bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust flow-guided neural prediction for sketch-based freeform surface modeling
Authors: Li Changjian , Pan Hao , Liu Yang , Tong Xin , Sheffer Alla , Wang Wenping 
Published: November 2018

Sketching provides an intuitive user interface for communicating free form shapes. While human observers can easily envision the shapes they intend to communicate, replicating this process algorithmically requires resolving numerous ambiguities. Existing sketch-based modeling methods resolve these ambiguities by either relying on expensive user annotations or by restricting the modeled shapes to specific narrow categories. We present an approach for modeling generic freeform 3D surfaces from sparse, expressive 2D sketches that overcomes both limitations by incorporating convolution neural networks (CNN) into the sketch processing workflow.
Given a 2D sketch of a 3D surface, we use CNNs to infer the depth and normal maps representing the surface. To combat ambiguity we introduce an intermediate CNN layer that models the dense curvature direction, or flow, field of the surface, and produce an additional output confidence map along with depth and normal. The flow field guides our subsequent surface reconstruction for improved regularity; the confidence map trained unsupervised measures ambiguity and provides a robust estimator for data fitting. To reduce ambiguities in input sketches users can refine their input by providing optional depth values at sparse points and curvature hints for strokes. Our CNN is trained on a large dataset generated by rendering sketches of various 3D shapes using non-photo-realistic line rendering (NPR) method that mimics human sketching of free-form shapes. We use the CNN model to process both single- and multi-view sketches. Using our multi-view framework users progressively complete the shape by sketching in different views, generating complete closed shapes. For each new view, the modeling is assisted by partial sketches and depth cues provided by surfaces generated in earlier views. The partial surfaces are fused into a complete shape using predicted confidence levels as weights.
We validate our approach, compare it with previous methods and alternative structures, and evaluate its performance with various modeling tasks. The results demonstrate our method is a new approach for efficiently modeling freeform shapes with succinct but expressive 2D sketches.
Differentiable Monte Carlo ray tracing through edge sampling
Authors: Li Tzu-Mao , Aittala Miika , Durand Frédo , Lehtinen Jaakko 
Published: November 2018

Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates.
We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision.
We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks.
A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT
Authors: Kossenkov Andrew V. , Qureshi Rehman , Dawany Noor B. , Wickramasinghe Jayamanna , Liu Qin , Majumdar R. Sonali , Chang Celia , Widura Sandy , Kumar Trisha , Horng Wen-Hwai , Konnisto Eric , Criner Gerard , Tsay Jun-Chieh J. , Pass Harvey , Yendamuri Sai , Vachani Anil , Bauer Thomas , Nam Brian , Rom William N. , Showe Michael K. , Showe Louise C. 
Published: November 2018

Abstract

Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high-risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. Malignant samples were predominantly stage 1 by pathologic diagnosis and 97% of the benign samples were confirmed by 4 years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data were then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC), which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed three clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6–20 mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, noninvasive method for the diagnosis of pulmonary nodules in patients with non–small cell lung cancer.

Significance:
These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.
Aspirin and Non-Aspirin NSAID Use and Prostate Cancer Incidence, Mortality, and Case Fatality in the Atherosclerosis Risk in Communities Study
Authors: Hurwitz Lauren M. , Joshu Corinne E. , Barber John R. , Prizment Anna E. , Vitolins Mara Z. , Jones Miranda R. , Folsom Aaron R. , Han Misop , Platz Elizabeth A. 
Published: November 2018

Abstract

Background:
NSAIDs appear to moderately reduce prostate cancer risk. However, evidence is limited on whether NSAIDs protect against prostate cancer mortality (death from prostate cancer among men without a cancer history) and case fatality (death from prostate cancer among men with prostate cancer), and whether benefits are consistent in white and black men. This study investigated associations of aspirin and non-aspirin (NA) NSAID use with prostate cancer incidence, mortality, and case fatality in a population-based cohort of white and black men.

Methods:
We included 6,594 men (5,060 white and 1,534 black) from the Atherosclerosis Risk in Communities study without a cancer history at enrollment from 1987 to 1989. NSAID use was assessed at four study visits (1987–1998). Cancer outcomes were ascertained through 2012. Cox proportional hazards regression was used to estimate adjusted HRs, overall and by race.

Results:
Aspirin use was not associated with prostate cancer incidence. However, aspirin use was inversely associated with prostate cancer mortality [HR, 0.59; 95% confidence interval (CI), 0.36–0.96]. This association was consistent among white and black men and appeared restricted to men using aspirin daily and/or for cardiovascular disease prevention. Aspirin use was inversely associated with case fatality (HR, 0.45; 95% CI, 0.22–0.94). NA-NSAID use was not associated with these endpoints.

