Predictions of semen production in ram using phenotypic traits by artificial neural network

Concentration of semen production is the most important fertility trait in ram and dimension of testis is a good criterion for identifying the quantity of semen production. Thus, prediction of that trait has important beneficial effect on the timely identification of genetically superior animals. Artificial neural network (ANN) system can be used as a decision making support system in ram industry as well as other industries. It can help breeders to predict future semen production based on phenotypic trait. Data from 24 rams of zandi breed in Tehran, Iran, were used. From 192 available data of phenotypic and semen concentration, 184 records were used for training a back propagation ANN system and 8 randomly chosen record (not used in the training process) were introduced to the trained neural network for evaluation. The result of the simulation showed that there was no significant difference between the observed and the predicted semen production (p > 0.05). The major use of this predictive system is to make accurate selection decision which is based on prior knowledge of the outcomes

Arginine vasopressin (AVP) and treatment with arginine vasopressin receptor antagonists (vaptans) in congestive heart failure, liver cirrhosis and syndrome of inappropriate antidiuretic hormone secretion (SIADH)

International audienceArginine vasopressin (AVP) is the major physiological regulator of renal water excretion and blood volume. The AVP pathways of VR-mediated vasoconstriction and VR-induced water retention represent a potentially attractive target of therapy for edematous diseases. Experimental and clinical evidence suggests beneficial effects of AVP receptor antagonists by increasing free water excretion and serum sodium levels. This review provides an update on the therapeutic implication of newly developed AVP receptor antagonists in respective disorders, such as chronic heart failure, liver cirrhosis and syndrome of inappropriate antidiuretic hormone secretion

Tip of the iceberg: 18F-FDG PET/CT diagnoses extensively disseminated coccidioidomycosis with cutaneous lesions

We present a case of an immunocompetent 27-year-old African American man who was initially diagnosed with diffuse pulmonary coccidioidomycosis and started on oral fluconazole. While his symptoms improved, he began to develop tender cutaneous lesions. Biopsies of the cutaneous lesions grew Coccidioides immitis. Subsequent 18F-FDG PET/CT revealed extensive multisystem involvement including the skin/subcutaneous fat, lungs, spleen, lymph nodes, and skeleton. This case demonstrates the utility of obtaining an 18F-FDG PET/CT to assess the disease extent and activity in patients with disseminated coccidioidomycosis who initially present with symptoms involving only the lungs

Targeting buyers of counterfeits of luxury brands: a study on attitudes of Singapore consumers

The paper examines the factors that influence Singaporean consumer's attitudes towards counterfeits of luxury brands. Data was collected from a convenience sample of postgraduate students of a large university using a self-administered questionnaire. Social influence, brand consciousness and price quality inference were found to significantly influence attitudes towards counterfeits of luxury brands. There is no significant relationship with personal gratification, value consciousness, and brand prestige. Attitudes towards counterfeits of luxury brands were found to influence purchase intention

Group communication analysis: A computational linguistics approach for detecting sociocognitive roles in multiparty interactions

Roles are one of the most important concepts in understanding human sociocognitive behavior. During group interactions, members take on different roles within the discussion. Roles have distinct patterns of behavioral engagement (i.e., active or passive, leading or following), contribution characteristics (i.e., providing new information or echoing given material), and social orientation (i.e., individual or group). Different combinations of roles can produce characteristically different group outcomes, and thus can be either less or more productive with regard to collective goals. In online collaborative-learning environments, this can lead to better or worse learning outcomes for the individual participants. In this study, we propose and validate a novel approach for detecting emergent roles from participants\u27 contributions and patterns of interaction. Specifically, we developed a group communication analysis (GCA) by combining automated computational linguistic techniques with analyses of the sequential interactions of online group communication. GCA was applied to three large collaborative interaction datasets (participant N = 2,429, group N = 3,598). Cluster analyses and linear mixed-effects modeling were used to assess the validity of the GCA approach and the influence of learner roles on student and group performance. The results indicated that participants\u27 patterns of linguistic coordination and cohesion are representative of the roles that individuals play in collaborative discussions. More broadly, GCA provides a framework for researchers to explore the micro intra- and interpersonal patterns associated with participants\u27 roles and the sociocognitive processes related to successful collaboration