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Finding novel promoter sequences is a cornerstone of synthetic biology. To contribute to the expanding catalog of biological parts, we employed a promoter-trap approach to identify novel sequences within an Antarctic microbial community that act as broad host-range promoters functional in diverse Pseudomonadota. Using Pseudomonas putida KT2440 as host, we generated a library comprising approximately 2,000 clones resulting in the identification of thirteen functional promoter sequences, thereby expanding the genetic toolkit available for this chassis. Some of the discovered promoter sequences prove to be broad host-range as they drove gene expression not only in P. putida KT2440 but also in Escherichia coli DH5α, Cupriavidus taiwanensis R1T, Paraburkholderia phymatum STM 815T, Ensifer meliloti 1021, and an indigenous Antarctic bacterium, Pseudomonas sp. UYIF39. Our findings enrich the existing catalog of biological parts, offering a repertoire of broad host-range promoter sequences that exhibit functionality across diverse members of the phylum Pseudomonadota, proving Antarctic microbial community as a valuable resource for prospecting new biological parts for synthetic biology.

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Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

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Soft pneumatic actuators (SPAs) produce motions for soft robots with simple pressure input, however, they require to be appropriately designed to fit the target application. Available design methods employ kinematic models and optimization to estimate the actuator response and the optimal design parameters to achieve a target actuator's shape. Within SPAs, bellow SPAs excel in rapid prototyping and large deformation, yet their kinematic models often lack accuracy due to the geometry complexity and the material nonlinearity. Furthermore, existing shape-matching algorithms are not providing an end-to-end solution from the desired shape to the actuator. In addition, despite the availability of computational design pipelines, an accessible and user-friendly toolbox for direct application remains elusive. This article addresses these challenges, offering an end-to-end shape-matching design framework for bellow SPAs to streamline the design process, and the open-source toolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing the framework with a graphic user interface for easy access. It provides a kinematic model grounded on a modular design to improve accuracy, finite element method (FEM) simulations, and piecewise constant curvature (PCC) approximation. An artificial neural network-trained surrogate model, based on FEM simulation data, is trained for fast computation in optimization. A shape-matching algorithm, merging three-dimensional (3D) PCC segmentation and a surrogate model-based genetic algorithm, identifies optimal actuator design parameters for desired shapes. The toolbox, implementing the proposed design framework, has proven its end-to-end capability in designing actuators to precisely match two-dimensional shapes with root-mean-squared-errors of 4.16, 2.70, and 2.51 mm, and demonstrating its potential by designing a 3D deformable actuator.

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Non-pharmacological interventions include physical activity, biofield energy therapy, reiki, Tai chi, and therapeutic touch. However, no reports analyzed the effectiveness of biofield therapy on cognition and motor function performance in adult subjects. The study aimed to investigate the impact of remote biofield energy healing therapy on cognition and motor functioning in adults with self-perceived neuropsychological impairments. This was a randomized double-blind clinical trial that involved 114 participants with self-perceived neuropsychological impairments. The participants were divided into three groups (control, sham control, and biofield intervention). Cognitive and motor function scores were assessed using the NIH Toolbox at baseline (day 0), day 90, and day 180. The biofield treatment group showed significant improvements in language function (p < 0.0001), working memory (p < 0.0001), and episodic memory (p < 0.0001) scores. Other cognitive functions also improved, although not statistically significant. The biofield intervention group also demonstrated significant enhancements (p < 0.05 to p < 0.0001) in locomotion, standing balance, dexterity, grip strength, and muscle endurance. No adverse effects were reported. The results suggest that remote biofield energy therapy is a safe, noninvasive intervention that improves cognitive and motor functions in adults. Further research is needed to understand its clinical benefits.

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Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample preparation altering brain morphology. The absence of an accessible toolbox for magnetic resonance imaging (MRI) data processing presents a challenge for assessing morphological changes in the mouse brain. To address this, we developed the MBV-Pipe (Mouse Brain Volumetric Statistics-Pipeline) toolbox, integrating the methods of Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL)-Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) to evaluate brain tissue volume and white matter integrity. To validate the reliability of MBV-Pipe, brain MRI data from seven mice at three time points (in vivo, post-perfusion, and post-fixation) were acquired using a 9.4T ultra-high MRI system. Employing the MBV-Pipe toolbox, we discerned substantial volumetric changes in the mouse brain following perfusion relative to the in vivo condition, with the fixation process inducing only negligible variations. Importantly, the white matter integrity was found to be largely stable throughout the sample preparation procedures. The MBV-Pipe source code is publicly available and includes a user-friendly GUI for facilitating quality control and experimental protocol optimization, which holds promise for advancing mouse brain research in the future.

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Validation of the Mobile Toolbox Faces and Names associative memory test is presented. Ninety-two participants self-administered Faces and Names in-person; 956 self-administered Faces and Names remotely but took convergent measures in person; and 123 self-administered Faces and Names remotely twice, 14 days apart. Internal consistency (.76-.79) and test-retest reliability (ICC = .73) were acceptable. Convergent validity with WMS-IV Verbal Paired Associates was satisfactory (immediate .54; delayed .58). The findings suggest the remotely administered Faces and Names is a reliable instrument.

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BACKGROUND: The type II based CRISPR-Cas system remains restrictedly utilized in archaea, a featured domain of life that ranks parallelly with Bacteria and Eukaryotes. Methanococcus maripaludis, known for rapid growth and genetic tractability, serves as an exemplary model for studying archaeal biology and exploring CO2-based biotechnological applications. However, tools for controlled gene regulation remain deficient and CRISPR-Cas tools still need improved in this archaeon, limiting its application as an archaeal model cellular factory. RESULTS: This study not only improved the CRISPR-Cas9 system for optimizing multiplex genome editing and CRISPR plasmid construction efficiencies but also pioneered an effective CRISPR interference (CRISPRi) system for controlled gene regulation in M. maripaludis. We developed two novel strategies for balanced expression of multiple sgRNAs, facilitating efficient multiplex genome editing. We also engineered a strain expressing Cas9 genomically, which simplified the CRISPR plasmid construction and facilitated more efficient genome modifications, including markerless and scarless gene knock-in. Importantly, we established a CRISPRi system using catalytic inactive dCas9, achieving up to 100-fold repression on target gene. Here, sgRNAs targeting near and downstream regions of the transcription start site and the 5'end ORF achieved the highest repression efficacy. Furthermore, we developed an inducible CRISPRi-dCas9 system based on TetR/tetO platform. This facilitated the inducible gene repression, especially for essential genes. CONCLUSIONS: Therefore, these advancements not only expand the toolkit for genetic manipulation but also bridge methodological gaps for controlled gene regulation, especially for essential genes, in M. maripaludis. The robust toolkit developed here paves the way for applying M. maripaludis as a vital model archaeal cell factory, facilitating fundamental biological studies and applied biotechnology development of archaea.

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The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using 2D images of GI wholemount preparations. It is developed in Fiji, has a user-friendly interface and offers rapid and accurate segmentation via custom deep learning (DL) based cell segmentation models developed using StarDist, and a ganglion segmentation model in deepImageJ. We use proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput allowing unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples rapidly.

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Lactiplantibacillus plantarum is a food-grade lactic acid bacterium widely used in the food and beverage industry. Recently, this probiotic organism has been applied as a biofactory for the production of pharmaceutical and food-related compounds, but existing promoters and expression vectors for the genetic engineering of L. plantarum rely on inefficient cloning strategies and are usually not well-characterized. We therefore developed a modular and standardized Golden Gate Assembly-based toolbox for the de novo assembly of shuttle vectors from Escherichia coli to L. plantarum. A collection of the most relevant genetic parts, e.g., different origins of replication and promoters, was incorporated in our toolbox and thoroughly characterized by flow cytometry and the fluorescence assay. Standardized fusion sites allow combining the genetic part freely into a plasmid in one step. This approach allows for the high-throughput assembly of numerous constructs in a standardized genetic context, thus improving the efficiency and predictability of metabolic engineering in L. plantarum. Using our toolbox, we were able to produce the aroma compounds linalool and geraniol in L. plantarum by extending its native mevalonate pathway with plant-derived monoterpenoid synthases.

