First there were vinyl records, then came cassettes, CDs, those failed audio-on-DVD formats, and then downloads. To quote the band, [Bateleur], "you can't pay someone to take a CD these days." So how do you release your new album? By hiding a Raspberry Pi in a semi-transparent fake rock on a mountainous cliff, and requiring a secret whistle to enable it, naturally.
Once activated, you'll be able to plug into the USB port and download the album, or sit there on a remote hillside cliff overlooking the ocean and enjoy the new tunes. Because there's a headphone jack in the rock, naturally. Besides being a cool hack, we think that putting people in the right physical and mental space for a serious listening is brilliant. Watch the video embedded below for an idea of the making of and a view from the site.
When you need a cold one and walking downstairs to your twin-keg refrigerator just won't do, it's time to break out the tools to deliver that frothy goodness where it's needed. And so began [DaveLondres'] inspiring tale of piping beer through the walls of his home.
Now we know what you're thinking… that beer is going to get mighty warm sitting in long lines from the fridge up to the ground floor. [Dave] thought about that too and designed a double-pipe system to overcome the issue. A run of PVC pipe for each keg connect the in-wall taps to holes drilled in the side of a second-hand fridge. An ingenious branching job yields an extra port for each run which was fitted with computer case fans to keep the cold air circulating. Plastic tubing is snaked inside of the PVC to carry the beer.
Rounding out the craftsmanship on this one is the inclusion of a plumbed drain to whisk away the drippings. If you're not going to have a beautifully adorned chest-freezer-gone-kegerator in your livingroom this is the best alternative we've seen.
You might look at the images above and think "oh neat" and then go about your business. But you'd be missing a great motorized hidden computer build. We simply must insist that you click on that link and look at all that went into it. Do it. DO. IT.
Still here? Okay, we'll give you the gist and then you won't be able to help yourself. First off, [Designforhire] built that door completely from scratch using skills that your average hacker wields. At first glance you'd think it was a retrofit or done with serious woodworking tools (quality table saw, router table, etc.). This actually started with a simple frame out of 2″x3″ pine studs. This is faced with Masonite which was affixed with glue and brads. From there the upper half was outfitted with a dry-erase panel, and trim pieces were added.
Now the hack really starts to get interesting. The opening for the monitor and the keyboard are both motorized. An old cordless drill (borked handle and dead battery) was cannibalized for its motor which is run using the two black switches just above the left corner of the monitor. When closed, a dry-erase calendar covers the monitor and a blank panel keeps the keyboard secret. The computer itself is actually in the basement, with cables running down the hinged side of the door and through a hole in the jamb.
We didn't see a video showing off the build, but you can satisfy that craving by looking back at the Kitchen HAL installation from a few years back.


