Search

A general method for synthesizing optically active, primary, secondary, and tertiary organofluorides was developed. This chiral pool synthesis utilized the skeleton of arabinose to generate diastereomerically pure 2-oxazolidinone-fused aziridines, which underwent ring opening with a fluoride anion. The adducts, polyoxygenated organofluorides, were useful precursors to various fluorinated compounds, such as fluorinated amino acids.

In this short note, we express our viewpoint regarding power considerations, via simulation studies, in clinical study design using hierarchical composite endpoint and Finkelstein-Schoenfeld test.

The document ICH E9 (R1) has brought much attention to the concept of estimand in the clinical trials community. ICH stands for International Conference for Harmonization. In this article, we draw attention to one facet of estimand that is not discussed in that document but is crucial in the context of observational studies, namely weighting for covariate balance. How weighting schemes are connected to estimand, or more specifically to one of its five attributes identified in ICH E9 (R1), the attribute of population, is illustrated using the Rubin Causal Model. Three estimands are examined from both theoretical and practical perspectives. Factors that may be considered in choosing among these estimands are discussed.

Learning from a few examples (one/few-shot learning) on the fly is a key challenge for on-device machine intelligence. We present the first chip-level demonstration of one-shot learning with Stanford Associative memory for Programmable, Integrated Edge iNtelligence via life-long learning and Search (SAPIENS), a resistive random access memory (RRAM)-based non-volatile associative memory (AM) chip that serves as the backend for memory-augmented neural networks (MANNs). The 64-kb fully integrated RRAM-CMOS AM chip performs long-term feature embedding and retrieval, demonstrated on a 32-way one-shot learning task on the Omniglot dataset. Using only one example per class for 32 unseen classes during on-chip learning, SAPIENS achieves 79% measured inference accuracy on Omniglot, comparable to edge software model accuracy using five-level quantization (82%). It achieves an energy efficiency of 118 GOPS/W at 200 MHz for in-memory L1 distance computation and prediction. Multi-bank measurements on the same chip show that increasing the capacity from three banks (24 kb) to eight banks (64 kb) improves the chip accuracy from 73.5% to 79%, while minimizing the accuracy excursion due to bank-to-bank variability.

One of the major roadblocks for filamentary type resistive random access memory is variations in both the write voltage and the read resistance. The variation is inevitable because of the stochastic nature of oxygen ion movement inside the metal oxide. In this letter, we show that by inserting a thin SnO 2 layer within the HfO 2 switching layer, these variations can be significantly reduced. The TiN/HfO 2 /SnO 2 /HfO 2 /Pt material stack achieves ~ 10 times smaller standard deviation for the high resistance state( $>100\text{k}\Omega $ ) cycle to cycle distribution compared with the control group without the SnO 2 layer. The tight high resistance state distribution retained after 10 7 pulse cycling. Incremental RESET pulse measurement indicates that the thin SnO 2 acts as an oxygen stopping layer and thus explains the origin of reduced variability. The concept of an oxygen stopping layer is promising for reducing variability for resistive memory.

In this paper, a propensity score-integrated power prior approach is developed to augment the control arm of a two-arm randomized controlled trial (RCT) with subjects from multiple external data sources such as real-world data (RWD) and historical clinical studies containing subject-level outcomes and covariates. The propensity scores for the subjects in the external data sources versus the subjects in the RCT are first estimated, and then subjects are placed in different strata based on their estimated propensity scores. Within each propensity score stratum, a power prior is formulated with the information contributed by the external data sources, and Bayesian inference on the treatment effect is obtained. The proposed approach is implemented under the two-stage study design framework utilizing the outcome-free principle to ensure the integrity of a study. An illustrative example is provided to demonstrate the implementation of the proposed approach.

This work demonstrates a trench isolated lateral double-implanted MOSFET (LDMOS) on Si-face in 4H-silicon carbide (SiC). A device $\vphantom {_{\int _{}}}$ where ${L}_{\textit {ch}} = 0.8\,\,\mu \text{m}$ and ${L}_{d} = 12\,\,\mu \text{m}$ shows an ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ of 22.9 $\text{m}\Omega $ cm2 at a ${V}_{\text {GS}}$ of 20 V and a breakdown voltage (BV) of 1100 V, corresponding to a high BV2/ ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ of 55.5 MW/cm2. Devices that have different ${L}_{\text {ch}}$ , ${L}_{\text {JFET}}$ , ${L}_{d}$ , and P-top dose values are measured in order to investigate the effects of geometry on the static performance. Operations at 150 °C are measured to evaluate the temperature performance. Gate charge waveforms are also measured in order to include the switching performance in the evaluation. The ${R}_{ \mathrm{\scriptscriptstyle ON}} \times {Q}_{G}$ and ${R}_{ \mathrm{\scriptscriptstyle ON}} \times {Q}_{\text {GD}}$ values are calculated as 17.7 and $9.0~\Omega $ nC, respectively, which are promising for power-integrated circuit applications.

Performance goals are numerical target values pertaining to effectiveness or safety endpoints in single-arm medical device clinical studies. Typically, performance goals are determined at the planning stage of the investigational study under consideration based on summarized outcome information from existing relevant clinical trials. In recent years, there is a growing interest in leveraging real-world evidence in medical product development. In this article, we introduce a new method for proposing performance goals by leveraging real-world evidence. The method applies entropy balancing to address possible patient dissimilarities between the study's target patient population and existing real-world patients, and can take into account operation differences between clinical studies and real-world clinical practice. An illustrative example is provided to demonstrate how to implement the proposed method for performance goal determination while leveraging real-world evidence.

Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint‐based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA‐Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue‐specific genome‐scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well‐known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin‐converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS‐CoV‐2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment., Identifying differentially expressed genes is critical in exploring molecular mechanisms of cancers. A high level of differential gene expression is not an essential factor for tumorigenesis. We developed an optimization platform that can identify oncogenes with low levels of differential expression and potential therapeutic targets for cancer treatment. The platform was applied on LUAD and LUSC to illustrate its performance.