Showing 21,001 - 21,020 results of 48,847 for search '(( significant decrease decrease ) OR ( significant ((use increased) OR (teer decrease)) ))', query time: 0.89s Refine Results
  1. 21001
  2. 21002

    pone.0329550.t006 - by Chukwuka Elendu (21865187)

    Published 2025
    “…Sub-group analyses highlighted higher adherence rates in urban areas (89%) compared to rural areas (73%) and significant reductions in malaria-related complications among pregnant women (78%). …”
  3. 21003

    Demographic breakdown and intervention coverage. by Chukwuka Elendu (21865187)

    Published 2025
    “…Sub-group analyses highlighted higher adherence rates in urban areas (89%) compared to rural areas (73%) and significant reductions in malaria-related complications among pregnant women (78%). …”
  4. 21004

    Malaria incidence reduction in Egypt. by Chukwuka Elendu (21865187)

    Published 2025
    “…Sub-group analyses highlighted higher adherence rates in urban areas (89%) compared to rural areas (73%) and significant reductions in malaria-related complications among pregnant women (78%). …”
  5. 21005

    Supplementary file 1_Tracing priming effects in palsa peat carbon dynamics using a stable isotope-assisted metabolomics approach.docx by Christian Ayala-Ortiz (15255371)

    Published 2025
    “…Pre-existing peat organic matter remained relatively stable; significant priming of its consumption was not observed, nor was its structural alteration.…”
  6. 21006

    Data Sheet 1_Student support during project-based learning using simulated automated formative feedback: an experimental evaluation.pdf by Kristina Murtazin (22020320)

    Published 2025
    “…Weekly surveys assessed perceptions of usefulness and motivation. No statistically significant differences were found between feedback conditions (Cohen's d = 0.16). …”
  7. 21007

    Image 4_Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.tif by Lin Wu (153444)

    Published 2025
    “…The gut microbial composition showed significant spatial heterogeneity. The diversity of the gut microbiota progressively increased along the intestinal tract. …”
  8. 21008

    Image 3_Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.tif by Lin Wu (153444)

    Published 2025
    “…The gut microbial composition showed significant spatial heterogeneity. The diversity of the gut microbiota progressively increased along the intestinal tract. …”
  9. 21009

    Image 1_Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.tif by Lin Wu (153444)

    Published 2025
    “…The gut microbial composition showed significant spatial heterogeneity. The diversity of the gut microbiota progressively increased along the intestinal tract. …”
  10. 21010

    Image 2_Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.tif by Lin Wu (153444)

    Published 2025
    “…The gut microbial composition showed significant spatial heterogeneity. The diversity of the gut microbiota progressively increased along the intestinal tract. …”
  11. 21011

    Table 1_Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.xlsx by Lin Wu (153444)

    Published 2025
    “…The gut microbial composition showed significant spatial heterogeneity. The diversity of the gut microbiota progressively increased along the intestinal tract. …”
  12. 21012

    Table 3_Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.xlsx by Lin Wu (153444)

    Published 2025
    “…The gut microbial composition showed significant spatial heterogeneity. The diversity of the gut microbiota progressively increased along the intestinal tract. …”
  13. 21013

    Table 2_Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.xlsx by Lin Wu (153444)

    Published 2025
    “…The gut microbial composition showed significant spatial heterogeneity. The diversity of the gut microbiota progressively increased along the intestinal tract. …”
  14. 21014

    Descriptive characteristics of study sample. by Aleksandra Jakubowski (4107814)

    Published 2025
    “…The risk of experiencing the death of a child increased from 3.4% among adult mothers to 15.3% among teenage mothers to 2 + children, a 4.5-fold increase (p < 0.001). …”
  15. 21015

    Health and economic outcomes by study groups. by Aleksandra Jakubowski (4107814)

    Published 2025
    “…The risk of experiencing the death of a child increased from 3.4% among adult mothers to 15.3% among teenage mothers to 2 + children, a 4.5-fold increase (p < 0.001). …”
  16. 21016

    Characteristics of the study participants. by Yasin Nasir (20721743)

    Published 2025
    “…Parasitemia was determined using 18S-based qPCR. The majority of participants were Duffy positive (96.8%, 421/435). …”
  17. 21017

    Train data annotation and classification. by Andrés Lozano (20489256)

    Published 2024
    “…In the same-dialect condition, <i>mfccNasalance</i> was more accurate than <i>eNasalance</i> independently of the CNN configuration; using a 1 × 1 kernel resulted in increased accuracy for +dynamic utterances (p < .000), though not for -dynamic utterances. …”
  18. 21018

    Kernel shapes and phonetic information. by Andrés Lozano (20489256)

    Published 2024
    “…In the same-dialect condition, <i>mfccNasalance</i> was more accurate than <i>eNasalance</i> independently of the CNN configuration; using a 1 × 1 kernel resulted in increased accuracy for +dynamic utterances (p < .000), though not for -dynamic utterances. …”
  19. 21019

    Utterances in test data. by Andrés Lozano (20489256)

    Published 2024
    “…In the same-dialect condition, <i>mfccNasalance</i> was more accurate than <i>eNasalance</i> independently of the CNN configuration; using a 1 × 1 kernel resulted in increased accuracy for +dynamic utterances (p < .000), though not for -dynamic utterances. …”
  20. 21020

    Train data annotation and classification. by Andrés Lozano (20489256)

    Published 2024
    “…In the same-dialect condition, <i>mfccNasalance</i> was more accurate than <i>eNasalance</i> independently of the CNN configuration; using a 1 × 1 kernel resulted in increased accuracy for +dynamic utterances (p < .000), though not for -dynamic utterances. …”