Search alternatives:
large decrease » marked decrease (Expand Search), large increases (Expand Search), large degree (Expand Search)
1 levels » _ levels (Expand Search)
i larger » _ larger (Expand Search), i large (Expand Search), _ large (Expand Search)
c large » _ large (Expand Search), i large (Expand Search), b large (Expand Search)
a large » _ large (Expand Search)
large decrease » marked decrease (Expand Search), large increases (Expand Search), large degree (Expand Search)
1 levels » _ levels (Expand Search)
i larger » _ larger (Expand Search), i large (Expand Search), _ large (Expand Search)
c large » _ large (Expand Search), i large (Expand Search), b large (Expand Search)
a large » _ large (Expand Search)
-
1
Pairwise correlations in APP/PS1 mice are decreased in dCA1, but increased in vCA1.
Published 2024Subjects: -
2
-
3
-
4
Spatial information is significantly decreased in dCA1 and vCA1 in APP/PS1 mice.
Published 2024Subjects: -
5
Spatial information of excitatory neurons in APP/PS1 mice are decreased in dCA1 and vCA1.
Published 2024Subjects: -
6
Population entropy in APP/PS1 mice is decreased in dCA1, but increased in vCA1.
Published 2024Subjects: -
7
Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas
Published 2025“…</p><p><br></p><p dir="ltr">#METADATA</p><p dir="ltr">#'data.frame': 472 obs. of 28 variables:</p><p dir="ltr"> #$ ID: Factor variable; a unique identity for the response to the survey</p><p dir="ltr"> #$ Year: Factor variable; six factors available (2016, 2017, 2018, 2019, 2020, 2021) representing the year for the response to the survey</p><p dir="ltr"> #$ N_dead_annual: Numeric variable; representing the number of colonies annually lost</p><p dir="ltr">#$ N_alive_annual: Numeric variable; representing the number of colonies annually alive</p><p dir="ltr"> #$ N_dead_dry: Numeric variable; representing the number of colonies lost during the dry season</p><p dir="ltr">#$ N_alive_dry: Numeric variable; representing the number of colonies alive during the dry season</p><p dir="ltr"> #$ N_dead_rainy: Numeric variable; representing the number of colonies lost during the rainy season</p><p dir="ltr">#$ N_alive_rainy: Numeric variable; representing the number of colonies alive during the rainy season</p><p dir="ltr"> #$ Education: Factor variable; four factors are available ("Self-taught","Learned from another melip","Intro training","Formal tech training"), representing the training level in meliponiculture</p><p dir="ltr"> #$ Operation_Size: Numeric variable; representing the number of colonies managed by the participant (in n)</p><p dir="ltr"> #$ propAgri: Numeric variable; representing the percentage of agricultural area surrounding the meliponary (in %)</p><p dir="ltr"> #$ propForest: Numeric variable; representing the percentage of forested area surrounding the meliponary (in %)</p><p dir="ltr">#$ temp.avg_annual: Numeric variable; representing the average annual temperature (in ºC)</p><p dir="ltr">#$ precip_annual_sum: Numeric variable; representing the total accumulated precipitation (in mm)</p><p dir="ltr">#$ precip_Oct_March_sum: Numeric variable; representing the total accumulated precipitation between October to March (in mm)</p><p dir="ltr">#$ precip_Apri_Sept_sum: Numeric variable; representing the total accumulated precipitation between April to September (in mm)</p><p dir="ltr">#$ temp.avg_Oct_March: Numeric variable; representing the total accumulated precipitation between October to March (in ºC)</p><p dir="ltr">#$ temp.avg_Apri_Sept: Numeric variable; representing the total accumulated precipitation between April to September (in ºC)</p><p dir="ltr"> #$ Importance_dead: Factor variable; three factors are available Normal","High","Very high"), representing the perception of the significance of annual colony losses</p><p dir="ltr"> #$ Climatic_environmental: Binary variable; representing if the participant considered climatic and environmental problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr"> #$ Contamination: Binary variable; representing if the participant considered contamination problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr"> #$ Nutritional: Binary variable; representing if the participant considered nutritional problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Sanitary: Binary variable; representing if the participant considered sanitary problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Queen: Binary variable; representing if the participant considered queen problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Time: Binary variable; representing if the participant considered time problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Economic: Binary variable; representing if the participant considered economic problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Attacks: Binary variable; representing if the participant considered time attacks as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Swarming: Binary variable; representing if the participant considered swarming problems as a potential driver (1) or not (0) of their annual colony losses</p><p><br></p>…”
-
8
-
9
-
10
-
11
-
12
-
13
-
14
-
15
-
16
-
17
-
18
Large Decreases in Tailpipe Criteria Pollutant Emissions from the U.S. Light-Duty Vehicle Fleet Expected in 2020–2040
Published 2024“…Reductions in CO<sub>2</sub> emissions follow a similar pattern. Large decreases in criteria pollutant and CO<sub>2</sub> emissions from light duty vehicles lie ahead.…”
-
19
Large Decreases in Tailpipe Criteria Pollutant Emissions from the U.S. Light-Duty Vehicle Fleet Expected in 2020–2040
Published 2024“…Reductions in CO<sub>2</sub> emissions follow a similar pattern. Large decreases in criteria pollutant and CO<sub>2</sub> emissions from light duty vehicles lie ahead.…”
-
20
Differential expression analysis of nuclear transcripts after siAPOBEC3A depletion reveals induction of TP53 targets and decreases in cell cycle and cell growth regulator transcrip...
Published 2024“…Data were analyzed by Fisher’s exact test, **** <i>p</i> ≤ 0.0001. (B) siAPOBEC3A treatment increases <i>CDKN1A</i> nuclear transcript levels in MCF10A cells by RNA-seq. …”