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A genome-wide association study identifies that the GDF5 and COL27A1 genes are associated with knee pain in UK Biobank (N = 171, 516)
Weihua Meng, Mark J Adams, Colin NA Palmer, The 23andMe Research Team, Jingchunzi Shi, Adam Auton, Kathleen A. Ryan, Joanne M. Jordan, Braxton D. Mitchell, Rebecca D. Jackson, Michelle S. Yau, Andrew M McIntosh, Blair H Smith
Weihua Meng
Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK, DD2 4BF
Mark J Adams
Division of Psychiatry, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK, EH10 5HF
Colin NA Palmer
Jingchunzi Shi
23andMe, Inc., Mountain View, CA 94061, USA
Adam Auton
Kathleen A. Ryan
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
Joanne M. Jordan
Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA
Braxton D. Mitchell
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USAGeriatric Research, Education and Clinical Center, Veterans Affairs Medical Center, Baltimore, MD, USA
Rebecca D. Jackson
Division of Endocrinology, Diabetes and Metabolism, The Ohio State University, Columbus, OH, USA
Michelle S. Yau
Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, Boston, MA, USA
Andrew M McIntosh
Blair H Smith
Objective Knee pain is one of the most common musculoskeletal complaints that brings people to medical attention. We sought to identify the genetic variants associated with knee pain in 171,516 subjects from the UK Biobank cohort and replicate them using cohorts from 23andMe, the Osteoarthritis Initiative (OAI), and the Johnston County Osteoarthritis Study (JoCo).
Methods We performed a genome-wide association study of knee pain in the UK Biobank, where knee pain was ascertained through self-report and defined as "knee pain in the last month interfering with usual activities". A total of 22,204 cases and 149,312 controls were included in the discovery analysis. We tested our top and independent SNPs (P < 5 × 10−8) for replication in 23andMe, OAI, and JoCo, then performed a joint meta-analysis between discovery and replication cohorts using GWAMA. We calculated the narrow-sense heritability of knee pain using Genome-wide Complex Trait Analysis (GCTA).
Results We identified 2 loci that reached genome-wide significance, rs143384 located in the GDF5 (P = 1.32 × 10−12), a gene previously implicated in osteoarthritis, and rs2808772, located near COL27A1 (P = 1.49 × 10−8). These findings were subsequently replicated in independent cohorts and increased in significance in the joint meta-analysis (rs143384: P = 4.64 × 10−18; rs2808772: P −11 = 2.56 × 10−1'). The narrow sense heritability of knee pain was 0.08.
Conclusion In this first reported genome-wide association meta-analysis of knee pain, we identified and replicated two loci in or near GDF5 and COL27A1 that are associated with knee pain.
The knee supports body weight when walking, standing upright and bending. Knee pain describes a specific area of pain inside the knee or diffuse pain around knee area1. It is one of the most common musculoskeletal complaints that brings people to medical attention2. The knee pain experience varies from person to person and can present as a dull ache to a sharp, stabbing pain and from intermittent weight bearing pain to persistent pain3.
Knee pain is highly prevalent in older individuals, with ~50% of individuals over the age of 50 reporting an experience of knee pain within the past 12 months4. In one US general population cohort, knee pain prevalence has increased from 15.7% to 32.9% in females and from 8.7% to 27.7% in males between 1983 to 2005, regardless of knee osteoarthritis status5. In another estimate, the prevalence of knee osteoarthritis in the US increased from 8% in 1950s to 16% currently6. There are currently over 8 million patients suffering from knee osteoarthritis in the UK7. According to the Global Burden of Diseases 2016, osteoarthritis including knee osteoarthritis is the 12th leading cause of years of life lived with disability (YLDs) globally8. It is estimated that ~ 50% of all people with knee osteoarthritis have reported knee pain symptoms and of those without knee osteoarthritis, 20% have reported knee pain5. There are many underlying mechanisms that can cause knee pain, including injuries, gout, and infection, as well as arthritis. Among these, osteoarthritis is the most common cause, particularly in people over the age of 505. People with knee pain will experience progressive loss of knee function and declining quality of daily life, and display increasing dependence in daily activities9. Further, knee pain caused by osteoarthritis frequently accompanies pain in other joints such as hips and hands, which further reduces quality of life10. The disease has generated huge economic burdens to the health care systems across the world.
For example, although no figures exist specifically on knee osteoarthritis, the total direct cost of osteoarthritis as a whole in the UK in 2010 was around £1 billion and the corresponding total indirect cost of osteoarthritis in 2010 was over £3.2 billion11.
Epidemiological studies have suggested multiple risk factors for knee pain, including female sex, age, obesity, previous knee injuries, knee-straining work, and smoking12. Similar risk factors are reported in studies of knee osteoarthritis specifically, which also included kneeling and squatting as further risk factors12,13. With aging populations and increasing rates of obesity, the prevalence of knee pain is likely to increase. Psychological factors are also important risk factors of knee pain14. These environmental and lifestyle factors are likely to interact with genetic factors, and are important to understand in genetic association studies.
Genetic studies to date have focused on knee osteoarthritis, but not knee pain more generally. Studies in siblings have reported heritabilities for knee osteoarthritis as high as 0.6215. The genetic architecture of knee osteoarthritis was considered to follow an additive genetic model, involving multiple genes or loci but each with small effect size16. Candidate genes including GDF5, COL9A1, IL1B, IL1RN, LRCH1, CLIP, TNA, and BMP2 have been reported to be associated with knee osteoarthritis17–21. Genome-wide association studies (GWAS) have also reported that the GDF5, DVWA, HLA-DQB1, BTNL2, COG5, MCF2L, TP63, FTO, SUPT3H/RUNX2, GLN3/GLT8D1, and LSP1P3 genes contribute specifically to knee osteoarthritis22–28. Recently, Zengini et al reported 9 novel genetic loci associated with osteoarthritis based on 5 different osteoarthritis definitions according to the self-reported status questionnaire and the Hospital Episode Statistics data from the UK Biobank cohort29. However, these analyses did not specifically focus on the knee area.
To identify the genetic variants associated with knee pain, we conducted a GWAS using the large UK Biobank cohort. We defined knee pain as 'knee pain in the last month interfering with usual activity', based on the information available from the study questionnaire. Since there are no well-defined knee pain cohorts available, we chose to replicate our genome-wide significant findings on knee pain using three independent cohorts that defined osteoarthritis using either questionnaire data (i.e., 23andMe) or radiographic criteria (i.e., the Osteoarthritis Initiative (OAI) and the Johnston County Osteoarthritis Study (JoCo)).
As far as we know, this is the first GWAS on knee pain to screen for genetic variants. Similar approaches have been taken for headache and back pain using the UK Biobank cohort30,31.
Participants and genetic information of the UK Biobank, 23andMe, OAI, and JoCo participants
Discovery cohort - UK Biobank: Over 500,000 people aged between 40 and 69 years were recruited by the UK Biobank cohort in 2006-2010 across England, Scotland and Wales. All participants provided informed consent that their health records could be accessed for research purposes. Further information about the UK Biobank cohort can be found at www.ukbiobank.ac.uk. Ethical approval was granted by the National Health Service National Research Ethics Service (reference 11/NW/0382).
DNA extraction and quality control (QC) were standardized and the detailed methods can be found at http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/DNA-Extraction-at-UK-Biobank-October-2014.pdf. The detailed QC steps can be found at http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=155580.
In July 2017, the UK Biobank released the genetic information (including directly genotyped genotypes and imputed genotypes) of 501,708 samples to all approved researchers. The detailed QC steps of imputation were described by Bycroft et al32.
Stage 1 replication cohort - 23andMe Inc: The 23andMe company is a privately held personal genomics and biotechnology company based in USA. It includes more than 1,500,000 genotyped subjects who have consented to participate in research. The DNA extraction from saliva and the QC of the genotyping and imputation results were all based on the company's standardised procedures. Further methodological details can be found in the supplementary file of a previous publication33.
Stage 2 replication cohorts - 1. The OAI is a prospective longitudinal study designed to identify risk factors for the incidence and progression of symptomatic tibiofemoral knee osteoarthritis. Participants aged between 45 and 79 years were recruited at four different clinical sites in the USA. Details of the study protocol, including recruitment procedures and eligibility criteria are available on the OAI web site. (http://oai.epi-ucsf.org/datarelease/docs/StudyDesignProtocol.pdf). 2. The JoCo is an ongoing, community-based study of the occurrence of knee and hip osteoarthritis in African American and Caucasian residents, aged 45 years and above from Johnston County, North Carolina in the US. A total of 3,068 individuals were recruited at baseline. A detailed description of the cohort has been reported34.
Standard procedures of imputation and QC were applied when genotyping OAI and JoCo samples. The detailed description of the cohorts has been previously published.28
Phenotypic information on knee pain of the UK Biobank, 23andMe, OAI, and JoCo
Discovery cohort - UK Biobank: We used a bespoke pain-related questionnaire adapted by the UK Biobank, which included the question: 'in the last month have you experienced any of the following that interfered with your usual activities?'. The options were: 1. Headache; 2. Facial pain; 3. Neck or shoulder pain; 4. Back pain; 5. Stomach or abdominal pain; 6. Hip pain; 7. Knee pain; 8. Pain all over the body; 9. None of the above; 10. Prefer not to say. More than one option could be selected. (UK Biobank Questionnaire field ID: 6159)
The knee pain cases in this study were those who selected the 'knee pain' option for the above question, regardless of whether they had selected other options.
The controls in this study were those who selected the 'None of the above' option.
Stage 1 replication cohort - 23andMe, Inc: The 23andMe cohort used an online survey to determine the phenotypic status of osteoarthritis of all participants.
Cases were defined as those self-reported having been diagnosed or treated for osteoarthritis.
Controls were defined as those self-reporting as having not been diagnosed or treated for osteoarthritis.
The cohort included 253,880 cases and 1,286,245 controls.
Stage 2 replication cohorts - OAI and JoCo:
Cases: Knee osteoarthritis was evaluated with fixed-flexion posteroanterior radiographs for OAI samples and for JoCo participants, weight-bearing anteroposterior extended radiographs were taken during initial recruitments and fixed-flexion posteroanterior radiographs were taken during follow up. Cases were those with definitive knee osteoarthritis, defined as radiographic evidence of the presence of definite osteophytes and possible joint space narrowing (Kellgren-Lawrence grade ≥ 2) or total joint replacement in one or both knees. Controls were those having no or doubtful evidence of OA (KL grade = 0 or 1) in both knees at all available time points.
These definitions were previously used by Yau et al28. In the current study, there were 2,672 cases (2,014 from OAI and 658 from JoCo) and 1,776 controls (953 from OAI and 823 from JoCo).
GWAS analysis: In the discovery stage, the BGENIE (https://jmarchini.org/bgenie/) was used as the main GWAS software recommended by the UK Biobank. Routine QC steps included removal of single nucleotide polymorphism (SNPs) with INFO scores less than 0.1, SNPs with minor allele frequency less than 0.5%, or SNPs that failed Hardy-Weinberg tests (P < 10−6). SNPs on the X and Y chromosomes and mitochondrial SNPs were also removed. We further removed data from individuals whose ancestry was not white British based on principal component analysis, those who were related to at least one other participant in the cohort (a cut-off value of 0.025 in the generation of the genetic relationship matrix), and those who failed QC. Association tests based on standard Frequentist association were performed using BGENIE adjusting for age, sex, body mass index (BMI), 9 population principal components, genotyping arrays, and assessment centres. A Chi-square test was used to test for gender differences between cases and controls. Age and BMI were compared using independent t testing in IBM SPSS 22 (IBM Corporation, New York). A P value less than 5 × 10−8 was considered to indicate a significant association. Independent SNPs were defined as those that were not correlated (r2<0.6) with any other significantly associated SNP. GCTA (https://cnsgenomics.com/software/gcta/#Overview) was used to calculate the narrow-sense heritability using a genomic relationship matrix calculated from genotyped autosomal SNPs.
In the replication stage, details of the identified significant and independent SNPs associated with knee pain in the discovery stage were sent to 23andMe Inc and the combined OAI and JoCo cohorts. The significant and independent SNPs were defined as those with P value < 5 × 10−8 and with linkage disequilibrium value r2 < 0.6. The 23andMe and the combined OAI and JoCo cohorts then extracted the summary statistics of these SNPs from their GWAS results, correspondingly.
23andMe performed GWAS on self-reported osteoarthritis in any joint using the logistic regression method assuming an additive genetic model for allelic effects adjusting for age, sex, 5 principal components, and 4 DNA chip platforms. Participants were restricted to a set of individuals who had >97% European ancestry, as determined through an analysis of local ancestry. A maximal set of unrelated individuals was chosen for the GWAS analysis using a segmental identity-by-descent (IBD) estimation algorithm. Further details can be found in the supplementary file of a previous publication33.
OAI and JoCo performed GWAS on radiographic knee osteoarthritis using logistic regression assuming an additive genetic model for allelic effects adjusting for age, sex, study site, and principal components. Summary statistics from both cohorts were then combined in a meta-analysis. Only participants of Caucasian origin were included in the GWAS study. Standard procedures were used to remove data from non-Caucasian individuals and related individuals. Further details can be found28.
The Genome-Wide Association Meta-Analysis (GWAMA) software (https://www.geenivaramu.ee/en/tools/gwama) was used to perform a meta-analysis combining the results from the UK Biobank, 23andMe, OAI, and JoCo cohorts.
GWAS-associated analysis: The FUMA web application was used as the main annotation tool, and a Manhattan plot and a Q-Q plot were also generated by this35. LocusZoom (http://locuszoom.org/) was used to provide regional visualization.
FUMA mainly provides 3 types of analysis: the gene analysis, the gene-set analysis and the tissue expression analysis. In gene analysis, summary statistics of SNPs were aggregated to the level of whole genes to test the associations between genes with the phenotype. In gene-set analysis, groups of genes sharing certain biological, functional or other characteristics were tested together to provide insight into the involvement of specific biological pathways or cellular functions in the genetic aetiology of a phenotype. The tissue expression analysis was based on GTEx (https://www.gtexportal.org/home/), which is integrated into FUMA.
To identify genetic correlations between knee pain and all other 234 complex traits, we used linkage disequilibrium score regression through LD Hub v1.9.0 (available at http://ldsc.broadinstitute.org/ldhub/) 36 The LD Hub estimates the bivariate genetic correlations of a phenotype with 234 traits using individual SNP allele effect sizes and the average linkage disequilibrium in a region. Those with P values less than 2.1 × 10−4 (0.05/234) were considered significant surviving Bonferroni correction for multiple testing.
GWAS analysis results (Discovery cohort – UK Biobank)
A total of 501,708 UK Biobank participants were invited to respond to the pain questionnaire during the initial assessment visit (2006-2010). Among those who responded, 29,995 participants selected the 'Knee pain' option (cases), and 197,149 participants selected the 'None of the above' option (controls). After removing samples from non-British participants, those who were related with another individual in the cohort and those who failed QC, we identified 22,204 cases (12,062 males and 10,142 females) and 149,312 controls (71,480 males and 77,832 females) for the GWAS association analysis and there were 15,377,520 SNPs available for the GWAS analysis. The genomic control value (lambda) was 1.06.
Table I summarises the clinical characteristics of these cases and controls. There were statistical differences (P<0.001) in sex, age and BMI between cases and controls in the UK Biobank samples.
Table I Clinical characteristics of knee pain and controls in the UK Biobank
We identified 2 SNP clusters that were associated with knee pain, with genome-wide significance (P < 5 × 10−8, Fig. 1, Table II). Four independent significantly associated SNPs within 2 clusters are shown in the Table II. All significantly associated SNPs (N=107) in the discovery stage are shown in Supplementary Table I.
Table II Summary of the 4 independent and significant SNPs associated with knee pain in the GDF5 and COL27A1 regions
Fig. 1. The Manhattan plot of the GWAS on knee pain using the UK Biobank cohort
The most significantly associated SNP cluster was in the GDF5 gene in the chromosome 20q11.22 region with a P value of 1.32 × 10−12 for rs143384 (A allele, odds ratio (OR): 0.992). The second most significantly associated cluster was in the LOC105376225 gene (near the COL27A1 gene) in chromosome 9 with a lowest P value of 1.49 × 10−8 for rs2808772 (A allele, OR: 1.006). The regional plots for loci in GDF5 and COL27A1 are shown in the Supplementary Fig. 1 and 2.
The Q-Q plot of the GWAS in the discovery stage is shown in the Supplementary Fig. 3. The SNP-based heritability of knee pain was 0.08 (standard error = 0.03).
Replication stage results (23andMe, OAI, JoCo)
In the stage 1 replication, the P value of rs143384 was 2.44 × 10−9 in the 23andMe cohort and in the stage 2 replication, the P value of rs143384 was 0.01 in the combined OAI and JoCo cohorts. The P values of rs2808772 were 4.43 × 10−5 in the 23andMe and 0. 36 in the combined OAI and JoCo cohorts. (Table II)
Meta-analysis results (UK Biobank, 23andMe, OAI, JoCo)
In the joint meta-analysis between the discovery and replication cohorts, the meta-analysis P values of all 4 independent and significant SNPs from 2 loci remained genome-wide significant and increased in significance. The meta-analysis results showed that the combined P values for rs143384 and rs2808772 were 4.64 × 10−18 (A allele, OR: 0.9905) and 2.56 × 10−11 (A allele, OR:1.007), respectively (Table II).
