituent atoms-affects their ability to capture experimental electronic differences. Density-based schemes, such as Hirshfeld charges, are more stable toward unphysical charge perturbations that result from nearby functional groups and outperform all other computational descriptors, including several commonly used basis set based schemes such as Natural Population Analysis. Using attached atoms also improves the statistical correlations. We obtained general linear relationships for the global prediction of experimental Hammett parameters from computed descriptors for use in statistical modeling studies.

The real-time infection hospitalisation and fatality risk across the COVID-19 pandemic in England.

The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution.

Late-stage benzenoid-to-troponoid skeletal modification of the cephalotanes exemplified by the total synthesis of harringtonolide.

Skeletal modifications enable elegant and rapid access to various derivatives of a compound that would otherwise be difficult to prepare. They are therefore a powerful tool, especially in the synthesis of natural products or drug discovery, to explore different natural products or to improve the properties of a drug candidate starting from a common intermediate. Inspired by the biosynthesis of the cephalotane natural products, we report here a single-atom insertion into the framework of the benzenoid subfamily, providing access to the troponoid congeners - representing the reverse of the proposed biosynthesis (i.e., a contra-biosynthesis approach). Computational evaluation of our designed transformation prompted us to investigate a Büchner-Curtius-Schlotterbeck reaction of a p-quinol methylether, which ultimately results in the synthesis of harringtonolide in two steps from cephanolide A, which we had previously prepared. Additional computational studies reveal that unconventional selectivity outcomes are driven by the choice of a Lewis acid and the nucleophile, which should inform further developments of these types of reactions.

A deconstruction-reconstruction strategy for pyrimidine diversification.

Structure-activity relationship (SAR) studies are fundamental to drug and agrochemical development, yet only a few synthetic strategies apply to the nitrogen heteroaromatics frequently encountered in small molecule candidates1-3. Here we present an alternative approach in which we convert pyrimidine-containing compounds into various other nitrogen heteroaromatics. Transforming pyrimidines into their corresponding N-arylpyrimidinium salts enables cleavage into a three-carbon iminoenamine building block, used for various heterocycle-forming reactions. This deconstruction-reconstruction sequence diversifies the initial pyrimidine core and enables access to various heterocycles, such as azoles4. In effect, this approach allows heterocycle formation on complex molecules, resulting in analogues that would be challenging to obtain by other methods. We anticipate that this deconstruction-reconstruction strategy will extend to other heterocycle classes.

Predicting Lewis Acidity: Machine Learning the Fluoride Ion Affinity of p-Block-Atom-Based Molecules.

"How strong is this Lewis acid?" is a question researchers often approach by calculating its fluoride ion affinity (FIA) with quantum chemistry. Here, we present FIA49k, an extensive FIA dataset with 48,986 data points calculated at the RI-DSD-BLYP-D3(BJ)/def2-QZVPP//PBEh-3c level of theory, including 13 different p-block atoms as the fluoride accepting site. The FIA49k dataset was used to train FIA-GNN, two message-passing graph neural networks, which predict gas and solution phase FIA values of molecules excluded from training with a mean absolute error of 14 kJ mol-1 (r2=0.93) from the SMILES string of the Lewis acid as the only input. The level of accuracy is notable, given the wide energetic range of 750 kJ mol-1 spanned by FIA49k. The model's value was demonstrated with four case studies, including predictions for molecules extracted from the Cambridge Structural Database and by reproducing results from catalysis research available in the literature. Weaknesses of the model are evaluated and interpreted chemically. FIA-GNN and the FIA49k dataset can be reached via a free web app (www.grebgroup.de/fia-gnn).

Understanding the infection severity and epidemiological characteristics of mpox in the UK.

