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The rapid, human-induced changes in the Earth system during the Anthropocene present humanity with critical sustainability challenges. Social-ecological systems (SES) research provides multiple approaches for understanding the complex interactions between humans, social systems, and environments and how we might direct them towards healthier and more resilient futures. However, general theories of SES change have yet to be fully developed. Formal evolutionary theory has been applied as a dynamic theory of change of complex phenomena in biology and the social sciences, but rarely in SES research. In this paper, we explore the connections between both fields, hoping to foster collaboration. After sketching out the distinct intellectual traditions of SES research and evolutionary theory, we map some of their terminological and theoretical connections. We then provide examples of how evolutionary theory might be incorporated into SES research through the use of systems mapping to identify evolutionary processes in SES, the application of concepts from evolutionary developmental biology to understand the connections between systems changes and evolutionary changes, and how evolutionary thinking may help design interventions for beneficial change. Integrating evolutionary theory and SES research can lead to a better understanding of SES changes and positive interventions for a more sustainable Anthropocene. This article is part of the theme issue Evolution and sustainability: gathering the strands for an Anthropocene synthesis.

On November 3, 2015, the majority (61%) of Houston voters voted to strike down Proposition 1: Houston’s Equal Rights Ordinance. Houston’s Equal Rights Ordinance (HERO) sought to expand anti-discrimination protections based on 15 demographic classifications including both sexual orientation and gender identity (Morris, 2014). The original passage of HERO in May 2014 by Houston’s City Council sparked not only a legal battle but also an 18-month-long media battle in which both supporters and opponents worked to sway voters to their side of the issue. This paper examines the political discourse surrounding the failed passage of HERO in three key arenas: media coverage of the event, campaign materials from both sides of the issue and politicians’ comments on the issue. In order to examine the discourse around the bill, I analyzed newspaper articles about the issues (N=358), campaign ads (N=29) and tweets from prominent local and state politicians (N= 776). Using the hybrid model of moral panics framework (Klocke and Muschert, 2010), this paper argues that the political discourse around HERO served as the catalyst for America’s newest moral panic: transgender bathroom access.

Spatio-temporal data analysis is crucial in many research fields. However, modelling large-scale spatio-temporal data presents challenges such as high computational demands, complex correlation structures, and the separation of mixed sources. To address these issues, we are developing 4DModeller (fdmr), a robust and user-friendly R package designed to model spatio-temporal data within a Bayesian framework. The software package offers a comprehensive solution for visualizing, analyzing and modelling different types of spatio-temporal data in various disciplines. By incorporating Bayesian hierarchical models, "fdmr" allows for the flexible integration of prior knowledge and data uncertainty into the modelling process. By utilizing the Integrated Nested Laplace Approximations (INLA) algorithm and the stochastic partial differential equations (SPDE) method for model inference, "fdmr" significantly reduces the computational complexity of handling high-resolution, highdimensional spatio-temporal data. Furthermore, "fdmr" provides intuitive and interactive visual analytics tools that facilitate the exploration of data patterns across both space and time. This paper aims to introduce the "fdmr" package, and outline its core modelling framework through an example study on the spread of COVID-19 infection rates in England from 19 December, 2020 to 20 March, 2021.

This paper proposes an efficient and compact hybrid switched-capacitor (SC) converter for direct 48-to-1-V power conversion in data centers. The proposed topology can be viewed as a 2-to-1 SC converter merged with two 8-to-1 series-capacitor-buck (SCB) modules through two switching buses. Different from the conventional DC bus architecture, the switching bus architecture removes the bulky DC bus capacitors and reduces the number of switches. Compared with existing 48-to-1-V hybrid SC demonstrations, the proposed converter achieves the highest SC stage conversion ratio with the lowest normalized switch volt-ampere (VA) rating, which promises lightened conversion burden on the following buck stage and reduced switch stress. A hardware prototype is designed, constructed and tested up to 500-A output current, achieving 94.7% peak power stage efficiency, 86.4% full-load efficiency and 464-W/in3 power density.

One-dimensional site response analysis (1D SRA) remains the state of practice to estimate site-specific seismic response, despite the ample evidence of discrepancies between observations and 1D SRA-based predictions. These discrepancies are due to errors in the input parameters, intrinsic limitations in the predicting capabilities of 1D SRAs even for sites relatively compliant with the 1D SRA assumptions, and the inability of 1D SRAs to model three-dimensional (3D) wave propagation phenomena. This article aims at reducing 1D SRA mispredictions using small-strain damping profiles factored by a damping multiplier (Dmul) and randomized shear-wave velocity (VS) profiles. An approach for conducting 1D SRAs for site-specific site response assessment is developed to reduce the 1D SRA errors in magnitude and variability. First, sites from a database of 534 downhole sites are classified as 1D- or 3D-like, depending on the substructure conditions inferred from observed transfer functions. Second, data from the 1D-like sites are compared against predictions from 1D SRAs conducted using various trials of Dmul and VS standard deviations (Formula presented.) for VS randomization. Third, Dmul and (Formula presented.) are selected based on their combined ability to reduce the root mean square error (RMSE) in SRA predictions. Results indicate that 1D SRAs conducted with Dmul = 3 and (Formula presented.) lead to an overall minimum RMSE and thus provide more accurate site response estimates. The use of these parameters in forward SRA predictions is discussed in a companion paper.

Site response estimates from one-dimensional (1D) site response analyses (SRAs) carry inaccuracies due to modeling and parametric errors. Modeling errors are due to the condensation of the three-dimensional (3D) wave propagation phenomenon to the vertical propagation of a horizontally polarized wave through a soil column, and parametric errors are due to the incomplete knowledge of the distributions of soil parameters, leading to the selection of nonoptimal input parameters for a site of interest. While parametric errors are traditionally handled using different soil parameters (e.g. alternative shear-wave velocity profiles), modeling errors are generally neglected. This paper proposes an approach for conducting linear elastic 1D SRAs to improve site response predictions and account for modeling errors. First, ground-motion data from borehole array sites are collected, processed, and screened for appropriateness (e.g. expected shear strains lower than 0.01%, signal-to-noise ratio higher than 3). Second, 1D SRA predictions in terms of transfer functions and amplification factors are compared against observations, and the discrepancies are quantified as residuals. Finally, the residuals are partitioned into a model bias term (Formula presented.), a site term (Formula presented.) with standard deviation (Formula presented.), and a event- and site-specific remaining residual (Formula presented.) with standard deviation (Formula presented.). Values for (Formula presented.) and (Formula presented.) for forward predictions are recommended. The sensitivity of the site response residuals to region, site type (1D- or 3D-like), and the applicability of findings to outcropping applications are discussed, and an example application for a hypothetical project site is presented.

