Unit Standard Deviation Research Articles

The effects of government policy on performance planning for implementation of performance contracting in the government ministries in Kenya

The study sought to analyse the effects of government policy on performance planning for the implementation of performance contracting in government ministries in Kenya. A mixed-method type of design was adopted. Stratified sampling was used to sample four government ministries. In sampling staff to be interviewed, top and middle-level management totalling 831 were identified. Thirty per cent of the 831 (249 staff members) were considered to be representative enough for the study. The study adopted descriptive and inferential statistics. The study used ANOVA, regression analysis, and correlation to analyse the relationship between variables. The study finding observed that there was a strong correlation coefficient of 0.866 at p-value of 0.035 between government policy and performance planning, which implied that employees' performance planning is significant in the implementation of performance contracting; a unit standard deviation increase in effective training and development was likely to increase the standard deviation of performance monitoring by 0.096 on implementation of performance contracting; a unit standard deviation increase in top management commitment was likely to increase the standard deviation of performance reporting on performance implementation by 0.337. The study concludes that government policies have an effect on performance contracting in government ministries in Kenya. Further, the conclusion was that government policy has minimally addressed issues of training and development. Therefore, it is recommended that transfer of training be included in the next reviews of the government policy and specifically addressed in the workplace that often affect the impact of training and development.

Establishing the effects of training and development on performance monitoring for implementation of performance contracting in the government ministries in Kenya

The study aimed to establish the effect of training on performance monitoring for implementation of performance contracting in the government ministries in Kenya. A mixed-method type of design was adopted. In sampling staff to be interviewed, top and middle-level management totalling 831 were identified. Thirty per cent of the 831 (249 staff members) considered it to be representative enough. The study adopted descriptive and inferential statistics. The study used ANOVA, regression analysis, and correlation to analyse the relationship between variables. Reliability of instruments was assessed using Cronbach's alpha coefficient, whose alpha value is equal to or greater than 0.70, which is considered sufficient. Content Validity Ratio (CVR) was calculated for each item, and items with a CVR value below 0.99 were eliminated. The final instrument comprised 22 items with CVR values ranging from 0.99 to 1.00. The study found that a unit standard deviation increase in effective training and development was likely to increase the standard deviation of performance monitoring by 0.096 on implementation of performance contracting. The study concludes that training and development have an effect on performance contracting in government ministries in Kenya. The study further recommends that transfer of training and development be included in the next review of the policy.

Abstract C009: An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities

Abstract Introduction: Obesity has been associated with aggressive prostate cancer (PCa). Environments that promote obesity, known as obesogenic environments, may also contribute to PCa progression at the population level. However, studies exploring these factors are limited. The purpose of this study was to examine the association between neighborhood obesogenic factors with PCa mortality rates in Chicago. Methods: We used an ecological study design with Chicago Community Areas (n=77) serving as the geographical area unit of analysis. All community area-level data were obtained from the Chicago Health Atlas public data portal. For each area, two obesogenic factors were examined: food environment and park access. The number of healthy and unhealthy grocery stores was used to characterize the food environment. The retail food establishments data was retrieved for the year 2013. The stores were classified into chain grocery stores, non-chain grocery stores, liquor stores, and convenience stores which included dollar tsores, corner stores and gas stations. Chain and non-chain grocery stores were grouped as healthy food access, and other (convenience store, dollar store and liquor store) as unhealthy food access. The area of public parks per 100,000 community area population was used as a measure of park access. An exclusive park area for each community was calculated by intersecting the community area and park area shape files from the year 2016. The outcome variable was community area-level PCa mortality rate age-adjusted to the community area male population. To normalize the data, all included variables were standardized to zero mean and unit standard deviation. Linear regression models analyzed the association of the standardized measures of obesogenic environment (individually and simultaneously) with age-adjusted rates using the Statistical Analysis Software 9.4. Results: The mean number of healthy (0 - 11) and unhealthy (0 - 10) food stores in each community area were 3.1 and 2.6 respectively and mean park area (14.0 - 4096.5 acres) available in each community area was 453.5 acres per 100,000 population. The mean (0 – 29.6) PCa mortality was 9.8 per 100,000 population. Healthy food access was inversely associated with PCa mortality rates, where for every for one 1-standard deviation increase in healthy food access standard deviation, there is a 0.40 unit decrease in community area PCa mortality rate standard deviation (p=0.002) while accounting for the other obesogenic factors (unhealthy food access and park access). Conclusion: The findings of this study suggest that the neighborhood access to healthy food may be a determinant of neighborhood PCa mortality rates. Ecological studies are helpful to understand the impact at the geographical level. Methodological challenges and opportunity in ecological analyses, such as addressing bias and multivariable control of confounding, will be discussed. Citation Format: Ravneet Kaur, Jiehuan Sun, Nebiyou Y. Tilahun, Vincent L. Freeman. An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C009.

