Miracle-dz/UniFAR-FTUs huggingface dataset:
Pain management in infant immunisation: A cross-sectional survey of UK primary care nurses

Abstract Background: Childhood immunisation is a critically important public health initiative. However, since most vaccines are administered by injection, it is associated with considerable pain and distress. Despite evidence demonstrating the efficacy of various pain management strategies, the frequency with which these are used during routine infant vaccinations in UK practice is unknown. Aim: This study aimed to explore primary care practice nurses’ (PNs) use of evidence-based pain management strategies during infant immunisation, as well as barriers to evidence-based practice. Methods: A questionnaire was developed and distributed to nurses throughout the UK via convenience sampling in paper and online formats. Questions assessed the frequency of pain management intervention use during infant immunisation and barriers to their use. Findings: A total of 255 questionnaire responses were received. Over 90% (n = 226) of respondents never used topical anaesthetics or sweet solutions during immunisations, while 41.9% advised breastfeeding occasionally (n = 103). Parent-/caregiver-led distraction was the most frequently used intervention, with most nurses using it occasionally (47.9%, n = 116) or often (30.6%, n = 74). Most practices had no immunisation pain management policy (81.1%, n = 184), and most PNs’ previous training had not included pain management (86.9%, n = 186). Barriers to intervention use included lack of time, knowledge and resources. Excluding distraction, pain management strategies were infrequently or never used during infant immunisation. Key barriers to using evidence-based strategies were lack of time, knowledge and resources.
Integrating desalination to reservoir operation to increase redundancy for more secure water supply

We investigate the potential of integrating desalination to existing reservoir systems to mitigate supply uncertainty. Desalinated seawater and wastewater are relatively reliable but expensive. Water from natural resources like reservoirs is generally cheaper but climate sensitive. We propose combining the operation of a reservoir and seawater and wastewater desalination plants for an overall system that is less vulnerable to scarcity and uncertainty, while constraining total cost. The joint system is modeled as a multiobjective optimization problem with the double objectives of minimizing risk and vulnerability, subject to a minimum limit on resilience. The joint model is applied to two cases, one based on the climate and demands of a location in India and the other of a location in California. The results for the Indian case indicate that it is possible for the joint system to reduce risk and vulnerability to zero given a budget increase of 20–120% under current climate conditions and 30–150% under projected future conditions. For the Californian case, this would require budget increases of 20–80% and 30–140% under current and future conditions, respectively. Further, our analysis shows a two‐way interaction between the reservoir and desalination plants where the optimal operation of the former is just as much affected by the latter as the latter by the former. This highlights the importance of an integrated management approach. This study contributes to a greater quantitative understanding of desalination as a redundancy measure for adapting water supply infrastructures for a future of greater scarcity and uncertainty.
A Machine Learning-Based Readability Model for Gujarati Texts

This study aims to develop a machine learning-based model to predict the readability of Gujarati texts. The dataset was fifty prose passages from Gujarati literature. Fourteen lexical and syntactic readability text features were extracted from the dataset using a machine learning algorithm of the unigram POS tagger and three Python programming scripts. Two samples of native Gujarati speaking secondary and higher education students rated the Gujarati texts for readability judgment on a 10-point scale of 'easy' to 'difficult' with the interrater agreement. After dimensionality reduction, seven text features as the independent variables and the mean readability rating as the dependent variable were used to train the readability model. As the students' level of education and gender were related to their readability rating, four readability models for school students, university students, male students, and female students were trained with a backward stepwise multiple linear regression algorithm of supervised machine learning. The trained model is comparable across the raters' groups. The best model is the university students' readability rating model. The model is cross-validated. It explains 91% and 88% of the variance in readability ratings at training and cross-validation, respectively, and its effect size and power are large and high.
Tracing Diurnal Differences in Brain Anatomy with Voxel-Based Morphometry – Associations with Sleep Characteristics

