bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fully Automated Multiplex Assay for Diagnosis of Lyme Disease with High Specificity and Improved Early Sensitivity
Authors: Hahm Johnnie B. , Breneman John W. , Liu Jing , Rabkina Svetlana , Zheng Weiming , Zhou Shuxia , Walker Roger P. , Kaul Ravi
Published: March 2020

Lyme borreliosis is a tick-borne disease caused by theBorrelia burgdorferisensu latocomplex. Bio-Rad Laboratories has developed a fully automated multiplex bead-based assay for the detection of IgM and IgG antibodies toB. burgdorferi. The BioPlex 2200 Lyme Total assay exhibits an improved rate of seropositivity in patients with early Lyme infection. Asymptomatic subjects from endemic and nonendemic origins demonstrated a seroreactivity rate of approximately 4% that was similar to other commercial assays evaluated in this study.
Complete Genome Sequence of an Alphabaculovirus from Choristoneura diversana
Authors: Takatsuka Jun
Published: March 2020

The genome sequence of a baculovirus isolated from
Choristoneura diversana
is 122,827 bp long and contains 150 putative open reading frames (ORFs). The virus is closely related to alphabaculoviruses isolated from insect species of the genus
Choristoneura
.
Fifteen-minute consultation: Imaging in paediatric major trauma
Authors: Abela Nikki , Herrieven Elizabeth
Published: March 2020

With trauma being a leading cause of death for children, identifying all sustained injuries remains a priority for clinicians, and imaging is a key diagnostic tool to ensure that is achieved. However, children have a greater risk of detrimental effects of ionising radiation than adults. Clinicians therefore have to balance limiting their patients’ radiation exposure to ‘as low as reasonably achievable’ with the need for diagnostic accuracy. But what is ‘reasonable’ in major trauma can be confusing. This article aims to clarify the current guidance on which body part to scan and when in paediatric major trauma.
Clinical validation of qPCR Target Selector™ assays using highly specific switch-blockers for rare mutation detection
Authors: Arnold Lyle , Alexiadis Vassilios , watanaskul Tim , Zarrabi Vahid , Poole Jason , Singh Veena
Published: March 2020

Aims

The identification of actionable DNA mutations associated with a patient’s tumour is critical for devising a targeted, personalised cancer treatment strategy. However, these molecular analyses are typically performed using tissue obtained via biopsy, which involves substantial risk and is often not feasible. In addition, biopsied tissue does not always reflect tumour heterogeneity, and sequential biopsies to track disease progression (eg, emergence of drug resistance mutations) are not well tolerated. To overcome these and other biopsy-associated limitations, we have developed non-invasive ‘liquid biopsy’ technologies to enable the molecular characterisation of a patient’s cancer using peripheral blood samples.

Methods

The Target Selector ctDNA platform uses a real-time PCR-based approach, coupled with DNA sequencing, to identify cancer-associated genetic mutations within circulating tumour DNA. This is accomplished via a patented blocking approach suppressing wild-type DNA amplification, while allowing specific amplification of mutant alleles.

Results

To promote the clinical uptake of liquid biopsy technologies, it is first critical to demonstrate concordance between results obtained via liquid and traditional biopsy procedures. Here, we focused on three genes frequently mutated in cancer:EGFR(Del19, L858, and T790),BRAF(V600) andKRAS(G12/G13). For each Target Selector assay, we demonstrated extremely high accuracy, sensitivity and specificity compared with results obtained from tissue biopsies. Overall, we found between 93% and 96% concordance to blinded tissue samples across 127 clinical assays.

Conclusions

The switch-blocker technology reported here offers a highly effective method for non-invasively determining the molecular signatures of patients with cancer.
Different CSF protein profiles in amyotrophic lateral sclerosis and frontotemporal dementia withC9orf72hexanucleotide repeat expansion
Authors: Barschke Peggy , Oeckl Patrick , Steinacker Petra , Al Shweiki MHD Rami , Weishaupt Jochen H , Landwehrmeyer G Bernhard , Anderl-Straub Sarah , Weydt Patrick , Diehl-Schmid Janine , Danek Adrian , Kornhuber Johannes , Schroeter Matthias L , Prudlo Johannes , Jahn Holger , Fassbender Klaus , Lauer Martin , van der Ende Emma Louise , van Swieten John Cornelis , Volk Alexander E , Ludolph Albert C , Otto Markus
Published: March 2020

Objectives

The hexanucleotide repeat expansion in theC9orf72gene is the most common mutation associated with amyotrophic lateral sclerosis (C9-ALS) and frontotemporal dementia (C9-FTD). Until now, it is unknown which factors define whetherC9orf72mutation carriers develop ALS or FTD. Our aim was to identify protein biomarker candidates in the cerebrospinal fluid (CSF) which differentiate between C9-ALS and C9-FTD and might be indicative for the outcome of the mutation.

Methods

We compared the CSF proteome of 16 C9-ALS and 8 C9-FTD patients and 11 asymptomaticC9orf72mutation carriers (CAR) by isobaric tags for relative and absolute quantitation. Eleven biomarker candidates were selected from the pool of differentially regulated proteins for further validation by multiple reaction monitoring and single-molecule array in a larger cohort (n=156).

