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>…”

Overview of the six original ACTIV-3/TICO trials. by Elizabeth S. Munroe (21568166)

Subjects:

Sleep deprivation increased inflammatory cytokines and decreased corticosterone in the blood. by Weiran Shan (16969688)

AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

Subjects:

The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

Data from: Spillage and food dust do not decrease efficacy of long-lasting insecticide-incorporated netting against stored product insects by Sabita Ranabhat (17484660)

KAP assessment scores (n = 422). by Chikondi Maluwa (20660522)

“…The baseline mean knowledge level score was 9.5 (38.0%) and rose to 21.08 (84.3%) p = 0.000 immediate post-health education and a 2.1% decrease 20.54 (82.2%) p<0.001 at week six from the immediate post health education score. …”

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". The following acronyms included in the CSV file are defined as follows: Hbt is total hemoglobin, Art is artery region, DV is draining vein region, WV is whisker vein region, SEM is standard error mean, TS is time series, max peak is maximum peak, min peak is minima, AUC is area under the curve, WT is wild-type, AD is Alzheimer's disease, ATH is atherosclerosis and MIX is mixed AD/atherosclerosis. …”

Mammary fistula (n(%)). by Na Wang (193263)

“…</p><p>Results</p><p>We found that numerical rating scale(NRS) score and incidence of breast fistula in group A were significantly lower than other, the continuous decrease of postoperative drainage in group A was higher than other, there were significant differences among groups (p<0.001). …”

FGF18 injections increase TIMP1 and decrease MMP13 and NOGGIN, suggesting healing of degeneration induction of anabolic effect. by Flavia Gomes Velasque Gama (21175112)

ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”

A flow diagram of the study entry. by Sakiko Fukui (387048)

“…Cluster analysis classified 4 clusters of decline in food intake changes during the last 6 months before death: immediate decrease (n = 14); decrease from 1 month before death (n = 24); decrease from 3 months before death (n = 7); and gradual decrease for 6 months before death (n = 24).…”

Changes in HSA outcomes between baseline and six months (N = 60). by Janelli Vallin (14540858)

A summary of the included study characteristics. by Zahra Tajik (20752452)

“…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. …”

Socio-demographic characteristics of hypertensive patients’ caregivers (n = 422). by Chikondi Maluwa (20660522)

Overview of A) precipitation, B) discharge and C) baseline PM water level data in the calibration period (April 2016 – April 2019) with six different scenario starting points, n = ... by Fabian Mitze (21084251)

Inhibition of NEAT1 decreased the miR-204-5p expression and increased Six1 expression. by Lei Li (29537)

“…<p>(A) NEAT1 expression following siRNA transfection in BEAS-2B cells; (B-D) The expression of NEAT1, miR-204-5p, and Six1 after decreasing NEAT1 expression. * p <0.05, *** p < 0.001, and **** p <0.0001.…”

Level classification and summary of total scores of KAP towards hypertension among caregivers at baseline assessment and at week six assessment (n = 422). by Chikondi Maluwa (20660522)

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

Comparison of knowledge retention among different socio-demographic factors using independent t-test (n = 422). by Chikondi Maluwa (20660522)