Breast cancer is the second most common cancer for women in the United States and, accurate identification of cancerous masses is critical to reduce death rates. This thesis explores several classification models that can accurately identify malignant and benign breast masses using data from the from the UCI Machine Learning Repository. In this paper, logistic regression with PCA, random forests, and Naive Bayes are compared to identify the best model for classification. Identifying a powerful classification model can help health care providers streamline screening and diagnosis processes.

This paper identifies and evaluates issues in traditional resource adequacy (RA) assessment practices, and how adjusting these practices may affect and depend on existing institutional arrangements for planning and procurement. The paper proposes a technical-institutional roadmap that would allow regulators in vertically-integrated jurisdictions and system planners and operators in restructured jurisdictions to revise RA practices across a range of components. First, we compile a critical review of current RA assessment practices based on (1) interviews with RA practitioners and (2) a review of recent technical literature. We find that (i) RA may need to expand beyond capacity adequacy to ensure energy adequacy – relevant for energy-limited resources such as storage – and potentially some form of ancillary service adequacy (e.g. enough ramping-up and ramping-down capability in the system); (ii) chronological hourly simulations for all hours in the year are the current best practice; (iii) metrics and models used do not reflect economic criteria in system operation and loss of load; and (iv) there is a need to improve representation of weather dependencies and weather data. Second, we review planning and RA reports for several private and public entities that plan generation and/or transmission infrastructure in the continental U.S. to look for existing practices involving resilience assessments. We find no systematic treatment of the costs of extreme weather and other hazards, the benefits of resilience, and resilience metrics in planning analyses and no systematic treatment of resilience metrics, methods, and outcomes for resource adequacy purposes. Third, we create a technical framework for probabilistic RA assessment and use it to study how key choices about how to model power system operations affect the values that are obtained for RA metrics. We find that (i) non-economic dispatch schemes that ignore economic objectives can lead to accurate RA assessments when

This paper identifies and evaluates issues in traditional resource adequacy (RA) assessment practices, and how adjusting these practices may affect and depend on existing institutional arrangements for planning and procurement. The paper proposes a technical-institutional roadmap that would allow regulators in vertically-integrated jurisdictions and system planners and operators in restructured jurisdictions to revise RA practices across a range of components. First, we compile a critical review of current RA assessment practices based on (1) interviews with RA practitioners and (2) a review of recent technical literature. We find that (i) RA may need to expand beyond capacity adequacy to ensure energy adequacy – relevant for energy-limited resources such as storage – and potentially some form of ancillary service adequacy (e.g. enough ramping-up and ramping-down capability in the system); (ii) chronological hourly simulations for all hours in the year are the current best practice; (iii) metrics and models used do not reflect economic criteria in system operation and loss of load; and (iv) there is a need to improve representation of weather dependencies and weather data. Second, we review planning and RA reports for several private and public entities that plan generation and/or transmission infrastructure in the continental U.S. to look for existing practices involving resilience assessments. We find no systematic treatment of the costs of extreme weather and other hazards, the benefits of resilience, and resilience metrics in planning analyses and no systematic treatment of resilience metrics, methods, and outcomes for resource adequacy purposes. Third, we create a technical framework for probabilistic RA assessment and use it to study how key choices about how to model power system operations affect the values that are obtained for RA metrics. We find that (i) non-economic dispatch schemes that ignore economic objectives can lead to accurate RA assessments when

Many types of bio-inspired appendages have been designed for locomotion on various terrains. In this paper, a novel Jagged Anisotropic Mechanically Jamming (JAMJam) Appendage is presented which is passively compliant and super-underactuated, needing only one actuator to power multiple appendages. The JAMJam's characteristics are studied, with 'straight' appendages generating less force than 'curved' appendages, but requiring smaller sweep angle. A robot utilizing the appendages is designed for shallow-water environments, which can both walk while semi-submerged at up to 9 cm/s and swim at 7.2 cm/s. The robot is capable of locomotion in both turf and sandy laboratory conditions, and can traverse over obstacles in its path.

The importance of studying suicidal behavior cannot be overstated given the concerning prevalence. Despite a wide range of studies on suicidal behaviors, three major limitations remain. First, most studies are cross-sectional. Second, most studies examined risk factors of suicidal ideation in isolation. A meta-analysis covering the past 50 years of suicidal research found that prediction was only slightly better than chance because researchers rarely tested the combined effect of multiple risk factors. Researchers recommended utilizing machine learning (ML) approaches to study suicidal ideation instead because ML can address complex classification problems. Third, many studies have ignored the possibility that suicidal ideation can shift throughout different developmental stages. Disregarding the fact that there are different trajectories of suicidal ideation can lead to biased results. In this paper, I addressed the limitations of past research by first investigating different trajectories of suicidal ideation using Latent Class Growth Analysis on a large, nationally representative longitudinal dataset (n = 7,295). I then used two ML approaches, classification trees and random forests, to examine which risk factors are predictive of the identified trajectories. Two hundred and eighty-one predictors were included in the ML models, spanning demographic, psychological, behavioral, economic, social, and environmental variables linked to suicidal behaviors, such as mental health, violence exposure, family, peer, and school functioning, neighborhood characteristics, community engagement, negative life events, expectations for the future, risky behaviors, self-esteem, and substance use. The Latent Class Growth Analysis identified three distinct trajectories: (a) Resilient Class, (b) Declining Class, and (c) Escalating Class. When comparing the predictive performance, machine learning models, specifically classification tree (AUC = 0.73) and random forest (AUC = 0.79), out

