OBJECTIVE:The growing demand for personalized treatment in multiple sclerosis (MS) highlights the need for more precise biomarkers that can outperform magnetic resonance imaging and clinical assessment in patient stratification. Advances in multiplex proteomic technologies suggest that cerebrospinal fluid (CSF) analysis at MS onset may not only improve diagnostic accuracy, but also offer prognostic and staging information, as well as insight into molecular therapeutic targets. METHODS:This multicenter study retrospectively analyzed cryopreserved CSF samples from 160 individuals undergoing diagnostic evaluation for possible neuroimmunological disorder, and among these, followed a cohort of 96 people with confirmed MS for at least 3 years. The goal was to externally validate previously published CSF-based diagnostic and prognostic classifiers. RESULTS:Upon unblinding, the CSF-based molecular diagnostic test distinguished 96 people with confirmed MS from 30 individuals with other inflammatory neurological diseases, and 34 individuals with non-inflammatory neurological diseases, achieving an area under the receiver operating characteristic curve of 0.94 (p = 4.7 × 10-21). The test also differentiated 65 individuals with relapsing-remitting MS from 31 individuals with progressive MS, with an area under the receiver operating characteristic curve of 0.76 (p = 1.4 × 10-5). The prognostic classifier predicted prospectively measured Expanded Disability Status Scale scores at follow up (rho = 0.43, p = 2.54 × 10-5). INTERPRETATION:This multicenter external validation study demonstrates that CSF-based molecular tests can robustly distinguish MS from other neurological conditions, stratify MS subtypes, and predict future disability progression in real-world settings. These results lay the groundwork for development of next-generation molecular tools to personalize care in MS. ANN NEUROL 2026;99:328-340.

Background: In agricultural communities in Central and South America, Egypt, India, and Sri Lanka, an unexplained form of chronic kidney disease affects agricultural workers. Termed chronic kidney disease of unknown origin (CKDu), it disproportionately affects young men in their 30s–40s and is unrelated to the traditional risk factors of diabetes, hypertension, and obesity [1–3]. Recent investigations suggest that agricultural work in the USA carries similar risks, as reduced kidney function has been found among those working in US agriculture [4–5]. However, researchers are yet to determine the etiology of the disease [6–8]. Central to the hypotheses of CKDu is the reduced blood flow to the kidneys due to inadequate hydration during periods of intense physical labor. Objectives: The primary aim of the current investigation was to identify if a relationship between hydration and kidney function exists among the general population by using the data from the National Health and Nutrition Examination Survey (NHANES). We hypothesize that reduced hydration will be associated with reduced kidney function. Methods: Data were retrieved from the NHANES dataset from 3 sample years 2005/2006, 2007/2008, and 2011/2012. Data were merged across all 3 periods with survey weights adjusted for combining across multiple years. Participants were excluded if they had missing data for hydration or kidney function, or if they were <19 year. Kidney function was categorized low risk, moderate risk, or high risk for impaired function based on estimated glomerular filtration rate and albumin creatinine ratio according to the National Kidney Foundation [9]. Hydration was classified based on total water intake (TWI) extracted from plain water intake and water from food. Participants were labeled as high if they met or exceeded sex-specific water recommendations, 3.7 and 2.7 L/day for men and women, respectively; otherwise they were labeled as low. A survey-weighted proportional odds logistic regression model was fitted to assess the association between water intake and kidney function, while controlling for other demographic, socio-economic, behavioral, and socio-economic risk factors [10–12]. Results: Of the 13,056 participants initially sampled, 10,651 participants are included in the analysis after cleaning and including survey weights. 9,125 (85.67%) of participants were in the low-risk group, 1,128 (10.59%) were classified as medium-risk, while the remaining 398 (3.74%) were high risk (Fig. 1). Adjusting for survey weights, results suggest that the estimated rate of high-risk kidney function was 5% more for low water drinkers compared to high water drinkers (Fig. 2). There is strong evidence of a difference in CKD risk categories based on TWI (χ2(1) = 13.1, p value <0.0001) from a survey-weighted proportional odds logistic regression model, but only moderate evidence of a difference when controlled for sodium/potassium ratio, education, age, gender, ethnicity, income, BMI, blood pressure, diabetes, smoking, and alcohol consumption (χ2(1) = 3.3, p value = 0.067). Conclusions: Not meeting recommended daily TWI was associated increased presentation of high-risk kidney function. Even though the NHANES data are not focused on areas where chronic kidney disease is prevalent, results from this are an indication that hydration does play a role in kidney function.

