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FE Finger V2 Script


Programmed ribosomal frameshifting (PRF) is a fundamental gene expression event in many viruses, including SARS-CoV-2. It allows production of essential viral, structural and replicative enzymes that are encoded in an alternative reading frame. Despite the importance of PRF for the viral life cycle, it is still largely unknown how and to what extent cellular factors alter mechanical properties of frameshift elements and thereby impact virulence. This prompted us to comprehensively dissect the interplay between the SARS-CoV-2 frameshift element and the host proteome. We reveal that the short isoform of the zinc-finger antiviral protein (ZAP-S) is a direct regulator of PRF in SARS-CoV-2 infected cells. ZAP-S overexpression strongly impairs frameshifting and inhibits viral replication. Using in vitro ensemble and single-molecule techniques, we further demonstrate that ZAP-S directly interacts with the SARS-CoV-2 RNA and interferes with the folding of the frameshift RNA element. Together, these data identify ZAP-S as a host-encoded inhibitor of SARS-CoV-2 frameshifting and expand our understanding of RNA-based gene regulation.




FE Finger V2 Script



U.R. and L.P. contributed equally to this work. We thank Dr. Zeljka Macak-Safranko and Prof. Alemka Markotic (University of Zagreb) for providing the SARS-CoV-2 virus isolate prior to publication. We thank Dr. Andreas Schlundt for kind gifts of IGF2BP3 and SUMO proteins (Goethe University, Frankfurt, Germany). We thank Dr. Joop van den Heuvel (HZI) for his helpful suggestions on protein purification. We thank Dr. Anke Sparmann, Prof. Jörg Vogel, Prof. Lars Dölken, Prof. Utz Fischer and Prof. Thomas Pietschmann for critical reading of the manuscript. We thank expert technical assistance by Tatyana Koch (HIRI-HZI). We thank Ayse Barut for cell maintenance for infection studies (HZI). We thank Dr. Andreas Schlosser and Stephanie Lamer from the Rudolf Virchow Center for the LC-MS/MS analysis. Figures were partially generated using BioRender.com (licensed for commercial printing to A.K.). This project is funded fully or in part by the Helmholtz Association. L.C.S. funded through MWK Niedersachsen Grant Nr. 14-76103-184 CORONA-2/20. N.C. received funding from the European Research Council (ERC) Grant Nr. 948636.


M.Z. and A.K. designed and cloned the constructs, purified proteins, and performed most of the biochemical experiments. L.P., S.B. and N.C. designed the OT constructs, L.P. performed most of the single molecule experiments and processed the data with the help of S.B. S.B. and L.P. have written the scripts for automatized analysis of the single molecule data. U.R. performed the SARS-CoV-2 infection assays and collected lysates for downstream biochemical analysis. L.Y. and R.S. performed DMS-MaPseq experiments and analyzed the data. L.C.-S., R.S., and N.C. supervised the study. M.Z., A.K., L.P. and N.C. wrote the paper. All authors contributed to the review and editing of the final paper.


Some customers find it faster to "type" on virtual keyboards by swiping the shape of the word they intend to type, and we're previewing this feature for the holographic keyboard. You can swipe one word at a time by passing the tip of your finger through the plane of the holographic keyboard, swiping the shape of the word, and then withdrawing the tip of your finger from the plane of the keyboard. You can swipe follow up words without needing to press the space bar by removing your finger from the keyboard between words. You will know the feature is working if you see a swipe trail following your finger's movement on the keyboard.


With this HoloLens update, Windows Holographic for Business enables delivery optimization settings to reduce bandwidth consumption for downloads from multiple HoloLens devices. A fuller description of this functionality along with the recommended network configuration is available here: Delivery Optimization for Windows 10 updates.


We present a QWERTY-based text entry system, TypeAnywhere, for use in off-desktop computing environments. Using a wearable device that can detect finger taps, users can leverage their touch-typing skills from physical keyboards to perform text entry on any surface. TypeAnywhere decodes typing sequences based only on finger-tap sequences without relying on tap locations. To achieve optimal decoding performance, we trained a neural language model and achieved a 1.6% character error rate (CER) in an offline evaluation, compared to a 5.3% CER from a traditional n-gram language model. Our user study showed that participants achieved an average performance of 70.6 WPM, or 80.4% of their physical keyboard speed, and 1.50% CER after 2.5 hours of practice over five days on a table surface. They also achieved 43.9 WPM and 1.37% CER when typing on their laps. Our results demonstrate the strong potential of QWERTY typing as a ubiquitous text entry solution.


