Ageing, as defined in terms of the slope of the probability of death versus time (hazard curve), is a generic phenomenon observed in nearly all complex systems. Theoretical models of ageing predict hazard curves that monotonically increase in time, in discrepancy with the peculiar ups and downs observed empirically. Here we introduce the concept of co-ageing, where the demographic trajectories of multiple cohorts couple together, and show that co-ageing dynamics can account for the anomalous hazard curves exhibited by some species. In our model, multiple interdependency networks inflict damage on one other proportional to their number of functional nodes. We then fit our model predictions to three datasets describing (i) co-ageing worm-pathogen populations and (ii) competing tree species. Lastly, we collect data on the mortality statistics of (iii) chess games to demonstrate that co-ageing dynamics is not exclusive to biological systems.

We present the first comprehensive study for signal growth in transmission lines (TL) with purely time-modulated characteristic impedance (infinite superluminality). This study pioneers the investigation into the effects of varying the cell's electrical length and the impact of loss on momentum bandgaps and amplification levels. It also thoroughly examines how time-modulated transmission line truncation by a static load influences the sensitivity of amplification gain to the relative phase between the incoming signal and modulation, comparing these findings with the case of parametric amplification. Varying is accomplished by loading TLs with a sinusoidally time-modulated capacitor (TMC). The study starts with a simple lumped model cell to facilitate understanding of the phenomena. Following this, transmission lines are introduced, and the effects of incorporating loss are examined. To accomplish this, three models are investigated: a lossless L-C TL lumped model loaded with a shunt lossless TMC and a TL loaded with a shunt lossless and lossy TMC. Dispersion diagrams are plotted and momentum bandgaps are identified at a modulation frequency double the signal frequency. Within the momentum bandgap, only imaginary frequencies are found and correlated to momentum bandgap width and signal growth level. Signal growth is confirmed using harmonic balance and transient simulations, and the results are consistent with the dispersion diagram outcomes.

The size of fruit bat colonies ranges from dozens to hundreds of thousands of individuals, depending on the species. While a deterministic modelling approach is appropriate for large colonies, the role of population fluctuations can be all-important for small colonies. From this perspective, we analyse the infection dynamics in small zoonotic niches due to filoviruses, e.g. Ebola. To this end, we perform stochastic numerical simulations and analytical calculations. The inherent stochasticity in ecological processes may play a significant role in driving small populations towards extinction. Here, we reveal that fluctuations can either lead to virus eradication or to sustain infection compared with the deterministic dynamics, depending on the size of the zoonotic niche. Altogether, our findings reveal non-trivial stochastic effects, which can shed light on the infection dynamics in small- and medium-sized bat colonies and help design preventive measures for zoonotic diseases.

Free will is assumed to be the core of an individual's self-concept. Belief in free will has been studied extensively and was found to be correlated with many behavioural and psychological outcomes. Although developed and validated in the West, the Free will and Determinism Plus (FAD-Plus) scale has been translated, used, and interpreted as a measurement of free will beliefs in multiple cultures. However, the cross-cultural measurement invariance of FAD-Plus has not been examined. Given the cultural differences in understanding the concept of 'free will', items of FAD-Plus may have different interpretations in different cultures, which may compromise its cross-cultural measurement invariance. To provide empirical evidence for the lack of cross-cultural measurement invariance, we collected data in China and analyzed these data together with open datasets of FAD-Plus in three other languages: Japanese, French and English. We only found partial measurement invariance between the Chinese and English datasets, as well as the Japanese and English datasets. These results provided the first assessment of cross-cultural measure invariance of FAD-Plus. We discussed the potential implications of the current study for future studies in the field.

Great musicians have a unique style and, with training, humans can learn to distinguish between these styles. What differences between performers enable us to make such judgements? We investigate this question by building a machine learning model that predicts performer identity from data extracted automatically from an audio recording. Such a model could be trained on all kinds of musical features, but here we focus specifically on rhythm, which (unlike harmony, melody and timbre) is relevant for any musical instrument. We demonstrate that a supervised learning model trained solely on rhythmic features extracted from 300 recordings of 10 jazz pianists correctly identified the performer in 59% of cases, six times better than chance. The most important features related to a performer's 'feel' (ensemble synchronization) and 'complexity' (information density). Further analysis revealed two clusters of performers, with those in the same cluster sharing similar rhythmic traits, and that the rhythmic style of each musician changed relatively little over the duration of their career. Our findings highlight the possibility that artificial intelligence can perform performer identification tasks normally reserved for experts. Links to each recording and the corresponding predictions are available on an interactive map to support future work in stylometry.

