cssi/SciSciGPT-SciSciNet huggingface dataset:
International Financial Integration, Sovereignty, and Constraints on Macroeconomic Policies

This paper considers the consequences of international financial market integration for national fiscal and monetary policies that derive from the absence of an international sovereign authority to define and enforce contractual obligations across borders. The sovereign immunity of national governments serves as a fundamental constraint on international finance and is used to derive intertemporal budget constraints for sovereign nations and their governments. It is shown that the appropriate debt limit for a country allows for state-contingent repayment. With non-contingent debt instruments, debt renegotiation occurs in equilibrium with positive probability. A model of tax smoothing is adopted to show how information imperfections lead to conventional bond contracts that are renegotiated when a critical level of indebtedness is reached. Renegotiation is interpreted in terms of nominal and real denominated bonds drawing implications of the intertemporal borrowing constraint for monetary policies, the accumulation of reserve assets and current account sustainability.
The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data

Goel et al. [14] examined diffusion data from Twitter to conclude that online petitions are shared more virally than other types of content. Their definition of structural virality, which measures the extent to which diffusion follows a broadcast model or is spread person to person (virally), depends on knowing the topology of the diffusion cascade. But often the diffusion structure cannot be observed directly. We examined time-stamped signature data from the Obama White House’s We the People petition platform. We developed measures based on temporal dynamics that, we argue, can be used to infer diffusion structure as well as the more intrinsic notion of virality sometimes known as infectiousness. These measures indicate that successful petitions are likely to be higher in both intrinsic and structural virality than unsuccessful petitions are. We also investigate threshold effects on petition signing that challenge simple contagion models, and report simulations for a theoretical model that are consistent with our data.
Wireless Power Transfer for Electric Vehicle Using an Adaptive Robot

Charging time and power transfer efficiency are the main challenges of wireless power transfer for electric vehicles. It is proposed in this paper to resolve both issues using the transformer induction concept as well as adaptive robotic technology. To this end, an autonomous robotic arm equipped with several sensors is mounted to the vehicle's undercarriage that carries a receiving coil. In case that the vehicle is parked in a parking spot equipped with wireless power transmitters, the robot will locate the receiving coil on the right place that is above the transmitting core when the vehicle is parked. When the vehicle is driven on a road equipped with the wireless power transmission system, the proposed mechanism provides the driver with signals that indicate the most efficient driving trajectory. To achieve the maximum efficiency, an advanced mathematical adaptive algorithm based on the extremum seeking method is employed. In addition, silicon carbide metal-oxide semiconductor field-effect transistor is used in power converter to retain the frequency as high as possible to decrease the charging time. Through this method, the above issues and the problem of large non-permeable air gap and accordingly electromagnetic interference are resolved; while, the charging time is decreased dramatically.
The Growing Problem of Humanizing Robots

The humanization of robots or, the representation of robots as humans, has contributed to the successful adoption of robots into our society. However, there are also drawbacks to the humanization of robots. Unfortunately, these drawbacks have received much less attention. It may now be time to consider these drawbacks. This article attempts to draw attention to such problems. The article concludes by not only recommending that scholars consider such problems but also consider, in some cases, the possibility of intentionally undermining the humanization of robots.
Plane-by-Plane Inscription of Grating Structures in Optical Fibers

Plane-by-plane fabrication of fiber Bragg gratings in optical fibers using a short-pulse femtosecond IR laser is proposed and demonstrated. By incorporating a cylindrical lens in the fabrication setup, a plane of index modification can be directly inscribed in the fiber core by a single laser pulse. The planes of index modification are produced in the core of a free-standing fiber without an oil immersion objective. This plane-by-plane method simplifies the direct grating inscription process and allows for the fabrication of complicated grating structures.
Near-Balanced Incomplete Block Designs, With an Application to Poster Competitions

Judging scholarly posters creates a challenge to assign the judges efficiently. If there are many posters and few reviews per judge, the commonly used balanced incomplete block design is not a feas...
Adsorption of TNT, DNAN, NTO, FOX7, and NQ onto cellulose, chitin, and cellulose triacetate. Insights from Density Functional Theory calculations

Abstract Insensitive munitions (IM) compounds such as DNAN (2,4-dinitroanisole), NTO (3-nitro-1,2,4-triazol-5-one), NQ (nitroguanidine), and FOX7 (1,1-diamino-2,2-dinitroethene) reduce the risk of accidental explosions due to shock and high temperature exposure. These compounds are being used as replacements for sensitive munition compounds such as TNT (2,4,6-trinitromethylbenzene) and RDX (1,3,5-hexahydro-1,3,5-trinitro-1,3,5-triazine). NTO and NQ in IM compounds are more soluble than TNT or RDX, hence they can easily spread in the environment and get dissolved if exposed to precipitation. DNAN solubility is comparable to TNT solubility. Cellulosic biomass, due to its abundance in the environment and its chemical structure, has a high probability of adsorbing these IM compounds, and thus, it is important to investigate the interactions between cellulose and cellulose like biopolymers (e.g. cellulose triacetate and chitin) with IM compounds. Using Density Functional Theory methods, we have studied the adsorption of TNT, DNAN, NTO, NQ, and FOX7 onto cellulose Iα and Iβ, chitin, and cellulose triacetate I (CTA I). Solvent effects on the adsorption were also investigated. Our results show that all contaminants are more strongly adsorbed onto chitin and cellulose Iα than onto CTA I and cellulose Iβ. Dispersion forces were found to be the predominant contribution to the adsorption energies of all contaminants.
Solving Equations of Random Convex Functions via Anchored Regression

