Topographical localisation of glucidic residues and their variations in the canine zona pellucida during folliculogenesis.

In the present ultrastructural study, horseradish peroxidase-labelled lectins, in conjunction with antiperoxidase antibody and protein A-gold, were used to characterise and localise the oligosaccharide sequences of zona pellucida glycoproteins at different stages of follicular development in the canine ovary. Deacetylation and sialidase digestion were also performed before lectin cytochemistry. The zona pellucida of oocytes present in unilaminar primary follicles reacts with WGA- and RCA-I-lectins. The zona pellucida of oocytes present in bilaminar and trilaminar secondary follicles displays positivity to WGA, RCA-I, Con-A, UEA-I, and sialidase/SBA. This labelling pattern persists in the zona pellucida of oocytes present in antral tertiary follicles with the exception of WGA and RCA-I reactive sites which are differently distributed throughout the zona pellucida. The topographical distribution of these carbohydrates is not uniform throughout the zona pellucida, indicating the regionalization of oligosaccharide chains within three concentric bands of the zona matrix: an inner surface close to the oocyte plasma membrane, an intermediate portion and an outer layer in contact with the follicular cells. Our results demonstrated variations in the presence and distribution of the carbohydrate residues in the canine zona pellucida during different stages of follicular growth. We also observed the presence of vesicles in both the ooplasm and granulosa cells, showing a similar lectin binding pattern to that of the zona pellucida

I Say “no”. You Say “it Isn't”. About a New Understanding of the Concept of Negation

Introduction: In our opinion there is still confusion about the exact meaning of the term negation. As a consequence of this the importance of negation in the psychotherapeutic work is until now underestimated. Objectives For psychiatrists who work according to Fagioli's human birth theory the concept of negation is fundamental. Negation is a notion that refers to unconscious reality. Aims: The task of the psychiatrist is to identify and to interpret the negation in the deformed dream images during the psychotherapeutic process. Methods: In contrast to an intentional lie, which is communicated through verbal speech, negation corresponds to unconscious thoughts, which we can find in dreams. During sleep a transformation occurs, language is altered and expressed through images. Negation deforms the image. This deformation of the image happens in an unconscious process. This negation distorts the reality of the patient and his ability to interact with the other. Corresponding the relationship will be aggressive/destructive. The only way to identify this deformation is dream interpretation. Results: Through this therapeutic process the patient will be able to intuit and realize instead of negate the positive qualities of the other and integrate these into his reality. Only human interaction that is free from negation enable the patient to overcome the ideo-affective splitting, which allows recovery of positive affects and the possibility of developing evolutive relationships. Conclusions: Only a clarification of the term negation allows a psychotherapeutic process with the aim of developing evolutive relationships

Signal enhancement and efficient DTW-based comparison for wearable gait recognition

The popularity of biometrics-based user identification has significantly increased over the last few years. User identification based on the face, fingerprints, and iris, usually achieves very high accuracy only in controlled setups and can be vulnerable to presentation attacks, spoofing, and forgeries. To overcome these issues, this work proposes a novel strategy based on a relatively less explored biometric trait, i.e., gait, collected by a smartphone accelerometer, which can be more robust to the attacks mentioned above. According to the wearable sensor-based gait recognition state-of-the-art, two main classes of approaches exist: 1) those based on machine and deep learning; 2) those exploiting hand-crafted features. While the former approaches can reach a higher accuracy, they suffer from problems like, e.g., performing poorly outside the training data, i.e., lack of generalizability. This paper proposes an algorithm based on hand-crafted features for gait recognition that can outperform the existing machine and deep learning approaches. It leverages a modified Majority Voting scheme applied to Fast Window Dynamic Time Warping, a modified version of the Dynamic Time Warping (DTW) algorithm with relaxed constraints and majority voting, to recognize gait patterns. We tested our approach named MV-FWDTW on the ZJU-gaitacc, one of the most extensive datasets for the number of subjects, but especially for the number of walks per subject and walk lengths. Results set a new state-of-the-art gait recognition rate of 98.82% in a cross-session experimental setup. We also confirm the quality of the proposed method using a subset of the OU-ISIR dataset, another large state-of-the-art benchmark with more subjects but much shorter walk signals