Machine Learning applied to student attentiveness detection: Using emotional and non-emotional measures

Electronic learning (e-learning) is considered the new norm of learning. One of the significant drawbacks of e-learning in comparison to the traditional classroom is that teachers cannot monitor the students' attentiveness. Previous literature used physical facial features or emotional states in detecting attentiveness. Other studies proposed combining physical and emotional facial features; however, a mixed model that only used a webcam was not tested. The study objective is to develop a machine learning (ML) model that automatically estimates students' attentiveness during e-learning classes using only a webcam. The model would help in evaluating teaching methods for e-learning. This study collected videos from seven students. The webcam of personal computers is used to obtain a video, from which we build a feature set that characterizes a student's physical and emotional state based on their face. This characterization includes eye aspect ratio (EAR), Yawn aspect ratio (YAR), head pose, and emotional states. A total of eleven variables are used in the training and validation of the model. ML algorithms are used to estimate individual students' attention levels. The ML models tested are decision trees, random forests, support vector machines (SVM), and extreme gradient boosting (XGBoost). Human observers' estimation of attention level is used as a reference. Our best attention classifier is the XGBoost, which achieved an average accuracy of 80.52%, with an AUROC OVR of 92.12%. The results indicate that a combination of emotional and non-emotional measures can generate a classifier with an accuracy comparable to other attentiveness studies. The study would also help assess the e-learning lectures through students' attentiveness. Hence will assist in developing the e-learning lectures by generating an attentiveness report for the tested lecture.

Machine learning prediction of mortality in Acute Myocardial Infarction

BACKGROUND

Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models.

METHODS

Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI.

RESULTS

Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data.

CONCLUSIONS

Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.

Big data and machine learning to tackle diabetes management

BACKGROUND

Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity.

METHODS

In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D.

RESULTS

To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co-morbidities, and biochemical factors, that is the millieu. Ultimately, the above-mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology.

CONCLUSION

Fully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications.

Object detection for automatic cancer cell counting in zebrafish xenografts

Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we implemented a fine-tuned architecture based on the Faster R-CNN using the Inception ResNet V2 feature extractor. Second, we performed several adjustments to optimize the process, paying attention to constraints such as the presence of overlapped cells, the high number of objects to detect, the heterogeneity of the cells' size and shape, and the small size of the data set. This method resulted in a median error of approximately 1% of the total number of cell units. These results demonstrate the potential of our novel approach for quantifying cells in poorly labeled images. Compared to traditional Faster R-CNN, our method improved the average precision from 71% to 85% on the studied data set.

Remote sensing depicts riparian vegetation responses to water stress in a humid Atlantic region

Riparian areas in the Cantabrian Atlantic ecoregion (northwest Portugal) play a key role in soil formation and conservation, regulation of nutrient and water cycle, creation of landscape aesthetic value and the preservation of biodiversity. The maintenance of their ecological integrity is crucial given the ever increase in multiple anthropogenic (water demand and agriculture) and climatic pressures (droughts and extreme events). We developed a transferable remote sensing approach, taking advantage of the latest freely available technologies (Sentinel-2 and Copernicus Land products), to detect intra-annual and inter-annual changes in riparian vegetation productivity at the river basin scale related to water stress. This study has used the normalized difference vegetation index (NDVI) to investigate riparian vegetation productivity dynamics on three different vegetation types (coniferous, broadleaved and grassland) over the past 5 years (2015-2019). Our results indicated that inter-annual seasonality differed between drier (2017) and wetter (2016) years. We found that intra-annual dynamics of NDVI were influenced by the longitudinal river zonation. Our model ranked first (r²m = 0.73) showed that the productivity of riparian vegetation during the dry season was positively influenced by annual rainfall and by the type of riparian vegetation. The emergent long lags between climatic variation and riparian plant productivity provides opportunities to forecast early warnings of climatically-driven impacts. In addition, the different average productivity levels among vegetation types should be considered when assessing climatic impacts on riparian vegetation. Future applications of Sentinel 2 products could seek to distinguish riparian areas that are likely to be more vulnerable to changes in the annual water balance from those that are more resistant under longer-term changes in climate.

Finding manoeuvre motifs in vehicle telematics

Driving behaviour has a great impact on road safety. A popular way of analysing driving behaviour is to move the focus to the manoeuvres as they give useful information about the driver who is performing them. In this paper, we investigate a new way of identifying manoeuvres from vehicle telematics data, through motif detection in time-series. We implement a modified version of the Extended Motif Discovery (EMD) algorithm, a classical variable-length motif detection algorithm for time-series and we applied it to the UAH-DriveSet, a publicly available naturalistic driving dataset. After a systematic exploration of the extracted motifs, we were able to conclude that the EMD algorithm was not only capable of extracting simple manoeuvres such as accelerations, brakes and curves, but also more complex manoeuvres, such as lane changes and overtaking manoeuvres, which validates motif discovery as a worthwhile line for future research.

Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning

AIMS

Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores - for example, the Dutch Lipid Score - are often employed as alternative, more accessible, albeit less accurate FH diagnostic tools. The aim of this study is to obtain a more reliable approach to FH diagnosis by a "virtual" genetic test using machine-learning approaches.