We consider the question of estimating a solution to a system of equations that involve convex nonlinearities, a problem that is common in machine learning and signal processing. Because of these nonlinearities, conventional estimators based on empirical risk minimization generally involve solving a non-convex optimization program. We propose anchored regression, a new approach based on convex programming that amounts to maximizing a linear functional (perhaps augmented by a regularizer) over a convex set. The proposed convex program is formulated in the natural space of the problem, and avoids the introduction of auxiliary variables, making it computationally favorable. Working in the native space also provides great flexibility as structural priors (e.g., sparsity) can be seamlessly incorporated. For our analysis, we model the equations as being drawn from a fixed set according to a probability law. Our main results provide guarantees on the accuracy of the estimator in terms of the number of equations we are solving, the amount of noise present, a measure of statistical complexity of the random equations, and the geometry of the regularizer at the true solution. We also provide recipes for constructing the anchor vector (that determines the linear functional to maximize) directly from the observed data.
Information vs. Automation and Implications for Dynamic Pricing

Essential resources, like electricity and water, can experience rapidly changing demand or supply while the other side of the market is unchanged. Short-run price variation could efficiently alloca...
A new slow control and run initialization byte-wise environment (SCRIBE) for the quality control of mass-produced CMS GEM detectors

The CMS collaboration aims at improving the muon trigger and tracking performance at the HL-LHC by installing new Gas Electron Multiplier (GEM) chambers in the endcaps of the CMS experiment. Construction and commissioning of GEM chambers for the first muon endcap stations is ramping up in several laboratories using common quality control protocols. The SCRIBE framework is a scalable and cross-platform web-based application for the RD51 Scalable Readout System (SRS) that controls data acquisition and analyzes data in near real time. It has been developed mainly to simplify and standardize measurements of the GEM chamber response uniformities with x-rays across all production sites. SCRIBE works with zero suppression of raw SRS pulse height data. This has increased acquisition rates to 5 kHz for a CMS GEM chamber with 3072 strips and allows strip-by-strip response comparisons with a few hours of data taking. SCRIBE also manages parallel data reconstruction to provide near real-time feedback on the chamber response to the user. Preliminary results on the response performance of the first mass-produced CMS GEM chambers commissioned with SCRIBE are presented.
Tensor geometry in the turbulent cascade

The defining characteristic of highly turbulent flows is the net directed transport of energy from the injection scales to the dissipation scales. This cascade is typically described in Fourier space, obscuring its connection to the mechanics of the flow. Here, we recast the energy cascade in mechanical terms, noting that for some scales to transfer energy to others, they must do mechanical work on them. This work can be expressed as the inner product of a turbulent stress and a rate of strain. But, as with all inner products, the relative alignment of these two tensors matters, and determines how strong the energy transfer will be. We show that this tensor alignment behaves very differently in two and three dimensions; in particular, the tensor eigenvalues affect the inner product in very different ways. By comparing the observed energy flux to the maximum possible if the tensors were in perfect alignment, we define an efficiency for the energy cascade. Using data from a direct numerical simulation of isotropic turbulence, we show that this efficiency is perhaps surprisingly low, with an average value of approximately 25 % in the inertial range, although it is spatially heterogeneous. Our results have implications for how the stress and strain-rate magnitudes influence the flux of energy between scales, and may help to explain why the energy cascades in two and three dimensions are different.
Can information outreach increase participation in community-driven development? A field experiment near Bwindi National Park, Uganda.

