Supplementary file 2_Quantile regression application to identify key determinants of malnutrition in five West African countries of Gabon, Gambia, Liberia, Mauritania, and Nigeria.... by Reshav Beni (21422981)

“…</p>Method<p>This study employed a quantile regression model to examine the determinants of malnutrition at various quantiles of interest across the Western African countries under consideration to facilitate focused policy measures and intervention strategies aimed at decreasing the prevalence.</p>Results<p>For the lower quantiles (0.1 and 0.25), which indicate severe malnutrition, significant variables included the child’s weight [quantile = 0.1, 95% CI(0.0063, 0.0103), quantile = 0.25, 95% CI(0.0054, 0.0107)], mother’s education level [No education: quantile = 0.1, 95% CI(−49.7471, −32.1376), quantile = 0.25, 95% CI(−38.1513, −22.4438) Primary: quantile = 0.1, 95% CI(−24.8095, −5.7693), quantile = 0.25, 95% CI(−19.5273, −6.3424) Higher: quantile = 0.1, 95% CI(5.6499, 40.3274), quantile = 0.25, 95% CI(21.8158, 40.278)], drinking water source [Natural Sources: quantile = 0.1, 95% CI(0.6877, 24.384),Piped: quantile = 0.1, 95% CI(25.578, 45.2368), quantile = 0.25, 95% CI(22.2782, 34.8212), Bottle/Sachet: quantile = 0.25, 95% CI(3.438, 98.1675)], toilet type [Flush: quantile = 0.25, 95% CI(2.2598, 18.3457),Other: quantile = 0.1, 95% CI(8.7863, 24.504), quantile = 0.25, 95% CI(7.0995, 20.1119)], household wealth index [Poorest: quantile = 0.1, 95% CI(−52.5112, −16.9197), quantile = 0.25, 95% CI(−48.3804, −23.0633),Poorer: quantile = 0.1, 95% CI(−38.8744, −4.7586), quantile = 0.25, 95% CI(−34.6993, −9.1766), Middle: quantile = 0.25, 95% CI(−28.9491, −6.5834)], health care visits [No: quantile = 0.1, 95% CI(−19.293, −3.6393), quantile = 0.25, 95% CI(−17.2342, −5.6411)], consumption of fortified foods and tubers [No: quantile = 0.1, 95% CI(−36.3898, −12.0378), quantile = 0.25, 95% CI(−17.8127, −1.2374)], anemia status [Anemic: quantile = 0.1, 95% CI(−15.9326, −1.1929), quantile = 0.25, 95% CI(−12.3361, −1.5516)], mosquito net usage [No: quantile = 0.1, 95% CI(−22.0323, −0.8033), quantile = 0.25, 95% CI(−13.8107, 1.1366)], child’s age [0 to 12 months: quantile = 0.1, 95% CI(81.6424, 105.7155), quantile = 0.25, 95% CI(61.4817, 78.5194),12 to 24 months: quantile = 0.1, 95% CI(0.5592, 24.933), 24 to 36 months: quantile = 0.1, 95% CI(7.9128, 40.2828)] and gender [Female: quantile = 0.1, 95% CI(4.5351, 17.9783), quantile = 0.25, 95% CI(5.0076, 15.4735)], and recent fever [No: quantile = 0.1, 95% CI(11.5663, 29.5984), quantile = 0.25, 95% CI(7.0313, 20.8918)]. …”

Supplementary file 1_Quantile regression application to identify key determinants of malnutrition in five West African countries of Gabon, Gambia, Liberia, Mauritania, and Nigeria.... by Reshav Beni (21422981)

