While England will definitely make one change for the series finale, Australia will also ponder alterations to their side after surrendering an 18-match unbeaten streak in home Ashes Tests.
Spinner Todd Murphy could come in for a pace bowler at…

While England will definitely make one change for the series finale, Australia will also ponder alterations to their side after surrendering an 18-match unbeaten streak in home Ashes Tests.
Spinner Todd Murphy could come in for a pace bowler at…

Mr Doyle was not the only person to potentially sell inside secrets on the development of Concorde.
In 1999, it was revealed a spy codenamed “Agent Ace” had also betrayed Britain.
The agent was an aeronautical engineer recruited in 1967, according to papers smuggled out of Russia by dissident KGB officer Vasili Mitrokhin.
It is thought Ace handed over more than 90,000 pages of detailed technical specifications.
The agent was one of more than a dozen spies operating within Britain and passing commercial and technological secrets to the Russians at the height of the Cold War, the papers revealed.
The secrets that made it out of Filton helped Russia build the Tupolev-144, nicknamed Concordski, and which was strikingly similar to Concorde.
It remains unclear whether Mr Doyle did, in fact, pass on the details he claimed to have done in the interview to the KGB or any other secrets about the Concorde programme.
For one, questions marks remain over why Mr Doyle was never prosecuted – despite admitting spying for Russia.
UK Parliament records seen by the BBC raised that very question on the 18 October 1971.
The Attorney General at the time said he had been consulted about the possibility of a prosecution under the Official Secrets Act, but a prosecution would be unlikely to succeed, based on the evidence, and criminal proceedings should not be started.

A surge in gift cards and vouchers is driving more customers into stores on Boxing Day, helping what has traditionally been one of the biggest sales for retailers compete with the increasing popularity of newer promotional events like the Black Friday and Cyber Monday juggernauts.
Gerry Harvey, the executive chairman of electronics and whitegoods giant Harvey Norman, said gift cards and vouchers now accounted for about one-fifth of all transactions, particularly on the other side of Christmas.
Loading…

Al-Marj – Marwan Al-Asbali is the head of the Veterinarians Syndicate in Al-Marj. He warned about inspection committees moving between poultry farms. They often fail to strictly adhere to biosecurity measures. This could spread epidemics…

Unlock the Editor’s Digest for free
Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.
China has fixed the renminbi at its strongest level against the dollar in 15 months, a move that analysts say signals its tolerance of a gradual appreciation as its soaring exports stoke tensions with trading partners.
The People’s Bank of China on Monday set the renminbi at 7.03 to the dollar, the strongest fix since September 30 2024. The currency has strengthened by almost 4 per cent this year against the greenback but has weakened against the euro and other currencies.
The relative weakness of the Chinese currency has been a bugbear for American and European leaders, who see it as unfairly advantaging their exporters and contributing to China’s enormous trade surplus.
“It’s clear we’re seeing an acceleration in renminbi strength to the end of year,” said Mansoor Mohi-uddin, chief economist at Bank of Singapore. “They’re clearly allowing the currency to rise but in a controlled way.”
Prior to a trade truce agreed in October, US tariffs on Chinese goods were at one stage as high as 145 per cent. “Now as the tariff situation becomes a lot clearer and less troublesome, you see the currency begin the rebound,” said Mohi-uddin.
The renminbi spot rate, which can fluctuate 2 per cent either way around the PBoC midpoint fix, has strengthened in recent weeks.
The Chinese central bank, in a statement released last week in the wake of a Monetary Policy Committee meeting on December 18, pledged to “maintain the basic stability of the [renminbi] exchange rate at a reasonable and balanced level”.
Even as the central bank has allowed the currency to strengthen, “the PBoC is also becoming more resistant to gains in the [renminbi], especially as they approach 7 [per dollar], which is both a psychologically important level for the PBoC and for exporters,” said Mitul Kotecha, head of foreign exchange and emerging markets macro strategy at Barclays.
“Never gamble on a one-way appreciation” of the renminbi, the state-owned Shanghai Securities News said in an article published on Monday.
Analysts doubted the PBoC would allow an appreciation that would undermine its export powerhouse economy, given its 5 per cent GDP growth target.
“The golden goose has been the exports trend, which has been the biggest contributor to growth this year,” said Kotecha.
He added that having a strong currency at a time when other drivers of growth, such as housing, are particularly weak will make it “tough for China to achieve its growth targets”.

