Antibiotic stress selects against cooperation in the pathogenic bacterium Pseudomonas aeruginosa

Cheats are a pervasive threat to public goods production in natural and human communities, as they benefit from the commons without contributing to it. Although ecological antagonisms such as predation, parasitism, competition, and abiotic environmental stress play key roles in shaping population biology, it is unknown how such stresses generally affect the ability of cheats to undermine cooperation. We used theory and experiments to address this question in the pathogenic bacterium, Pseudomonas aeruginosa Although public goods producers were selected against in all populations, our competition experiments showed that antibiotics significantly increased the advantage of nonproducers. Moreover, the dominance of nonproducers in mixed cultures was associated with higher resistance to antibiotics than in either monoculture. Mathematical modeling indicates that accentuated costs to producer phenotypes underlie the observed patterns. Mathematical analysis further shows how these patterns should generalize to other taxa with public goods behaviors. Our findings suggest that explaining the maintenance of cooperative public goods behaviors in certain natural systems will be more challenging than previously thought. Our results also have specific implications for the control of pathogenic bacteria using antibiotics and for understanding natural bacterial ecosystems, where subinhibitory concentrations of antimicrobials frequently occur

Abdominal organ motion during inhalation and exhalation breath-holds: pancreatic motion at different lung volumes compared

Contrary to what is commonly assumed, organs continue to move during breath-holding. We investigated the influence of lung volume on motion magnitude during breath-holding and changes in velocity over the duration of breath-holding. Sixteen healthy subjects performed 60-second inhalation breath-holds in room-air, with lung volumes of ∼100% and ∼70% of the inspiratory capacity, and exhalation breath-holds, with lung volumes of ∼30% and ∼0% of the inspiratory capacity. During breath-holding, we obtained dynamic single-slice magnetic-resonance images with a time-resolution of 0.6s. We used 2-dimensional image correlation to obtain the diaphragmatic and pancreatic velocity and displacement during breath-holding. Organ velocity was largest in the inferior-superior direction and was greatest during the first 10s of breath-holding, with diaphragm velocities of 0.41mm/s, 0.29mm/s, 0.16mm/s and 0.15mm/s during BH100%, BH70%, BH30% and BH0%, respectively. Organ motion magnitudes were larger during inhalation breath-holds (diaphragm moved 9.8 and 9.0mm during BH100% and BH70%, respectively) than during exhalation breath-holds (5.6 and 4.3mm during BH30% and BH0%, respectively). Using exhalation breath-holds rather than inhalation breath-holds and delaying irradiation until after the first 10s of breath-holding may be advantageous for irradiation of abdominal tumor

Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck Cancer Radiotherapy

Depending on the clinical situation, different combinations of lymph node (LN) levels define the elective LN target volume in head-and-neck cancer (HNC) radiotherapy. The accurate auto-contouring of individual LN levels could reduce the burden and variability of manual segmentation and be used regardless of the primary tumor location. We evaluated three deep learning approaches for the segmenting individual LN levels I–V, which were manually contoured on CT scans from 70 HNC patients. The networks were trained and evaluated using five-fold cross-validation and ensemble learning for 60 patients with (1) 3D patch-based UNets, (2) multi-view (MV) voxel classification networks and (3) sequential UNet+MV. The performances were evaluated using Dice similarity coefficients (DSC) for automated and manual segmentations for individual levels, and the planning target volumes were extrapolated from the combined levels I–V and II–IV, both for the cross-validation and for an independent test set of 10 patients. The median DSC were 0.80, 0.66 and 0.82 for UNet, MV and UNet+MV, respectively. Overall, UNet+MV significantly (p < 0.0001) outperformed other arrangements and yielded DSC = 0.87, 0.85, 0.86, 0.82, 0.77, 0.77 for the combined and individual level I–V structures, respectively. Both PTVs were also significantly (p < 0.0001) more accurate with UNet+MV, with DSC = 0.91 and 0.90, respectively. The accurate segmentation of individual LN levels I–V can be achieved using an ensemble of UNets. UNet+MV can further refine this result

Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an MRI technique for quantifying perfusion that can be used in clinical applications for classification of tumours and other types of diseases. Conventionally, the non-linear least squares (NLLS) methods is used for tracer-kinetic modelling of DCE data. However, despite promising results, NLLS suffers from long processing times (minutes-hours) and noisy parameter maps due to the non-convexity of the cost function. In this work, we investigated physics-informed deep neural networks for estimating physiological parameters from DCE-MRI signal-curves. Three voxel-wise temporal frameworks (FCN, LSTM, GRU) and two spatio-temporal frameworks (CNN, U-Net) were investigated. The accuracy and precision of parameter estimation by the temporal frameworks were evaluated in simulations. All networks showed higher precision than the NLLS. Specifically, the GRU showed to decrease the random error on ve by a factor of 4.8 with respect to the NLLS for noise (SD) of 1/20. The accuracy was better for the prediction of the ve parameter in all networks compared to the NLLS. The GRU and LSTM worked with arbitrary acquisition lengths. The GRU was selected for in vivo evaluation and compared to the spatio-temporal frameworks in 28 patients with pancreatic cancer. All neural network approaches showed less noisy parameter maps than the NLLS. The GRU had better test-retest repeatability than the NLLS for all three parameters and was able to detect one additional patient with significant changes in DCE parameters post chemo-radiotherapy. Although the U-Net and CNN had even better test-retest characteristics than the GRU, and were able to detect even more responders, they also showed potential systematic errors in the parameter maps. Therefore, we advise using our GRU framework for analysing DCE data