Genealogy and the contemporary of “orang rawang”: hidden population mapping on forest and land resource control practices in South Sumatera

The issuance of rights to manage and use forest and land resources by the government to large corporations usually incurs costs for the state and society. One of them is the emergence of hidden populations, namely people who are marginalized and even oppressed by development programs. Using the hidden population mapping method, this study reveals that hidden populations are born from the issuance of management and use rights on land they have relied on for their livelihoods. In this study, Orang Rawang is used as a term to represent a hidden population which the amounts is approximately about 30% of the population of Perigi Village and 35% of the population of Riding Village. The formation of Orang Rawang can be associated with a long-standing social stratification process that can now be identified from assets and survival strategies. Most of them do not have assets in the form of land ownership on mineral lands. Their main livelihood is fishing and collecting wood and non-timber products. Social networking in the community is carried out horizontally by dividing collective space for roaming areas, and vertically by forming patron-clan relationships with the Orang Risan and Orang Sungai.

Systematic screening of pulmonary tuberculosis among HIV high risk groups in two districts of Chhattisgarh: a feasibility study

Background: HIV is an important risk factor for the development of tuberculosis. People living with HIV are 21-34 times more likely to develop TB than their uninfected counterparts. Efficient approach for detecting more cases along with shortened duration of infectivity involves a systematic screening of pulmonary TB in settings where high risk groups are concentrated even before the diagnosis HIV infection. Lack of proper screening strategy for HRGs might result in their exclusion from timely intervention which may prove lethal without treatment.Methods: A cross sectional study was carried out in two districts of Chhattisgarh during September-December 2019.Training cum sensitization sessions were conducted for peer educators, outreach workers, counselors and project managers prior to the survey and were trained for systematic screening of pulmonary TB, sputum collection and transportation to GeneXpert®MTB/RIF lab and other relevant data collection for pulmonary TB diagnosis.Results: A total of 3963 HRGs were intended to be included in the study, 3418 were screened with 86.2% compliance rate. Out of all HRGs screened (3418), 81 (2.4%) were found presumptive pulmonary TB, of them 2 (0.05%) were microbiologically confirmed, 5 cases were found with incomplete treatment (all were IDUs). Prevalence of tobacco use, alcohol use, diabetes and hypertension were observed in 5.3% and 7.2%, 1.2% and 1.1 respectively.Conclusions: Although yield for pulmonary TB in this study was not much, the study has demonstrated that active case finding for accessing such a hidden population through existing manpower can assure better acceptability and feasibility in resource poor settings.

A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data

Background Low-depth sequencing allows researchers to increase sample size at the expense of lower accuracy. To incorporate uncertainties while maintaining statistical power, we introduce to analyze population structure of low-depth sequencing data. Results The method optimizes the choice of nonlinear transformations of dosages to maximize the Ky Fan norm of the covariance matrix. The transformation incorporates the uncertainty in calling between heterozygotes and the common homozygotes for loci having a rare allele and is more linear when both variants are common. Conclusions We apply to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The package is available on https://github.com/yiwenstat/MCPCA_PopGen.

Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects

Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method performed almost similar to LMM approach but much better than LRM approach in absence of outliers. In the case of real data analysis, our proposed method identified 11 SNPs that are significantly associated with the rice flowering time. Among the identified candidate SNPs, some were involved in seed development and flowering time pathways, and some were connected with flower and other developmental processes. These identified candidate SNPs could assist rice breeding programs effectively. Thus, our findings highlighted the importance of robust GWAS in identifying candidate genes.

The Limits of Labelling: Incidental Sex Work Among Gay, Bisexual, and Queer Young Men on Social Media

Introduction The term incidental sex work refers to forms of casual, occasional, unsolicited commercial sex, arranged between gay, bisexual, and queer men on social media platforms such as Grindr. This paper explores the limits of labelling sexual identities, and how definitions of “sex” and “work” have become increasingly unstable in the digital age. Methods This study used mixed methods, with the primary mode of data collection being qualitative interviews with young gay, bisexual, and queer men conducted between May 2015 and April 2016. The interviews incorporated a nine-point sexuality scale and photo-elicitation procedures to prompt further discussions. Through the participant recruitment process, the study also generated an informal survey of 1473 Grindr users aged 18 to 28, finding that 14.6% had been paid for sex, most of whom (8.2%) had done so “incidentally.” Results The 50 interview participants discussed being paid for sex 358 times. This paper focuses on their narratives of labelling, identity politics, sexual normativity, and social stigma. All participants distanced themselves from labels such as “prostitute,” “rent boy,” or “sex worker” given that their behaviours were not seen as “regular” or “professional” enough, alongside seeking to avoid association with stigmatising stereotypes of sex work. These results are compared with the participants’ experiences of coming out as gay, bisexual, and queer. Discussion These narratives are interpreted using queer theory to understand those whose behaviours and identities do not conform to normative (legal, medical, social) discourses of sex work. The implications of this hidden population for campaigners, policymakers, and healthcare practitioners are discussed, contributing to ongoing debates around harm reduction and social policy.

Estimating Population Size of Criminals: A New Horvitz–Thompson Estimator under One-Inflated Positive Poisson–Lindley Model

Many crime datasets often display an excess of “1” counts, arises when arrested criminals have the desire and ability to avoid subsequent arrests. In this study, a new Horvitz–Thompson (HT) estimator under one-inflated positive Poisson–Lindley (OIPPL) distribution which allow for one-inflation and the existence of heterogeneity in the data is developed to estimate the hidden population size of criminals. From the simulation study and applications to real crime datasets, the OIPPL is capable to provide an adequate fit to the datasets considered and the proposed HT estimator is found to produce a more precise estimate of the population size with a narrower 95% confidence interval as compared to several other contending estimators considered in this study.