Johnston JN, Kadriu B, Kraus C, Henter ID, Zarate CA Jr. Ketamine in neuropsychiatric disorders: an update. Neuropsychopharmacol. 2024;49:23–40.
Google Scholar
Conway CR, George MS, Sackeim HA….
Xu, Z. et al. NLRP inflammasomes in health and disease. Mol. Biomed. 5 (1), 14 (2024).
Carroll, K., Sawden, M. & Sharma, S. DAMPs, PAMPs, NLRs, RIGs, CLRs and TLRs–Understanding the alphabet soup in the context of bone biology. Curr. Osteoporos. Rep. 23 (1), 6 (2025).
Zheng, D., Liwinski, T. & Elinav, E. Inflammasome activation and regulation: toward a better Understanding of complex mechanisms. Cell. Discovery. 6 (1), 36 (2020).
Fusco, R. et al. Focus on the role of NLRP3 inflammasome in diseases. Int. J. Mol. Sci. 21 (12), 4223 (2020).
Van de Veerdonk, F. L. et al. Inflammasome activation and IL-1β and IL-18 processing during infection. Trends Immunol. 32 (3), 110–116 (2011).
Dadkhah, M. & Sharifi, M. The NLRP3 inflammasome: mechanisms of activation, regulation, and role in diseases. Int. Rev. Immunol. 44 (2), 98–111 (2025).
Jurcău, M. C. et al. The link between oxidative stress, mitochondrial dysfunction and neuroinflammation in the pathophysiology of alzheimer’s disease: therapeutic implications and future perspectives. Antioxidants 11 (11), 2167 (2022).
Li, Y. et al. Targeting microglial α-synuclein/TLRs/NF-kappaB/NLRP3 inflammasome axis in parkinson’s disease. Front. Immunol. 12, 719807 (2021).
Nasoohi, S., Parveen, K. & Ishrat, T. Metabolic syndrome, brain insulin resistance, and alzheimer’s disease: thioredoxin interacting protein (TXNIP) and inflammasome as core amplifiers. J. Alzheimer’s Disease. 66 (3), 857–885 (2018).
Boršić, E. et al. Clustering of NLRP3 induced by membrane or protein scaffolds promotes inflammasome assembly. Nat. Commun. 16 (1), 4887 (2025).
Fu, J. & Wu, H. Structural mechanisms of NLRP3 inflammasome assembly and activation. Annu. Rev. Immunol. 41 (1), 301–316 (2023).
Zhang, X. et al. Inhibitors of the NLRP3 inflammasome pathway as promising therapeutic candidates for inflammatory diseases. Int. J. Mol. Med. 51 (4), 35 (2023).
Kennedy, C. R. et al. A probe for NLRP3 inflammasome inhibitor MCC950 identifies carbonic anhydrase 2 as a novel target. ACS Chem. Biol. 16 (6), 982–990 (2021).
Patel, V. & Shah, M. Artificial intelligence and machine learning in drug discovery and development. Intell. Med. 2 (3), 134–140 (2022).
Daroch, A. & Purohit, R. MDbDMRP: A novel molecular descriptor-based computational model to identify drug-miRNA relationships. Int. J. Biol. Macromol. 287, 138580 (2025).
Sharma, B. & Purohit, R. Enhanced sampling simulations to explore Himalayan phytochemicals as potential phosphodiesterase-1 inhibitor for neurological disorders. Biochem. Biophys. Res. Commun. 758, 151614 (2025).
Singh, R. & Purohit, R. Determining the effect of natural compounds on mutations of Pyrazinamidase in multidrug-resistant tuberculosis: illuminating the dark tunnel. Biochem. Biophys. Res. Commun. 756, 151575 (2025).
Gupta, A., Thind, A. S. & Purohit, R. EGFR AP: a predictive machine learning model for assessing small molecule activity against the epidermal growth factor receptor. RSC Med. Chem. 16 (9), 4415–4426 (2025).
Hayat, C. et al. Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches. BMC Chem. 18 (1), 76 (2024).
Pinheiro, G. A. et al. Machine learning prediction of nine molecular properties based on the SMILES representation of the QM9 quantum-chemistry dataset. J. Phys. Chem. A. 124 (47), 9854–9866 (2020).
Kaneko, H. Molecular descriptors, structure generation, and inverse QSAR/QSPR based on SELFIES. ACS Omega. 8 (24), 21781–21786 (2023).
Samkhaniani, M. et al. A machine learning approach to feature selection and uncertainty analysis for biogas production in wastewater treatment plants. Waste Manage. 197, 14–24 (2025).
Pantic, I. & Paunovic Pantic, J. Artificial intelligence in chromatin analysis: A random forest model enhanced by fractal and wavelet features. Fractal Fract. 8 (8), 490 (2024).
