Enhancing Student Engagement in Online Learning through Facial Expression Analysis and Complex Emotion Recognition using Deep Learning

In response to the COVID-19 pandemic, traditional physical classrooms have transitioned to online environments, necessitating effective strategies to ensure sustained student engagement. A significant challenge in online teaching is the absence of real-time feedback from teachers on students learning progress. This paper introduces a novel approach employing deep learning techniques based on facial expressions to assess students engagement levels during online learning sessions. Human emotions cannot be adequately conveyed by a student using only the basic emotions, including anger, disgust, fear, joy, sadness, surprise, and neutrality. To address this challenge, proposed a generation of four complex emotions such as confusion, satisfaction, disappointment, and frustration by combining the basic emotions. These complex emotions are often experienced simultaneously by students during the learning session. To depict these emotions dynamically,utilized a continuous stream of image frames instead of discrete images. The proposed work utilized a Convolutional Neural Network (CNN) model to categorize the fundamental emotional states of learners accurately. The proposed CNN model demonstrates strong performance, achieving a 95% accuracy in precise categorization of learner emotions.Comment: Face emotion recognition wor

FGF23 expression is a promising immunohistochemical diagnostic marker for undifferentiated pleomorphic sarcoma of bone (UPSb)

© 2024 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/genes15020242Background: Undifferentiated pleomorphic sarcoma of bone (UPSb) is a rare primary bone sarcoma that lacks a specific line of differentiation. Distinguishing between UPSb and other malignant bone sarcomas, including dedifferentiated chondrosarcoma and osteosarcoma, is challenging due to their overlapping features. We have previously identified that UPSb tumours have elevated mRNA levels of Fibroblast Growth Factor 23 (FGF23) transcripts compared to other sarcomas including osteosarcoma. In the present study, we evaluated the specificity and practicality of FGF23 immunoreactivity as a specific diagnostic tool to differentiate UPSb tumours from osteosarcomas and dedifferentiated chondrosarcomas. Methods: A total of 10 UPSb, 10 osteosarcoma, and 10 dedifferentiated chondrosarcoma cases (all high-grade), were retrieved and immunohistochemistry for FGF23 was performed. Results: FGF23 protein was expressed at high levels in 80–90% of undifferentiated pleomorphic sarcoma of the bone cases, whereas it was expressed at significantly lower levels in dedifferentiated chondrosarcoma and osteosarcoma cases. A semiquantitative analysis, considering the intensity of immunoreactivity, confirmed significantly elevated FGF23 expression levels in UPSb tissues compared to those observed in osteosarcoma and dedifferentiated chondrosarcoma tissues. Conclusions: The results we present here suggest that FGF23 immunohistochemistry may be a useful tool to aid in differentiating UPSb from morphologically similar malignant bone sarcomas, especially in situations where sampling is restricted and there is limited clinical information available.Funding for the study was provided by the Bone Cancer Research Trust (BCRT) with grant code BCRT/7020. This research was also funded in part by the Kuwait Medical Genetics Centre (KMGC), Ministry of Health, Kuwait; the Italian Ministry of Health (Ricerca Corrente L4097 and L4135, IRCCS Istituto Ortopedico Galeazzi); and the Rotha Abraham Bequest, New Cross Hospital, Wolverhampton, UK.Published versio

Plasma chemokines CXCL10 and CXCL9 as potential diagnostic markers of drug-sensitive and drug-resistant tuberculosis

Tuberculosis (TB) diagnosis still remains to be a challenge with the currently used immune based diagnostic methods particularly Interferon Gamma Release Assay due to the sensitivity issues and their inability in differentiating stages of TB infection. Immune markers are valuable sources for understanding disease biology and are easily accessible. Chemokines, the stimulant, and the shaper of host immune responses are the vital hub for disease mediated dysregulation and their varied levels in TB disease are considered as an important marker to define the disease status. Hence, we wanted to examine the levels of chemokines among the individuals with drug-resistant, drug-sensitive, and latent TB compared to healthy individuals. Our results demonstrated that the differential levels of chemokines between the study groups and revealed that CXCL10 and CXCL9 as potential markers of drug-resistant and drug-sensitive TB with better stage discriminating abilities

