Sonosensitive Cavitation Nuclei-A Customisable Platform Technology for Enhanced Therapeutic Delivery.

Ultrasound-mediated cavitation shows great promise for improving targeted drug delivery across a range of clinical applications. Cavitation nuclei-sound-sensitive constructs that enhance cavitation activity at lower pressures-have become a powerful adjuvant to ultrasound-based treatments, and more recently emerged as a drug delivery vehicle in their own right. The unique combination of physical, biological, and chemical effects that occur around these structures, as well as their varied compositions and morphologies, make cavitation nuclei an attractive platform for creating delivery systems tuned to particular therapeutics. In this review, we describe the structure and function of cavitation nuclei, approaches to their functionalization and customization, various clinical applications, progress toward real-world translation, and future directions for the field.

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development.

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.

Creative, Thus Connected: The Power of Sociometric Creativity on Friendship Formation in Gifted Adolescents-A Longitudinal Network Analysis of Gifted Students.

The current study's purpose is to explore the influence of peer-perceived creativity (sociometric creativity) on the short-term development of friendships during a summer program for high ability students. Specifically, the two main objectives of our study are: (1) How did students' friendships network and sociometric creativity network evolve in the summer program? (2) How did sociometric creativity influence the friendship formation? The longitudinal study was conducted at the beginning, middle and the end of a 3-week long program for gifted students in Ireland. The sample consisted of Irish gifted students (N = 702, aged 13-18 years, 52% female, over thirty-one classes). Overall, our longitudinal multilevel and multigroup social network analysis shows that gifted adolescents formed reciprocated friendship ties and cohesive peer group structures in the investigated period; similar age and the same gender predicted friendship formation. Regarding the sociometric creativity, they tended to nominate a similar age and same gender student as very creative. Moreover, the sociometric creativity positively influenced adolescents' friendship networks on a dyadic level, indicating that adolescents select friends based on their perception of the other student's creativity. Further results, explanations, and implications are discussed.

Measuring vocal difference in bird population pairs.

Over time, a bird population's acoustic and morphological features can diverge from the parent species. A quantitative measure of difference between two populations of species/subspecies is extremely useful to zoologists. Work in this paper takes a dialect difference system first developed for speech and refines it to automatically measure vocalisation difference between bird populations by extracting pitch contours. The pitch contours are transposed into pitch codes. A variety of codebook schemes are proposed to represent the contour structure, including a vector quantization approach. The measure, called Bird Vocalisation Difference, is applied to bird populations with calls that are considered very similar, very different, and between these two extremes. Initial results are very promising, with the behaviour of the metric consistent with accepted levels of similarity for the populations tested to date. The influence of data size on the measure is investigated by using reduced datasets. Results of species pair classification using Gaussian mixture models with Mel-frequency cepstral coefficients is also given as a baseline indicator of class confusability.

A robust automatic birdsong phrase classification: A template-based approach.

Automatic phrase detection systems of bird sounds are useful in several applications as they reduce the need for manual annotations. However, birdphrase detection is challenging due to limited training data and background noise. Limited data occur because of limited recordings or the existence of rare phrases. Background noise interference occurs because of the intrinsic nature of the recording environment such as wind or other animals. This paper presents a different approach to birdsong phrase classification using template-based techniques suitable even for limited training data and noisy environments. The algorithm utilizes dynamic time-warping (DTW) and prominent (high-energy) time-frequency regions of training spectrograms to derive templates. The performance of the proposed algorithm is compared with the traditional DTW and hidden Markov models (HMMs) methods under several training and test conditions. DTW works well when the data are limited, while HMMs do better when more data are available, yet they both suffer when the background noise is severe. The proposed algorithm outperforms DTW and HMMs in most training and testing conditions, usually with a high margin when the background noise level is high. The innovation of this work is that the proposed algorithm is robust to both limited training data and background noise.