Gene analysis, gene-set analysis and tissue expression analysis by FUMA
In the gene analysis, all the SNPs that are located within genes were mapped to 19,436 protein coding genes. GDF5 demonstrated the most significant association, with a P value of 1.09 × 10−11. The 11 associated genes with P values less than 3 × 10−6 (0.05/19436) were GDF5, UQCC1, CEP250, PODXL, C20orf173, SPAG4, MTMR3, ERGIC3, FBLN2, CPNE1, CDC42SE2. The results are included in the Supplementary Table II.
In the gene-set analysis, a total of 10,894 gene sets were tested. The regulation pathway of breast_cancer_20q11_amplicon demonstrated a P value of 2.59 × 10−8 and this was the only gene set with a statistically significant association (P < 5 × 10−6 (0.05/10,894)). The top 10 gene sets from this analysis are shown in the Supplementary Table III.
In the tissue expression analysis, none of the tissue types demonstrated statistically significant associations (P < 0.001), either in the expression analysis of 30 general tissue types from multiple organs or in the 53 specific tissue types within some of these organs. See Supplementary Fig. 4 and 5.
Genetic correlation analysis by LD Hub
We identified multiple significant and negative genetic correlations for knee pain with all other traits (Supplementary Table IV). The genetic correlations (rg) surviving multiple testing correction were: Years of schooling 2016 (rg = −0.29, P = 4.97 × 10−8), College completion (rg = −0.36, P = 6.55 × 10−6), Age of having first baby (rg = −0.30, P = 1.92 × 10−5).
In the first reported GWAS of knee pain using the UK Biobank resource, we identified genome-wide significant variants in or near GDF5 and COL27A1, which were subsequently replicated in osteoarthritis cohorts from the 23andMe, OAI, and JoCo cohorts. In addition, we found that knee pain was genetically and negatively correlated with a number of socioeconomic factors such as years of schooling and college completion.
The generic pain question used by the UK Biobank is useful as a screening tool and a useful step to test whether heterogeneous pain phenotypes (such as knee pain) have genetic components at all. We have successfully used the same question to identify the genetic variants of broadly-defined headache, and our findings were similar to those for well-defined migraine phenotypes30,39. The benefit of using UK Biobank on heterogeneous phenotypes will allow researchers to overcome potential issues with reduced power due to heterogeneity by using very large numbers to cut through the statistical noise.
In this GWAS, we have identified 2 loci for knee pain. The top locus was in the GDF5 gene in chromosome20q11.2 with a lowest P value of 1.32 × 10−12 for rs143384 while the locus itself was 140kb long spanning from the UQCC1 gene to the GDF5 gene containing 104 significant SNPs (Supplementary Table I). The GDF5 gene encodes a secreted ligand of the transforming growth factor-beta (TGF-beta) superfamily of proteins40. This protein not only regulates the development of numerous tissue and cell types, but also promotes the maintenance and repair of synovial joint tissues, particularly cartilage and bones40,41. Mutations in the gene can cause cartilage or bone related disorders such as chondrodysplasia, acromesomelic dysplasia, and brachydactyly, suggesting a protective role in skeletal development40. The GDF5 gene has been repeatedly reported to be associated with osteoarthritis through genetic studies17,27. Functional studies have suggested that knee morphology is profoundly affected by Gdf5 absence in mice models, and downstream regulatory sequences mediate its effects by controlling Gdf5 expression in knee tissues42. It was also suggested that osteoarthritis susceptibility mediated by variants in the GDF5 gene was not restricted to cartilage but joint wide43. Recently, Terence et al combined transgenetic mice model with population genetic analyses in humans to identify a GDF5 enhancer that influences human growth and osteoarthritis risk44. Overall, there is sufficient and solid biological evidence relating the GDF5 gene with knee osteoarthritis, and we assume that this finding is due to detection of knee pain caused by osteoarthritis, rather than other pathologies.
The second SNP cluster was in the LOC105376225 area (which is next to the COL27A1 gene) in chromosome 6 with a lowest P value of 1.49 × 10−8 for rs2808772. There have been no specific studies published about LOC105376225 and its relationship with knee pain or knee osteoarthritis. However, the neighbouring COL27A1 gene is clearly a good candidate gene. This gene encodes a member of the fibrillar collagen family, and plays a role during the calcification of cartilage and the transition of cartilage to bone45. Mutations in the COL27A1 gene have been reported to be associated with the Steel syndrome. This syndrome is characterized by bone changes such as bilateral hip and radial head dislocations, short stature, characteristic facies, fusion of carpal bones, scoliosis, pes cavus, and cervical spine anomalies46. Further, the gene was reported to be associated with knee osteoarthritis in the first stage, but did not replicate in the second stage in a recent GWAS study on knee osteoarthritis28. Thus, our large study on knee pain has suggested that the COL27A1 gene might play a role in the knee area. Importantly, polymorphisms in the gene have been associated with tendinopathy around the ankle joint47. The concordance between radiographically defined knee osteoarthritis and knee pain is quite poor, with between 15% and 81% of patients diagnosed by radiographic methods having pain symptoms48. It is therefore likely that many people reporting knee pain have pain that is not bone or cartilage related, but tendon related.
Our study focused on knee pain as a broad phenotype and the genes that we identified and replicated are suggested to be related to knee osteoarthritis. This suggests that the phenotype we chose was genetically similar to the phenotype of knee osteoarthritis. The relationship between knee pain and knee osteoarthritis deserves further investigation. Studies have shown that people with end-stage knee osteoarthritis all presented with knee pain49, but this might not be the case for early stage knee osteoarthritis. As described above, 20% of knee pain was not caused by knee osteoarthritis and only 50% of knee osteoarthritis patients with radiographic evidence had knee pain symptoms5.
The gene-analysis by FUMA also supports our finding that GDF5 was the strongest gene for knee pain. The gene-set analysis by FUMA revealed that the regulation pathway of Nikolsky_breast_cancer_20q11_amplicon signaling was associated with the phenotype we use. We noticed that GDF5 and this amplicon were both located in chromosome 20q11 area and it was reported that GDF5 protein regulates TGF-beta dependent angiogenesis in breast carcinoma MCF-7 Cells50.
The SNP-based heritability for knee pain was 0.08 in our study, which is the first report of its kind. We identified that knee pain was genetically and negatively correlated with a number of phenotypes such as years of schooling, college completion and age of having first baby. This means that those with more years of schooling, those with completed college education, and those who were older when they had their first baby were less likely to report current troublesome knee pain. These factors could be related to lifestyle and occupation.
Using the CaTS power calculator (http://csg.sph.umich.edu/abecasis/cats/), we had 80% power to identify SNP associations with a significance level of 5 × 10−8, based on 22,204 cases and 149,312 controls, assuming an additive model, a minor disease allele frequency of 0.20, a genotypic relative risk of 1.06, and an estimated prevalence of knee pain in the general population of 0.2.
In this study, we used different but similar phenotypes in discovery and replication stages. We defined cases and controls based on the responses by UK Biobank participants to a specific pain question. This question focused on knee pain occurrence, sufficient to cause interference with activities, during the previous month. The question does not ask information of the severity and frequency and the exact area of knee pain. Therefore, our phenotyping should be considered as widely defined. The situation was similar for the 23andMe cohort, in which disease status was also self-reported via survey and self-reported osteoarthritis in the 23andMe cohort was not specific to the knee. Self-reported knee pain has been widely used in other studies as well, though not for studies of genetic associations37,38. The OAI and JoCo assessed radiographic evidence of knee osteoarthritis but with limited sample size.
In conclusion, we have identified 2 loci (GDF5 and COL27A1) for knee pain in a GWAS using the UK Biobank resource and replicated them in the 23andMe, OAI, and JoCo cohorts. In addition, we found several significant and negative genetic correlations between knee pain and a number of educational phenotypes, suggesting that the genetic aetiology of knee pain may also be related to these traits.
WM organised project, drafted the paper and contributed to the analysis. MA performed the main UK Biobank GWAS analysis. CP, BM, MY provided essential comments. KR, JJ, BM, RJ and MY provided the OAI and JoCo GWAS summary statistics on knee osteoarthritis. JS and AA performed replication in the 23andMe cohort. AM and BS organised the project and provided comments.
Role of the funding source
This work was supported by the STRADL project (Wellcome Trust, grant number: 104036/Z/14/Z). The Osteoarthritis Initiative (OAI) was public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the NIH. The the Johnston County Osteoarthritis Study (JoCo) was supported in part by S043, S1734, & S3486 from the CDC/Association of Schools of Public Health; 5-P60-AR30701 & 5-P60-AR49465-03 from NIAMS/NIH; genotyping was supported by Algynomics, Inc. Additional support was obtained from NIH grant P30-DK072488.
J.S., A.A., and members of the 23andMe Research Team are employees of 23andMe, Inc., and hold stock or stock options in 23andMe. Other coauthors have no conflicts of interest.
The summary statistics of the UK Biobank results on knee pain can be shared upon request to non - commercial researchers.
We would like to thank all participants of the UK Biobank, 23andMe and the OAI and JoCo cohorts who have provided necessary genetic and phenotypic information. The current study was conducted under approved UK Biobank data application number 4844.
Members of the 23andMe Research Team: Michelle Agee, Babak Alipanahi, Robert K. Bell, Katarzyna Bryc, Sarah L. Elson, Pierre Fontanillas, Nicholas A. Furlotte, Barry Hicks, David A. Hinds, Karen E. Huber, Ethan M. Jewett, Yunxuan Jiang, Aaron Kleinman, Keng-Han Lin, Nadia K. Litterman, Jennifer C. McCreight, Matthew H. McIntyre, Kimberly F. McManus, Joanna L. Mountain, Elizabeth S. Noblin, Carrie A.M. Northover, Steven J. Pitts, G. David Poznik, J. Fah Sathirapongsasuti, Janie F. Shelton, Suyash Shringarpure, Chao Tian, Joyce Y. Tung, Vladimir Vacic, Xin Wang, Catherine H. Wilson.
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Jinks C, Jordan K, Ong BN, Croft P. A brief screening tool for knee pain in primary care (KNEST). 2. Results from a survey in the general population aged 50 and over. Rheumatology (Oxford) 2004;43:55–61.
Nguyen US, Zhang Y, Zhu Y, Niu J, Zhang B, Felson DT. Increasing prevalence of knee pain and symptomatic knee osteoarthritis: survey and cohort data. Ann Intern Med 2011;155:725–32.
Wallace IJ, Worthington S, Felson DT, Jurmain RD, Wren KT, Maijanen H, et al. Knee osteoarthritis has doubled in prevalence since the mid-20th century. Proc Natl Acad Sci U S A 2017;114:9332–6.
National Institute for Health and Care Excellence (NICE). Clinical guideline CG177. Osteoarthritis: care and management in adults. 2014.
GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017;390:1211–59.
Bindawas SM, Vennu V, Al Snih S. Differences in health-related quality of life among subjects with frequent bilateral or unilateral knee pain: data from the Osteoarthritis Initiative study. J Orthop Sports Phys Ther 2015;45:128–36.
10.↵
Prieto-Alhambra D, Judge A, Javaid MK, Cooper C, Diez-Perez A, Arden NK. Incidence and risk factors for clinically diagnosed knee, hip and hand osteoarthritis: influences of age, gender and osteoarthritis affecting other joints. Ann Rheum Dis 2014;73:1659–64.
Chen A, Gupte C, Akhtar K, Smith P, Cobb J. The Global Economic Cost of Osteoarthritis: How the UK Compares. Arthritis 2012;2012:698709
Miranda H, Viikari-Juntura E, Martikainen R, Riihimäki H. A prospective study on knee pain and its risk factors. Osteoarthr itis Cartilage 2002;10:623–30.
Heidari B. Knee osteoarthritis prevalence, risk factors, pathogenesis and features: Part I. Caspian J Intern Med 2011;2:205–12.
Iijima H, Aoyama T, Fukutani N, Isho T, Yamamoto Y, Hiraoka M, et al. Psychological health is associated with knee pain and physical function in patients with knee osteoarthritis: an exploratory cross-sectional study. BMC Psychol 2018;6:19.
Neame RL, Muir K, Doherty S, Doherty M. Genetic risk of knee osteoarthritis: a sibling study. Ann Rheum Dis 2004;63:1022–7.
Ikegawa S. New gene associations in osteoarthritis: what do they provide, and where are we going? Curr Opin Rheumatol 2007;19:429–34.
Miyamoto Y, Mabuchi A, Shi D, Kubo T, Takatori Y, Saito S, et al. A functional polymorphism in the 5' UTR of GDF5 is associated with susceptibility to osteoarthritis. Nat Genet 2007;39:529–33.
Loughlin J, Mustafa Z, Dowling B, Southam L, Marcelline L, Räinä SS, et al. Finer linkage mapping of a primary hip osteoarthritis susceptibility locus on chromosome 6. Eur J Hum Genet 2002;10:562–8.
Meulenbelt I, Seymour AB, Nieuwland M, Huizinga TW, van Duijn CM, Slagboom PE. Association of the interleukin-1 gene cluster with radiographic signs of osteoarthritis of the hip. Arthritis Rheum 2004;50:1179–86.
Spector TD, Reneland RH, Mah S, Valdes AM, Hart DJ, Kammerer S, et al. Association between a variation in LRCH1 and knee osteoarthritis. Arthritis Rheum 2006;54:524–532.
Valdes AM, van Oene M, Hart DJ, Surdulescu GL, Loughlin J, Doherty M, et al. Reproducible genetic associations between candidate genes and clinical knee osteoarthritis in men and women. Arthritis Rheum 2006;54:533–9.
Nakajima M, Takahashi A, Kou I, Rodriguez-Fontenla C, Gomez-Reino JJ, Furuichi T, et al. New sequence variants in HLA class II/III region associated with susceptibility to knee osteoarthritis identified by genome-wide association study. PLoS One 2010;5.
Miyamoto Y, Shi D, Nakajima M, Ozaki K, Sudo A, Kotani A, et al. Common variants in DVWA on chromosome 3p24.3 are associated with susceptibility to knee osteoarthritis. Nat Genet 2008;40:994–8.
Evangelou E, Valdes AM, Kerkhof HJ, Styrkarsdottir U, Zhu Y, Meulenbelt I, et al. Meta-analysis of genome-wide association studies confirms a susceptibility locus for knee osteoarthritis on chromosome 7q22. Ann Rheum Dis 2011;70:349–55.
Day-Williams AG, Southam L, Panoutsopoulou K, Rayner NW, Esko T, Estrada K, et al. A variant in MCF2L is associated with osteoarthritis. Am J Hum Genet 2011;89:446–50.
arcOGEN Consortium; arcOGEN Collaborators, Zeggini E, Panoutsopoulou K, Southam L, Rayner NW, et al. Identification of new susceptibility loci for osteoarthritis (arcOGEN): a genome-wide association study. Lancet 2012;380:815–23.
Valdes AM, Evangelou E, Kerkhof HJ, Tamm A, Doherty SA, Kisand K, et al. The GDF5 rs143383 polymorphism is associated with osteoarthritis of the knee with genome-wide statistical significance. Ann Rheum Dis 2011;70:873–5.
Yau MS, Yerges-Armstrong LM, Liu Y, Lewis CE, Duggan DJ, Renner JB, et al. Genome-Wide Association Study of Radiographic Knee Osteoarthritis in North American Caucasians. Arthritis Rheumatol 2017;69:343–51.
Zengini E, Hatzikotoulas K, Tachmazidou I, Steinberg J, Hartwig FP, Southam L, et al. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Genet 2018;50:549–58.
Meng W, Adams MJ, Hebert HL, Deary IJ, McIntosh AM, Smith BH. A Genome-Wide Association Study Finds Genetic Associations with Broadly-Defined Headache in UK Biobank (N=223,773). EBioMedicine 2018;28:180–6.
Suri P, Palmer MR, Tsepilov YA, Freidin MB, Boer CG, Yau MS, et al. Genome-wide meta-analysis of 158,000 individuals of European ancestry identifies three loci associated with chronic back pain. PLoS Genet 2018;14:e1007601.
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. Genome-wide genetic data on ~500,000 UK Biobank participants. BioRxiv 2017. doi: https://doi.org/10.1101/166298.
Warrington NM, Shevroja E, Hemani G, Hysi PG, Jiang Y, Auton A, et al. Genome-wide association study identifies nine novel loci for 2D:4D finger ratio, a putative retrospective biomarker of testosterone exposure in utero. Hum Mol Genet 2018;27:2025–38.
Jordan JM, Helmick CG, Renner JB, Luta G, Dragomir AD, Woodard J, et al. Prevalence of knee symptoms and radiographic and symptomatic knee osteoarthritis in African Americans and Caucasians: the Johnston County Osteoarthritis Project. J Rheumatol 2007;34:172–80.
Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun 2017;8:1826.
Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 2017;33:272–9.
Baldwin JN, McKay MJ, Simic M, Hiller CE, Moloney N, Nightingale EJ, et al. Self-reported knee pain and disability among healthy individuals: reference data and factors associated with the Knee injury and Osteoarthritis Outcome Score (KOOS) and KOOS-Child. Osteoarthritis Cartilage 2017;25:1282–90.
Ho-Pham LT, Lai TQ, Mai LD, Doan MC, Pham HN, Nguyen TV. Prevalence of radiographic osteoarthritis of the knee and its relationship to self-reported pain. PLoS One 2014;9:e94563.
Gormley P, Anttila V, Winsvold BS, Palta P, Esko T, Pers TH, et al. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat Genet 2016;48:856–66.
https://www.ncbi.nlm.nih.gov/gene/8200
Mikic B. Multiple effects of GDF-5 deficiency on skeletal tissues: implications for therapeutic bioengineering. Ann Biomed Eng 2004;32:466–76.
Pregizer SK, Kiapour AM, Young M, Chen H, Schoor M, Liu Z, et al. Impact of broad regulatory regions on Gdf5 expression and function in knee development and susceptibility to osteoarthritis. Ann Rheum Dis 2018;77:450.
Egli RJ, Southam L, Wilkins JM, Lorenzen I, Pombo-Suarez M, Gonzalez A, et al. Functional analysis of the osteoarthritis susceptibility-associated GDF5 regulatory polymorphism. Arthritis Rheum 2009;60:2055–64.
Capellini TD, Chen H, Cao J, Doxey AC, Kiapour AM, Schoor M, et al. Ancient selection for derived alleles at a GDF5 enhancer influencing human growth and osteoarthritis risk. Nat Genet 2017;49:1202–10.
https://www.ncbi.nlm.nih.gov/gene/85301
Gonzaga-Jauregui C, Gamble CN, Yuan B, Penney S, Jhangiani S, Muzny DM, et al. Mutations in COL27A1 cause Steel syndrome and suggest a founder mutation effect in the Puerto Rican population. Eur J Hum Genet 2015;23:342–6.
Saunders CJ, van der Merwe L, Posthumus M, Cook J, Handley CJ, Collins M, et al. Investigation of variants within the COL27A1 and TNC genes and Achilles tendinopathy in two populations. J Orthop Res 2013;31:632–7.
Bedson J, Croft PR. The discordance between clinical and radiographic knee osteoarthritis: a systematic search and summary of the literature. BMC Musculoskelet Disord 2008;9:116.
Zeni JA Jr., Axe MJ, Snyder-Mackler L. Clinical predictors of elective total joint replacement in persons with end-stage knee osteoarthritis. BMC Musculoskelet Disord 2010;11:86.
Margheri F, Schiavone N, Papucci L, Magnelli L, Serratì S, Chillà A, et al. GDF5 regulates TGFß-dependent angiogenesis in breast carcinoma MCF-7 cells: in vitro and in vivo control by anti-TGFß peptides. PLoS One 2012;7:e50342.
Posted January 20, 2019.
You are going to email the following A genome-wide association study identifies that the GDF5 and COL27A1 genes are associated with knee pain in UK Biobank (N = 171, 516)