In May 2022, individuals infected with the monkeypox virus were detected in the UK without clear travel links to endemic areas. Understanding the clinical characteristics and infection severity of mpox is necessary for effective public health policy. The study period of this paper, from the 1st June 2022 to 30th September 2022, included 3,375 individuals that tested positive for the monkeypox virus. The posterior mean times from infection to hospital admission and length of hospital stay were 14.89 days (95% Credible Intervals (CrI): 13.60, 16.32) and 7.07 days (95% CrI: 6.07, 8.23), respectively. We estimated the modelled Infection Hospitalisation Risk to be 4.13% (95% CrI: 3.04, 5.02), compared to the overall sample Case Hospitalisation Risk (CHR) of 5.10% (95% CrI: 4.38, 5.86). The overall sample CHR was estimated to be 17.86% (95% CrI: 6.06, 33.11) for females and 4.99% (95% CrI: 4.27, 5.75) for males. A notable difference was observed between the CHRs that were estimated for each sex, which may be indicative of increased infection severity in females or a considerably lower infection ascertainment rate. It was estimated that 74.65% (95% CrI: 55.78, 86.85) of infections with the monkeypox virus in the UK were captured over the outbreak.

Designing solvent systems using self-evolving solubility databases and graph neural networks.

Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address deficiencies in the model's predictive accuracy and generalizability; this can be addressed by expanding and integrating experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be trained separately, and they should not be simply combined without reconciling the discrepancies from different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from quantum-mechanical calculations are referred to during semi-supervised learning, but they are not directly added to the experimental database. Dataset augmentation is performed from 11 637 experimental solubilities to >900 000 data points in the integrated database, while correcting for the discrepancies between experiment and computation. Our model was successfully applied to study solvent selection in organic reactions and separation processes. The accuracy (mean absolute error around 0.2 kcal mol-1 for the test set) is quantitatively useful in exploring Linear Free Energy Relationships between reaction rates and solvation free energies for 11 organic reactions. Our model also accurately predicted the partition coefficients of lignin-derived monomers and drug-like molecules. While there is room for expanding solubility predictions to transition states, radicals, charged species, and organometallic complexes, this approach will be attractive to predictive chemistry areas where experimental, computational, and other heterogeneous data should be combined.

Catalytic Effects of Active Site Conformational Change in the Allosteric Activation of Imidazole Glycerol Phosphate Synthase.

Imidazole glycerol phosphate synthase (IGPS) is a class-I glutamine amidotransferase (GAT) that hydrolyzes glutamine. Ammonia is produced and transferred to a second active site, where it reacts with N1-(5'-phosphoribosyl)-formimino-5-aminoimidazole-4-carboxamide ribonucleotide (PrFAR) to form precursors to purine and histidine biosynthesis. Binding of PrFAR over 25 Šaway from the active site increases glutaminase efficiency by ∼4500-fold, primarily altering the glutamine turnover number. IGPS has been the focus of many studies on allosteric communication; however, atomic details for how the glutamine hydrolysis rate increases in the presence of PrFAR are lacking. We present a density functional theory study on 237-atom active site cluster models of IGPS based on crystallized structures representing the inactive and allosterically active conformations and investigate the multistep reaction leading to thioester formation and ammonia production. The proposed mechanism is supported by similar, well-studied enzyme mechanisms, and the corresponding energy profile is consistent with steady-state kinetic studies of PrFAR + IGPS. Additional active site models are constructed to examine the relationship between active site structural change and transition-state stabilization via energy decomposition schemes. The results reveal that the inactive IGPS conformation does not provide an adequately formed oxyanion hole structure and that repositioning of the oxyanion strand relative to the substrate is vital for a catalysis-competent oxyanion hole, with or without the hVal51 dihedral flip. These findings are valuable for future endeavors in modeling the IGPS allosteric mechanism by providing insight into the atomistic changes required for rate enhancement that can inform suitable reaction coordinates for subsequent investigations.

Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models.

BACKGROUND: Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast. METHODS: We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models. RESULTS: Across the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend. CONCLUSIONS: This study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.

