Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Socioeconomic status, reserve capacity, and depressive symptoms predict pain in Rheumatoid Arthritis: an examination of the reserve capacity model.

BACKGROUND: Guided by the reserve capacity model, we evaluated the unique relationships between socioeconomic status (SES), reserve capacity (helplessness, self-efficacy, social support), and negative emotions on pain in patients with Rheumatoid Arthritis (RA). METHODS: The secondary analysis used baseline, cross-sectional data from 106 adults in a clinical trial comparing behavioral treatments for RA. Patients were eligible if they were ≥ 18 years old, met the ACR criteria for RA (determined by study rheumatologist), had stable disease and drug regimens for 3 months, and did not have a significant comorbid condition. Structural equation modeling evaluated the direct effects of SES, reserve capacity (helplessness- Arthritis Helplessness Index, self-efficacy -Personal Mastery Scale, social support- Social Provisions Scale) and negative emotions (stress and depressive symptoms- Perceived Stress Scale and Hamilton Depression Rating Scale) on pain (Rapid Assessment of Disease Activity in Rheumatology-RADAR & visual analog scale-VAS), and the indirect effects of SES as mediated by reserve capacity and negative emotions. The SEM model was evaluated using multiple fit criteria: χ2 goodness-of-fit statistic, the comparative fit index (CFI), the standardized root mean square residual (SRMR), and the root mean square error of approximation (RMSEA). RESULTS: Participants were mostly female (85%), 55.45 years old on average, self-identified as white (61%), Hispanic (16%), black (13%), and other (10%), and had RA for an average of 10.63 years. Results showed that low SES contributed to worse pain, through lower reserve capacity and higher negative emotions. Mediational analyses showed that reserve capacity and negative emotions partially mediated the effect of SES on pain. The final model explained 39% of the variance in pain. CONCLUSIONS: The findings indicate that lower SES was related to worse clinical pain outcomes and negative emotions and reserve capacity (helplessness, social support, and self-efficacy) mediated the effect of SES on pain. A primary limitation is the small sample size; future studies should evaluate this model further in larger, longitudinal approaches. Interventions that target negative emotions in patients with low SES may facilitate better pain control with RA. TRIAL REGISTRATION: clinicaltrials.gov NCT00072657 01/02/2004 20/03/2009

A Phase I Study of Milademetan (DS3032B) in Combination With Low Dose Cytarabine With or Without Venetoclax in Acute Myeloid Leukemia: Clinical Safety, Efficacy, and Correlative Analysis

In TP53 wild-type acute myeloid leukemia (AML), inhibition of MDM2 can enhance p53 protein expression and potentiate leukemic cell apoptosis. MDM2 inhibitor (MDM2i) monotherapy in AML has shown modest responses in clinical trials but combining options of MDM2i with other potent AML-directed agents like cytarabine and venetoclax could improve its efficacy. We conducted a phase I clinical trial (NCT03634228) to study the safety and efficacy of milademetan (an MDM2i) with low-dose cytarabine (LDAC)±venetoclax in adult patients with relapsed refractory (R/R) or newly diagnosed (ND; unfit) TP53 wild-type AML and performed comprehensive CyTOF analyses to interrogate multiple signaling pathways, the p53-MDM2 axis and the interplay between pro/anti-apoptotic molecules to identify factors that determine response and resistance to therapy. Sixteen patients (14 R/R, 2 N/D treated secondary AML) at a median age of 70 years (range, 23-80 years) were treated in this trial. Two patients (13%) achieved an overall response (complete remission with incomplete hematological recovery). Median cycles on trial were 1 (range 1-7) and at a median follow-up of 11 months, no patients remained on active therapy. Gastrointestinal toxicity was significant and dose-limiting (50% of patients ≥ grade 3). Single-cell proteomic analysis of the leukemia compartment revealed therapy-induced proteomic alterations and potential mechanisms of adaptive response to the MDM2i combination. The response was associated with immune cell abundance and induced the proteomic profiles of leukemia cells to disrupt survival pathways and significantly reduced MCL1 and YTHDF2 to potentiate leukemic cell death. The combination of milademetan, LDAC±venetoclax led to only modest responses with recognizable gastrointestinal toxicity. Treatment-induced reduction of MCL1 and YTHDF2 in an immune-rich milieu correlate with treatment response