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When whipping egg whites, first wash the mixing bowl with white vinegar. You will turn a 5-minute task into a 30-second one, and your egg whites will stay fluffier longer.
Good to know for this week's holiday cookie bake-a-thon.


text:
What percent of interracial marriages end in divorce?
An analysis conducted a decade ago found that 10 years after they married, interracial couples had a 41% chance of separation or divorce, compared with a 31% chance among couples who married within their race, according to a study based on the 1995 National Survey of Family Growth (NSFG).
Secondly, What ethnic group has the highest divorce rate?
All racial-ethnic groups had more marriages than divorces. …
Black women were the only group that had a higher divorce rate than marriage rate, with nearly 31 divorces per 1,000 married women aged 15 and older and only 17.3 marriages per 1,000 unmarried women.
Do interracial couples have higher divorce rates?
Divorce rates among interracial couples are slightly higher than divorce rates among same-race couples, but interracial marriages in the United States have climbed to 4.8 million – a record 1 in 12 – as a steady flow of new Asian and Hispanic immigrants expands the pool of prospective spouses.
Similarly, What interracial marriage has the highest divorce rate? Marital stability
Relative divorce rate by pairing
1 Black, Black 1.63
2 Black husband, White wife 1.62
3 Black, White 1.55
4 White husband, Black wife 1.44
1 What is the Black divorce rate?
2 Which gender initiates more divorce?
3 What is the #1 cause of divorce?
4 What are the benefits of interracial relationships?
4.1 What divorce does to a man?
5 What are the advantages of interracial marriage?
What is the Black divorce rate?
Black men and women both experience at least one divorce at a rate of about 42%.
What is the percent of interracial marriages? The percentage of married-couple households that are interracial or interethnic grew across the United States from 7.4 to 10.2 percent from 2000 to 2012-2016. This change varied across states and counties and for specific interracial/interethnic combinations.
Who is most likely to get divorced? Wives are the ones who most often file for divorce at 66 percent on average. That figure has soared to nearly 75 percent in some years.
Why do interracial relationships fail? The racial and cultural differences in your interracial marriage won't necessarily cause your relationship to fail. What can cause an interracial marriage to fall apart is the inability of a couple to handle their differences and a failure to talk about the stresses one or both of them are experiencing.
Which gender initiates more divorce?
In fact, nearly 70 percent of divorces are initiated by women. This is according to a 2015 research study conducted by the American Sociological Association (ASA) which suggests two-thirds of all divorces are initiated by women. Among college-educated women, this number jumps up to 90%.
What race has the lowest marriage rate? Contemporary Differences
At all ages, black Americans display lower marriage rates than do other racial and ethnic groups (see table 1, panel A). Consequently, a far lower proportion of black women have married at least once by age 40.
Which couple is the most likely to intermarry?
Asian and Hispanic newlyweds are by far the most likely to intermarry in the U.S. About three-in-ten Asian newlyweds 3 (29%) did so in 2015, and the share was 27% among recently married Hispanics. For these groups, intermarriage is even more prevalent among the U.S.
Why do interracial marriages fail? What can cause an interracial marriage to fall apart is the inability of a couple to handle their differences and a failure to talk about the stresses one or both of them are experiencing.
What is the #1 cause of divorce?
The most commonly reported major contributors to divorce were lack of commitment, infidelity, and conflict/arguing. The most common "final straw" reasons were infidelity, domestic violence, and substance use. More participants blamed their partners than blamed themselves for the divorce.
What year of marriage is most common for divorce?
While there are countless divorce studies with conflicting statistics, the data points to two periods during a marriage when divorces are most common: years 1 – 2 and years 5 – 8. Of those two high-risk periods, there are two years in particular that stand out as the most common years for divorce — years 7 and 8.
What is the average length of a second marriage? The median amount of time that it takes someone to get married after a divorce is 3.7 years, which has been fairly stable since 1950. Sadly, the average length of time for second marriages ending in divorce will typically just under eight years.
What are the advantages of mixed marriage? A benefit of interracial marriages is that it increases the opportunity for positive interracial encounters. Research has found a reduction in prejudice and discrimination towards members of an out-group (someone from whom one has a different racial identity) when one has positive interracial encounters.
What are the benefits of interracial relationships?
Over time, a lot of interracial marriage benefits have been observed all around the world along with a lot more support than hate experienced just decades ago!
Diverse Genetics (Less Recessive Diseases)
Exploring New Cultures.
Sharing Your Culture.
Traveling To Different Places.
Open-Mindedness.
Was interracial marriage ever illegal in Canada? Unlike the United States, Canada had no blatant laws banning interracial marriage. But while the stigma was more informal in this country, it could be just as terrifying.
What is the number 1 reason for divorce?
The most commonly reported major contributors to divorce were lack of commitment, infidelity, and conflict/arguing. The most common "final straw" reasons were infidelity, domestic violence, and substance use.
Who regrets divorce more? There are many beliefs, myths and misconceptions which surround divorce, one of which is who regrets divorce more — men or women. In a study conducted by legal website www.avvo.com, 73 percent of women reported having no regret over their divorce, compared to 61 percent of men.
What divorce does to a man?
Men experience more health problems in the process and after a divorce. The most common health problems include weight fluctuations, depression, anxiety, and insomnia. Men also have the added stress of handling all the finances and identity loss, which makes them much more susceptible to both stroke and heart disease.
Which religion has the highest divorce rate? People tend to throw around the "fact" that "50% of all marriages end in divorce." This popular myth has hooked its claws deep into the American mind, yet its claims are simply untrue.
Moving up the ladder, Barna's averages by group totaled to:
Born-Again Christian: 27%
Jewish: 30%
Muslim: 31%
Protestant: 34%
What race has the highest divorce rate 2021?
However, the 15-24 year old age range has the highest divorce rate of any age group.
Divorce rates by ethnicity were as follows in 2018:
Black women: 30.8 divorces per 1,000 people.
Hispanic women: 18.5 divorces per 1,000 people.
White women: 15.1 divorces per 1,000 people.
Others: 12.4 divorces per 1,000 people.
Which marriages last the longest? The average length of a marriage in the US is 8.2 years.
Whilst the national average marriage length is just over eight years, couples in New York typically have the longest-lasting unions. The typical marriage in the Empire State lasts for 12.2 years, which is significantly higher than the national average.
What are the advantages of interracial marriage?
A benefit of interracial marriages is that it increases the opportunity for positive interracial encounters. Research has found a reduction in prejudice and discrimination towards members of an out-group (someone from whom one has a different racial identity) when one has positive interracial encounters.
Which U.S. city has the most interracial couples? Honolulu has by far the highest share of intermarried newlyweds of any metro area analyzed – 42% of newlyweds living in and around that city were married to someone of a different race or ethnicity.
Where can I go for Easter in NZ?
How do you quantify impressions?


