cssi/SciSciGPT-SciSciNet huggingface dataset:
Intramolecular Anodic Carbon−Carbon Bond Formation from Oxidized Phenol Intermediates. Effect of Oxygenated Substituents on the Yields of Spiro Dienones in Electrochemical and Iodobenzene Diacetate Oxidations

The single-cell, constant-current anodic oxidation of a series of 4-(2-alkenylaryl)phenols was studied in which the aryl substituents were 2-vinyl-4,5-dimethoxy, 6a; 2-propenyl-4,5-dimethoxy, 9a; 2-vinyl-4,5-methylenedioxy, 6b; 2-propenyl-4,5-methylenedioxy, 9b; 2-vinyl-4-methoxy, 6c; 2-propenyl-4-methoxy, 9c; 2-vinyl-5-methoxy, 6d; and 2-propenyl-5-methoxy, 9d. Two compounds having methoxyl groups on the phenolic ring were also studied: 4-(2‘-propenylphenyl)-2-methoxyphenol, 9e, and 4-(2‘-propenylphenyl)-3-methoxyphenol, 9f. A novel synthetic route to the methoxy compounds 6c, 9c, 6d, and 9d was developed. This involved as a key step the MAD, bis(2,6-di-tert-butyl-4-methylphenoxide)methylaluminum, mediated addition of organolithium and Grignard reagents to functionalize quinol ether derivatives. Anodic oxidation of compounds 6a, 9a, 9b, 6c, 9c, and 9d gave good yields of spiro dienones arising from trapping of the phenoxonium ion by the alkenyl side chain followed by reaction of the resulting cation wit...
Ultrasound-promoted highly efficient reduction of aromatic nitro compounds to the aromatic amines by samarium/ammonium chloride

Abstract Ultrasound-promoted, highly efficient reduction of several aromatic nitro compounds to the aromatic amines was achieved by samarium/ammonium chloride mediated reaction.
Accurate Protein-Folding Transition-Path Statistics from a Simple Free-Energy Landscape

A central goal of protein-folding theory is to predict the stochastic dynamics of transition paths-the rare trajectories that transit between the folded and unfolded ensembles-using only thermodynamic information, such as a low-dimensional equilibrium free-energy landscape. However, commonly used one-dimensional landscapes typically fall short of this aim, because an empirical coordinate-dependent diffusion coefficient has to be fit to transition-path trajectory data in order to reproduce the transition-path dynamics. We show that an alternative, first-principles free-energy landscape predicts transition-path statistics that agree well with simulations and single-molecule experiments without requiring dynamical data as an input. This "topological configuration" model assumes that distinct, native-like substructures assemble on a time scale that is slower than native-contact formation but faster than the folding of the entire protein. Using only equilibrium simulation data to determine the free energies of these coarse-grained intermediate states, we predict a broad distribution of transition-path transit times that agrees well with the transition-path durations observed in simulations. We further show that both the distribution of finite-time displacements on a one-dimensional order parameter and the ensemble of transition-path trajectories generated by the model are consistent with the simulated transition paths. These results indicate that a landscape based on transient folding intermediates, which are often hidden by one-dimensional projections, can form the basis of a predictive model of protein-folding transition-path dynamics.
Characterizing building blocks of resource constrained biological networks.

Identification of motifs–recurrent and statistically significant patterns–in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over- or under-estimates), and thus, wrong biological interpretations. In this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs. Our experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cell’s organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.
Optimal Control of Molecular Motion: Design, Implementation, and Inversion

This paper reviews recent theoretical and experimental developments aimed at controlling molecular motion using tailored laser fields. Emphasis is given to seeking optimal designs for the laser controls and optimal implementation of the controls in the laboratory. Optimization on both counts provides a rigorous, flexible, and physically attractive means for obtaining the best possible control over molecular motion under any specified conditions. The theoretical design and laboratory implementation of control are best effected by a closed-loop process that draws on observations of the evolving molecular sample to steer it toward the desired target. Going beyond control, similar closed-loop laboratory learning concepts may lead to automated molecular monitors for inversion to systematically identify details of molecular Hamiltonians.
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg .
Tandem Hass–Bender/Henry reaction for the synthesis of dimethylnitro alcohols from benzylic halides

Abstract Dimethylnitro alcohols are constructed in a one-pot process from benzylic halides and 2-nitropropane. The use of tetrabutylammonium fluoride (TBAF) as the promoter is essential for this tandem Hass–Bender/Henry reaction to proceed.
Synthesis and reactivity of pyrrolo[3,2-e]indole: Removal of a N-BOM group from an unactivated indole