Differential Frequencies of Intermediate Monocyte Subsets Among Individuals Infected With Drug-Sensitive or Drug-Resistant Mycobacterium tuberculosis

The rampant increase in drug-resistant tuberculosis (TB) remains a major challenge not only for treatment management but also for diagnosis, as well as drug design and development. Drug-resistant mycobacteria affect the quality of life owing to the delayed diagnosis and require prolonged treatment with multiple and toxic drugs. The phenotypic modulations defining the immune status of an individual during tuberculosis are well established. The present study aims to explore the phenotypic changes of monocytes & dendritic cells (DC) as well as their subsets across the TB disease spectrum, from latency to drug-sensitive TB (DS-TB) and drug-resistant TB (DR-TB) using traditional immunophenotypic analysis and by uniform manifold approximation and projection (UMAP) analysis. Our results demonstrate changes in frequencies of monocytes (classical, CD14(++)CD16(-), intermediate, CD14(++)CD16(+) and non-classical, CD14(+/-)CD16(++)) and dendritic cells (DC) (HLA-DR(+)CD11c(+) myeloid DCs, cross-presenting HLA-DR(+)CD14(-)CD141(+) myeloid DCs and HLA-DR(+)CD14(-)CD16(-)CD11c(-)CD123(+) plasmacytoid DCs) together with elevated Monocyte to Lymphocyte ratios (MLR)/Neutrophil to Lymphocyte ratios (NLR) and alteration of cytokine levels between DS-TB and DR-TB groups. UMAP analysis revealed significant differential expression of CD14(+), CD16(+), CD86(+) and CD64(+) on monocytes and CD123(+) on DCs by the DR-TB group. Thus, our study reveals differential monocyte and DC subset frequencies among the various TB disease groups towards modulating the immune responses and will be helpful to understand the pathogenicity driven by Mycobacterium tuberculosis

Mesiodens with an unusual morphology and multiple impacted supernumerary teeth in a non-syndromic patient

Supernumerary teeth are a relatively frequent disorder of odontogenesis characterized by an excess number of teeth. Mesiodens is the most common type of supernumerary tooth found in the premaxilla between the two central incisors. They can be supplemental (resembling natural teeth), conical, tuberculate or molariform. We present the case of a 19 year-old girl who presented with a mesiodens of an unusual morphology and multiple impacted supernumerary teeth not associated with any syndrome

On the automatic scoring of handwritten essays

Automating the task of scoring handwritten student essays is a challenging problem of AI. The goal is to assign scores which are comparable to those of human scorers even though both human and machine handwriting recognition do not achieve perfect transcription. The research described is a first attempt based on coupling two AI technologies: optical handwriting recognition and automated essay scoring. The test-bed is that of essays written by children in statewide reading comprehension tests in schools. The process involves several image-level operations: removal of pre-printed matter, segmentation of handwritten text lines and extraction of words. Constraints provided by the reading passage, the question and the answer rubric help recognize handwritten words. The method of essay scoring is based on using a vector space model and a machine learning approach to learn scoring parameters from a set of human-scored samples. System performance is compared to scoring done by human raters. Testing on test-bed of 96 handwritten answers indicate that system performance is comparable to that of automatic scoring based on perfect manual transcription.

Development of transgenic pigeonpea (Cajanus cajan. L Millsp) overexpressing citrate synthase gene for high phosphorus uptake

493-501Plants have developed several adaptive strategies to enhance the availability and uptake of phosphorus (P) from the soil under conditions of P deficiency. Exudation of organic acids like citrate is one of the important strategies. In this study, we developed transgenic pigeonpea (Cajanus cajan) over-expressing Dacus carota citrate synthase (DcCs) gene to increase the synthesis and exudation of citrate. Transgenic plants were generated through agro bacterium mediated in-planta transformation technique. Integration and expression of the transgene was confirmed by genomic Southern and RT-PCR analysis. We observed that the transgenic lines had more tissue P and chlorophyll content, and also citrate synthase content higher in the roots. Further, transgenic lines had more vigorous root system both under P sufficient and deficient conditions with more lateral roots and root hairs under P deficient conditions. We conclude that the transgenic pigeonpea plants have the capacity to acquire more P under P deficient conditions