Maternal mortality remains a significant global health challenge, with roughly 300,000 maternal deaths occurring annually, most of which are preventable. The relationship between socioeconomic status (SES) and maternal mortality rates (MMR) has been widely studied, with a growing body of evidence suggesting that lower SES is associated with higher MMR. This meta-analysis aimed to investigate the relationship between SES and MMR across different countries. We conducted a comprehensive search of electronic databases and included studies that reported the association between SES and MMR. We used a random-effects model to estimate the overall effect size and explored potential sources of heterogeneity using subgroup analyses. Our findings suggest that there is a significant inverse association between SES and MMR, with higher SES being associated with lower MMR. However, the magnitude of the effect varied across different regions, with the strongest association observed in low and middle-income countries. While previous studies have examined the relationship between various variables, they often do so within a narrow context, focusing on specific regions or communities. As a result, the findings of such studies may not be generalizable or applicable to other settings. Our research, therefore, takes a more comprehensive approach, examining the interplay between SES and MMR across different regions and countries, and considering a range of social, economic, and health-related factors that could potentially influence this relationship. Our research employs a meta-analysis of research papers and scientific data, this approach allowed for a more comprehensive and rigorous examination of the research question and can help identify patterns and trends across studies. A systematic literature search/screening coupled with data extraction from online databases informed our results

A classifier may be limited by its conditional misclassification rates more than its overall misclassification rate. In the case that one or more of the conditional misclassification rates are high, a neutral zone may be introduced to decrease and possibly balance the misclassification rates. In this paper, a neutral zone is incorporated into a three‐class classifier with its region determined by controlling conditional misclassification rates. The neutral zone classifier is illustrated with a text mining application that classifies written comments associated with student evaluations of teaching.

The simultaneous optimization of multiple objective functions is needed in many particle accelerator applications. In this paper, we present a parallel evolution based multi-objective optimizer that uses a variable population from generation to generation and an external storage to save good solutions. Two heuristic optimization methods, one uses the unified differential evolution and the other uses the real-coded genetic algorithm, are included in the optimizer to generate next generation candidate solutions, and are compared in the test examples. As an application, we applied this optimizer to the beam dynamics design optimization of a photoinjector and attained the optimal front solutions after 200 generations with the unified differential evolution offspring production scheme.

Video Games are a media platform that cannot be ignored - yet when we ask what methods can we use to make a successful video game, the answers are blurry. Players seem to care about the emergent properties of games that science traditionally struggles with: fun, balance, feel, and so on. These terms tend to be stigmatized in a research community - yet they also seem to be some of the strongest indicators of a game’s success. This thesis presents two papers that offer a framework to understand how we might start getting good design recommendations for emergent properties through a deep analysis of the conscious experience of players. In the first, we present an automated game design system (AGD) that aims to generate “balanced” games. In the second, we provide a theoretical discussion on why we might need to take unconventional routes of analysis to produce impactful games. Together, these papers present a picture not only of how to think about design strategies of emergent properties in games, but also a case-study of developing such a system and deploying it amongst users.

This review aims to summarize the putative role of histone deacetylases (HDACs) in rhabdomyosarcoma (RMS) and the effects of HDAC inhibitors (HDACi) on RMS by elucidating and highlighting known oncogenic pathways, mechanisms of resistance, and the synergistic potential of histone deacetylase inhibitors. We searched two databases (PubMed and Google Scholar) for the keywords Rhabdomyosarcoma, histone deacetylase, histone deacetylase inhibitors. We excluded three publications that did not permit access to the full text to review and those that focus exclusively on pleiomorphic RMS in adults. Forty-seven papers met the inclusion criteria. This review highlights that HDACi induce cytotoxicity, cell-cycle arrest, and oxidative stress in RMS cells. Ultimately, HDACi have been shown to increase apoptosis and the cessation of embryonal and alveolar RMS proliferation in vivo and in vitro, both synergistically and on its own. HDACi contain potent therapeutic potential against RMS. This review discusses the significant findings and the biological mechanisms behind the anti-cancer effects of HDACi. Additionally, this review highlights important clinical trials assessing the efficacy of HDACi in sarcomas.

Youth in the United States are experiencing mental health concerns at alarming rates. Considering the nation's legacy of racism and growing recognition of the impact of social determinants of health on educational and mental health inequities, it is imperative to re-envision how we approach mental health screening in schools to center equity. A focus on mental health screening for the sole purpose of identifying individual at-risk students ignores key contextual considerations, is ineffective in addressing health and educational inequities, and has the potential to perpetuate oppressive practices in schools. Equity-focused mental health screening requires a shift from individual- and deficit-focused approaches to systems- and holistic-focused approaches that (a) identify strengths and stressors among individuals, groups, and communities; (b) dismantle structural forms of oppression; and (c) promote positive mental health outcomes for minoritized youth. Integrating recommendations from the educational equity literature and critical school mental health frameworks, this paper identifies core considerations for equitable school mental health screening and provides guiding principles for each phase of the screening process, from screening readiness to execution to follow up. To implement these recommendations and transform school-based mental health care, schools should (a) incorporate multiple perspectives; (b) prioritize student, family, and community voices; and (c) build collaborative partnerships to co-construct a vision for equitable school mental health.

This paper offers a model of the semantic content of spatial nouns as generic terms in place names (e.g. Square in Trafalgar Square) and as descriptors for places ("place nouns", e.g. street in the second street). The model is based on a variant of Frame Semantics in which different context- and community-based uses (e.g. general, daily uses; specialised uses; legal, normative uses) are modelled as as sets/matrices of attribute-value pairs, or frames. The attributes forming these frames are based on data extraction from corpora (general uses), Wikipedia articles (specialised uses), and professional geographical dictionary (legal uses) as contexts. It is shown that uses associated to each context define frames varying considerably in content; however, a semantic overlap relation connects these frames. Consequences for a general theory of the semantics of place and geonames are discussed.

We consider a spatial model of cancer in which cells are points on the d-dimensional torus $\mathcal{T}=[0,L]^d$ , and each cell with $k-1$ mutations acquires a kth mutation at rate $\mu_k$ . We assume that the mutation rates $\mu_k$ are increasing, and we find the asymptotic waiting time for the first cell to acquire k mutations as the torus volume tends to infinity. This paper generalizes results on waiting for $k\geq 3$ mutations in Foo et al. (2020), which considered the case in which all of the mutation rates $\mu_k$ are the same. In addition, we find the limiting distribution of the spatial distances between mutations for certain values of the mutation rates.

In 1879, James McNeill Whistler created The Gold Scab, a painting depicting Frederick R. Leyland, his former friend and patron, as an anthropomorphized peacock sitting atop a white house in front of a piano covered with bags of gold. The Gold Scab has been described as a self-portrait, a satirical portrait, a “Japanese grotesquerie,” and a monumental caricature. Regardless of what one categorizes it as, the work is an unsatisfactory misfit that deviates from the conventions of these genres. By analyzing how The Gold Scab borrows from the genres of portraiture and caricature yet deviates from the conventions and intentions of these genres, this paper situates The Gold Scab within a larger movement, away from the rigidity of the genre of nineteenth-century portraiture. In my examination of The Gold Scab through the lens of anti-portraiture, a framework that deprioritizes likeness as the defining convention of portraiture and instead prioritizes consideration of the artist’s intentions, it becomes clear that The Gold Scab is not a misfit; rather, it serves as a more than satisfactory example of the shift in the ideological discourse regarding likeness and portraiture taking place in the nineteenth century.