Depression and its associated factors among people living with HIV in the Volta region of Ghana

Depression among people living with HIV/AIDS in higher-income countries is associated with suboptimal adherence to antiretroviral therapy and though counterintuitive. Yet, less is known regarding how depression, social support, and other sociodemographic factors influence outcomes among people living with HIV, particularly in resource-limited settings like Ghana. In view of this gap, this study investigated factors associated with depressive symptoms among people living with HIV in the Volta region of Ghana. A total of 181 people living with HIV from a local antiretroviral clinic was purposively sampled for the study. The questionnaire included the Center for Epidemiologic Studies Depression Scale, the Internalized Stigma of HIV/AIDS Tool, and the Interpersonal Support Evaluation List-12. An independent student t-test, one-way analysis of variance, and chi-square test were conducted to ascertain the associations among the variables of interest. The magnitude of association was evaluated with multiple linear regression. The average depression score among the participants was 9.1±8.8 and 20.4% reported signs of depression. Majority (78%) of participants who were depressed were male compared to females (p = 0.031). In the multiple linear regression, every one-year increase in age was significantly associated with an estimated 0.012 standard deviation increase in depression scores (95% CI: 0.002–0.021) after adjusting for all other variables in the model. Every unit standard deviation increase in social support was significantly associated with an estimated 0.659 standard deviation increase in depression scores (95% CI:0.187–1.132), after adjusting for all other variables in the model. We found a high prevalence of depressive symptoms among people living with HIV especially among males. An increase in age and social support was associated with an increase in depressive symptoms among people living with HIV in this study. We recommend further study using longitudinal approach to understand this unexpected association between depression and social support among people living with HIV in Ghana.

Abstract 4969: Investigating digital pathology tumor microenvironment features for immunotherapy outcome in patients with advanced non-small cell lung cancer

Abstract Introduction The composition of the tumor microenvironment (TME) has been shown to influence response to immunotherapy (IO). In this study, we investigated digital pathology TME features of IO outcomes among patients with non-squamous, non-small cell lung cancer (NSCLC) within a real-world dataset. Methods From the nationwide de-identified Flatiron Health-Foundation Medicine NSCLC clinico-genomic database, a cohort of 50 EGFR/ALK-negative, non-squamous NSCLC patients treated with first-line pembrolizumab monotherapy (mono-IO) or pembrolizumab in combination with carboplatin/cisplatin and pemetrexed (chemo-IO) that had available whole slide images of H&E-stained tissue resection specimens were included. The de-identified data originated from approximately 280 US cancer clinics (~800 sites of care). AI-powered TME characterization models developed by PathAI (Boston, MA; commercially available as PathExplore™) were deployed on scanned H&E slides to extract a panel of cell- and tissue-level human interpretable features (HIFs). Cox proportional hazards regression was used to identify digital pathology features or HIFs associated with overall survival. Results 17 HIFs were associated with better survival and 19 HIFs were associated with worse survival among patients treated with mono-IO (p < 0.05). Lymphocyte features were among the HIFs associated with favorable survival, including density of lymphocyte cells in cancer epithelium (hazard ratio (HR) per unit standard deviation (SD) = 0.46 [0.24, 0.88], p-value = 0.02) and proportion of lymphocyte cells relative to total immune cells in cancer epithelium (HR per unit SD = 0.61 [0.39, 0.95], p-value = 0.03). Cell-level features associated with worse survival included density of fibroblast cells in stroma (HR per unit SD = 1.51 [1.01, 2.25], p-value = 0.05). These associations remained after adjusting for tumor mutational burden (TMB) (N = 29 low, 21 high) and PD-L1 status (N = 0 negative, 1 low-positive, 17 high-positive, 32 unknown). Additionally, similar associations were not observed among patients treated with chemo-IO. High lymphocyte density was associated with better survival compared to low lymphocyte density in mono-IO treated patients (adjusted HR per unit SD = 0.36 [0.18, 0.74], p-value = 0.01) but not in chemo-IO treated patients (adjusted HR per unit SD = 1.10 [0.76, 1.60], p-value = 0.6). Conclusion These results indicate that the composition of TME assessed via digital pathology may have utility in identifying NSCLC patients that will respond to first-line immune checkpoint inhibitors beyond the established IO biomarkers. Citation Format: Ericka M. Ebot, Kuei-Ting Chen, Mikayla Biggs, Douglas I. Lin, Julia A. Elvin, Garrett M. Frampton, James J. Pao. Investigating digital pathology tumor microenvironment features for immunotherapy outcome in patients with advanced non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4969.