Requests for repairs, complaints, problems or questions should be directed to Tina Schlemme, Drainage Clerk in the Auditor's Office, by calling 641-939-8111 or by email.
For more information on drainage district projects or to view online maps of the districts, visit the Drainage District Search website (Beacon). Be sure to select the 'Drainage Tiles' and 'Drainage Districts' check boxes on the left under Layers.
Can the construction company be on my property?
Can I be reimbursed for property damages caused by construction?
Click on the tab below for information about each drainage district.
April 11, 2018 - Trustees awarded bid to Hands On Excavating in the amount of $149,516.85 for the total project (See Bid Tab for individual DDs amounts).
Public Hearing on Classification Reports set for Wednesday, March 6, 2019 at 12:00 in the Hardin County Courthouse.
Wednesday, March 6, 2019 at 12:00 P.M.
Public Hearing on Engineer's Report on Repairs or Improvements to Main Tiles set for Wednesday, March 6, 2019 at 1:00 in the Hardin County Courthouse.
Wednesday, March 6, 2019 at 1:00 P.M.
Public Hearing on Engineer's Report on Improvements to Main Tile set for Wednesday, March 27, 2019 at 1:00 in the Hardin County Courthouse.
Wednesday, March 27, 2019 at 1:00 P.M.
Lateral 7 to be repaired.
Main tile project on hold while landowner determines wetland programs.
Landowner meeting to discuss trees within 50 feet of tile from west end of town to east end.
Project moving forward after being delayed from railroad.
Combined with DD 1 boring project.
Engineer creating report with options that include repair, replace and to improve tile. Once report is received, a hearing will be scheduled.
Completion of project accepted on December 12, 2018.
Project combined with DD 146 & 165 to obtain better pricing.


Setting up Concrete5 with Cherokee is really easy. A configuration wizard is provided that can either be used to install Concrete5 as a new dedicated Virtual Server, or to set it up inside a Directory.
This recipe contains three sections detailing how to: . Configure Cherokee for either installation mode using the Wizard. . Install Concrete5 v3.3.1 which is the last one available at the time of writing. . Configure Cherokee manually.
Two common scenarios are contemplated: installation on a dedicated virtual server (http://concrete5.example.net/, for instance) or under a subdirectory on an existing virtual server (as could be http://example.net/concrete5/).
The first step would be to go to the virtual server list page. Once in there, click on the Add button at the top of the panel to show the list of available wizards.
Now, you will have to select the CMS category, and run the Concrete5 wizard. At this stage the Concrete5 wizard will ask you for some basic parameters about the installation. It will try to auto-fill as many entries of the form as possible. For instance, if it finds Concrete5 installed in a standard location it will use that path as the default directory.
The new virtual server will be created after this form is submitted. The virtual server configuration will cover the vast majority of the cases. Most probably it will cover your needs, although you are free to modify it as you will.
Concrete5 can also be configured inside a directory. Let's suppose we want to set up Concrete5 inside the /blog directory of one of our virtual servers.
In this case, the first thing you will have to do is to enter the virtual server configuration you'd like to configure Concrete5 in. Visit the Behavior tab, use the Rule Management button and click on the Add button at the top of the panel.
As in the previous case, this wizard will ask some very basic information, including the public web directory where Concrete5 will be set up and the local Concrete5 source directory.
And create the database for Concrete5. We will be using the name concrete5, the user concrete5user and the password concrete5password, but you should set up your own.
Then point your web browser to the point specified when you ran the wizard (i.e. http://localhost/concrete5, http://example.com/, etc) and follow the instructions provided by the Concrete5 installer.
There is not much else you must do for Cherokee to work with Concrete5. If you follow the Concrete5 installer's instructions -providing valid database information in most cases will suffice-, you will be done in an instant.