Abstract A practical synthesis of pyrrolo[3,2-e]indole (1) is described. Different hydrogenation conditions of the indol-4-ylacetonitrile (3) afforded either 1-BOM-pyrrolo[3,2-e]indole (4, 42% from 5-nitroindole) or 1-hydroxymethylpyrrolo[3,2-e]indole (5, 46% from 5-nitroindole). Removal of the benzyl group was found to be problematic, but could be accomplished in moderate yield. Treatment of the resulting 1-hydroxymethylpyrrolo[3,2-e]indole (5) with NaOH in THF afforded 1 (94% from 5). Limited studies on the chemistry of 1 are also presented.
Spin dependent fragmentation functions for heavy flavor baryons and single heavy hyperon polarization

Spin dependent fragmentation functions for heavy flavor quarks to fragment into heavy baryons are carculated in a quark-diquark model. The production of intermediate spin 1/2 and 3/2 excited states is explicity included. Λb, Λc and Ξc production rate and polarization at LEP energies are calculated and, where possible, compared with experiment. A different approach, also relying on a heavy quark-diquark model, is proposed for the small momentum transfer inclusive production of polarized heavy flavor hyperons. The predicted Λc polarization is roughly in agreement with experiment.
Preparatory Encoding of the Fine Scale of Human Spatial Attention.

Our attentional focus is constantly shifting: In one moment, our attention may be intently concentrated on a specific spot, whereas in another moment we might spread our attention more broadly. Although much is known about the mechanisms by which we shift our visual attention from place to place, relatively little is known about how we shift the aperture of attention from more narrowly to more broadly focused. Here we introduce a novel attentional distribution task to examine the neural mechanisms underlying this process. In this task, participants are presented with an informative cue that indicates the location of an upcoming target. This cue can be perfectly predictive of the exact target location, or it can indicate—with varying degrees of certainty—approximately where the target might appear. This cue is followed by a preparatory period in which there is nothing on the screen except a central fixation cross. Using scalp EEG, we examined neural activity during this preparatory period. We find that, wi...
A NA I Absorption Map of the Small-Scale Structure in the Interstellar Gas toward M15

Using the DensePak fiber-optic array on the KPNO WIYN telescope, we have obtained high signal-to-noise ratio echelle spectra of the Na I D wavelength region toward the central 27'' × 43'' of the globular cluster M15 at a spatial resolution of 4''. The spectra exhibit significant interstellar Na I absorption at LSR velocities of 3 km s-1 (local interstellar medium [LISM] component) and 68 km s-1 (intermediate-velocity cloud [IVC] component). Both components vary appreciably in strength on these scales. The derived Na I column densities differ by a factor of 4 across the LISM absorption map and by a factor of 16 across the IVC map. Assuming distances of 500 and 1500 pc for the LISM and IV clouds, respectively, these maps show evidence of significant ISM structure down to the minimum scales of 2000 and 6000 AU probed in these absorbers. The smallest scale N(Na I) variations observed in the M15 LISM and IVC maps are typically comparable to or higher than the values found at similar scales in previous studies of interstellar Na I structure toward binary stars. The physical implications of the small and larger scale Na I features observed in the M15 maps are discussed in terms of variations in the H I column density as well as in the Na ionization equilibrium.
Ba4Cr2US9: The First Chalcogenide Analogue of the Perovskite-related (A3A′BO6)m(A3B3O9)n Family

The compound Ba4Cr2US9, which crystallizes in space group P321, consists of one-dimensional chains separated by Ba2+ cations. Each one-dimensional chain comprises face-sharing CrS6 octahedra and US6 trigonal prisms in the sequence oct oct tp oct oct tp with the U and Cr centers in a linear chain parallel to [001].
Gas-phase reactions of 1,4-benzodioxan, 2,3-dihydrobenzofuran, and 2,3-benzofuran with OH radicals and O3

The kinetics of the gas-phase reactions of 1,4-benzodioxan, 2,3-dihydrobenzofuran, and 2,3-benzofuran with OH radicals and O 3 have been studied at 298±2 K and atmospheric pressure of air and the products have also been investigated. 1,4-Benzodioxan and 2,3-dihydrobenzofuran were hosen as volatile model compounds for dibenzo-p-dioxin and dibenzofuran, respectively
Radical Substituent Effects of α-Fluorine and α-Trifluoromethyl Groups

