Adaptive LASSO estimation for functional hidden dynamic geostatistical models.

We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the regression coefficients are functions. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed effects. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire function for an irrelevant regressor. The algorithm is based on an adaptive LASSO penalty function, with weights obtained by the unpenalised f-HDGM maximum likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the log-likelihood. A Monte Carlo simulation study provides insight in prediction ability and parameter estimate precision, considering increasing spatiotemporal dependence and cross-correlations among predictors. Further, the algorithm behaviour is investigated when modelling air quality functional data with several weather and land cover covariates. Within this application, we also explore some scalability properties of our algorithm. Both simulations and empirical results show that the prediction ability of the penalised estimates are equivalent to those provided by the maximum likelihood estimates. However, adopting the so-called one-standard-error rule, we obtain estimates closer to the real ones, as well as simpler and more interpretable models. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-023-02466-5.

Age, Gender, and Disease as Determinants of Social Distancing: Germany as a Case Study.

A mix of guidance and mandated regulations during the coronavirus disease (COVID-19) pandemic served to reduce the number of social contacts, to ensure distancing in public spaces, and to maintain the isolation of infected individuals. Individual variation in compliance to social distancing in Germany, relating to age, gender, or the presence of pre-existing health conditions, was examined using results from a total of 39 375 respondents to a web-based behavioral survey.Older people and females were more willing to engage in social distancing. Those with chronic conditions showed overall higher levels of compliance, but those with cystic fibrosis, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), and epilepsy showed less adherence to general social distancing measures but were significantly more likely to isolate in their homes. Behavioral differences partly lie in the nature of each condition, especially with those conditions likely to be exacerbated by COVID-19. Compliance differences for age and gender are largely in line with previous studies.

Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy.

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016-2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research.

Spatiotemporal variable selection and air quality impact assessment of COVID-19 lockdown.

During the first wave of the COVID-19 pandemics in 2020, lockdown policies reduced human mobility in many countries globally. This significantly reduces car traffic-related emissions. In this paper, we consider the impact of the Italian restrictions (lockdown) on the air quality in the Lombardy Region. In particular, we consider public data on concentrations of particulate matters (PM10 and PM2.5) and nitrogen dioxide, pre/during/after lockdown. To reduce the effect of confounders, we use detailed regression function based on meteorological, land and calendar information. Spatial and temporal correlations are handled using a multivariate spatiotemporal model in the class of hidden dynamic geostatistical models (HDGM). Due to the large size of the design matrix, variable selection is made using a hybrid approach coupling the well known LASSO algorithm with the cross-validation performance of HDGM. The impact of COVID-19 lockdown is heterogeneous in the region. Indeed, there is high statistical evidence of nitrogen dioxide concentration reductions in metropolitan areas and near trafficked roads where also PM10 concentration is reduced. However, rural, industrial, and mountain areas do not show significant reductions. Also, PM2.5 concentrations lack significant reductions irrespective of zone. The post-lockdown restart shows unclear results.

Online network monitoring.

An important problem in network analysis is the online detection of anomalous behaviour. In this paper, we introduce a network surveillance method bringing together network modelling and statistical process control. Our approach is to apply multivariate control charts based on exponential smoothing and cumulative sums in order to monitor networks generated by temporal exponential random graph models (TERGM). The latter allows us to account for temporal dependence while simultaneously reducing the number of parameters to be monitored. The performance of the considered charts is evaluated by calculating the average run length and the conditional expected delay for both simulated and real data. To justify the decision of using the TERGM to describe network data, some measures of goodness of fit are inspected. We demonstrate the effectiveness of the proposed approach by an empirical application, monitoring daily flights in the United States to detect anomalous patterns.

Methocarbamol blocks muscular Nav 1.4 channels and decreases isometric force of mouse muscles.

