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Many pieces of advice, recommendations and warnings in Egyptian teaching texts are introduced by imperative verbal lexemes (which linguists call "directive speech acts"). Examples can be found, for example, in the teachings of Ptahhotep, Ani, and Amenemhat. The great numbers of imperative lexemes in these texts offer a wide range of spellings, syntactic constructions, and semantic contents. The same lexeme can appear in several forms within a single text, with various phonograms and classifiers. From a semantic point of view, there are also notable variations, such as the various meanings of sA(w), which is usually translated as "refrain from", although it is polysemous. This paper gives an overview of a project that is intended to fill a gap in the Egyptological literature on directive speech acts. Every aspect of the related verbal lexemes is studied, including grammatical analysis, graphemic comparisons, lexicographical and semantic study, as well as issues arising and potential conclusions.

In the course of sifting earth removed from the Temple Mount in Jerusalem, dozens of clay sealings from the First Temple period were recovered. Among them was a sealing bearing the name of the priestly family of Immer. In-depth study of the writing on the sealing, as well as the fabric imprint on its reverse, indicated with a high probability that this sealing was used in the Temple treasury. The article reviews the function and use of sealings in the administration of ancient Near Eastern treasuries and the significance of sealings with a textile imprint on their reverse. The study revealed similar patterns in the finds near the “Royal Building” exposed in the Ophel excavations, and we therefore suggest identifying it with Judah’s royal treasury.

Abscisic acid (ABA) is a phytohormone that plants utilize to coordinate responses to abiotic stress, modulate seed dormancy, and is central to plant development in several contexts. Chemicals that activate or block ABA signaling are useful as research tools and as potential agrochemical leads. Many successes have been reported for ABA activators (agonists), but existing ABA blockers (antagonists) are limited by modest in vivo activity. Here we report antabactin (ANT), a potent ABA blocker developed using “click chemistry”–based diversification of a known ABA activator. Structural studies reveal, ANT disrupts signaling by stabilizing ABA receptors in an unproductive form. ANT can accelerate seed germination in multiple species, making it a chemical tool for improving germination.Abscisic acid (ABA) is a key plant hormone that mediates both plant biotic and abiotic stress responses and many other developmental processes. ABA receptor antagonists are useful for dissecting and manipulating ABA’s physiological roles in vivo. We set out to design antagonists that block receptor–PP2C interactions by modifying the agonist opabactin (OP), a synthetically accessible, high-affinity scaffold. Click chemistry was used to create an \~4,000-member library of C4-diversified opabactin derivatives that were screened for receptor antagonism in vitro. This revealed a peptidotriazole motif shared among hits, which we optimized to yield antabactin (ANT), a pan-receptor antagonist. An X-ray crystal structure of an ANT–PYL10 complex (1.86 Å) reveals that ANT’s peptidotriazole headgroup is positioned to sterically block receptor–PP2C interactions in the 4' tunnel and stabilizes a noncanonical closed-gate receptor conformer that partially opens to accommodate ANT binding. To facilitate binding-affinity studies using fluorescence polarization, we synthesized TAMRA–ANT. Equilibrium dissociation constants for TAMRA–ANT binding to Arabidopsis receptors range from \~400 to 1,700 pM. ANT displays improved activity in vivo and disrupts ABA-mediated processes in multiple species. ANT is able to accelerate seed germination in Arabidopsis, tomato, and barley, suggesting that it could be useful as a germination stimulant in species where endogenous ABA signaling limits seed germination. Thus, click-based diversification of a synthetic agonist scaffold allowed us to rapidly develop a high-affinity probe of ABA–receptor function for dissecting and manipulating ABA signaling.The atomic coordinates and structure factors reported in this article have been deposited in the Protein Data Bank, https://www.wwpdb.org/ [PDB ID codes 7MLC (45) and 7MLD (46)].