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Cyanobacteria show promise as hosts for whole-cell biocatalysis. Their photoautotrophic metabolism can be leveraged for a sustainable production process. Despite advancements, performance still lags behind heterotrophic hosts. A key challenge is the limited ability to overexpress recombinant enzymes, which also hinders their biocatalytic efficiency. To address this, we generated large-scale expression libraries and developed a high-throughput method combining fluorescence-activated cell sorting (FACS) and deep sequencing in Synechocystis sp. PCC 6803 (Syn. 6803) to screen and optimize its genetic background. We apply this approach to enhance expression and biocatalyst performance for three enzymes: the ketoreductase LfSDR1M50, enoate reductase YqjM, and Baeyer-Villiger monooxygenase (BVMO) CHMOmut. Diverse genetic combinations yielded significant improvements: optimizing LfSDR1M50 expression showed a 17-fold increase to 39.2 U gcell dry weight (CDW)-1. In vivo activity of Syn. YqjM was improved 16-fold to 58.7 U gCDW-1 and, for Syn. CHMOmut, a 1.5-fold increase to 7.3 U gCDW-1 was achieved by tailored genetic design. Thus, this strategy offers a pathway to optimize cyanobacteria as expression hosts, paving the way for broader applications in other cyanobacteria strains and larger libraries.

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Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised multi-query similarity search models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models' robustness and reliability. The top-performing model, M13+, demonstrated impressive results, with an accuracy of 0.969 and a Matthew's correlation coefficient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine-learning and deep-learning AVP predictors. The MQSSMs outperformed these predictors, highlighting their efficiency in terms of resource demand and public accessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date. In general, these results suggest that MQSSMs are promissory tools to develop good alignment-based models that can be successfully applied in the screening of large datasets for new AVP discovery.

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While genome-wide transposon mutagenesis screens have identified numerous essential genes in the significant human pathogen Streptococcus pyogenes (group A Streptococcus or GAS), many of their functions remain elusive. This knowledge gap is attributed in part to the limited molecular toolbox for controlling GAS gene expression and the bacterium's poor genetic transformability. CRISPR interference (CRISPRi), using catalytically inactive GAS Cas9 (dCas9), is a powerful approach to specifically repress gene expression in both bacteria and eukaryotes, but ironically, it has never been harnessed for controlled gene expression in GAS. In this study, we present a highly transformable and fully virulent serotype M1T1 GAS strain and introduce a doxycycline-inducible CRISPRi system for efficient repression of bacterial gene expression. We demonstrate highly efficient, oligo-based single guide RNA cloning directly to GAS, enabling the construction of a gene knockdown strain in just 2 days, in contrast to the several weeks typically required. The system is shown to be titratable and functional both in vitro and in vivo using a murine model of GAS infection. Furthermore, we provide direct in vivo evidence that the expression of the conserved cell division gene ftsZ is essential for GAS virulence, highlighting its promise as a target for emerging FtsZ inhibitors. Finally, we introduce SpyBrowse (https://veeninglab.com/SpyBrowse), a comprehensive and user-friendly online resource for visually inspecting and exploring GAS genetic features. The tools and methodologies described in this work are poised to facilitate fundamental research in GAS, contribute to vaccine development, and aid in the discovery of antibiotic targets. IMPORTANCE: While group A Streptococcus (GAS) remains a predominant cause of bacterial infections worldwide, there are limited genetic tools available to study its basic cell biology. Here, we bridge this gap by creating a highly transformable, fully virulent M1T1 GAS strain. In addition, we established a tight and titratable doxycycline-inducible system and developed CRISPR interference (CRISPRi) for controlled gene expression in GAS. We show that CRISPRi is functional in vivo in a mouse infection model. Additionally, we present SpyBrowse, an intuitive and accessible genome browser (https://veeninglab.com/SpyBrowse). Overall, this work overcomes significant technical challenges of working with GAS and, together with SpyBrowse, represents a valuable resource for researchers in the GAS field.

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OBJECTIVE: The ability to remotely monitor cognitive skills is increasing with the ubiquity of smartphones. The Mobile Toolbox (MTB) is a new measurement system that includes measures assessing Executive Functioning (EF) and Processing Speed (PS): Arrow Matching, Shape-Color Sorting, and Number-Symbol Match. The purpose of this study was to assess their psychometric properties. METHOD: MTB measures were developed for smartphone administration based on constructs measured in the NIH Toolbox® (NIHTB). Psychometric properties of the resulting measures were evaluated in three studies with participants ages 18 to 90. In Study 1 (N = 92), participants completed MTB measures in the lab and were administered both equivalent NIH TB measures and other external measures of similar cognitive constructs. In Study 2 (N = 1,021), participants completed the equivalent NIHTB measures in the lab and then took the MTB measures on their own, remotely. In Study 3 (N = 168), participants completed MTB measures twice remotely, two weeks apart. RESULTS: All three measures exhibited very high internal consistency and strong test-retest reliability, as well as moderately high correlations with comparable NIHTB tests and moderate correlations with external measures of similar constructs. Phone operating system (iOS vs. Android) had a significant impact on performance for Arrow Matching and Shape-Color Sorting, but no impact on either validity or reliability. CONCLUSIONS: Results support the reliability and convergent validity of MTB EF and PS measures for use across the adult lifespan in remote, self-administered designs.

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Over fifty years have passed since the last large scale longitudinal study of individuals with PAH deficiency in the U.S. Since then, there have been significant changes in terms of treatment recommendations as well as treatment options. The Phenylalanine Families and Researchers Exploring Evidence (PHEFREE) Consortium was recently established to collect a more up-to-date and extensive longitudinal natural history in individuals with phenylketonuria across the lifespan. In the present paper, we describe the structure and methods of the PHEFREE longitudinal study protocol and report cross-sectional data from an initial sample of 73 individuals (5 months to 54 years of age) with PAH deficiency who have enrolled. Looking forward, the study holds the promise for advancing the field on several fronts including the validation of novel neurocognitive tools for assessment in individuals with PKU as well as evaluation of the long-term effects of changes in metabolic control (e.g., effects of Phe-lowering therapies) on outcome.

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BACKGROUND: Amyloid-beta (Aß) plaque is a neuropathological hallmark of Alzheimer's disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility. OBJECTIVE: To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study. DESIGN: ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type). SETTING: Participants across various sites were involved in the ARMADA study for validating the NIHTB. PARTICIPANTS: 199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers). MEASUREMENTS: We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity. RESULTS: The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 - 0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 - 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers. CONCLUSION: Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity).

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BACKGROUND CONTEXT: Degenerative cervical myelopathy (DCM) is characterized by spinal cord atrophy. Accurate estimation of spinal cord atrophy is key to the understanding of neurological diseases, including DCM. However, its clinical application is hampered by difficulties in its precise and consistent estimation due to significant variability in spinal cord morphometry along the cervical spine, both within and between individuals. PURPOSE: To characterize morphometrics of the compressed spinal cord in DCM patients. We employed our semiautomated analysis framework that incorporates the Spinal Cord Toolbox (SCT) and a normalization approach to effectively address the challenges posed by cord compression in these patients. Additionally, we examined the clinical relevance of these morphometric measures to enhance our understanding of DCM pathophysiology. STUDY DESIGN: Prospective study. PATIENT SAMPLE: This study investigated 36 DCM patients and 31 healthy controls (HCs). OUTCOME MEASURES: Clinical scores including 9-hole peg test for hand dexterity, hand grip strength, balance, gait speed, modified Japanese Orthopaedic Association (mJOA) score, and imaging-based spinal cord morphometrics. METHOD: Using the generic spine acquisition protocol and our semiautomated analysis pipeline, spinal cord morphometrics, including cross-sectional area (CSA), anterior-posterior (AP) and transverse (RL) diameters, eccentricity, and solidity, were estimated from sagittal T2w magnetic resonance imaging (MRI) images using the Spinal Cord Toolbox (SCT). Normalized metrics were extracted from the C1 to C7 vertebral levels and compared between DCM patients and HC. Morphometric data at regions of maximum spinal cord compression (MSCC) were correlated with the clinical scores. A subset of participants underwent follow-up scans at 6 months to monitor longitudinal changes in spinal cord atrophy. RESULTS: Spinal cord morphometric data were normalized against the healthy population morphometry (PAM50 database) and extracted for all participants. DCM patients showed a notable reduction in CSA, AP, and RL diameter across all vertebral levels compared to HC. MSCC metrics correlated significantly with clinical scores like dexterity, grip strength, and mJOA scores. Longitudinal analysis indicated a decrease in CSA and worsening clinical scores in DCM patients. CONCLUSION: Our processing pipeline offers a reliable method for assessing spinal cord compression in DCM patients. Normalized spinal cord morphometrics, particularly the CSA could have potential for monitoring DCM disease severity and progression, guiding treatment decisions. Furthermore, to our knowledge our study is the first to apply the generic spinal cord acquisition protocol, ensuring consistent imaging across different MRI scanners and settings. Coupled with our semiautomated analysis pipeline, this protocol is key for the detailed morphometric characterization of compressed spinal cords in patients with DCM, a disease that is both complex and heterogenous. This study was funded by the National Institute of Neurological Disorders and Stroke (NINDS) (K23:NS091430) and (R01: NS129852-01A1).

