bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep-learning-based detection of missing road lane markings using YOLOv5 algorithm
Authors: Azmi Nur Hanisah , Sophian Ali , Bawono Ali Aryo 
Published: July 2022

Abstract
Road lane markings are critical for ensuring road user safety. To improve their safety, there are even different types of road lane markings, such as single solid lines, double solid lines, dashed single line etc. Their colors generally are white and yellow. This road lane markings mainly used to provide guidance and information for road user to comply with the rule of the road. Unfortunately, these markings get worn out with time and may even disappear. In order to prevent this from happening, regular inspection and maintenance need to be conducted. Manual inspection is tedious, slow, and prone to human errors. With the recent technological advancement, especially in machine vision and artificial intelligence, automated or semi-automated missing road lane marking detection systems can potentially be developed. In this work, preliminary study of the implementation of one of the latest deep learning algorithms, i.e. YOLOv5, has been carried out in the detection and classification of missing road lane markings. This paper shows the preliminary results which look promising as the mean Average Precision (mAP@0.5) reaches 0.995.
Detection of unbalance and looseness faults in a ventilation turbine using vibration signature analysis
Authors: Djemana Mohamed , Benyoucef Rania , Hrairi Meftah 
Published: July 2022

Abstract
Heavy industry, which generally uses turbomachines, often uses both technologies to perform high-performance vibration monitoring of its production tool. Vibration analysis is one of the means used to monitor the health of rotating machinery in operation. This is part of a policy of forecast maintenance of the industrial production tool. This work is part of the monitoring and diagnosis of rotating machines by vibration analysis taking as an example the X205 circulation fan. Numerical simulation was done to test the capabilities and limitations of a dynamic simulation. The modelling of vibratory phenomena is developed using SolidWorks software to perform a dynamic simulation of the 3D model. The numerical simulations were performed to find the effect of different types of defects, such as defect of unbalance fin fan and bolt joint looseness, on the output of system. The numerical results were confirmed either by experimentally using employing results available in the open literature that existent at factory of cement plant (SCHS) and a good agreement was observed. The proposed and derived model has demonstrated the viability of dynamic simulation approach to rotating machines by vibration analysis and serves as a significant alternative approach to the direct experimentation on the same systems in terms of cost and time.
Machine Learning Model to Predict Slump, VEBE and Compaction Factor of M Sand and Shredded Pet Bottles Concrete
Authors: Nadimalla A.M , Masjuki S.A , Saad S.A , Ali M 
Published: July 2022

Abstract
A measure of how easy it is to transport, cast, compact and surface finish of fresh concrete without any segregation is directly proportional to workability of concrete. The consistency of fresh concrete indirectly measures its workability. VeBe test, slump test and compaction factor test are some of the methods that can be used to study the consistency of concrete. A quick determination of the concrete strength and fresh properties is an important element on large construction sites for mass concreting to save time and cost. It is important to develop a machine learning model to predict the strength and workability of concrete with the help of available data. This paper aims to develop the machine learning model with the help of experimental data such as slump, VeBe and compaction factor of concrete incorporated with shredded Polyethylene Terephthalate (PET) bottles, Manufactured Sand (M-sand) and River sand as fine aggregates replacement in concrete mixtures. The machine learning model is developed by using different machine learning techniques such as Multiple Linear Regression (MLR) and Decision Tree Regression (DTR). As per the results obtained the DTR model is performed well than MLR model.
Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters
Authors: Althomsons Sandy P , Winglee Kathryn , Heilig Charles M , Talarico Sarah , Silk Benjamin , Wortham Jonathan , Hill Andrew N , Navin Thomas R 
Published: July 2022

Abstract
The early identification of clusters of persons with tuberculosis (TB) that will grow to become outbreaks creates an opportunity for intervention in preventing future TB cases. We used surveillance data (2009–2018) from the United States, statistically derived definitions of unexpected growth, and machine-learning techniques to predict which clusters of genotype-matched TB cases are most likely to continue accumulating cases above expected growth within a 1-year follow-up period. We developed a model to predict which clusters are likely to grow on a training and testing data set that was generalizable to a validation data set. Our model showed that characteristics of clusters were more important than the social, demographic, and clinical characteristics of the patients in those clusters. For instance, the time between cases before unexpected growth was identified as the most important of our predictors. A faster accumulation of cases increased the probability of excess growth being predicted during the follow-up period. We have demonstrated that combining the characteristics of clusters and cases with machine learning can add to existing tools to help prioritize which clusters may benefit most from public health interventions. For example, consideration of an entire cluster, not only an individual patient, may assist in interrupting ongoing transmission.
AT2 Receptor Stimulation Inhibits Vascular Smooth Muscle Cell Senescence Induced by Angiotensin II and Hyperglycemia
Authors: Bai Hui-Yu , Li Hui , Zhou Xiang , Gu Hai-Bo , Shan Bao-Shuai 
Published: July 2022

Abstract

BACKGROUND
Hyperglycemia has been widely reported to induce vascular senescence. We have previously demonstrated that angiotensin II (Ang II) could promote brain vascular smooth muscle cell (VSMC) senescence, and its type 2 (AT2) receptor deletion could enhance VSMC senescence. Therefore, we examined the possible cross-talk between Ang II and hyperglycemia on VSMC senescence, and the roles of AT2 receptor agonist, compound 21 (C21) on it.

