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Evaluating the quantitative accuracy and application of DNA metabarcoding for dietary reconstruction in ruminants
Full text linkDNA metabarcoding offers a powerful, non-invasive tool to identify dietary composition with high taxonomic resolution, yet its quantitative accuracy and bias remain a well-recognised limitation across taxa and sample types. This universal challenge is particularly evident in herbivores, where plant material introduces additional amplification constraints. This study evaluates the accuracy of DNA metabarcoding in reconstructing the diets of sheep under controlled feeding trials involving high and low digestibility forage, using two widely used plant DNA barcodes (ITS2 and trnL). A secondary trial tested the detectability and proportional representation of a target species, Medicago sativa, when added to the diet in varying amounts (1%, 5%, 10%). ITS2 provided greater species-level resolution, while trnL showed broader taxonomic coverage but reduced precision. Both markers distinguished diet treatments effectively; however, faecal DNA showed proportional discrepancies from vegetation input, particularly under low-digestibility conditions. M. sativa was reliably detected even at 1% inclusion but was consistently overrepresented in sequence reads. Our findings highlight the strengths and limitations of DNA metabarcoding for herbivore diet studies and underscore the importance of marker choice and the effects of differential digestion biases. These findings demonstrate the need for multi-marker approaches and calibration controls in dietary studies, especially when quantitative interpretation is required. Despite limitations in quantitative accuracy, faecal DNA metabarcoding provides valuable insights into herbivore diet composition and preferences, with future refinements expected to improve its resolution and reliability for ecological monitoring and grazing management
Auxiliary-Label Enhanced Semi Supervised Learning With Selective Pseudolabeling for Battery Capacity Estimation
Full text linkRecent advances in data-driven methods have significantly improved battery capacity estimation, yet most existing approaches remain constrained by their reliance on supervised learning, requiring substantial amounts of labeled cycling data that are often costly to obtain. To address this challenge, this study proposes a dual-branch network-based semi supervised framework that integrates self-supervised learning and transfer learning mechanisms. First, the framework derives meaningful degradation-aware auxiliary labels from both labeled and unlabeled samples, creating reliable self-supervised signal for model training. Second, the designed dual-branch neural network architecture employs a shared feature extractor that processes input data for both the primary capacity estimation task and the auxiliary label prediction task, enabling effective knowledge transfer between labeled and unlabeled domains through their common representation space. Third, a pseudolabel filtering strategy is proposed to dynamically select high-confidence samples from the unlabeled dataset for self-training, thereby effectively expanding the training set with high-quality pseudolabels and enhancing the capacity estimation accuracy. Finally, extensive experiments validate the framework’s superior performance, achieving a worst-case root-mean-square error of only 0.0143 Ah with merely 5% labeled data, representing a 27.04% reduction compared with the best-performing semi supervised baseline (Co-training) under the same conditions
Identifying Subgroups of Frequent Emergency Department Users:A Latent Class Analysis with Linked Healthcare Utilisation, Cost, and Mortality Outcomes in the UK
No full textCan instrumental effects obscure the true photospheric wave spectrum?
Full text linkOptical aberrations and instrument resolution can affect the observed morphological properties of features in the solar atmosphere. However, little work has been done to study the effects of spatial resolution on the dynamical processes occurring in the Sun’s atmosphere. In this work, owing to the availability of high-resolution observations of a magnetic pore captured with the Interferometric BIdimensional Spectrometer mounted at the Dunn Solar Telescope, we studied the impact of the diffraction limit and the sampling of an instrument on line-of-sight Doppler velocity oscillations. We reported a noticeable shift in the dominant frequency band from 5 to 3 mHz, as both the angular and detector resolutions of the instruments were degraded. We argue that the observed behavior is a result of the increased contamination of straylight from neighboring quiet Sun regions, masking the true behavior of umbral oscillations. These results suggest that the wave energy contributions reported in the literature and based on low-resolution instrumentation may be fundamentally underestimated. As we move into the era of high-resolution instrumentation such as DKIST and MUSE, this paper will offer a critical baseline for interpreting new observations, especially in terms of distinguishing true dynamic behaviors from artifacts introduced by instrument-related limitations
Acceptability and effectiveness of cognitive analytic therapy (CAT) for depression for treatment returners to NHS talking therapies: A pilot evaluation
No full textObjectives
There is evidence that over one quarter of patients return to National Health Service (NHS) talking therapies (TT) services, and the needs of these patients are poorly understood and catered for. This project investigated the acceptability and effectiveness of delivering cognitive analytic therapy (CAT) for patients with depression returning to a TT service with childhood trauma and associated relational difficulties.