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BACKGROUND: Intellectual and developmental disabilities (IDDs) are associated with both cognitive challenges and difficulties in conceptual, social, and practical areas of living, commonly referred to as adaptive behavior (DSM-5). Although cross-sectional associations between intelligence or cognition and adaptive behavior have been reported in IDD populations, no study to date has examined whether developmental changes in cognition contribute to or track with changes in adaptive behavior. The present study sought to examine associations of longitudinal developmental change in domains of cognition (NIH Toolbox Cognition Battery, NIHTB-CB) and adaptive behavior domains (Vineland Adaptive Behavior Scales-3; VABS-3) including Socialization, Communication, and Daily Living Skills (DLS) over a two year period in a large sample of children, adolescents and young adults with IDD. METHODS: Three groups were recruited, including those with fragile X syndrome, Down syndrome, and other/idiopathic intellectual disability. Eligible participants (n = 263) included those who were between 6 and 26 years (mage = 15.52, sd = 5.17) at Visit 1, and who had a diagnosis of, or suspected intellectual disability (ID), including borderline ID, with a mental age of at least 3.0 years. Participants were given cognitive and adaptive behavior assessments at two time points over a two year period (m = 2.45 years, range = 1.27 to 5.56 years). In order to examine the association of developmental change between cognitive and adaptive behavior domains, bivariate latent change score (BLCS) models were fit to compare change in the three cognitive domains measured by the NIHTB-CB (Fluid Cognition, Crystallized Cognition, Total Cognition) and the three adaptive behavior domains measured by the VABS-3 (Communication, DLS, and Socialization). RESULTS: Over a two year period, change in cognition (both Crystallized and Total Composites) was significantly and positively associated with change in daily living skills. Also, baseline cognition level predicted growth in adaptive behavior, however baseline adaptive behavior did not predict growth in cognition in any model. CONCLUSIONS: The present study demonstrated that developmental changes in cognition and adaptive behavior are associated in children and young adults with IDD, indicating the potential for cross-domain effects of intervention. Notably, improvements in DLS emerged as a primary area of adaptive behavior that positively related to improvements in cognition. This work provides evidence for the clinical, "real life" meaningfulness of changes in cognition detected by the NIHTB-CB in IDD, and provides empirical support for the NIHTB-CB as a fit-for-purpose performance-based outcome measure for this population.

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Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

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BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

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Alcohol ethoxylates (AEs) are a well-known class of non-ionic surfactants widely used by the personal care market. The aim of this study was to evaluate and characterize the in vitro metabolism of AEs and identify metabolites. Five selected individual homologue AEs (C8EO4, C10EO5, C12EO4, C16EO8, and C18EO3) were incubated using human, rat, and hamster liver S9 fraction and cryopreserved hepatocytes. LC-MS was used to identify metabolites following the incubation of AEs by liver S9 and hepatocytes of all three species. All AEs were metabolized in these systems with a half-life ranging from 2 to 139 min. In general, incubation of AE with human liver S9 showed a shorter half-life compared to rat liver S9. While rat hepatocytes metabolized AEs faster than human hepatocytes. Both hydrophobic alkyl chain and hydrophilic EO head group groups of AEs were found to be target sites of metabolism. Metabolites were identified that show primary hydroxylation and dehydrogenation, followed by O-dealkylation (shortening of EO head groups) and glucuronidation. Additionally, the detection of whole EO groups indicates the cleavage of the ether bond between the alkyl chain and the EO groups as a minor metabolic pathway in the current testing system. Furthermore, no difference in metabolic patterns of each individual homologue AE investigated was observed, regardless of alkyl chain length or the number of EO groups. Moreover, there is an excellent agreement between the in vitro experimental data and the metabolite profile simulations using in silico approaches (OECD QSAR Toolbox). Altogether, these data indicate fast metabolism of all AEs with a qualitatively similar metabolic pathway with some quantitative differences observed in the metabolite profiles. These metabolic studies using different species can provide important reference values for further safety evaluation.

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