cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimism, Pessimism, and Short-Term Fluctuations*

Abstract Economic theory offers several explanations as to why shifting expectations about future economic activity affect current demand. Abstracting from whether changes in expectations originate from swings in beliefs or fundamentals, we test empirically whether optimism and pessimism about the economy trigger short-term fluctuations in private consumption and investment. Under the assumption that cyclical movements in private consumption and investment growth are exogenous to potential output growth forecasts far into the future, our results are consistent with the idea of private economic agents learning about future potential output growth and adjusting their current demand accordingly. We also propose a simple Keynesian model to illustrate that revisions in expected future income can affect short-term equilibria, in line with the results of the empirical analysis.
Potential high-frequency off-target mutagenesis induced by CRISPR/Cas9 in Arabidopsis and its prevention

We present novel observations of high-specificity SpCas9 variants, sgRNA expression strategies based on mutant sgRNA scaffold and tRNA processing system, and CRISPR/Cas9-mediated T-DNA integrations. Specificity of CRISPR/Cas9 tools has been a major concern along with the reports of their successful applications. We report unexpected observations of high frequency off-target mutagenesis induced by CRISPR/Cas9 in T1 Arabidopsis mutants although the sgRNA was predicted to have a high specificity score. We also present evidence that the off-target effects were further exacerbated in the T2 progeny. To prevent the off-target effects, we tested and optimized two strategies in Arabidopsis, including introduction of a mCherry cassette for a simple and reliable isolation of Cas9-free mutants and the use of highly specific mutant SpCas9 variants. Optimization of the mCherry vectors and subsequent validation found that fusion of tRNA with the mutant rather than the original sgRNA scaffold significantly improves editing efficiency. We then examined the editing efficiency of eight high-specificity SpCas9 variants in combination with the improved tRNA-sgRNA fusion strategy. Our results suggest that highly specific SpCas9 variants require a higher level of expression than their wild-type counterpart to maintain high editing efficiency. Additionally, we demonstrate that T-DNA can be inserted into the cleavage sites of CRISPR/Cas9 targets with high frequency. Altogether, our results suggest that in plants, continuous attention should be paid to off-target effects induced by CRISPR/Cas9 in current and subsequent generations, and that the tools optimized in this report will be useful in improving genome editing efficiency and specificity in plants and other organisms.
A low-power microcontroller having a 0.5-/spl mu/A standby current on-chip regulator with dual-reference scheme

We present a microcontroller having a 0.5-/spl mu/A standby current on-chip regulator. To break through the area overhead problem which a conventional regulator scheme suffers from to achieve small standby current, we propose a dual-reference scheme in which one voltage reference circuit is provided for active mode and another voltage reference circuit is provided for standby mode. For the voltage reference circuit for standby mode, a resistor-free circuit was used to achieve small current consumption without occupying large area. The microcontroller was fabricated in a 0.18-/spl mu/m CMOS process. The implementation and measurement results show that the dual-reference scheme achieves 0.5-/spl mu/A current consumption of the regulator in standby mode with 50% smaller area than the conventional scheme. The measured standby current of the whole chip was 2.0 /spl mu/A.
Rapid Multiplex Small DNA Sequencing on the MinION Nanopore Sequencing Platform

Real-time sequencing of short DNA reads has a wide variety of clinical and research applications including screening for mutations, target sequences and aneuploidy. We recently demonstrated that MinION, a nanopore-based DNA sequencing device the size of a USB drive, could be used for short-read DNA sequencing. In this study, an ultra-rapid multiplex library preparation and sequencing method for the MinION is presented and applied to accurately test normal diploid and aneuploidy samples' genomic DNA in under three hours, including library preparation and sequencing. This novel method shows great promise as a clinical diagnostic test for applications requiring rapid short-read DNA sequencing.
Taxon Disappearance from Microbiome Analysis Reinforces the Value of Mock Communities as a Standard in Every Sequencing Run

