Persistence of Environmental DNA in Freshwater Ecosystems

The precise knowledge of species distribution is a key step in conservation biology. However, species detection can be extremely difficult in many environments, specific life stages and in populations at very low density. The aim of this study was to improve the knowledge on DNA persistence in water in order to confirm the presence of the focus species in freshwater ecosystems. Aquatic vertebrates (fish: Siberian sturgeon and amphibian: Bullfrog tadpoles) were used as target species. In control conditions (tanks) and in the field (ponds), the DNA detectability decreases with time after the removal of the species source of DNA. DNA was detectable for less than one month in both conditions. The density of individuals also influences the dynamics of DNA detectability in water samples. The dynamics of detectability reflects the persistence of DNA fragments in freshwater ecosystems. The short time persistence of detectable amounts of DNA opens perspectives in conservation biology, by allowing access to the presence or absence of species e.g. rare, secretive, potentially invasive, or at low density. This knowledge of DNA persistence will greatly influence planning of biodiversity inventories and biosecurity surveys

ITALIAN CANCER FIGURES - REPORT 2015: The burden of rare cancers in Italy = I TUMORI IN ITALIA - RAPPORTO 2015: I tumori rari in Italia

OBJECTIVES: This collaborative study, based on data collected by the network of Italian Cancer Registries (AIRTUM), describes the burden of rare cancers in Italy. Estimated number of new rare cancer cases yearly diagnosed (incidence), proportion of patients alive after diagnosis (survival), and estimated number of people still alive after a new cancer diagnosis (prevalence) are provided for about 200 different cancer entities. MATERIALS AND METHODS: Data herein presented were provided by AIRTUM population- based cancer registries (CRs), covering nowadays 52% of the Italian population. This monograph uses the AIRTUM database (January 2015), which includes all malignant cancer cases diagnosed between 1976 and 2010. All cases are coded according to the International Classification of Diseases for Oncology (ICD-O-3). Data underwent standard quality checks (described in the AIRTUM data management protocol) and were checked against rare-cancer specific quality indicators proposed and published by RARECARE and HAEMACARE (www.rarecarenet.eu; www.haemacare.eu). The definition and list of rare cancers proposed by the RARECAREnet "Information Network on Rare Cancers" project were adopted: rare cancers are entities (defined as a combination of topographical and morphological codes of the ICD-O-3) having an incidence rate of less than 6 per 100,000 per year in the European population. This monograph presents 198 rare cancers grouped in 14 major groups. Crude incidence rates were estimated as the number of all new cancers occurring in 2000-2010 divided by the overall population at risk, for males and females (also for gender-specific tumours).The proportion of rare cancers out of the total cancers (rare and common) by site was also calculated. Incidence rates by sex and age are reported. The expected number of new cases in 2015 in Italy was estimated assuming the incidence in Italy to be the same as in the AIRTUM area. One- and 5-year relative survival estimates of cases aged 0-99 years diagnosed between 2000 and 2008 in the AIRTUM database, and followed up to 31 December 2009, were calculated using complete cohort survival analysis. To estimate the observed prevalence in Italy, incidence and follow-up data from 11 CRs for the period 1992-2006 were used, with a prevalence index date of 1 January 2007. Observed prevalence in the general population was disentangled by time prior to the reference date (≤2 years, 2-5 years, ≤15 years). To calculate the complete prevalence proportion at 1 January 2007 in Italy, the 15-year observed prevalence was corrected by the completeness index, in order to account for those cancer survivors diagnosed before the cancer registry activity started. The completeness index by cancer and age was obtained by means of statistical regression models, using incidence and survival data available in the European RARECAREnet data. RESULTS: In total, 339,403 tumours were included in the incidence analysis. The annual incidence rate (IR) of all 198 rare cancers in the period 2000-2010 was 147 per 100,000 per year, corresponding to about 89,000 new diagnoses in Italy each year, accounting for 25% of all cancer. Five cancers, rare at European level, were not rare in Italy because their IR was higher than 6 per 100,000; these tumours were: diffuse large B-cell lymphoma and squamous cell carcinoma of larynx (whose IRs in Italy were 7 per 100,000), multiple myeloma (IR: 8 per 100,000), hepatocellular carcinoma (IR: 9 per 100,000) and carcinoma of thyroid gland (IR: 14 per 100,000). Among the remaining 193 rare cancers, more than two thirds (No. 139) had an annual IR <0.5 per 100,000, accounting for about 7,100 new cancers cases; for 25 cancer types, the IR ranged between 0.5 and 1 per 100,000, accounting for about 10,000 new diagnoses; while for 29 cancer types the IR was between 1 and 6 per 100,000, accounting for about 41,000 new cancer cases. Among all rare cancers diagnosed in Italy, 7% were rare haematological diseases (IR: 41 per 100,000), 18% were solid rare cancers. Among the latter, the rare epithelial tumours of the digestive system were the most common (23%, IR: 26 per 100,000), followed by epithelial tumours of head and neck (17%, IR: 19) and rare cancers of the female genital system (17%, IR: 17), endocrine tumours (13% including thyroid carcinomas and less than 1% with an IR of 0.4 excluding thyroid carcinomas), sarcomas (8%, IR: 9 per 100,000), central nervous system tumours and rare epithelial tumours of the thoracic cavity (5%with an IR equal to 6 and 5 per 100,000, respectively). The remaining (rare male genital tumours, IR: 4 per 100,000; tumours of eye, IR: 0.7 per 100,000; neuroendocrine tumours, IR: 4 per 100,000; embryonal tumours, IR: 0.4 per 100,000; rare skin tumours and malignant melanoma of mucosae, IR: 0.8 per 100,000) each constituted <4% of all