When analysing the effect of negative temperature on overwintering pit constructions of unsaturated soil, using the mechanical parameter of saturated soil at room temperature leads to an inaccuracy in the research findings. The strength parameters are obtained through indoor experiments. The foundation pit model is created using FLAC3D numerical simulation software based on the indoor experimental data. The influence of different parameters on the stress and deformation of the overwintering deep foundation pit supporting structure is analysed. The numerical simulation results obtained are compared with the actual monitoring data. According to research, the matric suction of the silty clay in its natural state in the Changchun area is 70 kPa. As the temperature decreases, the total cohesion of the unsaturated soil increases, and the internal friction angle tends to decrease. The numerical simulation results are consistent with the actual monitoring data changes. With the excavation, the horizontal displacement of the supporting structure increases first and then decreases, reaching the maximum displacement at two-thirds of the foundation pit. Compared with room temperature, the deformation of the supporting structure is larger under a negative temperature condition. The deformation of the supporting structure simulated by the actual temperature mechanical parameters is larger than that under the condition of normal temperature mechanical parameters. The frost-heaving force increases with the overall excavation, and a surge occurs at the bottom of the pit. The frost-heaving force changes most significantly under the condition of freezing at -20°C for 30 days.

Inflammation is a complex physiological response associated with the onset and progression of various disorders, including diabetes. In this study, we synthesized a series of diclofenac acid derivatives and evaluated their potential anti-diabetic and anti-inflammatory activities. The compounds were specifically assessed for their ability to inhibit 15-lipoxygenase (15-LOX) and α-glucosidase enzymes. The structures of synthesized derivatives were confirmed through H nuclear magnetic resonance (NMR), C-NMR and high-resolution mass spectrometry (electron ionization) analysis. All these synthesized derivatives exhibited varying degrees of inhibitory activity against LOX, when compared with standard drugs, compounds (half-maximal inhibitory concentration (IC) 14 ± 1 µM), (IC 61 ± 1 µM) and (IC 67 ± 1 µM) showed good activity against the LOX enzyme. While the α-glucosidase inhibitory results revealed that most of the compounds exhibited significant activity when compared with the standard drug acarbose (376 ± 1 µM). The most potent compounds as αglucosidase inhibitors were (3 ± 1 µM), (5 ± 1 µM), (7 ± 1 µM) and (11 ± 1 µM). All these active compounds were found to be least toxic and maintained the mononuclear cells viability at 96-97% compared with that of controls as determined by multi-transaction translator assay. Molecular docking studies further reiterated the significance of these 'lead' compounds with great potential against the target enzymes in the process of drug discovery.

The origin of cellular life can be described in terms of the transition from inorganic matter to the emergence of cooperative assemblies of organic matter: DNA and proteins, capable of replication and metabolism. Directionality theory is a mathematical theory of the collective behaviour of networks of organic matter: activated macromolecules, cells and higher organisms. Evolutionary entropy, a generalization of the thermodynamic entropy of Boltzmann, is a statistical measure of the cooperativity of the biotic components. The cornerstone of Directionality theory is the evolutionary entropy in systems driven by a stable energy source, and in systems subject to a fluctuating energy source. This article invokes the Entropic Principle of Evolution-an extension to biological systems of the Second Law of Thermodynamics-to provide an adaptive rationale for the following sequence of transformations that define the emergence of cellular life: (i) the self-assembly of activated macromolecules from inorganic matter; (ii) the emergence of an RNA world, defined by RNA molecules with catalytic and replicative properties; and (iii) the origin of cellular life, the integration of the three carbon-based polymers-DNA, proteins and lipids, to generate a metabolic and replicative unit.

We develop a Huntington's disease (HD) progression model and integrate this with a novel economic model, accounting for the major factors of the HD's societal cost. Data from the Enroll-HD observational study were used to fit a continuous-time hidden Markov disease progression model, which identified five distinct states. The number of disease states was determined using a cross-validated maximum likelihood approach. A novel data augmentation method was used to correct the biased life expectancy of the progression model. Multiple sources of cost data were then mapped to Enroll-HD variables using expert experience. A simulation of a synthetic patient population was used to show the feasibility of the approach in estimating population costs and the impact of hypothetical intervention scenarios. Our results confirm that early cognitive decline, which is not captured by the total functional capacity score currently used by clinicians but flagged up in HD integrated staging system, can be quantified from participants' visits. Finally, the results of the UK cost modelling show that indirect costs of HD such as state benefits and lost gross domestic product contribution could be the driving factors for the societal cost, over and above health and social care costs.

