Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning.

Active Inference is a recently developed framework for modeling decision processes under uncertainty. Over the last several years, empirical and theoretical work has begun to evaluate the strengths and weaknesses of this approach and how it might be extended and improved. One recent extension is the "sophisticated inference" (SI) algorithm, which improves performance on multi-step planning problems through a recursive decision tree search. However, little work to date has been done to compare SI to other established planning algorithms in reinforcement learning (RL). In addition, SI was developed with a focus on inference as opposed to learning. The present paper therefore has two aims. First, we compare performance of SI to Bayesian RL schemes designed to solve similar problems. Second, we present and compare an extension of SI - sophisticated learning (SL) - that more fully incorporates active learning during planning. SL maintains beliefs about how model parameters would change under the future observations expected under each policy. This allows a form of counterfactual retrospective inference in which the agent considers what could be learned from current or past observations given different future observations. To accomplish these aims, we make use of a novel, biologically inspired environment that requires an optimal balance between goal-seeking and active learning, and which was designed to highlight the problem structure for which SL offers a unique solution. This setup requires an agent to continually search an open environment for available (but changing) resources in the presence of competing affordances for information gain. Our simulations demonstrate that SL outperforms all other algorithms in this context - most notably, Bayes-adaptive RL and upper confidence bound (UCB) algorithms, which aim to solve multi-step planning problems using similar principles (i.e., directed exploration and counterfactual reasoning about belief updates given different possible actions/observations). These results provide added support for the utility of Active Inference in solving this class of biologically-relevant problems and offer added tools for testing hypotheses about human cognition.

Understanding the widespread use of veterinary ivermectin for Chagas disease, underlying factors and implications for the COVID-19 pandemic: a convergent mixed-methods study.

OBJECTIVES: Veterinary ivermectin (vet-IVM) has been used widely in Latin America against COVID-19, despite the lack of scientific evidence and potential risks. Widespread vet-IVM intake was also discovered against Chagas disease during a study in Bolivia prior to the pandemic. All vet-IVM-related data were extracted to understand this phenomenon, its extent and underlying factors and to discuss potential implications for the current pandemic. DESIGN: A convergent mixed-methods study design including a survey, qualitative in-depth interviews (IDI) and focus group discussions (FGD). SETTING: A cross-sectional study conducted in 2018 covering the geographic area of Monteagudo, an endemic municipality for Chagas disease. PARTICIPANTS: A total of 669 adult household representatives from 26 communities participated in the survey, supplemented by 14 IDI and 2 FGD among patients, relatives and key informants. RESULTS: 9 IDI and 2 FGD contained narratives on vet-IVM use against Chagas disease. Five main themes emerged: (1) the extent of the vet-IVM phenomenon, (2) the perception of vet-IVM as a treatment for Chagas disease, (3) the vet-IVM market and the controversial role of stakeholders, (4) concerns about potential adverse events and (5) underlying factors of vet-IVM use against Chagas disease.In quantitative analysis, 28% of participants seropositive for Chagas disease had taken vet-IVM. Factors associated with multivariate analysis were advanced age (OR 17.01, 95 CI 1.24 to 36.55, p=0.027 for age above 60 years), the experience of someone close as information source (OR 3.13, 95 CI 1.62 to 5.02, p<0.001), seropositivity for Chagas disease (OR 3.89, 95 CI 1.39 to 6.20, p=0.005) and citing the unavailability of benznidazole as perceived healthcare barrier (OR 2.3, 95 CI 1.45 to 5.18, p=0.002). Participants with an academic education were less likely to report vet-IVM intake (OR 0.12, 95 CI 0.01 to 0.78, p=0.029). CONCLUSIONS: Social determinants of health, the unavailability of treatment and a wonder drug image might contribute to the phenomenon of vet-IVM.

Correlation Constraints for Regression Models: Controlling Bias in Brain Age Prediction.

In neuroimaging, the difference between chronological age and predicted brain age, also known as brain age delta, has been proposed as a pathology marker linked to a range of phenotypes. Brain age delta is estimated using regression, which involves a frequently observed bias due to a negative correlation between chronological age and brain age delta. In brain age prediction models, this correlation can manifest as an overprediction of the age of young brains and an underprediction for elderly ones. We show that this bias can be controlled for by adding correlation constraints to the model training procedure. We develop an analytical solution to this constrained optimization problem for Linear, Ridge, and Kernel Ridge regression. The solution is optimal in the least-squares sense i.e., there is no other model that satisfies the correlation constraints and has a better fit. Analyses on the PAC2019 competition data demonstrate that this approach produces optimal unbiased predictive models with a number of advantages over existing approaches. Finally, we introduce regression toolboxes for Python and MATLAB that implement our algorithm.

