Diet Mixing: Do Animals Integrate Growth or Resources across Temporal Heterogeneity?

2010 ◽  
Vol 176 (5) ◽  
pp. 651-663 ◽  
Author(s):  
James M. Hood ◽  
Robert W. Sterner
Keyword(s):  
Temporal Heterogeneity ◽  
Diet Mixing

scholarly journals Spatial–temporal trends in forced migrant mortality, 2014–2018

2020 ◽  
Vol 5 (10) ◽  
pp. e002885
Author(s):  
Danielle N Poole ◽  
Bethany Hedt-Gauthier ◽  
Till Bärnighausen ◽  
Stéphane Verguet ◽  
Marcia C Castro
Keyword(s):  
Temporal Trends ◽  
Forced Migration ◽  
Global Scale ◽  
International Organization ◽  
Temporal Heterogeneity ◽  
Percentage Point Increase ◽  
Humanitarian Response ◽  
Risk Of Mortality ◽  
Point Increase ◽  
Time Periods

IntroductionThe identification of spatial–temporal clusters of forced migrant mortality is urgently needed to inform preventative policies and humanitarian response. As a first step towards understanding the geography of forced migrant mortality, this study investigates spatial–temporal patterns in death at a global scale.MethodsWe used information on the location and dates of forced migrant deaths reported in the International Organization for Migration’s Missing Migrant Project from 2014 to 2018. Kulldorff’s spatial–temporal and seasonal scans were used to detect spatial–temporal and temporal heterogeneity in mortality.ResultsA total of 16 314 deaths were reported during the study period. A preponderance of deaths occurred at sea each year (range 26%–54% across 5 years). Twelve spatial–temporal clusters of forced migrant mortality were detected by maximum likelihood testing. Annually, the period of August–October was associated with a 40-percentage-point increase in the risk of mortality, relative to other time periods.ConclusionsDeath during forced migration occurs close to national borders and during periods of intense conflict. This evidence may inform the design of policies and targeting of interventions to prevent forced migration-related deaths.

scholarly journals Deciphering the temporal heterogeneity of cancer-associated fibroblast subpopulations in breast cancer

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Freja Albjerg Venning ◽  
Kamilla Westarp Zornhagen ◽  
Lena Wullkopf ◽  
Jonas Sjölund ◽  
Carmen Rodriguez-Cupello ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Flow Cytometry ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Ex Vivo ◽  
Smooth Muscle Actin ◽  
Mammary Tissue ◽  
Temporal Heterogeneity ◽  
Alpha Smooth Muscle Actin ◽  
Murine Tumours

Abstract Background Cancer-associated fibroblasts (CAFs) comprise a heterogeneous population of stromal cells within the tumour microenvironment. CAFs exhibit both tumour-promoting and tumour-suppressing functions, making them exciting targets for improving cancer treatments. Careful isolation, identification, and characterisation of CAF heterogeneity is thus necessary for ex vivo validation and future implementation of CAF-targeted strategies in cancer. Methods Murine 4T1 (metastatic) and 4T07 (poorly/non-metastatic) orthotopic triple negative breast cancer tumours were collected after 7, 14, or 21 days. The tumours were analysed via flow cytometry for the simultaneous expression of six CAF markers: alpha smooth muscle actin (αSMA), fibroblast activation protein alpha (FAPα), platelet derived growth factor receptor alpha and beta (PDGFRα and PDGFRβ), CD26/DPP4 and podoplanin (PDPN). All non-CAFs were excluded from the analysis using a lineage marker cocktail (CD24, CD31, CD45, CD49f, EpCAM, LYVE-1, and TER-119). In total 128 murine tumours and 12 healthy mammary fat pads were analysed. Results We have developed a multicolour flow cytometry strategy based on exclusion of non-CAFs and successfully employed this to explore the temporal heterogeneity of freshly isolated CAFs in the 4T1 and 4T07 mouse models of triple-negative breast cancer. Analysing 128 murine tumours, we identified 5–6 main CAF populations and numerous minor ones based on the analysis of αSMA, FAPα, PDGFRα, PDGFRβ, CD26, and PDPN. All markers showed temporal changes with a distinct switch from primarily PDGFRα+ fibroblasts in healthy mammary tissue to predominantly PDGFRβ+ CAFs in tumours. CD26+ CAFs emerged as a large novel subpopulation, only matched by FAPα+ CAFs in abundance. Conclusion We demonstrate that multiple subpopulations of CAFs co-exist in murine triple negative breast cancer, and that the abundance and dynamics for each marker differ depending on tumour type and time. Our results form the foundation needed to isolate and characterise specific CAF populations, and ultimately provide an opportunity to therapeutically target specific CAF subpopulations.

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