Journal articles on the topic 'Neuroscience informed algorithm'

Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems.

Adaptations or alternative versions of cognitive psychotherapy for posttraumatic stress disorder (PTSD) are needed because even the most efficacious cognitive or cognitive-behavioral psychotherapies for PTSD do not retain or achieve sustained clinically significant benefits for a majority of recipients. Cognitive affective neuroscience research is reviewed which suggests that it is not just memory (or memories) of traumatic events and related core beliefs about self, the world, and relationships that are altered in PTSD but also memory (and affective information) processing. A cognitive psychotherapy is described that was designed to systematically make explicit these otherwise implicit trauma-related alterations in cognitive emotion regulation and its application to the treatment of complex variants of PTSD—Trauma Affect Regulation: Guide for Education and Therapy (TARGET). TARGET provides therapists and clients with (a) a neurobiologically informed strengths-based meta-model of stress-related cognitive processing in the brain and how this is altered by PTSD and (b) a practical algorithm for restoring the executive functions that are necessary to make implicit trauma-related cognitions explicit (i.e., experiential awareness) and modifiable (i.e., planful refocusing). Results of randomized clinical trial studies and quasi-experimental effectiveness evaluations of TARGET with adolescents and adults are reviewed.

Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview), and CV-analysis (Textio), as well as estimation of psychometric characteristics through image-(Traitify) and game-based assessments (HireVue) and video interviews (Cammio). However, driven by concern that such high-impact technology must be used responsibly due to the potential for unfair hiring to result from the algorithms used by these tools, there is an active effort towards proving mechanisms of governance for such automation. In this article, we apply a systematic algorithm audit framework in the context of the ethically critical industry of algorithmic recruitment systems, exploring how audit assessments on AI-driven systems can be used to assure that such systems are being responsibly deployed in a fair and well-governed manner. We outline sources of risk for the use of algorithmic hiring tools, suggest the most appropriate opportunities for audits to take place, recommend ways to measure bias in algorithms, and discuss the transparency of algorithms.

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a “biologically plausible” region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.

Abstract Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four different geographical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method’s outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are effective in comparison.

Electrophysiology is entering the era of big data. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, that is, single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention (Rey, Pedreira, & Quian Quiroga, 2015 ; Rossant et al., 2016 ) but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grows exponentially. Here we introduce the [Formula: see text]-student stochastic neighbor embedding (t-SNE) dimensionality reduction method (Van der Maaten & Hinton, 2008 ) as a visualization tool in the spike sorting process. t-SNE embeds the [Formula: see text]-dimensional extracellular spikes ([Formula: see text] = number of features by which each spike is decomposed) into a low- (usually two-) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets from both hybrid (Rossant et al., 2016 ) and paired juxtacellular/extracellular recordings (Neto et al., 2016 ). We have released a graphical user interface (GUI) written in Python as a tool for the manual clustering of the t-SNE embedded spikes and as a tool for an informed overview and fast manual curation of results from different clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

Phylogenomic analyses of hundreds of protein-coding genes aimed at resolving phylogenetic relationships is now a common practice. However, no software currently exists that includes tools for dataset construction and subsequent analysis with diverse validation strategies to assess robustness. Furthermore, there are no publicly available high-quality curated databases designed to assess deep (>100 million years) relationships in the tree of eukaryotes. To address these issues, we developed an easy-to-use software package, PhyloFisher (https://github.com/TheBrownLab/PhyloFisher), written in Python 3. PhyloFisher includes a manually curated database of 240 protein-coding genes from 304 eukaryotic taxa covering known eukaryotic diversity, a novel tool for ortholog selection, and utilities that will perform diverse analyses required by state-of-the-art phylogenomic investigations. Through phylogenetic reconstructions of the tree of eukaryotes and of the Saccharomycetaceae clade of budding yeasts, we demonstrate the utility of the PhyloFisher workflow and the provided starting database to address phylogenetic questions across a large range of evolutionary time points for diverse groups of organisms. We also demonstrate that undetected paralogy can remain in phylogenomic “single-copy orthogroup” datasets constructed using widely accepted methods such as all vs. all BLAST searches followed by Markov Cluster Algorithm (MCL) clustering and application of automated tree pruning algorithms. Finally, we show how the PhyloFisher workflow helps detect inadvertent paralog inclusions, allowing the user to make more informed decisions regarding orthology assignments, leading to a more accurate final dataset.

In recent years, with the emergence of relatively inexpensive and accessible virtual reality technologies, it is now possible to deliver compelling and realistic simulations of human-to-human interaction. Neuroimaging studies have shown that, when participants believe they are interacting via a virtual interface with another human agent, they show different patterns of brain activity compared to when they know that their virtual partner is computer-controlled. The suggestion is that users adopt an “intentional stance” by attributing mental states to their virtual partner. However, it remains unclear how beliefs in the agency of a virtual partner influence participants’ behaviour and subjective experience of the interaction. We investigated this issue in the context of a cooperative “joint attention” game in which participants interacted via an eye tracker with a virtual onscreen partner, directing each other’s eye gaze to different screen locations. Half of the participants were correctly informed that their partner was controlled by a computer algorithm (“Computer” condition). The other half were misled into believing that the virtual character was controlled by a second participant in another room (“Human” condition). Those in the “Human” condition were slower to make eye contact with their partner and more likely to try and guide their partner before they had established mutual eye contact than participants in the “Computer” condition. They also responded more rapidly when their partner was guiding them, although the same effect was also found for a control condition in which they responded to an arrow cue. Results confirm the influence of human agency beliefs on behaviour in this virtual social interaction context. They further suggest that researchers and developers attempting to simulate social interactions should consider the impact of agency beliefs on user experience in other social contexts, and their effect on the achievement of the application’s goals.