Decentralization and community-driven development intend to bring public decisions closer to the people, yet elites often capture local institutions. One way that local elites capture community-driven development is to limit information about opportunities for citizens to shape group decisions. We investigate whether sending citizens targeted and timely information about when and how they can participate in the planning of community-driven development projects increases knowledge, participation, and satisfaction with local institutions. We implemented a pre-registered randomized field experiment in partnership with the Uganda Wildlife Authority that involved sending residents in randomly selected villages near Bwindi National Park approximately 60 messages by mobile phone over eight months about how a park-sponsored revenue sharing program worked and how and when residents could participate. We do not find evidence that the information increased perceived knowledge, participation, perceived efficacy, or satisfaction with local institutions. Exploratory findings suggest that among women, who are often disenfranchised in Ugandan society, the information treatment backfired related to perceived opportunities to participate. More positively, we find that reaching more people in a community with information led to promising results related to participation and satisfaction with the park. We conclude that informational treatments are unlikely to empower participation on average, unless they are deployed broadly and in ways that promote collective action.
Numb prevents a complete epithelial–mesenchymal transition by modulating Notch signalling

Epithelial-mesenchymal transition (EMT) plays key roles during embryonic development, wound healing and cancer metastasis. Cells in a partial EMT or hybrid epithelial/mesenchymal (E/M) phenotype exhibit collective cell migration, forming clusters of circulating tumour cells-the primary drivers of metastasis. Activation of cell-cell signalling pathways such as Notch fosters a partial or complete EMT, yet the mechanisms enabling cluster formation remain poorly understood. Using an integrated computational-experimental approach, we examine the role of Numb-an inhibitor of Notch intercellular signalling-in mediating EMT and clusters formation. We show via an mathematical model that Numb inhibits a full EMT by stabilizing a hybrid E/M phenotype. Consistent with this observation, knockdown of Numb in stable hybrid E/M cells H1975 results in a full EMT, thereby showing that Numb acts as a brake for a full EMT and thus behaves as a 'phenotypic stability factor' by modulating Notch-driven EMT. By generalizing the mathematical model to a multi-cell level, Numb is predicted to alter the balance of hybrid E/M versus mesenchymal cells in clusters, potentially resulting in a higher tumour-initiation ability. Finally, Numb correlates with a worse survival in multiple independent lung and ovarian cancer datasets, hence confirming its relationship with increased cancer aggressiveness.
Yeast interfering RNA larvicides targeting neural genes induce high rates of Anopheles larval mortality.

Although larviciding can reduce the number of outdoor biting malaria vector mosquitoes, which may help to prevent residual malaria transmission, the current larvicide repertoire is faced with great challenges to sustainability. The identification of new effective, economical, and biorational larvicides could facilitate maintenance and expansion of the practice of larviciding in integrated malaria vector mosquito control programmes. Interfering RNA molecules represent a novel class of larvicides with untapped potential for sustainable mosquito control. This investigation tested the hypothesis that short interfering RNA molecules can be used as mosquito larvicides. A small interfering RNA (siRNA) screen for larval lethal genes identified siRNAs corresponding to the Anopheles gambiae suppressor of actin (Sac1), leukocyte receptor complex member (lrc), and offtrack (otk) genes. Saccharomyces cerevisiae (baker’s yeast) was engineered to produce short hairpin RNAs (shRNAs) for silencing of these genes. Feeding larvae with the engineered yeasts resulted in silenced target gene expression, a severe loss of neural synapses in the larval brain, and high levels of larval mortality. The larvicidal activities of yeast interfering RNA larvicides were retained following heat inactivation and drying of the yeast into user-friendly tablet formulations that induced up to 100% larval mortality in laboratory trials. Ready-to-use dried inactivated yeast interfering RNA larvicide tablets may someday be an effective and inexpensive addition to malaria mosquito control programmes and a valuable, biorational tool for addressing residual malaria transmission.
US Implied Volatility as A predictor of International Returns

This study provides evidence of the US implied volatility’s e ect on international equitymarkets’ returns. This evidence has two main implications: i) investors may find that foreign equityreturns adjusting to US implied volatility may not provide true diversification benefits, and ii) foreignequity returns may be predicted using US implied volatility. Our sample includes US volatility index(VIX) and major equity indexes in twenty countries for the period between January, 2000 throughJuly, 2017. VIX leads eighteen of the international markets and Granger causes seventeen of themarkets after controlling for the S&P-500 index returns and the 2007/2008 US financial crisis. USinvestors looking to diversify US risk may find that international equities may not provide intendeddiversification benefits. Our evidence provides support for predictability of international equity returnsbased on US volatility.
Cytotoxic Escherichia coli strains encoding colibactin and cytotoxic necrotizing factor (CNF) colonize laboratory macaques.

