Signal Appropriation of Explicit HIV Status Disclosure Fields in Sex-Social Apps used by Gay and Bisexual Men

HIV status disclosure fields in online sex-social applications ("apps") are designed to help increase awareness, reduce stigma, and promote sexual health. Public disclosure could also help those diagnosed relate to others with similar statuses to feel less isolated. However, in our interview study (n=28) with HIV positive and negative men who have sex with men (MSM), we found some users preferred to keep their status private, especially when disclosure could stigmatise and disadvantage them, or risk revealing their status to someone they knew offline in a different context. How do users manage these tensions between health, stigma, and privacy? We analysed our interview data using signalling theory as a conceptual framework and identify participants developing 'signal appropriation' strategies, helping them manage the disclosure of their HIV status. Additionally, we propose a set of design considerations that explore the use of signals in the design of sensitive disclosure fields

A new data-driven model for post-transplant antibody dynamics in high risk kidney transplantation

The dynamics of donor specific human leukocyte antigen (HLA) antibodies during early stage after transplantation are of great clinical interest as they are considered to be associated with short and long term outcomes (graft function and rejection). However, the limited number of such detailed donor-specific antibody (DSA) time series currently available and their diverse patterns have made the task of modelling difficult. Focusing on one typical dynamic pattern with rapid falls and stable settling levels, a novel data-driven model in the form of a third order differential equation has been developed to describe such post-transplant dynamics in DSAs for the first time. A variational Bayesian inference method has been applied to select a model and learn its parameters for 39 time series from two groups of graft recipients, i.e. patients with and without acute antibody-mediated rejection (AMR) episodes. Linear and nonlinear dynamic models of different order were attempted to fit the time series, and the third order linear model provided the best description of the common features in both groups. Both deterministic and stochastic parameters are found to be significantly different in the AMR and no-AMR groups. Eigenvalues have been calculated for each fitting, and phase portraits have been plotted to show the trajectories of the system states for both groups. The results from our previous study with fewer cases have been further confirmed: the time series in the AMR group have significantly higher frequency of oscillations and faster dissipation rates, which may potentially lead to better laboratory measurement strategy and a better chance of understanding the underlying immunological mechanisms

Subclass analysis of donor HLA-specific IgG in antibody-incompatible renal transplantation reveals a significant association of IgG4 with rejection and graft failure

Donor HLA-specific antibodies (DSAs) can cause rejection and graft loss after renal transplantation, but their levels measured by the current assays are not fully predictive of outcomes. We investigated whether IgG subclasses of DSA were associated with early rejection and graft failure. DSA levels were determined pretreatment, at the day of peak pan-IgG level and at 30 days post-transplantation in eighty HLA antibody-incompatible kidney transplant recipients using a modified microbead assay. Pretreatment IgG4 levels were predictive of acute antibody-mediated rejection (P = 0.003) in the first 30 days post-transplant. Pre-treatment presence of IgG4 DSA (P = 0.008) and day 30 IgG3 DSA (P = 0.03) was associated with poor graft survival. Multivariate regression analysis showed that in addition to pan-IgG levels, total IgG4 levels were an independent risk factor for early rejection when measured pretreatment, and the presence of pretreatment IgG4 DSA was also an independent risk factor for graft failure. Pretreatment IgG4 DSA levels correlated independently with higher risk of early rejection episodes and medium-term death-censored graft survival. Thus, pretreatment IgG4 DSA may be used as a biomarker to predict and risk stratify cases with higher levels of pan-IgG DSA in HLA antibody-incompatible transplantation. Further investigations are needed to confirm our results

Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy.

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p < 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization

Class solutions for SABR-VMAT for high-risk prostate cancer with and without elective nodal irradiation

BACKGROUND: The purpose of this study is to find the optimal planning settings for prostate SABR-VMAT for high-risk prostate cancer patients irradiated to prostate only (PO) or prostate and pelvic lymph nodes (PPLN). METHODS: For 10 patients, plans using 6MV flattened, flattening-filter-free (FFF) 6MV (6 F) and FFF 10MV (10 F) photon beams with full and partial arc arrangements were generated and compared. The prescribed dose was 40Gy to the prostate with 25Gy to the PLN in 5 fractions. Plans were then evaluated for PTV coverage, dose fall-off, and OAR doses. The number of monitor units and the treatment delivery times were also compared. Statistical differences were evaluated using a paired sample Wilcoxon signed rank test with a significance level of 0.05%. RESULTS: A total of 150 plans were generated for this study. Acceptable PO plans were obtained using single arcs, while two arcs were necessary for PPLN. All plans were highly conformal (CI ≥1.3 and CN ≥0.90) with no significant differences in the PTV dose coverage. 6MV plans required significantly longer treatment time and had higher dose spillage compared to FFF plans. Superior plans were obtained using 10 F 300° partial arcs for PO with the lowest rectal dose, dose spillage and the shortest treatment times. For PPLN, 6 F and 10 F plans were equivalent. CONCLUSIONS: SABR-VMAT with FFF photon beams offers a clear benefit with respect to shorter treatment delivery times and reduced dose spillage. Class solutions using a single 10 F 300° arc for PO and two 10 F or 6 F partial 300° arcs for PPLN are proposed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13014-016-0730-7) contains supplementary material, which is available to authorized users