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters.

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

"We have already heard that the treatment doesn't do anything, so why should we take it?": A mixed method perspective on Chagas disease knowledge, attitudes, prevention, and treatment behaviour in the Bolivian Chaco.

BACKGROUND: Chagas disease (CD) is highly endemic in the Bolivian Chaco. The municipality of Monteagudo has been targeted by national interventions as well as by Médecins Sans Frontières to reduce infection rates, and to decentralize early diagnosis and treatment. This study seeks to determine the knowledge and attitudes of a population with increased awareness and to identify remaining factors and barriers for sustained vector control, health care seeking behaviour, and access, in order to improve future interventions. METHODOLOGY/PRINCIPAL FINDINGS: A cross-sectional survey was conducted among approximately 10% (n = 669) of the municipality of Monteagudo's households that were randomly selected. Additionally, a total of 14 in-depth interviews and 2 focus group discussions were conducted with patients and key informants. Several attitudes and practices were identified that could undermine effective control against (re-)infection. Knowledge of clinical symptoms and secondary prevention was limited, and revealed specific misconceptions. Although 76% of the participants had been tested for CD, only 18% of those who tested positive concluded treatment with benznidazole (BNZ). Sustained positive serologies after treatment led to perceived ineffectiveness of BNZ. Moreover, access barriers such as direct as well as indirect costs, BNZ stock-outs and a fear of adverse reactions triggered by other community members made patients opt for alternative treatments against CD such as veterinary ivermectin, used by 28% of infected participants in our study. The lack of accessible care for chronic complications as well as socioeconomic consequences, such as the exclusion from both job opportunities and bank loans contributed to the ongoing burden of CD. CONCLUSIONS/SIGNIFICANCE: Large scale interventions should be accompanied by operational research in order to identify misconceptions and unintended consequences early on, to generate accessible data for future interventions, and for rigorous evaluation. An integrated, community-based approach tackling social determinants and including both traditional and animal health sectors might help to overcome current barriers and advocate for patients' rights.

Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness.

BACKGROUND: The continuation of life-sustaining therapy in critical care patients with anoxic-ischemic disorders of consciousness (AI-DOC) depends on prognostic tests such as serum neuron-specific enolase (NSE) concentration levels. OBJECTIVES: To apply predictive models using machine learning methods to examine, one year after onset, the prognostic power of serial measurements of NSE in patients with AI-DOC. To compare the discriminative accuracy of this method to both standard single-day, absolute, and difference-between-days, relative NSE levels. METHODS: Classification algorithms were implemented and K-nearest neighbours (KNN) imputation was used to avoid complete case elimination of patients with missing NSE values. Non-imputed measurements from Day 0 to Day 6 were used for single day and difference-between-days. RESULTS: The naive Bayes classifier on imputed serial NSE measurements returned an AUC of (0.81±0.07) for n=126 patients (100 poor outcome). This was greater than logistic regression (0.73±0.08) and all other classifiers. Naive Bayes gave a specificity and sensitivity of 96% and 49%, respectively, for an (uncalibrated) probability decision threshold of 90%. The maximum AUC for a single day was Day 3 (0.75) for a subset of n=79 (61 poor outcome) patients, and for differences between Day 1 and Day 4 (0.81) for a subset of n=46 (39 poor outcome) patients. CONCLUSION: Imputation avoided the elimination of patients with missing data and naive Bayes outperformed all other classifiers. Machine learning algorithms could detect automatically discriminatory features and the overall predictive power increased from standard methods due to the larger data set. CODE AVAILABILITY: Data analysis code is available under GNU at: https://github.com/emilymuller1991/outcome_prediction_nse.

Consciousness Indexing and Outcome Prediction with Resting-State EEG in Severe Disorders of Consciousness.

