Flowering date of taxonomic families predicts phenological sensitivity to temperature: Implications for forecasting the effects of climate change on unstudied taxa.

PREMISE OF THE STUDY: Numerous long-term studies in seasonal habitats have tracked interannual variation in first flowering date (FFD) in relation to climate, documenting the effect of warming on the FFD of many species. Despite these efforts, long-term phenological observations are still lacking for many species. If we could forecast responses based on taxonomic affinity, however, then we could leverage existing data to predict the climate-related phenological shifts of many taxa not yet studied. METHODS: We examined phenological time series of 1226 species occurrences (1031 unique species in 119 families) across seven sites in North America and England to determine whether family membership (or family mean FFD) predicts the sensitivity of FFD to standardized interannual changes in temperature and precipitation during seasonal periods before flowering and whether families differ significantly in the direction of their phenological shifts. KEY RESULTS: Patterns observed among species within and across sites are mirrored among family means across sites; early-flowering families advance their FFD in response to warming more than late-flowering families. By contrast, we found no consistent relationships among taxa between mean FFD and sensitivity to precipitation as measured here. CONCLUSIONS: Family membership can be used to identify taxa of high and low sensitivity to temperature within the seasonal, temperate zone plant communities analyzed here. The high sensitivity of early-flowering families (and the absence of early-flowering families not sensitive to temperature) may reflect plasticity in flowering time, which may be adaptive in environments where early-season conditions are highly variable among years.

Soybean Planting Date, 2007

The planting date for soybeans should be based on seedbed conditions and calendar date rather than soil temperature. The optimum time to plant soybeans in Iowa is the last week of April for the southern two thirds of Iowa and the first week of May for the northern one third of Iowa.

Soybean Planting Date, 2007

The planting date for soybeans should be based on seedbed conditions and calendar date rather than soil temperature. The optimum time to plant soybeans in Iowa is the last week of April for the southern two thirds of Iowa and the first week of May for the northern one third of Iowa.

Clinical experience to date with nilotinib in gastrointestinal stromal tumors.

Nilotinib, a novel tyrosine kinase inhibitor (TKI) that inhibits BCR-ABL, the stem cell factor receptor (KIT), and platelet-derived growth factor receptor-alpha (PDGFRα), is approved for the treatment of patients with newly diagnosed Philadelphia chromosome-positive chronic myelogenous leukemia (CML) and those with CML that is imatinib-resistant or -intolerant. Due to its potent inhibition of KIT and PDGFRα--the two tyrosine kinases that are the central oncogenic mechanisms of gastrointestinal stromal tumors (GIST)--nilotinib also has been investigated for potential efficacy and safety in patients with GIST who have progressed on other approved treatments. Initial results have been encouraging, as nilotinib has demonstrated clinical efficacy and safety in a phase I trial as either a single agent or in combination with imatinib, as well as in heavily pretreated patients with GIST in a compassionate use program. In addition, the phase III trial of nilotinib versus best supportive care (with or without a TKI at the investigator's discretion) indicated that nilotinib may have efficacy in some third-line patients. Furthermore, the Evaluating Nilotinib Efficacy and Safety in Clinical Trials (ENEST g1 trial), a phase III randomized, open-label study comparing the safety and efficacy of imatinib versus nilotinib in the first-line treatment of patients with GIST, is currently under way. Other studies with nilotinib either have been initiated or are in development. Based on published and accruing clinical data, nilotinib shows potential as a new drug in the clinician's armamentarium for the management of GIST.

A joint back calculation model for the imputation of the date of HIV infection in a prevalent cohort.

In studies of the natural history of HIV-1 infection, the time scale of primary interest is the time since infection. Unfortunately, this time is very often unknown for HIV infection and using the follow-up time instead of the time since infection is likely to provide biased results because of onset confounding. Laboratory markers such as the CD4 T-cell count carry important information concerning disease progression and can be used to predict the unknown date of infection. Previous work on this topic has made use of only one CD4 measurement or based the imputation on incident patients only. However, because of considerable intrinsic variability in CD4 levels and because incident cases are different from prevalent cases, back calculation based on only one CD4 determination per person or on characteristics of the incident sub-cohort may provide unreliable results. Therefore, we propose a methodology based on the repeated individual CD4 T-cells marker measurements that use both incident and prevalent cases to impute the unknown date of infection. Our approach uses joint modelling of the time since infection, the CD4 time path and the drop-out process. This methodology has been applied to estimate the CD4 slope and impute the unknown date of infection in HIV patients from the Swiss HIV Cohort Study. A procedure based on the comparison of different slope estimates is proposed to assess the goodness of fit of the imputation. Results of simulation studies indicated that the imputation procedure worked well, despite the intrinsic high volatility of the CD4 marker.