Sign Language Motion Generation from Sign Characteristics.

This paper proposes, analyzes, and evaluates a deep learning architecture based on transformers for generating sign language motion from sign phonemes (represented using HamNoSys: a notation system developed at the University of Hamburg). The sign phonemes provide information about sign characteristics like hand configuration, localization, or movements. The use of sign phonemes is crucial for generating sign motion with a high level of details (including finger extensions and flexions). The transformer-based approach also includes a stop detection module for predicting the end of the generation process. Both aspects, motion generation and stop detection, are evaluated in detail. For motion generation, the dynamic time warping distance is used to compute the similarity between two landmarks sequences (ground truth and generated). The stop detection module is evaluated considering detection accuracy and ROC (receiver operating characteristic) curves. The paper proposes and evaluates several strategies to obtain the system configuration with the best performance. These strategies include different padding strategies, interpolation approaches, and data augmentation techniques. The best configuration of a fully automatic system obtains an average DTW distance per frame of 0.1057 and an area under the ROC curve (AUC) higher than 0.94.

Sign Language Dataset for Automatic Motion Generation.

Several sign language datasets are available in the literature. Most of them are designed for sign language recognition and translation. This paper presents a new sign language dataset for automatic motion generation. This dataset includes phonemes for each sign (specified in HamNoSys, a transcription system developed at the University of Hamburg, Hamburg, Germany) and the corresponding motion information. The motion information includes sign videos and the sequence of extracted landmarks associated with relevant points of the skeleton (including face, arms, hands, and fingers). The dataset includes signs from three different subjects in three different positions, performing 754 signs including the entire alphabet, numbers from 0 to 100, numbers for hour specification, months, and weekdays, and the most frequent signs used in Spanish Sign Language (LSE). In total, there are 6786 videos and their corresponding phonemes (HamNoSys annotations). From each video, a sequence of landmarks was extracted using MediaPipe. The dataset allows training an automatic system for motion generation from sign language phonemes. This paper also presents preliminary results in motion generation from sign phonemes obtaining a Dynamic Time Warping distance per frame of 0.37.

Tissue Distribution and Biochemical Changes in Response to Copper Accumulation in Erica australis L.

Copper uptake, accumulation in different tissues and organs and biochemical and physiological parameters were studied in Erica australis treated with different Cu concentrations (1, 50, 100 and 200 µM) under hydroponic culture. Copper treatments led to a significant reduction in growth rate, biomass production and water content in shoots, while photosynthetic pigments did not change. Copper treatments led to an increase in catalase and peroxidase activities. Copper accumulation followed the pattern roots > stems ≥ leaves, being roots the prevalent Cu sink. Analysis by scanning electron microscopy coupled with elemental X-ray analysis (SEM-EDX) showed a uniform Cu distribution in root tissues. On the contrary, in leaf tissues, Cu showed preferential storage in abaxial trichomes, suggesting a mechanism of compartmentation to restrict accumulation in mesophyll cells. The results show that the studied species act as a Cu-excluder, and Cu toxicity was avoided to a certain extent by root immobilization, leaf tissue compartmentation and induction of antioxidant enzymes to prevent cell damage.

Generating photo-realistic training data to improve face recognition accuracy.

Face recognition has become a widely adopted biometric in forensics, security and law enforcement thanks to the high accuracy achieved by systems based on convolutional neural networks (CNNs). However, to achieve good performance, CNNs need to be trained with very large datasets which are not always available. In this paper we investigate the feasibility of using synthetic data to augment face datasets. In particular, we propose a novel generative adversarial network (GAN) that can disentangle identity-related attributes from non-identity-related attributes. This is done by training an embedding network that maps discrete identity labels to an identity latent space that follows a simple prior distribution, and training a GAN conditioned on samples from that distribution. A main novelty of our approach is the ability to generate both synthetic images of subjects in the training set and synthetic images of new subjects not in the training set, both of which we use to augment face datasets. By using recent advances in GAN training, we show that the synthetic images generated by our model are photo-realistic, and that training with datasets augmented with those images can lead to increased recognition accuracy. Experimental results show that our method is more effective when augmenting small datasets. In particular, an absolute accuracy improvement of 8.42% was achieved when augmenting a dataset of less than 60k facial images.

Nefropatía Mesoamericana / Mesoamerican Nephropathy

Resumen Desde la década de 1990, se comenzó a notar un incremento en la prevalencia de enfermedad renal crónica (ERC) a nivel de Centroamérica. Este incremento se ha presentado principalmente en trabajadores de campos agrícolas en esa región, quienes se encuentran sometidos a elevadas temperaturas, lo que condujo a su designación como nefropatía mesoamericana (MeN por sus siglas en inglés). Aunque su etiología no está esclarecida, se considera que existe un componente ocupacional y ambiental involucrado. El presente artículo, describe sus principales características, su posible etiología, diagnóstico y estrategias de prevención y tratamiento.

Abstract Since the 1990s, an increase in the prevalence of chronic kidney disease (CKD) in several countries in Central America began to be noticed. This increase has occurred mainly in agricultural workers within that region, who are subjected to high temperatures, which led to its designation as Mesoamerican Nephropathy (MeN). Although its etiology is not clarified, it is considered that there is an occupational and environmental component involved. In this article, its main characteristics are described, including what is known about its possible etiology, diagnosis and prevention and treatment strategies.