IRTree models have been receiving increasing attention. However, to date, there are limited sources that provide a systematic introduction to Bayesian modeling techniques using modern probabilistic programming frameworks for the implementation of IRTree models. To facilitate the research and application of IRTree models, this paper introduces how to perform two families of Bayesian IRTree models (i.e., response tree models and latent tree models) in Stan and how to extend them in an explanatory way. Some suggestions on executing Stan codes and checking convergence are also provided. An empirical study based on the Oxford Achieving Resilience during COVID-19 data was conducted as an example to further illustrate how to apply Bayesian IRTree models to address research questions. Finally, strengths and future directions are discussed.

Despite an increased understanding of the pathophysiology of breast cancer in recent years, the disease remains largely prevalent in the female population due to its disordered process. Breast cancer is known to affect 1 in 8 women and is the second leading cause of cancer deaths in women as well. Treatments are currently being administered mostly in surgical procedures, chemotherapy, and hormone therapy, but current research indicates that there are potential remedies that could alter the metastasis of breast cancer on the DNA level. The usage of CRISPR(Clustered Regularly Interspaced Palindromic Repeats)/Cas9 within breast cancer treatments has grown in popularity immensely in the last decade. CRISPR/Cas9 is a system that utilizes a guide RNA to target and cut specific DNA sequences with the intention of genetically altering the genome. In cancer treatments, the knockout of specific genes utilizing the CRISPR/Cas9 system has proven variably successful in apoptosis by indirectly regulating tumor suppressor genes and causing gene knockouts. However, there are serious limitations and consequences that are being investigated within these therapies, as discussed in this paper.

Recent advances in Deep Neural Networks (DNN's) over the last decade have allowed modern neural networks to be reliably deployed "on the edge" in countless applications ranging from computer vision to natural language processing. Existing hardware is capable of running complex models with low latency, but a problem occurs when applications are scaled to require cheaper hardware with shallower memory resources or minimal latency. The goal of model compression is to take popular pre-trained deep neural networks and reduce their size to allow them to be readily deployed in areas requiring "on-device" inference such as self-driving vehicles and A.I. assistants. This paper covers recent advances in the field of model compression that has allowed us to create a 100x smaller model in terms of memory storage, while maintaining stable F1, Precision and Recall scores.

Improving the sustainability of cities is crucial for meeting climate goals in the next several decades. One way this is being tackled is through innovation in district energy systems, which can take advantage of local resources and economies of scale to improve the performance of whole neighborhoods in ways infeasible for individual buildings. These systems vary in physical size, end use services, primary energy resources, and sophistication of control. They also vary enormously in their choice of optimization metrics while all under the umbrella-goal of improved sustainability. This paper explores the implications of choice of metric on district energy systems using three case studies: Stanford University, the University of California at Merced, and the Richmond Bay campus of the University of California at Berkeley. They each have a centralized authority to implement large-scale projects quickly, while maintaining data records, which makes them relatively effective at achieving their respective goals. Comparing the systems using several common energy metrics reveals significant differences in relative system merit. Additionally, a novel bidirectional heating and cooling system is presented. This system is highly energy-efficient, and while more analysis is required, may be the basis of the next generation of district energy systems.

The Society of Abdominal Radiologys Colorectal and Anal Cancer Disease-Focused Panel (DFP) first published a rectal cancer lexicon paper in 2019. Since that time, the DFP has published revised initial staging and restaging reporting templates, and a new SAR user guide to accompany the rectal MRI synoptic report (primary staging). This lexicon update summarizes interval developments, while conforming to the original lexicon 2019 format. Emphasis is placed on primary staging, treatment response, anatomic terminology, nodal staging, and the utility of specific sequences in the MRI protocol. A discussion of primary tumor staging reviews updates on tumor morphology and its clinical significance, T1 and T3 subclassifications and their clinical implications, T4a and T4b imaging findings/definitions, terminology updates on the use of MRF over CRM, and the conundrum of the external sphincter. A parallel section on treatment response reviews the clinical significance of near-complete response and introduces the lexicon of regrowth versus recurrence. A review of relevant anatomy incorporates updated definitions and expert consensus of anatomic landmarks, including the NCCNs new definition of rectal upper margin and sigmoid take-off. A detailed review of nodal staging is also included, with attention to tumor location relative to the dentate line and locoregional lymph node designation, a new suggested size threshold for lateral lymph nodes and their indications for use, and imaging criteria used to differentiate tumor deposits from lymph nodes. Finally, new treatment terminologies such as organ preservation, TNT, TAMIS and watch-and-wait management are introduced. This 2023 version aims to serve as a concise set of up-to-date recommendations for radiologists, and discusses terminology, classification systems, MRI and clinical staging, and the evolving concepts in diagnosis and treatment of rectal cancer.

ISO 50001:2011 is a voluntary international standard that provides a flexible framework for implementing a continual improvement-based system to manage energy. Experience shows that institutionalizing ISO 50001’s proven best practices can lead to deep total energy savings of 5–20 percent annually. Based on an internationally developed methodology for predicting future impacts of ISO 50001 uptake within a region from McKane et al. (2017), we estimate that adopting ISO 50001 within the U.S. commercial buildings sector can generate annual energy savings of approximately 65 billion Btu in 2030. Commercial properties also could leverage ISO 50001 to unlock benefits through increased property values, rental premiums, company valuations, and access to new capital markets. Currently, within the United States, ISO 50001 has been primarily adopted in the industrial sector, with limited uptake in the commercial sector. This paper discusses the motivation, implementation approach, and achieved benefits of two ISO 50001-certified companies with commercial building portfolios in the United States. Anecdotal results from these companies show that the commercial buildings sector has an opportunity to implement ISO 50001 cost-effectively at the enterprise level and scale energy savings quickly—especially among organizations with campuses and/or chains of geographically scattered buildings with similar building energy systems. These early results, coupled with estimated impacts from ISO 50001 uptake within the U.S. commercial buildings sector, suggest that direct uptake and incorporation of the standard in utility and government programs will generate significant energy and greenhouse gas reductions.

