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Repression over responsibility: sanctioning of environmental activism
No full textConservation provides the scientific and ecological foundation for many environmental activism efforts, while aConservation offers evidence-based insights into the state of ecosystems and biodiversity, while activism amplifies conservation goals through public and political engagement. Despite the importance of this relationship, a troubling pattern has emerged: However, environmental activism is increasingly met with disproportionate criminalization and punitive responses currently disproportionately criminalized and punished by many governments worldwide. Such repression not only threatens individual activists but also undermines the broader conservation movement by discouraging public participation and stifling dissent, ultimately jeopardizing our collective ability to address the planet’s most pressing challenges. This short perspective provides examines the growing trend of targeting environmental activists an overview of this troubling trend and discusses the potential consequences this may have for conservationists and anyone else concerned about the profound, immediate threats of climate change and environmental exploitation.</p
Dynamic changes in <i>Ccn3</i> expression across the limbic forebrain through the mouse estrous cycle and during lactation
No full textAbstractCellular communication network factor 3 (CCN3), also known as nephroblastoma overexpressed (NOV), is an adipocytokine that has recently been suggested to be secreted selectively by hypothalamic arcuate nucleus kisspeptin (ARNKISS) neurons to protect bone density during lactation. Using RNAscope hybridization, we have examined the expression of Ccn3 transcripts in the forebrain of male mice and female mice across the estrous cycle and during lactation. Transcripts for Ccn3 are highly expressed in the cerebral cortex, hippocampus, subthalamic nucleus, and amygdala in both sexes. Lower levels of Ccn3 mRNA were detected within the hypothalamus of females but not males. During lactation (day 11), a substantial 6‐fold increase in the numbers of cells expressing Ccn3 mRNA was found in the arcuate and dorsomedial nuclei of the hypothalamus as well as the posterodorsal division of the medial amygdala. Approximately 50% of cells expressing Ccn3 in the ARN during lactation also contained Kiss1 transcripts. An increase in Ccn3 mRNA expression in ARNKISS neurons also occurred during proestrus. These observations demonstrate that multiple limbic brain regions and cell types coordinately up‐regulate their expression of Ccn3 during lactation in the mouse.</p
Visual perception and adaptive scene analysis with autonomous panoptic segmentation
No full textTechniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks. This article offers an intriguing architecture for semantic, instance, and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks (Bi-FPN). When implemented in place of the EfficientNet-B5 backbone, EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications. By ensuring superior multi-scale feature fusion, Bi-FPN integration enhances the segmentation of complex objects across various urban environments. The design suggested is examined on rigorous datasets, encompassing Cityscapes, Common Objects in Context, KITTI Karlsruhe Institute of Technology and Toyota Technological Institute, and Indian Driving Dataset, which replicate numerous real-world driving conditions. During extensive training, validation, and testing, the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic, instance, and panoptic segmentation tasks. Outperforming present methods, the recommended approach generates noteworthy gains in Panoptic Quality: +0.4% on Cityscapes, +0.2% on COCO, +1.7% on KITTI, and +0.4% on IDD. These changes show just how efficient it is in various driving circumstances and datasets. This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable, high-precision segmentation in computer vision applications, primarily autonomous driving. The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.</p
A modern twist on an old classic: innovative and transformative pedagogy for new trainee teachers
No full textTo train to become a teacher in England is straightforward yet complex. The requirements are clear and laid out annually for prospective entrants to the profession to understand, including qualifications in English, Mathematics and Science, an undergraduate degree and the award of qualified teacher status (QTS) which combine to officially classify teachers as eligible to begin their two year ‘Early Career Teacher’ probationary period. However, the ways in which new teachers are awarded each of the required qualifications can be complex. They can become teachers through undergraduate education courses that combine with QTS, through subject specific undergraduate degree courses combined with postgraduate qualifications that include QTS, through assessment only routes, workbased routes, salaried routes and now teacher apprenticeships. They can undertake these qualifications via an accredited provider, through a higher education institute or through school centred initial teacher training. From September 2024 there will be 179 accredited providers delivering courses leading to QTS in England. </p
The effects of altered distances between A-frame and the preceding jump on front limb dynamics in agility dogs
No full textA high proportion of agility injuries associated with an obstacle are due to the A-frame, however, there is limited research into the kinetics and kinematics of dogs traversing this type of equipment. The aim of this research was to study the kinematics and kinetics of agility dogs negotiating an A-frame when the preceding obstacle (in this case a jump) was placed at 10 m, 7.5 m and 5 m ahead of the A-frame. Six competition standard agility dogs were recorded negotiating an A-frame after completing a jump with each dog attempting each distance three times. Inertial measuring units attached to each dog gathered maximum velocity, acceleration and deceleration between jump landing and the A-frame. Video analysis and pressure sensors gathered carpal hyperextension and peak vertical forces for both forelimbs at the dogs’ contact with the A-frame. The study found no difference in either carpal extension or PVF data between the different distances. However, maximum approach velocity decreased (p < 0.05) with decreasing distance: 10 m (7.30 ± 0.40 m/s), 7 m (6.61 ± 0.34 m/s), and 5 m (5.74 ± 0.62 m/s). Acceleration was also decreased at the 5 m distance compared with 10 m distance (p < 0.05). A notable finding was the − 1.57 m/s2 decrease in deceleration found between the 10 m (-5.92 m/s2 ) and 5 m (-4.35 m/s2 ) distances (p < 0.05), with the 10 m distance having 36 % more deceleration than 5 m. As forelimbs have a role in deceleration, an increased distance between obstacles could be one of the factors involved in forelimbs injuries in agility dogs. In our study, positioning the preceding obstacle 5 m from the A-frame moderated speed, acceleration, and deceleration, and could potentially help to reduce reported injury rates, but additional studies are recommended to allow evidence-based guidelines.</p