Atmospheric carbon dioxide concentrations [CO2] are increasing steadily. Some reports have shown that root growth in grain crops is mostly stimulated in the topsoil rather than evenly throughout the soil profile by e[CO2], which is not optimal for crops grown in semi-arid environments with strong reliance on stored water. An experiment was conducted during the 2014 and 2015 growing seasons with two lentil (Lens culinaris) genotypes grown under Free Air CO2 Enrichment (FACE) in which root growth was observed non-destructively with mini-rhizotrons approximately every 2–3 weeks. Root growth was not always statistically increased by e[CO2] and not consistently between depths and genotypes. In 2014, root growth in the top 15 cm of the soil profile (topsoil) was indeed increased by e[CO2], but increases at lower depths (30–45 cm) later in the season were greater than in the topsoil. In 2015, e[CO2] only increased root length in the topsoil for one genotype, potentially reflecting the lack of plant available soil water between 30–60 cm until recharged by irrigation during grain filling. Our limited data to compare responses to e[CO2] showed that root length increases in the topsoil were correlated with a lower yield response to e[CO2]. The increase in yield response was rather correlated with increases in root growth below 30 cm depth.

A new set of methods are developed to perform cluster analysis of functions, motivated by a data set consisting of hydraulic gradients at several locations distributed across a wetland complex. The methods build on previous work on clustering of functions, such as Tarpey and Kinateder (2003) and Hitchcock et al. (2007), but explore functions generated from an additive model decomposition (Wood, 2006) of the original time series. Our decomposition targets two aspects of the series, using an adaptive smoother for the trend and circular spline for the diurnal variation in the series. Different measures for comparing locations are discussed, including a method for efficiently clustering time series that are of different lengths using a functional data approach. The complicated nature of these wetlands are highlighted by the shifting group memberships depending on which scale of variation and year of the study are considered.

We address the need for a model by considering two competing theories regarding the origin of life: (i) the Metabolism First theory and (ii) the RNA World theory. We discuss two inter-related points. (I) Models are valuable tools in understanding both the processes and intricacies of the origin of life issues. (II) Insights from models also help us to evaluate the core objection to origin of life theories called “the inefficiency objection” commonly raised by proponents of both the Metabolism First theory and the RNA World theory against each other. We use Simpson’s paradox as a tool for challenging this objection. We will use models in various senses ranging from taking them as representations of reality to treating them as theories/accounts that provide heuristics for probing reality. In this paper, we will frequently use models and theories interchangeably. Additionally, we investigate Conway’s Game of Life and contrast it with our Simpson’s Paradox (SP)-based approach to emergence of life issues. Finally, we discuss some of the consequences of our view. A scientific model is testable in three senses: (i) a logical sense, (ii) a nomological sense, and (iii) a current technological sense. The SP-based model is testable in the logical sense. It is also testable nomologically. However, it is not currently feasible to test it.

The Carnegie Classification of Institutions of Higher Education is a commonly used framework for institutional classification that classifies doctoral-granting schools into three groups based on research productivity. Despite its wide use, the Carnegie methodology involves several shortcomings, including a lack of thorough documentation, subjectively placed thresholds between institutions, and a methodology that is not completely reproducible. We describe the methodology of the 2015 and 2018 updates to the classification and propose an alternative method of classification using the same data that relies on structural equation modeling (SEM) of latent factors rather than principal component-based indices of productivity. In contrast to the Carnegie methodology, we use SEM to obtain a single factor score for each school based on latent metrics of research productivity. Classifications are then made using a univariate model-based clustering algorithm as opposed to subjective thresholding, as is done in the Carnegie methodology. Finally, we present a Shiny web application that demonstrates sensitivity of both the Carnegie Classification and SEM-based classification of a selected university and generates a table of peer institutions in line with the stated goals of the Carnegie Classification.

Against a backdrop of tensions related to EU membership, we find levels of online abuse toward UK MPs reach a new high. Race and religion have become pressing topics globally, and in the UK this interacts with "Brexit" and the rise of social media to create a complex social climate in which much can be learned about evolving attitudes. In 8 million tweets by and to UK MPs in the first half of 2019, religious intolerance scandals in the UK's two main political parties attracted significant attention. Furthermore, high profile ethnic minority MPs started conversations on Twitter about race and religion, the responses to which provide a valuable source of insight. We found a significant presence for disturbing racial and religious abuse. We also explore metrics relating to abuse patterns, which may affect its impact. We find "burstiness" of abuse doesn't depend on race or gender, but individual factors may lead to politicians having very different experiences online.

