Boxplots of average NH₃ ppm in cages with four female or four male mice (see key for male and female) across five cage changing cycles at KI, LUMC and UGA. by B. Ulfhake (6293984)

Species occurrence along the NO₃¯ gradient (sorted by decreasing 25th percentile values). by Willem Kaijser (12872371)

Global Land Use Change Impacts on Soil Nitrogen Availability and Environmental Losses by Jing Wang (6206297)

“…In contrast, reversing managed to natural ecosystems significantly increased NNM by 20% (9.7, 25.4%) and decreased NN by 89% (−125, −46%), indicating increasing N availability while decreasing potential N loss. …”

Detailed meta–analyses in untreated hypothyroid patients compared with controls for each HRV parameters: RR intervals, SDNN, RMSSD, pNN50, TP, LF, HF, LF/HF. by Valentin Brusseau (12769704)

GXM decreases the trans-endothelial electrical resistance (TEER) and transmigration of <i>C</i>. <i>neoformans</i> through HBECs in a blood brain barrier (BBB) model. by Hiu Ham Lee (15374677)

Dose- and time-dependent effect of IFN-γ and TNF-α on benzamil-sensitive <i>I</i>_sc and TEER in HBECs. by Anusree Sasidharan (19226521)

Dose- and time-dependent effect of IL-13, IL-4, and TGF-β1 on benzamil-sensitive <i>I</i>_sc and TEER in HBECs. by Anusree Sasidharan (19226521)

The decreased expression of <i>SNAP25</i> in Knight-C4 is replicated in MSBB and ROSMAP cohorts. by Abdallah M. Eteleeb (13866743)

Ammonia (NH₃) measurement (ppm) on the last day of a cage-change cycle in the front, middle and rear of the cage, for each site and sex. by B. Ulfhake (6293984)

Fig 25 - by Zhezhe Zhang (19704587)

Table_1_Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 25 Countries.XLSX by Josef Dolejs (5534258)

Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 12 covariates chosen using subjective feature selection. by Dennis Ochola (11626912)

Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 17 covariates selected after recursive feature selection. by Dennis Ochola (11626912)

Lifetime risk of developing type 2 diabetes, for cohorts of 25 years-old individuals according to scenarios. by Pierre Bauvin (18154818)

“…<p>Initial weight distribution is representative of the one of the 25 years-old individuals in 2022: A) men, 73.2% normal weight, 17.9% overweight, 6.6% obese class I, 1.9% obese class II, 0.5% obese class III, and B) women, 81.0% normal weight, 9.7% overweight, 5.4% obese class I, 2.6% obese class II, 1.3% obese class III. …”

Data_Sheet_1_Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 25 Countries.PDF by Josef Dolejs (5534258)

Video_7_Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 25 Countries.MP4 by Josef Dolejs (5534258)

Video_5_Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 25 Countries.MP4 by Josef Dolejs (5534258)

Video_4_Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 25 Countries.MP4 by Josef Dolejs (5534258)

Video_1_Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 25 Countries.MP4 by Josef Dolejs (5534258)

Video_6_Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 25 Countries.MP4 by Josef Dolejs (5534258)