cssi/SciSciGPT-SciSciNet huggingface dataset:
Intrinsic brain activity changes associated with adjuvant chemotherapy in older women with breast cancer: a pilot longitudinal study

Older cancer patients are at increased risk of cancer-related cognitive impairment. The purpose of this study was to assess the alterations in intrinsic brain activity associated with adjuvant chemotherapy in older women with breast cancer. Chemotherapy treatment (CT) group included sixteen women aged ≥ 60 years (range 60–82 years) with stage I–III breast cancers, who underwent both resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing with NIH Toolbox for Cognition before adjuvant chemotherapy, at time point 1 (TP1), and again within 1 month after completing chemotherapy, at time point 2 (TP2). Fourteen age- and sex-matched healthy controls (HC) underwent the same assessments at matched intervals. Three voxel-wise rs-fMRI parameters: amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity, were computed at each time point. The changes in rs-fMRI parameters from TP1 to TP2 for each group, the group differences in changes (the CT group vs. the HC group), and the group difference in the baseline rs-fMRI parameters were assessed. In addition, correlative analysis between the rs-fMRI parameters and neuropsychological testing scores was also performed. In the CT group, one brain region, which included parts of the bilateral subcallosal gyri and right anterior cingulate gyrus, displayed increased ALFF from TP1 to TP2 (cluster p-corrected = 0.024); another brain region in the left precuneus displayed decreased fALFF from TP1 to TP2 (cluster level p-corrected = 0.025). No significant changes in the rs-fMRI parameters from TP1 to TP2 were observed in the HC group. Although ALFF and fALFF alterations were observed only in the CT group, none of the between-group differences in rs-fMRI parameter changes reached statistical significance. Our study results of ALFF and fALFF alterations in the chemotherapy-treated women suggest that adjuvant chemotherapy may affect intrinsic brain activity in older women with breast cancer.
Estimating a sharp convergence bound for randomized ensembles

Abstract When randomized ensembles such as bagging or random forests are used for binary classification, the prediction error of the ensemble tends to decrease and stabilize as the number of classifiers increases. However, the precise relationship between prediction error and ensemble size is unknown in practice. In the standard case when classifiers are aggregated by majority vote, the present work offers a way to quantify this convergence in terms of “algorithmic variance,” i.e. the variance of prediction error due only to the randomized training algorithm. Specifically, we study a theoretical upper bound on this variance, and show that it is sharp — in the sense that it is attained by a specific family of randomized classifiers. Next, we address the problem of estimating the unknown value of the bound, which leads to a unique twist on the classical problem of non-parametric density estimation. In particular, we develop an estimator for the bound and show that its MSE matches optimal non-parametric rates under certain conditions. (Concurrent with this work, some closely related results have also been considered in Cannings and Samworth (2017) and Lopes (2019).)
The Burgeoning Health Care Needs of Aging Prisoners

We estimate the increasing health care needs of American inmates by combining data on inmate demographics and national health trends. Decades of harsh punishment combined with demographic shifts have led to a “greying” prison population that is a source of concern for policymakers. Our estimates reveal that inmate health care needs are significant and growing beyond what is predicted by their age profile. While the fraction of inmates aged 40 or more increased by 49 percent between 1996 and 2004, the prevalence of medical conditions such as diabetes, hypertension, asthma, and cancer among inmates grew between 177 and 268 percent.
A genome-wide association study-derived polygenic score for interleukin-1β is associated with hippocampal volume in two samples.

Accumulating research suggests that the pro-inflammatory cytokine interleukin-1β (IL-1β) has a modulatory effect on the hippocampus, a brain structure important for learning and memory as well as linked with both psychiatric and neurodegenerative disorders. Here, we used an imaging genetics strategy to test an association between an IL-1β polygenic score and hippocampal volume in two independent samples. Our polygenic score was derived using summary statistics from a recent genome-wide association study of circulating cytokines that included IL-1β (N = 3,309). In the first sample of 512 non-Hispanic Caucasian university students (274 women, mean age 19.78 ± 1.24 years) from the Duke Neurogenetics Study, we identified a significant positive correlation between IL-1β polygenic scores and hippocampal volume. This positive association was successfully replicated in a second sample of 7,960 white British volunteers (4,158 women, mean age 62.63 ± 7.45 years) from the UK Biobank. Our results lend further support in humans, to the link between IL-1β and the structure of the hippocampus.
Acoustic-to-Word Models with Conversational Context Information.

Conversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation. However, existing speech recognition models are typically built at a sentence level, and thus it may not capture important conversational context information. The recent progress in end-to-end speech recognition enables integrating context with other available information (e.g., acoustic, linguistic resources) and directly recognizing words from speech. In this work, we present a direct acoustic-to-word, end-to-end speech recognition model capable of utilizing the conversational context to better process long conversations. We evaluate our proposed approach on the Switchboard conversational speech corpus and show that our system outperforms a standard end-to-end speech recognition system.
A hierarchical approach to deep learning and its application to tomographic reconstruction

Deep learning (DL) has been successfully applied to many image analysis and image enhancement tasks, but applying DL to inverse problems such as tomographic reconstruction remains challenging due to its high dimensionality and non-local spatial relationship. This paper introduces a hierarchical network architecture that enables purely DL-based tomographic reconstruction for full-size computed tomography (CT) datasets. The proposed method recursively decomposes the reconstruction problem into hierarchical sub problems that can each be solved by a neural network. Overall, the hierarchical approach requires exponentially fewer parameters than a generic network would, and in theory, is of lower computational order of complexity than analytical filtered-backprojection (FBP) reconstruction. As an example, we built a hierarchical network to reconstruct 2D CT images directly from sinograms without relying on conventional analytical or iterative reconstruction components. The hierarchical approach is extensible to three dimensions and to other applications such as emission and magnetic resonance reconstruction. Such DL-based reconstruction opens the door to an entirely new type of reconstruction, which could potentially lead to a better tradeoff between image quality and computational complexity.
Stereospecific Palladium-Catalyzed Reactions of Siliranes with Alkynes

Palladium-catalyzed insertion of alkynes into siliranes and silylene extrusion reactions proceed with stereospecific retention of configuration at the carbon center.
Mkk4 and Mkk7 Are Important for Retinal Development and Axonal Injury-Induced Retinal Ganglion Cell Death

The mitogen-activated protein kinase (MAPK) pathway has been shown to be involved in both neurodevelopment and neurodegeneration. c-Jun N-terminal kinase (JNK), a MAPK important in retinal development and after optic nerve crush injury, is regulated by two upstream kinases: MKK4 and MKK7. The specific requirements of MKK4 and MKK7 in retinal development and retinal ganglion cell (RGC) death after axonal injury, however, are currently undefined. Optic nerve injury is an important insult in many neurologic conditions including traumatic, ischemic, inflammatory, and glaucomatous optic neuropathies. Mice deficient in Mkk4, Mkk7, and both Mkk4 and Mkk7 were generated. Immunohistochemistry was used to study the distribution and structure of retinal cell types and to assess RGC survival after optic nerve injury (mechanical controlled optic nerve crush (CONC)). Adult Mkk4- and Mkk7-deficient retinas had all retinal cell types, and with the exception of small areas of disrupted photoreceptor lamination in Mkk4-deficient mice, the retinas of both mutants were grossly normal. Deficiency of Mkk4 or Mkk7 reduced JNK signaling in RGCs after axonal injury and resulted in a significantly greater percentage of surviving RGCs 35 days after CONC as compared to wild-type controls (Mkk4: 51.5%, Mkk7: 29.1%, WT: 15.2%; p < 0.001). Combined deficiency of Mkk4 and Mkk7 caused failure of optic nerve formation, irregular retinal axonal trajectories, disruption of retinal lamination, clumping of RGC bodies, and dendritic fasciculation of dopaminergic amacrine cells. These results suggest that MKK4 and MKK7 may serve redundant and unique roles in molecular signaling important for retinal development and injury response following axonal insult.
Total Synthesis of Luminacin D

[reaction: see text] A highly convergent synthesis of the angiogenesis inhibitor luminacin D has been achieved in 13 linear steps (19 steps total, 5.3% overall yield) utilizing a samarium(II) iodide-mediated mixed tandem aldol/Evans-Tishchenko reaction to construct the carbohydrate precursor. The modular synthetic design will allow derivatization at key positions necessary for biochemical mode of action studies.
Orthoplatinated triarylphosphite as a highly efficient catalyst for addition reactions of arylboronic acids with aldehydes: low catalyst loading catalysis and a new tandem reaction sequence.

Readily available, air/moisture-stable orthoplatinated triarylphosphite catalyzes the addition reactions of arylboronic acids with aldehydes with the catalyst loading as low as 0.01%. It also catay...
ANTIFERROMAGNETIC RESONANCE AS A TOOL FOR INVESTIGATING MAGNETOSTRUCTURAL CORRELATIONS : THE CANTED ANTIFERROMAGNETIC STATE OF KMNPO4.H2O AND A SERIES OF MANGANESE PHOSPHONATES

