Global Measures Research Articles

Job Embeddedness Review: Presentation, Measurement and Development

Employee turnover to organization is large cost, how to retain employees is the key issue facing the organization. The explanation of why people turnover has always been the focus of scholars. After traditional turnover models failed to meet the need for explanation, job embeddedness was proposed, which, clearly, differs from elements like job choice or organizational commitment. Job embeddedness comprehensively considers the relationship between people and organization, community through three dimensions, they are links, fit as well as sacrifice. By reviewing the previous research, this study summarizes the concept of job embeddedness, the composite measure and the global measure of job embeddedness and empirical research in different directions. Job embeddedness is highly correlated with turnover, so organizations can use it to help predict employee turnover. Secondly, job embeddedness has a positive correlation with job performance. Employees who have higher job embeddedness will perform better in their work. On the other hand, job embeddedness can act as a moderating variable to study its relationship with job performance and other variables. Third, the relevant research on job embeddedness and gender are summarized. Due to cultural reasons, women are more likely to receive unfair treatment at high embeddedness. Finally, based on the existing studies, this study holds that the previous studies did not pay equal attention to the off-the-job embeddedness, and the use of cross-cutting research resulted in a lack of understanding of employees. Future scholars can develop a new scale to measure job embeddedness more accurately.

Microglial Activation Contributes to Neuropsychiatric Dysfunction in Alzheimer’s Disease

AbstractBackgroundPrevious studies have shown that microglial activation (MA) plays a key role in the pathophysiological and clinical progressions of Alzheimer’s disease (AD). However, little is known whether MA is also associated with the development of neuropsychiatric symptoms typically found in patients with AD. Thus, we aim to investigate here the association of MA with neuropsychiatric symptoms (NPS) of individuals across the AD continuum.MethodWe assessed 132 individuals (86 cognitively unimpaired (CU), 28 MCI, and 18 AD dementia) from the TRIAD cohort who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q), and had positron emission tomography (PET) for amyloid‐ß (Aß) ([18F]AZD4694), tau tangles ([18F]MK6240) and MA ([11C]PBR28) at the same visit. Regions were tailored using Desikan‐Killiany (DK) atlas. SUVRs were calculated using the cerebellum gray matter as a reference. Linear regression tested the association between biomarkers accounting for age, sex, and cognitive status.ResultNPI‐Q total score was significantly associated with [11C]PBR28 in the cingulate, inferior temporal, and precuneus accounting for age, sex, and after false discovery rate (FDR) correction for multiple comparisons (Figures 1A, 1B, 1C). This association was independent of Aß and tau levels (Table 1). Notably, MA predicted neuropsychiatric dysfunction with higher magnitude than Aß or tau using PET values from overlap region (regional SUVR) and if we use a global measure for all tracers (global SUVR) (Figure 2A). When we stratify NPI‐Q domains (agitation, irritability, motor disturbance, disinhibition, elation, delusion, hallucinations, nighttime disturbance, depression, anxiety, apathy, and appetite disturbance) severity score, we found that the hyperactivity subdomain (agitation, irritability, motor disturbance, disinhibition, and elation) showed the larger contribution to the results (Figure 2B).ConclusionOur results suggest MA as a key element associated with neuropsychiatric dysfunction in AD independent of Aß and tau pathologies. These findings provide additional rationale for the therapeutics targeting glial cells activation in AD patients.

Chromosome 10q24.32 Variants Associate With Brain Arterial Diameters in Diverse Populations: A Genome-Wide Association Study.

Brain arterial diameters (BADs) are novel imaging biomarkers of cerebrovascular disease, cognitive decline, and dementia. Traditional vascular risk factors have been associated with BADs, but whether there may be genetic determinants of BADs is unknown. The authors studied 4150 participants from 6 geographically diverse population-based cohorts (40% European, 14% African, 22% Hispanic, 24% Asian ancestries). Brain arterial diameters for 13 segments were measured and averaged to obtain a global measure of BADs as well as the posterior and anterior circulations. A genome-wide association study revealed 14 variants at one locus associated with global BAD at genome-wide significance (P<5×10-8) (top single-nucleotide polymorphism, rs7921574; β=0.06 [P=1.54×10-8]). This locus mapped to an intron of CNNM2. A trans-ancestry genome-wide association study meta-analysis identified 2 more loci at NT5C2 (rs10748839; P=2.54×10-8) and AS3MT (rs10786721; P=4.97×10-8), associated with global BAD. In addition, 2 single-nucleotide polymorphisms colocalized with expression of CNNM2 (rs7897654; β=0.12 [P=6.17×10-7]) and AL356608.1 (rs10786719; β=-0.17 [P=6.60×10-6]) in brain tissue. For the posterior BAD, 2 variants at one locus mapped to an intron of TCF25 were identified (top single-nucleotide polymorphism, rs35994878; β=0.11 [P=2.94×10-8]). For the anterior BAD, one locus at ADAP1 was identified in trans-ancestry genome-wide association analysis (rs34217249; P=3.11×10-8). The current study reveals 3 novel risk loci (CNNM2, NT5C2, and AS3MT) associated with BADs. These findings may help elucidate the mechanism by which BADs may influence cerebrovascular health.

Default mode network sub‐systems connectivity in atypical Alzheimer’s disease

AbstractBackgroundDisruption of default mode network (DMN) connectivity is common in Alzheimer’s disease. We investigated cross‐sectional and one‐year longitudinal changes in DMN sub‐systems in atypical clinical presentations of AD, in relation to age and tau.MethodSixty‐four amyloid‐positive atypical AD (Aty‐AD) patients diagnosed with posterior cortical atrophy (n = 35) or logopenic progressive aphasia (n = 29) were recruited by the Neurodegenerative Research Group. Patients underwent structural and resting‐state functional MRI, and [18F]flortaucipir PET at baseline and one‐year follow‐up. 140 amyloid‐negative cognitively unimpaired (CU) individuals from the Mayo Clinic Study of Aging were included as controls. Global tau SUVR was calculated in a meta‐ROI including parietal, temporal, and occipital regions. DMN connectivity was calculated in native space for posterior (pDMN), ventral (vDMN), anterior ventral (avDMN) and anterior dorsal (adDMN) DMN sub‐systems using the dual regression method. A global measure of DMN connectivity, the Network Failure Quotient (NFQ), was calculated as (vDMN_to_pDMN+ adDMN_to_pDMN)/(pDMN+vDMN). Linear mixed‐effects models were computed for each DMN connectivity measure, including diagnosis (Aty‐AD, CU), age, time and their interaction, and a per‐person random effect. SPM paired t‐tests were run to compare baseline and follow‐up images in Aty‐AD and CU separately. Linear mixed‐effect models were run in the Aty‐AD cohort only to investigate the effect of global baseline tau SUVR on each DMN connectivity measure.ResultMixed‐effects models showed evidence of lower cross‐sectional pDMN and higher adDMN connectivity in Aty‐AD relative to CU, with no significant time effects (Fig. 1). While connectivity in all DMN sub‐systems declined with age in CU, connectivity was higher in older Aty‐AD patients relative to younger, particularly in vDMN and pDMN. A similar trend was observed for NFQ, which was significantly higher in Aty‐AD, with a negative age effect (Fig. 1). In Aty‐AD, voxel‐based analyses showed moderate evidence of decline in vDMN connectivity and increase in avDMN connectivity over time (Fig. 2). Greater baseline global tau SUVR was associated with lower vDMN connectivity, cross‐sectionally and longitudinally (Fig. 3).ConclusionDMN connectivity was altered in Aty‐AD, with strongest alterations in younger patients. The vDMN declined over time, suggesting a shift in DMN connectivity with disease progression that was likely related to tau pathology.

Association of Epigenetic Aging with Cognitive Aging in Diverse Hispanics/Latinos

AbstractBackgroundAge‐related DNA methylation (DNAm) changes occur in specific regions of the genome and have been shown to be correlated with an individual’s chronological age. Multiple “epigenetic clocks” have been developed to estimate an individual’s DNAm age, which reflect different aspects of the multidimensional aging process. Age acceleration, the deviation of the DNAm‐estimated age from the chronological age, has been proposed as a novel biomarker of aging. We investigated the association between multiple measures of age acceleration and cognitive aging phenotypes in diverse Hispanic and Latino adults.MethodsWe estimated epigenetic age acceleration from 5 epigenetic clocks in 2188 Hispanic and Latino adults (mean age 56.7 years; 64.6% women) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) who had available blood DNA methylation data at the baseline visit and neurocognitive data. These included first generation clocks, Horvath and Hannum clocks; second generation PhenoAge and GrimAge; and third generation DunedinPACE (Pace of Aging). We used mixed models to test the association of each of these measures with a global measure of cognitive function (PC1) at the baseline visit and with mild cognitive impairment at a follow‐up visit approximately 7 years later.ResultsThere were significant cross‐sectional associations between lower global cognitive function and greater age acceleration based on Hannum, Horvath, PhenoAge, and DunedinPACE algorithms. In addition, a greater age acceleration based on DunedinPACE was strongly associated with a higher risk of MCI 7 years later (Table).ConclusionBiological aging is associated with lower cognitive function and risk of MCI in diverse Hispanic and Latino adults. DunedinPACE, a third generation epigenetic clock, may be a more sensitive predictor of cognitive aging outcomes than previous generation clocks.

Microglial Activation Contributes to Neuropsychiatric Dysfunction in Alzheimer’s Disease

AbstractBackgroundPrevious studies have shown that microglial activation (MA) plays a key role in the pathophysiological and clinical progressions of Alzheimer’s disease (AD). However, little is known whether MA is also associated with the development of neuropsychiatric symptoms typically found in patients with AD. Thus, we aim to investigate here the association of MA with neuropsychiatric symptoms (NPS) of individuals across the AD continuum.MethodWe assessed 132 individuals (86 cognitively unimpaired (CU), 28 MCI, and 18 AD dementia) from the TRIAD cohort who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q), and had positron emission tomography (PET) for amyloid‐β (Aβ) ([18F]AZD4694), tau tangles ([18F]MK6240) and MA ([11C]PBR28) at the same visit. Regions were tailored using Desikan‐Killiany (DK) atlas. SUVRs were calculated using the cerebellum gray matter as a reference. Linear regression tested the association between biomarkers accounting for age, sex, and cognitive status.ResultNPI‐Q total score was significantly associated with [11C]PBR28 in the cingulate, inferior temporal, and precuneus accounting for age, sex, and after false discovery rate (FDR) correction for multiple comparisons (Figures 1A, 1B, 1C). This association was independent of Aβ and tau levels (Table 1). Notably, MA predicted neuropsychiatric dysfunction with higher magnitude than Aß or tau using PET values from overlap region (regional SUVR) and if we use a global measure for all tracers (global SUVR) (Figure 2A). When we stratify NPI‐Q domains (agitation, irritability, motor disturbance, disinhibition, elation, delusion, hallucinations, nighttime disturbance, depression, anxiety, apathy, and appetite disturbance) severity score, we found that the hyperactivity subdomain (agitation, irritability, motor disturbance, disinhibition, and elation) showed the larger contribution to the results (Figure 2B).ConclusionOur results suggest MA as a key element associated with neuropsychiatric dysfunction in AD independent of Aβ and tau pathologies. These findings provide additional rationale for the therapeutics targeting glial cells activation in AD patients.

Brain Structure and Cognitive Dysfunction in Myotonic Dystrophy Type 2 (BraCE‐DM2)

AbstractBackgroundMyotonic dystrophy type 2 (DM2) is a pleiotropic multisystemic disease. Although progressive muscle weakness is the main symptom in DM2, almost two‐third of the patients report cognitive deficits as one of the most disabling symptoms. Emerging evidence has identified tau mis‐splicing and tangle pathology in DM2, which has prompted interest in elucidating the role of tau in DM2‐related cognitive impairment. Previous studies suggest that cerebral white matter (WM) is primarily affected in DM2. However, quantitative brain imaging studies incorporate with cognitive measures are extremely limited.Method3T brain MRIs were acquired in 10 adults with DM2 and 10 gender and age‐matched controls. Brain morphometry and WM integrity were assessed using T1‐MPRAGE and diffusion tensor imaging (DTI) sequences. DTI scalars, including fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and median diffusivity (MD) were compared between groups. A comprehensive battery of cognitive and behavioral measures was performed and correlated with MRI measures (Spearman’s Correlation).ResultOf 20 participants (60% female), mean age (62.2 vs. 62.6 years) and education (16.4 vs. 17.5 years) were similar between the two groups. Compared to controls, the DM2 group showed a significant reduction in global measures of cerebral gray matter (GM) volume in both cortex and deep gray structures (mean difference, ‐0.03; p&lt;0.01), but not the WM volume. Widespread abnormalities in DTI measures (lower FA, higher RD) were among the most robust findings in DM2 (mean differences of FA, 0.04; p&lt;0.01 and RD, 0.00004; p&lt;0.01), indicating that the pathology is within WM microstructures. Overall, the DM2 group scored lower than controls in all cognitive domains, particularly executive function (mean difference, ‐12.5; p = 0.04). We found a strong correlation between cerebral FA and a test of executive function (rho = 0.71, p = 0.02). Cerebral FA also showed a moderate correlation with processing speed (rho = 0.50) and apathy evaluation scale (rho = ‐0.43).ConclusionOur pilot data demonstrate robust differences of brain structure, explicitly measures of WM integrity and cerebral GM volume between the DM2 group vs. controls. The strong correlation between cerebral FA and executive function also supports the notion that WM integrity plays an important role in cognitive dysfunction in DM2.

The Generic Conspiracist Beliefs Scale-5: further psychometric evaluation using a United Kingdom-based sample

The 5-item Generic Conspiracist Beliefs Scale (GCB-5) is an abridged version of the 15-item GCBS. It was developed as a global measure of the tendency to engage in non-event-based, conspiracy-related ideation. The GCB-5 is appealing to researchers because of its brevity, which facilitates the measurement of belief in conspiracies alongside multiple constructs and/or in situations where resources are limited (time, etc.). Noting that several studies failed to find an adequate unidimensional fit in the parent GCBS measures across different contexts, the present study further assessed the psychometric properties of the GCB-5. This was necessary since the GCB-5 was validated using North American samples. Thus, to ensure that the GCB-5 was satisfactory for use with samples in the United Kingdom (UK), GCBS/GCB-5 items were administered to a large, representative UK-based sample (N = 1,331), alongside a range of validated conspiracy scales. Confirmatory factor analysis found that a one-factor GCB-5 model produced a good model fit. This specified that the GCB-5 was underpinned by a single dimension. Furthermore, the performance of the GCB-5 was equivalent to the longer GCBS. Both instruments produced similar mean item scores and standard deviations and were comparably positively correlated with concurrent measures. Although the GCB-5 internal reliability was lower than the GCBS, it was good. The GCB-5 also demonstrated configural, metric, and scalar invariance (among gender and age subgroups). This indicated that the GCB-5 was interpreted similarly by men and women and different age groups. Overall, results supported the assertion that the GCB-5 is a psychometrically satisfactory global measure of non-event-based, conspiratorial ideation.

Local but not global graph theoretic measures of semantic networks generalize across tasks.

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory.