Mock communities have been used in microbiome method development to help estimate biases introduced in PCR amplification and sequencing and to optimize pipeline outputs. Nevertheless, the strong value of routine mock community analysis beyond initial method development is rarely, if ever, considered. Here we report that our routine use of mock communities as internal standards allowed us to discover highly aberrant and strong biases in the relative proportions of multiple taxa in a single Illumina HiSeqPE250 run. In this run, an important archaeal taxon virtually disappeared from all samples, and other mock community taxa showed >2-fold high or low abundance, whereas a rerun of those identical amplicons (from the same reaction tubes) on a different date yielded "normal" results. Although obvious from the strange mock community results, we could have easily missed the problem had we not used the mock communities because of natural variation of microbiomes at our site. The "normal" results were validated over four MiSeqPE300 runs and three HiSeqPE250 runs, and run-to-run variation was usually low. While validating these "normal" results, we also discovered that some mock microbial taxa had relatively modest, but consistent, differences between sequencing platforms. We strongly advise the use of mock communities in every sequencing run to distinguish potentially serious aberrations from natural variations. The mock communities should have more than just a few members and ideally at least partly represent the samples being analyzed to detect problems that show up only in some taxa and also to help validate clustering. IMPORTANCE Despite the routine use of standards and blanks in virtually all chemical or physical assays and most biological studies (a kind of "control"), microbiome analysis has traditionally lacked such standards. Here we show that unexpected problems of unknown origin can occur in such sequencing runs and yield completely incorrect results that would not necessarily be detected without the use of standards. Assuming that the microbiome sequencing analysis works properly every time risks serious errors that can be detected by the use of mock communities.
Which linguistic features predict quality of argumentative writing for college basic writers, and how do those features change with instruction?

The study developed a model of linguistic constructs to predict writing quality for college basic writers and analyzed how those constructs changed following instruction. Analysis used a corpus of argumentative essays from a quasi-experimental, instructional study with 252 students (MacArthur, Philippakos, & Ianetta, 2015) that found large effects (ES = 1.22) on quality of argumentative writing. Coh-Metrix (McNamara, Graesser, McCarthy, & Cai, 2014) was used to analyze the essays for lexical and syntactic complexity and cohesion. Structural equation modeling found that referential cohesion (p < .001) and lexical complexity (p < .01) positively predicted quality on posttest essays while syntactic complexity (p < .001) was negatively related to quality. Length explained 30% of variance in quality; the full model explained 48.7%. Confirmatory factor analysis was used to impute factor scores for pretest and posttest essays. Analysis of covariance using these factors found that the treatment group wrote posttest essays with greater lexical complexity (p < .01) and referential cohesion (p < .01) and less use of connectives (p < .05) than a business-as-usual control group.
A Theory of Demographically Targeted Repression

People from specific ethnic, religious, or other externally identifiable groups are often subjected to harsher repression than others. This phenomenon of demographically targeted repression is ofte...
Non-negatively Curved 6-Manifolds with Almost Maximal Symmetry Rank

We classify closed, simply connected, non-negatively curved 6-manifolds of almost maximal symmetry rank up to equivariant diffeomorphism.
The signpost platform for city-scale sensing

City-scale sensing holds the promise of enabling a deeper understanding of our urban environments. However, a city-scale deployment requires physical installation, power management, and communications---all challenging tasks standing between a good idea and a realized one. This indicates the need for a platform that enables easy deployment and experimentation for applications operating at city scale. To address these challenges, we present Signpost, a modular, energy-harvesting platform for city-scale sensing. Signpost simplifies deployment by eliminating the need for connection to wired infrastructure and instead harvesting energy from an integrated solar panel. The platform furnishes the key resources necessary to support multiple, pluggable sensor modules while providing fair, safe, and reliable sharing in the face of dynamic energy constraints. We deploy Signpost with several sensor modules, showing the viability of an energy-harvesting, multi-tenant, sensing system, and evaluate its ability to support sensing applications. We believe Signpost reduces the difficulty inherent in city-scale deployments, enables new experimentation, and provides improved insights into urban health.
Corporate charitable foundations, executive entrenchment, and shareholder distributions

Abstract We show that firms with corporate charitable foundations increased shareholder distributions by less than one half as much as similar firms without foundations following the 2003 capital income tax cut, even after controlling for common explanatory factors such as executive shareholding. The findings are robust to alternative explanations and to common threats to causal identification. Further exploration reveals that our estimates capture a greater reluctance of foundation firms to initiate or rapidly increase shareholder payouts, but not a greater tendency to reduce or eliminate shareholder payouts. Additional analyses suggest managers direct funds that would have been paid out toward executive compensation and capital investment. In light of the fact that firms with foundations are more likely to exhibit a high degree of managerial entrenchment, we interpret these findings as evidence that foundations are both a sign of and vehicle for managerial self-dealing.
Testing general relativity in cosmology