Methods
A case-controlled pilot study using TT sessional outcome measures. A 16-session CAT was offered to N = 76 patients who had previously received a high-intensity intervention (mainly cognitive-behavioural therapy; CBT) in the same TT service. Dropout rates, recovery rates and when recovery occurred during the CAT treatment episode were calculated. CAT outcomes were compared against the previous treatment episodes (n = 47) and also benchmarked against the evidence base. Patients were followed up after receiving CAT (n = 16) to assess the durability of change. The number then returning to the TT service after receiving CAT was also tracked.
Results
The dropout rate for CAT was 16.9% and the reliable recovery rate was 40%. Reliable and/or clinically significant reductions in depression tended to occur during early CAT sessions. At a group level, there were significant reductions in depression during CAT. There was no evidence of relapse at follow-up. The return rate to the service following CAT was 28.94%.
Conclusions
CAT appears useful to consider in the offer for patients returning to TT services. Clinical trials now need to focus on the treatment return patient group in TT services across the stepped care model
Retreat, lowering and persistent supraglacial streams at the Barnes Ice Cap, Baffin Island, Canada
No full textSignificant and widespread surface melt is prevalent across glaciers and ice caps, and such surface melt is transmitted through complex supraglacial pathways. The efficiency with which this water transits across glaciers and ice caps is important since it is by these networks that water is removed from the system, constituting mass loss. Here, we use remote sensing to explore mass loss of the Barnes Ice Cap in Arctic Canada since the 1980s alongside a detailed investigation of supraglacial drainage evolution, focussing on the central-southern outlet which is the fastest moving part of the ice-cap. The Barnes Ice Cap is almost entirely covered in a network of supraglacial channels from the highest to lowest elevations. These channels exhibit extraordinary stability over many years and are re-used annually. We also observe significant and widespread surface lowering across most of the Barnes Ice Cap, yet erosion by flowing water in these channels is of sufficient magnitude to ensure they persist rather than re-establishing anew each year. As a result, efficient routing of water across and off the ice cap is likely re-initiated quickly each year, removing large volumes of water from the glacier system
A cross-national study examining imaginary companions and face pareidolia in British and Chinese adults
Full text linkAlthough imaginary companions are created by children and sometimes adults around the world, the prevalence of this play behaviour varies. Cross-nationally, imaginary companions are reported more frequently in Western countries. These imaginary entities have been speculated to be similar to hallucination-like-experiences, based on evidence for elevated top-down auditory processing in children who report them. Face pareidolia tasks engage visual top-down processing, and performance on them does not tend to vary across cultures. This study asked if: 1) there would be cross-national differences in imaginary companion creation in childhood and adulthood between Chinese and British adults, 2) whether those creating imaginary companions would see more face pareidolia and 3) if there would be cross-national differences in face pareidolia. 291 participants (185 Chinese) completed a questionnaire on their imagination followed by a face pareidolia task consisting of 36 image trials (24 containing face pareidolia). Results showed that including all participants (Chinese and British) 11% of the adults currently had an imaginary companion. Chinese adults were significantly less likely than British adults to report a childhood, but not adulthood, imaginary companion. There were significantly more reports of face pareidolia from participants with a current imaginary companion, but not those who remembered a companion in childhood. The pareidolia hits did not differ between country, but false alarms were experienced significantly more by the Chinese participants. Taken together, the results provide more information around imaginary companion creation in China and the UK as well as the role top-down processing may play in imaginary companion interactions
Families on the move – Latin American perspectives
