Macro Measures Research Articles

Effects of Aerobic Exercise on Brain Age and Health in Middle-Aged and Older Adults: A Single-Arm Pilot Clinical Trial.

Sleep disturbances are prevalent among elderly individuals. While polysomnography (PSG) serves as the gold standard for sleep monitoring, its extensive setup and data analysis procedures impose significant costs and time constraints, thereby restricting the long-term application within the general public. Our laboratory introduced an innovative biomarker, utilizing artificial intelligence algorithms applied to PSG data to estimate brain age (BA), a metric validated in cohorts with cognitive impairments. Nevertheless, the potential of exercise, which has been a recognized means of enhancing sleep quality in middle-aged and older adults to reduce BA, remains undetermined. We conducted an exploratory study to evaluate whether 12 weeks of moderate-intensity exercise can improve cognitive function, sleep quality, and the brain age index (BAI), a biomarker computed from overnight sleep electroencephalogram (EEG), in physically inactive middle-aged and older adults. Home wearable devices were used to monitor heart rate and overnight sleep EEG over this period. The NIH Toolbox Cognition Battery, in-lab overnight polysomnography, cardiopulmonary exercise testing, and a multiplex cytokines assay were employed to compare pre- and post-exercise brain health, exercise capacity, and plasma proteins. In total, 26 participants completed the initial assessment and exercise program, and 24 completed all procedures. Data are presented as mean [lower 95% CI of mean, upper 95% CI of mean]. Participants significantly increased maximal oxygen consumption (Pre: 21.11 [18.98, 23.23], Post 22.39 [20.09, 24.68], mL/kg/min; effect size: -0.33) and decreased resting heart rate (Pre: 66.66 [63.62, 67.38], Post: 65.13 [64.25, 66.93], bpm; effect size: -0.02) and sleeping heart rate (Pre: 64.55 [61.87, 667.23], Post: 62.93 [60.78, 65.09], bpm; effect size: -0.15). Total cognitive performance (Pre: 111.1 [107.6, 114.6], Post: 115.2 [111.9, 118.5]; effect size: 0.49) was significantly improved. No significant differences were seen in BAI or measures of sleep macro- and micro-architecture. Plasma IL-4 (Pre: 0.24 [0.18, 0.3], Post: 0.33 [0.24, 0.42], pg/mL; effect size: 0.49) was elevated, while IL-8 (Pre: 5.5 [4.45, 6.55], Post: 4.3 [3.66, 5], pg/mL; effect size: -0.57) was reduced. Cognitive function was improved by a 12-week moderate-intensity exercise program in physically inactive middle-aged and older adults, as were aerobic fitness (VO2max) and plasma cytokine profiles. However, we found no measurable effects on sleep architecture or BAI. It remains to be seen whether a study with a larger sample size and more intensive or more prolonged exercise exposure can demonstrate a beneficial effect on sleep quality and brain age.

The poverty of sustainability: behavioral choices of drinking water in Kenya and Germany

Worldwide countries are engaged in technological improvements, changes in legislation, economic incentives, strengthened administrative structures, and political strategies to allocate and distribute water among different sections of the population. These macro measures treat water as an object of social, cultural, and environmental production unit for distribution and allocation, distancing it from the micro behavioral practices of drinking water. The poverty of sustainability stems from the gap between macro instrumental measures and the inner human dimensions that determine the micro practices. This paper explores the disconnect between macro sustainability measures and the micro practice of drinking water choice among individuals and their motives in the city of Nakuru in Kenya and the state of North Rhine-Westphalia in Germany. Both Kenya and Germany have developed top-down policies with a fix-it mindset. Kenya aims to provide drinking water to its population through piped network technology despite being a water-scarce region with inadequate housing and governance. In contrast, Germany, which is rich in water resources with highly regulated governance arrangements, spends billions treating water to attain drinking water quality only for most citizens to buy bottled water to drink. Despite water resources being finite, both of these nations source additional water through instrumental approaches to satisfy the demands of the materialistic world. In this context, the challenge of overcoming the poverty of sustainability remains a topic of debate that can be ameliorated by complementing scientific approaches with nonscientific knowledge and practices.