Many Escherichia coli strains are considered to be a component of the normal flora found in the human and animal intestinal tracts. While most E. coli strains are commensal, some strains encode virulence factors that enable the bacteria to cause intestinal and extra-intestinal clinically-relevant infections. Colibactin, encoded by a genomic island (pks island), and cytotoxic necrotizing factor (CNF), encoded by the cnf gene, are genotoxic and can modulate cellular differentiation, apoptosis and proliferation. Some commensal and pathogenic pks+ and cnf+ E. coli strains have been associated with inflammation and cancer in humans and animals. In the present study, E. coli strains encoding colibactin and CNF were identified in macaque samples. We performed bacterial cultures utilizing rectal swabs and extra-intestinal samples from clinically normal macaques. A total of 239 E. coli strains were isolated from 266 macaques. The strains were identified biochemically and selected isolates were serotyped as O88:H4, O25:H4, O7:H7, OM:H14, and OM:H16. Specific PCR for pks and cnf1 gene amplification, and phylogenetic group identification were performed on all E. coli strains. Among the 239 isolates, 41 (17.2%) were pks+/cnf1−, 19 (7.9%) were pks−/cnf1+, and 31 (13.0%) were pks+/cnf1+. One hundred forty-eight (61.9%) E. coli isolates were negative for both genes (pks−/cnf1−). In total, 72 (30.1%) were positive for pks genes, and 50 (20.9%) were positive for cnf1. No cnf2+ isolates were detected. Both pks+ and cnf1+ E. coli strains belonged mainly to phylogenetic group B2, including B21. Colibactin and CNF cytotoxic activities were observed using a HeLa cell cytotoxicity assay in representative isolates. Whole genome sequencing of 10 representative E. coli strains confirmed the presence of virulence factors and antibiotic resistance genes in rhesus macaque E. coli isolates. Our findings indicate that colibactin- and CNF-encoding E. coli colonize laboratory macaques and can potentially cause clinical and subclinical diseases that impact macaque models.
Parallel Ellipsoidal Perfectly Matched Layers for Acoustic Helmholtz Problems on Exterior Domains

Exterior acoustic problems occur in a wide range of applications, making the finite element analysis of such problems a common practice in the engineering community. Various methods for truncating infinite exterior domains have been developed, including absorbing boundary conditions, infinite elements, and more recently, perfectly matched layers (PML). PML are gaining popularity due to their generality, ease of implementation, and effectiveness as an absorbing boundary condition. PML formulations have been developed in Cartesian, cylindrical, and spherical geometries, but not ellipsoidal. In addition, the parallel solution of PML formulations with iterative solvers for the solution of the Helmholtz equation, and how this compares with more traditional strategies such as infinite elements, has not been adequately investigated. In this paper, we present a parallel, ellipsoidal PML formulation for acoustic Helmholtz problems. To faciliate the meshing process, the ellipsoidal PML layer is generated with an on...
Physiological Contractility of Cardiomyocytes in the Wall of Mouse and Rat Azygos Vein

We recently demonstrated the abundant presence of cardiomyocytes in the wall of thoracic veins of adult mouse and rat (Kracklauer, Feng, Jiang, Lin, Lin, Jin, FEBS J. 280:880-91, 2013). The highly differentiated morphology and myofilament protein expression of the venous cardiomyocytes suggested contractile functions. Here we further investigated the contractility of mouse and rat azygos venous rings in comparison with that of atrial strips and ventricular papillary muscle. X-gal staining of transgenic mouse vessels expressing lacZ under cloned cardiac troponin T promoter demonstrated that the venous cardiomyocytes are discontinuous from atrial myocardium and longitudinally aligned in the wall of thoracic veins perpendicular to the vessel axis. Histology study showed striation patterns in the venous cardiomyocytes, which indicate encirclement orientation of myofibrils in the vessel wall. Mechanical studies found that mouse and rat azygos veins produce strong cardiac type twitch contractions when stimulated by electrical pacing in contrast to the weak and slow smooth muscle contractions induced using 90 mM KCl. The twitch contraction and relaxation of rodent azygos veins further exhibited cardiac type beta-adrenergic responses. Quantitative characterization showed that the contractions of venous cardiomyocytes are slightly slower than that of atrium muscle but significantly faster than that of ventricular papillary muscle. These novel findings indicate that the cardiomyocytes in rodent thoracic veins possess fully differentiated cardiac muscle phenotype despite their anatomical and functional separation from the heart.
Economic Viability of Software Defined Networking (SDN)

Abstract Economical and operational facets of networks drive the necessity for significant changes towards fundamentals of networking architectures. Recently, the momentum of programmable networking attempts illustrates the significance of economic aspects of network technologies. Software Defined Networking (SDN) has got the attention of researchers from both academia and industry as a means to decrease network costs and generate revenue for service providers due to features it promises in networking. In this article, we investigate how programmable network architectures, i.e. SDN technology, affect the network economics compared to traditional network architectures, i.e. MPLS technology. We define two metrics, Unit Service Cost Scalability and Cost-to-Service, to evaluate how SDN architecture performs compared to MPLS architecture. Also, we present mathematical models to calculate certain cost parts of a network. In addition, we compare different popular SDN control plane models, Centralized Control Plane (CCP), Distributed Control Plane (DCP), and Hierarchical Control Plane (HCP), to understand the economic impact of them with regards to the defined metrics. We use video traffic with different patterns for the comparison. This work aims at being a useful primer to providing insights regarding which technology and control plane model are appropriate for a specific service, i.e. video, for network owners to plan their investments.
High Power Density Z-Source Resonant Wireless Charger With Line Frequency Sinusoidal Charging