Results

In total, 2095 CSF proteins were identified and 236 proteins were significantly different in C9-ALS versus C9-FTD including neurofilament medium polypeptide (NEFM) and chitotriosidase-1 (CHIT1). Eight candidates were successfully validated including significantly increased ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) levels in C9-ALS compared with C9-FTD and controls and decreased neuronal pentraxin receptor (NPTXR) levels in C9-FTD versus CAR.

Conclusions

This study presents a deep proteomic CSF analysis of C9-ALS versus C9-FTD patients. As a proof of concept, we observed higher NEFM and CHIT1 CSF levels in C9-ALS. In addition, we also show clear upregulation of UCHL1 in C9-ALS and downregulation of NPTXR in C9-FTD. Significant differences in UCHL1 CSF levels may explain diverging ubiquitination and autophagy processes and NPTXR levels might reflect different synapses organisation processes.
A novel three-legged 6-DOF parallel robot with simple kinematics
Authors: Cherchelanov Edelvays , Bonev Ilian A.
Published: March 2020

This paper presents a novel three-legged six degrees of freedom (6-DOF) parallel robot with simple kinematics. The main idea behind this novel architecture is that each of the three identical legs is controlled by two prismatic actuators with parallel directions. As a result, it is possible to control simultaneously or separately the position and the orientation of a leg. The reduced number of legs leads to a simple mechanical design with reduced risk for mechanical interferences.
Analytical investigations on formations of hairy neutral reflecting shells in the scalar-Gauss–Bonnet gravity
Authors: Peng Yan
Published: March 2020

We study scalarization of spherically symmetric neutral reflecting shells in the scalar-tensor gravity. We consider neutral static massless scalar fields non-minimally coupled to the Gauss–Bonnet invariant. We obtain a relation representing the existence regime of hairy neutral reflecting shells. For parameters unsatisfying this relation, the massless scalar field cannot exist outside the neutral reflecting shell. In the parameter region where this relation holds, we get analytical solutions of scalar field hairs outside neutral reflecting shells.
Adaptive Exploration for Unsupervised Person Re-identification
Authors: Ding Yuhang , Fan Hehe , Xu Mingliang , Yang Yi
Published: March 2020

Due to domain bias, directly deploying a deep person re-identification (re-ID) model trained on one dataset often achieves considerably poor accuracy on another dataset. In this article, we propose an Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner. Specifically, in the target domain, the re-ID model is inducted to (1) maximize distances between all person images and (2) minimize distances between similar person images. In the first case, by treating each person image as an individual class, a non-parametric classifier with a feature memory is exploited to encourage person images to move far away from each other. In the second case, according to a similarity threshold, our method adaptively selects neighborhoods for each person image in the feature space. By treating these similar person images as the same class, the non-parametric classifier forces them to stay closer. However, a problem of the adaptive selection is that, when an image has too many neighborhoods, it is more likely to attract other images as its neighborhoods. As a result, a minority of images may select a large number of neighborhoods while a majority of images has only a few neighborhoods. To address this issue, we additionally integrate a balance strategy into the adaptive selection. We evaluate our methods with two protocols. The first one is called “target-only re-ID”, in which only the unlabeled target data is used for training. The second one is called “domain adaptive re-ID”, in which both the source data and the target data are used during training. Experimental results on large-scale re-ID datasets demonstrate the effectiveness of our method. Our code has been released at https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification.
MANDOLA
Authors: Paschalides Demetris , Stephanidis Dimosthenis , Andreou Andreas , Orphanou Kalia , Pallis George , Dikaiakos Marios D. , Markatos Evangelos
Published: March 2020

In recent years, the increasing propagation of hate speech in online social networks and the need for effective counter-measures have drawn significant investment from social network companies and researchers. This has resulted in the development of many web platforms and mobile applications for reporting and monitoring online hate speech incidents. In this article, we present MANDOLA, a big-data processing system that monitors, detects, visualizes, and reports the spread and penetration of online hate-related speech using big-data approaches. MANDOLA consists of six individual components that intercommunicate to consume, process, store, and visualize statistical information regarding hate speech spread online. We also present a novel ensemble-based classification algorithm for hate speech detection that can significantly improve the performance of MANDOLA’s ability to detect hate speech. To present the functionality and usability of our system, we present a use case scenario of real-life event annotation and data correlation. As shown from the performance of the individual modules, as well as the usability and functionality of the whole system, MANDOLA is a powerful system for reporting and monitoring online hate speech.
An Evaluation of Tile Selection Methods for Viewport-Adaptive Streaming of 360-Degree Video
Authors: Nguyen Duc V. , Tran Huyen T. T. , Thang Truong Cong
Published: March 2020