A Deep Learning-Based Pathomics Methodology for Quantifying and Characterizing Nucleated Cells in the Bone Marrow Microenvironment

Introduction: The overall cellularity of the bone marrow (BM) microenvironment is a WHO criterion for differentiating BCRABL- myeloproliferative neoplasms (MPNs) like polycythemia vera (PV) and myelofibrosis (MF) [1]. However, manual assessment by hematopathologists is subjective, not scalable, and only records a single global measure, disregarding BM regional differences. Leveraging pathology slide scanners and deep learning (DL) can address these limitations and reveal valuable insights into MPN subtypes and treatment effects. Despite its potential, computational pathology (Pathomics) in this area remains understudied [2-3]. Methods: After institutional review from Stony Brook University and Weill Cornell Medicine (WCM), we identified 54 patients (pts) with a manually confirmed PV diagnosis (dx) at WCM (17% newly dx'ed, 61% treated, 17% disease progression). The dataset includes hematoxylin &amp; eosin (H&amp;E) stained whole slide images (WSIs), labs, molecular studies, and pathology report elements (reference standard for biopsy core size and cellularity). Numeric cellularity measures the ratio of hematopoietic cells to fat cells and excludes inevaluable trabecular bone. Normal cellularity ranges from (100-pt age [y])±20% and PV is characterized by hypercellularity. We evaluated three DL algorithms for nuclear segmentation: 1) Our U-Net model trained on 5B nuclei from 10 cancer types [4], 2) HistoCartography (HC): a graph neural network trained on 235K nuclei (PanNuke/MoNuSac datasets) [5], and 3) CellPose (CP): a gradient-driven U-Net model trained on 1K nuclei of varied modalities [6]. A color/morphology-based segmenter identified fat cells and evaluable BM regions, excluding vessels/tissue tears. Cellular characteristics (shape, texture, color) were computed and clustered by k-nearest neighbors (kNN) and k-means to explore differences between PV stages. A tissue-based approach for cellularity estimation derived the proportion of non-fat tissue to evaluable regions. A cell-based approach derived the proportions of nucleated cell and fat cell areas. The study calculated mean differences between biopsy core size estimates, assessed correlation (r) and intra-class correlation (ICC) for numeric cellularity estimates, and Cohen's kappa (κ) for categorical cellularity estimates. Results: Measured and estimated biopsy core sizes differed by an acceptable margin (3±4 mm). Tissue-based cellularity estimator results: r=0.2, ICC=0.0, κ=0.2. Cell-based cellularity estimator result: our approach (r=0.7, ICC=0.6, κ=0.7), HC (r=0.7, ICC=0.5, κ=0.6), and CP (r=0.2, ICC=0.2, κ=0.7). Cell size clusters aligned with known distributions in PV, with the prominent Int1 class (5.1-9μm) likely containing erythrocytes and myeloid progenitors. Overall and cell-sized cellularity heatmaps visualize regional differences (Figure 1). K-means clustering separated new and progressing PV (Figure 2). Discussion: DL and classical image analysis were employed to segment nucleated cells, fat cells, and other evaluable regions of hematopoietic tissue. Extracting shape, texture, and color features from segmented nuclei enhances BM microenvironment characterization. This pilot study resulted in a practical and interpretable Pathomics pipeline and downstream analytics as a proof of concept for hypothesis-driven research. Future directions involve applying these insights to a large cohort of diagnosis WSIs from other MPN subtypes and correlating findings with other tools, like immune cell detection models.

