Abstract
DBS surgery is considered for Parkinson's Disease patients when motor complications and consequent quality of life is no longer acceptable on optimal medical therapy prescribed by neurologists. Within the operating room, the electrode placement with the best clinical outcome for the patient is quantitatively assessed via the wrist rigidity assessment. A subjective scale is used, influenced by the neurologists' perception and experience. Our research group has previously designed a novel, comfortable and wireless system aiming to tackle this subjectivity. This system comprised a gyroscope sensor in a textile band, placed in the patients' hand, which communicated its measurement to a Smartphone via Bluetooth. During the wrist rigidity evaluation exam, a signal descriptor was computed from angular velocity (omega) and a polynomial mathematical model was used to classify the signals using a quantitative scale of rigidity improvement. In this present work, we aim to develop models that consider the 3-gyroscope-axes to acquire the omega and the cogwheel rigidity. Our results showed that y-gyroscope-axis remains the best way to classify the rigidity reduction, showing an accuracy of 78% and a mean error of 3.5%. According to previous results, the performance was similar and the decrease of samples to extract the omega features did not compromise system performance. The cogwheel rigidity did not improve the previous model and other gyroscope-axis beyond the y-axis decreased system performance.

Abstract
A reflective fiber optic sensor based on a Fabry-Perot cavity made by splicing two sections of multimode fiber is demonstrated to measure the needle curvature. The sensing structure was incorporated into a medical needle and characterized for curvature and temperature measurements. The maximum sensitivity of -0.152dB/m(-1) was obtained to the curvature measurements, with a resolution of 0.089m(-1). When subjected to temperature, the sensing head presented a low temperature sensitivity, which resulted in a small cross-sensitivity.

Abstract
Information can be conveyed to the user by means of a narrative, modeled according to the user’s context. A case in point is the weather, which can be perceived differently and with distinct levels of importance according to the user’s context. For example, for a blind person, the weather is an important element to plan and move between locations. In fact, weather can make it very difficult or even impossible for a blind person to successfully negotiate a path and navigate from one place to another. To provide proper information, narrated and delivered according to the person’s context, this paper proposes a project for the creation of weather narratives, targeted at specific types of users and contexts. The proposal’s main objective is to add value to the data, acquired through the observation of weather systems, by interpreting that data, in order to identify relevant information and automatically create narratives, in a conversational way or with machine metadata language. These narratives should communicate specific aspects of the evolution of the weather systems in an efficient way, providing knowledge and insight in specific contexts and for specific purposes. Currently, there are several language generator’ systems, which automatically create weather forecast reports, based on previously processed and synthesized information. This paper, proposes a wider and more comprehensive approach to the weather systems phenomena, proposing a full process, from the raw data to a contextualized narration, thus providing a methodology and a tool that might be used for various contexts and weather systems. © 2019, Springer Nature Switzerland AG.

Abstract
Purpose: To investigate the frequency, localizing significance, and intensity characteristics of ictal vocalization in different focal epilepsy syndromes. Methods: Up to four consecutive focal seizures were evaluated in 277 patients with lesional focal epilepsy, excluding isolated auras and subclinical EEG seizure patterns. Vocalization was considered to be present if observed in at least one of the analyzed seizures and not being of speech quality. Intensity features of ictal vocalization were analyzed in a subsample of 17 patients with temporal and 19 with extratemporal epilepsy syndrome. Results: Ictal vocalization was observed in 37% of the patients (102/277) with similar frequency amongst different focal epilepsy syndromes. Localizing significance was found for its co-occurrence with ictal automatisms, which identified patients with temporal seizure onset with a sensitivity of 92% and specificity of 70%. Quantitative analysis of vocalization intensity allowed to distinguish seizures of frontal from temporal lobe origin based on the intensity range (p = 0.0003), intensity variation (p < 0.0001), as well as the intensity increase rate at the beginning of the vocalization (p = 0.003), which were significantly higher in frontal lobe seizures. No significant difference was found for mean intensity and mean vocalization duration. Conclusions: Although ictal vocalization is similarly common in different focal epilepsies, it shows localizing significance when taken into account the co-occurring seizure semiology. It especially increases the localizing value of automatisms, predicting a temporal seizure onset with a sensitivity of 92% and specificity of 70%. Quantitative parameters of the intensity dynamic objectively distinguished frontal lobe seizures, establishing an observer independent tool for semiological seizure evaluation.

Abstract
Bioinformatics Algorithms: Design and Implementation in Python provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover prototypes of bioinformatics applications that demonstrate the main principles underlying real world applications.

Abstract
The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost.