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Context-dependent coordination of movement in <i>Tribolium castaneum</i> larvae
No full textInsect pests, like the red flour beetle Tribolium castaneum, destroy up to 20% of stored grain products worldwide, making them a significant threat to food security. Their success hinges upon adapting their movements to unpredictable, heterogeneous environments like flour. Tribolium is well developed as a genetic model system; however, little is known about their natural locomotion and how their nervous systems coordinate adaptive movement. Here, we employed videographic whole-animal and leg tracking to assess how Tribolium larvae locomote over different substrates and analyze their gait kinematics across speeds. Unlike many hexapods, larvae employed a bilaterally symmetric, posterior-to-anterior wave gait during fast locomotion. At slower speeds, coordination within thoracic segments was disrupted, although intersegmental coordination remained intact. Moreover, larvae used terminal abdominal structures (pygopods) to support challenging movements, such as climbing overhangs. Pygopod placement coincided with leg swing initiation, suggesting a stabilizing role as adaptive anchoring devices. Surgically lesioning the connective between thoracic and abdominal ganglia impaired pygopod engagement and led to escalating impairments in flat-terrain locomotion, climbing and tunnelling. These results suggest that effective movement in Tribolium larvae requires thoracic-abdominal coordination, and that larval gait and limb recruitment is context-dependent. Our work provides the first kinematic analysis of Tribolium larval locomotion and gives insights into its neural control, creating a foundation for future motor control research in a genetically tractable beetle that jeopardizes global food security
A further twist to helicity
No full textIn fluid dynamics, helicity measures the correlation between velocity and its curl, vorticity, over a spatial volume. Under ‘ideal’ conditions (vanishing viscosity and either homogeneneous density or when pressure may be regarded as a function of density alone), helicity is a topological invariant closely related to the knottedness of vortex lines (Moffatt 1969 J. Fluid Mech. 35 (1), 117–129). Helicity is conserved following a material volume for compact vorticity distributions, i.e. when the vorticity field is tangent to the surface of the volume. There is a related helicity invariant in ideal magnetohydrodynamics involving the correlation between the magnetic potential and its curl, the magnetic field. Helicity is a fragile invariant in the sense that relaxing any one of the ideal conditions results in non-conservation. Unlike energy and enstrophy (mean-square vorticity), helicity is not positive (or sign) definite. Viscous diffusion can create both positive and negative helicity when vortex lines reconnect, something which is topologically forbidden in an ideal fluid where vortex lines move as material curves. Moreover, variable density or more generally compressibility destroys conservation and weakens the association between helicity and vortex-line topology. Furthermore, in compressible flows, the velocity field is not entirely determined from the vorticity field. A recent paper by Boutros & Gibbon (2025) J. Fluid Mech. in this journal explains how one can extend the definition of helicity to control and limit the non-conservation of helicity. This offers a promising way forward in using helicity to characterise flow properties in computational studies of high Reynolds number flows
Continual learning in sensor-based human activity recognition with dynamic mixture of experts
No full textHuman activity recognition (HAR) is a key enabler for many applications in healthcare, factory automation, and smart home. It detects and predicts human behaviours or daily activities via a range of wearable sensors or ambient sensors embedded in an environment. As more and more HAR applications are deployed in the real-world environments, there is a pressing need for the ability of continually and incrementally learning new activities over time without retraining the HAR model. Recently, various continual learning techniques have been applied to HAR; however, most of them commit to a large architecture, which might not suit to devices that deploy HAR models. In addition, these techniques often require to deploy the same large architecture on the devices and cannot customise the architecture for different requirements. To tackle this challenge, we present a dynamic mixture-of-experts approach, which grows an expert for each new task and allows flexible composition of experts to suit individual needs of applications. We have empirically evaluated our technique on 4 third-party, publicly available datasets and compared with 11 state-of-the-art continual learning techniques. Our results demonstrate that our technique can achieve better or comparable performance but with much less parameter spaces and training time
Large language models surpass human experts in predicting neuroscience results
No full textScientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs indicated high confidence in their predictions, their responses were more likely to be correct, which presages a future where LLMs assist humans in making discoveries. Our approach is not neuroscience specific and is transferable to other knowledge-intensive endeavours.</p
The clonality window:relatedness and the group covariance effect in the evolution of division of labour
No full textThe challenges and coping strategies of Sino-foreign EAP students
No full textEAP students face a plethora of diverse challenges which are intensified and expanded upon if they are at transnational institutions. This is especially true in China where Sino-Foreign tertiary programs (along with the difficulties faced by students) have continued to grow. In order to identify and explore the main challenges faced by Sino- Foreign EAP students as well as how they manage them, a survey was created which 528 students from 16 universities in China completed. The greatest challenges seemed to be related to the productive skills and vocabulary. There were also comments about difficulty adapting to a new learning culture along with EAP being dull. In order to overcome their difficulties, students seemed resourceful and resilient with most reporting doing so independently, sometimes with technology (such as AI) which also raised concerns about students using such tools poorly. Many discussed the affordances of reaching out to classmates and instructors with some also mentioning their limitations