Mean attractors and invariant measures of locally monotone and generally coercive SPDEs driven by superlinear noise

We study the global solvability, mean attractors and invariant measures for an abstract locally monotone and generally coercive SPDEs driven by infinite-dimensional superlinear noise defined in a dual space of intersection of finitely many Banach spaces. The main feature of this abstract system is that it covers a larger class of fundamental models which are included or not included previously. Under an extended locally monotone variational setting, we establish the global well-posedness, Itô's energy equality and existence of mean random attractors in some high-order Bochner spaces. The existence, uniqueness, support, (high-order and exponential) moment estimates, ergodicity, (pointwise and Wasserstein-type) exponentially mixing and asymptotic stability of invariant measures and evolution systems of measures are discussed for autonomous and nonautonomous stochastic equations. A stopping time technique is used to prove the convergence of solutions in probability in order to overcome the difficulty caused by the local monotonicity and superlinear growth of the coefficients. Our abstract results and unified methods are expected to be applied to various types of SPDEs like 2D Navier-Stokes equations, 2D MHD equations, 2D magnetic Bénard problem, Burgers type equations, 3D Leray α-model, convective Brinkman-Forchheimer equations, fractional (s,p)-Laplacian equations with monotone nonlinearities of polynomial growth of arbitrary order, and others.

The Relationship Between Patient Experience Special Care Measures and Joint Commission Accreditation Standards Compliance

Accreditation evaluates healthcare organization quality of care and patient safety processes, but the influence of this activity on the patient experience is not well understood. This study was designed to explore the relationship between accreditation survey scoring and patient experience of care using a subset of The Joint Commission (TJC) accreditation standards that are conceptually aligned with the Home Health Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) Special Care Issues composite. This was an observational study that used 2018 to 2019 CMS HHCAHPS data and TJC survey findings for 1464 accredited HHAs. The presence or absence of Requirements for Improvement (RFIs) indicating observed noncompliance and the Survey Analysis for Evaluating Risk (SAFER) score were used as independent variables in the analysis. Dependent variables included 3 HHCAHPS composite measures and 2 global rating measures. Data were analyzed using 2 sets of random effects logistic regression models. We found that an increase in RFIs scored on an HHA survey was associated with lower HHCAHPS score on all measures. When the frequency of RFIs and SAFER scores increased, there was a corresponding decline in HHCAHPS ratings, suggesting that patient experience ratings can potentially identify quality of care issues, when experience questions and quality concepts are sufficiently aligned. Adherence to Joint Commission accreditation standards may positively influence some aspects of the patient experience of care. While causality remains uncertain, accreditation appears to play a role in contributing to these outcomes.

Assessing heterogeneity on cardiovascular magnetic resonance imaging: a novel approach to diagnosis and risk stratification in cardiac diseases.

Cardiac disease affects the heart non-uniformly. Examples include focal septal or apical hypertrophy with reduced strain in hypertrophic cardiomyopathy, replacement fibrosis with akinesia in an infarct-related coronary artery territory, and a pattern of scarring in dilated cardiomyopathy. The detail and versatility of cardiovascular magnetic resonance (CMR) imaging mean it contains a wealth of information imperceptible to the naked eye and not captured by standard global measures. CMR-derived heterogeneity biomarkers could facilitate early diagnosis, better risk stratification, and a more comprehensive prediction of treatment response. Small cohort and case-control studies demonstrate the feasibility of proof-of-concept structural and functional heterogeneity measures. Detailed radiomic analyses of different CMR sequences using open-source software delineate unique voxel patterns as hallmarks of histopathological changes. Meanwhile, measures of dispersion applied to emerging CMR strain sequences describe variable longitudinal, circumferential, and radial function across the myocardium. Two of the most promising heterogeneity measures are the mean absolute deviation of regional standard deviations on native T1 and T2 and the standard deviation of time to maximum regional radial wall motion, termed the tissue synchronization index in a 16-segment left ventricle model. Real-world limitations include the non-standardization of CMR imaging protocols across different centres and the testing of large numbers of radiomic features in small, inadequately powered patient samples. We, therefore, propose a three-step roadmap to benchmark novel heterogeneity biomarkers, including defining normal reference ranges, statistical modelling against diagnosis and outcomes in large epidemiological studies, and finally, comprehensive internal and external validations.

