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Microbial genome-wide association study (GWAS) tools often require manual data processing steps, lack comprehensive workflows, and are limited by scalability issues, thus hindering the exploration of bacterial genetic traits. To address these challenges, we developed BaGPipe, an automated and flexible bacterial GWAS pipeline built using Nextflow and incorporating Pyseer for association analysis. BaGPipe integrates all essential components of a bacterial GWAS--spanning pre-processing, statistical analysis, and downstream visualisation--into a unified workflow that is reproducible and easy to deploy across diverse computational environments. BaGPipe was validated on a publicly available dataset of Streptococcus pneumoniae whole-genome sequences, and reproduced published findings with improved computational efficiency. BaGPipe was then applied to a dataset of Staphylococcus aureus whole-genome sequences, successfully identifying known and novel antibiotic resistance associations. By offering an accessible, efficient, and reproducible platform, BaGPipe accelerates bacterial GWAS and facilitates deeper exploration into the genetic underpinnings of phenotypic traits.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://zenodo.org/records/14947249

Genetic distances between bacterial DNA sequences can be used to cluster populations into closely related subpopulations, and as an additional source of information when detecting possible transmission events. Due to their variable gene content and order, reference-free methods offer more sensitive detection of genetic differences, especially among closely related samples found in outbreaks. However, across longer genetic distances, frequent recombination can make calculation and interpretation of these differences more challenging, requiring significant bioinformatic expertise and manual intervention during the analysis process. Here we present a Population analysis PIPEline (PopPIPE) which combines rapid reference-free genome analysis methods to analyse bacterial genomes across these two scales, splitting whole populations into subclusters and detecting plausible transmission events within closely related clusters. We use k-mer sketching to split populations into strains, followed by split k-mer analysis and recombination removal to create alignments and subclusters within these strains. We first show that this approach creates high quality subclusters on a population-wide dataset of Streptococcus pneumoniae. When applied to nosocomial vancomycin resistant Enterococcus faecium samples, PopPIPE finds transmission clusters which are more epidemiologically plausible than core genome or MLST-based approaches. Our pipeline is rapid and reproducible, creates interactive visualisations, and can easily be reconfigured and re-run on new datasets. Therefore PopPIPE provides a user-friendly pipeline for analyses spanning species-wide clustering to outbreak investigations.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/bacpop/PopPIPE

In the same year the world was thrown into turmoil with COVID-19, the USA also experienced a surge in attention given to the plight of Black people in the policing system, following the killing of George Floyd. Both the COVID-19 pandemic and the ongoing “pandemic” of police and White violence against Black people in the USA cause significant amounts of stress, disproportionately affecting Black people. Utilizing qualitative analysis of responses from 128 Black-identifying participants to an online survey, this investigation seeks to understand how the coping strategies of Black people in the USA compare between the racism-related stressor of police killings of Black people and the generalized stressor of the COVID-19 pandemic. Findings demonstrate that while Black people use overlapping strategies to deal with stress, clear patterns exist with regard to differences across racism-related and non-racism-related stressors. We report important implications for understanding the impact of COVID-19 on Black people, cultural understandings of research on coping, and Black mental health more broadly.

Live-attenuated bacterial vectors for antigens delivery have aroused growing interest in the field of cancer immunotherapy. Their potency to stimulate innate immunity and to promote intracellular antigen delivery into antigen-presenting cells could be exploited to elicit a strong and specific cellular immune response against tumor cells. We previously described genetically-modified and attenuated Pseudomonas aeruginosa vectors able to deliver in vivo protein antigens into antigen-presenting cells, through Type 3 secretion system of the bacteria. Using this approach, we managed to protect immunized mice against aggressive B16 melanoma development in both a prophylactic and therapeutic setting. In this study, we further investigated the antigen-specific CD8+ T cell response, in terms of phenotypic and functional aspects, obtained after immunizations with a killed but metabolically active P. aeruginosa attenuated vector. We demonstrated that P. aeruginosa vaccine induces a highly functional pool of antigen-specific CD8+ T cell able to infiltrate the tumor. Furthermore, multiple immunizations allowed the development of a long-lasting immune response, represented by a pool of predominantly effector memory cells which protected mice against late tumor challenge. Overall, killed but metabolically active P. aeruginosa vector is a safe and promising approach for active and specific antitumor immunotherapy.

