Purpose To assess the agreement of the optical low-coherence reflectometry (OLCR) device LENSTAR LS900 with partial coherence interferometry (PCI) device IOLMaster and applanation and immersion ultrasound biometry. Methods We conducted the study at the Ophthalmology Clinic, University of Malaya Medical Center, Malaysia. Phakic eyes of 76 consecutive cataract patients were measured using four different methods: IOLMaster, LENSTAR and A-scan applanation and immersion ultrasound biometry. We assessed the method agreement in the LENSTARIOLMaster, LENSTAR-applanation, and LENSTAR-immersion comparisons for axial length (AL) and intraocular lens (IOL) power using Bland-Altman plots. For average K, we compared LENSTAR with IOLMaster and the TOPCON KR-8100 autorefractor-keratometer. SRK/T formula was used to compute IOL power, with emmetropia as the target refractive outcome. Results For all the variables studied, LENSTAR agreement with IOLMaster is strongest, followed by those with immersion and applanation. For the LENSTARIOLMaster comparison, the estimated proportion of differences falling within 0.33 mm from zero AL and within 1D from zero IOL power is 100%. The estimated proportion of differences falling within 0.5 D from zero average K is almost 100% in the LENSTAR-IOLMaster comparison but 88% in the LENSTAR-TOPCON comparison. The proportion of differences falling within 0.10 mm (AL) and within 1D (IOL power) in the LENSTAR-IOLMaster comparison has practically significant discrepancy with that of LENSTAR-applanation and LENSTARimmersion comparisons.Conclusions In phakic eyes of cataract patients, measurements of AL, average K, and IOL power calculated using the SRK/T formula from LENSTAR are biometrically equivalent to those from IOLMaster, but not with those from applanation and immersion ultrasound biometry.

Biological data is huge and increasing rapidly therefore data storing and mining will become major challenges. We have encountered several key problems related to limitations in the database management system (DBMS) used and information retrieval in our in-house Monogenean-host database. In this paper we will be presenting the ontology developed that is specific to our dataset using semantic technology to overcome these problems. Our Taxonomy ontology is built based on accepted taxonomic classification system and semantic key identifier therefore problems in information retrieval are minimized. Our focus is on the images used in taxonomy and how to retrieve them based on semantic identifiers.

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PurposeBiodiversity resources are inevitably digital and stored in a wide variety of formats by researchers or stakeholders. In Malaysia, although digitizing biodiversity data has long been stressed, the interoperability of the biodiversity data is still an issue that requires attention. This is because, when data are shared, the question of copyright occurs, creating a setback among researchers wanting to promote or share data through online presentations. To solve this, the aim is to present an approach to integrate data through wrapping of datasets stored in relational databases located on networked platforms.Design/methodology/approachThe approach uses tools such as XML, PHP, ASP and HTML to integrate distributed databases in heterogeneous formats. Five current database integration systems were reviewed and all of them have common attributes such as query‐oriented, using a mediator‐based approach and integrating a structured data model. These common attributes were also adopted in the proposed solution. Distributed Generic Information Retrieval (DiGIR) was used as a model in designing the proposed solution.FindingsA new database integration system was developed, which is user‐friendly and simple with common attributes found in current integration systems.Originality/valueThe proposed system is unique in that it allows biodiversity data sharing, through the integration of biodiversity databases, hence enabling scientists to share information and generate knowledge. It also solves copyright problems by suggesting distributed warehouses, giving data owners the benefit of having their database under their own jurisdiction. It meets the requirements of querying heterogeneous and remote biodiversity databases.

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