We review recent developments and results in testing general relativity (GR) at cosmological scales. The subject has witnessed rapid growth during the last two decades with the aim of addressing the question of cosmic acceleration and the dark energy associated with it. However, with the advent of precision cosmology, it has also become a well-motivated endeavor by itself to test gravitational physics at cosmic scales. We overview cosmological probes of gravity, formalisms and parameterizations for testing deviations from GR at cosmological scales, selected modified gravity (MG) theories, gravitational screening mechanisms, and computer codes developed for these tests. We then provide summaries of recent cosmological constraints on MG parameters and selected MG models. We supplement these cosmological constraints with a summary of implications from the recent binary neutron star merger event. Next, we summarize some results on MG parameter forecasts with and without astrophysical systematics that will dominate the uncertainties. The review aims at providing an overall picture of the subject and an entry point to students and researchers interested in joining the field. It can also serve as a quick reference to recent results and constraints on testing gravity at cosmological scales.
Dynamics of Cas10 Govern Discrimination between Self and Non-self in Type III CRISPR-Cas Immunity.

Summary Adaptive immune systems must accurately distinguish between self and non-self in order to defend against invading pathogens while avoiding autoimmunity. Type III CRISPR-Cas systems employ guide RNA to recognize complementary RNA targets, which triggers the degradation of both the invader’s transcripts and their template DNA. These systems can broadly eliminate foreign targets with multiple mutations but circumvent damage to the host genome. To explore the molecular basis for these features, we use single-molecule fluorescence microscopy to study the interaction between a type III-A ribonucleoprotein complex and various RNA substrates. We find that Cas10—the DNase effector of the complex—displays rapid conformational fluctuations on foreign RNA targets, but is locked in a static configuration on self RNA. Target mutations differentially modulate Cas10 dynamics and tune the CRISPR interference activity in vivo. These findings highlight the central role of the internal dynamics of CRISPR-Cas complexes in self versus non-self discrimination and target specificity.
Efficient and Secure Group Key Management in IoT using Multistage Interconnected PUF

Secure group-oriented communication is crucial to a wide range of applications in Internet of Things (IoT). Security problems related to group-oriented communications in IoT-based applications placed in a privacy-sensitive environment have become a major concern along with the development of the technology. Unfortunately, many IoT devices are designed to be portable and light-weight; thus, their functionalities, including security modules, are heavily constrained by the limited energy resources (e.g., battery capacity). To address these problems, we propose a group key management scheme based on a novel physically unclonable function (PUF) design: multistage interconnected PUF (MIPUF) to secure group communications in an energy-constrained environment. Our design is capable of performing key management tasks such as key distribution, key storage and rekeying securely and efficiently. We show that our design is secure against multiple attack methods and our experimental results show that our design saves 47.33% of energy globally comparing to state-of-the-art Elliptic-curve cryptography (ECC)-based key management scheme on average.
The crossover effect: A review of racial/ethnic variations in risk for substance use and substance use disorder across development.

The “crossover” effect, a phenomenon by which some minority groups switch from low to high risk for substance use as a function of age, was first documented 25 years ago. However, rigorous methodological research examining the crossover effect has only recently emerged. The current paper reviews the past 25 years of research on the crossover effect, which has primarily examined the shift from low to high substance use risk among Blacks relative to Whites. Although findings regarding the crossover effect vary based on gender, socioeconomic status, and substance, Blacks and Hispanics appear to be at lower risk for some substance use—particularly binge drinking and cigarette smoking—than Whites during adolescence and early adulthood, but at higher risk for use in later life. Research regarding the crossover effect of substance use disorder and related problems is limited but more consistent with a similar pattern of effects observed. Due to significant limitations of the extant literature examining the crossover effect, it requires additional research to clarify sociodemographic differences in the effect, identify its mechanisms, and determine its clinical implications. Such research may have important implications for preventing racial/ethnic disparities in the consequences associated with disordered substance use.
Multiple Testing When Many p-Values are Uniformly Conservative, with Application to Testing Qualitative Interaction in Educational Interventions

In the evaluation of treatment effects, it is of major policy interest to know if the treatment is beneficial for some and harmful for others, a phenomenon known as qualitative interaction. We form...
Lightweight, obfuscation-resilient detection and family identification of Android malware

The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a security analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]
DOCK: Detecting Objects by transferring Common-sense Knowledge