Wireless charger for electric vehicles (EVs) is an offline application and it needs power factor correction (PFC) function, which usually consists of a front-end boost PFC and a cascaded dc–dc converter. Z-source resonant converter (ZSRC), a single-stage solution with low cost and high efficiency, was proposed for EV wireless charger lately. The Z-source capacitors in the ZSRC are designed to absorb the double-line frequency ripple in this single-phase application. Sinusoidal charging, which allows the double-line frequency ripple propagate to the output, is another solution aiming at reducing the bulky capacitors. Recently, a comparison of low-frequency (120 Hz) sinusoidal charging and dc charging shows a negligible impact on Li-ion batteries’ performance. Therefore, the ZSRC's capacitor, the biggest component, can be reduced dramatically from millifarad to several microfarads with a sinusoidal charging technique, which features the ZSRC high power density. Also, it keeps the Z-source's benefit of boost ability and being immune to shoot-through problems. In this paper, the sinusoidal charging behavior for the ZSRC is modeled, and a control scheme that has both PFC function and load regulation for sinusoidal charging is proposed. This control scheme can pass IEC61000-3-2 Class A criterion. Experimental results based on a 1-kW prototype with 20 cm air gap between the primary and secondary side are presented to illustrate the proposed control scheme.
The Fast and the Slow: Folding and Trapping of λ6-85

Molecular dynamics simulations that combine many μs trajectories have recently predicted that a very fast-folding protein like lambda repressor fragment λ6-85 D14A could have a millisecond kinetic phase. We investigated this possibility by detecting temperature jump relaxation to 5 ms. While λ6-85 D14A shows no significant slow phase, two even more stable mutants do. A slow phase of λ6-85 D14A does appear in mild denaturant. The experimental data (and we believe also the computational modeling) is consistent with the following hypothesis: λ6-85 takes only microseconds to reach its native state from an extensively unfolded state, while the latter takes milliseconds to reach compact traps containing beta sheet structure. λ6-85 is not only thermodynamically, but also kinetically protected from reaching intramolecular analogs of beta sheet aggregates while folding.View Large Image | View Hi-Res Image | Download PowerPoint Slide
Interdisciplinary Dissertation Research Among Public Health Doctoral Trainees, 2003-2015.

Objectives:Given the call for more interdisciplinary research in public health, the objectives of this study were to (1) examine the correlates of interdisciplinary dissertation completion and (2) ...
Two-Round Multiparty Secure Computation Minimizing Public Key Operations

We show new constructions of semi-honest and malicious two-round multiparty secure computation protocols using only (a fixed) \(\mathsf {poly}(n,\lambda )\) invocations of a two-round oblivious transfer protocol (which use expensive public-key operations) and \(\mathsf {poly}(\lambda , |C|)\) cheaper one-way function calls, where \(\lambda \) is the security parameter, n is the number of parties, and C is the circuit being computed. All previously known two-round multiparty secure computation protocols required \(\mathsf {poly}(\lambda ,|C|)\) expensive public-key operations.
A Cluster Elastic Net for Multivariate Regression

We propose a method for estimating coefficients in multivariate regression when there is a clustering structure to the response variables. The proposed method includes a fusion penalty, to shrink the difference in fitted values from responses in the same cluster, and an L1 penalty for simultaneous variable selection and estimation. The method can be used when the grouping structure of the response variables is known or unknown. When the clustering structure is unknown the method will simultaneously estimate the clusters of the response and the regression coefficients. Theoretical results are presented for the penalized least squares case, including asymptotic results allowing for p >> n. We extend our method to the setting where the responses are binomial variables. We propose a coordinate descent algorithm for both the normal and binomial likelihood, which can easily be extended to other generalized linear model (GLM) settings. Simulations and data examples from business operations and genomics are presented to show the merits of both the least squares and binomial methods.
Image Super-Resolution via Dual-State Recurrent Networks

Advances in image super-resolution (SR) have recently benefited significantly from rapid developments in deep neural networks. Inspired by these recent discoveries, we note that many state-of-the-art deep SR architectures can be reformulated as a single-state recurrent neural network (RNN) with finite unfoldings. In this paper, we explore new structures for SR based on this compact RNN view, leading us to a dual-state design, the Dual-State Recurrent Network (DSRN). Compared to its single-state counterparts that operate at a fixed spatial resolution, DSRN exploits both low-resolution (LR) and high-resolution (HR) signals jointly. Recurrent signals are exchanged between these states in both directions (both LR to HR and HR to LR) via delayed feedback. Extensive quantitative and qualitative evaluations on benchmark datasets and on a recent challenge demonstrate that the proposed DSRN performs favorably against state-of-the-art algorithms in terms of both memory consumption and predictive accuracy. The code for our method is publicly available1.
Adaptive TTL-Based Caching for Content Delivery

