<i>T</i>. <i>spiralis</i> Infection Enhanced Residential Tregs in Lamina Propria of the Large Intestine. by Xi-Meng Sun (17721428)

Brief Report: Increased technology use associated with lower A1C in a large pediatric clinical population by G. Todd Alonso (9568724)

<i>C</i>. <i>elegans</i> nictation ratio decreases over the course of our 30 min videos. by Patrick D. McClanahan (2385076)

An osprey nest with a large chick and an unhatched egg (photo P. Saurola). by Matti Viluksela (321291)

Subjects:

Effects of HDAC inhibitors on development of <i>S</i>. <i>exigua</i> and <i>T</i>. <i>ni</i> larvae. by Dana J. Somers (17240675)

“…<p>(A) The mass of <i>S</i>. <i>exigua</i> larvae was reduced by consumption of SFN at Day 11 (One-way ANOVA, P = 0.006; red bars). …”

Decreased nicotine mediated by increased auxin accounts for the susceptibility of SL-RNAi plants to <i>T</i>. <i>mucorea</i> attack. by Suhua Li (1314660)

Overexpression of <i>RECQL4</i> results in increased RAD51 foci and decreased tail moment. by Thong T. Luong (13833935)

Large <i>Bgl-</i>FaNaC neurons labeled with HCR <i>in situ</i> hybridization. by Laura C. Vicente-Rodríguez (16437803)

The HMR complex components BOH1 and BOH2 largely colocalize with HMR in proximity to centromeres and HP1a domains (related to Fig 1). by Andrea Lukacs (3752956)

Population entropy in APP/PS1 mice is decreased in dCA1, but increased in vCA1. by Udaysankar Chockanathan (18510288)

Subjects:

Pairwise correlations in APP/PS1 mice are decreased in dCA1, but increased in vCA1. by Udaysankar Chockanathan (18510288)

Subjects:

Average effect size decreased as sample size increased for all types of analyses (<i>p</i> < .001). by Luke J. Ney (12621627)

Innovation and night light in large and small cities. by Saul Estrin (8629173)

Geographical distribution of large cities and small cities. by Saul Estrin (8629173)

“…The Figure reveals two patterns: 1) the maximum level of innovation is higher in large cities (2.53) than in small cities (2.02); 2) among large cities in <b>a</b>, innovation levels in general decrease with nightlight density. …”

Bacterial abundance in L2 larvae collected from larval flasks inoculated with decreasing quantities of glycerol stock (bacteria source). by Margaret V. Becker (21387776)

Pairwise correlations of excitatory neurons in APP/PS1 mice are decreased in dCA1, but increased in vCA1. by Udaysankar Chockanathan (18510288)

Subjects:

The large subunit of TFIIF, Tfg1, is sumoylated at Lys 60/61. by Mohammad S. Baig (11502463)

“…A strain harboring a point mutation (I615N) in <i>ULP1</i> (mt), and an isogenic wild-type strain (WT), were engineered to express Tfg1-HA, then the strains were used for chromatin fractionation analysis. …”

Addition of <i>precA</i> decreases the sensitivity of <i>rarA ruvB</i> cells to DNA damage. by Kanika Jain (2884439)

Decreased MBP levels in the brain in <i>Large</i>^{<i>myd/myd</i>} mice. by Shigefumi Morioka (8893511)

“…<p><b>A,</b> Brain sections of eight-week-old control and <i>Large</i>^{<i>myd/myd</i>} mice at the level of the corpus callosum were obtained for immunostaining for MBP (Scale bars: 200 μm). …”

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>…”