We applied the following methods to resting-state EEG data from patients with disorders of consciousness (DOC) for consciousness indexing and outcome prediction: microstates, entropy (i.e. approximate, permutation), power in alpha and delta frequency bands, and connectivity (i.e. weighted symbolic mutual information, symbolic transfer entropy, complex network analysis). Patients with unresponsive wakefulness syndrome (UWS) and patients in a minimally conscious state (MCS) were classified into these two categories by fitting and testing a generalised linear model. We aimed subsequently to develop an automated system for outcome prediction in severe DOC by selecting an optimal subset of features using sequential floating forward selection (SFFS). The two outcome categories were defined as UWS or dead, and MCS or emerged from MCS. Percentage of time spent in microstate D in the alpha frequency band performed best at distinguishing MCS from UWS patients. The average clustering coefficient obtained from thresholding beta coherence performed best at predicting outcome. The optimal subset of features selected with SFFS consisted of the frequency of microstate A in the 2-20 Hz frequency band, path length obtained from thresholding alpha coherence, and average path length obtained from thresholding alpha coherence. Combining these features seemed to afford high prediction power. Python and MATLAB toolboxes for the above calculations are freely available under the GNU public license for non-commercial use ( https://qeeg.wordpress.com ).

Severe disorders of consciousness after acquired brain injury: A single-centre long-term follow-up study.

OBJECTIVES: To assess long-term clinical outcome, functional independence and health-related quality of life (HRQOL) in acquired brain injury (ABI) patients with a disorder of consciousness at admission to inpatient rehabilitation. METHODS: We selected patients from a cohort of ABI patients from a single centre. In addition to mortality, we measured level of consciousness with the Coma Remission Scale, functional independence with the Barthel Index, as well as generic and condition-specific HRQOL with the EQ5D and the "Quality of Life after Brain Injury" (QOLIBRI) respectively. RESULTS: Half of the obtained sample had died by follow-up. Survivors were younger at onset, in a minimally conscious state (MCS) at admission and had spent longer time in rehabilitation. Patients in a MCS were more likely to survive, and be in a state better than MCS over the follow-up time than patients with an unresponsive wakefulness syndrome (UWS). A small proportion of patients with UWS at admission emerged from MCS at follow-up. Emergence from MCS was associated with traumatic brain injury (TBI) and higher functional independence. CONCLUSION: Clinical outcome is mostly concordant with previous findings. Survivors' rehabilitation duration suggest revision of current standards. HRQOL results indicate a correlation with functional independence and that condition-specific HRQOL should not be neglected.

Cross-sectional, descriptive study of Chagas disease among citizens of Bolivian origin living in Munich, Germany.

PURPOSE: Chagas disease (CD) has become a global health issue mainly due to migration. Germany lacks surveillance data and is home to a large Latin American immigrant population. Recognising that Bolivia is the country with the highest CD prevalence in Latin America, this cross-sectional, descriptive pilot study investigated CD and associated factors among citizens of Bolivian origin living in Munich, Germany. METHODS: Participants completed a questionnaire in order to collect socioeconomic and health-related data. In addition, serology was performed. In case of positive serological tests, PCR diagnostic and clinical staging together with disease management was initiated. Qualitative research was conducted to identify personal and community barriers as well as strategies to increase CD awareness among the population at risk. RESULTS: Between June 2013 and June 2014, 43 people from Bolivia (or descendants) were enrolled. A total of 9.3% (4/43), of whom two women were of childbearing age, tested seropositive (ELISA and IFAT), and one also by PCR. For 2/4 positive participants, clinical evaluation was performed and the indeterminate form of CD was diagnosed. Knowledge about CD symptoms and ways of transmission were completely absent among 55.8% (24/43, 2/4 with CD) and 30.2% (13/43, 1/4 with CD) of participants, respectively. A total of 27.9% (12/43, 0/4 with CD) of participants had donated blood prior to the study, whereas 62.8% (27/43, 3/4 with CD) were motivated to donate blood in the future. The qualitative research identified lack of knowledge as well as stigma and fears related to CD. CONCLUSIONS: Despite the small number of participants, the prevalence of CD as well as the potential risk of non-vectorial transmission was alarming. Campaigns adapted for Latin American migrants as well as control strategies should be developed and put in place in order to prevent non-vectorial transmission and actively detect cases of CD in Germany.

Efficient quantification and characterization of bacterial outer membrane derived nano-particles with flow cytometric analysis.