Homolytic bond dissociation enthalpies (BDEs) of the C−H bonds in 30 fluorinated hydrocarbons have been obtained from ab initio CBS-4 (complete basis set) model calculation at both 0 and 298 K. The calculated C−H BDEs agree with the available literature experimental results within ±2 kcal/mol. Introduction of one or two α-fluorine (α-F) substituent(s) into methane, ethane, or isopropane was found to weaken the sp3 C−H bond strength by 1−4 kcal/mol primarily due to the resonance delocalization of the unpaired electron of the radicals formed by removal of one hydrogen atom. In contrast, introduction of three α-F substituents in trifluoromethane (CHF3) was found to strengthen the sp3 C−H bond by about 1 kcal/mol. The 4−6 kcal/mol bond-strengthening effects of α-trifluoromethyl groups were attributed exclusively to their inductive effects. The substituent effects of fluorine and trifluoromethyl groups have also been examined on the strength of the ethylene sp2 C−H bonds and the acetylene sp C−H bonds. The gro...
Joint Design of Multimodal Transit Networks and Shared Autonomous Mobility Fleets

Abstract Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this question, a joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP) is introduced, and a bi-level mathematical programming formulation and solution approach are presented. The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable and allowing the removal of bus routes. The lower-level problem consists of a dynamic combined mode choice-traveler assignment problem (DCMC-TAP) formulation. The heuristic solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based assignment-simulation approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the region’s existing multimodal transit network. The computational results indicate significant traveler benefits, in terms of improved average traveler wait times, associated with optimizing the joint design of multimodal transit networks and SAMS fleets compared with the initial transit network design.
Metaproteomics of Colonic Microbiota Unveils Discrete Protein Functions among Colitic Mice and Control Groups.

Metaproteomics can greatly assist established high-throughput sequencing methodologies to provide systems biological insights into the alterations of microbial protein functionalities correlated with disease-associated dysbiosis of the intestinal microbiota. Here, the authors utilize the well-characterized murine T cell transfer model of colitis to find specific changes within the intestinal luminal proteome associated with inflammation. MS proteomic analysis of colonic samples permitted the identification of ≈10 000-12 000 unique peptides that corresponded to 5610 protein clusters identified across three groups, including the colitic Rag1-/- T cell recipients, isogenic Rag1-/- controls, and wild-type mice. The authors demonstrate that the colitic mice exhibited a significant increase in Proteobacteria and Verrucomicrobia and show that such alterations in the microbial communities contributed to the enrichment of specific proteins with transcription and translation gene ontology terms. In combination with 16S sequencing, the authors' metaproteomics-based microbiome studies provide a foundation for assessing alterations in intestinal luminal protein functionalities in a robust and well-characterized mouse model of colitis, and set the stage for future studies to further explore the functional mechanisms of altered protein functionalities associated with dysbiosis and inflammation.
UWB Air-to-Ground Propagation Channel Measurements and Modeling Using UAVs

This paper presents an experimental study of the air-to-ground (AG) propagation channel through ultrawideband (UWB) measurements in an open area using unmanned-aerial-vehicles (UAVs). Measurements were performed using UWB radios operating in the frequency range of 3.1 GHz-4.8 GHz and UWB planar elliptical dipole antennas having an omni-directional pattern in the azimuth plane and typical donut shaped pattern in the elevation plane. Three scenarios were considered for the channel measurements: (i) two receivers (RXs) at different heights above the ground and placed close to each other in line-of-sight (LOS) with the transmitter (TX) on the UAV and the UAV is hovering; (ii) RXs in obstructed line-of-sight (OLOS) with the UAV TX due to foliage, and the UAV is hovering; and, (iii) UAV moving in a circular path. Different horizontal and vertical distances between the RXs and the TX were used in the measurements. In addition, two different antenna orientations were used on the UAV antennas (vertical and horizontal) to analyze the effects of antenna radiation patterns on the UWB AG propagation. From the empirical results, it was observed that the received power depends mainly on the antenna radiation pattern in the elevation plane when the antennas are oriented in the same direction, as expected for these omni-azimuth antennas. Moreover, the overall antenna gain at the TX and RX can be approximated using trigonometric functions of the elevation angle. The antenna orientation (polarization) mismatch increases path loss, and produces a larger number of weak multipath components (MPCs) than when aligned. Similarly, additional path loss and a larger number of MPCs were observed for the OLOS scenario. In the case of the UAV moving in a circular path, the antenna orientation mismatch has smaller effects on the path loss than when the UAV is hovering, because a larger number of cross polarized components are received during motion. A statistical channel model for UWB AG propagation is built from the empirical results.
Sagittamides A and B. Polyacetoxy long-chain acyl amino acids from a didemnid ascidian.

