Fatty liver is a metabolic disorder of dairy cows in early lactation, and approximately half of multi-parous dairy cows experience a moderate to severe fatty liver at calving. Since the occurrence of fatty liver in buffalo is not known, the aim of this study was to evaluate the hepatic content of triacylglycerols (TAG) and total lipids (TL), and the serum content of non-esterified fatty acids (...

background: the fc receptor like (fcrl) molecules belong to the immunoglobulin (ig) superfamily with potentially immunoregulatoryfunction. among the fcrl family fcrl2 and 4 are predominantly expressed on memory b cells and fcrl1 is a pan- b cell marker. to date, noligand has been identifid for the human fcrl1, 2 and 4 molecules.objectives: cloning, expression, purifiation and structural analysi...

the direct torque control (dtc) of interior permanent magnet synchronous motor (ipmsm) offers simple structure and fast torque response. the conventional switching table-based dtc (st-dtc) presents some disadvantages like high torque and flux ripple and also variable switching frequency. this paper investigates the improved st-dtc strategies to reduce both torque and flux ripple in dtc of ipmsm...

The Competitor Rule Suppression (CRS) effect is the performance impairment observed in task switching when the currently relevant task rule is the same rule that had generated a response conflict in the preceding trial. This effect could reflect (a) episodic tagging, in which a competitor rule is retrieved with relative difficulty in subsequent trials or (b) residual active inhibition of the co...

Community tagging offers valuable information for media search and retrieval, but new media items are at a disadvantage. Automated tagging may populate media items with few tags, thus enabling their inclusion into search results. In this paper, a multi-label decision tree is proposed and applied to the problem of automated tagging of media data. In addition to binary labels, the proposed Iterat...

In multi-label learning, each instance can be related with one or more binary target variables. Multi-label learning problems are commonly found in many applications, e.g. in text classification where a news article is possible to be both on politics and finance. The main motivation of multi-label learning algorithms is the exploitation of label dependencies in order to improve prediction accur...