METHODS
Aortic VSMCs were prepared from adult male mice and stimulated with Ang II and/or high glucose (Glu) and/or C21 and/or an autophagy inhibitor, 3-methyladenine (3-MA), and/or an autophagy agonist, rapamycin (RAP) for the indicated times. Cellular senescence, oxidative stress, and protein expressions were evaluated.

RESULTS
Combination treatment with Ang II and Glu synergistically increased the proportion of VSMC senescent area compared with control group and each treatment alone, which was almost completely attenuated by C21 treatment. Moreover, combination treatment induced significant changes in the levels of superoxide anion, the expressions of p21 and pRb, and the ratio of LC3B II/I expression, which were also significantly attenuated by C21 treatment. The proportion of VSMC senescent area and the levels of superoxide anion by combination treatment were increased after 3-MA treatment, and the proportion of senescent area and the expressions of p21 and pRb were decreased after RAP treatment, both of which were further attenuated by C21 treatment.

CONCLUSIONS
Ang II and hyperglycemia synergistically promoted VSMC senescence, at least partly through the participation by autophagy, oxidative stress, and p21-pRb pathway, which could be inhibited by C21.
Lie Detection: A Strategic Analysis of the Verifiability Approach
Authors: Ioannidis Konstantinos , Offerman Theo , Sloof Randolph 
Published: July 2022

Abstract
The Verifiability Approach is a lie detection method based on the insight that truth-tellers provide precise details whereas liars sometimes remain vague to avoid being exposed. We provide a game-theoretic foundation for the strategic effect that underlies this approach. We consider a speaker who wants to be acquitted and an investigator who prefers to find out the truth. The investigator can verify the speaker’s statement at some cost; verification gets more reliable the more details are provided. If, after a falsified statement, the investigator convicts, an additional penalty is imposed. Constructing precise but false statements is assumed to be cognitively costly. We derive all equilibria and thereby the conditions under which the investigator can infer valuable information from the speaker’s statement at face value. If cognitive costs are not prohibitively high, these require that liars are deterred from making false precise statements if always verified. Strategic information revelation by the speaker and verification by the investigator then necessarily work in tandem in a partially pooling equilibrium. Improvements in reliability result in more valuable information via the statements per se, whereas larger lying costs or a harsher penalty do not once the deterrence condition for the existence of this equilibrium is met.
Adaptation and Validation of Addenbrooke’s Cognitive Examination-III in Bengali for Screening MCI and Dementia
Authors: Bhattacharyya Bidisha , Mukherjee Ruchira , Mukherjee Adreesh , Das Gautam , Dogra Atanu Kumar , Das Sujata , Biswas Atanu 
Published: July 2022

Abstract

Objective
Bengali, the 6th most spoken language globally with 268 million speakers, demands a culturally appropriate tool for screening any cognitive compromise in this population. Addenbrooke Cognitive Examination-III (ACE-III) is a standardized tool used for screening and/or diagnostic purpose worldwide. The aim of the present study was to adapt and validate ACE-III into Bengali language.

Methods
The ACE-III UK Version A (2012) was adapted with linguistically and culturally appropriate items and validated on Bengali speakers. The participants consisted of 40 dementia and 22 Mild Cognitive Impairment (MCI) patients and 120 healthy-controls. Reliability and validity were examined. Discriminant function analysis was done. Sensitivity and specificity were evaluated and optimum cut-offs were established for MCI and dementia.

Results
Both sensitivity and specificity of ACE-III-Bengali of identifying dementia was 1; sensitivity for MCI ranged from 0.83 to 1, specificity from 0.76 to 1. Discriminant function analysis showed a significant difference in all domains of ACE-III-Bengali between healthy individuals and persons with neurocognitive impairment. Separate optimum ACE-III-Bengali cut-off scores were established according to level of education. For low education (&lt;Class 10) cut-off was 83 for dementia and 86 for MCI, whereas, for high education (≥Class 10) it was 85 and 88 for dementia and MCI, respectively. The area under curve for distinguishing dementia and MCI ranged from 0.949 to 0.955.