Abstract 18607: Sex-specific Metabolites Associated With Incident Stroke, Incident Coronary Heart Disease, Hypertension, And Chronic Kidney Disease In The Regards Cohort

Sex disparities continue to exist in cardiometabolic diseases. Metabolomic profiling offers the ability to gain insight into disease mechanisms. The purpose of the present analysis was to identify metabolites associated with sex and determine if sex-specific metabolites are associated with incident stoke, incident coronary heart disease (CHD), hypertension (HTN), and chronic kidney disease (CKD). Targeted metabolomics was conducted for 358 metabolites in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. Weighted logistic regression models were used to test the association between sex and metabolites. Metabolites meeting the Bonferroni-corrected threshold were selected for replication in the Hypertension Genetic Epidemiology Network study. Cox proportional hazard models were used to calculate hazard ratios (HRs) per unit standard deviation (SD) of each replicated metabolite with incident stroke and incident CHD. Weighted logistic regression models were used to calculate odds ratios (ORs) per unit SD of each replicated metabolite with prevalent HTN and prevalent CKD. A total of 1,898 individuals were included in the analysis and 50.47% of the sample was male sex. The mean age was 68.40 ± 10.55 years and 43% of the sample was Black race. Sixteen metabolites were associated with sex, meeting the Bonferroni-adjusted p-value of 1.4 x 10 -4 . These included 8 lipids, 6 amino acids, 1 indole, and 1 quinoline. Of these 16 metabolites, none were associated with incident stroke or incident CHD. Kynurenic acid and leucine were associated with HTN in the base model adjusting for age, sex, and race (kynurenic acid: OR 1.45, 95% CI [1.19, 1.76], p=2.36 x 10 -4 ; leucine: OR 1.41, 95% CI [1.14, 1.76], p=2.03 x 10 -3 ). Indole-3-lactic acid and kynurenic acid were associated with CKD in the full model adjusting for age, sex, race, smoking, alcohol use, and BMI (indole-3-lactic acid: OR 3.83, 95% CI [2.77, 5.29], p=5.93 x 10 -16 ; kynurenic acid: OR 6.63, 95% CI [4.67, 9.41], p=1.50 x 10 -25 ). Our findings shed light on the sexual dimorphism of the metabolome and suggest that differences in biochemical pathways may contribute to sex disparities in cardiometabolic disease risk.

Abstract 14347: Albuminuria, N-Terminal Pro-Brain Natriuretic Peptide and Incident Congestive Heart Failure in High-Risk Adults With Hypertension: A Post Hoc Analysis of the Systolic Blood Pressure Intervention Trial

Introduction: Abnormal levels of urinary albumin concentration (UAC) and N-terminal pro-Brain Natriuretic Peptide (NT-proBNP) have each been independently associated with an increased risk of congestive heart failure (CHF). It remains unknown whether the concomitant presence of abnormal UAC and NT-proBNP increases an individual’s risk for future CHF beyond that contributed by each biomarker. Objective: To examine the joint association between abnormal UAC and NT-ProBNP with incident CHF in participants from the SPRINT (Systolic Blood Pressure Intervention Trial) study. Methods: This analysis included 8,176 participants from SPRINT with available data on baseline UAC and NT-proBNP. UAC was measured by urine albumin-creatinine ratio (UACR). Albuminuria was defined as UACR ≥30 mg/g. Abnormal NT-proBNP was defined as levels ≥125 pg/ml. Cox proportional hazard models were used to examine the association of UAC and NT-proBNP, as well as albuminuria and abnormal NT-proBNP, with incident CHF. Results: Over a median follow-up of 3.2 years, 179 incident cases of CHF were observed. In adjusted models, the risk of incident CHF (HR 95% CI) associated with each 1 (one) unit standard deviation increase in the levels of UAC and NT-proBNP was 1.10 (1.04-1.16; p <0.001) and 1.13 (1.10-1.16; p <0.0001), respectively. A statistically significant multiplicative interaction between UAC*NT-proBNP was observed in the fully adjusted model (p <0.0001). The risk associated with the presence of abnormal UAC +/- abnormal NT-proBNP in the SPRINT cohort is depicted in the figure. Conclusions: Among SPRINT participants, we found a significant multiplicative interaction between UAC and NT-proBNP as predictors of incident CHF. Also, the risk of future CHF was over six times higher in individuals with abnormal UAC and NT-proBNP, compared with those with normal levels of both biomarkers. More studies are needed to further explore this interaction and the implications on CHF prevention.

Association between weight variability, weight change and clinical outcomes in hypertension

ObjectiveThe effect of body weight variability (BWV) and body weight change (BWC) in high-risk individuals with hypertension, but without diabetes mellitus (DM) remains unclear. We examined the effect of BWV and BWC on the primary outcome [the composite of myocardial infarction (MI), other acute coronary syndromes, stroke, acute decompensated heart failure (HF), or cardiovascular (CV) death] and all-cause mortality in the Systolic Blood Pressure Intervention Trial (SPRINT). MethodsIn this post-hoc analysis, we used multivariate Cox regression models to examine the risk associated with BWV and BWC for the primary outcome in SPRINT. BWV was defined as the intra-individual average successive variability (ASV). BWC was defined as baseline weight minus final weight. ResultsA total of 8714 SPRINT participants (mean age 67.8 ± 9.4 years, 35.1 % women, 58.9 % Whites) with available data on body weight were included. The median follow-up was about 3.9 years (IQR, 3.3–4.4). In multivariable-adjusted Cox models, each 1 unit standard deviation (SD) of BWV was significantly associated with a higher risk for the primary outcome, all-cause mortality, HF, MI, and stroke [HR(95 % CI)]: 1.13 (1.07–1.19; p < 0.0001), 1.22 (1.14–1.30; p < 0.0001), 1.16 (1.07–1.26; p < 0.001), 1.10 (1.00–1.20; p = 0.047), and 1.15 (1.05–1.27; p = 0.005), respectively. Similarly, each 1 unit SD of BWC was significantly associated with a higher risk of the primary outcome, all-cause mortality, MI, and HF: 1.11(1.02–1.21; p = 0.017), 1.44 (1.26–1.65; p < 0.0001), 1.16 (1.01–1.32; p = 0.041) and 1.19 (1.02–1.40; p = 0.031) respectively. However, there was no significant association with CV death (for both BWV and BWC) or stroke (BWC). ConclusionIn high-risk hypertension, BWV and BWC were both associated with higher risk of the primary outcome and all-cause mortality. These results further stress the clinical importance of sustained weight loss and minimizing fluctuations in weight in hypertension.

An environmental justice analysis of air pollution in India

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 μg/m3 (95% CI 0.062 μg/m3, 0.192 μg/m3) and 0.199 μg/m3 (95% CI 0.116 μg/m3, 0.283 μg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.

Development and analytical validation of a novel nasopharynx swab-based Epstein-Barr virus C promoter methylation quantitative assay for nasopharyngeal carcinoma detection.

Epstein-Barr virus (EBV) C promoter (Cp) hypermethylation, a crucial factor for EBV latent infection of nasopharyngeal epithelial cells, has been recognized as a promising biomarker for nasopharyngeal carcinoma (NPC) detection. In this study, we develop a novel EBV Cp methylation quantification (E-CpMQ) assay and evaluate its diagnostic performance for NPC detection. A novel qPCR assay for simultaneous quantification of methylated- and unmethylated EBV Cp was developed by the combinational modification of MethyLight and QASM, with an innovative calibrator to improve the detection accuracy and consistency. The NP swab samples and synthetic standards were used for the analytical validation of the E-CpMQ. The diagnostic efficacy of the developed E-CpMQ assay was validated in 137 NPC patients and 137 non-NPC controls. The E-CpMQ assay can detect the EBV Cp methylation ratio in one reaction system under 10 copies with 100 % recognition specificity, which is highly correlated to pyrosequencing with a correlation coefficient over 0.99. The calibrated E-CpMQ assay reduces the coefficient of variation by an average of 55.5 % with a total variance of less than 0.06 units standard deviation (SD). Linear methylation ratio detection range from 4.76 to 99.01 %. The sensitivity and specificity of the E-CpMQ respectively are 96.4 % (95 % CI: 91.7-98.8 %), 89.8 % (95 % CI: 83.5-94.3 %). The developed E-CpMQ assay with a calibrator enables accurate and reproducible EBV Cp methylation ratio quantification and offers a sensitive, specific, cost-effective method for NPC early detection.

Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study

ObjectiveWe aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk.MethodsThis study considered the 462 patients of the CORDIOPREV study (CHD patients) who were not diabetic at the beginning of the intervention. In total, 107 of them developed T2DM after a median follow-up of 60 months. They were diagnosed using the American Diabetes Association criteria. A novel lipidomic methodology employing liquid chromatography (LC) separation followed by HESI, and detection by mass spectrometry (MS) was used to annotate the lipids at the isomer level. The patients were then classified into a Training and a Validation Set (60–40). Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. Finally, a production model combining the clinical variables of interest, and the lipidic species was carried out.ResultsLC-tandem MS annotated 440 lipid species. From those, the RSF identified 15 lipid species with the lowest prediction error. These lipids were combined in an LR score which showed association with the development of T2DM. The LR hazard ratio per unit standard deviation was 2.87 and 1.43, in the Training and Validation Set respectively. Likewise, patients with higher LR Score values had lower insulin sensitivity (P = 0.006) and higher liver insulin resistance (P = 0.005). The receiver operating characteristic (ROC) curve obtained by combining clinical variables and the selected lipidic isomers using a generalised lineal model had an area under the curve (AUC) of 81.3%.ConclusionOur study showed the potential of comprehensive lipidomic analysis in identifying patients at risk of developing T2DM. In addition, the lipid species combined with clinical variables provided a new, highly sensitive model which can be used in clinical practice to identify patients at T2DM risk. Moreover, these results also indicate that we need to look closely at isomers to understand the role of this specific compound in T2DM development.Trials registration NCT00924937.

Toxic Tides and Environmental Injustice: Social Vulnerability to Sea Level Rise and Flooding of Hazardous Sites in Coastal California.

Sea level rise (SLR) and heavy precipitation events are increasing the frequency and extent of coastal flooding, which can trigger releases of toxic chemicals from hazardous sites, many of which are in low-income communities of color. We used regression models to estimate the association between facility flood risk and social vulnerability indicators in low-lying block groups in California. We applied dasymetric mapping techniques to refine facility boundaries and population estimates and probabilistic SLR projections to estimate facilities' future flood risk. We estimate that 423 facilities are at risk of flooding in 2100 under a high emissions scenario (RCP 8.5). One unit standard deviation increases in nonvoters, poverty rate, renters, residents of color, and linguistically isolated households were associated with a 1.5-2.2 times higher odds of the presence of an at-risk site within 1 km (ORs [95% CIs]: 2.2 [1.8, 2.8], 1.9 [1.5, 2.3], 1.7 [1.4, 1.9], 1.5 [1.2, 1.9], and 1.5 [1.2, 1.9], respectively). Among block groups near at least one at-risk site, the number of sites increased with poverty, proportion of renters and residents of color, and lower voter turnout. These results underscore the need for further research and disaster planning that addresses the differential hazards and health risks of SLR.

Child maltreatment and telomere length in middle and older age: retrospective cohort study of 141 748 UK Biobank participants.

There is evidence that child maltreatment is associated with shorter telomere length in early life. This study aims to examine if child maltreatment is associated with telomere length in middle- and older-age adults. This was a retrospective cohort study of 141 748 UK Biobank participants aged 37-73 years at recruitment. Leukocyte telomere length was measured with quantitative polymerase chain reaction, and log-transformed and scaled to have unit standard deviation. Child maltreatment was recalled by participants. Linear regression was used to analyse the association. After adjusting for sociodemographic characteristics, participants with three or more types of maltreatment presented with the shortest telomere lengths (β = -0.05, 95% CI -0.07 to -0.03; P < 0.0001), followed by those with two types of maltreatment (β = -0.02, 95% CI -0.04 to 0.00; P = 0.02), referent to those who had none. When adjusted for depression and post-traumatic stress disorder, the telomere lengths of participants with three or more types of maltreatment were still shorter (β = -0.04, 95% CI -0.07 to -0.02; P = 0.0008). The telomere lengths of those with one type of maltreatment were not significantly different from those who had none. When mutually adjusted, physical abuse (β = -0.05, 95% CI -0.07 to -0.03; P < 0.0001) and sexual abuse (β = -0.02, 95% CI -0.04 to 0.00; P = 0.02) were independently associated with shorter telomere length. Our findings showed that child maltreatment is associated with shorter telomere length in middle- and older-aged adults, independent of sociodemographic and mental health factors.

