Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of calcium stearate addition of ultrahigh molecular weight polyethylene in direct compression molding

The effects of calcium stearate addition in molding of ultrahigh molecular weight polyethylene (UHMWPE) were investigated by tensile testing, scanning electron microscope (SEM), Fourier transform infrared (FT-IR) spectroscopy, and elemental analysis. Four types of UHMWPE specimens with varying contents of calcium stearate (<5, 50, 100, and 1000 ppm) were used in this study. SEM observation revealed that calcium stearate added specimens have smoother and more homogeneous microstructures. After accelerated aging, subsurface oxidative degeneration was observed in all specimens; however, calcium stearate added specimens had less oxidative degeneration in comparison with specimens without additives. Specimens showed better mechanical properties with increasing content of calcium stearate. Though all specimens aggravated their mechanical properties due to oxidation after accelerated aging, the specimens of 100 ppm of calcium stearate showed better properties at 3.7 MPa larger yield strength and four times larger value of elongation at fracture than the specimens without additives. These results indicate that calcium stearate addition produces dense packing of UHMWPE particles, which brings about stronger fusion among the particles and fewer structural defects, and results in better mechanical properties and better resistance to oxidative degeneration. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 1602–1609, 2003
A 9‐bit successive approximation ADC in SOI CMOS operating up to 300 °C

Industrial electronics are in great demand for oil and gas exploration, well drilling, and automotive applications where the operating temperature goes beyond 200 °C. Circuit designs using conventional complementary metal–oxide semiconductor (CMOS) technology are mostly rated at maximum of 125 °C, which is not suitable for harsh environment. In this paper, a high‐temperature (HT) 9‐bit successive approximation register analog‐to‐digital converter (SAR ADC) designed in silicon‐on‐insolation CMOS technology with a sampling rate of 50 kS/s is presented. The design considerations of the HT SAR ADC are discussed from process selection, temperature‐aware circuit design, and measurement perspectives. The ADC achieves an effective number of bit (ENOB) of 8.35 bits and a figure of merit of 93 pJ/step at room temperature. Under HT test, ENOBs of 7.3 bits at 225 °C and 6.9 bits at 300 °C are obtained. The power consumption is 1.52 mW from a 5‐V supply at room temperature and only 2.17 mW at 300 °C. Copyright © 2015 John Wiley & Sons, Ltd.
Premature delivery in HIV-infected women starting protease inhibitor therapy during pregnancy: role of the ritonavir boost?

BACKGROUND The association between combination antiretroviral (cARV) therapy use by human immunodeficiency virus (HIV)-infected women during pregnancy and risk of prematurity is still controversial. We explored this question, focusing on the initiation of ritonavir-boosted protease inhibitors (PIs) during pregnancy, which is now standard care. METHODS Trends in prematurity (<37 gestational weeks) were studied among all singleton pregnancies in the Agence Nationale de Recherche sur le SIDA (ANRS) French Perinatal Cohort from 1990 through 2009 (n = 13 271). In-depth analysis was conducted in a more detailed substudy of the cohort, among women starting PI-based ARV therapy during pregnancy (n = 1253). Multivariable analysis adjusted for immunovirological status and known risk factors for prematurity. RESULTS Prematurity increased from 9.2% during 1990-1993 (no therapy) and 9.6% during 1994-1996 (mostly zidovudine monotherapy) to 12.4% during 1997-1999 (dual-nucleoside analog therapy) and 14.3% during 2005-2009 (routine cARV therapy; P < .01). Prematurity was associated with cARV therapy, compared with zidovudine monotherapy, with an adjusted odds ratio of 1.69 (95% confidence interval [CI], 1.38-2.07; P < .01) when accounting for maternal age, intravenous drug use, geographic origin, and CD4 cell count. During 2005-2009, the prematurity rate was higher with boosted than with nonboosted PI therapy started during pregnancy (14.4% vs 9.1% [P = .05]; adjusted hazard ratio, 2.03 [95% CI, 1.06-3.89; P = .03] in multivariate analysis). The difference concerned mainly induced preterm delivery for maternal or fetal indications (5.6% vs 1.6%; P = .02), CONCLUSIONS The prematurity rate among HIV-infected pregnant women was twice that in the general population in France; this was not entirely explained by sociodemographic characteristics. Prematurity was independently associated with cARV therapy and, particularly, with the initiation of ritonavir-boosted PI therapy during pregnancy.
A Planar Dipole Array Surface for Electromagnetic Energy Harvesting and Wireless Power Transfer

