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Background and aimsThe precise roles of screen media activity (SMA) and sleep problems in relation to child/adolescent psychopathology remain ambiguous. We investigated temporal relationships among sleep problems, SMA, and psychopathology and potential involvement of thalamus-prefrontal-cortex (PFC)-brainstem structural covariation.MethodsThis study utilized data from the Adolescent Brain Cognitive Development study (n = 4,641 ages 9-12) at baseline, Year1, and Year2 follow-up. Cross-Lagged Panel Models (CLPMs) investigated reciprocal predictive relationships between sleep duration/problems, SMA, and psychopathology symptoms. A potential mediating role of baseline Thalamus-PFC-brainstem covariation on SMA-externalizing relationships was examined.ResultsParticipants were divided into discovery (n = 2,359, 1,054 girls) and replication (n = 2,282, 997 girls) sets. CLPMs showed 1) bidirectional associations between sleep duration and SMA in late childhood, with higher frequency SMA predicting shorter sleep duration (β = -0.10 [95%CI: -0.16, -0.03], p = 0.004) and vice versa (β = -0.11 [95%CI: -0.18, -0.05], p < 0.001); 2) externalizing symptoms at age 10-11 predicting sleep problems (β = 0.11 [95%CI: 0.04, 0.19], p = 0.002), SMA (β = 0.07 [95%CI: 0.01, 0.13], p = 0.014), and internalizing symptoms (β = 0.09 [95%CI: 0.05, 0.13], p < 0.001) at age 11-12; and 3) externalizing behavior at age 10-11 partially mediating the relationship between baseline thalamus-PFC-brainstem covariation and SMA at age 11-12 (indirect effect = 0.032 [95%CI: 0.003, 0.067], p-value = 0.030). Findings were replicable.ConclusionWe found bi-directional SMA-sleep-duration associations in late childhood. Externalizing symptoms preceded future SMA and sleep disturbances and partially mediated relationships between structural brain covariation and SMA. The findings emphasize the need for understanding individual differences and developing and implementing integrated strategies addressing both sleep concerns and screen time to mitigate potential impacts on psychopathology.

ABSTRACT: The objective of this study was to determine whether a novel footbath solution containing stannous fluoride (SnF2) was superior to 5% copper sulfate solution for the treatment and prevention of digital dermatitis (DD) in dairy cattle. Study 1 was conducted over 4 wk in Missouri and involved 34 lactating Holstein-Friesian cows with hind feet DD lesions. Cows in group SF walked through a footbath containing a proprietary formulation of SnF2 once weekly, whereas cows in group CS walked through a 5% CuSO4 footbath once daily for 5 d each week. Study 2 was conducted over 8 wk in California and involved 40 lactating Holstein-Friesian cows with hind feet DD lesions. Cows in group SF walked through a SnF2 footbath for 3 consecutive days and then once a week for the following 7 wk. Cows in group CS walked through a 5% CuSO4 footbath 3 times each week for 8 wk. Data collection included lesion type, lesion area, locomotion score, and pain score. Digital dermatitis was actively transmitted in study 1, and lesion area and locomotion scores were lower in group SF than group CS. In contrast, DD was not actively transmitted in study 2, and lesion area and locomotion scores were similar in groups SF and CS. Stannous fluoride delayed the development of active DD lesions in study 1 compared with copper sulfate, with a lower relative risk (0.57) of a hind foot developing an active DD lesion over 28 d. However, SnF2 decreased the rate that active DD lesions transitioned to M3, M4, or M0 lesions compared with 5% copper sulfate in both studies, with the relative risk of a hind foot with an active DD lesion transitioning to M3, M4, or M0 in group SF being slightly lower in study 1 (0.83) and study 2 (0.90) than in group CS. Our findings demonstrated that walking cows through a stannous fluoride footbath once per week in a herd undergoing active transmission of infection was more effective in preventing active DD lesions, but less effective in treating active DD lesions, than walking cows through a copper sulfate footbath 4 times per week. The novel SnF2 footbath solution shows promise for controlling DD in dairy herds that want an alternative footbath solution to CuSO4 and are interested in limiting the environmental accumulation of copper.