Conclusions:
Aspirin use was inversely associated with prostate cancer mortality and case fatality among white and black men.

Impact:
If confirmed by additional studies, benefits of aspirin for preventing prostate cancer mortality may need to be factored into risk–benefit calculations of men considering an aspirin regimen.
BIRC3 Expression Predicts CLL Progression and Defines Treatment Sensitivity via Enhanced NF-κB Nuclear Translocation
Authors: Asslaber Daniela , Wacht Nathalie , Leisch Michael , Qi Yuan , Maeding Nicole , Hufnagl Clemens , Jansko Bettina , Zaborsky Nadja , Villunger Andreas , Hartmann Tanja N. , Greil Richard , Egle Alexander 
Published: November 2018

Abstract

Purpose:
Chronic lymphocytic leukemia (CLL) pathophysiology is characterized by a complex crosstalk of tumor cells with the microenvironment. In this regard, NF-κB signaling is considered as important signaling axis, with a variety of key molecules aberrantly expressed or genetically altered in patients with CLL. One of these molecules is BIRC3 (cIAP2), a central regulator of noncanonical NF-κB signaling that serves as pathway brake in the absence of microenvironmental signals. However, the contribution of BIRC3 expression to CLL progression and potential therapeutic implications is unknown.
Experimental Design: We analyzed the role of BIRC3 mRNA expression in primary CLL samples in correlation to clinical datasets and used ex vivo assays to investigate functional consequences on the level of NF-κB signaling and downstream target gene regulation. For proof-of-principle experiments, we used genetically modified cell lines.

Results:
We demonstrate that patients with CLL with low BIRC3 expression experience a more rapid disease progression, which coincides with an enhanced activation of canonical NF-κB target genes evidenced by an increased p65/Rel-B nuclear translocation ratio. As a consequence of enhanced canonical NF-κB target gene activation, both anti- and proapoptotic Bcl-2 family members were upregulated in BIRC3low primary CLL cells, which was associated with higher sensitivity to venetoclax treatment in vitro.

Conclusions:
Here we show the impact of BIRC3 expression in CLL disease progression in the absence of BIRC3 mutations and show altered canonical NF-κB target gene activation with therapeutic implications.
Targeted Therapy for EBV-Associated B-cell Neoplasms
Authors: Ganguly Siddhartha , Kuravi Sudhakiranmayi , Alleboina Satyanarayana , Mudduluru Giridhar , Jensen Roy A. , McGuirk Joseph P. , Balusu Ramesh 
Published: November 2018

Abstract

Epstein–Barr virus (EBV) is directly implicated in several B-cell lymphoid malignancies. EBV-associated lymphomas are characterized by prominent activation of the NF-κB pathway and targeting this pathway establishes a rationale for a therapeutic approach. The ubiquitin/proteasome signaling plays an essential role in the regulation of the NF-κB pathway. Ixazomib is an FDA-approved, orally bioavailable proteasome inhibitor. Here we report the first preclinical evaluation of ixazomib-mediated growth-inhibitory effects on EBV-infected B-lymphoblastoid cell lines Raji and Daudi. Ixazomib induced apoptosis in these cell lines in a dose-dependent manner. Cell-cycle analysis demonstrated ixazomib treatment induced cell-cycle arrest at the G2–M phase with a concomitant decrease in G0–G1 and S phases. The results further revealed an increase in p53, p21, and p27 levels and a decrease in survivin and c-Myc protein levels. Mechanistically, ixazomib treatment resulted in the accumulation of polyubiquitinated proteins, including phosphorylated IκBα with a significant reduction of p65 subunit nuclear translocation. Altogether, our preclinical data support the rationale for in vivo testing of ixazomib in EBV-associated B-cell neoplasms.