An unidentified tunicate from Pohnpei, Micronesia, yielded sagittamides A and Bcompounds comprising a long-chain C26 dicarboxylic acid that acylates terminal l-valine and l-ornithine groups. The structures, which contain an unprecedented internal O-hexacetyl-1,2,3,4,5,6-hexaol moiety, were determined by combined spectroscopic analysis including mass spectrometry and 1D and 2D NMR and chemical degradation. The partial absolute stereochemistry of the new compounds was addressed by Marfey's analysis.
Discovery of a phosphine-mediated cycloisomerization of alkynyl hemiketals: access to spiroketals and dihydropyrazoles via tandem reactions.

Reported here are details on the discovery of a phosphine-catalyzed isomerization of hemiketals and subsequent reactions of the cyclic keto enol ether products. The new cycloisomerization complements a previously reported amine-catalyzed process that gave oxepinones from the same hemiketal starting materials. In the absence of functionality (R2) on the cyclic keto enol ether, a rapid and facile dimerization occurs, giving spiroketal products. When the enone is substituted (i.e., R2 = Ph), the cyclic keto enol ether is sufficiently stable so that it can be isolated; it can then be further reacted in the same pot to provide the corresponding dihydropyrazoles. Both the spiroketal and dihydropyrazole products arise by a tandem reaction that begins with the novel cycloisomerization. The method allows for the rapid introduction of complexity in the products from relatively simple starting materials. It should find application in the synthesis of natural product-like molecules.
Gene-level differential analysis at transcript-level resolution.

Compared to RNA-sequencing transcript differential analysis, gene-level differential expression analysis is more robust and experimentally actionable. However, the use of gene counts for statistical analysis can mask transcript-level dynamics. We demonstrate that ‘analysis first, aggregation second,’ where the p values derived from transcript analysis are aggregated to obtain gene-level results, increase sensitivity and accuracy. The method we propose can also be applied to transcript compatibility counts obtained from pseudoalignment of reads, which circumvents the need for quantification and is fast, accurate, and model-free. The method generalizes to various levels of biology and we showcase an application to gene ontologies.
JIP2 haploinsufficiency contributes to neurodevelopmental abnormalities in human pluripotent stem cell–derived neural progenitors and cortical neurons

Phelan–McDermid syndrome (also known as 22q13.3 deletion syndrome) is a syndromic form of autism spectrum disorder and currently thought to be caused by heterozygous loss of SHANK3. However, patients most frequently present with large chromosomal deletions affecting several additional genes. We used human pluripotent stem cell technology and genome editing to further dissect molecular and cellular mechanisms. We found that loss of JIP2 (MAPK8IP2) may contribute to a distinct neurodevelopmental phenotype in neural progenitor cells (NPCs) affecting neuronal maturation. This is most likely due to a simultaneous down-regulation of c-Jun N-terminal kinase (JNK) proteins, leading to impaired generation of mature neurons. Furthermore, semaphorin signaling appears to be impaired in patient NPCs and neurons. Pharmacological activation of neuropilin receptor 1 (NRP1) rescued impaired semaphorin pathway activity and JNK expression in patient neurons. Our results suggest a novel disease-specific mechanism involving the JIP/JNK complex and identify NRP1 as a potential new therapeutic target.
Metal-free intramolecular cyclopropanation of alkenes through iodonium ylide methodology

Abstract Intramolecular cyclopropanation of alkenes occurs thermally with iodonium ylides in the absence of conventional metal catalysts such as Rh(II) and Cu(II). In rigid molecular systems conversions are near quantitative. A mechanism is proposed involving formal 2+2 cycloaddition followed by reductive elimination of PhI to yield the cyclopropane.
Certain 3,9-Dideazapurines as Inhibitors of Purine Nucleoside Phosphorylase

Abstract The synthesis of four 3,9-dideazapurines is described. In order to achieve the desired substitution pattern, a new approach to the pyrrolo[2, 3-c]pyridine ring system was devised utilizing the Gassman reaction as the key step for its construction. The four target heterocycles were all evaluated for their ability to inhibit human erythrocytic purine nucleoside phosphorylase.
Diene-ligated iridium catalyst for allylation reactions of ketones and imines.