No full textThis introduction to the Special Issue ‘Families on the Move – Latin American Perspectives’ explores the intersections of family life and migration within and toward Latin America. While existing scholarship has focused predominantly on migrant and transnational families in Europe and North America due to language barriers and epistemic exclusions, this Special Issue brings critical attention to the dynamics of intra-regional and South – South migration. Latin America presents a unique context for theorising family-related migration. This is due to factors such as porous borders, flexible and intergenerational care arrangements, heightened economic precarity in countries of origin and settlement, enduring colonial legacies, and migration policies that blend progressive legislation with restrictive implementation and practice. Drawing on theoretical contributions from scholars working on the region and literature written in English, Spanish, and Portuguese, this issue advances the international literature along three key dimensions: (1) rethinking binary notions of separation and reunification by highlighting fluid forms of family life that have movement and transit at their core; (2) examining how progressive policies alone cannot guarantee the right to family life, especially in the face of economic precarity and implementation gaps; and (3) interrogating how colonial, racial, and gendered legacies shape perceptions of family and care. By centreing on Latin America, this Special Issue fills a critical gap in migration literature while also challenging essentialised notions of ‘the South’, demonstrating that migrant families’ experiences are diverse, intersectional, and profoundly shaped by the specific socio-historical conditions of each local context
Converse or reverse? Machine-learning modeling for disease progression: A study based on Alzheimer’s disease continuum cohort
Full text linkIntroduction
Longitudinal trajectories from healthy aging to Mild Cognitive Impairment and Alzheimer’s Disease involve complex mechanisms.
Methods
We evaluated five machine learning approaches (Random Forest, Support Vector Machines, Radial Basis Function Networks, Backpropagation Networks, Convolutional Neural Network) to assess the importance of potential predictive markers across the health-to-dementia continuum. Using the ADNI cohort across four phases (ADNI1, ADNIGO, ADNI2, ADNI3), we analyzed participants with distinct trajectories: stable, convertible, and reverse progression.
Results
Random Forest outperformed other models across key effectiveness metrics and achieved a macro-averaged sensitivity of 70.8 % and specificity of 96.8 % across all participant groups. Random Forest identified visuospatial and memory-related cognitive dysfunction as key predictive clinical features and several amyloid-related neuroimaging biomarkers — including temporal variations of amyloid uptake within inferior lateral ventricles, para-hippocampus—for classifying participant groups. Additionally, plasma APOE4 and long neurofilament light chain levels emerged as promising predictors for tracking progression.
Conclusion
These findings highlight the potential of machine learning in classifying disease trajectories
Who’s coding? An analysis of UK bootcamp experiences and outcomes
Full text linkBootcamps are intensive training programs that aim to turn adults with little to no experience into employable software developers and data scientists, typically in 3–4 months. The bootcamp model has been highly touted in the UK, with significant government investment. However, it is unclear whether UK coding and data science bootcamps make good on their key claims, particularly around employment and salaries. There is a dearth of evidence on UK bootcamp attendees and their motivations, experiences and outcomes. This study begins to address this research gap, identifying who goes to coding & data science bootcamps in the UK, their motivations, and the key factors that impact bootcamp students’ experiences and outcomes. Our research demonstrates that there are several demographic factors that impact bootcamp experience and success in the UK, including age, disability, immigration history, caring responsibilities, and financial circumstances. Overall, coding and data science bootcamps tend to most significantly benefit younger, non-disabled, native British people from financially stable backgrounds who do not have caring responsibilities. These findings contradict claims that bootcamps offer an accessible gateway for marginalised groups looking to obtain coveted, well-remunerated roles in the UK digital and data industries
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