cssi/SciSciGPT-SciSciNet huggingface dataset:
Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum.

Background: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics—based on the Akaike information criterion values ranging from −642.75 to −412.32—were statistically significant. Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life.
RISE: Randomized Input Sampling for Explanation of Black-box Models.

Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to the end users. In this paper, we address the problem of Explainable AI for deep neural networks that take images as input and output a class probability. We propose an approach called RISE that generates an importance map indicating how salient each pixel is for the model's prediction. In contrast to white-box approaches that estimate pixel importance using gradients or other internal network state, RISE works on black-box models. It estimates importance empirically by probing the model with randomly masked versions of the input image and obtaining the corresponding outputs. We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments. Extensive experiments on several benchmark datasets show that our approach matches or exceeds the performance of other methods, including white-box approaches. Project page: this http URL
An Integrative Systems Biology and Experimental Approach Identifies Convergence of Epithelial Plasticity, Metabolism, and Autophagy to Promote Chemoresistance.

The evolution of therapeutic resistance is a major cause of death for cancer patients. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure of the host immune system. These selective forces often lead to evolutionary convergence on pathways or hallmarks that drive progression. Thus, a deeper understanding of the evolutionary convergences that occur could reveal vulnerabilities to treat therapy-resistant cancer. To this end, we combined phylogenetic clustering, systems biology analyses, and molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during transforming growth factor-β (TGF-β)-mediated epithelial–mesenchymal transition in lung cancer. Phylogenetic analyses of gene expression data from TGF-β-treated cells revealed convergence of cells toward amine metabolic pathways and autophagy during TGF-β treatment. Knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. In addition, high ATG16L expression was found to be a poor prognostic marker in multiple cancer types. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities for cancer.
Sample-Selection Bias and Height Trends in the Nineteenth-Century United States

After adjusting for sample-selection bias, I find a net decline in average stature of 0.64 inches in the birth cohorts of 1832–1860 in the United States. This result supports the veracity of the Antebellum Puzzle—a deterioration of health during early modern economic growth in the United States. However, this adjustment alters the trend in average stature in the same cohort range, validating concerns over bias in the historical heights literature. The adjustment is based on census-linked military height data and uses a two-step semi-parametric sample-selection model to adjust for selection on observables and unobservables.
When transparency fails: bias and financial incentives in ridesharing platforms

Providing transparency into operational processes can change consumer and worker behavior. However, it is unclear whether operational transparency is beneficial with potentially biased service prov...
Effects of urbanization on food demand in China

Urbanization in China has been on a steady rise recently, which has contributed to the changing consumer food preferences and consumption patterns. This carries significant implications for food security in China and the global food trade, given the role China plays on global food markets. This study investigates the effects of urbanization on food demand in China by adopting an empirical framework that incorporates urbanization into a theory plausible demand system. It also considers the effects of urbanization-induced loss of agricultural land and increased pollution on food production resource quality and food supply. Modeling the demand and supply components simultaneously allows us to undertake equilibrium analysis to determine prices. Based on the urbanization elasticities derived and estimated in this study, our findings indicate that urbanization has increased demand for meats, fruit, and eggs, while reducing demand for grains, vegetables, and fats and oils.
A review of the evidence on the relation between crude oil prices and petroleum product prices

Abstract We review a large body of the empirical literature focusing on the relation between petroleum product prices and oil prices and discuss the evidence on the direction of causality between crude oil prices and petroleum product prices. In addition, we survey the literature on the much-debated question of whether petroleum product prices respond differently to increases versus decreases in oil prices, which Bacon (1991) labeled the “rockets and feathers” phenomenon.
Hardware Conditioned Policies for Multi-Robot Transfer Learning

Deep reinforcement learning could be used to learn dexterous robotic policies but it is challenging to transfer them to new robots with vastly different hardware properties. It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexity of modern state-of-the-art algorithms. We propose a novel approach called Hardware Conditioned Policies where we train a universal policy conditioned on a vector representation of robot hardware. We considered robots in simulation with varied dynamics, kinematic structure, kinematic lengths and degrees-of-freedom. First, we use the kinematic structure directly as the hardware encoding and show great zero-shot transfer to completely novel robots not seen during training. For robots with lower zero-shot success rate, we also demonstrate that fine-tuning the policy network is significantly more sample-efficient than training a model from scratch. In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well. Videos of experiments are available at: https://sites.google.com/view/robot-transfer-hcp.
Distributed System Development Using Web Service and Enterprise Java Beans

