Nowcasting in a pandemic using non-parametric mixed frequency VARs

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their exibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR

Trans-cellular control of synapse properties by a cell type-specific splicing regulator

The recognition of synaptic partners and specification of synaptic properties are fundamental for the function of neuronal circuits. ‘Terminal selector’ transcription factors coordinate the expression of terminal gene batteries that specify cell type-specific properties. Moreover, pan-neuronal alternative splicing regulators have been implicated in directing neuronal differentiation. However, the cellular logic of how splicing regulators instruct specific synaptic properties remains poorly understood. Here, we combine genome-wide mapping of mRNA targets and cell type-specific loss-of-function studies to uncover the contribution of the nuclear RNA binding protein SLM2 to hippocampal synapse specification. Focusing on hippocampal pyramidal cells and SST-positive GABAergic interneurons, we find that SLM2 preferentially binds and regulates alternative splicing of transcripts encoding synaptic proteins, thereby generating cell type-specific isoforms. In the absence of SLM2, cell type-specification, differentiation, and viability are unaltered and neuronal populations exhibit normal intrinsic properties. By contrast, cell type-specific loss of SLM2 results in highly selective, non-cell autonomous synaptic phenotypes, altered synaptic transmission, and associated defects in a hippocampus-dependent memory task. Thus, alternative splicing provides a critical layer of gene regulation that instructs specification of neuronal connectivity in a trans-synaptic manner

A cell-type-specific alternative splicing regulator shapes synapse properties in a trans-synaptic manner

The specification of synaptic properties is fundamental for the function of neuronal circuits. "Terminal selector" transcription factors coordinate terminal gene batteries that specify cell-type-specific properties. Moreover, pan-neuronal splicing regulators have been implicated in directing neuronal differentiation. However, the cellular logic of how splicing regulators instruct specific synaptic properties remains poorly understood. Here, we combine genome-wide mapping of mRNA targets and cell-type-specific loss-of-function studies to uncover the contribution of the RNA-binding protein SLM2 to hippocampal synapse specification. Focusing on pyramidal cells and somatostatin (SST)-positive GABAergic interneurons, we find that SLM2 preferentially binds and regulates alternative splicing of transcripts encoding synaptic proteins. In the absence of SLM2, neuronal populations exhibit normal intrinsic properties, but there are non-cell-autonomous synaptic phenotypes and associated defects in a hippocampus-dependent memory task. Thus, alternative splicing provides a critical layer of gene regulation that instructs specification of neuronal connectivity in a trans-synaptic manner

Real-time estimation of EEG-based engagement in different tasks

: Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Synovial hemangioma of the knee joint in a 12-year-old boy: a case report

<p>Abstract</p> <p>Introduction</p> <p>Synovial hemangioma is a rare condition and is frequently misdiagnosed, leading to a diagnostic delay of many years.</p> <p>Case presentation</p> <p>We present a case of an atypical synovial hemangioma in a 12-year-old Caucasian boy with a diagnostic delay of 3 years.</p> <p>Conclusion</p> <p>It is important to know that synovial hemangioma mostly affects the knee joint, showing recurrent bloody effusions without a history of trauma. If there are no intermittent effusions, the diagnosis will be even more difficult. In cases of nonspecific symptoms and longstanding knee pain the diagnosis of a synovial hemangioma should also be considered in order to avoid diagnostic delay. Magnetic resonance imaging is the main diagnostic tool to evaluate patients with synovial hemangioma, showing characteristic lace-like or linear patterns.</p> <p>Angiography can identify feeder vessels and offers the possibility of embolisation in the same setting. Surgical excision, either done per arthroscopy or per arthrotomy, is recommended as soon as possible to avoid the risk of damage to the cartilage.</p

Modeling of GERDA Phase II data

The GERmanium Detector Array (GERDA) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double-beta ($0\nu\beta\beta$) decay of $^{76}$Ge. The technological challenge of GERDA is to operate in a "background-free" regime in the region of interest (ROI) after analysis cuts for the full 100$\,$kg$\cdot$yr target exposure of the experiment. A careful modeling and decomposition of the full-range energy spectrum is essential to predict the shape and composition of events in the ROI around $Q_{\beta\beta}$ for the $0\nu\beta\beta$ search, to extract a precise measurement of the half-life of the double-beta decay mode with neutrinos ($2\nu\beta\beta$) and in order to identify the location of residual impurities. The latter will permit future experiments to build strategies in order to further lower the background and achieve even better sensitivities. In this article the background decomposition prior to analysis cuts is presented for GERDA Phase II. The background model fit yields a flat spectrum in the ROI with a background index (BI) of $16.04^{+0.78}_{-0.85} \cdot 10^{-3}\,$cts/(kg$\cdot$keV$\cdot$yr) for the enriched BEGe data set and $14.68^{+0.47}_{-0.52} \cdot 10^{-3}\,$cts/(kg$\cdot$keV$\cdot$yr) for the enriched coaxial data set. These values are similar to the one of Gerda Phase I despite a much larger number of detectors and hence radioactive hardware components