Many ischaemic stroke patients who have a mechanical removal of their clot (thrombectomy) do not get reperfusion of tissue despite the thrombus being removed. One hypothesis for this ‘no-reperfusion’ phenomenon is micro-emboli fragmenting off the large clot during thrombectomy and occluding smaller blood vessels downstream of the clot location. This is impossible to observe in-vivo and so we here develop an in-silico model based on in-vitro experiments to model the effect of micro-emboli on brain tissue. Through in-vitro experiments we obtain, under a variety of clot consistencies and thrombectomy techniques, micro-emboli distributions post-thrombectomy. Blood flow through the microcirculation is modelled for statistically accurate voxels of brain microvasculature including penetrating arterioles and capillary beds. A novel micro-emboli algorithm, informed by the experimental data, is used to simulate the impact of micro-emboli successively entering the penetrating arterioles and the capillary bed. Scaled-up blood flow parameters–permeability and coupling coefficients–are calculated under various conditions. We find that capillary beds are more susceptible to occlusions than the penetrating arterioles with a 4x greater drop in permeability per volume of vessel occluded. Individual microvascular geometries determine robustness to micro-emboli. Hard clot fragmentation leads to larger micro-emboli and larger drops in blood flow for a given number of micro-emboli. Thrombectomy technique has a large impact on clot fragmentation and hence occlusions in the microvasculature. As such, in-silico modelling of mechanical thrombectomy predicts that clot specific factors, interventional technique, and microvascular geometry strongly influence reperfusion of the brain. Micro-emboli are likely contributory to the phenomenon of no-reperfusion following successful removal of a major clot.

Departures of normal blood flow and metabolite distribution from the cerebral microvasculature into neuronal tissue have been implicated with age-related neurodegeneration. Mathematical models informed by spatially and temporally distributed neuroimage data are becoming instrumental for reconstructing a coherent picture of normal and pathological oxygen delivery throughout the brain. Unfortunately, current mathematical models of cerebral blood flow and oxygen exchange become excessively large in size. They further suffer from boundary effects due to incomplete or physiologically inaccurate computational domains, numerical instabilities due to enormous length scale differences, and convergence problems associated with condition number deterioration at fine mesh resolutions. Our proposed simple finite volume discretization scheme for blood and oxygen microperfusion simulations does not require expensive mesh generation leading to the critical benefit that it drastically reduces matrix size and bandwidth of the coupled oxygen transfer problem. The compact problem formulation yields rapid and stable convergence. Moreover, boundary effects can effectively be suppressed by generating very large replica of the cortical microcirculation in silico using an image-based cerebrovascular network synthesis algorithm, so that boundaries of the perfusion simulations are far removed from the regions of interest. Massive simulations over sizeable portions of the cortex with feature resolution down to the micron scale become tractable with even modest computer resources. The feasibility and accuracy of the novel method is demonstrated and validated with in vivo oxygen perfusion data in cohorts of young and aged mice. Our oxygen exchange simulations quantify steep gradients near penetrating blood vessels and point towards pathological changes that might cause neurodegeneration in aged brains. This research aims to explain mechanistic interactions between anatomical structures and how they might change in diseases or with age. Rigorous quantification of age-related changes is of significant interest because it might aide in the search for imaging biomarkers for dementia and Alzheimer’s disease.

PurposeArtificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. Instead, this paper aims to explore the role of AI in educational leadership.Design/methodology/approachTo explore the role of AI in educational leadership, I synthesized the literature that intersects AI, decision-making, and educational leadership from multiple disciplines such as computer science, educational leadership, administrative science, judgment and decision-making and neuroscience. Grounded in the intellectual interrelationships between AI and educational leadership since the 1950s, this paper starts with conceptualizing decision-making, including both individual decision-making and organizational decision-making, as the foundation of educational leadership. Next, I elaborated on the symbiotic role of human-AI decision-making.FindingsWith its efficiency in collecting, processing, analyzing data and providing real-time or near real-time results, AI can bring in analytical efficiency to assist educational leaders in making data-driven, evidence-informed decisions. However, AI-assisted data-driven decision-making may run against value-based moral decision-making. Taken together, both leaders' individual decision-making and organizational decision-making are best handled by using a blend of data-driven, evidence-informed decision-making and value-based moral decision-making. AI can function as an extended brain in making data-driven, evidence-informed decisions. The shortcomings of AI-assisted data-driven decision-making can be overcome by human judgment guided by moral values.Practical implicationsThe paper concludes with two recommendations for educational leadership practitioners' decision-making and future scholarly inquiry: keeping a watchful eye on biases and minding ethically-compromised decisions.Originality/valueThis paper brings together two fields of educational leadership and AI that have been growing up together since the 1950s and mostly growing apart till the late 2010s. To explore the role of AI in educational leadership, this paper starts with the foundation of leadership—decision-making, both leaders' individual decisions and collective organizational decisions. The paper then synthesizes the literature that intersects AI, decision-making and educational leadership from multiple disciplines to delineate the role of AI in educational leadership.

Suicide is a complex phenomenon that has attracted attention throughout the times of humanity. Since ancient times, its history has been approached in a general way. Mesopotamian, Egyptian, Greek and Roman civilizations already considered it the product of a melancholic state of mind. Virtually all religions agree in their rejection as a means of ending life. The common basis for this rejection is that it is God who gives life and He is the only one capable of taking it away. Most writers agree when considering it as the result of an act resulting from a distressing situation. Carrying out this study is justified since it is a topic that draws attention worldwide, due to the increase in the registration of cases, becoming a Public Health problem. According to the World Health Organization (WHO), in 2020, 1.53 million people will die from suicide, one death every twenty seconds, and the number of attempts will be between ten and twenty times higher. Due to its seriousness, it requires our attention, although unfortunately, the large number of psychoeducational programs that exist for its prevention and control is not an easy task. With this work, we intend to understand its current reach in the young population and make known to what extent Artificial Intelligence (AI) and Neurocommunication with appropriate content on social networks could be applied to the management of Public Relations, to help alleviate, to a large extent, the envisaged attempts on the population concerned. Artificial Intelligence can be used to take advantage of real-time data to help us make more optimized and informed decisions. The advances made today in the field of advanced analytical techniques and statistical algorithms, to identify and obtain a better evaluation of what may happen in the future, processing data to identify patterns of behavior, managing with the media of communication, the issues derived from strategic consulting, academic research, can bring in various ways, great benefits in their application to Public Relations. This will increase the capacities that add value and can be considered as a prevention tool. New ways of acting that increase the efficiency between the sender and the receiver are necessary, through the contributions of neuroscience and the techniques of Public Relations so that their actions are more effective when the messages are directed towards reward systems of the brain. The new discipline of Neurocommunication as a meeting point between neurosciences and communication, tries to know the brain processes to carry out better strategies, in this case, of Public Relations, that allow decision-making in the adoption behaviour in various situations. In-depth knowledge of the processes of the human brain as a decision system in which individuals interpret their realities, depends on the way each subject decodes it, since there is a connection between how we act and the brain system. All this makes us foresee that its application in the field of Public Relations will be essential to mitigate the reality, in this case studied, in the affected groups.