acute intestinal intussusception is a life-threatening surgical condition. In some settings, rotavirus vaccines have been associated with a low-level increased risk of intussusception. We describe the epidemiology, clinical manifestations and management of intussusception in a tertiary referral hospital in Burkina Faso prior to the introduction of rotavirus vaccine in October 2013. we retrospectively reviewed medical records of all children under 5 years of age treated at the Charles de Gaulle Pediatric Hospital for intussusception meeting the Brighton level 1 diagnostic criteria, from October 31st, 2008 to October 30th, 2013. We report the incidence of intussusception as well as descriptive characteristics of these cases. a total of 107 Brighton level 1 intussusception cases were identified, representing a hospital incidence of 21.4 cases / year. There were 69 males and 38 females (sex ratio of 1.8), with a median age of 8 months (range 2 months to 4 years). Sixty-two percent of intussusception cases occurred among infants (n = 67 cases). The average time from symptom onset to seeking medical consultation was 3.8 days +/- 2.7 (range 0 to 14 days). Treatment was mainly surgical (105 patients, 98.1%) with 35 patients (32.7%) undergoing intestinal resection. Thirty-seven patients (35.5%) experienced post-operative complications. The mortality rate was 9.3%. Intestinal resection was a risk factor for death from intussusception. in this review of intussusception hospitalizations prior to rotavirus vaccine introduction in Burkina Faso, delays in seeking care were common and were associated with mortality.

Ultrasound (US) imaging is one of the most commonly used non-invasive imaging techniques. However, US image acquisition requires simultaneous guidance of the transducer and interpretation of images, which is a highly challenging task that requires years of training. Despite many recent developments in intra-examination US image analysis, the results are not easy to translate to a clinical setting. We propose a generic framework to extract the US images and superimpose the results of an analysis task, without any need for physical connection or alteration to the US system. The proposed method captures the US image by tracking the screen with a camera fixed at the sonographer's view point and reformats the captured image to the right aspect ratio, in 87.66 +- 3.73ms on average. It is hypothesized that this would enable to input such retrieved image into an image processing pipeline to extract information that can help improve the examination. This information could eventually be projected back to the sonographer's field of view in real time using, for example, an augmented reality (AR) headset.

Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are subject to large inter-observer variability, resulting in low early-detection rates of fetal abnormalities. To address this issue, we propose a novel probabilistic Deep Learning approach for real-time automated estimation of fetal HC. This system feeds back statistics on measurement robustness to inform users how confident a deep neural network is in evaluating suitable views acquired during free-hand ultrasound examination. In real-time scenarios, this approach may be exploited to guide operators to scan planes that are as close as possible to the underlying distribution of training images, for the purpose of improving inter-operator consistency. We train on free-hand ultrasound data from over 2000 subjects (2848 training/540 test) and show that our method is able to predict HC measurements within 1.81\\(\\pm\\)1.65mm deviation from the ground truth, with 50% of the test images fully contained within the predicted confidence margins, and an average of 1.82\\(\\pm\\)1.78mm deviation from the margin for the remaining cases that are not fully contained.

This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i.e. without any localisation or segmentation information. We examine the use of convolutional neural network architectures coupled with soft proposal layers. The resulting network simultaneously performs anatomical region detection (classification) and localisation tasks. We generate a proposal map describing the attention of the network for a particular class. The network is trained on 85,500 2D fetal Ultrasound images and their associated labels. Labels correspond to six anatomical regions: head, spine, thorax, abdomen, limbs, and placenta. Detection achieves an average accuracy of 90\\% on individual regions, and show that the proposal maps correlate well with relevant anatomical structures. This work presents itself as a powerful and essential step towards subsequent tasks such as fetal position and pose estimation, organ-specific segmentation, or image-guided navigation. Code and additional material is available at https://ntoussaint.github.io/fetalnav

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. Additionally, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation and etc. to verify the effectiveness of our method. Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. We further demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion and automated biometric measurements.

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.