Productive Women: Gender, Sex, and Labor in the Digital Cultural Economy

Explicit sex and sexuality have come to figure more prominently in mainstream media and popular culture in recent years, sparking public concern and scholarly debate on how the “sexualization” of culture particularly affects women. Against this backdrop and amidst digitally reconfigured circuits of media production and consumption, ordinary women participate in, and potentially profit from, the commodification of sexual interactions and relationships. This study compares women’s practices in cultures of cam modeling and sugar dating in order to better understand their meanings and contexts. Whether performed in tandem with romantic dating scripts and roles, as is common in sugar dating, or in the intimate yet solitary context of domestic spaces, as is common in cam modeling, both practices illustrate the ways in which digital media reconfigure the sites, contexts, and cultural practices of erotic labor. They also make salient the complex and constrained ways women claim sexual and economic sovereignty in the contemporary United States, and the resistances they continue to face. Therefore, ethnographic exploration of women’s lived experiences of cam modeling and sugar dating moves beyond critical assessments of commodified sex and intimacy as exploitative or oppressive. This study takes such an approach, exploring the ways in which women “make do” within commercial relationships, organizations, and networks of sexual cultural production, opening up possibilities for agency within structural constraints, pleasure under disciplinary regimes, creative sexual expression supported by online sexual labor, and new forms of intimacy in highly mediated social environments. It sheds light on how women organize, make sense of, and derive value from these practices. It also examines how their participation might expand, constrain, or complicate the realm of meanings and identities available to them in a changing cultural, political, and economic environment

Comparative Study of Physico- chemical Parameters of Ground water of Residential and Industrial area of Sirgitti in Bilaspur District

Water is available in abundance on the earth. It is one of the main reasons which make life possible on our planet. The quality strength and type of sewage depends upon the human population, industrialization, deforestation and life style of people. Analytical explorations of some selected physico- chemical parameter have been made on the ground water bodies of Sirgitti industrial area. In the present comparative study for physico-chemical analysis water samples were collected  from to different locations the residential and industrial area of  Sirgitti in Bilaspur district. The laboratory test of the collected water samples were performed for analysis of some selected physico-chemical parameters such as pH, EC, TDS, DO, COD, Total hardness, Fluoride, chloride etc. The methods employed for the analysis as per standard  methods recommended by APHA and WHO standard of drinking water. The obtained values are compared with the standard limit. The results of this study reveals that in the industrial area of the physico-chemical parameters higher in the maximum permissible limit of WHO with variations in some parameters. Both the sampling stations groundwater unsuitable for domestic, irrigation and drinking purposes but the industrial area of BEC fertilizers groundwater is more polluted than the Adarsh nagar residential area of Sirgitti. So it is essential that the quality of ground water should be regular checked and also needs treatment before direct use

PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski’s rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery

PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

Abstract Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski’s rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery

Short-acting β2-agonists over-prescription in patients with asthma: An Indian subset analysis of international SABINA III study

Objective: The SABINA (SABA use IN Asthma) program was initiated to describe short-acting β2-agonists (SABA) prescription patterns and assess the impact of its over-prescription on exacerbation risk and asthma control. We evaluated SABA prescription patterns in patients with asthma in the Indian cohort of SABINA III. Methods: This multi-centre, observational, cross-sectional study included retrospective and real-time electronic data collection. Data were extracted from medical records of patients with asthma (aged >12 years) having >3 consultations with the same healthcare practitioners between March 2019 and January 2020. The data included prescriptions of SABA and other asthma treatments and over-the-counter (OTC) purchases of SABA. SABA prescriptions were categorized by the number of SABA canisters prescribed in the 12 months preceding the study visit. Results: A total of 510 patients with asthma were included from specialist care (mean age 49.1 years; 57.65 females), with 8.2% classified with mild asthma and 91.8% with moderate-to-severe asthma. SABA as monotherapy and add-on to maintenance therapy was prescribed to 4.5% (n = 23) and 44.9% (n = 229) of patients, respectively. While ICS monotherapy and ICS/LABA were prescribed to 5.1% (n = 26) and 93.3% (n = 476) of patients, respectively. SABA was found to be over-prescribed (≥3 SABA canisters/year) among 21.6% of patients (n = 110). Additionally, 8% of patients (n = 41) purchased SABA OTC without prescription. Conclusions: Nearly one-fourth of patients with asthma in India were over-prescribed SABA. Educational programmes targeted at national and regional levels should be expanded to raise greater asthma awareness and encourage the adoption of guideline-directed asthma treatment plans among healthcare practitioners.</p