Lesbian, gay, bisexual, and transgender (LGBT) health services in the United States: Origins, evolution, and contemporary landscape

Background LGBT community organizations in the United States have been providing health services since at least the 1970s. However, available explanations for the origins of LGBT health services do not sufficiently explain why health in particular has been so closely and consistently linked to LGBT activism. Little is also known regarding how LGBT health services may have evolved over time with the growing scientific understanding of LGBT health needs. Methods This study begins with a review of the early intersections of sexuality and health that led to an LGBT health movement in the United States, as well as the evolution of LGBT health services over time. Informed by this, an asset map displaying the location and types of services provided by “LGBT community health centers” today in relation to the population density of LGBT people was explored. An online search of LGBT community health centers was conducted between September–December, 2015. Organizational details, including physical addresses and the services provided, were confirmed via an online database of federally-registered non-profit organizations and organizational websites. The locations and types of services provided were analyzed and presented alongside county-level census data of same-sex households using geographic information system (GIS) software ArcGIS for Desktop. Findings LGBT community health centers are concentrated within urban hubs and coastal states, and are more likely to be present in areas with a high density of same-sex couples. LGBT community health centers do not operate in 13 states. The most common health services provided are wellness programs, HIV/STI services, and counseling services. Conclusions LGBT community health centers have adapted over time to meet the needs of LGBT people. However, significant gaps in service remain in the United States, and LGBT community health centers may require significant transformations going forward in order to continue serving LGBT people

Identification of Drosophila Gene Products Required for Phagocytosis of Candida albicans

Phagocytosis is a highly conserved aspect of innate immunity. We used Drosophila melanogaster S2 cells as a model system to study the phagocytosis of Candida albicans, the major fungal pathogen of humans, by screening an RNAi library representing 7,216 fly genes conserved among metazoans. After rescreening the initial genes identified and eliminating certain classes of housekeeping genes, we identified 184 genes required for efficient phagocytosis of C. albicans. Diverse biological processes are represented, with actin cytoskeleton regulation, vesicle transport, signaling, and transcriptional regulation being prominent. Secondary screens using Escherichia coli and latex beads revealed several genes specific for C. albicans phagocytosis. Characterization of one of those gene products, Macroglobulin complement related (Mcr), shows that it is secreted, that it binds specifically to the surface of C. albicans, and that it promotes its subsequent phagocytosis. Mcr is closely related to the four Drosophila thioester proteins (Teps), and we show that TepII is required for efficient phagocytosis of E. coli (but not C. albicans or Staphylococcus aureus) and that TepIII is required for the efficient phagocytosis of S. aureus (but not C. albicans or E. coli). Thus, this family of fly proteins distinguishes different pathogens for subsequent phagocytosis

Spatial Variations of Jovian Tropospheric Ammonia via Ground-Based Imaging

Optical bandpass-filter observations can be simply processed to determine similar horizontal ammonia distributions above the Jovian cloud tops as mid-infrared and microwave observations. Current understanding of this distribution and its relationship to aerosol opacity, cloud-top pressure, and circulation is provided by atmospheric retrieval models using observations from major ground-based facilities and spacecraft. These techniques recover high fidelity information on the ammonia distribution but are limited in spatial and temporal coverage. Part of this coverage gap - upper tropospheric abundance - can be bridged by using continuum-divided ammonia and methane absorption images as suggested by Combes and Encrenaz [1979]. In 2020-21, Jupiter was imaged in the 645 nm ammonia absorption band and adjacent continuum bands, demonstrating that the spatially-resolved optical depth in that band could be determined with a 0.28-m Schmidt-Cassegrain telescope (SCT). In 2022, a 620 nm filter was added to include methane absorption images in the same wavelength range. Methane abundance provides a constant reference against which to determine the ammonia abundance, specifically the column-averaged mole fraction above the clouds. VLT/MUSE results are compared to these SCT results and those from the TEXES mid-infrared spectrometer used on the IRTF and the Gemini telescopes. Meridional and longitudinal features are examined, including the Equatorial Zone (EZ) ammonia enhancement, the North Equatorial Belt (NEB) depletion, depletion above the Great Red Spot (GRS), and suggested enhancements over bright plumes in the northern EZ. This work demonstrates meaningful ammonia monitoring that can provide synoptic coverage and continuity between spacecraft or major ground-based facility campaigns.Comment: 23 pages, 8 figure

Language Models as Knowledge Bases?

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as "fill-in-the-blank" cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https://github.com/facebookresearch/LAMA.Comment: accepted at EMNLP 201

S=1/2 chains and spin-Peierls transition in TiOCl

We study TiOCl as an example of an S=1/2 layered Mott insulator. From our analysis of new susceptibility data, combined with LDA and LDA+U band structure calculations, we conclude that orbital ordering produces quasi-one-dimensional spin chains and that TiOCl is a new example of Heisenberg-chains which undergo a spin-Peierls transition. The energy scale is an order of magnitude larger than that of previously known examples. The effects of non-magnetic Sc impurities are explained using a model of broken finite chains.Comment: 5 pages, 5 figures (color); details on crystal growth added; to be published in Phys. Rev.

A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka

Grübner O, Khan MH, Lautenbach S, et al. A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka. International Journal of Health Geographics. 2011;10(1): 36.Background: The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF). Methods: We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's / statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health. Results: We found that poor mental health (WHO-5 scores = 15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment. Conclusions: Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies

Impact of Biofluid Viscosity on Size and Sedimentation Efficiency of the Isolated Microvesicles

Microvesicles are nano-sized lipid vesicles released by all cells in vivo and in vitro. They are released physiologically under normal conditions but their rate of release is higher under pathological conditions such as tumors. Once released they end up in the systemic circulation and have been found and characterized in all biofluids such as plasma, serum, cerebrospinal fluid, breast milk, ascites, and urine. Microvesicles represent the status of the donor cell they are released from and they are currently under intense investigation as a potential source for disease biomarkers. Currently, the “gold standard” for isolating microvesicles is ultracentrifugation, although alternative techniques such as affinity purification have been explored. Viscosity is the resistance of a fluid to a deforming force by either shear or tensile stress. The different chemical and molecular compositions of biofluids have an effect on its viscosity and this could affect movements of the particles inside the fluid. In this manuscript we addressed the issue of whether viscosity has an effect on sedimentation efficiency of microvesicles using ultracentrifugation. We used different biofluids and spiked them with polystyrene beads and assessed their recovery using the Nanoparticle Tracking Analysis. We demonstrate that MVs recovery inversely correlates with viscosity and as a result, sample dilutions should be considered prior to ultracentrifugation when processing any biofluids