Publications

Using digital standard cells and digital place-and-route (PnR) tools, we created a 20 GS/s, 8-bit analog-to-digital converter (ADC) for use in high-speed serial link applications with an ENOB of 5.6, a DNL of 0.96 LSB, and an INL of 2.39 LSB, which dissipated 175 mW in 0.102 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in a 16nm technology. The design is entirely described by HDL so that it can be ported to other processes with minimal effort and shared as open source.

The increasing availability of personal genomic tests has led to discussions about the validity and utility of such tests and the balance of benefits and harms. A multidisciplinary workshop was convened by the National Institutes of Health and the Centers for Disease Control and Prevention to review the scientific foundation for using personal genomics in risk assessment and disease prevention and to develop recommendations for targeted research. The clinical validity and utility of personal genomics is a moving target with rapidly developing discoveries but little translation research to close the gap between discoveries and health impact. Workshop participants made recommendations in five domains: (1) developing and applying scientific standards for assessing personal genomic tests; (2) developing and applying a multidisciplinary research agenda, including observational studies and clinical trials to fill knowledge gaps in clinical validity and utility; (3) enhancing credible knowledge synthesis and information dissemination to clinicians and consumers; (4) linking scientific findings to evidence-based recommendations for use of personal genomics; and (5) assessing how the concept of personal utility can affect health benefits, costs, and risks by developing appropriate metrics for evaluation. To fulfill the promise of personal genomics, a rigorous multidisciplinary research agenda is needed.

&lt;b&gt;&lt;i&gt;Introduction:&lt;/i&gt;&lt;/b&gt; Repetitive transcranial magnetic stimulation (rTMS) has been widely tested and promoted for use in multiple neuropsychiatric conditions, but as for many other medical devices, some gaps may exist in the literature and the evidence base for the clinical efficacy of rTMS remains under debate. &lt;b&gt;&lt;i&gt;Objective:&lt;/i&gt;&lt;/b&gt; We aimed to test for an excess number of statistically significant results in the literature on the therapeutic efficacy of rTMS across a wide range of meta-analyses and to characterize the power of studies included in these meta-analyses. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; Based on power calculations, we computed the expected number of “positive” datasets for a medium effect size (standardized mean difference, SMD = 0.30) and compared it with the number of observed “positive” datasets. Sensitivity analyses considered small (SMD = 0.20), modest (SMD = 0.50), and large (SMD = 0.80) effect sizes. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; A total of 14 meta-analyses with 228 datasets (110 for neurological disorders and 118 for psychiatric disorders) were assessed. For SMD = 0.3, the number of observed “positive” studies (&lt;i&gt;n&lt;/i&gt; = 94) was larger than expected (&lt;i&gt;n&lt;/i&gt; = 35). We found evidence for an excess of significant findings overall (&lt;i&gt;p&lt;/i&gt; &amp;#x3c; 0.0001) and in 8/14 meta-analyses. Evidence for an excess of significant findings was also observed for SMD = 0.5 for neurological disorders. Of the 228 datasets, 0 (0%), 0 (0%), 3 (1%), and 53 (23%) had a power &amp;#x3e;0.80, respectively, for SMDs of 0.30, 0.20, 0.50, and 0.80. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; Most studies in the rTMS literature are underpowered. This results in fragmentation and waste of research efforts. The somewhat high frequency of “positive” results seems spurious and may reflect bias. Caution is warranted in accepting rTMS as an established treatment for neuropsychiatric conditions.

FFT design requires an exhaustive recoupling of data across successive stages of computation. The resulting memory access patterns have constantly-changing strides, making it hard to interleave the data for reliable conflict-free access of operand pairs. We modify an existing method of "swizzling" data locations so as to guarantee conflict-free access within any given stage and, with minimal support for buffering, we provide conflict-free access across the boundaries of adjoining stages as well. As a result, implementations that would naively require either a fully-associative, or at the very least a multiported register file, can be implemented using four single-ported banks of memory per butterfly unit, plus one bypass buffer. Because fewer ports means less area, and given that a butterfly must read two inputs and write two results for each cycle of operation, this solution should represent the least-area memory configuration for a resource-constrained FFT. Using this scheme, we show examples including a minimal one-butterfly FFT having 9% less area versus a competing equal-performance design and 20% better throughput versus a competing equal-area design.