Summary form only given. Web services and Enterprise Java Beans are evolving technology that allows the development and publication of software components. These components can be integrated together in plug and play manner. It provides a generic component based framework on the Web for large and complex distributed application development. This tutorial provides a detailed explanation of how you can build different types of EJBs to implement your distributed application. It will also provide details of publishing, accessing and innovation of Web service functions. Moreover, it will give you some dos and don'ts for developing Web services and EJBs. This tutorial is valuable for those involved in designing and deploying distributed B2B solutions such as Web developers, architects, consultants, engineers and analysts and programmers. The basic programming experience is assumed.
General properties of the evolution of research fields: a scientometric study of human microbiome, evolutionary robotics and astrobiology

How do research fields evolve? This study confronts this question here by developing an inductive analysis based on emerging research fields of human microbiome, evolutionary robotics and astrobiology (also called exobiology). Data analysis considers papers associated with subject areas of authors from starting years to 2017 per each research field under study. Findings suggest some empirical properties of the evolution of research fields: the first property states that the evolution of a research field is driven by few disciplines (3–5) that generate more than 80% of documents (concentration of scientific production); the second property states that the evolution of research fields is path-dependent of critical disciplines: they can be parent disciplines that have originated the research field or new disciplines emerged during the evolution of science; the third property states that the evolution of research fields can be also due to a new discipline originated from a process of specialization within applied or basic sciences and/or convergence between disciplines. Finally, the fourth property states that the evolution of specific research fields can be due to both applied and basic sciences. These results here can explain and generalize some characteristics of the evolution of scientific fields in the dynamics of science. Overall, then, this study begins the process of clarifying and generalizing, as far as possible, the general properties of the evolution of research fields to lay a foundation for the development of sophisticated theories of the evolution of science.
Multi-thread optimization for the calibration of microscopic traffic simulation model

This paper proposes an innovative multi-thread stochastic optimization approach for the calibration of microscopic traffic simulation models. Combining Quasi-Monte Carlo (QMC) sampling and the Part...
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization

Responses generated by neural conversational models tend to lack informativeness and diversity. We present Adversarial Information Maximization (AIM), an adversarial learning framework that addresses these two related but distinct problems. To foster response diversity, we leverage adversarial training that allows distributional matching of synthetic and real responses. To improve informativeness, our framework explicitly optimizes a variational lower bound on pairwise mutual information between query and response. Empirical results from automatic and human evaluations demonstrate that our methods significantly boost informativeness and diversity.
Probing the link between residual entropy and viscosity of molecular fluids and model potentials.

This work investigates the link between residual entropy and viscosity based on wide-ranging, highly accurate experimental and simulation data. This link was originally postulated by Rosenfeld in 1977 [Rosenfeld Y (1977) Phys Rev A 15:2545–2549], and it is shown that this scaling results in an approximately monovariate relationship between residual entropy and reduced viscosity for a wide range of molecular fluids [argon, methane, C O 2 , S F 6 , refrigerant R-134a (1,1,1,2-tetrafluoroethane), refrigerant R-125 (pentafluoroethane), methanol, and water] and a range of model potentials (hard sphere, inverse power, Lennard-Jones, and Weeks–Chandler–Andersen). While the proposed “universal” correlation of Rosenfeld is shown to be far from universal, when used with the appropriate density scaling for molecular fluids, the viscosity of nonassociating molecular fluids can be mapped onto the model potentials. This mapping results in a length scale that is proportional to the cube root of experimentally measurable liquid volume values.
Credit Rationing and Effective Supply Failures

The topic of this paper is among the oldest and most fundamental in monetary theory: how and why does monetary policy affect real economic activity? Traditional answers hold that the central bank can manipulate aggregate demand by engineering expansions or contractions of the medium of exchange. In its monetarist variant, this story posits a direct link between something called M and aggregate spending. In its Keynesian variant, the story holds that adjustments in asset prices brought about by a change in M lead to more spending, especially on capital goods. In either case, short-run stickiness of prices is needed to translate some of the changes in demand into movements of real output. In recent years, these conventional stories have become increasingly implausible, as Stiglitz and I (Blinder and Stiglitz, I 983) have argued elsewhere. With 'money' becoming more and more difficult to define, it is becoming increasingly difficult to believe that the central bank can cause a recession by contriving a shortage of an arbitrary subset of assets called M. Put differently, the point seems both simple and compelling: if there are ready substitutes for money, control of money will not give the authorities much leverage over the real economy. Hence this paper develops an explanation for how central bank policy affects
RESPONSE: Collaboration and Collegiality: The Dual Pillars of Cardiovascular Imaging Now and in the Future.

Dr. Parwani and colleagues have laid out an important vision of the future of cardiovascular imaging, especially for fellows-in-training and early career cardiologists. The future of cardiovascular care is an emphasis on value of care rather than volume, and it is in this light that collaboration
Taking the Scenic Route: Automatic Exploration for Videogames

Machine playtesting tools and game moment search engines require exposure to the diversity of a game's state space if they are to report on or index the most interesting moments of possible play. Meanwhile, mobile app distribution services would like to quickly determine if a freshly-uploaded game is fit to be published. Having access to a semantic map of reachable states in the game would enable efficient inference in these applications. However, human gameplay data is expensive to acquire relative to the coverage of a game that it provides. We show that off-the-shelf automatic exploration strategies can explore with an effectiveness comparable to human gameplay on the same timescale. We contribute generic methods for quantifying exploration quality as a function of time and demonstrate our metric on several elementary techniques and human players on a collection of commercial games sampled from multiple game platforms (from Atari 2600 to Nintendo 64). Emphasizing the diversity of states reached and the semantic map extracted, this work makes productive contrast with the focus on finding a behavior policy or optimizing game score used in most automatic game playing research.
The educational emphases of science teachers in US Evangelical Protestant high schools

I examine the levels of educational emphases that science teachers in Evangelical Protestant (EP) schools place on (i) teaching basic content knowledge, (ii) improving scientific reasoning skills, ...
The Tumor Microenvironment of High Grade Serous Ovarian Cancer

The Special Issue on high grade serous ovarian cancer (HGSOC) and the contribution of the tumor micro-environment (TME) consisted of reviews contributed by leaders in the ovarian cancer (OC) field. [...].
Testing Spatial Dependence in Spatial Models with Endogenous Weights Matrices

In this study, we propose simple test statistics for identifying the source of spatial dependence in spatial autoregressive models with endogenous weights matrices. Elements of the weights matrices are modelled in such a way that endogenity arises when the unobserved factors that affect elements of the weights matrices are correlated with the unobserved factors in the outcome equation. The proposed test statistics are robust to the presence of endogeneity in the weights and can be used to detect spatial dependence in the dependent variable and/or the disturbance terms. The robust test statistics are easy to calculate as computationally simple estimations are needed for their calculations. Our Monte Carlo results indicate that these tests have good size and power properties in finite samples. We also provide an empirical illustration to demonstrate the usefulness of the robust tests in identifying the source of spatial dependence.
Large-sample estimation and inference in multivariate single-index models

Abstract By optimizing index functions against different outcomes, we propose a multivariate single-index model (SIM) for development of medical indices that simultaneously work with multiple outcomes. Fitting of a multivariate SIM is not fundamentally different from fitting a univariate SIM, as the former can be written as a sum of multiple univariate SIMs with appropriate indicator functions. What have not been carefully studied are the theoretical properties of the parameter estimators. Because of the lack of asymptotic results, no formal inference procedure has been made available for multivariate SIMs. In this paper, we examine the asymptotic properties of the multivariate SIM parameter estimators. We show that, under mild regularity conditions, estimators for the multivariate SIM parameters are indeed n -consistent and asymptotically normal. We conduct a simulation study to investigate the finite-sample performance of the corresponding estimation and inference procedures. To illustrate its use in practice, we construct an index measure of urine electrolyte markers for assessing the risk of hypertension in individual subjects.
Automated detection of celiac disease on duodenal biopsy slides: A deep learning approach