This study investigated relationships between changes in certain types of coaching knowledge and practices among mathematics classroom coaches and how these explain changes in the attitudes, knowledge, and practice of the teachers they coach. Participants in this study were 51 school-based mathematics classroom coaches in the USA and 180 of the teachers whom they coached between 2009 and 2014. The participating coaches were recruited from schools that hired their own coaches independently from this research project. This study found evidence that improvements in coaches’ use of practices recommended by particular coaching models are related to improvements in teachers’ mathematical knowledge for teaching. The study also found that improvements in coaches’ self-assessment of their own coaching skills are related to improvements in teachers’ mathematics content knowledge for teaching, mathematics teaching practices, and attitudes about self-efficacy for teaching mathematics. The study did not detect relationships between changes in coaches’ mathematics knowledge and changes in teachers’ knowledge or practices.

Objective To perform a meta-analysis of randomized, blinded, multiple sclerosis (MS) clinical trials, to test the hypothesis that efficacy of immunomodulatory disease-modifying therapies (DMTs) on MS disability progression is strongly dependent on age. Methods We performed a literature search with pre-defined criteria and extracted relevant features from 38 clinical trials that assessed efficacy of DMTs on disability progression. We fit a linear regression, weighted for trial sample size, and duration, to examine the hypothesis that age has a defining effect on the therapeutic efficacy of immunomodulatory DMTs. Results More than 28,000 MS subjects participating in trials of 13 categories of immunomodulatory drugs are included in the meta-analysis. The efficacy of immunomodulatory DMTs on MS disability strongly decreased with advancing age (R2 = 0.6757, p = 6.39e−09). Inclusion of baseline EDSS did not significantly improve the model. The regression predicts zero efficacy beyond approximately age 53 years. The comparative efficacy rank derived from the regression residuals differentiates high- and low-efficacy drugs. High-efficacy drugs outperform low-efficacy drugs in inhibiting MS disability only for patients younger than 40.5 years. Conclusion The meta-analysis supports the notion that progressive MS is simply a later stage of the MS disease process and that age is an essential modifier of a drug efficacy. Higher efficacy treatments exert their benefit over lower efficacy treatments only during early stages of MS, and, after age 53, the model suggests that there is no predicted benefit to receiving immunomodulatory DMTs for the average MS patient.

Objective: To investigate the hypothesis that the efficacy of immunomodulatory disease-modifying therapies (DMTs) is dependent on age in patients with multiple sclerosis (MS). Background: As new therapies are approved for the treatment of MS, it becomes more difficult to select the treatment option that will best limit progression of the disease. Design/Methods: We performed a meta-analysis of blinded, randomized clinical trials involving 28,000 patients with MS and identified 38 clinical trials that reported EDSS outcomes on disability progression. We fit a linear regression, weighted for sample size and trial duration, to FDA-approved drugs (in approved indications) from these trials and calculated the mean of the weighted residuals for each drug type. The sign, positive or negative, of this mean was used to classify the drugs as low- or high-efficacy. We then refit the regression (using a step-down testing procedure) by including possible interactions between age, efficacy classification, and baseline EDSS. Results: The model fit improved significantly by including the interaction between age and efficacy classification. The resulting model predicted a strong decline in therapeutic efficacy (R 2 = 0.6757, p = 6.39e-09) until age 53 years, at which point there was no predicted benefit to receiving DMT. Furthermore, high-efficacy therapy was more beneficial than low-efficacy therapy only in patients younger than 40.5 years. Conclusions: The data derived from this meta-analysis demonstrate that there is a significant decrease in the efficacy of immunomodulatory DMTs with age. The model results are derived from regressions representing the disability progression of an average patient on an average DMT. This progression can be reduced by administering high-efficacy therapy during early stages of the disease, and, for patients over the age of 53 years, progression is unaffected by immunomodulatory DMTs. Study Supported by: The study was supported by the intramural research program of the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH). Disclosure: Dr. Weideman has nothing to disclose. Dr. Tapia-Maltos has nothing to disclose. Dr. Johnson has nothing to disclose. Dr. Greenwood has nothing to disclose. Dr. Bielekova has nothing to disclose.

