Search

The existence of functional endometrial tissue outside the uterus is known as endometriosis. It is a benign estrogen-dependent gynecological condition that affects 5%-10% of women who are of reproductive age. Endometriosis often affects the ovary and fallopian tubes, although it can also occur in nonpelvic areas. The most typical location for extra-pelvic endometriosis is the abdomen. Umbilical endometriosis is a rare condition accounting for 0.5%-1.0% of all cases of endometriosis. In 3% of cases, there is a chance of malignant change. This disorder's precise etiology is uncertain. Recurrent discomfort and swelling around the umbilicus are the classic manifestations. In this instance, we describe a patient with primary umbilical endometriosis (PUE) who had cyclical bleeding and swelling over the umbilicus and was surgically naive. This case will demonstrate how, particularly in surgically naïve instances, diagnosis of PUE is frequently delayed due to ignorance of the entity. [ABSTRACT FROM AUTHOR]

Nitrogen (N), the most vital nutrient, undergoes a transformation in the soil and was subjected to loss through ammonia volatilization, nitrate leaching, and denitrification, which lower the soil fertility, crop yield and pollute the environment. Integrated nutrient management (INM) improves the nutrient supply system by integrating recommended dose fertilizers and biofertilizers, lessens the nitrogen losses, and synchronizes with the nutrient demand of the crop. To evaluate the impact of INM on minimization of N losses and improvement of wheat (Triticum aestivum L.) yield and growth parameters, a field experiment was conducted with different combinations of biofertilizers along with the recommended dosage of fertilizers. Application of NPK + Azotobacter + mycorrhiza (Tram) in combination was found to improve soil nitrogen dynamics, availability of P, K, microbial activity, root growth, urease activity, and nitrogen use efficiency as compared to NPK (Tr) only. The cumulative emission from ammonia flux in the INM plot varied from 10.2 to 10.6 kg ha−1 and denitrification losses varied from 2.4 to 2.5 kg ha−1. At the same time, these were recorded significantly higher in Tr plot. The yield obtained under the INM plot receiving Tram was recorded as 6.4 t ha−1, which is higher than other treatments. [ABSTRACT FROM AUTHOR]

Underutilized fruits like kadam are bio-reserves of numerous biochemical and minerals and have the potential to be used as food alternatives and supplements to fulfill the needs of malnourished populations. The production of quality fruits needs monitoring throughout the growth and ripening in order to build consumer and industry confidence. The nondestructive optical spectroscopic techniques have immense potential to monitor the growth and ripening indices of the fruits. Hence, this study explores the potential of laser-induced fluorescence and confocal micro Raman spectroscopy for the reliable and cost effective monitoring of growth and ripening of kadam fruits. The analysis of the laser-induced fluorescence measurements reveals that the ripening of the kadam fruits may be monitored by analyzing the fluorescence emission of chlorophyll and carotenoids. The decrease in the intensity of chlorophyll fluorescence accompanied by the increase in the intensity of the carotenoid bands is the characteristic fluorescence signature associated with the ripening of the kadam fruits. Hence, upon the ripening of the fruits, the concentration of chlorophyll decreases considerably with the accumulation of carotenoids. This fact is further complimented by confocal micro Raman measurements, which show that as the fruit ripens, the intensity of carotenoid bands increases dramatically. The observed spectral features may be used by the horticulture scientists and nutritionists to monitor the growth, maturity, and ripening of underutilized fruits like kadam in order to maximize the quality and minimize postharvest losses. [ABSTRACT FROM AUTHOR]

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the components. In this paper, we adapt the variable neighborhood search (VNS) paradigm to develop two heuristics for variable selection in PCA. The performances of these heuristics were compared to those obtained by a branch-and-bound algorithm, as well as forward stepwise, backward stepwise, and tabu search heuristics. In the first experiment, which considered candidate pools of 18 to 30 variables, the VNS heuristics matched the optimal subset obtained by the branch-and-bound algorithm more frequently than their competitors. In the second experiment, which considered candidate pools of 54 to 90 variables, the VNS heuristics provided better solutions than their competitors for a large percentage of the test problems. An application to a real-world data set is provided to demonstrate the importance of variable selection in the context of PCA.