Context: Celiac disease (CD) prevalence and diagnosis have increased substantially in recent years. The current gold standard for CD confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose CD more efficiently. Subjects and Methods: In this study, we trained a deep learning model to detect CD on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Results: Our model identified CD, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was >0.95 for all classes. Conclusions: We have developed an automated biopsy analysis system that achieves high performance in detecting CD on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies before review by pathologists. This technology has great potential for improving the accuracy and efficiency of CD diagnosis.
Improving police officer and justice personnel attitudes and de-escalation skills: A pilot study of Policing the Teen Brain

This pilot study assessed whether police officers and juvenile justice personnel reported improved attitudes toward youth and knowledge about de-escalation skills after attending Policing the Teen ...
Mechanistic Assessment of Extrahepatic Contributions to Glucuronidation of Integrase Strand Transfer Inhibitors.

Integrase strand transfer inhibitor (INSTI)–based regimens dominate initial human immunodeficiency virus treatment. Most INSTIs are metabolized predominantly via UDP-glucuronosyltransferases (UGTs). For drugs predominantly metabolized by UGTs, including INSTIs, in vitro data recovered from human liver microsomes (HLMs) alone often underpredict human oral clearance. While several factors may contribute, extrahepatic glucuronidation may contribute to this underprediction. Thus, we comprehensively characterized the kinetics for the glucuronidation of INSTIs (cabotegravir, dolutegravir, and raltegravir) using pooled human microsomal preparations from liver (HLMs), intestine (HIMs), and kidney (HKMs) tissues; human embryonic kidney 293 cells expressing individual UGTs; and recombinant UGTs. In vitro glucuronidation of cabotegravir (HLMs≈HKMs>>>HIMs), dolutegravir (HLMs>HIMs>>HKMs), and raltegravir (HLMs>HKMs>> HIMs) occurred in hepatic and extrahepatic tissues. The kinetic data from expression systems suggested the major enzymes in each tissue: hepatic UGT1A9 > UGT1A1 (dolutegravir and raltegravir) and UGT1A1 (cabotegravir), intestinal UGT1A3 > UGT1A8 > UGT1A1 (dolutegravir) and UGT1A8 > UGT1A1 (raltegravir), and renal UGT1A9 (dolutegravir and raltegravir). Enzymes catalyzing cabotegravir glucuronidation in the kidney and intestine could not be identified unequivocally. Using data from dolutegravir glucuronidation as a prototype, a “bottom-up” physiologically based pharmacokinetic model was developed in a stepwise approach and predicted dolutegravir oral clearance within 4.5-fold (hepatic data only), 2-fold (hepatic and intestinal data), and 32% (hepatic, intestinal, and renal data). These results suggest clinically meaningful glucuronidation of dolutegravir in tissues other than the liver. Incorporation of additional novel mechanistic and physiologic underpinnings of dolutegravir metabolism along with in silico approaches appears to be a powerful tool to accurately predict the clearance of dolutegravir from in vitro data.
A Bias Aggregation Theorem

Abstract In a market where some traders are rational (maximize expected utility) and others are systematically biased (deviate from expected utility due to some bias parameter, θ ), do equilibrium prices necessarily depend on θ ? Focusing on the case where there is an aggregate and systematic bias in the population, we show that market prices can still be unbiased. Hence, we establish that systematically biased agents do not necessarily imply biased market prices.
Chondrichthyes (Sharks, Rays, and Chimaeras)

First page of article Keywords: chondrichthyes (sharks, rays and chimaeras); special sensory adaptations
Second Primary Non-Small-Cell Lung Cancer After Head and Neck Cancer: A Population-Based Study of Clinical and Pathologic Characteristics and Survival Outcomes in 3597 Patients.

