No abstract available.
This paper presents a novel 3D-SRAM architecture that can be used to extend the scaling of SRAM. This architecture significantly reduces the bit-line capacitance, achieves 3.4 times reduction in active power consumption and 1.8 times reduction in access time. In this architecture, local bit-lines are vertical and connect through select transistors to the global bit-lines routed on the bottom level. A proof-of-concept 32Kb sub-array emulating the critical path of the 3D-SRAM has demonstrated about 5 times improvement in power-delay over conventional 2D-SRAM.
Abstract Statistical tests of heterogeneity and bias, in particular publication bias, are very popular in meta‐analyses. These tests use statistical approaches whose limitations are often not recognized. Moreover, it is often implied with inappropriate confidence that these tests can provide reliable answers to questions that in essence are not of statistical nature. Statistical heterogeneity is only a correlate of clinical and pragmatic heterogeneity and the correlation may sometimes be weak. Similarly, statistical signals may hint to bias, but seen in isolation they cannot fully prove or disprove bias in general, let alone specific causes of bias, such as publication bias in particular. Both false‐positive and false‐negative signals of heterogeneity and bias can be common and their prevalence may be anticipated based on some rational considerations. Here I discuss the major common challenges and flaws that emerge in using and interpreting statistical tests of heterogeneity and bias in meta‐analyses. I discuss misinterpretations that can occur at the level of statistical inference, clinical/pragmatic inference and specific cause attribution. Suggestions are made on how to avoid these flaws, use these tests properly and learn from them.
We performed a meta-analysis of the predictive value of maternal cell-free viral load in vertical HIV-1 transmission, including 9 cohorts with 1115 mother-infant pairs (696 untreated and 419 treated women). The pooled rate of transmission in untreated women was 21.3% (95% confidence interval [CI], 18.3%-24.5%). The rates of transmission for untreated women in the <1000 copies/ml, 1000 to 9999 copies/ml, and > or = 10,000 copies/ml categories were 5% (95% CI, 2%-11%), 15% (95% CI, 11%-20%) and 37% (95% CI, 29%-46% by random effects), respectively. The area under the receiver operating characteristic (ROC) curve in individual studies ranged from 0.67 to 1.00. The predictive performance of RNA differed between cohorts in which different percentages of transmitters had RNA values >10,000 copies/ml. When 95% of transmitters have RNA values >1000 copies/ml, 77% of nontransmitters would also have values above this cutoff. Transmission rates for treated women in the 1000 to 9999 copies/ml category (7%; 95% CI, 4%-11%,) and > or = 10,000 copies/ml category (18%; 95% CI, 12%-27%) were probably lower than those for untreated women, whereas the transmission rate for treated women with <1000 copies/ml was 5% (95% CI, 2%-11 %). Thus, the risk gradient between RNA categories seems attenuated in treated women. Several aspects of the design, analysis, and reporting of research in this area may be improved in the future with attention to selection and observer biases, multivariate adjustment, and technical consistency. Maternal HIV-1 RNA is a modest predictor of transmission for individual mothers, but a strong predictor of the average risk in groups of untreated mothers. Its discriminatory power is better in untreated than in treated populations and is better in cohorts with a high prevalence of elevated viral load values than in cohorts with generally low levels of viremia.
Abstract Social media and new tools for engagement offer democratic platforms for enhancing constructive scientific criticism which had previously been limited. Constructive criticism can now be massive, timely and open. However, new options have also enhanced obsessive criticism. Obsessive criticism tends to focus on one or a handful of individuals and their work, often includes ad hominem aspects, and the critics often lack field‐specific skills and technical expertise. Typical behaviours include: repetitive and persistent comments (including sealioning), lengthy commentaries/tweetorials/responses often longer than the original work, strong degree of moralizing, distortion of the underlying work, argumentum ad populum, calls to suspend/censor/retract the work or the author, guilt‐by‐association, reputational tarnishing, large gains in followers specifically through attacks, finding and positing sensitive personal information, anonymity or pseudonymity, social media campaigning, and unusual ratio of criticism to pursuit of one's research agenda. These behaviours may last months or years. Prevention and treatment options may include awareness, identifying and working around aggravating factors, placing limits on the volume by editors, constructive pairing of commissioned editorials, incorporation of some hot debates from unregulated locations such as social media or PubPeer to the pages of scientific journals, preserving decency and focusing on evidence and arguments and avoiding personal statements, or (in some cases) ignoring. We need more research on the role of social media and obsessive criticism on an evolving cancel culture, the social media credibility, the use/misuse of anonymity and pseudonymity, and whether potential interventions from universities may improve or further weaponize scientific criticism.