“…</p>Method<p>This study employed a quantile regression model to examine the determinants of malnutrition at various quantiles of interest across the Western African countries under consideration to facilitate focused policy measures and intervention strategies aimed at decreasing the prevalence.</p>Results<p>For the lower quantiles (0.1 and 0.25), which indicate severe malnutrition, significant variables included the child’s weight [quantile = 0.1, 95% CI(0.0063, 0.0103), quantile = 0.25, 95% CI(0.0054, 0.0107)], mother’s education level [No education: quantile = 0.1, 95% CI(−49.7471, −32.1376), quantile = 0.25, 95% CI(−38.1513, −22.4438) Primary: quantile = 0.1, 95% CI(−24.8095, −5.7693), quantile = 0.25, 95% CI(−19.5273, −6.3424) Higher: quantile = 0.1, 95% CI(5.6499, 40.3274), quantile = 0.25, 95% CI(21.8158, 40.278)], drinking water source [Natural Sources: quantile = 0.1, 95% CI(0.6877, 24.384),Piped: quantile = 0.1, 95% CI(25.578, 45.2368), quantile = 0.25, 95% CI(22.2782, 34.8212), Bottle/Sachet: quantile = 0.25, 95% CI(3.438, 98.1675)], toilet type [Flush: quantile = 0.25, 95% CI(2.2598, 18.3457),Other: quantile = 0.1, 95% CI(8.7863, 24.504), quantile = 0.25, 95% CI(7.0995, 20.1119)], household wealth index [Poorest: quantile = 0.1, 95% CI(−52.5112, −16.9197), quantile = 0.25, 95% CI(−48.3804, −23.0633),Poorer: quantile = 0.1, 95% CI(−38.8744, −4.7586), quantile = 0.25, 95% CI(−34.6993, −9.1766), Middle: quantile = 0.25, 95% CI(−28.9491, −6.5834)], health care visits [No: quantile = 0.1, 95% CI(−19.293, −3.6393), quantile = 0.25, 95% CI(−17.2342, −5.6411)], consumption of fortified foods and tubers [No: quantile = 0.1, 95% CI(−36.3898, −12.0378), quantile = 0.25, 95% CI(−17.8127, −1.2374)], anemia status [Anemic: quantile = 0.1, 95% CI(−15.9326, −1.1929), quantile = 0.25, 95% CI(−12.3361, −1.5516)], mosquito net usage [No: quantile = 0.1, 95% CI(−22.0323, −0.8033), quantile = 0.25, 95% CI(−13.8107, 1.1366)], child’s age [0 to 12 months: quantile = 0.1, 95% CI(81.6424, 105.7155), quantile = 0.25, 95% CI(61.4817, 78.5194),12 to 24 months: quantile = 0.1, 95% CI(0.5592, 24.933), 24 to 36 months: quantile = 0.1, 95% CI(7.9128, 40.2828)] and gender [Female: quantile = 0.1, 95% CI(4.5351, 17.9783), quantile = 0.25, 95% CI(5.0076, 15.4735)], and recent fever [No: quantile = 0.1, 95% CI(11.5663, 29.5984), quantile = 0.25, 95% CI(7.0313, 20.8918)]. …”

Data from: Temporal niche partitioning by nocturnal arboreal mammals increases the modularity of plant-frugivore networks in a fragmented subtropical landscape by Wande Li (12480883)

“…Island area positively predicted nocturnal interaction richness and its proportional contribution, whereas isolation had no significant effect. Incorporating nocturnal interactions increased network modularity and decreased nestedness, indicating temporal niche partitioning, while connectance remained unchanged. …”

Data Sheet 1_Detection of fusion events by RNA sequencing in FFPE versus freshly frozen colorectal cancer tissue samples.pdf by Maxim Sorokin (4379848)

“…We detected no statistically significant difference in the number of chimeric transcripts in FFPE and FF RNAseq profiles. …”

Table 1_Detection of fusion events by RNA sequencing in FFPE versus freshly frozen colorectal cancer tissue samples.xlsx by Maxim Sorokin (4379848)

“…We detected no statistically significant difference in the number of chimeric transcripts in FFPE and FF RNAseq profiles. …”

Table 2_Detection of fusion events by RNA sequencing in FFPE versus freshly frozen colorectal cancer tissue samples.xlsx by Maxim Sorokin (4379848)

“…We detected no statistically significant difference in the number of chimeric transcripts in FFPE and FF RNAseq profiles. …”

Sex-specific responses of mosquitoes to <i>Bti</i> pesticide and odonate predators by Jaclyn Everly (22623200)