Operculo-insular epilepsy (OIE) is an under-recognized condition that can mimic temporal and extratemporal epilepsies. Previous studies have revealed structural connectivity changes in the epileptic network of focal epilepsy. However, most reports use the debated streamline-count to quantify ‘connectivity strength’ and rely on standard tracking algorithms. We propose a sophisticated cutting-edge method that is robust to crossing fibers, optimizes cortical coverage, and assigns an accurate microstructure-reflecting quantitative conectivity marker, namely the COMMIT (Convex Optimization Modeling for Microstructure Informed Tractography)-weight. Using our pipeline, we report the connectivity alterations in OIE. COMMIT-weighted matrices were created in all participants (nine patients with OIE, eight patients with temporal lobe epilepsy (TLE), and 22 healthy controls (HC)). In the OIE group, widespread increases in ‘connectivity strength’ were observed bilaterally. In OIE patients, ‘hyperconnections’ were observed between the insula and the pregenual cingulate gyrus (OIE group vs. HC group) and between insular subregions (OIE vs. TLE). Graph theoretic analyses revealed higher connectivity within insular subregions of OIE patients (OIE vs. TLE). We reveal, for the first time, the structural connectivity distribution in OIE. The observed pattern of connectivity in OIE likely reflects a diffuse epileptic network incorporating insular-connected regions and may represent a structural signature and diagnostic biomarker.

Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms. This approach led to the identification of Neutrophils Extracellular Traps (NETs) as plausible mediators of vascular thrombosis in severe COVID-19 in children and adults. Taken together, the network-informed analysis led us to propose the following model: the release of NETs in response to inflammatory signals acting in concert with SARS-CoV-2 damage the endothelium and direct platelet-activation promoting abnormal coagulation leading to serious complications of COVID-19. The underlying hypothesis is that genetic and/or environmental conditions that favor the release of NETs may predispose individuals to thrombotic complications of COVID-19 due to an increase risk of abnormal coagulation. This would be a common pathogenic mechanism in conditions including autoimmune/infectious diseases, hematologic and metabolic disorders.

We revisit the oft-studied asymptotic (in sample size) behavior of the parameter or weight estimate returned by any member of a large family of neural network training algorithms. By properly accounting for the characteristic property of neural networks that their empirical and generalization errors possess multiple minima, we rigorously establish conditions under which the parameter estimate converges strongly into the set of minima of the generalization error. Convergence of the parameter estimate to a particular value cannot be guaranteed under our assumptions. We then evaluate the asymptotic distribution of the distance between the parameter estimate and its nearest neighbor among the set of minima of the generalization error. Results on this question have appeared numerous times and generally assert asymptotic normality, the conclusion expected from familiar statistical arguments concerned with maximum likelihood estimators. These conclusions are usually reached on the basis of somewhat informal calculations, although we shall see that the situation is somewhat delicate. The preceding results then provide a derivation of learning curves for generalization and empirical errors that leads to bounds on rates of convergence.

Characterising gene function for the ever-increasing number and diversity of species with annotated genomes relies almost entirely on computational prediction methods. These software are also numerous and diverse, each with different strengths and weaknesses as revealed through community benchmarking efforts. Meta-predictors that assess consensus and conflict from individual algorithms should deliver enhanced functional annotations. To exploit the benefits of meta-approaches, we developed CrowdGO, an open-source consensus-based Gene Ontology (GO) term meta-predictor that employs machine learning models with GO term semantic similarities and information contents. By re-evaluating each gene-term annotation, a consensus dataset is produced with high-scoring confident annotations and low-scoring rejected annotations. Applying CrowdGO to results from a deep learning-based, a sequence similarity-based, and two protein domain-based methods, delivers consensus annotations with improved precision and recall. Furthermore, using standard evaluation measures CrowdGO performance matches that of the community’s best performing individual methods. CrowdGO therefore offers a model-informed approach to leverage strengths of individual predictors and produce comprehensive and accurate gene functional annotations.

Neural machine translation technologies are having increasing applications in clinical and healthcare settings. In multicultural countries, automatic translation tools provide critical support to medical and health professionals in their interaction and exchange of health messages with migrant patients with limited or non-English proficiency. While research has mainly explored the usability and limitations of state-of-the-art machine translation tools in the detection and diagnosis of physical diseases and conditions, there is a persistent lack of evidence-based studies on the applicability of machine translation tools in the delivery of mental healthcare services for vulnerable populations. Our study developed Bayesian machine learning algorithms using relevance vector machine to support frontline health workers and medical professionals to make better informed decisions between risks and convenience of using online translation tools when delivering mental healthcare services to Spanish-speaking minority populations living in English-speaking countries. Major strengths of the machine learning classifier that we developed include scalability, interpretability, and adaptability of the classifier for diverse mental healthcare settings. In this paper, we report on the process of the Bayesian machine learning classifier development through automatic feature optimisation and the interpretation of the classifier-enabled assessment of the suitability of original English mental health information for automatic online translation. We elaborate on the interpretation of the assessment results in clinical settings using statistical tools such as positive likelihood ratios and negative likelihood ratios.