Voting for trade protectionist parties: Evidence from nine waves of the European Social Survey

This article empirically investigates the impact of globalization on voting behavior. Specifically, combining individual-level data from the first nine waves of the European Social Survey, party-level information from the Comparative Manifesto Project, and country-level data from Eurostat, we study the individual determinants of the vote for trade protectionist parties. Our findings show, firstly, that protectionist parties mainly receive electoral support from less-educated voters, unemployed individuals, and members of labor unions. Secondly, we test the compensation principle using a macro measure of a country's compensation potential and find, contrary to expectations, no significant evidence that a greater potential to mitigate the labor market adjustment costs resulting from economic openness deters the propensity to vote for protectionist parties in national elections.

A Study on Macro Measurement Methods and Volatility Characteristics of Interest Rate Risk of Commercial Banks under the New LPR Mechanism

Abstract The interest rate risk of commercial banks is closely related to the effectiveness of the LPR mechanism. In this paper, the interest rate market is divided into three stages according to the process of interest rate marketization, and a six-sector New Keynesian MS-DSGE model is constructed by adding monetary regulation variables. The parameters in the model that are influenced by interest rate marketization and the new LPR mechanism are transformed by Markov zone transformation. The interest rate risk of commercial banks is measured by the impulse response functions of uncertain interest rate shocks to the interest rate sensitivity gap and the duration gap, and the volatility characteristics of interest rate risk in different stages of interest rate marketization are compared and analyzed. The results show that in the third stage (2019-present), the LPR mechanism is transformed from old to new. Under the δR = 0.5 shock, the commercial lending interest rate tends to stabilize around 0.00038, and the borrowing interest rate tends to 0 in all 3 differentiated target parameters in the 10th period. For this reason, the differentiated interest rate pricing behaviors of commercial banks should be considered comprehensively, and the LPR target should be set reasonably so as to get the optimal policy effect.

Assessing the barriers and enablers to the implementation of the diagnostic radiographer musculoskeletal X-ray reporting service within the NHS in England: a systematic literature review

IntroductionThe United Kingdom (UK) government's healthcare policy in the early 1990s paved the way adoption of the skills mix development and implementation of diagnostic radiographers' X-ray reporting service. Current clinical practice within the public UK healthcare system reflects the same pressures of increased demand in patient imaging and limited capacity of the reporting workforce (radiographers and radiologists) as in the 1990s. This study aimed to identify, define and assess the longitudinal macro, meso, and micro barriers and enablers to the implementation of the diagnostic radiographer musculoskeletal X-ray reporting service in the National Healthcare System (NHS) in England.MethodsMultiple independent databases were searched, including PubMed, Ovid MEDLINE; Embase; CINAHL, and Google Scholar, as well as journal databases (Scopus, Wiley), healthcare databases (NHS Evidence Database; Cochrane Library) and grey literature databases (OpenGrey, GreyNet International, and the British Library EthOS depository) and recorded in a PRISMA flow chart. A combination of keywords, Boolean logic, truncation, parentheses and wildcards with inclusion/exclusion criteria and a time frame of 1995–2022 was applied. The literature was assessed against Joanna Briggs Institute's critical appraisal checklists. With meta-aggregation to synthesize each paper, and coded using NVivo, with context grouped into macro, meso, and micro-level sources and categorised into subgroups of enablers and barriers.ResultsThe wide and diverse range of data (n = 241 papers) identified barriers and enablers of implementation, which were categorised into measures of macro, meso, and micro levels, and thematic categories of context, culture, environment, and leadership.ConclusionThe literature since 1995 has reframed the debates on implementation of the radiographer reporting role and has been instrumental in shaping clinical practice. There has been clear influence upon both meso (professional body) and macro-level (governmental/health service) policies and guidance, that have shaped change at micro-level NHS Trust organisations. There is evidence of a shift in culturally intrenched legacy perspectives within and between different meso-level professional bodies around skills mix acceptance and role boundaries. This has helped shape capacity building of the reporting workforce. All of which have contributed to conceptual understandings of the skills mix workforce within modern radiology services.