We present a scalable approach for Detecting Objects by transferring Common-sense Knowledge (DOCK) from source to target categories. In our setting, the training data for the source categories have bounding box annotations, while those for the target categories only have image-level annotations. Current state-of-the-art approaches focus on image-level visual or semantic similarity to adapt a detector trained on the source categories to the new target categories. In contrast, our key idea is to (i) use similarity not at the image-level, but rather at the region-level, and (ii) leverage richer common-sense (based on attribute, spatial, etc.) to guide the algorithm towards learning the correct detections. We acquire such common-sense cues automatically from readily-available knowledge bases without any extra human effort. On the challenging MS COCO dataset, we find that common-sense knowledge can substantially improve detection performance over existing transfer-learning baselines.
Effects of Cyclosporine on the Renin-Angiotensin-Aldosterone System and Potassium Excretion in Renal Transplant Recipients

To evaluate the mechanism of cyclosporine-induced hyperkalemia, the renin-angiotensin-aldosterone system and renal potassium clearance were compared in ten renal transplant recipients treated with cyclosporine and treated with azathioprine. After stimulation by a low-sodium diet and furosemide, cyclosporine-treated patients demonstrated lower plasma renin activity when supine (1.9 +/- 0.3 v 7.8 +/- 1.4 ng/mL/hr) and after standing (3.0 +/- 0.7 v 12.2 +/- 1.5 ng/mL/hr). Supine plasma aldosterone levels tended to be lower in cyclosporine-treated patients, (4.8 +/- v 10.5 +/- 2.6 ng/dL), although standing plasma aldosterone levels were not different (10.8 +/- 3.0 v 12.3 +/- 2.0 ng/dL). After administration of 0.75 mEq of potassium chloride per kilogram of body weight, cyclosporine-treated patients excreted 52% +/- 7.1% of the potassium load in six hours compared with excretion of 67% +/- 7.0% by the azathioprine-treated patients, although there was no difference in plasma aldosterone levels in response to the potassium load in the two groups. These data suggest that cyclosporine causes suppression of plasma renin activity and a tubular insensitivity to aldosterone, both of which may impair potassium excretion.
Myopia, education, and social security

A potential role of social security is to protect individuals who have accumulated little or no assets for retirement. Yet, this type of social safety net could reduce human capital formation by making the life-cycle financial rewards from education less attractive. For example, social security tax rates are correlated negatively with tertiary educational attainment across OECD countries. We construct a continuous-time overlapping-generations model with endogenous school duration that can account for this correlation, and we use the model to compute the social security tax rate that maximizes steady-state social welfare in general equilibrium. Social security in the model provides protection for retirees who arrive at retirement with no assets, and it can also redistribute wealth toward those with low earnings, but the program distorts the level of human and physical capital accumulation. In the presence of these characteristics, the social security tax rate that maximizes steady-state welfare is approximately 10%, which is about half of the average rate of 21% across the OECD. This result is robust to whether the social security program is redistributive or earnings based.
How Secure are Multicarrier Communication Systems Against Signal Exploitation Attacks

In this paper, robustness of non-contiguous orthogonal frequency division multiplexing (NC-OFDM) transmissions is investigated and contrasted to OFDM transmissions for fending off signal exploitation attacks. In contrast to ODFM transmissions, NC-OFDM transmissions take place over a subset of active sub carriers to either avoid incumbent transmissions or for strategic considerations. A point-to-point communication system is considered in this paper in the presence of an adversary (exploiter) that aims to infer transmission parameters (e.g., the subset of active sub carriers and duration of the signal) using a deep neural network (DNN). This method has been proposed since the existing methods for exploitation, which are based on cyclostationary analysis, have been shown to have limited success in NC-OFDM systems. A good estimation of the transmission parameters allows the adversary to transmit spurious data and attack the legitimate receiver. Simulation results show that the DNN can infer the transmit parameters of OFDM signals with very good accuracy. However, NC-OFDM with fully random selection of active sub carriers makes it difficult for the adversary to exploit the waveform and thus for the receiver to be affected by the spurious data. Moreover, the more structured the set of active subcarriers selected by the transmitter is, the easier it is for the adversary to infer the transmission parameters and attack the receiver using a DNN.
WHAT DRIVES CAPITAL FLOWS TO EMERGING MARKETS? A SURVEY OF THE EMPIRICAL LITERATURE

This paper reviews the rapidly growing empirical literature on the drivers of capital flows to emerging markets. The empirical evidence is structured based on the recognition that the drivers of capital flows vary over time and across different types of capital flows. The drivers are classified using the traditional “push vs. pull” framework, which is augmented by a distinction between cyclical and structural factors. Push factors are found to matter most for portfolio flows, somewhat less for banking flows, and least for FDI. Pull factors matter for all three components, but most for banking flows. A historical perspective suggests that the recent literature may have overemphasized the importance of cyclical factors at the expense of longer-term structural trends.
Impact of a Neuroscience-Based Health Education Course on High School Students' Health Knowledge, Beliefs, and Behaviors.

