Interactive data exploration websites for large-scale electrophysiology.

Methodological advances in neuroscience have enabled the collection of massive datasets which demand innovative approaches for scientific communication. Existing platforms for data storage lack intuitive tools for data exploration, limiting our ability to interact effectively with these brain-wide datasets. We introduce two public websites: (Data and Atlas) developed for the International Brain Laboratory which provide access to millions of behavioral trials and hundreds of thousands of individual neurons. These interfaces allow users to discover both the raw and processed brain-wide data released by the IBL at the scale of the whole brain, individual sessions, trials, and neurons. By hosting these data interfaces as websites they are available cross-platform with no installation. By releasing each site's code as a modular open-source framework, other researchers can easily develop their own web interfaces and explore their own data. As neuroscience datasets continue to expand, customizable web interfaces offer a glimpse into a future of streamlined data exploration and act as blueprints for future tools.

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools.

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce 'Lightning Pose', an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We released a cloud application that allows users to label data, train networks and process new videos directly from the browser.

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools.

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.

One-Pot and Catalyst-Free Transformation of N-Protected 1-Amino-1-Ethoxyalkylphosphonates into Bisphosphonic Analogs of Protein and Non-Protein α-Amino Acids.

Herein, we describe the development of one-pot transformation of α-ethoxy derivatives of phosphorus analogs of protein and non-protein α-amino acids into biologically important N-protected 1-aminobisphosphonates. The proposed strategy, based on the three-component reaction of 1-(N-acylamino)-1-ethoxyphosphonates with triphenylphosphonium tetrafluoroborate and triethyl phosphite, facilitates good to excellent yields under mild reaction conditions. The course of the reaction was monitored by 31P NMR spectroscopy, allowing the identification of probable intermediate species, thus making it possible to propose a reaction mechanism. In most cases, there is no need to use a catalyst to provide transformation efficiency, which increases its attractiveness both in economic and ecological terms. Furthermore, we demonstrate that the one-pot procedure can be successfully applied for the synthesis of structurally diverse N-protected bisphosphonic analogs of α-amino acids. As shown, the indirect formation of the corresponding phosphonium salt as a reactive intermediate during the conversion of 1-(N-acylamino)-1-ethoxyphosphonate into a 1-aminobisphosphonate derivative is a crucial component of the developed methodology.

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.

Standardized and reproducible measurement of decision-making in mice.

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.

In science, it is of vital importance that multiple studies corroborate the same result. Researchers therefore need to know all the details of previous experiments in order to implement the procedures as exactly as possible. However, this is becoming a major problem in neuroscience, as animal studies of behavior have proven to be hard to reproduce, and most experiments are never replicated by other laboratories. Mice are increasingly being used to study the neural mechanisms of decision making, taking advantage of the genetic, imaging and physiological tools that are available for mouse brains. Yet, the lack of standardized behavioral assays is leading to inconsistent results between laboratories. This makes it challenging to carry out large-scale collaborations which have led to massive breakthroughs in other fields such as physics and genetics. To help make these studies more reproducible, the International Brain Laboratory (a collaborative research group) et al. developed a standardized approach for investigating decision making in mice that incorporates every step of the process; from the training protocol to the software used to analyze the data. In the experiment, mice were shown images with different contrast and had to indicate, using a steering wheel, whether it appeared on their right or left. The mice then received a drop of sugar water for every correction decision. When the image contrast was high, mice could rely on their vision. However, when the image contrast was very low or zero, they needed to consider the information of previous trials and choose the side that had recently appeared more frequently. This method was used to train 140 mice in seven laboratories from three different countries. The results showed that learning speed was different across mice and laboratories, but once training was complete the mice behaved consistently, relying on visual stimuli or experiences to guide their choices in a similar way. These results show that complex behaviors in mice can be reproduced across multiple laboratories, providing an unprecedented dataset and open-access tools for studying decision making. This work could serve as a foundation for other groups, paving the way to a more collaborative approach in the field of neuroscience that could help to tackle complex research challenges.

Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models.

Sensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the stimulus information contained in those responses. This information can in principle be preserved if variability is shared across many neurons, but depends on the structure of the shared variability and its relationship to sensory encoding at the population level. The structure of this shared variability in neural activity can be characterized by latent variable models, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron's stimulus selectivity and a set of latent variables that modulate these stimulus-driven responses both additively and multiplicatively. While these approaches can detect very general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an "affine model". In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.

Dual practitioners are as engaged in their primary job as their senior colleagues.

INTRODUCTION: Dual practice--the combination of a public hospital job with a job held in private health care--is often a source of controversy. Physicians involved in dual practice (dual practitioners) are believed to provide less work input in their public employment than physicians who are not involved in dual practice (single practitioners). This paper compares work behaviour of dual and single practitioners in the public hospitals. We focus on senior physicians in anaesthesiology and surgery. MATERIAL AND METHODS: Data were collected in a survey of public hospital physicians in Denmark. Bivariate analyses--two-sample Kolmogorov-Smirnov and Fisher's exact tests--were used to test for differences between dual and single practitioners. RESULTS: The sample represents 45% of senior public hospital physicians in 2008. Dual and single practitioners did not differ significantly in terms of the average length of work week, participation in non-mandatory activities or duties outside normal working hours, including duties accepted with short notice. Furthermore, no significant differences were ascertained in their preferences for working hours or turnover intention (i.e. their intention to leave the current workplace) for their public hospital positions. The two groups also did not differ significantly in terms of scholarly activity, viz. the number of research projects in which they participated or the number of publications issued. CONCLUSION: The revealed profile of a dual practitioner is significantly different from that suggested in the current debate. The findings suggest that the dual practice implications for the functioning of the public health-care system are less problematic than expected. FUNDING: Not relevant. TRIAL REGISTRATION: Not relevant.

Physician dual practice: a review of literature.

OBJECTIVES: A combination of public and private practice by physicians, referred to as physician dual practice, has been receiving attention in connection with arguments about its negative impact for the public health care. This paper aims to review and critically discuss findings on the subject of dual practice effects for the public health care. METHODS: A systematic literature review identified 23 positions on the subject consisting of journal articles, academic working papers, book chapter, and publications of the WHO. RESULTS: The subject is short on evidence. Theoretical analyses indicate both positive and negative effects of dual practice. Some of the effects depend, however, on assumptions that are undermined in the broader literature. The analyses assume that the dual practitioners' objective is to maximise income. Yet, while physicians seem to engage in a private practice on top of the public one mainly to increase income, it remains uncertain whether dual practice is an income-maximising combination of jobs. Moreover, costs of enforcing restrictions on dual practice are rarely considered. CONCLUSIONS: Further research is needed that compares dual practitioners and other physicians in uniform settings, investigates how the dual practitioners divide labour between the two jobs, and analyses the costs of enforcing restrictions on dual practice.