Genetic Diversity And Population Structure of Indian Mustard (Brassica Juncea) Based On Morphological and Molecular Markers

Rapeseed-mustard is one of the most important oilseed crops and providing a major source of edible oil in the world besides having other economic importance like leafy vegetables, ornamentals, and hedge crops. However, the genetic diversity present in the Brassica gene pool has not been investigated in detail. To address this problem, a study was conducted on 76 genotypes of B. juncea including cultivars, exotic lines, registered genetic stocks, advanced breeding lines, and germplasm lines. The genetic diversity was analyzed with the help of 50 polymorphic SSR and EST-SSR markers. For these genotype-marker combinations, a total of 126 alleles were amplified. Using molecular and phenotypic data, the dendrogram was constructed based on Jaccard’s similarity coefficient and Manhattan dissimilarity coefficient and linkage algorithm UPGMA. All the genotypes were grouped into 5 clusters based on their dissimilarity matrix. Population structure analysis grouped the genotypes in 8 clusters and various degrees of admixture was also observed. The grouping of genotypes appears effective as per their pedigree. The marker data was found more accurate to characterize the diversity and study the population structure than the quantitative trait data. The results of the present investigation will provide useful information for the identification of important alleles for future studies and pave the way to enhance genetic gains in Indian mustard.

Time-trends and predictors of interhospital transfers and 30-day rehospitalizations after acute coronary syndrome from 2000-2015

Aims Assess trends and factors associated with interhospital transfers (IHT) and 30-day acute coronary syndrome (ACS) rehospitalizations in a national administrative database of patients admitted with an ACS between 2000–2015. Methods and results Cohort study of patients hospitalized with ACS from 2000 to 2015, using a validated linkage algorithm to identify and link patient-level sequential hospitalizations occurring within 30 days from first admission (considering all hospitalizations within the 30-day timeframe as belonging to the same ACS episode of care-ACS-EC). From 212,481 ACS-EC, 42,670 (20.1%) had more than one hospitalization. ACS-EC hospitalization rates decreased throughout the study period (2000: 207.7/100.000 person-years to 2015: 185,8/100,000 person-years, p for trend <0.05). Proportion of IHT increased from 10.5% in 2000 to 20.1% in 2015 compared to a reduction in both planned and unplanned 30-day ACS rehospitalization from 9.0% in 2000 to 2.7% in 2015. After adjusting for patient and first admission hospital’s characteristics, compared to 2000–2003, in 2012–2015 the odds of IHT increased by 3.81 (95%CI: 3.65–3.98); the odds of unplanned and planned 30-day ACS rehospitalization decreased by 0.36 (95%CI: 0.33; 0.39) and 0.47 (95%CI: 0.43; 0.53), respectively. Female sex, older age and the presence and severity of comorbidities were associated with lower likelihood of being transferred or having a planned 30-day ACS rehospitalization. Unplanned 30-day ACS rehospitalization was more likely in patients with higher comorbidity burden. Conclusion IHT and 30-day ACS rehospitalization reflect coronary referral network efficiency and access to specialized treatment. Identifying factors associated with higher likelihood of IHT and 30-day ACS rehospitalization may allow heightened surveillance and interventions to reduce rehospitalizations and inequities in access to specialized treatment.

Pengelompokkan Tingkat Kriminalitas di Indonesia Menggunakan Algoritma Average Linkage

Crime needs to be analyzed and grouped so that the act does not cause harm either ecologically or psychologically. The statistical method that can be used to classify crime is the Average Linkage Algorithm. The study aims to group and analyze the characteristics of criminal cases in Indonesia. From the results of the analysis, 3 clusters were formed based on the average of each cluster. Cluster 1 consists of Aceh, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep. Bangka Belitung, Kep. Riau, West Java, Central Java, DI Yogyakarta, East Java, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, Maluku, North Maluku and Papua. Cluster 2 consists of North Sumatra while Cluster 3 consists of Metro Jaya. The grouping results are the basis of the government, apparatus, and the community in implementing the handling of criminal acts that occur in each cluster area so that prevention can minimize the losses caused by these crimes.