To develop a sensitive neurological disability scale for broad utilization in clinical practice. We employed advances of mobile computing to develop an iPad-based App for convenient documentation of the neurological examination into a secure, cloud-linked database. We included features present in four traditional neuroimmunological disability scales and codified their automatic computation. By combining spatial distribution of the neurological deficit with quantitative or semiquantitative rating of its severity we developed a new summary score (called NeurEx; ranging from 0 to 1349 with minimal measurable change of 0.25) and compared its performance with clinician- and App-computed traditional clinical scales. In the cross-sectional comparison of 906 neurological examinations, the variance between App-computed and clinician-scored disability scales was comparable to the variance between rating of the identical neurological examination by multiple sclerosis (MS)-trained clinicians. By eliminating rating ambiguity, App-computed scales achieved greater accuracy in measuring disability progression over time ( n = 191 patients studied over 880.6 patient-years). The NeurEx score had no apparent ceiling effect and more than 200-fold higher sensitivity for detecting a measurable yearly disability progression (i.e., median progression slope of 8.13 relative to minimum detectable change of 0.25) than Expanded Disability Status Scale (EDSS) with a median yearly progression slope of 0.071 that is lower than the minimal measurable change on EDSS of 0.5. NeurEx can be used as a highly sensitive outcome measure in neuroimmunology. The App can be easily modified for use in other areas of neurology and it can bridge private practice practitioners to academic centers in multicenter research studies.

To develop multiple sclerosis (MS) severity model that can predict future development of disability.

The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n=133) and validation (n=68) sub-cohorts, to test four hypotheses: 1) There is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. 2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration. 3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-adjusted Disability Score (CombiWISE), and substituting age for disease duration will significantly improve predictions of future accumulation of disability. 4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS), derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score (ARMSS) in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e-06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web-application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.

Therapeutic advance in progressive multiple sclerosis (MS) has been very slow. Based on the transformative role magnetic resonance imaging (MRI) contrast-enhancing lesions had on drug development for relapsing-remitting MS, we consider the lack of sensitive outcomes to be the greatest barrier for developing new treatments for progressive MS. The purpose of this study was to compare 58 prospectively acquired candidate outcomes in the real-world situation of progressive MS trials to select and validate the best-performing outcome. The 1-year pre-treatment period of adaptively designed IPPoMS (ClinicalTrials.gov #NCT00950248) and RIVITaLISe (ClinicalTrials.gov #NCT01212094) Phase II trials served to determine the primary outcome for the subsequent blinded treatment phase by comparing 8 clinical, 1 electrophysiological, 1 optical coherence tomography, 7 MRI volumetric, 9 quantitative T1 MRI, and 32 diffusion tensor imaging MRI outcomes. Fifteen outcomes demonstrated significant progression over 1 year (Δ) in the predetermined analysis and seven out of these were validated in two independent cohorts. Validated MRI outcomes had limited correlations with clinical scales, relatively poor signal-to-noise ratios (SNR) and recorded overlapping values between healthy subjects and MS patients with moderate-severe disability. Clinical measures correlated better, even though each reflects a somewhat different disability domain. Therefore, using machine-learning techniques, we developed a combinatorial weight-adjusted disability score (CombiWISE) that integrates four clinical scales: expanded disability status scale (EDSS), Scripps neurological rating scale, 25 foot walk and 9 hole peg test. CombiWISE outperformed all clinical scales (Δ = 9.10%; p = 0.0003) and all MRI outcomes. CombiWISE recorded no overlapping values between healthy subjects and disabled MS patients, had high SNR, and predicted changes in EDSS in a longitudinal assessment of 98 progressive MS patients and in a cross-sectional cohort of 303 untreated subjects. One point change in EDSS corresponds on average to 7.50 point change in CombiWISE with a standard error of 0.10. The novel validated clinical outcome, CombiWISE, outperforms the current broadly utilized MRI brain atrophy outcome and more than doubles sensitivity in detecting clinical deterioration in progressive MS in comparison to the scale traditionally used for regulatory approval, EDSS.

Monothetic clustering is a divisive clustering method based on recursive bipartitions of the data set determined by choosing splitting rules from any of the variables to conditionally optimally partition the multivariate responses. Like in other clustering methods, the choice of the number of clusters is important in this method. Connections between monothetic clustering and decision trees motivate the consideration of pruning methods as aids in selecting the number of clusters. We apply different cross-validation techniques to find the number of clusters that optimize prediction error and compare that approach to permutation-based hypothesis tests at each bi-splitting step, retaining splits with “small” p-values. A simulation study is performed to evaluate the performance of the new methods and compare to some other existing techniques.