solid rare cancers. Patients with rare cancers were on average younger than those with common cancers. Essentially, all childhood cancers were rare, while after age 40 years, the common cancers (breast, prostate, colon, rectum, and lung) became increasingly more frequent. For 254,821 rare cancers diagnosed in 2000-2008, 5-year RS was on average 55%, lower than the corresponding figures for patients with common cancers (68%). RS was lower for rare cancers than for common cancers at 1 year and continued to diverge up to 3 years, while the gap remained constant from 3 to 5 years after diagnosis. For rare and common cancers, survival decreased with increasing age. Five-year RS was similar and high for both rare and common cancers up to 54 years; it decreased with age, especially after 54 years, with the elderly (75+ years) having a 37% and 20% lower survival than those aged 55-64 years for rare and common cancers, respectively. We estimated that about 900,000 people were alive in Italy with a previous diagnosis of a rare cancer in 2010 (prevalence). The highest prevalence was observed for rare haematological diseases (278 per 100,000) and rare tumours of the female genital system (265 per 100,000). Very low prevalence (<10 prt 100,000) was observed for rare epithelial skin cancers, for rare epithelial tumours of the digestive system and rare epithelial tumours of the thoracic cavity. COMMENTS: One in four cancers cases diagnosed in Italy is a rare cancer, in agreement with estimates of 24% calculated in Europe overall. In Italy, the group of all rare cancers combined, include 5 cancer types with an IR>6 per 100,000 in Italy, in particular thyroid cancer (IR: 14 per 100,000).The exclusion of thyroid carcinoma from rare cancers reduces the proportion of them in Italy in 2010 to 22%. Differences in incidence across population can be due to the different distribution of risk factors (whether environmental, lifestyle, occupational, or genetic), heterogeneous diagnostic intensity activity, as well as different diagnostic capacity; moreover heterogeneity in accuracy of registration may determine some minor differences in the account of rare cancers. Rare cancers had worse prognosis than common cancers at 1, 3, and 5 years from diagnosis. Differences between rare and common cancers were small 1 year after diagnosis, but survival for rare cancers declined more markedly thereafter, consistent with the idea that treatments for rare cancers are less effective than those for common cancers. However, differences in stage at diagnosis could not be excluded, as 1- and 3-year RS for rare cancers was lower than the corresponding figures for common cancers. Moreover, rare cancers include many cancer entities with a bad prognosis (5-year RS <50%): cancer of head and neck, oesophagus, small intestine, ovary, brain, biliary tract, liver, pleura, multiple myeloma, acute myeloid and lymphatic leukaemia; in contrast, most common cancer cases are breast, prostate, and colorectal cancers, which have a good prognosis. The high prevalence observed for rare haematological diseases and rare tumours of the female genital system is due to their high incidence (the majority of haematological diseases are rare and gynaecological cancers added up to fairly high incidence rates) and relatively good prognosis. The low prevalence of rare epithelial tumours of the digestive system was due to the low survival rates of the majority of tumours included in this group (oesophagus, stomach, small intestine, pancreas, and liver), regardless of the high incidence rate of rare epithelial cancers of these sites. This AIRTUM study confirms that rare cancers are a major public health problem in Italy and provides quantitative estimations, for the first time in Italy, to a problem long known to exist. This monograph provides detailed epidemiologic indicators for almost 200 rare cancers, the majority of which (72%) are very rare (IR<0.5 per 100,000). These data are of major interest for different stakeholders. Health care planners can find useful information herein to properly plan and think of how to reorganise health care services. Researchers now have numbers to design clinical trials considering alternative study designs and statistical approaches. Population-based cancer registries with good quality data are the best source of information to describe the rare cancer burden in a population

Genetic diversity and connectivity in a brooding reef coral at the limit of its distribution

Remote populations are predicted to be vulnerable owing to their isolation from potential source reefs, and usually low population size and associated increased extinction risk. We investigated genetic diversity, population subdivision and connectivity in the brooding reef coral Seriatopora hystrix at the limits of its Eastern Australian (EA) distribution and three sites in the southern Great Barrier Reef (GBR). Over the approximately 1270 km survey range, high levels of population subdivision were detected (global FST = 0.224), with the greatest range in pairwise FST values observed among the three southernmost locations: Lord Howe Island, Elizabeth Reef and Middleton Reef. Flinders Reef, located between the GBR and the more southerly offshore reefs, was highly isolated and showed the signature of a recent bottleneck. High pairwise FST values and the presence of multiple genetic clusters indicate that EA subtropical coral populations have been historically isolated from each other and the GBR. One putative first-generation migrant was detected from the GBR into the EA subtropics. Occasional long-distance dispersal is supported by changes in species composition at these high-latitude reefs and the occurrence of new species records over the past three decades. While subtropical populations exhibited significantly lower allelic richness than their GBR counterparts, genetic diversity was still moderately high. Furthermore, subtropical populations were not inbred and had a considerable number of private alleles. The results suggest that these high-latitude S. hystrix populations are supplemented by infrequent long-distance migrants from the GBR and may have adequate population sizes to maintain viability and resist severe losses of genetic diversity