The housing integration of refugees and asylum seekers in Germany
No full textIn recent years, refugee migrants have become a significant population in many European countries, yet little is known about their housing market integration post-arrival. Our study, using data from the German Socio-Economic Panel (GSOEP), examines the residential mobility and homeownership of refugees, asylum seekers, and other immigrants. Analyzing housing trajectories for refugees from Syria, Afghanistan, and Iraq, we consider time-varying partnership and employment status, along with their duration of stay. We find that refugees initially exhibit higher mobility rates when compared to other immigrant groups. However, their mobility rates become similar to those of other groups after five years of residence. Refugees are consistently more likely to move into government housing and less likely to transition into homeownership compared to other groups, irrespective of their duration of stay. The results highlight the housing insecurity faced by refugee migrants, providing valuable insights into their unique challenges within the housing market
GnRH pulse generator activity in mouse models of polycystic ovary syndrome
No full textOne in ten women in their reproductive age suffer from polycystic ovary syndrome (PCOS) that, alongside subfertility and hyperandrogenism, typically presents with increased luteinizing hormone (LH) pulsatility. As such, it is suspected that the arcuate kisspeptin (ARNKISS) neurons that represent the GnRH pulse generator are dysfunctional in PCOS. We used here in vivo GCaMP fiber photometry and other approaches to examine the behavior of the GnRH pulse generator in two mouse models of PCOS. We began with the peripubertal androgen (PPA) mouse model of PCOS but found that it had a reduction in the frequency of ARNKISS neuron synchronization events (SEs) that drive LH pulses. Examining the prenatal androgen (PNA) model of PCOS, we observed highly variable patterns of pulse generator activity with no significant differences detected in ARNKISS neuron SEs, pulsatile LH secretion, or serum testosterone, estradiol, and progesterone concentrations. However, a machine learning approach identified that the ARNKISS neurons of acyclic PNA mice continued to exhibit cyclical patterns of activity similar to that of normal mice. The frequency of ARNKISS neuron SEs was significantly increased in algorithm-identified 'diestrous stage' PNA mice compared to controls. In addition, ARNKISS neurons exhibited reduced feedback suppression to progesterone in PNA mice and their gonadotrophs were also less sensitive to GnRH. These observations demonstrate the importance of understanding GnRH pulse generator activity in mouse models of PCOS. The existence of cyclical GnRH pulse generator activity in the acyclic PNA mouse indicates the presence of a complex phenotype with deficits at multiple levels of the hypothalamo-pituitary-gonadal axis.</p
Big Five traits predict between- and within-person variation in loneliness
No full textPast research has linked individual differences in loneliness to Big Five personality traits. However, experience sampling studies also show intrapersonal fluctuations in loneliness. These may reflect situational factors as well as stable individual differences. Here, for the first time, we study the relationship between personality traits and within-person variation in loneliness. In a one-week experience sampling study, n = 285 Nepali participants reported feelings of loneliness three times a day (3597 observations). We use Bayesian mixed-effects location scale models to simultaneously estimate the relationship between Big Five personality traits and (a) mean levels and (b) within-person variability in loneliness. We also test whether these relationships vary depending on whether participants were alone or in the company of others. More neurotic individuals felt lonelier, especially (but not only) when they were alone. These individuals also experienced greater intrapersonal fluctuations in loneliness. These findings extend the differential reactivity hypothesis, according to which individuals vary in loneliness due to differential reactivity to social situations, and accord with the conceptual view of neuroticism as hyperreactivity to social stressors. In addition, we document the role of personality and social context in people’s everyday experience of loneliness in a non-WEIRD population.</p
A framework for assessing the habitat correlates of spatially explicit population trends
No full textAim: Halting widespread biodiversity loss will require detailed information on species' trends and the habitat conditions correlated with population declines. However, constraints on conventional monitoring programs and commonplace approaches for trend estimation can make it difficult to obtain such information across species' ranges. Here, we demonstrate how recent developments in machine learning and model interpretation, combined with data sources derived from participatory science, enable landscape-scale inferences on the habitat correlates of population trends across broad spatial extents.Location: Worldwide, with a case study in the western United States.Methods: We used interpretable machine learning to understand the relationships between land cover and spatially explicit bird population trends. Using a case study with three passerine birds in the western U.S. and spatially explicit trends derived from eBird data, we explore the potential impacts of simulated land cover modification while evaluating potential co-benefits among species.Results: Our analysis revealed complex, non-linear relationships between land cover variables and species' population trends as well as substantial interspecific variation in those relationships. Areas with the most positive impacts from a simulated land cover modification overlapped for two species, but these changes had little effect on the third species.Main Conclusions: This framework can help conservation practitioners identify important relationships between species trends and habitat while also highlighting areas where potential modifications to the landscape could bring the biggest benefits. The analysis is transferable to hundreds of species worldwide with spatially explicit trend estimates, allowing inference across multiple species at scales that are tractable for management to combat species declines.<div/