The survey of investigation about Mesopotamichthys sharpeyi projects in development program in Iranian Fisheries Research

Attention to the aquaculture capacity in inland water is needed to reach production of 262072 tons which was implicited in sixth economical and social development program of culture and production warmwater fishes industry. On of the most important aim of progressive management in fisheries program is native aquaculture activity with emphasis on Barbus fishes in Iran.Benni fish is one of the species of this genus despit of low relative growth rate, but is as a desire fishes in market. This fish not only has high fecondity and feed phytoplankton during the larval stages but also is very delicious for consumers. During the fifth economical and social development program there were so many projects have been done on aquaculture of this species but needs to analysis of all data which is important for successive future its aquaculture commercially. The precent analytical investigation base on three decades data, was done for completeing that gap including of analytical activities of researches were done, developmental access to fish aqu culture in six proficiency subjects , propagation and cultue, nutrition, genetic, health and disease biology, physiologt and determination of weak points. Then base on guideline studies on warmwater aquaculture and developmental road map, several suggestion for doing apply research were adopted

Image1_Evaluating the effectiveness of a novel somatostatin receptor 2 antagonist, ZT-01, for hypoglycemia prevention in a rodent model of type 2 diabetes.TIF

Background: Elevated levels of somatostatin blunt glucagon counterregulation during hypoglycemia in type 1 diabetes (T1D) and this can be improved using somatostatin receptor 2 (SSTR2) antagonists. Hypoglycemia also occurs in late-stage type 2 diabetes (T2D), particularly when insulin therapy is initiated, but the utility of SSTR2 antagonists in ameliorating hypoglycemia in this disease state is unknown. We examined the efficacy of a single-dose of SSTR2 antagonists in a rodent model of T2D.Methods: High-fat fed (HFF), low dose streptozotocin (STZ, 35 mg/kg)-induced T2D and HFF only, nondiabetic (controls-no STZ) rats were treated with the SSTR2 antagonists ZT-01/PRL-2903 or vehicle (n = 9–11/group) 60 min before an insulin tolerance test (ITT; 2–12 U/kg insulin aspart) or an oral glucose tolerance test (OGTT; 2 g/kg glucose via oral gavage) on separate days.Results: This rodent model of T2D is characterized by higher baseline glucose and HbA1c levels relative to HFF controls. T2D rats also had lower c-peptide levels at baseline and a blunted glucagon counterregulatory response to hypoglycemia when subjected to the ITT. SSTR2 antagonists increased the glucagon response and reduced incidence of hypoglycemia, which was more pronounced with ZT-01 than PRL-2903. ZT-01 treatment in the T2D rats increased glucagon levels above the control response within 60 min of dosing, and values remained elevated during the ITT (glucagon Cmax: 156 ± 50 vs. 77 ± 46 pg/mL, p Conclusion: Treatment with SSTR2 antagonists increases glucagon responses in a rat model of T2D and results in less hypoglycemia exposure. Future studies are required to determine the best dosing periods for chronic SSTR2 antagonism treatment in T2D.</p

Image1_Evaluating the effectiveness of a novel somatostatin receptor 2 antagonist, ZT-01, for hypoglycemia prevention in a rodent model of type 2 diabetes.TIF

Background: Elevated levels of somatostatin blunt glucagon counterregulation during hypoglycemia in type 1 diabetes (T1D) and this can be improved using somatostatin receptor 2 (SSTR2) antagonists. Hypoglycemia also occurs in late-stage type 2 diabetes (T2D), particularly when insulin therapy is initiated, but the utility of SSTR2 antagonists in ameliorating hypoglycemia in this disease state is unknown. We examined the efficacy of a single-dose of SSTR2 antagonists in a rodent model of T2D.Methods: High-fat fed (HFF), low dose streptozotocin (STZ, 35 mg/kg)-induced T2D and HFF only, nondiabetic (controls-no STZ) rats were treated with the SSTR2 antagonists ZT-01/PRL-2903 or vehicle (n = 9–11/group) 60 min before an insulin tolerance test (ITT; 2–12 U/kg insulin aspart) or an oral glucose tolerance test (OGTT; 2 g/kg glucose via oral gavage) on separate days.Results: This rodent model of T2D is characterized by higher baseline glucose and HbA1c levels relative to HFF controls. T2D rats also had lower c-peptide levels at baseline and a blunted glucagon counterregulatory response to hypoglycemia when subjected to the ITT. SSTR2 antagonists increased the glucagon response and reduced incidence of hypoglycemia, which was more pronounced with ZT-01 than PRL-2903. ZT-01 treatment in the T2D rats increased glucagon levels above the control response within 60 min of dosing, and values remained elevated during the ITT (glucagon Cmax: 156 ± 50 vs. 77 ± 46 pg/mL, p Conclusion: Treatment with SSTR2 antagonists increases glucagon responses in a rat model of T2D and results in less hypoglycemia exposure. Future studies are required to determine the best dosing periods for chronic SSTR2 antagonism treatment in T2D.</p

Belief revision in structured probabilistic argumentation: Model and application to cyber security

In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.Fil: Shakarian, Paulo. Arizona State University; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Moores, Geoffrey. U.S. Military Academy; Estados UnidosFil: Paulo, Damon. U.S. Military Academy; Estados UnidosFil: Parsons, Simon. University of Liverpool; Reino UnidoFil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Aleali, Ashkan. Arizona State University; Estados Unido