Prioritization of knowledge-needs to achieve best practices for bottom trawling in relation to seabed habitats

Management and technical approaches that achieve a sustainable level of fish production while at the same time minimizing or limiting the wider ecological effects caused through fishing gear contact with the seabed might be considered to be ‘best practice’. To identify future knowledge-needs that would help to support a transition towards the adoption of best practices for trawling, a prioritization exercise was undertaken with a group of 39 practitioners from the seafood industry and management, and 13 research scientists who have an active research interest in bottom-trawl and dredge fisheries. A list of 108 knowledge-needs related to trawl and dredge fisheries was developed in conjunction with an ‘expert task force’. The long list was further refined through a three stage process of voting and scoring, including discussions of each knowledge-need. The top 25 knowledge-needs are presented, as scored separately by practitioners and scientists. There was considerable consistency in the priorities identified by these two groups. The top priority knowledge-need to improve current understanding on the distribution and extent of different habitat types also reinforced the concomitant need for the provision and access to data on the spatial and temporal distribution of all forms of towed bottom-fishing activities. Many of the other top 25 knowledge-needs concerned the evaluation of different management approaches or implementation of different fishing practices, particularly those that explore trade-offs between effects of bottom trawling on biodiversity and ecosystem services and the benefits of fish production as food.Fil: Kaiser, Michel J.. Bangor University; Reino UnidoFil: Hilborn, Ray. University of Washington; Estados UnidosFil: Jennings, Simon. Fisheries and Aquaculture Science; Reino UnidoFil: Amaroso, Ricky. University of Washington; Estados UnidosFil: Andersen, Michael. Danish Fishermen; DinamarcaFil: Balliet, Kris. Sustainable Fisheries Partnership; Estados UnidosFil: Barratt, Eric. Sanford Limited; Nueva ZelandaFil: Bergstad, Odd A. Institute of Marine Research; NoruegaFil: Bishop, Stephen. Independent Fisheries Ltd; Nueva ZelandaFil: Bostrom, Jodi L. Marine Stewardship Council; Reino UnidoFil: Boyd, Catherine. Clearwater Seafoods; CanadáFil: Bruce, Eduardo A. Friosur S.A.; ChileFil: Burden, Merrick. Marine Conservation Alliance; Estados UnidosFil: Carey, Chris. Independent Fisheries Ltd.; Estados UnidosFil: Clermont, Jason. New England Aquarium; Estados UnidosFil: Collie, Jeremy S. University of Rhode Island,; Estados UnidosFil: Delahunty, Antony. National Federation of Fishermen; Reino UnidoFil: Dixon, Jacqui. Pacific Andes International Holdings Limited; ChinaFil: Eayrs, Steve. Gulf of Maine Research Institute; Estados UnidosFil: Edwards, Nigel. Seachill Ltd.; Reino UnidoFil: Fujita, Rod. Environmental Defense Fund; Reino UnidoFil: Gauvin, John. Alaska Seafood Cooperative; Estados UnidosFil: Gleason, Mary. The Nature Conservancy; Estados UnidosFil: Harris, Brad. Alaska Pacific University; Estados UnidosFil: He, Pingguo. University of Massachusetts Dartmouth; Estados UnidosFil: Hiddink, Jan G. Bangor University; Reino UnidoFil: Hughes, Kathryn M. Bangor University; Reino UnidoFil: Inostroza, Mario. EMDEPES; ChileFil: Kenny, Andrew. Fisheries and Aquaculture Science; Reino UnidoFil: Kritzer, Jake. Environmental Defense Fund; Estados UnidosFil: Kuntzsch, Volker. Sanford Limited; Estados UnidosFil: Lasta, Mario. Diag. Montegrande N° 7078. Mar del Plata; ArgentinaFil: Lopez, Ivan. Confederacion Española de Pesca; EspañaFil: Loveridge, Craig. South Pacific Regional Fisheries Management Organisation; Nueva ZelandaFil: Lynch, Don. Gorton; Estados UnidosFil: Masters, Jim. Marine Conservation Society; Reino UnidoFil: Mazor, Tessa. CSIRO Marine and Atmospheric Research; AustraliaFil: McConnaughey, Robert A. US National Marine Fisheries Service; Estados UnidosFil: Moenne, Marcel. Pacificblu; ChileFil: Francis. Marine Scotland Science; Reino UnidoFil: Nimick, Aileen M. Alaska Pacific University; Estados UnidosFil: Olsen, Alex. A. Espersen; DinamarcaFil: Parker, David. Young; Reino UnidoFil: Parma, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; ArgentinaFil: Penney, Christine. Clearwater Seafoods; CanadáFil: Pierce, David. Massachusetts Division of Marine Fisheries; Estados UnidosFil: Pitcher, Roland. CSIRO Marine and Atmospheric Research; AustraliaFil: Pol, Michael. Massachusetts Division of Marine Fisheries; Estados UnidosFil: Richardson, Ed. Pollock Conservation Cooperative; Estados UnidosFil: Rijnsdorp, Adriaan D. Wageningen IMARES; Países BajosFil: Rilatt, Simon. A. Espersen; DinamarcaFil: Rodmell, Dale P. National Federation of Fishermen's Organisations; Reino UnidoFil: Rose, Craig. FishNext Research; Estados UnidosFil: Sethi, Suresh A. Alaska Pacific University; Estados UnidosFil: Short, Katherine. F.L.O.W. Collaborative; Nueva ZelandaFil: Suuronen, Petri. Fisheries and Aquaculture Department; ItaliaFil: Taylor, Erin. New England Aquarium; Estados UnidosFil: Wallace, Scott. The David Suzuki Foundation; CanadáFil: Webb, Lisa. Gorton's Inc.; Estados UnidosFil: Wickham, Eric. Unit four –1957 McNicoll Avenue; CanadáFil: Wilding, Sam R. Monterey Bay Aquarium; Estados UnidosFil: Wilson, Ashley. Department for Environment; Reino UnidoFil: Winger, Paul. Memorial University Of Newfoundland; CanadáFil: Sutherland, William J. University of Cambridge; Reino Unid