Antiferromagnetic resonance (AFMR) is used to characterize the canted antiferromagnetic state and to probe magnetostructural correlations within a series of layered solids including KMnPO4·H2O, four manganese alkylphosphonates, Mn(O3PCnH2n+1)·H2O (n = 3−6), and manganese phenylphosphonate. All samples were investigated as powders. A complete set of magnetic parameters, determined from AFMR and SQUID magnetometry, are presented for each material. The high field electron magnetic resonance facility at the National High Magnetic Field Laboratory in Tallahassee, Florida, was used to acquire AFMR spectra. Source frequencies ranged from 24 to 380 GHz, and the magnetic field range was 0−14.5 T. Correlations between the magnetic exchange and the electronic structure of the bridging ligand as well as measurements of the magnetic anisotropy in the ordered state are determined from analysis of the frequency and field dependence of the AFMR signals.
Evaluation of Chelation Effects Operative during Diastereoselective Addition of the Allylindium Reagent to 2- and 3-Hydroxycyclohexanones in Aqueous, Organic, and Mixed Solvent Systems

The unprotected 2- and 3-hydroxycyclohexanones 1−8 were prepared by methods that skirted as much as possible their proclivity for α-ketol rearrangement (where the possibility for such isomerization...
Amine-controlled assembly of metal-sulfite architecture from 1D chains to 3D framework.

Whereas open-framework materials have been made in a variety of chemical compositions, few are known in which 3-connected SO32- anions serve as basic building units. Here, we report four new metal−...
AN IMPROVED SYNTHESIS OF 9-BENZYLADENINE: A MODEL FOR ADENOSINE AND ITS ANALOGUES

An improved synthesis of 9-benzyladenine (I), a model for a variety of biologically and therapeutically significant adenine nucleosides, has been reported. The target compound was synthesized by condensation of 1-benzyl-4-cyano-5-methoxymethyleneimino-imidazole (14) with guanidine. Compound 14 was prepared from 5-amino-1-benzyl-4-cyanoimidazole (5), which in turn was prepared by reaction of benzylamine with ethyl (Z)-N-(2-amino-1,2-dicyanovinyl) formimidate (13). The latter was synthesized by reaction of diaminomaleonitrile (6) with triethyl orthoformate.
Approximate Multiparametric Mixed-Integer Convex Programming

We propose an algorithm for generating explicit solutions of multiparametric mixed-integer convex programs to within a given suboptimality tolerance. The algorithm is applicable to a very general class of optimization problems, but is most useful for hybrid model predictive control, where online implementation is hampered by the worst-case exponential complexity of mixed-integer solvers. The output is a simplicial partition which defines a static map from the current state to a suboptimal solution. The primary theoretical contribution of this letter is to introduce a non-zero optimal cost overlap metric which is necessary and sufficient for convergence. The overlap size is also linked to partition complexity. The algorithm is massively parallelizable and our implementation, which is publicly available, is run on a cluster of several hundred processors. Not only does our solution have a deterministic runtime, simulations show that our approach is faster than online optimization by up to three orders of magnitude.
Proline‐Based P,N Ligands in Palladium‐Catalyzed Asymmetric π‐Allyl Additions

Excellent yields and at least 95 % ee can be achieved for the addition of dimethyl malonate to cycloalkenyl acetates by using a palladium complex of the new phosphanyldihydrooxazole ligand L as a catalyst (see scheme). The ligand L can be synthesized from commercially available trans 4-hydroxy-L-proline in four steps. BOC=tert-butoxycarbonyl.
The Synthesis, Structure, and Bonding of Sc8Te3 and Y8Te3. Cooperative Matrix and Bonding Effects in the Solid State

Sc8Te3 and Y8Te3 have been prepared by high-temperature solid-state techniques. The structures of both were determined from single-crystal and powder X-ray diffraction methods to be monoclinic, C2/...
Working with Zika and Usutu Viruses In Vitro.

Usutu (USUV) and Zika (ZIKV) viruses are emerging arboviruses of significant medical and veterinary importance. These viruses have not been studied as well as other medically important arboviruses such as West Nile (WNV), dengue (DENV), or chikungunya (CHIKV) viruses. As such, information regarding the behavior of ZIKV and USUV viruses in the laboratory is dated. Usutu virus re-emerged in Austria in 2001 and has since spread throughout the European and Asian continents causing significant mortality among birds. Zika virus has recently appeared in the Western Hemisphere and has exhibited high rates of birth defects and sexual transmission. Information about the characteristics of USUV and ZIKV viruses are needed to better understand the transmission, dispersal, and adaptation of these viruses in new environments. Since their initial characterization in the middle of last century, technologies and reagents have been developed that could enhance our abilities to study these pathogens. Currently, standard laboratory methods for these viruses are limited to 2–3 cell lines and many assays take several days to generate meaningful data. The goal of this study was to characterize these viruses in cells from multiple diverse species. Cell lines from 17 species were permissive to both ZIKV and USUV. These viruses were able to replicate to significant titers in most of the cell lines tested. Moreover, cytopathic effects were observed in 8 of the cell lines tested. These data indicate that a variety of cell lines can be used to study ZIKV and USUV infection and may provide an updated foundation for the study of host-pathogen interactions, model development, and the development of therapeutics.
Preparation of 7-Arylmethyl-1H-pyrrolo[3,4-c]pyridine-1,3-(2H)-diones and α-aryl-3-hydroxy-5-pyridylacetonitriles using arynic methodology