text:
Japan - Front
Japan - Back
ラジアータ ストーリーズ
09/06/05 Square Enix
Owners: 114
Radiata Stories (PS2)
In a world where humans and fairies live side-by-side, lies the human kingdom called "Radiata". There, a young boy, Jack, an aspiring knight meets a girl, Ridley, an obligated knight. The outbreak of war between humans and fairies awaits the two, as they become the knights of the kingdom. As their world heads down the path of doom, the two must face an uncertain future. A fate clouded by shadow...
tri-Ace Corporation
Critic Review Added to Database Son1x 15th Dec 2009
Game Information Updated Son1x 15th Dec 2009
Game Information Updated oliist 29th Aug 2009
Yamaneko22 posted 07/05/2011, 09:10
One of my fav. games as well:) Too bad that it wasn't released in Europe:/
DarkFury posted 13/09/2009, 06:46
I loved this game. I would love to know the total sales figure. Here is for hoping it was at least 1M.
zexen_lowe posted 07/12/2008, 06:51
It's a good game, maybe a high seven or a low 8, it becomes too repetitive at the end, and the story, while funny, falters at some point. Still, it's worth playing, specially if you play it with the amazing Japanese VA (Junko Takeuchi as Jack Russell =WIN)
ms07 posted 11/08/2008, 02:11
Great game, and surprisingly good dub.
Riachu posted 21/06/2008, 05:31
Radiata Stories did get hype. It's just that all of it was from Japan and not the US unlike SO3, another tri-Ace game
Dark Odin posted 19/05/2008, 11:11
It was good. I´d say a 8.
maybe sold goos in NA... who knows.


text:
15-та дивізія — найменування дивізії в збройних силах різних держав.

Кавалерійські дивізії

СРСР
 15-та гвардійська кавалерійська Мозирська Червонопрапорна ордена Суворова дивізія — кавалерійська дивізія РСЧА.

Російська імперія
 15-та кавалерійська дивізія (Російська імперія) — кавалерійська дивізія Російської імператорської армії.

Моторизовані дивізії

СРСР
 15-та моторизована дивізія (СРСР) — моторизована дивізія РСЧА з 1941 року.

Піхотні дивізії

Німеччина
 15-та авіапольова дивізія (Третій Рейх) — піхотна дивізія Вермахту в 1942—1943 роках.
 15-та піхотна дивізія (Третій Рейх) — піхотна дивізія Вермахту в 1934—1945 роках.
 15-та гренадерська дивізія СС (1-ша латвійська) — піхотна дивізія Ваффен-CC в 1943—1945 роках.

Російська імперія
 15-та піхотна дивізія (Російська імперія) — піхотна дивізія Російської імператорської армії в 1831—1918 роках.

СРСР
 15-та стрілецька дивізія (РСЧА) — піхотна дивізія РСЧА в 1919—1957 роках.
 15-та гвардійська Харківсько-Празька ордена Леніна двічі Червонопрапорна орденів Суворова і Кутузова стрілецька дивізія — піхотна дивізія РСЧА в 1942—1991 роках.

Танкові дивізії

Німеччина
 15-та танкова дивізія (Третій Рейх) — танкова дивізія Вермахту в 1940—1943 роках.
 15-та панцергренадерська дивізія (Третій Рейх) — танкова дивізія Вермахту в 1943—1945 роках.

СРСР
 15-та танкова дивізія (СРСР) — танкова дивізія РСЧА в 1940—1941 роках.
 15-та гвардійська танкова Мозирська Червонопрапорна ордена Суворова дивізія — танкова дивізія РСЧА.

redpajama_set_name: RedPajamaWikipedia


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Watlington Business Association are inviting residents and businesses to a public meeting on the Government's Gigabit voucher scheme which could upgrade your broadband connection completely free of charge.
View the full details of the meeting here.
The Gigabit Broadband Voucher Scheme is part of the Local Full Fibre Networks programme, launched in late 2017, which has the strategic objective of increasing full fibre coverage across the UK. Full fibre broadband connections offer the fastest and most reliable speeds available. More information on the scheme and how it can benefit your business.


The AR-ARCH and AR-GARCH models, which allow for conditional heteroskedasticity and autoregression, reduce to random walk or white noise for some values of the parameters. We consider generalised versions of the AR-ARCH(1) and AR-GARCH(1,1) models, and, under mild assumptions, calculate the asymptotic distributions of pseudo-likelihood ratio statistics for testing hypotheses that reect these reductions. These hypotheses are of two kinds: the conditional volatility parameters may take their boundary values of zero, or the autoregressive component may take the form of a unit root process or not in fact be present. The limiting distributions of the resulting test statistics can be expressed in terms of functionals of Brownian motion related to the Dickey-Fuller statistic, together with independent chi-square components. The finite sample performances of the test statistics are assessed by simulations, and percentiles are tabulated. The results have applications in the analysis of financial time series and random coefficient models.


text:
<?php
namespace Aws\Pinpoint;

use Aws\Api\ApiProvider;
use Aws\Api\DocModel;
use Aws\Api\Service;
use Aws\AwsClient;

