Toroidal topology of population activity in grid cells

The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment1. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations2, and are organized in modules3 that collectively form a population code for the animal’s allocentric position1. The invariance of the correlation structure of this population code across environments4,5 and behavioural states6,7, independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern1,8–11. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models12. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.

An Automatic Evaluation Method for Parkinson's Dyskinesia Using Finger Tapping Video for Small Samples

PurposeThe assessment of dyskinesia in Parkinson's disease (PD) based on Artificial Intelligence technology is a significant and challenging task. At present, doctors usually use MDS-UPDRS scale to assess the severity of patients. This method is time-consuming and laborious, and there are subjective differences. The evaluation method based on sensor equipment is also widely used, but this method is expensive and needs professional guidance, which is not suitable for remote evaluation and patient self-examination. In addition, it is difficult to collect patient data in medical research, so it is of great significance to find an objective and automatic assessment method for Parkinson's dyskinesia based on small samples.MethodsIn this study, we design an automatic evaluation method combining manual features and convolutional neural network (CNN), which is suitable for small sample classification. Based on the finger tapping video of Parkinson's patients, we use the pose estimation model to obtain the action skeleton information and calculate the feature data. We then use the 5-folds cross validation training model to achieve optimum trade-of between bias and variance, and finally make multi-class prediction through fully connected network (FCN). ResultsOur proposed method achieves the current optimal accuracy of 79.7% in this research. We have compared with the latest methods of related research, and our method is superior to them in terms of accuracy, number of parameters and FLOPs. ConclusionThe method in this paper does not require patients to wear sensor devices, and has obvious advantages in remote clinical evaluation. At the same time, the method of using motion feature data to train CNN model obtains the optimal accuracy, effectively solves the problem of difficult data acquisition in medicine, and provides a new idea for small sample classification.

Using Plane Strain Compression Test to Evaluate the Mechanical Behavior of Magnesium Processed by HPT

There is a great interest in improving mechanical testing of small samples produced in the laboratory. Plane strain compression is an effective test in which the workpiece is a thin sheet. This provides great potential for testing samples produced by high-pressure torsion. Thus, a custom tool was designed with the aim to test 10 mm diameter discs processed by this technique. Finite element analysis is used to evaluate the deformation zone, stress and strain distribution, and the accuracy in the estimation of stress–strain curves. Pure magnesium and a magnesium alloy processed by high-pressure torsion are tested using this custom-made tool. The trends observed in strength and ductility agree with trends reported in the literature for these materials.

Voice phenomenology as a mirror of the past

Background Post-traumatic mechanisms are theorised to contribute to voice-hearing in people with psychosis and a history of trauma. Phenomenological links between trauma and voices support this hypothesis, as they suggest post-traumatic processes contribute to the content of, and relationships with, voices. However, research has included small samples and lacked theory-based comprehensive assessments. Method In people with distressing voices (n = 73) who experienced trauma prior to voice-hearing, trauma–voice links were assessed both independently and dependently (descriptions were presented and rated separately and together, respectively) by both participants and researchers. A structured coding frame assessed four types of independent links (i.e. victimisation type, physiological-behavioural, emotional, and cognitive response themes including negative self-beliefs) and three types of dependent links: relational (similar interaction with/response to, voice and trauma); content (voice and trauma content are exactly the same); and identity (voice identity is the same as perpetrator). Results Independent links were prevalent in participants (51–58%) and low to moderately present in researcher ratings (8–41%) for significant themes. Identification of negative self-beliefs in trauma was associated with a significantly higher likelihood of negative self-beliefs in voices [participants odds ratio (OR) 9.8; researchers OR 4.9]. Participants and researchers also reported many dependent links (80%, 66%, respectively), most frequently relational links (75%, 64%), followed by content (60%, 25%) and identity links (51%, 22%). Conclusion Trauma appears to be a strong shaping force for voice content and its psychological impact. The most common trauma–voice links involved the experience of cognitive-affective psychological threat, embodied in relational experiences. Trauma-induced mechanisms may be important intervention targets.

Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.

A Comparison of Covariate Adjustment Approaches Under Model Misspecification In Individually Randomized Trials

Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and can protect against chance imbalances in covariates. For continuous covariates, there is a risk that the the form of the relationship between the covariate and outcome is misspecified when taking an adjusted approach. Using a simulation study focusing on small to medium-sized individually randomized trials, we explore whether a range of adjustment methods are robust to misspecification, either in the covariate-outcome relationship or through an omitted covariate-treatment interaction. Specifically, we aim to identify potential settings where G-computation, Inverse Probability of Treatment Weighting ( IPTW ), Augmented Inverse Probability of Treatment Weighting ( AIPTW ) and Targeted Maximum Likelihood Estimation ( TMLE ) offer improvement over the commonly used Analysis of Covariance ( ANCOVA ). Our simulations show that all adjustment methods are generally robust to model misspecification if adjusting for a few covariates, sample size is 100 or larger, and there are no covariate-treatment interactions. When there is a non-linear interaction of treatment with a skewed covariate and sample size is small, all adjustment methods can suffer from bias; however, methods that allow for interactions (such as G-computation with interaction and IPTW ) show improved results compared to ANCOVA . When there are a high number of covariates to adjust for, ANCOVA retains good properties while other methods suffer from under- or over-coverage. An outstanding issue for G-computation, IPTW and AIPTW in small samples is that standard errors are underestimated; development of small sample corrections is needed.

Testing the Fit of Regression Models Estimated with Extremely Small Samples: Application in Pharmaceutical Stability Studies

Pharmaceutical stability studies are conducted to estimate the shelf life, i.e. the period during which the drug product maintains its identity and stability. In the evaluation of process, regression curve is fitted on the data obtained during the study and the shelf life is determined using the fitted curve. The evaluation process suggested by ICH considers only the case of the true relationship between the measured attribute and time being linear. However, no method is suggested for the practitioner to decide if the linear model is appropriate for their dataset. This is a major problem, as a falsely selected model may distort the estimated shelf life to a great extent, resulting in unreliable quality control. The difficulty of model misspecification detection in stability studies is that very few observations are available. The conventional methods applied for model verification might not be appropriate or efficient due to the small sample size. In this paper, this problem is addressed and some developed methods are proposed to detect model misspecification. The methods can be applied for any process where the regression estimation is performed on independent small samples. Besides stability studies, frequently performed construction of single calibration curves for an analytical measurement is another case where the methods may be applied. It is shown that our methods are statistically appropriate and some of them have high efficiency in the detection of model misspecification when applied in simulated situations which resemble pre-approval and post-approval stability studies.

Advances in Forensic Sedimentology

Forensic sedimentology is a relatively recently realized field. Sedimentological methods used to solve cases have evolved as the field has developed, beginning with simple identification of minerals and progressing to the examination of individual grains using highly advanced scanning electron microscopes. More simple methods, such as color analysis, are still used today, but in addition, forensic sedimentologists look at surface textures and grain size distribution. For instance, quartz grains were used in a forensic technique as sediment fingerprint. The particle size distribution is one of the important tests when analysing sediments and soils in geological studies. For forensic work, the particle size distribution of sometimes very small samples requires precise determination using a rapid and reliable method with a high resolution. FRITSCH laser granulometer offers rapid and accurate sizing of particles in the range 0.04–2000 μm for a variety of sample types, including soils, unconsolidated sediments, dusts, powders, and other particulate materials.