Abstract 7-Arylmethyl-1H-pyrrolo[3,4-c]pyridine-1,3-(2H)-diones and α-aryl-3-hydroxy-5-pyridylacetonitriles can be prepared in modest yields from the respective reactions of 5-bromonicotinamide and 5-chloro-3-pyridinol with arylacetonitriles and LDA.
Placental DNA methylation levels at CYP2E1 and IRS2 are associated with child outcome in a prospective autism study

DNA methylation acts at the interface of genetic and environmental factors relevant for autism spectrum disorder (ASD). Placenta, normally discarded at birth, is a potentially rich source of DNA methylation patterns predictive of ASD in the child. Here, we performed whole methylome analyses of placentas from a prospective study MARBLES (Markers of Autism Risk in Babies-Learning Early Signs) of high-risk pregnancies. A total of 400 differentially methylated regions (DMRs) discriminated placentas stored from children later diagnosed with ASD compared to typically developing controls. These ASD DMRs were significantly enriched at promoters, mapped to 596 genes functionally enriched in neuronal development, and overlapped genetic ASD risk. ASD DMRs at CYP2E1 and IRS2 reached genome-wide significance, replicated by pyrosequencing and correlated with expression differences in brain. Methylation at CYP2E1 associated with both ASD diagnosis and genotype within the DMR. In contrast, methylation at IRS2 was unaffected by within DMR genotype but modified by preconceptional maternal prenatal vitamin use. This study therefore identified two potentially useful early epigenetic markers for ASD in placenta.
Shape Optimization of Reentry Vehicles to Minimize Heat Loading

The objective of the current study is to design an optimum reentry vehicle shape that minimizes heat loading subject to constraints on the maximum values of surface heat flux and temperature. A new...
Mass spectrometric studies of organic ion/molecule reactions.

In the past 25 years, a tremendous amount of work has been published on the ion/molecule reactions of organic species. This review provides an overview of the areas where gas phase ion chemistry has made a contribution to our understanding of fundamental organic reaction processes. It is clear that the gas phase work can provide insights into subtle features of reaction mechanisms that could not be addressed by conventional condensed phase methods. The study of ion/molecule reactions has already had a major impact on the way that organic chemists think about reaction mechanisms and interpret substituent effects. Moreover, it has heightened our awareness of the importance of solvation effects and how they can alter not only absolute rates but also relative rates, leading in some cases to complete reversals in reactivity patterns. A large body of work could not be included in this review due to space limitations. For example, the study of thermochemistry in the gas phase (i.e., acidities, basicities, bond strengths, binding energies, etc.) has provided a wealth of data that has been exceptionally useful in interpreting organic reaction mechanisms. This has spilled over into the study of organometallic systems, and several groups are making major headway in using mass spectrometry to probe the stability and reactivity of transition metal species. Finally, work involving chemical ionization has provided abundant information on gas phase reaction mechanisms. The future appears to be very promising for the study of gas phase organic reaction mechanisms. In particular, the emergence of new ionization techniques and more powerful mass analyzers will allow chemists to explore a wider range of species. Although still at an early stage, the gas phase study of biochemical transformations offers great promise and has been facilitated by electrospray and matrix assisted laser desorption ionization methods. In addition, these techniques provide a means for introducing important, metal-centered catalytic species into the gas phase and exploring the details of their reactivity. Finally, mass spectrometry continues to play a major role in the study of atmospheric ion chemistry and is providing important kinetic as well as mechanistic data.
Expanding the Crystal Chemistry of Uranyl Peroxides: Synthesis and Structures of Di- and Triperoxodioxouranium(VI) Complexes