/**
 * This client is used to interact with the **Amazon Pinpoint** service.
 * @method \Aws\Result createApp(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createAppAsync(array $args = [])
 * @method \Aws\Result createCampaign(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createCampaignAsync(array $args = [])
 * @method \Aws\Result createEmailTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createEmailTemplateAsync(array $args = [])
 * @method \Aws\Result createExportJob(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createExportJobAsync(array $args = [])
 * @method \Aws\Result createImportJob(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createImportJobAsync(array $args = [])
 * @method \Aws\Result createJourney(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createJourneyAsync(array $args = [])
 * @method \Aws\Result createPushTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createPushTemplateAsync(array $args = [])
 * @method \Aws\Result createSegment(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createSegmentAsync(array $args = [])
 * @method \Aws\Result createSmsTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createSmsTemplateAsync(array $args = [])
 * @method \Aws\Result createVoiceTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise createVoiceTemplateAsync(array $args = [])
 * @method \Aws\Result deleteAdmChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteAdmChannelAsync(array $args = [])
 * @method \Aws\Result deleteApnsChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteApnsChannelAsync(array $args = [])
 * @method \Aws\Result deleteApnsSandboxChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteApnsSandboxChannelAsync(array $args = [])
 * @method \Aws\Result deleteApnsVoipChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteApnsVoipChannelAsync(array $args = [])
 * @method \Aws\Result deleteApnsVoipSandboxChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteApnsVoipSandboxChannelAsync(array $args = [])
 * @method \Aws\Result deleteApp(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteAppAsync(array $args = [])
 * @method \Aws\Result deleteBaiduChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteBaiduChannelAsync(array $args = [])
 * @method \Aws\Result deleteCampaign(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteCampaignAsync(array $args = [])
 * @method \Aws\Result deleteEmailChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteEmailChannelAsync(array $args = [])
 * @method \Aws\Result deleteEmailTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteEmailTemplateAsync(array $args = [])
 * @method \Aws\Result deleteEndpoint(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteEndpointAsync(array $args = [])
 * @method \Aws\Result deleteEventStream(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteEventStreamAsync(array $args = [])
 * @method \Aws\Result deleteGcmChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteGcmChannelAsync(array $args = [])
 * @method \Aws\Result deleteJourney(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteJourneyAsync(array $args = [])
 * @method \Aws\Result deletePushTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deletePushTemplateAsync(array $args = [])
 * @method \Aws\Result deleteSegment(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteSegmentAsync(array $args = [])
 * @method \Aws\Result deleteSmsChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteSmsChannelAsync(array $args = [])
 * @method \Aws\Result deleteSmsTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteSmsTemplateAsync(array $args = [])
 * @method \Aws\Result deleteUserEndpoints(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteUserEndpointsAsync(array $args = [])
 * @method \Aws\Result deleteVoiceChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteVoiceChannelAsync(array $args = [])
 * @method \Aws\Result deleteVoiceTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise deleteVoiceTemplateAsync(array $args = [])
 * @method \Aws\Result getAdmChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getAdmChannelAsync(array $args = [])
 * @method \Aws\Result getApnsChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getApnsChannelAsync(array $args = [])
 * @method \Aws\Result getApnsSandboxChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getApnsSandboxChannelAsync(array $args = [])
 * @method \Aws\Result getApnsVoipChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getApnsVoipChannelAsync(array $args = [])
 * @method \Aws\Result getApnsVoipSandboxChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getApnsVoipSandboxChannelAsync(array $args = [])
 * @method \Aws\Result getApp(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getAppAsync(array $args = [])
 * @method \Aws\Result getApplicationDateRangeKpi(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getApplicationDateRangeKpiAsync(array $args = [])
 * @method \Aws\Result getApplicationSettings(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getApplicationSettingsAsync(array $args = [])
 * @method \Aws\Result getApps(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getAppsAsync(array $args = [])
 * @method \Aws\Result getBaiduChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getBaiduChannelAsync(array $args = [])
 * @method \Aws\Result getCampaign(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getCampaignAsync(array $args = [])
 * @method \Aws\Result getCampaignActivities(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getCampaignActivitiesAsync(array $args = [])
 * @method \Aws\Result getCampaignDateRangeKpi(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getCampaignDateRangeKpiAsync(array $args = [])
 * @method \Aws\Result getCampaignVersion(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getCampaignVersionAsync(array $args = [])
 * @method \Aws\Result getCampaignVersions(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getCampaignVersionsAsync(array $args = [])
 * @method \Aws\Result getCampaigns(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getCampaignsAsync(array $args = [])
 * @method \Aws\Result getChannels(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getChannelsAsync(array $args = [])
 * @method \Aws\Result getEmailChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getEmailChannelAsync(array $args = [])
 * @method \Aws\Result getEmailTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getEmailTemplateAsync(array $args = [])
 * @method \Aws\Result getUserEndpoint(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getUserEndpointAsync(array $args = [])
 * @method \Aws\Result getEventStream(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getEventStreamAsync(array $args = [])
 * @method \Aws\Result getExportJob(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getExportJobAsync(array $args = [])
 * @method \Aws\Result getExportJobs(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getExportJobsAsync(array $args = [])
 * @method \Aws\Result getGcmChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getGcmChannelAsync(array $args = [])
 * @method \Aws\Result getImportJob(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getImportJobAsync(array $args = [])
 * @method \Aws\Result getImportJobs(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getImportJobsAsync(array $args = [])
 * @method \Aws\Result getJourney(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getJourneyAsync(array $args = [])
 * @method \Aws\Result getJourneyDateRangeKpi(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getJourneyDateRangeKpiAsync(array $args = [])
 * @method \Aws\Result getJourneyExecutionActivityMetrics(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getJourneyExecutionActivityMetricsAsync(array $args = [])
 * @method \Aws\Result getJourneyExecutionMetrics(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getJourneyExecutionMetricsAsync(array $args = [])
 * @method \Aws\Result getPushTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getPushTemplateAsync(array $args = [])
 * @method \Aws\Result getSegment(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getSegmentAsync(array $args = [])
 * @method \Aws\Result getSegmentExportJobs(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getSegmentExportJobsAsync(array $args = [])
 * @method \Aws\Result getSegmentImportJobs(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getSegmentImportJobsAsync(array $args = [])
 * @method \Aws\Result getSegmentVersion(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getSegmentVersionAsync(array $args = [])
 * @method \Aws\Result getSegmentVersions(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getSegmentVersionsAsync(array $args = [])
 * @method \Aws\Result getSegments(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getSegmentsAsync(array $args = [])
 * @method \Aws\Result getSmsChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getSmsChannelAsync(array $args = [])
 * @method \Aws\Result getSmsTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getSmsTemplateAsync(array $args = [])
 * @method \Aws\Result getUserEndpoints(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getUserEndpointsAsync(array $args = [])
 * @method \Aws\Result getVoiceChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getVoiceChannelAsync(array $args = [])
 * @method \Aws\Result getVoiceTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise getVoiceTemplateAsync(array $args = [])
 * @method \Aws\Result listJourneys(array $args = [])
 * @method \GuzzleHttp\Promise\Promise listJourneysAsync(array $args = [])
 * @method \Aws\Result listTagsForResource(array $args = [])
 * @method \GuzzleHttp\Promise\Promise listTagsForResourceAsync(array $args = [])
 * @method \Aws\Result listTemplateVersions(array $args = [])
 * @method \GuzzleHttp\Promise\Promise listTemplateVersionsAsync(array $args = [])
 * @method \Aws\Result listTemplates(array $args = [])
 * @method \GuzzleHttp\Promise\Promise listTemplatesAsync(array $args = [])
 * @method \Aws\Result phoneNumberValidate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise phoneNumberValidateAsync(array $args = [])
 * @method \Aws\Result putEventStream(array $args = [])
 * @method \GuzzleHttp\Promise\Promise putEventStreamAsync(array $args = [])
 * @method \Aws\Result putEvents(array $args = [])
 * @method \GuzzleHttp\Promise\Promise putEventsAsync(array $args = [])
 * @method \Aws\Result removeAttributes(array $args = [])
 * @method \GuzzleHttp\Promise\Promise removeAttributesAsync(array $args = [])
 * @method \Aws\Result sendMessages(array $args = [])
 * @method \GuzzleHttp\Promise\Promise sendMessagesAsync(array $args = [])
 * @method \Aws\Result sendUsersMessages(array $args = [])
 * @method \GuzzleHttp\Promise\Promise sendUsersMessagesAsync(array $args = [])
 * @method \Aws\Result tagResource(array $args = [])
 * @method \GuzzleHttp\Promise\Promise tagResourceAsync(array $args = [])
 * @method \Aws\Result untagResource(array $args = [])
 * @method \GuzzleHttp\Promise\Promise untagResourceAsync(array $args = [])
 * @method \Aws\Result updateAdmChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateAdmChannelAsync(array $args = [])
 * @method \Aws\Result updateApnsChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateApnsChannelAsync(array $args = [])
 * @method \Aws\Result updateApnsSandboxChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateApnsSandboxChannelAsync(array $args = [])
 * @method \Aws\Result updateApnsVoipChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateApnsVoipChannelAsync(array $args = [])
 * @method \Aws\Result updateApnsVoipSandboxChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateApnsVoipSandboxChannelAsync(array $args = [])
 * @method \Aws\Result updateApplicationSettings(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateApplicationSettingsAsync(array $args = [])
 * @method \Aws\Result updateBaiduChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateBaiduChannelAsync(array $args = [])
 * @method \Aws\Result updateCampaign(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateCampaignAsync(array $args = [])
 * @method \Aws\Result updateEmailChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateEmailChannelAsync(array $args = [])
 * @method \Aws\Result updateEmailTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateEmailTemplateAsync(array $args = [])
 * @method \Aws\Result updateUserEndpoint(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateUserEndpointAsync(array $args = [])
 * @method \Aws\Result updateUserEndpointsBatch(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateUserEndpointsBatchAsync(array $args = [])
 * @method \Aws\Result updateGcmChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateGcmChannelAsync(array $args = [])
 * @method \Aws\Result updateJourney(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateJourneyAsync(array $args = [])
 * @method \Aws\Result updateJourneyState(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateJourneyStateAsync(array $args = [])
 * @method \Aws\Result updatePushTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updatePushTemplateAsync(array $args = [])
 * @method \Aws\Result updateSegment(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateSegmentAsync(array $args = [])
 * @method \Aws\Result updateSmsChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateSmsChannelAsync(array $args = [])
 * @method \Aws\Result updateSmsTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateSmsTemplateAsync(array $args = [])
 * @method \Aws\Result updateTemplateActiveVersion(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateTemplateActiveVersionAsync(array $args = [])
 * @method \Aws\Result updateVoiceChannel(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateVoiceChannelAsync(array $args = [])
 * @method \Aws\Result updateVoiceTemplate(array $args = [])
 * @method \GuzzleHttp\Promise\Promise updateVoiceTemplateAsync(array $args = [])
 */
class PinpointClient extends AwsClient {}

redpajama_set_name: RedPajamaGithub


In 2015, UN member states approved the 2030 Agenda for Sustainable Development – a transformative global agenda that integrates the economic, social and environmental pillars of development within 17 intricately interdependent Sustainable Development Goals (SDGs). Central to the 2030 Agenda is a pledge to meet the Goals for all, leaving no one behind, and endeavouring to reach the furthest behind first. The Development Co-operation Report 2018 unpacks the meaning of this pledge with a specific focus on the unique role and added value of development co-operation and official development assistance (ODA). This report responds to demand from members of the OECD Development Assistance Committee for greater clarity on how to answer the pledge.


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Guam's ethics commission takes oath of office
The commission is tasked to ensure that GovGuam employees are held to the highest ethical standards.
Guam's ethics commission takes oath of office The commission is tasked to ensure that GovGuam employees are held to the highest ethical standards. Check out this story on guampdn.com: https://www.guampdn.com/story/news/2019/06/27/guams-ethics-commission-takes-oath-office-govguam/1581213001/
Haidee Eugenio Gilbert, Pacific Daily News Published 12:16 a.m. ChT June 28, 2019 | Updated 12:17 a.m. ChT June 28, 2019
Gov. Lou Leon Guerrero with members of the newly sworn in Guam Ethics Commission on Thursday. (Photo: Courtesy of the governor's office)
Five members of the revived Guam Ethics Commission took their oath of office on Thursday.
Gov. Lou Leon Guerrero, whose administration revived the commission after a 23-year hiatus, administered the oath of office to:
Dr. Robert Jack, an ophthalmologist at Island Eye Center;
Shannon Murphy, a former journalist and managing editor at Guampedia;
Meg Tyquiengco, president of Energetic Presence and retired manager of Xerox Corporation Guam;
Donnalyn Camacho, certified fraud examiner and board-designated Bank Secrecy Act officer for ANZ Guam Inc.;
Marilyn Roberto, vice president of Enterprise Sales at Docomo Pacific.
The governor said the appointees represent a very talented and diverse group of private sector professionals. She said she's confident they will perform as an independent, qualified body "to ensure each of us is held to the highest ethical standards."
More: Administration revives dormant ethics commission
"Our administration is delivering on our commitment to transparency and accountability, and the revival of the Guam Ethics Commission is just one means of ensuring we stay true to the government we promised to the people," the governor said in a statement.
Members of the commission cannot be government of Guam employees or have any immediate family employed by the Government of Guam.
During their first meeting, which will be announced at a later date, members will draw for their term lengths.
A sixth member is pending confirmation by the Legislature while a final seat remains
vacant.
Read or Share this story: https://www.guampdn.com/story/news/2019/06/27/guams-ethics-commission-takes-oath-office-govguam/1581213001/
Grand jury indicts Gerald Cruz in dog's death
EX-DOC officer sentenced to 12 months home detention
Man accused of jury tampering seeks judge's recusal
Megabyte seeks help in store burglary
Allen Jesse Chaco Charfauros arrested in stabbing
3 bills to help deter repeat sex crimes


