STUDY OF FINGERPRINT PATTERN IN RELATION TO GENDER AND BLOOD GROUP IN JHALAWAR REGION

Dermatoglyphics, the study of epidermal ridges on palm, sole, and digits, is considered as most effective and reliable evidence of identification. Finger prints are the impressions made by fine ridges present on finger tips which are highly individualistic. The fingerprint ridges develop between 2nd and 3rd months of intra uterine life and remain unchanged in an individual throughout life. Out of many blood grouping systems available, ABO and Rh systems are the most important and are considered for the present study. Due to the immense potential of fingerprints as an effective method of identification an attempt has been made in the present work to analyse their correlation with gender and blood group of an individual. This study is carried out on 100 subjects (50 male and 50 female) having different ABO blood groups and belonging to different age groups. All the 10 fingerprints are taken and divided into loops, whorls, arches and composite. The results show that majority of the subjects belonged to blood group O. The fingerprint pattern of loops is most commonly found followed by whorls, arches and composite. Loops are higher in males whereas whorls and arches are found more in females. Composites are being in same proportion in both sexes. Highest frequency of loops is seen in O positive blood group followed by B positive. Among loops ulnar loops are predominant. Keywords: Fingerprints, Loops, Whorls, Arches, Blood Group, Pattern, Gender.

Can bryophyte groups increase functional resolution in tundra ecosystems?

The relative contribution of bryophytes to plant diversity, primary productivity, and ecosystem functioning increases towards colder climates. Bryophytes respond to environmental changes at the species level, but because bryophyte species are relatively difficult to identify, they are often lumped into one functional group. Consequently, bryophyte function remains poorly resolved. Here, we explore how higher resolution of bryophyte functional diversity can be encouraged and implemented in tundra ecological studies. We briefly review previous bryophyte functional classifications and the roles of bryophytes in tundra ecosystems and their susceptibility to environmental change. Based on shoot morphology and colony organization, we then propose twelve easily distinguishable bryophyte functional groups. To illustrate how bryophyte functional groups can help elucidate variation in bryophyte effects and responses, we compiled existing data on water holding capacity, a key bryophyte trait. Although plant functional groups, can mask potentially high inter- and intraspecific variability, we found better separation of bryophyte functional group means compared to previous grouping systems regarding water holding capacity. This suggests that our bryophyte functional groups truly represent variation in the functional roles of bryophytes in tundra ecosystems. Lastly, we provide recommendations to improve monitoring of bryophyte community changes in tundra study sites.

Innovations from Iran: Resolving quality issues in the integration of administrative and big data in official statistics

In a modernized statistical production process, non-traditional data sources such as ‘big’ data are increasingly being considered either as the main or as a supplementary source for official statistics. Their use however has brought new sorts of challenges: messy datasets, duplicate entries, missing information and misspellings to name a few. In many cases, there is also no unique identifier which can be used to unambiguously identify a record for the purpose of data integration. These challenges can be compounded by a non-English based alphabet like the Persian/Farsi alphabet used in Iran. In this paper, two innovative methods have been elaborated to address such data challenges. More specifically, the application of probabilistic record linkage using an ACSII coding system is an innovative way to deal with both data challenges and lack of unique identifier simultaneously. Moreover, text mining is an innovative way to address categorization and grouping systems that are not suitable for statistical purposes. Both innovative approaches can improve the accuracy and coherency of datasets and for data integration result in higher quality datasets. Results of research undertaken by the authors show the innovations lead to more effective data integration and improve the quality of the resulting official statistics. The innovations have wide applicability especially in non-English alphabet countries.