Ishfaq, M. et al. Multinomial classification of NLRP3 inhibitory compounds based on large scale machine learning approaches. Mol. Diversity. 28 (4), 1849–1868 (2024).
Mehrabinezhad, A., Teshnehlab, M. & Sharifi, A. A comparative study to examine principal component analysis and kernel principal component analysis-based weighting layer for convolutional neural networks. Comput. Methods Biomech. Biomedical Engineering: Imaging Visualization. 12 (1), 2379526 (2024).
Abdul-Al, M. et al. A novel approach to enhancing multi-modal facial recognition: integrating convolutional neural networks, principal component analysis, and sequential neural networks. IEEE Access. 12 (2024).
Haji, A. Comparative analysis of autoencoder and PCA for dimensionality reduction in gene expression data. (2024).
Kaib, M. T. H. et al. Data size reduction approach for nonlinear process monitoring refinement using kernel PCA technique. Expert Syst. Appl. 274, 126975 (2025).
Makkulau, M. et al. Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis. in ITM Web of Conferences. EDP Sciences. (2024).
Frost, H. R. Eigenvectors from eigenvalues sparse principal component analysis (EESPCA). J. Comput. Graphical Statistics: Joint Publication Am. Stat. Association Inst. Math. Stat. Interface Foundation North. Am. 31 (2), 486 (2021).
Eze, N. M., Asogwa, O. C. & Eze, C. M. Principal component factor analysis of some development factors in Southern Nigeria and its extension to regression analysis. J. Adv. Math. Comput. Sci. 36 (3), 132–160 (2021).
Abdulhafedh, A. Incorporating k-means, hierarchical clustering and Pca in customer segmentation. J. City Dev. 3 (1), 12–30 (2021).
Niazi, S. K. & Mariam, Z. Recent advances in machine-learning-based chemoinformatics: a comprehensive review. Int. J. Mol. Sci. 24 (14), 11488 (2023).
Wani, M. A. & Roy, K. K. Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents. Mol. Diversity. 26 (3), 1345–1356 (2022).
Shrivastava, T., Singh, V. & Agrawal, A. Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening. Health Inform. Sci. Syst. 12 (1), 18 (2024).
Almatroudi, A. Integrative machine learning, virtual screening, and molecular modeling for BacA-Targeted Anti-Biofilm drug discovery against Staphylococcal infections. Crystals 14 (12), 1057 (2024).
Zhang, H. et al. Machine learning methods for weather forecasting: A survey. Atmosphere 16 (1), 82 (2025).
Salama, M. Optimization of regression models using machine learning: A comprehensive study with scikit-learn. Optimization of Regression Models Using Machine Learning: A Comprehensive Study with Scikit-learn| IUSRJ, 5. (2024).
Alemerien, K., Alsarayreh, S. & Altarawneh, E. Diagnosing cardiovascular diseases using optimized machine learning algorithms with GridSearchCV. J. Appl. Data Sci. 5 (4), 1539–1552 (2024).
Padhy, S. & SMOTE-based Deep, L. S. T. M. System with GridSearchCV optimization for intelligent diabetes diagnosis. J. Electr. Syst. 20 (7s), 804–815 (2024).
Mumtaz, A. et al. MPD3: a useful medicinal plants database for drug designing. Nat. Prod. Res. 31 (11), 1228–1236 (2017).
Aloufi, B. H., Snoussi, M. & Sulieman, A. M. E. Antiviral efficacy of selected natural phytochemicals against SARS-CoV-2 Spike glycoprotein using structure-based drug designing. Molecules 27 (8), 2401 (2022).
El-Hachem, N. et al. AutoDock and AutoDockTools for protein-ligand docking: beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) as a case study, in Neuroproteomics: Methods and Protocols. Springer.391–403. (2017).
Zayed, A. O. H. Optimizing protein-ligand Docking through machine learning: algorithm selection with AutoDock Vina. Discover Chem. 2 (1), 164 (2025).
Kaur, J., Kaur, S. & andSingh Rational modification of the lead molecule: enhancement in the anticancer and dihydrofolate reductase inhibitory activity. Bioorg. Med. Chem. Lett. 26 (8), 1936–1940 (2016).
Berendsen, H. J., van der Spoel, D. & van Drunen, R. A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91 (1–3), 43–56 (1995).
