We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com.

[Abstract unavailable

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

Rechargeable lithium metal batteries are considered the "Holy Grail" of energy storage systems. Unfortunately, uncontrollable dendritic lithium growth inherent in these batteries (upon repeated charge/discharge cycling) has prevented their practical application over the past 40 years. We show a novel mechanism that can fundamentally alter dendrite formation. At low concentrations, selected cations (such as cesium or rubidium ions) exhibit an effective reduction potential below the standard reduction potential of lithium ions. During lithium deposition, these additive cations form a positively charged electrostatic shield around the initial growth tip of the protuberances without reduction and deposition of the additives. This forces further deposition of lithium to adjacent regions of the anode and eliminates dendrite formation in lithium metal batteries. This strategy may also prevent dendrite growth in lithium-ion batteries as well as other metal batteries and transform the surface uniformity of coatings deposited in many general electrodeposition processes.

Exosomes are 40–100 nm nano-sized vesicles that are released from many cell types into the extracellular space. Such vesicles are widely distributed in various body fluids. Recently, mRNAs and microRNAs (miRNAs) have been identified in exosomes, which can be taken up by neighboring or distant cells and subsequently modulate recipient cells. This suggests an active sorting mechanism of exosomal miRNAs, since the miRNA profiles of exosomes may differ from those of the parent cells. Exosomal miRNAs play an important role in disease progression, and can stimulate angiogenesis and facilitate metastasis in cancers. In this review, we will introduce the origin and the trafficking of exosomes between cells, display current research on the sorting mechanism of exosomal miRNAs, and briefly describe how exosomes and their miRNAs function in recipient cells. Finally, we will discuss the potential applications of these miRNA-containing vesicles in clinical settings.