Fatigue condition diagnosis of rolling bearing based on normalized balanced multiscale sample entropy

Rolling bearing is a key component of machinery, its fatigue failure will affect the reliability of machinery. The bearing vibration signal has strong nonlinearity, resulting in weak fault information. Multiscale sample entropy (MSE) is an effective technique for analyzing nonlinear time series. However, MSE has limitations, such as a large deviation and weak information mining abilities, and even the calculation results have no definition. To this end, a normalized balanced multiscale sample entropy (NBMSE) is proposed in this study. For NBMSE, the data are first normalized to zero mean and unit standard deviation by zero-mean normalization, which eliminates the influence of abnormal data. Secondly, the balanced multiscale approach is advanced to coarse-grain the time series, which takes into account the uniqueness of different amplitudes and the globality of the time series. The method not only avoids no-definition results, but also reduces the entropy calculation error, thus mining useful amplitude information. The comparison of synthetic signals shows that the proposed NBMSE is more robust than other methods. Furthermore, the results of two bearing cases show that NBMSE not only provides sensitive features for fatigue diagnosis but also has higher diagnostic accuracy.

Weight variability and cardiovascular outcomes: a systematic review and meta-analysis

The association between body weight variability and the risk of cardiovascular disease (CVD) has been investigated previously with mixed findings. However, there has been no extensive study which systematically evaluates the current evidence. Furthermore, the impact of ethnicity and type 2 diabetes on this phenomena has not yet been investigated. Therefore, the aim of this study was to comprehensively evaluate the effect of weight variability on risk of CVD (any cardiovascular (CV) event, composite CV outcome, CV death, Stroke, Myocardial Infarction) and the influence of ethnicity and type 2 diabetes status on the observed association. A systematic review and meta-analysis was performed according to the meta-analyses of observational studies in epidemiology (MOOSE) guidelines. The electronic databases PubMed, Web of Science, and the Cochrane Library were searched for studies that investigated the relationship between body weight or BMI variability and CV diseases using Medical Subject Headings (MeSH) terms and keywords. The relative risks (RRs) for the outcomes were collected from studies, pooled, and analysed using a random-effects model to estimate the overall relative risk. Of 5645 articles screened, 23 studies with a total population of 15,382,537 fulfilled the prespecified criteria and were included. Individuals in the highest strata of body weight variability were found to have significantly increased risk of any CV event (RR = 1.27; 95% Confidence Interval (CI) 1.17–1.38; P < 0.0001; I2 = 97.28%), cardiovascular death (RR = 1.29; 95% CI 1.03–1.60; P < 0.0001; I2 = 55.16%), myocardial infarction (RR = 1.32; 95% CI 1.09–1.59; P = 0.0037; I2 = 97.14%), stroke (RR = 1.21; 95% CI 1.19–1.24; P < 0.0001; I2 = 0.06%), and compound CVD outcomes (RR = 1.36; 95% CI 1.08–1.73; P = 0.01; I2 = 92.41%). Similar RRs were observed regarding BMI variability and per unit standard deviation (SD) increase in body weight variability. Comparable effects were seen in people with and without diabetes, in White Europeans and Asians. In conclusion, body weight variability is associated with increased risk of CV diseases regardless of ethnicity or diabetes status. Future research is needed to prove a causative link between weight variability and CVD risk, as appropriate interventions to maintain stable weight could positively influence CVD.

Tumor Connectomics: A Novel MRI-Based Framework to Distinguish Pathologically-Proven Tumor Progression and Radiation Necrosis in Brain Metastases Treated with Radiosurgery