360-degree video has become increasingly popular nowadays. For effective transmission of bandwidth-intensive 360-degree video over networks, viewport-adaptive streaming has been introduced. In this article, we evaluate, for the first time, ten existing methods to understand the effectiveness of tile-based viewport adaptive streaming of 360-degree video. Experimental results show that tile-based methods can improve the average V-PSNR by up to 4.3 dB compared to a non-tiled method under low delay settings. Here, the V-PSNR is computed as the peak signal-to-noise ratio of the adapted viewport compared to the corresponding origin viewport. Also, different methods show different tradeoffs between average viewport quality and viewport quality variations. Especially, the performances of most tile-based methods decrease quickly as the segment duration and/or buffer size increase for the content with no main focus. Even, under long delay settings like HTTP Adaptive Streaming, it is found that the simple non-tiled method appears to be the best one. For the content with a strong viewing focus, it is found that the tile-based methods are less influenced by the segment duration and the buffer size. In addition, a comparison of the performances of the tile selection methods using two popular viewport estimation methods is conducted. It is interesting that there is only little difference found in performances of tile selection methods. The findings of this study are useful for service providers to make decisions on deployment of streaming solutions.
Joint Stacked Hourglass Network and Salient Region Attention Refinement for Robust Face Alignment
Authors: Zhang Junfeng , Hu Haifeng , Shen Guobin
Published: March 2020

Facial landmark detection aims to locate keypoints for facial images, which typically suffer from variations caused by arbitrary pose, diverse facial expressions, and partial occlusion. In this article, we propose a coarse-to-fine framework that joins a stacked hourglass network and salient region attention refinement for robust face alignment. To achieve this goal, we first present a multi-scale region learning module to analyze the structure information at a different facial region and extract a strong discriminative deep feature. Then we employ a stacked hourglass network for heatmap regression and initial facial landmarks prediction. Specifically, the stacked hourglass network introduces an improved Inception-ResNet unit as a basic building block, which can effectively improve the receptive field and learn contextual feature representations. Meanwhile, a novel loss function takes into account global weights and local weights to make the heatmap regression more accurate. Different from existing heatmap regression models, we present a salient region attention refinement module to extract a precise feature based on the heatmap regression, and utilize the filtered feature for landmarks refinement to achieve accurate prediction. Extensive experimental results of several challenging datasets (including 300 Faces in the Wild, Caltech Occluded Faces in the Wild, and Annotated Facial Landmarks Faces in the Wild) confirm that our approach can achieve more competitive performance than the most advanced algorithms.
Quality Indicators in Search-based Software Engineering
Authors: Ali Shaukat , Arcaini Paolo , Pradhan Dipesh , Safdar Safdar Aqeel , Yue Tao
Published: March 2020

Search-Based Software Engineering (SBSE) researchers who apply multi-objective search algorithms (MOSAs) often assess the quality of solutions produced by MOSAs with one or more quality indicators (QIs). However, SBSE lacks evidence providing insights on commonly used QIs, especially about agreements among them and their relations with SBSE problems and applied MOSAs. Such evidence about QIs agreements is essential to understand relationships among QIs, identify redundant QIs, and consequently devise guidelines for SBSE researchers to select appropriate QIs for their specific contexts. To this end, we conducted an extensive empirical evaluation to provide insights on commonly used QIs in the context of SBSE, by studying agreements among QIs with and without considering differences of SBSE problems and MOSAs. In addition, by defining a systematic process based on three common ways of comparing MOSAs in SBSE, we present additional observations that were automatically produced based on the results of our empirical evaluation. These observations can be used by SBSE researchers to gain a better understanding of the commonly used QIs in SBSE, in particular, regarding their agreements. Finally, based on the results, we also provide a set of guidelines for SBSE researchers to select appropriate QIs for their particular context.
A Model-Based Software Solution for Simultaneous Multiple Kernels on GPUs
Authors: Wu Hao , Liu Weizhi , Lin Huanxin , Wang Cho-Li
Published: March 2020

As a critical computing resource in multiuser systems such as supercomputers, data centers, and cloud services, a GPU contains multiple compute units (CUs). GPU Multitasking is an intuitive solution to underutilization in GPGPU computing. Recently proposed solutions of multitasking GPUs can be classified into two categories: (1) spatially partitioned sharing (SPS), which coexecutes different kernels on disjointed sets of compute units (CU), and (2) simultaneous multikernel (SMK), which runs multiple kernels simultaneously within a CU. Compared to SPS, SMK can improve resource utilization even further due to the interleaving of instructions from kernels with low dynamic resource contentions.

However, it is hard to implement SMK on current GPU architecture, because (1) techniques for applying SMK on top of GPU hardware scheduling policy are scarce and (2) finding an efficient SMK scheme is difficult due to the complex interferences of concurrently executed kernels. In this article, we propose a lightweight and effective performance model to evaluate the complex interferences of SMK. Based on the probability of independent events, our performance model is built from a totally new angle and contains limited parameters. Then, we propose a metric,
symbiotic factor
, which can evaluate an SMK scheme so that kernels with complementary resource utilization can corun within a CU. Also, we analyze the advantages and disadvantages of kernel slicing and kernel stretching techniques and integrate them to apply SMK on GPUs instead of simulators. We validate our model on 18 benchmarks. Compared to the optimized hardware-based concurrent kernel execution whose kernel launching order brings fast execution time, the results of corunning kernel pairs show 11%, 18%, and 12% speedup on AMD R9 290X, RX 480, and Vega 64, respectively, on average. Compared to the Warped-Slicer, the results show 29%, 18%, and 51% speedup on AMD R9 290X, RX 480, and Vega 64, respectively, on average.
Spatio-temporal Segmentation Based Adaptive Compression of Dynamic Mesh Sequences
Authors: Luo Guoliang , Deng Zhigang , Zhao Xin , Jin Xiaogang , Zeng Wei , Xie Wenqiang , Seo Hyewon
Published: March 2020