[Ir(cod)Cl]2 is a highly reactive catalyst for allylation reactions of ketones using allylboronic ester. Mechanistic experiments are consistent with formation of a nucleophilic allyliridium(I) complex that is activated by the diene ligand toward attack of a ketone. Aryl and alkyl ketones react smoothly at room temperature. Aldimines also undergo allylation under these reaction conditions, requiring increased reaction times relative to the corresponding ketones.
(R,R)-2,5-Diphenylpyrrolidine: Diastereoselective Radical Addition to the Derived Methacrylamide

Abstract Radical addition of thiophenol to the methacrylamide 1 proceeds with high diastereoselectivity, to give 2 and 3 in a ratio of 25:1. © 1997 Elsevier Science Ltd.
[MoO2(acac)2], a versatile precursor for diazenido-and hydrazido-complexes

Abstract In this review, we present the most significant aspects of the reactivity of [MoO2(acac)2] toward (i) phenylhydrazine and its p-substituted derivatives, R-p-C6H4NHNH2 (R=Me, MeO, Cl, NO2), (ii) N,N-disubstituted hydrazines, PhRNNH2 (R=Me, Ph), and (iii) N,N-disubstituted hydrazinium halides, PhRNNH3+X– (R=Me, Ph; X=Cl, Br, I) in alcohols and acetonitrile. In particular, we emphasize the versatility of [MoO2(acac)2] as a precursor for a number of mono-, di- and tetranuclear organodinitrogen molybdenum complexes containing diazenido, NNR, hydrazido(2-), NNRR′, and hydrazido(1-), NHNRR′, ligands. © 2000 Academie des sciences / Editions scientifiques et medicales Elsevier SAS molybdenum / diazenido / hydrazido(2-) / hydrazido(1-) / alkoxo / dinuclear complexes / tetranuclear complexes
Image velocimetry and spectral analysis enable quantitative characterization of larval zebrafish gut motility.

Background Normal gut function requires rhythmic and coordinated movements that are affected by developmental processes, physical and chemical stimuli, and many debilitating diseases. The imaging and characterization of gut motility, especially regarding periodic, propagative contractions driving material transport, are therefore critical goals. Previous image analysis approaches have successfully extracted properties related to the temporal frequency of motility modes, but robust measures of contraction magnitude, especially from in vivo image data, remain challenging to obtain. Methods We developed a new image analysis method based on image velocimetry and spectral analysis that reveals temporal characteristics such as frequency and wave propagation speed, while also providing quantitative measures of the amplitude of gut motion. Key results We validate this approach using several challenges to larval zebrafish, imaged with differential interference contrast microscopy. Both acetylcholine exposure and feeding increase frequency and amplitude of motility. Larvae lacking enteric nervous system gut innervation show the same average motility frequency, but reduced and less variable amplitude compared to wild types. Conclusions & inferences Our image analysis approach enables insights into gut dynamics in a wide variety of developmental and physiological contexts and can also be extended to analyze other types of cell movements.
Spartan: Efficient and General-Purpose zkSNARKs Without Trusted Setup

This paper introduces Spartan, a new family of zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs) for the rank-1 constraint satisfiability (R1CS), an NP-complete language that generalizes arithmetic circuit satisfiability. A distinctive feature of Spartan is that it offers the first zkSNARKs without trusted setup (i.e., transparent zkSNARKs) for NP where verifying a proof incurs sub-linear costs—without requiring uniformity in the NP statement’s structure. Furthermore, Spartan offers zkSNARKs with a time-optimal prover, a property that has remained elusive for nearly all zkSNARKs in the literature.
A statistical simulator scDesign for rational scRNA-seq experimental design.

Author(s): Li, Wei Vivian; Li, Jingyi Jessica | Abstract: MotivationSingle-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information.ResultsHere we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA-seq computational methods based on specific research goals.Availability and implementationWe have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign.Supplementary informationSupplementary data are available at Bioinformatics online.
Case Volume-to-Outcome Relationship in Minimally Invasive Esophagogastrectomy.

Background Outcomes after open esophagogastrectomy (OE) have been shown to depend on institution case volume. We aim to determine whether a similar relationship exists for minimally invasive esophagogastrectomy (MIE). Methods Patients who had OE or MIE (excluding robotic procedures) between 2010 and 2013 in the National Cancer Database were included. Outcomes included 30-day and 90-day mortality, length of stay, hospital readmission, margin positivity, and number of lymph nodes harvested. Logistic and linear regression were used to adjust for possible confounders including age, sex, tumor size, Charlson score, induction therapy, and type of institution (academic versus community based). Results We identified 2371 patients in the MIE group and 6285 patients in the OE group. In multivariate analysis, high case volume was an independent predictor for lower 30-day mortality and 90-day mortality, shorter length of stay, and higher rate of negative-margin resection in OE (P Conclusions In this dataset, MIE postoperative outcomes, unlike OE postoperative outcomes, did not correlate with hospital case volume. Volume-outcome relationships may be affected by surgical approach. The effect of case volume on long-term outcomes after MIE warrants further study.
Output Observability of Systems Over Finite Alphabets With Linear Internal Dynamics