Abstract Introduction Retrospective studies have shown an increased risk of second primary lung cancer in patients with a history of head and neck cancer (HNC). No population-based study has examined the overall survival (OS) outcomes of patients with second primary non–small-cell lung cancer (NSCLC) after HNC comparison with patients with first primary NSCLC. Patients and Methods Individuals with histologically confirmed NSCLC diagnosed after nonmetastatic squamous-cell carcinoma of the head and neck (HNC-NSCLC; n = 3597) were identified in Surveillance, Epidemiology, and End Results 18 registries (1988-2013). OS and baseline characteristics were compared in patients with first primary NSCLC (NSCLC-1; n = 365,551) in the same registries. Results Squamous NSCLC was more common in HNC-NSCLC (n = 745 [64.1%] localized, n = 833 [71.9%] regional, and n = 811 [63.5%] distant) than in the NSCLC-1 (n = 30,901 [38.3%] localized, n = 50,557 [48.2%] regional, and n = 53,720 [29.8%] distant; P Conclusion A history of HNC adversely affects OS in patients who subsequently develop NSCLC. This OS decrement might have implications for NSCLC surveillance and NSCLC therapy selection in this population.
The North Dakota integrated carbon storage complex feasibility study

Abstract The Energy & Environmental Research Center is investigating the feasibility of safely, permanently, and economically storing 50 million tonnes of CO2 in central North Dakota, United States, over a 25-year operational period, should a business case for CO2 storage emerge. The study is part of the U.S. Department of Energy (DOE) National Energy Technology Laboratory CarbonSAFE initiative and addresses the technical and nontechnical challenges of commercially deploying a CO2 storage project. Evaluation of cores from two stratigraphic test wells demonstrate that the Broom Creek Formation (sandstone) is an excellent candidate for the geologic storage of CO2 and the overlying Opeche Formation a competent cap rock.
Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series

Estimating the dependence structure of multidimensional time series data in real-time is challenging. With large volumes of streaming data, the problem becomes more difficult when the multidimensional data are collected asynchronously across distributed nodes, which motivates us to sample representative data points from streams. We propose a leverage score sampling (LSS) method for efficient online inference of the streaming vector autoregressive (VAR) model. We define the leverage score for the streaming VAR model so that the LSS method selects informative data points in real-time with statistical guarantees of parameter estimation efficiency. Moreover, our LSS method can be directly deployed in an asynchronous decentralized environment, e.g., a sensor network without a fusion center, and produce asynchronous consensus online parameter estimation over time. By exploiting the temporal dependence structure of the VAR model, the LSS method selects samples independently on each dimension and thus is able to update the estimation asynchronously. We illustrate the effectiveness of the LSS method in synthetic, gas sensor and seismic datasets.
Evolution of social norms and correlated equilibria.

Social norms regulate and coordinate most aspects of human social life, yet they emerge and change as a result of individual behaviors, beliefs, and expectations. A satisfactory account for the evolutionary dynamics of social norms, therefore, has to link individual beliefs and expectations to population-level dynamics, where individual norms change according to their consequences for individuals. Here, we present a model of evolutionary dynamics of social norms that encompasses this objective and addresses the emergence of social norms. In this model, a norm is a set of behavioral prescriptions and a set of environmental descriptions that describe the expected behaviors of those with whom the norm holder will interact. These prescriptions and descriptions are functions of exogenous environmental events. These events have no intrinsic meaning or effect on the payoffs to individuals, yet beliefs/superstitions regarding them can effectuate coordination. Although a norm’s prescriptions and descriptions are dependent on one another, we show how they emerge from random accumulations of beliefs. We categorize the space of social norms into several natural classes and study the evolutionary competition between these classes of norms. We apply our model to the Game of Chicken and the Nash Bargaining Game. Furthermore, we show how the space of norms and evolutionary stability are dependent on the correlation structure of the environment and under which such correlation structures social dilemmas can be ameliorated or exacerbated.
A new station-enabled multi-sensor integrated index for drought monitoring

Abstract Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available.
Large-Scale Qualitative and Quantitative Top-Down Proteomics Using Capillary Zone Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry with Nanograms of Proteome Samples.

Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has attracted attention recently for top-down proteomics because it can achieve highly efficient separation and very sensitive detection of proteins. However, separation window and sample loading volume of CZE need to be boosted for a better proteome coverage using CZE-MS/MS. Here, we present an improved CZE-MS/MS system that achieved a 180-min separation window and a 2-μL sample loading volume for top-down characterization of protein mixtures. The system obtained highly efficient separation of proteins with nearly one million theoretical plates for myoglobin and enabled baseline separation of three different proteoforms of myoglobin. The CZE-MS/MS system identified 797 ± 21 proteoforms and 258 ± 7 proteins (n = 2) from an Escherichia coli (E. coli) proteome sample in a single run with only 250 ng of proteins injected. The system still identified 449 ± 40 proteoforms and 173 ± 6 proteins (n = 2) from the E. coli sample when only 25 ng of proteins were injected per run. Single-shot CZE-MS/MS analyses of zebrafish brain cerebellum (Cb) and optic tectum (Teo) regions identified 1730 ± 196 proteoforms (n = 3) and 2024 ± 255 proteoforms (n = 3), respectively, with only 500-ng proteins loaded per run. Label-free quantitative top-down proteomics of zebrafish brain Cb and Teo regions revealed significant differences between Cb and Teo regarding the proteoform abundance. Over 700 proteoforms from 131 proteins had significantly higher abundance in Cb compared to Teo, and these proteins were highly enriched in several biological processes, including muscle contraction, glycolytic process, and mesenchyme migration.
Metrics Towards Measuring Cyber Agility

In cyberspace, evolutionary strategies are commonly used by both attackers and defenders. For example, an attacker’s strategy often changes over the course of time, as new vulnerabilities are discovered and/or mitigated. Similarly, a defender’s strategy changes over time. These changes may or may not be in direct response to a change in the opponent’s strategy. In any case, it is important to have a set of quantitative metrics to characterize and understand the effectiveness of attackers’ and defenders’ evolutionary strategies, which reflect their cyber agility . Despite its clear importance, few systematic metrics have been developed to quantify the cyber agility of attackers and defenders. In this paper, we propose the first metric framework for measuring cyber agility in terms of the effectiveness of the dynamic evolution of cyber attacks and defenses. The proposed framework is generic and applicable to transform any relevant, quantitative, and/or conventional static security metrics (e.g., false positives and false negatives) into dynamic metrics to capture dynamics of system behaviors. In order to validate the usefulness of the proposed framework, we conduct case studies on measuring the evolution of cyber attacks and defenses using two real-world datasets. We discuss the limitations of the current work and identify future research directions.
X-DeepSCA: Cross-Device Deep Learning Side Channel Attack

This article, for the first time, demonstrates Cross-device Deep Learning Side-Channel Attack (X-DeepSCA), achieving an accuracy of > 99.9%, even in presence of significantly higher inter-device variations compared to the inter-key variations. Augmenting traces captured from multiple devices for training and with proper choice of hyper-parameters, the proposed 256-class Deep Neural Network (DNN) learns accurately from the power side-channel leakage of an AES-128 target encryption engine, and an N-trace (N ≤ 10) X-DeepSCA attack breaks different target devices within seconds compared to a few minutes for a correlational power analysis (CPA) attack, thereby increasing the threat surface for embedded devices significantly. Even for low SNR scenarios, the proposed X-DeepSCA attack achieves ~ 10× lower minimum traces to disclosure (MTD) compared to a traditional CPA. CCS Concepts • Security and privacy $\rightarrow $ Embedded systems security; Side-channel analysis and countermeasures.
Microwave assisted synthesis of 10b-aza-10c-borapyrene

The synthesis of 10b-aza-10c-borapyrene, an analog of the polycyclic aromatic hydrocarbon pyrene with an internal boron–nitrogen bond, has been improved. We have dramatically reduced the reaction times and ease of workup for the synthesis of this molecule by using a microwave reactor and carefully controlling reaction conditions.
Investigations of human myosin VI targeting using optogenetically controlled cargo loading

Myosins play countless critical roles in the cell, each requiring it to be activated at a specific location and time. To control myosin VI with this specificity, we created an optogenetic tool for activating myosin VI by fusing the light-sensitive Avena sativa phototropin1 LOV2 domain to a peptide from Dab2 (LOVDab), a myosin VI cargo protein. Our approach harnesses the native targeting and activation mechanism of myosin VI, allowing direct inferences on myosin VI function. LOVDab robustly recruits human full-length myosin VI to various organelles in vivo and hinders peroxisome motion in a light-controllable manner. LOVDab also activates myosin VI in an in vitro gliding filament assay. Our data suggest that protein and lipid cargoes cooperate to activate myosin VI, allowing myosin VI to integrate Ca2+, lipid, and protein cargo signals in the cell to deploy in a site-specific manner.
Two-directional synthesis of the non-adjacent bis(tetrahydrofuran) core of cis-sylvaticin