Background Understanding the effects of herd management practices on the prevalence of multidrug-resistant pathogenic Salmonella and commensals Enterococcus spp. and Escherichia coli in dairy cattle is key in reducing antibacterial resistant infections in humans originating from food animals. Our objective was to explore the herd and cow level features associated with the multi-drug resistant, and resistance phenotypes shared between Salmonella, E. coli and Enterococcus spp. using machine learning algorithms. Methods Randomly collected fecal samples from cull dairy cows from six dairy farms in central California were tested for multi-drug resistance phenotypes of Salmonella, E. coli and Enterococcus spp. Using data on herd management practices collected from a questionnaire, we built three machine learning algorithms (decision tree classifier, random forest, and gradient boosting decision trees) to predict the cows shedding multidrug-resistant Salmonella and commensal bacteria. Results The decision tree classifier identified rolling herd average milk production as an important feature for predicting fecal shedding of multi-drug resistance in Salmonella or commensal bacteria. The number of culled animals, monthly culling frequency and percentage, herd size, and proportion of Holstein cows in the herd were found to be influential herd characteristics predicting fecal shedding of multidrug-resistant phenotypes based on random forest models for Salmonella and commensal bacteria. Gradient boosting models showed that higher culling frequency and monthly culling percentages were associated with fecal shedding of multidrug resistant Salmonella or commensal bacteria. In contrast, an overall increase in the number of culled animals on a culling day showed a negative trend with classifying a cow as shedding multidrug-resistant bacteria. Increasing rolling herd average milk production and spring season were positively associated with fecal shedding of multidrug- resistant Salmonella. Only six individual cows were detected sharing tetracycline resistance phenotypes between Salmonella and either of the commensal bacteria. Discussion Percent culled and culling rate reflect the increase in culling over time adjusting for herd size and were associated with shedding multidrug resistant bacteria. In contrast, number culled was negatively associated with shedding multidrug resistant bacteria which may reflect producer decisions to prioritize the culling of otherwise healthy but low-producing cows based on milk or beef prices (with respect to dairy beef), amongst other factors. Using a data-driven suite of machine learning algorithms we identified generalizable and distant associations between antimicrobial resistance in Salmonella and fecal commensal bacteria, that can help develop a producer-friendly and data-informed risk assessment tool to reduce shedding of multidrug-resistant bacteria in cull dairy cows.

According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date and confirmed-case count, using the RKI’s EBS database as labels which performed modestly. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using bag-of-words, document and word embeddings. The best classifier, a logistic regression, achieved a sensitivity of 0.82 and an index balanced accuracy of 0.61. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code and data are publicly available under open licenses.