Abstract Purpose The purpose of this pilot study was to evaluate the potential of an innovative high school neuroscience-based health course for implementation feasibility and impact on student outcomes. Methods Thirteen teachers from two high schools participated in this quasi-experimental pilot study including 395 students (202 in the intervention classes and 193 in the comparison classes). Students completed pre/post online surveys assessing their knowledge, beliefs, and behaviors. Our analysis strategy for multi-item measures was to estimate the effects of the intervention on latent change scores in structural equation models. Results Students in the neuroscience health classes showed a significant increase in neuroscience knowledge as compared to students in the comparison group (difference estimate in proportion correct metric, adjusted for covariates = .04; 95% confidence interval [.01, .06]). However, none of the other primary outcomes showed a significant difference between conditions. Teachers in the intervention group were observed implementing the neuroscience and health components more often than the self-regulation and growth mindset components. Students in the neuroscience group were more likely to mention the importance of caring for their brain and its link to health behaviors. Conclusions Findings demonstrate that information about the link between health behaviors and brain functioning can be successfully integrated into a high school health education course, although effects on student health beliefs and behaviors were not observed. Additional development work should focus on clarifying the theoretical mechanisms of change, integrating the neuroscience content with self-regulation and growth mindset, and providing additional professional development for teachers.
Learning To Segment Corneal Tissue Interfaces In Oct Images

Accurate and repeatable delineation of corneal tissue interfaces is necessary for surgical planning during anterior segment interventions, such as Keratoplasty. Designing an approach to identify interfaces, which generalizes to datasets acquired from different Optical Coherence Tomographic (OCT) scanners, is paramount. In this paper, we present a Convolutional Neural Network (CNN) based framework called CorNet that can accurately segment three corneal interfaces across datasets obtained with different scan settings from different OCT scanners. Extensive validation of the approach was conducted across all imaged datasets. To the best of our knowledge, this is the first deep learning based approach to segment both anterior and posterior corneal tissue interfaces. Our errors are $2 \times $ lower than non-proprietary state-of-the-art corneal tissue interface segmentation algorithms, which include image analysis-based and deep learning approaches.
A Spatial Latent Profile Analysis to Classify Land Uses for Population Synthesis Methods in Travel Demand Forecasting

In this paper, a new land use classification method is explored for its utility in explaining travel behavior and as a new dimension in population synthesis for travel demand forecasting. This meth...
OLDER LESBIANS RECEIVING HOME CARE: FORMAL AND INFORMAL DIMENSIONS OF CAREGIVING

Older lesbians face the triple jeopardy of ageism, sexism, and heterosexism, and their experiences are largely invisible. This qualitative, exploratory study examines the formal and informal caregiving experiences of 20 lesbians, 65 and older, who had utilized home care services due to acute illness or chronic disabilities. Half of those not partnered reported some level of isolation from support networks. Nearly all study participants eventually found home care workers with whom they were satisfied and even quite connected. Practice implications are discussed in context of study participants' views of how being lesbian affects their aging process and day-to-day lives.
Teaming up in child welfare: The perspective of guardians ad litem on the components of interprofessional collaboration

Abstract Policies and researchers have emphasized the need for interprofessional collaboration (IPC) in the child welfare system. However, few child welfare studies have sought to identify the components of IPC and no studies have been conducted in the U.S. to examine the perspective of guardians ad litem (GALs) on these components. Understanding the GAL viewpoint is warranted as they are mandated to be appointed in court-involved child welfare cases. This qualitative study addresses these gaps by exploring the GAL perspective on the main components of IPC. Interviews were conducted with 12 GALs in a mountain region state. Nine components of IPC emerged from the analysis, including communication and information sharing; problem-solving; respect and appreciation; joint decision-making; clarifying roles, responsibilities and expectations; sharing ideas and perspectives; mutual trust; shared responsibility; and establishing shared goals. The findings can inform the development of strategies to improve IPC in child welfare and guide future research.
Cardiovascular Health Disparities in Underserved Populations

