Metabolic enzymes are the catalysts that drive the biochemical reactions essential for sustaining life. Many of these enzymes are tightly regulated by feedback mechanisms. To fully understand their roles and modulation, it is crucial to investigate the relationship between their structure, catalytic mechanism, and function. In this perspective, by using three examples from our studies on Mycobacterium tuberculosis (Mtb) isocitrate lyase and related proteins, we highlight how an integrated approach combining structural, activity, and biophysical data provides insights into their biological functions. These examples underscore the importance of employing fast-fail experiments at the early stages of a research project, emphasise the value of complementary techniques in validating findings, and demonstrate how in vitro data combined with chemical, biochemical, and physiological knowledge can lead to a broader understanding of metabolic adaptations in pathogenic bacteria. Finally, we address the unexplored questions in Mtb metabolism and discuss how we expand our approach to include microbiological and bioanalytical techniques to further our understanding. Such an integrated and interdisciplinary strategy has the potential to uncover novel regulatory mechanisms and identify new therapeutic opportunities for the eradication of tuberculosis. The approach can also be broadly applied to investigate other biochemical networks and complex biological systems.

The Plane or Plain Scale is a navigational device that dates back to the early 1600s but has long since ceased to be used in practice. It could perform the function of a protractor and be used to solve problems in plane trigonometry. In addition, coupled with a suite of remarkable geometric constructions based on stereographic projection, the Plane Scale could efficiently solve problems in spherical trigonometry and hence navigation on a sphere. The methods used seem today to be largely unknown. This paper describes the Plane Scale and how it was used.

Metabolism is at the core of all functions of living cells as it provides Gibbs free energy and building blocks for synthesis of macromolecules, which are necessary for structures, growth, and proliferation. Metabolism is a complex network composed of thousands of reactions catalyzed by enzymes involving many co-factors and metabolites. Traditionally it has been difficult to study metabolism as a whole network and most traditional efforts were therefore focused on specific metabolic pathways, enzymes, and metabolites. By using engineering principles of mathematical modeling to analyze and study metabolism, as well as engineer it, that is, design and build, new metabolic features, it is possible to gain many new fundamental insights as well as applications in biotechnology. Here, we present the history and basic principles of engineering metabolism, as well as the newest developments in the field. We are using examples of applications in: (1) production of protein pharmaceuticals and chemicals; (2) basic studies of metabolism; and (3) impacting health care. We will end by discussing how engineering metabolism can benefit from advances in artificial intelligence (AI)-based models.

The optimisation of inter-island transportation systems constitutes a critical determinant of regional economic development and the efficacy of mobility infrastructure. This study presents a comparative analysis of passenger mode selection between short-sea shipping (SSS) and road transport alternatives through stated preference surveys conducted via anonymised questionnaires. Employing advanced discrete choice modelling techniques – specifically the multinomial logit (MNL), random parameter logit (RPL) and latent class (LC) frameworks – we quantitatively disentangle the complex determinants influencing modal preferences. Our systematic sensitivity analysis reveals distinct behavioural patterns: passengers opting for SSS prioritise journey convenience, whereas road transport users exhibit stronger cost sensitivity. These findings provide actionable insights for formulating evidence-based policies to enhance intermodal transportation networks in the Zhoushan Archipelago of China. Beyond its immediate geographical focus, this research contributes methodological innovations by applying finite mixture models to capture unobserved heterogeneity in maritime transport decisions. The framework demonstrates significant transferability potential for island territories globally and urban freight corridor optimisation challenges, particularly in contexts requiring trade-off analyses between maritime efficiency and terrestrial logistics constraints.