On-Chip Quantization- and Correlation-Robust Jitter Measurement for Low Phase Noise and High Frequency Oscillators

For on-chip oscillator phase noise characterization and monitoring a common technique is the measurement of the equivalent cycle-to-cycle jitter. In this letter we show that the quantization introduced to the recorded jitter by the measurement with a time-to-digital converter can be prohibitive for calculating the phase noise, especially at high oscillation frequencies and for spectrally pure oscillators. A second drawback of this method is that supply voltage noise can mask the phase noise due to intrinsic device noise. To avoid these limitations, we investigate an approach where the oscillator under test is compared to an identical replica. The resulting time difference measurements are introduced as delta jitter. The validity of the method is proven in simulation and measurement of an on-chip jitter measurement macro designed in 16 nm FinFET CMOS technology. The measurement results indicate both robustness against quantization error and a self-canceling of correlated influences on phase noise in delta jitter measurements.

Application of Support Vector Machine Regression and Partial Least-Square Regression Models for Predicting Sugarcane Leaf Nutrients Content Using Near Infra-Red Spectroscopy

ABSTRACT Near infra-red spectroscopy (NIRS) has been suggested as a rapid, cost-effective, and accurate diagnostic tool for leaf nutrient analysis that could replace more traditional laboratory diagnostics. To ease operational workflows, there would advantage in estimating nutrients using a single method, namely NIRS. This study evaluated the potential of NIRS as a diagnostic method for the measurement of key macro and micronutrients in sugarcane leaf samples. Three hundred and fifty-one sugarcane leaf samples used in quality control reference analysis in Fertiliser Advisory Service (FAS) were used for model calibration. About 35% of the samples were from growers within South Africa, while the remainder were from estates across southern and eastern Africa. Dried and milled leaf material was scanned on a Bruker MPA-NIRS instrument, and spectral pre-processing was performed. Support vector machine regression (SVMR) and partial least squares regression (PLSR) were used for calibrating the estimation models with the test validation(Tval) procedure. The results showed both the PLSR and SVMR model resulted in the best calibration for (K, Ca, Mg)(R2 >87% and the ratio of performance to inter-quartile distance(RPIQ) > 2.1). With a test validation dataset, the SVMR yielded relatively high accuracy(R2 >85% and the RPIQ > 2.5, while PLSR yielded poor accuracy. The SVMR also outperforms Zn(R2 = 0.86; and RPIQ = 2.5) for model calibration. NIR Spectrometry combined with the SVMR technique resulted in a practical option to accurately measure leaf nutrient concentrations and evaluate sugarcane mineral contents accurately. These results indicate that NIRS can replace the current analytical methods used in FAS.

Can we count on the commitment of European SMEs to achieve SGD12? An exploratory study of business sustainability

The United Nations created the 17 Sustainable Development Goals (SDG) to serve as a compass for a better future, i.e., for a sustainable future, a green, socially and financially prosperous tomorrow. Due to the significant responsibility of small and medium-sized enterprises (SMEs) to environmental dilapidation and destruction, they are an essential engine to the green transition. Business activities are central to achieving SDG12. This article focuses on the contribution of European SMEs to carbon neutrality. We explore the influence of SMEs’ investment in micro resource-efficiency actions on adopting macro measures of carbon neutrality and whether investments, financial, administrative, and regulatory requirements influence the implementation of resource efficiency practices. This is the first attempt to explore the interrelationship between micro resource efficiency and macro climate change measures. A quantitative analysis was performed using descriptive statistics, factor analysis (exploratory and confirmatory factor analysis) and the partial least squares method. Data from 2021 included 17,144 European SMEs and were retrieved from the fifth wave of the Flash Eurobarometer 498. The results reveal that SMEs' implementation of resource-efficient actions at the micro level positively influenced the adoption of macro-level measures for decarbonization. Furthermore, implementing these micro-measures was positively influenced by business investment and negatively affected by external sources of finance and regulatory and administrative requirements. Overall, the findings indicate that European SMEs still have a long way to go toward the twelfth SDG and decarbonization.

Profiling intra- and inter-individual differences in brain development across early adolescence

As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9–13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom.