We present a design of an electromagnetic energy harvesting surface inspired by an array of printed metallic dipolar elements. The unit cell of the proposed harvester is based on two printed asymmetric off-center fed dipoles. As a proof of concept, a finite array of $9 \times 3$ unit cells was analyzed numerically and experimentally. The array was first analyzed for maximizing radiation to ac absorption where each dipole was terminated by a resistor across its gap. For dc conversion, the resistors were the replaced by Schottky diodes. The simulation results show radiation to ac harvesting efficiency of 99%. An overall radiation to dc harvesting efficiency of 76% was obtained experimentally, which, to the best of the authors’ knowledge, exceeds the performance of all previous energy harvesting surfaces. Another critical feature of the proposed designs is enhancing the power per diode in order to maximize its turn-ON time.
A novel ALS-associated variant in UBQLN4 regulates motor axon morphogenesis

The etiological underpinnings of amyotrophic lateral sclerosis (ALS) are complex and incompletely understood, although contributions to pathogenesis by regulators of proteolytic pathways have become increasingly apparent. Here, we present a novel variant in UBQLN4 that is associated with ALS and show that its expression compromises motor axon morphogenesis in mouse motor neurons and in zebrafish. We further demonstrate that the ALS-associated UBQLN4 variant impairs proteasomal function, and identify the Wnt signaling pathway effector beta-catenin as a UBQLN4 substrate. Inhibition of beta-catenin function rescues the UBQLN4 variant-induced motor axon phenotypes. These findings provide a strong link between the regulation of axonal morphogenesis and a new ALS-associated gene variant mediated by protein degradation pathways. DOI: http://dx.doi. org/10.7554/eLife.25453. 001
Outcomes of a Shoulder Treatment Flowchart in Patients With Axillary Burns

The purpose of this study was to evaluate the effects of a structured shoulder treatment flowchart on range of motion (ROM) and function of the upper limb in patients at high and low risk of contracture after an axillary burn injury. Consecutive patients with axillary burns were managed according to a structured pathway based on risk of contracture. Those patients with deep partial- or full-thickness burns to more than one area of the axilla or requiring more than one split skin graft were classified as high risk; these patients underwent a more aggressive approach to splinting and exercise. Measurements of shoulder ROM and functional ability (upper extremity functional index) were made at admission, hospital discharge, and at 12 weeks postdischarge. Twenty patients (19 men) with an average age of 39 years and a median TBSA of 20% were recruited. Median length of stay was 18 days. Ten patients were classified as high risk. Both low-risk and high-risk patients showed good range of movement outcomes at discharge and 12 weeks, with no significant differences between risk groups (abduction at 12 weeks, mean [SD]: 168° [22°] vs 166° [28°], P = .60; flexion at 12 weeks, mean [SD]: 172° [20°] vs 167° [31°], P = .60, respectively). There were no differences in functional outcomes at 12 weeks postdischarge. Use of a shoulder treatment pathway was associated with good shoulder ROM and functional capacity in patients at high and low risk of axillary contracture.
Cardiovascular Disease : A Randomized Controlled Trial in Young Adults Docosahexaenoic Acid Supplementation , Vascular Function and Risk Factors for Online

Cardiovascular Disease: A Randomized Controlled Trial in Young Adults Docosahexaenoic Acid Supplementation, Vascular Function and Risk Factors for Online ISSN: 2047-9980 Dallas, TX 75231 is published by the American Heart Association, 7272 Greenville Avenue, Journal of the American Heart Association The doi: 10.1161/JAHA.113. 000283 2013;2:e000283; originally published July 1, 2013; J Am Heart Assoc. http://jaha.ahajournals. org/content/2/4/e000283 World Wide Web at: The online version of this article, along with updated information and services, is located on the
Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification

Convolutional Neural Networks (CNNs) have gained remarkable success in computer vision, which is mostly owe to their ability that enables learning rich image representations from large-scale annotated data. In the field of medical image analysis, large amounts of annotated data may be not always available. The number of acquired ground-truth data is sometimes insufficient to train the CNNs without overfitting and convergence issues from scratch. Hence application of the deep CNNs is a challenge in medical imaging domain. However, transfer learning techniques are shown to provide solutions for this challenge. In this paper, our target task is to implement diabetic retinopathy fundus image classification using CNNs based transfer learning. Experiments are performed on 1014 and 1200 fundus images from two publicly available DR1 and MESSIDOR datasets. In order to complete the target task, we carry out experiments using three different methods: 1) fine-tuning all network layers of each of different pre-trained CNN models; 2) fine-tuning a pre-trained CNN model in a layer-wise manner; 3) using pre-trained CNN models to extract features from fundus images, and then training support vector machines using these features. Experimental results show that convolutional neural networks based transfer learning can achieve better classification results in our task with small datasets (target domain), by taking advantage of knowledge learned from other related tasks with larger datasets (source domain). Transfer learning is a promising technique that promotes the use of deep CNNs in medical field with limited amounts of data.
Modeling the Cost Effectiveness of Malaria Control Interventions in the Highlands of Western Kenya

Introduction Tools that allow for in silico optimization of available malaria control strategies can assist the decision-making process for prioritizing interventions. The OpenMalaria stochastic simulation modeling platform can be applied to simulate the impact of interventions singly and in combination as implemented in Rachuonyo South District, western Kenya, to support this goal. Methods Combinations of malaria interventions were simulated using a previously-published, validated model of malaria epidemiology and control in the study area. An economic model of the costs of case management and malaria control interventions in Kenya was applied to simulation results and cost-effectiveness of each intervention combination compared to the corresponding simulated outputs of a scenario without interventions. Uncertainty was evaluated by varying health system and intervention delivery parameters. Results The intervention strategy with the greatest simulated health impact employed long lasting insecticide treated net (LLIN) use by 80% of the population, 90% of households covered by indoor residual spraying (IRS) with deployment starting in April, and intermittent screen and treat (IST) of school children using Artemether lumefantrine (AL) with 80% coverage twice per term. However, the current malaria control strategy in the study area including LLIN use of 56% and IRS coverage of 70% was the most cost effective at reducing disability-adjusted life years (DALYs) over a five year period. Conclusions All the simulated intervention combinations can be considered cost effective in the context of available resources for health in Kenya. Increasing coverage of vector control interventions has a larger simulated impact compared to adding IST to the current implementation strategy, suggesting that transmission in the study area is not at a level to warrant replacing vector control to a school-based screen and treat program. These results have the potential to assist malaria control program managers in the study area in adding new or changing implementation of current interventions.
Improving the electrochemical performance by sulfonation of polyaniline-graphene-silica composite for high performance supercapacitor

ABSTRACT In this work, to increase the specific capacitance and energy density of reduced graphene oxide–silica composite (RGO-SiO2), pseudocapacitive behaviour of polyaniline is introduced to RGO-SiO2(PANI-RGO-SiO2) and further improved by sulfonating the polyaniline system (SPANI-RGO-SiO2). Formation of SPANI-RGO-SiO2 is confirmed from IR and XRD analyses. SPANI-RGO-SiO2 shows nanorods morphology with less crystalline nature. The specific capacitance of RGO-SiO2 is increases from 24 to 780 F g−1 with sulfonation of polyaniline in RGO-SiO2. Retention in the specific capacitance value of SPANI-RGO-SiO2 is 85% with that of its original value of 780 F g−1 with coulombic efficiency (96–99%). GRAPHICAL ABSTRACT
GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics

In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyze, to modify, and to tease apart the inherent principles that lead to the desired collective behavior. In contrast, our representation is based on completely readable and analyzable individual-level rules that lead to a desired collective behavior. The core of our method is a grammar that can generate a rich variety of collective behaviors. We test GESwarm by evolving a foraging strategy using a realistic swarm robotics simulator. We then systematically compare the evolved collective behavior against an hand-coded one for performance, scalability and flexibility, showing that collective behaviors evolved with GESwarm can outperform the hand-coded one.
High-throughput single-molecule telomere characterization

We have developed a novel method that enables global subtelomere and haplotype-resolved analysis of telomere lengths at the single-molecule level. An in vitro CRISPR/Cas9 RNA-directed nickase system directs the specific labeling of human (TTAGGG)n DNA tracts in genomes that have also been barcoded using a separate nickase enzyme that recognizes a 7-bp motif genome-wide. High-throughput imaging and analysis of large DNA single molecules from genomes labeled in this fashion using a nanochannel array system permits mapping through subtelomere repeat element (SRE) regions to unique chromosomal DNA while simultaneously measuring the (TTAGGG)n tract length at the end of each large telomere-terminal DNA segment. The methodology also permits subtelomere and haplotype-resolved analyses of SRE organization and variation, providing a window into the population dynamics and potential functions of these complex and structurally variant telomere-adjacent DNA regions. At its current stage of development, the assay can be used to identify and characterize telomere length distributions of 30–35 discrete telomeres simultaneously and accurately. The assay's utility is demonstrated using early versus late passage and senescent human diploid fibroblasts, documenting the anticipated telomere attrition on a global telomere-by-telomere basis as well as identifying subtelomere-specific biases for critically short telomeres. Similarly, we present the first global single-telomere-resolved analyses of two cancer cell lines.
Genetic variants in regulatory regions of microRNAs are associated with lung cancer risk

Genetic variants in regulatory regions of some miRNAs might be associated with lung cancer risk and survival. We performed a case-control study including 1341 non-small cell lung cancer (NSCLC) cases and 1982 controls to evaluate the associations of 7 potentially functional polymorphisms in several differently expressed miRNAs with NSCLC risk. Each SNP was also tested for the association with overall survival of 1001 NSCLC patients. We identified that rs9660710 in miR-200b/200a/429 cluster and rs763354 in miR-30a were significantly associated with NSCLC risk [odds ratio (OR) = 1.17, 95% confidence interval (CI) = 1.06–1.30, P = 0.002; OR = 0.88, 95% CI = 0.80–0.98, P = 0.017; respectively]. However, no significant association between variants and NSCLC death risk was observed in survival analysis. Functional annotation showed that both rs9660710 and rs763354 were located in regulatory elements in lung cancer cells. Compared to normal tissues, miR-200a-3p, miR-200a-5p, miR-200b-3p, miR-200b-5p and miR-429 were significantly increased in The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma (LUAD) tumors, whereas miR-30a-3p and miR-30a-5p were significantly decreased in tumors (all P < 0.05). Furthermore, we observed that rs9660710 is an expression quantitative trait locus (eQTL) or methylation eQTL for miR-429 expression in TCGA normal tissues. Our results indicated that rs9660710 in miR-200b/200a/429 cluster and rs763354 in miR-30a might modify the susceptibility to NSCLC.
Data motifs: a lens towards fully understanding big data and AI workloads