African Americans are at increased risk for hypertension, hyperlipidemia, obesity, and diabetes, which contribute to the burden of cardiovascular disease (CVD). The disparities of CVD in underserved populations require targeted attention from primary care clinicians to eliminate. Primary care can provide this targeted care for their patients by assessing cardiovascular risk, addressing blood pressure control, and selecting appropriate intervention strategies. Using community resources is also effective for addressing CVD disparities in the underserved population.
Targeting NAD+ Metabolism to Enhance Radiation Therapy Responses

Nicotinamide adenine dinucleotide (NAD+) metabolism is integrally connected with the mechanisms of action of radiation therapy and is altered in many radiation-resistant tumors. This makes NAD+ metabolism an ideal target for therapies that increase radiation sensitivity and improve patient outcomes. This review provides an overview of NAD+ metabolism in the context of the cellular response to ionizing radiation, as well as current therapies that target NAD+ metabolism to enhance radiation therapy responses. Additionally, we summarize state-of-the-art methods for measuring, modeling, and manipulating NAD+ metabolism, which are being used to identify novel targets in the NAD+ metabolic network for therapeutic interventions in combination with radiation therapy.
Strongly Mismatched Regime of Nonlinear Laser–Plasma Acceleration: Optimization of Laser-to-Energetic Particle Efficiency

Laser electron accelerators utilize a bubble regime of nonlinear plasma waves driven as laser wakefields that, from theoretical considerations, require a matched laser spot size incident on plasma. A strongly mismatched regime of nonlinear laser–plasma acceleration in the bubble regime, favored by experiments, is introduced and modeled for optimization of laser-to-particle energy efficiency with application to the recently proposed laser positron accelerator. Strong mismatch, in contrast with the matched condition, arises from the incident laser spot size being much larger than that needed for equilibration of the laser ponderomotive and electron-ion charge-separation forces in the nonlinearly driven density structure of a plasma bubble. This is shown to be favorable for optimization of large self-injected electron charge and ultralow transverse emittance without precluding beam spectral shaping. It is shown that there are prominent signatures of the mismatched regime, strong optical-shock excitation, and bubble elongation, which are validated using multidimensional particle-in-cell simulations. This paper thus uncovers a generalized regime that apart from being used in many laser–plasma acceleration experiments also opens a novel pathway for a wide range of future applications.
Iso-Seq Allows Genome-Independent Transcriptome Profiling of Grape Berry Development

Transcriptomics has been widely applied to study grape berry development. With few exceptions, transcriptomic studies in grape are performed using the available genome sequence, PN40024, as reference. However, differences in gene content among grape accessions, which contribute to phenotypic differences among cultivars, suggest that a single reference genome does not represent the species' entire gene space. Though whole genome assembly and annotation can reveal the relatively unique or "private" gene space of any particular cultivar, transcriptome reconstruction is a more rapid, less costly, and less computationally intensive strategy to accomplish the same goal. In this study, we used single molecule-real time sequencing (SMRT) to sequence full-length cDNA (Iso-Seq) and reconstruct the transcriptome of Cabernet Sauvignon berries during berry ripening. In addition, short reads from ripening berries were used to error-correct low-expression isoforms and to profile isoform expression. By comparing the annotated gene space of Cabernet Sauvignon to other grape cultivars, we demonstrate that the transcriptome reference built with Iso-Seq data represents most of the expressed genes in the grape berries and includes 1,501 cultivar-specific genes. Iso-Seq produced transcriptome profiles similar to those obtained after mapping on a complete genome reference. Together, these results justify the application of Iso-Seq to identify cultivar-specific genes and build a comprehensive reference for transcriptional profiling that circumvents the necessity of a genome reference with its associated costs and computational weight.
scruff : an R/Bioconductor package for preprocessing single-cell RNA-sequencing data

Single-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking. Here we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package. scruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.
Quantized consensus

We study the distributed averaging problem on arbitrary connected graphs, with the additional constraint that the value at each node is an integer. This discretized distributed averaging problem models several problems of interest, such as averaging in a network with finite capacity channels and load balancing in a processor network. We describe simple randomized distributed algorithms which achieve consensus to the extent that the discrete nature of the problem permits. We give bounds on the convergence time of these algorithms for fully connected networks and linear networks.
Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies

Whole-genome sequencing (WGS) studies are being widely conducted in order to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set-based analyses are commonly used by researchers for analyzing rare variants. However, existing variant-set-based approaches need to pre-specify genetic regions for analysis; hence, they are not directly applicable to WGS data because of the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding-window method requires the pre-specification of fixed window sizes, which are often unknown as a priori, are difficult to specify in practice, and are subject to limitations given that the sizes of genetic-association regions are likely to vary across the genome and phenotypes. We propose a computationally efficient and dynamic scan-statistic method (Scan the Genome [SCANG]) for analyzing WGS data; this method flexibly detects the sizes and the locations of rare-variant association regions without the need to specify a prior, fixed window size. The proposed method controls for the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected sizes of rare-variant association regions to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative methods for detecting rare-variant-associations while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.
Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion

We describe a novel pipeline to automatically discover hierarchies of repeated sections in musical audio. The proposed method uses similarity network fusion (SNF) to combine different frame-level features into clean affinity matrices, which are then used as input to spectral clustering. While prior spectral clustering approaches to music structure analysis have pre-processed affinity matrices with heuristics specifically designed for this task, we show that the SNF approach directly yields segmentations which agree better with human annotators, as measured by the "L-measure" metric for hierarchical annotations. Furthermore, the SNF approach immediately supports arbitrarily many input features, allowing us to simultaneously discover structure encoded in timbral, harmonic, and rhythmic representations without any changes to the base algorithm.
Information value of property description: A Machine learning approach

Abstract This paper employs machine learning to quantify the value of “soft” information contained in real estate property descriptions. Textual descriptions contain information that traditional hedonic attributes cannot capture. A one standard deviation increase in the uniqueness of a property based on this “soft” information leads to a 15% increase in property sale price in a hedonic price model and a 10% increase in a repeat sales price model. The effects in the hedonic model appear to arise through two channels: the unobserved quality of the housing unit, and the market power of the housing unit relative to competing properties. The effects in the repeat sales model appear to be driven entirely by the market power of the unit. Further, an annual hedonic price index ignoring our measure of unobserved quality overstates real estate prices by between 10% to 23% and mistimes the stabilization of housing prices following the Great Recession. Similar, but smaller effects, are observed for the repeat sales price index.
Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people’s activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 min. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps.
Epistemic norms: truth conducive enough

Epistemology needs to account for the success of science. In True Enough (2017), Catherine Elgin argues that a veritist epistemology is inadequate to this task. She advocates shifting epistemology’s focus away from true belief and toward understanding, and further, jettisoning truth from its privileged place in epistemological theorizing. Pace Elgin, I argue that epistemology’s accommodation of science does not require rejecting truth as the central epistemic value. Instead, it requires understanding veritism in an ecumenical way that acknowledges a rich array of truth-oriented values. In place of veritism, Elgin offers a holistic epistemology that takes epistemic norms to have their genesis in our collective practice of deliberation. The acceptability of epistemic norms turns on epistemic responsibility, as opposed to reliability, and truth-conduciveness is rejected as the standard of evaluation for arguments and methods of inquiry. I argue, by way of an extended discussion of a high-profile and controversial criminal case, that this leaves epistemic practices and their products inadequately grounded. I offer an alternative, veritistic account of epistemic norms that retains a modified version of truth-conduciveness as a standard of evaluation. However, my alternative account of epistemic norms is congenial to Elgin’s holistic epistemology, and, I suggest, could be incorporated within it.
Sensory nutrition: The role of taste in the reviews of commercial food products.

Abstract Many factors play a role in choosing what to eat or drink. We explored the role of sensation to explain these differences, drawing on consumer reviews for commercially available food products sold through an online retailer. We analyzed 393,568 unique food product reviews from Amazon customers with a total of 256,043 reviewers rating 67,553 products. Taste-associated words were mentioned more than words associated with price, food texture, customer service, nutrition, smell, or those referring to the trigeminal senses, e.g., “spicy”. We computed the overall number of reviews that mentioned taste qualities: the word taste was mentioned in over 30% of the reviews (N = 142,768), followed by sweet (10.7%, N = 42,315), bitter (2.9%, N = 11,424), sour (2.1%, N = 8252) and salty (1.4%, N = 5688). We identified 38 phrases used to describe the evaluation of sweetness, finding that ‘too sweet’ was used in nearly 0.8% of the reviews and oversweetness was mentioned over 25 times more often than under-sweetness. We then focused on ‘polarizing’ products, those that elicited a wide difference of opinion (as measured by the ranges of the product ratings). Using the products that had more than 50 reviews, we identified the top 10 most polarizing foods and provide representative comments about the polarized taste experience of consumers. Overall, these results support the primacy of taste in real-world food ratings and individualized taste experience, such as whether a product is ‘too sweet’. Analysis of consumer review data sets can provide information about purchasing decisions and customer sensory responses to particular commercially available products and represents a promising methodology for the emerging field of sensory nutrition.
Crystal Structures of Potassium Tetratellurite, K2Te4O9, and Potassium Ditellurite, K2Te2O5, and Structural Trends in Solid Alkali Tellurites

