Learning to rank from medical imaging data

Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order. While a standard classifier discards this information, we would like to take it into account in order to improve prediction performance. A standard linear regression does model such information, however the linearity assumption is likely not be satisfied when predicting from pixel intensities in an image. In this paper we address these modeling challenges with a supervised learning procedure where the model aims to order or rank images. We use a linear model for its robustness in high dimension and its possible interpretation. We show on simulations and two fMRI datasets that this approach is able to predict the correct ordering on pairs of images, yielding higher prediction accuracy than standard regression and multiclass classification techniques

Estimating a sub-mesoscale diffusivity using a roughness measure applied to a tracer release experiment in the Southern Ocean

We test the use of a measure to diagnose a sub-mesoscale isopycnal diffusivity by determining the best match between observations of a tracer and simulations with varying small-scale diffusivities. Specifically, the robustness of a ‘roughness’ measure to discriminate between tracer fields experiencing different sub-mesoscale isopycnal diffusivities and advected by scaled altimetric velocity fields is investigated. We use the measure to compare numerical simulations of the tracer released at a depth of about 1.5 km in the Pacific sector of the Southern Ocean during the Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES) field campaign with observations of the tracer taken on DIMES cruises. We find that simulations with an isopycnal diffusivity of ~20 m2s−1 best match observations in the Pacific sector of the ACC, rising to ~20-50 m2s−1 through Drake Passage, representing sub-mesoscale processes and any mesoscale processes unresolved by the advecting altimetry fields. The roughness measure is demonstrated to be a statistically robust way to estimate a small-scale diffusivity when measurements are relatively sparse in space and time, although it does not work if there are too few measurements overall. The planning of tracer measurements during a cruise in order to maximise the robustness of the roughness measure is also considered. It is found that the robustness is increased if the spatial resolution of tracer measurements is increased with the time since tracer release

COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease

One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0–15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge

Crossing Boundaries with Web-Based Tools for Learning Object Evaluation

Abstract. Learning object repositories and evaluation tools have the potential to serve as sites for interaction among different cultures and communities of prac-tice. This paper outlines the web-based learning object evaluation tools we have developed, describes our current efforts to extend those tools to a wider range of user communities, and considers methods for fostering interaction among user communities. The approaches considered include establishing shared but differentiated learning object evaluation standards, mapping between local lan-guages, ontologies and practices, and recommending objects across community boundaries

Functional connectome fingerprinting and stability in multiple sclerosis

BACKGROUND: Functional connectome fingerprinting can identify individuals based on their functional connectome. Previous studies relied mostly on short intervals between fMRI acquisitions. OBJECTIVE: This cohort study aimed to determine the stability of connectome-based identification and their underlying signatures in patients with multiple sclerosis and healthy individuals with long follow-up intervals. METHODS: We acquired resting-state fMRI in 70 patients with multiple sclerosis and 273 healthy individuals with long follow-up times (up to 4 and 9 years, respectively). Using functional connectome fingerprinting, we examined the stability of the connectome and additionally investigated which regions, connections and networks supported individual identification. Finally, we predicted cognitive and behavioural outcome based on functional connectivity. RESULTS: Multiple sclerosis patients showed connectome stability and identification accuracies similar to healthy individuals, with longer time delays between imaging sessions being associated with accuracies dropping from 89% to 76%. Lesion load, brain atrophy or cognitive impairment did not affect identification accuracies within the range of disease severity studied. Connections from the fronto-parietal and default mode network were consistently most distinctive, i.e., informative of identity. The functional connectivity also allowed the prediction of individual cognitive performances. CONCLUSION: Our results demonstrate that discriminatory signatures in the functional connectome are stable over extended periods of time in multiple sclerosis, resulting in similar identification accuracies and distinctive long-lasting functional connectome fingerprinting signatures in patients and healthy individuals

Role of fractal dimension in random walks on scale-free networks

Fractal dimension is central to understanding dynamical processes occurring on networks; however, the relation between fractal dimension and random walks on fractal scale-free networks has been rarely addressed, despite the fact that such networks are ubiquitous in real-life world. In this paper, we study the trapping problem on two families of networks. The first is deterministic, often called $(x,y)$-flowers; the other is random, which is a combination of $(1,3)$-flower and $(2,4)$-flower and thus called hybrid networks. The two network families display rich behavior as observed in various real systems, as well as some unique topological properties not shared by other networks. We derive analytically the average trapping time for random walks on both the $(x,y)$-flowers and the hybrid networks with an immobile trap positioned at an initial node, i.e., a hub node with the highest degree in the networks. Based on these analytical formulae, we show how the average trapping time scales with the network size. Comparing the obtained results, we further uncover that fractal dimension plays a decisive role in the behavior of average trapping time on fractal scale-free networks, i.e., the average trapping time decreases with an increasing fractal dimension.Comment: Definitive version published in European Physical Journal

Safety and Immunogenicity of a Recombinant Adenovirus Serotype 35-Vectored HIV-1 Vaccine in Adenovirus Serotype 5 Seronegative and Seropositive Individuals.