Four compounds containing tri- and diperoxodioxouranium(VI) complexes have been synthesized under ambient conditions and structurally characterized. The crystal structures of Na4(UO2)(O2)3(H2O)12 (monoclinic, P21/c, a = 6.7883(6) A, b = 16.001(2) A, c = 16.562(2) A, β = 91.917(2)°, V = 1797.9(3) A3, Z = 4) and Ca2(UO2)(O2)3(H2O)9 (orthorhombic, Pbcn, a = 9.576(3) A, b = 12.172(3) A, c = 12.314(2) A, V = 1435.4(6) A3, Z = 4) contain clusters of triperoxodioxouranium(VI). These clusters are bonded through a network of H bonding to H2O groups and in the Ca compound by bonds to Ca2+ cations. In the crystal structure of Na2Rb4(UO2)2(O2)5(H2O)14 (orthorhombic, Pbcm, a = 6.808(2) A, b = 16.888(6) A, c = 23.286(8) A, V = 2677.5(16) A3, Z = 4), triperoxodioxouranium(VI) polyhedra share a peroxide edge, forming dimers of polyhedra of composition (UO2)2(O2)56-. Adjacent dimers are linked through bonding to Rb+ cations and by H bonds to H2O groups. The crystal structure of K6[(UO2)(O2)2(OH)]2(H2O)7 (orthorhombic, Pcc...
Synthesis of β-hydroxy esters using highly active manganese

Abstract A modified Reformatsky reaction is reported using highly reactive manganese (Mn*). The active manganese was found to readily react with α-haloester in the presence of aldehydes and ketones to yield the corresponding β-hydroxy esters. The reaction is carried out at room temperature in the absence of Lewis acid or trapping agents.
Molybdenum-Based Diazotrophy in a Sphagnum Peatland in Northern Minnesota.

Microbial N2 fixation (diazotrophy) represents an important nitrogen source to oligotrophic peatland ecosystems, which are important sinks for atmospheric CO2 and are susceptible to the changing climate. The objectives of this study were (i) to determine the active microbial group and type of nitrogenase mediating diazotrophy in an ombrotrophic Sphagnum-dominated peat bog (the S1 peat bog, Marcell Experimental Forest, Minnesota, USA); and (ii) to determine the effect of environmental parameters (light, O2, CO2, and CH4) on potential rates of diazotrophy measured by acetylene (C2H2) reduction and 15N2 incorporation. A molecular analysis of metabolically active microbial communities suggested that diazotrophy in surface peat was primarily mediated by Alphaproteobacteria (Bradyrhizobiaceae and Beijerinckiaceae). Despite higher concentrations of dissolved vanadium ([V] 11 nM) than molybdenum ([Mo] 3 nM) in surface peat, a combination of metagenomic, amplicon sequencing, and activity measurements indicated that Mo-containing nitrogenases dominate over the V-containing form. Acetylene reduction was only detected in surface peat exposed to light, with the highest rates observed in peat collected from hollows with the highest water contents. Incorporation of 15N2 was suppressed 90% by O2 and 55% by C2H2 and was unaffected by CH4 and CO2 amendments. These results suggest that peatland diazotrophy is mediated by a combination of C2H2-sensitive and C2H2-insensitive microbes that are more active at low concentrations of O2 and show similar activity at high and low concentrations of CH4IMPORTANCE Previous studies indicate that diazotrophy provides an important nitrogen source and is linked to methanotrophy in Sphagnum-dominated peatlands. However, the environmental controls and enzymatic pathways of peatland diazotrophy, as well as the metabolically active microbial populations that catalyze this process, remain in question. Our findings indicate that oxygen levels and photosynthetic activity override low nutrient availability in limiting diazotrophy and that members of the Alphaproteobacteria (Rhizobiales) catalyze this process at the bog surface using the molybdenum-based form of the nitrogenase enzyme.
Catalysis by Zeolites – Science and Technology

Publisher Summary The use of zeolites as catalysts was first announced in 1960. Technically important paraffin isomerization and cracking reactions occurring in the intracrystalline cavity of zeolites X and Y were later reported in publications, and the shape-selective properties of the molecular sieve CaA were demonstrated. The first commercialization of a zeolite-catalyzed process in 1962 in gas–oil cracking, which was the largest volume catalytic process with a worldwide capacity of over 40 million barrels per day, required support from more accurate and advanced characterization methods and from better understanding of the physics and chemistry of the catalytic process. This process and the Shell Hysomer process for the isomerization of paraffins used as catalysts rare earth-stabilized X or Y and platinum/mordenite, respectively, which were both large-pore zeolites with pore openings described by a ring of 12 tetrahedra. In the laboratory, a great variety of acid- and some base-catalyzed reactions were found to be readily catalyzable by the hydrogen or alkali form of zeolites. This chapter describes a large number of potential zeolite applications in the synthesis of intermediates and fine chemicals and discusses some fundamental aspects of zeolite catalysis. The chapter explains the extent to which catalysis by zeolites can, at least in some cases, be called a “science.” It describes the fundamental principles and their quantitative effects on catalytic parameters.
Preparations of Trisubstituted Hydrazines and Pyrazolidines from N-(1-Benzotriazolylalkyl)hydrazines

Reactions of N,N'-disubstituted hydrazines with benzotriazole and aldehydes give N-(alpha-benzotriazoylalkyl)-N,N'-disubstituted hydrazines which on treatment with organometallic reagents form N-alkyl-N,N'-disubstituted hydrazines in good yields. N-(Benzotriazolylalkyl)-N,N'-disubstituted hydrazines and electron-rich olefins, in the presence of zinc bromide catalyst, generate N,N'-disubstituted pyrazolidines in moderate to good yields.
integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth.