Abscisic acid (ABA) is a phytohormone that plants utilize to coordinate responses to abiotic stress, modulate seed dormancy, and is central to plant development in several contexts. Chemicals that activate or block ABA signaling are useful as research tools and as potential agrochemical leads. Many successes have been reported for ABA activators (agonists), but existing ABA blockers (antagonists) are limited by modest in vivo activity. Here we report antabactin (ANT), a potent ABA blocker developed using “click chemistry”–based diversification of a known ABA activator. Structural studies reveal, ANT disrupts signaling by stabilizing ABA receptors in an unproductive form. ANT can accelerate seed germination in multiple species, making it a chemical tool for improving germination.Abscisic acid (ABA) is a key plant hormone that mediates both plant biotic and abiotic stress responses and many other developmental processes. ABA receptor antagonists are useful for dissecting and manipulating ABA’s physiological roles in vivo. We set out to design antagonists that block receptor–PP2C interactions by modifying the agonist opabactin (OP), a synthetically accessible, high-affinity scaffold. Click chemistry was used to create an ∼4,000-member library of C4-diversified opabactin derivatives that were screened for receptor antagonism in vitro. This revealed a peptidotriazole motif shared among hits, which we optimized to yield antabactin (ANT), a pan-receptor antagonist. An X-ray crystal structure of an ANT–PYL10 complex (1.86 Å) reveals that ANT’s peptidotriazole headgroup is positioned to sterically block receptor–PP2C interactions in the 4′ tunnel and stabilizes a noncanonical closed-gate receptor conformer that partially opens to accommodate ANT binding. To facilitate binding-affinity studies using fluorescence polarization, we synthesized TAMRA–ANT. Equilibrium dissociation constants for TAMRA–ANT binding to Arabidopsis receptors range from ∼400 to 1,700 pM. ANT displays improved activity in vivo and disrupts ABA-mediated processes in multiple species. ANT is able to accelerate seed germination in Arabidopsis, tomato, and barley, suggesting that it could be useful as a germination stimulant in species where endogenous ABA signaling limits seed germination. Thus, click-based diversification of a synthetic agonist scaffold allowed us to rapidly develop a high-affinity probe of ABA–receptor function for dissecting and manipulating ABA signaling.The atomic coordinates and structure factors reported in this article have been deposited in the Protein Data Bank, https://www.wwpdb.org/ [PDB ID codes 7MLC (45) and 7MLD (46)].

Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

With the development of the economy and technology, people's requirement for communication is also increasing. Satellite communication networks have been paid more and more attention because of their broadband service capability and wide coverage. In this paper, we investigate the scheme of convolutional long short term memory (CLSTM) network and transfer learning (TL) based combined free/demand assignment multiple access (CFDAMA) scheme (CFDAMA-CLSTMTL), which is a new multiple access scheme in the satellite communication networks. Generally, there is a delay time T between sending a request from the user to the satellite and receiving a reply from the satellite. So far, the traditional multiple access schemes have not processed the data generated in this period. So, in order to transmit the data in time, we propose a new prediction method CLSTMTL, which can be used to predict the data generated in this period. We introduce the prediction method into the CFDAMA scheme so that it can reduce data accumulation by the way of sending the slots request which is the sum of slots requested by the user and the predicted slots generated in the delay time. A comparison with CFDAMA-PA and CFDAMA-PB is provided through simulation results, which gives the effect of the CFDAMA-CLSTMTL in a satellite communication network.

Abstract Intrusion detection as well distributed denial of service (DDoS) are vital in ensuring computer network security. Some researchers claim that current approaches cannot meet the requirements of today's networks are either not workable or sustainable. In a more specific sense, these concerns are related to an increasing number of human interactions, along with reducing levels of detection ability. With our study, a novel deep learning model for intrusion detection is developed for addressing these issues. We proposed a novel deep learning classification algorithm constructed using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) named CLSTMNet. Our proposed model has been implemented and evaluated using the benchmark NSL-KDD datasets. Compared with many conventional machine learning algorithms, the satisfied outcomes have been obtained from our model.

Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary questions. Here, we develop a machine-learning approach for utilizing phylogenetic profiles across 1154 eukaryotic species. This method integrates co-evolution across eukaryotic clades to predict functional interactions between human genes and the context for these interactions. We benchmark our approach showing a 14% performance increase (auROC) compared to previous methods. Using this approach, we predict functional annotations for less studied genes. We focus on DNA repair and verify that 9 of the top 50 predicted genes have been identified elsewhere, with others previously prioritized by high-throughput screens. Overall, our approach enables better annotation of function and functional interactions and facilitates the understanding of evolutionary processes underlying co-evolution. The manuscript is accompanied by a webserver available at: https://mlpp.cs.huji.ac.il.

Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary questions. Here, we develop a machine-learning approach for utilizing phylogenetic profiles across 1154 eukaryotic species. This method integrates co-evolution across eukaryotic clades to predict functional interactions between human genes and the context for these interactions. We benchmark our approach showing a 14% performance increase (auROC) compared to previous methods. Using this approach, we predict functional annotations for less studied genes. We focus on DNA repair and verify that 9 of the top 50 predicted genes have been identified elsewhere, with others previously prioritized by high-throughput screens. Overall, our approach enables better annotation of function and functional interactions and facilitates the understanding of evolutionary processes underlying co-evolution. The manuscript is accompanied by a webserver available at: https://mlpp.cs.huji.ac.il.