BACKGROUND: Recombinant adenovirus serotype 5 (rAd5)-vectored HIV-1 vaccines have not prevented HIV-1 infection or disease and pre-existing Ad5 neutralizing antibodies may limit the clinical utility of Ad5 vectors globally. Using a rare Ad serotype vector, such as Ad35, may circumvent these issues, but there are few data on the safety and immunogenicity of rAd35 directly compared to rAd5 following human vaccination. METHODS: HVTN 077 randomized 192 healthy, HIV-uninfected participants into one of four HIV-1 vaccine/placebo groups: rAd35/rAd5, DNA/rAd5, and DNA/rAd35 in Ad5-seronegative persons; and DNA/rAd35 in Ad5-seropositive persons. All vaccines encoded the HIV-1 EnvA antigen. Antibody and T-cell responses were measured 4 weeks post boost immunization. RESULTS: All vaccines were generally well tolerated and similarly immunogenic. As compared to rAd5, rAd35 was equally potent in boosting HIV-1-specific humoral and cellular immunity and responses were not significantly attenuated in those with baseline Ad5 seropositivity. Like DNA, rAd35 efficiently primed rAd5 boosting. All vaccine regimens tested elicited cross-clade antibody responses, including Env V1/V2-specific IgG responses. CONCLUSIONS: Vaccine antigen delivery by rAd35 is well-tolerated and immunogenic as a prime to rAd5 immunization and as a boost to prior DNA immunization with the homologous insert. Further development of rAd35-vectored prime-boost vaccine regimens is warranted

Workplace productivity and office type: an evaluation of office occupier differences based on age and gender

Purpose Open plan office environments are considered to offer workplace productivity benefits because of the opportunities that they create for interaction and knowledge exchange, but more recent research has highlighted noise, distraction and loss of privacy as significant productivity penalties with this office layout. This study aims to investigate if the purported productivity benefits of open plan outweigh the potential productivity penalties. Design/methodology/approach Previous research suggests that office environments are experienced differently according to the gender and age of the occupier across both open-plan and enclosed configurations. Empirical research undertaken with office occupiers in the Middle East (N=220) led to evaluations to establish the impact different offices had on perceived productivity. Factor analysis was used to establish five underlying components of office productivity. The five factors are subsequently used as the basis for comparison between office occupiers based on age, gender and office type. Findings This research shows that benefits and penalties to workplace productivity are experienced equally across open-plan and enclosed office environments. The greatest impact on perceived workplace productivity however was availability of a variety of physical layouts, control over interaction and the 'downtime' offered by social interaction points. Male occupiers and those from younger generations were also found to consider the office environment to have more of a negative impact on their perceived workplace productivity compared to female and older occupiers. Originality/value The originality of this paper is that it develops the concept of profiling office occupiers with the aim of better matching office provision. This paper aims to establish different occupier profiles based on age, gender and office type. Data analysis techniques such as factor analysis and t-test analysis identify the need for different spaces so that occupiers can choose the most appropriate space to best undertake a particular work task. In addition, it emphasises the value that occupiers place on ‘downtime’ leading to the need for appropriate social space

Central stress processing, T-cell responsivity to stress hormones, and disease severity in multiple sclerosis

Epidemiological, clinical and neuroscientific studies support a link between psychobiological stress and multiple sclerosis. Neuroimaging suggests that blunted central stress processing goes along with higher multiple sclerosis severity, neuroendocrine studies suggest that blunted immune system sensitivity to stress hormones is linked to stronger neuroinflammation. Until now, however, no effort has been made to elucidate whether central stress processing and immune system sensitivity to stress hormones are related in a disease-specific fashion, and if so, whether this relation is clinically meaningful. Consequently, we conducted two functional MRI analyses based on a total of 39 persons with multiple sclerosis and 25 healthy persons. Motivated by findings of an altered interplay between neuroendocrine stress processing and T-cell glucocorticoid sensitivity in multiple sclerosis, we searched for neural networks whose stress task-evoked activity is differentially linked to peripheral T-cell glucocorticoid signalling in patients versus healthy persons as a potential indicator of disease-specific CNS–immune crosstalk. Subsequently, we tested whether this activity is simultaneously related to disease severity. We found that activity of a network comprising right anterior insula, right fusiform gyrus, left midcingulate and lingual gyrus was differentially coupled to T-cell glucocorticoid signalling across groups. This network’s activity was simultaneously linked to patients’ lesion volume, clinical disability and information-processing speed. Complementary analyses revealed that T-cell glucocorticoid signalling was not directly linked to disease severity. Our findings show that alterations in the coupling between central stress processing and T-cell stress hormone sensitivity are related to key severity measures of multiple sclerosis