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Funding: H2020 European Research Council (Grant Number(s): GA 101044975, GA 101098020).Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables: Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain: Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain: The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement: The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format: csv and. SQL.Peer reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Metabolism and functional effects of plant-derived omega-3 fatty acids in humans

Alpha-linolenic acid (ALA) is an essential fatty acid and the substrate for the synthesis of longer-chain, more un- saturated ω-3 fatty acids, eicosapentaenoic acid (EPA), docosapentaenoic acid and docosahexaenoic acid (DHA). EPA and DHA are associated with human health benefits. The primary source of EPA and DHA is seafood. There is a need for sustainable sources of biologically active ω-3 fatty acids. Certain plants contain high concentrations of ALA and stearidonic acid (SDA). Here we review the literature on the metabolism of ALA and SDA in humans, the impact of increased ALA and SDA consumption on concentrations of EPA and DHA in blood and cell lipid pools, and the extent to which ALA and SDA might have health benefits. Although it is generally considered that humans have limited capacity for conversion of ALA to EPA and DHA, sex differences in conversion to DHA have been identified. If conversion of ALA to EPA and DHA is limited, then ALA may have a smaller health benefit than EPA and DHA. SDA is more readily converted to EPA and appears to offer better potential for health improvement than ALA. However, conversion of both ALA and SDA to DHA is limited in most humans

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Motivation. Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included. The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain. Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain. The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement. The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format. csv and. SQL