With the recent advances in data acquisition techniques, the compression of various dynamic mesh sequence data has become an important topic in the computer graphics community. In this article, we present a new spatio-temporal segmentation-based approach for the adaptive compression of the dynamic mesh sequences. Given an input dynamic mesh sequence, we first compute an initial temporal cut to obtain a small subsequence by detecting the temporal boundary of dynamic behavior. Then, we apply a two-stage vertex clustering on the resulting subsequence to classify the vertices into groups with optimal intra-affinities. After that, we design a temporal segmentation step based on the variations of the principal components within each vertex group prior to performing a PCA-based compression. Furthermore, we apply an extra step on the lossless compression of the PCA bases and coefficients to gain more storage saving. Our approach can adaptively determine the temporal and spatial segmentation boundaries to exploit both temporal and spatial redundancies. We have conducted extensive experiments on different types of 3D mesh animations with various segmentation configurations. Our comparative studies show the advantages of our approach for the compression of 3D mesh animations.
A Deep Learning Approach for Face Hallucination Guided by Facial Boundary Responses
Authors: Li Mengyan , Zhang Zhaoyu , Xie Guochen , Yu Jun
Published: March 2020

Face hallucination is a domain-specific super-resolution (SR) problem of learning a mapping between a low-resolution (LR) face image and its corresponding high-resolution (HR) image. Tremendous progress on deep learning has shown exciting potential for a variety of face hallucination tasks. However, most deep-learning–based methods are limited to handle facial appearance information without paying attention to facial structure priors. In this article, we propose an open source
1
Boundary-aware Dual-branch Network (BDN) for face hallucination, which simultaneously extracts face features and estimates facial boundary responses from LR inputs, ultimately fusing them to reconstruct HR results. Specifically, we first upsample LR face images to HR feature maps, and then feed the upsampled HR features into a memory unit and an attention unit synchronously to obtain the refined features and predict facial boundary responses. Next, they are fed into a feature map fusion unit to combine facial appearance and structure information by a spatial attention mechanism. Moreover, we employ a series of stacked units to boost performance before recovering HR face images. Finally, a discriminative network is developed to improve visual quality by introducing adversarial learning strategy. Extensive experiments show that the proposed approach achieves superior face hallucination results against the state-of-the-art ones.
Enabling Highly Efficient Batched Matrix Multiplications on SW26010 Many-core Processor
Authors: Jiang Lijuan , Yang Chao , Ma Wenjing
Published: March 2020

We present a systematic methodology for optimizing batched matrix multiplications on SW26010 many-core processor of the Sunway TaihuLight supercomputer. Five surrogate algorithms and a machine learning–based algorithm selector are proposed to fully exploit the computing capability of SW26010 and cope with the sophisticated algorithm characteristics of batched matrix multiplications. Experiment results show that the algorithm selector is able to adaptively choose the appropriate algorithm for various matrix shapes and batch sizes with low overhead and high accuracy. In particular, the optimized batched matrix multiplications can substantially outperform the non-batched version and reach around 84.8% of the performance upper bound.
Pseudorandom Bits for Oblivious Branching Programs
Authors: Gurjar Rohit , Volk Ben Lee
Published: March 2020

We construct a pseudorandom generator that fools known-order read-
k
oblivious branching programs and, more generally, any linear length oblivious branching program. For polynomial width branching programs, the seed lengths in our constructions are
Õ
(
n
1−1/2

k−1

) (for the read-
k
case) and
O
(
n
/ log log
n
) (for the linear length case). Previously, the best construction for these models required seed length (1 − Ω(1))
n
.
Interhemispheric modulations of motor outputs by the rostral and caudal forelimb areas in rats
Authors: Touvykine Boris , Elgbeili Guillaume , Quessy Stephan , Dancause Numa
Published: March 2020

In rats, forelimb movements are evoked from two cortical regions, the caudal and rostral forelimb areas (CFA and RFA, respectively). These areas are densely interconnected and RFA induces complex and powerful modulations of CFA outputs. CFA and RFA also have interhemispheric connections, and these areas from both hemispheres send projections to common targets along the motor axis, providing multiple potential sites of interactions for movement production. Our objective was to characterize how CFA and RFA in one hemisphere can modulate motor outputs of the opposite hemisphere. To do so, we used paired-pulse protocols with intracortical microstimulation techniques (ICMS), while recording electromyographic (EMG) activity of forelimb muscles in sedated rats. A subthreshold conditioning stimulation was applied in either CFA or RFA in one hemisphere simultaneously or before a suprathreshold test stimulation in either CFA or RFA in the opposite hemisphere. Both CFA and RFA tended to facilitate motor outputs with short (0–2.5 ms) or long (20–35 ms) delays between the conditioning and test stimuli. In contrast, they tended to inhibit motor outputs with intermediate delays, in particular 10 ms. When comparing the two areas, we found that facilitatory effects from RFA were more frequent and powerful than the ones from CFA. In contrast, inhibitory effects from CFA on its homolog were more frequent and powerful than the ones from RFA. Our results demonstrate that interhemispheric modulations from CFA and RFA share some similarities but also have clear differences that could sustain specific functions these cortical areas carry for the generation of forelimb movements.

