Progressive ShallowNet for large scale dynamic and spontaneous facial behaviour analysis in children

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively

Comparison of American orthopedic foot and ankle society score between anterior to posterior lag screws versus posterior buttress plating for posterior malleolus fixation in tri-malleolar ankle fracture

Background: Posterior malleolar fractures are frequent, occurring in up to 44% of all ankle fractures. Surgical management typically involves anterior to posterior (AP) lag screws or posterior buttress plates. This study aimed to compare the AOFAS (American orthopedic foot and ankle society) score between AP lag screw and posterior buttress plating for posterior malleolus fixation in tri-malleolar ankle fracture. Methods: This quasi-experimental study was conducted at the department of orthopedics and traumatology, Chittagong medical college hospital, Chattogram, Bangladesh, from November 2020 to October 2021. It involved 28 patients with ankle fractures, equally divided into two groups. Group A received AP lag screw fixation, and group B underwent posterior buttress plating. Data analysis was performed using MS office tools and SPSS version 23.0. Results: The posterior plating group demonstrated superior postoperative AOFAS scores compared with the AP screw group with statistically significant differences (88 versus 81, p=0.008).  Significant differences were noted in the AOFAS pain score (p=0.045) and function score (p=0.019) at the final follow-up between the two groups. Conclusions: Patients with tri-malleolar ankle fractures in whom the posterior malleolus is treated with posterior buttress plating show superior postoperative AOFAS scores at follow-up compared to those treated with AP screws

Precursor- route ZnO films from mixed casting solvent for high performance aqueous electrolyte- gated transistors

We significantly improved the properties of semiconducting zinc oxide (ZnO) films resulting from the thermal conversion of a soluble precursor, zinc acetate (ZnAc), by using a mixed casting solvent for the precursor. ZnAc dissolves more readily in a 1:1 mix of ethanol (EtOH) and acetone than in either pure EtOH, pure acetone, or pure isopropanol, and ZnO films converted from mixed solvent cast ZnAc are more homogeneous. When gated with a biocompatible electrolyte, phosphate buffered saline (PBS), ZnO thin film transistors (TFTs) derived from mixed solvent cast ZnAc give 7 times larger field effect current than similar films derived from ZnAc cast from pure EtOH. Sheet resistance at VG = VD = 1V is 18 kΩ/▢, lower than for any organic TFT, and lower than for any water- gated ZnO TFT, reported to date

‘The Mosques Are the Biggest Problem We’ve Got Right Now’ – Key Agent and Survivor Accounts of Engaging Mosques with Domestic and Honour-Based Violence in the United Kingdom.

This article considers the role of mosques in addressing domestic violence (DV) and honor-based violence (HBV) in the United Kingdom. Utilizing data extracted from interviews with 38 key agents and survivors, this article will highlight that some mosques can be difficult to engage with when attempting to raise awareness on violence against women (VAW). Participants explained that the patriarchal nature of mosques contributes to this difficulty together with their exclusion of women within organizational structures. Some mosques also deny that VAW is even a problem within their communities. This is a worrying trend as those on the pulpit often possess significant powers of influence across large congregations and are perfectly placed to help provoke dialogue on these issues. Furthermore, it adds yet another layer of inequality experienced by Muslim women that makes reporting abuse and seeking intervention that much more difficult. In the face of this resistance, this article will consider some ways in which mosques can raise awareness about VAW and where Muslim women can access support. It will also explore additional strategies and recommendations in relation to overcoming mosques unwilling to support VAW initiatives

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero

A miniaturised integrated biophotonic point-of care genotyping system.

This paper reports the development of a novel genotyping device specifically designed for point-of-care applications. As the results of the human genome project are applied to clinical practice there is an increasing requirement for simple to operate high-speed, potentially low-cost genotyping devices for use in the clinic. The aim of such devices is not to specifically detect a full gene sequence but to monitor the presence of specific Single Nucleotide Polymorphisms (SNPs). The instrument is designed to fulfil this specific clinical requirement. Using a FRET-based assay the instrument completes a full PCR process and then performs a melting point test to determine the exact SNPs present in the sample. Results are presented in which the instrument produces results within 18 min based upon saliva samples provided by the patient. The paper also reports successful results both with purified DNA samples and saliva-based samples which were taken from subjects after experiments deliberately aimed at confusing the instrument

Progressive ShallowNet for large scale dynamic and spontaneous facial behaviour analysis in children

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively