cssi/SciSciGPT-SciSciNet huggingface dataset:
Kaposi’s sarcoma-associated herpesvirus ORF68 is a DNA binding protein required for viral genome cleavage and packaging

Herpesviral DNA packaging into nascent capsids requires multiple conserved viral proteins that coordinate genome encapsidation. Here, we investigated the role of the ORF68 protein of Kaposi's sarcoma-associated herpesvirus (KSHV), a protein required for viral DNA encapsidation whose function remains largely unresolved across the herpesviridae. We found that KSHV ORF68 is expressed with early kinetics and localizes predominantly to viral replication compartments, although it is dispensable for viral DNA replication and gene expression. However, in agreement with its proposed role in viral DNA packaging, KSHV-infected cells lacking ORF68 failed to cleave viral DNA concatemers, accumulated exclusively immature B capsids, and released no infectious progeny virions. ORF68 has no predicted domains aside from a series of putative zinc finger motifs. However, in vitro biochemical analyses of purified ORF68 protein revealed that it robustly binds DNA and is associated with nuclease activity. These activities provide new insights into the role of KSHV ORF68 in viral genome encapsidation.IMPORTANCE Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi's sarcoma and several B-cell cancers, causing significant morbidity and mortality in immunocompromised individuals. A critical step in the production of infectious viral progeny is the packaging of the newly replicated viral DNA genome into the capsid, which involves coordination between at least seven herpesviral proteins. While the majority of these packaging factors have been well studied in related herpesviruses, the role of the KSHV ORF68 protein and its homologs remains unresolved. Here, using a KSHV mutant lacking ORF68, we confirm its requirement for viral DNA processing and packaging in infected cells. Furthermore, we show that the purified ORF68 protein directly binds DNA and is associated with a metal-dependent cleavage activity on double-stranded DNA in vitro These activities suggest a novel role for ORF68 in herpesviral genome processing and encapsidation.
Hypophosphorous acid–iodine: a novel reducing system. Part 2: Reduction of benzhydrols to diarylmethylene derivatives

Abstract A mixture of hypophosphorous acid (H 3 PO 2 ) and iodine in acetic acid reduces a variety of substituted benzhydrols to the corresponding methylene derivatives in very high yields. The active reducing agent is hydrogen iodide generated by reaction between iodine and hypophosphorous acid.
Catalytic, enantioselective aldol additions to ketones.

Catalytic, enantioselective additions of a trichlorosilyl ketene acetal to ketones have been demonstrated. The trichlorosilyl enolate of methyl acetate undergoes a rapid and high-yielding aldol addition to a wide range of ketones (aromatic, olefinic, acetylenic, aliphatic) in the presence of a catalytic amount of pyridine N-oxide. Moreover, in the presence of a catalytic amount (10 mol %) of a chiral bispyridine bis N-oxide (possessing both axial and central chiral elements) the aldol addition takes place again in excellent yield and with good stereoselectivity. The enantioselectivities of the additions are highly variable (7−86% ee) and are strongly dependent on the structure of the ketone acceptor. Aromatic methyl ketones gave the highest selectivity, whereas olefinic ketones were the least selective.
Control of thiol-maleimide reaction kinetics in PEG hydrogel networks.

Abstract Michael-type addition reactions are widely used to polymerize biocompatible hydrogels. The thiol-maleimide modality achieves the highest macromer coupling efficiency of the reported Michael-type pairs, but the resulting hydrogel networks are heterogeneous because polymerization is faster than the individual components can be manually mixed. The reactivity of the thiol dictates the overall reaction speed, which can be slowed in organic solvents and acidic buffers. Since these modifications also reduce the biocompatibility of resulting hydrogels, we investigated a series of biocompatible buffers and crosslinkers to decelerate gelation while maintaining high cell viability. We found that lowering the polymer weight percentage (wt%), buffer concentration, and pH slowed gelation kinetics, but crosslinking with an electronegative peptide was optimal for both kinetics and cell viability. Including a high glucose medium supplement in the polymer solvent buffer improved the viability of the cells being encapsulated without impacting gelation time. Slowing the speed of polymerization resulted in more uniform hydrogels, both in terms of visual inspection and the diffusion of small molecules through the network. However, reactions that were too slow resulted in non-uniform particle dispersion due to settling, thus there is a trade-off in hydrogel network uniformity versus cell distribution in the hydrogels when using these networks in cell applications. Statement of Significance The polymer network of thiol-maleimide hydrogels assembles faster than individual components can be uniformly mixed due to their fast gelation kinetics. The lack of homogeneity can result in variable cell-based assay results, resulting in batch-to-batch variability and limiting their use in predictive screening assays. Although these hydrogels are incredibly useful in tissue engineering, this network heterogeneity is a known problem in the field. We screened a variety of possible techniques to slow down the reaction speed and improve the homogeneity of these hydrogels, without sacrificing the viability and distribution of encapsulated cells. As others have reported, an electronegative crosslinker was the most effective technique to slow the reaction, but the chemical modification required is technically challenging. Of interest to a broad community, we screened buffer type, strength, and crosslinker electronegativity to find an optimal reaction speed that allows for high cell viability and small molecule diffusion, without allowing cells to settle during gelation, allowing application of these materials to the drug screening industry and tissue engineering community.
Catalytic allylic oxidation with a recyclable, fluorous seleninic acid.

In conjunction with iodoxybenzene as reoxidant perfluorooctylseleninic acid catalyzes the allylic oxidation of alkenes to enones in trifluoromethylbenzene at reflux in moderate to good yield. After a reductive workup with sodium metabisulfite, the catalyst is recovered by fluorous extraction in the form of bis(perfluorooctyl) diselenide, which, itself, serves as a convenient catalyst precursor.
Diastereoselective Petasis Mannich reactions accelerated by hexafluoroisopropanol: a pyrrolidine-derived arylglycine synthesis

Abstract A diastereoselective synthesis of pyrrolidine-derived arylglycines has been developed using the Petasis boronic acid Mannich reaction. High diastereoselectivities in the reactions of chiral amines, aryl boronic acids, and glyoxylic acid monohydrate have been demonstrated for the first time. Key to the implementation of this method is the discovery that hexafluoroisopropanol accelerates the Petasis process, reducing reaction times from multiple days to less than 24 h.
BIOREDUCTION OF (R)-CARVONE AND REGIOSELECTIVE BAEYER-VILLIGER OXIDATIONS : APPLICATION TO THE ASYMMETRIC SYNTHESIS OF CRYPTOPHYCIN FRAGMENT A

Abstract Cryptophycin fragment A ( 1 ) was prepared in high enantiomeric purity in 10 steps from ( R )-carvone. A stereoselective bioreduction of ( R )-carvone to neodihydrocarveol and a regioselective Baeyer-Villiger oxidation of cyclohexanone 8 with pertrifluoroacetic acid were employed in this synthesis.
Recent advances in phosphorus-carbon bond formation: synthesis of H-phosphinic acid derivatives from hypophosphorous compounds

Abstract This account summarizes the research conducted in our laboratory over the past five years. New methodologies were devised for the formation of P–C bonds with a focus on the reactions of hypophosphorous acid derivatives. Three types of reactions have been developed: palladium-catalyzed cross-coupling, room-temperature radical addition, and palladium-catalyzed addition. Our results are summarized in each of these areas and include some of our most recent data. (1) Our palladium-catalyzed cross-coupling has been extended to the direct coupling of alkyl phosphinates with a variety of aryl, heteroaryl, and even alkenyl electrophiles. (2) The addition of sodium hypophosphite under radical conditions is extended from alkenes to alkynes. (3) The catalytic addition of hypophosphorous compounds using palladium catalysts (hydrophosphinylation) is also discussed.
Colloidal CIGS and CZTS nanocrystals: A precursor route to printed photovoltaics

Abstract This review article summarizes our research focused on Cu(In1–xGax)Se2 (CIGS) nanocrystals, including their synthesis and implementation as the active light absorbing material in photovoltaic devices (PVs). CIGS PV layers are typically made using a high temperature (>450 °C) process in which Cu, In and Ga are sequentially or co-evaporated and selenized. We have sought to use CIGS nanocrystals synthesized with the desired stoichiometry to deposit PV device layers without high temperature processing. This approach, using spray deposition of the CIGS light absorber layers, without high temperature selenization, has enabled up to 3.1% power conversion efficiency under AM 1.5 solar illumination. Although the device efficiency is too low for commercialization, these devices provide a proof-of-concept that solution-deposited CIGS nanocrystal films can function in PV devices, enabling unconventional device architectures and materials combinations, including the use of flexible, inexpensive and light-weight plastic substrates.
Genomic insights into adaptive divergence and speciation among malaria vectors of the Anopheles nili group

Ongoing speciation in most African malaria vectors gives rise to cryptic populations, which differ remarkably in their behaviour, ecology and capacity to vector malaria parasites. Understanding the population structure and the drivers of genetic differentiation among mosquitoes is crucial for effective disease control because heterogeneity within species contribute to variability in malaria cases and allow fractions of vector populations to escape control efforts. To examine the population structure and the potential impacts of recent large-scale control interventions, we have investigated the genomic patterns of differentiation in mosquitoes belonging to the Anopheles nili group — a large taxonomic group that diverged ~3Myr ago. Using 4343 single nucleotide polymorphisms (SNPs), we detected strong population structure characterized by high FST values between multiple divergent populations adapted to different habitats within the Central African rainforest. Delineating the cryptic species within the Anopheles nili group is challenging due to incongruence between morphology, ribosomal DNA and SNP markers consistent with incomplete lineage sorting and/or interspecific gene flow. A very high proportion of loci are fixed (FST = 1) within the genome of putative species, which suggests that ecological and/or reproductive barriers are maintained by strong selection on a substantial number of genes. This article is protected by copyright. All rights reserved.
SYNTHESIS OF 4-BORONO-2-FLUOROPHENYLALANINE

(2000). SYNTHESIS OF 4-BORONO-2-FLUOROPHENYLALANINE. Organic Preparations and Procedures International: Vol. 32, No. 3, pp. 290-293.
diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals.

Summary: We present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing single-nucleotide polymorphism–based IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM. Availability: http://sourceforge.net/projects/dical-ibd Contact: ude.yelekreb.scee@ssy Supplementary information: Supplementary data are available at Bioinformatics online.
A phosphoranide (10‐P‐4) anion coordinating to transition metals, as a Lewis base and a π‐acceptor

The reaction of 10-P-5 hydridophosphorane 6 with KH provides the 10-P-4 potassium phosphoranide anion 7a. Anion 7a reacts with CpFe(CO)2I and BrAu(PEt3) to replace the halide with the phosphoranide as a monodentate ligand forming 8 and 9. The X-ray structure of 8 shows that the equatorial—Fe(CO)2Cp ligand of 8 makes the apical PO bonds (1.824 A) longer than the PO bonds (1.80 A) of the phosphoranide oxide anion 10. The PO bonds (1.75 A) of 6, with an equatorial proton ligand, are still shorter. This provides evidence that the—Fe(CO)2Cp ligand is not only a sigma-acceptor Lewis acid ligand, but also a pi-donor that transfers electrons into the OPO three-center bond's anti-bonding orbital to increase its P—O bond lengths. It is a better pi-donor than the O− pi-donor of phosphoranide oxide 10.
How to delegate computations publicly

We construct a delegation scheme for all polynomial time computations. Our scheme is publicly verifiable and completely non-interactive in the common reference string (CRS) model. Our scheme is based on an efficiently falsifiable decisional assumption on groups with bilinear maps. Prior to this work, publicly verifiable non-interactive delegation schemes were only known under knowledge assumptions (or in the Random Oracle model) or under non-standard assumptions related to obfuscation or multilinear maps. We obtain our result in two steps. First, we construct a scheme with a long CRS (polynomial in the running time of the computation) by following the blueprint of Paneth and Rothblum (TCC 2017). Then we bootstrap this scheme to obtain a short CRS. Our bootstrapping theorem exploits the fact that our scheme can securely delegate certain non-deterministic computations.
Intramolecular Coupling Reactions of Allylic Thioesters with Diene Iron Tricarbonyl Systems

Cyclization reactions are reported for a series of allylic thioester derivatives of cyclohexadiene iron tricarbonyl systems, leading to the formation of spiro thialactone derivatives. The overall reaction process is equivalent to a [6π + 2π] ene reaction in which the pendant alkene, derived from the allyl thioester, is coupled with the diene of the diene−Fe(CO)3 moiety. While simple substrates were found to undergo this cyclization readily, increased substitution on the pendant double bond led to lower, or zero, yields. Methodology for ring opening of the product thialactones, with or without reductive extrusion of sulfur, is described. The X-ray structure of one of the products (21b) is reported: C15H14FeO4, Mr = 346.17. Crystal dimensions: 0.20 × 0.20 × 0.10 mm. Yelow, clear prism. Mo Kα radiation (λ = 0.71073 A). Monoclinic space group P2(1)/n; a = 11.355(2) A, b = 6.4943(13) A, c = 20.060(4) A, β = 103.30(3)°, V = 1439.6 (5) A3. Z = 4. ϑ = 2.35−27.06°. T = 150 (2) K.
Spherical and Aspherical Intermolecular Force Fields for Sulfur Allotropes

Elemental sulfur exists in many crystalline forms, with many of these forms being composed of discrete Sn cyclic molecules. Eleven experimentally determined Sn crystal structures are used to define a new spherical S···S intermolecular force field of the (exp-6) type. Evidence is presented that the bonded sulfur atom in these structures is not spherical. The asphericity was modeled by placing repulsion bumps at optimized locations on the sulfur atoms to represent lone pair electron repulsion. The bump locations deviate from perfect tetrahedral geometry, being located more perpendicular to the S−S−S plane. A new aspherical sulfur intermolecular force field was derived from eleven Sn crystal structures. The aspherical force field gives an improved prediction of the Sn crystal structures.
Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High-Performance Computer Simulations:

Cancer chemotherapy dose schedules are conventionally applied intermittently, with dose duration of the order of hours, intervals between doses of days or weeks, and cycles repeated for weeks. The ...
Control of Lineage-Specific Gene Expression by Functionalized gRNA Barcodes

Lineage tracking delivers essential quantitative insight into dynamic, probabilistic cellular processes, such as somatic tumor evolution and differentiation. Methods for high diversity lineage quantitation rely on sequencing a population of DNA barcodes. However, manipulation of specific individual lineages is not possible with this approach. To address this challenge, we developed a functionalized lineage tracing tool, Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT), that enables high diversity lineage tracing and lineage-specific manipulation of gene expression. This modular platform utilizes expressed barcode gRNAs to both track cell lineages and direct lineage-specific gene expression.
Aromatic amines as nucleophiles in the Bargellini reaction

Aromatic amines can be employed in the Bargellini reaction to generate useful intermediates. Rapid, practical access to functionalised, privileged structures may have significant utility in the synthesis of drug-like molecules. An improved synthesis of carfentanil analogues illustrates this point.
A gene regulatory network for apical organ neurogenesis and its spatial control in sea star embryos

How neural stem cells generate the correct number and type of differentiated neurons in appropriate places remains an important question. Although nervous systems are diverse across phyla, in many taxa the larva forms an anterior concentration of serotonergic neurons, or apical organ. The sea star embryo initially has a pan-neurogenic ectoderm, but the genetic mechanism that directs a subset of these cells to generate serotonergic neurons in a particular location is unresolved. We show that neurogenesis in sea star larvae begins with soxc -expressing multipotent progenitors. These give rise to restricted progenitors that express lhx2/9 . soxc- and lhx2/9 -expressing cells can undergo both asymmetric divisions, allowing for progression towards a particular neural fate, and symmetric proliferative divisions. We show that nested concentric domains of gene expression along the anterior-posterior (AP) axis, which are observed in a great diversity of metazoans, control neurogenesis in the sea star larva by promoting particular division modes and progression towards becoming a neuron. This work explains how spatial patterning in the ectoderm controls progression of neurogenesis in addition to providing spatial cues for neuron location. Modification to the sizes of these AP territories provides a simple mechanism to explain the diversity of neuron number among apical organs.
Methyl 2,3,4-Tri-O-benzyl-α-d-glucopyranosyl-Derived γ-Silyloxy Allylic Stannanes as Reagents for SE2‘ Additions to Aldehydes†

The methyl α-d-glucopyranoside allylic stannanes 3.1 and 3.2 were prepared from the 3-(5-pyranosyl)-2-propenal 2.1 and the cuprate Bu3Sn(Bu)CuCNLi2, followed by trapping of the derived enolate with TBSCl. The major stannane, 3.1, underwent BF3-promoted addition to 2-nonynal to afford a single syn adduct 4.1 in 70−90% yield. The minor stannane, 3.2, gave rise to a 70:30 mixture of adduct 4.1 and the diastereomeric syn adduct 5.1 under these conditions. The stereochemistry of the adduct 4.1 was proven by degradation of the bis-TBS derivative 4.2 to aldehyde 4.3, prepared independently from the (R,Z)-γ-OTBS crotyl stannane 4.4 and 2-nonynal along similar lines. Analogous degradation of the adducts 4.1 and 5.1 led to a ca. 65:35 mixture of aldehydes 4.3 and its enantiomer. Accordingly, it can be surmised that the addition of stannane 3.2 to 2-nonynal takes place mainly by a syn SE2‘ pathway. BF3-promoted additions to enal 6.1 proceeded as expected. Stannane 3.1 afforded the syn adduct 6.2 in 87−97% yield, and...
Crystal Growth of New Hexahydroxometallates Using a Hydroflux

A series of seven compounds, Sr2Mn(OH)6, Ba2Mn(OH)6, Sr2Co(OH)6, Ba2Co(OH)6, Sr2Ni(OH)6, Ba2Ni(OH)6, and Ba2Cu(OH)6, were synthesized using a low-melting hydroflux, a hybrid approach between aqueous hydrothermal and molten hydroxide flux techniques. Crystals of the hexahydroxometallates were obtained by dissolving appropriate amounts of alkaline-earth nitrates or hydroxides and transition-metal oxides, acetates, or chlorides in the hydroflux and reacting at 180–230 °C. The isostructural compounds all crystallize in the monoclinic space group P21/n. The monoclinic structure consists of isolated transition-metal octahedra within a three-dimensional framework of corner- and edge-shared eight-coordinate, alkaline-earth polyhedra. Magnetic susceptibility data show that all compounds are simple paramagnets. Thermogravimetric analysis indicates that these hydroxides lose water between 215 and 350 °C and transform into oxide products, the identity of which depends on the metal cations present in the parent hexahy...
Analysis of novel domain-specific mutations in the zebrafish ndr2/cyclops gene generated using CRISPR-Cas9 RNPs

Nodal-related protein (ndr2) is a member of the transforming growth factor type $$\upbeta $$ superfamily of factors and is required for ventral midline patterning of the embryonic central nervous system in zebrafish. In humans, mutations in the gene encoding nodal cause holoprosencephaly and heterotaxy. Mutations in the ndr2 gene in the zebrafish (Danio rerio) lead to similar phenotypes, including loss of the medial floor plate, severe deficits in ventral forebrain development and cyclopia. Alleles of the ndr2 gene have been useful in studying patterning of ventral structures of the central nervous system. Fifteen different ndr2 alleles have been reported in zebrafish, of which eight were generated using chemical mutagenesis, four were radiation-induced and the remaining alleles were obtained via random insertion, gene targeting (TALEN) or unknown methods. Therefore, most mutation sites were random and could not be predicted a priori. Using the CRISPR-Cas9 system from Streptococcus pyogenes, we targeted distinct regions in all three exons of zebrafish ndr2 and observed cyclopia in the injected ( $$\hbox {G}_{0}$$ ) embryos. We show that the use of sgRNA-Cas9 ribonucleoprotein (RNP) complexes can cause penetrant cyclopic phenotypes in injected ( $$\hbox {G}_{0}$$ ) embryos. Targeted polymerase chain reaction amplicon analysis using Sanger sequencing showed that most of the alleles had small indels resulting in frameshifts. The sequence information correlates with the loss of ndr2 activity. In this study, we validate multiple CRISPR targets using an in vitro nuclease assay and in vivo analysis using embryos. We describe one specific mutant allele resulting in the loss of conserved terminal cysteine-coding sequences. This study is another demonstration of the utility of the CRISPR-Cas9 system in generating domain-specific mutations and provides further insights into the structure–function of the ndr2 gene.
α-Na6Pb3(PS4)4, a noncentrosymmetric thiophosphate with the novel, saucer-shaped [Pb3(PS4)4]6- cluster, and its metastable, 3-dimensionally polymerized allotrope β-Na6Pb3(PS4)4

Discovered in a molten polythiophosphate salt reaction, the compound α-Na6Pb3(PS4)4 is a salt containing the molecular cluster [Pb3(PS4)4].6- The C3v symmetry and the saucer shape of this cluster e...
Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences.

Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale. The availability of labelled training data is one of the practical obstacles towards wide application of machine learning models in medicine. Here the authors develop a weakly supervised deep learning model for the classification of aortic malformations using unlabelled cardiac MRI sequences from the UK biobank.
Multi-state Perfect Phylogeny Mixture Deconvolution and Applications to Cancer Sequencing.

The reconstruction of phylogenetic trees from mixed populations has become important in the study of cancer evolution, as sequencing is often performed on bulk tumor tissue containing mixed populations of cells. Recent work has shown how to reconstruct a perfect phylogeny tree from samples that contain mixtures of two-state characters, where each character/locus is either mutated or not. However, most cancers contain more complex mutations, such as copy-number aberrations, that exhibit more than two states. We formulate the Multi-State Perfect Phylogeny Mixture Deconvolution Problem of reconstructing a multi-state perfect phylogeny tree given mixtures of the leaves of the tree. We characterize the solutions of this problem as a restricted class of spanning trees in a graph constructed from the input data, and prove that the problem is NP-complete. We derive an algorithm to enumerate such trees in the important special case of cladisitic characters, where the ordering of the states of each character is given. We apply our algorithm to simulated data and to two cancer datasets. On simulated data, we find that for a small number of samples, the Multi-State Perfect Phylogeny Mixture Deconvolution Problem often has many solutions, but that this ambiguity declines quickly as the number of samples increases. On real data, we recover copy-neutral loss of heterozygosity, single-copy amplification and single-copy deletion events, as well as their interactions with single-nucleotide variants.
User Interest and Interaction Structure in Online Forums

We present a new similarity measure tailored to posts in an online forum. Our measure takes into account all the available information about user interest and interaction — the content of posts, the threads in the forum, and the author of the posts. We use this post similarity to build a similarity between users, based on principal coordinate analysis. This allows easy visualization of the user activity as well. Similarity between users has numerous applications, such as clustering or classification. We show that including the author of a post in the post similarity has a smoothing effect on principal coordinate projections. We demonstrate our method on real data drawn from an internal corporate forum, and compare our results to those given by a standard document classification method. We conclude our method gives a more detailed picture of both the local and global network structure.
The Power of Waiting for More Than One Response in Minimizing the Age-of-Information

The Age-of-Information (AoI) has recently been proposed as an important metric for investigating the timeliness performance in information-update systems. Prior studies on AoI optimization often consider a Push model, which is concerned about when and how to "push" (i.e., generate and transmit) the updated information to the user. In stark contrast, in this paper we introduce a new Pull model, which is more relevant for certain applications (such as the real-time stock quotes service), where a user sends requests to the servers to proactively "pull" the information of interest. Moreover, we propose to employ request replication to reduce the AoI. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process at the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Finally, we conduct numerical simulations to elucidate our theoretical results. Our findings show that waiting for more than one response can significantly reduce the AoI in most scenarios.
FID: Function Modeling-based Data-Independent and Channel-Robust Physical-Layer Identification

Trusted identification is critical to secure IoT devices. However, the limited memory and computation power of low-end IoT devices prevent the direct usage of conventional identification systems. RF fingerprinting is a promising technique to identify low-end IoT devices since it only requires the RF signals that most IoT devices can produce for communication. However, most existing RF fingerprinting systems are data-dependent and/or not robust to impacts from wireless channels. To address the above problems, we propose to exploit the mathematical expression of the physical-layer process, regarded as a function $\mathcal{F} (\cdot )$ for device identification. $\mathcal{F} (\cdot )$ is not directly derivable, so we further propose a model to learn it and employ this function model as the device fingerprint in our system, namely $\mathcal{F}{\rm{ID}}$. Our proposed function model characterizes the unique physical-layer process of a device that is independent of the transmitted data, and hence, our system $\mathcal{F}{\rm{ID}}$ is data-independent and thus resilient against signal replay attacks. Modeling and further separating channel effects from the function model makes $\mathcal{F}{\rm{ID}}$ channel-robust. We evaluate $\mathcal{F}{\rm{ID}}$ on thousands of random signal packets from 33 different devices in different environments and scenarios, and the overall identification accuracy is over 99%.
On associative algebras, modules and twisted modules for vertex operator algebras

Abstract We give a new construction of functors from the category of modules for the associative algebras A n ( V ) and A g ( V ) associated with a vertex operator algebra V, defined by Dong, Li and Mason, to the category of admissible V-modules and admissible twisted V-modules, respectively, using the method developed in the joint work [5] with Y.-Z. Huang. The functors were first constructed by Dong, Li and Mason, but the importance of the new method, as in [5] , is that we can apply the method to study objects without the commutator formula in the representation theory of vertex operator algebras.
Online estimation and adaptive control for a class of history dependent functional differential equations

This paper presents sufficient conditions for the convergence of online estimation methods and the stability of adaptive control strategies for a class of history-dependent, functional differential equations. The study is motivated by the increasing interest in estimation and control techniques for robotic systems whose governing equations include history-dependent nonlinearities. The functional differential equations in this paper are constructed using integral operators that depend on distributed parameters. As a consequence, the resulting estimation and control equations are examples of distributed parameter systems whose states and distributed parameters evolve in finite and infinite dimensional spaces, respectively. Well-posedness, existence, and uniqueness are discussed for the class of fully actuated robotic systems with history-dependent forces in their governing equation of motion. By deriving rates of approximation for the class of history-dependent operators in this paper, sufficient conditions are derived that guarantee that finite dimensional approximations of the online estimation equations converge to the solution of the infinite dimensional, distributed parameter system. The convergence and stability of a sliding mode adaptive control strategy for the history-dependent, functional differential equations is established using Barbalat’s lemma.
A Strong Form of the Quantitative Wulff Inequality

Quantitative isoperimetric inequalities are shown for anisotropic surface energies where the isoperimetric deficit controls both the Fraenkel asymmetry and a measure of the oscillation of the boundary with respect to the boundary of the corresponding Wulff shape.
On Counting Triangles Through Edge Sampling in Large Dynamic Graphs

Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes incident to the edges. In this chapter, we propose a new edge sampling framework for big-graph analytics in dynamic graphs which enhances the traditional model by enabling the use of additional related information. To demonstrate the advantages of this framework, we present a new sampling algorithm, called Edge Sample and Discard (esd). It generates an unbiased estimate of the total number of triangles, which can be continuously updated in response to both edge additions and deletions. We provide a comparative analysis of the accuracy and computational complexity of esd under the new framework against two current state-of-the-art algorithms operating under the traditional framework. The results of the experiments performed on real graphs show that, with the help of the neighborhood information of the sampled edges, the accuracy achieved by our algorithm is substantially better. We also characterize the impact of properties of the graph on the performance of our algorithm by testing on several Barabasi–Albert graphs.
Smoothing dynamic systems with state-dependent covariance matrices

Kalman filtering and smoothing algorithms are used in many areas, including tracking and navigation, medical applications, and financial trend filtering. One of the basic assumptions required to apply the Kalman smoothing framework is that error covariance matrices are known and given. In this paper, we study a general class of inference problems where covariance matrices can depend functionally on unknown parameters. In the Kalman framework, this allows modeling situations where covariance matrices may depend functionally on the state sequence being estimated. We present an extended formulation and generalized Gauss-Newton (GGN) algorithm for inference in this context. When applied to dynamic systems inference, we show the algorithm can be implemented to preserve the computational efficiency of the classic Kalman smoother. The new approach is illustrated with a synthetic numerical example.
Existence, uniqueness and anisotropic-decay-caused Lifshitz tails of the integrated density of surface states for random surface models

Thecurrentpaperisdevotedtothestudyofexistence,uniquenessandLifshitztailsoftheintegrated densityofsurfacestates(IDSS)forSchrodingeroperatorswithalloytyperandomsurfacepotentials.Weprove the existence and uniqueness of the IDSS for negative energies, which is dened as the thermodynamic limit of the normalized eigenvalue counting functions of localized operators on strips with sections being special cuboids. Under the additional assumption that the single-site impurity potential decays anisotropically, we also prove that the IDSS for negative energies exhibits Lifshitz tails near the bottom of the almost sure spectrum in the following three regimes: the quantum regime, the quantum-classical/classical-quantum regime and the classical regime. We point out that the quantum-classical/classical-quantum regime is new for random surface models.
Estimating accuracy from unlabeled data: a probabilistic logic approach

We propose an efficient method to estimate the accuracy of classifiers using only unlabeled data. We consider a setting with multiple classification problems where the target classes may be tied together through logical constraints. For example, a set of classes may be mutually exclusive, meaning that a data instance can belong to at most one of them. The proposed method is based on the intuition that: (i) when classifiers agree, they are more likely to be correct, and (ii) when the classifiers make a prediction that violates the constraints, at least one classifier must be making an error. Experiments on four real-world data sets produce accuracy estimates within a few percent of the true accuracy, using solely unlabeled data. Our models also outperform existing state-of-the-art solutions in both estimating accuracies, and combining multiple classifier outputs. The results emphasize the utility of logical constraints in estimating accuracy, thus validating our intuition.
C^{2,\alpha } estimates for nonlinear elliptic equations of twisted type

We prove a priori interior \(C^{2,\alpha }\) estimates for solutions of fully nonlinear elliptic equations of twisted type. For example, our estimates apply to equations of the type convex + concave. These results are particularly well suited to equations arising from elliptic regularization. As application, we obtain a new proof of an estimate of Streets and Warren on the twisted real Monge–Ampere equation.
Photoperiod-induced neurotransmitter plasticity declines with aging: An epigenetic regulation?

Neuroplasticity has classically been understood to arise through changes in synaptic strength or synaptic connectivity. A newly discovered form of neuroplasticity, neurotransmitter switching, involves changes in neurotransmitter identity. Chronic exposure to different photoperiods alters the number of dopamine (tyrosine hydroxylase, TH+) and somatostatin (SST+) neurons in the paraventricular nucleus (PaVN) of the hypothalamus of adult rats and results in discrete behavioral changes. Here, we investigate whether photoperiod-induced neurotransmitter switching persists during aging and whether epigenetic mechanisms of histone acetylation and DNA methylation may contribute to this neurotransmitter plasticity. We show that this plasticity in rats is robust at 1 and at 3 months but reduced in TH+ neurons at 12 months and completely abolished in both TH+ and SST+ neurons by 18 months. De novo expression of DNMT3a catalyzing DNA methylation and anti-AcetylH3 assessing histone 3 acetylation were observed following short-day photoperiod exposure in both TH+ and SST+ neurons at 1 and 3 months while an overall increase in DNMT3a in SST+ neurons paralleled neuroplasticity reduction at 12 and 18 months. Histone acetylation increased in TH+ neurons and decreased in SST+ neurons following short-day exposure at 3 months while the total number of anti-AcetylH3+ PaVN neurons remained constant. Reciprocal histone acetylation in TH+ and SST+ neurons indicates the importance of studying epigenetic regulation at the circuit level for identified cell phenotypes. The findings may be useful for developing approaches for noninvasive treatment of disorders characterized by neurotransmitter dysfunction.
An exponential speedup in parallel running time for submodular maximization without loss in approximation

In this paper we study the adaptivity of submodular maximization. Adaptivity quantifies the number of sequential rounds that an algorithm makes when function evaluations can be executed in parallel. Adaptivity is a fundamental concept that is heavily studied across a variety of areas in computer science, largely due to the need for parallelizing computation. For the canonical problem of maximizing a monotone submodular function under a cardinality constraint, it is well known that a simple greedy algorithm achieves a 1 - 1/e approximation [NWF78] and that this approximation is optimal for polynomial-time algorithms [NW78]. Somewhat surprisingly, despite extensive efforts on submodular optimization for large-scale datasets, until very recently there was no known algorithm that achieves a constant factor approximation for this problem whose adaptivity is sublinear in the size of the ground set n.Recent work by [BS18] describes an algorithm that obtains an approximation arbitrarily close to 1/3 in O(logn) adaptive rounds and shows that no algorithm can obtain a constant factor approximation in o(log n) adaptive rounds. This approach achieves an exponential speedup in adaptivity (and parallel running time) at the expense of approximation quality.In this paper we describe a novel approach that yields an algorithm whose approximation is arbitrarily close to the optimal 1 - 1/eguarantee in O(log n) adaptive rounds. This algorithm therefore achieves an exponential speedup in parallel running time for submodular maximization at the expense of an arbitrarily small loss in approximation quality. This guarantee is optimal in both approximation and adaptivity, up to lower order terms.
Parameter selection for HOTV regularization

Abstract Popular methods for finding regularized solutions to inverse problems include sparsity promoting l 1 regularization techniques, one in particular which is the well known total variation (TV) regularization. More recently, several higher order (HO) methods similar to TV have been proposed, which we generally refer to as HOTV methods. In this letter, we investigate the problem of the often debated selection of λ, the parameter used to carefully balance the interplay between data fitting and regularization terms. We theoretically argue for a scaling of the operators for a uniform parameter selection for all orders of HOTV regularization. In particular, parameter selection for all orders of HOTV may be determined by scaling an initial parameter for TV, which the imaging community may be more familiar with. We also provide several numerical results which justify our theoretical findings.
Machine learning applications in estimating transformer loss of life

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies/Ambient temperature and load profile are the main factors which affect aging of the transforiner insulation, and consequently, the transformer lifetime. The IEEE Std. C57.91-1995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.
Communication vs distributed computation: An alternative trade-off curve

In this paper, we revisit the communication vs. distributed computing trade-off, studied within the framework of MapReduce in [1]. An implicit assumption in the aforementioned work is that each server performs all possible computations on all the files stored in its memory. Our starting observation is that, if servers can compute only the intermediate values they need, then storage constraints do not directly imply computation constraints. We examine how this affects the communication-computation trade-off and suggest that the trade-off be studied with a predetermined storage constraint. We then proceed to examine the case where servers need to perform computationally intensive tasks, and may not have sufficient time to perform all computations required by the scheme in [1]. Given a threshold that limits the computational load, we derive a lower bound on the associated communication load, and propose a heuristic scheme that achieves in some cases the lower bound.
Physical Multi-Layer Phantoms for Intra-Body Communications

This paper presents approaches for creating tissue mimicking materials that can be used as phantoms for evaluating the performance of the body area networks (BANs). The main goal of this paper is to describe a methodology to create a repeatable experimental BAN platform that can be customized depending on the BAN scenario under test. Comparisons between different material compositions and percentages are shown along with the resulting electrical properties of each mixture over the frequency range of interest for intra-body communications, 100 KHz–100 MHz. Test results on a composite multi-layer sample are presented confirming the efficacy of the proposed methodology. To date, this is the first paper that provides guidance on how to decide on the concentration levels of ingredients, depending on the exact frequency range of operation, and the desired matched electrical characteristics (conductivity versus permittivity), to create multi-layer phantoms for the intra-body communication applications.
Signal-based Bayesian Seismic Monitoring

This thesis presents a new approach to seismic monitoring, the task of detecting seismic events from potentially noisy and cluttered signals recorded across multiple stations. Unlike previous work, which represents seismic signals by a lossy set of discrete detections, we specify a generative probability model of raw seismic waveforms, incorporating a rich representation of the physics underlying the signal generation process, including source mechanisms, wave propagation, and station response. Inference in this model recovers the qualitative behavior of geophysical methods including waveform matching and double-differencing, all as part of a unified Bayesian monitoring system that simultaneously detects and locates events from a network of stations.Our model of seismic signals combines physically meaningful latent variables such as phase travel times, amplitudes, and signal decay rates, with data-driven models based on historical signals. Detailed waveform structure is represented using Gaussian process models of wavelet coefficients, encoding a general assumption that seismic signals are spatially corre- lated, and allowing us to detect and locate events even from weak signals at a single station. We show that the wavelet coefficients can be marginalized out using message passing applied to a state-space representation of the signal model, allowing for practical inference using a reversible jump Metropolis-Hastings algorithm.We evaluate our system, SIGVISA (Signal-based Vertically Integrated Seismic Analysis), on a task of monitoring the western United States for a two-week period following the magnitude 6.0 event in Wells, NV in February 2008. During this period, SIGVISA detects between two to three times as many events as detection-based systems, while reducing mean location errors by a factor of four. We provide evidence that SIGVISA detects some events that are missed even by the regional monitoring networks that we use as a ground-truth comparison. A primary driver of monitoring research is the verification of nuclear test ban treaties, which are particularly concerned with detecting events in regions with no nearby historical seismicity. In our experiments, SIGVISA matches or exceeds the detection rates of existing systems for such events, and even detects a number of such events missed by human analysts.
Community detection with spiking neural networks for neuromorphic hardware

We present results related to the performance of an algorithm for community detection which incorporates event-driven computation. We define a mapping which takes a graph G to a system of symmetrically connected, spiking neurons and use spike train similarities to identify vertex communities. On a random graph with 128 vertices and known community structure we show how our approach can be used to identify individual communities from spiking neuron responses.