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D.C. Politics
Court delivers blow to D.C.'s plan to redevelop Barry Farm public housing complex
Housing blocks in Barry Farm, most of them empty, line Sumner Road in Washington in 2017. (Michael Robinson Chavez/The Washington Post)
By Fenit Nirappil
The highest court in D.C. on Thursday halted the city's plans to raze and redevelop Barry Farm, a public housing complex in the poorest part of Washington, vacating the zoning commission's approval of the project and sending it back for reconsideration.
The D.C. Court of Appeal's ruling is a win for tenants of the dilapidated complex in Southeast, who feared they would lose their subsidized apartments and be left scrambling for housing in a quickly gentrifying city.
And it's a setback for the District's decade-long effort to break up concentrated poverty by replacing public housing with residential development that attracts a mix of renters and owners of different income levels.
Under the New Communities Initiative, the 444-unit Barry Farm complex is slated to become a 1400-unit development with market-rate apartments, town homes, retail and other amenities, in addition to public housing.
Officials had assured the remaining tenants that they would be relocated during demolition and reconstruction, and would have an opportunity to move back.
But residents have argued that they would be displaced because plans call for the creation of 344 units for low-income residents — 100 fewer than exist now. To compensate, the developer pledged to construct 100 "off-site" units for low-income residents.
The court seemed to side with residents, noting there would be a "net loss" of 100 affordable units at Barry Farm.
Ari Theresa, an attorney for the tenants, said he's hopeful the zoning commission will now have to address the risk of displacement in light of the court's decision. He said he would seek an injunction to block demolition.
"The ability to develop in place is the ultimate goal, so people don't have to leave the project site for the 10 to 12 years the development is supposed to be taking place, so they can stay within their community as a new community develops," Theresa said.
More than half the tenants at Barry Farm have already moved out; 120 families remain, according to the D.C. Housing Authority.
A spokesman for the agency, Richard A. White, said officials were reviewing Thursday's ruling.
"However, our first concern is the impact on the hundreds of people who planned to return to the redeveloped site in 2020," White said. "Over the last several months, Ward 8 families made the decision to relocate from the Barry Farm community while demolition and redevelopment are underway. These families should not be overlooked or forgotten. DCHA remains committed to the families who desire to return to the new development, which is guaranteed by DCHA policy."
City officials vowed to press on with the project. "We owe it to our residents and the District to rebuild Barry Farm with safe, affordable housing and amenities which residents in other parts of the District enjoy," Deputy Mayor for Planning and Economic Development Brian Kenner said in a statement. "D.C. Housing Authority and its partners must carefully review the decision and address issues in order to move ahead with much-needed housing for District residents."
The developer, Baltimore-based A & R Development, did not immediately return requests for comment.
Robert Marus, a spokesman for D.C. Attorney General Karl A. Racine, said the developer could request a rehearing from the Court of Appeals, or could opt to return to the zoning commission for a reconsideration of the project.
Opponents of the plan have also argued that in addition to losing 100 affordable units at Barry Farm, families will be displaced because the renovation plans include fewer multi-bedroom units.
In gentrifying D.C., large apartments for families are disappearing
The court concluded the zoning commission failed to properly consider that and other issues when approving plans in 2014 to redevelop Barry Farm.
But the court also made clear its decision wasn't a fatal blow.
"We are not necessarily holding that the development may not go forward on this site, but rather, are simply requiring that the Commission give fuller consideration to and explain its determinations on the issues that we have identified," wrote Chief Judge Anna Blackburne-Rigsby in the court's opinion.
She faulted the plan to relocate residents as insufficient.
"Given the dramatic effect that a forced relocation can have on a family's well-being, such families are entitled to some semblance of predictability," Blackburne-Rigsby wrote.
"For Barry Farm residents, the relocation plan must be attuned to the realities of the D.C. housing market, and sensitive to the fact that many residents in Ward 8 have already been displaced from other parts of the District."
Barry Farm residents are angry they must move during redevelopment
The judge also found city officials failed to weigh how redevelopment would hurt residents.
While the zoning commission considered new housing, a retail corridor and other changes as benefits to Barry Farm residents, the judge said it failed to address how gentrification, the loss of grassy yards and fewer affordable units would hurt the community.
At a 2014 hearing, a city planning official was unable to answer questions about the demographics of Barry Farm residents and the gentrification pressures the project would bring to the neighboring communities.
That, Blackburne-Rigsby, said, "suggested a gap in knowledge with regard to the current Barry Farm community, which precluded a comprehensive understanding of all adverse effects."
The opinion also comes as Mayor Muriel E. Bowser (D) is trying to revise the city's comprehensive plan to limit the ability of opponents to delay or block development.
D.C. mayor tries to halt legal avenue to delay development projects


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What You Need To Know About Recreational Marijuana In Mass.
<iframe width="100%" height="124" scrolling="no" frameborder="no" src="https://player.wbur.org/news/2018/06/25/recreational-marijuana-massachusetts-faq"></iframe>
Steve BrownTwitter
Ally JarmanningTwitter
A budding mature marijuana plant. (Jesse Costa/WBUR)
This post, originally published on June 25, has been updated.
It's been almost two years since voters passed the law legalizing adult-use recreational marijuana in Massachusetts. And while it's legal to use cannabis, there's still nowhere to legally buy it.
That's expected to change in the next few weeks, as shops get up and running.
So what's this new industry going to look like? What do users need to know? We asked you for your questions, and we have some (updated) answers.
Where can I buy marijuana?
Starting Tuesday, Nov. 20, two shops will be open: New England Treatment Access in Northampton and Cultivate in Leicester. More are expected to open in the coming weeks.
Part of the holdup was the lack of testing labs to inspect the product, but now two labs — MCR in Framingham and CDX Analytics in Salem — have the final OK from the CCC.
If you have a medical card, there are 40 open registered medical marijuana dispensaries in the state. NETA and Cultivate Holdings are also medical marijuana dispensaries.
I thought retail shops could open July 1?
Under the law, they could have, but none had final licences from the CCC on that day. The commission has long said not to expect Massachusetts to look like Colorado — which has a robust retail industry -- right away.
What towns, as of now, are open to having marijuana shops opening up? — Kevin
That number is shifting. As of now, there are about 70 communities that have a ban on the books, and about 160 more cities and towns have a moratorium of some sort in place, though most of those moratoriums are expiring at the end of this year.
Will towns that ban cannabis businesses or enact moratoriums still receive tax dollars generated from sales of cannabis products within Massachusetts? — Joe Crinkley
Yes. They'll share in the statewide 17 percent sales and marijuana excise taxes that are generated from the sale of cannabis, because that money goes into the state's general fund, and all communities benefit from that. The communities that have enacted bans and moratoriums of course will not get the optional local tax, which can be up to 3 percent for a marijuana-friendly town.
Those city and town positions could change. As we said, most of those moratoriums expire at the end of the year.
What does the law say about possession and consumption for adults over 18 but under 21? — Dave
Only adults 21 or older can legally purchase and possess marijuana, except for those who are registered patients. If you're over 18 but under 21, you face a civil penalty of $100, and need to complete a drug awareness program.
I live in New Hampshire, work in Massachusetts. Can I buy marijuana for recreational use in Mass.? -- Anonymous
Yes, but you may have a hard time finding some place where you can legally consume the product you purchase. Taking it home over the state line isn't legal under federal law, and consuming it in Massachusetts at a public place is illegal under state law. If you live in New Hampshire and have a friend who owns a home in Massachusetts, you could legally consume it on your buddy's couch.
For medical marijuana, there was a required registration into a database of users. Will the state be keeping track of who buys [recreational]? — Leah
Cannabis businesses are not required to capture any vital information, other than to check an ID to make sure a customer is 21 -- sort of like they do at a liquor store.
How does this affect medical marijuana? I am beginning the process of getting a medical marijuana card. Would this still be necessary? — Anonymous
That's a personal call. Medical marijuana is currently tax free. You'd have to do the math to figure out if the cost of maintaining your card is less than what you would pay in tax for your supply. Medical patients will also have access to separate lines and a separate supply. (Here's more on medical marijuana.)
When dispensaries start selling recreational cannabis, will there be a shortage of medicinal cannabis to registered medical marijuana patients? — Max
There likely will be a rush on supply once the adult-use sales are available. But the Cannabis Control Commission has instructed medical dispensaries to reserve 35 percent of their inventory for sale only through the medical marijuana program.
Once I finally am able to buy marijuana, where can I use it?
At home -- that is, if you own your house. If you have roommates or a landlord who doesn't want you smoking there, you might be out of luck. Landlords can place restrictions on whether you can smoke in your home, just like they can with cigarettes. And you can't smoke in public, or anywhere where you're prohibited from smoking tobacco.
What about bringing a cannabis-infused brownie into a movie theater, and snacking on it during the previews?
Setting aside the movie theater policy of no outside food for a moment, eating that cannabis-infused brownie is public consumption of cannabis, and that is not allowed under the law.
Will cannabis "cafes" — i.e., places to both buy and consume — be legal? — Tim Cox
After initially putting the question about so-called "social use" on hold, a cannabis commissioner now says it'll be years before any cafes or other gathering spots for using marijuana will open.
The commission has the authority to license these types of businesses, but it says it wants to wait to see how other states and cities are handling it. A decision is expected early next year.
What about other ways to consume cannabis?
Smoking is probably the most obvious, old school way of getting high. There's vaping, where cannabis in the form of flower, oil or wax is heated up to a vapor that's nearly odorless. There are edibles, which have THC infused in them. And there's all sorts of pills and topical creams and tinctures that are really unobtrusive.
Any recommendations if I haven't gotten high in awhile?
We asked Cannabis Control Commissioner Shaleen Title for her advice. Here's what she said:
I would recommend starting low. The benefit of vaping or smoking is you can very consciously adjust the amount you're using because you'll feel it right away. So you can take one puff. And then slow down and see how you feel and then decide.
If you want to take an edible I would start at one serving, which has been designated at 5 milligrams. Give it two hours and see how you feel. And the next time you can take a little more if you want. But it's much better to start low than accidentally take too much and not feel well.
For more updates on the industry, join our Facebook group, Green Rush: Cannabis in Massachusetts.
This article was originally published on June 25, 2018.
This segment aired on June 25, 2018.
Complete Coverage: Recreational Marijuana
Steve Brown Twitter Senior Reporter/Anchor
Steve Brown is a veteran broadcast journalist who serves as WBUR's senior State House reporter.
Ally Jarmanning Twitter Senior Reporter
Ally is a senior reporter focused on criminal justice and police accountability.


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