E-learning in higher education in times of pandemic: A bibliographic review

The research is a bibliographic review on e-learning at the higher level in times of pandemic, e-learning. Databases were reviewed Eric, Scopus, Web of science, Dialnet, Scielo, Google scholar, digital books Likewise, the article aims to explain higher education and the pandemic, the conceptualization of e-learnig, b-learnig, connectivism theory, pedagogical dimensions of the E -Learnig, online activities, and the educational inequalities in times of pandemic. The review of this literature allowed us to reach the following conclusions: e-learning are applications and tools that rely on ICT to facilitate learning, from a pedagogical approach through e-learning, students learn autonomously and independently. The impact on higher education has been dramatic and transformative, and a common trend in education systems around the world has been to respond to the pandemic with “emergency e-learning” protocols, marking a rapid transition from face-to-face classes to learning systems online.

Reinforcement Learning and Physics

Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of reinforcement learning and physics. We include standard reinforcement learning techniques in the computer science community for enhancing physics research, as well as the more recent and emerging area of quantum reinforcement learning, inside quantum machine learning, for improving reinforcement learning computations.

SARS-CoV-2: A Professional and social gamechanger - Medical and dental aspects

This study reports the SARS-CoV-2 outbreak and its impact on dental practice and education in Brazil. A literature review involving medical and dental interests was performed based on recent general findings about the infection (research and relevant guidelines). COVID-19 is a high transmissible, unpredictable systemic disease, involving a viral replication phase, followed by an inflammatory phase that can evolve into hyperinflammation that leads to a cytokine storm and other serious issues including sepsis, shock and multiple organ failure. The dentists are directly impacted by the new coronavirus as they work with the oral cavity that is irrigated by the saliva and receive the respiratory aerosols and droplets from the patient. In conclusion, the world is facing a completely new situation that deserves the comprehension of the population and close attention of the authorities. Following protocols to attend patients can prevent the dissemination of the virus, cross-infection, and the contamination of health care professionals. New strategies need to be developed to enhance the existing teaching and learning protocols in Universities and to allow research to continue.

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

An artificial spiking quantum neuron

Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.

American Mammals Susceptibility to Dengue According to Geographical, Environmental, and Phylogenetic Distances

Many human emergent and re-emergent diseases have a sylvatic cycle. Yet, little effort has been put into discovering and modeling the wild mammal reservoirs of dengue (DENV), particularly in the Americas. Here, we show a species-level susceptibility prediction to dengue of wild mammals in the Americas as a function of the three most important biodiversity dimensions (ecological, geographical, and phylogenetic spaces), using machine learning protocols. Model predictions showed that different species of bats would be highly susceptible to DENV infections, where susceptibility mostly depended on phylogenetic relationships among hosts and their environmental requirement. Mammal species predicted as highly susceptible coincide with sets of species that have been reported infected in field studies, but it also suggests other species that have not been previously considered or that have been captured in low numbers. Also, the environment (i.e., the distance between the species' optima in bioclimatic dimensions) in combination with geographic and phylogenetic distance is highly relevant in predicting susceptibility to DENV in wild mammals. Our results agree with previous modeling efforts indicating that temperature is an important factor determining DENV transmission, and provide novel insights regarding other relevant factors and the importance of considering wild reservoirs. This modeling framework will aid in the identification of potential DENV reservoirs for future surveillance efforts.