Subsystem Rényi Entropy of Thermal Ensembles for SYK-like models

The Sachdev-Ye-Kitaev model is an N-modes fermionic model with infinite range random interactions. In this work, we study the thermal Rényi entropy for a subsystem of the SYK model using the path-integral formalism in the large-N limit. The results are consistent with exact diagonalization [1] and can be well approximated by thermal entropy with an effective temperature [2] when subsystem size M ≤ N/2. We also consider generalizations of the SYK model with quadratic random hopping term or U(1) charge conservation

The distribution and origin of keratin 20-containing taste buds in rat and human

Sections of tissues containing lingual and extra-lingual taste buds were evaluated with monoclonal antibodies against cytokeratins. In the caudal third of the rat's tongue, keratin 20 immunoreactivity was restricted to taste buds, whereas keratins 7, 8, 18, and 19 were expressed in vallate and foliate taste buds and in cells of salivary ducts that merge with these taste epithelia. Hence, antibodies against keratin 20 most clearly distinguished differentiated taste cells from all other cells. In rat epiglottis, taste buds and isolated bipolar cells were keratin-20-positive. In rat nasopalatine papilla and palate, antibodies against keratin 20 identified Merkel cells, none of which was near to the keratin-20-negative taste buds. Nor were Merkel cells present at epiglottal taste buds or the keratin-20-negative fungiform taste buds or elsewhere in rat tongue. Hence, Merkel cells make no contribution to rat fungiform, epiglottal, nasopalatine, or palatal taste buds. Human and rat keratin-20-positive tissues are reported to be endodermal derivatives with the exception of Merkel cells and luminal urothelial cells. In rats the distribution of keratin-20-positive taste buds was in full agreement with the classical view that the posterior third of the tongue is derived from endoderm (keratin-20-positive taste buds), whereas the anterior two-thirds of the tongue is derived from stomadeal ectoderm (keratin-20-negative taste buds). The equally intense keratin 20 immunoreactivity of human fungiform and vallate taste buds violates this traditional rostro-caudal segregation and suggests that endodermally derived tissues may be present in the tip of the human tongue.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75260/1/j.1432-0436.1996.6120121.x.pd

Methylated DNMT1 and E2F1 Are Targeted for Proteolysis by L3MBTL3 and CRL4DCAF5 Ubiquitin Ligase

Many non-histone proteins are lysine methylated and a novel function of this modification is to trigger the proteolysis of methylated proteins. Here, we report that the methylated lysine 142 of DNMT1, a major DNA methyltransferase that preserves epigenetic inheritance of DNA methylation patterns during DNA replication, is demethylated by LSD1. A novel methyl-binding protein, L3MBTL3, binds the K142-methylated DNMT1 and recruits a novel CRL4DCAF5 ubiquitin ligase to degrade DNMT1. Both LSD1 and PHF20L1 act primarily in S phase to prevent DNMT1 degradation by L3MBTL3-CRL4DCAF5. Mouse L3MBTL3/MBT-1 deletion causes accumulation of DNMT1 protein, increased genomic DNA methylation, and late embryonic lethality. DNMT1 contains a consensus methylation motif shared by many non-histone proteins including E2F1, a key transcription factor for S phase. We show that the methylation-dependent E2F1 degradation is also controlled by L3MBTL3-CRL4DCAF5. Our studies elucidate for the first time a novel mechanism by which the stability of many methylated non-histone proteins are regulated

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Digital Technology-driven Business Model Innovations: A Bibliometric Analysis

With the advent of the data age, digital technology has been widely used in business model innovation. To understand the current research situation in the field of digital technology-driven business model innovation and reveal the knowledge structure, research hotspots, and development trends in this research field, this paper adopts statistical analysis, co-citation analysis, cluster analysis and other methods to carry out bibliometric analysis and knowledge mapping on the relevant literature included in the Web of Science database. The research results show that customer relationship management, digital economy and financial service system, sustainable development and digital service innovation, and the competition and cooperation mechanism of enterprises are hot topics in this field. Moreover, digital platform, firm performance, and value creation are the main research directions in the future