We present a re-discovery of G278.94+1.35a as possibly one of the largest known Galactic supernova remnants (SNRs) – that we name Diprotodon. While previously established as a Galactic SNR, Diprotodon is visible in our new Evolutionary Map of the Universe (EMU) and GaLactic and Extragalactic All-sky MWA (GLEAM) radio continuum images at an angular size of $3{{{{.\!^\circ}}}}33\times3{{{{.\!^\circ}}}}23$, much larger than previously measured. At the previously suggested distance of 2.7 kpc, this implies a diameter of 157$\times$152 pc. This size would qualify Diprotodon as the largest known SNR and pushes our estimates of SNR sizes to the upper limits. We investigate the environment in which the SNR is located and examine various scenarios that might explain such a large and relatively bright SNR appearance. We find that Diprotodon is most likely at a much closer distance of $\sim$1 kpc, implying its diameter is 58$\times$56 pc and it is in the radiative evolutionary phase. We also present a new Fermi-LAT data analysis that confirms the angular extent of the SNR in gamma rays. The origin of the high-energy emission remains somewhat puzzling, and the scenarios we explore reveal new puzzles, given this unexpected and unique observation of a seemingly evolved SNR having a hard GeV spectrum with no breaks. We explore both leptonic and hadronic scenarios, as well as the possibility that the high-energy emission arises from the leftover particle population of a historic pulsar wind nebula.

The Gaia optical astrometric mission has measured the precise positions of millions of objects in the sky, including extragalactic sources also observed by Very Long Baseline Interferometry (VLBI). In the recent Gaia EDR3 release, an effect of negative parallax with a magnitude of approximately $-17$ $\mu$as was reported, presumably due to technical reasons related to the relativistic delay model. A recent analysis of a 30-yr set of geodetic VLBI data (1993–2023) revealed a similar negative parallax with an amplitude of $-15.8 \pm 0.5$ $\mu$as. Since both astrometric techniques, optical and radio, provide consistent estimates of this negative parallax, it is necessary to investigate the potential origin of this effect.

We developed the extended group relativistic delay model to incorporate the additional parallactic effect for radio sources at distances less than 1 Mpc and found that the apparent annual signal might appear due the non-orthogonality of the fundamental axes, which are defined by the positions of the reference radio sources themselves. Unlike the conventional parallactic ellipse, the apparent annual effect in this case appears as a circular motion for all objects independently of their ecliptic latitude. The measured amplitude of this circular effect is within a range of 10–15 $\mu$as that is consistent with the ICRF3 stability of the fundamental axis. This annual circular effect could also arise if a Gödel-type cosmological metric were applied, suggesting that, in the future, this phenomenon could be used to indicate global cosmic rotation.

In this study, the results obtained using GOES satellite X-ray data and MWO and WSO measurements of the solar magnetic field between 1976 and 2022 are compared and discussed. By analysing GOES satellite X-ray data in 47 different time periods of one month long, 7 500 solar flares are obtained, the flare equivalent duration distributions against the total duration of the flare are statistically modelled, and then their variation via time is examined. The variations of the model parameters such as the Plateau, which is considered as an indicator of the stellar saturation level in an observation season, and the flare timescales via time are examined. We noticed that the variation found in the solar magnetic field and the variation determined in the flare saturation levels are very similar. As a result, it is well known that the solar magnetic dipole moment measured from the solar poles steadily decreased from 1976 to 2022. We revealed that the solar X-ray flare energies are also generally decreasing in the same trend. This decrease is also evident in flare timescales, indicating that the geometry of solar magnetic loops is getting smaller over time.

High resolution structures of protein complexes provide a wealth of information on protein structure and function. Databases of these protein structures are also used for artificial-intelligence (AI)-based methods of structural modelling. Despite the wealth of protein structures that have been determined by structural biologists, there are still gaps, or missing pieces in the puzzle of protein structural biology. Highly flexible regions may be missing from protein structures and conformational changes of different protein complex states may not be captured by current databases. In this perspective, I sketch out several ways that cross-linking mass spectrometry can contribute to filling in some of these missing pieces: Identification of cross-linked interactions in highly flexible protein regions not captured by other structural techniques; capturing conformational changes of protein complexes in different functional states; serving as distance constraints in integrative structural modelling and providing structural information of in cellulo proteins. The myriad ways in which cross-linking mass spectrometry contributes to filling in missing pieces in structural biology makes it a powerful technique in structural biology.