NEW & NOTEWORTHY We show that caudal and rostral forelimb areas (CFA and RFA) have distinct effects on motor outputs from the opposite hemisphere, supporting that they are distinct nodes in the motor network of rats. However, the pattern of interhemispheric modulations from RFA has no clear equivalent among premotor areas in nonhuman primates, suggesting they contribute differently to the generation of ipsilateral hand movements. Understanding these interspecies differences is important given the common use of rodent models in motor control and recovery studies.
Medical Waste-Sorting and Management Practices in Five Hospitals in Ghana
Authors: Adu Robert Ohene , Gyasi Samuel Fosu , Essumang David Kofi , Otabil Kenneth Bentum
Published: March 2020

Hospital waste management in Ghana faces the risk of cross-contamination from the lack of thorough sorting of the waste at the points of generation, codisposal of hazardous and nonhazardous waste types, and use of open-fire pits and substandard incinerators for burning infectious waste. This has increased the potential for the spread of infections and chemical pollutants. A cross-sectional study was conducted in five hospitals in Ghana to assess behavioral patterns on waste sorting and the effectiveness of hospital waste management in Ghana. A total of 250 questionnaires were distributed purposively to some staff of the five hospitals to assess workers’ perceptions on medical waste sorting and handling. Additionally, focused group discussions and transect walks were adopted to examine the current collection, storage, treatment, and disposal methods used in the health facilities. Chi-square analyses showed significant differences in waste-sorting behavior based only on occupation (p<0.0001, n=180) and not on gender, education, or experience in the health sector. Even though contaminated sharps were separated into brown safety boxes, color coding for other infectious waste containers was inconsistent across the health facilities. The study revealed that incineration is still the modal method of treatment in Ghanaian hospitals and therefore new approaches such as an engineering approach were required to minimize its environmental effects. It is recommended that periodic in-service training workshops be held for healthcare staff on the right source-segregation of medical waste, in order to facilitate the effective and safe handling, transport, treatment, and disposal of waste from health facilities.
Aloe‐Emodin Induces Breast Tumor Cell Apoptosis through Upregulation of miR‐15a/miR‐16‐1 That Suppresses BCL2
Authors: Jiang Xuefeng , Liu Yusheng , Zhang Guijuan , Lin Shujun , Wu Jieyan , Yan Xianxin , Ma Yi , Ma Min
Published: March 2020

Purpose. Aloe‐emodin (AE) is a natural compound derived from aloe vera and palmatum rhubarb and shows anticancer activities in various cancers. Bcl‐2 family is the main regulator of cell death or cell survival. This study describes the effects of AE on proliferation of breast tumor (BT) cells. Methods. MCF‐10A, MCF‐10AT, MCF‐7, and MDA‐MB‐231 cell lines were exposed to AE. Cell proliferation and apoptosis were assessed by CCK‐8 and flow cytometry. Protein levels were measured by Western blotting. The levels of mRNA and miRNA were examined by RT‐PCR. Bioinformatics was applied to screen miRNAs that bind to 3′‐UTR of mRNA. Results. The results showed that AE selective activity inhibited the proliferation and induced apoptosis of MCF‐10AT and MCF‐7 cells but exhibited no significant inhibition in MCF10A and MDA‐MB‐231 cells. Mechanistically, AE dose‐dependently decreased the protein expression of Bcl‐2 and Bcl‐xl, while it increased Bax protein expression in MCF‐10AT and MCF‐7 cells. The levels of Bcl‐xl and Bax mRNA were altered by AE treatment, which was consistent with the protein expression results. However, Bcl‐2 mRNA levels were not affected in either cell line, suggesting that AE may modulate the protein translation of Bcl‐2 through miRNAs. In all candidate miRNAs that bind to 3′‐UTR of Bcl‐2, miR‐15a and miR‐16‐1 were dose‐dependently downregulated by AE. Moreover, inhibition of miR‐15a/16‐1 could eliminate the inhibition of MCF‐10AT and MCF‐7 cells growth by AE and could reverse the downregulation of AE‐induced Bcl‐2 protein level. Conclusion. Our research provides an important basis that AE induces BT cell apoptosis through upregulation of miR‐15a/miR‐16‐1 that suppresses BCL2.
Design and Development of Weigh-In-Motion Using Vehicular Telematics
Authors: Kirushanth Sivaramalingam , Kabaso Boniface
Published: March 2020

