Abstract
The Biot-Granier (Gbt) is a new thermal dissipation-based sap flow measurement methodology, comprising sensors, data management and automatic data processing. It relies on the conventional Granier (Gcv) methodology upgraded with a modified Granier sensor set, as well as on an algorithm to measure the absolute temperatures in the two observation points and perform the Biot number approach. The work described herein addresses the construction details of the Gbt sensors and the characterization of the overall performance of the Gbt method after comparison with a commercial sap flow sensor and independent data (i.e., volumetric water content, vapor pressure deficit and eddy covariance technique). Its performance was evaluated in three trials: potted olive trees in a greenhouse and two vineyards. The trial with olive trees in a greenhouse showed that the transpiration measures provided by the Gbt sensors showed better agreement with the gravimetric approach, compared to those provided by the Gcv sensors. These tended to overestimate sap flow rates as much as 4 times, while Gbt sensors overestimated gravimetric values 1.5 times. The adjustments based on the Biot equations obtained with Gbt sensors contribute to reduce the overestimates yielded by the conventional approach. On the other hand, the heating capacity of the Gbt sensor provided a minimum of around 7 degrees C and maximum about 9 degrees C, contrasting with a minimum around 6 degrees C and a maximum of 12 degrees C given by the Gcv sensors. The positioning of the temperature sensor on the tip of the sap flow needle proposed in the Gbt sensors, closer to the sap measurement spot, allow to capture sap induced temperature variations more accurately. This explains the higher resolution and sensitivity of the Gbt sensor. Overall, the alternative Biot approach showed a significant improvement in sap flow estimations, contributing to adjust the Granier sap flow index, a vulnerability of that methodology.

Abstract
Cancer has an important and considerable gender differential susceptibility confirmed by several epidemiological studies. Gastric (GC) and thyroid cancer (TC) are examples of malignancies with a higher incidence in males and females, respectively. Beyond environmental predisposing factors, it is expected that gender-specific gene deregulation contributes to this differential incidence. We performed a detailed characterization of the transcriptomic differences between genders in normal and tumor tissues from stomach and thyroid using Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data. We found hundreds of sex-biased genes (SBGs). Most of the SBGs shared by normal and tumor belong to sexual chromosomes, while the normal and tumor-specific tend to be found in the autosomes. Expression of several cancer-associated genes is also found to differ between sexes in both types of tissue. Thousands of differentially expressed genes (DEGs) between paired tumor-normal tissues were identified in GC and TC. For both cancers, in the most susceptible gender, the DEGs were mostly under-expressed in the tumor tissue, with an enrichment for tumor-suppressor genes (TSGs). Moreover, we found gene networks preferentially associated to males in GC and to females in TC and correlated with cancer histological subtypes. Our results shed light on the molecular differences and commonalities between genders and provide novel insights in the differential risk underlying these cancers.