Multiple aspects of brain functioning, including arousal, motivation, and cognitive performance, are governed by circadian rhythmicity. Although the recent rise in the use of magnetic resonance imaging (MRI) has enabled investigations into the macroscopic correlates of the diurnal brain processes, neuroanatomical studies are scarce. The current work investigated how time-of-day (TOD) impacts white (WM) and grey matter (GM) volumes using voxel-based morphometry (VBM) in a large dataset (N = 72) divided into two equal, comparable subsamples to assess the replicability of effects. Furthermore, we aimed to assess how the magnitude of these diurnal differences was related to actigraphy-derived indices of sleep health. The results extend the current knowledge by reporting that TOD is predominantly associated with regional WM volume decreases. Additionally, alongside corroborating previously observed volumetric GM decreases, we provide the first evidence for positive TOD effects. Higher replicability was observed for WM, with the only two replicated GM clusters being volumetric increases in the amygdala and hippocampus, and decreases in the retrosplenial cortex, with the latter more pronounced in individuals with shorter sleep times. These findings implicate the existence of region-specific mechanisms behind GM effects, which might be related to cognitive processes taking place during wakefulness and homeostatic sleep pressure.
Application of Finite Difference Technique to Raman Lidar Signals to Derive the Altitude Profiles of Atmospheric Aerosol Extinction

Lidars (Laser radars) are the best suitable instruments to derive the range resolved parameters of atmosphere. Single wavelength and simple backscatter lidars have been widely used to study the height profiles of particle scattering and extinction in the atmosphere. However, atmospheric extinction derived using these lidars data undergo several assumptions and hence involve a significant amount of error in estimation of extinction. The Raman lidar methodology of deriving particle extinction in the atmosphere is a simplified straight-forward method that does not involve any assumptions. The Raman lidar method of atmospheric extinction computation employs derivative of logarithm of normalized range corrected Raman backscattered signal. Usually this causes gaps in the height profiles wherever there is a gradient in the signal under examination. In the present study, a new method is proposed to derive the particle extinction in the atmospheric boundary layer. In this new method, a scheme of alternative solution methodology has been proposed using "Finite Difference Technique". The method has an advantage that, it does not involve the gradient as compared to conventional technique and hence reduces the error. Using this method, the height profiles of particle extinction has been derived. A code in MATLAB is developed to derive the altitude distribution of aerosol extinction. In this connection, the NOAA-REDY site data has been used as the reference data for calculating the molecular extinction in the lower atmosphere.
How Do Adults with Dyslexia Recognize Spoken Words? Evidence from Behavioral and EEG Data

ABSTRACT Purpose In adults with dyslexia (DYS), the persistent influence of phonological deficits on spoken language processing has mainly been examined in either perceptual tasks or those tapping complex cognitive operations. Much less attention is devoted to spoken word recognition per se. Our study aimed to fill this gap. Method Adults with and without dyslexia (for both groups: N = 30, mean age = 21 years, 50% female, 100% white European) performed an auditory lexical decision task. Performance and ERP were recorded. Results Reaction times showed a lexicality effect in both groups although they differed in ERP responses to stimulus lexicality. Skilled readers showed the typical amplitude enhancement for pseudowords compared to words in a late phase of N400 (414-581msec) whereas DYS showed the opposite pattern in an earlier phase of N400 (246-413msec). Both groups showed a stronger negativity during pseudowords processing in the late post-lexical stage (582-800msec). Conclusions ERP data showed subtle differences between the two populations during the lexical stage of word recognition despite their comparable behavioral outcomes. We hypothesized that a stronger reliance on intact semantic knowledge might contribute to the general enhanced and sustained ERP responses to words in DYS across different phases of lexical processing, although confirmation is needed.
Reduced Lateralization of Multiple Functional Brain Networks in Autistic Males

Background Autism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly. Methods In the present study, we estimated brain hemispheric lateralization in autism based on each participant’s unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups. Results We found significant group differences in lateralization for the left-lateralized Language (d = −0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic individuals. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic individuals with language delay and neurotypical individuals. Limitations The generalizability of our findings is restricted due to the male-only sample and greater representation of individuals with high verbal and cognitive performance. Conclusions These findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Furthermore, a differential relationship was identified between Language network lateralization and specific symptom profiles (namely, language delay) of autism.
In situ structural determination of cyanobacterial phycobilisome-PSII supercomplex by STAgSPA strategy