<p><strong>1. How can we maintain a sustainable private practice in the current political and economic climate?</strong></p><p>Eugene Allers</p><p><strong>2. SASOP Clinical guidelines, protocols and algorithms: Development of treatment guidelines for bipolar mood disorder and major depression</strong></p><p> Eugene Allers, Margaret Nair, Gerhard Grobler</p><p><strong>3. The revolving door phenomenon in psychiatry: Comparing low-frequency and high-frequency users of psychiatric inpatient services in a developing country</strong></p><p>U A Botha, P Oosthuien, L Koen, J A Joska, J Parker, N Horn</p><p><strong>4. Neurophysiology of emotion and senses - The interface between psyche and soma</strong></p><p>Eugene Allers</p><p><strong>5. Suicide prevention: From and beyond the psychiatrist's hands</strong></p><p>O Alonso Betanourt, M Morales Herrera</p><p><strong>6. Treatment of first-episod psychosis: Efficacy and toleabilty of a long-acting typical antipsychotic </strong></p><p>B Chiliza, R Schoeman, R Emsey, P Oosthuizen, L KOen, D Niehaus, S Hawkridge</p><p><strong>7. Treatment of attention deficit hyperactivity disorder in the young child</strong></p><p>Helen Clark</p><p><strong>8. Holistic/ Alternative treatment in psychiatry: The value of indigenous knowledge systems in cllaboration with moral, ethical and religious approaches in the military services</strong></p><p>J Dill</p><p><strong>9. Treating Schizophrenia: Have we got it wrong?</strong></p><p>Robin Emsley</p><p><strong>10.Terminal questions in the elderly</strong></p><p>Mike Ewart Smith</p><p><strong>11. Mental Health Policy development and implementation in Ghana, South Africa, Uganda and Zambia</strong></p><p>Alan J Flisher, Crick Lund, Michelle Frank, Arvin Bhana, Victor Doku, Natalie Drew, Fred N Kigozi, Martin Knapp, Mayeh Omar, Inge Petersen, Andrew Green andthe MHaPP Research Programme Consortium</p><p><strong>12. What indicators should be used to monitor progress in scaling uo services for people with mental disorders?</strong></p><p>Lancet Global Mental Health Group (Alan J Flisher, Dan Chisholm, Crick Lund, Vikram Patel, Shokhar Saxena, Graham Thornicroft, Mark Tomlinson)</p><p><strong>13. Does unipolar mania merit research in South Africa? A look at the literature</strong></p><p>Christoffel Grobler</p><p><strong>14. Revisiting the Cartesian duality of mind and body</strong></p><p>Oye Gureje</p><p><strong>15. Child and adolescent psychopharmacology: Current trends and complexities</strong></p><p>S M Hawkridge</p><p><strong>16. Integrating mental illness, suicide and religion</strong></p><p>Volker Hitzeroth</p><p><strong>17. Cost of acute inpatient mental health care in a 72-hour assessment uniy</strong></p><p>A B R Janse van Rensburg, W Jassat</p><p><strong>18. Management of Schizophrenia according to South African standard treatment guidelines</strong></p><p>A B R Janse van Rensburg</p><p><strong>19. Structural brain imaging in the clinical management of psychiatric illness</strong></p><p>F Y Jeenah</p><p><strong>20. ADHD: Change in symptoms from child to adulthood</strong></p><p>S A Jeeva, A Turgay</p><p><strong>21. HIV-Positive psychiatric patients in antiretrovirals</strong></p><p>G Jonsson, F Y Jeenah, M Y H Moosa</p><p><strong>22. A one year review of patients admitted to tertiary HIV/Neuropsychiatry beds in the Western Cape</strong></p><p>John Joska, Paul Carey, Ian Lewis, Paul Magni, Don Wilson, Dan J Stein</p><p><strong>23. Star'd - Critical review and treatment implications</strong></p><p>Andre Joubert</p><p><strong>24. Options for treatment-resistent depression: Lessons from Star'd; an interactive session</strong></p><p>Andre Joubert</p><p><strong>25. My brain made me do it: How Neuroscience may change the insanity defence</strong></p><p>Sean Kaliski</p><p><strong>26. Child andadolescent mental health services in four African countries</strong></p><p>Sharon Kleintjies, Alan Flisher, Victoruia Campbell-Hall, Arvin Bhana, Phillippa Bird, Victor Doku, Natalie, Drew, Michelle Funk, Andrew Green, Fred Kigozi, Crick Lund, Angela Ofori-Atta, Mayeh Omar, Inge Petersen, Mental Health and Poverty Research Programme Consortium</p><p><strong>27. Individualistic theories of risk behaviour</strong></p><p>Liezl Kramer, Volker Hitzeroth</p><p><strong>28. Development and implementation of mental health poliy and law in South Africa: What is the impact of stigma?</strong></p><p>Ritsuko Kakuma, Sharon Kleintjes, Crick Lund, Alan J Flisher, Paula Goering, MHaPP Research Programme Consortium</p><p><strong>29. Factors contributing to community reintegration of long-term mental health crae users of Weskoppies Hospital</strong></p><p>Carri Lewis, Christa Kruger</p><p><strong>30. Mental health and poverty: A systematic review of the research in low- and middle-income countries</strong></p><p>Crick Lund, Allison Breen, Allan J Flisher, Ritsuko Kakuma, Leslie Swartz, John Joska, Joanne Corrigall, Vikram Patel, MHaPP Research Programe Consortium</p><p><strong>31. The cost of scaling up mental health care in low- and middle-income countries</strong></p><p>Crick Lund, Dan Chishlom, Shekhar Saxena</p><p><strong>32. 'Tikking'Clock: The impact of a methamphetamine epidemic at a psychiatric hospital in the Western Cape</strong></p><p>P Milligan, J S Parker</p><p><strong>33. Durban youth healh-sk behaviour: Prevalence f Violence-related behaviour</strong></p><p>D L Mkize</p><p><strong>34. Profile of morality of patients amitted Weskoppies Psychiatric Hospital in Sout frican over a 5-Year period (2001-2005)</strong></p><p>N M Moola, N Khamker, J L Roos, P Rheeder</p><p><strong>35. One flew over Psychiatry nest</strong></p><p>Leverne Mountany</p><p><strong>36. The ethical relationship betwe psychiatrists and the pharmaceutical indutry</strong></p><p>Margaret G Nair</p><p><strong>37. Developing the frameor of a postgraduate da programme in mental health</strong></p><p>R J Nichol, B de Klerk, M M Nel, G van Zyl, J Hay</p><p><strong>38. An unfolding story: The experience with HIV-ve patients at a Psychiatric Hospital</strong></p><p>J S Parker, P Milligan</p><p><strong>39. Task shifting: A practical strategy for scalingup mental health care in developing countries</strong></p><p>Vikram Patel</p><p><strong>40. Ethics: Informed consent and competency in the elderly</strong></p><p>Willie Pienaar</p><p><strong>41. Confronting ommonmoral dilemmas. Celebrating uncertainty, while in search patient good</strong></p><p>Willie Pienaar</p><p><strong>42. Moral dilemmas in the treatment and repatriation of patients with psychtorders while visiting our country</strong></p><p>Duncan Ian Rodseth</p><p><strong>43. Geriatrics workshop (Psegal symposium): Medico-legal issuess in geriatric psyhiatry</strong></p><p>Felix Potocnik</p><p><strong>44. Brain stimulation techniques - update on recent research</strong></p><p>P J Pretorius</p><p><strong>45. Holistic/Alternative treatments in psychiatry</strong></p><p>T Rangaka, J Dill</p><p><strong>46. Cognitive behaviour therapy and other brief interventions for management of substances</strong></p><p>Solomon Rataemane</p><p><strong>47. A Transtheoretical view of change</strong></p><p>Nathan P Rogerson</p><p><strong>48. Profile of security breaches in longerm mental health care users at Weskoppies Hospital over a 6-month period</strong></p><p>Deleyn Rema, Lindiwe Mthethwa, Christa Kruger</p><p><strong>49. Management of psychogenic and chronic pain - A novel approach</strong></p><p>M S Salduker</p><p><strong>50. Childhood ADHD and bipolar mood disorders: Differences and similarities</strong></p><p>L Scribante</p><p><strong>51. The choice of antipsychotic in HIV-infected patients and psychopharmacocal responses to antipsychotic medication</strong></p><p>Dinesh Singh, Karl Goodkin</p><p><strong>52. Pearls in clinical neuroscience: A teaching column in CNS Spectrums</strong></p><p><strong></strong>Dan J Stein</p><p><strong>53. Urinary Cortisol secretion and traumatics in a cohort of SA Metro policemen A longitudinal study</strong></p><p>Ugash Subramaney</p><p><strong>54. Canabis use in Psychiatric inpatients</strong></p><p><strong></strong>M Talatala, G M Nair, D L Mkize</p><p><strong>55. Pathways to care and treatmt in first and multi-episodepsychosis: Findings fm a developing country</strong></p><p>H S Teh, P P Oosthuizen</p><p><strong>56. Mental disorders in HIV-infected indivat various HIV Treatment sites in South Africa</strong></p><p>Rita Thom</p><p><strong>57. Attendanc ile of long-term mental health care users at ocupational therapy group sessions at Weskoppies Hospital</strong></p><p>Ronel van der Westhuizen, Christa Kruger</p><p><strong>58. Epidemiological patterns of extra-medical drug use in South Africa: Results from the South African stress and health study</strong></p><p>Margaretha S van Heerden, Anna Grimsrud, David Williams, Dan Stein</p><p><strong>59. Persocentred diagnosis: Where d ps and mental disorders fit in the International classificaton of diseases (ICD)?</strong></p><p>Werdie van Staden</p><p><strong>60. What every psychiatrist needs to know about scans</strong></p><p>Herman van Vuuren</p><p><strong>61. Psychiatric morbidity in health care workers withle drug-resistant erulosis (MDR-TB) A case series</strong></p><p>Urvashi Vasant, Dinesh Singh</p><p><strong>62. Association between uetrine artery pulsatility index and antenatal maternal psychological stress</strong></p><p>Bavanisha Vythilingum, Lut Geerts, Annerine Roos, Sheila Faure, Dan J Stein</p><p><strong>63. Approaching the dual diagnosis dilemma</strong></p><p>Lize Weich</p><p><strong>64. Women's mental health: Onset of mood disturbance in midlife - Fact or fiction</strong></p><p>Denise White</p><p><strong>65. Failing or faking: Isses in the fiagnosis and treatment of adult ADHD</strong></p><p>Dora Wynchank</p>

AbstractEvidence-based algorithms can improve both lay and professional judgements and decisions, yet they remain underutilised. Research on advice taking established that humans tend to discount advice—especially when it contradicts their own judgement (“egocentric advice discounting”)—but this can be mitigated by knowledge about the advisor’s past performance. Advice discounting has typically been investigated using tasks with outcomes of low importance (e.g. general knowledge questions) and students as participants. Using the judge-advisor framework, we tested whether the principles of advice discounting apply in the clinical domain. We used realistic patient scenarios, algorithmic advice from a validated cancer risk calculator, and general practitioners (GPs) as participants. GPs could update their risk estimates after receiving algorithmic advice. Half of them received information about the algorithm’s derivation, validation, and accuracy. We measured weight of advice and found that, on average, GPs weighed their estimates and the algorithm equally—but not always: they retained their initial estimates 29% of the time, and fully updated them 27% of the time. Updating did not depend on whether GPs were informed about the algorithm. We found a weak negative quadratic relationship between estimate updating and advice distance: although GPs integrate algorithmic advice on average, they may somewhat discount it, if it is very different from their own estimate. These results present a more complex picture than simple egocentric discounting of advice. They cast a more optimistic view of advice taking, where experts weigh algorithmic advice and their own judgement equally and move towards the advice even when it contradicts their own initial estimates.