In the wake of the COVID-19 pandemic, many journals swiftly changed their editorial policies and peer-review processes to accelerate the provision of knowledge about COVID-related issues to a wide audience. These changes may have favoured speed at the cost of accuracy and methodological rigour. In this study, we compare 100 COVID-related articles published in four major psychological journals between 2020 and 2022 with 100 non-COVID articles from the same journal issues and 100 pre-COVID articles published between 2017 and 2019. Articles were coded with regard to design features, sampling and recruitment features, and openness and transparency practices. Even though COVID research was, by and large, more 'observational' in nature and less experimentally controlled than non- or pre-COVID research, we found that COVID-related studies were more likely to use 'stronger' (i.e. more longitudinal and fewer cross-sectional) designs, larger samples, justify their sample sizes based on power analysis, pre-register their hypotheses and analysis plans and make their data, materials and code openly available. Thus, COVID-related psychological research does not appear to be less rigorous in these regards than non-COVID research.

Event segmentation is a neurocognitive process bridging perception and episodic memory. To our knowledge, almost all segmentation work is framed towards humans, yet evolutionarily conserved mechanisms in event cognition exist across species. Here, we addressed segmentation in a way that is applicable to humans and non-human animals, inspired by research in rats; specifically, the fragmentation of grid-cell spatial representations following the insertion of boundaries into an environment (forming a corridor maze). Participants indicated when they felt a meaningful unit of activity ended and another began, while watching an agent traverse from a first-person perspective. A virtual corridor maze (experiment 1) and two other mazes were used (experiment 2), with participants viewing/segmenting the same stimuli twice. We found that people segmented more during turns relative to corridors, with elevated segmentation occurring in discrete moments around turns. Interestingly, we also found that boundaries of the corridor maze facilitated an increase in segmentation within and across viewings. These results suggest that segmentation can be driven by recognized repeating activity that can become more meaningful over time, highlighting an important link between event segmentation and pattern separation that is relevant to many species in their formation of episodic-(like) memory.

In this study, we investigate the impact of demographic characteristics on Middle East respiratory syndrome coronavirus (MERS-CoV) cases in Saudi Arabia, specifically focusing on the time intervals between symptom onset and key events such as hospitalization, case confirmation, reporting and death. We estimate these intervals using data from 2196 cases occurring between June 2012 and January 2020, partitioning the data into four age groups (0-24 years, 25-49 years, 50-74 years and 75-100 years). The duration from symptom onset to hospitalization varies between age cohorts, ranging from 4.03 to 4.75 days, with the 75-100 age group experiencing the longest delay. The interval from symptom onset to case confirmation spans 5.83-8.24 days, and again, the 75-100 age group faces the lengthiest delay. The interval from symptom onset and case reporting ranges from 7.0 to 9.8 days, with the 75-100 age group experiencing the longest delay. The period from symptom onset to death varies across age groups (12.3-16.1 days), with elevated mortality rates during outbreaks. Importantly, we observe age-based differences in the risk of hospitalization and other measures of infection severity, including the probability of death conditional on hospitalization. Careful quantification of epidemiological characteristics, including inference of key epidemiological periods and assessments of differences between cases of different ages, plays a crucial role in understanding the progression of MERS-CoV outbreaks and formulating effective public health strategies to mitigate their impact.

