bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 21, 2024
Abstract
Hyperspectral
imaging
provides
a
powerful
tool
for
analyzing
above-ground
plant
characteristics
in
fabricated
ecosystems,
offering
rich
spectral
information
across
diverse
wavelengths.
This
study
presents
an
efficient
workflow
hyperspectral
data
segmentation
and
subsequent
analytics,
minimizing
the
need
user
annotation
through
use
of
ensembles
sparse
mixed-scale
convolution
neural
networks.
The
process
leverages
diversity
to
achieve
high
accuracy
with
minimal
labeled
data,
reducing
labor-intensive
efforts.
To
further
enhance
robustness,
we
incorporate
image
alignment
techniques
address
spatial
variability
dataset.
Down-stream
analysis
focuses
on
using
segmented
processing
enabling
monitoring
health.
approach
not
only
scalable
solution
but
also
facilitates
actionable
insights
into
conditions
complex,
controlled
environments.
Our
results
demonstrate
utility
combining
advanced
machine
learning
analytics
high-throughput
monitoring.
Summary
The
plant
microbiota
research
field
has
rapidly
shifted
from
efforts
aimed
at
gaining
a
descriptive
understanding
of
composition
to
focus
on
acquiring
mechanistic
insights
into
functions
and
assembly
rules.
This
evolution
was
driven
by
our
ability
establish
comprehensive
collections
plant‐associated
microbes
reconstruct
meaningful
microbial
synthetic
communities
(SynComs).
We
argue
that
this
powerful
deconstruction–reconstruction
strategy
can
be
used
reconstitute
increasingly
complex
ecosystems
(SynEcos)
mechanistically
understand
high‐level
biological
organization.
transitioning
simple
more
advanced,
fully
tractable
programmable
gnotobiotic
SynEcos
is
ongoing
aims
rationally
simplifying
natural
engineering
them.
Such
reconstitution
ecology
approaches
represent
an
untapped
for
bridging
the
gap
between
functional
biology
unraveling
plant–microbiota–environment
mechanisms
modulate
ecosystem
health,
assembly,
functioning.
Frontiers in High Performance Computing,
Год журнала:
2025,
Номер
3
Опубликована: Фев. 17, 2025
Hyperspectral
imaging
provides
a
powerful
tool
for
analyzing
above-ground
plant
characteristics
in
fabricated
ecosystems,
offering
rich
spectral
information
across
diverse
wavelengths.
This
study
presents
an
efficient
workflow
hyperspectral
data
segmentation
and
subsequent
analytics,
minimizing
the
need
user
annotation
through
use
of
ensembles
sparse
mixed
scale
convolution
neural
networks.
The
process
leverages
diversity
to
achieve
high
accuracy
with
minimal
labeled
data,
reducing
labor-intensive
efforts.
To
further
enhance
robustness,
we
incorporate
image
alignment
techniques
address
spatial
variability
dataset.
Downstream
analysis
focuses
on
using
segmented
processing
enabling
monitoring
health.
approach
scalable
solution
segmentation,
facilitates
actionable
insights
into
conditions
complex,
controlled
environments.
Our
results
demonstrate
utility
combining
advanced
machine
learning
analytics
high-throughput
monitoring.
Plant Cell & Environment,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 19, 2025
ABSTRACT
Bacteriophages
impact
soil
bacteria
through
lysis,
altering
the
availability
of
organic
carbon
and
plant
nutrients.
However,
magnitude
nutrient
uptake
by
plants
from
lysed
remains
unknown,
partly
because
this
process
is
challenging
to
investigate
in
field.
In
study,
we
extend
ecosystem
fabrication
(EcoFAB
2.0)
approaches
study
plant−bacteria−phage
interactions
comparing
virocell
(phage‐lysed)
uninfected
15
N‐labelled
bacterial
necromass
on
nitrogen
acquisition
rhizosphere
exometabolites
composition.
We
show
that
grass
Brachypodium
distachyon
derives
some
amino
acids
Pseudomonas
putida
sonication
but
not
necromass.
Additionally,
elicits
formation
exometabolites,
which
(guanosine),
alongside
tested
aromatic
(
p
‐coumaric
benzoic
acid),
bacterium‐specific
effects
bacteriophage‐induced
lysis
when
vitro.
The
highlights
dynamic
feedback
between
suggests
root
exudate
metabolites
can
bacteriophage
infection
dynamics.