In multisite observational studies where level-one units are nested within level-two groups and treatment assignment occurs within (as opposed to between) groups, treatment assignment processes may vary between groups. A possible consequence of such group-varying treatment assignment processes is that the conditional exchangeability assumption may hold for some groups in a given sample while other groups are susceptible to omitted variable bias. This paper employs a simulation study to explore the potential for leveraging information about group-specific treatment assignment processes in order to mitigate omitted variable bias in hierarchical linear models with random intercepts and treatment effect slopes. The simulation demonstrates that an “augmented” model that incorporates information about group-varying treatment assignment can substantially reduce bias in treatment main effect estimates and also reduce the mean squared error of treatment random effect variance estimates.

Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models.

Nafion ionomer, composed of hydrophobic perfluorocarbon backbones and hydrophilic sulfonic acid side chains, is the most widely used additive for preparing catalyst layers (CLs) for electrochemical CO2 reduction, but its impact on the performance of CO2 electrolysis remains poorly understood. Here, we systematically investigate the role of the catalyst ink formulation on CO2 electrolysis using commercial CuO nanoparticles as the model pre-catalyst. We find that the presence of Nafion is essential for achieving stable product distributions due to its ability to stabilize the catalyst morphology under reaction conditions. Moreover, the Nafion content and solvent composition (water/alcohol fraction) regulate the internal structure of Nafion coatings, as well as the catalyst morphology, thereby significantly impacting CO2 electrolysis performance, resulting in variations of C2+ product Faradaic efficiency (FE) by >3×, with C2+ FE ranging from 17 to 54% on carbon paper substrates. Using a combination of ellipsometry and in situ Raman spectroscopy during CO2 reduction, we find that such selectivity differences stem from changes to the local reaction microenvironment. In particular, the combination of high water/alcohol ratios and low Nafion fractions in the catalyst ink results in stable and favorable microenvironments, increasing the local CO2/H2O concentration ratio and promoting high CO surface coverage to facilitate C2+ production in long-term CO2 electrolysis. Therefore, this work provides insights into the critical role of Nafion binders and underlines the importance of optimizing Nafion/solvent formulations as a means of enhancing the performance of electrochemical CO2 reduction systems.

Analytical limitations make it challenging to develop effective methodologies for understanding glyphosate-based herbicide levels in drinking water and groundwater. Due to their lack of chromophores and zwitterionic nature, glyphosate-based herbicides are difficult to detect using traditional methods. This paper offers a straightforward method for quantifying glyphosate, glufosinate, and aminomethylphosphonic acid (AMPA) via 9-fluorenylmethylchloroformate (FMOC-Cl) pre-column derivatization and analysis by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Method development was focused on optimizing the critical variables for optimal derivatization using a 24-factorial design. We found that complete derivatization significantly depends on the inclusion of borate buffer to create the alkaline conditions necessary for aminolysis. Ethylenediaminetetraacetic acid (EDTA) addition was critical to minimize metallic chelation and ensure reproducible retention times and peaks. However, EDTA concentrations ≥5% decreased peak intensity due to ion suppression. The FMOC-Cl concentration and derivatization time exhibited a direct proportional relationship, with the complete reaction achieved with 2.5 mM FMOC-Cl after 4 h. Concentrations of FMOC-Cl greater than 2.5 mM led to the formation of oxides, which interfere with the detection sensitivity and selectivity. Desirable results were achieved with 1% EDTA, 5% borate, and 2.5 mM FMOC-Cl, which led to complete derivatization after 4 h.

One-pot syntheses and in situ ultrasonication have been optimized to generate tunable and highly hydrophobic 2,7-octadienyl-ether (ODE) cellulose nanofibrils (CNFs). Using only butadiene sulfone to serve as dual reaction medium and precursor to 1,3-butadiene (1,3-BD) for telomerization with cellulose hydroxyls, the OH-to-ODE conversion significantly improved from ca. 0.7 to 1.2, 1.8, 3.1, and 4.1 mmol of ODE/g-cellulose at 103-110 °C for 1-2 h. Ultrasonication was highly effective and versatile in the direct disintegration of ODE-cellulose (ODE-cell) in nonpolar organic solvents and plant oils into ODE-CNFs as well as pretreating cellulose for improved functionalization. Mixing 2 wt % in situ ultrasonicated 1.2 mmol of ODE/g-cell improved the modulus of polybutadiene six times and the strength of poly(styrene-b-isoprene-b-styrene) three times, whereas in situ ultrasonication of 1.8 mmol/g-cell directly with linseed oil at merely 0.07 wt % created holistic biobased hydrophobic cellulose paper to resist water penetration while also significantly improving the tensile strength. This optimized telomerization and streamlined in situ ultrasonication approach has presented a sustainable synthesis and processing strategy to generate highly hydrocarbon-compatible nanocelluloses previously unattainable.

Over the past two decades, serial X-ray crystallography has enabled the structure determination of a wide range of proteins. With the advent of X-ray free-electron lasers (XFELs), ever-smaller crystals have yielded high-resolution diffraction and structure determination. A crucial need to continue advancement is the efficient delivery of fragile and micrometre-sized crystals to the X-ray beam intersection. This paper presents an improved design of an all-polymer microfluidic `chip for room-temperature fixed-target serial crystallography that can be tailored to broadly meet the needs of users at either synchrotron or XFEL light sources. The chips are designed to be customized around different types of crystals and offer users a friendly, quick, convenient, ultra-low-cost and robust sample-delivery platform. Compared with the previous iteration of the chip [Gilbile et al. (2021), Lab Chip, 21, 4831-4845], the new design eliminates cleanroom fabrication. It has a larger imaging area to volume, while maintaining crystal hydration stability for both in situ crystallization or direct crystal slurry loading. Crystals of two model proteins, lysozyme and thaumatin, were used to validate the effectiveness of the design at both synchrotron (lysozyme and thaumatin) and XFEL (lysozyme only) facilities, yielding complete data sets with resolutions of 1.42, 1.48 and 1.70 Å, respectively. Overall, the improved chip design, ease of fabrication and high modifiability create a powerful, all-around sample-delivery tool that structural biologists can quickly adopt, especially in cases of limited sample volume and small, fragile crystals.