This work reports a detailed theoretical study of the molecular parameters, harmonic vibrational frequencies, UV absorption spectra and standard enthalpies of formation for the radicals CX (with X = F, Cl and Br) and a comparison with the corresponding determinations for the rest of the members of the family CX (with = 2-4). Molecular properties were calculated using different levels of theory: density functional theory employing the B3LYP, X3LYP, BMK, M06-2X and M08-HX functionals combined with the basis sets 6-311++G(3df,3pd) and aug-cc-pVTZ, and the composite models G3B3 and G4. Structural and spectroscopic characterization of the CF, CCl and CBr radicals, along with the estimation of the enthalpies of formation of CF and CCl, were derived here for the first time, to our knowledge. In particular, values of -220.9 ± 2.9, 230.8 ± 3.8 and 375.4 ± 5.9 kJ mol were computed for enthalpies of formation of CF, CCl and CBr, respectively. Additionally, enthalpies of formation for related closed-shell molecules were obtained with less uncertainty compared to those found in the literature. The recommended values of -669.6 ± 3.8, -23.0 ± 4.6 and 155.3 ± 5.0 kJ mol were derived for CF, CCl and CBr, while corresponding values of 0.6 ± 6.3, 228.1 ± 2.1 and 319.6 ± 5.4 kJ mol were estimated for CF, CCl and CBr, respectively.

In animal models, social isolation impacts threat responding and threat learning, especially during development. This study examined the effects of acute social isolation on threat learning in human adolescents using an experimental, within-participant design. Participants aged 16-19 years underwent a session of complete isolation and a separate session of isolation with virtual social interactions, counterbalanced between participants, as well as a baseline session. At baseline and following each isolation session, participants reported their psychological state and completed a threat learning task in which self-report ratings and physiological responses to learned threat and safety cues were measured. Threat learning increased after both isolation sessions in two ways. First, participants found the learned threat cue more anxiety-inducing and unpleasant after isolation compared with baseline. Second, during threat extinction, electrodermal activity was partially elevated after isolation compared with baseline. Further, the results suggested that isolation influenced threat learning through state loneliness. Threat learning is central to threat-related disorders including anxiety, phobias, obsessive-compulsive disorder (OCD) and post-traumatic stress disorder (PTSD), and our findings suggest that isolation and loneliness in adolescence might increase vulnerability to the emergence of these disorders through increased threat learning.

It has been demonstrated that moving together in synchrony to music makes us feel connected. Yet, little is known about the individual differences that shape the relationship between interpersonal synchronization to music and social bonding. The present research tests the hypothesis that this association is influenced by differences in empathy and creativity-two highly relevant factors in many musical activities. We implemented a synchronization task featuring a virtual drummer and measured self-other integration (SOI), a core component of social bonding. We employed a dual-measurement paradigm, incorporating both an explicit assessment ( scale) and an implicit assessment () of SOI. Surprisingly, our analysis did not reveal explicit and implicit measurements correlating, nor were they similarly affected by interpersonal synchronization. This raises questions about the assessment of SOI in interpersonal synchronization experiments. Furthermore, we observed no moderating role of empathy or creativity in the association between interpersonal synchronization and SOI. Nevertheless, we found creativity to correlate with SOI. In light of this finding, we recommend placing greater emphasis on creativity as a decisive factor in the study of musical interaction.

The COVID-19 vaccine has been available in India since January 2021, although many individuals have refused to take the vaccine for various reasons. Vaccination plays a crucial role in disease control by preventing a substantial number of cases and associated disabilities. However, vaccine hesitancy poses a barrier that hinders these efforts. Our article presents a novel approach by proposing a mathematical model for COVID-19 that incorporates vaccine hesitancy, vaccine efficacy and behaviour compensation post-vaccination. The model is calibrated with COVID-19 incidence data for India from 13 February 2021 to 12 January 2022, using the Markov chain Monte Carlo method. The analysis examines the effects of hesitancy and social interventions through a series of practical simulations. The simulation results show that while COVID-19-infected individuals may have natural immunity, vaccination post-recovery is crucial to reduce cases by up to 64.1%. Social interventions, such as face masks and distancing, remain essential to prevent a rise in cases and ensure effective disease control. The model demonstrates that vaccination, combined with continued social interventions, is crucial for effectively reducing COVID-19 cases and preventing future outbreaks. Addressing vaccine hesitancy and maintaining preventive measures are key to successfully controlling the pandemic.

Predators can improve prey capture using a search image, and recent prey provide a visual template with which subsequent prey are compared. Considering trout feeding responses to mayfly prey of different sizes and phenological availability across years, we tested if changing relative abundances (ratios) of prey of the same species, but different body sizes, shifted trout feeding behaviour. For example, we hypothesized that a feeding switch from larger to smaller prey required continuous exposure to the novel smaller prey. The hypothesis that continuous exposure to novel small prey results in their acceptance was not supported. Rather, we discovered that trout identify novel prey using a dynamic stepwise visual neural template prey matching process, which involves the formation of focal prey template based on size or type, rejection of novel prey that do not match the size or type templates and modification of the existing or development of multiple prey templates that eventually enabled recognition of novel, small prey. We also discovered trout store multiple visual prey templates in memory. These results have implications for predator and prey dynamics, optimal foraging, the persistence of rare prey, prey species coexistence and predator selection on prey phenology.