A Federated Record Linkage Algorithm for Secure Medical Data Sharing

The process of consolidating medical records from multiple institutions into one data set makes privacy-preserving record linkage (PPRL) a necessity. Most PPRL approaches, however, are only designed to link records from two institutions, and existing multi-party approaches tend to discard non-matching records, leading to incomplete result sets. In this paper, we propose a new algorithm for federated record linkage between multiple parties by a trusted third party using record-level bloom filters to preserve patient data privacy. We conduct a study to find optimal weights for linkage-relevant data fields and are able to achieve 99.5% linkage accuracy testing on the Febrl record linkage dataset. This approach is integrated into an end-to-end pseudonymization framework for medical data sharing.

Recovering records on cancer of the larynx from anonymous health information databases

ABSTRACT: Objective: To develop a linkage algorithm to match anonymous death records of cancer of the larynx (ICD-10 C32X), retrieved from the Mortality Information System (SIM) and the Hospital Information System of the Brazilian Unified National Health System (SIH-SUS) in Brazil. Methodology: Death records containing ICD-10 C32X codes were retrieved from SIM and SIH-SUS, limited to individuals aged 30 years and over, between 2002 and 2012, in the state of São Paulo. The databases were linked using a unique key identifier developed with sociodemographic data shared by both systems. Linkage performance was ascertained by applying the same procedure to similar non-anonymous databases. True pairs were those having the same identification variables. Results: A total of 14,311 eligible death records were found. Most records, 10,674 (74.6%), were exclusive to SIM. Only 1,853 (12.9%) deaths were registered in both systems, representing true pairs. A total of 1,784 (12.5%) cases of laryngeal cancer in the SIH-SUS database were tracked in SIM with different causes of death. The linkage failed to match 167 (9.4%) records due to inconsistencies in the key identifier. Conclusion: The authors found that linking anonymous data from mortality and hospital records is a feasible measure to track missing records and may improve cancer statistics.

Linkage of primary care prescribing records and pharmacy dispensing Records in the Salford Lung Study: application in asthma

Background Records of medication prescriptions can be used in conjunction with pharmacy dispensing records to investigate the incidence of adherence, which is defined as observing the treatment plans agreed between a patient and their clinician. Using prescribing records alone fails to identify primary non-adherence; medications not being collected from the dispensary. Using dispensing records alone means that cases of conditions that resolve and/or treatments that are discontinued will be unaccounted for. While using a linked prescribing and dispensing dataset to measure medication non-adherence is optimal, this linkage is not routinely conducted. Furthermore, without a unique common event identifier, linkage between these two datasets is not straightforward. Methods We undertook a secondary analysis of the Salford Lung Study dataset. A novel probabilistic record linkage methodology was developed matching asthma medication pharmacy dispensing records and primary care prescribing records, using semantic (meaning) and syntactic (structure) harmonization, domain knowledge integration, and natural language feature extraction. Cox survival analysis was conducted to assess factors associated with the time to medication dispensing after the prescription was written. Finally, we used a simplified record linkage algorithm in which only identical records were matched, for a naïve benchmarking to compare against the results of our proposed methodology. Results We matched 83% of pharmacy dispensing records to primary care prescribing records. Missing data were prevalent in the dispensing records which were not matched – approximately 60% for both medication strength and quantity. A naïve benchmarking approach, requiring perfect matching, identified one-quarter as many matching prescribing records as our methodology. Factors associated with delay (or failure) to collect the prescribed medication from a pharmacy included season, quantity of medication prescribed, previous dispensing history and class of medication. Our findings indicate that over 30% of prescriptions issued were not collected from a dispensary (primary non-adherence). Conclusions We have developed a probabilistic record linkage methodology matching a large percentage of pharmacy dispensing records with primary care prescribing records for asthma medications. This will allow researchers to link datasets in order to extract information about asthma medication non-adherence.

Hierarchical Clustering for Functionalities E-Commerce Adoption

Web functionality is one driver for e-commerce adoption. It is appeared the level of technological capabilities as well as the accentuation of the strategy put on e-commerce by the organization. Web functionality is related to the level of e-commerce relocation. Website with more functionality will give way better benefits for shoppers and trade partners. Functionalities of web are components that support the achievement of adoption benefits. Hierarchical clustering and ranking availability of e-commerce functionality is a challenging task. Ward Linkage algorithm was used to measure distance. This study proposed to get a grouping of e-commerce functionalities that influence e-commerce adoption and to get the ranking of the groups that most influence the achievement of these benefits. Result shows that functionalities that supports the achievement of every benefit of e-commerce has been clustered into two or three clusters, where each cluster also has been ranked to facilitate the achievement of these benefits