The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates. To better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB). We developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub ( https://github.com/haleyeidem/integRATE ). Desirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.
Reductive lithiation of alkyl 2-thiopyridyl ethers to generate optically pure α-alkoxylithium reagents

Abstract Enantiomerically pure 4-lithio-2-alkyl-1,3-dioxanes, 11 and 13 , were prepared by reductive lithiation of alkyl 2-thiopyridyl ether 10 , which was in turn prepared by Barton radical decarboxylation of the homologous acid. The optical activity was introduced by chirality transfer to and from the stereogenic acetal center.
P(CH3NCH2CH2)3N: A Nonionic Superbase for Efficient Dehydrohalogenation

The commercially available nonionic superbase P(CH3NCH2CH2)3N (1) is far superior to DBU for the conversion of primary and secondary alkyl halides to alkenes. A reason for the efficacy of acetonitrile as a solvent for the halides requiring extended reaction times is presented.
Ozonides, Epoxides, and Oxidoannulenes of C70

Six new monoadducts of C70 with oxygen species have been prepared, isolated, and characterized following ozonation of C70 solutions. The initial products are two ozonide monoadducts, identified as a,b- and c,c-C70O3. These ozonides lose O2 through thermolysis or photolysis to form various isomers of C70O. The a,b-C70O3 isomer dissociates through thermolysis with a decay time of 14 min at 296 K to form the [6,6]-closed epoxide a,b-C70O. When photolyzed, it instead forms a [5,6]-open oxidoannulene identified as a,a-C70O. These reactions mimic those seen for C60O3. By contrast, the c,c-C70O3 isomer, which has a thermolysis lifetime of 650 min at 296 K, decays thermally only to an oxidoannulene deduced to be d,d-C70O. Photolysis of c,c-C70O3 produces a mixture of the oxidoannulenes b,c-C70O and c,d-C70O plus a minor amount of the c,c-epoxide. All four C70O oxidoannulene isomers undergo photoisomerization, giving eventually the a,b- and c,c-C70O epoxides.
A general synthesis of 4-alkyl/aryl substituted saccharins ☆

Abstract A new method for the synthesis of 4-alky/aryl substituted saccharins (2) has been developed. The key steps include conjugate addition/acylation of cyclohexenone (3) and reaction of the resulting β-ketoesters 4 with benzyl mercaptan to give the vinyl sulfides 5 and 6. The latter were converted to the saccharins 2 in high yield.
Accelerating silicon photonic parameter extraction using artificial neural networks

We present a novel silicon photonic parameter extraction tool that uses artificial neural networks. While other parameter extraction methods are restricted to relatively simple devices whose responses are easily modeled by analytic transfer functions, this method is capable of extracting parameters for any device with a discrete number of design parameters. To validate the method, we design and fabricate integrated chirped Bragg gratings. We then estimate the actual device parameters by iteratively fitting the simultaneously measured group delay and reflection profiles to the artificial neural network output. The method is fast, accurate, and capable of modeling the complicated chirping and index contrast.
TIN(IV) CHLORIDE MEDIATED GLYCOSYLATION IN ARABINOFURANOSE, GALACTOFURANOSE AND RHAMNOPYRANOSE

Abstract Using tin(IV) chloride, O -glycosylation reactions were performed on peracylated D-arabinofuranose, d -galactofuranose and l -rhamnofuranose as well as 1-bromo- d -arabinofuranoses at room temperature in good anomeric purities and yields. In these circumstances, this coupling method has certain advantages over standard glycosylation reactions, such as the Koenigs-Knorr methods which use the 1-halosugar (synthesized from the 1-acyl derivative) and toxic mercury salts as the coupling agent.
3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.
Do cells use passwords in cell-state transitions? Is cell signaling sometimes encrypted?