Abstract Functional coactivation between human brain regions is partly explained by white matter connections; however, how the structure-function relationship varies by function remains unclear. Here, we reference large data repositories to compute maps of structure-function correspondence across hundreds of specific functions and brain regions. We use natural language processing to accurately predict structure-function correspondence for specific functions and to identify macroscale gradients across the brain that correlate with structure-function correspondence as well as cortical thickness. Our findings suggest structure-function correspondence unfolds along a sensory-fugal organizational axis, with higher correspondence in primary sensory and motor cortex for perceptual and motor functions, and lower correspondence in association cortex for cognitive functions. Our study bridges neuroscience and natural language to describe how structure-function coupling varies by region and function in the brain, offering insight into the diversity and evolution of neural network properties.

Climate distress in young people is very likely to increase in coming years, and young people’s mental health services need to be prepared to meet the demand. This paper reports a qualitative pilot study to establish the views of three stakeholder groups involved in youth mental health counselling services in England: young people, mental health counsellors, and strategic partners. We find broad agreement amongst youth mental health service users and providers that climate distress comprises a range of emotional responses to climate change, including anxiety, hopelessness, isolation, guilt and injustice, exacerbated by developmental, social and informational contexts. Service providers can support young people by surfacing, validating, and channelling these emotional responses, but they themselves need training and support to do this effectively.

Abstract Background Childhood adversities worsen physical and mental health across the lifespan. Health and social care practitioners play a key role in identifying and responding to childhood adversity, however, may be reluctant to do so due to a perceived lack of services to refer to, time pressures and a deficit of training and confidence. We aimed to (1) quantify changes in practitioner comfort and confidence to identify and respond to childhood adversity following a multimodal intervention within an integrated child and family health and social care hub and (2) to understand barriers and facilitators of practice change. Methods Hub practitioners were surveyed about their competence and comfort to directly ask about and confidence to respond to adversity at baseline and then at six and twelve months post training. Interviews were undertaken to explore practitioner barriers and enablers of practice change. Interviews were recorded, transcribed verbatim, and analysed using reflexive thematic analysis. The theoretical domains framework was used to identify the key drivers of practice change. Results Fifteen of 18 practitioners completed all three surveys and 70% reported increased competence and comfort to directly ask, and confidence to respond across a range of adversities over the 12-month intervention. Twenty-one practitioners completed interviews. Six themes were identified as either facilitators or barriers to practice change. Facilitator themes included (1) connection matters, (2) knowledge provides assurance, (3) confidence in ability and (4) choosing change. Barrier themes were (1) never enough time and (2) opening Pandora’s box. Following analysis, key drivers of practice change were ‘social influence’, ‘belief in capability’, ‘knowledge’ and ‘behaviour regulation’ while barriers to practice change were ‘environmental context and resources’ and ‘emotion’. Conclusions Practitioners reported improved confidence in identifying and responding to adversity through a multimodal intervention delivered in an integrated Child and Family Hub. Changing practice requires more than just education and training. Opportunities for social connection and coaching to improve self-confidence and perceived competence are needed to overcome the fear of opening Pandora’s box.

Abstract Gut microbiomes of mammals carry a complex symbiotic assemblage of microorganisms. Feeding newborn infants milk from the mammary gland allows vertical transmission of the parental milk microbiome to the offspring’s gut microbiome. This has benefits, but also has hazards for the host population. Using mathematical models, we demonstrate that biparental vertical transmission enables deleterious microbial elements to invade host populations. In contrast, uniparental vertical transmission acts as a sieve, preventing these invasions. Moreover, we show that deleterious symbionts generate selection on host modifier genes that keep uniparental transmission in place. Since microbial transmission occurs during birth in placental mammals, subsequent transmission of the milk microbiome needs to be maternal to avoid the spread of deleterious elements. This paper therefore argues that viviparity and the hazards from biparental transmission of the milk microbiome, together generate selection against male lactation in placental mammals.