Extracellular vesicles (EVs), including exosomes, microvesicles, and oncosomes, are nano-sized particles enclosed by a lipid bilayer. EVs are released by virtually all eukaryotic cells and have been shown to contribute to intercellular communication by transporting proteins, lipids, and nucleic acids. In the context of neurodegenerative diseases, EVs may carry toxic, misfolded forms of amyloidogenic proteins and facilitate their spread to recipient cells in the central nervous system (CNS). CNS-originating EVs can cross the blood-brain barrier into the bloodstream and may be found in other body fluids, including saliva, tears, and urine. EVs originating in the CNS represent an attractive source of biomarkers for neurodegenerative diseases, because they contain cell- and cell state-specific biological materials. In recent years, multiple papers have reported the use of this strategy for identification and quantitation of biomarkers for neurodegenerative diseases, including Parkinson's disease and atypical parkinsonian disorders. However, certain technical issues have yet to be standardized, such as the best surface markers for isolation of cell type-specific EVs and validating the cellular origin of the EVs. Here, we review recent research using CNS-originating EVs for biomarker studies, primarily in parkinsonian disorders, highlight technical challenges, and propose strategies for overcoming them.

In this paper we conduct transfer learning on the Faster R-CNN ResNet-50 FPN V2 object detection model from Benchmarking Detection Transfer Learning with Vision Transformers [12] to a new problem space: detecting what parts of a webpage can be clicked on. The model predicts the bounding boxes and class labels for what part of a website image can be clicked on, and what the result will be. We found that training the feature extraction layers of the model was detrimental to the learning (354% higher loss after 10 epochs), but that training the RPN layers was not detrimental. We additionally found that the most effective resizing for website imagery was to 224 x 224 pixels. Since the meta trainer we developed did not produce usable data, we experiment on using the data diversity approach in active learning to select the most representative images to train on. Compared to our control, the model trained on the active learning dataset scored 12.5% better on the validation data. However, with such a small dataset of 10 labeled images, the model was unable to generalize and did not predict any of the 15 objects on a labeled image outside the training set.

Innovations in commercial building control sequences (ASHRAE guideline 36) and fault detection and diagnostics (FDD) technology have the potential to transform existing building operational efficiency by realizing whole-building level savings on the order of 15% and higher. However, several technical and market barriers related to up-front estimation of achievable savings, verifying implementation, and ensuring persistence are slowing down their adoption as a core offering through diverse owner-funded initiatives and rate-payer funded programs. This paper describes a suite of solutions to overcome these barriers to program delivery of advanced HVAC controls integrated with FDD energy management and information system (EMIS) technology. First, the Guideline 36 conformance test offers an automated method to confirm correct sequence implementation by the manufacturer, before field deployment. A successful test of a controller was conducted using a new manufacturer-independent hardware-in-the-loop testbed demonstrating the viability and scalability of this approach. Second, the Guideline 36 energy savings calculator uses modeling paired with high-level user inputs to estimate building-specific savings potential, target cost effective implementation and minimize uncertainty. Initial results indicate the baseline control sequences significantly impact energy savings, however, as much as 50% savings may be possible for the worst-case baseline scenario. Third, a functional specification provides minimum recommended EMIS-FDD capabilities, and best practices to integrate the technology into organizational practice and ensure that advanced control sequences provide persistent savings.

PURPOSE: Skin and soft tissue aging has been an important topic of discussion among plastic surgeons and their patients. While botulinum toxin, facial fillers, chemical peels, and surgical lifts preside as the mainstay of treatment to restore appearance of youth, emergent technologies, such as CRISPR-Cas9, proteostasis, flap biology, and stem cell therapies, have gained traction in addressing the aging process of skin and soft tissue. Several studies have introduced these advancements, but it remains unclear how safe and effective these therapeutics are in facial rejuvenation, and how they may fit in the existent treatment workflow for soft tissue aging. MATERIALS/METHODS: A systematic literature review was conducted to identify and assess therapeutics utilized in addressing skin and soft tissue aging. Variables collected included year of publication, journal, article title, organization of study, patient sample, treatment modality, associated outcomes. In addition, we performed a market analysis of companies involved in promoting technologies and therapeutics within this space. PitchBook (Seattle, WA), a public market database, was utilized to classify companies, and record the amount of venture capital funding allocated to these categories. RESULTS: Initial review yielded four hundred and two papers. Of these, thirty-five were extracted after applying inclusion and exclusion criteria. Though previous literature regards CRISPR-Cas9 technology as the most favorable anti-aging innovation, after reviewing the current literature, stem cell therapies utilizing recipient chimerism appeared to be the superior skin anti-aging technique when accounting for possible disadvantages of various techniques. The psychosocial and cosmetic outcomes from using cell therapy to modulate allograft survival and tolerance may confer more long-term proposed benefits than the technologies in CRISPR-Cas9, flap biology innovations, and autologous platelet-rich plasma use. Market analysis

For decades, activists, organizers, and scientists across the Global South have warned about the pronounced impacts of the climate crisis on their countries, communities, and ecosystems, and have done so while working tirelessly to develop resilience against such impacts. This is especially true for the African continent, which has contributed the least to the climate crisis, is among the most impacted by the climate crisis, and has given rise to demonstrably robust and far-reaching strategies for climate resilience.In our research for this paper, we focus on how African climate, agri-food, and environmental organizations are combating the drivers of the climate crisis, managing the impacts of the climate crisis, and forging strategies to build climate resilience. We do so to help ensure that efforts to support climate justice in Africa are accountable to the objectives, strategies, and activities of African organizations and African peoples themselves, and to build robust and impactful relationships with African organizations.&nbsp

My interdisciplinary research project contributes to the field of Octavia E. Butler Studies by documenting how Butler’s work bridges the theories of Afrofuturism, Afro-pessimism, and Black Anarchism. In my dissertation, I connect my work as a Black Studies cultural practitioner and scholar to the MacArthur award-winning science fiction writer Octavia E. Butler’s deployment of Black speculative fiction and neo-slave narratives as a highly generative and necessary space for knowledge production. The analysis of Butler’s fiction and the Butler Papers, housed at the Huntington Library in San Marino California, reveals how Afrofuturism, Afro-pessimism and Black Anarchism are incredibly compatible when used to support each other and deeply generative when their tensions produce rigorous questions. Questions that by their nature cannot always be answered, but in that unanswerable tension, we have much to consider. Each of the three theories are fundamentally concerned with the violence of the human category in modernity, anti-Blackness, white supremacy, and civil liberties, even as they enter these conversations differently. I argue each of these theories are also in line with Butler’s mission as a writer. Afrofuturism, Afro-pessimism, and Black Anarchism are political theories which seek to educate, disrupt thinking, abate imperial global destruction, consider possibilities, and always remain in reflection of the past, present, and future. Due to this I not only analyze Black speculative fiction, via Butler’s work, I also contribute to creating it in multiple forms in dialogue with Butler’s work and Afrofuturist cultural production, which often takes the form of visual culture, such as comics and graphic novels, as well as literary fiction. My dissertation will be a synthesis of three creative projects and an analysis of Butler’s fiction and archival material, with an introduction and conclusion connecting the individual chapters. My dissertation constructs the theoretica