Conclusion
The ACE-III-Bengali is found to have high diagnostic accuracy in identifying dementia and MCI in the Bengali population.
A Validity Measure for the Automated Neuropsychological Assessment Metrics
Authors: Meyers John E , Miller Ronald Mellado , Vincent Andrea S 
Published: July 2022

Abstract
The Automated Neuropsychological Assessment Metrics (ANAM) is one of the most widely used and validated neuropsychological instruments for assessing cognition. The ANAM Test System includes a reporting tool, the ANAM Validity Indicator Report that generates scores for the embedded effort measure, the ANAM Performance Validity Index (APVI). The current study seeks to develop a proxy for the APVI, using raw subtest summary test scores. This would be useful for situations where the APVI score is unavailable (e.g., validity report not generated at the time of the assessment) or when the item level data needed to generate this score are inaccessible. ANAM scores from a large data set of 1,000,000+ observations were used for this retrospective analysis. Results of linear regression analysis suggest that the APVI can be reasonably estimated from the raw subtest summary test scores that are presented on the ANAM Performance Report. Clinically, this means that an important step in the interpretation process, checking the validity of test data, can still be performed even when the APVI is not available.
LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays
Authors: Thanh Nguyen Dat , Hoang Nguyen Quan , Thuy Duong Nguyen , Vo Nam S 
Published: July 2022

Abstract
Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs not included in the array (imputed SNPs) can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the array. It is those functional SNPs that are most likely associated with traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited. $\textrm{LmTag}$ is available at: https://github.com/datngu/LmTag.
MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network
Authors: Dong Guiying , Zhang Zi-Chao , Feng Jianfeng , Zhao Xing-Ming 
Published: July 2022

Abstract
Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.
Doubly robust evaluation of high-dimensional surrogate markers
Authors: Agniel Denis , Hejblum Boris P , Thiébaut Rodolphe , Parast Layla 
Published: July 2022

SummaryWhen evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference—namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.
Constrained groupwise additive index models
Authors: Masselot Pierre , Chebana Fateh , Campagna Céline , Lavigne Éric , Ouarda Taha B M J , Gosselin Pierre 
Published: July 2022

SummaryIn environmental epidemiology, there is wide interest in creating and using comprehensive indices that can summarize information from different environmental exposures while retaining strong predictive power on a target health outcome. In this context, the present article proposes a model called the constrained groupwise additive index model (CGAIM) to create easy-to-interpret indices predictive of a response variable, from a potentially large list of variables. The CGAIM considers groups of predictors that naturally belong together to yield meaningful indices. It also allows the addition of linear constraints on both the index weights and the form of their relationship with the response variable to represent prior assumptions or operational requirements. We propose an efficient algorithm to estimate the CGAIM, along with index selection and inference procedures. A simulation study shows that the proposed algorithm has good estimation performances, with low bias and variance and is applicable in complex situations with many correlated predictors. It also demonstrates important sensitivity and specificity in index selection, but non-negligible coverage error on constructed confidence intervals. The CGAIM is then illustrated in the construction of heat indices in a health warning system context. We believe the CGAIM could become useful in a wide variety of situations, such as warning systems establishment, and multipollutant or exposome studies.
Fast and functionally specific cortical thickness changes induced by visual stimulation
Authors: Zaretskaya Natalia , Fink Erik , Arsenovic Ana , Ischebeck Anja 
Published: July 2022

AbstractStructural characteristics of the human brain serve as important markers of brain development, aging, disease progression, and neural plasticity. They are considered stable properties, changing slowly over time. Multiple recent studies reported that structural brain changes measured with magnetic resonance imaging (MRI) may occur much faster than previously thought, within hours or even minutes. The mechanisms behind such fast changes remain unclear, with hemodynamics as one possible explanation. Here we investigated the functional specificity of cortical thickness changes induced by a flickering checkerboard and compared them to blood oxygenation level-dependent (BOLD) functional MRI activity. We found that checkerboard stimulation led to a significant thickness increase, which was driven by an expansion at the gray–white matter boundary, functionally specific to V1, confined to the retinotopic representation of the checkerboard stimulus, and amounted to 1.3% or 0.022 mm. Although functional specificity and the effect size of these changes were comparable to those of the BOLD signal in V1, thickness effects were substantially weaker in V3. Furthermore, a comparison of predicted and measured thickness changes for different stimulus timings suggested a slow increase of thickness over time, speaking against a hemodynamic explanation. Altogether, our findings suggest that visual stimulation can induce structural gray matter enlargement measurable with MRI.
Inferior temporal cortex leads prefrontal cortex in response to a violation of a learned sequence
Authors: Esmailpour Hamideh , Raman Rajani , Vogels Rufin 
Published: July 2022

AbstractPrimates learn statistical regularities that are embedded in visual sequences, a form of statistical learning. Single-unit recordings in macaques showed that inferior temporal (IT) neurons are sensitive to statistical regularities in visual sequences. Here, we asked whether ventrolateral prefrontal cortex (VLPFC), which is connected to IT, is also sensitive to the transition probabilities in visual sequences and whether the statistical learning signal in IT originates in VLPFC. We recorded simultaneously multiunit activity (MUA) and local field potentials (LFPs) in IT and VLPFC after monkeys were exposed to triplets of images with a fixed presentation order. In both areas, the MUA was stronger to images that violated the learned sequence (deviants) compared to the same images presented in the learned triplets. The high-gamma and beta LFP power showed an enhanced and suppressed response, respectively, to the deviants in both areas. The enhanced response was present also for the image following the deviant, suggesting a sensitivity for temporal adjacent dependencies in IT and VLPFC. The increased response to the deviant occurred later in VLPFC than in IT, suggesting that the deviant response in IT was not inherited from VLPFC. These data support predictive coding theories that propose a feedforward flow of prediction errors.