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>…”

NK cells are activated, proliferating and decreased in frequency in peripheral blood of HFRS patients. by Marina García (694440)

Nightlight densities in the lowest, around the median and the highest ranked cities in the sample and the distribution of firms in each city. by Saul Estrin (8629173)

BNT162b2-induced antibody levels 2 weeks, 3 and 8 months after first vaccination. by Olaf Nickel (9524791)

Subjects:

Large state-wide variation (8.6-fold) in number of clozapine prescriptions per thousand Medicare Part D enrollees in 2018 ^a indicates ≥1.50 SD (17.9) from the mean (32.9... by Luke R. Cavanah (19022435)

Large state-wide variation (8.5-fold) in number of clozapine prescriptions per thousand Medicare Part D enrollees in 2019 ^a indicates ≥1.50 SD (16.8) from the mean (31.0... by Luke R. Cavanah (19022435)

Large state-wide variation (10.2-fold) in number of clozapine prescriptions per thousand Medicare Part D enrollees in 2016 ^a indicates ≥1.50 SD (19.2) from the mean (34.... by Luke R. Cavanah (19022435)

Large state-wide variation (8.6-fold) in number of clozapine prescriptions per thousand Medicare Part D enrollees in 2020 ^a indicates ≥1.50 SD (16.0) from the mean (29.9... by Luke R. Cavanah (19022435)

Table 1_Cholesterol metabolic reprogramming drives the onset of DLBCL and represents a promising therapeutic target.docx by Lili Zhou (265076)

“…</p>Methods<p>We retrospectively analyzed clinical data from 200 DLBCL patients and 185 healthy controls, focusing on lipid and lipoprotein levels, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and apolipoprotein E (ApoE). …”

STAT6-driven immunity decreases <i>N</i>. <i>brasiliensis</i> reproductive output and survival within host. by Annabel A. Ferguson (17616286)

Extreme morphological adaptations to accommodate large teeth in fish jaws. by Nick Peoples (21593888)

The HMR C-terminus is required for HMR localization in proximity to centromeres and HP1a-bound chromatin (related to Fig 4A and 5). by Andrea Lukacs (3752956)

The percentages of genes with largely changed expression after AdNKX2-1 infection. by Yuko Ito (1976692)

Salivary IgA specific to SARS-CoV-2 RBD in N = 40 individuals prior first (V1), three weeks after first (V2) and to weeks after second vaccination (P1) in comparison with COVID-19... by Olaf Nickel (9524791)

Subjects:

Data Sheet 1_Cholesterol metabolic reprogramming drives the onset of DLBCL and represents a promising therapeutic target.docx by Lili Zhou (265076)

“…</p>Methods<p>We retrospectively analyzed clinical data from 200 DLBCL patients and 185 healthy controls, focusing on lipid and lipoprotein levels, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and apolipoprotein E (ApoE). …”

Table_3_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.XLS by Chengliang Yin (7545749)

“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”

Table_5_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.csv by Chengliang Yin (7545749)

“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”

Table_1_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.xls by Chengliang Yin (7545749)

“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”

Image_1_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.TIF by Chengliang Yin (7545749)

“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”

Image_1_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.TIF by Chengliang Yin (7545749)

“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”