scholarly journals High F-score Model for Recognizing Object Visibility in Images with Occluded Objects of Interest

2021 ◽  
Vol 9 (1) ◽  
pp. 35-48
Author(s):  
Bernardas Ciapas ◽  
Povilas Treygis
Keyword(s):  
GeroPsych ◽  
2020 ◽  
Vol 33 (4) ◽  
pp. 235-244
Author(s):  
Boo Johansson ◽  
Marcus Praetorius Björk ◽  
Valgeir Thorvaldsson

Abstract. In 1987, we administered a subjective memory questionnaire to 143 40-year-old men, and 30 years later 67 of them again responded to the same questionnaire at age 70. At the follow-up, we also instructed participants to answer the questionnaire in the same manner as they thought they did at age 40 and to perform a picture recognition and a public event test. We employed confirmatory factor analysis to model a latent subjective memory construct. A single-factor solution provided acceptable model fit to data (χ2(12) = 9.33, p = .68; χ2(12) = 10.48, p = .57) and a decent reliability at both ages for the subjective memory measurements (omega = .82 and .93, respectively). Our longitudinal invariance testing revealed only a partial weak invariance. We also fitted a latent change-score model to the data. As expected, participants on average rated their memory as poorer at age 70 than at 40. Those who reported better overall health and less anxiety reported less memory decline up to age 70. Notably, this was also the case for those who rated memory as worse at age 40. Higher stress and depression at age 70, however, were associated with worse subjective memory already at age 40. The correspondences between memory ratings and tests were low. The correlation between the subjective memory factors at age 40 and 70 was 0.58, while the correlation between the memory factor at age 70 and the retrospective subjective memory factor was 0.87. Our findings suggest that subjective memory is quite consistent, and that we are inclined to preserve the continuity of our own memory functioning over the adult lifespan.


2020 ◽  
Vol 25 (1) ◽  
pp. 29-44
Author(s):  
Mariati ◽  
Emmy Indrayani

Company’s financial condition reflected in the financial statements. However, there are many loopholes in the financial statements which can become a chance for the management and certain parties to commit fraud on the financial statements. This study aims to detect financial statement fraud as measured using fraud score model that occurred in issuers entered into the LQ-45 index in 2014-2016 with the use of six independent variables are financial stability, external pressure, financial target, nature of industry, ineffective monitoring and rationalization. This study using 27 emiten of LQ-45 index during 2014-2016. However, there are some data outlier that shall be removed, thus sample results obtained 66 data from 25 companies. Multiple linear regression analysis were used in this study. The results showed that the financial stability variables (SATA), nature of industry (RECEIVBLE), ineffective monitoring (IND) and rationalization (ITRENDLB) proved to be influential or have the capability to detect financial statement fraud. While the external pressure variables (DER) and financial target (ROA) are not able to detect the existence of financial statement fraud. Simultaneously all variables in this study were able to detect significantly financial statement fraud.


2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 684.1-684
Author(s):  
J. Q. Zhang ◽  
S. X. Zhang ◽  
R. Zhao ◽  
J. Qiao ◽  
M. T. Qiu ◽  
...  

Background:Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.Objectives:To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.Methods:Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.Results:We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).Conclusion:The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.References:[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].Acknowledgements:This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared


2021 ◽  
Vol 14 (5) ◽  
pp. 199
Author(s):  
Mahfuzur Rahman ◽  
Cheong Li Sa ◽  
Md. Abdul KaiumMasud

Financial performance of firms is very important to bankers, shareholders, potential investors, and creditors. The inability of firms to meet their liabilities will affect all its stakeholders and will result in negative consequences in the wider economy. The objective of the study is to explore the applicability of a distress prediction model which uses the F-Score and its components to identify firms which are at high risk of going into default. The study incorporates a prediction model and vast literature to address the research questions. The sample of the study is collected from publicly listed firms of the United States. In total, 81 financially distressed firms wereextracted from the UCLA-LoPucki Bankruptcy Research Database during 2009–2017. This study found that the relationship of the F-Score and probability of firms going into financial distress is significant. This study also demonstrated that firms which are at risk of distress tend to record a negative cash flow from operations (CFO) and showed a greater decline in return on assets (ROA) in the year prior to default. This study extends the existing literature by supporting a model which has not been widely used in the area of financial distress predictions.


2020 ◽  
Vol 41 (35) ◽  
pp. 3325-3333 ◽  
Author(s):  
Taavi Tillmann ◽  
Kristi Läll ◽  
Oliver Dukes ◽  
Giovanni Veronesi ◽  
Hynek Pikhart ◽  
...  

Abstract Aims Cardiovascular disease (CVD) risk prediction models are used in Western European countries, but less so in Eastern European countries where rates of CVD can be two to four times higher. We recalibrated the SCORE prediction model for three Eastern European countries and evaluated the impact of adding seven behavioural and psychosocial risk factors to the model. Methods and results We developed and validated models using data from the prospective HAPIEE cohort study with 14 598 participants from Russia, Poland, and the Czech Republic (derivation cohort, median follow-up 7.2 years, 338 fatal CVD cases) and Estonian Biobank data with 4632 participants (validation cohort, median follow-up 8.3 years, 91 fatal CVD cases). The first model (recalibrated SCORE) used the same risk factors as in the SCORE model. The second model (HAPIEE SCORE) added education, employment, marital status, depression, body mass index, physical inactivity, and antihypertensive use. Discrimination of the original SCORE model (C-statistic 0.78 in the derivation and 0.83 in the validation cohorts) was improved in recalibrated SCORE (0.82 and 0.85) and HAPIEE SCORE (0.84 and 0.87) models. After dichotomizing risk at the clinically meaningful threshold of 5%, and when comparing the final HAPIEE SCORE model against the original SCORE model, the net reclassification improvement was 0.07 [95% confidence interval (CI) 0.02–0.11] in the derivation cohort and 0.14 (95% CI 0.04–0.25) in the validation cohort. Conclusion Our recalibrated SCORE may be more appropriate than the conventional SCORE for some Eastern European populations. The addition of seven quick, non-invasive, and cheap predictors further improved prediction accuracy.


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