There currently exists no efficient and easy method for size profiling and counting of membranous nano-scale particles, such as bacterial outer membrane vesicles (OMVs). We present here a cost-effective and fast method capable of profiling and counting small sample volumes of nano-scale membranous vesicles with standard laboratory equipment without the need for any washing steps. OMV populations of different bacterial species are compared and even subpopulations of OMVs can be identified after a simple labelling procedure. Counting is possible over three orders of magnitude without any changes to the protocol. Protein contaminations do not alter the described measurements.

Evaluation of Plasmodium falciparum gametocyte detection in different patient material.

BACKGROUND: For future eradication strategies of malaria it is important to control the transmission of gametocytes from humans to the anopheline vector which causes the spread of the disease. Sensitive, non-invasive methods to detect gametocytes under field conditions can play a role in monitoring transmission potential. METHODS: Microscopically Plasmodium falciparum-positive patients from Jimma, Ethiopia donated finger-prick blood, venous blood, saliva, oral mucosa and urine samples that were spotted on filter paper or swabs. All samples were taken and stored under equal, standardized conditions. RNA was extracted from the filter paper and detected by real-time QT-NASBA. Pfs16-mRNA and Pfs25-mRNA were measured with a time to positivity to detect gametocyte specific mRNA in different gametocyte stages. They were compared to 18S-rRNA, which is expressed in all parasite stages. Results were quantified via a known dilution series of artificial RNA copies. RESULTS: Ninety-six samples of 16 uncomplicated malaria patients were investigated. 10 (66.7%) of the slides showed gametocyte densities between 0.3-2.9 gametocytes/µl. For all RNA-targets, molecular detection in blood samples was most sensitive; finger-prick sampling required significantly smaller amounts of blood than venous blood collection. Detection of asexual 18S-rRNA in saliva and urine showed sensitivities of 80 and 67%, respectively. Non-invasive methods to count gametocytes proved insensitive. Pfs16-mRNA was detectable in 20% of urine samples, sensitivities for other materials were lower. Pfs25-mRNA was not detectable in any sample. CONCLUSIONS: The sensitivity of non-invasively collected material such as urine, saliva or mucosa seems unsuitable for the detection of gametocyte-specific mRNA. Sensitivity in asymptomatic carriers might be generally even lower. Finger-prick testing revealed the highest absolute count of RNA copies per µL, especially for Pfs25-mRNA copies. The method proved to be the most effective and should preferably be applied in future transmission control and eradication plans. A rapid test for gametocyte targets would simplify efforts.

Stability of gametocyte-specific Pfs25-mRNA in dried blood spots on filter paper subjected to different storage conditions.

BACKGROUND: Real-time quantitative nucleic acid sequence-based amplification (QT-NASBA) is a sensitive method for detection of sub-microscopic gametocytaemia by measuring gametocyte-specific mRNA. Performing analysis on fresh whole blood samples is often not feasible in remote and resource-poor areas. Convenient methods for sample storage and transport are urgently needed. METHODS: Real-time QT-NASBA was performed on whole blood spiked with a dilution series of purified in-vitro cultivated gametocytes. The blood was either freshly processed or spotted on filter papers. Gametocyte detection sensitivity for QT-NASBA was determined and controlled by microscopy. Dried blood spot (DBS) samples were subjected to five different storage conditions and the loss of sensitivity over time was investigated. A formula to approximate the loss of Pfs25-mRNA due to different storage conditions and time was developed. RESULTS: Pfs25-mRNA was measured in time to positivity (TTP) and correlated well with the microscopic counts and the theoretical concentrations of the dilution series. TTP results constantly indicated higher amounts of RNA in filter paper samples extracted after 24 hours than in immediately extracted fresh blood. Among investigated storage conditions freezing at -20°C performed best with 98.7% of the Pfs25-mRNA still detectable at day 28 compared to fresh blood samples. After 92 days, the RNA detection rate was only slightly decreased to 92.9%. Samples stored at 37°C showed most decay with only 64.5% of Pfs25-mRNA detectable after one month. The calculated theoretical detection limit for 24 h-old DBS filter paper samples was 0.0095 (95% CI: 0.0025 to 0.0380) per µl. CONCLUSIONS: The results suggest that the application of DBS filter papers for quantification of Plasmodium falciparum gametocytes with real-time QT-NASBA is practical and recommendable. This method proved sensitive enough for detection of sub-microscopic densities even after prolonged storage. Decay rates can be predicted for different storage conditions as well as durations.