Al-Sheikh Muwannis, where Tel Aviv University was later established, was one of four villages founded in the Ottoman period next to the shores of the Sharon (along with the villages of Ijlil, Al-Haram Sidna ‘Ali and Umm Khalid). The article traces the village's history within its social and spatial contexts. At the historiographical level, the establishment of Al-Sheikh Muwannis as discussed in the article demonstrates that the expansion of settlement in the southern Sharon was the result of the internal expansion of the core settlement by residents of the mountainous highlands of Samaria, and not by Egyptian ‘penetrators’ as previously claimed. Furthermore, the article outlines the complex relationships that developed between the original village population, new migrants, and nomadic populations camping on its lands; relationships that were exploited to further the growth and crystallization of Jewish settlement in the Yarkon Basin and around Tel Aviv. The close proximity of al-Sheikh Muwannis to the countryside of the Sharon and to Tel Aviv-Jaffa, led to the development of a space expressing architectural, social, linguistic and technological concepts derived from both countryside and city alike. The rich and complex picture presented in this paper can provide a framework to understand the development of other villages like al-Haram Sidna ‘Ali, Jalil, al-Jammasin, Sumayil (al-Mas‘udiya) and Salama.

Elements of a high-latitude (∼60–70°S) and low-diversity early Kungurian (Cisuralian/Early Permian) brachiopod fauna have been sporadically reported from the sandstone-dominated Snapper Point Formation (SPF) in the southern Sydney Basin of southeastern Australia for more than a half-century, but a detailed description of this fauna is not yet available. In this paper we describe 12 brachiopod species and an indeterminate ingelarellid from the SPF, including one new species (Tasmanospirifer jervisbayensis sp. nov. Waterhouse & Lee). Though this brachiopod fauna is evidently associated with an interglacial stratigraphic interval, its taxonomic characteristics overall resemble those from stratigraphically bounding glacial intervals. This association is interpreted to indicate persistence and the strong endemic nature of the Permian Eastern Australian biogeographic province in high-latitude eastern Gondwana, regardless of glacial/interglacial climate states during the Cisuralian. Biostratigraphically, the SPF brachiopod fauna is divisible into two distinctive stratigraphic assemblages: the Notospirifer cf. triplicata–Simplicisulcus sp. Assemblage in the lower part of the formation and the Johndearia brevis–Sulciplica transversa Assemblage in the upper part, each distinguished by a set of unique species.

This paper examines how female Daoists achieved ideological political power within the Confucian feudal society in the Tang Dynasty in ancient China. It compares the influence of the three core religions Ru Shi Dao to show the social background of the Tang Dynasty, serving as a basis for discussing women’s empowerment. The paper demonstrates that Daoism played a significant role in helping women obtain the liberation of their physicality, step out of their nuclear family, and obtain social power. The paper employs Michael Mann’s theory on sources of social power to understand the power structure in the Tang Dynasty. It examines classical texts on Confucianism and Daoism, as well as Chinese poems, providing both theoretical support to the social order of the Tang Dynasty and individual experience in the specific social environment.

Located at the heart of the Amazonian-Andean geobiological interface, Bolivia is uniquely situated with an ecologically diverse landscape, a politically active Indigenous population, and natural resources that attract foreign interest. As climate change mitigation gains international traction, Bolivia’s potential to provide lithium globally and hydropower regionally has prompted exploitation of the land by the Bolivian government. In a nation where the Indigenous majority has defined the rights of Mother Earth in its Constitution, the destructive nature of these projects calls attention to the novel issue of justifying environmental degradation with a promise to save the world from climate change. By examining primary and secondary sources, this paper explores the friction between development for sustainability and Indigenous environmental justice in Bolivia. In my investigation, I question whether investing in renewables that cause environmental degradation is inherently contradictory in the context of Bolivian Indigenous tradition. I consistently find that the methods of development and consultation for these projects are flawed. I suggest that a framework of temporal environmental justice is needed to fully understand this friction between environmental and climate justice.

In this paper, I explore how Filipinx settlers in occupied Hawaiʻi are racialized in proximity to Blackness, in relation to US-centric and colonial articulations of Blackness, and within the settler colonial system of power and domination in the transpacific. Across the Filipinx diaspora, critiques of white skin valorization are conceptualized primarily as colorism where Asian beauty and desirability are routed through the white colonial imagination. However, drawing from ethnographic research on local skin whitening discourse among first and second-generation Filipinx-American settlers in Hawaiʻi, I consider how these stories of skin reveal how Filipinx settlers are racialized in proximity to Blackness, where Blackness is denigrated, whiteness reigns supreme, and Kanaka Maoli are entangled in US racial binaries. As such, I move beyond colorism to argue that the processes of racialization indexed by skin whitening is an anti-Black project of US empire that renders dark-skin bodies abject and undesirable. In confronting anti-Blackness in Hawaiʻi, I contend that Afro-Asian-Indigenous relationalities challenge enduring racial colonial discourses and contribute to alternative possibilities for Blackness, Indigeneity, and settler allyship to become entwined components of an anti-racist and decolonial Hawaiʻi.

Mixed-effects models for repeated measures and longitudinal data include random coefficients that are unique to the individual, and thus permit subject-specific growth trajectories, as well as direct study of how the coefficients of a growth function vary as a function of covariates. Although applications of these models often assume homogeneity of the within-subject residual variance that characterizes within-person variation after accounting for systematic change and the variances of the random coefficients of a growth model that quantify individual differences in aspects of change, alternative covariance structures can be considered. These include allowing for serial correlations between the within-subject residuals to account for dependencies in data that remain after fitting a particular growth model or specifying the within-subject residual variance to be a function of covariates or a random subject effect to address between-subject heterogeneity due to unmeasured influences. Further, the variances of the random coefficients can be functions of covariates to relax the assumption that these variances are constant across subjects and to allow for the study of determinants of these sources of variation. In this paper, we consider combinations of these structures that permit flexibility in how mixed-effects models are specified to understand within- and between-subject variation in repeated measures and longitudinal data. Data from three learning studies are analyzed using these different specifications of mixed-effects models.