<h3>Purpose/Objective(s)</h3> Distinguishing true progression (TP) from radionecrosis (RN) in brain metastases after stereotactic radiosurgery (SRS) remains difficult. While traditional radiomics focuses on characterizing individual voxels, we applied tumor connectomics, a novel MRI-based complex graph framework that describes the intricate network of relationships within the tumor and surrounding tissue, to identifying TP vs. RN after SRS. <h3>Materials/Methods</h3> Brain metastases treated with SRS that underwent biopsy or resection for pathologic assessment of TP vs. RN were included from a single institution. The regions of interest were manually segmented based on the single largest diameter of the T1 post-contrast (T1C) enhancing lesion plus the corresponding area of T2 FLAIR hyperintensity, on the MRI images preceding surgical assessment typically within 5 days. The data was randomly allocated into training (80%) and test (20%) sets. The training data was standardized to zero mean and unit standard deviation. The standardization parameters were then applied to the test set to prevent any data leakage. We trained a linear support vector machine classifier using the connectomics graph theoretic metrics (consisting of node strength, degree centrality, betweenness centrality, average path length, and eigenvector centrality) in combination with biologically effective dose (BED10) to classify TP from RN. Class imbalance was resolved using the Synthetic Minority Over-sampling Technique (SMOTE). Area Under the Curve (AUC) analysis was performed. Statistical significance was set as p<0.05. <h3>Results</h3> We analyzed 135 SRS-treated lesions in 110 patients. The most common histologies were NSCLC (35.6%), breast (20.7%), and melanoma (20.0%). The median BED10 for SRS was 43.2 Gy (range: 28-60). There were 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP after SRS. The highest-performing connectomics features were degree centrality and average path length. Degree centrality was increased while average path length was decreased in RN cases compared to TP cases. This suggests a higher degree of connectivity in RN, with greater similarity in intralesional features between regions represented by the T1C and FLAIR signal. The model built using these two connectomics metrics had a sensitivity, specificity, and AUC-ROC of 0.85, 0.57, and 0.81 (95% CI: 0.61-0.93), respectively. Incorporating BED10 into the model improved performance to a sensitivity of 0.8 and specificity of 0.85, with AUC-ROC of 0.89 (95% CI: 0.7-0.98). <h3>Conclusion</h3> Our novel tumor connectomics machine-learning framework was able to distinguish pathologically-proven TP from RN with excellent discrimination, which may ultimately serve as a useful advanced imaging metric to guide clinical decision-making for radiographically progressive brain metastases.

SIMULATION MODELING OF THE SYSTEM OF RELATIONS BETWEEN THE PARTICIPANTS OF THE TRANSPORT AND LOGISTICS PROCESS AT THE SEA CARGO TERMINAL

In order to analyze the effectiveness of interaction of the cargo terminal operator with customs authorities and participants in foreign economic activity, a four-sector model of the network diagram of the interaction stages between participants in the transport and logistics process at the sea cargo terminal is presented in the paper; the model allows visualizing and controlling the process of aggregated relationships during cargo operations and the provision of customs services in the area of seaports activity. Calculations of works time parameters are performed on the basis of a four-sector model, in which the event is divided into four sectors, where the number of the event is indicated on the top, the early start of work is indicated on the left, the late end of work is marked in the right sector, and the number of the initial event of the previous work is indicated in the lower sector, according to which the critical path passes. At the same time, simulation modeling of the system of relations between the participants in the transport and logistics process at the sea cargo terminal is carried out. When modeling by the Monte Carlo method, a set of parametric values presented in the form of a probability distribution is used. It is noted that the operations performed in the simulation are subject to the normal distribution law, since a large number of participants are involved in the transport and logistics system. The results of the algorithm operation are presented in the form of a data array, which is a sequence of works performed during the interaction of participants in the transport and logistics process at the sea cargo terminal. One of the methods for modeling randomly distributed variables is the Box-Muller generation method. The use of the Box-Muller method in the work makes it possible to apply the fundamental law of probability transformation to obtain two independent normal variables that have a standard normal distribution. Thus, the chosen method gives a set of random numbers that have a normal distribution with zero mean and unit standard deviation. The results of simulation modeling allow us to conclude that the aggregated system of relationship between participants in the transport and logistics process, which uses the technology of preliminary declaration in order to make a decision on the early placement of goods in the inspection zone, is effective.

Can digital economy alleviate CO2 emissions in the transport sector? Evidence from provincial panel data in China

AbstractThe existing literature mainly focuses on the impact of information communication technologies on carbon emissions, but little attention has been paid to the role of the digital economy in transporting carbon emissions. This paper calculates the index of the digital economy through the entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method and constructs the panel data of 30 provinces in China from 2008 to 2017. By extending the Stochastic Impacts by Regression on Population, Affluence and Technology model, this study explores the potential linear and non‐linear relationship between digital economy and carbon emissions in the transport sector. Empirical results show that the digital economy has the alleviating effect on carbon emissions in the transportation sector, and a change in the digital economy of one unit standard deviation resulted in a 6.14% reduction in carbon emissions. In terms of sub‐regions, the digital economy has a significant negative impact on transport carbon emissions in the eastern and central regions, while it is insignificant in the western regions. This paper further investigates the threshold effect of urbanization on the relationship between the digital economy and transportation‐related carbon emissions. The digital economy accelerates the transport sector's carbon emissions in the low urbanization stage, while it reduces the carbon emissions in the high urbanization stage.