The crystal structures of potassium tetratellurite and potassium ditellurite are reported, completing the series of known phases of alkali tellurites including lithium, sodium, potassium and cesium...
A new method for the preparation of ent-cholesterol from ent-testosterone

Abstract An efficient total synthesis of the enantiomer of cholesterol is reported. The enantiomer of testosterone was prepared and converted into a C21 3-silyloxy-Δ5,17(20)-diene according to literature procedures. The additional five carbon atoms of the cholesterol side chain were introduced by an ene reaction. Selective hydrogenation of the resultant Δ16 double bond and removal of the 22-hydroxyl group by a mesylation and demesylation sequence gave ent-cholesterol in 9.7% overall yield from ent-testosterone
Novel Routes for the Preparation of a Range of Germanium Containing Zeolites

Three aluminogermanate materials have been synthesized at temperatures below 100 °C and identified as analogues of RHO, GIS and NAT type zeolites. In addition, microwave heating has yielded phase-pure Ge-X after only 10 s, and Ge-RHO is the dominant product after several days reaction at ambient temperature. This demonstrates the relative ease of formation of aluminogermanate zeolites, provided a suitable starting stoichiometry is chosen.
20-Oxopregnacalciferols: Vitamin D compounds that bind the progesterone receptor

Abstract 20-Oxopregnacaliferols and 19-nor-20-oxopregnacalciferol were prepared from calciferol 22-aldehydes by an oxygenation procedure.
ENOLIZATION AND HYDROLYSIS OF 7-NITROISOCHROMAN-3-ONE IN AQUEOUS SOLUTION : GENERATION OF A RELATIVELY STABLE LACTONE ENOLATE

The enolization and hydrolysis of the lactone 7-nitroisochroman-3-one (7-nitro-1,4-dihydro-3H-2-benzopyran-3-one, 1) has been studied in aqueous sodium hydroxide. In solutions of sufficient basicity ([NaOH] ≥ ca. 0.1 mM), 1 undergoes reversible deprotonation to form the corresponding enolate. Although ester hydrolysis accompanies enolization, observable quantities of the enolate persist for several seconds. Rate constants for deprotonation by hydroxide ion (k1 = 1.31(±0.06) × 104 M-1 s-1), protonation of the enolate by water (k-1 = 212 ± 24 s-1), and lactone hydrolysis (kOH = 19.0 ± 0.3 M-1 s-1) have been determined by monitoring the rates of formation and disappearance of the enolate. The kinetic data can be used to calculate the acid dissociation constant for 1 (pKa 11.98).
PRX9 and PRX40 Are Extensin Peroxidases Essential for Maintaining Tapetum and Microspore Cell Wall Integrity during Arabidopsis Anther Development

Pollen and microspore development are essential steps in the life cycle of all land plants that generate male gametes. Within flowering plants, pollen development occurs inside of the anther. Here, we report the identification of two class III peroxidase-encoding genes, PEROXIDASE9 (PRX9) and PRX40, that are genetically redundant and essential for proper anther and pollen development in Arabidopsis (Arabidopsis thaliana). Arabidopsis double mutants devoid of functional PRX9 and PRX40 are male sterile. The mutant anthers display swollen, hypertrophic tapetal cells and pollen grains, suggesting disrupted cell wall integrity. These phenotypes lead to nearly 100%-penetrant pollen degeneration upon anther maturation. Using immunochemical and biochemical approaches, we show that PRX9 and PRX40 likely cross-link extensins to contribute to tapetal cell wall integrity during anther development. This work suggests that PRX9 and PRX40 encode Arabidopsis extensin peroxidases and highlights the importance of extensin cross-linking during pollen development.