Beyond hunger: uncovering the link between food insecurity and depression, anxiety, and stress in adolescents
No full textBackgroundFood insecurity (FI) represents a critical public health concern, particularly for adolescents, as it compromises nutritional intake and mental health during crucial developmental stages.ObjectivesThis study examines the associations between FI and symptoms of depression, anxiety, and stress in a sample of 712 adolescents aged 12–17 y from Valle de Ricote, Region of Murcia, Spain.MethodsData were sourced from the cross-sectional “Eating Healthy and Daily Life Activities” study. FI was assessed via the Child Food Security Survey Module, whereas mental health symptoms were evaluated via the Depression, Anxiety, and Stress Scale. Generalized linear models adjusted for socioeconomic status, lifestyle factors, and anthropometric variables were employed to estimate the relationships between FI and psychological outcomes.ResultsOf the 712 adolescents (median age 14 y; 56% girls), 16.2% experienced FI. These adolescents had significantly greater risks of mental health symptoms: the likelihood of experiencing depression, anxiety, and stress was 2–3 times greater than that of their food-secure peers (odds ratios ranging from 2.45 to 3.35). Notably, the predicted probabilities of experiencing symptoms of anxiety and stress among food-insecure adolescents were 39.2% and 43.5%, respectively, whereas they were 16.1% and 19.8%, respectively, among their food-secure peers (P ConclusionsThese results underscore the profound psychological toll of FI and highlight the necessity of targeted interventions to address this issue. Addressing FI through public health policies and psychosocial programs is essential for mitigating its detrimental impact on adolescent mental health.</p
The Quiet Eye Period and its Relationship With Task Complexity: Is the Ceiling Effect an Indicator of Expertise?
No full textBackground:
The quiet eye (QE) period is defined as the final fixation prior to movement initiation for a minimum period of 100 ms. In golf the QE period continues beyond the ball being struck and typically lasts approximately 2-3s. Longer total-QE, pre-QE (pre-ball contact), and post-QE (post-ball contact) are associated with more complex tasks and higher-skilled golfers. However, excessively long QE durations are most likely counterproductive.
Purpose:
We aimed to use an in-situ design to investigate how task complexity affects QE duration in thirty golfers (10 Sub-elites, 10 Intermediates and 10 Novices).
Data Collection:
Participants performed four shots from increasing distances (greater task complexity). Total-QE, QE-pre, and QE-post were measured.
Results:
Novice total-QE indicated a significant moderate linear relationship with complexity, whereas no relationship was observed in intermediate and sub-elites. Novice QE increased by 53% between the simplest and the most complex condition.
Conclusions:
A ceiling effect may be a discriminating factor between skilled and less-skilled golfers, which could suggest that the mechanisms underpinning the QE in higher-skilled golfers are independent of task demands. The consistent QE durations observed in sub-elite golfers may imply that this period is used to parameterise the optimal movement variant or that this group did not find the most complex task more effortful. Novice golfers were more sensitive to alterations in tasks demands, which may indicate that the QE period reflects a higher cognitive load, explaining a linear increase in QE duration. A ceiling effect may indicate a difference in the purpose of QE between skilled and less skilled golfers.</p
Music Therapy for Procedural Support
No full textRich with case material, the second edition of this respected text has been thoroughly revised with many new contributing authors and 85% new material. The Handbook comprehensively explores music therapy theory, research, and practice.</p
Constructing the Self: Investigating how the Brain creates Self-consciousness
No full textNo description supplied</p
Machine learning applications in suicide prediction and prevention: a narrative review
No full textBackground: Suicide is a complex and preventable public health issue where traditional statistical techniques have shown limited effectiveness in predicting future suicide deaths. Machine learning offers promising approaches to identify complex patterns and improve prediction accuracy. Methods: This narrative review examined the application of machine learning in suicide prediction by searching academic databases (PubMed, CINAHL Plus, IEEE Xplore) using MeSH terms 'Machine Learning' and 'Suicide.' English-language articles published within the last five years focusing on suicide, suicide deaths, and prevention were included. The final selection comprised 18 articles after removing duplicates. Results: Key risk factors identified included mental health conditions (particularly depression), socioeconomic factors (unemployment and financial difficulties), family-related issues, and demographic characteristics (age, gender). Various machine learning approaches demonstrated effectiveness in predicting suicide risk. K-Nearest Neighbors and ensemble models (combining Random Forest and XGBoost) showed particularly strong performance. Time series models like ARIMA variants excelled at temporal predictions, while ensemble methods demonstrated versatility with multiple data sources. Conclusion: Machine learning techniques offer substantial improvements over traditional approaches for suicide prediction, with model selection dependent on data availability, geographical scale, and temporal requirements. Ensemble methods perform best with multiple data sources, while time series models excel with temporal data.</p
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