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1. Airborne DNA reveals predictable spatial and seasonal dynamics of fungi.

2. Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA.

3. Expert elicitation of state shifts and divergent sensitivities to climate warming across northern ecosystems

4. Herbivore diversity effects on Arctic tundra ecosystems: a systematic review

8. Computing H-Partitions in ASP and Datalog

9. Factors influencing mercury exposure in Arctic-breeding shorebirds

12. A circumpolar study unveils a positive non‐linear effect of temperature on arctic arthropod availability that may reduce the risk of warming‐induced trophic mismatch for breeding shorebirds

13. Ordered and deterministic cancer genome evolution after p53 loss

15. BRCA mutational status shapes the stromal microenvironment of pancreatic cancer linking clusterin expression in cancer associated fibroblasts with HSF1 signaling

16. Harnessing large language models for coding, teaching and inclusion to empower research in ecology and evolution.

18. Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer.

19. Documenting lemming population change in the Arctic : Can we detect trends?

22. A circumpolar study unveils a positive non-linear effect of temperature on arctic arthropod availability that may reduce the risk of warming-induced trophic mismatch for breeding shorebirds

23. Properties of vertebrate predator–prey networks in the high Arctic

24. Global Spore Sampling Project:A global, standardized dataset of airborne fungal DNA

25. Population cycles and outbreaks of small rodents: ten essential questions we still need to solve

26. Concurrent Germline BRCA1/2 and Mismatch Repair Mutations in Young-Onset Pancreatic and Colorectal Cancer: The Importance of Comprehensive Germline and Somatic Characterization to Inform Therapeutic Options

30. The collective application of shorebird tracking data to conservation

31. Figure 7 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

32. Supplementary Material 1 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

33. Figure 3 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

34. Figure 6 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

35. Figure 2 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

36. Figure 9 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

38. Supplementary Material 2 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

39. Figure 5 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

40. Figure 4 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

41. Figure 1 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

42. Figure 8 from: Villeneuve C-A, Snyman LP, Jenkins EJ, Lecomte N, Dusfour I, Leighton PA (2024) Variable performance of DNA barcoding and morpholo­gical characteristics for the identification of Arctic black-legged Aedes (Diptera: Culicidae), with a focus on the Punctor subgroup. Arthropod Systematics & Phylogeny 82: 17-34. https://doi.org/10.3897/asp.82.e111985

43. Patient-derived biliary tract cancer organoid biobank: Is personalized medicine utilizing organoids feasible for patients with cholangiocarcinoma?

44. Abstract C115: Genomic evolution of pancreatic cancer at single-cell resolution

45. Garbage consumption by Arctic terrestrial predators in one of the most pristine land areas on Earth

46. Correction to: Documenting lemming population change in the Arctic: Can we detect trends?

48. Why do avian responses to change in Arctic green‐up vary?

50. Deep learning for passive acoustic monitoring: how to study changing phenology in remote areas

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