METHODS AND RESULTS

We used three machine-learning algorithms (a classification tree (CT), a gradient boosting machine (GBM), a neural network (NN)) to predict the presence of FH-causative genetic mutations in two independent FH cohorts: the FH Gothenburg cohort (split into training data (N = 174) and internal test (N = 74)) and the FH-CEGP Milan cohort (external test, N = 364). By evaluating their area under the receiver operating characteristic (AUROC) curves, we found that the three machine-learning algorithms performed better (AUROC 0.79 (CT), 0.83 (GBM), and 0.83 (NN) on the Gothenburg cohort, and 0.70 (CT), 0.78 (GBM), and 0.76 (NN) on the Milan cohort) than the clinical Dutch Lipid Score (AUROC 0.68 and 0.64 on the Gothenburg and Milan cohorts, respectively) in predicting carriers of FH-causative mutations.

CONCLUSION

In the diagnosis of FH-causative genetic mutations, all three machine-learning approaches we have tested outperform the Dutch Lipid Score, which is the clinical standard. We expect these machine-learning algorithms to provide the tools to implement a virtual genetic test of FH. These tools might prove particularly important for lipid clinics without access to genetic testing.

Clustering Clinical Data in R

We are currently witnessing a paradigm shift from evidence-based medicine to precision medicine, which has been made possible by the enormous development of technology. The advances in data mining algorithms will allow us to integrate trans-omics with clinical data, contributing to our understanding of pathological mechanisms and massively impacting on the clinical sciences. Cluster analysis is one of the main data mining techniques and allows for the exploration of data patterns that the human mind cannot capture.This chapter focuses on the cluster analysis of clinical data, using the statistical software, R. We outline the cluster analysis process, underlining some clinical data characteristics. Starting with the data preprocessing step, we then discuss the advantages and disadvantages of the most commonly used clustering algorithms and point to examples of their applications in clinical work. Finally, we briefly discuss how to perform validation of clusters. Throughout the chapter we highlight R packages suitable for each computational step of cluster analysis.

Impact of cooking and handling conditions on furanic compounds in breaded fish products

This study evaluates the influence of cooking and handling conditions on the quantity of furanic compounds (furan, 2-furfural, furfuryl alcohol, 2-pentylfuran, 5-hydroxymethylfurfural) in breaded fish products. Oven-baking and reheating in the microwave lead to low furanic compounds formation in comparison with deep-frying. The use of olive oil for deep-frying promoted higher levels of furanic compounds than sunflower oil. The amounts of these compounds diminished as the temperature and time of deep-frying decreased as well as after a delay after deep-frying. Thus, the generation of furanic compounds can be minimized by adjusting the cooking method and conditions, such as using an electric oven, deep-frying in sunflower oil at 160°C during 4min, or waiting 10min after cooking. However, these conditions that reduce furanic compounds levels also reduce the content of volatile compounds related to the aroma and flavour of fried foods. In this sense, new efforts should be done to reduce the formation of furanic compounds without being detrimental to the volatile profile.

Chronic arthritis directly induces quantitative and qualitative bone disturbances leading to compromised biomechanical properties

OBJECTIVES

Rheumatoid arthritis (RA) is associated with an increased risk of fragility fractures. In RA patients, the direct effect of inflammation on bone is difficult to study because their skeleton is also affected by medication with corticosteroids and other drugs as well as aging and menopause, which contribute to bone fragility. This study used an animal model of chronic arthritis to evaluate the direct impact of chronic inflammation on biomechanical properties and structure of bone.

METHODS

In the SKG mouse chronic arthritis model three point bending tests were performed on femoral bones and compression tests on vertebral bodies. Collagen structure was analysed using second-harmonic generation (SHG) imaging with a two-photon microscope, ultramorphology by scanning electron microscopy (SEM) coupled with energy dispersive x-ray spectroscopy (EDS) and bone density using water pycnometer.

RESULTS

Arthritic bones had poor biomechanical quality compared to control bones. SHG, SEM and pycnometry disclosed variable signs of impaired collagen organization, poor trabecular architecture and low bone density.

CONCLUSION

Present data demonstrate for the first time that chronic inflammation per se, without confounding influence of drugs and aging, leads to impairment of bone biomechanics in terms of stiffness, ductility and ultimate strength (fracture).

Upper limb tremor induced by peripheral nerve injury

We report a patient with proximal right upper limb tremor, secondary to direct peripheral nerve lesion caused by prior thoracic surgery. Electromyography demonstrated neurogenic abnormalities and tremor in muscles innervated by the thoracodorsal and long thoracic nerves. Somatosensory evoked potentials, transcranial magnetic stimulation, and MRI of the cervical and thoracic spine were normal. Tremor persisted in REM and non-REM sleep. These findings suggest a peripheral generator.

[Primary immunodeficiency secondary to ZAP-70 deficiency]

The authors present the case of a child with recurrent infections since the age of 4 months, including bilateral pneumonia by Pneumocystis carinii and protracted varicella. Serum immunoglobulin values (when 10 months old), and B cell values were normal. There was persistent lymphocytic leucocytosis, near absence of CD8+ cells, and an increased CD4/CD8 ratio. The percentage of activated T cells and the expression of HLA class I were normal. Proliferation, activation and IL-2 synthesis studies in T cells showed a TCR/CD3-associated signal transduction deficit. ZAP-70 cDNA sequencing showed a mutation, and no ZAP-70 protein was detected in T cells. ZAP-70 deficiency is associated with a rare immune deficiency with absence of CD8+ T cells as well as a functional deficiency in T cells. Seven months after bone marrow transplantation the child is clinically well and immunologically recovered.