Abstract Background and Aims The most restrictive nonpharmaceutical interventions (NPIs) for controlling the spread of COVID‐19 are mandatory stay‐at‐home and business closures. Given the consequences of these policies, it is important to assess their effects. We evaluate the effects on epidemic case growth of more restrictive NPIs (mrNPIs), above and beyond those of less‐restrictive NPIs (lrNPIs). Methods We first estimate COVID‐19 case growth in relation to any NPI implementation in subnational regions of 10 countries: England, France, Germany, Iran, Italy, Netherlands, Spain, South Korea, Sweden and the United States. Using first‐difference models with fixed effects, we isolate the effects of mrNPIs by subtracting the combined effects of lrNPIs and epidemic dynamics from all NPIs. We use case growth in Sweden and South Korea, 2 countries that did not implement mandatory stay‐at‐home and business closures, as comparison countries for the other 8 countries (16 total comparisons). Results Implementing any NPIs was associated with significant reductions in case growth in 9 out of 10 study countries, including South Korea and Sweden that implemented only lrNPIs (Spain had a nonsignificant effect). After subtracting the epidemic and lrNPI effects, we find no clear, significant beneficial effect of mrNPIs on case growth in any country. In France, for example, the effect of mrNPIs was +7% (95% CI: −5%‐19%) when compared with Sweden and + 13% (−12%‐38%) when compared with South Korea (positive means pro‐contagion). The 95% confidence intervals excluded 30% declines in all 16 comparisons and 15% declines in 11/16 comparisons. Conclusions While small benefits cannot be excluded, we do not find significant benefits on case growth of more restrictive NPIs. Similar reductions in case growth may be achievable with less‐restrictive interventions.

Abstract Importance Genetics and other -omics’ technologies have long been proposed for medical research use. Objective To assess how genetics and other -omics information’ are used in the most cited recent clinical trials, and to evaluate industry involvement and transparency patterns. Design, Setting, Participants Meta-research evaluation using a previously constructed database of the 600 most cited clinical trials published from 2019 to 2022. Main Outcome(s) and Measure(s) Utilization of genetic or other -omics information in the trial design, analysis, and results; industry involvement and transparency. Results 132 (22%) trials used genetic or other -omics information, predominantly for detection of cancer mutations (n=101). Utilization included eligibility criteria (n=59), subgroup analysis (n=82), and stratification factor in randomization (n=14). Authors addressed the relevance in the conclusions in 82 studies (62%). 102 studies (77%) provided data availability statements and 6 had data already available. Most studies had industry funding (n=111 [84.0%]). Oncology trials were more likely to be industry-funded (90.1% vs 64.5%, p=0.001), to have industry-affiliated analysts (43.6% vs 22.6%, p=0.036) and to favor industry-sponsored interventions (83.2% vs 58.1% p=0.004). When compared to other trials, genetic and other -omics trials were more likely to be funded by industry (84% vs 63.9%, p&lt;0.001) and tended to be less likely to have full protocols (p=0.018) and statistical plans (p=0.04) available. Conclusions and Relevance Our study highlights the current underutilization of genetic and other-omics technologies beyond testing for cancer mutations. Industry involvement in these trials appears to be more substantial and transparency is more limited, raising concerns about potential bias. Key Points Question How are genetics and other -omics information used in highly impactful clinical trials, and what is the involvement of the industry in their development? Findings 22% of the trials employed genetic or -omics information, with the vast majority being oncology-related and focusing on testing for mutations. Industry was more heavily involved in the funding and in the design of these trials, in particular for oncology trials. Meaning The use of genetics and -omics is limited and needs to become more common in non-oncology trials. Industry involvement is intense in this context, highlighting the need for improvements in transparency and independence.

Background: Recent GWAS revealed single nucleotide polymorphisms (SNPs) that cumulatively increase risk of CHD in those of European ancestry. We quantified the magnitude of preventive effects of healthy lifestyle against the countervailing effects of genetic risk on incident CHD, and assessed potential interactions. Methods: We extracted SNPs of genome-wide significance (&lt;5 x 10 -8 ) for CHD from CARDIoGRAMplusC4 GWAS, and created a weighted 57-SNP genetic risk score (GRS). Baseline covariates, healthy lifestyle score (including diet pattern, physical activity, smoking and BMI), and incident CHD were harmonized across 4 US cohorts (WHI, ARIC, CHS, and MESA) using dbGaP data. Pooled analyses were run using one-step individual participant data multivariate regressions in Caucasians without prevalent CVD. Results: Among 22,412 adults, the GRS was associated with incident CHD (n = 3001 cases) across each cohort (pooled GRS quintile (Q)5 vs Q1, RR (95% CI): 1.59 (1.41-1.79)), independent of all demographic and lifestyle variables. The lifestyle score was more strongly associated with CHD (pooled Q1 vs Q5, RR: 1.94 (1.71-2.20)) than the GRS, and better discriminated those who had a CHD event ( P =0.025 for difference in AUROC curves). When compared to population-level analysis, joint effects analysis revealed significant heterogeneity within quartiles of lifestyle score on risk of incident CHD ( Table ). Interactions between the GRS and diet pattern ( P =0.022) and trans fats ( P =0.046) were observed. Effects of trans fats on CHD risk appear to be concentrated in those with a higher GRS: for trans fats &gt;2.4% of energy, RRs for increasing GRS quartiles are 0.92 (0.62-1.36), 1.13 (0.79-1.62), 1.43 (1.01-2.01), and 1.84 (1.30-2.61), compared to those in the lowest quartile of trans fat (&lt;1.3% of energy) and lowest GRS quartile. Conclusion: A joint effects approach reveals significant inter-individual variability in lifestyle on CHD risk by underlying genetic risk that is obscured by a conventional population-level effects approach.