Juvenile root vigour improves phosphorus use efficiency of potato

Aims Potato (Solanum tuberosum L.) has a large phosphorus (P)-fertiliser requirement. This is thought to be due to its inability to acquire P effectively from the soil. This work tested the hypothesis that early proliferation of its root system would enhance P acquisition, accelerate canopy development, and enable greater yields. Methods Six years of field experiments characterised the relationships between (1) leaf P concentration ([P]leaf), tuber yield, and tuber P concentration ([P]tuber) among 27 Tuberosum, 35 Phureja and 4 Diploid Hybrid genotypes and (2) juvenile root vigour, P acquisition and tuber yield among eight Tuberosum genotypes selected for contrasting responses to P-fertiliser. Results Substantial genetic variation was observed in tuber yield, [P]leaf and [P]tuber. There was a strong positive relationship between tuber yields and P acquisition among genotypes, whether grown with or without P-fertiliser. Juvenile root vigour was correlated with accelerated canopy development and both greater P acquisition and tuber biomass accumulation early in the season. However, the latter relationships became weaker during the season. Conclusions Increased juvenile root vigour accelerated P acquisition and initial canopy cover and, thereby, increased tuber yields. Juvenile root vigour is a heritable trait and can be selected to improve P-fertiliser use efficiency of potato

Big Data Clustering Algorithm and Strategies

In current digital era extensive volume ofdata is being generated at an enormous rate. The data are large, complex and information rich. In order to obtain valuable insights from the massive volume and variety of data, efficient and effective tools are needed. Clustering algorithms have emerged as a machine learning tool to accurately analyze such massive volume of data. Clustering is an unsupervised learning technique which groups data objects in such a way that objects in the same group are more similar as much as possible and data objects in different groups are dissimilar. But, traditional algorithm cannot cope up with huge amount of data. Therefore efficient clustering algorithms are needed to analyze such a big data within a reasonable time. In this paper we have discussed some theoretical overview and comparison of various clustering techniques used for analyzing big data

A Hybrid Approach of DenseNet121 with Attention and Bi-LSTM for Yoga Pose Estimation

This study presents an Automated Pose Recognition system using Enhanced Chicken Swarm Optimization with Deep Learning (APR-ECSODL), a cutting-edge solution for identifying and categorizing human postures from images and videos with high accuracy. The system is designed to integrate advanced AI techniques, providing an innovative approach to pose recognition that leverages several sophisticated machine learning models and algorithms to enhance performance. The pre-processing stage involves applying a Wiener Filter (WF) for effective noise removal, ensuring that the data is clean and ready for analysis. Dynamic Histogram Equalization (DHE) is then employed to enhance image contrast, improving the visibility of key features within the images. For segmentation, the YOLOv8 model is used to isolate relevant regions of interest, providing a precise input for the next phase. Feature extraction is conducted using OpenPose, a widely recognized tool for obtaining key human body points. This step is crucial for capturing detailed information about the postures. The classification of these poses is performed using a Self-Attention Based Gated Recurrent Unit (SA-GRU) model. This model enhances accuracy by incorporating self-attention mechanisms, allowing the system to focus on significant features within the data. Performance optimization is achieved through the Enhanced Chicken Swarm Optimization (ECSO) method, which fine-tunes the parameters of the system to ensure optimal results. The APR-ECSODL technique was rigorously tested on a posture image classification dataset from Kaggle, demonstrating its effectiveness in categorizing various poses. By integrating these cutting-edge deep learning and AI methodologies, the APR-ECSODL system sets a new standard in pose recognition, offering a robust tool for applications in fields such as fitness monitoring, rehabilitation, and human-computer interaction. This approach not only ensures accurate pose identification but also enhances practicing quality and helps prevent errors, making it a valuable asset in diverse domains

Retroperitoneal pelvic schwannoma in pregnancy: a case report

Solitary nerve sheath tumor such as Benign schwannomas arising in the pelvic retro peritoneum is infrequently reported. Retroperitoneal location accounts for 0.3-3.2% of primary schwannomas. We report a case of benign retroperitoneal pelvic schwannoma in pregnancy that was incidentally diagnosed when it presented with Preterm premature rupture of membranes and mechanical obstruction for labour. She underwent caesarean section and delivered a healthy baby. She was evaluated in the postoperative period by computerized tomography (CT) imaging studies and CT guided fine needle aspiration cytology (FNAC) was not diagnostic. Complete surgical excision of the tumor was achieved in the postpartum period. The adjacent vascular and urinary channels sustained no injuries and she had no neurologic deficit. Histology revealed spindle cell neoplasm composed of interlacing fascicles and sheets of spindle cell with focal areas of nuclear palisading and thick walled blood vessels. Immunohistochemistry was positive for S 100 suggesting schwannoma. Retroperitoneal location of schwannomas is rare and surgery is curative. Prognosis is good, since recurrence is rare.