Content delivery networks (CDNs) cache and serve a majority of the user-requested content on the Internet. Designing caching algorithms that automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work. While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited. Consequently, most prior models are inaccurate for non-stationary production CDN traffic. We propose two TTL-based caching algorithms that provide provable performance guarantees for request traffic that is bursty and non-stationary. The first algorithm called d-TTL dynamically adapts a TTL parameter using stochastic approximation. Given a feasible target hit rate, we show that d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals. The second algorithm called f-TTL uses two caches, each with its own TTL. The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic. Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets. We evaluate both d-TTL and f-TTL using an extensive trace containing more than 500 million requests from a production CDN server. We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%. But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out non-stationary content.
PARPi Triggers the STING-Dependent Immune Response and Enhances the Therapeutic Efficacy of Immune Checkpoint Blockade Independent of BRCAness

Poly-(ADP-ribose) polymerase (PARP) inhibitors (PARPi) have shown remarkable therapeutic efficacy against BRCA1/2 mutant cancers through a synthetic lethal interaction. PARPi exert their therapeutic effects mainly through the blockade of single-stranded DNA damage repair, which leads to the accumulation of toxic DNA double-strand breaks specifically in cancer cells with DNA repair deficiency (BCRAness), including those harboring BRCA1/2 mutations. Here we show that PARPi-mediated modulation of the immune response contributes to their therapeutic effects independently of BRCA1/2 mutations. PARPi promoted accumulation of cytosolic DNA fragments due to unresolved DNA lesions, which in turn activated the DNA sensing cGAS-STING pathway and stimulated production of type I interferons to induce antitumor immunity independent of BRCAness. These effects of PARPi were further enhanced by immune checkpoint blockade. Overall, these results provide a mechanistic rationale for using PARPi as immunomodulatory agents to harness the therapeutic efficacy of immune checkpoint blockade.
Large-scale patterning of living colloids for dynamic studies of neutrophil–microbe interactions

Neutrophils are the first white blood cells to respond to microbes and to limit their invasion of the body. However, the growth of the microbes into colonies often challenges the neutrophils ability to contain them. To study the interactions between neutrophils and microbial colonies, we designed an assay for arranging microbes in clusters of controlled size (i.e. living colloids). The patterned microbes in the living colloid are mechanically trapped inside the wells and fully accessible to neutrophils. Using the assay, we studied the interactions between human neutrophils and Candida albicans and Staphylococcus aureus, two common human pathogens. We also probed the susceptibility of C. albicans colloids to caspofungin, a common antifungal drug.
ZIONIST MASTER OF “THREE JIHADS”

The essays, “ Three jihads : Islamic, Christian, and Jewish ” by Laura Nader (Anthropology Today 31[2]: 1-2) and “ Fictions and artifices ” by Adam Blitz together with Laura Nader’s elusive response (Anthropology Today 31[6]: 16-19) speak, either deliberately or unintentionally, past each other. In order to place the core subject of these voices on an interactive track we need an explicit and non-evasive encounter with the Zionist jihad which Laura Nader does not coherently and forcefully address and which Adam Blitz, in his hazy “wandering and wordy” text, either fails to understand or tactfully disguises with the declaration that “Judaism, though not pacifistic , calls for minimization of violence. It does not possess a biblical Hebrew equivalent of either ‘just’ or ‘holy’ war” (AT 31[6]: 17, italics added).
Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index.

Summary Sequence-level searches on large collections of RNA sequencing experiments, such as the NCBI Sequence Read Archive (SRA), would enable one to ask many questions about the expression or variation of a given transcript in a population. Existing approaches, such as the sequence Bloom tree, suffer from fundamental limitations of the Bloom filter, resulting in slow build and query times, less-than-optimal space usage, and potentially large numbers of false-positives. This paper introduces Mantis, a space-efficient system that uses new data structures to index thousands of raw-read experiments and facilitates large-scale sequence searches. In our evaluation, index construction with Mantis is 6× faster and yields a 20% smaller index than the state-of-the-art split sequence Bloom tree (SSBT). For queries, Mantis is 6–108× faster than SSBT and has no false-positives or -negatives. For example, Mantis was able to search for all 200,400 known human transcripts in an index of 2,652 RNA sequencing experiments in 82 min; SSBT took close to 4 days.
Estimating risk-return relations with analysts price targets

Abstract Asset pricing tests often replace ex ante return expectation with ex post realization. The large deviation between the two drastically weakens the power of these tests. This paper proposes to use analysts consensus price target for a stock as the market expectation of the stock’s future price to directly construct the stock’s expected excess return. Analyzing the expected excess return behavior both over time and across different stocks shows that classic asset pricing theory works much better on ex ante return expectations than on ex post realizations. The analysis also provides new insights on the pricing of common equity risk factors.
Learning to Align Images Using Weak Geometric Supervision