Photosynthesis converting solar energy to chemical energy is one of the most important chemical reactions on earth1. In cyanobacteria, light energy is captured by antenna system phycobilisomes (PBSs) and transferred to photosynthetic reaction centers of photosystem II (PSII) and photosystem I (PSI)2. While most of the protein complexes involved in photosynthesis have been characterized by in vitro structural analyses, how these protein complexes function together in vivo is not well understood. Here we developed an in situ structural analysis strategy “STAgSPA” to successfully solve the in situ structure of PBS-PSII supercomplex from the cyanobacteria Spirulina platensis FACHB-439 at resolution of ∼3. 5Å. The structure reveals the unprecedented coupling details among adjacent PBSs and PSII dimers, and the collaborative energy transfer mechanism mediated by multiple “super-PBS” in cyanobacteria. Our results not only provide the insights for understanding the diversity of photosynthesis-related systems between prokaryotic cyanobacteria and eukaryotic red algae, but also a valuable methodological demonstration for in situ high-resolution structural analysis in cellular or tissue samples.
Simultaneous PET and molecular MR imaging of cardiopulmonary fibrosis in a mouse model of left ventricular dysfunction

Background Aging-associated left ventricular (LV) dysfunction promotes cardiopulmonary fibrogenic remodeling, Group 2 pulmonary hypertension (PH), and right ventricular failure. At the time of diagnosis, cardiac function has declined, and cardiopulmonary fibrosis has often developed. Here, we sought to develop a molecular positron emission tomography (PET)-magnetic resonance imaging (MRI) protocol to detect both cardiopulmonary fibrosis and fibrotic disease activity in an LV dysfunction model. Methods LV dysfunction was induced by transverse aortic constriction (TAC) in 6-month-old senescence-accelerated prone (SAMP8) mice, a subset of mice received sham surgery. Three weeks post-surgery, mice underwent simultaneous PET-MR imaging at 4.7 T. Collagen–targeted PET and fibrogenesis MR probes were intravenously administered. PET signal was computed as myocardium- or lung-to-muscle ratio (MMR, LMR). Percent signal increase (%SI) and ΔLMR were computed from the pre-/post-injection MR images. Tissue specimens were analyzed for hydroxyproline (Hyp) and allysine content. Ventricular structure and function were measured by echocardiography and hemodynamic pressure-volume (PV) loop analysis. Results Allysine in the heart (22±5 (TAC), 13±5 (sham) nmol/g, P=0.02) and lungs (7.5±4 (TAC), 5.8±2 (sham) nmol/lung, P=0.17) of TAC mice corresponded to an increase in myocardial MRI %SI (29±15 (TAC), 6.1±4 (sham), P<0.0001) and ΔLMR (0.3±0.1 (TAC), 0.08±0.1 (sham), P<0.0001). Hyp in the heart (555±90 (TAC), 400±80 (sham) µg/g, P<0.0001) and lungs (189±63 (TAC), 143±31 (sham) µg/lung, P<0.01) were elevated in TAC mice, which corresponded to an increase in PET signal (MMR: 1.8±0.1 (TAC), 1.6±0.2 (sham), P=0.02; LMR: 1.5±0.1 (TAC), 1.2±0.1 (sham), P<0.001). PV loop and echocardiography demonstrated adverse LV remodeling, function, and increased right ventricular systolic pressure in TAC mice. Conclusions Administration of collagen-targeted PET and allysine-targeted MR probes led to elevated PET-MRI signals in the myocardium and lungs of TAC mice. The study demonstrates the potential to detect fibrosis and fibrogenesis in cardiopulmonary disease through a dual molecular PET-MR imaging protocol.
Estimating the frequency of virulence against an Stb gene in Zymoseptoria tritici populations by bulk phenotyping on checkerboard microcanopies of wheat NILs