Abstract
Abstract Background Recently, cardiac shear wave (SW) elastography, based on high frame rate (HFR) echocardiography, has been proposed as new non-invasive technique for assessing myocardial stiffness. As myocardial stiffness increases with increasing wall stress, differences in measured operating myocardial stiffness do not necessarily reflect differences in intrinsic myocardial properties, but can also be caused by mere changes in loading or chamber geometry. This complicates myocardial stiffness interpretation for different types of pathologic hypertrophy. Purpose To explore the relationship between myocardial stiffness and underlying pathological substrates for cardiac hypertrophy. Methods We included 20 patients with hypertension (HT) and myocardial remodelling (59±14 years, 75% male), 20 patients with hypertrophic cardiomyopathy (HCM) (59±16 years, 60% male) and 20 healthy controls (56±14 years, 75% male). Left ventricular (LV) parasternal long axis views were acquired with an experimental HFR scanner at 1293±362 frames per seconds. Propagation velocity of SW occurring after mitral valve closure in the interventricular septum (IVS) served as measure of operating myocardial stiffness (Figure A). To compare myocardial stiffness among hearts with differing loading conditions and chamber geometry, SW velocities were normalized to end-diastolic wall stress, estimated at IVS from regional wall thickness, longitudinal and circumferential regional radii of curvature, and non-invasively estimated LV end-diastolic pressure (EDP). Results SW velocities differed significantly between groups (p<0.001). The controls had the lowest SW velocities (4.02±0.97 m/s), whereas values between HT and HCM group were comparable (6.46±0.99 m/s vs. 7.00±2.10 m/s; p=0.738). Considering end-diastolic wall stress, HCM patients had the same SW velocity at lower wall stress compared to HT (Figure B), indicating higher myocardial stiffness in the HCM group. SW velocities normalized for wall stress indicated significantly different myocardial stiffness among all groups (p<0.001) (Figure C). In a multiple linear regression model, the underlying pathological substrate independently influenced SW velocity (beta 1.37, 95% CI (0.78–1.96); p<0.001), while wall stress did not significantly affect its value (p=0.479). Conclusions Our study demonstrated that SW elastography can detect differences in myocardial stiffness in hypertensive heart and hypertrophic cardiomyopathy. Additionally, our results suggest that SW velocity is dominated by underlying myocardial tissue properties. We hypothesize that differential changes in cardiomyocytes and/or the extracellular matrix contribute to the differential myocardial stiffening in different pathologic entities of LV hypertrophy. Thus, SW elastography could provide useful novel diagnostic information in the evaluation of LV hypertrophy. Figure A, B, C Funding Acknowledgement Type of funding source: None

Abstract
Glycosomes of trypanosomatids are peroxisome-like organelles comprising unique metabolic features, among which the lack of the hallmark peroxisomal enzyme catalase. The absence of this highly efficient peroxidase from glycosomes is presumably compensated by other antioxidants, peroxidases of the peroxiredoxin (PRX) family being the most promising candidates for this function. Here, we follow on this premise and investigate the product of a Leishmania infantum gene coding for a putative glycosomal PRX (LigPRX). First, we demonstrate that LigPRX localizes to glycosomes, resorting to indirect immunofluorescence analysis. Second, we prove that purified recombinant LigPRX is an active peroxidase in vitro. Third, we generate viable LigPRX-depleted L. infantum promastigotes by classical homologous recombination. Surprisingly, phenotypic analysis of these knockout parasites revealed that promastigote survival, replication, and protection from oxidative and nitrosative insults can proceed normally in the absence of LigPRX. Noticeably, we also witness that LigPRX-depleted parasites can infect and thrive in mice to the same extent as wild type parasites. Overall, by disclosing the dispensable character of the glycosomal peroxiredoxin in L. infantum, this work excludes this enzyme from being a key component of the glycosomal hydroperoxide metabolism and contemplates alternative players for this function.

Abstract
Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients' survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter's support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus being a more reliable approach for the proposed exercise. The novel network revealed similar scores to the U-Net, while at the same time reducing computational cost and improving memory efficiency. Consequently, such study may contribute to the possible implementation of this model in a decision support system, assisting the physicians in establishing a reliable diagnosis of lung pathologies based on this segmentation task.

Abstract
Currently, lung cancer is the most lethal in the world. In order to make screening and follow-up a little more systematic, guidelines have been proposed. Therefore, this study aimed to create a diagnostic support approach by providing a patient label based on the LUNG-RADSTM guidelines. The only input required by the system is the nodule centroid to take the region of interest for the input of the classification system. With this in mind, two deep learning networks were evaluated: a Wide Residual Network and a DenseNet. Taking into account the annotation uncertainty we proposed to use sample weights that are introduced in the loss function, allowing nodules with a high agreement in the annotation process to take a greater impact on the training error than its counterpart. The best result was achieved with the Wide Residual Network with sample weights achieving a nodule-wise LUNG-RADSTM labelling accuracy of 0.735 +/- 0.003.