Objectives: The objective of this study was to develop Service, Research and Policy priorities to prevent the impact of family adversity on child mental health and determine comparative priorities of diverse stakeholders to those with lived experience of adversity. Methods: Value-weighting approach conducted in a staged process: (i) professionals and experts with lived experience from health, education, justice and social care sectors attended a national symposium to identify priorities for family adversity and mental health and (ii) a subsequent resource allocation survey gathered views from participants and external experts on symposium priorities. Results: Consensus was reached on priorities. Service priorities included establishing intersectoral hubs for children and families and early childhood nurse home-visiting programs. Research priorities included scaling up evidence-based interventions and evaluating cross-sector, flexible funding models for services addressing childhood adversity. Policy priorities included developing evidence-based policies with evaluation and implementation plans and flexible funding models to support integrated care. Conclusions: Our results provide detailed and actionable clarity on next steps to address family adversities. Implications for Public Health: The priorities call for a focus on cross-sectoral approaches to preventing or mitigating the effects of family adversity. The current Australian policy environment provides a timely opportunity to action the proposed interventions.

Abstract Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

Abstract A sub-terahertz holographic image of a two-dimensional 576-bit data code is produced using a diffractive phase-plate element. The phase plate was designed using a modified Gerchberg-Saxton iterative algorithm to encode a focused image of the data code into a phase modulation profile. The complex phase plate structure is fabricated from polylactic acid using fused deposition modeling, a common three-dimensional-printing technique. The design achieves a significantly simplified optical setup, consisting of a 0.14 THz diverging source, the holographic phase plate and a scanning detector, without the need for additional optical elements. The information stored in the data code is an example of a cryptographic private key. Specifically, the private key for a Bitcoin wallet address. Successful retrieval of the encoded information demonstrates a potential use case for terahertz holographic memory, using a storage medium that can be fabricated with consumer-level three-dimensional-printing techniques.

Abstract Eliminating conventional pulsed B0-gradient coils for magnetic resonance imaging (MRI) can significantly reduce the cost of and increase access to these devices. Phase shifts induced by the Bloch-Siegert shift effect have been proposed as a means for gradient-free, RF spatial encoding for low-field MR imaging. However, nonlinear phasor patterns like those generated from loop coils have not been systematically studied in the context of 2D spatial encoding. This work presents an optimization algorithm to select an efficient encoding trajectory among the nonlinear patterns achievable with a given hardware setup. Performance of encoding trajectories or projections was evaluated through simulated and experimental image reconstructions. Results show that the encodings schemes designed by this algorithm provide more efficient spatial encoding than comparison encoding sets, and the method produces images with the predicted spatial resolution and minimal artifacts. Overall, the work demonstrates the feasibility of performing 2D gradient-free, low-field imaging using the Bloch-Siegert shift which is an important step towards creating low-cost, point-of-care MR systems.

Abstract When technical requirements are high, and patient outcomes are critical, opportunities for monitoring and improving surgical skills via objective motion analysis feedback may be particularly beneficial. This narrative review synthesises work on technical and non-technical surgical skills, collaborative task performance, and pose estimation to illustrate new opportunities to advance cardiothoracic surgical performance with innovations from computer vision and artificial intelligence. These technological innovations are critically evaluated in terms of the benefits they could offer the cardiothoracic surgical community, and any barriers to the uptake of the technology are elaborated upon. Like some other specialities, cardiothoracic surgery has relatively few opportunities to benefit from tools with data capture technology embedded within them (as is possible with robotic-assisted laparoscopic surgery, for example). In such cases, pose estimation techniques that allow for movement tracking across a conventional operating field without using specialist equipment or markers offer considerable potential. With video data from either simulated or real surgical procedures, these tools can (1) provide insight into the development of expertise and surgical performance over a surgeon’s career, (2) provide feedback to trainee surgeons regarding areas for improvement, (3) provide the opportunity to investigate what aspects of skill may be linked to patient outcomes which can (4) inform the aspects of surgical skill which should be focused on within training or mentoring programmes. Classifier or assessment algorithms that use artificial intelligence to ‘learn’ what expertise is from expert surgical evaluators could further assist educators in determining if trainees meet competency thresholds. With collaborative efforts between surgical teams, medical institutions, computer scientists and researchers to ensure this technology is developed with usability and ethics in mind, the developed feedback tools could improve cardiothoracic surgical practice in a data-driven way.