Identifying overloaded vehicles on a highway is essential for the safety of vehicles on the road as well as for the performance monitoring of highway infrastructure and planning. Traffic enforcement uses various weigh-in-motion (WIM) methods. Since Vehicular Telematics (VT) is favoured in the transport industry, using it for building a new WIM system to infer the payload of a vehicle at any road segment would be beneficial for the transport industry. This paper presents the effort taken to use VT data from onboard diagnostics modules and smartphones to infer the payload of a vehicle. The experiment done to find the correlation between VT data and the payload of a vehicle is discussed. Feature engineering was done; nine different settings were tested to find the best regression model. A multiple nonlinear regression model produced significant a p value of 6.322e-08 and an R-squared value of 0.8736. Results support the notion of using the VT data for nonintrusive measurement of the weight of a vehicle in motion.
Assessing the Oxidative Degradation of N-Methylpyrrolidone (NMP) in Microelectronic Fabrication Processes by Using a Multiplatform Analytical Approach
Authors: Lennon Gavin , Willox Shannon , Ramdas Ragini , Funston Scott J. , Klun Matthew , Pieh Robert , Fairlie Stewart , Dobbin Sara , Cobice Diego F.
Published: March 2020

During the construction of recording head devices, corrosion of metal features and subsequent deposition of corrosion by-products have been observed. Previous studies have determined that the use of N-methylpyrrolidone (NMP) may be a contributing factor. In this study, we report the use of a novel multiplatform analytical approach comprising of pH, liquid chromatography/UV detection (LC/UV), inductively coupled plasma optical emission spectroscopy (ICP-OES), and LC/mass spectrometry (LC/MS) to demonstrate that reaction conditions mimicking those of general photoresist removal processes can invoke the oxidation of NMP during the photolithography lift-off process. For the first time, we have confirmed that the oxidation of NMP lowers the pH, facilitating the dissolution of transition metals deposited on wafer substrates during post-mask and pre-lift-off processes in microelectronic fabrication. This negatively impacts upon the performance of the microelectronic device. Furthermore, it was shown that, by performing the process in an inert atmosphere, the oxidation of NMP was suppressed and the pH was stabilized, suggesting an affordable modification of the photolithography lift-off stage to enhance the quality of recording heads. This novel study has provided key data that may have a significant impact on current and future fabrication process design, optimization, and control. Results here suggest the inclusion of pH as a key process input variable (KPIV) during the design of new photoresist removal processes.
Environmental Carcinogenesis at the Single-Cell Level
Authors: Chang Gregory , Saeki Kohei , Mori Hitomi , Chen Shiuan
Published: March 2020

Elucidating the mechanisms behind how exposure to environmental chemicals can lead to cancer is not easy due to the complex natures of these compounds and the challenges to establish biologically relevant experimental models to study them. Environmental chemicals often present selective mechanisms of action on different cell types and can be involved in the modulation of targeted cells and their microenvironment, including immune cells. Currently, the limitations of traditional epidemiologic correlation analyses, in vitro cell-based assays, and animal models are that they are unable to comprehensively examine cellular heterogeneity and the tissue-selective influences. To this end, we propose utilizing single-cell RNA-sequencing (scRNA-seq) to more effectively capture the subtle and complex effects of environmental chemicals and how their exposure could lead to cancer. scRNA-seq's capabilities for studying gene expression level data at a significantly higher resolution relative to bulk RNA-sequencing (RNA-seq) enable studies to evaluate how environmental chemicals regulate gene transcription on different cell types as well as how these compounds impact signaling pathways and interactions between cells in the tissue microenvironment. These studies will be valuable for evaluating environmental chemicals' carcinogenic properties at the individual cell level.

See all articles in this CEBP Focus section, “Environmental Carcinogenesis: Pathways to Prevention.”
Reducing Ovarian Cancer Mortality Through Early Detection: Approaches Using Circulating Biomarkers
Authors: Trinidad Camille V. , Tetlow Ashley L. , Bantis Leonidas E. , Godwin Andrew K.
Published: March 2020

More than two-thirds of all women diagnosed with epithelial ovarian cancer (EOC) will die from the disease (>14,000 deaths annually), a fact that has not changed considerably in the last three decades. Although the 5-year survival rates for most other solid tumors have improved steadily, ovarian cancer remains an exception, making it the deadliest of all gynecologic cancers and five times deadlier than breast cancer. When diagnosed early, treatment is more effective, with a 5-year survival rate of up to 90%. Unfortunately, most cases are not detected until after the cancer has spread, resulting in a dismal 5-year survival rate of less than 30%. Current screening methods for ovarian cancer typically use a combination of a pelvic examination, transvaginal ultrasonography, and serum cancer antigen 125 (CA125), but these have made minimal impact on improving mortality. Thus, there is a compelling unmet need to develop new molecular tools that can be used to diagnose early-stage EOC and/or assist in the clinical management of the disease after a diagnosis, given that more than 220,000 women are living with ovarian cancer in the United States and are at risk of recurrence. Here, we discuss the state of advancing liquid-based approaches for improving the early detection of ovarian cancer.

