Abstract
How we perceive and experience the world around us is inherently multisensory. Most of the Virtual Reality (VR) literature is based on the senses of sight and hearing. However, there is a lot of potential for integrating additional stimuli into Virtual Environments (VEs), especially in a training context. Identifying the relevant stimuli for obtaining a virtual experience that is perceptually equivalent to a real experience will lead users to behave the same across environments, which adds substantial value for several training areas, such as firefighters. In this article, we present an experiment aiming to assess the impact of different sensory stimuli on stress, fatigue, cybersickness, Presence and knowledge transfer of users during a firefighter training VE. The results suggested that the stimulus that significantly impacted the user's response was wearing a firefighter's uniform and combining all sensory stimuli under study: heat, weight, uniform, and mask. The results also showed that the VE did not induce cybersickness and that it was successful in the task of transferring knowledge.

Abstract
Using surface resonance (SPR) as a sensitivity enhancer, this work describes the development of a transmissive multimode optical fiber sensor with a gold (Au) thin film that measures glucose concentration. The fiber's cladding was initially removed, and an Au layer was then sputtered onto its surface to simultaneously excite SPR and reflect light, making the SPR sensor extremely sensitive to changes in the environment's refractive index. A range of glucose concentrations, from 0.0001 to 0.5000 g/ml, were tested on the sensor. A maximum sensitivity of 161.302 nm/(g/mL) was attained for the lowest glucose concentration, while the highest concentration yielded a sensitivity of 312.000 nm/(g/mL). The proposed sensor's compact size, high sensitivity, good stability and practicality make it a promising candidate for a range of applications, including detecting diabetes.

Abstract
A paracetamol concentration-sensing scheme based on a linear cavity fiber laser configuration is demonstrated experimentally. The laser cavity has a fiber sensor at one end, that allows refractive index measurements. The refractometer consists of a cleaved fiber tip combined with an FBG functioning as a reflecting mirror. The combination of a fiber loop mirror at the other end allows to reflect all the light from the FBG and refractometer, forming a linear cavity. By measuring the intensity variation of the Fresnel reflection at the fiber-to-liquid interface, the measured concentration is linear and have a concentration sensitivity of [( - 8.74 & PLUSMN; 0.34) x 10-5 ] & mu;W/(g/kg), over a range of 52.61 to 219.25 g/kg, and with a resolution of 2.77 g/kg. The results obtained present high stability and prove the potential of the fiber laser system to performed realtime measurements of concentration, in a non-invasive way.

Abstract

Abstract
Author summaryCancer therapies often fail because tumor cells become resistant to treatment. One way to overcome resistance is by treating patients with a combination of two or more drugs. Some combinations may be more effective than when considering individual drug effects, a phenomenon called drug synergy. Computational drug synergy prediction methods can help to identify new, clinically relevant drug combinations. In this study, we developed several deep learning models for drug synergy prediction. We examined the effect of using different types of deep learning architectures, and different ways of representing drugs and cancer cell lines. We explored the use of biological prior knowledge to select relevant cell line features, and also tested data-driven feature reduction methods. We tested both precomputed drug features and deep learning methods that can directly learn features from raw representations of molecules. We also evaluated whether including genomic features, in addition to gene expression data, improves the predictive performance of the models. Through these experiments, we were able to identify strategies that will help guide the development of new deep learning models for drug synergy prediction in the future. One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R-2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R-2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R-2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R-2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Abstract
In this article, the use of diffuse reflectance (R-d) spectroscopy is explored to evaluate the diffusion properties of water and sucrose in skeletal muscle during optical clearing treatments. Treating muscle samples with sucrose-water solutions with different osmolarities, collimated transmittance (T-c) and R-d measurements were performed to obtain the diffusion time (t) and the diffusion coefficient (D) values that characterize the unique water and sucrose fluxes in the muscle and also the optical clearing mechanisms designated as tissue dehydration and refractive index matching. Considering the R-d measurements, the estimated t and D values for water in the muscle were 63.1s and 1.72x10(-6) cm(2)/s, while the ones estimated for sucrose were 261s and 4.86x10(-7) cm(2)/s. Comparing these values with the ones estimated from the T-c measurements, the relative differences observed for t and D were 1.6% and 2.8% in the case of water and 0.3% and 0.4% in the case of sucrose.