Seasonal influenza causes a burden for hospitals and therefore it is useful to be able to accurately predict how many patients might be admitted with the disease. We attempted to predict influenza admissions up to 14 days in the future by creating a computational model that incorporates how the disease is reported and how it spreads. We evaluated our optimised model on data acquired during the winter of 2022-2023 data in England and compared it with previously developed models. Our model was better at modelling how influenza spreads and predicting future hospital admissions than the models we compared it to. Improving how influenza admissions are forecast can enable hospitals to prepare better for increased admissions, enabling improved treatment and reduced death for all patients in hospital over winter.

Electrochemical Modification of Polypeptides at Selenocysteine.

Mild strategies for the selective modification of peptides and proteins are in demand for applications in therapeutic peptide and protein discovery, and in the study of fundamental biomolecular processes. Herein, we describe the development of an electrochemical selenoetherification (e-SE) platform for the efficient site-selective functionalization of polypeptides. This methodology utilizes the unique reactivity of the 21st amino acid, selenocysteine, to effect formation of valuable bioconjugates through stable selenoether linkages under mild electrochemical conditions. The power of e-SE is highlighted through late-stage C-terminal modification of the FDA-approved cancer drug leuprolide and assembly of a library of anti-HER2 affibody conjugates bearing complex cargoes. Following assembly by e-SE, the utility of functionalized affibodies for in vitro imaging and targeting of HER2 positive breast and lung cancer cell lines is also demonstrated.

Iridium-Catalyzed Asymmetric Difunctionalization of C-C σ-Bonds Enabled by Ring-Strained Boronate Complexes.

Enantioenriched organoboron intermediates are important building blocks in organic synthesis and drug discovery. Recently, transition metal-catalyzed enantioselective 1,2-metalate rearrangements of alkenylboronates have emerged as an attractive protocol to access these valuable reagents by installing two different carbon fragments across C═C π-bonds. Herein, we report the development of an iridium-catalyzed asymmetric allylation-induced 1,2-metalate rearrangement of bicyclo[1.1.0]butyl (BCB) boronate complexes enabled by strain release, which allows asymmetric difunctionalization of C-C σ-bonds, including dicarbonation and carboboration. This protocol provides a variety of enantioenriched three-dimensional 1,1,3-trisubstituted cyclobutane products bearing a boronic ester that can be readily derivatized. Notably, the reaction gives trans diastereoisomers that result from an anti-addition across the C-C σ-bond, which is in contrast to the syn-additions observed for reactions promoted by PdII-aryl complexes and other electrophiles in our previous works. The diastereoselectivity has been rationalized based on a combination of experimental data and density functional theory calculations, which suggest that the BCB boronate complexes are highly nucleophilic and react via early transition states with low activation barriers.

Exploring Cuneanes as Potential Benzene Isosteres and Energetic Materials: Scope and Mechanistic Investigations into Regioselective Rearrangements from Cubanes.

Cuneane is a strained hydrocarbon that can be accessed via metal-catalyzed isomerization of cubane. The carbon atoms of cuneane define a polyhedron of the C2v point group with six faces─two triangular, two quadrilateral, and two pentagonal. The rigidity, strain, and unique exit vectors of the cuneane skeleton make it a potential scaffold of interest for the synthesis of functional small molecules and materials. However, the limited previous synthetic efforts toward cuneanes have focused on monosubstituted or redundantly substituted systems such as permethylated, perfluorinated, and bis(hydroxymethylated) cuneanes. Such compounds, particularly rotationally symmetric redundantly substituted cuneanes, have limited potential as building blocks for the synthesis of complex molecules. Reliable, predictable, and selective syntheses of polysubstituted cuneanes bearing more complex substitution patterns would facilitate the study of this ring system in myriad applications. Herein, we report the regioselective, AgI-catalyzed isomerization of asymmetrically 1,4-disubstituted cubanes to cuneanes. In-depth DFT calculations provide a charge-controlled regioselectivity model, and direct dynamics simulations indicate that the nonclassical carbocation invoked is short-lived and dynamic effects augment the charge model.