Organisms must maintain proper regulation including defense and healing. Life-threatening problems may be caused by pathogens or by a multicellular organism’s own cells through cancer or autoimmune disorders. Life evolved solutions to these problems that can be conceptualized through the lens of information security, which is a well-developed field in computer science. Here I argue that taking an information security view of cells is not merely semantics, but useful to explain features of signaling, regulation, and defense. An information security perspective also offers a conduit for cross-fertilization of advanced ideas from computer science and the potential for biology to inform computer science. First, I consider whether cells use passwords, i.e., initiation sequences that are required for subsequent signals to have effects, by analyzing the concept of pioneer transcription factors in chromatin regulation and cellular reprogramming. Second, I consider whether cells may encrypt signal transduction cascades. Encryption could benefit cells by making it more difficult for pathogens or oncogenes to hijack cell networks. By using numerous molecules, cells may gain a security advantage in particular against viruses, whose genome sizes are typically under selection pressure. I provide a simple conceptual argument for how cells may perform encryption through posttranslational modifications, complex formation, and chromatin accessibility. I invoke information theory to provide a criterion of an entropy spike to assess whether a signaling cascade has encryption-like features. I discuss how the frequently invoked concept of context dependency may oversimplify more advanced features of cell signaling networks, such as encryption. Therefore, by considering that biochemical networks may be even more complex than commonly realized we may be better able to understand defenses against pathogens and pathologies.
Excluded minors in cubic graphs

Abstract Let G be a cubic graph, with girth at least five, such that for every partition X , Y of its vertex set with | X | , | Y | ≥ 7 there are at least six edges between X and Y. We prove that if there is no homeomorphic embedding of the Petersen graph in G, and G is not one particular 20-vertex graph, then either • G ∖ v is planar for some vertex v; or • G can be drawn with crossings in the plane, but with only two crossings, both on the infinite region. We also prove several other theorems of the same kind.
Effective divisors on the Hilbert scheme of points in the plane and interpolation for stable bundles

We compute the cone of effective divisors on the Hilbert scheme of points in the projective plane. We show the sections of many stable vector bundles satisfy a natural interpolation condition, and that these bundles always give rise to the edge of the effective cone of the Hilbert scheme. To do this, we give a generalization of Gaeta's theorem on the resolution of the ideal sheaf of a general collection of points in the plane. This resolution has a natural interpretation in terms of Bridgeland stability, and we observe that ideal sheaves of collections of points are destabilized by exceptional bundles. By studying the Bridgeland stability of exceptional bundles, we also show that our computation of the effective cone of the Hilbert scheme is consistent with a conjecture which predicts a correspondence between Mori and Bridgeland walls for the Hilbert scheme.
Distributed apportioning in a power network for providing demand response services

Greater penetration of Distributed Energy Resources (DERs) in power networks requires coordination strategies that allow for self-adjustment of contributions in a network of DERs, owing to variability in generation and demand. In this article, a distributed scheme is proposed that enables a DER in a network to arrive at viable power reference commands that satisfies the DERs local constraints on its generation and loads it has to service, while, the aggregated behavior of multiple DERs in the network and their respective loads meet the ancillary services demanded by the grid. The Net-load Management system for a single unit is referred to as the Local Inverter System (LIS) in this article. A distinguishing feature of the proposed consensus based solution is the distributed finite time termination of the algorithm that allows each LIS unit in the network to determine power reference commands in the presence of communication delays in a distributed manner. The proposed scheme allows prioritization of Renewable Energy Sources (RES) in the network and also enables auto-adjustment of contributions from LIS units with lower priority resources (non-RES). The methods are validated using hardware-in-the-loop simulations with Raspberry PI devices as distributed control units, implementing the proposed distributed algorithm and responsible for determining and dispatching real-time power reference commands to simulated power electronics interface emulating LIS units for demand response.
Prediction, expectation, and surprise: methods, designs, and study of a deployed traffic forecasting service

We present research on developing models that forecast traffic flow and congestion in the Greater Seattle area. The research has led to the deployment of a service named JamBayes, that is being actively used by over 2,500 users via smartphones and desktop versions of the system. We review the modeling effort and describe experiments probing the predictive accuracy of the models. Finally, we present research on building models that can identify current and future surprises, via efforts on modeling and forecasting unexpected situations.
Metasurface Freeform Nanophotonics

Freeform optics aims to expand the toolkit of optical elements by allowing for more complex phase geometries beyond rotational symmetry. Complex, asymmetric curvatures are employed to enhance the performance of optical components while minimizing their size. Unfortunately, these high curvatures and complex forms are often difficult to manufacture with current technologies, especially at the micron scale. Metasurfaces are planar sub-wavelength structures that can control the phase, amplitude, and polarization of incident light, and can thereby mimic complex geometric curvatures on a flat, wavelength-scale thick surface. We present a methodology for designing analogues of freeform optics using a silicon nitride based metasurface platform for operation at visible wavelengths. We demonstrate a cubic phase plate with a point spread function exhibiting enhanced depth of field over 300 micron along the optical axis with potential for performing metasurface-based white light imaging, and an Alvarez lens with a tunable focal length range of over 2.5 mm corresponding to a change in optical power of ~1600 diopters with 100 micron of total mechanical displacement. The adaptation of freeform optics to a sub-wavelength metasurface platform allows for further miniaturization of optical components and offers a scalable route toward implementing near-arbitrary geometric curvatures in nanophotonics.
Optimally Learning Populations of Parameters