IntroductionThis culturally tailored enrollment effort aims to determine the feasibility of enrolling 5000 older Latino adults from California into the Brain Health Registries (BHR) over 2.25 years.MethodsThis paper describes (1) the development and deployment of culturally tailored BHR websites and digital ads, in collaboration with a Latino community science partnership board and a marketing company; (2) an interim feasibility analysis of the enrollment efforts and numbers, and participant characteristics (primary aim); as well as (3) an exploration of module completion and a preliminary efficacy evaluation of the culturally tailored digital efforts compared to BHR's standard non-culturally tailored efforts (secondary aim).ResultsIn 12.5 months, 3603 older Latino adults were enrolled (71% of the total California Latino BHR initiative enrollment goal). Completion of all BHR modules was low (6%).DiscussionTargeted ad placement, culturally tailored enrollment messaging, and culturally tailored BHR websites increased enrollment of Latino participants in BHR, but did not translate to increased module completion.HighlightsCulturally tailored social marketing and website improvements were implemented. The efforts enrolled 5662 Latino individuals in 12.5 months. The number of Latino Brain Health Registry (BHR) participants increased by 122.7%. We failed to adequately enroll female Latinos and Latinos with lower education. Future work will evaluate effects of a newly released Spanish-language BHR website.

INTRODUCTION: This perspective paper addresses the US Hispanic/Latino (herein, Latino) experience with regards to a significant public health concern-the underrepresentation of Latino persons in Alzheimers disease and related dementias (AD/ADRD) clinical trials. Latino individuals are at increased risk for AD/ADRD, experience higher disease burden, and low receipt of care and services. We present a novel theoretical framework-the Micro-Meso-Macro Framework for Diversifying AD/ADRD Trial Recruitment-which considers multi-level barriers and their impact on Latino trial recruitment. METHODS: Based on a review of the peer-reviewed literature and our lived experience with the Latino community, we drew from our interdisciplinary expertise in health equity and disparities research, Latino studies, social work, nursing, political economy, medicine, public health, and clinical AD/ADRD trials. We discuss factors likely to impede or accelerate Latino representation, and end with a call for action and recommendations for a bold path forward. RESULTS: In the 200+ clinical trials conducted with over 70,000 US Americans, Latino participants comprise a fraction of AD/ADRD trial samples. Efforts to recruit Latino participants typically address individual- and family-level factors (micro-level) such as language, cultural beliefs, knowledge of aging and memory loss, limited awareness of research, and logistical considerations. Scientific efforts to understand recruitment barriers largely remain at this level, resulting in diminished attention to upstream institutional- and policy-level barriers, where decisions around scientific policies and funding allocations are ultimately made. These structural barriers are comprised of inadequacies or misalignments in trial budgets, study protocols, workforce competencies, healthcare-related barriers, criteria for reviewing and approving clinical trial funding, criteria for disseminating findings, etiological focus and social determinants of heal

The goal of this literature review is to analyze various treatment options for Alzheimer’s Disease, a neurodegenerative disorder that affects many Americans. Four drugs being studied in the paper include Donepezil, Memantine, Lecanemab, and Rivastigmine. Donepezil is an acetylcholinesterase inhibitor that is used for patients with mild or moderate AD. Memantine is used to block the overflow of glutamate through NMDA receptors and to treat patients with moderate to severe AD. It can be used in combination therapy with Donepezil and Galantamine. Lecanemab is used to target amyloid-beta plaques and to reduce aggregates. Rivastigmine is a carbamate cholinesterase inhibitor that can be used to inhibit both acetylcholinesterase and butyrylcholinesterase for mild to moderate cases of AD. All four drugs have been shown to slow down the progression of AD by increasing cognitive performance and to improve the quality of life for patients and their caregivers

BACKGROUND: There has been increasing interest in cortical microstructure as a complementary and earlier measure of neurodegeneration than macrostructural atrophy, but few papers have related cortical diffusion imaging to post-mortem neuropathology. This study aimed to characterise the associations between the main Alzheimers disease (AD) neuropathological hallmarks and multiple cortical microstructural measures from in vivo diffusion MRI. Comorbidities and co-pathologies were also investigated. METHODS: Forty-three autopsy cases (8 cognitively normal, 9 mild cognitive impairment, 26 AD) from the National Alzheimers Coordinating Center and Alzheimers Disease Neuroimaging Initiative databases were included. Structural and diffusion MRI scans were analysed to calculate cortical minicolumn-related measures (AngleR, PerpPD+, and ParlPD) and mean diffusivity (MD). Neuropathological hallmarks comprised Thal phase, Braak stage, neuritic plaques, and combined AD neuropathological changes (ADNC-the ABC score from NIA-AA recommendations). Regarding comorbidities, relationships between cortical microstructure and severity of white matter rarefaction (WMr), cerebral amyloid angiopathy (CAA), atherosclerosis of the circle of Willis (ACW), and locus coeruleus hypopigmentation (LCh) were investigated. Finally, the effect of coexistent pathologies-Lewy body disease and TAR DNA-binding protein 43 (TDP-43)-on cortical microstructure was assessed. RESULTS: Cortical diffusivity measures were significantly associated with Thal phase, Braak stage, ADNC, and LCh. Thal phase was associated with AngleR in temporal areas, while Braak stage was associated with PerpPD+ in a wide cortical pattern, involving mainly temporal and limbic areas. A similar association was found between ADNC (ABC score) and PerpPD+. LCh was associated with PerpPD+, ParlPD, and MD. Co-existent neuropathologies of Lewy body disease and TDP-43 exhibited significantly reduced AngleR and MD compared to ADNC cases without c

Charismatic leaders are often desired. At the same time, experience, especially with demagogues, as well as social science studies, raise doubts about such leaders. This paper offers explanations for charismatic leadership’s “mixed report card.” It offers insights into why and when charismatic leadership can be effective; which, when, and why certain groups will prefer more to less charismatic leaders; and how being more charismatic can make leaders worse in other dimensions, particularly causing them to work less hard on their followers’ behalf. (JEL D72, D83, M54)