Efficacy of HIV/STI behavioral interventions for heterosexual African American men in the United States: a meta-analysis

This meta-analysis estimates the overall efficacy of HIV prevention interventions to reduce HIV sexual risk behaviors and sexually transmitted infections (STIs) among heterosexual African American men. A comprehensive search of the literature published during 1988–2008 yielded 44 relevant studies. Interventions significantly reduced HIV sexual risk behaviors and STIs. The stratified analysis for HIV sexual risk behaviors indicated that interventions were efficacious for studies specifically targeting African American men and men with incarceration history. In addition, interventions that had provision/referral of medical services, male facilitators, shorter follow-up periods, or emphasized the importance of protecting family and significant others were associated with reductions in HIV sexual risk behaviors. Meta-regression analyses indicated that the most robust intervention component is the provision/referral of medical services. Findings indicate that HIV interventions for heterosexual African American men might be more efficacious if they incorporated a range of health care services rather than HIV/STI-related services alone

Assessment of facial wrinkles as a soft biometrics

International audienceThis paper presents results on the assessment of facial wrinkles as a soft biometrics. Recently, several micro features such as moles, scars, freckles, etc. have been used in addition to more common facial features for face recognition. The discriminative power of facial wrinkles has not been evaluated. In this paper we present results of our experiments on assessment of discriminative power of wrinkles in recognizing subjects. We treat a set of facial wrinkles from an image as a curve pattern and find similarity between curve patterns from two subjects. Several metrics based on Hausdorff distance and curve-to-curve correspondences are introduced to quantify the similarity. A simple bipartite graph matching algorithm is introduced to find correspondences between curves from two patterns. We present experiments on data sets using manually extracted and automatically detected wrinkles. The recognition rate for these data sets using only the binary forehead wrinkle curve patterns exceeds 65% at rank 1 and 90% at rank 4

New perspectives on radium behavior within a subterranean estuary

Over the past decade, radium isotopes have been frequently applied as tracers of submarine groundwater discharge (SGD). The unique radium signature of SGD is acquired within the subterranean estuary, a mixing zone between fresh groundwater and seawater in coastal aquifers, yet little is known about what controls Ra cycling in this system. The focus of this study was to examine controls on sediment and groundwater radium activities within permeable aquifer sands (Waquoit Bay, MA, USA) through a combination of field and laboratory studies. In the field, a series of sediment cores and corresponding groundwater profiles were collected for analysis of the four radium isotopes, as well as dissolved and sediment associated manganese, iron, and barium. We found that in addition to greater desorption at increasing salinity, radium was also closely tied to manganese and iron redox cycling within these sediments. A series of laboratory adsorption/desorption experiments helped elucidate the importance of 1) contact time between sediment and water, 2) salinity of water in contact with sediment, 3) redox conditions of water in contact with sediment, and 4) the chemical characteristics of sediment on radium adsorption/desorption. We found that these reactions are rapid (on the order of hours), desorption increases with increasing salinity and decreasing pH, and the presence of Fe and Mn (hydr)oxides on the sediment inhibit the release of radium. These sediments have a large capacity to sorb radium from fresh water. Combined with these experimental results, we present evidence from time series groundwater sampling that within this subterranean estuary there are cyclic periods of Ra accumulation and release controlled by changing salinity and redox conditions

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p