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marker decrease » marked decrease (Expand Search), marked increase (Expand Search), markedly decreased (Expand Search)
larger decrease » marked decrease (Expand Search)
large decrease » marked decrease (Expand Search), large increases (Expand Search), large degree (Expand Search)
n decrease » nn decrease (Expand Search), _ decrease (Expand Search), a decrease (Expand Search)
a large » _ large (Expand Search)
marker decrease » marked decrease (Expand Search), marked increase (Expand Search), markedly decreased (Expand Search)
larger decrease » marked decrease (Expand Search)
large decrease » marked decrease (Expand Search), large increases (Expand Search), large degree (Expand Search)
n decrease » nn decrease (Expand Search), _ decrease (Expand Search), a decrease (Expand Search)
a large » _ large (Expand Search)
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Image 7_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif
Published 2025“…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …”
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Image 6_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif
Published 2025“…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …”
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Image 3_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif
Published 2025“…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …”
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Image 1_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif
Published 2025“…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …”
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Image 2_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif
Published 2025“…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …”
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Image 4_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif
Published 2025“…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …”
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Image 5_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif
Published 2025“…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …”
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Image 8_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif
Published 2025“…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …”
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The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness.
Published 2025“…When they stop being introduced in further assembly events (i.e. introduced species do not carry any mutualistic interactions), their proportion slowly decreases with successive invasions. (B) Even though higher proportions of mutualism promote higher richness, introducing this type of interaction into already assembled large communities promotes a sudden drop in richness, while stopping mutualism promotes a slight boost in richness increase. …”
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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>…”
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Group-level narrow- and broad-band spectral changes after hemispherotomy reveal a marked EEG slowing of the isolated cortex, robust across patients.
Published 2025“…This decrease was larger in the disconnected than in the contralateral cortex. …”
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<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>"
Published 2025“…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”
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A summary of the included study characteristics.
Published 2025“…There is no significant difference one month after NSPT in diabetic patients (SMD: -5.83, 95%CI: -15.5, 3.83, p = 0.237, I-square, 97.4%, random effects model, n = 2), but three (SMD: -2.44, 95%CI: -3.37, -1.15, p = 0.001, I-square, 75.9%, random effects model, n = 3) and six months (SMD: -2.41, 95%CI: -3.81, -1.01, p = 0.001, I-square, 78.7%, random effects model, n = 2) after the treatment, a significant decrease is observed in the mean GCF visfatin level. …”
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