The complexity and diversity of big data and AI workloads make understanding them difficult and challenging. This paper proposes a new approachto modelling and characterizing big data and AI workloads. We consider each big data and AI workload as a pipeline of one or more classes of units of computation performed on different initial or intermediate data inputs. Each class of unit of computation captures the common requirements while being reasonably divorced from individual implementations, and hence we call it a data motif. For the first time, among a wide variety of big data and AI workloads, we identify eight data motifs that take up most of the run time of those workloads, including Matrix, Sampling, Logic, Transform, Set, Graph, Sort and Statistic. We implement the eight data motifs on different software stacks as the micro benchmarks of an open-source big data and AI benchmark suite --- BigDataBench 4.0 (publicly available from http://prof.ict.ac.cn/BigDataBench), and perform comprehensive characterization of those data motifs from perspective of data sizes, types, sources, and patterns as a lens towards fully understanding big data and AI workloads. We believe the eight data motifs are promising abstractions and tools for not only big data and AI benchmarking, but also domain-specific hardware and software co-design.
Characterization of Recycled Carbon Fibers and Their Formation of Composites Using Injection Molding

CONNOR, MYLES LINDEN. Characterization of Recycled Carbon Fibers and Their Formation of Composites Using Injection Molding. (Under the direction of Dr. Jerome Cuomo.) This study investigates the properties of two recycled carbon fiber sets which have been reclaimed using different techniques and their introduction into composites using extrusion and injection molding technology. This research involves the characterization and comparison of the reclaimed fibers to virgin carbon fiber controls via; optical microscopy, scanning electron microscopy, energy dispersive spectroscopy, x-ray photoelectron spectroscopy, single fiber tensile tests, and single fiber composite fragmentation tests. Characterization proved the reclaimed carbon fibers had comparable properties to virgin carbon fibers. Reclaimed carbon fibers were then compounded via extrusion, with polycarbonate resin and test coupons were fabricated using injection molded technology. The mechanical and electrical properties of the recycled carbon fiber reinforced plastics were then characterized and compared to a commercially compounded virgin carbon fiber and polycarbonate control. From this research it can be concluded that the reclaimed carbon fiber surface properties compare favorably to the virgin controls. The results indicated that the reclamation techniques did not damage the surface of the fibers. The individual reclaimed fibers exhibited an 81% or better retention of tensile properties and performed as good as or better than virgin control fibers during fiber fragmentation measurements. With the exception of one sample, the compounded and injection molded reclaimed fiber sets compared favorably to the virgin control in mechanical properties testing, showing 88% retained tensile strength, 98% flexural strength and 136% impact resistance with respect to the commercially compounded control. From these results it can be stated that current carbon fiber recycling techniques yield fibers with desirable properties. Also, data gathered in this research has shown that recycled carbon fibers can be utilized in various composite applications due to the retained integrity of their mechanical properties. Characterization of Recycled Carbon Fibers and Their Formation of Composites Using Injection Molding by Myles Linden Connor A thesis submitted to the Graduate Faculty of North Carolina State University In partial fulfillment of the Requirements for the degree of Master of Science Materials Science and Engineering
Clinical Pregenetic Screening for Stroke Monogenic Diseases: Results From Lombardia GENS Registry

Background and Purpose— Lombardia GENS is a multicentre prospective study aimed at diagnosing 5 single-gene disorders associated with stroke (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, Fabry disease, MELAS [mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes], hereditary cerebral amyloid angiopathy, and Marfan syndrome) by applying diagnostic algorithms specific for each clinically suspected disease Methods— We enrolled a consecutive series of patients with ischemic or hemorrhagic stroke or transient ischemic attack admitted in stroke units in the Lombardia region participating in the project. Patients were defined as probable when presenting with stroke or transient ischemic attack of unknown etiopathogenic causes, or in the presence of <3 conventional vascular risk factors or young age at onset, or positive familial history or of specific clinical features. Patients fulfilling diagnostic algorithms specific for each monogenic disease (suspected) were referred for genetic analysis. Results— In 209 patients (57.4±14.7 years), the application of the disease-specific algorithm identified 227 patients with possible monogenic disease. Genetic testing identified pathogenic mutations in 7% of these cases. Familial history of stroke was the only significant specific feature that distinguished mutated patients from nonmutated ones. The presence of cerebrovascular risk factors did not exclude a genetic disease. Conclusions— In patients prescreened using a clinical algorithm for monogenic disorders, we identified monogenic causes of events in 7% of patients in comparison to the 1% to 5% prevalence reported in previous series.