Case studies of clinical decision-making through prescriptive models based on machine learning.

Background: The development of computational methodologies to support clinical decision-making is of vital importance to reduce morbidity and mortality rates. Specifically, prescriptive analytic is a promising area to support decision-making in the monitoring, treatment and prevention of diseases. These aspects remain a challenge for medical professionals and health authorities.

An autonomous cycle of data analysis tasks for the clinical management of dengue.

Dengue is the most widespread vector-borne disease worldwide. Timely diagnosis and treatment of dengue is the main objective of medical professionals to decrease mortality rates. In this paper, we propose an autonomous cycle that integrates data analysis tasks to support decision-making in the clinical management of dengue.

A clinical decision-support system for dengue based on fuzzy cognitive maps.

Dengue is a viral infection widely distributed in tropical and subtropical regions of the world. Dengue is characterized by high fatality rates when the diagnosis is not made promptly and effectively. To aid in the diagnosis of dengue, we propose a clinical decision-support system that classifies the clinical picture based on its severity, and using causal relationships evaluates the behavior of the clinical and laboratory variables that describe the signs and symptoms related to dengue.

Dengue models based on machine learning techniques: A systematic literature review.

Background: Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention.

Does vaporized hydrogen peroxide sterilization affect the geometrical properties of anatomic models and guides 3D printed from computed tomography images?

Background: Material extrusion is used to 3D print anatomic models and guides. Sterilization is required if a 3D printed part touches the patient during an intervention. Vaporized Hydrogen Peroxide (VHP) is one method of sterilization.

A Cyber-Physical Data Collection System Integrating Remote Sensing and Wireless Sensor Networks for Coffee Leaf Rust Diagnosis.

Coffee Leaf Rust (CLR) is a fungal epidemic disease that has been affecting coffee trees around the world since the 1980s. The early diagnosis of CLR would contribute strategically to minimize the impact on the crops and, therefore, protect the farmers' profitability. In this research, a cyber-physical data-collection system was developed, by integrating Remote Sensing and Wireless Sensor Networks, to gather data, during the development of the CLR, on a test bench coffee-crop.

Unsupervised visual discrimination learning of complex stimuli: Accuracy, bias and generalization.

Through same-different judgements, we can discriminate an immense variety of stimuli and consequently, they are critical in our everyday interaction with the environment. The quality of the judgements depends on familiarity with stimuli. A way to improve the discrimination is through learning, but to this day, we lack direct evidence of how learning shapes the same-different judgments with complex stimuli.

AtHMA1 is a thapsigargin-sensitive Ca2+/heavy metal pump.

The Arabidopsis thaliana AtHMA1 protein is a member of the P(IB)-ATPase family, which is implicated in heavy metal transport. However, sequence analysis reveals that AtHMA1 possesses a predicted stalk segment present in SERCA (sarcoplasmic/endoplasmic reticulum Ca(2+) ATPase)-type pumps that is involved in inhibition by thapsigargin. To analyze the ion specificity of AtHMA1, we performed functional complementation assays using mutant yeast strains defective in Ca(2+) homeostasis or heavy metal transport.