Implications:
This preclinical study supports the use of oral proteasome inhibitor ixazomib for targeting NF-κB signaling in the treatment of EBV-associated B-cell neoplasms.
Visual Overview: http://mcr.aacrjournals.org/content/molcanres/17/4/839/F1.large.jpg.
A Phase I/Ib Trial of the VEGFR-Sparing Multikinase RET Inhibitor RXDX-105
Authors: Drilon Alexander , Fu Siqing , Patel Manish R. , Fakih Marwan , Wang Ding , Olszanski Anthony J. , Morgensztern Daniel , Liu Stephen V. , Cho Byoung Chul , Bazhenova Lyudmila , Rodriguez Cristina P. , Doebele Robert C. , Wozniak Antoinette , Reckamp Karen L. , Seery Tara , Nikolinakos Petros , Hu Zheyi , Oliver Jennifer W. , Trone Denise , McArthur Katherine , Patel Rupal , Multani Pratik S. , Ahn Myung-Ju 
Published: November 2018

Abstract

RET fusions are oncogenic drivers of various tumors, including non–small cell lung cancers (NSCLC). The safety and antitumor activity of the multikinase RET inhibitor RXDX-105 were explored in a phase I/Ib trial. A recommended phase II dose of 275 mg fed daily was identified. The most common treatment-related adverse events were fatigue (25%), diarrhea (24%), hypophosphatemia (18%), maculopapular rash (18%), and nonmaculopapular rash (17%). In the phase Ib cohort of RET inhibitor–naïve patients with RET fusion–positive NSCLCs, the objective response rate (ORR) was 19% (95% CI, 8%–38%, n = 6/31). Interestingly, the ORR varied significantly by the gene fusion partner (P &lt; 0.001, Fisher exact test): 0% (95% CI, 0%–17%, n = 0/20) with KIF5B (the most common upstream partner for RET fusion–positive NSCLC), and 67% (95% CI, 30%–93%, n = 6/9) with non-KIF5B partners. The median duration of response in all RET fusion–positive NSCLCs was not reached (range, 5 to 18+ months).

Significance:
Although KIF5B–RET is the most common RET fusion in NSCLCs, RET inhibition with RXDX-105 resulted in responses only in non–KIF5B–RET-containing cancers. Novel approaches to targeting KIF5B–RET-containing tumors are needed, along with a deeper understanding of the biology that underlies the differential responses observed.
This article is highlighted in the In This Issue feature, p. 305
Vertical Profiles of Aerosol Composition over Beijing, China: Analysis of In Situ Aircraft Measurements
Authors: Liu Quan , Quan Jiannong , Jia Xingcan , Sun Zhaobin , Li Xia , Gao Yang , Liu Yangang 
Published: November 2018

AbstractAerosol samples were collected over Beijing, China, during several flights in November 2011. Aerosol composition of nonrefractory submicron particles (NR-PM1) was measured by an Aerodyne compact time-of-flight aerosol mass spectrometer (C-ToF-AMS). This measurement on the aircraft provided vertical distribution of aerosol species over Beijing, including sulfate (SO4), nitrate (NO3), ammonium (NH4), chloride (Chl), and organic aerosols [OA; hydrocarbon-like OA (HOA) and oxygenated OA (OOA)]. The observations showed that aerosol compositions varied drastically with altitude, especially near the top of the planetary boundary layer (PBL). On average, organics (34%) and nitrate (32%) were dominant components in the PBL, followed by ammonium (15%), sulfate (14%), and chloride (4%); in the free troposphere (FT), sulfate (34%) and organics (28%) were dominant components, followed by ammonium (20%), nitrate (19%), and chloride (1%). The dominant OA species was primarily HOA in the PBL but changed to OOA in the FT. For sulfate, nitrate, and ammonium, the sulfate mass fraction increased from the PBL to the FT, nitrate mass fraction decreased, and ammonium remained relatively constant. Analysis of the sulfate-to-nitrate molar ratio further indicated that this ratio was usually less than one in the FT but larger than one in the PBL. Further analysis revealed that the vertical aerosol composition profiles were influenced by complex processes, including PBL structure, regional transportation, emission variation, and the aging process of aerosols and gaseous precursors during vertical diffusion.
The Effect of Wind Stress Anomalies and Location in Driving Pacific Subtropical Cells and Tropical Climate
Authors: Graffino Giorgio , Farneti Riccardo , Kucharski Fred , Molteni Franco 
Published: November 2018

