5 research outputs found
Temperature drives variation in flying insect biomass across a German malaise trap network
1. Among the many concerns for biodiversity in the Anthropocene, recent reports of flying insect loss are particularly alarming, given their importance as pollinators, pest control agents, and as a food source. Few insect monitoring programmes cover the large spatial scales required to provide more generalizable estimates of insect responses to global change drivers.
2. We ask how climate and surrounding habitat affect flying insect biomass using data from the first year of a new monitoring network at 84 locations across Germany comprising a spatial gradient of land cover types from protected to urban and crop areas.
3. Flying insect biomass increased linearly with temperature across Germany. However, the effect of temperature on flying insect biomass flipped to negative in the hot months of June and July when local temperatures most exceeded long-term averages.
4. Land cover explained little variation in insect biomass, but biomass was lowest in forests. Grasslands, pastures, and orchards harboured the highest insect biomass. The date of peak biomass was primarily driven by surrounding land cover, with grasslands especially having earlier insect biomass phenologies.
5. Standardised, large-scale monitoring provides key insights into the underlying processes of insect decline and is pivotal for the development of climate-adapted strategies to promote insect diversity. In a temperate climate region, we find that the positive effects of temperature on flying insect biomass diminish in a German summer at locations where temperatures most exceeded long-term averages. Our results highlight the importance of local adaptation in climate change-driven impacts on insect communities
Failure of adaptive self-organized criticality during epileptic seizure attacks
Critical dynamics are assumed to be an attractive mode for normal brain
functioning as information processing and computational capabilities are found
to be optimized there. Recent experimental observations of neuronal activity
patterns following power-law distributions, a hallmark of systems at a critical
state, have led to the hypothesis that human brain dynamics could be poised at
a phase transition between ordered and disordered activity. A so far unresolved
question concerns the medical significance of critical brain activity and how
it relates to pathological conditions. Using data from invasive
electroencephalogram recordings from humans we show that during epileptic
seizure attacks neuronal activity patterns deviate from the normally observed
power-law distribution characterizing critical dynamics. The comparison of
these observations to results from a computational model exhibiting
self-organized criticality (SOC) based on adaptive networks allows further
insights into the underlying dynamics. Together these results suggest that
brain dynamics deviates from criticality during seizures caused by the failure
of adaptive SOC.Comment: 7 pages, 5 figure
Reproducible image-based profiling with Pycytominer
Technological advances in high-throughput microscopy have facilitated the
acquisition of cell images at a rapid pace, and data pipelines can now extract
and process thousands of image-based features from microscopy images. These
features represent valuable single-cell phenotypes that contain information
about cell state and biological processes. The use of these features for
biological discovery is known as image-based or morphological profiling.
However, these raw features need processing before use and image-based
profiling lacks scalable and reproducible open-source software. Inconsistent
processing across studies makes it difficult to compare datasets and processing
steps, further delaying the development of optimal pipelines, methods, and
analyses. To address these issues, we present Pycytominer, an open-source
software package with a vibrant community that establishes an image-based
profiling standard. Pycytominer has a simple, user-friendly Application
Programming Interface (API) that implements image-based profiling functions for
processing high-dimensional morphological features extracted from microscopy
images of cells. Establishing Pycytominer as a standard image-based profiling
toolkit ensures consistent data processing pipelines with data provenance,
therefore minimizing potential inconsistencies and enabling researchers to
confidently derive accurate conclusions and discover novel insights from their
data, thus driving progress in our field.Comment: 13 pages, 4 figure