Abstract Synthesis of the C 2 -symmetric, non-adjacent bis(tetrahydrofuran) core of cis -sylvaticin in seven steps and 24% overall yield from (2 R ,3 S )-1,2-epoxy-4-penten-3-ol is reported. A strategy involving assembly of the central 1,4-diol unit by silicon-tethered ring-closing metathesis and subsequent two-directional functionalization, including establishment of the cis/threo stereochemical relationships of the tetrahydrofuran rings by Sharpless asymmetric dihydroxylation/S N 2 cyclization, is employed.
Synthesis and Structure−Activity Profiles of A-Homoestranes, the Estratropones

2-Methoxyestradiol, a mammalian metabolite of estradiol, has reported antiangiogenic activity which has been proposed to be mediated through interaction at the colchicine binding site on the tubulin monomer. Subsequent structure−activity studies of 2-methoxyestradiol have yielded highly potent steroidal inhibitors of tubulin polymerization. In an effort to probe the scope of binding at the colchicine binding site and the nature of the relationship between 2-methoxyestradiol and colchicine, a series of colchicine/2-methoxyestradiol hybrids was synthesized. These A-homoestrane hybrid systems, collectively termed estratropones, possessed an A-ring tropone system with the keto functionality at either the C-2, C-3, or C-4 position of the steroid nucleus. The estratropones were evaluated for their ability to inhibit the polymerization of tubulin using an in vitro purified bovine brain assay. Most of these hybrids inhibit polymerization with greater potency than either of the natural products. The most potent of...
Total synthesis of (+/-)-methyl rishirilide B.

A regio- and stereospecific total synthesis of (±)-methyl rishirilide B (2b), an (α)2-macroglobulin inhibitor, is described. A key feature of the synthetic plan was regiospecific construction of a hydroanthracenone intermediate through condensation of a phenylsulfonyl isobenzofuranone with a functionalized 2-cyclohexen-1-one. Introduction of the vicinal trans-hydroxyl groups in the densely functionalized A-ring was accomplished via a novel one-pot procedure that involved oxidation of enolate anions with the Davis reagent.
Cyclodextrin-mediated regioselective photo-Fries reaction of 1-naphthyl phenyl acylates

1-Naphthyl phenyl acylates upon irradiation in solution yield eight products via β-cleavage process. However, excitation of these molecules as included in γ-cyclodextrin results in a single product (>95%). This medium dependent product selectivity is attributed to conformational and translational restrictions enforced on the reactant as well as intermediates by the cyclodextrin cavity.
Building blocks for peptide and carbamate libraries

Abstract A building block containing the 1,4-benzodiazepine-2,5-diazepine pharmacophore has been synthesized for use in constructing both peptide and carbamate combinatorial libraries.
Progress toward the synthesis of sarain A: An unanticipated rearrangement

Abstract An unanticipated rearrangement of a bicyclo[3.2.1]octane to a bicyclo[2.2.2]octane occurred in attempt to prepare the core of sarain A.
A distributed algorithm to maintain and repair the trail networks of arboreal ants

We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithm's success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.
A 3D topographical model of parenchymal infiltration and perivascular invasion in glioblastoma.

Glioblastoma (GBM) is the most common and invasive primary brain cancer. GBM tumors are characterized by diffuse infiltration, with tumor cells invading slowly through the hyaluronic acid (HA)-rich parenchyma toward vascular beds and then migrating rapidly along microvasculature. Progress in understanding local infiltration, vascular homing, and perivascular invasion is limited by the absence of culture models that recapitulate these hallmark processes. Here, we introduce a platform for GBM invasion consisting of a tumor-like cell reservoir and a parallel open channel “vessel” embedded in the 3D HA-RGD matrix. We show that this simple paradigm is sufficient to capture multi-step invasion and transitions in cell morphology and speed reminiscent of those seen in GBM. Specifically, seeded tumor cells grow into multicellular masses that expand and invade the surrounding HA-RGD matrices while extending long (10–100 μm), thin protrusions resembling those observed for GBM in vivo. Upon encountering the channel, cells orient along the channel wall, adopt a 2D-like morphology, and migrate rapidly along the channel. Structured illumination microscopy reveals distinct cytoskeletal architectures for cells invading through the HA matrix versus those migrating along the vascular channel. Substitution of collagen I in place of HA-RGD supports the same sequence of events but with faster local invasion and a more mesenchymal morphology. These results indicate that topographical effects are generalizable across matrix formulations, but the mechanisms underlying invasion are matrix-dependent. We anticipate that our reductionist paradigm should speed the development of mechanistic hypotheses that could be tested in more complex tumor models.
Biaryl cannabinoid mimetics--synthesis and structure-activity relationship.