Causal analysis of SDG achievements

The United Nations Sustainable Development Goals (SDGs) set common targets for countries all over the world for their economy and society. This research uses popular indices as antecedents (independent variables) to analyze the causal complexity of SDG achievements (outcome or dependent variable). For the antecedents, Inclusive Internet Index measures digital technology, Corruption Perception Index measures transparency and integrity, Global Innovation Index measures innovation, Global Entrepreneurship Monitor measures entrepreneurship, GDP measures economic growth, and Global Findex measures financial technology (fintech). It takes fuzzy set/Qualitative Comparative Analysis (fsQCA) as the research method and applies it to data spanning the years 2020 (30 common countries) and 2021 (28 common countries), respectively. The results show multiple relationships for both 2020 and 2021, and each relationship consists of multiple antecedents. In addition, the causal relationships of the two calendar years appear similar. Implications are discussed herein, and following the research results, countries with different competitive advantages can choose the proper causal relationship to facilitate their own SDG achievements.

Global Well-Posedness, Mean Attractors and Invariant Measures of Generalized Reversible Gray–Scott Lattice Systems Driven by Nonlinear Noise

Global Well-Posedness, Mean Attractors and Invariant Measures of Generalized Reversible Gray–Scott Lattice Systems Driven by Nonlinear Noise

Burden of Ischemic Heart Disease and Its Attributable Risk Factors in North Africa and the Middle East, 1990 to 2019: Results From the GBD Study 2019.

The North Africa and Middle East (NAME) region has one of the highest burdens of ischemic heart disease (IHD) worldwide. This study reports the contemporary epidemiology of IHD in NAME. We estimated the incidence, prevalence, deaths, years of life lost, years lived with disability, disability-adjusted life years (DALYs), and premature mortality of IHD, and its attributable risk factors in NAME from 1990 to 2019 using the results of the GBD (Global Burden of Disease study 2019). In 2019, 0.8 million lives and 18.0 million DALYs were lost due to IHD in NAME. From 1990 to 2019, the age-standardized DALY rate of IHD significantly decreased by 33.3%, mostly due to the reduction of years of life lost rather than years lived with disability. In 2019, the proportion of premature death attributable to IHD was higher in NAME compared with global measures: 26.8% versus 16.9% for women and 18.4% versus 14.8% for men, respectively. The age-standardized DALY rate of IHD attributed to metabolic risks, behavioral risks, and environmental/occupational risks significantly decreased by 28.7%, 37.8%, and 36.4%, respectively. Dietary risk factors, high systolic blood pressure, and high low-density lipoprotein cholesterol were the top 3 risks contributing to the IHD burden in most countries of NAME in 2019. In 2019, IHD was the leading cause of death and lost DALYs in NAME, where premature death due to IHD was greater than the global average. Despite the great reduction in the age-standardized DALYs of IHD in NAME from 1990 to 2019, this region still had the second-highest burden of IHD in 2019 globally.

Are preferences for soil-based ecosystem services driven by spatial phenomena?

Agricultural soils provide multiple ecosystem services that affect human well-being. Soils’ potential to provide these ecosystem services varies spatially. Socio-demographic and other drivers of environmental preferences are also spatially variable. Therefore, preferences for soil-based ecosystem services are likely to be spatially heterogeneous, which may result in different policy priorities across locations. Understanding this spatial heterogeneity of preferences is therefore essential to guide public policy to protect healthy soils. We present a study that combines explorative and hypothesis-driven approaches to understand the spatial heterogeneity of preferences for four soil-based ecosystem services: climate regulation, clean water provision, drought protection and flood protection. Based on the results of a discrete choice experiment conducted on a representative sample of the German public, we first use global and local spatial autocorrelation measures to test whether there are any obvious patterns in the spatial distribution of preferences. Second, we use spatial lag models to test a number of hypotheses to explain the observed preference heterogeneity. We particularly focus on the spatial variability of relevant phenomena such as floods, droughts or nitrate pollution of groundwater, and their effects on the studied preferences. Lastly, we compare the results from both approaches in order to see whether the identified patterns are consistent with each other. We find weak patterns of spatial heterogeneity, but our hypotheses are all rejected. This suggests that salience of relevant phenomena and individual affectedness do not have an effect on preferences for soil-based ecosystem services.