Image alignment tasks require accurate pixel correspondences, which are usually recovered by matching local feature descriptors. Such descriptors are often derived using supervised learning on existing datasets with ground truth correspondences. However, the cost of creating such datasets is usually prohibitive. In this paper, we propose a new approach to align two images related by an unknown 2D homography where the local descriptor is learned from scratch from the images and the homography is estimated simultaneously. Our key insight is that a siamese convolutional neural network can be trained jointly while iteratively updating the homography parameters by optimizing a single loss function. Our method is currently weakly supervised because the input images need to be roughly aligned. We have used this method to align images of different modalities such as RGB and near-infra-red (NIR) without using any prior labeled data. Images automatically aligned by our method were then used to train descriptors that generalize to new images. We also evaluated our method on RGB images. On the HPatches benchmark, our method achieves comparable accuracy to deep local descriptors that were trained offline in a supervised setting.
Towards In-baggage Suspicious Object Detection Using Commodity WiFi

The growing needs of public safety urgently require scalable and low-cost techniques on detecting dangerous objects (e.g., lethal weapons, homemade-bombs, explosive chemicals) hidden in baggage. Traditional baggage check involves either high manpower for manual examinations or expensive and specialized instruments, such as X-ray and CT. As such, many public places (i.e., museums and schools) that lack of strict security check are exposed to high risk. In this work, we propose to utilize the finegrained channel state information (CSI) from off-the-shelf WiFi to detect suspicious objects that are suspected to be dangerous (i.e., defined as any metal and liquid object) without penetrating into the user’s privacy through physically opening the baggage. Our suspicious object detection system significantly reduces the deployment cost and is easy to set up in public venues. Towards this end, our system is realized by two major components: it first detects the existence of suspicious objects and identifies the dangerous material type based on the reconstructed CSI complex value (including both amplitude and phase information); it then determines the risk level of the object by examining the object’s dimension (i.e., liquid volume and metal object’s shape) based on the reconstructed CSI complex of the signals reflected by the object. Extensive experiments are conducted with 15 metal and liquid objects and 6 types of bags in a 6-month period. The results show that our system can detect over 95% suspicious objects in different types of bags and successfully identify 90% dangerous material types. In addition, our system can achieve the average errors of 16ml and 0.5cm when estimating the volume of liquid and shape (i.e., width and height) of metal objects, respectively.
Experimental Study of Material Removal at Nanoscale

Abstract In order to develop nano-machining into a viable and efficient process, there is a need to achieve a better understand the relation between process parameters (such as feed, speed, and depth of cut) and resulting geometry. In this study, a comprehensive experimental parametric study was conducted to produce a database that is used to select proper machining conditions for guiding the fabrication of precise nano-geometries. The parametric studies conducted using AFM nanosize tips showed the following: normal forces for both nano-indentation and nano-scratching increase as the depth of cut increases. The indentation depth increases with tip speed, but the depth of scratch decrease with increasing tip speed. The width and depth of scratched groove also depend on the scratch angle. The recommended scratch angle is at 90°. The surface roughness increases with step over, especially when the step over is larger than the tip radius. The depth of cut also increases as the step over decreases.
Structural basis for substrate gripping and translocation by the ClpB AAA+ disaggregase.

Bacterial ClpB and yeast Hsp104 are homologous Hsp100 protein disaggregases that serve critical functions in proteostasis by solubilizing protein aggregates. Two AAA+ nucleotide binding domains (NBDs) power polypeptide translocation through a central channel comprised of a hexameric spiral of protomers that contact substrate via conserved pore-loop interactions. Here we report cryo-EM structures of a hyperactive ClpB variant bound to the model substrate, casein in the presence of slowly hydrolysable ATPγS, which reveal the translocation mechanism. Distinct substrate-gripping interactions are identified for NBD1 and NBD2 pore loops. A trimer of N-terminal domains define a channel entrance that binds the polypeptide substrate adjacent to the topmost NBD1 contact. NBD conformations at the seam interface reveal how ATP hydrolysis-driven substrate disengagement and re-binding are precisely tuned to drive a directional, stepwise translocation cycle. Bacterial ClpB is a disaggregase that solubilizes protein aggregates. Here the authors present the 2.9 A cryo-EM structure of a hyperactive variant of ClpB bound to the substrate casein in active translocation states and discuss its polypeptide translocation mechanism.
Training Set Camouflage

We introduce a form of steganography in the domain of machine learning which we call training set camouflage. Imagine Alice has a training set on an illicit machine learning classification task. Alice wants Bob (a machine learning system) to learn the task. However, sending either the training set or the trained model to Bob can raise suspicion if the communication is monitored. Training set camouflage allows Alice to compute a second training set on a completely different – and seemingly benign – classification task. By construction, sending the second training set will not raise suspicion. When Bob applies his standard (public) learning algorithm to the second training set, he approximately recovers the classifier on the original task. Training set camouflage is a novel form of steganography in machine learning. We formulate training set camouflage as a combinatorial bilevel optimization problem and propose solvers based on nonlinear programming and local search. Experiments on real classification tasks demonstrate the feasibility of such camouflage.
A GAL80 Collection To Inhibit GAL4 Transgenes in Drosophila Olfactory Sensory Neurons