We motivate the need for a new notion of observability for systems over finite alphabets and propose three new notions of output observability, thereby shifting our attention to the problem of state estimation for output prediction. We then consider a class of systems over finite alphabets with linear internal dynamics, finite-valued control inputs, and finitely quantized outputs. We derive a set of sufficient conditions and a set of necessary conditions for these systems to be output observable, propose an algorithmic procedure to verify one of these conditions, and construct finite memory output observers when certain conditions are met.
Modeling cooperative navigation in dense human crowds

For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with one another to avoid collisions, while heading toward their respective destinations. Previous approaches have used handcrafted functions based on proximity to model human-human and human-robot interactions. However, these approaches can only model simple interactions and fail to generalize for complex crowded settings. In this paper, we develop an approach that models the joint distribution over future trajectories of all interacting agents in the crowd, through a local interaction model that we train using real human trajectory data. The interaction model infers the velocity of each agent based on the spatial orientation of other agents in his vicinity. During prediction, our approach infers the goal of the agent from its past trajectory and uses the learned model to predict its future trajectory. We demonstrate the performance of our method against a state-of-the-art approach on a public dataset and show that our model outperforms when predicting future trajectories for longer horizons.
Teaching Data Science

We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general programming concepts by using the Python programming language with an emphasis on data preparation, processing, and presentation. The course had no prerequisites, and students were not expected to have any programming experience. This introductory course was designed to cover a wide range of topics, from the nature of data, to storage, to visualization, to probability and statistical analysis, to cloud and high performance computing, without becoming overly focused on any one subject. We conclude this article with a discussion of lessons learned and our plans to develop new data science courses.
An overview of the NIRSPEC upgrade for the Keck II telescope

Author(s): Martin, EC; Fitzgerald, MP; McLean, IS; Doppmann, G; Kassis, M; Aliado, T; Canfield, J; Johnson, C; Kress, E; Lanclos, K; Magnone, K; Sohn, JM; Wang, E; Weiss, J | Abstract: NIRSPEC is a 1-5 um echelle spectrograph in use on the Keck II Telescope since 1999. The spectrograph is capable of both moderate (R=λ/Δλ∼2000) and high (R∼25,000) resolution observations and has been a workhorse instrument across many astronomical fields, from planetary science to extragalactic observations. In the latter half of 2018, we will upgrade NIRSPEC to improve the sensitivity and stability of the instrument and increase its lifetime. The major components of the upgrade include replacing the spectrometer and slit-viewing camera detectors with Teledyne H2RG arrays and replacing all transputer-based electronics. We present detailed design, testing, and analysis of the upgraded instrument, including the finalized optomechanical design of the new 1-5 μm slit-viewing camera, detector characterization of the science and Engineering A grade arrays, electronics systems, and updated software design. The optomechanical design of the slit-viewing camera and replacement detector head assembly have both been assembled and cold-tested in our lab. We also show results from the GigE interface to the SAM/ASIC boards to control the H2RG. The upgrade will continue NIRSPEC's legacy as a powerful near-infrared spectrograph behind one of the world's most scientifically productive telescopes.
Integral points of bounded degree on the projective line and in dynamical orbits

Let $D$ be a non-empty effective divisor on $\mathbb{P}^1$. We show that when ordered by height, any set of $(D,S)$-integral points on $\mathbb{P}^1$ of bounded degree has relative density zero. We then apply this to arithmetic dynamics: let $\varphi(z)\in \overline{\mathbb{Q}}(z)$ be a rational function of degree at least two whose second iterate $\varphi^2(z)$ is not a polynomial. We show that as we vary over points $P\in\mathbb{P}^1(\overline{\mathbb{Q}})$ of bounded degree, the number of algebraic integers in the forward orbit of $P$ is absolutely bounded and zero on average.
Simple groups of dynamical origin

We associate with every etale groupoid G two normal subgroups S(G) and A(G) of the topological full group of G, which are analogs of the symmetric and alternating groups. We prove that if G is a minimal groupoid of germs (e.g., of a group action), then A(G) is simple and is contained in every non-trivial normal subgroup of the full group. We show that if G is expansive (e.g., is the groupoid of germs of an expansive action of a group), then A(G) is finitely generated. We also show that S(G)/A(G) is a quotient of H_0(G, Z/2Z).
Variance reduction for faster non-convex optimization