Innovative standard degree-day indicator (SDI) concept and application for monthly energy consumption control

In practice, monthly temperature records play a major role calculating the heating and cooling degree-days of the air conditioner management in buildings. Classically, heating and cooling degree calculations are based on the comparison of outdoor temperature measurements with a standard level called the base temperature. Truncating the temperature series at a constant base temperature causes heating (deficits) and cooling (surpluses) degrees. Since outdoor temperature records are of random character, future predictions with probabilistic and statistical methods are convenient means for calculations. All previous methodologies in the literature are based on crisp logic with simple mathematical calculations. This paper proposes a new methodological approach by converting the actual temperature record probability distribution function (PDF) into a probabilistic standardized degree indicator (SDI) in normal (Gaussian) PDF form with zero mean and unit standard deviation. Furthermore, as a new evaluation methodology fuzzy logic inference calculations are provided. SDI is divided into seven classes, three for cooling, heating, and a single class for uncooled-unheated states. Heating and cooling degree changes according to SDI values are presented with crisp and fuzzy sets. The application of the methodology is made for Kadıköy urban area on the Asian side of Istanbul, Turkey. In the case of fuzzy set SDI air conditioned operation, it has been determined that there is cooling energy saving of approximately 25% compared to the crisp set. The same is true for the heating degree-day side.

A breast cancer (BC) risk model incorporating Tyrer-Cuzick version 8 (TCv8) and a polygenic risk score (PRS) for diverse ancestries.

557 Background: BC risk assessment is important for guiding personalized screening and risk-reducing interventions. TCv8 is used to estimate BC risk based on age, breast density, family cancer history and other clinical factors. Accuracy may be improved by combining TCv8 with a PRS. We developed and validated a PRS for diverse ancestries based on 149 common genetic variants (PRS-149) comprised of 56 ancestry-informative and 93 BC-associated variants. Here, we describe a BC risk model that combines PRS-149 with TCv8. Methods: Subjects had multigene panel testing for hereditary cancer and were negative for pathogenic variants in known BC susceptibility genes. A combined risk score (CRS), incorporating PRS-149 and TCv8, was developed based on 189,230 women, including 43,444 (23%) with a history of BC. Breast Imaging Reporting and Data System (BI-RADS) breast density measurements were available for 12,363 women. We used multivariable logistic regression to test breast density and PRS-149 for association with risk of BC. An independent test cohort of 6,030 BC-unaffected women with BI-RADS assessment was used to evaluate the effect of PRS-149 on risk stratification. Relative contributions of family history, breast density, other clinical factors in TCv8 and PRS-149 were examined by adding terms sequentially to an ANOVA model. We compared differences in classification of women as high (20%) or low/moderate (20%) remaining lifetime risk according to TCv8 versus CRS. Results: In the development cohort, increased breast density was significantly associated with higher risk of BC (p=3.0x10-6) with an effect size consistent with TCv8. PRS-149 improved BC risk prediction over age, breast density and family history (OR per unit standard deviation: 1.41, 95% CI: 1.37 – 1.46; p: 1.8x10-105). PRS-149 was weakly but significantly correlated with both family history (r=0.09) and breast density (r=0.01). After adjusting for multiple testing, no other factors were correlated with PRS-149. In the independent test cohort, PRS-149 explained 27% of CRS variability after accounting for family history, breast density and other clinical factors. Adding PRS-149 to TCv8 significantly altered risk estimates, with 16.3% (983/6,030) of patients classified differently by CRS versus TCv8. By TCv8 alone, 38.0% (2,289/6,030) of patients were classified as high-risk. Among patients who were high-risk by TCv8, 25.2% (576/2,289) were downgraded by CRS. Conclusions: This is the first BC risk model that includes breast density, family history, and a PRS based on genetically determined ancestry that is validated for diverse populations. Addition of PRS-149 improved risk prediction and substantially modified risk stratification compared to TCv8 alone. Implementation of CRS may therefore lead to improved identification of women who are likely to benefit from increased surveillance and preventive medications.