The white matter is organized into “tracts” or “bundles,” which connect different parts of the central nervous system. Knowing where these tracts are located in each individual is important for understanding the cause of potential sensorial, motor or cognitive deficits and for developing appropriate treatments. Traditionally, tracts are found using tracer injection, which is a difficult, slow and poorly scalable technique. However, axon populations from a given tract exhibit specific characteristics in terms of morphometrics and myelination. Hence, the delineation of tracts could, in principle, be done based on their morphometry. The objective of this study was to generate automatic parcellation of the rat spinal white matter tracts using the manifold information from scanning electron microscopy images of the entire spinal cord. The axon morphometrics (axon density, axon diameter, myelin thickness and g-ratio) were computed pixelwise following automatic axon segmentation using AxonSeg. The parcellation was based on an agglomerative clustering algorithm to group the tracts. Results show that axon morphometrics provide sufficient information to automatically identify some white matter tracts in the spinal cord, however, not all tracts were correctly identified. Future developments of microstructure quantitative MRI even bring hope for a personalized clustering of white matter tracts in each individual patient. The generated atlas and the associated code can be found at https://github.com/neuropoly/tract-clustering.

Abstract Objective Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians’ experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data which can effectively assist the clinical programming using automated algorithms. Approach This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex (SCC) and ventral capsule/ventral striatum (VC/VS) using tractography optimized targeting with an imaging defined target previously described. Main results Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (P=0.002). Significance The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point for the clinicians to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.

Abstract Background Amyloid-β (Aβ) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aβ deposition is a necessary cause or response to the cellular pathology of Alzheimer’s disease (AD). Usual clinical and research interpretation of amyloid PET does not fully utilise all information regarding the spatial distribution of signal. We present a data-driven, spatially informed classifier to boost the diagnostic power of amyloid PET in AD. Methods Voxel-wise k-means clustering of amyloid-positive voxels was performed; clusters were mapped to brain anatomy and tested for their associations by diagnostic category and disease severity with 758 amyloid PET scans from volunteers in the AD continuum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A machine learning approach based on this spatially constrained model using an optimised quadratic support vector machine was developed for automatic classification of scans for AD vs non-AD pathology. Results This classifier boosted the accuracy of classification of AD scans to 81% using the amyloid PET alone with an area under the curve (AUC) of 0.91 compared to other spatial methods. This increased sensitivity to detect AD by 15% and the AUC by 9% compared to the use of a composite region of interest SUVr. Conclusions The diagnostic classification accuracy of amyloid PET was improved using an automated data-driven spatial classifier. Our classifier highlights the importance of considering the spatial variation in Aβ PET signal for optimal interpretation of scans. The algorithm now is available to be evaluated prospectively as a tool for automated clinical decision support in research settings.

Abstract Objective: Accurate modeling of transcranial magnetic stimulation (TMS) coils with the magnetic core is largely an open problem since commercial (quasi)magnetostatic solvers do not output specific field characteristics (e.g., induced electric field) and have difficulties when incorporating realistic head models. Many open-source TMS softwares do not include magnetic cores into consideration. This present study reports an algorithm for modeling TMS coils with a (nonlinear) magnetic core and validates the algorithm through comparison with finite-element method (FEM) simulations and experiments. Approach: The algorithm uses the boundary element fast multipole method (BEM-FMM) applied to all facets of a tetrahedral core mesh for a single-state solution and the successive substitution method for nonlinear convergence of the subsequent core states. The algorithm also outputs coil inductances, with or without magnetic cores. The coil--core combination is solved only once i.e., before incorporating the head model. The resulting primary TMS electric field is proportional to the total vector potential in the quasistatic approximation; it therefore also employs the precomputed core magnetization. Main results: The solver demonstrates excellent convergence for typical TMS field strengths and for analytical B-H approximations of experimental magnetization curves such as Froelich's equation or an arctangent equation. Typical execution times are 1--3 min on a common multicore workstation. For a simple test case of a cylindrical core within a one-turn coil, our solver computed the small-signal inductance nearly identical to that from ANSYS Maxwell. For a multiturn rodent TMS coil with a core, the modeled inductance matched the experimental measured value to within 5%. Significance: Incorporating magnetic core in TMS coil design has advantages of field shaping and energy efficiency. Our software package can facilitate model-informed design of more efficiency TMS systems and guide selection of core material. These models can also inform dosing with existing clinical TMS systems that use magnetic cores.

AbstractDepression in late-life is associated with increased risk of cognitive decline and development of all-cause dementia. The neurobiology of late-life depression (LLD) may involve both neurochemical and neurodegenerative mechanisms that are common to depression and dementia. Transgenic amyloid mouse models show evidence of early degeneration of monoamine systems. Informed by these preclinical data, the hypotheses were tested that a spatial covariance pattern of higher beta-amyloid (Aβ) and lower serotonin transporter availability (5-HTT) in frontal, temporal, and parietal cortical regions would distinguish LLD patients from healthy controls and the expression of this pattern would be associated with greater depressive symptoms. Twenty un-medicated LLD patients who met DSM-V criteria for major depression and 20 healthy controls underwent PET imaging with radiotracers for Aβ ([11C]-PiB) and 5-HTT ([11C]-DASB). A voxel-based multi-modal partial least squares (mmPLS) algorithm was applied to the parametric PET images to determine the spatial covariance pattern between the two radiotracers. A spatial covariance pattern was identified, including higher Aβ in temporal, parietal and occipital cortices associated with lower 5-HTT in putamen, thalamus, amygdala, hippocampus and raphe nuclei (dorsal, medial and pontine), which distinguished LLD patients from controls. Greater expression of this pattern, reflected in summary 5-HTT/Aβ mmPLS subject scores, was associated with higher levels of depressive symptoms. The mmPLS method is a powerful approach to evaluate the synaptic changes associated with AD pathology. This spatial covariance pattern should be evaluated further to determine whether it represents a biological marker of antidepressant treatment response and/or cognitive decline in LLD patients.

Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods.

The exact solution of the NP-hard (nondeterministic polynomial-time hard) maximum cut problem is important in many applications across, for example, physics, chemistry, neuroscience, and circuit layout—which is also due to its equivalence to the unconstrained binary quadratic optimization problem. Leading solution methods are based on linear or semidefinite programming and require the separation of the so-called odd-cycle inequalities. In their groundbreaking research, F. Barahona and A. R. Mahjoub have given an informal description of a polynomial-time algorithm for this problem. As pointed out recently, however, additional effort is necessary to guarantee that the inequalities obtained correspond to facets of the cut polytope. In this paper, we shed more light on a so enhanced separation procedure and investigate experimentally how it performs in comparison with an ideal setting where one could even employ the sparsest, most violated, or geometrically most promising facet-defining odd-cycle inequalities. Summary of Contribution: This paper aims at a better capability to solve binary quadratic optimization or maximum cut problems and their various applications using integer programming techniques. To this end, the paper describes enhancements to a well-known algorithm for the central separation problem arising in this context; it is demonstrated experimentally that these enhancements are worthwhile from a computational point of view. The linear relaxations of the aforementioned problems are typically solved using fewer iterations and cutting planes than with a nonenhanced approach. It is also shown that the enhanced procedure is only slightly inferior to an ideal, enumerative, and, in practice, intractable global cutting-plane selection.

State standards include fluent use of standard computational algorithms. However, learning and using them require conceptual understanding of numbers and operations. Previous research using the concrete–representational–abstract (CRA) sequence has been effective in teaching computational algorithms to students at risk of and students with learning disabilities by emphasizing conceptual understanding. However, all the research was face-to-face and few captured the impact of the intervention on students’ conceptual understanding. The current study occurred during a pandemic, so researchers modified CRA for remote instruction. This study investigated the effects of modified CRA on sixth-grade students with learning disabilities’ fluency and accuracy. They also included an assessment of conceptual understanding. The researchers used a multiple probe across participants design, demonstrating a functional relation between CRA and students’ fluency and accuracy. Researchers assessed conceptual understanding with informal measures that required application of their conceptual understanding. The results and implications will be discussed.

