Music medicine and music therapy in neonatal care: a scoping review of passive music listening research applications and findings on infant development and medical practice.

Background: In recent years, the use of music as a therapeutic and developmental tool for infants, especially within neonatal intensive care units (NICUs), has seen a surge in interest. Despite a growing body of research underscoring the potential benefits of music therapy and music medicine in enhancing infant development and aiding medical practices, the specific characteristics of music that maximize these benefits remain poorly understood.

Objectives: This scoping review aims to provide a comprehensive foundation by mapping the existing literature on passive music listening and identifying gaps, trends, and patterns that are crucial precursors to the development of best practices.

Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep.

Computationally expensive data processing in neuroimaging research places demands on energy consumption-and the resulting carbon emissions contribute to the climate crisis. We measured the carbon footprint of the functional magnetic resonance imaging (fMRI) preprocessing tool fMRIPrep, testing the effect of varying parameters on estimated carbon emissions and preprocessing performance. Performance was quantified using (a) statistical individual-level task activation in regions of interest and (b) mean smoothness of preprocessed data.

Updating predictions in a complex repertoire of actions and its neural representation.

Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure.

High-throughput classification of S. cerevisiae tetrads using deep learning.

Meiotic crossovers play a vital role in proper chromosome segregation and evolution of most sexually reproducing organisms. Meiotic recombination can be visually observed in Saccharomyces cerevisiae tetrads using linked spore-autonomous fluorescent markers placed at defined intervals within the genome, which allows for analysis of meiotic segregation without the need for tetrad dissection. To automate the analysis, we developed a deep learning-based image recognition and classification pipeline for high-throughput tetrad detection and meiotic crossover classification.

Stabilization of V1 interneuron-motor neuron connectivity ameliorates motor phenotype in a mouse model of ALS.

Loss of connectivity between spinal V1 inhibitory interneurons and motor neurons is found early in disease in the SOD1 mice. Such changes in premotor inputs can contribute to homeostatic imbalance of motor neurons. Here, we show that the Extended Synaptotagmin 1 (Esyt1) presynaptic organizer is downregulated in V1 interneurons.

Spinal inhibitory neurons degenerate before motor neurons and excitatory neurons in a mouse model of ALS.

Amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of somatic motor neurons. A major focus has been directed to motor neuron intrinsic properties as a cause for degeneration, while less attention has been given to the contribution of spinal interneurons. In the present work, we applied multiplexing detection of transcripts and machine learning-based image analysis to investigate the fate of multiple spinal interneuron populations during ALS progression in the SOD1 mouse model.

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.

The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry.

Photocatalytic degradation of fluoroquinolone antibiotics using chitosan biopolymer functionalized copper oxide nanoparticles prepared by facile sonochemical method.

Photocatalytic degradation is an excellent method for removing pharmaceutical residues due to their simplicity, ecological benignity, high efficiency, and exceptional stability. Herein, we demonstrate the sonochemically synthesised chitosan biopolymer functionalized copper oxide nanoparticles as an efficient photocatalyst for the degradation of fluoroquinolone-based antibiotics. The X-ray diffraction Rietveld refinement revealed the formation of single-phase copper oxide (CuO) with a monoclinic structure.

Pedunculopontine Chx10 neurons control global motor arrest in mice.

Arrest of ongoing movements is an integral part of executing motor programs. Behavioral arrest may happen upon termination of a variety of goal-directed movements or as a global motor arrest either in the context of fear or in response to salient environmental cues. The neuronal circuits that bridge with the executive motor circuits to implement a global motor arrest are poorly understood.

DeepStruc: towards structure solution from pair distribution function data using deep generative models.

Structure solution of nanostructured materials that have limited long-range order remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple monometallic nanoparticle structure directly from a Pair Distribution Function (PDF) obtained from total scattering data by using a conditional variational autoencoder. We first apply DeepStruc to PDFs from seven different structure types of monometallic nanoparticles, and show that structures can be solved from both simulated and experimental PDFs, including PDFs from nanoparticles that are not present in the training distribution.

Quality standards and recommendations for research in music and neuroplasticity.

Research on how music influences brain plasticity has gained momentum in recent years. Considering, however, the nonuniform methodological standards implemented, the findings end up being nonreplicable and less generalizable. To address the need for a standardized baseline of research quality, we gathered all the studies in the music and neuroplasticity field in 2019 and appraised their methodological rigor systematically and critically.

Green synthesis of multifunctional carbon quantum dots: An approach in cancer theranostics.

