Biology subjects

Berndt, M.

Publications and source records attributed to Berndt, M..

3 recordsLinked to original sources

bioRxiv2023

Quantifying the linkages between California sea lion (Zalophus californianus) strandings and particulate domoic acid concentrations at piers across Southern California

Domoic acid producing blooms of the diatom genus Pseudo-nitzschia are pervasive in coastal environments globally. Domoic acid, a neurotoxin, accumulates via trophic transfer into marine food webs and are often associated with mass marine mammal mortality and stranding events. In Southern California, California sea lions (Zalophus californiaus) are an indicator species for food web impacts of domoic acid because they are abundant secondary consumers, sensitive to domoic acid intoxication, and are actively monitored by stranding networks. However, domoic acid exposure may occur a distance from where a sea lion ultimately strands. This spatiotemporal variation complicates coupling domoic acid observations in water to strandings. Therefore, we sought to quantify whether monitoring data from four pier sites across the region, covering nearly 700 km of coastline from 2015-2019, could be used to predict adult and subadult sea lion strandings along the 68 km Orange County coastline surveyed by the Pacific Marine Mammal Center. We found that increased sea lion strandings were often observed just prior to an increase in particulate domoic acid at the piers, confirming that clusters of subadult and adult sea lion strandings with clinical signs of domoic acid intoxication a serve as indicators of bloom events. In addition, domoic acid concentrations at Stearns Wharf, nearly 200 km from stranding locations, best predicted increased total sea lion strandings, and strandings of sea lions with domoic acid intoxication symptoms. Particulate domoic acid concentrations greater than 0.05 g/L at Stearns Wharf led to a detectable increase in stranding probability in Orange County, and concentrations over 0.25 g/L resulted in a nearly 1.6-fold increase in stranding probabilities for a given week.

ecology↗

bioRxiv2022

Bidirectional synaptic changes in deep and superficial hippocampal neurons following in vivo activity

Neuronal activity during experience is thought to induce plastic changes within the hippocampal network which underlie memory formation, although the extent and details of such changes in vivo remain unclear. Here, we employed a temporally precise marker of neuronal activity, CaMPARI2, to label active CA1 hippocampal neurons in vivo, followed by immediate acute slice preparation and electrophysiological quantification of synaptic properties. Recently active neurons in the superficial sublayer of stratum pyramidale displayed larger post-synaptic responses at excitatory synapses from area CA3, with no change in pre-synaptic release probability. In contrast, in vivo activity weakened both pre- and post-synaptic excitatory weights onto pyramidal cells in the deep sublayer. In vivo activity of deep and superficial neurons within sharp-wave ripples was bidirectionally changed across experience, consistent with the observed changes in synaptic weights. These findings reveal novel, fundamental mechanisms through which the hippocampal network is modified by experience to store information.

neuroscience↗

bioRxiv2021

AI for predicting chemical-effect associations at the universe level - deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods - even if high throughput - are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn. Supplementary informationSupplementary data are available at bioRxiv online. Contactjana.schor@ufz.de

bioinformatics↗