Feature fusion network based on few-shot fine-grained classification.

The objective of few-shot fine-grained learning is to identify subclasses within a primary class using a limited number of labeled samples. However, many current methodologies rely on the metric of singular feature, which is either global or local. In fine-grained image classification tasks, where the inter-class distance is small and the intra-class distance is big, relying on a singular similarity measurement can lead to the omission of either inter-class or intra-class information. We delve into inter-class information through global measures and tap into intra-class information via local measures. In this study, we introduce the Feature Fusion Similarity Network (FFSNet). This model employs global measures to accentuate the differences between classes, while utilizing local measures to consolidate intra-class data. Such an approach enables the model to learn features characterized by enlarge inter-class distances and reduce intra-class distances, even with a limited dataset of fine-grained images. Consequently, this greatly enhances the model's generalization capabilities. Our experimental results demonstrated that the proposed paradigm stands its ground against state-of-the-art models across multiple established fine-grained image benchmark datasets.

The Updated Global Malnutrition Composite Score Clinical Quality Measure: Its Relevance to Improving Inpatient Clinical Outcomes and Health Equity

Quality improvement has existed in health care for centuries with a dramatic transformation over time, largely motivated by the academic health quality movement. Throughout this evolution, the Centers for Medicare and Medicaid Services have been at the forefront of the development and provision of quality measures for health care in a variety of settings, including acute care. Quality initiatives aid in the evaluation of patient care to encourage quality improvement efforts, determine pay-for-performance rates, and help patients and consumers evaluate their care providers. The addition of the Global Malnutrition Composite Score as an electronic Clinical Quality Measure in 2022 highlights the key role nutrition plays in outcomes and quality of hospitalized patients. With this, credentialed nutrition and dietetics practitioners lie front and center for the development of quality improvement processes to help promote high quality standards of nutrition care, improve length of stay, and reduce health care costs and readmissions while addressing malnutrition, health equity, and nutrition care as a human right. As the Global Malnutrition Composite Score steward, it is the obligation of the Academy of Nutrition and Dietetics and the Commission on Dietetic Registration to promote the measure and support credentialed nutrition and dietetics practitioners in advocating for the implementation of this measure. Therefore, the purpose of this practice update is to provide necessary information to credentialed nutrition and dietetics practitioners and other health care leaders related to the history and implementation of the Global Malnutrition Composite Score, along with relevant updates to the measure and practice implications.

Ranking trees based on global centrality measures

Trees, or connected graphs with no cycles, are a commonly studied combinatorial family. When many natural metrics on networks are applied to the set of all trees of a fixed order, the extremal values are realized at the star and path graphs. In this paper, we prove inequalities for several global centrality measures, such as global closeness and betweenness centralities, and for graphical Stirling numbers of the first kind that interpolate these extremes. Moreover, we provide two algorithms that allow us to traverse the space of non-isomorphic trees of a fixed order, one towards the star graph of the same order and the other towards the path. Furthermore, we investigate the relationship between these global centrality measures on the one hand and the (n−2)nd Stirling numbers of the first kind for small trees on the other hand, demonstrating a strong association between them, in particular with respect to the hierarchical structures obtained from applying our two interpolating algorithms. Based on our observations from these small trees, we prove general bounds that relate the (n−2)nd Stirling numbers of the first kind of trees of order n to these global centrality measures. Finally, we provide two related approaches to totally order the set of all non-isomorphic trees of fixed order. We show that the totally ordering obtained from one of these approaches is consistent with the hierarchical structure obtained from our two tree interpolation algorithms in addition to being one of the features to use for predicting the (n−2)nd Stirling numbers of the first kind for small trees.