Arctigenin decreased proliferation in colorectal cancer cells with no significant effect on normal cells. by Xiang-Fan Chen (13932983)

Changes in sodium levels, serving sizes, and calories in matched restaurant menu items (2016–2020) by sodium reduction or increase. by Caroline G. Middleton (22041479)

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Analysis of Significantly Different Bacteria. by Xu Jia (187142)

Linear covariate analysis of prognostically significant metabolites. Presenting the effect sizes of metabolites that showed significant differences among prognostic groups in ICU-treated COVID-19 patients.... by Emir Matpan (21655811)

“…<p>Linear covariate analysis of prognostically significant metabolites. Presenting the effect sizes of metabolites that showed significant differences among prognostic groups in ICU-treated COVID-19 patients. …”

Transition probabilities and class sizes for three-step LTA model (N = 432). by Carolin Oetzmann (11397968)

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Optimal proportion under comprehensive balance analysis (vertical "*" indicates the number of occurrences for a certain factor at a certain level, horizontal "*" denotes the degree... by Junxia Zhou (14381598)

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Comparative classification and characterisation of weathered levels. by Dong Feng (5375471)

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Aripiprazole significantly decreases phosphorylated Src (Y416) levels in dHL-60 cells. by Courtney A. Swain (15170264)

Sediment particle size curve. by Hui Zhang (7197)

“…The highest average relative flows across three sediment levels occurred under the F1 treatment, measuring 0.699, 0.681, and 0.668 for type-H₁, type-H₂, and type-H₃ tapes, respectively. …”

Map with color coding showing the relative risk of loss at the regional level for each country in Sub-Saharan Africa. by Beatrice T. Nganso (4114768)

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Particle size distribution table of sand samples. by Kui Suo (20614613)

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Learn!Bio longitudinal, cross-sectional study 2020-2023: Cohort sizes and participants – overview. by Katy Andrews (22311170)

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Effect of sample size on reproducibility. by Jan Willem Koten (17743224)

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The level of miRNA 221 in the myocardium of MIRI mice was decreased. by Hongyu Li (1332669)

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Metabolites with decreasing levels during the development. by Martin Sládek (130663)

“…<p>Temporal profiles of polar metabolites and lipids with SCN levels significantly decreasing from E19 to P28. Rhythmicity was determined by eJTK; full or dashed lines depict the profiles that either did or did not pass the significance threshold (FDR-adjusted <i>P</i> < 0.05), respectively.…”

Each subfigure shows a different local size (LS) and context size (CS) configuration, where the reliability of the model across different initializations is measured in R-squared. by Eloy Geenjaar (21533195)

“…<p>In most cases where both the local size and context size is small, the DSVAE model is significantly more reliable than the IDSVAE method. …”

Knockdown of MRN complex decreases HBV cccDNA level. by Kaitao Zhao (3617825)

Knockdown of MRN complex decreases HBV cccDNA level. by Kaitao Zhao (3617825)

Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

“…</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>…”

Conceptual framework of relationships between remittances and other individual, household and community-level factors and outcome measures, with potential confounders and unobserva... by Chelsea Ducille (21540080)

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