Survey of spiking in the mouse visual system reveals functional hierarchy.

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically1. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory2-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas3. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

Food Targeting: Determination of the Cocoa Shell Content (Theobroma cacao L.) in Cocoa Products by LC-QqQ-MS/MS.

A targeted metabolomics LC-ESI-QqQ-MS/MS application for the determination of cocoa shell based on 15 non-polar key metabolites was developed, validated according to recognized guidelines, and used to predict the cocoa shell content in various cocoa products. For the cocoa shell prediction, different PLSR models based on different cocoa shell calibration series were developed and their suitability and prediction quality were compared. By analysing samples from different origins and harvest years with known shell content, the prediction model could be confirmed. The predicted shell content could be verified with a deviation of about 1% cocoa shell. The presented method demonstrates the suitability of the targeted application of metabolomic profiling for the determination of cocoa shell and its applicability in routine analysis is discussed.

Food fingerprinting: Mass spectrometric determination of the cocoa shell content (Theobroma cacao L.) in cocoa products by HPLC-QTOF-MS.

The determination of cocoa shell content (Theobroma cacao L.) in cocoa products using a metabolomics approach was accomplished via high performance liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS). The developed method was used to separately analyze the polar and non-polar metabolome of the cocoa testa (cocoa shell) and the cocoa cotyledons (cocoa nibs) of cocoa samples from 15 different geographic origins, harvest years, and varieties in positive and negative ion mode. Potential key metabolites were selected which are exclusively contained in the cocoa shell or with significant higher concentration in the cocoa shell than in the cocoa nibs. The pool of potential key metabolites was filtered by established selection criteria, such as temperature stability, fermentations stability, and independence from the geographic origin. Based on these key metabolites an inverse sparse partial least square regression (SPLS) was used for the prediction of the cocoa shell content.

Food Targeting: Geographical Origin Determination of Hazelnuts (Corylus avellana) by LC-QqQ-MS/MS-Based Targeted Metabolomics Application.

A targeted metabolomics LC-ESI-QqQ-MS application for geographical origin discrimination based on 20 nonpolar key metabolites was developed, validated according to accepted guidelines and used for quantitation via stable isotope labeled internal standards in 202 raw authentic hazelnut samples from six countries (Turkey, Italy, Georgia, Spain, France, and Germany) of harvest years 2014 and 2015. Multivariate statistics were used for detection of significant variations in metabolite levels between countries and, moreover, a prediction model using support vector machine classification (SVM) was calculated yielding 100% training accuracy and 97% cross-validation accuracy, which was subsequently applied to 55 hazelnut samples for the confectionary industry gaining up to 80% correct classifications compared to declared origin. The present method demonstrates the great suitability for targeted metabolomics applications in the geographical origin determination of hazelnuts and their applicability in routine analytics.