Carbon quantum dots (CQDs) have gained significant growing attention in the recent past due to their peculiar characteristics including smaller size, high surface area, photoluminescence, chemical stability, facile synthesis and functionalization possibilities. They are carbon nanostructures having less than 10 nm size with fluorescent properties. In recent years, the scientific community is curiously adopting biomass precursors for the preparation of CQDs over the chemical compounds.

Performance characteristics of virus neutralization test (VNT) and liquid phase blocking ELISA (LPBE) and their relationship in the cattle immunized with trivalent foot and mouth disease vaccine.

Virus neutralization test (VNT) and liquid phase blocking ELISA (LPBE) are accepted tests for screening and as in vitro alternativ to challenge in FMD vaccine potency testing. To replace VNT by LPBE for the screening of cattle, the optimized tests need to be first evaluated for their diagnostic performances. To replace it with LPBE in the absence of protection data, the interrelationship between VNT and LPBE have to be established to find out LPBE cut‑off titer corresponding to the currently used VNT titers.

Key Challenges and Future Directions When Running Auditory Brainstem Response (ABR) Research Protocols with Newborns: A Music and Language EEG Feasibility Study.

Although many musical intervention studies exist in the wider framework of neuroscience and psychology, the preliminary importance of feasibility studies is rarely discussed. Adding to this fact the limited research existing on the therapeutic and restorative potential of music exposure during early developmental periods, pushed us to concentrate on investigating newborns' perception of music and its impact on the brain. Here, we explore the feasibility of a randomized controlled trial (RCT) approach when measuring and comparing the neurophysiological perception of music versus language on the brainstem of newborns using auditory brainstem response (ABR).

Infection and protection responses of deletion mutants of non-structural proteins of foot-and-mouth disease virus serotype Asia1 in guinea pigs.

The development of a negative marker vaccine against the foot-and-mouth disease virus (FMDV) will enhance the capabilities to differentiate vaccinated from infected animals and move forward in the progressive control pathway for the control of FMD. Here, we report the development of mutant FMDV of Asia1 with partial deletion of non-structural proteins 3A and 3B and characterization of their infectivity and protection response in the guinea pig model. The deleted FMDV Asia1/IND/63/1972 mutants, pAsia and pAsia were constructed from the full-length infectious clone pAsia, the viable virus was rescued, and the genetic stability of the mutants was confirmed by 20 monolayer passages in BHK21 cells.

Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks.

This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient.

Locomotor deficits in a mouse model of ALS are paralleled by loss of V1-interneuron connections onto fast motor neurons.

ALS is characterized by progressive inability to execute movements. Motor neurons innervating fast-twitch muscle-fibers preferentially degenerate. The reason for this differential vulnerability and its consequences on motor output is not known.

A sustainable green synthesis of functionalized biocompatible carbon quantum dots from Aloe barbadensis Miller and its multifunctional applications.

Herein, we demonstrated a sustainable green approach for the preparation of fluorescent biocompatible carbon quantum dots by microwave-assisted reflux synthesis from Aloe barbadensis Miller (Aloe vera) extract. The Transmission Electron Microscopic images reveal that the as-prepared CQDs are spherical with less than 5 nm in size. The CQDs are amorphous, showed an excitation-independent behaviour, emitted blue fluorescence and have a fluorescence quantum yield of 31%.

Short-term effect of foot-and-mouth disease (FMD) vaccination on the milk yield in the Deoni and crossbred cows.

Biannual vaccination of the cattle with inactivated foot-and-mouth disease (FMD) vaccine is the control strategy in endemic countries. Reduction in the milk yield is one of the main reasons for poor compliance of the cattle owners to FMD vaccination. As it can adversely affect the herd immunity, the present study aimed to quantify the losses in the milk yield post-FMD vaccination.

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx.

Graph refinement based airway extraction using mean-field networks and graph neural networks.

Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement.

Segmentation of roots in soil with U-Net.

Background: Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces.

Correction: Estimation of membrane bending modulus of stiffness tuned human red blood cells from micropore filtration studies.

[This corrects the article DOI: 10.1371/journal.pone.

Estimation of membrane bending modulus of stiffness tuned human red blood cells from micropore filtration studies.

Human red blood cells (RBCs) need to deform in order to pass through capillaries in human vasculature with diameter smaller than that of the RBC. An altered RBC cell membrane stiffness (CMS), thereby, is likely to have consequences on their flow rate. RBC CMS is known to be affected by several commonly encountered disease conditions.