First, found Atmel SOC (system on chip), and since Linux was available for it, tried that. It would have been a great solution. It has gigabit Ethernet, which would have been ideal to transfer 2 megabytes per second off of that unit to a PC style Quad Core workstation.
MEGA SAMPLES VOL-99
Even so, we were so close, we decided to keep going. We could probably make 500,000 samples per second work, with some loss of capabilities. However, that rate would have allowed a successful product.
DMA was tried, but DMA priority conflict either killed the ADC or killed the transfer off chip. At 1 mega-sample per second you can only use one DMA engine and *keep* getting 1 mega-sample per second transferred into memory.
We decided to write out the data in software, as we could theoretically run 62 instructions per microsecond. 700,000 bytes per second (350,000 samples per second), using CPU writes in a loop, nothing else going on. (Measured on an oscilloscope.)
Closer! So, we put two of these devices and changed the bus to be 16 bits. (Two USB streams into the PC) 1,400,000 bytes per second. Closer. 200,000 samples faster than USB, so our product would work better. However, we could taste blood, so we kept going.
We started looking at the structure carefully. What if we did not do software loops but fell through writing a word at a time. 2048 repeated lines of code. Since the PSoC has 1/4 megabyte of flash, no problem. Suddenly, about three megabytes per second!
The thing I am left with is the realization that everyone who plays in this bathtub touts a 1 mega-sample per second A/D. No one has put features in their device for getting that data rate off of their device. I don't think the developers of the devices realize that if you get a mega-sample per second, you might want to use it. I feel that realization would have led them to provide either high speed USB 2.0, or Ethernet, along with the development environment support. Lack of development environment support nearly killed this beast.
L.L., B.S., F.F., R.M., J.R. designed the study, F.F., J.A., S.H., S.C., A.S., A.L., K.N., P.D., H.M., R.S.-A., R.B., J.R. processed samples, L.L., B.S.-R. developed the model, L.L., J.R. wrote the manuscript, L.L., J.A., S.H., J.R. prepared figures and tables. All authors reviewed the manuscript.
Studies of posttraumatic stress disorder (PTSD) report volume abnormalities in multiple regions of the cerebral cortex. However, findings for many regions, particularly regions outside commonly studied emotion-related prefrontal, insular, and limbic regions, are inconsistent and tentative. Also, few studies address the possibility that PTSD abnormalities may be confounded by comorbid depression. A mega-analysis investigating all cortical regions in a large sample of PTSD and control subjects can potentially provide new insight into these issues. Given this perspective, our group aggregated regional volumes data of 68 cortical regions across both hemispheres from 1379 PTSD patients to 2192 controls without PTSD after data were processed by 32 international laboratories using ENIGMA standardized procedures. We examined whether regional cortical volumes were different in PTSD vs. controls, were associated with posttraumatic stress symptom (PTSS) severity, or were affected by comorbid depression. Volumes of left and right lateral orbitofrontal gyri (LOFG), left superior temporal gyrus, and right insular, lingual and superior parietal gyri were significantly smaller, on average, in PTSD patients than controls (standardized coefficients = -0.111 to -0.068, FDR corrected P values
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Macrocytosis is a term used to describe red blood cells that are larger than normal. Also known as megalocytosis or macrocythemia, this condition typically causes no signs or symptoms and is usually detected incidentally on routine blood tests.
In the replication stage, the two novel loci and the four novel associations at established loci were examined in independent populations from the Blood Cell Consortium (BCX) [23] after excluding the overlapped BioMe multi-ethnic and WHI EA samples. BCX represents the largest published trans-ethnic meta-analysis of blood cell traits, with a total of 746,667 participants (76% EA, 20% East Asian, 2% AA, 1% HL, and 1% South Asian). None of the six loci showed evidence of association in the replication stage (Supplemental Table 5). All variants showed consistent directions except for the MED13L variant.
Compared to the PAGE global paper [28], the current analyses included more samples and evaluated more phenotypes. We included samples genotyped on the MEGA array as well as additional samples genotyped on other Illumina or Affymetrix arrays from the participating studies, leading to more than a 128% increase in sample size compared to the PAGE global paper. In addition, the current analyses included eight phenotypes while the global paper focused on WBC and PLT. Our study has several limitations. First, the sample sizes of the underrepresented AA and HL populations remained limited compared to sample sizes available in Euro-centric GWAS (with over 500,000 EA participants in the BCX Consortium [23]). The relatively modest sample sizes limited the power to identify additional novel loci in the univariate association analyses and the multi-trait association analysis. Second, we were unable to examine the underrepresented Native American and Hawaiian populations. These participants were included in our PAGE Study but had limited numbers of white blood cell and platelet trait measurements. Studies on these ancestral groups currently are extremely sparse and continued efforts to include them in genetic association analyses are needed. Third, the usage of the European reference transcriptome may have introduced bias and the relatively limited sample sizes may have contributed to the absence of novel gene findings in the PrediXcan analysis, reinforcing the need to collect transcriptomics data and construct tailored models in minority populations.
In the discovery stage, we performed both univariate GWAS analysis for each of the eight traits and aSPU simulation-based method which jointly tested all eight traits [40]. For WBC and the five subtypes (BAS, EOS, LYM, MON, and NEU), values were log10 transformed before association analysis. For PLT and MPV, raw values were used. For samples genotyped on the MEGA array, residual values for each trait were calculated from linear regression models after adjustment for age, age2, sex (when applicable), center (when applicable), and the first 10 principal components (PC). For samples previously genotyped on either other Illumina or Affymetrix arrays, residual values for each trait were calculated from linear regression models after adjustment for age, age2, sex (when applicable), center (when applicable), and the first 10 PCs calculated from an LD-pruned set of genotypes in each individual study. In the univariate GWAS analysis, we tested the association of each genetic variant with the rank-based inverse-normally transformed residual values in MEGA samples and in each individual study, respectively. All MEGA samples were pooled together for testing while association testing was performed by study and ancestral group in non-MEGA samples. These association analyses were performed using SUGEN, which is based on generalized estimating equations (GEE) allowing correlated errors for first or second-degree relatives and independent error distributions by self-reported race/ethnic group [41]. Association results from these studies were then combined through fixed-effect inverse-variance-weighted meta-analysis in METAL for each trait [42]. Both ancestry-combined and ancestry-specific meta-analyses were performed. Complete summary level results are available through dbGaP (phs000356). 2ff7e9595c
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