INTRODUCTION: Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan. METHODS: We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds. RESULTS: In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults. DISCUSSION: We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

BackgroundApproximately 1 in 5 adolescents in the United States has prediabetes, and racially and ethnically minoritized youths are disproportionately impacted. Unfortunately, there are few effective youth diabetes prevention programs, and in-person interventions are challenging because of barriers to access and engagement. ObjectiveWe aimed to develop and assess the preliminary feasibility and acceptability of a youth-informed SMS text messaging platform to provide additional support and motivation to adolescents with prediabetes participating in a diabetes prevention workshop in East Harlem, New York City, New York, United States. We collaborated with our youth action board and a technology partner (mPulse Mobile) to develop and pilot-test the novel interactive platform. MethodsThe technology subcommittee of our community action board (comprising youths and young adults) used the results from focus groups that we had previously conducted with youths from our community to develop 5 message types focused on healthy eating and active living: goal setting, behavior tracking, individually tailored guidance, motivational messages, and photo diary. We used an iterative process to develop and pilot the program with our internal study team, including youths from our community action board and mPulse Mobile developers. We then conducted a pilot of the 12-week SMS text messaging program with 13 youths with prediabetes. ResultsParticipants (aged 15-21 years; 10/13, 77% female; 3/10, 23% Black and 10/13, 77% Hispanic or Latinx) received an average of 2 automated messages per day. The system correctly sent 84% (2231/2656) of the messages at the time intended; the remaining 16% (425/2656) of the messages were either sent at the incorrect time, or the system did not recognize a participant response to provide the appropriate reply. The level of engagement with the program ranged from 1 (little to no response) to 5 (highly responsive) based on how frequently participants responded to the interactive (2-way) messages. Highly responsive participants (6/13, 46%) responded >75% (1154/1538) of the time to interactive messages sent over 12 weeks, and 69% (9/13) of the participants were still engaged with the program at week 12. During a focus group conducted after program completion, the participants remarked that the message frequency was appropriate, and those who had participated in our in-person workshops reflected that the messages were reminiscent of the workshop content. Participants rated goal setting, behavior tracking, and tailored messages most highly and informed planned adaptations to the platform. Participants described the program as: “interactive, informative, enjoyable, very convenient, reliable, motivational, productive, and reflective.” ConclusionsWe partnered with youths in the initial content development and pilot testing of a novel SMS text messaging platform to support diabetes prevention. This study is unique in the triple partnership we formed among researchers, technology experts, and diverse youths to develop a mobile health platform to address diabetes-related disparities.

Abstract Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employ wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determine cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks exhibit overlapping organization. We find that there is considerable network overlap (both modalities) in addition to disjoint organization. Our results show that multiple BOLD networks are detected via Ca2+ signals, and networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks. In addition, the principal gradient of functional connectivity is nearly identical for BOLD and Ca2+ signals. Despite similarities, important differences are also detected across modalities, such as in measures of functional connectivity strength and diversity. In conclusion, Ca2+ imaging uncovers overlapping functional cortical organization in the mouse that reflects several, but not all, properties observed with fMRI-BOLD signals.

The electroretinogram (ERG) is a clinical test that records the retina’s electrical response to a brief flash of light as a waveform signal. Analysis of the ERG signal offers a promising non-invasive method for studying different neurodevelopmental and neurodegenerative disorders. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by poor communication, reduced reciprocal social interaction, and restricted and repetitive stereotyped behaviors that should be detected as early as possible to ensure timely and appropriate intervention to support the individual and their family. In this study, we applied gated Multilayer Perceptron (gMLP) for the light-adapted ERG waveform classification as an effective alternative to Transformers. This study presents the first application of gMLP for ASD classification, which employs basic multilayer perceptrons with fewer parameters than Transformers. We compared the performance of different time-series models on an ASD-Control dataset and found that the superiority of gMLP in classification accuracy was the best at 89.7% compared to alternative models and supports the use of gMLP in classification models based on ERG recordings involving case-control comparisons.