Huang, J. & MacKerell, A. D. Jr CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J. Comput. Chem. 34 (25), 2135–2145 (2013).
Mishra, S. et al. Classical molecular dynamics simulation identifies catechingallate as a promising antiviral polyphenol against MPOX palmitoylated surface protein. Comput. Biol. Chem. 110, 108070 (2024).
Ramsey, I. S. et al. An aqueous H + permeation pathway in the voltage-gated proton channel Hv1. Nat. Struct. Mol. Biol. 17 (7), 869–875 (2010).
Kognole, A. A. et al. CHARMM-GUI Drude Prepper for molecular dynamics simulation using the classical Drude polarizable force field. J. Comput. Chem. 43 (5), 359–375 (2022).
Jawad, B. et al. Key interacting residues between RBD of SARS-CoV-2 and ACE2 receptor: combination of molecular dynamics simulation and density functional calculation. J. Chem. Inf. Model. 61 (9), 4425–4441 (2021).
Gilson, M. K. & Zhou, H. X. Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct. 36 (1), 21–42 (2007).
Du, X. et al. Insights into protein–ligand interactions: mechanisms, models, and methods. Int. J. Mol. Sci. 17 (2), 144 (2016).
Yasir, M. et al. Investigating the inhibitory potential of flavonoids against aldose reductase: insights from molecular docking, dynamics simulations, and gmx_MMPBSA analysis. Curr. Issues. Mol. Biol. 46 (10), 11503–11518 (2024).
Kadhum, L. H. Geometry optimization of coupling allin-metformin using dft/b3lyp molecular modelling technique: geometry optimization of coupling allin-metformin using dft/b3lyp molecular modelling technique. Iraqi J. Market Res. Consumer Prot. 13 (2), 89–100 (2021).
El Addali, A. et al. Theoretical study of the phosphate units stability by the Dft b3lyp/6-311 g quantum method. J. Chem. Technol. 31 (3), 477–485 (2023).
Mackay, A. et al. Discovery of NP3-253, a potent brain penetrant inhibitor of the NLRP3 inflammasome. J. Med. Chem. 67 (23), 20780–20798 (2024).
Bağlan, M., Gören, K. & Yıldıko, Ü. MEP analysis and molecular Docking using DFT calculations in DFPA molecule. Int. J. Chem. Technol. 7 (1), 38–47 (2023).
Taher, S. R. & Hamad, W. M. Synthesis, characterization, density functional theory (DFT) analysis, and mesomorphic study of new thiazole derivatives. Bull. Chem. Soc. Ethiop. 38 (6), 1827–1842 (2024).
Stuart, J. G. & Jebaraj, J. W. Synthesis, characterisation, in Silico molecular Docking and DFT studies of 2, 6-bis (4-hydroxy-3-methoxyphenyl)-3, 5-dimethylpiperidin-4-one. Indian J. Chem. (IJC). 62 (10), 1061–1080 (2023).
Andonova, V. et al. Spectral characteristics, in Silico perspectives, density functional theory (DFT), and therapeutic potential of green-extracted phycocyanin from spirulina. Int. J. Mol. Sci. 25 (17), 9170 (2024).
Wu, S. et al. Theoretical study on the adsorption of Sulforaphane on B 12 N 12-related nanocages based on density functional theory. New J. Chem. 47 (47), 21743–21752 (2023).
Khalid, M. et al. Exploration of noncovalent interactions, chemical reactivity, and nonlinear optical properties of Piperidone derivatives: a concise theoretical approach. ACS Omega. 5 (22), 13236–13249 (2020).
Solgun, D. G. et al. Synthesis of axially silicon phthalocyanine substituted with bis-(3, 4-dimethoxyphenethoxy) groups, DFT and molecular Docking studies. J. Incl. Phenom. Macrocyclic Chem. 102 (11), 851–860 (2022).
Ganiev, B., Mardonov, U. & Kholikova, G. Molecular structure, HOMO-LUMO, MEP-–Analysis of triazine compounds using DFT (B3LYP) calculations. Materials Today: Proceedings, (2023).
Pardridge, W. M. Drug transport across the blood–brain barrier. J. Cereb. Blood flow. Metabolism. 32 (11), 1959–1972 (2012).
Leeson, P. D. & Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat. Rev. Drug Discovery. 6 (11), 881–890 (2007).

In 2019, musical theatre accounted for a third of all performances, half of all attendances, and almost 60% of all box office income.
In 2023, those proportions rose to 40% of all performances, over half of all attendances, and nearly 66% of all…