Monitoring virulent strains within fungal pathogen populations is crucial to improve host resistance deployment strategies. Such monitoring increasingly involves field pathogenomics studies of molecular polymorphisms in genomes based on high-throughput screening technologies. However, it is not always straightforward to predict virulence phenotypes from these polymorphisms and in planta phenotyping remains necessary. We developed a method for ‘bulk phenotyping on checkerboard microcanopies of wheat near-isogenic lines’ (BPC) for estimating the frequency of virulence against an Stb gene in populations of Zymoseptoria tritici, the causal agent of Septoria tritici blotch in wheat, without the need for strain-by-strain phenotyping. Our method involves the uniform inoculation of a microcanopy of two wheat lines – one with the resistance gene and the other without it – with a multi-strain cocktail representative of the population to be characterized, followed by the differential quantification of infection points (lesions). Using Stb16q, a resistance gene that has recently broken down in Europe, we found a robust correlation between the ratio of the mean number of lesions on each wheat line and the frequency of virulent strains in the inoculum. Using pairs of virulent and avirulent strains, and synthetic populations consisting of 10 virulent strains and 10 avirulent strains mixed in different proportions, we validated the principle of the method and established standard curves at virulence frequencies close to those observed in natural conditions. We discuss the potential of this method for virulence monitoring in combination with recently developed molecular methods.
A functional parcellation of the whole brain in individuals with autism spectrum disorder reveals atypical patterns of network organization

BACKGROUND Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy individuals with ASD and a group of seventy typically developing (TD) individuals. METHODS The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates stability and replicability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. RESULTS We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: 1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, 2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and 3) subcortical structures and the hippocampus are atypically integrated with the neocortex. CONCLUSIONS These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.
A Mathematical Framework for Measuring and Tuning Tempo in Developmental Gene Regulatory Networks

Embryo development is a dynamic process governed by the regulation of timing and sequences of gene expression, which control the proper growth of the organism. While many genetic programs coordinating these sequences are common across species, the timescales of gene expression can vary significantly among different organisms. Currently, substantial experimental efforts are focused on identifying molecular mechanisms that control these temporal aspects. In contrast, the capacity of established mathematical models to incorporate tempo control while maintaining the same dynamical landscape remains less understood. This manuscript addresses this gap by developing a mathematical framework that links the functionality of developmental programs to the corresponding gene expression orbits (or landscapes). This unlocks the ability to find tempo differences as perturbations in the dynamical system that preserve its orbits. We demonstrate that this framework allows for the prediction of molecular mechanisms governing tempo, through both numerical and analytical methods. Our exploration includes two case studies: a generic network featuring coupled production and degradation, and the repressilator. In the latter, we illustrate how altering the dimerisation rates of transcription factors can decouple the tempo from the shape of the resulting orbits. The manuscript concludes by highlighting how the identification of orthogonal molecular mechanisms for tempo control can inform the design of circuits with specific orbits and tempos.
Softening in two-component lipid mixtures by spontaneous curvature variance

The bending modulus of a lipid bilayer quantifies its mechanical resistance to curvature. It is typically understood in terms of thickness, e.g., thicker bilayers are stiffer. Here, we describe an additional and powerful molecular determinant of stiffness — the variance in the distribution of curvature sensitivity of lipids and lipid conformations. Zwitterionic choline and ethanolamine headgroups of glycero-phospholipids dynamically explore inter- and intra-species interactions, leading to transient clustering. We demonstrate that these clusters couple strongly to negative curvature, exciting undulatory membrane modes and reducing the apparent bending modulus. Three forcefields (Martini 2, Martini 3, and all-atom CHARMM C36) each show the effect to a different extent, with the coarse-grained Martini models showing the most clustering and thus the most softening. The theory is a guide to understanding the stiffness of biological membranes with their complex composition, as well as how choices of forcefield parameterization are translated into mechanical stiffness.