Abstract
The importance of subtropical and extratropical zonal wind stress anomalies on Pacific subtropical cell (STC) strength is assessed through several idealized and realistic numerical experiments with a global ocean model. Different zonal wind stress anomalies are employed, and their intensity is strengthened or weakened with respect to the climatological value throughout a suite of simulations. Subtropical strengthened (weakened) zonal wind stress anomalies result in increased (decreased) STC meridional mass and energy transport. When upwelling of subsurface water into the tropics is intensified (reduced), a distinct cold (warm) anomaly appears in the equatorial thermocline and up to the surface, resulting in significant tropical sea surface temperature (SST) anomalies. The use of realistic wind stress anomalies also suggests a potential impact of midlatitude atmospheric modes of variability on tropical climate through STC dynamics. The remotely driven response is compared with a set of simulations where an equatorial zonal wind stress anomaly is imposed. A dynamically distinct response is achieved, whereby the equatorial thermocline adjusts to the wind stress anomaly, resulting in significant equatorial SST anomalies as in the remotely forced simulations but with no role for STCs. Significant anomalies in Indonesian Throughflow transport are generated only when equatorial wind stress anomalies are applied, leading to remarkable heat content anomalies in the Indian Ocean. Equatorial wind stress anomalies do not involve modifications of STC transport but could set up the appropriate initial conditions for a tropical–extratropical teleconnection involving Hadley cells, exciting an STC anomalous transport, which ultimately feeds back on the tropics.
A Framework for Diagnosing Factors Degrading the Streamflow Performance of a Soil Moisture Data Assimilation System
Authors: Mao Yixin , Crow Wade T. , Nijssen Bart 
Published: November 2018

Abstract
Data assimilation (DA) techniques have been widely applied to assimilate satellite-based soil moisture (SM) measurements into hydrologic models to improve streamflow simulations. However, past studies have reached mixed conclusions regarding the degree of runoff improvement achieved via SM state updating. In this study, a synthetic diagnostic framework is designed to 1) decompose the random error components in a hydrologic simulation, 2) quantify the error terms that originate from SM states, and 3) assess the effectiveness of SM DA to correct these random errors. The general framework is illustrated through a case study in which surface Soil Moisture Active Passive (SMAP) data are assimilated into a large-scale land surface model in the Arkansas–Red River basin. The case study includes systematic error in the simulated streamflow that imposes a first-order limit on DA performance. In addition, about 60% of the random runoff error originates directly from rainfall and cannot be corrected by SM DA. In particular, fast-response runoff dominates in much of the basin but is relatively unresponsive to state updating. Slow-response runoff is strongly controlled by the bottom-layer SM and therefore only modestly improved via the assimilation of surface measurements. Combined, the total runoff improvement in the synthetic analysis is small (&lt;10% over the basin). Improvements in the real SMAP-assimilated case are further limited due to systematic error and other factors such as inaccurate error assumptions and SMAP rescaling. Findings from the diagnostic framework suggest that SM DA alone is insufficient to substantially improve streamflow estimates in large basins.
Using Operational Radar to Identify Deep Hail Accumulations from Thunderstorms
Authors: Wallace Robinson , Friedrich Katja , Kalina Evan A. , Schlatter Paul 
Published: November 2018

Abstract
Thunderstorms that produce surface hail accumulations, sometimes as large as 60 cm in depth, have significantly affected the residents of the Front Range and High Plains of Colorado and Wyoming by creating hazardous road conditions and endangering lives and property. To date, surface hail accumulation is not part of a routine forecasting or monitoring system. Extensive coordinated hail accumulation reports and operational products designed to identify deep hail accumulating storms in real time are lacking. Kalina et al. used dual-polarization WSR-88D radar observations to calculate hail depth and hail accumulations but never validated the algorithm. This study shows how 20 quality-controlled hail depth reports from the hail depth database built by the Colorado Hail Accumulation from Thunderstorms (CHAT) project are being used to validate the Kalina et al. radar-based hail accumulation algorithm for operational application. The validated algorithm shows increased correlations between radar-derived and reported accumulations for hail depth reports not included in the validation. Furthermore, increases in computational efficiency have allowed the improved algorithm to be used operationally. With an improved hail accumulation algorithm, thunderstorms that produce hail accumulations are more frequently detected than previously reported.
Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm
Authors: Balli Serkan , Sağbaş Ensar Arif , Peker Musa 
Published: November 2018

Background: Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch. Methods: The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class. Results and Discussion: After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.
Advertising, Mass Communication, and Tax Deduction
Authors: Wedding Nugent 
Published: November 2018

Recent regulations of the Internal Revenue Service regarding tax deductibility of expenditures for “institutional” advertising tend to restrict and discourage the use of advertising by business management as a method of mass communication. The author points out that the concept of “institutional” advertising which the Internal Revenue Service has followed for forty years is narrow and, for many purposes, outdated. Attempts to modify present regulations under the old concept are not satisfactory. According to the author, there is need for a recognition on the part of Treasury officials of the growing responsibilities of management and of the expanded function of advertising to carry messages, of a public-relations and public-interest nature, as a means of informing and influencing public attitudes and opinions.