BACKGROUND: The muscle relaxant methocarbamol is widely used for the treatment of muscle spasms and pain syndromes. To elucidate molecular mechanisms of its action, we studied its influence on neuromuscular transmission, on isometric muscle force, and on voltage-gated Na+ channels. METHODS: Neuromuscular transmission was investigated in murine diaphragm-phrenic nerve preparations and muscle force studied on mouse soleus muscles. Nav 1.4 channels and Nav 1.7 channels were functionally expressed in eukaryotic cell lines. RESULTS: Methocarbamol, at 2 mM, decreased the decay of endplate currents, slowed the decay of endplate potentials and reduced tetanic force of soleus muscles. The drug reversibly inhibited current flow through muscular Nav 1.4 channels, while neuronal Nav 1.7 channels were unaffected. CONCLUSIONS: The study provides evidence for peripheral actions of methocarbamol on skeletal muscle. Muscular Na+ channels are a molecular target of methocarbamol. Since Nav 1.7 currents were unaffected, methocarbamol is unlikely to exert its analgesic effect by directly blocking Nav 1.7 channels.

webKnossos: efficient online 3D data annotation for connectomics.

We report webKnossos, an in-browser annotation tool for 3D electron microscopic data. webKnossos provides flight mode, a single-view egocentric reconstruction method enabling trained annotator crowds to reconstruct at a speed of 1.5 ± 0.6 mm/h for axons and 2.1 ± 0.9 mm/h for dendrites in 3D electron microscopic data from mammalian cortex. webKnossos accelerates neurite reconstruction for connectomics by 4- to 13-fold compared with current state-of-the-art tools, thus extending the range of connectomes that can realistically be mapped in the future.

Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models.

In this paper, we propose a test procedure to detect change points of multidimensional autoregressive processes. The considered process differs from typical applied spatial autoregressive processes in that it is assumed to evolve from a predefined center into every dimension. Additionally, structural breaks in the process can occur at a certain distance from the predefined center. The main aim of this paper is to detect such spatial changes. In particular, we focus on shifts in the mean and the autoregressive parameter. The proposed test procedure is based on the likelihood-ratio approach. Eventually, the goodness-of-fit values of the estimators are compared for different shifts. Moreover, the empirical distribution of the test statistic of the likelihood-ratio test is obtained via Monte Carlo simulations. We show that the generalized Gumbel distribution seems to be a suitable limiting distribution of the proposed test statistic. Finally, we discuss the detection of lung cancer in computed tomography scans and illustrate the proposed test procedure.

Small increase in substratum [corrected] pH causes the dieback of one of Europe's most common lichens, Lecanora conizaeoides.

BACKGROUNDS AND AIMS: Lecanora conizaeoides was until recently western and central Europe's most abundant epiphytic lichen species or at least one of the most common epiphytes. The species is adapted to very acidic conditions at pH values around 3 and high concentrations of SO(2) and its derivatives formed in aqueous solution, and thus spread with increasing SO(2) deposition during the 19th and 20th centuries. With the recent decrease of SO(2) emissions to nearly pre-industrial levels within 20 years, L. conizaeoides declined from most of its former range. If still present, the species is no longer the dominant epiphyte, but is occurring in small densities only. The rapid spread of the L. conizaeoides in Europe from an extremely rare species to the probably most frequent epiphytic lichen and the subsequent rapid dieback are unprecedented by any other organism. The present study aimed at identifying the magnitude of deacidification needed to cause the dieback of the lichen. METHODS: The epiphytic lichen diversity and bark chemistry of montane spruce forests in the Harz Mountains, northern Germany, were studied and the results were compared with data recorded with the same methods 13-15 years ago. KEY RESULTS: Lecanora conizaeoides, which was the dominant epiphyte of the study area until 15 years ago, is still found on most trees, but only with small cover values of ≤1 %. The bark pH increased by only 0·4 pH units. CONCLUSIONS: The data suggest that only slight deacidification of the substratum causes the breakdown of the L. conizaeoides populations. Neither competitors nor parasites of L. conizaeoides that may have profited from reduced SO(2) concentrations are likely causes of the rapid dieback of the species.

SSL: a theory of how people learn to select strategies.

The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis of reinforcement learning. The theory assumes that individuals develop subjective expectations for the strategies they have and select strategies proportional to their expectations, which are then updated on the basis of subsequent experience. The learning assumption was supported in 4 experimental studies. Participants substantially improved their inferences through feedback. In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities. When testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted the inferences most accurately.