A STUDY OF 24HOUR CENTRAL BLOOD PRESSURE VARIABILITY WITH MEASURES OF MACRO- AND MICROVASCULAR FUNCTION IN A POPULATION OF UNTREATED HYPERTENSIVE AND NORMOTENSIVE INDIVIDUALS

Objective: Central blood pressure (cBP) has been more closely associated with target-organ damage compared with brachial BP, although a bidirectional interplay between central and peripheral arteries exists. Brachial short term BP variability (BPV) has been increasingly related to impaired macro- and microvascular function. However, scarce data exists regarding 24hour central BPV (cBPV) and its impact on organ damage. This study aimed to measure 24h cBPV in a population of untreated essential hypertensive (UHTs) and normotensive (NTs) individuals and evaluate its association with measures of macro- and microvascular function. Design and method: Newly-diagnosed UHTs and NTs were enrolled. 24hour cBP and 24h pulse wave velocity (PWV) were measured with the Mobil-O-Graph device. BPV including average real variability (ARV) of 24h central systolic BP (cSBP) and diastolic BP (cDBP) was calculated according to a standardized formula based on the ambulatory BP monitoring (ABPM) measurements. Urine albumin excretion (UAE) was measured in 24h samples. Results: We recruited 119 individuals including 79 UHTs and 40 NTs that didn’t differ in baseline characteristics. As expected, PWV was increased in UHTs compared to NTs [7.7 (1.5) vs 6.7 (1.2) m/s, p<0.001]. Similarly, in a subset of 60 individuals, UAE was increased [12.1 (9.7) vs 6.4 (4.5) mg/24h, p = 0.008]. UHTs presented higher ARV of 24h, daytime and nighttime cSBP/cDBP compared to NTs (p<0.05 for all comparisons). In total population, 24h PWV positively correlated with ARV of 24h cSBP (r = 0.353, p<0.001) and cDBP (r = 0.235, p = 0.010), ARV of daytime cSBP (r = 0.342, p<0.001) and cDBP (r = 0.235, p = 0.010), and nighttime cSBP (r = 0.297, p = 0.007). Furthermore, UAE correlated with ARV of nighttime cDBP (r = 0.372, p = 0.017). In multivariate analysis after adjusting for age, sex, and hypertension, ARV of 24h cSBP remained an independent predictor of PWV (beta = 0.109, p<0.001). Likewise, ARV of nighttime cDBP independently predicted UAE (beta = 0.480, p = 0.002). Conclusions: In a population of untreated hypertensives and normotensives, cARV indices are significantly and independently associated with measures of macro- and microvascular function including PWV and UAE, respectively. Further studies are warranted to ascertain the clinical value of cARV in terms of risk stratification and management.

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

The latest information technologies have greatly accelerated the digitalization progress of Human Resource Management (HRM) and many useful techniques and tools have been developed for that purpose. However, in terms of risk management, effective enough tools and methods are still insufficient. Existing studies generally fail to give a turnkey solution to the operational risks in digital HRM system, and the macro measurement models are not suitable for dealing with the risks in the digital HRM system of each single enterprise. In view of these defects, this paper studied the prediction of risks in digital HRM systems based on Artificial Intelligence (AI). Firstly, the paper outlined the functions of a digital HRM system, defined the risk management mechanism of a HRM system, and built a conceptual model for it. Then, this paper proposed a novel method for predicting the risks in the digital HRM system, which innovatively integrates the digital HRM risk event chains with the risk event graph. After that, the paper elaborated on the structures and building principles of the risk event representation layer, risk event chain module, risk event graph module, and attention fusion module. At last, experimental results verified that the proposed model has obvious advantages in digital HRM risk prediction in terms of both stability and accuracy.

Improving quantification of myotube width and nuclear/cytoplasmic ratio in myogenesis research

Background and Objective: The culture of skeletal muscle cells is particularly relevant to basic biomedical research and translational medicine. The incubation of dissociated cells under controlled conditions has helped to dissect several molecular mechanisms associated with muscle cell differentiation, in addition to contributing for the evaluation of drug effects and prospective cell therapies for patients with degenerative muscle pathologies. The formation of mature multinucleated myotubes is a stepwise process involving well defined events of cell proliferation, commitment, migration, and fusion easily identified through optical microscopy methods including immunofluorescence and live cell imaging. The characterization of each step is usually based on muscle cell morphology and nuclei number, as well as the presence and intracellular location of specific cell markers. However, manual quantification of these parameters in large datasets of images is work-intensive and prone to researcher's subjectivity, mostly because of the extremely elongated cell shape of large myotubes and because myotubes are multinucleated. Methods: Here we provide two semi-automated ImageJ macros aimed to measure the width of myotubes and the nuclear/cytoplasmic localization of molecules in fluorescence images. The width measuring macro automatically determines the best angle, perpendicular to most cells, to draw a profile plot and identify and measure individual myotubes. The nuclear/cytoplasmic ratio macro compares the intensity values along lines, drawn by the user, over cytoplasm and nucleus. Results: We show that the macro measurements are more consistent than manual measurements by comparing with our own results and with the literature. Conclusions: By relying on semi-automated muscle specific ImageJ macros, we seek to improve measurement accuracy and to alleviate the laborious routine of counting and measuring muscle cell features.