We consider the fundamental problem in nonconvex optimization of efficiently reaching a stationary point. In contrast to the convex case, in the long history of this basic problem, the only known theoretical results on first-order nonconvex optimization remain to be full gradient descent that converges in O(1/e) iterations for smooth objectives, and stochastic gradient descent that converges in O(1/e2) iterations for objectives that are sum of smooth functions. We provide the first improvement in this line of research. Our result is based on the variance reduction trick recently introduced to convex optimization, as well as a brand new analysis of variance reduction that is suitable for non-convex optimization. For objectives that are sum of smooth functions, our first-order minibatch stochastic method converges with an O(1/e) rate, and is faster than full gradient descent by Ω(n1/3). We demonstrate the effectiveness of our methods on empirical risk minimizations with nonconvex loss functions and training neural nets.
Honeycomb lattice potentials and Dirac points

We prove that the two-dimensional Schroedinger operator with a potential having the symmetry of a honeycomb structure has dispersion surfaces with conical singularities (Dirac points) at the vertices of its Brillouin zone. No assumptions are made on the size of the potential. We then prove the robustness of such conical singularities to a restrictive class of perturbations, which break the honeycomb lattice symmetry. General small perturbations of potentials with Dirac points do not have Dirac points; their dispersion surfaces are smooth. The presence of Dirac points in honeycomb structures is associated with many novel electronic and optical properties of materials such as graphene.
Spectre returns! speculation attacks using the return stack buffer

The recent Spectre attacks exploit speculative execution, a pervasively used feature of modern microprocessors, to allow the exfiltration of sensitive data across protection boundaries. In this paper, we introduce a new Spectreclass attack that we call SpectreRSB. In particular, rather than exploiting the branch predictor unit, SpectreRSB exploits the return stack buffer (RSB), a common predictor structure in modern CPUs used to predict return addresses. We show that both local attacks (within the same process such as Spectre 1) and attacks on SGX are possible by constructing proof of concept attacks. We also analyze additional types of the attack on the kernel or across address spaces and show that under some practical and widely used conditions they are possible. Importantly, none of the known defenses including Retpoline and Intel's microcode patches stop all SpectreRSB attacks. We believe that future system developers should be aware of this vulnerability and consider it in developing defenses against speculation attacks. In particular, on Core-i7 Skylake and newer processors (but not on Intel's Xeon processor line), a patch called RSB refilling is used to address a vulnerability when the RSB underfills; this defense interferes with SpectreRSB's ability to launch attacks that switch into the kernel. We recommend that this patch should be used on all machines to protect against SpectreRSB.
Distributed UAV Placement Optimization for Cooperative Line-of-sight MIMO Communications

IEEECooperative communication using unmanned aerial vehicles (UAVs) is a promising technology for infrastructureless wireless networks. One of the key challenges in UAV based communications is the backhaul throughput. In this paper, we propose optimization of the UAV swarm positions to achieve a high mulitplexing gain in line-of-sight (LoS) MIMO back-haul. We develop two distributed algorithms to position the UAVs such that each UAV moves a minimal distance to realize the highest capacity LoS MIMO channel. The first approach uses iterative gradient descent (GD) and the second uses iterative brute force (BF). Simulations show that both algorithms can achieve up to 6 times higher capacity compared to the approach relying on random UAV placement, earlier proposed in the literature. BF has the advantage of not requiring any location information, while GD is less sensitive to errors in motion.
Very ample polarized self maps extend to projective space

Abstract Let X be a projective variety defined over an infinite field, equipped with a line bundle L, giving an embedding of X into P m and let ϕ : X → X be a morphism such that ϕ ⁎ L ≅ L ⊗ q , q ⩾ 2 . Then there exists an integer r > 0 extending ϕ r to P m .
Dual pairs of quantum moment maps and doubles of Hopf algebras

Abstract Given a dual pair of topological Hopf algebras A , A ⁎ , under mild conditions there exists a natural associative algebra homomorphism D ( A ) → H ( A ) between the corresponding Drinfeld double D ( A ) and Heisenberg double H ( A ) . We construct this homomorphism using a pair of commuting quantum moment maps, and then use it to provide a homomorphism of certain reflection equation algebras. We also explain how the quantization of the Grothendieck–Springer resolution arises in this context.
Statistical Inference for Perturbed Multiscale Dynamical Systems