QWERTY-based touch-typing on physical keyboards remains the most common text input skill of computer users [11], utilizing multiple hands and fingers and relying on tacit knowledge of learned key positions. Although the QWERTY layout was not invented for optimal speed [9], the fact that people are so familiar with the layout has motivated text entry researchers to build upon QWERTY [2, 19, 53, 60, 61, 65]. We therefore want to leverage the physical QWERTY keyboard typing experience in our effort to discover a solution for ubiquitous computing text entry, but without presuming the availability of mechanical keys. What if we could type on an imaginary QWERTY keyboard on any surface, just like we type a physical keyboard?


To explore this question, our work developed TypeAnywhere, a QWERTY-based text entry solution for everyday use in ubiquitous computing contexts (Figure 1). TypeAnywhere employs wearable sensors on two hands that detect finger-tap actions, a decoder that converts tap sequences into text, and a corresponding interface for text editing. TypeAnywhere's hardware is the commercial Tap Strap1 product, which uses accelerometers for tap detection. We feed a detected finger-tap sequence to a neural decoder modified from the BERT model [10], which then displays the output text on the typing interface. Similar to Type, then Correct [71], we also designed a text correction interaction for TypeAnywhere that avoids the need for cursor navigation.


An important feature of TypeAnywhere is that it does not rely on position information to decode the intended letters being typed by the user. Rather, TypeAnywhere relies only on the index of the finger being tapped and the context provided by the text entered thus far. Because TypeAnywhere only uses the index of the tapping finger without relying on (x, y) position information, the user can perform the tap at any location, so long as they use the correct finger. This scheme simplifies the design space, enabling us to both generalize the decoder easily for different finger-to-key mappings, and to achieve high accuracy on text decoding. Unlike other QWERTY-based text entry research projects [14, 15, 63, 65, 76], which had to conduct user studies to collect spatial information for model-building, we were able to train the model without collecting data from user studies. Instead, we curated the training data by converting each letter in a phrase set to its corresponding finger ID. In this way, we were able to train the model on a large text corpus containing over 3.6 million samples [75] and adapt different finger-to-key mappings by altering the finger IDs in the training set. We performed computational evaluations on the neural decoder, achieving a 1.6% character error rate (CER) on the Cornell Movie-Dialogue Corpus [8], compared to a 5.3% CER using a conventional n-gram language model.


Ten-finger touch-typing on a QWERTY layout has been studied extensively since at least the 1970s [20, 43, 44, 45]. To understand how people perform touch typing with different finger-to-key mappings, Feit et al. [13] observed 30 participants typing on a physical keyboard. They found that people who did not type with the standard finger-to-key mapping (Figure 2)3 could also reach high levels of performance, over 70 WPM. Through clustering they identified six mapping strategies for the right hand and four strategies for the left. In a follow-up study [11], they found that rollover key-pressing was a key factor for fast typing, and faster typists often used more fingers to type. In other work, Findlater et al. [15] performed empirical studies of ten-finger typing on touch screens and found that the typing speed (58.5 WPM) was 31% slower than the physical keyboard without any feedback.


There are several projects also trying to enable QWERTY-style 10-finger typing without using a keyboard. The most similar concept to TypeAnywhere is from Goldstein et al. [16], where they proposed a QWERTY-style typing interaction using a pair of gloves. However, their evaluation was only conducted on a mock-up without a functioning prototype. In our work, we realized the TypeAnywhere concept in a fully working system, including with personalization, editing interactions, and advanced language models. The Canesta prototype [42] projected a keyboard on a table to enable QWERTY-style typing. TOAST [46] was an eyes-free keyboard that enabled the user to type on large touch screens without a visible keyboard. With its Markov-Bayesian algorithm for spatial decoding, participants reached an average speed of 44.6 WPM. However, this technique could only be applied on devices with large touch screens. Relatedly, Findlater et al. [14] showed how machine learning could be used to design touch screen keyboards on interactive tabletops that adapt at runtime to evolving finger positions. ATK [65] provided for mid-air typing using computer vision hand-tracking, reaching an average speed of 29.2 WPM. However, mid-air typing lacks tactile feedback and can easily cause fatigue. Richardson et al. [41] implemented a vision-based text entry system relevant to this project, where hand motion captured by cameras was fed into a neural network decoder that then output the decoded text corrected by another neural language model. Although they did not perform a user study to evaluate their system, the offline CER was reported to be 2.22%. While promising, such computer vision-based systems still require fixed-position cameras overlooking the hands wearing arrays of markers, making such systems impractical for ubiquitous computing. 041b061a72


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