Teratoma Associated With Testicular Tissue in a Female-Like Horse With 64,XY (SRY-Positive) Disorder of Sex Development.

A four-year-old female-like Quarter Horse was admitted for clinical evaluation because of masculinized-aggressive behavior and lack of estrous signs. On external inspection, a hypertrophied clitoris and prominent muscles were observed. On gynecological examination, apparently normal mammary glands, vulva, vagina, and cervix were noted. On the other hand, the uterus had no tone and was smaller than normal. The left gonad was very soft on palpation and the right gonad was mostly firm, irregular, and wider than the left gonad. On ultrasound examination, there were no signs of regular ovarian structure or follicular activity. Because of the different shapes and consistencies of the gonads and a suspicion of increased testosterone production, a bilateral gonadectomy was recommended. Blood was collected for testosterone levels quantification and for cytogenetic and molecular investigations. After removal, gonads were analyzed macroscopically and sections were sent for histopathological examination. A final diagnosis of benign adult teratoma associated with seminiferous tubules and Leydig cells was made. On cytogenetic and molecular analyses, a normal diploid number of 64 chromosomes and the presence of the XY sex chromosomes were seen in all cells, as well as the SRY gene. Testosterone levels were higher than normal before surgery and were reduced after gonads removal. In conclusion, the masculinized behavior was probably caused by increased testosterone levels produced by testicular tissue, in a female-like horse with 64,XY SRY-positive disorder of sex development, which was associated with a teratoma.

Simultaneously estimating the thermal conductivity and thermal diffusivity of a poorly conducting solid material using single surface measurements.

This paper presents a new experimental method that simultaneously obtains the thermal conductivity and thermal diffusivity of a solid. This technique uses a new surface probe with a resistance heater, two thermocouples, and a heat flux sensor. In this method, part of the top of the sample is heated, and two regions on this surface (one heated and the other not) are used to estimate the thermal properties of the material. This technique minimizes two distinct functions that are defined from surface temperature measurements, using, however, different thermal concepts. The first uses the gain ratio between two surface temperatures to determine the thermal diffusivity. The second uses the well-known square quadratic error function calculated with the same experimental and theoretical values of the temperatures of the surface to estimate the thermal conductivity.

Breast tumor localization using skin surface temperatures from a 2D anatomic model without knowledge of the thermophysical properties.

Breast cancer is the second most common type of cancer among women after nonmelanoma skin cancer. Use of mammography, the main method to diagnose the disease, has several limitations in parts of the population. The primary goal of this work was to detect and localize the geometric centers of mammary tumors using only superficial temperatures of the breast skin. The 2D anatomic geometry of the breast was simulated using the commercial software COMSOL to obtain the distribution of skin temperature in the three main types of breast cancer. Random errors of  ±â€¯2% were added to the simulated temperatures. The temperature variation caused by each type of cancer on the healthy tissue was correlated with auxiliary temperature profiles. These auxiliary temperature profiles were obtained with no prior knowledge of the thermophysical properties of the tumor apart from the mean values for thermal conductivity and blood perfusion of the layers of healthy breast tissue. The results showed that the maximum error for geometric center estimation was 0.32 cm for invasive lobular carcinoma, with a diameter of 1 cm, positioned 5 cm from the skin surface. Thus, this work contributes to studies aiming to improve the use of infrared thermography for early breast cancer diagnosis, as the results showed that localization of tumors using only superficial temperature profiles does not require prior knowledge of the thermophysical properties of the tissues.

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts.

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.