Abstract Background Data on the factors affecting blood ionized calcium concentration (ciCa2+) and diagnostic performance of serum total calcium concentration (ctCa) measurements to detect abnormal blood iCa2+ status are lacking in sick adult cattle. Objective Assess the association of ciCa2+ with venous blood pH, plasma concentrations of chloride (cCl), sodium (cNa), and potassium (cK), and ctCa, and total protein, albumin, and globulin concentrations in sick adult cattle. Animals Two‐hundred and sixty‐five adult cattle (≥1‐year‐old) with different diseases. Methods Prospective study. Whole blood pH, ciCa2+, cNa, cK, and cCl were measured using a blood gas and electrolyte analyzer, whereas ctCa, and total protein, and albumin concentrations were determined using an autoanalyzer. The relationship between ciCa2+ and venous blood pH, plasma cCl, cNa, cK, and ctCa, and total protein, albumin, and globulin concentrations was investigated. Sensitivity and specificity were calculated for ctCa for diagnosis of abnormal ciCa2+. Results Sensitivity of ctCa measurements to detect abnormal ciCa2+ was 66.0% whereas specificity of ctCa measurements was 72.3%. Serum total calcium concentration measurements accounted for 42% of adjusted blood ionized calcium (iCa2+7.40) concentration variance. Plasma cCl, and cK had explanatory power of ciCa2+7.40, accounting for an additional 21% and 9% of the variance, respectively. Conclusions and Clinical Importance Serum tCa measurements failed to accurately predict blood iCa2+ status in ill adult cattle. Serum tCa concentrations and plasma cCl were the strongest predictors of ciCa2+ in sick adult cattle.

Heusler compounds with semiconducting properties represent an important class of functional materials. Usually, research on these systems is guided by simple electron-counting rules, such as the Slater-Pauling principle. Here, we report on the discovery of Heusler-type semiconductors, significantly deviating from the Slater-Pauling rule. We theoretically predict the occurrence of nonmagnetic semiconducting ground states in various highly off-stoichiometric full-Heusler alloys, where self-substitution leads to a band-gap opening. This unexpected trend is confirmed experimentally by thermoelectric transport measurements on a multitude of Fe_{2−2x}V_{1−x}Al_{1+3x} samples with up to 20% substitution of Fe and V atoms. The band-gap opening leads to an exceptionally large Seebeck coefficient in p-type Fe_{2}VAl thermoelectrics, previously limited by bipolar conduction and low-density-of-states effective mass. Consequently, our work presents a paradigm to tune the band gap of Heusler compounds by self-substitution and introduces a hitherto unexplored class of semiconductors with exceptional thermoelectric properties, offering significant potential for advancements in energy science and sustainable-energy technologies.

Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina’s response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window functions of different sizes and window overlaps were compared to enhance feature extraction concerning specific ML algorithms. The obtained spectrograms were employed to train deep learning models alongside manual feature extraction for more classical ML models. Our findings demonstrated the superiority of utilizing the Visual Transformer architecture with a Hamming window function, showcasing its advantage in ERG signal classification. Also, as a result, we recommend the RF algorithm for scenarios necessitating manual feature extraction, particularly with the Boxcar (rectangular) or Bartlett window functions. By elucidating the optimal methodologies for feature extraction and classification, this study contributes to advancing the diagnostic capabilities of ERG analysis in clinical settings.

The Adolescent Brain Cognitive Development (ABCD) Study® is a 10-year longitudinal study of children recruited at ages 9 and 10. A battery of neuroimaging tasks are administered biennially to track neurodevelopment and identify individual differences in brain function. This study reports activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N = 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N = 6,657) and working memory and emotion reactivity with an emotional N-back (EN-back) task (N = 6,009). Further, we report the spatial reproducibility of activation patterns by assessing between-group vertex/voxelwise correlations of blood oxygen level-dependent (BOLD) activation. Analyses reveal robust brain activations that are consistent with the published literature, vary across fMRI tasks/contrasts and slightly correlate with individual behavioral performance on the tasks. These results establish the preadolescent brain function baseline, guide interpretation of cross-sectional analyses and will enable the investigation of longitudinal changes during adolescent development.