Abstract Synthesis, in vitro biological evaluation, and structure–activity relationships of a biaryl cannabinoid mimetic 2 are reported. Variations in the substitution pattern yielded a number of agonists with low nanomolar affinity. Replacing the phenol group by a methyl morpholino acetate group led to compound 28, a 500-fold selective CB2 receptor agonist.
Perovskite catalysts for methane combustion

We have investigated the methane oxidation activity and structure of a number of complex metal oxides with the objective of determining the relationship between catalytic and such solid-state parameters as the type of transition metal cation incorporated in the oxide crystal structure, the metal cation valence state, oxygen stoichiometry, and defect structure
Indium (III) mediated Markovnikov addition of malonates and β-ketoesters to terminal alkynes and the formation of Knoevenagel condensation products

The indium(III) triflate mediated addition of active methylene compounds to terminal alkynes has been expanded to use malonates and low boiling terminal alkynes to form the Markovnikov addition products. Indium(III) chloride and indium(III) bromide were also found to be efficient catalysts. Knoevenagel condensation products were isolated when reactions involved a simple malonate or β-ketoester.
Formal Cycloaddition Reactions of Vinylogous Amides with α,β-Unsaturated Iminiums. A Strategy for Constructing Piperidinyl Heterocycles

A formal [3 + 3] cycloaddition strategy for constructing piperidinyl heterocycles from vinylogous amides and α,β-unsaturated iminiums is described here. These reactions proceed well, leading to various heterocycles that could serve as useful synthetic building blocks. Comparative studies between the reactivities of vinylogous amides and other 1,3-diketo systems have also been examined. Preferences for the nitrogen atom during the electrocyclic ring closure step are noted in these reactions, and preliminary calculations suggest that these preferences could result from electronic factors.
Small-Volume and High-Sensitivity NMR Probes

Aside from efforts to develop capillary nuclear magnetic resonance (NMR) probes during the 1970s, most NMR samples were traditionally examined using 5 mm probes. Due to low gyromagnetic ratio and/or natural abundance, some larger format NMR probes were developed, these including 8 and 12 mm, and large volume probes of 18 and 22 mm. Large format probes addressed the difficulties of acquiring NMR data for insensitive nuclides when an investigator was not sample limited or dealing with highly insoluble materials. In contrast, for the characterization of scarce samples of natural products, impurities or degradants of pharmaceuticals, metabolites, forensic samples, and other severely sample-limited applications, even conventional 5 mm probes were not well suited to the task of acquiring data for such samples. These considerations led to the development of the first of the 3 mm NMR probes in the early 1990s followed by a succession of progressively smaller format NMR probes down to the 1 mm tube format probes currently available. In parallel, μCoil probes were also developed allowing the use of still smaller sample volumes, albeit in a flow cell rather than a “tube” format, as were magic angle sideways spinning liquids probes such as the Varian Nano-probe ™ . In the late 1990s, the first examples of cryogenic NMR probe technology became available, and these extremely high-sensitivity NMR probes are now becoming more readily available to investigators faced with undertaking the structural characterization of scarce samples. The development of small-volume and high-sensitivity NMR probes is described in this contribution and examples of the application of these probe technologies are reviewed.
Fundamental Principles of Selective Heterogeneous Oxidation Catalysis

Seven principles (“seven pillars”) underpinning selective heterogeneous oxidation catalysis, comprising lattice oxygen, metal-oxygen bond strength, host structure, redox, multifunctionality of active sites, site isolation, and phase cooperation are described. Their intended utilization towards a deeper understanding of metal oxide catalyst behavior and application towards catalyst design are indicated.