Biology subjects

Mahler, L.

Publications and source records attributed to Mahler, L..

5 recordsLinked to original sources

bioRxiv2025

Beyond ROIs: Laminar fMRI Mapping with Cylarim

Laminar fMRI offers a powerful approach to investigate brain function by resolving neural activity across the distinct layers of the cortical gray matter. However, current laminar fMRI analysis methods often rely on predefined regions of interest (ROIs), which introduce subjectivity and bias due to arbitrary spatial delineation. To address this limitation, we present "Cylarim", a novel software package that eliminates dependence on rigid ROI definitions. Cylarim employs small, overlapping cylinders that systematically traverse the cortical ribbon, enabling unbiased, large-scale mapping of laminar-specific hemodynamic activity. This approach supports comprehensive sampling of cortical depth-resolved signals across extensive regions. By circumventing the need for manual or atlas-driven ROI selection, Cylarim facilitates the study of feedforward and feedback pathways in diverse cortical areas without compromising spatial coverage. Preliminary results demonstrate Cylarims ability to accurately localize layer-dependent responses, providing a robust framework for advancing ultrahigh-resolution fMRI research in neuroscience.

neuroscience↗

bioRxiv2024

UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations

The UltraCortex repository (https://www.ultracortex.org) houses magnetic resonance imaging data of the human brain obtained at an ultra-high field strength of 9.4 T. It contains 86 structural MR images with spatial resolutions ranging from 0.6 to 0.8 mm. Additionally, the repository includes segmentations of 12 brains into gray and white matter compartments. These segmentations have been independently validated by two expert neuroradiologists, thus establishing them as a reliable gold standard. This resource provides researchers with access to high-quality brain imaging data and validated segmentations, facilitating neuroimaging studies and advancing our understanding of brain structure and function. Existing repositories do not accommodate field strengths beyond 7 T, nor do they offer validated segmentations, underscoring the significance of this new resource.

neuroscience↗

bioRxiv2023

Novel nitrifying symbiont lineages are vertically inherited and widespread in marine sponges

Ammonia-oxidising archaea and nitrite-oxidising bacteria are common members of marine sponge microbiomes. They derive energy for carbon fixation and growth from nitrification - the oxidation of ammonia to nitrite and further to nitrate - and are proposed to play essential roles in the carbon and nitrogen cycling of sponge holobionts. In this study, we characterise two novel nitrifying symbiont lineages, Ca. Nitrosokoinonia and Ca. Nitrosymbion in the marine sponge Coscinoderma matthewsi using a combination of molecular tools, single-cell imaging techniques, and physiological rate measurements. Both represent a new genus in the ammonia-oxidising archaeal class Nitrososphaeria and the nitrite-oxidising bacterial order Nitrospirales, respectively. Furthermore, we show that larvae of this viviparous sponge are densely colonised by representatives of Ca. Nitrosokoinonia and Ca. Nitrosymbion indicating vertical transmission. In adults, the representatives of both symbiont genera are located extracellularly in the mesohyl. Comparative metagenome analyses and physiological data suggest that ammonia-oxidising archaeal symbionts of the genus Ca. Nitrosokoinonia strongly rely on endogenously produced nitrogenous compounds (i.e., ammonium, urea, nitriles/cyanides, and creatinine) rather than on exogenous ammonium sources taken up by the sponge. Additionally, the nitrite-oxidising bacterial symbionts Ca. Nitrosymbion may reciprocally support the ammonia-oxidisers with ammonia via the utilisation of sponge-derived urea and cyanate. Interestingly, comparative analyses of published environmental 16S rRNA amplicon data revealed that Ca. Nitrosokoinonia and Ca. Nitrosymbion are widely distributed and predominantly associated with marine sponges and corals, suggesting a broad relevance of our findings.

microbiology↗

bioRxiv2023

Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning

Predicting neuromarkers for cognitive abilities using fMRI has been a major focus of research in the past few years. However, it has recently been reported that many thousands of participants are required to obtain reproducible results (Marek et al (2022)). This appears to be a major impediment to obtaining neuromarkers from fMRI because large sample sizes are typically not available in neuroimaging studies. Here we show that the out-of-sample prediction accuracy can be dramatically improved by supplementing fMRI with readily available non-imaging information so that reliable predictive modeling becomes feasible even for small sample sizes. Specifically, we introduce a novel machine learning method that predicts intelligence from resting-state fMRI data, leveraging educational level as supplementary information. We refer to our approach as "semi-blind machine learning (SML)" because it operates under the assumption that supplementary information, such as educational level, is available for subjects in both the training and test sets. This setup closely mirrors real-world scenarios, especially in clinical contexts, where patient background information typically exists and can be utilized to boost prediction accuracy. However, guarding against bias is crucial. Subjects should not be categorized as more intelligent simply based on their higher education levels. Therefore, our approach contains a component explicitly designed for bias control. We have applied our method to three different data collections and observed marked improvements in prediction accuracies across a wide range of sample sizes. We anticipate that semi-blind machine learning provides a promising approach to fMRI-based predictive modelling with the potential for a wide range of future applications.

neuroscience↗

bioRxiv2019

Highly parallelized microfluidic droplet cultivation and prioritization on antibiotic producers from complex natural microbial communities

To investigate the overwhelming part of the bacterial diversity still evading standard cultivation for its potential use in antibiotic synthesis, we have compiled a microscale-cultivation and screening system. We devised a strategy based on droplet-microfluidics taking advantage of the inherent miniaturization and high throughput. Single cells of natural samples were confined in 9 x 106 aqueous droplets and subjected to long-term incubation under controlled conditions. Subsequent a high-throughput screening for antimicrobial natural products was implemented, employing a whole cell reporting system using the viability of reporter strains as a probe for antimicrobial activity. Due to the described microscale cultivation a novel subset of bacterial strains was made available for the following screening for antimicrobials. We demonstrate the merits of the in-droplet cultivation by comparing the cultivation outcome in microfluidic droplets and on conventional agar plates for a bacterial community derived from soil by 16S rRNA gene amplicon sequencing. In-droplet cultivation resulted in a significantly higher bacterial diversity without the common overrepresentation of Firmicutes. Natural strains able to inhibit either a Gram-positive or a Gram-negative reporter strain were isolated from the microscale system and further cultivated. Thereby a variety of rare isolates was obtained. The natural products with antimicrobial activity were elucidated for the most promising candidate. Our method combines a new cultivation approach with a high-throughput search for antibiotic producers to increase the chances of finding new lead substances.

microbiology↗