ForewordThe only time is now. Every “now” is unique. Responsible persons ask themselves, “How can I act well now?” The answers will differ for every person, because just as every situation is unique, so is every person different from every other person. But surely there must be some algorithm that will assist us in coming to the right answer. Unfortunately, no, for there is no right answer. There is only an answer that is as appropriate as we can conclude at that moment in that situation. No written guidelines can apply appropriately to every unique situation.Unfortunately we physicians have been suckled on a fallacy: “What’s good for the goose is good for the gander.” Phrased in medical terms, “normal findings are good, and abnormal findings are bad.” This is too simple, and often wrong.Good clinicians know that care must be personalized for it to be optimal. So-called normal findings give rough guidance, sometimes applicable to groups, but frequently wrong for individuals. Consider intraocular pressure (IOP). A normal IOP of 15 mmHg good for some and bad for others, and an abnormal IOP of 30 mmHg is good for some and bad for others. We are so bombarded by the myth of the sanctity of the standard distribution curve that it is hard to think independently and specifically. Also, unfortunately, doctors are prone to decide for patients, often on the basis of normative data that is not relevant or important for the particular patient. That we do this is not surprising, as we want to help, and so we default to what seems to be the easy, safe (non-thinking) way, in which we do not have to hold ourselves accountable for the outcome.Somebody HAS to decide, or else we would be living in an anarchical world. Also true. And because none of us knows as much as we need to know to act appropriately, we seek advice from so-called “experts.”For us to care for people well it is essential that we consider what others recommend. So we look to experts, as we should. However, experts are sometimes right and sometimes wrong. Remember that von Graefe in 1860 recommended surgical iridectomy for all glaucoma, Elliot recommended mustard plaster between the shoulders for glaucoma, Becker based treatment on tonographic findings, Weve reported 100% success with penetrating cyclodiathermy in glaucoma, Lichter advised against laser trabeculoplasty, many thought Cypass was great, and the investigators in the Advanced Glaucoma Intervention Study indicated that an IOP usually around 12 mmHg was better than one usually around 20 mmHg. All wrong. What the authors of these guidelines have done excellently, is to provide a general framework on which ophthalmologists can hang pieces of evidence, so as to be able to evaluate the validity and the importance of that evidence. In doing this meticulously they have provided a valuable service to all ophthalmologists, none of whom individually have either the time or the skill to be fully informed. In their own practices the authors consider whether valid information is relevant for the particular person being considered. That process of considering relevance is essential, always. And relevance is based on the particular unique patient, unique doctor and unique situation. The only guideline the authors can provide in this regard is to remind us all to consider relevance with all patients in all situations, and from the patient’s perspective. Even more important than the service to ophthalmologists is the benefit to patients that will result from thoughtful use of these guidelines.We need, also, to remember that diagnoses are generic, and that within every diagnosis there are differences. For example what does a diagnosis of primary open angle mean? Some of those affected will rapidly go blind despite the most thoughtful treatment and others will keep their sight even without treatment. What does a diagnosis of Chandler’s Syndrome mean? In some, surgery works well, and, in others, poorly. So one never directs diagnosis and treatment at a condition, but rather at the person, the objective being the wellness of that person.The previous European Glaucoma Society Guidelines are used internationally. It is good that the EGS is again providing updated, useful information.The Guidelines are a practical, inspirational contribution.George L. Spaeth, BA, MD.Esposito Research Professor, Wills Eye Hospital/Sidney Kimmel Medical College/Thomas Jefferson Universitywww.eugs.orgThe Guidelines writers, authors and contributorsAugusto Azuara-Blanco (Editor)Luca BagnascoAlessandro BagnisJoao Barbosa BredaChiara BonzanoAndrei BrezhnevAlain BronCarlo A. CutoloBarbara CvenkelStefano GandolfiTed Garway HeathIlmira GazizovaGus GazzardFranz GrehnAnders HeijlCornelia HirnGábor HollóAnton HommerMichele IesterIngrida JanulevicieneGauti JóhannessonMiriam KolkoTianjing LiJosé Martínez de la CasaFrances Meier-GibbonsMaria MusolinoMarta PazosNorbert PfeifferSergey PetrovLuis Abegao PintoRiccardo ScottoIngeborg StalmansGordana SunaricMégevandErnst TammJohn ThygesenFotis TopouzisMarc Töteberg-HarmsCarlo E. Traverso (Editor)Anja TuulonenZoya VeselovskayaAnanth ViswanathanIlgaz YalvacThierry ZeyenGuidelines CommitteeAugusto Azuara-Blanco (Chair)Carlo E. Traverso (Co-chair)Manuele Michelessi (NGP Co-chair)Luis Abegao PintoMichele IesterJoao BredaCarlo A. CutoloPanayiota FountiGerhard GarhoeferAndreas KatsanosMiriam KolkoFrancesco OddoneMarta PazosVerena Prokosch-WillingCedric SchweitzerAndrew TathamMarc Toteberg-HarmsAcknowledgementsAnja TuulonenTed Garway HeathRichard WormaldTianjing LiManuele MichelessiJenny BurrAzuara-Blanco for their methodological oversight.Tianjing Li and Riaz Qureshi (US Cochrane Eye and Vision Group) and Manuele Michelessi (EGS) for leading the evidence review.Manuele MichelessiGianni VirgiliJoao Barbosa BredaCarlo A. CutoloMarta PazosAndreas KatsanosGerhard GarhoferMiriam KolkoVerena ProkoschPanayota FountiFrancesco OddoneAli Ahmed Al RajhiTianjing LiRiaz Qureshi and Azuara-Blanco for their contribution to the evidence review.Karen Osborn and Joanna Bradley from Glaucoma UK charity for their contribution to the section: ‘What matters to patients’ (https://glaucoma.uk)Additional contributions were made by the following people on specific topicsEleftherios AnastasopoulosPanayiota FountiGus GazzardFranz GrehnAnders HeijlGábor HollóFotis TopouzisAnja TuulonenAnanth ViswanathamThe team of Clinica Oculistica of the University of Genoa for medical editing and illustrationsLuca BagnascoAlessandro BagnisChiara BonzanoCarlo A. CutoloMichele LesterMaria MusolinoRoberta ParodiRiccardo ScottoWe would like to thank the following colleagues for their help in reviewing/editing section I.7. Landmark randomised controlled trials for glaucomaJoe CaprioliTed Garway Heath Gus Gazzard Divakar Gupta Anders Heijl Michael Kass Stefano Miglior David Musch Norbert Pfeiffer Thierry ZeyenExternal reviewsWe would like to thank the following societies and experts:World Glaucoma Association:Parul IchhpujaniMonisha NongpiurTanuj DadaSola OlawoyeJayme ViannaMin Hee SuhFarouk GarbaSimon SkalickyAlex HuangFarouk GarbaPradeep RamuluVerena ProkoschCarolina Gracitelli;American Glaucoma Society:Josh Stein;and Latin-American Glaucoma Society:Daniel GrigeraWe would like to thank the external reviewers whose comments are listed on https://www.eugs.org/eng/guidelines.aspThe EGS executive committeeTed Garway Heath (President)Fotis Topouzis (Vice President)Ingeborg Stalmans (Treasurer)Anja Tuulonen (Past President)Luis Abegao PintoAndrei BrezhnevAlain BronGauti JóhannessonNorbert PfeifferThe board of the European Glaucoma Society FoundationCarlo E. Traverso (Chair)Fotis Topouzis (Vice Chair)Franz GrehnAnders HeijlJohn ThygesenThierry ZeyenGlossary5-FU 5-fluorouracilAAC Acute angle closureACG Angle closure glaucomaAGIS Advanced glaucoma intervention studyAH Aqueous humourAI Artificial intelligenceALT Argon laser trabeculoplastyBAC Benzalkalonium chlorideCCT Central corneal thicknessCDR Cup to disc ratioCIGTS Initial glaucoma treatment studyCNTGS Collaborative normal tension glaucoma studyDCT Dynamic contour tonometryEAGLE Effectiveness of early lens extraction for the treatment of primary angle closure glaucomaEGPS European glaucoma prevention studyEGS European glaucoma societyEMA The european medicines agencyEMGT Early manifest glaucoma trialFC Flow chartFDT Frequency doubling technologyFC Fixed combinationFL Fixation lossesFN False negativesFP False positiveGAT Goldmann applanation tonometryGHT The glaucoma hemifield testGRADE Grading of recommendations, assessment, development and evaluationsHRT Heidelberg retina tomographyICE Irido-corneal endothelial syndromeIOL Intraocular lensIOP Intraocular pressureITC Iridotrabecular contactIV IntravenousLIGHT Laser in glaucoma and ocular hypertension trialLPI Laser peripheral iridotomyLV Loss varianceMD Mean defect or mean deviationMMC Mitomycin CNCT Non-contact tonometryNd:YAG Neodymium-doped yttrium aluminum garnetNTG Normal tension glaucomaOAG Open angle glaucomaOCT Optical coherence tomographyOHT Ocular hypertensionOHTS The ocular hypertension treatment studyONH Optic nerve headORA Ocular response analyserOSD Ocular surface diseasePAC Primary angle closurePACG Primary angle closure glaucomaPACS Primary angle closure suspectPAS Peripheral anterior synechiaePCG Primary congenital glaucomaPDS Pigment dispersion syndromePGA Prostaglandin analoguePOAG Primary open angle glaucomaPG Pigmentary glaucomaPSD Pattern standard deviationPXF Pseudoexfoliation syndromePXFG Pseudoexfoliation glaucomaRCT Randomised controlled trialRNFL Retinal nerve fiber layerRoP Rate of progressionSAP Standard automated perimetrySITA Swedish interactive threshold algorithmSLT Selective laser trabeculoplastySWAP Short-wavelength automated perimetryTLPI Thermal laser peripheral iridoplastyTM Trabecular meshworkUBM Ultrasound biomicroscopyUGH Uveitis-glaucoma-hyphema syndromeUKGTS United Kingdom glaucoma treatment studyVEGF Vascular endothelial growth factorVF Visual filedVFI Visual field indexZAP Zhongshan angle closure prevention trial

Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.