Abstract We study statistical inference for small-noise-perturbed multiscale dynamical systems. We prove consistency, asymptotic normality, and convergence of all scaled moments of an appropriately constructed maximum likelihood estimator (MLE) for a parameter of interest, identifying precisely its limiting variance. We allow full dependence of coefficients on both slow and fast processes, which take values in the full Euclidean space; coefficients in the equation for the slow process need not be bounded and there is no assumption of periodic dependence. The results provide a theoretical basis for calibration of small-noise-perturbed multiscale dynamical systems. Data from numerical simulations are presented to illustrate the theory.
Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations

This paper presents a novel regrasp control policy that makes use of tactile sensing to plan local grasp adjustments. Our approach determines regrasp actions by virtually searching for local transformations of tactile measurements that improve the quality of the grasp. First, we construct a tactile-based grasp quality metric using a deep convolutional neural network trained on over 2800 grasps. The quality of each grasp, a continuous value between 0 and 1, is determined experimentally by measuring its resistance to external perturbations. Second, we simulate the tactile imprints associated with robot motions relative to the initial grasp by performing rigid-body transformations of the given tactile measurements. The newly generated tactile imprints are evaluated with the learned grasp quality network and the regrasp action is chosen to maximize the grasp quality. Results show that the grasp quality network can predict the outcome of grasps with an average accuracy of 85% on known objects and 75% on novel objects. The regrasp control policy improves the success rate of grasp actions by an average relative increase of 70% on a test set of 8 objects. We provide a video summarizing our approach at https://youtu.be/gjn7DmfpwDk.
The multilinear restriction estimate: a short proof and a refinement

We provide an alternative and self contained proof of the main result of Bennett, Carbery, Tao regarding the multilinear restriction estimate. The approach is inspired by the recent result of Guth about the Kakeya version of multilinear restriction estimate. At lower levels of multilinearity we provide a refined estimate in the context of small support for one of the terms involved.
UNIFORMLY PERFECT ANALYTIC AND CONFORMAL ATTRACTOR SETS

Conditions are given which imply that analytic iterated function systems (IFSs) in the complex plane [Copf ] have uniformly perfect attractor sets. In particular, it is shown that the attractor set of a finitely generated conformal IFS is uniformly perfect when it contains two or more points. Also, an example is given of a finitely generated analytic attractor set which is not uniformly perfect.
Blind Signal Separation in the Presence of Gaussian Noise

A prototypical blind signal separation problem is the so-called cocktail party problem, with n people talking simultaneously and n dierent microphones within a room. The goal is to recover each speech signal from the microphone inputs. Mathematically this can be modeled by assuming that we are given samples from an n-dimensional random variable X = AS, where S is a vector whose coordinates are independent random variables corresponding to each speaker. The objective is to recover the matrix A 1 given random samples from X. A range of techniques collectively known as Independent Component Analysis (ICA) have been proposed to address this problem in the signal processing and machine learning literature. Many of these techniques are based on using the kurtosis or other cumulants to recover the components. In this paper we propose a new algorithm for solving the blind signal separation problem in the presence of additive Gaussian noise, when we are given samples from X = AS + , where is drawn from an unknown, not necessarily spherical n-dimensional Gaussian distribution. Our approach is based on a method for decorrelating a sample with additive Gaussian noise under the assumption that the underlying distribution is a linear transformation of a distribution with independent components. Our decorrelation routine is based on the properties of cumulant tensors and can be combined with any standard cumulant-based method for ICA to get an algorithm that is provably robust in the presence of Gaussian noise. We derive polynomial bounds for the sample complexity and error propagation of our method.
Maximizing social influence in nearly optimal time

Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by word-of-mouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion. Runtime is a primary consideration for this problem due to the massive size of the relevant input networks.We provide a fast algorithm for the influence maximization problem, obtaining the near-optimal approximation factor of (1--1/e -- e), for any e > 0, in time O((m + n)e-3 log n). Our algorithm is runtime-optimal (up to a logarithmic factor) and substantially improves upon the previously best-known algorithms which run in time Ω(mnk · POLY(e-1)). Furthermore, our algorithm can be modified to allow early termination: if it is terminated after O(β(m + n) log n) steps for some β
Revisiting canonical quantization

Conventional canonical quantization procedures directly link various c-number and q-number quantities. Here, we advocate a different association of classical and quantum quantities that renders classical theory a natural subset of quantum theory with ℏ > 0, in conformity with the real world wherein nature has chosen ℏ > 0 rather than ℏ = 0. While keeping the good results of conventional procedures, some examples are presented for which the new procedures offer better results than the conventional ones.