With the low Earth orbit environment becoming increasingly populated with artificial satellites, rockets, and debris, it is important to understand the effects they have on radio astronomy. In this work, we undertake a multi-frequency, multi-epoch survey with two SKA-Low station prototypes located at the SKA-Low site, to identify and characterise radio frequency emission from orbiting objects and consider their impact on radio astronomy observations. We identified 152 unique satellites across multiple passes in low and medium Earth orbits from 1.6 million full-sky images across 13 selected ${\approx}1$ MHz frequency bands in the SKA-Low frequency range, acquired over almost 20 days of data collection. Our algorithms significantly reduce the rate of satellite misidentification, compared to previous work, validated through simulations to be $ \lt 1\%$. Notably, multiple satellites were detected transmitting unintended electromagnetic radiation, as well as several decommissioned satellites likely transmitting when the Sun illuminates their solar panels. We test alternative methods of processing data, which will be deployed for a larger, more systematic survey at SKA-Low frequencies in the near future. The current work establishes a baseline for monitoring satellite transmissions, which will be repeated in future years to assess their evolving impact on radio astronomy observations.

Galaxy Zoo is an online project to classify morphological features in extra-galactic imaging surveys with public voting. In this paper, we compare the classifications made for two different surveys, the Dark Energy Spectroscopic Instrument (DESI) imaging survey and a part of the Kilo-Degree Survey (KiDS), in the equatorial fields of the Galaxy And Mass Assembly (GAMA) survey. Our aim is to cross-validate and compare the classifications based on different imaging quality and depth. We find that generally the voting agrees globally but with substantial scatter, that is, substantial differences for individual galaxies. There is a notable higher voting fraction in favour of ‘smooth’ galaxies in the DESI+zoobot classifications, most likely due to the difference between imaging depth. DESI imaging is shallower and slightly lower resolution than KiDS and the Galaxy Zoo images do not reveal details such as disc features and thus are missed in the zoobot training sample. We check against expert visual classifications and find good agreement with KiDS-based Galaxy Zoo voting. We reproduce the results from Porter-Temple+ (2022), on the dependence of stellar mass, star formation, and specific star formation on the number of spiral arms. This shows that once corrected for redshift, the DESI Galaxy Zoo and KiDS Galaxy Zoo classifications agree well on population properties. The zoobot cross-validation increases confidence in its ability to compliment Galaxy Zoo classifications and its ability for transfer learning across surveys.

Fast radio burst (FRB) science primarily revolves around two facets: the origin of these bursts and their use in cosmological studies. This work follows from previous redshift–dispersion measure (z–DM) analyses in which we model instrumental biases and simultaneously fit population parameters and cosmological parameters to the observed population of FRBs. This sheds light on both the progenitors of FRBs and cosmological questions. Previously, we have completed similar analyses with data from the Australian Square Kilometer Array Pathfinder (ASKAP) and the Murriyang (Parkes) Multibeam system. In this manuscript, we use 119 FRBs with 29 associated redshifts by additionally modelling the Deep Synoptic Array (DSA) and the Five-hundred-metre Aperture Spherical radio Telescope (FAST). We also invoke a Markov chain Monte Carlo (MCMC) sampler and implement uncertainty in the Galactic DM contributions. The latter leads to larger uncertainties in derived model parameters than previous estimates despite the additional data and indicate that precise measurements of DM$_\textrm{ISM}$ will be important in the future. We provide refined constraints on FRB population parameters and derive a new constraint on the minimum FRB energy of log $E_{\mathrm{min}}$(erg)=39.47$^{+0.54}_{-1.28}$ which is significantly higher than bursts detected from strong repeaters. This result likely indicates a low-energy turnover in the luminosity function or may alternatively suggest that strong repeaters have a different luminosity function to single bursts. We also predict that FAST will detect 25–41% of their FRBs at $z \gtrsim 2$ and DSA will detect 2–12% of their FRBs at $z \gtrsim 1$.