See all articles in this Special Collection Honoring Paul F. Engstrom, MD, Champion of Cancer Prevention
Addressing Disparities in Cancer Screening among U.S. Immigrants: Progress and Opportunities
Authors: Fang Carolyn Y. , Ragin Camille C.
Published: March 2020

The United States is home to 47 million foreign-born individuals, which currently represents over 14% of the U.S. population. With greater length of U.S. residence, immigrants experience increased risk for chronic disease including selected cancers; yet, they are less likely to access preventive health care services and undergo cancer screening. As a result, there have been concerted efforts to address disparities in cancer screening in immigrant populations. This minireview describes current progress in promoting participation in cancer screening among U.S. immigrants and explores potential opportunities for improving impact. Of the 42 studies included in the review, the majority targeted Asian and Latino immigrant populations and included some form of culturally specific educational programming, often delivered in-person by community health workers and/or using a multimedia format. Twenty-eight of the 42 studies also offered navigation assistance to help overcome logistical and access barriers to care, and these studies yielded somewhat greater increases in screening. Yet, despite considerable effort over the past 20+ years, screening rates remain well below national goals. Opportunities to harness digital health tools to increase awareness and engagement, evaluating nonclinic-based screening paradigms to promote greater participation, and increasing efforts to address the needs of other immigrant subgroups are likely to have beneficial outcomes. Together, these strategies may help reduce inequities in access and uptake of cancer screening in U.S. immigrant populations.

See all articles in this Special Collection Honoring Paul F. Engstrom, MD, Champion of Cancer Prevention
Navigating the Intersection between Genomic Research and Clinical Practice
Authors: Daly Mary B.
Published: March 2020

The Risk Assessment Program (RAP) at Fox Chase Cancer Center (Philadelphia, PA) is a multi-generational prospective cohort, enhanced for personal and family history of cancer, consisting of over 10,000 individuals for whom data on personal and family history of cancer, risk factors, genetic and genomic data, health behaviors, and biospecimens are available. The RAP has a broad research agenda including the characterization of genes with known or potential relevance to cancer, gene–gene and gene–environment interactions, and their contribution to clinically useful risk assessment and risk reduction strategies. Increasingly, this body of research is identifying genetic changes which may have clinical significance for RAP research participants, leading us to confront the issue of whether to return genetic results emerging from research laboratories. This review will describe some of the important fundamental points that must be debated as we develop a paradigm for return of research results. The key issues to address as the scientific community moves toward adopting a policy of return of research results include the best criteria for determining which results to offer, the consent document components necessary to ensure that the participant makes a truly informed decision about receiving their results, and associated logistical and cost challenges.

See all articles in this Special Collection Honoring Paul F. Engstrom, MD, Champion of Cancer Prevention
Relative Contribution of Magnetic Resonance Imaging, Microelectrode Recordings, and Awake Test Stimulation in Final Lead Placement during Deep Brain Stimulation Surgery of the Subthalamic Nucleus in Parkinson’s Disease
Authors: Frequin Henrieke L. , Bot Maarten , Dilai José , Scholten Marije N. , Postma Miranda , Bour Lodewijk J. , Contarino Maria Fiorella , de Bie Rob M.A. , Schuurman P. Rick , van den Munckhof Pepijn
Published: March 2020

Introduction: For deep brain stimulation (DBS) surgery of the subthalamic nucleus (STN) in Parkinson’s disease (PD), many centers employ visualization of the nucleus on magnetic resonance imaging (MRI), intraoperative microelectrode recordings (MER), and test stimulation in awake patients. The value of these steps is a subject for ongoing debate. In the current study, we determined the relative contribution of MRI targeting, multitrack MER, and awake test stimulation in final lead placement during STN DBS surgery for PD. Methods: Data on PD patients undergoing MRI-targeted STN DBS surgery with three-channel MER and awake test stimulation between February 2010 and January 2014 were analyzed to determine in which MER trajectory final leads were implanted and why this tract was chosen. Results: Seventy-six patients underwent implantation of 146 DBS leads. In 92% of the STN, the final leads were implanted in one of the three planned channels. In 6%, additional channels were needed. In 2%, surgery was aborted before final lead implantation due to anxiety or fatigue. The final leads were implanted in the channels with the longest STN MER signal trajectory in 60% of the STN (38% of the bilaterally implanted patients). This was the central channel containing the MRI target in 39% of the STN (18% bilaterally). The most frequently noted reasons why another channel than the central channel was chosen for final lead placement were (1) a lower threshold for side effects (54%) and (2) no or a too short trajectory of the STN MER signal (40%) in the central channel. The latter reason correlated with larger 2D (x and y) errors in our stereotactic method. Conclusions: STN DBS leads were often not implanted in the MRI-planned trajectory or in the trajectory with the longest STN MER signal. Thresholds for side effects during awake test stimulation were decisive for final target selection in the majority of patients.
Implementation of Sustainable Development Concept in Hotel A Surabaya
Authors: Puspawardani Nuke , Mukti Kresno Eka , Himawan Angsar Nur , Sentosa Happy Ratna
Published: March 2020

Hospitality is one of the important sectors in the growth of the Indonesian Economy, which it continues to increase each year. But on the other hand it also has a negative impact. The higher use of the hotel, the higher the resource and energy expended to serve the guests of the hotel. This study aims to determine the implementation of sustainable development concept of Hotel A in Surabaya. This hotel is under auspices of the Accor Group, one of international hotel management groups, which already implemented the concept of sustainable development. To achieve the objectives of the study, the researcher using qualitative analysis techniques to process the primary and secondary data obtained, to find the answer on how far the application of sustainable development at the Hotel A. The research results show that Hotel A has conducted various measures to support sustainable development in several aspects. But they just apply 3 of 7 pillars of ecology as envisioned by the Accor Group. This study shows the aspects of eco-friendly management that can be carried out by a hotel.
 