“…Rather than mitigating the effects of Bti, the interaction of predators and Bti synergistically decreased adult male size. We discuss the roles of competition, phenotypic plasticity, selective predation, and sublethal Bti exposure potentially contributing to these results, emphasizing the complex interactions among sources of extrinsic and intrinsic mortality and their divergent effects across taxa, sexes, and environments that have important implications for both theoretical and applied ecology.…”

Image 1_Detection of fusion events by RNA sequencing in FFPE versus freshly frozen colorectal cancer tissue samples.pdf by Maxim Sorokin (4379848)

“…We detected no statistically significant difference in the number of chimeric transcripts in FFPE and FF RNAseq profiles. …”

Image 2_Restoring natural killer cell activity in lung injury with 1,25-hydroxy vitamin D3: a promising therapeutic approach.jpeg by Johnny Amer (495285)

“…However, the extent to which ALI regulates lung tissue-resident NK (trNK) activity and their molecular phenotypic alterations are not well defined. We aimed to assess the impact of 1,25-hydroxy-vitamin-D3 [1,125(OH)₂D] on ALI clinical outcome in a mouse model and effects on lung trNK cell activations.…”

Image 3_Restoring natural killer cell activity in lung injury with 1,25-hydroxy vitamin D3: a promising therapeutic approach.jpeg by Johnny Amer (495285)

“…However, the extent to which ALI regulates lung tissue-resident NK (trNK) activity and their molecular phenotypic alterations are not well defined. We aimed to assess the impact of 1,25-hydroxy-vitamin-D3 [1,125(OH)₂D] on ALI clinical outcome in a mouse model and effects on lung trNK cell activations.…”

Table 1_Restoring natural killer cell activity in lung injury with 1,25-hydroxy vitamin D3: a promising therapeutic approach.docx by Johnny Amer (495285)

“…However, the extent to which ALI regulates lung tissue-resident NK (trNK) activity and their molecular phenotypic alterations are not well defined. We aimed to assess the impact of 1,25-hydroxy-vitamin-D3 [1,125(OH)₂D] on ALI clinical outcome in a mouse model and effects on lung trNK cell activations.…”

Image 1_Restoring natural killer cell activity in lung injury with 1,25-hydroxy vitamin D3: a promising therapeutic approach.jpg by Johnny Amer (495285)

“…However, the extent to which ALI regulates lung tissue-resident NK (trNK) activity and their molecular phenotypic alterations are not well defined. We aimed to assess the impact of 1,25-hydroxy-vitamin-D3 [1,125(OH)₂D] on ALI clinical outcome in a mouse model and effects on lung trNK cell activations.…”

Image 5_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…Background<p>We used an algorithm to determine age- and sex-specific reference intervals (RIs) for copper, zinc, calcium, magnesium, iron, and lead in blood. …”

Image 3_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…Background<p>We used an algorithm to determine age- and sex-specific reference intervals (RIs) for copper, zinc, calcium, magnesium, iron, and lead in blood. …”

Image 2_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…Background<p>We used an algorithm to determine age- and sex-specific reference intervals (RIs) for copper, zinc, calcium, magnesium, iron, and lead in blood. …”

Image 4_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…Background<p>We used an algorithm to determine age- and sex-specific reference intervals (RIs) for copper, zinc, calcium, magnesium, iron, and lead in blood. …”

Table 1_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.docx by Junqiang Li (491954)

“…Background<p>We used an algorithm to determine age- and sex-specific reference intervals (RIs) for copper, zinc, calcium, magnesium, iron, and lead in blood. …”

Image 6_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg by Junqiang Li (491954)

“…Background<p>We used an algorithm to determine age- and sex-specific reference intervals (RIs) for copper, zinc, calcium, magnesium, iron, and lead in blood. …”

SEAwise report on the impact of socioeconomic effects of fisheries management adapted according to input from review workshops by Isabella Bitetto (20145735)

“…For demersal fleets, variable prices caused significant income shifts, though operational effort was independent of price. …”

Image 1_The gut microbiota in liver transplantation recipients during the perioperative and postoperative recovery period.png by Zhongyuan Bai (8859077)

“…Using 16S rRNA sequencing technology, we compared the characteristics and changes of their microbiota. …”