Consider the following fundamental estimation problem: there are $n$ entities, each with an unknown parameter $p_i \in [0,1]$, and we observe $n$ independent random variables, $X_1,\ldots,X_n$, with $X_i \sim $ Binomial$(t, p_i)$. How accurately can one recover the ``histogram'' (i.e. cumulative density function) of the $p_i$s? While the empirical estimates would recover the histogram to earth mover distance $\Theta(\frac{1}{\sqrt{t}})$ (equivalently, $\ell_1$ distance between the CDFs), we show that, provided $n$ is sufficiently large, we can achieve error $O(\frac{1}{t})$ which is information theoretically optimal. We also extend our results to the multi-dimensional parameter case, capturing settings where each member of the population has multiple associated parameters. Beyond the theoretical results, we demonstrate that the recovery algorithm performs well in practice on a variety of datasets, providing illuminating insights into several domains, including politics, and sports analytics.
When Are Finite Projective Planes Magic

We study a generalization of magic squares, where the entries come from the natural numbers, to magic finite projective planes, where the entries come from Abelian groups. For each finite projectiv...
Graph-Based Radio Resource Management for Vehicular Networks

This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph- based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm.
The nonlinear future stability of the FLRW family of solutions to the Euler-Einstein system with a positive cosmological constant

In this article, we study small perturbations of the family of Friedmann–Lemaitre–Robertson–Walker cosmological background solutions to the 1 + 3 dimensional Euler–Einstein system with a positive cosmological constant. These background solutions describe an initially uniform quiet fluid of positive energy density evolving in a spacetime undergoing accelerated expansion. Our nonlinear analysis shows that under the equation of state $${p = c^2_s \rho}$$ , $${0 < c^2_s < 1/3}$$ , the background solutions are globally future-stable. In particular, we prove that the perturbed spacetime solutions, which have the topological structure $${[0,\infty) \times \mathbb{T}^3}$$ , are future-causally geodesically complete. These results are extensions of previous results derived by the author in a collaboration with I. Rodnianski, in which the fluid was assumed to be irrotational. Our novel analysis of a fluid with non-zero vorticity is based on the use of suitably defined energy currents.
Asymptotics of Selective Inference

In this paper, we seek to establish asymptotic results for selective inference procedures removing the assumption of Gaussianity. The class of selection procedures we consider are determined by affine inequalities, which we refer to as affine selection procedures. Examples of affine selection procedures include post-selection inference along the solution path of the LASSO, as well as post-selection inference after fitting the LASSO at a fixed value of the regularization parameter. We also consider some tests in penalized generalized linear models. Our method of proof adapts a method of Chatterjee (2005).
Fine-to-coarse knowledge transfer for low-res image classification

We address the difficult problem of distinguishing fine-grained object categories in low resolution images. We propose a simple an effective deep learning approach that transfers fine-grained knowledge gained from high resolution training data to the coarse low-resolution test scenario. Such fine-to-coarse knowledge transfer has many real world applications, such as identifying objects in surveillance photos or satellite images where the image resolution at the test time is very low but plenty of high resolution photos of similar objects are available. Our extensive experiments on two standard benchmark datasets containing fine-grained car models and bird species demonstrate that our approach can effectively transfer fine-detail knowledge to coarse-detail imagery.
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions

Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our proposed solution represents the treatment response curves using linear time-invariant dynamical systems---this provides a flexible means for modeling response over time to highly variable dose curves. Moreover, for multivariate data, the proposed method: uncovers shared structure in treatment response and the baseline across multiple markers; and, flexibly models challenging correlation structure both across and within signals over time. For this, we build upon the framework of multiple-output Gaussian Processes. On simulated and a challenging clinical dataset, we show significant gains in accuracy over state-of-the-art models.
A matrix weighted $T1$ theorem for matrix kernelled CZOs and a matrix weighted John-Nirenberg theorem

In this paper, we will prove a matrix weighted T1 theorem regarding the boundedness of certain matrix kernelled CZOs on matrix weighted $$L^p(W)$$ for matrix A $${}_p$$ weights W. Using some of the ideas from the proof, we will also establish a natural matrix weighted John–Nirenberg result that extends to the matrix setting (in the case when one of the weights is the identity) a very recent characterization of both S. Bloom’s BMO space and the two weight boundedness of commutators by I. Holmes, M. Lacey, and B. Wick.