Scientists and philosophers have long struggled with the question of whether non-human animals experience emotions or consciousness. Yet, it is unclear where the scientific consensus on these topics lies today. To address this gap, we administered a survey of professional animal behaviour researchers to assess perceptions regarding (i) the taxonomic distribution of emotions and consciousness in non-human animals, (ii) respondents' confidence in this assessment, and (iii) attitudes towards pitfalls and potential for progress when addressing these questions. Respondents ( = 100) ascribe emotionality and consciousness to a broad swath of the animal taxonomy, including non-human primates, other mammals, birds and cephalopods. Respondents' attribution of these phenomena was strongly associated with their confidence in their assessments ( > 0.9), with respondents assuming an absence of emotions and consciousness when they were unsure. We also identify an emergent consensus of the components involved in a functional definition of emotions. Researchers are optimistic that tools either currently exist or will exist in the future to rigorously address these questions (>85%) and that animal behaviour, as a field, should do more to encourage research works on emotions (>70%). We discuss implications for publication bias and future work in this area as well as ethical considerations regarding animal care and use.

The development of laser-driven accelerators-on-chip has provided an opportunity to miniaturize devices for electron radiotherapy delivery. Laser-driven accelerators produce highly time-compressed electron pulses, on the 100 fs to 1 ps scale. This delivers electrons at high peak power yet low average beam current compared with conventional delivery devices, which generate pulses of approximately 3 µs. The biophysical effects of this time structure, however, are unclear. Here, we use a Monte Carlo simulation approach to explore the effects of the electron beam time structure on the production of reactive oxygen species (ROS) in water. Our results show a power law increase in the generation of hydroxyl ions per deposited electron with decreasing pulse length over the pulse length range of 10 µs to 100 fs. Similar trends were observed for hydrogen peroxide, superoxide, hydroperoxyl, hydronium and solvated electrons. In practical terms, this indicates a fourfold increase in the efficiency of free radical production for sub-picosecond pulses, relative to that of conventional microsecond pulses, for the same number of deposited electrons.

Caregivers often modulate their speech when interacting with infants, adapting a register that has been suggested to have attentional, affective and didactic purposes. The present preregistered study examined the longitudinal trajectories of a diverse range of acoustic features of infant-directed speech (IDS) and compared these with adult-directed speech (ADS), in Norwegian parents of 6- to 18-month-old infants. Sixty-nine families participated. Throughout five laboratory visits across one year, parents were recorded reading a picture-book to their infant (IDS) and an experimenter (ADS). The book was designed to tightly control for the linguistic content and context of speech between participants, timepoints and registers. Analyses of a total of 54 594 vowels and 22 958 phrases revealed, first, an overall effect of register: parents used higher pitch, wider pitch range, slower articulation rate, longer vowel duration and more variable and less distinct vowels in IDS than in ADS. Second, significant register-by-age interactions indicated that parents' IDS, compared with their ADS, featured wider pitch range, larger vowel space and shorter vowel duration in older as compared with younger infants, while pitch, articulation rate and vowel variability and distinctiveness remained relatively stable with age. Results are discussed in the context of the proposed functions of IDS.

Fitting statistical models to aggregate data is still the dominant approach in many demographic and biodemographic applications. Although these macro-level models have proven useful for a variety of tasks, they often have no demographic interpretation. Individual-level modelling, on the other hand, offers a deeper understanding of the mechanisms underlying observed patterns. Their parameters represent quantities in the real world, instead of pure mathematical abstractions. However, estimating these parameters using real-world data has remained a challenge. The approach we introduce in this article attempts to overcome this limitation. Using a likelihood-free inference technique, we show that it is possible to estimate the parameters of a simple but demographically interpretable individual-level model of the reproductive process by exclusively relying on the information contained in a set of age-specific fertility rates. By estimating individual-level models from widely available aggregate data, this approach can contribute to a better understanding of reproductive behaviour and its driving mechanisms, bridging the gap between individual-level and population-level processes. We illustrate our approach using data from three natural fertility populations.