Boron-doped diamond growth on carbon fibre: Enhancing the electrical conductivity

Carbon fibre reinforced polymers (CRFP) are extensively used in many industrial applications thanks to its mechanical properties and its low weight. Nevertheless, the orthotropic character of CRFP highly reduces its applications. The transversal electrical conductivity in CRFP is two orders poorer than in the longitudinal direction. To improve their electrical properties, this work proposes the use of polycrystalline boron doped diamond (BDD) as coating of the carbon fibres (CF). BDD coating is deposited on CF surface using microwave plasma enhanced chemical vapor deposition (MPCVD) system. The BDD coating forms a rigid conductive coating around the CF as a core–shell structure. Here, an electrical characterization of both, 12,000 filaments (a tow) and a single coated filament, are carried out in the longitudinal and cross-section directions. Macro, micro and local analysis using the Kelvin method, Conductive Atomic Force Microscopy (C-AFM), and Scanning Microwave Impedance Microscopy (sMIM), were carried out to evidence the improvement of the electrical properties. Macro measurement reveals that the BDD coating decreases to half the resistivity of the CF. The BDD coating raises the local electrical conductivity of the CF by an order of magnitude with respect to the uncoated ones. sMIM maps identified BDD locations in ring-like configurations.

Failure analysis of CK45 steel crankshaft in light truck diesel engine

Failure analysis was performed on the light truck diesel engine crankshaft made of CK45 steel. Premature failure has been reported in the fillet zones of the main journal-to-crankpin. This study included chemical composition determination, macro and microstructural analysis, mechanical properties evaluation, fractography, and numerical analysis. Fractographic studies using the scanning electron microscope (SEM) identified fatigue as the main mechanism of crankshaft failure. Numerical results showed that the maximum value of stress has occurred in the crack initiation zone. The results of macro measurements showed that the fillet radius was much more than the standard size (r≫0.015dcrankpin). In addition, the hardness results monitored that the main journal surface was slightly reduced to 0.1 mm (550HV difference at 0.1 mm) of induced hardness that a non-uniform distribution of high-slope hardness was observed throughout the cross-section in the main journal. In general, the experimental results showed that the high-slope hardness, the fillet radius more than the standard size, and the presence of stress concentration may be caused the crack to initiate in the fillet zone and thus the cyclic load of combustion pressure has been propagated the crack growth.

The effect of macro-control and organizational support perception on nurses and physicians intention to quit during the COVID-19 pandemic.

The aim of this study is to determine the effect of organizational support and COVID-19 macro-control perception levels on intention to quit in physicians and nurses. During the pandemic process, healthcare workers faced great health risks and workloads. Many health workers are considering quitting their job. A cross-sectional and explanatory research. The sample of the study consisted of 300 nurses and 73 physicians. To collect data, COVID-19 perception of macro-control, perceived organizational support, and intention to quit scales were used. Structural equation modeling was used. COVID-19 macro-control perception and perceived organizational support have a negative effect on the intention to quit. The perceived organizational support has a partial mediating role, on the COVID-19 macro-control perception and the intention to quit. This study demonstrated that the macro measures taken against the pandemic and the organizational support received by the nurses and physicians, who are the main actors in the fight against the epidemic, negatively affected their intention to leave. To reduce the intention to quit health workers during the COVID-19 pandemic, scientific-based macro-control measures should be taken, and organizational support should be provided.