Broadband Antenna for LTE Operation: A Novel Design Approach Using Meandered Strip Line
Authors: Kumaran M.
Published: March 2020

The broadband performance of an antenna for the LTE operation is presented in this paper. A novel design approach used to design the antenna using meandered strip line can be treated as a simplest and a cost effective approach. Due to the requirement of the bandwidth hungry applications, there is a smooth migration of the mobile technology from the existing services like GSM; UMTS to LTE is in progress. Thus a compact radiating structure can be preferred in many applications to improve the throughput performance of the multimedia service. The proposed antenna operates in the LTE 42 band. It shows the broadband performance centred at 3533 MHz with 112 MHz bandwidth. Bandwidth performance at this frequency is also found to be well suited to operate in the downlink frequency band at LTE 22. To determine the optimal performance of the antenna, a suitable threshold (S11 < 10 dB) is imposed. Gain and directivity of the antenna are 6.6 dB and 7.8 dBi respectively. Efficiency is achieved to 74%. Radiation pattern shows the best performance of the antenna. With the best performance achieved at this frequency, the antenna is advantageous to exploit in small cell coverage, LTE Hotspot and Indoor enhancement solutions. CST Microwave Studio Suite software is used to design the antenna. An ordinary 50 Ω transmission line is fed to the antenna. Other antenana parameters are also discussed for its performance.
Novel Design of Reversible MUX and DEMUX using GDI Techinque
Authors: Naveenkumar Majety
Published: March 2020

Now a day’s Reversible logic is playing a crucial role in designing of digital circuits and it is used in reducing power consumption in digital design. By regaining the bit loss it reduces the power consumption in digital circuits. Gate diffusion input (GDI) is a technique of low-power digital circuit design. This technique reduces the power consumption, delay, and transistor count by maintaining the complexity very low of logic design. In these paper a novel MUX and DEMUX has been presented, which can be extended up to 1:2n and 2n:1 respectively  and these are developed by using only one type of Reversible gate i.e. Fredkin Gate (FRG) and Not Gate. The simulations are done in H-Spice using 90nm technology.
Design of A New Three Phase Hybrid H-bridge and H-Type FCMLI for Various PWM Strategies
Authors: Balamurugan Chinnapettai Ramalingam , Natarajan S.P. , Padmathilagam V. , Anandhi T.S.
Published: March 2020

The inverters have to be designed to obtain a quality output voltage or a current waveform with a minimum amount of ripple content. In high power and high voltage applications the conventional two level inverters, however, have some limitations in operating at high frequency mainly due to switching losses and constraints of the power device ratings. Series and parallel combination of power switches in order to achieve the power handling voltages and currents. The conventional two level inverters produce THD levels around 60% even under normal operating conditions which are undesirable and cause more losses and other power quality problems too on the AC drives and utilities. Nowadays, multilevel inverters are widely used in power industry. Voltage unbalance problem is one of the major issues in working of multilevel inverter. In this paper, a three phase H-bridge + H-type FCMLI using sinusoidal reference, third harmonic injection reference, 60 degree reference and stepped wave reference are initially developed using SIMULINK and then implemented in real time environment using dSPACE. The five level output voltages of the chosen MLIs obtained using the MATLAB and dSPACE based PWM strategies and the corresponding % THD, VRMS (fundamental) , CF and FF are presented and analyzed. It is observed that bipolar COPWM-C provides output with relatively low distortion for sine reference and bipolar COPWM-B strategy is found to perform better since it provides relatively higher fundamental RMS output voltage for 60 degree reference.
Machine Learning in Big Data
Authors: Wang Lidong
Published: March 2020

Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.
Commodity Price Data Analysis Using Web Scraping
Authors: Rao M. Kameswara , Lagisetty Rohit , Maniraj M.S.V.K. , Dattu K.N.S. , Ganga B. Sneha
Published: March 2020

Today, analysis of data which is available on the web has become more popular, by using such data we are capable to solve many issues. Our project deals with the analysis of commodity price data available on the web. In general, commodity price data analysis is performed to know inflation rate prevailing in the country and also to know cost price index (CPI). Presently in some countries this analysis is done manually by collecting data from different cities, then calculate inflation and CPI using some predefined formulae. To make this entire process automatic we are developing this project. Now a day’s most of the customers are depending on online websites for their day to day purchases. This is the reason we are implementing a system to collect the data available in various e-commerce sites for commodity price analysis. Here, we are going to introduce a data scraping technique which enables us to collect data of various products available online and then store it in a database there after we perform analysis on them. By this process we can reduce the burden of collecting data manually by reaching various cities. The system consists of web module which perform analysis and visualization of data available in the database.