Fruit flies recognize hundreds of ecologically relevant odors and respond appropriately to them. The complexity, redundancy and interconnectedness of the olfactory machinery complicate efforts to pinpoint the functional contributions of any component neuron or receptor to behavior. Some contributions can only be elucidated in flies that carry multiple mutations and transgenes, but the production of such flies is currently labor-intensive and time-consuming. Here, we describe a set of transgenic flies that express the Saccharomyces cerevisiae GAL80 in specific olfactory sensory neurons (OrX-GAL80s). The GAL80s effectively and specifically subtract the activities of GAL4-driven transgenes that impart anatomical and physiological phenotypes. OrX-GAL80s can allow researchers to efficiently activate only one or a few types of functional neurons in an otherwise nonfunctional olfactory background. Such experiments will improve our understanding of the mechanistic connections between odorant inputs and behavioral outputs at the resolution of only a few functional neurons.
Co-manifold learning with missing data

Representation learning is typically applied to only one mode of a data matrix, either its rows or columns. Yet in many applications, there is an underlying geometry to both the rows and the columns. We propose utilizing this coupled structure to perform co-manifold learning: uncovering the underlying geometry of both the rows and the columns of a given matrix, where we focus on a missing data setting. Our unsupervised approach consists of three components. We first solve a family of optimization problems to estimate a complete matrix at multiple scales of smoothness. We then use this collection of smooth matrix estimates to compute pairwise distances on the rows and columns based on a new multi-scale metric that implicitly introduces a coupling between the rows and the columns. Finally, we construct row and column representations from these multi-scale metrics. We demonstrate that our approach outperforms competing methods in both data visualization and clustering.
CELLULAR SENESCENCE, AGING, AND CANCER

INTRODUCTION. Mammalian cells can respond to damage or stress by entering a state of irreversibly arrested growth and altered function termed cellular senescence. The senescence response can be elicited by one or more critically short telomere (replicative senescence), certain types of DNA damage or changes in chromatin structure, the expression of certain oncogenes, and supraphysiological mitogenic signals. Several lines of evidence suggest that cellular senescence evolved to suppress the development of cancer. Cellular senescence has also been proposed to contribute to aging, although strong evidence for this idea has been lacking.
Cuprous Oxide Electrodeposited with Nickel for the Oxygen Evolution Reaction in 1 M NaOH

The electrolysis of water is limited by the high overpotential of the oxygen evolution reaction (OER). In search of an inexpensive and efficient catalyst for OER, we construct electrocatalysts usin...
Chapter 13 – Formulation and Solution Procedure

The determination of temperature distribution in heated solid bodies and structures plays an important role in determining thermal stresses. This chapter presents the basic equations of heat transfer, formulation of heat transfer problems, and procedures for finding the temperature distribution. The basic equations of heat transfer—energy balance equation, rate equations for conduction, convection and radiation heat transfer, energy generated and energy stored in a solid body—are given. The governing equation for heat transfer in a three-dimensional body is derived in the Cartesian coordinate system, and its equivalence equations in cylindrical and spherical coordinate systems are also given. The problem of finding the temperature distribution inside a solid body is presented in differential equation form and also in variational form. The finite element equations are derived for heat transfer analysis using variational and Galerkin approaches.
Incidence Density Ratio

The incidence density ratio is the ratio of the incidence density in one group to that in another group. Keywords: incidence density ratio; incidence density
Self-Reported Everyday Psychosocial Stressors Are Associated With Greater Impairments in Endothelial Function in Young Adults With Major Depressive Disorder.

Background Despite the epidemiological associations between psychological stress, depression, and increased cardiovascular disease risk, no studies have examined the relation between naturally occu...
Predicting Virality on Networks Using Local Graphlet Frequency Distribution

The task of predicting virality has far-reaching consequences, from the world of advertising to more recent attempts to reduce the spread of fake news. Previous work has shown that graphlet distribution is an effective feature for predicting virality. Here, we investigate the use of aggregated edge-centric local graphlets around source nodes as features for virality prediction. These prediction features are used to predict expected virality for both a time-independent Hawkes model and an independent cascade model of virality. In the Hawkes model, we use linear regression to predict the number of Hawkes events and node ranking, while in the independent cascade model we use logistic regression to predict whether a k-size cascade will multiply by a factor X in size. Our study indicates that local graphlet frequency distribution can effectively capture the variances of the viral processes simulated by Hawkes process and independent-cascade process. Furthermore, we identify a group of local graphlets which might be significant in the viral processes. We compare the effectiveness of our methods with eigenvector centrality-based node choice.