P096 Objective Measures of Sleep Architecture in Adults and Older Adults with Depression: A Systematic Review and Meta-Analysis

Abstract Depression is intrinsically linked to sleep, however, the exact nature of sleep disturbances has been poorly defined, especially for older adults over 50 years with depression. We examined published articles with overnight EEG recordings in adults with and without depression to determine if objective measures of sleep macro and micro architecture differ. We also examined differences in these outcomes in older adults. 2135 papers were identified through four databases. Two reviewers, using the PICO structure, removed 2062 papers at abstract review and 58 at full text review. Fifteen articles met inclusion criteria and reported macroarchitecture. Three reported microarchitectural outcomes but were not meta-analysed. RevMan 5.4 software was used to perform a random effects model meta-analysis. NICE case-control guidelines were used for risk of bias. Fifteen studies were assessed (406 depressed and 432 control). Two papers examined older adults (31 depression and 31 controls). Within the total population, adults with depression had less total sleep time, lower sleep efficiency(SE), delayed sleep onset latency, higher wake after sleep onset, shorter rapid eye movement latency(ROL) and greater REM density than controls. No differences in stage 1(N1), stage 2(N2), stage 3(N3) or REM sleep were observed. In older adults, the depressed group had greater N1, less N2, shorter ROL, greater REM density and no difference in SE or in REM sleep duration. REM latency and REM density outcomes were consistent across older and younger depressed groups, whereas younger adults with depression had worse sleep continuity. Differences in sleep phenotype may relate to underlying neuropathology.

Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis.

Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.

Pandemic, poverty and education: A challenge for the sustainable development goals

COVID-19 has changed the socio-economic and educational landscape, increasing poverty rates and generating psychosocial problems. It has also conditioned the achievement of the Sustainable Development Goals (SDGs). Thus, this article pursues three-fold objectives: to describe the situation generated worldwide by the increase in poverty due to COVID-19; to analyse the degree of compliance with some post-pandemic SDGs and to establish educational measures to alleviate the psycho-pedagogical consequences. Interpretative hermeneutics is applied as it makes it possible to interpret and understand the socio-economic-educational reality and establish intervention measures adapted to the educational context. Amongst the macro measures designed, we can mention core actions such as establishing legal and motivational mechanisms to re-engage students at risk of dropping out of school; generating policies of economic and cultural compensation for possible educational inequalities; implementing school support programmes for families and students in vulnerable situations; carrying out socio-emotional learning programmes to combat anxiety, stress and self-esteem among students; establishing policies to raise awareness and appreciation of the teaching profession and stimulate collaboration between family and school; developing teacher training programmes in ICTs. At the micro level, the core actions applied are target vulnerable groups of students to overcome learning losses; promote emotional education; establish initiatives to reduce stigmatisation and discrimination; improve educational environments; and ensure equal opportunities at school.

Fiscal Consolidation and Automatic Stabilization: New Results

We derive a novel measure of household income stabilization and analyze how reforms of tax-benefit systems in the period 2007–2014 have affected the workings of automatic stabilizers in the EU-27. Our results reveal that the heterogeneity in automatic stabilizers across EU countries has become slightly smaller over the period under consideration. With a few exceptions, automatic stabilizers could operate freely in the early phase of the financial and economic crisis, but were constrained in several EU countries by subsequent fiscal consolidation measures. A comparison of our estimates of automatic stabilizers inherent in tax-benefit systems with macro measures such as changes in cyclically adjusted budget balances reveals that micro-based estimates provide more precise information about the degree of household income stabilization.

Machine-Learning-Based Return Predictors and the Spanning Controversy in Macro-Finance

We propose a two-step machine learning algorithm—the Supervised Adaptive Group LASSO (SAGLasso) method—that is suitable for constructing parsimonious return predictors from a large set of macro variables. We apply this method to government bonds and a set of 917 macro variables and construct a new, transparent, and easy-to-interpret macro variable with significant out-of-sample predictive power for excess bond returns. This new macro factor, termed the SAGLasso factor, is a linear combination of merely 30 selected macro variables out of 917. Furthermore, it can be decomposed into three sublevel factors: a novel housing factor, an employment factor, and an inflation factor. Importantly, the predictive power of the SAGLasso factor is robust to bond yields, namely, the SAGLasso factor is not spanned by bond yields. Moreover, we show that the unspanned variation of the SAGLasso factor cannot be attributed to yield measurement error or macro measurement error. The SAGLasso factor therefore provides a potential resolution to the spanning controversy in the macro-finance literature. This paper was accepted by Haoxiang Zhu, finance. Funding: This work was supported by a grant from the Penn State Institute for Real Estate Studies. Supplemental Material: Data and the internet appendices are available at https://doi.org/10.1287/mnsc.2022.4386 .