Saving Darwin's muse: evolutionary genetics for the recovery of the Floreana mockingbird

The distribution of mockingbird species among the Galápagos Islands prompted Charles Darwin to question, for the first time in writing, the ‘stability of species’. Some 50 years after Darwin's visit, however, the endemic Floreana mockingbird (Mimus trifasciatus) had become extinct on Floreana Island and, today, only two small populations survive on two satellite islets. As Darwin noted, rarity often precedes extinction. To avert extinction, plans are being developed to reintroduce M. trifasciatus to Floreana. Here, we integrate evolutionary thinking and conservation practice using coalescent analyses and genetic data from contemporary and museum samples, including two collected by Darwin and Robert Fitzroy on Floreana in 1835. Our microsatellite results show substantial differentiation between the two extant populations, but our coalescence-based modelling does not indicate long, independent evolutionary histories. One of the populations is highly inbred, but both harbour unique alleles present on Floreana in 1835, suggesting that birds from both islets should be used to establish a single, mixed population on Floreana. Thus, Darwin's mockingbird specimens not only revealed to him a level of variation that suggested speciation following geographical isolation but also, more than 170 years later, return important information to their place of origin for the conservation of their conspecifics

Solvent-Controlled, Site-Selective <i>N</i>‑Alkylation Reactions of Azolo-Fused Ring Heterocycles at N1‑, N2‑, and N3-Positions, Including Pyrazolo[3,4‑<i>d</i>]pyrimidines, Purines, [1,2,3]Triazolo[4,5]pyridines, and Related Deaza-Compounds

Alkylation of 4-methoxy-1<i>H</i>-pyrazolo­[3,4-<i>d</i>]­pyrimidine (<b>1b</b>) with iodomethane in THF using NaHMDS as base selectively provided N2-methyl product 4-methoxy-2-methyl-2<i>H</i>-pyrazolo­[3,4-<i>d</i>]­pyrimidine (<b>3b</b>) in an 8/1 ratio over N1-methyl product (<b>2b</b>). Interestingly, conducting the reaction in DMSO reversed selectivity to provide a 4/1 ratio of N1/N2 methylated products. Crystal structures of product <b>3b</b> with N1 and N7 coordinated to sodium indicated a potential role for the latter reinforcing the N2-selectivity. Limits of selectivity were tested with 26 heterocycles which revealed that N7 was a controlling element directing alkylations to favor N2 for pyrazolo- and N3 for imidazo- and triazolo-fused ring heterocycles when conducted in THF. Use of ¹H-detected pulsed field gradient-stimulated echo (PFG-STE) NMR defined the molecular weights of ionic reactive complexes. This data and DFT charge distribution calculations suggest close ion pairs (CIPs) or tight ion pairs (TIPs) control alkylation selectivity in THF and solvent-separated ion pairs (SIPs) are the reactive species in DMSO

Solvent-Controlled, Site-Selective <i>N</i>‑Alkylation Reactions of Azolo-Fused Ring Heterocycles at N1‑, N2‑, and N3-Positions, Including Pyrazolo[3,4‑<i>d</i>]pyrimidines, Purines, [1,2,3]Triazolo[4,5]pyridines, and Related Deaza-Compounds

Alkylation of 4-methoxy-1<i>H</i>-pyrazolo­[3,4-<i>d</i>]­pyrimidine (<b>1b</b>) with iodomethane in THF using NaHMDS as base selectively provided N2-methyl product 4-methoxy-2-methyl-2<i>H</i>-pyrazolo­[3,4-<i>d</i>]­pyrimidine (<b>3b</b>) in an 8/1 ratio over N1-methyl product (<b>2b</b>). Interestingly, conducting the reaction in DMSO reversed selectivity to provide a 4/1 ratio of N1/N2 methylated products. Crystal structures of product <b>3b</b> with N1 and N7 coordinated to sodium indicated a potential role for the latter reinforcing the N2-selectivity. Limits of selectivity were tested with 26 heterocycles which revealed that N7 was a controlling element directing alkylations to favor N2 for pyrazolo- and N3 for imidazo- and triazolo-fused ring heterocycles when conducted in THF. Use of ¹H-detected pulsed field gradient-stimulated echo (PFG-STE) NMR defined the molecular weights of ionic reactive complexes. This data and DFT charge distribution calculations suggest close ion pairs (CIPs) or tight ion pairs (TIPs) control alkylation selectivity in THF and solvent-separated ion pairs (SIPs) are the reactive species in DMSO